31:[] 2f:{"title":"Exponential Regression","cells":[{"id":0,"value":"md`# Exponential Regression\n\nExponential regressions take the form ${tex`y = ae^{bx}`}. Use [d3-regression](https://github.com/HarryStevens/d3-regression)'s d3.[regressionExp()](https://github.com/HarryStevens/d3-regression#regressionExp) like this:`","pinned":false,"hidden":false,"mode":"ojs"},{"id":36,"value":"regressionGenerator = d3.regressionExp()\n .x(d => d.x)\n .y(d => d.y)\n .domain([-2, 5.06]);","pinned":true,"hidden":false,"mode":"ojs"},{"id":200,"value":"md`d3.regressionExp() returns a configurable function with x- and y- accessors (which default to d => d[0] and d => d[1], respectively). If you want the resulting line to extend beyond the extents of your input data, you can specify a domain.\n\nWhen you pass data to the regression generator, you'll get back an array of points representing a curved line. You can use a d3.[line()](https://github.com/d3/d3-shape#line) to render it in SVG or Canvas:`","pinned":false,"hidden":false,"mode":"ojs"},{"id":32,"value":"lineGenerator = d3.line()\n .x(d => xScale(d[0]))\n .y(d => yScale(d[1]));","pinned":true,"hidden":false,"mode":"ojs"},{"id":39,"value":"{\n const svg = d3.select(DOM.svg(innerWidth + margin.left + margin.right, innerHeight + margin.top + margin.bottom))\n \n const g = svg.append(\"g\")\n .attr(\"transform\", `translate(${margin.left}, ${margin.top})`);\n\n g.append(\"g\")\n .attr(\"class\", \"axis\")\n .call(xAxisGenerator)\n .selectAll(\".tick line\")\n .classed(\"baseline\", d => d === 0);\n\n g.append(\"g\")\n .attr(\"class\", \"axis\")\n .attr(\"transform\", `translate(${innerWidth})`)\n .call(yAxisGenerator)\n .selectAll(\".tick line\")\n .classed(\"baseline\", d => d === 0);\n \n g.selectAll(\"circle\")\n .data(data)\n .enter().append(\"circle\")\n .attr(\"r\", 3)\n .attr(\"cx\", d => xScale(d.x))\n .attr(\"cy\", d => yScale(d.y));\n\n g.append(\"path\")\n .attr(\"class\", \"regression\")\n .datum(regressionGenerator(data))\n .attr(\"d\", lineGenerator);\n\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":230,"value":"md`This example comes from [Varsity Tutors](https://www.varsitytutors.com/hotmath/hotmath_help/topics/exponential-regression). The regression is described by the equation ${tex`y = ${regressionGenerator(data).a.toFixed(3)}e^{${regressionGenerator(data).b.toFixed(3)}x}`}. The coefficient of determination, or ${tex`R^2`}, is ${regressionGenerator(data).rSquared.toFixed(3)}.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":58,"value":"md`[**d3-regression**](https://github.com/harrystevens/d3-regression) is a [D3.js](https://d3js.org/) module for calculating statistical regressions from two-dimensional data. It is dependency-free, and its API exposes configurable functions that transform input data, in the manner of other D3 modules.\n\nFor an explanation of all of d3-regression's capabilities, see [this tutorial](https://observablehq.com/d/7b51129a2feec1d0).`","pinned":false,"hidden":false,"mode":"ojs"},{"id":149,"value":"md`## Data`","pinned":false,"hidden":false,"mode":"ojs"},{"id":9,"value":"data = [{x: 0, y: 3}, {x: 1, y: 7}, {x: 2, y: 10}, {x: 3, y: 24}, {x: 4, y: 50}, {x: 5, y: 95}]","pinned":false,"hidden":false,"mode":"ojs"},{"id":70,"value":"md`## Dimensions`","pinned":false,"hidden":false,"mode":"ojs"},{"id":16,"value":"margin = ({left: 29, right: 8, top: 11, bottom: 26});","pinned":false,"hidden":false,"mode":"ojs"},{"id":17,"value":"base = Math.min(width, 400)","pinned":false,"hidden":false,"mode":"ojs"},{"id":19,"value":"innerWidth = base - margin.left - margin.right;","pinned":false,"hidden":false,"mode":"ojs"},{"id":21,"value":"innerHeight = base - margin.top - margin.bottom;","pinned":false,"hidden":false,"mode":"ojs"},{"id":143,"value":"md`## Scales`","pinned":false,"hidden":false,"mode":"ojs"},{"id":23,"value":"xScale = d3.scaleLinear()\n .domain([-2, 10])\n .range([0, innerWidth]);","pinned":false,"hidden":false,"mode":"ojs"},{"id":26,"value":"yScale = d3.scaleLinear()\n .domain([-10, 100])\n .range([innerHeight, 0]);","pinned":false,"hidden":false,"mode":"ojs"},{"id":145,"value":"md`## Axes`","pinned":false,"hidden":false,"mode":"ojs"},{"id":28,"value":"xAxisGenerator = d3.axisBottom(xScale)\n .tickSize(innerHeight + 10)","pinned":false,"hidden":false,"mode":"ojs"},{"id":30,"value":"yAxisGenerator = d3.axisLeft(yScale)\n .tickSize(innerWidth + 10)","pinned":false,"hidden":false,"mode":"ojs"},{"id":147,"value":"md`## Styles`","pinned":false,"hidden":false,"mode":"ojs"},{"id":34,"value":"html`\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":151,"value":"md`## Imports\n\nTo make the scatterplot, I used [d3-axis](https://github.com/d3/d3-axis), [d3-scale](https://github.com/d3/d3-scale), and [d3-selection](https://github.com/d3/d3-selection), and [d3-shape](https://github.com/d3/d3-shape). I also am loading [d3-regression](https://github.com/HarryStevens/d3-regression) with the rest of the D3.js modules.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":13,"value":"d3 = require(\"d3-array@3\", \"d3-axis@3\", \"d3-regression@2\", \"d3-scale@4\", \"d3-shape@3\", \"d3-selection@3\")","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W31","stdlib":"1","titling":"auto"}