Section 12.1: The Simple Linear Regression Model

1. State how a population-level regression model parallels a sample-level regression model in terms of parameters and errors / residuals.

Section 12.2: Estimating Model Parameters

1. State the objective function that is minimized to find the least squares solution for the regression parameters \(b_{0}\) and \(b_{1}\).
2. State the least squares solution for the coefficients of a simple linear regression in terms of the sample means, variances, covariance, and correlation.

Section 12.3: Inferences About the Regression Coefficient \(\beta_{1}\)

1. State the assumptions of the Simple Linear Regression with Gaussian Noise (SLRGN) Model necessary to make cookbook inferences for the parameters from a simple linear regression.
2. State the sampling distribution of the intercept and slope estimators under the SLRGN model.
3. Construct confidence intervals and bounds for the population intercept and slope under the SLRGN model.
4. Perform hypothesis tests for the population intercept or slope under the SLRGN model.

Section 12.6: Aptness of the Model and Model Checking

1. Explain the role of diagnostic plots in checking model assumptions before performing an inference based on the SLRGN model.
2. Identify how deviations from the SLRGN model show up in diagnostic plots.