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Search, Evaluate, and Read Evidence","\u002Fen\u002Facademic-writing\u002F02-reading-and-literature-matrix","en\u002Facademic-writing\u002F02-reading-and-literature-matrix",{"title":27,"path":28,"stem":29},"3. From Sources to Synthesis and Argument","\u002Fen\u002Facademic-writing\u002F03-arguments-outlines-and-paragraphs","en\u002Facademic-writing\u002F03-arguments-outlines-and-paragraphs",{"title":31,"path":32,"stem":33},"4. Literature Review and a Defensible Gap","\u002Fen\u002Facademic-writing\u002F04-writing-the-literature-review","en\u002Facademic-writing\u002F04-writing-the-literature-review",{"title":35,"path":36,"stem":37},"5. Research Design, Evidence, and Core Sections","\u002Fen\u002Facademic-writing\u002F05-core-sections-and-evidence","en\u002Facademic-writing\u002F05-core-sections-and-evidence",{"title":39,"path":40,"stem":41},"6. Citation, Paraphrasing, Integrity, and AI","\u002Fen\u002Facademic-writing\u002F06-citation-paraphrasing-and-integrity","en\u002Facademic-writing\u002F06-citation-paraphrasing-and-integrity",{"title":43,"path":44,"stem":45},"7. Revision, Review, and Submission","\u002Fen\u002Facademic-writing\u002F07-revision-style-and-submission","en\u002Facademic-writing\u002F07-revision-style-and-submission",{"title":47,"path":48,"stem":49},"8. Research Workbook","\u002Fen\u002Facademic-writing\u002F08-literature-review-checklist-and-template","en\u002Facademic-writing\u002F08-literature-review-checklist-and-template",{"title":51,"path":52,"stem":53},"9. Research Workflow and Tools","\u002Fen\u002Facademic-writing\u002F09-tools-for-academic-writing-and-literature-management","en\u002Facademic-writing\u002F09-tools-for-academic-writing-and-literature-management",{"title":55,"path":56,"stem":57},"10. Worked Project — From Question to Defensible Conclusion","\u002Fen\u002Facademic-writing\u002F10-worked-example-from-block-structure-to-question-chain","en\u002Facademic-writing\u002F10-worked-example-from-block-structure-to-question-chain",{"title":59,"path":60,"stem":61,"children":62,"page":249},"Accounting","\u002Fen\u002Faccounting","en\u002Faccounting",[63,69,87,93,193],{"title":64,"path":65,"stem":66,"children":67},"Accounting — From Evidence to Decisions","\u002Fen\u002Faccounting\u002F00-index","en\u002Faccounting\u002F00-index",[68],{"title":64,"path":65,"stem":66},{"title":70,"path":71,"stem":72,"children":73},"Accounting Appendix","\u002Fen\u002Faccounting\u002Fappendix","en\u002Faccounting\u002Fappendix\u002Findex",[74,75,79,83],{"title":70,"path":71,"stem":72},{"title":76,"path":77,"stem":78},"Worked Examples and Error Diagnosis","\u002Fen\u002Faccounting\u002Fappendix\u002F24-examples-pitfalls","en\u002Faccounting\u002Fappendix\u002F24-examples-pitfalls",{"title":80,"path":81,"stem":82},"Accounting Glossary","\u002Fen\u002Faccounting\u002Fappendix\u002F26-glossary","en\u002Faccounting\u002Fappendix\u002F26-glossary",{"title":84,"path":85,"stem":86},"Reading and Evidence Map","\u002Fen\u002Faccounting\u002Fappendix\u002F27-reading-map","en\u002Faccounting\u002Fappendix\u002F27-reading-map",{"title":88,"path":89,"stem":90,"children":91},"Northstar Record-to-Decision Capstone","\u002Fen\u002Faccounting\u002Fcapstone","en\u002Faccounting\u002Fcapstone\u002Findex",[92],{"title":88,"path":89,"stem":90},{"title":94,"path":95,"stem":96,"children":97},"Financial Accounting","\u002Fen\u002Faccounting\u002Ffinancial-accounting","en\u002Faccounting\u002Ffinancial-accounting\u002Findex",[98,99,117,131,165,179],{"title":94,"path":95,"stem":96},{"title":100,"path":101,"stem":102,"children":103},"1. Foundations","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations","en\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002Findex",[104,105,109,113],{"title":100,"path":101,"stem":102},{"title":106,"path":107,"stem":108},"Objectives and Qualitative Characteristics","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F01-objectives-qualitative-characteristics","en\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F01-objectives-qualitative-characteristics",{"title":110,"path":111,"stem":112},"Equation and Elements","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F02-equation-elements","en\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F02-equation-elements",{"title":114,"path":115,"stem":116},"Accrual Basis, Estimates and Periods","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F03-accounting-bases-principles","en\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F03-accounting-bases-principles",{"title":118,"path":119,"stem":120,"children":121},"2. Recording Transactions","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions","en\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002Findex",[122,123,127],{"title":118,"path":119,"stem":120},{"title":124,"path":125,"stem":126},"Double-Entry and Debit\u002FCredit","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F04-double-entry-dr-cr","en\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F04-double-entry-dr-cr",{"title":128,"path":129,"stem":130},"Accounting Cycle","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F05-accounting-cycle","en\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F05-accounting-cycle",{"title":132,"path":133,"stem":134,"children":135},"3. Measurement and Adjustments","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002Findex",[136,137,141,145,149,153,157,161],{"title":132,"path":133,"stem":134},{"title":138,"path":139,"stem":140},"Revenue Recognition","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F07-revenue-recognition","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F07-revenue-recognition",{"title":142,"path":143,"stem":144},"Inventory","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F08-inventory","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F08-inventory",{"title":146,"path":147,"stem":148},"Receivables and Expected Credit Losses","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F09-receivables","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F09-receivables",{"title":150,"path":151,"stem":152},"Property, Plant and Equipment","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F10-ppe","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F10-ppe",{"title":154,"path":155,"stem":156},"Intangible Assets","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F11-intangibles","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F11-intangibles",{"title":158,"path":159,"stem":160},"Leases","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F12-leases","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F12-leases",{"title":162,"path":163,"stem":164},"Current and Deferred Income Tax","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F13-income-tax","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F13-income-tax",{"title":166,"path":167,"stem":168,"children":169},"4. Reporting and Cash","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash","en\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002Findex",[170,171,175],{"title":166,"path":167,"stem":168},{"title":172,"path":173,"stem":174},"Linked Financial Statements","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F06-financial-statements","en\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F06-financial-statements",{"title":176,"path":177,"stem":178},"Cash, Reconciliation and Internal Control","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F14-cash-control","en\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F14-cash-control",{"title":180,"path":181,"stem":182,"children":183},"5. Analysis and Comparison","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison","en\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002Findex",[184,185,189],{"title":180,"path":181,"stem":182},{"title":186,"path":187,"stem":188},"Ratio Analysis","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F15-ratio-analysis","en\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F15-ratio-analysis",{"title":190,"path":191,"stem":192},"IFRS versus US GAAP","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F25-ifrs-gaap","en\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F25-ifrs-gaap",{"title":194,"path":195,"stem":196,"children":197},"Management Accounting","\u002Fen\u002Faccounting\u002Fmanagement-accounting","en\u002Faccounting\u002Fmanagement-accounting\u002Findex",[198,199,217,235],{"title":194,"path":195,"stem":196},{"title":200,"path":201,"stem":202,"children":203},"1. Cost Foundations","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations","en\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002Findex",[204,205,209,213],{"title":200,"path":201,"stem":202},{"title":206,"path":207,"stem":208},"Cost Concepts and Behaviour","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F16-cost-concepts","en\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F16-cost-concepts",{"title":210,"path":211,"stem":212},"Costing Systems","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F17-costing-systems","en\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F17-costing-systems",{"title":214,"path":215,"stem":216},"Variable and Absorption Costing","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F19-variable-vs-absorption","en\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F19-variable-vs-absorption",{"title":218,"path":219,"stem":220,"children":221},"2. Planning and Control","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control","en\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002Findex",[222,223,227,231],{"title":218,"path":219,"stem":220},{"title":224,"path":225,"stem":226},"Cost–Volume–Profit Analysis","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F18-cvp-analysis","en\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F18-cvp-analysis",{"title":228,"path":229,"stem":230},"Budgeting and Variance Analysis","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F20-budgeting-variances","en\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F20-budgeting-variances",{"title":232,"path":233,"stem":234},"Performance Measurement","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F21-performance-measurement","en\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F21-performance-measurement",{"title":236,"path":237,"stem":238,"children":239},"3. Decisions