31:T5ed,html` ${['A', 'B', 'C', 'D'].map(d => html``)} ${ data .map(set => math.mean(set.map(d => d[0]))) .map(d => html``) } ${ data .map(set => math.var(set.map(d => d[0]))) .map(d => html``) } ${ data .map(set => math.mean(set.map(d => d[1]))) .map(d => Math.round(d * 100) / 100) .map(d => html``) } ${ data .map(set => math.var(set.map(d => d[1]))) .map(d => Math.round(d * 100) / 100) .map(d => html``) } ${ data .map(set => set.reduce(({ x, y }, d) => ({ x: [...x, d[0]], y: [...y, d[1]] }), {x: [], y: []})) .map(({ x, y }) => getCorrelation(x, y)) .map(d => Math.round(d * 100) / 100) .map(d => html``) } ${ data .map(set => regression.linear(set).string) .map(d => html``) } ${ data .map(set => regression.linear(set).r2) .map(d => html``) }

	${d}
Moyenne des X	${d}
Variance des X	${d}
Moyenne des Y	${d}
Variance des Y	${d}
Coefficient de corrélation	${d}
Régression linéaire	${d}
Coefficient de détermination	${d}

`32:[] 2f:{"title":"Quartet d'Anscombe","cells":[{"id":0,"value":"md`# Quartet d'Anscombe\n\nLe quartet d'Anscombe est constitué de quatre ensembles de données qui ont les mêmes propriétés statistiques simples mais qui sont en réalité très différents, ce qui se voit facilement lorsqu'on les représente sous forme de graphiques. Ils ont été construits en 1973 par le statisticien Francis Anscombe dans le but de démontrer l'importance de tracer des graphiques avant d'analyser des données, car cela permet notamment d'estimer l'incidence des données aberrantes sur les différentes indices statistiques que l'on pourrait calculer.\n\nDans la première page du premier chapitre de son ouvrage, The Visual Display of Quantitative Information, Edward Tufte utilise le quartet d'Anscombe pour démontrer l'importance de l'exploration graphique avant d'analyser un ensemble de données.\n\n[wikipedia](https://fr.wikipedia.org/wiki/Quartet_d%27Anscombe)\n\n## Les quatre ensembles de données\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":103,"value":"html`\n \n \n ${ ['A', 'B', 'C', 'D'].map(d => html``) }\n \n \n \n \n \n \n ${\n data[0].map((d,i) => ([...d, ...data[1][i], ...data[2][i], ...data[3][i]]))\n .map(line => html`\n ${line.map(cell => html``)}\n `)\n }\n \n

${d}
x	y	x	y	x	y	x	y
${cell}

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":262,"value":"md`## Les propriétés statistiques`","pinned":false,"hidden":false,"mode":"ojs"},{"id":190,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":264,"value":"md`## Les graphiques`","pinned":false,"hidden":false,"mode":"ojs"},{"id":44,"value":"svg``","pinned":false,"hidden":false,"mode":"ojs"},{"id":88,"value":"style = ({\n width: width / 2 * 0.8,\n height: (width / 2 * 0.8) * 0.5,\n dot: {\n radius: 5,\n fill: 'indianred',\n },\n line: {\n stroke: 'steelblue',\n },\n axis: {\n stroke: 'gray',\n fontSize: width / 90\n }\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":35,"value":"drawGraph = (dataIndex, x, y) => svg`\n\n ${ drawAxisX() }\n ${ drawAxisY() }\n ${ drawRegressionLine(data[dataIndex]) }\n ${ drawDots(data[dataIndex]) }\n \n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":137,"value":"drawAxisX = () => svg`\n \n ${\n [2,4,6,8,10,12,14,16,18]\n .map(d => svg`\n ${d}\n `)\n }\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":162,"value":"drawAxisY = () => svg`\n \n ${\n [4,6,8,10,12]\n .map(d => svg`\n ${d}\n `)\n }\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":74,"value":"drawRegressionLine = data => {\n const { x1, y1, x2, y2 } = getLinearRegression(data)\n return svg``\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":85,"value":"drawDots = data =>\n data\n .map(([x, y]) => ([xScale(x), yScale(y)]))\n .map(([cx, cy]) => svg``)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2,"value":"data = [\n [[10,8.04],[8,6.95],[13,7.58],[9,8.81],[11,8.33],[14,9.96],[6,7.24],[4,4.26],[12,10.84],[7,4.82],[5,5.68]],\n [[10,9.14],[8,8.14],[13,8.74],[9,8.77],[11,9.26],[14,8.1],[6,6.13],[4,3.1],[12,9.13],[7,7.26],[5,4.74]],\n [[10,7.46],[8,6.77],[13,12.74],[9,7.11],[11,7.81],[14,8.84],[6,6.08],[4,5.39],[12,8.15],[7,6.42],[5,5.73]],\n [[8,6.58],[8,5.76],[8,7.71],[8,8.84],[8,8.47],[8,7.04],[8,5.25],[19,12.5],[8,5.56],[8,7.91],[8,6.89]]\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":13,"value":"xScale = d3.scaleLinear().range([0, style.width]).domain([0, 20])","pinned":false,"hidden":false,"mode":"ojs"},{"id":18,"value":"yScale = d3.scaleLinear().range([style.height, 0]).domain([3, 13])","pinned":false,"hidden":false,"mode":"ojs"},{"id":69,"value":"getLinearRegression = dataset => {\n const [x1, y1] = regression.linear(dataset).predict(0)\n const [x2, y2] = regression.linear(dataset).predict(20)\n return { x1, y1, x2, y2 }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":206,"value":"getCorrelation = (x, y) => {\n // https://memory.psych.mun.ca/tech/js/correlation.shtml\n // refactored\n if (x.length !== y.length) { throw new Error('Length of x and y do not match') }\n const length = x.length\n\n const sum = arr => arr.reduce((r, c) => r + c, 0)\n const sum_x = sum(x)\n const sum_y = sum(y)\n const sum_xy = sum(x.map((d, i) => d * y[i]))\n const sum_x2 = sum(x.map(d => d * d))\n const sum_y2 = sum(y.map(d => d * d))\n \n const step1 = (length * sum_xy) - (sum_x * sum_y)\n const step2 = (length * sum_x2) - (sum_x * sum_x)\n const step3 = (length * sum_y2) - (sum_y * sum_y)\n const step4 = Math.sqrt(step2 * step3)\n return step1 / step4\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":8,"value":"d3 = require('d3-scale')","pinned":false,"hidden":false,"mode":"ojs"},{"id":61,"value":"regression = require('https://bundle.run/regression@2.0.1')","pinned":false,"hidden":false,"mode":"ojs"},{"id":184,"value":"math = require('https://bundle.run/mathjs@5.4.1')","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W32","stdlib":"1","titling":"auto"}