STAT 6120
Linear Models
Course Description
Prerequisites
Graduate standing in Statistics, or instructor permission
Course develops fundamental methodology to regression and linear-models analysis in general. Topics include model fitting and inference, partial and sequential testing, variable selection, transformations, diagnostics for influential observations, multicollinearity, and regression in nonstandard settings. Conceptual discussion in lectures is supplemented withhands-on practice in applied data-analysis tasks using SAS or R statistical software.Prerequisite: Graduate standing in Statistics, or instructor permission.
Instructors
Lingxiao Wang
Fall 2026
tuth 9:30am - 10:45am
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Rating
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Difficulty
3.56
GPA
To Announced
Fall 2023
TuTh 3:30pm - 4:45pm
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Rating
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Difficulty
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GPA
Shan Yu
Fall 2022
MoWeFr 11:00am - 11:50am
2.7
Rating
3.0
Difficulty
3.58
GPA
Tianxi Li
Fall 2020
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Rating
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Difficulty
3.54
GPA
Tingting Zhang
Fall 2017
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Rating
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Difficulty
3.52
GPA
Faculty Staff
Fall 2015
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Rating
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Difficulty
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GPA
Jeffrey Holt
Fall 2012
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Rating
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Difficulty
3.72
GPA
Dan Spitzner
Fall 2010
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Rating
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Difficulty
3.56
GPA