and Investment","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment","en\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002Findex",[240,241,245],{"title":236,"path":237,"stem":238},{"title":242,"path":243,"stem":244},"Short-Term Decisions","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F22-short-term-decisions","en\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F22-short-term-decisions",{"title":246,"path":247,"stem":248},"Capital Budgeting","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F23-capital-budgeting","en\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F23-capital-budgeting",false,{"title":251,"path":252,"stem":253,"children":254,"page":249},"Business Analytics","\u002Fen\u002Fbusiness-analytics","en\u002Fbusiness-analytics",[255,261,279,305,335,365,383,400],{"title":256,"path":257,"stem":258,"children":259},"Business Analytics — From Data to Defensible Action","\u002Fen\u002Fbusiness-analytics\u002F00-index","en\u002Fbusiness-analytics\u002F00-index",[260],{"title":256,"path":257,"stem":258},{"title":262,"path":263,"stem":264,"children":265},"0. Decision and Data Foundations","\u002Fen\u002Fbusiness-analytics\u002F00-intro","en\u002Fbusiness-analytics\u002F00-intro\u002Findex",[266,267,271,275],{"title":262,"path":263,"stem":264},{"title":268,"path":269,"stem":270},"Decision Framing","\u002Fen\u002Fbusiness-analytics\u002F00-intro\u002F01-decision-framing","en\u002Fbusiness-analytics\u002F00-intro\u002F01-decision-framing",{"title":272,"path":273,"stem":274},"Data Contracts","\u002Fen\u002Fbusiness-analytics\u002F00-intro\u002F02-data-contracts","en\u002Fbusiness-analytics\u002F00-intro\u002F02-data-contracts",{"title":276,"path":277,"stem":278},"Reproducible Analytics Workflow","\u002Fen\u002Fbusiness-analytics\u002F00-intro\u002F03-reproducible-workflow","en\u002Fbusiness-analytics\u002F00-intro\u002F03-reproducible-workflow",{"title":280,"path":281,"stem":282,"children":283},"1. Descriptive Analytics","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive","en\u002Fbusiness-analytics\u002F01-descriptive\u002Findex",[284,285,289,293,297,301],{"title":280,"path":281,"stem":282},{"title":286,"path":287,"stem":288},"Distributions and Exploratory Data Analysis","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F04-distributions-eda","en\u002Fbusiness-analytics\u002F01-descriptive\u002F04-distributions-eda",{"title":290,"path":291,"stem":292},"KPIs and Denominators","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F05-kpis-denominators","en\u002Fbusiness-analytics\u002F01-descriptive\u002F05-kpis-denominators",{"title":294,"path":295,"stem":296},"Segments, Cohorts and Funnels","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F06-segmentation-cohorts-funnels","en\u002Fbusiness-analytics\u002F01-descriptive\u002F06-segmentation-cohorts-funnels",{"title":298,"path":299,"stem":300},"Visual Evidence and Data Stories","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F07-visualisation-story","en\u002Fbusiness-analytics\u002F01-descriptive\u002F07-visualisation-story",{"title":302,"path":303,"stem":304},"Experiments and Causal Boundaries","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F08-experiments-causal-boundary","en\u002Fbusiness-analytics\u002F01-descriptive\u002F08-experiments-causal-boundary",{"title":306,"path":307,"stem":308,"children":309},"2. Predictive Analytics","\u002Fen\u002Fbusiness-analytics\u002F02-predictive","en\u002Fbusiness-analytics\u002F02-predictive\u002Findex",[310,311,315,319,323,327,331],{"title":306,"path":307,"stem":308},{"title":312,"path":313,"stem":314},"Validation, Baselines and Leakage","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F09-validation-leakage","en\u002Fbusiness-analytics\u002F02-predictive\u002F09-validation-leakage",{"title":316,"path":317,"stem":318},"Regression and Forecasting","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F10-regression-forecasting","en\u002Fbusiness-analytics\u002F02-predictive\u002F10-regression-forecasting",{"title":320,"path":321,"stem":322},"Classification and Calibration","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F11-classification-calibration","en\u002Fbusiness-analytics\u002F02-predictive\u002F11-classification-calibration",{"title":324,"path":325,"stem":326},"Trees and Ensembles","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F12-trees-ensembles","en\u002Fbusiness-analytics\u002F02-predictive\u002F12-trees-ensembles",{"title":328,"path":329,"stem":330},"Decision Metrics and Thresholds","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F13-decision-metrics-thresholds","en\u002Fbusiness-analytics\u002F02-predictive\u002F13-decision-metrics-thresholds",{"title":332,"path":333,"stem":334},"Explainability, Monitoring and Drift","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F14-explainability-drift","en\u002Fbusiness-analytics\u002F02-predictive\u002F14-explainability-drift",{"title":336,"path":337,"stem":338,"children":339},"3. Prescriptive Analytics","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive","en\u002Fbusiness-analytics\u002F03-prescriptive\u002Findex",[340,341,345,349,353,357,361],{"title":336,"path":337,"stem":338},{"title":342,"path":343,"stem":344},"Decision-Making under Uncertainty","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F15-decision-uncertainty","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F15-decision-uncertainty",{"title":346,"path":347,"stem":348},"Linear Optimisation","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F16-linear-optimization","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F16-linear-optimization",{"title":350,"path":351,"stem":352},"Inventory and Allocation","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F17-inventory-allocation","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F17-inventory-allocation",{"title":354,"path":355,"stem":356},"Queueing and Simulation","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F18-queueing-simulation","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F18-queueing-simulation",{"title":358,"path":359,"stem":360},"Pricing and Revenue Management","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F19-pricing-experimentation","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F19-pricing-experimentation",{"title":362,"path":363,"stem":364},"Causal Targeting and Policy Learning","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F20-causal-targeting","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F20-causal-targeting",{"title":366,"path":367,"stem":368,"children":369},"4. Deployment and Governance","\u002Fen\u002Fbusiness-analytics\u002F04-deployment","en\u002Fbusiness-analytics\u002F04-deployment\u002Findex",[370,371,375,379],{"title":366,"path":367,"stem":368},{"title":372,"path":373,"stem":374},"Data Products and Monitoring","\u002Fen\u002Fbusiness-analytics\u002F04-deployment\u002F21-data-products-monitoring","en\u002Fbusiness-analytics\u002F04-deployment\u002F21-data-products-monitoring",{"title":376,"path":377,"stem":378},"Governance, Fairness and Privacy","\u002Fen\u002Fbusiness-analytics\u002F04-deployment\u002F22-governance-fairness-privacy","en\u002Fbusiness-analytics\u002F04-deployment\u002F22-governance-fairness-privacy",{"title":380,"path":381,"stem":382},"Adoption and Business Value","\u002Fen\u002Fbusiness-analytics\u002F04-deployment\u002F23-adoption-value","en\u002Fbusiness-analytics\u002F04-deployment\u002F23-adoption-value",{"title":384,"path":385,"stem":386,"children":387},"Appendix and Revision Tools","\u002Fen\u002Fbusiness-analytics\u002Fappendix","en\u002Fbusiness-analytics\u002Fappendix\u002Findex",[388,389,393,397],{"title":384,"path":385,"stem":386},{"title":390,"path":391,"stem":392},"Formula and Decision Map","\u002Fen\u002Fbusiness-analytics\u002Fappendix\u002F24-formula-map","en\u002Fbusiness-analytics\u002Fappendix\u002F24-formula-map",{"title":394,"path":395,"stem":396},"Worked Examples and Pitfalls","\u002Fen\u002Fbusiness-analytics\u002Fappendix\u002F25-worked-examples-pitfalls","en\u002Fbusiness-analytics\u002Fappendix\u002F25-worked-examples-pitfalls",{"title":84,"path":398,"stem":399},"\u002Fen\u002Fbusiness-analytics\u002Fappendix\u002F26-reading-map","en\u002Fbusiness-analytics\u002Fappendix\u002F26-reading-map",{"title":401,"path":402,"stem":403,"children":404},"Capstone — The Evening Delivery Promise","\u002Fen\u002Fbusiness-analytics\u002Fcapstone","en\u002Fbusiness-analytics\u002Fcapstone\u002Findex",[405],{"title":401,"path":402,"stem":403},{"title":407,"path":408,"stem":409,"children":410,"page":249},"Financial Economic Time Series","\u002Fen\u002Ffinancial-economic-time-series","en\u002Ffinancial-economic-time-series",[411,417,423,429,435,441,447,453,459,465,471],{"title":412,"path":413,"stem":414,"children":415},"Financial and Economic Time Series — Course Guide","\u002Fen\u002Ffinancial-economic-time-series\u002F00-intro","en\u002Ffinancial-economic-time-series\u002F00-intro\u002Findex",[416],{"title":412,"path":413,"stem":414},{"title":418,"path":419,"stem":420,"children":421},"Three Versions of Time Series","\u002Fen\u002Ffinancial-economic-time-series\u002F01-bridge","en\u002Ffinancial-economic-time-series\u002F01-bridge\u002Findex",[422],{"title":418,"path":419,"stem":420},{"title":424,"path":425,"stem":426,"children":427},"Data, Clocks, and Transformations","\u002Fen\u002Ffinancial-economic-time-series\u002F02-data-transformations","en\u002Ffinancial-economic-time-series\u002F02-data-transformations\u002Findex",[428],{"title":424,"path":425,"stem":426},{"title":430,"path":431,"stem":432,"children":433},"Predictive Regressions and Persistent Predictors","\u002Fen\u002Ffinancial-economic-time-series\u002F03-predictive-regressions","en\u002Ffinancial-economic-time-series\u002F03-predictive-regressions\u002Findex",[434],{"title":430,"path":431,"stem":432},{"title":436,"path":437,"stem":438,"children":439},"Volatility, Tails, and Financial Risk","\u002Fen\u002Ffinancial-economic-time-series\u002F04-volatility-risk","en\u002Ffinancial-economic-time-series\u002F04-volatility-risk\u002Findex",[440],{"title":436,"path":437,"stem":438},{"title":442,"path":443,"stem":444,"children":445},"Unit Roots, Cointegration, and Error