# Companion to BER 642: Advanced Regression Methods

*2020-10-27*

# Chapter 1 Introduction

This book is still in progress !!! This is a draft. Several sections are still incomplete or unedited.

This book is written to help students enrolled in the University of Alabama, Advanced Regression Method (BER 642) course led by Professor Dr.Youn-Jeng (Joy) Choi.

We hope that the book will be a useful resource to help you learn both R and statistics.

If you have any questions concerning your homework in R, please contact your TA: Qingzhou, at: qshi7@crimson.ua.edu

If you have any suggestions or improvements to this R-book, or, if you have any R issues that could not be solved, please get in touch with your TA: Cheng Hua, at: chua@crimson.ua.edu

## 1.1 Course Description

Course Description and Credit Hours

Different multiple regression methods are presented including an overview of ordinary least squares regression, ordinal regression, logistic and probit regression, loglinear, mixed, and regression discontinuity. Interpretation of results diagnostics, and appications are covered for the several glm models.

This course is designed to learn advanced regression methods such as logistic regression, ordinal logistic regression, probit analysis, and multilevel modeling. This course focuses on understanding, conceptualizing, designing, and interpreting advanced regression methods.

## 1.2 Course Information

Instructor: Dr. Youn-Jeng Choi (Joy)

- Class Time: Every Thursday, 1:00 pm to 3:50 pm (face to face & virtual)
- Classroom: 109 Graves Hall (face to face)
- Virtual Classroom: Zoom Link
- E-mail: ychoi26@ua.edu
- Office hour: Wednesday 10:00 am to 12:00 am, or by Appointment

## 1.3 Student Learning Outcomes

By the end of this course, students will be able to:

- Apply advanced regression analysis (ARA) techniques to their research area.

- Judge the technical quality of ARA.

- Critically review ARA articles on journals.

- Run correlation and simple linear regression using R.

- Run multiple regression using R.

- Be familiar with diagnostics and model fit on regression.

- Run curvilinear regression using R.

- Run logistic regression using R.

- Run probit analysis using R.

- Run multilevel modeling using R.

- Correctly interpret regression coefficients.

- Write up papers using regression analyses for publication.

## 1.4 Other Course Materials

Google it Please

Agresti, A. (1996). An Introduction to Categorical Data Analysis. Wiley & Sons, NY.

Bickel, R. (2007). Multilevel Analysis for Applied Research: It’s just regression. New York: Guilford.

Field, A. (2018).Discovering Statistics using IBM SPSS Statistics (5th ed.). London: Sage publications.

Pedhazur, E. (1997). Multiple Rregression in Behavioral Research (3rd ed.). Wadsworth.