9.24: Simple Linear Regression
Upcoming deadlines
- HW 2 9/29 11:59 PM
Learning Objectives
By the end of the class, students should be able to:
describe the linear regression model with statistical terminology (population parameter, estimate, random variable, probability distribution)
define “least squares estimation”
interpret regression output (estimates, standard errors, test statistics, p-values, and confidence intervals)
Class prep
Introduction to Statistical Learning with Applications in R (ISLR) 2nd edition: Chapter 3 up to 3.1.3 (i.e., introduction through “Assessing the accuracy of the coefficient estimates”)
By the end of the reading, you should be able to answer the following questions:
What is an example of a type of question that can be answered with linear regression?
What is a residual in linear regression? How does the concept of a residual connect to least squares estimation?
How do the population regression line and least squares line connect to the concepts of parameters and sample estimates that we’ve previously covered?
What are the null and alternative hypotheses for regression coefficients?