Correction","\u002Fen\u002Ffinancial-economic-time-series\u002F05-unit-roots-cointegration","en\u002Ffinancial-economic-time-series\u002F05-unit-roots-cointegration\u002Findex",[446],{"title":442,"path":443,"stem":444},{"title":448,"path":449,"stem":450,"children":451},"VAR, Structural Identification, and Local Projections","\u002Fen\u002Ffinancial-economic-time-series\u002F06-var-identification","en\u002Ffinancial-economic-time-series\u002F06-var-identification\u002Findex",[452],{"title":448,"path":449,"stem":450},{"title":454,"path":455,"stem":456,"children":457},"State Space, Mixed Frequency, and Nowcasting","\u002Fen\u002Ffinancial-economic-time-series\u002F07-state-space-nowcasting","en\u002Ffinancial-economic-time-series\u002F07-state-space-nowcasting\u002Findex",[458],{"title":454,"path":455,"stem":456},{"title":460,"path":461,"stem":462,"children":463},"Forecast Evaluation for Decisions","\u002Fen\u002Ffinancial-economic-time-series\u002F08-forecast-evaluation","en\u002Ffinancial-economic-time-series\u002F08-forecast-evaluation\u002Findex",[464],{"title":460,"path":461,"stem":462},{"title":466,"path":467,"stem":468,"children":469},"Integrated R Laboratory","\u002Fen\u002Ffinancial-economic-time-series\u002F09-r-laboratory","en\u002Ffinancial-economic-time-series\u002F09-r-laboratory\u002Findex",[470],{"title":466,"path":467,"stem":468},{"title":472,"path":473,"stem":474,"children":475},"Capstones, Data, and Reading Ladder","\u002Fen\u002Ffinancial-economic-time-series\u002F10-capstone-readings","en\u002Ffinancial-economic-time-series\u002F10-capstone-readings\u002Findex",[476],{"title":472,"path":473,"stem":474},{"title":478,"path":479,"stem":480,"children":481,"page":249},"Intro To Economics","\u002Fen\u002Fintro-to-economics","en\u002Fintro-to-economics",[482,486,490,494,498,502,506,510,514,518,522,526,530],{"title":483,"path":484,"stem":485},"Introduction to Economics — Decisions, Markets, and the Macroeconomy","\u002Fen\u002Fintro-to-economics\u002F00-intro","en\u002Fintro-to-economics\u002F00-intro",{"title":487,"path":488,"stem":489},"Chapter 1 — Choice, Opportunity Cost, and Trade","\u002Fen\u002Fintro-to-economics\u002F01-foundations","en\u002Fintro-to-economics\u002F01-foundations",{"title":491,"path":492,"stem":493},"Chapter 2 — Demand, Supply, Equilibrium, and Welfare","\u002Fen\u002Fintro-to-economics\u002F02-demand-and-supply","en\u002Fintro-to-economics\u002F02-demand-and-supply",{"title":495,"path":496,"stem":497},"Chapter 3 — Elasticity, Revenue, and Tax Incidence","\u002Fen\u002Fintro-to-economics\u002F03-elasticity","en\u002Fintro-to-economics\u002F03-elasticity",{"title":499,"path":500,"stem":501},"Chapter 4 — Firms, Market Power, and Market Failure","\u002Fen\u002Fintro-to-economics\u002F04-market-structures","en\u002Fintro-to-economics\u002F04-market-structures",{"title":503,"path":504,"stem":505},"Chapter 5 — GDP, Income, Wealth, and Welfare","\u002Fen\u002Fintro-to-economics\u002F05-gdp-and-wealth","en\u002Fintro-to-economics\u002F05-gdp-and-wealth",{"title":507,"path":508,"stem":509},"Chapter 6 — Inflation, Purchasing Power, and Labour Markets","\u002Fen\u002Fintro-to-economics\u002F06-inflation-and-unemployment","en\u002Fintro-to-economics\u002F06-inflation-and-unemployment",{"title":511,"path":512,"stem":513},"Chapter 7 — Productivity, Technology, and Economic Growth","\u002Fen\u002Fintro-to-economics\u002F07-economic-growth","en\u002Fintro-to-economics\u002F07-economic-growth",{"title":515,"path":516,"stem":517},"Chapter 8 — Money, Credit, and Banking","\u002Fen\u002Fintro-to-economics\u002F08-money-and-banking","en\u002Fintro-to-economics\u002F08-money-and-banking",{"title":519,"path":520,"stem":521},"Chapter 9 — Business Cycles, AD–AS, and Monetary Policy","\u002Fen\u002Fintro-to-economics\u002F09-monetary-policy-and-ad-as","en\u002Fintro-to-economics\u002F09-monetary-policy-and-ad-as",{"title":523,"path":524,"stem":525},"Chapter 10 — Fiscal Policy, Distribution, and Public Debt","\u002Fen\u002Fintro-to-economics\u002F10-fiscal-policy","en\u002Fintro-to-economics\u002F10-fiscal-policy",{"title":527,"path":528,"stem":529},"Chapter 11 — Trade, Capital Flows, and Exchange Rates","\u002Fen\u002Fintro-to-economics\u002F11-open-economy-and-exchange-rates","en\u002Fintro-to-economics\u002F11-open-economy-and-exchange-rates",{"title":531,"path":532,"stem":533},"Chapter 12 — Integrated Economic Analysis Studio","\u002Fen\u002Fintro-to-economics\u002F12-review-and-case-studies","en\u002Fintro-to-economics\u002F12-review-and-case-studies",{"title":535,"path":536,"stem":537,"children":538,"page":249},"Microeconometrics","\u002Fen\u002Fmicroeconometrics","en\u002Fmicroeconometrics",[539,557,563,581,603,629,651,673,691,709],{"title":540,"path":541,"stem":542,"children":543},"0. Causal Questions and Designs","\u002Fen\u002Fmicroeconometrics\u002F00-foundations","en\u002Fmicroeconometrics\u002F00-foundations\u002Findex",[544,545,549,553],{"title":540,"path":541,"stem":542},{"title":546,"path":547,"stem":548},"Causal Questions and Estimands","\u002Fen\u002Fmicroeconometrics\u002F00-foundations\u002F01-causal-question-estimands","en\u002Fmicroeconometrics\u002F00-foundations\u002F01-causal-question-estimands",{"title":550,"path":551,"stem":552},"Potential Outcomes and Experiments","\u002Fen\u002Fmicroeconometrics\u002F00-foundations\u002F02-potential-outcomes-experiments","en\u002Fmicroeconometrics\u002F00-foundations\u002F02-potential-outcomes-experiments",{"title":554,"path":555,"stem":556},"Causal Diagrams and Controls","\u002Fen\u002Fmicroeconometrics\u002F00-foundations\u002F03-dags-controls","en\u002Fmicroeconometrics\u002F00-foundations\u002F03-dags-controls",{"title":558,"path":559,"stem":560,"children":561},"Microeconometrics — Designing Credible Counterfactuals","\u002Fen\u002Fmicroeconometrics\u002F00-index","en\u002Fmicroeconometrics\u002F00-index",[562],{"title":558,"path":559,"stem":560},{"title":564,"path":565,"stem":566,"children":567},"1. Regression and Inference","\u002Fen\u002Fmicroeconometrics\u002F01-regression","en\u002Fmicroeconometrics\u002F01-regression\u002Findex",[568,569,573,577],{"title":564,"path":565,"stem":566},{"title":570,"path":571,"stem":572},"OLS as a Projection","\u002Fen\u002Fmicroeconometrics\u002F01-regression\u002F04-ols-projection","en\u002Fmicroeconometrics\u002F01-regression\u002F04-ols-projection",{"title":574,"path":575,"stem":576},"FWL, Selection and Controls","\u002Fen\u002Fmicroeconometrics\u002F01-regression\u002F05-fwl-selection-controls","en\u002Fmicroeconometrics\u002F01-regression\u002F05-fwl-selection-controls",{"title":578,"path":579,"stem":580},"Inference and Clustering","\u002Fen\u002Fmicroeconometrics\u002F01-regression\u002F06-inference-clustering","en\u002Fmicroeconometrics\u002F01-regression\u002F06-inference-clustering",{"title":582,"path":583,"stem":584,"children":585},"2. Instruments and Panel Data","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel","en\u002Fmicroeconometrics\u002F02-iv-panel\u002Findex",[586,587,591,595,599],{"title":582,"path":583,"stem":584},{"title":588,"path":589,"stem":590},"Instrumental Variables","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F07-iv-identification","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F07-iv-identification",{"title":592,"path":593,"stem":594},"Weak Instruments and LATE","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F08-weak-iv-late","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F08-weak-iv-late",{"title":596,"path":597,"stem":598},"Panel Fixed Effects","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F09-panel-fixed-effects","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F09-panel-fixed-effects",{"title":600,"path":601,"stem":602},"Dynamic Panels and Limits","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F10-dynamic-panel","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F10-dynamic-panel",{"title":604,"path":605,"stem":606,"children":607},"3. Policy Evaluation Designs","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs","en\u002Fmicroeconometrics\u002F03-policy-designs\u002Findex",[608,609,613,617,621,625],{"title":604,"path":605,"stem":606},{"title":610,"path":611,"stem":612},"Difference-in-Differences","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F11-did-core","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F11-did-core",{"title":614,"path":615,"stem":616},"Staggered DID and Event Studies","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F12-staggered-event-studies","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F12-staggered-event-studies",{"title":618,"path":619,"stem":620},"Regression Discontinuity","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F13-rdd","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F13-rdd",{"title":622,"path":623,"stem":624},"Matching, Weighting and Overlap","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F14-matching-weighting","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F14-matching-weighting",{"title":626,"path":627,"stem":628},"Synthetic Control","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F15-synthetic-control","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F15-synthetic-control",{"title":630,"path":631,"stem":632,"children":633},"Module 4 — Choice and Limited Outcomes","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002Findex",[634,635,639,643,647],{"title":630,"path":631,"stem":632},{"title":636,"path":637,"stem":638},"Binary Choice","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F16-binary-choice","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F16-binary-choice",{"title":640,"path":641,"stem":642},"Multinomial and Ordered Choice","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F17-multinomial-choice","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F17-multinomial-choice",{"title":644,"path":645,"stem":646},"Count Outcomes","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F18-count-outcomes","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F18-count-outcomes",{"title":648,"path":649,"stem":650},"Censoring, Truncation and Selection","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F19-censoring-selection","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F19-censoring-selection",{"title":652,"path":653,"stem":654,"children":655},"Module 