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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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m;",[1840,2701,2616],{"className":2702},[2703],"mbin",[1840,2705],{"className":2706,"style":2699},[2110],[1840,2708,2710,2714,2717,2749,2752,2755,2758],{"className":2709},[1881],[1840,2711],{"className":2712,"style":2713},[1885],"height:1.0641em;vertical-align:-0.25em;",[1840,2715,1864],{"className":2716,"style":1895},[1890,1894],[1840,2718,2721,2724],{"className":2719},[2720],"mclose",[1840,2722,2623],{"className":2723},[2720],[1840,2725,2727],{"className":2726},[1899],[1840,2728,2730],{"className":2729},[1903],[1840,2731,2733],{"className":2732},[1908],[1840,2734,2737],{"className":2735,"style":2736},[1912],"height:0.8141em;",[1840,2738,2740,2743],{"style":2739},"top:-3.063em;margin-right:0.05em;",[1840,2741],{"className":2742,"style":1921},[1920],[1840,2744,2746],{"className":2745},[1925,1926,1927,1928],[1840,2747,2431],{"className":2748},[1890,1928],[1840,2750,2628],{"className":2751},[2720],[1840,2753],{"className":2754,"style":2111},[2110],[1840,2756,2032],{"className":2757},[2115],[1840,2759],{"className":2760,"style":2111},[2110],[1840,2762,2764,2768],{"className":2763},[1881],[1840,2765],{"className":2766,"style":2767},[1885],"height:0.6444em;",[1840,2769,2633],{"className":2770},[1890],[1984,2772,2773,2776,2926],{},[2002,2774,2775],{},"依分布收敛",[2002,2777,2778],{},[1840,2779,2781,2810],{"className":2780},[1843],[1840,2782,2784],{"className":2783},[1847],[1849,2785,2786],{"xmlns":1851},[1853,2787,2788,2807],{},[1856,2789,2790,2796,2805],{},[1859,2791,2792,2794],{},[1862,2793,1864],{},[1862,2795,1867],{},[2026,2797,2798,2800],{},[2029,2799,2032],{"stretchy":1877,"minsize":2031},[2034,2801,2802],{"width":2036,"lspace":2037},[1862,2803,2804],{},"d",[1862,2806,1864],{},[1869,2808,2809],{"encoding":1871},"X_n\\xrightarrow{d}X",[1840,2811,2813,2917],{"className":2812,"ariaHidden":1877},[1876],[1840,2814,2816,2820,2860,2863,2914],{"className":2815},[1881],[1840,2817],{"className":2818,"style":2819},[1885],"height:1.2581em;vertical-align:-0.15em;",[1840,2821,2823,2826],{"className":2822},[1890],[1840,2824,1864],{"className":2825,"style":1895},[1890,1894],[1840,2827,2829],{"className":2828},[1899],[1840,2830,2832,2852],{"className":2831},[1903,1904],[1840,2833,2835,2849],{"className":2834},[1908],[1840,2836,2838],{"className":2837,"style":1913},[1912],[1840,2839,2840,2843],{"style":1916},[1840,2841],{"className":2842,"style":1921},[1920],[1840,2844,2846],{"className":2845},[1925,1926,1927,1928],[1840,2847,1867],{"className":2848},[1890,1894,1928],[1840,2850,1936],{"className":2851},[1935],[1840,2853,2855],{"className":2854},[1908],[1840,2856,2858],{"className":2857,"style":1943},[1912],[1840,2859],{},[1840,2861],{"className":2862,"style":2111},[2110],[1840,2864,2866],{"className":2865},[2115,2116],[1840,2867,2869,2906],{"className":2868},[1903,1904],[1840,2870,2872,2903],{"className":2871},[1908],[1840,2873,2876,2890],{"className":2874,"style":2875},[1912],"height:1.1081em;",[1840,2877,2878,2881],{"style":2129},[1840,2879],{"className":2880,"style":1921},[1920],[1840,2882,2884],{"className":2883},[1925,1926,1927,1928,2136],[1840,2885,2887],{"className":2886},[1890,1928],[1840,2888,2804],{"className":2889},[1890,1894,1928],[1840,2891,2893,2896],{"className":2892,"style":2156},[2155],[1840,2894],{"className":2895,"style":1921},[1920],[1840,2897,2899],{"className":2898,"style":2164},[2163],[2166,2900,2901],{"xmlns":2168,"width":2169,"height":2170,"viewBox":2171,"preserveAspectRatio":2172},[2174,2902],{"d":2176},[1840,2904,1936],{"className":2905},[1935],[1840,2907,2909],{"className":2908},[1908],[1840,2910,2912],{"className":2911,"style":2186},[1912],[1840,2913],{},[1840,2915],{"className":2916,"style":2111},[2110],[1840,2918,2920,2923],{"className":2919},[1881],[1840,2921],{"className":2922,"style":1972},[1885],[1840,2924,1864],{"className":2925,"style":1895},[1890,1894],[2002,2927,2928],{},"CDF 在连续点收敛",[1796,2930,2931],{},"常见关系：",[1840,2933,2936],{"className":2934},[2935],"katex-display",[1840,2937,2939,2987],{"className":2938},[1843],[1840,2940,2942],{"className":2941},[1847],[1849,2943,2945],{"xmlns":1851,"display":2944},"block",[1853,2946,2947,2984],{},[1856,2948,2949,2955,2958,2960,2962,2964,2967,2970,2972,2974,2976,2978,2980,2982],{},[2423,2950,2951,2953],{},[1862,2952,2427],{},[2429,2954,2431],{},[2029,2956,2957],{},"⇒",[1862,2959,1796],{},[2029,2961,2957],{},[1862,2963,2804],{},[2029,2965,2966],{"separator":1877},",",[2110,2968],{"width":2969},"2em",[1862,2971,2042],{},[1862,2973,2046],{"mathvariant":2045},[1862,2975,2049],{},[1862,2977,2046],{"mathvariant":2045},[2029,2979,2957],{},[1862,2981,1796],{},[1862,2983,2046],{"mathvariant":2045},[1869,2985,2986],{"encoding":1871},"L^2\\Rightarrow p\\Rightarrow d,\n\\qquad a.s.