25:["$","title",null,{"children":"Data Literacy: On the Table! (Performance Dashboard) | Zsolt Olah's Data Literacy Workspace | Observable"}] 49:T472,learnPerChangeCompManager = addTooltips( Plot.plot({ title: "Normalized learning change (%) vs. normalized performance change (%)", subtitle: "Reps whose manager reportedly completed the pilot, by region (○ West, + East, ◇ North, □ South)", grid: true, marks: [ Plot.dot(pilotCompManager, { x: "adj_learning_gain", y: "adj_performance_gain", fill: (d) => REGION[String(d.region).trim().toUpperCase()] ?? d.region, stroke: (d) => REGION[String(d.region).trim().toUpperCase()] ?? d.region, symbol: (d) => REGION[String(d.region).trim().toUpperCase()] ?? d.region, title: (d) => `${REGION[String(d.region).trim().toUpperCase()] ?? d.region}\nNormalized learning change: ${d.adj_learning_gain}%\nNormalized performance change: ${d.adj_performance_gain}%` }) ], x: { label: "Normalized learning change (%)" }, y: { label: "Normalized performance change (%)" }, symbol: { legend: true, label: "Region", domain: ["West", "East", "North", "South"] }, color: { domain: ["West", "East", "North", "South"] } }), { fill: "black" } )4a:Tab3,// ───────────────────────────────────────────────────────────── // learnPerf2 — learning change vs. performance change, by region // Observable Notebooks 2.0 (TypeScript) // // Two changes from the 1.0 cell: // • addTooltips() is gone. Plot 0.6.11+ has tooltips built in via // the mark's `tip` option, so the @mkfreeman/plot-tooltip import // is no longer needed — and the result stays a plain

, // which is what downloadPNG() knows how to export. // • Axis labels and zero rules added. With signed percentages the // zero lines carry meaning: they split the chart into learned- // and-improved, learned-but-didn't, and so on. Delete the two // Plot.rule marks if you want it exactly as it was. // // pilotCompManager comes from your existing data cell. // ───────────────────────────────────────────────────────────── interface ManagerRow { manager?: string; region: string; adj_learning_gain: number; adj_performance_gain: number; } const learnPerf2 = Plot.plot({ width: 700, height: 470, marginLeft: 62, marginBottom: 52, title: "Individual Normalized Learning Change (%) vs. Normalized Performance Change (%)", subtitle: "Manager completion across regions", grid: true, symbol: { legend: true }, color: { legend: false }, // one legend, not two: symbol already names the regions x: { label: "Normalized learning change (%) →" }, y: { label: "↑ Normalized performance change (%)" }, marks: [ Plot.ruleY([0], { stroke: "#0b0b0b", strokeOpacity: 0.3 }), Plot.ruleX([0], { stroke: "#0b0b0b", strokeOpacity: 0.3 }), Plot.dot(pilotCompManager as ManagerRow[], { x: "adj_learning_gain", y: "adj_performance_gain", fill: "region", stroke: "region", symbol: "region", r: 4.5, fillOpacity: 0.65, strokeWidth: 1.4, channels: { Manager: "manager", Region: "region" }, tip: { format: { x: (d: number) => `${d}%`, y: (d: number) => `${d}%`, fill: false, stroke: false, symbol: false } } }) ] }); display(learnPerf2); display( Object.assign(document.createElement("button"), { textContent: "Download PNG", onclick: () => downloadPNG(learnPerf2, "learn_vs_performance") }) ); // If `manager` is not a column in pilotCompManager, drop it from // `channels` — an unknown field makes the tip show "undefined" // rather than throwing, which is easy to miss. 4b:T55f,hist3 = addTooltips( Plot.plot({ title: "Distribution of normalized performance gain (%) by pilot completion", subtitle: html`Blue = Completed pilot | Orange = Did not complete
Statistical mean: +${roundMe(meanPerformanceGain,2)}% (black dotted line) | Median: +${roundMe(medPerformanceGain,2)}% (green dash)`, marks: [ Plot.axisX({ ticks: [-50,-40,-30,-20,-10,0,10,20,30,40,50,60,70,80,90,100] }), // Color by pilot completion Plot.rectY( salesfinal_dataset, Plot.binX( { y: "count", title: elems => `${elems.length} salesfolks` }, { x: "adj_performance_gain", interval: 10, fill: d => d.complete_training === 1 ? "steelblue" : "orange" } ) ), // Mean and median reference lines Plot.ruleX([meanPerformanceGain], { stroke: "black", strokeDasharray: "2,2", markerEnd: "dot" }), Plot.ruleX([medPerformanceGain], { stroke: "green", strokeDasharray: "8,8", markerEnd: "dot" }) ], x: { label: "Normalized Performance Change (%)", domain: [-50, 100] }, y: { label: "Number of Salesfolks" }, // Hide color legend (no gradient shading) color: { legend: false } }), { fill: "black" } ); 4c:T5bf, hist4 = addTooltips ( Plot.plot({ title: "Distribution of normalized performance change (%) for those who completed the pilot", subtitle: "Statistical mean: "+roundMe(meanPerformanceGainComp,2)+"% (solid line) | Median: "+roundMe(medPerformanceGainComp,2)+"% (dotted line)", marks: [ Plot.axisX({ticks: [-50,-40,-30, -20.00001, -10.00001, 0.00001, 10.00001, 20.00001, 30.00001, 40.00001, 50.00001, 60.00001, 70.00001,80.00001,90.00001,100.00001]}), Plot.rectY( pilotComp, Plot.binX({ y: "count",title: (elems) => `${elems.length} salesfolks` }, { x: "adj_performance_gain", fill: "steelblue",interval: 10.0001 }) // Bars colored in steelblue ), Plot.text( pilotComp, Plot.binX({ y: "count"}, { x: "adj_performance_gain", y:"count", text: (d) => (d.length), dy: -6, textAnchor: "middle",interval: 10.0001 }) ), Plot.ruleX(pilotComp,{ x: [meanPerformanceGainComp], y:[maxPerformanceGainComp+10],stroke: "black", strokeDasharray: "0,0", markerEnd: "dot" }), // Dotted line for mean Plot.ruleX(pilotComp,{ x: [medPerformanceGainComp], y:[maxPerformanceGainComp+10],stroke: "green", strokeDasharray: "2,2", markerEnd: "dot" }), // Label for the mean line Plot.ruleY[0] ], x: { label: "Normalized performance change (%)", domain: [-50, 100.00001] }, y: { domain: [0, [maxPerformanceGainComp+10]], label: "Number of Salespeople" // Y-axis title } }), { fill: "black" }) 4d:T609, hist4Control = addTooltips ( Plot.plot({ title: "Distribution of normalized performance change (%) for the control group", subtitle: "Statistical mean: "+roundMe(meanPerformanceGainControl,2)+"% (solid line) | Median: "+roundMe(medPerformanceGainControl,2)+"% (dotted line)", marks: [ Plot.axisX({ticks: [-50,-40,-30, -20.00001, -10.00001, 0.00001, 10.00001, 20.00001, 30.00001, 40.00001, 50.00001, 60.00001, 70.00001,80.00001,90.00001,100.00001]}), Plot.rectY( pilotControlPerformanceChange, Plot.binX({ y: "count",title: (elems) => `${elems.length} salesfolks` }, { x: "adj_performance_gain", fill: "steelblue",interval: 10.0001 }) // Bars colored in steelblue ), Plot.text( pilotControlPerformanceChange, Plot.binX({ y: "count"}, { x: "adj_performance_gain", y:"count", text: (d) => (d.length), dy: -6, textAnchor: "middle",interval: 10.0001 }) ), Plot.ruleX(pilotControlPerformanceChange,{ x: [meanPerformanceGainControl], y:[maxPerformanceGain+10],stroke: "black", strokeDasharray: "0,0", markerEnd: "dot" }), // Dotted line for mean Plot.ruleX(pilotControlPerformanceChange,{ x: [medPerformanceGainControl], y:[maxPerformanceGain+10],stroke: "green", strokeDasharray: "2,2", markerEnd: "dot" }), // Label for the mean line Plot.ruleY[0] ], x: { label: "Normalized performance change (%)", domain: [-50, 100.00001] }, y: { domain: [0, [maxPerformanceGain+50]], label: "Number of Salesfolks" // Y-axis title } }), { fill: "black" }) 4e:T733,perf_by_group = Plot.plot({ title: "Performance (%) Comparing Pre- and Post-Pilot", subtitle: "Comparing the pre- and post-performance for pilot participants and the control group.", marginLeft: 60, // more room for facet header + extra headroom inside the chart facet: { marginTop: 36 }, y: { grid: true, inset: 6, insetTop: 24, insetBottom: 12, label: "Performance (%)" }, x: { label: "Phase", domain: ["Pre", "Post"] }, color: { legend: true, domain: ["Pre", "Post"] }, fx: { label: "Group (Completed the pilot?) →", labelAnchor: "right", domain: [-1, 1], tickFormat: d => (d === 1 ? "Pilot (completed)" : "Control (did not)") }, marks: [ Plot.ruleY([0]), Plot.boxY(perf_long, { fx: "group", x: "phase", y: "value", fill: "phase", stroke: "phase" }), // --- overlays on top --- // Min–max whisker (neutral) Plot.ruleY(stats, { fx: "group", x: "phase", y1: "min", y2: "max", stroke: "#444", strokeOpacity: 0.35 }), // Mean dotted line (white on Pre, dark on Post) Plot.ruleY(meanLines, { fx: "group", x1: "x1", x2: "x2", y: "median", stroke: d => d.phase === "Pre" ? "#fff" : "#222", strokeWidth: 1.5, strokeDasharray: "4,4" }), // Mean label (white on Pre, dark on Post) Plot.text(stats, { fx: "group", x: "phase", y: "median", text: d => `med ${d3.format(".1f")(d.median)}`, dy: -8, fontSize: 11, fill: d => d.phase === "Pre" ? "#fff" : "#222" }), // Min label (always black) Plot.text(stats, { fx: "group", x: "phase", y: "min", text: d => `min ${d3.format(".1f")(d.min)}`, dy: 12, fontSize: 10, fill: "#000" }), // Max label (always black) Plot.text(stats, { fx: "group", x: "phase", y: "max", text: d => `max ${d3.format(".1f")(d.max)}`, dy: -6, fontSize: 10, fill: "#000" }) ] }); 4f:T6a5, perf_pre_by_group = Plot.plot({ title: "Pre-pilot performance: is there a selection bias?", subtitle: "Quota attainment (%) one quarter before the pilot, by whether managers selected the rep for the pilot.", marginLeft: 60, marginBottom: 40, y: { grid: true, domain: [0, 100], insetTop: 24, insetBottom: 12, label: "Performance (%)" }, x: { label: null, domain: [1, -1], tickFormat: groupLabel, padding: 0.4 }, color: { domain: [1, -1], range: ["#2f6fdb", "#9aa3ad"] }, marks: [ Plot.ruleY([0]), Plot.ruleY([50], { stroke: "#c0392b", strokeDasharray: "3,3", strokeOpacity: 0.7 }), Plot.text([50], { y: d => d, frameAnchor: "right", text: () => "50% low-performance line", dy: -6, fontSize: 10, fill: "#c0392b" }), Plot.boxY(prePerf, { x: "group", y: "value", fill: "group", stroke: "group", fillOpacity: 0.85 }), // Min–max whisker Plot.ruleX(preStats, { x: "group", y1: "min", y2: "max", stroke: "#444", strokeOpacity: 0.35 }), // Median dotted line + label Plot.tickY(preStats, { x: "group", y: "median", stroke: "#fff", strokeWidth: 1.5, strokeDasharray: "4,4" }), Plot.text(preStats, { x: "group", y: "median", text: d => `med ${d3.format(".1f")(d.median)}`, dy: -8, fontSize: 11, fill: "#fff" }), // Min / max labels Plot.text(preStats, { x: "group", y: "min", text: d => `min ${d3.format(".1f")(d.min)}`, dy: 12, fontSize: 10, fill: "#000" }), Plot.text(preStats, { x: "group", y: "max", text: d => `max ${d3.format(".1f")(d.max)}`, dy: -6, fontSize: 10, fill: "#000" }), // Group size under each box Plot.text(preStats, { x: "group", y: 0, text: d => `n = ${d.n}`, dy: 14, fontSize: 10, fill: "#555" }) ] })50:T730,perf_by_group1 = Plot.plot({ title: "Performance (%) Comparing Pre- and Post-Pilot", subtitle: "Comparing the pre- and post-performance for pilot participants and the control group.", marginLeft: 60, // more room for facet header + extra headroom inside the chart facet: { marginTop: 36 }, y: { grid: true, inset: 6, insetTop: 24, insetBottom: 12, label: "Performance (%)" }, x: { label: "Phase", domain: ["Pre", "Post"] }, color: { legend: true, domain: ["Pre", "Post"] }, fx: { label: "Group (Completed the pilot?) →", labelAnchor: "right", domain: [-1, 1], tickFormat: d => (d === 1 ? "Pilot (completed)" : "Control (did not)") }, marks: [ Plot.ruleY([0]), Plot.boxY(perf_long, { fx: "group", x: "phase", y: "value", fill: "phase", stroke: "phase" }), // --- overlays on top --- // Min–max whisker (neutral) Plot.ruleY(stats, { fx: "group", x: "phase", y1: "min", y2: "max", stroke: "#444", strokeOpacity: 0.35 }), // Mean dotted line (white on Pre, dark on Post) Plot.ruleY(meanLines, { fx: "group", x1: "x1", x2: "x2", y: "median", stroke: d => d.phase === "Pre" ? "#fff" : "#222", strokeWidth: 1.5, strokeDasharray: "4,4" }), // Mean label (white on Pre, dark on Post) Plot.text(stats, { fx: "group", x: "phase", y: "mean", text: d => `med ${d3.format(".1f")(d.mean)}`, dy: -8, fontSize: 11, fill: d => d.phase === "Pre" ? "#fff" : "#222" }), // Min label (always black) Plot.text(stats, { fx: "group", x: "phase", y: "min", text: d => `min ${d3.format(".1f")(d.min)}`, dy: 12, fontSize: 10, fill: "#000" }), // Max label (always black) Plot.text(stats, { fx: "group", x: "phase", y: "max", text: d => `max ${d3.format(".1f")(d.max)}`, dy: -6, fontSize: 10, fill: "#000" }) ] }); 51:T7c7,perfall = { // --- compute phase-level stats (Pre/Post) --- const stats_phase = d3.rollups( perf_long, v => ({ min: d3.min(v, d => d.value), max: d3.max(v, d => d.value), median: d3.median(v, d => d.value) }), d => d.phase ).map(([phase, s]) => ({ phase, ...s })); return Plot.plot({ title: "Performance (%) — Pre vs Post", subtitle: "All participants combined (pilot + control together)", marginLeft: 60, y: { grid: true, inset: 6, insetTop: 24, insetBottom: 12, label: "Performance (%)" }, x: { label: "Phase", domain: ["Pre", "Post"] }, color: { legend: true, domain: ["Pre", "Post"] }, marks: [ Plot.ruleY([0]), // Boxplots (combined) Plot.boxY(perf_long, { x: "phase", y: "value", fill: "phase", stroke: "phase" }), // Min–max whisker Plot.ruleY(stats_phase, { x: "phase", y1: "min", y2: "max", stroke: "#444", strokeOpacity: 0.35 }), /* // Median marker (dot) — reliable in one paste Plot.dot(stats_phase, { x: "phase", y: "median", r: 4, fill: d => d.phase === "Pre" ? "#fff" : "#222", stroke: d => d.phase === "Pre" ? "#fff" : "#222" }), */ // Median label Plot.text(stats_phase, { x: "phase", y: "median", text: d => `med ${d3.format(".1f")(d.median)}`, dy: -10, fontSize: 11, fill: d => d.phase === "Pre" ? "#fff" : "#222" }), // Min label Plot.text(stats_phase, { x: "phase", y: "min", text: d => `min ${d3.format(".1f")(d.min)}`, dy: 12, fontSize: 10, fill: "#000" }), // Max label Plot.text(stats_phase, { x: "phase", y: "max", text: d => `max ${d3.format(".1f")(d.max)}`, dy: -6, fontSize: 10, fill: "#000" }) ] }); }52:T426,perfRegion = addTooltips( Plot.plot({ title: "Average normalized performance change by region", subtitle: "Pilot completers only (n = 115)", x: { label: "Region", tickFormat: (k) => `${k} (n=${perfAvgAmount.find((d) => d.key === k).n})` }, y: { label: "Normalized performance change (%)", grid: true, domain: [-30, 30] }, color: { legend: false }, marks: [ Plot.barY(perfAvgAmount, { x: "key", y: "value", fill: "key", title: (d) => `${d.key}: ${d.value}% mean normalized performance change (n=${d.n})`, sort: { x: "y", reverse: true } }), // labels above positive bars Plot.text(perfAvgAmount.filter((d) => d.value >= 0), { x: "key", y: "value", text: (d) => `${d.value}%`, dy: -8, fill: "black" }), // labels below negative bars Plot.text(perfAvgAmount.filter((d) => d.value < 0), { x: "key", y: "value", text: (d) => `${d.value}%`, dy: 12, fill: "black" }), Plot.ruleY([0]) ] }), { fill: "black" } )53:T491,lch4 = Plot.plot({ color: { legend: true }, title: "Distribution of learning gain (unmatched data)", subtitle: "Statistical mean: "+roundMe([meanLearningGain],2)+" (dotted line)", marks: [ Plot.rectY( salesfinal_dataset, Plot.binX({ y: "count",title: (elems) => `${elems.length} salesfolks`}, { x: "learn_gain",fill: "steelblue",tip: true ,thresholds:[-46,-36,-26,-16,-6,4,14,24,34,44,54,64,74,84]}) // Bars colored in steelblue ), Plot.text( salesfinal_dataset, Plot.binX({ y: "count",text: (d) => d.length}, {x: "learn_gain",thresholds:[-46,-36,-26,-16,-6,4,14,24,34,44,54,64,74,84],dy:-8}) ), Plot.ruleX(sales_clean,{ x: [meanLearningGain], y:[maxLearnGain+20],stroke: "black", strokeDasharray: "2,2", markerEnd: "dot" }), // Dotted line for mean Plot.text( { x: 80, y: 50, text: "Mean", fill:"black"}), // Label for the mean line Plot.ruleY[0] ], x: { label: "Learning Gain (%)", domain: [-46, 84], ticks: [-46, -36, -26, -16, -6, 4, 14, 24, 34, 44, 54, 64, 74, 84] }, y: { label: "Number of Salesfolks", domain: [0, [maxLearnGain+20]] } }) 54:T401,// 2) Ordinary Least Squares for y ~ a + b x (with errors) regfiltered = { const n = xyfiltered.length; const meanX = d3.mean(xyfiltered, d => d.x); const meanY = d3.mean(xyfiltered, d => d.y); const Sxx = d3.sum(xyfiltered, d => (d.x - meanX) ** 2); const Sxy = d3.sum(xyfiltered, d => (d.x - meanX) * (d.y - meanY)); const slope = Sxy / Sxx; // b const intercept = meanY - slope * meanX; // a // fitted values & residuals const yhat = xyfiltered.map(d => intercept + slope * d.x); const resid = xyfiltered.map((d, i) => d.y - yhat[i]); const SSE = d3.sum(resid, e => e * e); const SST = d3.sum(xyfiltered, d => (d.y - meanY) ** 2); const sigma2 = SSE / (n - 2); // residual variance const rmse = Math.sqrt(sigma2); // standard errors for coefficients const seSlope = Math.sqrt(sigma2 / Sxx); const seIntercept = Math.sqrt(sigma2 * (1/n + (meanX ** 2) / Sxx)); const r2 = 1 - SSE / SST; return { n, slope, intercept, r2, rmse, seSlope, seIntercept }; } 55:Te69,statsRegression = html`

