31:Tffe,function normal_pic(z1, z2, size) { let width, height; if (size) { width = size; height = size / 2; } else { width = 400; height = 200; } let pad = 5; let div = d3 .create('div') .attr('class', 'pic') .style('width', `${width}px`) .style('height', `${height + 40}px`) .style('text-align', 'center'); let pic_container = div .append('div') .style('width', `${width + 1}px`) .style('height', `${height + 1}px`); let pic = pic_container .append('svg') .attr('width', width) .attr('height', height) .style('background-color', 'white'); let xmin = -3.4; let xmax = 3.4; let ymin = -0.05; let ymax = 0.5; let x_scale = d3 .scaleLinear() .domain([xmin, xmax]) .range([pad, width - pad]); let xmin2 = 49; let xmax2 = 151; let x_scale2 = d3 .scaleLinear() .domain([xmin2, xmax2]) .range([pad, width - pad]); let y_scale = d3 .scaleLinear() .domain([ymin, ymax]) .range([height - pad, pad]); let pts_to_path = d3 .line() .x(function(d) { return x_scale(d[0]); }) .y(function(d) { return y_scale(d[1]); }); let dx = 0.01; let fill_pts; if (!(isNaN(parseFloat(z1)) || isNaN(parseFloat(z2)))) { let Z1, Z2; if (z1 < xmin) { Z1 = xmin; } else { Z1 = z1; } if (z1 > xmax) { Z1 = xmax; } else { Z1 = z1; } if (z1 === -Infinity) { Z1 = -4; } if (z2 < xmin) { Z2 = xmin; } else { Z2 = z2; } if (z2 > xmax) { Z2 = xmax; } else { Z2 = z2; } fill_pts = d3.range(Z1, Z2, dx).map(x => [x, normal_pdf(x)]); fill_pts = [[Z1, 0]].concat(fill_pts).concat([[Z2, 0]]); pic .append('path') .attr('class', 'fill') .attr('d', pts_to_path(fill_pts)) .attr('stroke', 'none') .attr('fill', 'lightblue'); } pic .append('line') .attr('x1', x_scale(xmin)) .attr('x2', x_scale(xmax)) .attr('y1', y_scale(0)) .attr('y2', y_scale(0)) .attr('stroke', 'black'); pic .append('line') .attr('x1', x_scale(0)) .attr('x2', x_scale(0)) .attr('y1', y_scale(ymin)) .attr('y2', y_scale(ymax)) .attr('stroke', 'black'); let x_ticks = [-3, -2, -1, 1, 2, 3]; pic .selectAll('line.xtick') .data(x_ticks) .join('line') .attr('class', 'xtick') .attr('x1', x => x_scale(x)) .attr('x2', x => x_scale(x)) .attr('y1', y_scale(0)) .attr('y2', y_scale(ymin / 3)) .attr('stroke', 'black'); pic .selectAll('text.xtick') .data(x_ticks) .join('text') .attr('class', 'xtick') .attr('x', x => x_scale(x) - width / 100) .attr('y', y_scale(1.05 * ymin)) .text(x => x); let x_ticks2 = [55, 70, 85, 100, 115, 130, 145]; pic .selectAll('line.xtick') .data(x_ticks2) .join('line') .attr('class', 'xtick') .attr('x1', x => x_scale2(x)) .attr('x2', x => x_scale2(x)) .attr('y1', y_scale(0)) .attr('y2', y_scale(ymin / 3)) .attr('stroke', 'black'); pic .selectAll('text.xtick') .data(x_ticks2) .join('text') .attr('class', 'xtick') .attr('x', x => x_scale2(x) - width / 100) .attr('y', y_scale(1.05 * ymin)) .text(x => x); let pts = d3.range(xmin, xmax + dx, dx).map(x => [x, normal_pdf(x)]); console.log([xmin, xmax, dx]); pic .append('path') .attr('class', 'curve') .attr('d', pts_to_path(pts)) .attr('stroke', 'black') .attr('stroke-width', 2.5) .attr('fill', 'none'); if (!isNaN(parseFloat(z2))) { let p, tex_string; if (z1 === -Infinity) { p = 0.5 + 0.5 * ss.errorFunction(z2 / Math.sqrt(2)); tex_string = `have IQ =< ${Math.round(ff(z2*15+100))}`; } else { p = .5 * (ss.errorFunction(z2 / Math.sqrt(2)) - ss.errorFunction(z1 / Math.sqrt(2))); tex_string = `have IQ between ${Math.round(ff(z1*15+100))} and ${Math.round(ff(z2)*15+100)}`; } div.append(() => md`${ff(p*100)}% people ${tex_string}`); } return div.node(); }32:[] 2f:{"title":"Intelligence Quotient (IQ)","cells":[{"id":186,"value":"zstar_description = md`# Intelligence Quotient (IQ)\n\n## IQ test\n\nIQ Test, or intelligence quotient test, is a measure of your ability to reason and solve problems. It essentially reflects how well you did on a specific test as compared to other people of your age group. While tests may vary, the average IQ on many tests is 100 and 68.2% of scores lie somewhere between 85 and 115 (1 standard deviation or -1< Z-score <1) and 95.4% of scores lie somewhere between 70 and 130 (2 standard deviations or -2< Z-score <2). \n\nIn other word, IQ test results follow Gaussian Distribution with the mean value = 100 and standard deviation = 15. Z-score is the number of standard deviations from the mean which is also called the \"Standard Score\" or \"Sigma\".