\\Rightarrow p.",[1840,2988,2990,3036,3055,3098],{"className":2989,"ariaHidden":1877},[1876],[1840,2991,2993,2997,3027,3030,3033],{"className":2992},[1881],[1840,2994],{"className":2995,"style":2996},[1885],"height:0.8641em;",[1840,2998,3000,3003],{"className":2999},[1890],[1840,3001,2427],{"className":3002},[1890,1894],[1840,3004,3006],{"className":3005},[1899],[1840,3007,3009],{"className":3008},[1903],[1840,3010,3012],{"className":3011},[1908],[1840,3013,3015],{"className":3014,"style":2996},[1912],[1840,3016,3018,3021],{"style":3017},"top:-3.113em;margin-right:0.05em;",[1840,3019],{"className":3020,"style":1921},[1920],[1840,3022,3024],{"className":3023},[1925,1926,1927,1928],[1840,3025,2431],{"className":3026},[1890,1928],[1840,3028],{"className":3029,"style":2111},[2110],[1840,3031,2957],{"className":3032},[2115],[1840,3034],{"className":3035,"style":2111},[2110],[1840,3037,3039,3043,3046,3049,3052],{"className":3038},[1881],[1840,3040],{"className":3041,"style":3042},[1885],"height:0.625em;vertical-align:-0.1944em;",[1840,3044,1796],{"className":3045},[1890,1894],[1840,3047],{"className":3048,"style":2111},[2110],[1840,3050,2957],{"className":3051},[2115],[1840,3053],{"className":3054,"style":2111},[2110],[1840,3056,3058,3062,3065,3069,3073,3077,3080,3083,3086,3089,3092,3095],{"className":3057},[1881],[1840,3059],{"className":3060,"style":3061},[1885],"height:0.8889em;vertical-align:-0.1944em;",[1840,3063,2804],{"className":3064},[1890,1894],[1840,3066,2966],{"className":3067},[3068],"mpunct",[1840,3070],{"className":3071,"style":3072},[2110],"margin-right:2em;",[1840,3074],{"className":3075,"style":3076},[2110],"margin-right:0.1667em;",[1840,3078,2042],{"className":3079},[1890,1894],[1840,3081,2046],{"className":3082},[1890],[1840,3084,2049],{"className":3085},[1890,1894],[1840,3087,2046],{"className":3088},[1890],[1840,3090],{"className":3091,"style":2111},[2110],[1840,3093,2957],{"className":3094},[2115],[1840,3096],{"className":3097,"style":2111},[2110],[1840,3099,3101,3104,3107],{"className":3100},[1881],[1840,3102],{"className":3103,"style":3042},[1885],[1840,3105,1796],{"className":3106},[1890,1894],[1840,3108,2046],{"className":3109},[1890],[1796,3111,3112],{},"反向一般不成立。若极限是常数，依分布收敛到该常数等价于依概率收敛。",[1807,3114,3116],{"id":3115},"_2-大数定律平均值稳定","2. 大数定律：平均值稳定",[1796,3118,3119,3120,3265,3266,3378],{},"若 ",[1840,3121,3123,3155],{"className":3122},[1843],[1840,3124,3126],{"className":3125},[1847],[1849,3127,3128],{"xmlns":1851},[1853,3129,3130,3152],{},[1856,3131,3132,3139,3141,3144,3146],{},[1859,3133,3134,3136],{},[1862,3135,1864],{},[2429,3137,3138],{},"1",[2029,3140,2966],{"separator":1877},[2029,3142,3143],{},"…",[2029,3145,2966],{"separator":1877},[1859,3147,3148,3150],{},[1862,3149,1864],{},[1862,3151,1867],{},[1869,3153,3154],{"encoding":1871},"X_1,\\ldots,X_n",[1840,3156,3158],{"className":3157,"ariaHidden":1877},[1876],[1840,3159,3161,3165,3206,3209,3212,3216,3219,3222,3225],{"className":3160},[1881],[1840,3162],{"className":3163,"style":3164},[1885],"height:0.8778em;vertical-align:-0.1944em;",[1840,3166,3168,3171],{"className":3167},[1890],[1840,3169,1864],{"className":3170,"style":1895},[1890,1894],[1840,3172,3174],{"className":3173},[1899],[1840,3175,3177,3198],{"className":3176},[1903,1904],[1840,3178,3180,3195],{"className":3179},[1908],[1840,3181,3184],{"className":3182,"style":3183},[1912],"height:0.3011em;",[1840,3185,3186,3189],{"style":1916},[1840,3187],{"className":3188,"style":1921},[1920],[1840,3190,3192],{"className":3191},[1925,1926,1927,1928],[1840,3193,3138],{"className":3194},[1890,1928],[1840,3196,1936],{"className":3197},[1935],[1840,3199,3201],{"className":3200},[1908],[1840,3202,3204],{"className":3203,"style":1943},[1912],[1840,3205],{},[1840,3207,2966],{"className":3208},[3068],[1840,3210],{"className":3211,"style":3076},[2110],[1840,3213,3143],{"className":3214},[3215],"minner",[1840,3217],{"className":3218,"style":3076},[2110],[1840,3220,2966],{"className":3221},[3068],[1840,3223],{"className":3224,"style":3076},[2110],[1840,3226,3228,3231],{"className":3227},[1890],[1840,3229,1864],{"className":3230,"style":1895},[1890,1894],[1840,3232,3234],{"className":3233},[1899],[1840,3235,3237,3257],{"className":3236},[1903,1904],[1840,3238,3240,3254],{"className":3239},[1908],[1840,3241,3243],{"className":3242,"style":1913},[1912],[1840,3244,3245,3248],{"style":1916},[1840,3246],{"className":3247,"style":1921},[1920],[1840,3249,3251],{"className":3250},[1925,1926,1927,1928],[1840,3252,1867],{"className":3253},[1890,1894,1928],[1840,3255,1936],{"className":3256},[1935],[1840,3258,3260],{"className":3259},[1908],[1840,3261,3263],{"className":3262,"style":1943},[1912],[1840,3264],{}," iid 且 ",[1840,3267,3269,3301],{"className":3268},[1843],[1840,3270,3272],{"className":3271},[1847],[1849,3273,3274],{"xmlns":1851},[1853,3275,3276,3298],{},[1856,3277,3278,3280,3283,3290,3292,3295],{},[1862,3279,2600],{},[1862,3281,3282],{"mathvariant":2045},"∣",[1859,3284,3285,3287],{},[1862,3286,1864],{},[1862,3288,3289],{},"i",[1862,3291,3282],{"mathvariant":2045},[2029,3293,3294],{},"\u003C",[1862,3296,3297],{"mathvariant":2045},"∞",[1869,3299,3300],{"encoding":1871},"E|X_i|\u003C\\infty",[1840,3302,3304,3369],{"className":3303,"ariaHidden":1877},[1876],[1840,3305,3307,3310,3313,3316,3357,3360,3363,3366],{"className":3306},[1881],[1840,3308],{"className":3309,"style":2646},[1885],[1840,3311,2600],{"className":3312,"style":2650},[1890,1894],[1840,3314,3282],{"className":3315},[1890],[1840,3317,3319,3322],{"className":3318},[1890],[1840,3320,1864],{"className":3321,"style":1895},[1890,1894],[1840,3323,3325],{"className":3324},[1899],[1840,3326,3328,3349],{"className":3327},[1903,1904],[1840,3329,3331,3346],{"className":3330},[1908],[1840,3332,3335],{"className":3333,"style":3334},[1912],"height:0.3117em;",[1840,3336,3337,3340],{"style":1916},[1840,3338],{"className":3339,"style":1921},[1920],[