28:null 29:["$","title",null,{"children":"Hypotestest för ett väntevärde - Typ I och II fel | Mattias Villani | Observable"}] 2e:T52a,plt_pdf = Plot.plot({ caption:html` sannolikheten för typ I fel: α = Pr(förkasta H0 | H0 sann)
sannolikheten för typ II fel: β = Pr(inte förkasta H0 | H0 inte sann)
testets styrka: 1-β = Pr(förkasta H0 | H0 inte sann) `, color: { legend: true }, x: { label: "xBar", axis: true }, y: { axis: false, domain: [0,1.02*maxpdf] }, marks: [ Plot.ruleY([0]), Plot.areaY(pdfdata, {filter: d => d.x <= zcrit_right && d.x >= zcrit_left && d.type == "HA", x: "x", y: "pdf", fill: "#C0C0C0", opacity: 0.3}), Plot.areaY(pdfdata, {filter: d => d.x > zcrit_right && d.type == "HA", x: "x", y: "pdf", fill: "orange", opacity: 0.5}), Plot.areaY(pdfdata, {filter: d => d.x < zcrit_left && d.type == "HA", x: "x", y: "pdf", fill: "orange", opacity: 0.5}), Plot.areaY(pdfdata, {filter: d => d.x >= zcrit_right && d.type == "H0", x: "x", y: "pdf", fill: "steelblue", opacity: 0.7}), Plot.areaY(pdfdata, {filter: d => d.x <= zcrit_left && d.type == "H0", x: "x", y: "pdf", fill: "steelblue", opacity: 0.7}), Plot.line(pdfdata, {x: "x", y: "pdf", stroke : "type", strokeWidth: 2}) ] })2f:[] 2c:{"id":"81609ad4e25e6f58","slug":"typ1och2fel","title":"Hypotestest för ett väntevärde - Typ I och II fel","thumbnail":"bc5db32d485a24e68810574ee111187c228b2a182f395c27247263fe6c0d285a","access_level":"public","create_time":"$D2023-02-26T15:01:21.137Z","update_time":"$D2023-09-27T10:55:32.512Z","view_time":null,"first_public_time":"$D2023-02-26T15:01:21.137Z","has_unpublished_edits":null,"fork":{"id":"32c30160ed8888fa","slug":"normalfordelningen","title":"Normalfördelningen","owner":{"id":"7592a7ab121f74a0","login":"mattiasvillani","name":"Mattias Villani"}},"stars":0,"starred":false,"owner":{"type":"team","id":"7592a7ab121f74a0","login":"mattiasvillani","name":"Mattias Villani","avatar_url":"https://avatars-next.observableusercontent.com/a440aaf39a3abefb86cbcc758fd7a5ab42555ae68d6825b52ec006f385a94d1d","tier":"education_2026"},"version":878,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"56499be8cf43f0ef","login":"mattiasvillani","name":"Mattias Villani","avatar_url":"https://avatars-next.observableusercontent.com/a440aaf39a3abefb86cbcc758fd7a5ab42555ae68d6825b52ec006f385a94d1d","tier":null}],"members":[{"type":"individual","id":"56499be8cf43f0ef","login":"mattiasvillani","name":"Mattias Villani","avatar_url":"https://avatars-next.observableusercontent.com/a440aaf39a3abefb86cbcc758fd7a5ab42555ae68d6825b52ec006f385a94d1d","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":2,"collection":{"id":"209e13808f44ed11","slug":"sda1","title":"SDA1","owner":{"id":"7592a7ab121f74a0","login":"mattiasvillani","name":"Mattias Villani"}},"type":"notebook","body":{"title":"Hypotestest för ett väntevärde - Typ I och II fel","cells":[{"id":38,"value":"# Hypotestest för ett väntevärde - Typ I och II fel","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":496,"value":"modelltext = md`### Modell och hypoteser\nLåt ${tex`X_1,X_2,\\ldots,X_n \\overset{iid}{\\sim}N(\\mu,\\sigma)`} med ${tex`\\sigma`} känd.\n\nVi testar:\n\n${tex`\nH_0:\\mu = \\mu_0 \\\\\nH_A:\\mu \\neq \\mu_0\n`}\n
\nmed teststatistiskan
\n\n${tex`Z = \\frac{\\bar X -\\mu_0}{\\sigma/\\sqrt n}`}\n
\nsom följer en standard normalfördelning under ${tex`H_0`}.\n\nUnder alternativhypotesen ${tex`H_A`} antar vi att väntevärdet är ${tex`\\mu_A`}.\n\n${tex`\nH_0: \\bar X \\sim N(\\mu_0,\\sigma/\\sqrt n) \\\\\nH_A: \\bar X \\sim N(\\mu_A,\\sigma/\\sqrt n)\n`}\n
