STSCI 4030
Last Updated
- Schedule of Classes - January 19, 2016 6:14PM EST
- Course Catalog - January 19, 2016 6:21PM EST
Classes
STSCI 4030
Course Description
Course information provided by the Courses of Study 2015-2016.
The focus of this course is the theory and application of the general linear model expressed in its matrix form. Topics will include: least squares estimation, multiple linear regression, coding for categorical predictors, residual diagnostics, anova decomposition, polynomial regression, model selection techniques, random effects and mixed models, maximum likelihood estimation and distributional theory assuming normal errors. Homework assignments will involve computation using the R statistical package.
When Offered Fall.
Prerequisites/Corequisites Prerequisites: A two-semester sequence on statistical methods (e.g. BTRY 3010-BTRY 3020), a course on probability and distribution theory (e.g. BTRY 3080 or MATH 4710), multivariable calculus, and linear/matrix algebra.
Outcomes
- Students will be able to discuss the mathematical foundations of linear statistical models using matrix algebra.
- Students will be able to use diagnostic measures to assess the validity of a given statistical model.
- Students will be able to analyze data involving both fixed and random factors.
Regular Academic Session. Choose one lecture and one laboratory. Combined with: BTRY 4030
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Credits and Grading Basis
4 Credits Stdnt Opt(Student Option)
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Class Number & Section Details
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Meeting Pattern
- MW Phillips Hall 101
Instructors
Booth, J
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Additional Information
A two-semester sequence on statistical methods (e.g. BTRY 3010-BTRY 3020), a course on probability and distribution theory (e.g. BTRY 3080 or Math 4710), multivariable calculus, and linear/matrix algebra.
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Class Number & Section Details
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Meeting Pattern
- M Mann Library B30A
Instructors
Booth, J
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Class Number & Section Details
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Meeting Pattern
- W Mann Library B30A
Instructors
Booth, J
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Class Number & Section Details
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Meeting Pattern
- R Mann Library B30A
Instructors
Booth, J
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