Regression Comparison: All Data vs. No Outliers vs. ≥20% Learning Change (West Region)

Metric	All Data	No Outliers	≥20% Learners TYG threshold
Reps (n)	35	33	31
Equation	ŷ = 2.624 + 0.432·x	ŷ = −6.041 + 0.571·x	ŷ = −29.261 + 0.958·x
Slope (β₁)	0.432	0.571	0.958
Slope 95% CI	[0.240, 0.625]	[0.389, 0.753]	[0.853, 1.063]
Intercept (β₀)	2.624	−6.041	−29.261
Intercept 95% CI	[−7.819, 13.067]	[−16.223, 4.141]	[−35.297, −23.225]
R²	0.389	0.568	0.924
RMSE	12.435	10.684	4.631
Degrees of Freedom	33	31	29
t*	2.035	2.040	2.045

Insights

• Slope strengthens as you clean/focus the data: 0.43 → 0.57 → 0.96. Among ≥20% learners, learning change translates to performance change almost 1:1.

• Model fit improves: R² rises from 0.389 → 0.568 → 0.924, while RMSE drops from 12.4 → 10.7 → 4.6. The relationship becomes both tighter and more predictive.

• Intercepts remain non-actionable in the first two models (CIs span 0). After thresholding, the negative intercept reflects extrapolation outside the observed range and isn’t decision-critical; the slope is the story.

• Interpretation: Outliers were masking the core signal, and people who didn’t learn enough dilute the learning→performance relationship. Focusing on those with meaningful learning (≥20%) reveals a robust transfer effect.

`56:T1301,// ───────────────────────────────────────────────────────────── // West region: one regression on everything, one without the // off-trend managers. Observable Notebooks 2.0 (TypeScript) // // Uses your existing xy, line_all and line_no_out. // // Three fixes from the version you pasted: // • `label:` on a mark does nothing in Plot — it is silently // ignored — and `stroke: "steelblue"` is a literal colour, not // a channel, so `color: {legend: true}` had nothing to build a // legend from. The two fits are combined into one array with a // `series` field, and stroke is mapped to it. Now the legend // appears, and the series names carry solid/dashed themselves. // • strokeDasharray is a constant, not a channel: a function // there is ignored and both lines render solid. So the dashed // fit is its own mark. // • The excluded points are drawn as hollow rings rather than // hidden, so "without outliers" shows which four it means. // ───────────────────────────────────────────────────────────── interface XYRow { x: number; y: number; outlier?: boolean; // optional; see the note at the bottom } interface FitPoint { x: number; yhat: number; } const westFit = (() => { const INK = "#0b0b0b"; const MUTED = "#6b6a66"; const DARK = "#184f95"; const ACCENT = "#b8460f"; const SURFACE = "#fcfcfb"; const halo = { stroke: SURFACE, strokeWidth: 5 } as const; const THRESHOLD = 20; // TYG data strategy: the point L&D calls significant const ALL = "All data (solid)"; const NO_OUT = "Outliers removed (dashed)"; const rows = xy as XYRow[]; const fits: (FitPoint & { series: string })[] = [ ...(line_all as FitPoint[]).map((d) => ({ ...d, series: ALL })), ...(line_no_out as FitPoint[]).map((d) => ({ ...d, series: NO_OUT })) ]; // if the rows carry no `outlier` flag, everything renders as a plain dot const kept = rows.filter((d) => !d.outlier); const dropped = rows.filter((d) => d.outlier); return Plot.plot({ width: 700, height: 440, marginLeft: 58, marginRight: 24, marginTop: 34, marginBottom: 50, style: { background: SURFACE, color: INK, fontFamily: "system-ui, -apple-system, 'Segoe UI', Roboto, sans-serif", fontSize: "12px" }, title: "Normalized learning change vs. Normalized performance change, West region", subtitle: "One regression on all the data, one with the off-trend managers removed.", x: { label: "Normalized learning change (%) →", grid: true }, y: { label: "↑ Normalized performance change (%)", grid: true }, color: { domain: [ALL, NO_OUT], range: [DARK, ACCENT], legend: true }, marks: [ // the threshold the whole figure is building towards Plot.ruleX([THRESHOLD], { stroke: MUTED, strokeWidth: 1.5, strokeDasharray: "3 4" }), Plot.text([`${THRESHOLD}% — TYG "meaningful change" threshold`], { x: THRESHOLD, frameAnchor: "top", dy: 6, dx: 6, textAnchor: "start", fill: MUTED, fontSize: 11, ...halo }), Plot.dot(kept, { x: "x", y: "y", r: 3.5, fill: MUTED, fillOpacity: 0.45 }), Plot.dot(dropped, { x: "x", y: "y", r: 5, fill: "none", stroke: MUTED, strokeWidth: 1.5, strokeDasharray: "2 2" }), Plot.line(fits.filter((d) => d.series === ALL), { x: "x", y: "yhat", stroke: "series", strokeWidth: 2.5 }), Plot.line(fits.filter((d) => d.series === NO_OUT), { x: "x", y: "yhat", stroke: "series", strokeWidth: 2.5, strokeDasharray: "7 5" }), Plot.ruleY([0], { stroke: INK, strokeOpacity: 0.3 }) ] }); })(); display(westFit); display( Object.assign(document.createElement("button"), { textContent: "Download PNG", onclick: () => downloadPNG(westFit, "west_regression") }) ); // ── two things to check against your own data ──────────────── // // 1. The hollow rings need an `outlier: true` field on the rows you // excluded. If xy has no such field, every point draws as a plain // grey dot and the chart still works — you just lose the "these // four" cue. Adding it is one line where you build line_no_out. // // 2. Check the x-range of line_no_out. If you fitted it on the // trimmed data but evaluated it across the FULL x-extent, the // dashed line runs out to where no data supports it — mine falls // to -35% at the left edge, which reads as a prediction rather // than a fit. Evaluate each fit only over the range it was fitted // on and both lines stop where their evidence stops. 57:T481,lch = Plot.plot({ color: { legend: true }, title: "Distribution of learning gain (matched data)", subtitle: "Statistical mean: "+roundMe([meanLearningGain5],2)+" (dotted line)", marks: [ Plot.rectY( pilotComp, Plot.binX({ y: "count",title: (elems) => `${elems.length} salesfolks`}, { x: "learn_gain",fill: "steelblue",tip: true ,thresholds:[-46,-36,-26,-16,-6,4,14,24,34,44,54,64,74,84]}) // Bars colored in steelblue ), Plot.text( pilotComp, Plot.binX({ y: "count",text: (d) => d.length}, {x: "learn_gain",thresholds:[-46,-36,-26,-16,-6,4,14,24,34,44,54,64,74,84],dy:-8}) ), Plot.ruleX(sales_clean,{ x: [meanLearningGain5], y:[maxLearnGain5+20],stroke: "black", strokeDasharray: "2,2", markerEnd: "dot" }), // Dotted line for mean Plot.text( { x: 80, y: 50, text: "Mean", fill:"black"}), // Label for the mean line Plot.ruleY[0] ], x: { label: "Learning Gain (%)", domain: [-46, 84], ticks: [-46, -36, -26, -16, -6, 4, 14, 24, 34, 44, 54, 64, 74, 84] }, y: { label: "Number of Salesfolks", domain: [0, [maxLearnGain5+20]] } }) 58:T402,const lo = Math.min(0, d3.min(gainMedians, (d) => d.median)) - 2; const hi = Math.max(0, d3.max(gainMedians, (d) => d.median)) + 2; const chart = Plot.plot({ height: 360, marginLeft: 65, marginBottom: 44, x: { domain: ["Pilot", "Control"], label: null, tickFormat: (g) => { const row = gainMedians.find((d) => d.group === g); return `${g} (n=${row.n})`; } }, y: { grid: true, domain: [lo, hi], label: "Average normalized performance change" }, color: {domain: ["Pilot", "Control"], range: ["#184f95", "#86b6ef"], legend: false}, marks: [ Plot.barY(gainMedians, {x: "group", y: "median", fill: "group"}), Plot.text(gainMedians.filter((d) => d.median >= 0), { x: "group", y: "median", text: (d) => "+" + d.median.toFixed(1), dy: -8, fontWeight: 600 }), Plot.text(gainMedians.filter((d) => d.median < 0), { x: "group", y: "median", text: (d) => d.median.toFixed(1), dy: 16, fontWeight: 600 }), Plot.ruleY([0]) ] }); display(chart);59:T430,const lo = Math.min(0, d3.min(gainMediansControl, (d) => d.mean)) - 2; const hi = Math.max(0, d3.max(gainMediansControl, (d) => d.mean)) + 2; const chartMeanControl = Plot.plot({ height: 360, marginLeft: 65, marginBottom: 44, x: { domain: ["Pilot", "Control"], label: null, tickFormat: (g) => { const row = gainMediansControl.find((d) => d.group === g); return `${g} (n=${row.n})`; } }, y: { grid: true, domain: [lo, hi], label: "Average normalized performance change" }, color: {domain: ["Pilot", "Control"], range: ["#184f95", "#86b6ef"], legend: false}, marks: [ Plot.barY(gainMediansControl, {x: "group", y: "mean", fill: "group"}), Plot.text(gainMediansControl.filter((d) => d.mean >= 0), { x: "group", y: "mean", text: (d) => "+" + d.mean.toFixed(1), dy: -8, fontWeight: 600 }), Plot.text(gainMediansControl.filter((d) => d.mean < 0), { x: "group", y: "mean", text: (d) => d.mean.toFixed(1), dy: 16, fontWeight: 600 }), Plot.ruleY([0]) ] }); display(chartMeanControl);5a:T1f32,// ───────────────────────────────────────────────────────────────────────────── // exportPNG — download a Plot figure as a PNG that INCLUDES its title, // subtitle and caption. // // Why the old exporter loses them: Plot's `title`, `subtitle` and `caption` // options are not drawn inside the SVG. Plot returns a