1840,3341,3343],{"className":3342},[1925,1926,1927,1928],[1840,3344,3289],{"className":3345},[1890,1894,1928],[1840,3347,1936],{"className":3348},[1935],[1840,3350,3352],{"className":3351},[1908],[1840,3353,3355],{"className":3354,"style":1943},[1912],[1840,3356],{},[1840,3358,3282],{"className":3359},[1890],[1840,3361],{"className":3362,"style":2111},[2110],[1840,3364,3294],{"className":3365},[2115],[1840,3367],{"className":3368,"style":2111},[2110],[1840,3370,3372,3375],{"className":3371},[1881],[1840,3373],{"className":3374,"style":2386},[1885],[1840,3376,3297],{"className":3377},[1890],"，弱大数定律给出：",[1840,3380,3382],{"className":3381},[2935],[1840,3383,3385,3421],{"className":3384},[1843],[1840,3386,3388],{"className":3387},[1847],[1849,3389,3390],{"xmlns":1851,"display":2944},[1853,3391,3392,3418],{},[1856,3393,3394,3405,3413,3416],{},[1859,3395,3396,3403],{},[2026,3397,3398,3400],{"accent":1877},[1862,3399,1864],{},[2029,3401,3402],{},"ˉ",[1862,3404,1867],{},[2026,3406,3407,3409],{},[2029,3408,2032],{"stretchy":1877,"minsize":2031},[2034,3410,3411],{"width":2036,"lspace":2037},[1862,3412,1796],{},[1862,3414,3415],{},"μ",[1862,3417,2046],{"mathvariant":2045},[1869,3419,3420],{"encoding":1871},"\\bar X_n\\xrightarrow{p}\\mu.",[1840,3422,3424,3561],{"className":3423,"ariaHidden":1877},[1876],[1840,3425,3427,3430,3505,3508,3558],{"className":3426},[1881],[1840,3428],{"className":3429,"style":2066},[1885],[1840,3431,3433,3471],{"className":3432},[1890],[1840,3434,3437],{"className":3435},[1890,3436],"accent",[1840,3438,3440],{"className":3439},[1903],[1840,3441,3443],{"className":3442},[1908],[1840,3444,3447,3457],{"className":3445,"style":3446},[1912],"height:0.8201em;",[1840,3448,3450,3454],{"style":3449},"top:-3em;",[1840,3451],{"className":3452,"style":3453},[1920],"height:3em;",[1840,3455,1864],{"className":3456,"style":1895},[1890,1894],[1840,3458,3460,3463],{"style":3459},"top:-3.2523em;",[1840,3461],{"className":3462,"style":3453},[1920],[1840,3464,3468],{"className":3465,"style":3467},[3466],"accent-body","left:-0.1667em;",[1840,3469,3402],{"className":3470},[1890],[1840,3472,3474],{"className":3473},[1899],[1840,3475,3477,3497],{"className":3476},[1903,1904],[1840,3478,3480,3494],{"className":3479},[1908],[1840,3481,3483],{"className":3482,"style":1913},[1912],[1840,3484,3485,3488],{"style":1916},[1840,3486],{"className":3487,"style":1921},[1920],[1840,3489,3491],{"className":3490},[1925,1926,1927,1928],[1840,3492,1867],{"className":3493},[1890,1894,1928],[1840,3495,1936],{"className":3496},[1935],[1840,3498,3500],{"className":3499},[1908],[1840,3501,3503],{"className":3502,"style":1943},[1912],[1840,3504],{},[1840,3506],{"className":3507,"style":2111},[2110],[1840,3509,3511],{"className":3510},[2115,2116],[1840,3512,3514,3550],{"className":3513},[1903,1904],[1840,3515,3517,3547],{"className":3516},[1908],[1840,3518,3520,3534],{"className":3519,"style":2126},[1912],[1840,3521,3522,3525],{"style":2129},[1840,3523],{"className":3524,"style":1921},[1920],[1840,3526,3528],{"className":3527},[1925,1926,1927,1928,2136],[1840,3529,3531],{"className":3530},[1890,1928],[1840,3532,1796],{"className":3533},[1890,1894,1928],[1840,3535,3537,3540],{"className":3536,"style":2156},[2155],[1840,3538],{"className":3539,"style":1921},[1920],[1840,3541,3543],{"className":3542,"style":2164},[2163],[2166,3544,3545],{"xmlns":2168,"width":2169,"height":2170,"viewBox":2171,"preserveAspectRatio":2172},[2174,3546],{"d":2176},[1840,3548,1936],{"className":3549},[1935],[1840,3551,3553],{"className":3552},[1908],[1840,3554,3556],{"className":3555,"style":2186},[1912],[1840,3557],{},[1840,3559],{"className":3560,"style":2111},[2110],[1840,3562,3564,3567,3570],{"className":3563},[1881],[1840,3565],{"className":3566,"style":3042},[1885],[1840,3568,3415],{"className":3569},[1890,1894],[1840,3571,2046],{"className":3572},[1890],[1796,3574,3575],{},"它说明一致性，不给出收敛速度或有限样本误差分布。独立同分布只是经典版本；更一般结果可以允许弱依赖或异质性，但要替换相应条件。",[1807,3577,3579],{"id":3578},"_3-中心极限定理缩放后的误差近似正态","3. 中心极限定理：缩放后的误差近似正态",[1796,3581,3582,3583,3691,3692,3799],{},"若 iid、",[1840,3584,3586,3615],{"className":3585},[1843],[1840,3587,3589],{"className":3588},[1847],[1849,3590,3591],{"xmlns":1851},[1853,3592,3593,3612],{},[1856,3594,3595,3597,3599,3605,3607,3610],{},[1862,3596,2600],{},[2029,3598,2604],{"stretchy":2603},[1859,3600,3601,3603],{},[1862,3602,1864],{},[1862,3604,3289],{},[2029,3606,2628],{"stretchy":2603},[2029,3608,3609],{},"=",[1862,3611,3415],{},[1869,3613,3614],{"encoding":1871},"E[X_i]=\\mu",[1840,3616,3618,3682],{"className":3617,"ariaHidden":1877},[1876],[1840,3619,3621,3624,3627,3630,3670,3673,3676,3679],{"className":3620},[1881],[1840,3622],{"className":3623,"style":2646},[1885],[1840,3625,2600],{"className":3626,"style":2650},[1890,1894],[1840,3628,2604],{"className":3629},[2654],[1840,3631,3633,3636],{"className":3632},[1890],[1840,3634,1864],{"className":3635,"style":1895},[1890,1894],[1840,3637,3639],{"className":3638},[1899],[1840,3640,3642,3662],{"className":3641},[1903,1904],[1840,3643,3645,3659],{"className":3644},[1908],[1840,3646,3648],{"className":3647,"style":3334},[1912],[1840,3649,3650,3653],{"style":1916},[1840,3651],{"className":3652,"style":1921},[1920],[1840,3654,3656],{"className":3655},[1925,1926,1927,1928],[1840,3657,3289],{"className":3658},[1890,1894,1928],[1840,3660,1936],{"className":3661},[1935],[1840,3663,3665],{"className