Extra credit opportunities: if you found an error or a typo in the lecture notes below, please email me the information. You will receive 1 point of extra credit for one error you find.

Lecture notes:

• Chapter 1 Introduction

  • Data Set: data_delivery.csv

• R Programming

• Chapter 2 Simple Linear Regression Part I, Part II, Part III

  • Data Set: data_RocketProp.csv

• Chapter 3 Multiple Linear Regression

  • Data Set: data_AirlinesCost.csv

• Chapter 4 Model Adequacy Checking

• Chapter 5 Model Validation

  • Data Sets: data_delivery_additional.csv

• Real Data Example: Moneyball

• Chapter 6 Confounder

• Chapter 7 Transformation

  • Data Sets: data_ElectricUtility.csv, data_Windmill.csv

• Chapter 8 Diagnostics for Leverage and Influence

• Chapter 9 Polynomial Regression

  • Data Sets: data_Hardwood.csv, data_VoltageDrop.csv

• Chapter 10 Indicator Variable

  • Data Sets: data_Life.csv, data_City.csv

• Chapter 11 Multicollinearity

  • Data Sets: data_BodyFat.csv, data_Webster.csv

• Chapter 12 Variable Selection

  • Data Sets: data_HaldCement.csv, data_Asphalt.csv

• Chapter 13 Logistic Regression

  • Data Sets: data_challenger.csv

In Class Exercises:

• In Class Exercises

  • Data Sets: data_chicago.csv, data_delta.csv, data_vote.csv

Homework:

• Please check homework in canopy.uc.edu and submit all your homework through Canopy.

Final Project:

• House prices in Cincinnati.