whose children // are

,

,

The answer is a range

${fmt.min(plan.typicalLo)}–${fmt.min(plan.typicalHi)} minutes

is what the middle ${fmt.pct(coverage)} of people take. To get ${fmt.pct(coverage)} of them finished, book ${fmt.min(plan.budget)} minutes.

Book the average instead (${fmt.min(meanMin)} min) and ${fmt.people(plan.unfinishedAtMean)} of ${fmt.people(cohort)} people are still working when you send them back to the floor.

`);62:T1059,const curveLo = Math.max(0, dist.quantile(0.0005)); const curveHi = dist.quantile(0.9995); const curveGrid = d3 .range(curveLo, curveHi, (curveHi - curveLo) / 600) .map((t) => ({ t, d: dist.pdf(t) })); const curveInside = curveGrid.filter( (g) => g.t >= plan.typicalLo && g.t <= plan.typicalHi ); const curvePeak = d3.max(curveGrid, (g) => g.d); // The frame always reaches +3 SD, so the empirical rule is visible end to end. const curveDomainMax = Math.max( dist.quantile(0.99), plan.budget * 1.12, meanMin + 3 * sdMin ); // ±1, ±2, ±3 SD posts. Each post stops at the curve rather than running the full // height of the frame, so its height shows how few people are out there. Short // posts get a floor of 8% so the far ones stay legible above the axis. Posts that // fall off either edge are dropped rather than drawn half-clipped. const sdPosts = [-3, -2, -1, 1, 2, 3] .map((k) => { const t = meanMin + k * sdMin; return { k, t, h: Math.max(dist.pdf(t), curvePeak * 0.08) }; }) .filter((p) => p.t > curveLo && p.t <= curveDomainMax); const swatch = (color, label) => html` ${label}`; const lineSwatch = (css, label) => html` ${label}`; display(html`
${swatch("#9ec5f4", `Middle ${fmt.pct(coverage)} of people`)} ${swatch("#e8e7e1", "Everyone else")} ${lineSwatch("2px dashed #0b0b0b", "Average")} ${lineSwatch("2px solid #d03b3b", "Book until")} ${lineSwatch("1px dashed #898781", "1, 2, 3 SD")}
`); const curveChart = Plot.plot({ width: Math.min(width, 860), height: 320, marginTop: 26, marginLeft: 24, marginRight: 24, marginBottom: 42, style: { background: "#fcfcfb", color: "#52514e", fontFamily: "system-ui, -apple-system, 'Segoe UI', sans-serif" }, x: { label: "Minutes to complete the training →", labelAnchor: "left", grid: false, // keep the frame readable when a long tail runs off to the right domain: [curveLo, curveDomainMax] }, y: { axis: null }, caption: ``, marks: [ Plot.areaY(curveGrid, { x: "t", y: "d", fill: "#e8e7e1" }), Plot.areaY(curveInside, { x: "t", y: "d", fill: "#9ec5f4" }), Plot.lineY(curveGrid, { x: "t", y: "d", stroke: "#1c5cab", strokeWidth: 2 }), Plot.ruleX(sdPosts, { x: "t", y1: 0, y2: "h", stroke: "#898781", strokeWidth: 1, strokeDasharray: "2 3" }), Plot.text(sdPosts, { x: "t", y: "h", text: (p) => `${p.k > 0 ? "+" : "−"}${Math.abs(p.k)} SD`, dy: -7, fill: "#52514e", fontSize: 10, stroke: "#fcfcfb", strokeWidth: 3 // halo, so labels stay legible over the fill }), Plot.ruleY([0], { stroke: "#c3c2b7" }), Plot.ruleX([meanMin], { stroke: "#0b0b0b", strokeWidth: 2, strokeDasharray: "5 4" }), Plot.ruleX([plan.budget], { stroke: "#d03b3b", strokeWidth: 2 }), Plot.text([{ t: meanMin, d: curvePeak }], { x: "t", y: "d", text: [`average ${fmt.min(meanMin)} min`], textAnchor: "end", dx: -8, dy: -12, fill: "#0b0b0b", fontSize: 12, fontWeight: 600 }), Plot.text([{ t: plan.budget, d: curvePeak * 0.55 }], { x: "t", y: "d", text: [`book ${fmt.min(plan.budget)} min`], textAnchor: "start", dx: 6, fill: "#d03b3b", fontSize: 12, fontWeight: 600 }), Plot.ruleX(curveGrid, Plot.pointerX({ x: "t", stroke: "#898781", strokeWidth: 1 })), Plot.tip(curveGrid, Plot.pointerX({ x: "t", y: "d", title: (g) => `${fmt.min1(g.t)} minutes\n${fmt.pct1(dist.cdf(g.t))} of people are done by here` })) ] }); display(curveChart);63:T7fd,const draws = (() => { const rng = d3.randomLcg(20260910); const times = d3 .range(cohort) .map(() => dist.quantile(Math.min(0.99999, Math.max(0.00001, rng())))); const binWidth = Math.max(0.5, (d3.max(times) - d3.min(times)) / 60); const counts = new Map(); return times .slice() .sort(d3.ascending) .map((t) => { const key = Math.floor(t / binWidth); const row = (counts.get(key) ?? 0) + 1; counts.set(key, row); return { t, row, status: t <= plan.budget ? "Finished inside the booked window" : "Still going" }; }); })(); const stillGoing = draws.filter((d) => d.status === "Still going").length; const cohortChart = Plot.plot({ width: Math.min(width, 860), height: 300, marginLeft: 24, marginRight: 24, marginBottom: 42, style: { background: "#fcfcfb", color: "#52514e", fontFamily: "system-ui, -apple-system, 'Segoe UI', sans-serif" }, x: { label: "Minutes to complete the training →", labelAnchor: "left" }, y: { axis: null }, color: { domain: ["Finished inside the booked window", "Still going"], range: ["#2a78d6", "#eb6834"], legend: true }, caption: `One dot is one person, drawn from the same distribution. In this cohort ${fmt.people(stillGoing)} of ${fmt.people(cohort)} run past the ${fmt.min(plan.budget)}-minute window. The math says to expect ${fmt.people(plan.unfinishedAtBudget)}, the ${fmt.pct(1 - coverage)} you chose to accept, and any one cohort lands near that number rather than on it.`, marks: [ Plot.dot(draws, { x: "t", y: "row", fill: "status", r: 3.5, stroke: "#fcfcfb", strokeWidth: 0.75, tip: true, title: (d) => `${fmt.min1(d.t)} minutes\n${d.status}` }), Plot.ruleX([plan.budget], { stroke: "#d03b3b", strokeWidth: 2 }), Plot.ruleX([meanMin], { stroke: "#0b0b0b", strokeWidth: 2, strokeDasharray: "5 4" }), Plot.ruleY([0], { stroke: "#c3c2b7" }) ] }); display(cohortChart);64:T58d,const tile = (kicker, value, unit, note, accent) => html`

