Description

• First, the course will review basic linear models in statistics including simple regression, multiple regression, regression with categorical variables and polynomials, etc.
• After studying basic linear regression models, we will focus on general F-test, basic model selection methods, and building appropriate models.
• Time permitting, we will also consider how to deal with outliers and influential observations.
• The popular R statistical language, Python3, and Minitab will be handled in this class.
• We will also consider various practical applications widely used for engineering.

Objectives

Upon successful completion of this course, students will be able to:

1. Program statistical softwares (Minitab and R).
2. Derive parameter estimates under the simple linear regression model.
3. Do basic statistical inference for the simple linear regression model.
4. Know how to use matrix algebra in regression models.
5. Extend the simple linear regression model to the multiple linear regression model using the matrix algebra.
6. Set up polynomial regression models.
7. Analyze and infer the multiple linear regression model.
8. Understand how to diagnose the problems from regression models.
9. Know the general linear F-test.
10. Use categorical predictor variables in the regression model setup.
11. Use all possible regression.
12. Understand several model selection procedures.
13. Build an appropriate model.
14. Detect outliers and influential observations.

Why regression?

• Key Types of Regressions: Which One to Use?
• What is “linear” regression model?
• R Vs Python: What’s the Difference?
• Why R for Data Science – and not Python?

Links

• Syllabus
• Class Notes
• R and Minitab Codes
• R4pda (R written in Korean)
• Minitab (trial SW)
• Minitab (manual)