STAT429
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Linear Models and Their Applications
Mathematics and Statistics
SC - Faculty of Science
Subject
STAT - Statistics
Description
Multiple linear regression model, parameter estimation, simultaneous confidence intervals and general linear hypothesis testing. Residual analysis and outliers. Model selection: best regression, stepwise regression algorithms. Transformation of variables and non-linear regression. Applications to forecasting. Variable selection in high-dimensional data using linear regression. Computer analysis of practical real world data.
Prerequisite(s): Statistics 323 or Data Science 305; and Mathematics 211 or 213.
Prerequisite(s): Statistics 323 or Data Science 305; and Mathematics 211 or 213.
Course Attributes
Fee Rate Group(Domestic) - A, Fee Rate Group(International) -A, GFC Hours (3-1T)
Courses may consist of a Lecture, Lab, Tutorial, and/or Seminar. Students will be required to register in each component that is required for the course as indicated in the schedule of classes. Practicums, internships or other experiential learning modalities are typically indicated as a Lab component.
Component
LEC
Component
TUT
Units
3
Repeat for Credit
No
Subject code
STAT
Understanding Course Information
Please refer to Course Terminology and Description to better understand how to interpret course information such as GFC Hours, pre-requisites, course levels, etc.