${kicker}

${value} ${unit}

${note}

`; display(html`
${tile("Book the average", fmt.people(plan.unfinishedAtMean), "people cut off", html`${fmt.hours(plan.overrunHoursAtMean)} agent-hours of unfinished work walks back onto the floor.`, "#eb6834")} ${tile(`Book to ${fmt.pct(coverage)}`, fmt.min(plan.budget), "minutes", html`${fmt.people(plan.unfinishedAtBudget)} people still run over. That is the tail you chose to accept.`, "#2a78d6")} ${tile("Cost of that safety", fmt.hours(plan.idleHoursAtBudget), "agent-hours idle", html`Time between finishing and the window closing, if the window is a fixed block. Let people return as they finish and most of this comes back.`, "#898781")}
`);65:T834,## What to say in the room The average answers a question nobody asked. On a symmetric distribution, booking the mean fails half your cohort by construction. Not because the estimate was wrong. Because a midpoint is not a deadline. Standard deviation is the width of "normal". One SD either side of the mean covers about 68% of people, two SD about 95%, three SD about 99.7%, on a normal shape. That is the rule of thumb that turns a single number into a planning window. Two different questions, two different answers. The typical person takes ${fmt.min(plan.typicalLo)}–${fmt.min(plan.typicalHi)} minutes, a centered range. The booking is ${fmt.min(plan.budget)} minutes, a one-sided cutoff. Confusing the two is how a range gets quoted and a schedule still blows up. Coverage is a business decision, not a statistical one. Going from 90% to 99% buys down the tail and pays for it in idle seat time. The distribution tells you the price. It does not tell you which price to pay. Check the shape before you trust the rule. Flip the shape control. The mean and the SD do not move, the same two summary numbers, because real completion times have a floor at zero and a long tail of people who got interrupted. Around 80–90% coverage the two shapes give nearly the same booking, which is why the normal assumption survives most of the time. Set coverage to 99% and watch them split: at a 45-minute mean and a 12-minute SD, normal says 73 minutes and skewed says 80. The rule of thumb is safe in the middle and wrong in the tail, and the tail is where escalations live. On skewed data the tidy percentages stop holding. More than half the cohort finishes before the mean, and the 1/2/3 SD windows no longer land on 68/95/99.7. The table above shows the live numbers. When that happens, take the planning number from the percentile, not the rule of thumb. The one-line version. "Average 45 minutes" is a fact about the data. "Book ${fmt.min(plan.budget)} minutes and expect ${fmt.people(plan.unfinishedAtBudget)} stragglers" is a fact about your Tuesday. <<< /66:T461e,// ───────────────────────────────────────────────────────────────────────────── // pilot-story-numbers — every figure the data story and the money passages use, // computed from salesfinal_dataset instead of from a simulated stand-in. // // One JavaScript cell for Observable Notebooks 2.0. It outputs: // 1. the headline figures (counts, medians, coins) // 2. the region table // 3. the manager-completion table // 4. the scatter's fit and correlation // 5. a JSON block you can paste back so the dashboard figure uses real rows // // It deliberately does NOT use num(): that helper turns blanks into zeros, and // the 21 control reps with an undefined normalized change would quietly become // 21 zeros. Everything here parses the raw value and keeps blanks as blanks. // ───────────────────────────────────────────────────────────────────────────── // ── pricing, from Sales ────────────────────────────────────────────────────── const COINS_PER_POINT_GAINED = 20_000; const COINS_PER_POINT_LOST = 40_000; // a point lost goes to a rival // TWO thresholds, both 20, and they are not the same thing: // LEARN_THRESHOLD is TYG's documented standard ("learning is meaningful at // 20% normalized learning change"), quoted in Ch 3, Ch 11 and Appendix B. // PERF_THRESHOLD is the cut the data story uses to define "the twenty-five". // The book does not currently document a performance standard at all. // Keep them separate in the output so nobody borrows one's authority for the // other. const LEARN_THRESHOLD = 20; const PERF_THRESHOLD = 20; const PROFICIENCY_LINE = 70; // post-pilot performance const CRITICAL_LINE = 50; // bonus eligibility // ── column resolution ──────────────────────────────────────────────────────── // The dataset has carried several names for these. Resolve once, loudly. const pick = (...names) => { const row = salesfinal_dataset[0] ?? {}; const found = names.find((n) => n in row); if (!found) throw new Error(`None of these columns exist: ${names.join(", ")}`); return found; }; const COL = { normPerf: pick("normalized_performance_change", "adj_performance_gain", "performance_gain"), normLearn: pick("normalized_learning_change", "adj_learning_gain", "learn_gain"), perfPre: pick("performance_before_training", "performance_before"), perfPost: pick("performance"), region: pick("region"), completed: pick("complete_training"), mgrCompleted: pick("manager_completed_training", "manager_comp"), mgrId: pick("managerid", "manager_id") }; // ── parsing that keeps a blank a blank ─────────────────────────────────────── const val = (d, key) => { const v = d?.[key]; if (v === null || v === undefined || v === "") return null; const n = +v; return Number.isFinite(n) ? n : null; }; const truthy = (v) => v === 1 || v === "1" || v === true || v === "TRUE" || v === "True" || v === "Yes" || v === "yes" || v === "Completed"; // Region values have to be read through one accessor, not compared against a // hardcoded literal. "West ", "west" and "WEST" are all different strings to // ===, which is how a headline figure can say 0 while the table says 21. // The region column stores CODES (W, E, N, S), not names. regionOf maps them to // the labels the book and the figure use, and also accepts full names in case a // later export changes its mind. The audit table below still shows the RAW // values, so the mess stays visible even though the counts stop depending on it. const REGION_LABELS = { w: "West", e: "East", n: "North", s: "South", west: "West", east: "East", north: "North", south: "South" }; const regionOf = (d) => { const raw = String(d?.[COL.region] ?? "").trim().replace(/\s+/g, " ").toLowerCase(); if (!raw) return "(blank)"; return REGION_LABELS[raw] ?? raw[0].toUpperCase() + raw.slice(1); }; const isWest = (d) => regionOf(d) === "West"; const isPilot = (d) => { const c = d[COL.completed]; return c === 1 || c === "1" || c === "Completed" || Number(c) === 1; }; // ── the two groups ─────────────────────────────────────────────────────────── const pilot = salesfinal_dataset.filter(isPilot); const control = salesfinal_dataset.filter((d) => !isPilot(d)); // a rep enters a calculation only if the measure is defined for them const withPerf = (rows) => rows.filter((d) => val(d, COL.normPerf) !== null); const undefinedFor = (rows) => rows.filter((d) => { if (val(d, COL.normPerf) !== null) return false; const pre = val(d, COL.perfPre), post = val(d, COL.perfPost); return pre !== null && pre === post && (pre === 100 || pre === 0); }).length; const pilotP = withPerf(pilot); const controlP = withPerf(control); // ── money ──────────────────────────────────────────────────────────────────── const coins = (v) => v (v >= 0 ? COINS_PER_POINT_GAINED : COINS_PER_POINT_LOST); const coinsOf = (rows) => d3.sum(rows, (d) => coins(val(d, COL.normPerf))); const gainers = pilotP.filter((d) => val(d, COL.normPerf) >= PERF_THRESHOLD); const others = pilotP.filter((d) => val(d, COL.normPerf) < PERF_THRESHOLD); const mgrYes = pilotP.filter((d) => truthy(d[COL.mgrCompleted])); const mgrNo = pilotP.filter((d) => !truthy(d[COL.mgrCompleted])); // ── correlation and least-squares fit, manager-completed group ─────────────── const fitOn = (rows) => { const pts = rows .map((d) => [val(d, COL.normLearn), val(d, COL.normPerf)]) .filter(([x, y]) => x !== null && y !== null); const mx = d3.mean(pts, (p) => p[0]), my = d3.mean(pts, (p) => p[1]); const sxy = d3.sum(pts.map((p) => (p[0] - mx) * (p[1] - my))); const sxx = d3.sum(pts.map((p) => (p[0] - mx) 2)); const syy = d3.sum(pts.map((p) => (p[1] - my) 2)); const slope = sxy / sxx; return { n: pts.length, r: sxy / Math.sqrt(sxx * syy), slope, intercept: my - slope * mx }; }; const fitMgr = fitOn(mgrYes); const fitAll = fitOn(pilotP); // ── headline figures ───────────────────────────────────────────────────────── const f0 = d3.format(",.0f"); const f1 = d3.format("+.1f"); const money = (v) => `${v < 0 ? "−" : "+"}${f0(Math.abs(v))}`; const moneyM = (v) => `${v < 0 ? "−" : "+"}${(Math.abs(v) / 1e6).toFixed(1)}M`; const headline = { "Assessed": salesfinal_dataset.length, "Pilot group": pilot.length, "Control group": control.length, "Pilot rows with a defined normalized change": pilotP.length, "Control rows with a defined normalized change": controlP.length, "Control rows undefined at the ceiling/floor": undefinedFor(control), "Pilot rows undefined at the ceiling/floor": undefinedFor(pilot), [`Reps at ${PERF_THRESHOLD}%+ normalized PERFORMANCE change`]: gainers.length, "... of them in West": gainers.filter(isWest).length, "... whose manager completed the pilot": gainers.filter((d) => truthy(d[COL.mgrCompleted])).length, "... by region": d3 .rollups(gainers, (v) => v.length, regionOf) .sort((a, b) => d3.descending(a[1], b[1])) .map(([r, n]) => `${r} ${n}`) .join(" · "), [`Reps at ${LEARN_THRESHOLD}%+ normalized LEARNING change (TYG's documented standard)`]: pilotP.filter((d) => val(d, COL.normLearn) >= LEARN_THRESHOLD).length, "Reps clearing BOTH thresholds": pilotP.filter( (d) => val(d, COL.normLearn) >= LEARN_THRESHOLD && val(d, COL.normPerf) >= PERF_THRESHOLD ).length, "Cleared learning but NOT performance": pilotP.filter( (d) => val(d, COL.normLearn) >= LEARN_THRESHOLD && val(d, COL.normPerf) < PERF_THRESHOLD ).length, [`... above ${PROFICIENCY_LINE} post-pilot`]: gainers.filter((d) => val(d, COL.perfPost) > PROFICIENCY_LINE).length, [`Pilot reps above ${CRITICAL_LINE} post-pilot (all 115)`]: pilotP.filter((d) => val(d, COL.perfPost) > CRITICAL_LINE).length, "Pilot mean normalized change": d3.mean(pilotP, (d) => val(d, COL.normPerf)), "Pilot median normalized change": d3.median(pilotP, (d) => val(d, COL.normPerf)), "Control mean normalized change": d3.mean(controlP, (d) => val(d, COL.normPerf)), "Control median normalized change": d3.median(controlP, (d) => val(d, COL.normPerf)), "Median, manager completed": d3.median(mgrYes, (d) => val(d, COL.normPerf)), "Median, manager did not": d3.median(mgrNo, (d) => val(d, COL.normPerf)), "Coins, the gainers": coinsOf(gainers), "Coins, everyone else in the pilot": coinsOf(others), "Coins, pilot net (the 115 completers, this is the story's number)": coinsOf(pilotP), // NOT in the data story figure. Here so the contrast is available if you want // it, and so nobody reconstructs it by hand and gets a different answer. "Coins, control group (not in the story)": coinsOf(controlP), "Coins per rep, pilot": coinsOf(pilotP) / pilotP.length, "Coins per rep, control": coinsOf(controlP) / controlP.length, "Coins per rep, manager completed": coinsOf(mgrYes) / mgrYes.length, "Coins per rep, manager did not": coinsOf(mgrNo) / mgrNo.length, "Coins gap per rep": coinsOf(mgrYes) / mgrYes.length - coinsOf(mgrNo) / mgrNo.length, "r, manager-completed group": fitMgr.r, "r, all pilot reps": fitAll.r }; display( Inputs.table( Object.entries(headline).map(([Figure, Value]) => ({ Figure, Value: typeof Value === "number" && Math.abs(Value) > 10000 ? f0(Value) : typeof Value === "number" ? +Value.toFixed(2) : Value })), { select: false, width: Math.min(width, 760), layout: "auto" } ) ); // ── what the region column actually contains ───────────────────────────────── // If any row here shows quotes with a space inside, or two rows that look the // same, that is your bug and it will silently break any hardcoded comparison. display( Inputs.table( d3 .rollups(salesfinal_dataset, (v) => v.length, (d) => d[COL.region]) .map(([raw, rows]) => ({ "Stored exactly as": JSON.stringify(raw), Rows: rows, "Trimmed": regionOf({ [COL.region]: raw }), "Clean?": JSON.stringify(raw) === JSON.stringify(regionOf({ [COL.region]: raw })) ? "yes" : "NO" })), { select: false, width: Math.min(width, 560) } ) ); // ── managers, counted once each ────────────────────────────────────────────── // A manager can have reps in more than one region. Counting distinct ids INSIDE // each region and then reading down the column adds that manager once per region // they touch — which is how a true count of 5 completed managers was reported as // 8. Managers are therefore counted globally here, and each one is assigned to a // single home region (the region most of their reps sit in) so the per-region // column still sums to the global total. const mgrIndex = d3.rollup( salesfinal_dataset.filter((d) => d[COL.mgrId] !== null && d[COL.mgrId] !== undefined && d[COL.mgrId] !== ""), (rows) => { const byRegion = d3.rollups(rows, (v) => v.length, regionOf).sort( (a, b) => d3.descending(a[1], b[1]) || d3.ascending(a[0], b[0]) ); return { id: rows[0][COL.mgrId], home: byRegion[0][0], regions: byRegion.map(([r, n]) => `${r} ${n}`).join(" · "), spans: byRegion.length, reps: rows.length, pilotReps: rows.filter(isPilot).length, completed: rows.some((d) => truthy(d[COL.mgrCompleted])) }; }, (d) => d[COL.mgrId] ); const managers = [...mgrIndex.values()]; const managersWithPilot = managers.filter((m) => m.pilotReps > 0); const managersDone = managers.filter((m) => m.completed); // the ids themselves, so this can be checked against the spreadsheet by eye display( html`
${managersDone.length} distinct managers completed the training: `${managersDone.map((m) => m.id).sort((a, b) => d3.ascending(+a || a, +b || b)).join(", ")}` — out of ${managers.length} managers in the dataset (${managersWithPilot.length} have at least one pilot rep). ${managers.filter((m) => m.spans > 1).length} manager(s) have reps in more than one region.
` ); display( Inputs.table( managersDone.map((m) => ({ "Manager": m.id, "Home region": m.home, "Reps by region": m.regions, "Reps (all)": m.reps, "Pilot reps": m.pilotReps })), { select: false, width: Math.min(width, 700) } ) ); // ── region table ───────────────────────────────────────────────────────────── const regions = [...new Set(salesfinal_dataset.map(regionOf))].sort(); const regionTable = regions.map((region) => { const inRegion = pilotP.filter((d) => regionOf(d) === region); const g = inRegion.filter((d) => val(d, COL.normPerf) >= PERF_THRESHOLD); return { Region: region, "Pilot reps": inRegion.length, [`At ${PERF_THRESHOLD}%+ performance`]: g.length, Share: inRegion.length ? d3.format(".0%")(g.length / inRegion.length) : "—", // counted by home region, so these three columns sum to the global totals "Managers (home region)": managers.filter((m) => m.home === region).length, "... with a pilot rep": managersWithPilot.filter((m) => m.home === region).length, "... who completed": managersDone.filter((m) => m.home === region).length, // this one is a count of REPS, not managers, so it is safe to read per region "Reps whose manager completed": inRegion.filter((d) => truthy(d[COL.mgrCompleted])).length, "Coins": f0(coinsOf(inRegion)) }; }); display(Inputs.table(regionTable, { select: false, width: Math.min(width, 860) })); // ── sentences you can paste into the manuscript ────────────────────────────── const sentences = [ `${gainers.length} reps closed ${PERF_THRESHOLD}% or more of the PERFORMANCE gap they had left. ` + `${headline["... of them in West"]} of them work in West.`, `Priced at ${f0(COINS_PER_POINT_GAINED)} coins a point gained and ${f0(COINS_PER_POINT_LOST)} a point lost, ` + `the ${gainers.length} generated ${moneyM(coinsOf(gainers))} coins, the other ${others.length} ` + `${coinsOf(others) < 0 ? "lost" : "added"} ${moneyM(Math.abs(coinsOf(others)))}, and the pilot ` + `${coinsOf(pilotP) < 0 ? "nets a loss of" : "nets"} ${moneyM(Math.abs(coinsOf(pilotP)))}.`, `Reps whose manager completed the pilot averaged ${money(coinsOf(mgrYes) / mgrYes.length)} coins; ` + `reps whose manager did not averaged ${money(coinsOf(mgrNo) / mgrNo.length)}. ` + `The gap is ${money(coinsOf(mgrYes) / mgrYes.length - coinsOf(mgrNo) / mgrNo.length)} coins a rep.`, `${managersDone.length} of the ${managers.length} managers completed the training themselves. ` + `Their reps account for ${pilotP.filter((d) => truthy(d[COL.mgrCompleted])).length} of the ${pilotP.length} pilot completers.`, `The control group's normalized performance change is ${f1(d3.mean(controlP, (d) => val(d, COL.normPerf)))}% ` + `over ${controlP.length} reps, not ${control.length}: ${undefinedFor(control)} sat at 100 before and after, ` + `so the measure is undefined for them.` ]; display( html`
${sentences.map((t) => html`
${t}
`)}
` ); // ── rows for the dashboard figure ──────────────────────────────────────────── // Paste this output back and the data story can be rebuilt on real rows rather // than a simulation. 115 small objects, no names or ids. const dashboardRows = pilotP.map((d) => ({ region: regionOf(d), learn: +val(d, COL.normLearn)?.toFixed(1), perf: +val(d, COL.normPerf).toFixed(1), post: +val(d, COL.perfPost).toFixed(1), mgr: truthy(d[COL.mgrCompleted]) })); const payload = JSON.stringify( { columnsUsed: COL, rates: { COINS_PER_POINT_GAINED, COINS_PER_POINT_LOST }, headline, managerSummary: { total: managers.length, withPilotRep: managersWithPilot.length, completed: managersDone.length, completedIds: managersDone.map((m) => m.id), spanningMoreThanOneRegion: managers.filter((m) => m.spans > 1).length }, regionTable, fitMgr, rows: dashboardRows }, null, 0 ); display( html`

JSON for rebuilding the data story figure (${dashboardRows.length} rows) — click, then copy
${payload}
` ); 67:T1fcf,// ───────────────────────────────────────────────────────────────────────────── // downloadPNG — download a Plot figure as a PNG that INCLUDES its title, // subtitle and caption. // // Why the old exporter loses them: Plot's `title`, `subtitle` and `caption` // options are not drawn inside the SVG. Plot returns a
whose children // are
,
,
,
, ${r.why(s)}` : ""} ` )} ` ); const excluded = stats.map((s) => s.missing).reduce((a, b) => a + b, 0); return html`

Normalized performance change, pilot group and control group

One quarter before the pilot and one quarter after. Every number in this table is calculated on the reps the measure is defined for, not on everyone in the group.
${stats.map((s) => html``)} ${body}

${s.name}
${s.note}

The histograms show the shape of each group. This table shows the part a chart cannot: the size of each group, and the ${n0(excluded)} rows that are in the data but not in any of the calculations. A rep who scored 100 before the pilot and 100 after it did not fail to improve. There was nothing left to improve, so the measure has no answer for them and they are set aside. If they were quietly counted as zero, the control group's center would move and nothing on the chart would look any different.

