MATH0211A-S22
Regression
Regression Theory and Applications
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 MATH 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 MATH 0311) 3 hrs lect./disc.
- Term:
- Spring 2022
- Location:
- Munroe Hall 317(MNR 317)
- Schedule:
- 1:45pm-2:35pm on Monday, Wednesday, Friday (Feb 14, 2022 to May 16, 2022)
- Type:
- Lecture
- Course Modality:
- In-Person
- Instructors:
- Kara Karpman
- Subject:
- Mathematics
- Department:
- Mathematics
- Division:
- Natural Sciences
- Requirements Fulfilled:
- DED
- Levels:
- Undergraduate
- Availability:
- View availability, prerequisites, and other requirements.
- Course Reference Number (CRN):
- 22749
- Subject Code:
- MATH
- Course Number:
- 0211
- Section Identifier:
- A