":3664},[1908],[1840,3666,3668],{"className":3667,"style":1943},[1912],[1840,3669],{},[1840,3671,2628],{"className":3672},[2720],[1840,3674],{"className":3675,"style":2111},[2110],[1840,3677,3609],{"className":3678},[2115],[1840,3680],{"className":3681,"style":2111},[2110],[1840,3683,3685,3688],{"className":3684},[1881],[1840,3686],{"className":3687,"style":3042},[1885],[1840,3689,3415],{"className":3690},[1890,1894],"、",[1840,3693,3695,3722],{"className":3694},[1843],[1840,3696,3698],{"className":3697},[1847],[1849,3699,3700],{"xmlns":1851},[1853,3701,3702,3719],{},[1856,3703,3704,3706,3708,3715,3717],{},[2429,3705,2633],{},[2029,3707,3294],{},[2423,3709,3710,3713],{},[1862,3711,3712],{},"σ",[2429,3714,2431],{},[2029,3716,3294],{},[1862,3718,3297],{"mathvariant":2045},[1869,3720,3721],{"encoding":1871},"0\u003C\\sigma^2\u003C\\infty",[1840,3723,3725,3744,3790],{"className":3724,"ariaHidden":1877},[1876],[1840,3726,3728,3732,3735,3738,3741],{"className":3727},[1881],[1840,3729],{"className":3730,"style":3731},[1885],"height:0.6835em;vertical-align:-0.0391em;",[1840,3733,2633],{"className":3734},[1890],[1840,3736],{"className":3737,"style":2111},[2110],[1840,3739,3294],{"className":3740},[2115],[1840,3742],{"className":3743,"style":2111},[2110],[1840,3745,3747,3751,3781,3784,3787],{"className":3746},[1881],[1840,3748],{"className":3749,"style":3750},[1885],"height:0.8532em;vertical-align:-0.0391em;",[1840,3752,3754,3758],{"className":3753},[1890],[1840,3755,3712],{"className":3756,"style":3757},[1890,1894],"margin-right:0.0359em;",[1840,3759,3761],{"className":3760},[1899],[1840,3762,3764],{"className":3763},[1903],[1840,3765,3767],{"className":3766},[1908],[1840,3768,3770],{"className":3769,"style":2736},[1912],[1840,3771,3772,3775],{"style":2739},[1840,3773],{"className":3774,"style":1921},[1920],[1840,3776,3778],{"className":3777},[1925,1926,1927,1928],[1840,3779,2431],{"className":3780},[1890,1928],[1840,3782],{"className":3783,"style":2111},[2110],[1840,3785,3294],{"className":3786},[2115],[1840,3788],{"className":3789,"style":2111},[2110],[1840,3791,3793,3796],{"className":3792},[1881],[1840,3794],{"className":3795,"style":2386},[1885],[1840,3797,3297],{"className":3798},[1890],"，则",[1840,3801,3803],{"className":3802},[2935],[1840,3804,3806,3867],{"className":3805},[1843],[1840,3807,3809],{"className":3808},[1847],[1849,3810,3811],{"xmlns":1851,"display":2944},[1853,3812,3813,3864],{},[1856,3814,3815,3820,3841,3849,3852,3854,3856,3858,3860,3862],{},[3816,3817,3818],"msqrt",{},[1862,3819,1867],{},[3821,3822,3823,3839],"mfrac",{},[1856,3824,3825,3835,3837],{},[1859,3826,3827,3833],{},[2026,3828,3829,3831],{"accent":1877},[1862,3830,1864],{},[2029,3832,3402],{},[1862,3834,1867],{},[2029,3836,2616],{},[1862,3838,3415],{},[1862,3840,3712],{},[2026,3842,3843,3845],{},[2029,3844,2032],{"stretchy":1877,"minsize":2031},[2034,3846,3847],{"width":2036,"lspace":2037},[1862,3848,2804],{},[1862,3850,3851],{},"N",[2029,3853,2607],{"stretchy":2603},[2429,3855,2633],{},[2029,3857,2966],{"separator":1877},[2429,3859,3138],{},[2029,3861,2623],{"stretchy":2603},[1862,3863,2046],{"mathvariant":2045},[1869,3865,3866],{"encoding":1871},"\\sqrt n\\frac{\\bar X_n-\\mu}{\\sigma}\n\\xrightarrow{d}N(0,1).",[1840,3868,3870,4134],{"className":3869,"ariaHidden":1877},[1876],[1840,3871,3873,3877,3931,4078,4081,4131],{"className":3872},[1881],[1840,3874],{"className":3875,"style":3876},[1885],"height:2.1831em;vertical-align:-0.686em;",[1840,3878,3881],{"className":3879},[1890,3880],"sqrt",[1840,3882,3884,3922],{"className":3883},[1903,1904],[1840,3885,3887,3919],{"className":3886},[1908],[1840,3888,3891,3901],{"className":3889,"style":3890},[1912],"height:0.8492em;",[1840,3892,3894,3897],{"className":3893,"style":3449},[2155],[1840,3895],{"className":3896,"style":3453},[1920],[1840,3898,1867],{"className":3899,"style":3900},[1890,1894],"padding-left:0.833em;",[1840,3902,3904,3907],{"style":3903},"top:-2.8092em;",[1840,3905],{"className":3906,"style":3453},[1920],[1840,3908,3911],{"className":3909,"style":3910},[2163],"min-width:0.853em;height:1.08em;",[2166,3912,3916],{"xmlns":2168,"width":2169,"height":3913,"viewBox":3914,"preserveAspectRatio":3915},"1.08em","0 0 400000 1080","xMinYMin slice",[2174,3917],{"d":3918},"M95,702\nc-2.7,0,-7.17,-2.7,-13.5,-8c-5.8,-5.3,-9.5,-10,-9.5,-14\nc0,-2,0.3,-3.3,1,-4c1.3,-2.7,23.83,-20.7,67.5,-54\nc44.2,-33.3,65.8,-50.3,66.5,-51c1.3,-1.3,3,-2,5,-2c4.7,0,8.7,3.3,12,10\ns173,378,173,378c0.7,0,35.3,-71,104,-213c68.7,-142,137.5,-285,206.5,-429\nc69,-144,104.5,-217.7,106.5,-221\nl0 -0\nc5.3,-9.3,12,-14,20,-14\nH400000v40H845.2724\ns-225.272,467,-225.272,467s-235,486,-235,486c-2.7,4.7,-9,7,-19,7\nc-6,0,-10,-1,-12,-3s-194,-422,-194,-422s-65,47,-65,47z\nM834 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X_n\\approx 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在 ",[1840,5940,5942,5956],{"className":5941},[1843],[1840,5943,5945],{"className":5944},[1847],[1849,5946,5947],{"xmlns":1851},[1853,5948,5949,5953],{},[1856,5950,5951],{},[1862,5952,5671],{},[1869,5954,5955],{"encoding":1871},"\\theta",[1840,5957,5959],{"className":5958,"ariaHidden":1877},[1876],[1840,5960,5962,5966],{"className":5961},[1881],[1840,5963],{"className":5964,"style":5965},[1885],"height:0.6944em;",[1840,5967,5671],{"className":5968,"style":5783},[1890,1894]," 