`; })(); display(comparisonTable); 6a:T71e,function gauge({value, target, width = 210}) { const h = width * 0.6, r = width * 0.42, inner = r * 0.66; const angle = d3.scaleLinear([0, 100], [-Math.PI / 2, Math.PI / 2]); const arc = d3.arc().innerRadius(inner).outerRadius(r); const svg = d3.create("svg").attr("viewBox", [0, 0, width, h]).attr("width", width) .style("font-family", "system-ui, sans-serif"); const g = svg.append("g").attr("transform", `translate(${width / 2},${h - 22})`); g.append("path").attr("d", arc({startAngle: angle(0), endAngle: angle(100)})).attr("fill", "#ececec"); g.append("path").attr("d", arc({startAngle: angle(0), endAngle: angle(value)})) .attr("fill", value >= target ? "#555" : "#a6a6a6"); // target tick (white halo + dark line so it shows on both fills) const t = angle(target), pt = rad => [Math.sin(t) * rad, -Math.cos(t) * rad]; const [x1, y1] = pt(inner - 3), [x2, y2] = pt(r + 3); g.append("line").attr("x1", x1).attr("y1", y1).attr("x2", x2).attr("y2", y2).attr("stroke", "white").attr("stroke-width", 5); g.append("line").attr("x1", x1).attr("y1", y1).attr("x2", x2).attr("y2", y2).attr("stroke", "#222").attr("stroke-width", 1.5); const [tx, ty] = pt(r + 14); g.append("text").attr("x", tx).attr("y", ty).attr("font-size", 11).attr("font-weight", 600) .attr("text-anchor", "start").attr("dominant-baseline", "middle").text(`${target}%`); g.append("text").attr("y", -6).attr("text-anchor", "middle").attr("font-size", r * 0.45) .attr("font-weight", 700).attr("fill", "#444").text(`${Math.round(value)}%`); g.append("text").attr("x", -r).attr("y", 16).attr("font-size", 10).attr("font-weight", 600).text("0%"); g.append("text").attr("x", r).attr("y", 16).attr("font-size", 10).attr("font-weight", 600) .attr("text-anchor", "end").text("100%"); return svg.node(); }6b:T978,function surveyBars(data, scores, {width = 760} = {}) { const xMax = Math.ceil(d3.max(data, d => d.pct) / 10) * 10; const shade = d => { const n = scores.filter(s => s.desired.includes(d.option)).length; return n === scores.length ? "#555" : n > 0 ? "#a6a6a6" : "#cfcfcf"; }; return Plot.plot({ width, height: 34 * data.length + 40, marginLeft: 360, marginRight: 60, style: {fontFamily: "system-ui, sans-serif", fontSize: "12px"}, x: {domain: [0, xMax], label: null, tickFormat: d => d + "%"}, y: {domain: data.map(d => d.label), label: null, tickSize: 0, tickFormat: d => d.length > 60 ? d.slice(0, 59) + "…" : d}, marks: [ Plot.gridX({strokeDasharray: "2,3", strokeOpacity: 0.6}), Plot.barX(data, {x: "pct", y: "label", fill: shade, insetTop: 5, insetBottom: 5}), Plot.text(data, {x: "pct", y: "label", text: d => d.pct.toFixed(2) + "%", textAnchor: "start", dx: 6, fontWeight: 600}), Plot.ruleX([0]) ] }); } function surveyPanel({question, note, data, scores, total, width = 1100}) { const scored = scores.map(s => ({ ...s, value: d3.sum(data.filter(d => s.desired.includes(d.option)), d => d.pct) })); const card = (head, body) => html`

${head}

${body}
`; return html`

${card("Total Responses", html`
${total.toLocaleString("en-US")}
`)}
${scored.map(s => card(html`${s.name}
Health: ${s.value >= s.target ? "Good" : "Caution"}`, gauge(s)))}

