15: Linear Regression
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The goal in this chapter is to introduce linear regression, the standard tool that statisticians rely on when analysing the relationship between interval scale predictors and interval scale outcomes. Stripped to its bare essentials, linear regression models are basically a slightly fancier version of the Pearson correlation (Section 5.7) though as we’ll see, regression models are much more powerful tools.
- 15.1: What Is a Linear Regression Model?
- 15.2: Estimating a Linear Regression Model
- 15.3: Multiple Linear Regression
- 15.4: Quantifying the Fit of the Regression Model
- 15.5: Hypothesis Tests for Regression Models
- 15.6: Testing the Significance of a Correlation
- 15.7: Regarding Regression Coefficients
- 15.8: Assumptions of Regression
- 15.9: Model Checking
- 15.10: Model Selection
- 15.11: Summary