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Applied Regression Analysis

605

An introduction to regression analysis and statistical learning with an emphasis on mathematical understanding and its software implementation. Programming uses Python.

Text: 

The Elements of Statistical Learning, Hastie, Tibshirani and Friedman, Springer, corrected 12th printing, 2017.

Prerequisite: 
Credit Hours: 
3

TOPICS:

  • Linear regression: parameter estimation, confidence ellipsoids and prediction intervals, hypothesis tests.
  • Classification: logistic regression, linear discriminant analysis.
  • Basis expansion: polynomial regression, regression splines.
  • Resampling methods: cross-validation, bootstrap.
  • Shrinkage methods.
  • Model selection: information criteria, forward and backward selection, lasso.
  • Decision trees and random forests - bagging, boosting.

(Talata 2020 )

 

Frequency: 
Even Fall Semesters Only

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