25:["$","title",null,{"children":"Data Literacy: On the Table! (Learning Dashboard) | Zsolt Olah's Data Literacy Workspace | Observable"}] 49:T572,Number of sales folks started the Pilot: **${maxRows}** | Participation target (${targetParticipants}): **${textcolor(((maxRows/targetParticipants)*100)+"%","green")}**

Number of sales folks completed the Pilot: **${maxRowsComp}** | Completion target (80): **${textcolor(((maxRowsComp/targetParticipantsComp)*100)+"%","green")}**

Average post-assessment score: **${roundMe(meanPostScore1,2)}** | Target average post-assessment score (${roundMe(targetParticipantsScore,2)}): **${textcolor((roundMe((meanPostScore1/targetParticipantsScore)*100,2))+"%","green")}**

Average 5-star rating of the Pilot\*: **${roundMe(starRatingNoOutlier,1)}** | Average 5-star rating Pilot target (4.4): **${textcolor(roundMe((starRatingNoOutlier/4.4)*100,2)+"%","orange")}**
\**A single outlier was removed because the 1-star review was about the Internet connection, not the content. Exact average rating without the outlier: ${starRatingNoOutlier}; with the outlier: ${averageStar}, which would be ${roundMe((averageStar/4.4)*100,2)}%.
Additional information the team did not consider: 95% confidence interval for the mean with total population of target salesfolks of 2,000: 4.2148659444648535 - 4.548020653473292*

Number of participating managers: **${maxManagers}** | Number of managers completing the Pilot with their direct reports (it was optional for managers): **${compManagers}** 4a:T451, hist3 = addTooltips ( Plot.plot({ title: "5-star participant satisfaction feedback", subtitle: "Statistical mean: "+roundMe(averageStar,4)+" (dotted line)", marks: [ Plot.axisX({ticks: [0,1, 2, 3, 4, 5]}), Plot.rectY( salesfinal_dataset, Plot.binX({ y: "count",title: (elems) => `${elems.length} salesfolks` }, { x: "feedback_star", fill: "manager_completed_training",interval: 1 }) // Bars colored in steelblue ), Plot.text( salesfinal_dataset, Plot.binX({ y: "count"}, { x: "feedback_star", y:"count", text: (d) => (d.length), dy: -6, textAnchor: "middle",interval: 1 }) ), Plot.ruleX(salesfinal_dataset,{ x: [averageStar], y:100,stroke: "black", strokeDasharray: "2,2", markerEnd: "dot" }), // Dotted line for mean Plot.text( { x: 80, y: 50, text: "Mean", fill:"black"}), // Label for the mean line Plot.ruleY[0] ], x: { label: "5-star rating", domain: [0, 5] }, y: { domain: [1, 100], label: "Number of Salesfolks" // Y-axis title } }), { fill: "black" }) 4b:T64e, hist4 = addTooltips ( Plot.plot({ title: "Distribution of hours spent in Pilot", subtitle: "Statistical mean: "+roundMe(meanTimeSpent,2)+" (black dotted line) | Statistical median: "+roundMe(medianTimeSpent,2)+" (green dotted line) |", marks: [ Plot.axisX({ticks: [1.4, 2.8, 4.2, 5.6, 7.0, 8.4, 9.8, 11.2, 12.6, 14],tickFormat: d => d.toFixed(1)}), Plot.rectY( salesfinal_dataset, Plot.binX({ y: "count",title: (elems) => `${elems.length} sales folks` }, { x: "Duration", fill: "steelblue",thresholds:[0, 1.4, 2.8, 4.2, 5.6, 7.0, 8.4, 9.8, 11.2, 12.6, 14.0001] }) // Bars colored in steelblue ), Plot.text( salesfinal_dataset, Plot.binX({ y: "count"}, { x: "Duration", y:"count", text: (d) => (d.length), dy: -6, textAnchor: "middle",thresholds:[0, 1.4, 2.8, 4.2, 5.6, 7.0, 8.4, 9.8, 11.2, 12.6, 14.0001] }) ), Plot.ruleX(salesfinal_dataset,{ x: [meanTimeSpent], y:[maxDuration+10],stroke: "black", strokeDasharray: "2,2", markerEnd: "dot" }), // Dotted line for mean Plot.text( { x: 80, y: 50, text: "Mean", fill:"black"}), Plot.ruleX(salesfinal_dataset,{ x: [medianTimeSpent], y:[maxDuration+10],stroke: "green", strokeDasharray: "4,4", markerEnd: "dot" }), // Dotted line for mean Plot.text( { x: 80, y: 50, text: "Median", fill:"black"}), // Label for the mean line Plot.ruleY[0] ], x: { label: "Hours spent in the pilot", domain: [0, 14] }, y: { domain: [0, [maxDuration+10]], label: "Number of Salesfolks" // Y-axis title } }), { fill: "black" }) 4c:T492, hist = addTooltips ( Plot.plot({ title: "Distribution of pre-assessment scores for those who completed the pilot", subtitle: "Statistical mean: "+roundMe([meanPreScore],2)+" (solid line)"+" | Median: "+roundMe([medianPreScore],2)+" (dotted line)", marks: [ Plot.rectY( pilotComp, Plot.binX({ y: "count",title: (elems) => `${elems.length} salesfolks` }, { x: "pre_score", fill: "steelblue",interval: 5 }) // Bars colored in steelblue ), Plot.text( pilotComp, Plot.binX({ y: "count"}, { x: "pre_score", y:"count", text: (d) => (d.length), dy: -6, textAnchor: "middle",interval: 5 }) ), Plot.ruleX(pilotComp,{ x: [meanPreScore], y:30,stroke: "black", strokeDasharray: "0,0", markerEnd: "dot" }), Plot.ruleX(pilotComp,{ x: [medianPreScore], y:30,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: "Pre-assessment Score", domain: [0, 100] }, y: { label: "Number of Salespeople" // Y-axis title } }), { fill: "black" }) 4d: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

,

Depending on how you measure the impact, you may get different results!

*Normalized learning change is computed from individual gains first, and then the average of those. It requires matched data where participants completed both assessments.
**Normalized learning gain is computed from pre and post group averages. It can be run on unmatched data where participants completed either the pre or the post assessment.
Metric	TYG Calculation	Matched Data	Unmatched Data (both pilot and control groups)
Group size	n = ${gains.matched.n}	n = ${gains.matched.n}	nPre = ${gains.unmatched.nPre}, nPost = ${gains.unmatched.nPost}
Pre average	${roundMe(gains.matched.preAvg,2)}	${roundMe(gains.matched.preAvg,2)}	${roundMe(gains.unmatched.preAvg,2)}
Post average	${roundMe(gains.matched.postAvg,2)}	${roundMe(gains.matched.postAvg,2)}	${roundMe(gains.unmatched.postAvg,2)}
Raw difference	${roundMe(gains.matched.postAvg-gains.matched.preAvg,2)}	${roundMe(gains.matched.postAvg-gains.matched.preAvg,2)}	${roundMe(gains.unmatched.postAvg-gains.unmatched.preAvg,2)}
Normalized learning change (c)* or learning gain (g)**	(c) ${learnGain5}%	(g) ${roundMe(gains.matched.nGain*100,0)}%	(g) ${roundMe(gains.unmatched.nGain*100,0)}%

