stats-pratices

Statistics

• Part I: Design of Experiments
  • 1: Controlled Experiments
    • 1.1 The Salk Vaccine Field Trial
    • 1.2 The Portacaval Shunt
    • 1.3 Historical Controls
  • 2: Observational Studies
    • 2.1 Introduction
    • 2.2 The Clofibrate Trial
    • 2.3 More Examples
    • 2.4 Sex Bias in Graduate Admissions
    • 2.5 Confounding
• Part II: Descriptive Statistics
  • 3: The Histogram
    • 3.1 Introduction
    • 3.2 Drawing a Histogram
    • 3.3 The Density Scale
    • 3.4 Variables
    • 3.5 Controlling for a Variable
    • 3.6 Cross-Tabulation
    • 3.7 Selective Breeding
  • 4: The Average and the Standard Deviation
    • 4.1 Introduction
    • 4.2 the Average and the Histogram
    • 4.3 The Root-Mean-Square
    • 4.5 The Standard Deviation
    • 4.6 Computing the Standard Deviation
    • 4.7 Using a Statistical Calculator
  • 5: The Normal Approximation for Data
    • 5.1 The Normal Curve
    • 5.2 Finding Areas Under the Normal Curve
    • 5.3 The Normal Approximation for Data
    • 5.4 Percentiles
    • 5.6 Change of Scale
    • 5.7 Review Exercises
    • 5.8 Summary
  • 6: Measurement Error
    • 6.1 Introduction
    • 6.2 Chance Error
    • 6.3 Outliers
    • 6.4 Bias
    • 6.7 Summary
  • 7: Plotting Points and Lines
    • 7.1 Reading Points Off a Graph
    • 7.2 Plotting Points
    • 7.3 Slope and Intercept
    • 7.4 Plotting Lines
    • 7.5 The Algebraic Equation for a Line
• Part III: Correlation and Regression
  • 8: Correlation
    • 8.1 The Scatter Diagram
    • 8.2 The correlation Coefficient
    • 8.3 The SD Line
    • 8.4 Computing the Correlation Coefficient
  • 9: More about Correlation
    • 9.1 Features of the Correlation Coefficient
    • 9.2 Chaning SDs
    • 9.3 Some Exceptional Cases
    • 9.4 Ecological Correlatons
    • 9.5 Assoclation is Not Causation
  • 10: Regression
    • 10.1 Introduction
    • 10.2 The Graph of Averages
    • 10.3 The Regression Method for Individuals
    • 10.4 The Regression Fallacy
    • 10.5 There are Two Regression Lines
  • 11: The R.M.S. Error for Regression
    • 10.1 Introduction
    • 10.2 Computing the R.M.S. Error
    • 10.3 Plotting the Residuals
    • 10.4 Looking at Vertical Strips
    • 10.5 Using the Normative Curve Inside a Vertical Strip
  • 12: The Regression Line
    • 12.1 Slope and Intercept
    • 12.2 The Method of Least Squares
    • 12.3 Does the Regression Make Sense?
• Part IV: Probability
  • 13: What are the Chances?
    • 13.1 Introduction
    • 13.2 conditional Probabilities
    • 13.3 The Multiplication Rule
    • 13.4 Independence
    • 13.5 The Collins Case
  • 14: More about Chance.
    • 14.1 Listing the Ways
    • 14.2 TheAddition Rule
    • 14.3 Two FAQs (Frequently Asked Questions)
    • 14.4 The Paradox of Chevalier de Méré
    • 14.5 Are Real Dice Fair?
  • 15: The Binomial Formula
    • 15.1 Introduction
    • 15.2 The BinomialFormula
    • 15.3 ReviewExercises
    • 15.4 Special Review Exercises
    • 15.5 Summary and Overview
• Part V: Chance Variability
  • 16: The Law of Averages
    • 16.1 What Does the Law of Averages Say?