5 — Modern Causal Analysis","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal","en\u002Fmicroeconometrics\u002F05-modern-causal\u002Findex",[656,657,661,665,669],{"title":652,"path":653,"stem":654},{"title":658,"path":659,"stem":660},"Double Machine Learning","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F20-dml","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F20-dml",{"title":662,"path":663,"stem":664},"Heterogeneity and Policy Learning","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F21-heterogeneity-policy","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F21-heterogeneity-policy",{"title":666,"path":667,"stem":668},"Sensitivity and Partial Identification","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F22-sensitivity-partial-id","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F22-sensitivity-partial-id",{"title":670,"path":671,"stem":672},"External Validity and Transport","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F23-external-validity","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F23-external-validity",{"title":674,"path":675,"stem":676,"children":677},"Module 6 — From Data to Auditable Evidence","\u002Fen\u002Fmicroeconometrics\u002F06-workflow","en\u002Fmicroeconometrics\u002F06-workflow\u002Findex",[678,679,683,687],{"title":674,"path":675,"stem":676},{"title":680,"path":681,"stem":682},"Data Provenance","\u002Fen\u002Fmicroeconometrics\u002F06-workflow\u002F24-data-provenance","en\u002Fmicroeconometrics\u002F06-workflow\u002F24-data-provenance",{"title":684,"path":685,"stem":686},"Reproducibility and Reporting","\u002Fen\u002Fmicroeconometrics\u002F06-workflow\u002F25-reproducibility-reporting","en\u002Fmicroeconometrics\u002F06-workflow\u002F25-reproducibility-reporting",{"title":688,"path":689,"stem":690},"Paper and Evidence Audit","\u002Fen\u002Fmicroeconometrics\u002F06-workflow\u002F26-paper-audit","en\u002Fmicroeconometrics\u002F06-workflow\u002F26-paper-audit",{"title":692,"path":693,"stem":694,"children":695},"Appendix — Revision and Evidence Map","\u002Fen\u002Fmicroeconometrics\u002Fappendix","en\u002Fmicroeconometrics\u002Fappendix\u002Findex",[696,697,701,705],{"title":692,"path":693,"stem":694},{"title":698,"path":699,"stem":700},"Formula and Design Map","\u002Fen\u002Fmicroeconometrics\u002Fappendix\u002F27-formula-design-map","en\u002Fmicroeconometrics\u002Fappendix\u002F27-formula-design-map",{"title":702,"path":703,"stem":704},"Ten Worked Microeconometric Pitfalls","\u002Fen\u002Fmicroeconometrics\u002Fappendix\u002F28-worked-pitfalls","en\u002Fmicroeconometrics\u002Fappendix\u002F28-worked-pitfalls",{"title":706,"path":707,"stem":708},"Reading and Software Map","\u002Fen\u002Fmicroeconometrics\u002Fappendix\u002F29-reading-software-map","en\u002Fmicroeconometrics\u002Fappendix\u002F29-reading-software-map",{"title":710,"path":711,"stem":712,"children":713},"Capstone — Should Northbridge Expand Pathways?","\u002Fen\u002Fmicroeconometrics\u002Fcapstone","en\u002Fmicroeconometrics\u002Fcapstone\u002Findex",[714],{"title":710,"path":711,"stem":712},{"title":716,"path":717,"stem":718,"children":719,"page":249},"Microeconomics","\u002Fen\u002Fmicroeconomics","en\u002Fmicroeconomics",[720,726,732,738,744,750,756,762,768,774,780,786,792],{"title":721,"path":722,"stem":723,"children":724},"Advanced Microeconomics — Course Guide","\u002Fen\u002Fmicroeconomics\u002F00-intro","en\u002Fmicroeconomics\u002F00-intro\u002Findex",[725],{"title":721,"path":722,"stem":723},{"title":727,"path":728,"stem":729,"children":730},"Module 1 — Choice, Duality, and Revealed Preference","\u002Fen\u002Fmicroeconomics\u002F01-fundations","en\u002Fmicroeconomics\u002F01-fundations\u002Findex",[731],{"title":727,"path":728,"stem":729},{"title":733,"path":734,"stem":735,"children":736},"Module 2 — Comparative Statics and Welfare Measurement","\u002Fen\u002Fmicroeconomics\u002F02-comparative-statics","en\u002Fmicroeconomics\u002F02-comparative-statics\u002Findex",[737],{"title":733,"path":734,"stem":735},{"title":739,"path":740,"stem":741,"children":742},"Module 3 — Choice under Risk and Insurance","\u002Fen\u002Fmicroeconomics\u002F03-uncertainty","en\u002Fmicroeconomics\u002F03-uncertainty\u002Findex",[743],{"title":739,"path":740,"stem":741},{"title":745,"path":746,"stem":747,"children":748},"Module 4 — General Equilibrium and Welfare","\u002Fen\u002Fmicroeconomics\u002F04-general-equilibrium","en\u002Fmicroeconomics\u002F04-general-equilibrium\u002Findex",[749],{"title":745,"path":746,"stem":747},{"title":751,"path":752,"stem":753,"children":754},"Module 5 — Static, Dynamic, and Repeated Games","\u002Fen\u002Fmicroeconomics\u002F05-game-theory","en\u002Fmicroeconomics\u002F05-game-theory\u002Findex",[755],{"title":751,"path":752,"stem":753},{"title":757,"path":758,"stem":759,"children":760},"Module 6 — Oligopoly, Entry, and Algorithmic Pricing","\u002Fen\u002Fmicroeconomics\u002F06-oligopoly","en\u002Fmicroeconomics\u002F06-oligopoly\u002Findex",[761],{"title":757,"path":758,"stem":759},{"title":763,"path":764,"stem":765,"children":766},"Module 7 — Information Economics and Contracts","\u002Fen\u002Fmicroeconomics\u002F07-information-economics","en\u002Fmicroeconomics\u002F07-information-economics\u002Findex",[767],{"title":763,"path":764,"stem":765},{"title":769,"path":770,"stem":771,"children":772},"Module 8 — Mechanism Design and Auctions","\u002Fen\u002Fmicroeconomics\u002F08-mechanism-design","en\u002Fmicroeconomics\u002F08-mechanism-design\u002Findex",[773],{"title":769,"path":770,"stem":771},{"title":775,"path":776,"stem":777,"children":778},"Module 9 — Behavioural and Experimental Microeconomics","\u002Fen\u002Fmicroeconomics\u002F09-behavioural-economics","en\u002Fmicroeconomics\u002F09-behavioural-economics\u002Findex",[779],{"title":775,"path":776,"stem":777},{"title":781,"path":782,"stem":783,"children":784},"Module 10 — Externalities, Public Goods, and Collective Action","\u002Fen\u002Fmicroeconomics\u002F10-externalities-public-goods","en\u002Fmicroeconomics\u002F10-externalities-public-goods\u002Findex",[785],{"title":781,"path":782,"stem":783},{"title":787,"path":788,"stem":789,"children":790},"Module 11 — Matching and Market Design","\u002Fen\u002Fmicroeconomics\u002F11-market-design","en\u002Fmicroeconomics\u002F11-market-design\u002Findex",[791],{"title":787,"path":788,"stem":789},{"title":793,"path":794,"stem":795,"children":796},"Module 12 — Integrated Microeconomic Design Studio","\u002Fen\u002Fmicroeconomics\u002F12-review","en\u002Fmicroeconomics\u002F12-review\u002Findex",[797],{"title":793,"path":794,"stem":795},{"title":799,"path":800,"stem":801,"children":802},"Playground","\u002Fen\u002Fplayground","en\u002Fplayground\u002Findex",[803,804,808,812,816,820],{"title":799,"path":800,"stem":801},{"title":805,"path":806,"stem":807},"Typst Plugin Playground","\u002Fen\u002Fplayground\u002F01-typstex","en\u002Fplayground\u002F01-typstex",{"title":809,"path":810,"stem":811},"Citation Plugin Test","\u002Fen\u002Fplayground\u002F02-citation","en\u002Fplayground\u002F02-citation",{"title":813,"path":814,"stem":815},"Pyodide Playground","\u002Fen\u002Fplayground\u002F03-pyodide","en\u002Fplayground\u002F03-pyodide",{"title":817,"path":818,"stem":819},"WebR Playground","\u002Fen\u002Fplayground\u002F04-webr","en\u002Fplayground\u002F04-webr",{"title":821,"path":822,"stem":823},"Chart.js 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文献综述的构建与写作","\u002Fzh\u002Facademic-writing\u002F04-writing-the-literature-review","zh\u002Facademic-writing\u002F04-writing-the-literature-review",{"title":1056,"path":1057,"stem":1058},"8. 文献综述清单与模板","\u002Fzh\u002Facademic-writing\u002F08-literature-review-checklist-and-template","zh\u002Facademic-writing\u002F08-literature-review-checklist-and-template",{"title":1060,"path":1061,"stem":1062},"10. 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",[1830,4947,4949,4966],{"className":4948},[1833],[1830,4950,4952],{"className":4951},[1837],[1839,4953,4954],{"xmlns":1841},[1843,4955,4956,4964],{},[1846,4957,4958],{},[2255,4959,4960,4962],{"accent":1869},[1849,4961,1959],{},[1853,4963,2261],{},[1861,4965,2264],{"encoding":1863},[1830,4967,4969],{"className":4968,"ariaHidden":1869},[1868],[1830,4970,4972,4975],{"className":4971},[1873],[1830,4973],{"className":4974,"style":2274},[1877],[1830,4976,4978],{"className":4977},[1882,2278],[1830,4979,4981],{"className":4980},[2130],[1830,4982,4984],{"className":4983},[2135],[1830,4985,4987,4995],{"className":4986,"style":2274},[2139],[1830,4988,4989,4992],{"style":2290},[1830,4990],{"className":4991,"style":2294},[2147],[1830,4993,1959],{"className":4994,"style":1975},[1882,1883],[1830,4996,4997,5000],{"style":2300},[1830,4998],{"className":4999,"style":2294},[2147],[1830,5001,5003],{"className":5002,"style":2308},[2307],[1830,5004,2261],{"className":5005},[1882]," 与 ",[1830,5008,5010,5028],{"className":5009},[1833],[1830,5011,5013],{"className":5012},[1837],[1839,5014,5015],{"xmlns":1841},[1843,5016,5017,5025],{},[1846,5018,5019],{},[2613,5020,5021,5023],{},[1849,5022,4040],{},[1857,5024,2620],{},[1861,5026,5027],{"encoding":1863},"S^2",[1830,5029,5031],{"className":5030,"ariaHidden":1869},[1868],[1830,5032,5034,5037],{"className":5033},[1873],[1830,5035],{"className":5036,"style":2702},[1877],[1830,5038,5040,5043],{"className":5039},[1882],[1830,5041,4040],{"className":5042,"style":2513},[1882,1883],[1830,5044,5046],{"className":5045},[2126],[1830,5047,5049],{"className":5048},[2130],[1830,5050,5052],{"className":5051},[2135],[1830,5053,5055],{"className":5054,"style":2702},[2139],[1830,5056,5057,5060],{"style":2724},[1830,5058],{"className":5059,"style":2148},[2147],[1830,5061,5063],{"className":5062},[2152,2153,2154,2155],[1830,5064,2620],{"className":5065},[1882,2155]," 独立。