`51: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) => `${elems.length} salesfolks`}, { x: "learn_gain",fill: "steelblue",tip: true ,thresholds:[-46,-36,-26,-16,-6,4,14,24,34,44,54,64,74,84]}) // Bars colored in steelblue ), Plot.text( salesfinal_dataset, Plot.binX({ y: "count",text: (d) => d.length}, {x: "learn_gain",thresholds:[-46,-36,-26,-16,-6,4,14,24,34,44,54,64,74,84],dy:-8}) ), Plot.ruleX(sales_clean,{ x: [meanLearningGain], y:[maxLearnGain+20],stroke: "black", strokeDasharray: "2,2", markerEnd: "dot" }), // Dotted line for mean Plot.text( { x: 80, y: 50, text: "Mean", fill:"black"}), // Label for the mean line Plot.ruleY[0] ], x: { label: "Learning Gain (%)", domain: [-46, 84], ticks: [-46, -36, -26, -16, -6, 4, 14, 24, 34, 44, 54, 64, 74, 84] }, y: { label: "Number of Salesfolks", domain: [0, [maxLearnGain+20]] } }) 52:T573,lch = { const edges = [-10, 0, 10, 20, 30, 40, 50, 60, 70, 80, 90]; const yMax = 40; // tallest bar is 33 (60–70) return Plot.plot({ title: "Distribution of normalized learning change (matched data)", subtitle: "Statistical mean: " + roundMe(meanLearningGain5, 2) + " (solid line) | Median: " + roundMe(medianLearningGain5, 2) + " (dotted line)", marks: [ Plot.rectY(pilotComp, Plot.binX( { y: "count", title: (elems) => `${elems.length} salespeople` }, { x: "adj_learning_gain", fill: "steelblue", thresholds: edges, tip: true } )), Plot.text(pilotComp, Plot.binX( { y: "count", text: (d) => d.length }, { x: "adj_learning_gain", thresholds: edges, dy: -8 } )), Plot.ruleX([meanLearningGain5], { y1: 0, y2: yMax - 2, stroke: "black", markerEnd: "dot" }), Plot.ruleX([medianLearningGain5], { y1: 0, y2: yMax - 2, stroke: "black", strokeDasharray: "2,2", markerEnd: "dot" }), Plot.text([meanLearningGain5], { x: d => d, y: yMax, text: () => "Mean", textAnchor: "end", dx: -3 }), Plot.text([medianLearningGain5], { x: d => d, y: yMax, text: () => "Median", textAnchor: "start", dx: 3 }), Plot.ruleY([0]) ], x: { label: "Normalized learning change (%)", domain: [-10, 90], ticks: edges }, y: { label: "Number of salespeople", domain: [0, yMax + 2] } }); }53:T1440,viewof annotatedPilot = { annotationCSS; const container = html`

`; Object.assign(container.style, { position: "relative", display: "inline-block", maxWidth: "100%" }); const img = pilotprocess.cloneNode(true); img.style.display = "block"; img.style.maxWidth = "100%"; img.alt ||= "Pilot process diagram"; container.append(img); const svgNS = "http://www.w3.org/2000/svg"; const svg = document.createElementNS(svgNS, "svg"); Object.assign(svg.style, { position: "absolute", left: 0, top: 0, overflow: "visible", pointerEvents: "none" }); container.append(svg); const tip = html``; tip.style.position = "absolute"; tip.style.display = "none"; tip.style.zIndex = 2; container.append(tip); const GAP = 12; // default offset from marker const PAD = 8; // inner margin from container edges let lastOpenIdx = null; // remember which marker is open so we can re-clamp on resize // helpers: compute px position for an annotation (center of icon) const posPx = (a) => { const w = img.clientWidth, h = img.clientHeight; return { cx: (a.x / 100) * w, cy: (a.y / 100) * h }; }; // place & clamp tooltip inside container function placeTipNear(cx, cy) { // default target (to the bottom-right of marker) let left = cx + GAP, top = cy + GAP; // measure container and tip const rect = container.getBoundingClientRect(); // make visible temporarily to measure if hidden const wasHidden = tip.style.display === "none"; if (wasHidden) { tip.style.visibility = "hidden"; tip.style.display = "block"; } const tw = tip.offsetWidth; const th = tip.offsetHeight; if (wasHidden) { tip.style.display = "none"; tip.style.visibility = ""; } // If overflows right/bottom, try flipping to left/up if (left + tw + PAD > rect.width) left = cx - GAP - tw; if (top + th + PAD > rect.height) top = cy - GAP - th; // Final clamp so it never clips out of frame left = Math.max(PAD, Math.min(left, rect.width - tw - PAD)); top = Math.max(PAD, Math.min(top, rect.height - th - PAD)); tip.style.left = `${left}px`; tip.style.top = `${top}px`; } // single open/close function openForIndex(i) { const a = annotations[i]; if (!a) return; const { cx, cy } = posPx(a); tip.innerHTML = `${a.title}

