In the final unit of the course, students conclude their study of inference by applying it to linear regression models. This chapter bridges the gap between descriptive bivariate data and statistical inference, allowing students to determine if a significant linear relationship exists between two quantitative variables.

Students will learn to construct confidence intervals for slope and perform t-tests for the population slope (β). A major focus is placed on interpreting computer regression output (such as Minitab or Excel tables) to identify the standard error of the slope (SE_b), the t-statistic, and the P-value.

Mastery of the LINER conditions—Linear, Independent, Normal, Equal Variance, and Random—is essential for verifying the appropriateness of the model. This unit empowers students to make data-based conclusions about the rate of change in real-world relationships.