MATH 423: Applied Regression

Fall 2020

Class Information

Syllabus

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Textbook

Preliminaries

Lecture Notes

Lecture Schedule

Date Topics Notes Reading Assignments
Lecture 1 Optimal prediction Part I Ch 1
Lecture 2 Regression function Part I Ch 2.1 2.2
Lecture 3 KNN Part I -
Lecture 4 Optimal linear prediction Part I -
Lecture 5 Plug-in estimation Part I -
Lecture 6 SLR assumptions Part I -
Lecture 7 SLR simulation Part I -
Lecture 8 Least squares estimator Part I -
Lecture 9 Statistical properties of LS Part I -
Lecture 10 Estimation of sigma Part I -
Lecture 11 Confidence intervals for beta Part I Ch 2.4
Lecture 12 Confidence intervals simulation Part I Ch 2.4
Lecture 13 Hypothesis test and CI Part I Ch 2.3
Lecture 14 t-test Part I Ch 2.3
Lecture 15 Permutation test and R squared Part I Ch 2.6
Lecture 16 Permutation test Part I -
Lecture 17 F test Part I -
Lecture 18 Predictive inference Part I Ch 2.5
Lecture 19 Multiple linear regression Part II Ch 3.1
Lecture 20 Estimation of the model parameters Part II Ch 3.2
Lecture 21 MLR t-test Part II Ch 3.3
Lecture 22 MLR F-test Part II Ch 3.3
Lecture 23 MLR confidence intervals Part II Ch 3.4-3.5
Lecture 24 Diagnostics Part II Ch 4
Lecture 25 Model selection Part II Ch 10

Handouts

Optimal prediction -
KNN R code
Plug-in estimation R code
SLR simulation R code
SLR theory -
Least squares in R R code
Linear algebra of LS R code
Statistical properties of LS R code
Estimation of Sigma R code
CI and Hypothesis testing for beta R code
Hypothesis testing theory -
Permutation test R code
Sum of Squares Decomposition -
F test R code
CI for prediction R code
Predictions interval simulation R code
CI theory -
R squared -
Mutiple regression R code
F test in R R code
Diagnostics R code
Transformation R code
Ploynomial factor R code
Factor predictors
Factor predictor example

Links

R Code Examples

  1. An R Markdown Example .Rmd file

  2. Simple Linear Regression .Rmd file

  3. Prediction .Rmd file

  4. Prediction (Simulation) .Rmd file

Exams