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Informatics and Computing
Description: Matrix-based coverage of linear statistical models for independent data from frequentist and Bayesian perspectives, including regression, analysis of variance (ANOVA), estimation, testing, selection, diagnostics, and associated random variables and probability distributions. Letter grade only.
Units: 3
Sections offered: Fall 2024 Winter 2024 Spring 2025
Prerequisite: Admission to Informatics and Computing Ph.D., STA 270, ((MAT 136, MAT 137) or MAT 131))
Informatics and Computing
Term : Summer 2020
Catalog Year : 2019-2020
INF 511 - Modern Regression I
Description: Matrix-based coverage of linear statistical models for independent data from frequentist and Bayesian perspectives, including regression, analysis of variance (ANOVA), estimation, testing, selection, diagnostics, and associated random variables and probability distributions. Letter grade only.
Units: 3
Sections offered: Fall 2024 Winter 2024 Spring 2025
Prerequisite: Admission to Informatics and Computing Ph.D., STA 270, ((MAT 136, MAT 137) or MAT 131))