Statistics for Advanced Quantitative Methods in Social Science

This repo contains course materials for a Graduate Level class teaching Statistics and Applied Regression Models for Social Science Major Graduate Students.

Subject

The subject covered in this course includes:

1. Elementary Probability Theory
2. Foundamentals for Mathmatical Statistics
3. Random Sampling and Hypothesis Testing
4. Simple and Multiple Regression Models
5. Regression Model Diagostics and Treatments
6. Advanced Statistics a. Logistic Regression b. Ridge and LASSO Regression c. Time Series Regression d. Mixed Effect Regression
7. Tutorials for Statistical Programming in R and Python

Textbook Recommended

1. Regression Analysis By Example Using R Sixth Edition Hadi and Chatterjee
2. Statistics Fifth Edition Hays
3. A First Course in Probability Theory Ninth Edition Ross
4. Statistical Inference Second Edition Casella and Berger
5. Applied Linear Statistical Models Fifth Edition Kutner, Nachtsheim, Neter & Li

1 & 2 are required textbook, 3-5 are recommended textbook.

Acknowledgment

This course is developed from several statistical courses I have taken at UC, Davis and University of Illinois, Urbana Champaign.

These courses are remarkably great, allowing me to learn the foundamental knowledge about statistics from a novice.

I hereby appreciate my instructors in these courses and their developed course materials, and highly recommend these courses to students who have an opportunity.

A list of courses that I borrowed:

1. PSYC 506 Statistical Methods I UIUC (taught by Dr. Koehn)
2. PSYC 507 Statistical Methods II UIUC (taught by Dr. Koehn)
3. STAT 200A Introduction to Probability Theory UC, Davis (taught by Dr. Lopes)
4. STAT 200B Introduction to Mathematical Statistics I UC, Davis (taught by Dr. Wang)
5. STAT 206 Statistical Methods for Research I UC, Davis (taught by Dr. Peng)