${question}

${note}

${surveyBars(data, scores)}

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Beyond learning, what happened to sales performance?"},{"id":"e168","lane":"rooms","type":"room","label":"Shape of U by Edward Shearhands and Eda Snow","chapter":"ch9","chapter_title":"Exploratory Data Analysis (EDA) Part 2","position":40683,"pct":53.64,"under_the_hood":false,"room_type":"Conference Room"},{"id":"e169","lane":"lifecycle","type":"you_are_here","label":"Data Lifecycle - EDA Part 2","chapter":"ch9","chapter_title":"Exploratory Data Analysis (EDA) Part 2","position":40751,"pct":53.73,"under_the_hood":false},{"id":"e170","lane":"concepts","type":"concept","label":"What to do with Outliers?","chapter":"ch9","chapter_title":"Exploratory Data Analysis (EDA) Part 2","position":40758,"pct":53.74,"under_the_hood":false},{"id":"e171","lane":"pilot","type":"dial_challenge","label":"Outliers","chapter":"ch9","chapter_title":"Exploratory Data Analysis (EDA) Part 2","position":41132,"pct":54.23,"under_the_hood":false,"statement":"It was acceptable to exclude the 1-star satisfaction response because the open-text comment clearly indicated that the complaint was about the internet connection, not the pilot program. 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"},{"id":"e173","lane":"pilot","type":"investigation_step","label":"How You Measure Changes","chapter":"ch9","chapter_title":"Exploratory Data Analysis (EDA) Part 2","position":41927,"pct":55.28,"under_the_hood":false},{"id":"e174","lane":"pilot","type":"dial_challenge","label":"Measuring Change","chapter":"ch9","chapter_title":"Exploratory Data Analysis (EDA) Part 2","position":42023,"pct":55.41,"under_the_hood":false,"statement":"Measuring the raw difference between the pre- and post-assessment averages would have been less complicated than calculating the normalized learning change and would have produced the same insights. True, False, or Unknown? Write your answer and one sentence of reasoning before reading on. 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Many survey results look like reporting out question by question: \"Participants rated the program 4.2 out of 5 on overall satisfaction. 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"" : d.label.length > max ? d.label.slice(0, max - 1) + "…" : d.label}; }); return Plot.plot({ width: W, height: 50 + lanes.length * 130, marginLeft: ML, marginRight: MR, marginTop: 34, style: {fontFamily: "system-ui, sans-serif", fontSize: "11px"}, x: {domain: [x0, x1], label: "Position in the book (%) →"}, fy: {domain: lanes, label: null, tickFormat: (id) => laneLabel.get(id), padding: 0.06}, color: {domain: GROUPS, scheme: "tableau10"}, symbol: {domain: GROUPS, range: ["circle", "diamond", "square", "star", "triangle", "cross", "wye"], legend: true}, marks: [ Plot.rect(chapters.filter((d) => !d.odd), {x1: (d) => clamp(d.start_pct), x2: (d) => clamp(d.end_pct), fill: "#fafafa"}), Plot.rect(chapters.filter((d) => d.odd), {x1: (d) => clamp(d.start_pct), x2: (d) => clamp(d.end_pct), fill: "#efefef"}), Plot.text(chapters, { x: (d) => (clamp(d.start_pct) + clamp(d.end_pct)) / 2, fy: () => lanes[0], text: (d) => (d.kind === "chapter" ? "" : "App ") + d.number, frameAnchor: "top", dy: -16, fontWeight: 600 }), Plot.rect(hood, {x1: (d) => clamp(d.start_pct), x2: (d) => clamp(d.end_pct), fill: "#000", fillOpacity: 0.08, title: (d) => `Under the hood (${d.chapter})`}), Plot.rect(spanLabels, {x1: (d) => clamp(d.start_pct), x2: (d) => clamp(d.end_pct), fy: "lane", fill: "#9db4c0", fillOpacity: 0.28, stroke: "white", strokeWidth: 2, title: (d) => `${d.label}\n${chLabel(chMap.get(d.chapter))}`}), Plot.text(spanLabels, {x: (d) => clamp(d.start_pct), fy: "lane", text: "short", frameAnchor: "top", textAnchor: "start", dx: 4, dy: 6, fontSize: 10, fill: "#33444d"}), Plot.dot(events, Plot.dodgeY({ anchor: "bottom", padding: 1.5, x: "pct", fy: "lane", r: 4, symbol: "group", fill: "group", stroke: "white", strokeWidth: 0.6, title: tipText, tip: {lineWidth: 40, fontSize: 11} })) ] }); }6e:Tc1d,satisfactionScales = { const codeX = [0, 0.25, 0.5, 0.75, 1]; // what the file stores: equal steps const feltX = [0, 0.2, 0.48, 0.62, 1]; // illustrative "felt" positions, not measured const labels = ["Very\nDissatisfied", "Dissatisfied", "Neutral", "Satisfied", "Very\nSatisfied"]; const opts = d3.range(5).map((i) => ({code: i + 1, cx: codeX[i], fx: feltX[i], label: labels[i]})); // 5 small stars per option, k of them filled const stars = opts.flatMap((o) => d3.range(5).map((j) => ({ x: o.cx + (j - 2) * 0.032, filled: j < o.code }))); const Y = {stars: 3, label: 2.1, code: 1}; const gaps = [ {x1: feltX[2], x2: feltX[3], text: "small step?"}, {x1: feltX[3], x2: feltX[4], text: "big step?"} ]; return Plot.plot({ width: 820, height: 380, marginLeft: 10, marginRight: 20, marginTop: 20, marginBottom: 30, style: {fontFamily: "system-ui, sans-serif", fontSize: "12px"}, x: {domain: [-0.42, 1.08], axis: null}, y: {domain: [0.45, 3.4], axis: null}, marks: [ // row titles Plot.text([ {y: Y.stars, t: "What respondents\nsee"}, {y: Y.label, t: "What the label\nmeans"}, {y: Y.code, t: "What the file\nstores"} ], {x: -0.4, y: "y", text: "t", textAnchor: "start", fontWeight: 700, fontSize: 13, lineHeight: 1.15}), // connectors: stars -> labels -> codes Plot.link(opts, {x1: "cx", y1: Y.stars - 0.22, x2: "fx", y2: Y.label + 0.32, stroke: "#9a9a9a", strokeDasharray: "3,3"}), Plot.link(opts, {x1: "fx", y1: Y.label - 0.28, x2: "cx", y2: Y.code + 0.24, stroke: "#9a9a9a", strokeDasharray: "3,3"}), // row 1: stars Plot.dot(stars, {x: "x", y: Y.stars, symbol: "star", r: 7, fill: (d) => d.filled ? "#222" : "white", stroke: "#222", strokeWidth: 1}), // row 2: labels at felt positions Plot.dot(opts, {x: "fx", y: Y.label, r: 5, fill: "#222"}), Plot.text(opts, {x: "fx", y: Y.label, text: "label", dy: -24, fontWeight: 600, lineHeight: 1.1}), // gap brackets under the labels Plot.link(gaps, {x1: "x1", x2: "x2", y1: Y.label - 0.16, y2: Y.label - 0.16, stroke: "#222", strokeWidth: 1.2, markerStart: "tick", markerEnd: "tick"}), Plot.text(gaps, {x: (d) => (d.x1 + d.x2) / 2, y: Y.label - 0.16, text: "text", dy: 12, fontStyle: "italic", fill: "#444"}), // row 3: codes, equal spacing Plot.dot(opts, {x: "cx", y: Y.code, r: 15, fill: "white", stroke: "#222", strokeWidth: 1.5}), Plot.text(opts, {x: "cx", y: Y.code, text: "code", fontSize: 15, fontWeight: 700}), Plot.text(opts.slice(1), {x: (d) => d.cx - 0.125, y: Y.code, text: () => "+1", dy: 26, fill: "#666", fontSize: 11}), // footnote Plot.text([0], {x: -0.4, y: 0.5, textAnchor: "start", fill: "#666", fontSize: 10.5, text: () => "Illustration only: the label positions show the idea of uneven emotional distances, not measured values."}) ] }); }6f:[{"id":"dc33528c93b2cc612ddf587c06f1d428c59c2c592211ddaa9e367d91594a7f3534e0db8a4412185a97300c0a761988518a944f40c5b9ab5b196c1fa60920b909","href":"https://static.observableusercontent.com/files/dc33528c93b2cc612ddf587c06f1d428c59c2c592211ddaa9e367d91594a7f3534e0db8a4412185a97300c0a761988518a944f40c5b9ab5b196c1fa60920b909","name":"LTEM-Image-Version-13.webp","type":"image/webp","lastModified":1758377676109.782,"size":115672},{"id":"404b14477c31bc2e87cd422218b71590e781bff8e073e20267478e123154c49999dec9c199663eac9957f98925cc4604fa3ebac0740b190a6c1da37cb9566690","href":"https://static.observableusercontent.com/files/404b14477c31bc2e87cd422218b71590e781bff8e073e20267478e123154c49999dec9c199663eac9957f98925cc4604fa3ebac0740b190a6c1da37cb9566690","name":"book_timeline_v4_2.json","type":"application/json","lastModified":1791548330081,"size":131447},{"id":"0f3d07f1760ecf4ff9a5b23eb5d13534af63f5c0f61ce3edb62cdad945b29d5a1aef8c088f24d0e900845277c1a6e229c6b779bffe8e2ae3a1ebd984cf8a4a35","href":"https://static.observableusercontent.com/files/0f3d07f1760ecf4ff9a5b23eb5d13534af63f5c0f61ce3edb62cdad945b29d5a1aef8c088f24d0e900845277c1a6e229c6b779bffe8e2ae3a1ebd984cf8a4a35","name":"salesfinal_tyg.csv","type":"text/csv","lastModified":1791165001858,"size":23165}] 29:{"id":"1556310a0d99b0c9","slug":null,"title":"Data Literacy: On the Table! 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`","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":147,"value":"function roundMe(num,place){\n let digits = 10**place;\n return Math.round(numdigits)/digits;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":89,"value":"mutable suska = ({\"coins\":1000,\"cost\":0});","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":106,"value":"function trade(what,amount){\n suska[what]+=amount;\n return suska;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":142,"value":"function getSuska(what){\n return suska[what];\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":135,"value":"trade(\"cost\",15)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":648,"value":"uniqueWhile = (arr, fn) => {\n let out = [],\n i = -1,\n n = arr.length;\n \n while (++i < n){\n const d = fn ? fn(arr[i], i, arr) : arr[i];\n if (!out.includes(d)) out.push(d);\n }\n \n return out;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":0,"value":"# The start","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":6,"value":"salesfinal_dataset = await FileAttachment(\"salesfinal_dataset_perfgain@2.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"salesfinal_dataset","database":"$undefined"},{"id":405,"value":"mean = Math.round((d3.mean(salesfinal_dataset,d => d.pre_score)100))/100","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":998,"value":"maxP = d3.count(salesfinal_dataset,d => d.adj_performance_gain==90)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":979,"value":"
Performance Dashboard: Chart 1
","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":890,"value":"learnPerfComp = addTooltips(\n Plot.plot({\n title: \"Normalized learning change (%) vs. normalized performance change (%)\",\n subtitle: \"With manager completion (+) and without (o)\",\n grid: true,\n marks: [\n Plot.dot(pilotComp, {\n x: \"adj_learning_gain\",\n y: \"adj_performance_gain\",\n fill: \"manager_completed_training\",\n stroke: \"manager_completed_training\",\n symbol: \"manager_completed_training\",\n title: (d) =>\n `Normalized learning change: ${d.adj_learning_gain}%\\nNormalized performance change: ${d.adj_performance_gain}%`\n })\n ],\n x: { label: \"Normalized learning change (%)\" },\n y: { label: \"Normalized performance change (%)\" },\n symbol: {\n legend: true,\n label: \"Manager completion\",\n domain: [\"Manager completed\", \"Manager did not complete\"],\n range: [\"plus\", \"circle\"]\n }\n }),\n { fill: \"black\" }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1478,"value":"display(dlButton(learnPerfComp, \"learn_perf_comp_manager\"))","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1480,"value":"REGION = ({ W: \"West\", E: \"East\", N: \"North\", S: \"South\" })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1479,"value":"$49","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1481,"value":"display(dlButton(learnPerChangeCompManager, \"learn_perf_comp_managercompleted\"))","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1399,"value":"learnPerChange = addTooltips (\n Plot.plot({\n title: \"Normalized Learning Change (%) vs. Normalized Performance change (%)\",\n subtitle: \"Manager Completion Across regions\",\n symbol: {legend: true},\n grid: true,\n marks: [\n Plot.dot(pilotCompManager, {x: \"learn_gain\", fill: \"region\", stroke:\"region\",symbol:\"region\" ,y: \"performance_gain\",title: (elems) => `Learn: ${elems.learn_gain}%\\nPerform: ${elems.performance_gain}%` }),\n\n ]\n}),\n { fill: \"black\" })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1440,"value":"$4a","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":958,"value":"learnPerf1 = addTooltips(\n Plot.plot({\n title: \"Time spent in the pilot (h) vs. Normalized learning change (%)\",\n grid: true,\n marks: [\n Plot.dot(pilotComp, {\n x: \"Duration\",\n y: \"learn_gain\",\n fill: \"steelblue\",\n title: (d) => `Time in pilot: ${d.Duration} hour(s)\\nNormalized learning change: ${d.learn_gain}%`\n })\n ],\n x: { label: \"Time spent in the pilot (hours)\" },\n y: { label: \"Normalized learning change (%)\" }\n }),\n { fill: \"black\" }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1474,"value":"display(dlButton(learnPerf1, \"time_vs_learning_change\"))","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":544,"value":"Plot.plot({\n x: { label: \"Pre-assessment score\" },\n color: { legend: true},\n \n title: \"Pre-assessment score\",\n height: 300,\n subtitle: \"Test\",\n caption: \"Caption\",\n y: { label: \"Number of salesfolks\" },\n marks: [\n Plot.rectY(\n sales_clean,\n Plot.binX(\n { y: \"count\" },\n { x: \"pre_score\", fill: \"complete_training\", tip: true }\n )\n ),\n Plot.ruleY([0])\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":548,"value":"sales_clean = salesfinal_dataset.map(d => ({\n ...d,\n complete_training: d.complete_training === 1 ? \"Completed\" : \"Did not complete\"\n})).map(d => ({\n ...d,\n manager_completed_training: d.manager_completed_training === 1 ? \"Manager completed\" : \"Manager did not complete\"\n}));\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":168,"value":"post_assessment_score = Plot.plot({\n title: \"Post-assessment score\",\n height: 300,\ncolor: {\n legend: true\n },\n marks: [\n Plot.auto(salesfinal_dataset, {x: \"score\", color: \"region\"})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"post_assessment_score","database":"$undefined"},{"id":430,"value":"Plot.plot({\n color: { legend: true },\n height: 300,\n title: \"Post-assessment score\",\n marks: [\n Plot.rectY(\n salesfinal_dataset,\n Plot.binX({ y: \"count\" }, { x: \"score\", fill: \"region\", tip: true })\n ),\n Plot.ruleY([0])\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":317,"value":"import {addTooltips} from \"@mkfreeman/plot-tooltip\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":409,"value":"pilotComp = sales_clean.filter((element) => {\n return element.complete_training == \"Completed\"\n});","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1417,"value":"pilotControl = sales_clean.filter((element) => {\n return element.complete_training !== \"Completed\"\n});","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1461,"value":"pilotControlPerformanceChange = pilotControl.filter((d) =>\n !(+d.performance_before_training === 100 && +d.performance === 100)\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":466,"value":"pilotCompManager = salesfinal_dataset.filter((element) => {\n return (element.complete_training == 1) && (element.manager_completed_training == 1)\n});","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1262,"value":"pilotCompManagerWest = salesfinal_dataset.filter((element) => {\n return (element.complete_training == 1) && (element.manager_completed_training == 1) && (element.region =='W')\n});","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1464,"value":"pilotCompTopList = salesfinal_dataset.filter((element) => {\n return (element.complete_training == 1)\n});","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":365,"value":"meanPreScore = d3.mean(pilotComp, d => d.pre_score);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1339,"value":"medPreScore = d3.median(pilotComp, d => d.pre_score);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":437,"value":"meanPreScoreAll = d3.mean(salesfinal_dataset, d => d.pre_score);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1341,"value":"medPreScoreAll = d3.median(salesfinal_dataset, d => d.pre_score);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":417,"value":"meanPostScore1 = d3.mean(pilotComp, d => d.score);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1344,"value":"medPostScore1 = d3.median(pilotComp, d => d.score);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":598,"value":"meanPerformanceGain = d3.mean(salesfinal_dataset, d => d.adj_performance_gain);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1346,"value":"medPerformanceGain = d3.median(salesfinal_dataset, d => d.adj_performance_gain);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1041,"value":"meanPerformanceGainComp = d3.mean(pilotComp, d => d.adj_performance_gain);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1423,"value":"meanPerformanceGainControl = d3.mean(pilotControlPerformanceChange, d => d.adj_performance_gain);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1348,"value":"medPerformanceGainComp = d3.median(pilotComp, d => d.adj_performance_gain);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1424,"value":"medPerformanceGainControl = d3.median(pilotControlPerformanceChange, d => d.adj_performance_gain);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":608,"value":"totalHours = html`${comma(d3.sum(salesfinal_dataset,d=>d.Duration))}`;","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":616,"value":"comma = d3.format(\",\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":629,"value":"maxRows = d3.count(salesfinal_dataset,d=>d.salesid)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":589,"value":"