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n\\{g(\\widehat\\theta)-g(\\theta)\\}\n\\xrightarrow{d}N(0,[g'(\\theta)]^2V).",[1840,6065,6067,6170,6245],{"className":6066,"ariaHidden":1877},[1876],[1840,6068,6070,6073,6117,6120,6123,6126,6158,6161,6164,6167],{"className":6069},[1881],[1840,6071],{"className":6072,"style":5714},[1885],[1840,6074,6076],{"className":6075},[1890,3880],[1840,6077,6079,6109],{"className":6078},[1903,1904],[1840,6080,6082,6106],{"className":6081},[1908],[1840,6083,6085,6094],{"className":6084,"style":3890},[1912],[1840,6086,6088,6091],{"className":6087,"style":3449},[2155],[1840,6089],{"className":6090,"style":3453},[1920],[1840,6092,1867],{"className":6093,"style":3900},[1890,1894],[1840,6095,6096,6099],{"style":3903},[1840,6097],{"className":6098,"style":3453},[1920],[1840,6100,6102],{"className":6101,"style":3910},[2163],[2166,6103,6104],{"xmlns":2168,"width":2169,"height":3913,"viewBox":3914,"preserveAspectRatio":3915},[2174,6105],{"d":3918},[1840,6107,1936],{"className":6108},[1935],[1840,6110,6112],{"className":6111},[1908],[1840,6113,6115],{"className":6114,"style":3928},[1912],[1840,6116],{},[1840,6118,5991],{"className":6119},[2654],[1840,6121,5923],{"className":6122,"style":3757},[1890,1894],[1840,6124,2607],{"className":6125},[2654],[1840,6127,6129],{"className":6128},[1890,3436],[1840,6130,6132],{"className":6131},[1903],[1840,6133,6135],{"className":6134},[1908],[1840,6136,6138,6146],{"className":6137,"style":5774},[1912],[1840,6139,6140,6143],{"style":3449},[1840,6141],{"className":6142,"style":3453},[1920],[1840,6144,5671],{"className":6145,"style":5783},[1890,1894],[1840,6147,6149,6152],{"className":6148,"style":5787},[2155],[1840,6150],{"className":6151,"style":3453},[1920],[1840,6153,6154],{"style":5793},[2166,6155,6156],{"xmlns":2168,"width":5796,"height":5797,"viewBox":5798,"preserveAspectRatio":5799},[2174,6157],{"d":5802},[1840,6159,2623],{"className":6160},[2720],[1840,6162],{"className":6163,"style":2699},[2110],[1840,6165,2616],{"className":6166},[2703],[1840,6168],{"className":6169,"style":2699},[2110],[1840,6171,6173,6176,6179,6182,6185,6189,6192,6242],{"className":6172},[1881],[1840,6174],{"className":6175,"style":5377},[1885],[1840,6177,5923],{"className":6178,"style":3757},[1890,1894],[1840,6180,2607],{"className":6181},[2654],[1840,6183,5671],{"className":6184,"style":5783},[1890,1894],[1840,6186,6188],{"className":6187},[2720],")}",[1840,6190],{"className":6191,"style":2111},[2110],[1840,6193,6195],{"className":6194},[2115,2116],[1840,6196,6198,6234],{"className":6197},[1903,1904],[1840,6199,6201,6231],{"className":6200},[1908],[1840,6202,6204,6218],{"className":6203,"style":2875},[1912],[1840,6205,6206,6209],{"style":2129},[1840,6207],{"className":6208,"style":1921},[1920],[1840,6210,6212],{"className":6211},[1925,1926,1927,1928,2136],[1840,6213,6215],{"className":6214},[1890,1928],[1840,6216,2804],{"className":6217},[1890,1894,1928],[1840,6219,6221,6224],{"className":6220,"style":2156},[2155],[1840,6222],{"className":6223,"style":1921},[1920],[1840,6225,6227],{"className":6226,"style":2164},[2163],[2166,6228,6229],{"xmlns":2168,"width":2169,"height":2170,"viewBox":2171,"preserveAspectRatio":2172},[2174,6230],{"d":2176},[1840,6232,1936],{"className":6233},[1935],[1840,6235,6237],{"className":6236},[1908],[1840,6238,6240],{"className":6239,"style":2186},[1912],[1840,6241],{},[1840,6243],{"className":6244,"style":2111},[2110],[1840,6246,6248,6252,6255,6258,6261,6264,6267,6270,6303,6306,6309,6312,6341,6344,6347],{"className":6247},[1881],[1840,6249],{"className":6250,"style":6251},[1885],"height:1.1141em;vertical-align:-0.25em;",[1840,6253,3851],{"className":6254,"style":4143},[1890,1894],[1840,6256,2607],{"className":6257},[2654],[1840,6259,2633],{"className":6260},[1890],[1840,6262,2966],{"className":6263},[3068],[1840,6265],{"className":6266,"style":3076},[2110],[1840,6268,2604],{"className":6269},[2654],[1840,6271,6273,6276],{"className":6272},[1890],[1840,6274,5923],{"className":6275,"style":3757},[1890,1894],[1840,6277,6279],{"className":6278},[1899],[1840,6280,6282],{"className":6281},[1903],[1840,6283,6285],{"className":6284},[1908],[1840,6286,6289],{"className":6287,"style":6288},[1912],"height:0.8019em;",[1840,6290,6291,6294],{"style":3017},[1840,6292],{"className":6293,"style":1921},[1920],[1840,6295,6297],{"className":6296},[1925,1926,1927,1928],[1840,6298,6300],{"className":6299},[1890,1928],[1840,6301,6042],{"className":6302},[1890,1928],[1840,6304,2607],{"className":6305},[2654],[1840,6307,5671],{"className":6308,"style":5783},[1890,1894],[1840,6310,2623],{"className":6311},[2720],[1840,6313,6315,6318],{"className":6314},[2720],[1840,6316,2628],{"className":6317},[2720],[1840,6319,6321],{"className":6320},[1899],[1840,6322,6324],{"className":6323},[1903],[1840,6325,6327],{"className":6326},[1908],[1840,6328,6330],{"className":6329,"style":2996},[1912],[1840,6331,6332,6335],{"style":3017},[1840,6333],{"className":6334,"style":1921},[1920],[1840,6336,6338],{"className":6337},[1925,192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",[1840,6354,6356,6386],{"className":6355},[1843],[1840,6357,6359],{"className":6358},[1847],[1849,6360,6361],{"xmlns":1851},[1853,6362,6363,6383],{},[1856,6364,6365,6367,6369,6371,6373,6375,6378,6381],{},[1862,6366,5923],{},[2029,6368,2607],{"stretchy":2603},[1862,6370,5671],{},[2029,6372,2623],{"stretchy":2603},[2029,6374,3609],