${a.desc}

`; tip.style.display = "block"; placeTipNear(cx, cy); lastOpenIdx = i; } function closeTip() { tip.style.display = "none"; lastOpenIdx = null; } // markers const markers = annotations.map((a, i) => { const g = document.createElementNS(svgNS, "g"); g.setAttribute("data-idx", i); g.setAttribute("tabindex", "0"); g.setAttribute("role", "button"); g.setAttribute("aria-label", `${a.title}: ${a.desc}`); g.style.cursor = "pointer"; g.style.pointerEvents = "auto"; const r = 12; const circle = document.createElementNS(svgNS, "circle"); circle.setAttribute("r", r); circle.setAttribute("class", "anno-icon"); const text = document.createElementNS(svgNS, "text"); text.setAttribute("text-anchor", "middle"); text.setAttribute("dominant-baseline", "middle"); text.setAttribute("class", "anno-icon-char"); text.textContent = "i"; g.append(circle, text); svg.append(g); // Interactions: always position using marker center (not mouse), then clamp g.addEventListener("click", (ev) => { ev.stopPropagation(); const idx = +g.getAttribute("data-idx"); if (tip.style.display === "block" && lastOpenIdx === idx) closeTip(); else openForIndex(idx); }); g.addEventListener("keydown", (ev) => { if (ev.key === "Enter" || ev.key === " ") { ev.preventDefault(); const idx = +g.getAttribute("data-idx"); if (tip.style.display === "block" && lastOpenIdx === idx) closeTip(); else openForIndex(idx); } if (ev.key === "Escape") closeTip(); }); // Optional hover open (comment out if you prefer click-only) g.addEventListener("mouseenter", () => openForIndex(+g.getAttribute("data-idx"))); g.addEventListener("mouseleave", () => { if (document.activeElement !== g) closeTip(); }); return { g, a, r }; }); document.addEventListener("click", (e) => { if (!container.contains(e.target)) closeTip(); }); function layout() { const w = img.clientWidth, h = img.clientHeight; svg.setAttribute("width", w); svg.setAttribute("height", h); markers.forEach(({ g, a }) => { const { cx, cy } = posPx(a); g.setAttribute("transform", `translate(${cx},${cy})`); }); // If a tooltip is open, re-clamp it after resize/layout changes if (lastOpenIdx != null) { const { cx, cy } = posPx(annotations[lastOpenIdx]); placeTipNear(cx, cy); } } const ro = new ResizeObserver(layout); ro.observe(img); if (img.complete) layout(); else img.addEventListener("load", layout); return container; } 54:T4be,// Usage examples: // quartiles(pilotComp, "adj_performance_gain") // quartiles(pilotComp, d => d.score) // quartiles([1,2,3,4,5], d => d) // array of numbers function quartiles(data, columnOrAccessor, {sorted = false, filterZeros = false} = {}) { const accessor = typeof columnOrAccessor === "function" ? columnOrAccessor : typeof columnOrAccessor === "string" ? (d) => d[columnOrAccessor] : (d) => d; // data is already numeric array let values = (Array.isArray(data) ? data : []) .map(accessor) .filter(v => v != null && v !== "" && !Number.isNaN(+v)) .map(Number); if (filterZeros) values = values.filter(v => v !== 0); if (values.length === 0) return null; if (!sorted) values.sort((a, b) => a - b); const q = (p) => d3.quantile(values, p); // Observable includes d3 const q1 = q(0.25); const median = q(0.5); const q3 = q(0.75); const iqr = q3 - q1; // Tukey fences (handy for boxplots/outliers) const lowerFence = q1 - 1.5 * iqr; const upperFence = q3 + 1.5 * iqr; return { q1, median, q3, iqr, min: values[0], max: values[values.length - 1], lowerFence, upperFence, count: values.length }; } 55:T87e,// Usage: // quartilesWithOutliers(pilotComp, "adj_performance_gain") // quartilesWithOutliers(pilotComp, d => d.score, { filterZeros: true }) // quartilesWithOutliers([1,2,3,100,105], d => d) function quartilesWithOutliers(data, columnOrAccessor, { filterZeros = false } = {}) { const accessor = typeof columnOrAccessor === "function" ? columnOrAccessor : typeof columnOrAccessor === "string" ? (d) => d[columnOrAccessor] : (d) => d; // numeric array // Keep links to original rows for reporting let mapped = (Array.isArray(data) ? data : []).map((row, i) => ({ value: Number(accessor(row)), index: i, row })) .filter(d => d.value != null && d.value !== "" && !Number.isNaN(d.value)); if (filterZeros) mapped = mapped.filter(d => d.value !== 0); if (mapped.length === 0) return null; // Sorted numeric array for quantiles const values = mapped.map(d => d.value).sort((a, b) => a - b); const q = (p) => d3.quantile(values, p); // d3 is available in Observable const q1 = q(0.25); const median = q(0.5); const q3 = q(0.75); const iqr = q3 - q1; // Tukey fences const lowerFence = q1 - 1.5 * iqr; const upperFence = q3 + 1.5 * iqr; const lowerExtreme = q1 - 3 * iqr; const upperExtreme = q3 + 3 * iqr; // Classify points using fences const isExtreme = (v) => v < lowerExtreme || v > upperExtreme; const isMild = (v) => (v < lowerFence && v >= lowerExtreme) || (v > upperFence && v <= upperExtreme); const isInlier = (v) => v >= lowerFence && v <= upperFence; const mildOutliers = mapped.filter(d => isMild(d.value)); const extremeOutliers = mapped.filter(d => isExtreme(d.value)); const inliers = mapped.filter(d => isInlier(d.value)); return { q1, median, q3, iqr, min: values[0], max: values[values.length - 1], count: values.length, fences: { lowerFence, upperFence, lowerExtreme, upperExtreme }, outliers: { mild: mildOutliers, // [{ value, index, row }, ...] extreme: extremeOutliers // [{ value, index, row }, ...] }, inliers // [{ value, index, row }, ...] }; } 56:T715, # Data Classification – "Grammar Layer" of Data Literacy *Levels are **accumulative**: each level includes everything above it.* | Data Classification Type | Mathematical Operations Allowed | Measures of Central Tendency | Measures of Variability | Examples | |---|---|---|---|---| | **Level 1 – Qualitative: Nominal** | Equality (=, ≠); Proportions | **Mode** (most common value) | N/A | Region; Job Title; Name; **Location**; Site | | **Level 2 – Qualitative: Ordinal** | Equality (=, ≠); Proportions; **Comparison** (>, <) | Mode; **Median** (middle after sorting) | **Range** (min, max); **IQR*** | Job levels; Likert-scale values; Tenure brackets (0–1 yr, 1–2 yrs, 3–5 yrs, …) | | **Level 3 – Quantitative: Interval** | Equality (=, ≠); Proportion; Comparison (>, <); **Addition, subtraction** (+, –) | Mode; Median; **Mean** (arithmetic average) | Range; IQR*; **Standard Deviation** (spread from the mean) | Temperature in °F/°C (not “twice as hot”); Test scores (calculation vs interpretation); Engagement scores | | **Level 4 – Quantitative: Ratio** | Equality (=, ≠); Proportions; Comparison (>, <); Addition, subtraction (+, –); **Multiplication, division** (×, ÷) | Mode; Median; Mean | Range; IQR*; **Standard Deviation**; **Relative Deviation** (STD / mean) | Temperature in K (yes to “twice as hot”); Age; Tenure; Time spent in training; Salary | **Continuous data** — measured numeric values (any value, including fractional numbers; value depends on the measuring tool). **Discrete data** — counted whole numbers with gaps between fixed values (permitted math depends on context). **Binary data** — only two values (True/False, 1/0, Yes/No). \* **IQR (interquartile range)** = 75th percentile (Q3) − 25th percentile (Q1). Q2 = median. ` 57:T3079,// ───────────────────────────────────────────────────────────── // Five chart types, one per job — TYG pilot data // Observable Notebooks 2.0 (TypeScript) // ───────────────────────────────────────────────────────────── import * as Plot from "npm:@observablehq/plot"; import * as d3 from "npm:d3"; // ── shared look ────────────────────────────────────────────── const INK = "#0b0b0b"; const MUTED = "#6b6a66"; const DARK = "#184f95"; const LIGHT = "#86b6ef"; const ACCENT = "#b8460f"; const SURFACE = "#fcfcfb"; const halo = { stroke: SURFACE, strokeWidth: 5 } as const; const base = { width: 680, marginLeft: 56, marginRight: 24, marginTop: 34, marginBottom: 48, style: { background: SURFACE, color: INK, fontFamily: "system-ui, -apple-system, 'Segoe UI', Roboto, sans-serif", fontSize: "12px" } }; // ═════════════════════════════════════════════════════════════ // 1 — COMPARISON: how do these groups differ? // Grouped horizontal bars. Four regions × two measures is the // ceiling; bars sorted by the story, not the alphabet, and the // x axis starts at zero because bar length IS the quantity. // ═════════════════════════════════════════════════════════════ interface RegionScore { region: string; n: number; pre: number; post: number; } const REGIONS: RegionScore[] = [ { region: "East", n: 35, pre: 65.2, post: 85.4 }, { region: "North", n: 34, pre: 65.3, post: 85.4 }, { region: "West", n: 36, pre: 69.2, post: 85.4 }, { region: "South", n: 10, pre: 67.0, post: 85.8 } ]; interface StageScore { region: string; stage: "Before" | "After"; score: number; } const scores: StageScore[] = REGIONS.flatMap((r) => [ { region: r.region, stage: "Before" as const, score: r.pre }, { region: r.region, stage: "After" as const, score: r.post } ]); const comparison = Plot.plot({ ...base, height: 300, title: "Assessment score before and after the pilot, by region", subtitle: "Four regions, two measures. Any more of either and the comparison stops working.", x: { label: "Assessment score →", domain: [0, 100], ticks: 5 }, y: { label: null, axis: null, domain: ["Before", "After"] }, fy: { label: null, domain: ["East", "North", "West", "South"] }, color: { domain: ["Before", "After"], range: [LIGHT, DARK], legend: true }, marks: [ Plot.barX(scores, { fy: "region", y: "stage", x: "score", fill: "stage", insetTop: 1.5, insetBottom: 1.5 }), Plot.text(scores, { fy: "region", y: "stage", x: "score", text: (d: StageScore) => d.score.toFixed(1), dx: -6, textAnchor: "end", fill: "#fff", fontSize: 11, fontWeight: 600 }), Plot.ruleX([0], { stroke: INK, strokeOpacity: 0.35 }) ] }); display(comparison); // ═════════════════════════════════════════════════════════════ // 2 — CHANGE OVER TIME: what is the shape of the movement? // A line, because time is continuous and the eye should follow // slope, not compare heights. Planned pace is dashed and grey so // it reads as reference, not as a second finding. // ═════════════════════════════════════════════════════════════ const WEEKS: number[] = [1, 2, 3, 4, 5, 6, 7, 8]; const actual: number[] = [12, 28, 47, 66, 84, 97, 100, 100]; const target: number[] = [12, 25, 38, 50, 63, 76, 88, 100]; interface PacePoint { week: number; pct: number; series: "Actual completion" | "Planned pace"; } const pace: PacePoint[] = WEEKS.flatMap((w, i) => [ { week: w, pct: actual[i], series: "Actual completion" as const }, { week: w, pct: target[i], series: "Planned pace" as const } ]); const time = Plot.plot({ ...base, height: 330, title: "Pilot completion, week by week", subtitle: "The y axis starts at zero. It has to, or the gap tells a story it has not earned.", x: { label: "Week of the pilot →", domain: [1, 8], ticks: 8 }, y: { label: "↑ Completed (%)", domain: [0, 100], grid: true, ticks: 5, tickSize: 0 }, color: { domain: ["Actual completion", "Planned pace"], range: [DARK, MUTED], legend: true }, marks: [ Plot.line(pace, { x: "week", y: "pct", stroke: "series", strokeWidth: 2.5, strokeDasharray: (d: PacePoint) => (d.series === "Planned pace" ? "5 4" : null) }), Plot.dot(pace.filter((d) => d.series === "Actual completion"), { x: "week", y: "pct", fill: DARK, r: 4 }), Plot.text([{ week: 6, pct: 97 }], { x: "week", y: "pct", text: ["97% by week 6"], dy: -16, fill: DARK, fontWeight: 600, ...halo }), Plot.ruleY([0], { stroke: INK, strokeOpacity: 0.35 }) ] }); display(time); // ═════════════════════════════════════════════════════════════ // 3 — DISTRIBUTION: what does the spread look like? // A histogram of every response, with the mean drawn on top so // the reader can see that the average sits in a gap. Note the // x scale is linear (not band) — that is what lets the mean rule // land at 4.35 instead of snapping to a category. // ═════════════════════════════════════════════════════════════ interface StarCount { stars: number; n: number; } const SAT: StarCount[] = [ { stars: 1, n: 1 }, { stars: 2, n: 0 }, { stars: 3, n: 23 }, { stars: 4, n: 14 }, { stars: 5, n: 60 } ]; const satTotal: number = d3.sum(SAT, (d) => d.n); const satMean: number = d3.sum(SAT, (d) => d.stars * d.n) / satTotal; const distribution = Plot.plot({ ...base, height: 330, title: `Pilot satisfaction: ${satTotal} responses`, subtitle: `The average is ${satMean.toFixed(2)} stars. Nobody gave ${satMean.toFixed(2)} stars.`, x: { label: "Stars given →", domain: [0.45, 5.55], ticks: [1, 2, 3, 4, 5], tickFormat: "d" }, y: { label: "↑ Responses", domain: [0, 72], grid: true, ticks: 4, tickSize: 0 }, marks: [ Plot.rectY(SAT, { x1: (d: StarCount) => d.stars - 0.4, x2: (d: StarCount) => d.stars + 0.4, y: "n", fill: DARK }), Plot.text(SAT.filter((d) => d.n > 0), { x: "stars", y: "n", text: "n", dy: -10, fill: INK, fontWeight: 600 }), Plot.ruleX([satMean], { stroke: ACCENT, strokeWidth: 2, strokeDasharray: "5 4" }), Plot.text([`Average ${satMean.toFixed(2)} stars`], { x: satMean, y: 68, textAnchor: "middle", fill: ACCENT, fontWeight: 600, ...halo }), Plot.ruleY([0], { stroke: INK, strokeOpacity: 0.35 }) ] }); display(distribution); // ═════════════════════════════════════════════════════════════ // 4 — RELATIONSHIP: does one thing move with another? // A scatterplot with one dot per person. The regression line is // there to be doubted, not believed: r = 0.28 means the line // explains under 8% of what you are looking at. The cloud is // the finding. // ═════════════════════════════════════════════════════════════ interface Participant { rep: number; hours: number; gain: number; } const rng = d3.randomLcg(4242); // seeded → the figure is reproducible const nz = d3.randomNormal.source(rng); const reps: Participant[] = d3.range(115).map((i) => { const hours = Math.max(2, Math.round(nz(11, 3.4)() * 10) / 10); const gain = Math.max( -20, Math.min(95, Math.round((18 + 1.45 * (hours - 11) + nz(0, 17)()) * 10) / 10) ); return { rep: i + 1, hours, gain }; }); function pearson(rows: Participant[]): number { const mx = d3.mean(rows, (d) => d.hours)!; const my = d3.mean(rows, (d) => d.gain)!; const num = d3.sum(rows, (d) => (d.hours - mx) * (d.gain - my)); const den = Math.sqrt( d3.sum(rows, (d) => (d.hours - mx) ** 2) * d3.sum(rows, (d) => (d.gain - my) ** 2) ); return num / den; } const r: number = pearson(reps); const relationship = Plot.plot({ ...base, height: 360, title: "Time in huddles against learning gain", subtitle: "One dot per participant. The cloud, not the line, is the finding.", x: { label: "Hours in huddles →", domain: [0, 24], ticks: 6, grid: true }, y: { label: "↑ Adjusted learning gain", domain: [-30, 100], ticks: 5, grid: true, tickSize: 0 }, marks: [ Plot.linearRegressionY(reps, { x: "hours", y: "gain", stroke: ACCENT, strokeWidth: 2, ci: 0, strokeOpacity: 0.8 }), Plot.dot(reps, { x: "hours", y: "gain", fill: DARK, fillOpacity: 0.55, r: 4, stroke: SURFACE, strokeWidth: 1 }), Plot.text([`r = ${r.toFixed(2)}`], { frameAnchor: "top-right", dx: -8, dy: 10, fill: ACCENT, fontWeight: 600, ...halo }), Plot.text(["a weak positive trend,\nand a very wide cloud"], { frameAnchor: "bottom-right", dx: -8, dy: -8, fill: MUTED, fontSize: 11.5, lineHeight: 1.35, textAnchor: "end", ...halo }), Plot.ruleY([0], { stroke: INK, strokeOpacity: 0.35 }) ] }); display(relationship); // ═════════════════════════════════════════════════════════════ // 5 — COMPOSITION: what are the parts of the whole? // 100% stacked bars: two parts, four wholes. The segments are // computed explicitly rather than left to the stack transform, // so the labels can be placed at known midpoints and the counts // (not the percentages) stay visible. // ═════════════════════════════════════════════════════════════ interface RegionPop { region: string; selected: number; not: number; } const POP: RegionPop[] = [ { region: "East", selected: 44, not: 50 }, { region: "North", selected: 31, not: 45 }, { region: "West", selected: 28, not: 33 }, { region: "South", selected: 12, not: 18 } ]; const SEL = "Selected for the pilot"; const NOT = "Not selected"; interface Segment { region: string; group: string; x1: number; x2: number; mid: number; count: number; } const comp: Segment[] = POP.flatMap((d) => { const tot = d.selected + d.not; const cut = (d.selected / tot) * 100; return [ { region: d.region, group: SEL, x1: 0, x2: cut, mid: cut / 2, count: d.selected }, { region: d.region, group: NOT, x1: cut, x2: 100, mid: (cut + 100) / 2, count: d.not } ]; }); const headcount: number = d3.sum(POP, (d) => d.selected + d.not); const composition = Plot.plot({ ...base, height: 300, title: "Who was selected, region by region", subtitle: `Every bar is 100% of that region's salesforce. ${headcount} people in total.`, x: { label: "Share of the region (%) →", domain: [0, 100], ticks: 5 }, y: { label: null, domain: ["East", "North", "West", "South"] }, color: { domain: [SEL, NOT], range: [DARK, LIGHT], legend: true }, marks: [ Plot.rectX(comp, { y: "region", x1: "x1", x2: "x2", fill: "group", insetTop: 4, insetBottom: 4, insetLeft: 1, insetRight: 1 }), Plot.text(comp, { y: "region", x: "mid", text: (d: Segment) => String(d.count), fill: (d: Segment) => (d.group === SEL ? "#fff" : INK), fontSize: 11, fontWeight: 600 }), Plot.ruleX([0], { stroke: INK, strokeOpacity: 0.35 }) ] }); display(composition); 58:[{"id":"0f3d07f1760ecf4ff9a5b23eb5d13534af63f5c0f61ce3edb62cdad945b29d5a1aef8c088f24d0e900845277c1a6e229c6b779bffe8e2ae3a1ebd984cf8a4a35","href":"https://static.observableusercontent.com/files/0f3d07f1760ecf4ff9a5b23eb5d13534af63f5c0f61ce3edb62cdad945b29d5a1aef8c088f24d0e900845277c1a6e229c6b779bffe8e2ae3a1ebd984cf8a4a35","name":"salesfinal_dataset_perfgain.csv","type":"text/csv","lastModified":1791165001858,"size":23165},{"id":"bbdc394f83bfae19170211f200d1f3488823c8e9293aed711e47a91a13cc75d8aa9f0abe979fd000dab18a7b89d19a00cc6c8680815cbab0189e8d5efc836e6f","href":"https://static.observableusercontent.com/files/bbdc394f83bfae19170211f200d1f3488823c8e9293aed711e47a91a13cc75d8aa9f0abe979fd000dab18a7b89d19a00cc6c8680815cbab0189e8d5efc836e6f","name":"pilotprocess.png","type":"image/png","lastModified":1758156110194.245,"size":60843},{"id":"12587a65ab4e77a89d23791f8e0f58dd208d0d9942a6753edfcdceb3176d9181e0d13aad0c68eced4611b9a3e27888b062074686b1235ef6f08cc0ce77179983","href":"https://static.observableusercontent.com/files/12587a65ab4e77a89d23791f8e0f58dd208d0d9942a6753edfcdceb3176d9181e0d13aad0c68eced4611b9a3e27888b062074686b1235ef6f08cc0ce77179983","name":"salesfinal_dataset_perfgain@1.csv","type":"text/csv","lastModified":1757274301505.247,"size":22580},{"id":"12587a65ab4e77a89d23791f8e0f58dd208d0d9942a6753edfcdceb3176d9181e0d13aad0c68eced4611b9a3e27888b062074686b1235ef6f08cc0ce77179983","href":"https://static.observableusercontent.com/files/12587a65ab4e77a89d23791f8e0f58dd208d0d9942a6753edfcdceb3176d9181e0d13aad0c68eced4611b9a3e27888b062074686b1235ef6f08cc0ce77179983","name":"salesfinal_dataset_perfgain_25.csv","type":"text/csv","lastModified":1757274386733.675,"size":22580},{"id":"cd5ead9a36b0fd495b383eed5bd1158b248c4fa4f2b6a02259ebc853d9f1033ed981ea81ea0247837b14a23973348224ae5419476d671e32b5eb8189c3fa5258","href":"https://static.observableusercontent.com/files/cd5ead9a36b0fd495b383eed5bd1158b248c4fa4f2b6a02259ebc853d9f1033ed981ea81ea0247837b14a23973348224ae5419476d671e32b5eb8189c3fa5258","name":"salesfinal_dataset_perfgain_25_1.csv","type":"text/csv","lastModified":1757274629839.213,"size":22583},{"id":"12587a65ab4e77a89d23791f8e0f58dd208d0d9942a6753edfcdceb3176d9181e0d13aad0c68eced4611b9a3e27888b062074686b1235ef6f08cc0ce77179983","href":"https://static.observableusercontent.com/files/12587a65ab4e77a89d23791f8e0f58dd208d0d9942a6753edfcdceb3176d9181e0d13aad0c68eced4611b9a3e27888b062074686b1235ef6f08cc0ce77179983","name":"salesfinal_dataset_perfgain_261.csv","type":"text/csv","lastModified":1757274939938.609,"size":22580},{"id":"87aad1d4b3f359ee15e65e1ebef5faf7cca816c71fe0f7fe89d8fb1a4d886320e1932818598eb7226e4013e168e5d70d03b7d422d39877c3992bb0c3bad155ea","href":"https://static.observableusercontent.com/files/87aad1d4b3f359ee15e65e1ebef5faf7cca816c71fe0f7fe89d8fb1a4d886320e1932818598eb7226e4013e168e5d70d03b7d422d39877c3992bb0c3bad155ea","name":"salesfinal_dataset@2.csv","type":"text/csv","lastModified":1706147939878.169,"size":21023},{"id":"a5a03d88d143704869f9be16895f8a6e6c9171ca52a6211be0cf81e4ede0f3afa75421a84bc50597961dafb619a6251a0d8ab1a5d171aa7f2a0626438376d518","href":"https://static.observableusercontent.com/files/a5a03d88d143704869f9be16895f8a6e6c9171ca52a6211be0cf81e4ede0f3afa75421a84bc50597961dafb619a6251a0d8ab1a5d171aa7f2a0626438376d518","name":"dash1.jpg","type":"image/jpeg","lastModified":1759583933724.23,"size":135969},{"id":"a5e226a470cbff7384b849864d13d8064a1ebd799e4f98dcd4afb0965c177567d7df02c1ca6adb52f6b392c8699675d0c8958d09f895fd1015f11e1a421b4304","href":"https://static.observableusercontent.com/files/a5e226a470cbff7384b849864d13d8064a1ebd799e4f98dcd4afb0965c177567d7df02c1ca6adb52f6b392c8699675d0c8958d09f895fd1015f11e1a421b4304","name":"DataClassificationSheet (2).jpg","type":"image/jpeg","lastModified":1760052078086.004,"size":1481413},{"id":"a6ab73992ae9dc776cfd5d70d398b6cc68e8353295101e75a1112a6ea5426a1398d289cc10db768de8bf43fb69b2927b29f7e0d233609577777f6e5698b25239","href":"https://static.observableusercontent.com/files/a6ab73992ae9dc776cfd5d70d398b6cc68e8353295101e75a1112a6ea5426a1398d289cc10db768de8bf43fb69b2927b29f7e0d233609577777f6e5698b25239","name":"learn_chart1.png","type":"image/png","lastModified":1706277302853.034,"size":4499717},{"id":"3ddde91c37ba1349af37356c4df0c035ea68e98b6deff6b54b7fc3ad72c7457aada82066f7592448af082978b3a172ed4effd946b7bfaced980e3289b3719020","href":"https://static.observableusercontent.com/files/3ddde91c37ba1349af37356c4df0c035ea68e98b6deff6b54b7fc3ad72c7457aada82066f7592448af082978b3a172ed4effd946b7bfaced980e3289b3719020","name":"salesfinal_dataset@1.csv","type":"text/csv","lastModified":1706112620164.148,"size":21028}] 29:{"id":"fb670ca5f330a7e9","slug":null,"title":"Data Literacy: On the Table! 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(Learning Dashboard)","cells":[{"id":501,"value":"\n

