STAT 8678 - SAS Programming & Data Analysis

Preface

Thank you

The course is finished (except of the final project). Thank you everyone for the participation and attention. Hope you all have learned something from this course, and have fun. Wish everyone has a good summer vacation! 🏀😼😊

Description

This course covers programming using the SAS statistical software package, and it provides an introduction to data analysis stressing the implementation using SAS.

Topics include two main parts:

1. SAS Programming: data management and manipulation, basic procedures, macro programming;
2. Data Analysis: descriptive statistical analysis, one- and two-sample inference, basic categorical data analysis, regression analysis, and other selected topics.

Prerequisites

MATH 4544/6544 – Biostatistics, or equivalent.

Instructor

Chi-Kuang Yeh, Assistant Professor in the Department of Mathematics and Statistics, Georgia State University.

• Office: Suite 1407, 25 Park Place.
• Email: cyeh@gsu.edu.

Office Hour

10:00–13:00 on Monday, or by appointment.

Grade Distribution

• Assignments: 60%
• Exam: 20%
• Project: 20%

Assignment

• A1, due on Jan 28, 2026
• A2, due on Feb 8, 2026
• A3, due on Feb 15, 2026
• A4, due on Feb 28, 2026
• A5, due on Mar 18, 2026
• A6, due on Apr 18, 2026

Midterm

• March 4, 2026

Final Project

• Due on May 1, 2026

More information can be found on the project page

Topics and Corresponding Lectures

Those chapters are based on the lecture notes. This part will be updated frequently.

Status	Lecture	Topic
✅	1	Welcome and Overview
		📘 Part 1: Basics
✅	2	Basic SAS Operation
✅	3	SAS Syntax
✅	4	Import and Export Data
✅	5	Random Variable
		📘 Part 2: Statistical Analysis
✅	6	Introduction to Statistical Inference I
✅	7	Introduction to Statistical Inference II
✅	8	One Sample Nonparametric Test
✅	9	One Sample Proportion Test
✅	10	Introduction to SAS Marco
✅	11	Chi-Square GoF test
✅	12	Power Analysis and Calculation (Recorded)
✅	13	🛠 Class Activity I
	14	📜 Exam 🖌️
✅	15	Two Sample (t)-test for Independent and Paired Datasets
		📘 Part 3: Regression Analysis
✅	16	Analysis of Variance
		⛱ Spring Break 🌊
✅	17	🛠 Class Activity II
✅	18	Intro to Regression Analysis
✅	19–20	Regression with Interaction Effects
✅	20	🛠 Class Activity III
✅	21	Linear Mixed Effects Model I
✅	22	Linear Mixed Effects Model II
✅	23	🛠 Class Activity IV
✅	24	Model Selection
✅	25	Nested Model
		📘 Part 4: Advanced Topic
✅	26	Predictive Modelling
✅	27	Machine Learning

Recommended Textbooks

• Statistics 480: Introduction to SAS, The Pennsylvania State University.

• SAS Training, SAS Institute.

• SAS Resources, University of California, Los Angeles.

Acknowledgments

Special thanks to Li-Hsiang Lin for providing the base materials given on this website.