\nI figuren nedan ritar vi upp fördelningen för ${tex`\\bar X`} under ${tex`H_0`} och ${tex`H_A`} på originalskalan (dvs inte standardiserat).\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":118,"value":"viewof inputs = Inputs.form([\n Inputs.range([-3, 3], {value: 0, step: 0.1, label: tex`\\mu_0`}),\n Inputs.range([-5, 5], {value: 2, step: 0.1, label: tex`\\mu_A`}),\n Inputs.range([0.01, 2], {value: 1, step: 0.01, label: tex`\\sigma`}),\n Inputs.range([1, 20], {value: 1, step: 1, label: \"n\"}),\n Inputs.range([0, 1], {value: 0.05, step: 0.001, label: tex`\\alpha`}),\n ])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":529,"value":"probserrors = tex`\n\\begin{aligned}\n&\\text{Typ I fel: }\\alpha = P(\\text{förkasta } H_0 \\vert H_0 \\text{ sann}) =${alpha.toPrecision(3)} \\\\[0.2em]\n&\\text{Typ II fel: }\\beta = P(\\text{inte förkasta } H_0 \\vert H_0 \\text{ inte sann}) =${beta.toPrecision(3)} \\\\[0.2em]\n&\\text{Testets styrka: }1-\\beta = P(\\text{förkasta } H_0 \\vert H_0 \\text{ inte sann}) =${(1-beta).toPrecision(3)} \n\\end{aligned}\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":305,"value":"$2e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":109,"value":"
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\n\n---\n## 🙈 Appendix 🙈\n\nHär följer bara kod och detaljer som du antagligen inte vill läsa.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":221,"value":"#### Imports","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":26,"value":"jstat = require('jstat')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":223,"value":"#### Data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":699,"value":"mu0 = inputs[0]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":703,"value":"muA = inputs[1]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":707,"value":"sigma = inputs[2]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":705,"value":"n = inputs[3]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":736,"value":"alpha = inputs[4]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":17,"value":"pdfdata = {\n const x = d3.range(-10, 10, 0.01);\n const normpdfH0 = x.map(x => ({x: x, pdf: jstat.normal.pdf(x, mu0, sigma/Math.sqrt(n)), type: \"H0\"}));\n const normpdfHA = x.map(x => ({x: x, pdf: jstat.normal.pdf(x, muA, sigma/Math.sqrt(n)), type: \"HA\"}));\n return normpdfH0.concat(normpdfHA)\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":723,"value":"zcrit_right = mu0 + jstat.normal.inv(1-alpha/2, 0, 1)*(sigma/Math.sqrt(n))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":806,"value":"zcrit_left = mu0 - jstat.normal.inv(1-alpha/2, 0, 1)*(sigma/Math.sqrt(n))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":19,"value":"beta = jstat.normal.cdf(zcrit_right, muA, sigma/Math.sqrt(n)) - jstat.normal.cdf(zcrit_left, muA, sigma/Math.sqrt(n));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":621,"value":"maxpdf = Math.max(jstat.normal.pdf(mu0, mu0, sigma/Math.sqrt(n)), jstat.normal.pdf(muA, muA, sigma/Math.sqrt(n)))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":229,"value":"#### Style","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":202,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":58,"value":"viewof md_colour = Inputs.color({label: \"Highlight color cells\", value: \"#F5F5DC\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W2f","stdlib":"1","titling":"auto"}} 2d:"$2c:body"