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Applied Regression Analysis

605

An introduction to linear regressive theory, least-squares methods for model fitting, hypothesis testing, estimation, prediction.

Text: 

An Introduction to Statistical Learning: with Applications in R, James, et al, Springer, 2017.

Prerequisite: 
Credit Hours: 
3

TOPICS:

A mathematical introduction to statistical regression analysis - linear models, logistic regression and more advanced topics - using the software R.

- Linear regression
- Classification
- Resampling methods
- Linear model selection and regularization
- Moving beyond linearity
- Tree-based methods
- Selected advanced topics as time allows

(Talata 2018 )

Frequency: 
Even Fall Semesters Only

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Using Math

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