\n\nJust plug in the percentage of people to see their IQ range:`","pinned":false,"hidden":false,"mode":"ojs"},{"id":197,"value":"viewof confidence_level = Range([0.1, 100], {label: md`Enter % of People:`,step:.1, value: \"95.4\"});","pinned":false,"hidden":false,"mode":"ojs"},{"id":677,"value":"zstar = {\n if (parseFloat(confidence_level) && !isNaN(confidence_level)) {\n let zstar = Math.sqrt(2) * erfi(2 * (1 - (1 - confidence_level/100) / 2 - 0.5));\n return normal_pic(-zstar, zstar);\n } else {\n return normal_pic();\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":292,"value":"inversion = md`## How Smart Are You?\n\nIf you have taken an IQ test then simply plug your score in the box below to see how smart you are in comparison to the rest of the people :D. \n\nFYI, Bill Gates and Steve Jobs both are told to have IQ of 160 while Elon Musk's IQ is 155. `","pinned":false,"hidden":false,"mode":"ojs"},{"id":143,"value":"viewof Z = Range([0, 185], {label: md`Enter Your IQ:`,step: 1, value: \"130\" })","pinned":false,"hidden":false,"mode":"ojs"},{"id":721,"value":"cfp = {\n let div = d3.create('div');\n div.append(() => normal_pic(-Infinity, (Z-100)/15));\n return div.node();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":305,"value":"viewof P = text({ title: '% People' })","pinned":false,"hidden":false,"mode":"ojs"},{"id":567,"value":"ZScore = {\n // let div = d3.create('div');\n let result;\n if (P === 0) {\n result = -Infinity;\n } else if (parseFloat(P) && !isNaN(P)) {\n if (P == 100) {\n result = Infinity;\n } else if (P < 0 || P > 100) {\n result = NaN;\n } else {\n result = Math.sqrt(2) * erfi(2 * P/100 - 1);\n }\n } else {\n result = '';\n }\n let z_pic = normal_pic(-Infinity, result);\n return z_pic; //(-4, result);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":308,"value":"// ZScore = {\n// let div = d3.create('div');\n// let result;\n// if (P === 0) {\n// result = -Infinity;\n// } else if (parseFloat(P) && !isNaN(P)) {\n// if (P == 1) {\n// result = Infinity;\n// } else if (P < 0 || P > 1) {\n// result = NaN;\n// } else {\n// result = Math.sqrt(2) * erfi(2 * P - 1);\n// }\n// } else {\n// result = '';\n// }\n// if (!(result === '')) {\n// div.append(() => tex`Z= ${result}`);\n// }\n// return div.node();\n// }","pinned":false,"hidden":false,"mode":"ojs"},{"id":705,"value":"md`## Code`","pinned":false,"hidden":false,"mode":"ojs"},{"id":277,"value":"function normal_pdf(t) {\n return Math.exp((-t * t) / 2) / Math.sqrt(2 * Math.PI);\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":387,"value":"// ss.inverseErrorFunction does not seem to be too accurate.\n// Here's an implementation based on Newton's method.\nfunction erfi(y) {\n let sqrtPiOver2 = Math.sqrt(Math.PI) / 2;\n if (y == 0) {\n return 0;\n } else if (y == 1) {\n return Infinity;\n } else if (y == -1) {\n return -Infinity;\n } else if (Math.abs(y) > 1) {\n return NaN;\n } else {\n let x = 0.;\n for (let i = 0; i < 8; i++) {\n x = x - sqrtPiOver2 * Math.exp(x * x) * (ss.errorFunction(x) - y);\n }\n return x;\n }\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":702,"value":"$31","pinned":true,"hidden":false,"mode":"ojs"},{"id":696,"value":"ff = d3.format('0.6~f')","pinned":true,"hidden":false,"mode":"ojs"},{"id":224,"value":"md`## Imports`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2,"value":"ss = require('simple-statistics')","pinned":true,"hidden":false,"mode":"ojs"},{"id":94,"value":"d3 = require('d3@6')","pinned":true,"hidden":false,"mode":"ojs"},{"id":66,"value":"import { pic } from '@mcmcclur/simple-normal-table'","pinned":true,"hidden":false,"mode":"ojs"},{"id":141,"value":"import { text } from \"@jashkenas/inputs\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":868,"value":"import {Range} from \"@observablehq/inputs\"","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W32","stdlib":"1","titling":"auto"}