Performance Dashboard - Chart 1
","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":610,"value":"Average performance before the pilot: ${avgPreperformance}%
\nAverage performance after the pilot: ${avgPostperformance}%","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1021,"value":"avgPreperformance = roundMe(d3.mean(salesfinal_dataset,d=>d.performance_before_training),1) ","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1037,"value":"avgPreperformanceComp = roundMe(d3.mean(pilotComp,d=>d.performance_before_training),2) ","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1421,"value":"avgPreperformanceControl = roundMe(d3.mean(pilotControl,d=>d.performance_before_training),2)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1027,"value":"avgPostperformance = roundMe(d3.mean(salesfinal_dataset,d=>d.performance),2) ","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1039,"value":"avgPostperformanceComp = roundMe(d3.mean(pilotComp,d=>d.performance),2) ","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1418,"value":"avgPostperformanceControl = roundMe(d3.mean(pilotControl,d=>d.performance),2)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":633,"value":"maxPerformanceGain = {\n return d3.max(\n d3\n .bin()\n .value((d) => d.adj_performance_gain)\n .thresholds(13)(salesfinal_dataset),\n (d) => d.length || NaN \n)\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1044,"value":"maxPerformanceGainComp = {\n return d3.max(\n d3\n .bin()\n .value((d) => d.adj_performance_gain)\n .thresholds(13)(pilotComp),\n (d) => d.length || NaN \n)\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":440,"value":"$4b","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":593,"value":"
Performance Dashboard - Chart 2
","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1057,"value":"Average performance before the pilot: ${avgPreperformanceComp}%
\nAverage performance after the pilot: ${avgPostperformanceComp}%","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1033,"value":"$4c","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1473,"value":"display(downloadButton(\"Hist4\", hist4, \"perf_distribute_comp\"));","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1466,"value":"display(downloadButton(\"Hist4Control\", hist4Control, \"perf_distribute_control\"));","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1416,"value":"$4d","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1471,"value":"display(dlButton(hist4Control, \"performance_change_control\"))","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1062,"value":"
Performance Dashboard - Chart 3
","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1231,"value":"perf_long = salesfinal_dataset.flatMap(d => ([\n { group: d.complete_training, phase: \"Pre\", value: d.performance_before_training },\n { group: d.complete_training, phase: \"Post\", value: d.performance }\n]));\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1236,"value":"stats = d3.rollups(\n perf_long,\n v => ({\n mean: d3.mean(v, d => d.value),\n median: d3.median(v, d => d.value), // <— add this\n min: d3.min(v, d => d.value),\n max: d3.max(v, d => d.value)\n }),\n d => d.group,\n d => d.phase\n).flatMap(([group, entries]) =>\n entries.map(([phase, s]) => ({ group, phase, ...s }))\n);\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1247,"value":"// Build spans: each row has group, phase, mean, and the x extent\nmeanLines = stats.map(d => ({\n ...d,\n x1: d.phase, // start of the category\n x2: d.phase // end of the category (Plot.ruleY will span the band)\n}));\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1229,"value":"$4e","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1484,"value":"display(dlButton(perf_by_group, \"perf_comparison_pre_post\"))","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1489,"value":"display(dlButton(perf_pre_by_group, \"perf_comparison_pre_post\"))","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1486,"value":"prePerf = perf_long.filter(d => d.phase === \"Pre\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1487,"value":"\npreStats = d3.rollups(\n prePerf,\n v => ({\n min: d3.min(v, d => d.value),\n max: d3.max(v, d => d.value),\n median: d3.median(v, d => d.value),\n n: v.length\n }),\n d => d.group\n).map(([group, s]) => ({ group, ...s }))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1488,"value":"groupLabel = d => (d === 1 ? \"Pilot (selected)\" : \"Control (not selected)\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1485,"value":"$4f","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1406,"value":"$50","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":918,"value":"perf1 = Plot.plot({\n title: \"Pre-performance (%)\",\n subtitle: \"Did not complete pilot (-1) vs. completed pilot (1)\",\n marginLeft: 60,\n y: {\n grid: true,\n inset: 6\n },\n fx: {\n label: \"Completed the pilot? →\",\n labelAnchor: \"right\",\n tickFormat: (x) => x.toFixed(1)\n \n },\n marks: [\n Plot.ruleY([0]),\n Plot.boxY(salesfinal_dataset, {fx: \"complete_training\", fill: \"steelblue\", y: \"performance_before_training\"})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":943,"value":"perf2 = Plot.plot({\n title: \"Post-performance (%)\",\n subtitle: \"Did not complete pilot (-1) vs. completed pilot (1)\",\n marginLeft: 60,\n y: {\n grid: true,\n inset: 6\n },\n fx: {\n label: \"Completed the pilot? →\",\n labelAnchor: \"right\",\n tickFormat: (x) => x.toFixed(1)\n \n },\n marks: [\n Plot.ruleY([0]),\n Plot.boxY(salesfinal_dataset, {fx: \"complete_training\", fill: \"steelblue\", y: \"performance\"})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1213,"value":"\n \n \n \n
\n
\n ${perf1}\n
\n
\n ${perf2}\n
\n
\n \n","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1217,"value":"$${perf1}","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"box1","database":"$undefined"},{"id":1221,"value":"$${perf2}","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"box2","database":"$undefined"},{"id":1140,"value":" perfAvgAmount = Array.from(\n d3.rollup(\n pilotComp,\n v => ({ value: roundMe(d3.mean(v, d => +d.adj_performance_gain), 2), n: v.length }),\n d => d.region\n ),\n ([key, s]) => ({ key, ...s })\n );","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1491,"value":"perfAvgAmountForAll = Array.from(d3.rollup(\n pilot,\n v => roundMe(d3.mean(v, d => d.adj_performance_gain),2), // aggregate by the mean of amount\n d => d.region // group by name\n),([key, value]) => ({ key, value }));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1182,"value":"perfAvgAmount.forEach(d => {\n d.dy = d.value > 0 ? -1 : 1; \n});","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1382,"value":"$51","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":421,"value":"$52","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1475,"value":"display(dlButton(perfRegion, \"performance_change_region\"))","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":663,"value":"
Learning Dashboard - Chart 4
","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":668,"value":"learnGain = roundMe(d3.mean(salesfinal_dataset,d=>d.learn_gain),0)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":673,"value":"avgPre = roundMe(d3.mean(salesfinal_dataset,d=>d.pre_score),2)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":677,"value":"avgPost = roundMe(d3.mean(salesfinal_dataset,d=>d.score),2)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":679,"value":"learnGainDiff = roundMe(((learnGain-30)/30)100,2)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":687,"value":"scoreGainDiff = roundMe(((avgPost-85)/85)100,2)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":704,"value":"mybins = {// Assuming `sales_clean` is your dataset\nconst start = -46; // Starting point of the first bin\nconst end = 84; // End point (should be beyond the maximum value in your dataset)\nconst interval = 10; // Interval between bins\n\n// Create thresholds array starting from `start` to `end` with steps of `interval`\nconst thresholds = d3.range(start, end+10, interval);\nreturn thresholds\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":30,"value":"viewof select = Inputs.select([\"A\", \"B\"], {label: \"Select one\"})\n\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":739,"value":"maxLearnGain = {\n return d3.max(\n d3\n .bin()\n .value((d) => d.learn_gain)\n .thresholds([-46,-36,-26,-16,-6,4,14,24,34,44,54,64,74,84])(salesfinal_dataset),\n (d) => d.length || NaN \n)\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":746,"value":"meanLearningGain = d3.mean(salesfinal_dataset,d=>d.learn_gain)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":693,"value":"$53","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":780,"value":"[maxLearnGain+20]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":446,"value":"function customTickFormat(d) {\n // Format the tick labels to show bin ranges\n return `${d.toFixed(2)} - ${(d + 1.41).toFixed(2)}`;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":36,"value":"cost = { trade(\"cost\",10);\n if(select == \"A\")\n {\n var c= trade(\"cost\",20)\n }else{\n var c = trade(\"cost\",100)\n }\n return suska; \n }","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":158,"value":"filteredStars = salesfinal_dataset.filter((element) => {\n return element.feedback_star > 1\n});\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":57,"value":"Average post-assessment score: **${Math.round((d3.mean(salesfinal_dataset,d => d.score)100))/100}\n\nAverage post-assessment score target: 85. \n\nResult: ${Math.round(((Math.round((d3.mean(salesfinal_dataset,d => d.score)100))/100)/85.00)*10000)/100}%**
\nAverage 5-star rating: ${roundMe(d3.mean(salesfinal_dataset,d=>d.feedback_star),1)}
\nIgnoring an outlier: ${roundMe(d3.mean(filteredStars,d=>d.feedback_star),1)}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":122,"value":"Your current coins: ${getSuska(\"coins\")-getSuska(\"cost\")}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":475,"value":"Plot.plot({\n marks: [\n Plot.rectY(pilotComp, Plot.binX({y: \"count\"}, {x: \"feedback_star\"}, {fill:'blue'})),\n Plot.ruleY([0])\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":471,"value":"Plot.plot({\n marks: [\n Plot.boxY(salesfinal_dataset, {x: \"complete_training\", y: \"pre_score\", sort: {y: \"x\"}})\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":462,"value":"Plot.plot({\n marks: [\n Plot.linearRegressionY(pilotCompManager, {x: \"adj_learning_gain\", y: \"adj_performance_gain\"})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":820,"value":"Plot.plot({\n marks: [\n Plot.linearRegressionY(pilotCompManager, {x: \"adj_learning_gain\", y: \"adj_performance_gain\"})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1253,"value":"// 1) Clean x/y from your table\nxy = pilotCompManagerWest\n .map(d => ({ \n x: +d.adj_learning_gain, \n y: +d.adj_performance_gain \n }))\n .filter(d => Number.isFinite(d.x) && Number.isFinite(d.y));\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1318,"value":"// 1) Clean x/y from your table\nxy20plus = xy.filter(d => d.x >= 20); \n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1277,"value":"xyfiltered = {\n const residuals = xy20plus.map(d => {\n const yhat = reg.intercept + reg.slope * d.x;\n return {...d, resid: d.y - yhat};\n });\n\n const meanResid = d3.mean(residuals, d => d.resid);\n const sdResid = d3.deviation(residuals, d => d.resid);\n\n // keep points with standardized residuals < 2\n return residuals.filter(d => Math.abs((d.resid - meanResid) / sdResid) < 2);\n}\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1255,"value":"// 2) Ordinary Least Squares for y ~ a + b x (with errors)\nreg = {\n const n = xy.length;\n const meanX = d3.mean(xy, d => d.x);\n const meanY = d3.mean(xy, d => d.y);\n const Sxx = d3.sum(xy, d => (d.x - meanX) ** 2);\n const Sxy = d3.sum(xy, d => (d.x - meanX) * (d.y - meanY));\n\n const slope = Sxy / Sxx; // b\n const intercept = meanY - slope * meanX; // a\n\n // fitted values & residuals\n const yhat = xy.map(d => intercept + slope * d.x);\n const resid = xy.map((d, i) => d.y - yhat[i]);\n\n const SSE = d3.sum(resid, e => e * e);\n const SST = d3.sum(xy, d => (d.y - meanY) ** 2);\n const sigma2 = SSE / (n - 2); // residual variance\n const rmse = Math.sqrt(sigma2);\n\n // standard errors for coefficients\n const seSlope = Math.sqrt(sigma2 / Sxx);\n const seIntercept = Math.sqrt(sigma2 * (1/n + (meanX ** 2) / Sxx));\n\n const r2 = 1 - SSE / SST;\n\n return { n, slope, intercept, r2, rmse, seSlope, seIntercept };\n}\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1290,"value":"$54","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1265,"value":"// Try to get exact t* from jStat; otherwise fall back to ~1.96\ntstar = {\n const df = reg.n - 2;\n try {\n const mod = await import(\"https://cdn.skypack.dev/jstat@1.9.6\");\n return mod.studentt.inv(1 - 0.05/2, df);\n } catch (e) {\n console.warn(\"jStat unavailable; using normal approx 1.96\");\n return 1.96;\n }\n}\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1292,"value":"// Try to get exact t* from jStat; otherwise fall back to ~1.96\ntstarfiltered = {\n const df = regfiltered.n - 2;\n try {\n const mod = await import(\"https://cdn.skypack.dev/jstat@1.9.6\");\n return mod.studentt.inv(1 - 0.05/2, df);\n } catch (e) {\n console.warn(\"jStat unavailable; using normal approx 1.96\");\n return 1.96;\n }\n}\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1268,"value":"reg_ci = {\n const t = tstar;\n const ciSlope = [reg.slope - t * reg.seSlope, reg.slope + t * reg.seSlope];\n const ciIntercept = [reg.intercept - t * reg.seIntercept, reg.intercept + t * reg.seIntercept];\n\n return {\n tstar: t,\n slope: reg.slope,\n slope_ci: ciSlope,\n intercept: reg.intercept,\n intercept_ci: ciIntercept\n };\n}\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1294,"value":"reg_cifiltered = {\n const t = tstarfiltered;\n const ciSlope = [regfiltered.slope - t * regfiltered.seSlope, regfiltered.slope + t * regfiltered.seSlope];\n const ciIntercept = [regfiltered.intercept - t * regfiltered.seIntercept, regfiltered.intercept + t * regfiltered.seIntercept];\n\n return {\n tstar: t,\n slope: regfiltered.slope,\n slope_ci: ciSlope,\n intercept: regfiltered.intercept,\n intercept_ci: ciIntercept\n };\n}\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1270,"value":"html`
\n
ŷ = ${reg.intercept.toFixed(3)} + ${reg.slope.toFixed(3)}·x
\n
R² = ${reg.r2.toFixed(3)}, RMSE = ${reg.rmse.toFixed(3)}
\n
\n t* (df=${reg.n-2}) = ${reg_ci.tstar.toFixed(3)}\n
\n
Slope 95% CI: [${reg_ci.slope_ci[0].toFixed(3)}, ${reg_ci.slope_ci[1].toFixed(3)}]
\n
Intercept 95% CI: [${reg_ci.intercept_ci[0].toFixed(3)}, ${reg_ci.intercept_ci[1].toFixed(3)}]
\n
`\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1296,"value":"html`
\n
ŷ = ${regfiltered.intercept.toFixed(3)} + ${regfiltered.slope.toFixed(3)}·x
\n
R² = ${regfiltered.r2.toFixed(3)}, RMSE = ${regfiltered.rmse.toFixed(3)}
\n
\n t* (df=${regfiltered.n-2}) = ${reg_cifiltered.tstar.toFixed(3)}\n
\n
Slope No Outliers 95% CI: [${reg_cifiltered.slope_ci[0].toFixed(3)}, ${reg_cifiltered.slope_ci[1].toFixed(3)}]
\n
Intercept No Outliers 95% CI: [${reg_cifiltered.intercept_ci[0].toFixed(3)}, ${reg_cifiltered.intercept_ci[1].toFixed(3)}]
\n
`\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1272,"value":"// Build prediction line with 95% CI around it\npreds = {\n const xs = d3.range(d3.min(xy, d => d.x), d3.max(xy, d => d.x), 1); // grid of x\n const df = reg.n - 2;\n const t = tstar; // from your CI cell\n const Sxx = d3.sum(xy, d => (d.x - d3.mean(xy, d => d.x))**2);\n\n return xs.map(x => {\n const yhat = reg.intercept + reg.slope * x;\n\n // Standard error of the fit at x\n const seFit = reg.rmse * Math.sqrt(1/reg.n + ((x - d3.mean(xy, d => d.x))**2) / Sxx);\n\n return {\n x,\n yhat,\n ylo: yhat - t * seFit,\n yhi: yhat + t * seFit\n };\n });\n}\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1300,"value":"// Build prediction line with 95% CI around it\npredsfiltered = {\n const xs = d3.range(d3.min(xyfiltered, d => d.x), d3.max(xyfiltered, d => d.x), 1); // grid of x\n const df = regfiltered.n - 2;\n const t = tstarfiltered; // from your CI cell\n const Sxx = d3.sum(xyfiltered, d => (d.x - d3.mean(xyfiltered, d => d.x))**2);\n\n return xs.map(x => {\n const yhat = regfiltered.intercept + regfiltered.slope * x;\n\n // Standard error of the fit at x\n const seFit = regfiltered.rmse * Math.sqrt(1/regfiltered.n + ((x - d3.mean(xyfiltered, d => d.x))**2) / Sxx);\n\n return {\n x,\n yhat,\n ylo: yhat - t * seFit,\n yhi: yhat + t * seFit\n };\n });\n}\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1274,"value":"Plot.plot({\n title: \"Learning Gain vs Performance Gain (West Region)\",\n subtitle: \"Linear regression with 95% confidence interval band\",\n \n x: {label: \"Learning gain\"},\n y: {label: \"Performance gain\"},\n marks: [\n // raw points\n Plot.dot(xy, {x: \"x\", y: \"y\", r: 3, opacity: 0.7}),\n \n // confidence band\n Plot.areaY(preds, {\n x: \"x\",\n y1: \"ylo\",\n y2: \"yhi\",\n fill: \"steelblue\",\n fillOpacity: 0.2\n }),\n\n // regression line\n Plot.line(preds, {\n x: \"x\",\n y: \"yhat\",\n stroke: \"steelblue\",\n strokeWidth: 2\n })\n ]\n})\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1299,"value":"Plot.plot({\n title: \"Learning Gain vs Performance Gain (West Region) without Outliers\",\n subtitle: \"Linear regression with 95% confidence interval band\",\n \n x: {label: \"Learning gain\"},\n y: {label: \"Performance gain\"},\n marks: [\n // raw points\n Plot.dot(xyfiltered, {x: \"x\", y: \"y\", r: 3, opacity: 0.7}),\n \n // confidence band\n Plot.areaY(predsfiltered, {\n x: \"x\",\n y1: \"ylo\",\n y2: \"yhi\",\n fill: \"steelblue\",\n fillOpacity: 0.2\n }),\n\n // regression line\n Plot.line(predsfiltered, {\n x: \"x\",\n y: \"yhat\",\n stroke: \"steelblue\",\n strokeWidth: 2\n })\n ]\n})\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1393,"value":"scatter = Plot.plot({\n title: \"Relationship between the normalized learning change and the normalized performance change\",\n subtitle: \"\",\n\n x: {label: \"Normalized learning change\"},\n y: {label: \"Normalized performance change\"},\n marks: [\n // raw points\n Plot.dot(xyfiltered, {x: \"x\", y: \"y\", r: 3, opacity: 0.7}),\n\n\n\n\n ]\n})\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1472,"value":"display(dlButton(scatter, \"scatter_plot_example\"))","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1257,"value":"// 3) Pretty-print the formula & errors\nhtml`
