Course Description
Linear regression, analysis of variance, and related methodology for students in quantitative disciplines other than statistics. Topics include multiple regression; associated estimation and inference methods; model building, selection, and diagnostics; the analysis of variance; completely randomized and block designs; the analysis of covariance, and relevant statistical computing packages.
Athena Title
Statistical Analysis II
Equivalent Courses
Not open to students with credit in none
Prerequisite
STAT 6310 or permission of department
Semester Course Offered
Not offered on a regular basis.
Grading System
A - F (Traditional)
Course Objectives
This is a second course in statistics for quantitatively oriented students from disciplines other than statistics. It is suitable for students who intend to take two or more courses in statistical methodology and/or put statistical techniques into practice to analyze real data. The goal of this course is for students to learn the basic form, interpretation and methodology of multiple linear regression and the analysis of variance. Upon completion, students will be familiar with the multiple linear regression model, will understand the associated methodology for estimation and inference, will understand the analysis of variance model and methodology, will know how to apply linear regression and analysis of variance for the purpose of data analysis using the statistical software package SAS. Students will also be introduced to the analysis of covariance.
Topical Outline
Course topics will include simple linear regression, multiple linear regression, least squares estimation, prediction, confidence and prediction intervals, hypothesis testing, model building and selection, model diagnostics, the analysis of variance, completely randomized and randomized block designs. Logistic regression and/or nonlinear regression may be covered as time allows. Students will learn how to implement the methods using the statistical software package SAS.