2d:null 2e:["$","title",null,{"children":"Untitled | Office for Statistics and Data of the Canton of Zurich | Observable"}] 33:T810,data = FileAttachment("freundschaftsbuch@1.tsv") .tsv() .then(function (data) { data.forEach(function (d) { d.Alter = +d.Alter; d.Pendelzeit = +d.Pendelzeit; d.Kurznachrichten = +d.Kurznachrichten; d.Kochen.toLowerCase() == "sehr gut" ? (d.Kochen = 4) : d.Kochen.toLowerCase() == "gut" ? (d.Kochen = 3) : d.Kochen.toLowerCase() == "nicht so gut" ? (d.Kochen = 2) : d.Kochen.toLowerCase() == "gar nicht" ? (d.Kochen = 1) : (d.Kochen = 0); d.Witze_erzählen.toLowerCase() == "sehr gut" ? (d.Witze_erzählen = 4) : d.Witze_erzählen.toLowerCase() == "gut" ? (d.Witze_erzählen = 3) : d.Witze_erzählen.toLowerCase() == "nicht so gut" ? (d.Witze_erzählen = 2) : d.Witze_erzählen.toLowerCase() == "gar nicht" ? (d.Witze_erzählen = 1) : (d.Witze_erzählen = 0); d.Zelt_aufbauen.toLowerCase() == "sehr gut" ? (d.Zelt_aufbauen = 4) : d.Zelt_aufbauen.toLowerCase() == "gut" ? (d.Zelt_aufbauen = 3) : d.Zelt_aufbauen.toLowerCase() == "nicht so gut" ? (d.Zelt_aufbauen = 2) : d.Zelt_aufbauen.toLowerCase() == "gar nicht" ? (d.Zelt_aufbauen = 1) : (d.Zelt_aufbauen = 0); d.Notlügen_erfinden.toLowerCase() == "sehr gut" ? (d.Notlügen_erfinden = 4) : d.Notlügen_erfinden.toLowerCase() == "gut" ? (d.Notlügen_erfinden = 3) : d.Notlügen_erfinden == "Nicht so gut" ? (d.Notlügen_erfinden = 2) : d.Notlügen_erfinden.toLowerCase() == "gar nicht" ? (d.Notlügen_erfinden = 1) : (d.Notlügen_erfinden = 0); d.Stricken.toLowerCase() == "sehr gut" ? (d.Stricken = 4) : d.Stricken.toLowerCase() == "gut" ? (d.Stricken = 3) : d.Stricken.toLowerCase() == "nicht so gut" ? (d.Stricken = 2) : d.Stricken.toLowerCase() == "gar nicht" ? (d.Stricken = 1) : (d.Stricken = 0); }); return data; })34:T5d7,## Standardmasse ### Lagemasse: - Modus
Der am häufigsten vorkommende Wert
- Median
Der Wert in der Mitte
- Arithmetisches Mittel (Mittelwert, Durchschnitt)
Summe aller Werte dividiert durch Anzahl Werte
|Skalenniveau der Variable |Modus |Median |Mittelwert | |---|---|---|---| |Nominal |ja |nein |nein | |Ordinal |ja |ja |nein | |Intervall |ja |ja |ja | ### Streuungsmasse - Spannweite/Range
Differenz zwischen dem größten (Maximum) und dem kleinsten Wert (Minimum) - Interquartilsabstand
50% der Werte in der Mitte der Verteilung (1. 2. 3. und 4. Quartil) - Varianz
Die Abstände aller Werte zum Mittelwert werden einfach aufsummiert. Aber -> aufheben plus und minus; -> quadrat; dann aber: je emhr Messungen desto grösser -> dividiert durch Anzahl. Die Varianz ist also die Summe der quadrierten Abweichungen vom Mittelwert, dividiert durch die Anzahl der Messwerte n. ![image@1.png](${await FileAttachment("image@1.png").url()}) - Standardabweichung
Wurzel aus der Varianz ![image@2.png](${await FileAttachment("image@2.png").url()}) Die Standardabweichung ist eine Kenngröße, die angibt, wie gut ein Mittelwert eine Verteilung repräsentiert. (Beispiel: Alter der Klasse) |Skalenniveau der Variable |Modus |Median |Mittelwert | |---|---|---|---| |Nominal |nein |nein |nein | |Ordinal |ja |ja |nein | |Intervall |ja |ja |ja |35:T93f,sameStats={ let margin = {top: 10, bottom:10,left:20,right:10}; let width = d3.min([450,(widthOrg+margin.left+margin.right)]); let height = width; const container = d3.select(DOM.svg(width+margin.left+margin.right, height+margin.top+margin.bottom)); const svg = container.append('g') .attr('width', width) .attr('height', height) .attr('transform', 'translate(' + margin.left + '.' + margin.top + ')'); let xScale = d3.scaleLinear() let rgX = d3.extent(datasaurus.filter(a => a.dataset === selectData), d => +d.x).reduce((a,b) => b-a)*0.1; xScale.domain( [ 0, 100 ] ).nice(); xScale.range([margin.left, width - margin.right]); let