{},[1862,6376,6377],{},"log",[2029,6379,6380],{},"⁡",[1862,6382,5671],{},[1869,6384,6385],{"encoding":1871},"g(\\theta)=\\log\\theta",[1840,6387,6389,6416],{"className":6388,"ariaHidden":1877},[1876],[1840,6390,6392,6395,6398,6401,6404,6407,6410,6413],{"className":6391},[1881],[1840,6393],{"className":6394,"style":2646},[1885],[1840,6396,5923],{"className":6397,"style":3757},[1890,1894],[1840,6399,2607],{"className":6400},[2654],[1840,6402,5671],{"className":6403,"style":5783},[1890,1894],[1840,6405,2623],{"className":6406},[2720],[1840,6408],{"className":6409,"style":2111},[2110],[1840,6411,3609],{"className":6412},[2115],[1840,6414],{"className":6415,"style":2111},[2110],[1840,6417,6419,6422,6430,6433],{"className":6418},[1881],[1840,6420],{"className":6421,"style":3061},[1885],[1840,6423,6426,6427],{"className":6424},[6425],"mop","lo",[1840,6428,5923],{"style":6429},"margin-right:0.0139em;",[1840,6431],{"className":6432,"style":3076},[2110],[1840,6434,5671],{"className":6435,"style":5783},[1890,1894],"，渐近方差乘以 ",[1840,6438,6440,6462],{"className":6439},[1843],[1840,6441,6443],{"className":6442},[1847],[1849,6444,6445],{"xmlns":1851},[1853,6446,6447,6459],{},[1856,6448,6449,6451,6453],{},[2429,6450,3138],{},[1862,6452,5344],{"mathvariant":2045},[2423,6454,6455,6457],{},[1862,6456,5671],{},[2429,6458,2431],{},[1869,6460,6461],{"encoding":1871},"1\u002F\\theta^2",[1840,6463,6465],{"className":6464,"ariaHidden":1877},[1876],[1840,6466,6468,6471,6475],{"className":6467},[1881],[1840,6469],{"className":6470,"style":2713},[1885],[1840,6472,6474],{"className":6473},[1890],"1\u002F",[1840,6476,6478,6481],{"className":6477},[1890],[1840,6479,5671],{"className":6480,"style":5783},[1890,1894],[1840,6482,6484],{"className":6483},[1899],[1840,6485,6487],{"className":6486},[1903],[1840,6488,6490],{"className":6489},[1908],[1840,6491,6493],{"className":6492,"style":2736},[1912],[1840,6494,6495,6498],{"style":2739},[1840,6496],{"className":6497,"style":1921},[1920],[1840,6499,6501],{"className":6500},[1925,1926,1927,1928],[1840,6502,2431],{"className":6503},[1890,1928],"。当 ",[1840,6506,6508,6536],{"className":6507},[1843],[1840,6509,6511],{"className":6510},[1847],[1849,6512,6513],{"xmlns":1851},[1853,6514,6515,6533],{},[1856,6516,6517,6523,6525,6527,6529,6531],{},[2423,6518,6519,6521],{},[1862,6520,5923],{},[2029,6522,6042],{"mathvariant":2045,"lspace":6041,"rspace":6041},[2029,6524,2607],{"stretchy":2603},[1862,6526,5671],{},[2029,6528,2623],{"stretchy":2603},[2029,6530,3609],{},[2429,6532,2633],{},[1869,6534,6535],{"encoding":1871},"g'(\\theta)=0",[1840,6537,6539,6597],{"className":6538,"ariaHidden":1877},[1876],[1840,6540,6542,6546,6579,6582,6585,6588,6591,6594],{"className":6541},[1881],[1840,6543],{"className":6544,"style":6545},[1885],"height:1.0019em;vertical-align:-0.25em;",[1840,6547,6549,6552],{"className":6548},[1890],[1840,6550,5923],{"className":6551,"style":3757},[1890,1894],[1840,6553,6555],{"className":6554},[1899],[1840,6556,6558],{"className":6557},[1903],[1840,6559,6561],{"className":6560},[1908],[1840,6562,6565],{"className":6563,"style":6564},[1912],"height:0.7519em;",[1840,6566,6567,6570],{"style":2739},[1840,6568],{"className":6569,"style":1921},[1920],[1840,6571,6573],{"className":6572},[1925,1926,1927,1928],[1840,6574,6576],{"className":6575},[1890,1928],[1840,6577,6042],{"className":6578},[1890,1928],[1840,6580,2607],{"className":6581},[2654],[1840,6583,5671],{"className":6584,"style":5783},[1890,1894],[1840,6586,2623],{"className":6587},[2720],[1840,6589],{"className":6590,"style":2111},[2110],[1840,6592,3609],{"className":6593},[2115],[1840,6595],{"className":6596,"style":2111},[2110],[1840,6598,6600,6603],{"className":6599},[1881],[1840,6601],{"className":6602,"style":2767},[1885],[1840,6604,2633],{"className":6605},[1890],"、参数在边界或函数不光滑时，一阶 Delta 方法可能失效。",[1807,6608,6610],{"id":6609},"_6-可运行案例指数均值与柯西均值","6. 可运行案例：指数均值与柯西均值",[1796,6612,6613],{},"指数分布有有限均值和方差，标准化样本均值逐渐接近正态。标准柯西分布没有有限均值；不同样本量下样本均值仍是柯西分布，不会向常数集中。",[6615,6616],"pyodide",{"code64":6617,"layout":6618,"locale":7,"packages":6619,"title":6620},"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","vertical","numpy","Python：CLT 的成功与重尾失败",[1796,6622,6623,6624,6707],{},"指数样本的标准化分位数逐渐靠近正态参考值；柯西样本均值的宽度没有按 ",[1840,6625,6627,6647],{"className":6626},[1843],[1840,6628,6630],{"className":6629},[1847],[1849,6631,6632],{"xmlns":1851},[1853,6633,6634,6644],{},[1856,6635,6636,6638,6640],{},[2429,6637,3138],{},[1862,6639,5344],{"mathvariant":2045},[3816,6641,6642],{},[1862,6643,1867],{},[1869,6645,6646],{"encoding":1871},"1\u002F\\sqrt n",[1840,6648,6650],{"className":6649,"ariaHidden":1877},[1876],[1840,6651,6653,6657,6660],{"className":6652},[1881],[1840,6654],{"className":6655,"style":6656},[1885],"height:1.0503em;vertical-align:-0.25em;",[1840,6658,6474],{"className":6659},[1890],[1840,6661,6663],{"className":6662},[1890,3880],[1840,6664,6666,6698],{"className":6665},[1903,1904],[1840,6667,6669,6695],{"className":6668},[1908],[1840,6670,6673,6682],{"className":6671,"style":6672},[1912],"height:0.8003em;",[1840,6674,6676,6679],{"className":6675,"style":3449},[2155],[1840,6677],{"className":6678,"style":3453},[1920],[1840,6680,1867],{"className":6681,"style":3900},[1890,1894],[1840,6683,6685,6688],{"style":6684},"top:-2.7603em;",[1840,6686],{"className":6687,"style":3453},[1920],[1840,6689,6691],{"className":6690,"style":3910},[2163],[2166,6692,6693],{"xmlns":2168,"width":2169,"height":3913,"viewBox":3914,"preserveAspectRatio":3915},[2174,6694],{"d":3918},[1840,6696,1936],{"className":6697},[1935],[1840,6699,6701],{"className":6700},[1908],[1840,6702,6705],{"className":6703,"style":6704},[1912],"height:0.2397em;",[1840,6706],{}," 收缩。