05.03APage 1Estimation
05.03A · Cours 3 — Estimation
Course 3 — Estimation
Course and questions · 67 pages
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05.03APage 9Learning Objectives
05.03APage 10InferencePrinciple: Grape Analogy
05.03APage 11InferenceSampling and Random Selection
05.03APage 12InferenceStatistical Inference Workflow
05.03APage 13InferenceSix Steps of Statistical Inference
05.03APage 16From Parameter to EstimateSimulation Setup
05.03APage 3295% Confidence Interval of a ProportionSummary
05.03APage 35Other Parameters to Estimate
05.03APage 36Estimating a Population MeanMean Estimation Concept
05.03APage 39Estimating a Population MeanOnline Simulation Tool
05.03APage 4195% CI of a MeanFinding the Lower Bound
05.03APage 4295% CI of a MeanFinding the Upper Bound
05.03APage 4395% CI of a MeanCentral Limit Theorem
05.03APage 4495% CI of a MeanDeriving the CI Formula
05.03APage 4595% CI of a MeanCI Bounds Visual: Cholesterol
05.03APage 4695% CI of a MeanFormula: m ± 2s/√n
05.03APage 4795% CI of a MeanStandard Error
05.03APage 4895% CI of a MeanExample: Student Heights
05.03APage 4995% CI of a MeanSample Size: n=5 vs n=25
05.03APage 5095% CI of a MeanCI Width Decreases with n
05.03APage 5195% CI of a MeanRepeated CIs: 95% Coverage for Means
05.03APage 5295% CI of a MeanCI Works for Non-Normal Populations
05.03APage 5395% CI of a MeanSummary
05.03APage 5495% CI: General Principles
05.03APage 56Types of ErrorRandom vs Systematic Errors
05.03APage 57Types of ErrorSelection Bias
05.03APage 58Case Study: Boston MarathonArticle Introduction
05.03APage 59Case Study: Boston MarathonRecruitment Method
05.03APage 60Case Study: Boston MarathonRecruitment Flow Diagram
05.03APage 64Case Study: Boston MarathonResults and Bias Question
05.03APage 66Key Concepts Summary
05.03APage 67Next Session Preview
