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  1. Introduction
  2. Graphing Distributions
  3. Summarizing Distributions
  4. Describing Bivariate Data
  5. Probability
  6. Research Design
  7. Normal Distribution
  8. Advanced Graphs
  9. Sampling Distributions
  10. Estimation
  11. Logic of Hypothesis Testing
  12. Tests of Means
  13. Power

  14. Regression
    1. Contents
      Standard  
    2. Introduction to Linear Regression
      Standard         Video
    3. Linear Fit Demo
      Standard
    4. Partitioning Sums of Squares
      Standard         Video
    5. Standard Error of the Estimate
      Standard         Video
    6. Inferential Statistics for b and r
      Standard         Video
    7. Influential Observations
      Standard         Video
    8. Regression Toward the Mean
      Standard         Video
    9. Introduction to Multiple Regression
      Standard         Video
    10. Statistical Literacy
      Standard
    11. Exercises
      Standard

  15. Analysis of Variance
  16. Transformations
  17. Chi Square
  18. Distribution Free Tests
  19. Effect Size
  20. Case Studies
  21. Calculators
  22. Glossary
 

Regression

Author(s)

David M. Lane

Prerequisites

Specified in individual sections
  1. Introduction to Simple Linear Regression
  2. Linear Fit Demo
  3. Partitioning Sums of Squares
  4. Standard Error of the Estimate
  5. Inferential Statistics for b and r
  6. Influential Observations
  7. Regression Toward the Mean
  8. Introduction to Multiple Regression
  9. Statistical Literacy
  10. Exercises

PDF (A good way to print the chapter.)

Statisticians are often called upon to develop methods to predict one variable from other variables. For example, one might want to predict college grade point average from high school grade point average. Or, one might want to predict income from the number of years of education.