Section 12.1: The Simple Linear Regression Model

1. Define a response and a predictor in the context of predicting one variable using another.
2. Distinguish between a response \(y\) and a prediction \(\widehat{y}(x) = f(x)\) of the response.
3. Give the form of the prediction model used in a simple linear regression.
4. Recognize the terminology “performing a regression” or “fitting a regression” for determining the parameters of a regression model using data.

Section 12.2: Estimating Model Parameters

1. Fit a simple linear regression model to data using R’s lm function.
2. Recognize the slope and intercept of a simple linear regression model, and interpret the values of a slope and intercept in the context of a particular problem.
3. Use a regression model to predict a response given a value of the predictor.
4. Define the residual (error) of a prediction in a simple linear regression.
5. Explain why the distribution of the residuals provides information about how well a regression function predicts a response.
6. Define the residual standard error (standard error of prediction).
7. Define the median absolute error of prediction, and interpret the median absolute error in the context of a particular problem.