Data Literacy: On the Table! (Learning Dashboard)

`","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(num*digits)/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":"# Raw data for the sales Pilot","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.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":1348,"value":"\nsalesfinal_readonly = Inputs.table(salesfinal_dataset, {\n columns: Object.keys(salesfinal_dataset[0] ?? {}),\n rows: 300\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","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":1448,"value":"perf_mean = Math.round((d3.mean(salesfinal_dataset,d => d.performance)*100))/100","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":8,"value":"pre_assessment_score = Plot.plot({\n title: \"Pre-assessment score\",\n subtitle: \"Subtitle\",\n caption: \"Caption\",\n height: 300,\nx: {\n label: \"Pre-assessment score\"\n },\n y: {\n label: \"Number of salesfolks\"\n },\n color: {\n legend: true\n },\n marks: [\n Plot.auto(salesfinal_dataset, {x: \"pre_score\", color: \"complete_training\"})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"pre_assessment_score","database":"$undefined"},{"id":544,"value":"Plot.plot({ x: { label: \"Pre-assessment score\" }, color: { legend: true}, title: \"Pre-assessment score\", height: 300, subtitle: \"Test\", caption: \"Caption\", y: { label: \"Number of salesfolks\" }, marks: [ Plot.rectY( sales_clean, Plot.binX( { y: \"count\" }, { x: \"pre_score\", fill: \"complete_training\", tip: true } ) ), Plot.ruleY([0]) ] })","pinned":true,"hidden":false,"mode":"ojs","author":"$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}));\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":"$undefined"},{"id":409,"value":"pilotComp = salesfinal_dataset.filter((element) => {\n return element.complete_training == 1\n});","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1452,"value":"pilotControl = 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":"$undefined"},{"id":1453,"value":"pilotControlMeanPerf = Math.round((d3.mean(pilotControl,d => d.performance)*100))/100","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1454,"value":"pilotControlMedianPerf = Math.round((d3.median(pilotControl,d => d.performance)*100))/100","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":466,"value":"pilotCompManager = salesfinal_dataset.filter((element) => {\n return (element.complete_training == 1) && (element.manager_completed_training == 1)\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1010,"value":"pilotCompManagerFocus = salesfinal_dataset.filter((element) => {\n return 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":"$undefined"},{"id":365,"value":"meanPreScore = d3.mean(pilotComp, d => d.pre_score);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1441,"value":"medianPreScore = 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":1442,"value":"medianPreScoreAll = 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":1443,"value":"medianPostScore1 = 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":"$undefined"},{"id":598,"value":"meanTimeSpent = d3.mean(salesfinal_dataset, d => d.Duration);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1180,"value":"medianTimeSpent = d3.median(salesfinal_dataset, d => d.Duration);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":608,"value":"totalHours = html`${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":"$undefined"},{"id":915,"value":"maxRowsComp = d3.count(pilotComp,d=>d.salesid)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":899,"value":"targetParticipants = 261","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":919,"value":"targetParticipantsComp = 80","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":921,"value":"targetParticipantsScore = 85","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":903,"value":"import {textcolor} from \"@observablehq/text-color-annotations-in-markdown\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":930,"value":"averageStar = d3.mean(pilotComp,d => d.feedback_star)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1249,"value":"numberStars = pilotComp.filter(d =>\n d.feedback_star != null && // not null/undefined\n !Number.isNaN(d.feedback_star) && // not NaN\n d.feedback_star > 0 // not zero\n).length","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1254,"value":"numberStarsResponseRate = roundMe((numberStars / 115)*100,0)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1260,"value":"

Satisfaction Data

Number of participants completing the pilot: 115

Number of received surveys: ${numberStars}

Response rate: ${numberStarsResponseRate}%

Statistical mean: ${roundMe(averageStar,1)}*

* By removing an outlier, this metric was presented as 4.4 in the final dashboard.

Learning Dashboard - Chart 1

\nNumber of sales folks completed the Pilot: **${maxRowsComp}** | Completion target (80): **${textcolor(((maxRowsComp/targetParticipantsComp)*100)+\"%\",\"green\")}**

\nNumber of participating managers: **${maxManagers}** | Number of managers completing the Pilot with their direct reports (it was optional for managers): **${compManagers}**\n\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"Completion","database":"$undefined"},{"id":942,"value":"starRatingAll = d3.mean(salesfinal_dataset,d=>d.feedback_star)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":945,"value":"starRatingNoOutlier = d3.mean(filteredStars,d=>d.feedback_star)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1058,"value":"starFeedback = pilotComp.filter(row => !isNaN(row.feedback_star))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":440,"value":"$4a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1434,"value":"\nhist6 = addTooltips (\n Plot.plot({\n title: \"5-star participant satisfaction feedback\",\n\n subtitle: \"Statistical mean: \"+roundMe(averageStar,4)+\" (solid line)\",\n\n marks: [\n Plot.rectY(\n salesfinal_dataset,\n Plot.binX({ y: \"count\", title: (elems) => `${elems.length} salesfolks` },\n { x: \"feedback_star\", interval: 1 })\n ),\n Plot.text(\n salesfinal_dataset,\n Plot.binX({ y: \"count\", text: \"count\" },\n { x: \"feedback_star\", interval: 1, dy: -6, textAnchor: \"middle\" })\n ),\n Plot.ruleX([averageStar], { stroke: \"black\", strokeDasharray: \"0,0\" }),\n Plot.text([{x: averageStar, label: \"Mean\"}],\n { x: \"x\", y: 95, text: \"label\", dx: 16, fontWeight: 600 }),\n Plot.ruleY([0])\n ],\n x: {\n label: \"5-star rating\",\n domain: [0, 6]\n\n },\n y: {\n domain: [1, 100],\n label: \"Number of Salesfolks\" // Y-axis title\n }\n\n}),\n { fill: \"black\" })\n\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1446,"value":"display(dlButton(hist6, \"5star_satisfaction_rating\"))","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1236,"value":"Average 5-star rating of the Pilot\\*: **${roundMe(starRatingNoOutlier,1)}** | Average 5-star rating Pilot target (4.4): **${textcolor(roundMe((starRatingNoOutlier/4.4)*100,2)+\"%\",\"orange\")}**

\n\n\\**A single outlier was removed because the 1-star review was about the Internet connection, not the content. Exact average rating without the outlier: ${starRatingNoOutlier}; with the outlier: ${averageStar}, which would be ${roundMe((averageStar/4.4)*100,2)}%.