样本量大不能弥补不存在的矩。",[1807,6709,6711],{"id":6710},"_7-渐近理论的边界","7. 渐近理论的边界",[1978,6713,6714,6727],{},[1981,6715,6716],{},[1984,6717,6718,6721,6724],{},[1987,6719,6720],{},"风险",[1987,6722,6723],{},"为什么经典结论会失效",[1987,6725,6726],{},"可考虑",[1997,6728,6729,6813,6824,6835,6846,6886],{},[1984,6730,6731,6734,6810],{},[2002,6732,6733],{},"无限方差重尾",[2002,6735,6736,6809],{},[1840,6737,6739,6755],{"className":6738},[1843],[1840,6740,6742],{"className":6741},[1847],[1849,6743,6744],{"xmlns":1851},[1853,6745,6746,6752],{},[1856,6747,6748],{},[3816,6749,6750],{},[1862,6751,1867],{},[1869,6753,6754],{"encoding":1871},"\\sqrt n",[1840,6756,6758],{"className":6757,"ariaHidden":1877},[1876],[1840,6759,6761,6765],{"className":6760},[1881],[1840,6762],{"className":6763,"style":6764},[1885],"height:1.04em;vertical-align:-0.2397em;",[1840,6766,6768],{"className":6767},[1890,3880],[1840,6769,6771,6801],{"className":6770},[1903,1904],[1840,6772,6774,6798],{"className":6773},[1908],[1840,6775,6777,6786],{"className":6776,"style":6672},[1912],[1840,6778,6780,6783],{"className":6779,"style":3449},[2155],[1840,6781],{"className":6782,"style":3453},[1920],[1840,6784,1867],{"className":6785,"style":3900},[1890,1894],[1840,6787,6788,6791],{"style":6684},[1840,6789],{"className":6790,"style":3453},[1920],[1840,6792,6794],{"className":6793,"style":3910},[2163],[2166,6795,6796],{"xmlns":2168,"width":2169,"height":3913,"viewBox":3914,"preserveAspectRatio":3915},[2174,6797],{"d":3918},[1840,6799,1936],{"className":6800},[1935],[1840,6802,6804],{"className":6803},[1908],[1840,6805,6807],{"className":6806,"style":6704},[1912],[1840,6808],{}," 缩放与正态极限不再合适",[2002,6811,6812],{},"稳健估计、稳定分布理论",[1984,6814,6815,6818,6821],{},[2002,6816,6817],{},"时间\u002F空间依赖",[2002,6819,6820],{},"有效信息量小于观测数",[2002,6822,6823],{},"mixing 条件、HAC、块 Bootstrap",[1984,6825,6826,6829,6832],{},[2002,6827,6828],{},"聚类数据",[2002,6830,6831],{},"组内观测不独立",[2002,6833,6834],{},"按独立分配单元推断",[1984,6836,6837,6840,6843],{},[2002,6838,6839],{},"参数在边界",[2002,6841,6842],{},"极限分布可能非正态",[2002,6844,6845],{},"专门边界理论、模拟",[1984,6847,6848,6880,6883],{},[2002,6849,6850,6851,6879],{},"维度随 ",[1840,6852,6854,6867],{"className":6853},[1843],[1840,6855,6857],{"className":6856},[1847],[1849,6858,6859],{"xmlns":1851},[1853,6860,6861,6865],{},[1856,6862,6863],{},[1862,6864,1867],{},[1869,6866,1867],{"encoding":1871},[1840,6868,6870],{"className":6869,"ariaHidden":1877},[1876],[1840,6871,6873,6876],{"className":6872},[1881],[1840,6874],{"className":6875,"style":2386},[1885],[1840,6877,1867],{"className":6878},[1890,1894]," 增长",[2002,6881,6882],{},"固定维渐近失效",[2002,6884,6885],{},"高维理论、正则化",[1984,6887,6888,6891,6894],{},[2002,6889,6890],{},"弱识别",[2002,6892,6893],{},"正态近似不均匀",[2002,6895,6896],{},"弱识别稳健推断",[1807,6898,6900],{"id":6899},"_8-诊断清单","8. 诊断清单",[6902,6903,6904,6907,6910,6913,6916,6919,6922],"ul",{},[1817,6905,6906],{},"渐近序列是什么：单位数、时间长度还是两者？",[1817,6908,6909],{},"有限的一阶、二阶或更高矩是否存在？",[1817,6911,6912],{},"独立性或弱依赖条件是否可信？",[1817,6914,6915],{},"标准误是否反映聚类与序列相关？",[1817,6917,6918],{},"变换点是否可微且不在边界？",[1817,6920,6921],{},"样本量相对参数维数是否足够？",[1817,6923,6924],{},"是否用模拟检查有限样本覆盖和偏差？",[1807,6926,6927],{"id":6927},"课堂任务",[1796,6929,6930,6931,1976],{},"对 Bernoulli 样本比例 ",[1840,6932,6934,6952],{"className":6933},[1843],[1840,6935,6937],{"className":6936},[1847],[1849,6938,6939],{"xmlns":1851},[1853,6940,6941,6949],{},[1856,6942,6943],{},[2026,6944,6945,6947],{"accent":1877},[1862,6946,1796],{},[2029,6948,5674],{"stretchy":1877},[1869,6950,6951],{"encoding":1871},"\\widehat p",[1840,6953,6955],{"className":6954,"ariaHidden":1877},[1876],[1840,6956,6958,6962],{"className":6957},[1881],[1840,6959],{"className":6960,"style":6961},[1885],"height:0.865em;vertical-align:-0.1944em;",[1840,6963,6965],{"className":6964},[1890,3436],[1840,6966,6968,6999],{"className":6967},[1903,1904],[1840,6969,6971,6996],{"className":6970},[1908],[1840,6972,6975,6983],{"className":6973,"style":6974},[1912],"height:0.6706em;",[1840,6976,6977,6980],{"style":3449},[1840,6978],{"className":6979,"style":3453},[1920],[1840,6981,1796],{"className":6982},[1890,1894],[1840,6984,6987,6990],{"className":6985,"style":6986},[2155],"width:calc(100% - 0.1667em);margin-left:0.1667em;top:-3.4306em;",[1840,6988],{"className":6989,"style":3453},[1920],[1840,6991,6992],{"style":5793},[2166,6993,6994],{"xmlns":2168,"width":5796,"height":5797,"viewBox":5798,"preserveAspectRatio":5799},[2174,6995],{"d":5802},[1840,6997,1936],{"className":6998},[1935],[1840,7000,7002],{"className":7001},[1908],[1840,7003,7006],{"className":7004,"style":7005},[1912],"height:0.1944em;",[1840,7007],{},[1814,7009,7010,7013,7142,7174],{},[1817,7011,7012],{},"写出 LLN 与 CLT；",[1817,7014,7015,7016,7141],{},"对 ",[1840,7017,7019,7063],{"className":7018},[1843],[1840,7020,7022],{"className":7021},[1847],[1849,7023,7024],{"xmlns":1851},[1853,7025,7026,7060],{},[1856,7027,7028,7030,7032,7034,7036,7038,7040,7042,7044,7046,7048,7050,7052,7054,7056,7058],{},[1862,7029,5923],{},[2029,7031,2607],{"stretchy":2603},[1862,7033,1796],{},[2029,7035,2623],{"stretchy":2603},[2029,7037,3609],{},[1862,7039,6377],{},[2029,7041,6380],{},[2029,7043,2604],{"stretchy":2603},[1862,7045,1796],{},[1862,7047,5344],{"mathvariant":2045},[2029,7049,2607],{"stretchy":2603},[2429,7051,3138],{},[2029,7053,2616],{},[1862,7055,1796],{},[2029,7057,2623],{"stretchy":2603},[2029,7059,2628],{"stretchy":2603},[1869,7061,7062],{"encoding":1871},"g(p)=\\log[p\u002F(1-p)]",[1840,7064,7066,7093,7128],{"className":7065,"ariaHidden":1877},[1876],[1840,7067,7069,7072,7075,7078,7081,7084,7087,7090],{"className":7068},[1881],[1840,7070],{"className":7071,"style":2646},[1885],[1840,7073,5923],{"className":7074,"style":3757},[1890,1894],[1840,7076,2607],{"className":7077},[2654],[1840,7079,1796],{"className":7080},[1890,1894],[1840,7082,2623],{"className":7083},[2720],[1840,7085],{"className":7086,"style":2111},[2110],[1840,7088,3609],{"className":7089},[2115],[1840,7091],{"className":7092,"style":2111},[2110],[1840,7094,7096,7099,7104,7107,7110,7113,7116,7119,7122,7125],{"className":7095},[1881],[1840,7097],{"className":7098,"style":2646},[1885],[1840,7100,6426,7102],{"className":7101},[6425],[1840,7103,5923],{"style":6429},[1840,7105,2604],{"className":7106},[2654],[1840,7108,1796],{"className":7109},[1890,1894],[1840,7111,5344],{"className":7112},[1890],[1840,7114,2607],{"className":7115},[2654],[1840,7117,3138],{"className":7118},[1890],[1840,7120],{"className":7121,"style":2699},[2110],[1840,7123,2616],{"className":7124},[2703],[1840,7126],{"className":7127,"style":2699},[2110],[1840,7129,7131,7134,7137],{"className":7130},[1881],[1840,7132],{"className":7133,"style":2646},[1885],[1840,7135,1796],{"className":7136},[1890,1894],[1840,7138,7140],{"className":7139},[2720],")]"," 使用 Delta 方法；",[1817,7143,7144,7145,7173],{},"解释 ",[1840,7146,7148,7161],{"className":7147},[1843],[1840,7149,7151],{"className":7150},[1847],[1849,7152,7153],{"xmlns":1851},[1853,7154,7155,7159],{},[1856,7156,7157],{},[1862,7158,1796],{},[1869,7160,1796],{"encoding":1871},[1840,7162,7164],{"className":7163,"ariaHidden":1877},[1876],[1840,7165,7167,7170],{"className":7166},[1881],[1840,7168],{"className":7169,"style":3042},[1885],[1840,7171,1796],{"className":7172},[1890,1894]," 接近 0 或 1 时近似为何变差；",[1817,7175,7176,7177,1946,7229,7281],{},"设计模拟比较 ",[1840,7178,7180,7199],{"className":7179},[1843],[1840,7181,7183],{"className":7182},[1847],[1849,7184,7185],{"xmlns":1851},[1853,7186,7187,7196],{},[1856,7188,7189,7191,7193],{},[1862,7190,1796],{},[2029,7192,3609],{},[2429,7194,7195],{},"0.5",[1869,7197,7198],{"encoding":1871},"p=0.5",[1840,7200,7202,7220],{"className":7201,"ariaHidden":1877},[1876],[1840,7203,7205,7208,7211,7214,7217],{"className":7204},[1881],[1840,7206],{"className":7207,"style":3042},[1885],[1840,7209,1796],{"className":7210},[1890,1894],[1840,7212],{"className":7213,"style":2111},[2110],[1840,7215,3609],{"className":7216},[2115],[1840,7218],{"className":7219,"style":2111},[2110],[1840,7221,7223,7226],{"className":7222},[1881],[1840,7224],{"className":7225,"style":2767},[1885],[1840,7227,7195],{"className":7228},[1890],[1840,7230,7232,7251],{"className":7231},[1843],[1840,7233,7235],{"className":7234},[1847],[1849,7236,7237],{"xmlns":1851},[1853,7238,7239,7248],{},[1856,7240,7241,7243,7245],{},[1862,7242,1796],{},[2029,7244,3609],{},[2429,7246,7247],{},"0.01",[1869,7249,7250],{"encoding":1871},"p=0.01",[1840,7252,7254,7272],{"className":7253,"ariaHidden":1877},[1876],[1840,7255,7257,7260,7263,7266,7269],{"className":7256},[1881],[1840,7258],{"className":7259,"style":3042},[1885],[1840,7261,1796],{"className":7262},[1890,1894],[1840,7264],{"className":7265,"style":2111},[2110],[1840,7267,3609],{"className":7268},[2115],[1840,7270],{"className":7271,"style":2111},[2110],[1840,7273,7275,7278],{"className":7274},[1881],[1840,7276],{"className":7277,"style":2767},[1885],[1840,7279,7247],{"className":7280},[1890]," 的区间覆盖。",[1807,7283,7284],{"id":7284},"核心阅读",[6902,7286,7287,7295,7302],{},[1817,7288,7289,7290,7294],{},"van der Vaart, ",[7291,7292,7293],"em",{},"Asymptotic Statistics","，第 2–3 章。",[1817,7296,7297,7298,7301],{},"Billingsley, ",[7291,7299,7300],{},"Probability and Measure","，极限定理章节。",[1817,7303,7304,7305,7308],{},"Wasserman, ",[7291,7306,7307],{},"All of Statistics","，第 5–6 章。",[1796,7310,7311,7312,7316,7317,5532],{},"上一章：",[2042,7313,7315],{"href":7314},"..\u002F04-families\u002F","常见分布族","｜下一章：",[2042,7318,7320],{"href":7319},"..\u002F..\u002F02-statistics\u002F01-sampling\u002F","抽样分布",{"title":10,"searchDepth":7322,"depth":7322,"links":7323},2,[7324,7325,7326,7327,7328,7329,7330,7331,7332,7333,7334],{"id":1809,"depth":7322,"text":1809},{"id":1834,"depth":7322,"text":1835},{"id":3115,"depth":7322,"text":3116},{"id":3578,"depth":7322,"text":3579},{"id":4505,"depth":7322,"text":4506},{"id":5641,"depth":7322,"text":5642},{"id":6609,"depth":7322,"text":6610},{"id":6710,"depth":7322,"text":6711},{"id":6899,"depth":7322,"text":6900},{"id":6927,"depth":7322,"text":6927},{"id":7284,"depth":7322,"text":7284},"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。","md",{"sidebar":7338},{"order":7339},6,true,{"title":1675,"description":7335},"dzgyOj05C3N58bACT1N3Q8gPwb9yA8PT43PCm8akOhM",[7344,7346],{"title":1669,"path":1670,"stem":1671,"description":7345,"children":-1},"按重复次数、事件率、等待时间和比例机制选择常见离散与连续分布。",{"title":1686,"path":1687,"stem":1688,"description":7347,"children":-1},"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。",1785754756721]