STAT 3032: Regression and Correlated Data

4 Credits

This is a second course in statistics with a focus on linear regression and correlated data. The intent of this course is to prepare statistics, economics and actuarial science students for statistical modeling needed in their discipline. The course covers the basic concepts of linear algebra and computing in R, simple linear regression, multiple linear regression, statistical inference, model diagnostics, transformations, model selection, model validation, and basics of time series and mixed models. Numerous datasets will be analyzed and interpreted using the open-source statistical software R. prereq: STAT 3011 or STAT 3021

View on University Catalog

All Instructors

B+ Average (3.179)Most Common: A (32%)

This total also includes data from semesters with unknown instructors.

2182 students
SNWFDCBA
  • 4.09

    /5

    Recommend
  • 4.35

    /5

    Effort
  • 4.24

    /5

    Understanding
  • 3.99

    /5

    Interesting
  • 4.36

    /5

    Activities


      Contribute on our Github

      Gopher Grades is maintained by Social Coding with data from Summer 2017 to Fall 2023 provided by the Office of Institutional Data and Research

      Privacy Policy