\nAdditional information the team did not consider: 95% confidence interval for the mean with total population of target salesfolks of 2,000: 4.2148659444648535 - 4.548020653473292*

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"hist3_1","database":"$undefined"},{"id":1052,"value":"counts = d3.rollups(starFeedback.map(row => row.feedback_star), v => v.length, d => d)\n .map(([value, count]) => ({value, count}));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1049,"value":"\nhist5 = addTooltips (\n Plot.plot({\n marks: [\n Plot.barY(counts, {x: \"value\", y: \"count\", title: (elems) => `${elems.count} salesfolks`, fill: \"value\"}),\n Plot.text(counts, { x: \"value\", y:\"count\", text: (d) => (d.count), dy: -6, textAnchor: \"middle\" }),\n \n ],\n x: {\n // Assuming your values are integers and you know the range\n domain: [1,2,3,4,5], // Adjust based on your actual data range\n label: \"Value\"\n },\n y: {\n label: \"Count\"\n }\n}),\n { fill: \"black\" })\n ","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":589,"value":"

Learning Dashboard - Chart 2

Learning Dashboard - Chart 3

Learning Dashboard - Chart 4

Normalized Learning Gain

Matched (pilotComp)\n — n=${g.matched.n},\n pre ${fmt(g.matched.preAvg)},\n post ${fmt(g.matched.postAvg)},\n g=${fmt(g.matched.nGain, 3)}\n

Unmatched (salesfinal_dataset)\n — nPre=${g.unmatched.nPre}, nPost=${g.unmatched.nPost},\n pre ${fmt(g.unmatched.preAvg)},\n post ${fmt(g.unmatched.postAvg)},\n g=${fmt(g.unmatched.nGain, 3)}\n

`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1312,"value":"$50","pinned":true,"hidden":false,"mode":"ojs","author":"$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":1133,"value":"Normalized learning gain average: **${learnGain}** | Target (30%) difference: **${textcolor(learnGainDiff+\"%\",\"red\")}**

\nAverage pre-assessment score: **${avgPre5}**

\nAverage post-assessment score: **${avgPost}** | Target (85) difference: **${textcolor(\"+\"+(roundMe(((avgPost-85)/85)*100,2))+\"%\",\"green\")}**

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"unmatchedLearning","database":"$undefined"},{"id":693,"value":"$51","pinned":false,"hidden":false,"mode":"ojs","author":"$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":822,"value":"

Learning Dashboard - Chart 5

\nAverage pre-assessment score: **${avgPre5}**

\nAverage post-assessment score: **${avgPost5}** | Target (85) difference: **${textcolor(\"+\"+(roundMe(((avgPost5-85)/85)*100,2))+\"%\",\"green\")}**