因此",[1830,5068,5070],{"className":5069},[2738],[1830,5071,5073,5130],{"className":5072},[1833],[1830,5074,5076],{"className":5075},[1837],[1839,5077,5078],{"xmlns":1841,"display":2747},[1843,5079,5080,5127],{},[1846,5081,5082,5084,5086,5110,5112,5125],{},[1849,5083,2068],{},[1853,5085,2494],{},[2790,5087,5088,5100],{},[1846,5089,5090,5096,5098],{},[2255,5091,5092,5094],{"accent":1869},[1849,5093,1959],{},[1853,5095,2261],{},[1853,5097,1855],{},[1849,5099,2497],{},[1846,5101,5102,5104,5106],{},[1849,5103,4040],{},[1849,5105,3061],{"mathvariant":2595},[3063,5107,5108],{},[1849,5109,1851],{},[1853,5111,1995],{},[2074,5113,5114,5117],{},[1849,5115,5116],{},"t",[1846,5118,5119,5121,5123],{},[1849,5120,1851],{},[1853,5122,1855],{},[1857,5124,1859],{},[1849,5126,2802],{"mathvariant":2595},[1861,5128,5129],{"encoding":1863},"T=\\frac{\\bar X-\\mu}{S\u002F\\sqrt n}\\sim t_{n-1}.",[1830,5131,5133,5151,5319],{"className":5132,"ariaHidden":1869},[1868],[1830,5134,5136,5139,5142,5145,5148],{"className":5135},[1873],[1830,5137],{"className":5138,"style":1971},[1877],[1830,5140,2068],{"className":5141,"style":2034},[1882,1883],[1830,5143],{"className":5144,"style":2017},[1887],[1830,5146,2494],{"className":5147},[2021],[1830,5149],{"className":5150,"style":2017},[1887],[1830,5152,5154,5158,5310,5313,5316],{"className":5153},[1873],[1830,5155],{"className":5156,"style":5157},[1877],"height:2.4374em;vertical-align:-0.9403em;",[1830,5159,5161,5164,5307],{"className":5160},[1882],[1830,5162],{"className":5163},[2116,2949],[1830,5165,5167],{"className":5166},[2790],[1830,5168,5170,5298],{"className":5169},[2130,2131],[1830,5171,5173,5295],{"className":5172},[2135],[1830,5174,5177,5236,5244],{"className":5175,"style":5176},[2139],"height:1.4971em;",[1830,5178,5180,5183],{"style":5179},"top:-2.3097em;",[1830,5181],{"className":5182,"style":2294},[2147],[1830,5184,5186,5189,5192],{"className":5185},[1882],[1830,5187,4040],{"className":5188,"style":2513},[1882,1883],[1830,5190,3061],{"className":5191},[1882],[1830,5193,5195],{"className":5194},[1882,3089],[1830,5196,5198,5228],{"className":5197},[2130,2131],[1830,5199,5201,5225],{"className":5200},[2135],[1830,5202,5204,5213],{"className":5203,"style":3099},[2139],[1830,5205,5207,5210],{"className":5206,"style":2290},[3103],[1830,5208],{"className":5209,"style":2294},[2147],[1830,5211,1851],{"className":5212,"style":3110},[1882,1883],[1830,5214,5215,5218],{"style":3113},[1830,5216],{"className":5217,"style":2294},[2147],[1830,5219,5221],{"className":5220,"style":3121},[3120],[3123,5222,5223],{"xmlns":3125,"width":3126,"height":3127,"viewBox":3128,"preserveAspectRatio":3129},[3131,5224],{"d":3133},[1830,5226,2163],{"className":5227},[2162],[1830,5229,5231],{"className":5230},[2135],[1830,5232,5234],{"className":5233,"style":3143},[2139],[1830,5235],{},[1830,5237,5238,5241],{"style":2977},[1830,5239],{"className":5240,"style":2294},[2147],[1830,5242],{"className":5243,"style":2985},[2984],[1830,5245,5246,5249],{"style":2988},[1830,5247],{"className":5248,"style":2294},[2147],[1830,5250,5252,5283,5286,5289,5292],{"className":5251},[1882],[1830,5253,5255],{"className":5254},[1882,2278],[1830,5256,5258],{"className":5257},[2130],[1830,5259,5261],{"className":5260},[2135],[1830,5262,5264,5272],{"className":5263,"style":2274},[2139],[1830,5265,5266,5269],{"style":2290},[1830,5267],{"className":5268,"style":2294},[2147],[1830,5270,1959],{"className":5271,"style":1975},[1882,1883],[1830,5273,5274,5277],{"style":2300},[1830,5275],{"className":5276,"style":2294},[2147],[1830,5278,5280],{"className":5279,"style":2308},[2307],[1830,5281,2261],{"className":5282},[1882],[1830,5284],{"className":5285,"style":1888},[1887],[1830,5287,1855],{"className":5288},[1892],[1830,5290],{"className":5291,"style":1888},[1887],[1830,5293,2497],{"className":5294},[1882,1883],[1830,5296,2163],{"className":5297},[2162],[1830,5299,5301],{"className":5300},[2135],[1830,5302,5305],{"className":5303,"style":5304},[2139],"height:0.9403em;",[1830,5306],{},[1830,5308],{"className":5309},[2238,2949],[1830,5311],{"className":5312,"style":2017},[1887],[1830,5314,1995],{"className":5315},[2021],[1830,5317],{"className":5318,"style":2017},[1887],[1830,5320,5322,5326,5377],{"className":5321},[1873],[1830,5323],{"className":5324,"style":5325},[1877],"height:0.8234em;vertical-align:-0.2083em;",[1830,5327,5329,5332],{"className":5328},[1882],[1830,5330,5116],{"className":5331},[1882,1883],[1830,5333,5335],{"className":5334},[2126],[1830,5336,5338,5368],{"className":5337},[2130,2131],[1830,5339,5341,5365],{"className":5340},[2135],[1830,5342,5344],{"className":5343,"style":2140},[2139],[1830,5345,5347,5350],{"style":5346},"top:-2.55em;margin-left:0em;margin-right:0.05em;",[1830,5348],{"className":5349,"style":2148},[2147],[1830,5351,5353],{"className":5352},[2152,2153,2154,2155],[1830,5354,5356,5359,5362],{"className":5355},[1882,2155],[1830,5357,1851],{"className":5358},[1882,1883,2155],[1830,5360,1855],{"className":5361},[1892,2155],[1830,5363,1859],{"className":5364},[1882,2155],[1830,5366,2163],{"className":5367},[2162],[1830,5369,5371],{"className":5370},[2135],[1830,5372,5375],{"className":5373,"style":5374},[2139],"height:0.2083em;",[1830,5376],{},[1830,5378,2802],{"className":5379},[1882],[1796,5381,5382],{},"这里的独立性是正态分布的特殊性质，不对任意总体精确成立。",[1807,5384,5386,5387],{"id":5385},"_4-自由度为什么是-n1n-1n1","4. 自由度为什么是 ",[1830,5388,5390,5407],{"className":5389},[1833],[1830,5391,5393],{"className":5392},[1837],[1839,5394,5395],{"xmlns":1841},[1843,5396,5397,5405],{},[1846,5398,5399,5401,5403],{},[1849,5400,1851],{},[1853,5402,1855],{},[1857,5404,1859],{},[1861,5406,1864],{"encoding":1863},[1830,5408,5410,5428],{"className":5409,"ariaHidden":1869},[1868],[1830,5411,5413,5416,5419,5422,5425],{"className":5412},[1873],[1830,5414],{"className":5415,"style":1878},[1877],[1830,5417,1851],{"className":5418},[1882,1883],[1830,5420],{"className":5421,"style":1888},[1887],[1830,5423,1855],{"className":5424},[1892],[1830,5426],{"className":5427,"style":1888},[1887],[1830,5429,5431,5434],{"className":5430},[1873],[1830,5432],{"className":5433,"style":1902},[1877],[1830,5435,1859],{"className":5436},[1882],[1796,5438,5439],{},"残差满足：",[1830,5441,5443],{"className":5442},[2738],[1830,5444,5446,5495],{"className":5445},[1833],[1830,5447,5449],{"className":5448},[1837],[1839,5450,5451],{"xmlns":1841,"display":2747},[1843,5452,5453,5492],{},[1846,5454,5455,5469,5471,5477,5479,5485,5487,5489],{},[4058,5456,5457,5459,5467],{},[1853,5458,3197],{},[1846,5460,5461,5463,5465],{},[1849,5462,2488],{},[1853,5464,2494],{},[1857,5466,1859],{},[1849,5468,1851],{},[1853,5470,2072],{"stretchy":2071},[2074,5472,5473,5475],{},[1849,5474,1959],{},[1849,5476,2488],{},[1853,5478,1855],{},[2255,5480,5481,5483],{"accent":1869},[1849,5482,1959],{},[1853,5484,2261],{},[1853,5486,2096],{"stretchy":2071},[1853,5488,2494],{},[1857,5490,5491],{},"0.",[1861,5493,5494],{"encoding":1863},"\\sum_{i=1}^n(X_i-\\bar X)=0.",[1830,5496,5498,5620,5669],{"className":5497,"ariaHidden":1869},[1868],[1830,5499,5501,5504,5568,5571,5611,5614,5617],{"className":5500},[1873],[1830,5502],{"className":5503,"style":4154},[1877],[1830,5505,5507],{"className":5506},[2636,3441],[1830,5508,5510,5560],{"className":5509},[2130,2131],[1830,5511,5513,5557],{"className":5512},[2135],[1830,5514,5516,5536,5546],{"className":5515,"style":4245},[2139],[1830,5517,5518,5521],{"style":3454},[1830,5519],{"className":5520,"style":3458},[2147],[1830,5522,5524],{"className":5523},[2152,2153,2154,2155],[1830,5525,5527,5530,5533],{"className":5526},[1882,2155],[1830,5528,2488],{"className":5529},[1882,1883,2155],[1830,5531,2494],{"className":5532},[2021,2155],[1830,5534,1859],{"className":5535},[1882,2155],[1830,5537,5538,5541],{"style":3467},[1830,5539],{"className":5540,"style":3458},[2147],[1830,5542,5543],{},[1830,5544,3197],{"className":5545},[2636,3476,3477],[1830,5547,5548,5551],{"style":4278},[1830,5549],{"className":5550,"style":3458},[2147],[1830,5552,5554],{"className":5553},[2152,2153,2154,2155],[1830,5555,1851],{"className":5556},[1882,1883,2155],[1830,5558,2163],{"className":5559},[2162],[1830,5561,5563],{"className":5562},[2135],[1830,5564,5566],{"className":5565,"style":3487},[2139],[1830,5567],{},[1830,5569,2072],{"className":5570},[2116],[1830,5572,5574,5577],{"className":5573},[1882],[1830,5575,1959],{"className":5576,"style":1975},[1882,1883],[1830,5578,5580],{"className":5579},[2126],[1830,5581,5583,5603],{"className":5582},[2130,2131],[1830,5584,5586,5600],{"className":5585},[2135],[1830,5587,5589],{"className":5588,"style":2535},[2139],[1830,5590,5591,5594],{"style":2143},[1830,5592],{"className":5593,"style":2148},[2147],[1830,5595,5597],{"className":5596},[2152,2153,2154,2155],[1830,5598,2488],{"className":5599},[1882,1883,2155],[1830,5601,2163],{"className":5602},[2162],[1830,5604,5606],{"className":5605},[2135],[1830,5607,5609],{"className":5608,"style":2170},[2139],[1830,5610],{},[1830,5612],{"className":5613,"style":1888},[1887],[1830,5615,1855],{"className":5616},[1892],[1830,5618],{"className":5619,"style":1888},[1887],[1830,5621,5623,5626,5657,5660,5663,5666],{"className":5622},[1873],[1830,5624],{"className":5625,"style":2815},[1877],[1830,5627,5629],{"className":5628},[1882,2278],[1830,5630,5632],{"className":5631},[2130],[1830,5633,5635],{"className":5634},[2135],[1830,5636,5638,5646],{"className":5637,"style":2274},[2139],[1830,5639,5640,5643],{"style":2290},[1830,5641],{"className":5642,"style":2294},[2147],[1830,5644,1959],{"className":5645,"style":1975},[1882,1883],[1830,5647,5648,5651],{"style":2300},[1830,5649],{"className":5650,"style":2294},[2147],[1830,5652,5654],{"className":5653,"style":2308},[2307],[1830,5655,2261],{"className":5656},[1882],[1830,5658,2096],{"className":5659},[2238],[1830,5661],{"className":5662,"style":2017},[1887],[1830,5664,2494],{"className":5665},[2021],[1830,5667],{"className":5668,"style":2017},[1887],[1830,5670,5672,5675],{"className":5671},[1873],[1830,5673],{"className":5674,"style":1902},[1877],[1830,5676,5491],{"className":5677},[1882],[1796,5679,5680,5681,5731,5732,5782,5783,5833,5834,5892,5893,5952],{},"知道前 ",[1830,5682,5684,5701],{"className":5683},[1833],[1830,5685,5687],{"className":5686},[1837],[1839,5688,5689],{"xmlns":1841},[1843,5690,5691,5699],{},[1846,5692,5693,5695,5697],{},[1849,5694,1851],{},[1853,5696,1855],{},[1857,5698,1859],{},[1861,5700,1864],{"encoding":1863},[1830,5702,5704,5722],{"className":5703,"ariaHidden":1869},[1868],[1830,5705,5707,5710,5713,5716,5719],{"className":5706},[1873],[1830,5708],{"className":5709,"style":1878},[1877],[1830,5711,1851],{"className":5712},[1882,1883],[1830,5714],{"className":5715,"style":1888},[1887],[1830,5717,1855],{"className":5718},[1892],[1830,5720],{"className":5721,"style":1888},[1887],[1830,5723,5725,5728],{"className":5724},[1873],[1830,5726],{"className":5727,"style":1902},[1877],[1830,5729,1859],{"className":5730},[1882]," 个残差后，最后一个由约束决定，只有 ",[1830,5733,5735,5752],{"className":5734},[1833],[1830,5736,5738],{"className":5737},[1837],[1839,5739,5740],{"xmlns":1841},[1843,5741,5742,5750],{},[1846,5743,5744,5746,5748],{},[1849,5745,1851],{},[1853,5747,1855],{},[1857,5749,1859],{},[1861,5751,1864],{"encoding":1863},[1830,5753,5755,5773],{"className":5754,"ariaHidden":1869},[1868],[1830,5756,5758,5761,5764,5767,5770],{"className":5757},[1873],[1830,5759],{"className":5760,"style":1878},[1877],[1830,5762,1851],{"className":5763},[1882,1883],[1830,5765],{"className":5766,"style":1888},[1887],[1830,5768,1855],{"className":5769},[1892],[1830,5771],{"className":5772,"style":1888},[1887],[1830,5774,5776,5779],{"className":5775},[1873],[1830,5777],{"className":5778,"style":1902},[1877],[1830,5780,1859],{"className":5781},[1882]," 个自由方向。