\n
ŷ = ${reg.intercept.toFixed(3)} + ${reg.slope.toFixed(3)}·x
\n
R² = ${reg.r2.toFixed(3)}, RMSE = ${reg.rmse.toFixed(3)}
\n
SE(slope) = ${reg.seSlope.toFixed(3)}, SE(intercept) = ${reg.seIntercept.toFixed(3)}
\n
`\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1306,"value":"// Helper to build predicted line given regression coefficients\nfunction buildLine(data, reg) {\n const xs = d3.range(d3.min(data, d => d.x), d3.max(data, d => d.x), 1);\n return xs.map(x => ({x, yhat: reg.intercept + reg.slope * x}));\n}\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1309,"value":"line_all = buildLine(xy, reg);\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1312,"value":"line_no_out = buildLine(xyfiltered, regfiltered);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1314,"value":"reg1 = Plot.plot({\n title: \"Normalized learning change vs Normalized performance change (West region)\",\n subtitle: \"Regression with all data (solid) vs. >=20% normalized learning change TYG standard (dash)\",\n x: {label: \"Normalized learning change\"},\n y: {label: \"Normalized performance change\"},\n marks: [\n // All raw points (faint gray)\n Plot.dot(xy, {x: \"x\", y: \"y\", r: 3, fill: \"gray\", opacity: 0.5}),\n\n // Regression line: all data\n Plot.line(line_all, {\n x: \"x\",\n y: \"yhat\",\n stroke: \"steelblue\",\n strokeWidth: 2,\n label: \"All data\"\n }),\n\n // Regression line: no outliers\n Plot.line(line_no_out, {\n x: \"x\",\n y: \"yhat\",\n stroke: \"orange\",\n strokeWidth: 2,\n strokeDasharray: \"4,4\",\n label: \"No outliers\"\n })\n ],\n color: {legend: true}\n})\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1482,"value":"display(dlButton(reg1, \"west_regression\"))","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1324,"value":"$55","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1260,"value":"// 4) Chart with the same regression line (for visualization)\nPlot.plot({\n marks: [\n Plot.dot(xy, {x: \"x\", y: \"y\", r: 3, opacity: 0.7}),\n Plot.linearRegressionY(xy, {x: \"x\", y: \"y\", stroke: \"currentColor\"})\n ],\n x: {label: \"adj_learning_gain\"},\n y: {label: \"adj_performance_gain\"}\n})\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1441,"value":"$56","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":822,"value":"
Learning Dashboard - Chart 5
","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":823,"value":"learnGain5 = roundMe(d3.mean(pilotComp,d=>d.learn_gain),0)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":831,"value":"avgPre5 = roundMe(d3.mean(pilotComp,d=>d.pre_score),2)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":834,"value":"avgPost5 = roundMe(d3.mean(pilotComp,d=>d.score),2)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":837,"value":"learnGainDiff5 = roundMe(((learnGain5-30)/30)100,2)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":840,"value":"scoreGainDiff5 = roundMe(((avgPost5-85)/85)100,2)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":845,"value":"maxLearnGain5 = {\n return d3.max(\n d3\n .bin()\n .value((d) => d.learn_gain)\n .thresholds([-46,-36,-26,-16,-6,4,14,24,34,44,54,64,74,84])(pilotComp),\n (d) => d.length || NaN \n)\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":849,"value":"meanLearningGain5 = d3.mean(pilotComp,d=>d.learn_gain)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":852,"value":"$57","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":883,"value":"
","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1410,"value":"gainMeans = [1, -1].map((code) => {\n const rows = salesfinal_dataset.filter(\n (d) => Number(d.complete_training) === code &&\n Number.isFinite(num(d.adj_performance_gain))\n );\n return {\n group: code === 1 ? \"Pilot\" : \"Control\",\n mean: d3.mean(rows, (d) => num(d.adj_performance_gain)),\n n: rows.length\n };\n})","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1332,"value":"ltemImageVersion13 = {\n const img = await FileAttachment(\"LTEM-Image-Version-13.webp\").image();\n img.style.width = \"100%\"; // scales with cell/iframe width\n img.style.maxWidth = \"500px\"; // but never exceed 500px\n img.style.height = \"auto\";\n img.loading = \"lazy\";\n img.decoding = \"async\";\n return img;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":881,"value":"
SUPPORTING FUNCTIONS
","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1413,"value":"const num = (v) => {\n if (v === null || v === undefined || v === \"\") return NaN;\n return +String(v).replace(/[^0-9.\\-]/g, \"\");\n};","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1411,"value":"const gainMedians = [1, -1].map((code) => {\n const values =salesfinal_dataset\n .filter((d) => Number(d.complete_training) === code)\n // drop reps at 100% both before and after (no room to gain, undefined normalized change)\n .filter((d) => !(Number(d.performance_before_training) === 100 && Number(d.performance) === 100))\n .map((d) => d.adj_performance_gain)\n .filter((v) => v !== null && v !== undefined && v !== \"\" && Number.isFinite(+v))\n .map(Number);\n\n return {\n group: code === 1 ? \"Pilot\" : \"Control\",\n median: d3.median(values),\n mean: d3.mean(values),\n n: values.length\n };\n});","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1456,"value":"const gainMediansControl = [\"Completed\",\"Did not complete\"].map((code) => {\n const values = pilotControlPerformanceChange\n .filter((d) => d.complete_training === code && d.adj_performance_gain!=NaN)\n .map((d) => num(d.adj_performance_gain))\n\n\n return {\n group: code === \"Completed\" ? \"Pilot\" : \"Control\",\n median: d3.median(values),\n mean: d3.mean(values),\n n: values.length\n };\n});\ndisplay(gainMediansControl)","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1412,"value":"$58","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1455,"value":"const lo = Math.min(0, d3.min(gainMedians, (d) => d.mean)) - 2;\nconst hi = Math.max(0, d3.max(gainMedians, (d) => d.mean)) + 2;\n\nconst chartMean = Plot.plot({\n height: 360,\n marginLeft: 65,\n marginBottom: 44,\n x: {\n domain: [\"Pilot\", \"Control\"],\n label: null,\n tickFormat: (g) => {\n const row = gainMedians.find((d) => d.group === g);\n return `${g} (n=${row.n})`;\n }\n },\n y: {\n grid: true,\n domain: [lo, hi],\n label: \"Average normalized performance change\"\n },\n color: {domain: [\"Pilot\", \"Control\"], range: [\"#184f95\", \"#86b6ef\"], legend: false},\n marks: [\n Plot.barY(gainMedians, {x: \"group\", y: \"mean\", fill: \"group\"}),\n Plot.text(gainMedians.filter((d) => d.mean >= 0), {\n x: \"group\", y: \"mean\", text: (d) => \"+\" + d.mean.toFixed(1), dy: -8, fontWeight: 600\n }),\n Plot.text(gainMedians.filter((d) => d.mean < 0), {\n x: \"group\", y: \"mean\", text: (d) => d.mean.toFixed(1), dy: 16, fontWeight: 600\n }),\n Plot.ruleY([0])\n ]\n});\n\ndisplay(chartMean);","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1457,"value":"$59","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1453,"value":"display(Object.assign(document.createElement(\"button\"), {\n textContent: \"Download PNG\",\n onclick: () => downloadPNG(chart, \"pilot-vs-control\")\n}));\ndisplay(Object.assign(document.createElement(\"button\"), {\n textContent: \"Download Mean PNG\",\n onclick: () => downloadPNG(chartMean, \"pilot-vs-control-mean\")\n}));","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1414,"value":"$5a","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1415,"value":"display(Object.assign(document.createElement(\"button\"), {\n textContent: \"Download PNG\",\n onclick: () => downloadPNG(chart, \"pilot-vs-control\")\n}));","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1425,"value":"display(Object.assign(document.createElement(\"button\"), {\n textContent: \"Download PNG\",\n onclick: () => downloadPNG(hist4Control, \"hist4Control\")\n}));","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1426,"value":"$5b","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1427,"value":"display(Object.assign(document.createElement(\"button\"), {\n textContent: \"Download PNG\",\n onclick: () => downloadPNG(rainfallBimodal, \"rainfallBimodal\")\n}));","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1428,"value":"$5c","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1429,"value":"display(Object.assign(document.createElement(\"button\"), {\n textContent: \"Download PNG\",\n onclick: () => downloadPNG(crossingTimesNormal, \"crossingTimesNormal\")\n}));","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1430,"value":"$5d","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1431,"value":"display(Object.assign(document.createElement(\"button\"), {\n textContent: \"Download PNG\",\n onclick: () => downloadPNG(sameMeanDifferentSpread, \"sameMeanDifferentSpread\")\n}));","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1432,"value":"$5e","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1433,"value":"display(Object.assign(document.createElement(\"button\"), {\n textContent: \"Download PNG\",\n onclick: () => downloadPNG(composition, \"visual_composition\")\n}));","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1435,"value":"display(Object.assign(document.createElement(\"button\"), {\n textContent: \"Download PNG\",\n onclick: () => downloadPNG(relationship, \"visual_relationship\")\n}));","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1436,"value":"display(Object.assign(document.createElement(\"button\"), {\n textContent: \"Download PNG\",\n onclick: () => downloadPNG(distribution, \"visual_distribution\")\n}));","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1438,"value":"display(Object.assign(document.createElement(\"button\"), {\n textContent: \"Download PNG\",\n onclick: () => downloadPNG(time, \"visual_time\")\n}));","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1439,"value":"display(Object.assign(document.createElement(\"button\"), {\n textContent: \"Download PNG\",\n onclick: () => downloadPNG(comparison, \"visual_comparison\")\n}));","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1442,"value":"const meanMin = view(\n Inputs.range([15, 120], {\n value: 45,\n step: 1,\n label: \"Average completion time (min)\"\n })\n);\n\nconst sdMin = view(\n Inputs.range([2, 40], {\n value: 12,\n step: 0.5,\n label: \"Standard deviation (min)\"\n })\n);\n\nconst coverage = view(\n Inputs.select([0.5, 0.68, 0.8, 0.9, 0.95, 0.99], {\n value: 0.9,\n label: \"Plan to cover\",\n format: (p) => `${Math.round(p * 100)}% of people`\n })\n);\n\nconst cohort = view(\n Inputs.range([20, 1000], {\n value: 420,\n step: 10,\n label: \"People in the cohort\"\n })\n);\n\nconst shapeChoice = view(\n Inputs.radio(\n [\"Normal (textbook)\", \"Right-skewed (what training data usually looks like)\"],\n { value: \"Normal (textbook)\", label: \"Shape of the distribution\" }\n )\n);","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1443,"value":"$5f","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1444,"value":"const dist = (() => {\n const m = meanMin;\n const s = sdMin;\n\n if (shapeChoice.startsWith(\"Normal\")) {\n return {\n label: \"Normal\",\n pdf: (x) => Math.exp(-0.5 * ((x - m) / s) ** 2) / (s * Math.sqrt(2 * Math.PI)),\n cdf: (x) => normCdf((x - m) / s),\n quantile: (p) => m + s * normQuantile(p)\n };\n }\n\n // Log-normal matched to the same mean and SD (method of moments)\n const sg = Math.sqrt(Math.log(1 + (s * s) / (m * m)));\n const mu = Math.log(m) - (sg * sg) / 2;\n return {\n label: \"Right-skewed\",\n pdf: (x) =>\n x <= 0\n ? 0\n : Math.exp(-0.5 * ((Math.log(x) - mu) / sg) ** 2) /\n (x * sg * Math.sqrt(2 * Math.PI)),\n cdf: (x) => (x <= 0 ? 0 : normCdf((Math.log(x) - mu) / sg)),\n quantile: (p) => Math.exp(mu + sg * normQuantile(p))\n };\n})();","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1445,"value":"$60","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1446,"value":"$61","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1447,"value":"$62","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1448,"value":"$63","pinned":false,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1449,"value":"$64","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1450,"value":"const ruleRows = [1, 2, 3].map((k) => {\n const lo = Math.max(0, meanMin - k * sdMin);\n const hi = meanMin + k * sdMin;\n return {\n Window: `${k} SD either side of the average`,\n Minutes: `${fmt.min(lo)}–${fmt.min(hi)}`,\n \"Share of people inside\": fmt.pct1(dist.cdf(hi) - dist.cdf(lo)),\n \"Textbook value\": [\"68.3%\", \"95.4%\", \"99.7%\"][k - 1]\n };\n});\n\ndisplay(\n Inputs.table(ruleRows, {\n width: Math.min(width, 860),\n layout: \"auto\",\n select: false\n })\n);\n","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1452,"value":"const downloadButton = (label, node, filename) =>\n Object.assign(document.createElement(\"button\"), {\n textContent: label,\n style:\n \"font:14px system-ui,-apple-system,'Segoe UI',sans-serif;padding:6px 12px;\" +\n \"margin-right:8px;border:1px solid rgba(11,11,11,0.20);border-radius:6px;\" +\n \"background:#fcfcfb;color:#0b0b0b;cursor:pointer;\",\n onclick: () => downloadPNG(node, filename)\n });\n\ndisplay(downloadButton(\"Download distribution PNG\", curveChart, \"training_time_distribution\"));\ndisplay(downloadButton(\"Download cohort PNG\", cohortChart, \"training_cohort_dots\"));","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1451,"value":"$65","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1465,"value":"$66","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1468,"value":"$67","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1469,"value":"$68","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1476,"value":"$69","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1492,"value":"recData = [\n {option: 1, label: \"1. No, I would not recommend it even with major improvements.\", pct: 4.22},\n {option: 2, label: \"2. Yes, I would recommend it with major improvements.\", pct: 8.66},\n {option: 3, label: \"3. Yes, I would recommend it with some minor improvements.\", pct: 16.63},\n {option: 4, label: \"4. Yes, I would recommend it as is without any changes.\", pct: 70.49}\n]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1493,"value":"mojoData = [\n {option: 1, label: \"1. I did not learn anything new.\", pct: 4.49},\n {option: 2, label: \"2. My current role does not enable me to use what I learned.\", pct: 1.94},\n {option: 3, label: \"3. I am uncertain about what I need to do as next steps.\", pct: 1.66},\n {option: 4, label: \"4. I need more guidance before I can use what I learned.\", pct: 3.88},\n {option: 5, label: \"5. 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I can perform now at an expert level.\", pct: 22.88}\n]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1494,"value":"$6a","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1495,"value":"$6b","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1500,"value":"htmlToImage = import(\"https://cdn.jsdelivr.net/npm/html-to-image@1.11.11/+esm\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1501,"value":"function downloadPanel(node, name, {scale = 3} = {}) {\n return DOM.download(\n () => htmlToImage.toBlob(node, {pixelRatio: scale, backgroundColor: \"white\"}),\n `${name}.png`,\n `Download ${name}.png`\n );\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1496,"value":"recPanelImage = surveyPanel({\n question: \"Would you recommend this course to a colleague?\",\n note: \"Self-paced experiences or when class scheduling is not a factor. 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