Pilot Process

\n\n","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1188,"value":"pilotprocess = FileAttachment(\"pilotprocess.png\").image()\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1194,"value":"annotations = [\n // x,y are percentages (0–100) relative to the image\n { x: 5, y: 24, title: \"Pre-Performance\", desc: \"Baseline of the performance of the 261 sales folks.\" },\n { x: 15, y: 24, title: \"Pre-Assessment\", desc: \"All 261 sales folks takes the pre-assessment.\" },\n { x: 25, y: 38, title: \"Manager Decision\", desc: \"Managers decide which of their direct reports should go through the pilot training.\" },\n { x: 45, y: 38, title: \"Pilot Training\", desc: \"115 sales folks go through the spaced learning program.\" },\n { x: 45, y: 88, title: \"Control Group\", desc: \"The rest of the sales folks go back to work.\" },\n { x: 80, y: 24, title: \"Post-Assessment\", desc: \"All 115 sales folks who went through the pilot training take the post-assessment.\" },\n { x: 100, y: 24, title: \"Post-Peformance\", desc: \"After a full quarter, sales takes a snapshot of the 261 sales folks' performance.\" }\n]\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1197,"value":"$53","pinned":false,"hidden":false,"mode":"ojs","author":"$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":1221,"value":"annotationCSS = {\n const style = document.createElement(\"style\");\n style.textContent = `\n .anno-container { position: relative; }\n .anno-tip {\n position: absolute;\n min-width: 220px;\n max-width: 320px;\n padding: 10px 12px;\n font: 14px/1.35 system-ui, -apple-system, Segoe UI, Roboto, Helvetica, Arial, sans-serif;\n background: rgba(20, 20, 20, 0.92);\n color: #fff;\n border-radius: 8px;\n box-shadow: 0 8px 24px rgba(0,0,0,0.25);\n }\n svg .anno-icon { fill: white; stroke: #111; stroke-width: 2; filter: drop-shadow(0 2px 4px rgba(0,0,0,.3)); }\n svg .anno-icon-char { font: 700 12px/1 system-ui, -apple-system, Segoe UI, Roboto, Helvetica, Arial, sans-serif; fill: #111; }\n svg g[role=\"button\"]:focus circle { outline: 3px solid #4c9ffe; outline-offset: 2px; }\n `;\n document.head.appendChild(style);\n return true; // any value; the cell’s value won’t be shown if you don’t embed it\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1270,"value":"**Success Criteria for the Pilot**\n\n100% of low-tenure sales folks (261) to take pre-assessment\n\n30% of sales folks (80) to complete the Pilot\n\n85 or above average post-assessment score\n\nMinimum of 4.4 satisfaction rate (1 - 5 scale where 1 is very dissatisfied and 5 is very satisfied)*\n\n30% average learning gain minimum (calculated as normalized learning change with matched data, Marx and Cummings 2007)\n\nMatched data means that you should only include sales folks who completed BOTH the pre- and post-assessments as the normalized learning change comes from individual learning gains. \n\n\\*Note: The TYG team has excluded one outlier from the feedback data. The 99.58% is due to a rounding mistake. The team learned the hard way that they should never round numbers if they're planning to do more calculations with them. \n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"criteria","database":"$undefined"},{"id":1336,"value":"dash1 = FileAttachment(\"dash1.jpg\").image()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1277,"value":"$54","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1283,"value":"$55","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1279,"value":"quartiles(pilotComp, \"feedback_star\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1285,"value":"quartilesWithOutliers(pilotComp, \"feedback_star\")","pinned":true,"hidden":false,"mode":"ojs","author":"$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":"$undefined","output":"shaqsong","database":"$undefined"},{"id":1340,"value":"$56","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"grammarLayerText","database":"$undefined"},{"id":1343,"value":"grammarLayer = FileAttachment(\"DataClassificationSheet (2).jpg\").image()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1366,"value":"corrPlot = Plot.plot({\n title: \"Learning Gain vs. Time Spent in Pilot\",\n subtitle: \"Scatter + linear regression\",\n grid: true,\n marks: [\n Plot.dot(pilotComp, {\n x: \"Duration\",\n y: \"learn_gain\",\n opacity: 0.6,\n r: 4,\n tip: true\n }),\n\n Plot.linearRegressionY(pilotComp, {\n x: \"Duration\",\n y: \"learn_gain\",\n stroke: \"black\",\n strokeWidth: 2\n })\n ],\n x: { label: \"Hours spent in pilot\" },\n y: { label: \"Learning gain (%)\" }\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1368,"value":"Plot.linearRegressionY(pilotComp, {\n x: \"Duration\",\n y: \"learn_gain\",\n stroke: \"black\"\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1373,"value":"longScores = salesfinal_dataset\n .flatMap(d => [\n {phase: \"Pre\", score: d.pre_score},\n {phase: \"Post\", score: d.score}\n ])\n .filter(d => d.score != null)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1387,"value":"num = (v) => {\n if (v === null || v === undefined || v === \"\") return NaN;\n return +String(v).replace(/[^0-9.-]/g, \"\");\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1390,"value":"peek = {\n const d = pilotComp[0];\n return {\n keys: Object.keys(d),\n pre_score: d.pre_score, pre_type: typeof d.pre_score,\n score: d.score, score_type: typeof d.score,\n nonNullScores: pilotComp.filter(d => d.score != null).length\n };\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1382,"value":"longScoresMatched = pilotComp\n .map(d => ({pre: num(d.pre_score), post: num(d.score)}))\n .filter(d => Number.isFinite(d.pre) && Number.isFinite(d.post))\n .flatMap(d => [\n {phase: \"Pre\", score: d.pre},\n {phase: \"Post\", score: d.post}\n ])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1375,"value":"Plot.plot({\n height: 340,\n marginLeft: 55,\n marginBottom: 45,\n marginTop: 45,\n x: {domain: [\"Pre\", \"Post\"], label: null},\n y: {domain: [0, 100], grid: true, label: \"Pre-Assessment and Post-Assessment Mean Scores\"},\n color: {domain: [\"Pre\", \"Post\"], range: [\"#86b6ef\", \"#184f95\"], legend: false},\n marks: [\n Plot.barY(phaseMeans, {x: \"phase\", y: \"mean\", fill: \"phase\"}),\n Plot.text(phaseMeans, {\n x: \"phase\", y: \"mean\",\n text: (d) => d.mean.toFixed(1),\n dy: -8, fontWeight: 600\n })\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1384,"value":"Plot.plot({\n height: 340,\n marginLeft: 55,\n marginTop: 45,\n x: {domain: [\"Pre\", \"Post\"], label: null},\n y: {domain: [0, 100], grid: true, label: \"Pre-Assessment and Post-Assessment Mean Scores (Pilot Group\"},\n color: {domain: [\"Pre\", \"Post\"], range: [\"#86b6ef\", \"#184f95\"], legend: false},\n marks: [\n Plot.barY(longScoresMatched, Plot.groupX(\n {y: \"mean\"},\n {x: \"phase\", y: \"score\", fill: \"phase\"}\n )),\n Plot.text(longScoresMatched, Plot.groupX(\n {y: \"mean\", text: (S) => d3.mean(S).toFixed(1)},\n {x: \"phase\", y: \"score\", text: \"score\", dy: -8, fontWeight: 600}\n )),\n Plot.ruleY([0])\n ]\n})\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1400,"value":"phaseMeans = [\n {phase: \"Pre\", mean: d3.mean(longScores.filter(d => d.phase === \"Pre\"), d => d.score)},\n {phase: \"Post\", mean: d3.mean(longScores.filter(d => d.phase === \"Post\"), d => d.score)}\n]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1378,"value":"Plot.plot({\n height: 340,\n marginLeft: 55,\n marginBottom: 45,\n marginTop: 45,\n x: {domain: [\"Pre\", \"Post\"], label: null},\n y: {domain: [70, 100], grid: true, label: \"Pre-Assessment and Post-Assessment Mean Scores\"},\n color: {domain: [\"Pre\", \"Post\"], range: [\"#86b6ef\", \"#184f95\"], legend: false},\n marks: [\n Plot.barY(phaseMeans, {x: \"phase\", y: \"mean\", fill: \"phase\"}),\n Plot.text(phaseMeans, {\n x: \"phase\", y: \"mean\",\n text: (d) => d.mean.toFixed(1),\n dy: -8, fontWeight: 600\n }),\n Plot.text(phaseMeans, {\n x: \"phase\",\n text: \"phase\",\n frameAnchor: \"bottom\",\n dy: 24,\n fontSize: 14,\n fill: \"white\" ,\n fontWeight: 600\n })\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1416,"value":"regionMeans = d3.groups(\n pilotComp.filter(d => Number.isFinite(num(d.pre_score)) && Number.isFinite(num(d.score))),\n d => d.region\n )\n .flatMap(([region, rows]) => [\n {region, phase: \"Pre\", mean: d3.mean(rows, (d) => num(d.pre_score)), n: rows.length},\n {region, phase: \"Post\", mean: d3.mean(rows, (d) => num(d.score)), n: rows.length}\n ])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1423,"value":"regionMeansPerformance = d3.groups(\n salesfinal_dataset.filter(d => Number.isFinite(num(d.performance_before_training)) && Number.isFinite(num(d.performance))),\n d => d.region\n )\n\n .flatMap(([region, rows]) => [\n {region, phase: \"Pre\", mean: d3.mean(rows, (d) => num(d.performance_before_training)), n: rows.length},\n {region, phase: \"Post\", mean: d3.mean(rows, (d) => num(d.performance)), n: rows.length}\n ])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1418,"value":"Plot.plot({\n height: 360,\n marginLeft: 55,\n marginBottom: 55,\n marginTop: 40,\n fx: {label: null},\n x: {domain: [\"Pre\", \"Post\"], axis: null},\n y: {domain: [0, 100], grid: true, label: \"Assessment Score Mean\"},\n color: {domain: [\"Pre\", \"Post\"], range: [\"#86b6ef\", \"#184f95\"], legend: true},\n marks: [\n Plot.barY(regionMeans, {fx: \"region\", x: \"phase\", y: \"mean\", fill: \"phase\"}),\n Plot.text(regionMeans, {\n fx: \"region\", x: \"phase\", y: \"mean\",\n text: (d) => d.mean.toFixed(1),\n dy: 18,\n fill: (d) => d.phase === \"Post\" ? \"white\" : \"#0b0b0b\",\n fontWeight: 600\n }),\n Plot.text(regionMeans, {\n fx: \"region\", x: \"phase\",\n text: \"phase\",\n frameAnchor: \"bottom\",\n dy: 24,\n fontSize: 12,\n fontWeight: 600\n })\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1427,"value":"Plot.plot({\n height: 360,\n marginLeft: 55,\n marginBottom: 55,\n marginTop: 40,\n fx: {label: null},\n x: {domain: [\"Pre\", \"Post\"], axis: null},\n y: {domain: [0, 100], grid: true, label: \"Assessment Score Mean\"},\n color: {domain: [\"Pre\", \"Post\"], range: [\"#86b6ef\", \"#184f95\"], legend: true},\n marks: [\n Plot.barY(regionMeansPerformance, {fx: \"region\", x: \"phase\", y: \"mean\", fill: \"phase\"}),\n Plot.text(regionMeansPerformance, {\n fx: \"region\", x: \"phase\", y: \"mean\",\n text: (d) => d.mean.toFixed(1),\n dy: 5,\n fill: (d) => d.phase === \"Post\" ? \"white\" : \"#0b0b0b\",\n fontWeight: 600\n }),\n Plot.text(regionMeansPerformance, {\n fx: \"region\", x: \"phase\",\n text: \"phase\",\n frameAnchor: \"bottom\",\n dy: 24,\n fontSize: 12,\n fontWeight: 600\n })\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1436,"value":"$57","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1455,"value":"display(dlButton(comparison, \"visual_comparison\"))","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1457,"value":"display(dlButton(relationship, \"visual_time\"))","pinned":true,"hidden":false,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W58","stdlib":"1"}} 24:null