Linear Regression

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Choosing Good Predictors It's tempting to just throw in hundreds of predictors and see which ones contribute most ● Don't do this! There are requirements on how the predictors interact with each other! ● Also, more predictors → less power Have a theoretical/prior justification for them Example: what's a DV you are interested in? ● Come up with as many possible good IVs as you can – have a justification! Background, internal personal, current external environment CPSY501: multiple regression 2 Oct 2009 20
Using Derived Variables You may want to use derived variables in regression, for example: Transformed variables (to satisfy assumptions) Interaction terms: (“moderating” variables) ● e.g., Airplay * Advertising Budget Dummy variables: ● e.g., coding for categorical predictors Curvilinear variables (non-linear regression) ● e.g., looking at X 2 instead of X CPSY501: multiple regression 2 Oct 2009 21
Page 1 and 2: Linear Regression 2 Oct 2009 CPSY50
Page 3 and 4: Causation from correlation? Ancien
Page 5 and 6: Potential hidden factors What othe
Page 7 and 8: Partial Correlation Purpose: to me
Page 9 and 10: Practise: Partial Correlation Data
Page 11 and 12: Simple Regression: 1 predictor Simp
Page 13 and 14: Assessing Fit of a Model R 2 : pro
Page 15 and 16: Example: Interpreting Results Mode
Page 17 and 18: General Linear Model Actually, nea
Page 19: Selecting Variables According to y
Page 23 and 24: Requirements on DV Must be interva
Page 25 and 26: Requirements on IV Scale-level ●

Linear Regression

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