除以 ",[1830,5784,5786,5803],{"className":5785},[1833],[1830,5787,5789],{"className":5788},[1837],[1839,5790,5791],{"xmlns":1841},[1843,5792,5793,5801],{},[1846,5794,5795,5797,5799],{},[1849,5796,1851],{},[1853,5798,1855],{},[1857,5800,1859],{},[1861,5802,1864],{"encoding":1863},[1830,5804,5806,5824],{"className":5805,"ariaHidden":1869},[1868],[1830,5807,5809,5812,5815,5818,5821],{"className":5808},[1873],[1830,5810],{"className":5811,"style":1878},[1877],[1830,5813,1851],{"className":5814},[1882,1883],[1830,5816],{"className":5817,"style":1888},[1887],[1830,5819,1855],{"className":5820},[1892],[1830,5822],{"className":5823,"style":1888},[1887],[1830,5825,5827,5830],{"className":5826},[1873],[1830,5828],{"className":5829,"style":1902},[1877],[1830,5831,1859],{"className":5832},[1882]," 使 ",[1830,5835,5837,5854],{"className":5836},[1833],[1830,5838,5840],{"className":5839},[1837],[1839,5841,5842],{"xmlns":1841},[1843,5843,5844,5852],{},[1846,5845,5846],{},[2613,5847,5848,5850],{},[1849,5849,4040],{},[1857,5851,2620],{},[1861,5853,5027],{"encoding":1863},[1830,5855,5857],{"className":5856,"ariaHidden":1869},[1868],[1830,5858,5860,5863],{"className":5859},[1873],[1830,5861],{"className":5862,"style":2702},[1877],[1830,5864,5866,5869],{"className":5865},[1882],[1830,5867,4040],{"className":5868,"style":2513},[1882,1883],[1830,5870,5872],{"className":5871},[2126],[1830,5873,5875],{"className":5874},[2130],[1830,5876,5878],{"className":5877},[2135],[1830,5879,5881],{"className":5880,"style":2702},[2139],[1830,5882,5883,5886],{"style":2724},[1830,5884],{"className":5885,"style":2148},[2147],[1830,5887,5889],{"className":5888},[2152,2153,2154,2155],[1830,5890,2620],{"className":5891},[1882,2155]," 对 ",[1830,5894,5896,5914],{"className":5895},[1833],[1830,5897,5899],{"className":5898},[1837],[1839,5900,5901],{"xmlns":1841},[1843,5902,5903,5911],{},[1846,5904,5905],{},[2613,5906,5907,5909],{},[1849,5908,2617],{},[1857,5910,2620],{},[1861,5912,5913],{"encoding":1863},"\\sigma^2",[1830,5915,5917],{"className":5916,"ariaHidden":1869},[1868],[1830,5918,5920,5923],{"className":5919},[1873],[1830,5921],{"className":5922,"style":2702},[1877],[1830,5924,5926,5929],{"className":5925},[1882],[1830,5927,2617],{"className":5928,"style":2709},[1882,1883],[1830,5930,5932],{"className":5931},[2126],[1830,5933,5935],{"className":5934},[2130],[1830,5936,5938],{"className":5937},[2135],[1830,5939,5941],{"className":5940,"style":2702},[2139],[1830,5942,5943,5946],{"style":2724},[1830,5944],{"className":5945,"style":2148},[2147],[1830,5947,5949],{"className":5948},[2152,2153,2154,2155],[1830,5950,2620],{"className":5951},[1882,2155]," 无偏：",[1830,5954,5956],{"className":5955},[2738],[1830,5957,5959,5993],{"className":5958},[1833],[1830,5960,5962],{"className":5961},[1837],[1839,5963,5964],{"xmlns":1841,"display":2747},[1843,5965,5966,5990],{},[1846,5967,5968,5970,5972,5978,5980,5982,5988],{},[1849,5969,2478],{},[1853,5971,2481],{"stretchy":2071},[2613,5973,5974,5976],{},[1849,5975,4040],{},[1857,5977,2620],{},[1853,5979,2491],{"stretchy":2071},[1853,5981,2494],{},[2613,5983,5984,5986],{},[1849,5985,2617],{},[1857,5987,2620],{},[1849,5989,2802],{"mathvariant":2595},[1861,5991,5992],{"encoding":1863},"E[S^2]=\\sigma^2.",[1830,5994,5996,6049],{"className":5995,"ariaHidden":1869},[1868],[1830,5997,5999,6002,6005,6008,6037,6040,6043,6046],{"className":5998},[1873],[1830,6000],{"className":6001,"style":4357},[1877],[1830,6003,2478],{"className":6004,"style":2513},[1882,1883],[1830,6006,2481],{"className":6007},[2116],[1830,6009,6011,6014],{"className":6010},[1882],[1830,6012,4040],{"className":6013,"style":2513},[1882,1883],[1830,6015,6017],{"className":6016},[2126],[1830,6018,6020],{"className":6019},[2130],[1830,6021,6023],{"className":6022},[2135],[1830,6024,6026],{"className":6025,"style":4108},[2139],[1830,6027,6028,6031],{"style":4129},[1830,6029],{"className":6030,"style":2148},[2147],[1830,6032,6034],{"className":6033},[2152,2153,2154,2155],[1830,6035,2620],{"className":6036},[1882,2155],[1830,6038,2491],{"className":6039},[2238],[1830,6041],{"className":6042,"style":2017},[1887],[1830,6044,2494],{"className":6045},[2021],[1830,6047],{"className":6048,"style":2017},[1887],[1830,6050,6052,6055,6084],{"className":6051},[1873],[1830,6053],{"className":6054,"style":4108},[1877],[1830,6056,6058,6061],{"className":6057},[1882],[1830,6059,2617],{"className":6060,"style":2709},[1882,1883],[1830,6062,6064],{"className":6063},[2126],[1830,6065,6067],{"className":6066},[2130],[1830,6068,6070],{"className":6069},[2135],[1830,6071,6073],{"className":6072,"style":4108},[2139],[1830,6074,6075,6078],{"style":4129},[1830,6076],{"className":6077,"style":2148},[2147],[1830,6079,6081],{"className":6080},[2152,2153,2154,2155],[1830,6082,2620],{"className":6083},[1882,2155],[1830,6085,2802],{"className":6086},[1882],[1796,6088,6089],{},"无偏不代表 MSE 最小；分母选择取决于估计目标。",[1807,6091,6093],{"id":6092},"_5-t-与-f-分布","5. t 与 F 分布",[6095,6096,6097,6100],"ul",{},[1817,6098,6099],{},"t 分布是标准正态除以独立卡方平方根；",[1817,6101,6102,6103],{},"F 分布是两个独立、按自由度标准化的卡方比：",[1830,6104,6106],{"className":6105},[2738],[1830,6107,6109,6163],{"className":6108},[1833],[1830,6110,6112],{"className":6111},[1837],[1839,6113,6114],{"xmlns":1841,"display":2747},[1843,6115,6116,6160],{},[1846,6117,6118,6120,6122,6158],{},[1849,6119,1998],{},[1853,6121,2494],{},[2790,6123,6124,6142],{},[1846,6125,6126,6133,6135],{},[2074,6127,6128,6131],{},[1849,6129,6130],{},"U",[1857,6132,1859],{},[1849,6134,3061],{"mathvariant":2595},[2074,6136,6137,6140],{},[1849,6138,6139],{},"ν",[1857,6141,1859],{},[1846,6143,6144,6150,6152],{},[2074,6145,6146,6148],{},[1849,6147,6130],{},[1857,6149,2620],{},[1849,6151,3061],{"mathvariant":2595},[2074,6153,6154,6156],{},[1849,6155,6139],{},[1857,6157,2620],{},[1849,6159,2802],{"mathvariant":2595},[1861,6161,6162],{"encoding":1863},"F=\\frac{U_1\u002F\\nu_1}{U_2\u002F\\nu_2}.",[1830,6164,6166,6184],{"className":6165,"ariaHidden":1869},[1868],[1830,6167,6169,6172,6175,6178,6181],{"className":6168},[1873],[1830,6170],{"className":6171,"style":1971},[1877],[1830,6173,1998],{"className":6174,"style":2034},[1882,1883],[1830,6176],{"className":6177,"style":2017},[1887],[1830,6179,2494],{"className":6180},[2021],[1830,6182],{"className":6183,"style":2017},[1887],[1830,6185,6187,6191,6418],{"className":6186},[1873],[1830,6188],{"className":6189,"style":6190},[1877],"height:2.363em;vertical-align:-0.936em;",[1830,6192,6194,6197,6415],{"className":6193},[1882],[1830,6195],{"className":6196},[2116,2949],[1830,6198,6200],{"className":6199},[2790],[1830,6201,6203,6406],{"className":6202},[2130,2131],[1830,6204,6206,6403],{"className":6205},[2135],[1830,6207,6210,6304,6312],{"className":6208,"style":6209},[2139],"height:1.427em;",[1830,6211,6212,6215],{"style":2965},[1830,6213],{"className":6214,"style":2294},[2147],[1830,6216,6218,6259,6262],{"className":6217},[1882],[1830,6219,6221,6224],{"className":6220},[1882],[1830,6222,6130],{"className":6223,"style":3975},[1882,1883],[1830,6225,6227],{"className":6226},[2126],[1830,6228,6230,6251],{"className":6229},[2130,2131],[1830,6231,6233,6248],{"className":6232},[2135],[1830,6234,6236],{"className":6235,"style":2140},[2139],[1830,6237,6239,6242],{"style":6238},"top:-2.55em;margin-left:-0.109em;margin-right:0.05em;",[1830,6240],{"className":6241,"style":2148},[2147],[1830,6243,6245],{"className":6244},[2152,2153,2154,2155],[1830,6246,2620],{"className":6247},[1882,2155],[1830,6249,2163],{"className":6250},[2162],[1830,6252,6254],{"className":6253},[2135],[1830,6255,6257],{"className":6256,"style":2170},[2139],[1830,6258],{},[1830,6260,3061],{"className":6261},[1882],[1830,6263,6265,6269],{"className":6264},[1882],[1830,6266,6139],{"className":6267,"style":6268},[1882,1883],"margin-right:0.0637em;",[1830,6270,6272],{"className":6271},[2126],[1830,6273,6275,6296],{"className":6274},[2130,2131],[1830,6276,6278,6293],{"className":6277},[2135],[1830,6279,6281],{"className":6280,"style":2140},[2139],[1830,6282,6284,6287],{"style":6283},"top:-2.55em;margin-left:-0.0637em;margin-right:0.05em;",[1830,6285],{"className":6286,"style":2148},[2147],[1830,6288,6290],{"className":6289},[2152,2153,2154,2155],[1830,6291,2620],{"className":6292},[1882,2155],[1830,6294,2163],{"className":6295},[2162],[1830,6297,6299],{"className":6298},[2135],[1830,6300,6302],{"className":6301,"style":2170},[2139],[1830,6303],{},[1830,6305,6306,6309],{"style":2977},[1830,6307],{"className":6308,"style":2294},[2147],[1830,6310],{"className":6311,"style":2985},[2984],[1830,6313,6314,6317],{"style":2988},[1830,6315],{"className":6316,"style":2294},[2147],[1830,6318,6320,6360,6363],{"className":6319},[1882],[1830,6321,6323,6326],{"className":6322},[1882],[1830,6324,6130],{"className":6325,"style":3975},[1882,1883],[1830,6327,6329],{"className":6328},[2126],[1830,6330,6332,6352],{"className":6331},[2130,2131],[1830,6333,6335,6349],{"className":6334},[2135],[1830,6336,6338],{"className":6337,"style":2140},[2139],[1830,6339,6340,6343],{"style":6238},[1830,6341],{"className":6342,"style":2148},[2147],[1830,6344,6346],{"className":6345},[2152,2153,2154,2155],[1830,6347,1859],{"className":6348},[1882,2155],[1830,6350,2163],{"className":6351},[2162],[1830,6353,6355],{"className":6354},[2135],[1830,6356,6358],{"className":6357,"style":2170},[2139],[1830,6359],{},[1830,6361,3061],{"className":6362},[1882],[1830,6364,6366,6369],{"className":6365},[1882],[1830,6367,6139],{"className":6368,"style":6268},[1882,1883],[1830,6370,6372],{"className":6371},[2126],[1830,6373,6375,6395],{"className":6374},[2130,2131],[1830,6376,6378,6392],{"className":6377},[2135],[1830,6379,6381],{"className":6380,"style":2140},[2139],[1830,6382,6383,6386],{"style":6283},[1830,6384],{"className":6385,"style":2148},[2147],[1830,6387,6389],{"className":6388},[2152,2153,2154,2155],[1830,6390,1859],{"className":6391},[1882,2155],[1830,6393,2163],{"className":6394},[2162],[1830,6396,6398],{"className":6397},[2135],[1830,6399,6401],{"className":6400,"style":2170},[2139],[1830,6402],{},[1830,6404,2163],{"className":6405},[2162],[1830,6407,6409],{"className":6408},[2135],[1830,6410,6413],{"className":6411,"style":6412},[2139],"height:0.936em;",[1830,6414],{},[1830,6416],{"className":6417},[2238,2949],[1830,6419,2802],{"className":6420},[1882],[1796,6422,6423,6424,6454],{},"它们连接均值、方差比、ANOVA 与回归检验。