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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
 
Learning Objectives
  1. State the regression equation
  2. Define "regression coefficient"
  3. Define "beta weight"
  4. Explaine what R is and how it is related to r
  5. Explain why a regression weight is called a "partial slope"
  6. Explain why the sum of squares explained in a multiple regression model is usually less than the sum of the sums of squares in simple regression
  7. Define R2 in terms of proportion explained
  8. Test R2 for significance
  9. Test the difference between a complete and reduced model for significance
  10. State the assumptions of multiple regression and specify which aspects of the analysis require assumptions