STAT3301
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STAT 3301 - Regression and Statistical Computing (4 Cr.)
Statistics, School of (10991)
TCLA - College of Liberal Arts
Course description
This is a second course in statistics for students that have completed a calculus-based introductory course. Students will learn to analyze data with the multiple linear regression model. This will include inference, diagnostics, validation, transformations, and model selection.
Students will also design and perform Monte Carlo simulation studies to improve their understanding of statistical concepts like coverage probability, Type I error probability, and power. This will allow students to understand the impacts of model misspecification and the quality of approximate inference.
prereq: Stat 3021 and (CSci 1113 or CSci 1133), and co-requisite (CSci 2033 or Math 2142 or Math 2243 or Math 2373 or Math 2574H or Math 2471)
Students will also design and perform Monte Carlo simulation studies to improve their understanding of statistical concepts like coverage probability, Type I error probability, and power. This will allow students to understand the impacts of model misspecification and the quality of approximate inference.
prereq: Stat 3021 and (CSci 1113 or CSci 1133), and co-requisite (CSci 2033 or Math 2142 or Math 2243 or Math 2373 or Math 2574H or Math 2471)
Minimum credits
4
Maximum credits
4
Is this course repeatable?
No
Grading basis
A-F - A-F Grade Basis
Discussion
Lecture
Requirements
010735
Fulfills the writing intensive requirement?
No
Typically offered term(s)
Every Fall & Spring