Reproducible Research Using R An Open Educational Resource

Explain the purpose of linear regression as a tool for modeling and prediction

Visualize and assess linear relationships between numeric variables using scatterplots

Use correlation to evaluate whether variables are appropriate for regression modeling

Fit a simple linear regression model using lm() and interpret the slope and intercept

Interpret key model outputs including coefficients, p-values, R², adjusted R², and the F-statistic

Generate predicted values and calculate residuals from a fitted regression model

Diagnose model assumptions by visually and statistically evaluating residuals

Test for heteroscedasticity using the Breusch–Pagan test

Extend simple regression to multiple regression by adding additional predictors

Compare competing regression models using adjusted R² and AIC

Select a parsimonious model that balances explanatory power and complexity