STAT0211A-F23
Regression
Regression Theory and Applications (formerly MATH 0211)
Regression is a popular statistical technique for making predictions and for modeling relationships between variables. In this course we will discuss the theory and practical applications of linear, log-linear, and logistic regression models. Topics include least squares estimation, coding for categorical predictors, analysis of variance, and model diagnostics. We will apply these concepts to real datasets using R, a statistical programming language. (MATH 0200; and MATH 0116 or STAT 0116, or MATH 0311 or STAT 0311) 3 hrs lect./disc.
Regression is a popular statistical technique for making predictions and for modeling relationships between variables. In this course we will discuss the theory and practical applications of linear, log-linear, and logistic regression models. Topics include least squares estimation, coding for categorical predictors, analysis of variance, and model diagnostics. We will apply these concepts to real datasets using R, a statistical programming language. (MATH 0200; and MATH 0116 or STAT 0116, or MATH 0311 or STAT 0311) 3 hrs lect./disc.
- Term:
- Fall 2023
- Location:
- Warner Hall 011(WNS 011)
- Schedule:
- 9:45am-11:00am on Tuesday, Thursday (Sep 11, 2023 to Dec 11, 2023)
- Type:
- Lecture
- Course Modality:
- In-Person
- Instructors:
- Emily Malcolm-White
- Subject:
- Statistics
- Department:
- Mathematics & Statistics
- Division:
- Natural Sciences
- Requirements Fulfilled:
- DED
- Levels:
- Undergraduate
- Availability:
- View availability, prerequisites, and other requirements.
- Course Reference Number (CRN):
- 92857
- Subject Code:
- STAT
- Course Number:
- 0211
- Section Identifier:
- A