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Applied Regression Analysis
Release Date: 2017-09-221. Basic Course Information
Course Title: Applied Regression Analysis
Course Code:102254
Credit Hours/Credits:64/4
Target Students:Undergraduate Students who major in Statistics and Applied Statistics
Prerequisites: Probability Theory and Mathematical Statistics
Subsequent Course(s):Applied Stochastic Processes
Course Category:Fundamental Course
Assessment: final exam (70%) + Participation & Assignments (30%)
2. Course Introduction
This is a core statistical course for both Statistics and Applied Statistics. Regression analysis is an important statistical method for discovering for the relationships amongst different variables. This course introduces statistical frameworks, analytical tools and some application of OLS (Ordinary Least Squares) regression models, weighted least-square regression, logistic regression models and generalized linear models. On completion of the course, students will be able to understand the type and nature of research questions and data that are suitable for regression analysis and know how to use statistical computing software package to manage and analyze data with regression models, interpret results of regression analysis, furthermore students will understand some limitations of the regression models and common pitfalls in using these models.