自由度越大，t 越接近标准正态；小样本时厚尾反映估计 ",[1830,6425,6427,6441],{"className":6426},[1833],[1830,6428,6430],{"className":6429},[1837],[1839,6431,6432],{"xmlns":1841},[1843,6433,6434,6438],{},[1846,6435,6436],{},[1849,6437,2617],{},[1861,6439,6440],{"encoding":1863},"\\sigma",[1830,6442,6444],{"className":6443,"ariaHidden":1869},[1868],[1830,6445,6447,6451],{"className":6446},[1873],[1830,6448],{"className":6449,"style":6450},[1877],"height:0.4306em;",[1830,6452,2617],{"className":6453,"style":2709},[1882,1883]," 的额外不确定性。",[1807,6456,6458],{"id":6457},"_6-可运行案例小样本中-z-临界值覆盖不足","6. 可运行案例：小样本中 z 临界值覆盖不足",[1796,6460,6461,6462,6514,6515,6590],{},"对 ",[1830,6463,6465,6484],{"className":6464},[1833],[1830,6466,6468],{"className":6467},[1837],[1839,6469,6470],{"xmlns":1841},[1843,6471,6472,6481],{},[1846,6473,6474,6476,6478],{},[1849,6475,1851],{},[1853,6477,2494],{},[1857,6479,6480],{},"12",[1861,6482,6483],{"encoding":1863},"n=12",[1830,6485,6487,6505],{"className":6486,"ariaHidden":1869},[1868],[1830,6488,6490,6493,6496,6499,6502],{"className":6489},[1873],[1830,6491],{"className":6492,"style":6450},[1877],[1830,6494,1851],{"className":6495},[1882,1883],[1830,6497],{"className":6498,"style":2017},[1887],[1830,6500,2494],{"className":6501},[2021],[1830,6503],{"className":6504,"style":2017},[1887],[1830,6506,6508,6511],{"className":6507},[1873],[1830,6509],{"className":6510,"style":1902},[1877],[1830,6512,6480],{"className":6513},[1882]," 的正态样本，使用估计标准差后的枢轴量服从 ",[1830,6516,6518,6537],{"className":6517},[1833],[1830,6519,6521],{"className":6520},[1837],[1839,6522,6523],{"xmlns":1841},[1843,6524,6525,6534],{},[1846,6526,6527],{},[2074,6528,6529,6531],{},[1849,6530,5116],{},[1857,6532,6533],{},"11",[1861,6535,6536],{"encoding":1863},"t_{11}",[1830,6538,6540],{"className":6539,"ariaHidden":1869},[1868],[1830,6541,6543,6547],{"className":6542},[1873],[1830,6544],{"className":6545,"style":6546},[1877],"height:0.7651em;vertical-align:-0.15em;",[1830,6548,6550,6553],{"className":6549},[1882],[1830,6551,5116],{"className":6552},[1882,1883],[1830,6554,6556],{"className":6555},[2126],[1830,6557,6559,6582],{"className":6558},[2130,2131],[1830,6560,6562,6579],{"className":6561},[2135],[1830,6563,6565],{"className":6564,"style":2140},[2139],[1830,6566,6567,6570],{"style":5346},[1830,6568],{"className":6569,"style":2148},[2147],[1830,6571,6573],{"className":6572},[2152,2153,2154,2155],[1830,6574,6576],{"className":6575},[1882,2155],[1830,6577,6533],{"className":6578},[1882,2155],[1830,6580,2163],{"className":6581},[2162],[1830,6583,6585],{"className":6584},[2135],[1830,6586,6588],{"className":6587,"style":2170},[2139],[1830,6589],{},"。代码比较错误使用 1.96 与正确使用约 2.201 的覆盖率。",[6592,6593],"pyodide",{"code64":6594,"layout":6595,"locale":7,"packages":6596,"title":6597},"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","vertical","numpy","Python：t 抽样分布与区间覆盖",[1796,6599,6600],{},"t 区间的精确覆盖依赖 iid 正态样本。偏态、小样本、聚类或选择性缺失下，应重新审查抽样机制，而不只是换一个临界值。",[1807,6602,6604],{"id":6603},"_7-次序统计量","7. 次序统计量",[1796,6606,6607],{},"排序后：",[1830,6609,6611],{"className":6610},[2738],[1830,6612,6614,6660],{"className":6613},[1833],[1830,6615,6617],{"className":6616},[1837],[1839,6618,6619],{"xmlns":1841,"display":2747},[1843,6620,6621,6657],{},[1846,6622,6623,6635,6638,6641,6643,6655],{},[2074,6624,6625,6627],{},[1849,6626,1959],{},[1846,6628,6629,6631,6633],{},[1853,6630,2072],{"stretchy":2071},[1857,6632,1859],{},[1853,6634,2096],{"stretchy":2071},[1853,6636,6637],{},"≤",[1853,6639,6640],{},"⋯",[1853,6642,6637],{},[2074,6644,6645,6647],{},[1849,6646,1959],{},[1846,6648,6649,6651,6653],{},[1853,6650,2072],{"stretchy":2071},[1849,6652,1851],{},[1853,6654,2096],{"stretchy":2071},[1849,6656,2802],{"mathvariant":2595},[1861,6658,6659],{"encoding":1863},"X_{(1)}\\le\\cdots\\le X_{(n)}.",[1830,6661,6663,6731,6750],{"className":6662,"ariaHidden":1869},[1868],[1830,6664,6666,6670,6722,6725,6728],{"className":6665},[1873],[1830,6667],{"className":6668,"style":6669},[1877],"height:1.0385em;vertical-align:-0.3552em;",[1830,6671,6673,6676],{"className":6672},[1882],[1830,6674,1959],{"className":6675,"style":1975},[1882,1883],[1830,6677,6679],{"className":6678},[2126],[1830,6680,6682,6713],{"className":6681},[2130,2131],[1830,6683,6685,6710],{"className":6684},[2135],[1830,6686,6689],{"className":6687,"style":6688},[2139],"height:0.3448em;",[1830,6690,6692,6695],{"style":6691},"top:-2.5198em;margin-left:-0.0785em;margin-right:0.05em;",[1830,6693],{"className":6694,"style":2148},[2147],[1830,6696,6698],{"className":6697},[2152,2153,2154,2155],[1830,6699,6701,6704,6707],{"className":6700},[1882,2155],[1830,6702,2072],{"className":6703},[2116,2155],[1830,6705,1859],{"className":6706},[1882,2155],[1830,6708,2096],{"className":6709},[2238,2155],[1830,6711,2163],{"className":6712},[2162],[1830,6714,6716],{"className":6715},[2135],[1830,6717,6720],{"className":6718,"style":6719},[2139],"height:0.3552em;",[1830,6721],{},[1830,6723],{"className":6724,"style":2017},[1887],[1830,6726,6637],{"className":6727},[2021],[1830,6729],{"className":6730,"style":2017},[1887],[1830,6732,6734,6738,6741,6744,6747],{"className":6733},[1873],[1830,6735],{"className":6736,"style":6737},[1877],"height:0.7719em;vertical-align:-0.136em;",[1830,6739,6640],{"className":6740},[2184],[1830,6742],{"className":6743,"style":2017},[1887],[1830,6745,6637],{"className":6746},[2021],[1830,6748],{"className":6749,"style":2017},[1887],[1830,6751,6753,6756,6805],{"className":6752},[1873],[1830,6754],{"className":6755,"style":6669},[1877],[1830,6757,6759,6762],{"className":6758},[1882],[1830,6760,1959],{"className":6761,"style":1975},[1882,1883],[1830,6763,6765],{"className":6764},[2126],[1830,6766,6768,6797],{"className":6767},[2130,2131],[1830,6769,6771,6794],{"className":6770},[2135],[1830,6772,6774],{"className":6773,"style":6688},[2139],[1830,6775,6776,6779],{"style":6691},[1830,6777],{"className":6778,"style":2148},[2147],[1830,6780,6782],{"className":6781},[2152,2153,2154,2155],[1830,6783,6785,6788,6791],{"className":6784},[1882,2155],[1830,6786,2072],{"className":6787},[2116,2155],[1830,6789,1851],{"className":6790},[1882,1883,2155],[1830,6792,2096],{"className":6793},[2238,2155],[1830,6795,2163],{"className":6796},[2162],[1830,6798,6800],{"className":6799},[2135],[1830,6801,6803],{"className":6802,"style":6719},[2139],[1830,6804],{},[1830,6806,2802],{"className":6807},[1882],[1796,6809,6810,6811,6839],{},"对连续 CDF ",[1830,6812,6814,6827],{"className":6813},[1833],[1830,6815,6817],{"className":6816},[1837],[1839,6818,6819],{"xmlns":1841},[1843,6820,6821,6825],{},[1846,6822,6823],{},[1849,6824,1998],{},[1861,6826,1998],{"encoding":1863},[1830,6828,6830],{"className":6829,"ariaHidden":1869},[1868],[1830,6831,6833,6836],{"className":6832},[1873],[1830,6834],{"className":6835,"style":1971},[1877],[1830,6837,1998],{"className":6838,"style":2034},[1882,1883],"，最大值满足：",[1830,6841,6843],{"className":6842},[2738],[1830,6844,6846,6898],{"className":6845},[1833],[1830,6847,6849],{"className":6848},[1837],[1839,6850,6851],{"xmlns":1841,"display":2747},[1843,6852,6853,6895],{},[1846,6854,6855,6858,6860,6872,6874,6877,6879,6881,6883,6885,6887,6893],{},[1849,6856,6857],{},"P",[1853,6859,2072],{"stretchy":2071},[2074,6861,6862,6864],{},[1849,6863,1959],{},[1846,6865,6866,6868,6870],{},[1853,6867,2072],{"stretchy":2071},[1849,6869,1851],{},[1853,6871,2096],{"stretchy":2071},[1853,6873,6637],{},[1849,6875,6876],{},"x",[1853,6878,2096],{"stretchy":2071},[1853,6880,2494],{},[1849,6882,1998],{},[1853,6884,2072],{"stretchy":2071},[1849,6886,6876],{},[2613,6888,6889,6891],{},[1853,6890,2096],{"stretchy":2071},[1849,6892,1851],{},[1849,6894,2802],{"mathvariant":2595},[1861,6896,6897],{"encoding":1863},"P(X_{(n)}\\le x)=F(x)^n.",[1830,6899,6901,6972,6993],{"className":6900,"ariaHidden":1869},[1868],[1830,6902,6904,6908,6911,6914,6963,6966,6969],{"className":6903},[1873],[1830,6905],{"className":6906,"style":6907},[1877],"height:1.1052em;vertical-align:-0.3552em;",[1830,6909,6857],{"className":6910,"style":2034},[1882,1883],[1830,6912,2072],{"className":6913},[2116],[1830,6915,6917,6920],{"className":6916},[1882],[1830,6918,1959],{"className":6919,"style":1975},[1882,1883],[1830,6921,6923],{"className":6922},[2126],[1830,6924,6926,6955],{"className":6925},[2130,2131],[1830,6927,6929,6952],{"className":6928},[2135],[1830,6930,6932],{"className":6931,"style":6688},[2139],[1830,6933,6934,6937],{"style":6691},[1830,6935],{"className":6936,"style":2148},[2147],[1830,6938,6940],{"className":6939},[2152,2153,2154,2155],[1830,6941,6943,6946,6949],{"className":6942},[1882,2155],[1830,6944,2072],{"className":6945},[2116,2155],[1830,6947,1851],{"className":6948},[1882,1883,2155],[1830,6950,2096],{"className":6951},[2238,2155],[1830,6953,2163],{"className":6954},[2162],[1830,6956,6958],{"className":6957},[2135],[1830,6959,6961],{"className":6960,"style":6719},[2139],[1830,6962],{},[1830,6964],{"className":6965,"style":2017},[1887],[1830,6967,6637],{"className":6968},[2021],[1830,6970],{"className":6971,"style":2017},[1887],[1830,6973,6975,6978,6981,6984,6987,6990],{"className":6974},[1873],[1830,6976],{"className":6977,"style":2109},[1877],[1830,6979,6876],{"className":6980},[1882,1883],[1830,6982,2096],{"className":6983},[2238],[1830,6985],{"className":6986,"style":2017},[1887],[1830,6988,2494],{"className":6989},[2021],[1830,6991],{"className":6992,"style":2017},[1887],[1830,6994,6996,6999,7002,7005,7008,7038],{"className":6995},[1873],[1830,6997],{"className":6998,"style":2109},[1877],[1830,7000,1998],{"className":7001,"style":2034},[1882,1883],[1830,7003,2072],{"className":7004},[2116],[1830,7006,6876],{"className":7007},[1882,1883],[1830,7009,7011,7014],{"className":7010},[2238],[1830,7012,2096],{"className":7013},[2238],[1830,7015,7017],{"className":7016},[2126],[1830,7018,7020],{"className":7019},[2130],[1830,7021,7023],{"className":7022},[2135],[1830,7024,7027],{"className":7025,"style":7026},[2139],"height:0.7144em;",[1830,7028,7029,7032],{"style":4129},[1830,7030],{"className":7031,"style":2148},[2147],[1830,7033,7035],{"className":7034},[2152,2153,2154,2155],[1830,7036,1851],{"className":7037},[1882,1883,2155],[1830,7039,2802],{"className":7040},[1882],[1796,7042,7043,7044,7075],{},"第 ",[1830,7045,7047,7061],{"className":7046},[1833],[1830,7048,7050],{"className":7049},[1837],[1839,7051,7052],{"xmlns":1841},[1843,7053,7054,7059],{},[1846,7055,7056],{},[1849,7057,7058],{},"k",[1861,7060,7058],{"encoding":1863},[1830,7062,7064],{"className":7063,"ariaHidden":1869},[1868],[1830,7065,7067,7071],{"className":7066},[1873],[1830,7068],{"className":7069,"style":7070},[1877],"height:0.6944em;",[1830,7072,7058],{"className":7073,"style":7074},[1882,1883],"margin-right:0.0315em;"," 个次序统计量连接样本分位数、容忍区间与保形预测中的校准秩。