xAxis = g => g .attr("transform", `translate(0,${height - margin.bottom})`) .call(d3.axisBottom(xScale).ticks(width / 80)); svg.append("g") .call(xAxis); const yScale = d3.scaleLinear() let rgY = d3.extent(datasaurus.filter(a => a.dataset === selectData), d => +d.y).reduce((a,b) => b-a)*0.1 yScale.domain( [ 0, 100 ] ).nice() yScale.range([height - margin.bottom, margin.top]); let yAxis = g => g .attr("transform", `translate(${margin.left},0)`) .call(d3.axisLeft(yScale).ticks(6)) .call(g => g.append("text") .attr("fill", "#000") .attr("x", 5) .attr("y", margin.top) .attr("dy", "0.32em") .attr("text-anchor", "start") .attr("font-weight", "bold") .text(`${selectData} Data`)) svg.append("g") .call(yAxis); let g = svg.append("g") .attr("stroke", "#000") .attr("stroke-opacity", 0.2); let kreise = g .selectAll("circle") .data(datasaurus.filter(a => a.dataset === selectData)) kreise.enter().append("circle") .attr("cx", (d,i) => xScale(+d.x)) .attr("cy", (d,i) => yScale(+d.y)) .attr("fill", "black") .attr("r", 3.5); kreise.transition() .duration(900) .attr("cx", (d,i) => xScale(+d.x)) .attr("cy", (d,i) => yScale(+d.y)) .attr("fill", "black") .attr("r", 3.5); let line = d3.line() .x(d => xScale(d.x)) .y(d => yScale(d.y)) svg.append('path') .datum(regressionPoints) .attr('d', line) .attr('stroke','tomato') .attr('transform','translate(-50,0)'); return container.node(); }36:T5ed,function input(config) { let {form, type = "text", attributes = {}, action, getValue, title, description, submit, options} = config; if (!form) form = html``; const input = form.input; Object.keys(attributes).forEach(key => { const val = attributes[key]; if (val != null) input.setAttribute(key, val); }); if (submit) form.append(html``); form.append(html``); if (title) form.prepend(html`

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31:{"id":"c943dfdaaae3fc53","slug":"hslu_tag3","title":"","thumbnail":"4e87bc3128b6b9b3e45a00d5df2e4b8dca5e260b308cfd3238db17b6b36fab7b","access_level":"public","create_time":"$D2020-09-25T14:55:15.786Z","update_time":"$D2021-10-05T13:47:40.034Z","view_time":null,"first_public_time":"$D2020-09-25T16:05:58.928Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"4b23743d6fe05d13","login":"statistikzh","name":"Office for Statistics and Data of the Canton of Zurich","avatar_url":"https://avatars-next.observableusercontent.com/61364635cd5db7a914727e7625ac41daa90fedeba239e3d2ce93b57a9ad3bce9","tier":"pro"},"version":691,"publish_version":691,"role":null,"authored":false,"authors":[{"type":"individual","id":"37f1612f224a0555","login":"mmznrstat","name":"Matthias 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FileAttachment(\"freundschaftsbuch@1.tsv\").tsv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":555,"value":"Rekodierung\n\nSehr gut = 4, Gut = 3, Nicht so gut = 2, gar nicht gut = 1","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":498,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":666,"value":"anz_kochen = d3.rollups(\n data,\n (v) => d3.count(v, (d) => d.Kochen),\n (d) => d.Kochen\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":680,"value":"scale = d3.scaleOrdinal()\n .domain([4,3,2,1])\n .range([\"sehr gut\",\"gut\",\"sicht so gut\",\"gar nicht\"])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":674,"value":"anz_k = (function () {\n let anz = [];\n for (let i = 0; i < anz_kochen.length; i++) {\n let obj = {};\n obj.label = scale(anz_kochen[i][0]);\n obj.value = anz_kochen[i][1];\n anz.push(obj);\n }\n return anz;\n})()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":632,"value":"Plot.plot({\n marks: [Plot.barY(anz_k, { x: \"label\", y: \"value\" })]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":518,"value":"Plot.plot({\n x: {\n domain: d3.sort(data, (d) => -d.Pendelzeit).map((d) => d.Name)\n },\n marks: [Plot.barY(data, { x: \"Name\", y: \"Pendelzeit\" })]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":540,"value":"Plot.plot({\n x: {\n domain: d3.sort(data, (d) => -d.Kurznachrichten).map((d) => d.Name)\n },\n marks: [Plot.barY(data, { x: \"Name\", y: \"Kurznachrichten\" })]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":453,"value":"$34","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":596,"value":"variable = \"Kurznachrichten\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":576,"value":"Mittelwert:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":551,"value":"d3.sum(data, (d) => d[variable]) / d3.count(data, (d) => d[variable])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":579,"value":"Median ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":560,"value":"d3.median(data, (d) => d[variable])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":582,"value":"Varianz","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":571,"value":"d3.variance(data, (d) => d[variable])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":592,"value":"Standardabweichung","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":566,"value":"d3.deviation(data, (d) => d[variable])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":266,"value":"md`## Eindimensional`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":155,"value":"viewof sample2 = select_distribution(\"Skewed left\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":35,"value":"viewof STATchart1 = {\n const height = 200;\n const svg = d3\n .create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height])\n .attr('id', 'STATchart1');\n \n let scaleX = d3.scaleLinear() \n .domain(range)\n .range([margin.left,width-margin.left -margin.right])\n \n let xAxis = svg.append('g')\n .attr(\"transform\", `translate(0,${height - margin.bottom})`)\n .call(d3.axisBottom(scaleX))\n \n let scaleY = d3.scaleLinear() \n .domain([0,1])\n .range([margin.top,height-margin.top -margin.bottom])\n \n let circles = svg.selectAll('circle')\n .data(sample2);\n let y = [];\n for (let i = 0;i scaleX(d))\n .attr('cy', (d,i) => scaleY(y[i]))\n .attr('r',2);\n \naddLine('Mittelwert',mittelwert,svg,scaleX,height)\n \naddLine('Median',median,svg,scaleX,height)\n \n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":90,"value":"viewof STATchart12 = {\n const height = 250;\n const svg = d3\n .create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height])\n .attr('id', 'STATchart1');\n \n \n let yMax = d3.max(häufigkeiten, d=> d.length)\n \n let yScale = d3.scaleLinear()\n .domain([yMax,0])\n .range([margin.top,height])\n \n let balken = svg.selectAll('rect')\n .data(häufigkeiten);\n \n let weite = (width-margin.left-margin.right)/häufigkeiten.length;\n \n balken.enter()\n .append('rect')\n .attr('x',(d,i) => margin.left+i*weite)\n .attr('y',d => yScale(d.length))\n .attr('width',weite)\n .attr('height',d=> height-yScale(d.length));\n \n \n return svg.node();\n \n \n \n \n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":509,"value":"Plot.plot({\n marks: [Plot.dot(data, { x: \"Pendelzeit\", y: \"Kurznachrichten\" })]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":475,"value":"## Zwei Variabeln\n### Kovarianz:\nhttps://www.scribbr.de/statistik/kovarianz/\n![image@3.png](${await FileAttachment(\"image@3.png\").url()})\n\n
\n### Korrelationskoeffizient:
\n![image@4.png](${await FileAttachment(\"image@4.png\").url()})\nKovarianz xy / (Standardabweichung x * Standardabweichung y)\n\n