    • 16.2 Chance Processes
    • 16.3 The Sum of Draws
    • 16.4 Making a Box Model
    • 16.5 Review Exercises
    • 16.6. Summary
  • 17: The Expected Value and Standard Error
    • 17.1 The Expected Value
    • 17.2 The Standard Error
    • 17.3 Using the Normal Curve
    • 17.4 A Short-Cut
    • 17.5 Classifying and Counting
    • 17.6 Review Exercises
    • 17.7 Postscript
    • 17.8 Summary
  • 18: The Normal Approximation for Probability Histograms
    • 18.1 Introduction
    • 18.2 Probability Histograms
    • 18.3 Probability Histograms and the Normal Curve
    • 18.4 The Normal Approximation.
    • 18.5 The Scope of the Normal Approximation
    • 18.6 Conclusion
    • 18.7 Review Exercises
    • 18.8 Summary
• Part VI: Sampling
  • 19: Sample Surveys
    • 19.1 Introduction
    • 19.2 The Literary Digest Poll
    • 19.3 The Year the Polls Elected Dewey
    • 19.4 Using Chance in Survey Work
    • 19.5 How Well Do Probability Methods Work?
    • 19.6 A Closer Look at the Gallup Poll
    • 19.7 Telephone Surveys
    • 19.8 Chance Error and Bias
    • 19.9 Review Exercises
    • 19.10 Summary
  • 20: Chance Errors in Sampling
    • 20.1 Introduction
    • 20.2 The Expected Value and Standard Error
    • 20.3 Using the Normal Curve
    • 20.4 The Correction Factor
    • 20.5 The Gallup Poll
    • 20.6 Review Exercises
    • 20.7 Summary
  • 21: The Accuracy of Percentages
    • 21.1 Introduction
    • 21.2 Confidence Intervals
    • 21.3 Interpreting a Confidence Interval
    • 21.4 Caveat Emptor
    • 21.5 The Gallup Poll
    • 21.6 Review Exercises
    • 21.7 Summary
  • 22: Measuring Employment and Unemployment
    • 22.1 Introduction
    • 22.2 The Design of the Current Population Survey
    • 22.3 Carrying Out the Survey
    • 22.4 Weighting the Sample
    • 22.5 Standard Errors
    • 22.6 The Quality of the Data
    • 22.7 Bias
    • 22.8 Review Exercises
    • 22.9 Summary
  • 23: The Accuracy of Averages
    • 23.1 Introduction
    • 23.2 The Sample Average
    • 23.3 Which SE?
    • 23.4 A Reminder
    • 23.5 Review Exercises
    • 23.6 Special Review Exercises
    • 23.7 Summary and Overview
• Part VII: Chance Models
  • 24: A Model For Measurement Error
    • 24.1 Estimating the Accuracy of an Average
    • 24.2 Chance Models
    • 24.3 The Gauss Model.
    • 24.4 Conclusion
    • 24.5 Review Exercises
    • 24.6 Summary
  • 25: Chance Models in Genetics
    • 25.1 How Mendel Discovered Genetics
    • 25.2 Did Mendel's Facts Fit His Model?
    • 25.3 The Law of Regression
    • 25.4 An Appreciation of the Model
• Part VIII: Tests of Significance
  • 26: Tests of Significance
    • 26.1 Introduction
    • 26.2 The Null and the Alternative
    • 26.3 Test Statistics and Significance Levels
    • 26.4 Making a Test of Significance
    • 26.5 Zero-One Boxes
    • 26.6 The t-Test
    • 26.7 Review Exercises
    • 26.8 Summary
  • 27: More Tests for Averages
    • 27.1 The Standard Error for a Difference
    • 27.2 Comparing Two Sample Averages
    • 27.3 Experiments
    • 27.4 More on Experiments
    • 27.5 When Does the z-Test Apply?
  • 28: The Chi-Square Test
    • 28.1 Introduction
    • 28.2 The Structure of the x2-Test
    • 28.3 How Fisher Used the x2-Test
    • 28.4 TestingIndependence
  • 29: A Closer Look at Tests of Significance
    • 29.1 Was the Result Significant?
    • 29.2 Data Snooping
    • 29.3 Was the Result Important?
    • 29.4 The Role of the Model
    • 29.5 Does the Difference Prove the Point?