• 6.2: Fitting the Model

      • 6.2.1: Standard Errors of the Least Squares Estimates

        • Standard Error of the Estimate Book

          This section discusses how to compute the standard error of the estimate based on errors of prediction as well as how to compute the standard error of the estimate based on a sample.

      • 6.2.2: Statistical Inference for the Slope and Correlation

        • Inferential Statistics for b and r Book

          This section starts with assumptions on the errors that are necessary for statistical inference. Then, it gives an example of a significance test for the slope. Finally, it talks about constructing confidence intervals for the slope and closes with a significance test for the correlation.

        • Statistical Inference about Slope Book

          This section further details two types of inferences on the slope parameter, considering both confidence intervals and hypothesis testing.

      • 6.2.3: Influential Observations

        • Influential Observations Book

          This section discusses the notion of influence and describes what makes a point influential. It introduces the concepts of leverage and distance, which are useful to detect influential observations.

        • A Complete Example Book

          This section explains linear regression, from presenting the data to using scatter plots to identify the linear pattern. It then fits a linear model using least squares estimation and addresses statistical inferences on correlation coefficient and slope parameter.