Module 1: Inference for Linear Regression
Module Overview
In this module, you will learn about inference for linear regression. You'll learn how to test for statistical significance between quantitative variables, conduct t-tests for slope parameters, build confidence intervals, and identify violations of linear regression assumptions.
Learning Objectives
- Identify the Appropriate Hypotheses to Test for a Statistically Significant Association Between Two Quantitative Variables
- Conduct and Interpret a t-test for the Slope Parameter and Make the Connection Between the t-test for a Population Mean and Slope Coefficient
- Identify the Appropriate Parts of the Output of a Linear Regression Model and Use Them to Build a Confidence Interval for the Slope Term
- Identify Violations of the Assumptions for Linear Regression and Articulate Why Correlation Does Not Imply Causation Inference for Linear Regression
Guided Project
Open DS_131_Inference_For_Regression.ipynb in the GitHub repository below to follow along with the guided project:
Guided Project Video
Module Assignment
Complete the Module 1 assignment to practice inference for linear regression techniques you've learned.