极值的分布随样本量显著移动，不能把样本最大值当总体上界。",[1807,7077,7079],{"id":7078},"_8-诊断清单","8. 诊断清单",[6095,7081,7082,7085,7088,7091,7094,7097],{},[1817,7083,7084],{},"独立抽样单位到底是个人、家庭、学校还是时间块？",[1817,7086,7087],{},"是否同分布，还是分层\u002F加权抽样？",[1817,7089,7090],{},"统计量的精确分布还是渐近分布？",[1817,7092,7093],{},"方差是已知、估计还是由设计给定？",[1817,7095,7096],{},"正态假设用于数据本身，还是只用于均值近似？",[1817,7098,7099],{},"自由度是否与估计约束一致？",[1807,7101,7102],{"id":7102},"课堂任务",[1796,7104,6461,7105,7256],{},[1830,7106,7108,7144],{"className":7107},[1833],[1830,7109,7111],{"className":7110},[1837],[1839,7112,7113],{"xmlns":1841},[1843,7114,7115,7141],{},[1846,7116,7117,7123,7125,7127,7129,7131,7133,7139],{},[2074,7118,7119,7121],{},[1849,7120,1959],{},[1849,7122,2488],{},[1853,7124,1995],{},[1849,7126,3847],{},[1853,7128,2072],{"stretchy":2071},[1849,7130,2497],{},[1853,7132,2082],{"separator":1869},[2613,7134,7135,7137],{},[1849,7136,2617],{},[1857,7138,2620],{},[1853,7140,2096],{"stretchy":2071},[1861,7142,7143],{"encoding":1863},"X_i\\sim N(\\mu,\\sigma^2)",[1830,7145,7147,7203],{"className":7146,"ariaHidden":1869},[1868],[1830,7148,7150,7154,7194,7197,7200],{"className":7149},[1873],[1830,7151],{"className":7152,"style":7153},[1877],"height:0.8333em;vertical-align:-0.15em;",[1830,7155,7157,7160],{"className":7156},[1882],[1830,7158,1959],{"className":7159,"style":1975},[1882,1883],[1830,7161,7163],{"className":7162},[2126],[1830,7164,7166,7186],{"className":7165},[2130,2131],[1830,7167,7169,7183],{"className":7168},[2135],[1830,7170,7172],{"className":7171,"style":2535},[2139],[1830,7173,7174,7177],{"style":2143},[1830,7175],{"className":7176,"style":2148},[2147],[1830,7178,7180],{"className":7179},[2152,2153,2154,2155],[1830,7181,2488],{"className":7182},[1882,1883,2155],[1830,7184,2163],{"className":7185},[2162],[1830,7187,7189],{"className":7188},[2135],[1830,7190,7192],{"className":7191,"style":2170},[2139],[1830,7193],{},[1830,7195],{"className":7196,"style":2017},[1887],[1830,7198,1995],{"className":7199},[2021],[1830,7201],{"className":7202,"style":2017},[1887],[1830,7204,7206,7209,7212,7215,7218,7221,7224,7253],{"className":7205},[1873],[1830,7207],{"className":7208,"style":3767},[1877],[1830,7210,3847],{"className":7211,"style":3975},[1882,1883],[1830,7213,2072],{"className":7214},[2116],[1830,7216,2497],{"className":7217},[1882,1883],[1830,7219,2082],{"className":7220},[2176],[1830,7222],{"className":7223,"style":2180},[1887],[1830,7225,7227,7230],{"className":7226},[1882],[1830,7228,2617],{"className":7229,"style":2709},[1882,1883],[1830,7231,7233],{"className":7232},[2126],[1830,7234,7236],{"className":7235},[2130],[1830,7237,7239],{"className":7238},[2135],[1830,7240,7242],{"className":7241,"style":2702},[2139],[1830,7243,7244,7247],{"style":2724},[1830,7245],{"className":7246,"style":2148},[2147],[1830,7248,7250],{"className":7249},[2152,2153,2154,2155],[1830,7251,2620],{"className":7252},[1882,2155],[1830,7254,2096],{"className":7255},[2238],"：",[1814,7258,7259,7420,7532,7535],{},[1817,7260,7261,7262,7338,7339,1906],{},"推导 ",[1830,7263,7265,7289],{"className":7264},[1833],[1830,7266,7268],{"className":7267},[1837],[1839,7269,7270],{"xmlns":1841},[1843,7271,7272,7286],{},[1846,7273,7274,7276,7278,7284],{},[1849,7275,2478],{},[1853,7277,2481],{"stretchy":2071},[2255,7279,7280,7282],{"accent":1869},[1849,7281,1959],{},[1853,7283,2261],{},[1853,7285,2491],{"stretchy":2071},[1861,7287,7288],{"encoding":1863},"E[\\bar X]",[1830,7290,7292],{"className":7291,"ariaHidden":1869},[1868],[1830,7293,7295,7298,7301,7304,7335],{"className":7294},[1873],[1830,7296],{"className":7297,"style":2815},[1877],[1830,7299,2478],{"className":7300,"style":2513},[1882,1883],[1830,7302,2481],{"className":7303},[2116],[1830,7305,7307],{"className":7306},[1882,2278],[1830,7308,7310],{"className":7309},[2130],[1830,7311,7313],{"className":7312},[2135],[1830,7314,7316,7324],{"className":7315,"style":2274},[2139],[1830,7317,7318,7321],{"style":2290},[1830,7319],{"className":7320,"style":2294},[2147],[1830,7322,1959],{"className":7323,"style":1975},[1882,1883],[1830,7325,7326,7329],{"style":2300},[1830,7327],{"className":7328,"style":2294},[2147],[1830,7330,7332],{"className":7331,"style":2308},[2307],[1830,7333,2261],{"className":7334},[1882],[1830,7336,2491],{"className":7337},[2238]," 和 ",[1830,7340,7342,7368],{"className":7341},[1833],[1830,7343,7345],{"className":7344},[1837],[1839,7346,7347],{"xmlns":1841},[1843,7348,7349,7365],{},[1846,7350,7351,7353,7355,7357,7363],{},[1849,7352,2596],{"mathvariant":2595},[1853,7354,2599],{},[1853,7356,2072],{"stretchy":2071},[2255,7358,7359,7361],{"accent":1869},[1849,7360,1959],{},[1853,7362,2261],{},[1853,7364,2096],{"stretchy":2071},[1861,7366,7367],{"encoding":1863},"\\operatorname{Var}(\\bar X)",[1830,7369,7371],{"className":7370,"ariaHidden":1869},[1868],[1830,7372,7374,7377,7383,7386,7417],{"className":7373},[1873],[1830,7375],{"className":7376,"style":2815},[1877],[1830,7378,7380],{"className":7379},[2636],[1830,7381,2596],{"className":7382},[1882,2640],[1830,7384,2072],{"className":7385},[2116],[1830,7387,7389],{"className":7388},[1882,2278],[1830,7390,7392],{"className":7391},[2130],[1830,7393,7395],{"className":7394},[2135],[1830,7396,7398,7406],{"className":7397,"style":2274},[2139],[1830,7399,7400,7403],{"style":2290},[1830,7401],{"className":7402,"style":2294},[2147],[1830,7404,1959],{"className":7405,"style":1975},[1882,1883],[1830,7407,7408,7411],{"style":2300},[1830,7409],{"className":7410,"style":2294},[2147],[1830,7412,7414],{"className":7413,"style":2308},[2307],[1830,7415,2261],{"className":7416},[1882],[1830,7418,2096],{"className":7419},[2238],[1817,7421,7422,7423,7481,7482,1906],{},"解释 ",[1830,7424,7426,7443],{"className":7425},[1833],[1830,7427,7429],{"className":7428},[1837],[1839,7430,7431],{"xmlns":1841},[1843,7432,7433,7441],{},[1846,7434,7435],{},[2613,7436,7437,7439],{},[1849,7438,4040],{},[1857,7440,2620],{},[1861,7442,5027],{"encoding":1863},[1830,7444,7446],{"className":7445,"ariaHidden":1869},[1868],[1830,7447,7449,7452],{"className":7448},[1873],[1830,7450],{"className":7451,"style":2702},[1877],[1830,7453,7455,7458],{"className":7454},[1882],[1830,7456,4040],{"className":7457,"style":2513},[1882,1883],[1830,7459,7461],{"className":7460},[2126],[1830,7462,7464],{"className":7463},[2130],[1830,7465,7467],{"className":7466},[2135],[1830,7468,7470],{"className":7469,"style":2702},[2139],[1830,7471,7472,7475],{"style":2724},[1830,7473],{"className":7474,"style":2148},[2147],[1830,7476,7478],{"className":7477},[2152,2153,2154,2155],[1830,7479,2620],{"className":7480},[1882,2155]," 为何除以 ",[1830,7483,7485,7502],{"className":7484},[1833],[1830,7486,7488],{"className":7487},[1837],[1839,7489,7490],{"xmlns":1841},[1843,7491,7492,7500],{},[1846,7493,7494,7496,7498],{},[1849,7495,1851],{},[1853,7497,1855],{},[1857,7499,1859],{},[1861,7501,1864],{"encoding":1863},[1830,7503,7505,7523],{"className":7504,"ariaHidden":1869},[1868],[1830,7506,7508,7511,7514,7517,7520],{"className":7507},[1873],[1830,7509],{"className":7510,"style":1878},[1877],[1830,7512,1851],{"className":7513},[1882,1883],[1830,7515],{"className":7516,"style":1888},[1887],[1830,7518,1855],{"className":7519},[1892],[1830,7521],{"className":7522,"style":1888},[1887],[1830,7524,7526,7529],{"className":7525},[1873],[1830,7527],{"className":7528,"style":1902},[1877],[1830,7530,1859],{"className":7531},[1882],[1817,7533,7534],{},"写出均值与方差的两个枢轴量；",[1817,7536,7537],{},"将 iid 改为同班学生，说明方差公式需要怎样变化。",[1807,7539,7540],{"id":7540},"核心阅读",[6095,7542,7543,7551,7558],{},[1817,7544,7545,7546,7550],{},"Casella & Berger, ",[7547,7548,7549],"em",{},"Statistical Inference","，抽样分布章节。",[1817,7552,7553,7554,7557],{},"Lehmann & Romano, ",[7547,7555,7556],{},"Testing Statistical Hypotheses","，分布理论部分。",[1817,7559,7560,7561,7564],{},"Wasserman, ",[7547,7562,7563],{},"All of Statistics","，第 6–7 章。",[1796,7566,7567,7568,7573,7574,3803],{},"上一章：",[7569,7570,7572],"a",{"href":7571},"..\u002F..\u002F01-probability\u002F05-asymptotics\u002F","渐近理论","｜下一章：",[7569,7575,7577],{"href":7576},"..\u002F02-interval-estimation\u002F","区间估计",{"title":10,"searchDepth":7579,"depth":7579,"links":7580},2,[7581,7582,7583,7584,7585,7587,7588,7589,7590,7591,7592],{"id":1809,"depth":7579,"text":1809},{"id":1912,"depth":7579,"text":1913},{"id":2317,"depth":7579,"text":2318},{"id":3806,"depth":7579,"text":3807},{"id":5385,"depth":7579,"text":7586},"4. 自由度为什么是 n−1n-1n−1",{"id":6092,"depth":7579,"text":6093},{"id":6457,"depth":7579,"text":6458},{"id":6603,"depth":7579,"text":6604},{"id":7078,"depth":7579,"text":7079},{"id":7102,"depth":7579,"text":7102},{"id":7540,"depth":7579,"text":7540},"把统计量视为随机变量，连接样本均值、样本方差、卡方、t、F 与次序统计量。","md",{"sidebar":7596},{"order":7597},7,true,{"title":1686,"description":7593},"RZiRFMy1soIMK2a2Mrq3KUMXx3YHs6jv1wMfSC5774k",[7602,7604],{"title":1675,"path":1676,"stem":1677,"description":7603,"children":-1},"连接大数定律、中心极限定理、Slutsky 定理与 Delta 方法，并识别重尾和依赖下的失败。",{"title":1692,"path":1693,"stem":1694,"description":7605,"children":-1},"从枢轴量、t 区间和 Wilson 区间进入渐近、似然与 Bootstrap 置信区间，并检查重复抽样覆盖率。",1785754756885]