Week 2

Review of Regression

Week Learning Objectives

By the end of this module, you will be able to

Task Lists

  1. If you have questions, attend the Tuesday Q&A session
  2. Complete the assigned readings
  3. Attend the Thursday session and participate in the class exercise
  4. Complete Homework 2

Lecture

Slides

You can view and download the slides here: HTML PDF

Statistical Model

Check your learning: In the example in the video, why do we need a random component?





Import Data

Check your learning: What is the coding for the sex variable?





Take a pause and look at the scatterplot matrix. Ask yourself the following:


Linear Regression

Sample regression line

Check your learning: How would you translate the regression line \(y = \beta_0 + \beta_1 \text{predictor1}\) into R?




Centering

Check your learning: The mean of the pub variable is 18.2. If we call the mean-centered version of it as pub_c, what should be the value of pub_c for someone with 10 publications?



Categorical Predictor

Check your learning: In a regression analysis, assume that there is a binary predictor that indicates whether a school is public (coded as 0) or private (coded as 1). If the coefficient for that predictor is 1.5, which category has a higher predicted score?



Multiple Regression

Think more: the coefficient of pub_c becomes smaller after adding time into the equation. Why do you think that is the case?


Interaction

Pratice yourself: from the interaction model obtain the regression line when pub = 50.