\n### Regression\nIst der vermutete lineare Zusammenhang zweier intervallskalierter Merkmale gerichtet, kann eine einfache Regressionsanalyse durchführt werden. Im Falle einer gerichteten Hypothese wird angenommen, dass eine Variable durch die andere bedingt wird. \nIntelligenz -> Schulnoten","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":0,"value":"md`# Statistik kann in die Irre führen`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":237,"value":"md`Play @ HSLU, Data Literacy, Tag 2\n\nLaure Stadler, Matthias Mazenauer`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":270,"value":"md`## Zweidimensional`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":403,"value":"md`### Anscombe-Quartett`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":393,"value":"await FileAttachment(\"image.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":399,"value":"md`\n- Mittelwert von x: 9\n- Varianz von x: 11\n- Mittelwert von y: 7,5 (auf zwei Nachkommastellen exakt)\n- Varianz von y: 4,1 (auf eine Nachkommastelle exakt)\n- Korrelation zwischen x und y: 0,816 (auf 3 Nachkommastellen exakt)\n- Lineare Regression: y = 3 + 0,3x (auf 2, bzw. 3 Nachkommastellen exakt)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":259,"value":"md`### Datasaurus by Alberto Cairo`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":284,"value":"viewof selectData = radio({\n title: 'Data sets',\n description: \"Choose a data set to view.\",\n options: dataSets,\n value: dataSets[1]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":283,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":358,"value":"viewof sumTable = html`\n \n

Descriptive Statistik:

\n \n\n\n\n\n\n\n\n\n\n\n\n\n

Mittelwert	Median	Standard Abweichung	Varianz
${descript[0]}	${descript[1]}	${descript[5]}	${descript[4]}

\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":438,"value":"md`## Anhang`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":251,"value":"datasaurus = d3.tsv(\"https://statistikzh.static.observableusercontent.com/files/a80ffb112818e46f0ed399a28a5d3a47f4548711fcde8299717b57711420a4a57052c74957783ed1daf86fc760474c1c76b54bb3d73a684bec67d39d15e0c1e1?response-content-disposition=attachment%3Bfilename*%3DUTF-8%27%27DatasaurusDozen.tsv&Expires=1601337600000&Key-Pair-Id=APKAJCHFJLLLU4Y2WVSQ&Signature=lisILQ5szrPopXRlsfSKFJ6qwHvV5HUFo2RE2s6sCQSIhy3IteApPfZ31WYOlhsOF-ZVqxqThqt3l9zlWx5vLizGk6U5AJYUjZQ4PkOG-W3J7Jit8~~8-COFckUXPAtY-9tDVtGFAc4Tq9JY43Y0B8ma-CZHDFk92a1RLq7NkiF-Ic9tieMs7jJNjUsMolDZrWxSxuVj-yQJizOtjmwpCGUVtb2FXJGEXiqGk~5QUFjsP4rJCHYfzcnYmRrxZm5Bt8spzIQU7UavwkQNcRKoMSq7KmBgcwQT3sOztydvfr1KhzacFdKFE5SysBVIk6shq3O6BR6feA1vZqm58gsSGg__\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":367,"value":"linearRegression = ss.linearRegression(datasaurus.filter(a => a.dataset === selectData).map(d => [+d.x, +d.y]))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":366,"value":"linearRegressionLine = ss.linearRegressionLine(linearRegression)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":373,"value":"regressionPoints = {\n const firstX = +datasaurus.filter(a => a.dataset === selectData)[0].x;\n const lastX = +datasaurus.filter(a => a.dataset === selectData).slice(-1)[0].x;\n const xCoordinates = [margin.left, width-margin.right];\n \n return xCoordinates.map(d => ({\n x: d, // We pick x and y arbitrarily, just make sure they match d3.line accessors\n y: linearRegressionLine(d)\n }));\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":347,"value":"descript = math.round([d3.mean(datasaurus.filter(a => a.dataset === selectData), d => +d.x),\n d3.median(datasaurus.filter(a => a.dataset === selectData), d => +d.x),\n ss.mode(datasaurus.filter(a => a.dataset === selectData).map(a => +a.x)),\n d3.max(datasaurus.filter(a => a.dataset === selectData), d => +d.x) \n - d3.min(datasaurus.filter(a => a.dataset === selectData), d => +d.x),\n d3.variance(datasaurus.filter(a => a.dataset === selectData), d => +d.x),\n d3.deviation(datasaurus.filter(a => a.dataset === selectData), d => +d.x),\n ss.sampleKurtosis(datasaurus.filter(a => a.dataset === selectData).map(a => +a.x)),\n ss.sampleSkewness(datasaurus.filter(a => a.dataset === selectData).map(a => +a.x))\n ],1)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":321,"value":"datasaurus.filter(a => a.dataset === \"dino\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":287,"value":"dataSets = distincts(datasaurus, \"dataset\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":294,"value":"function distincts(array,element) {\n var flags = [], output = [], l = array.length, i;\n \n for( i=0; i (Math.random() > 0.3 ? r20() : r21()) },\n { label: \"Ausreisser\", random: () => (Math.random() > 0.1 ? r30() : r31()) },\n { label: \"Linksschief\", random: d3.randomLogNormal(2) },\n { label: \"Rechtsschief\", random: () => max - r4() }\n ];\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":153,"value":"function distribution(label) {\n return select_distribution(label).value\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":214,"value":"addLine = function(text,value,container,xScale,height) {\n container\n .append('text')\n .attr('y', '-.2em')\n .attr('x', -4)\n .attr('font-size', '12px')\n .attr('text-anchor', 'start')\n .attr(\"fill\", \"darkorange\")\n .text(text)\n .attr(\n 'transform',\n 'translate(' +\n xScale(value) +\n ',' +\n (height-10) +\n ')rotate(-90)'\n )\n .style('font-family', 'Helvetica, Arial, sans-serif');\n \n container\n .append('line')\n .attr('x1', xScale(value))\n .attr('x2', xScale(value))\n .attr('y1', margin.top)\n .attr('y2', height)\n .attr(\"stroke\", \"darkorange\")\n .attr(\"stroke-width\", 1.5)\n .attr(\"stroke-linejoin\", \"round\")\n .attr(\"stroke-linecap\", \"round\");\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":160,"value":"max = range[1]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":158,"value":"function select_distribution(\n label,\n description = \"Random distribution\",\n length = anzahl\n) {\n const form = html``;\n form.oninput = () => {\n form.value = Float32Array.from(\n { length },\n randomDistributions[+form.radio.value].random\n );\n };\n form.onchange = () => {\n // Safari…\n form.value = form.radio.value;\n form.dispatchEvent(new CustomEvent(\"input\"));\n };\n form.oninput();\n return form;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":45,"value":"margin = ({left:20,right:20,top:10,bottom:60})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":14,"value":"import {slider} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5,"value":"d3 = require('d3')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":354,"value":"math =require(\"https://cdnjs.cloudflare.com/ajax/libs/mathjs/5.10.3/math.min.js\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":349,"value":"ss = require(\"simple-statistics@6.0.0/dist/simple-statistics.min.js\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":299,"value":"function radio(config = {}) {\n let {value: formValue, title, description, submit, options} = config;\n if (Array.isArray(config)) options = config;\n options = options.map(o => typeof o === \"string\" ? {value: o, label: o} : o);\n const form = input({\n type: \"radio\", title, description, submit, \n getValue: input => {\n const checked = Array.prototype.find.call(input, radio => radio.checked);\n return checked ? checked.value : undefined;\n }, \n form: html`\n \n `\n });\n form.output.remove();\n return form;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":428,"value":"widthOrg = width","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":301,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":230,"value":"html`