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MATH 5358: Regression analysis
Spring 2018

Table of Contents


Announcement

Please attend the lecture on Monday 4/9. I will let you choose a final project.

The midterm exam II has been posted.

Upon installing the R, please download .Rprofile and put it in your HOME directory.

Prior to Lab 3, please install TeX Live for Windows or MacTeX for OS X.

About this course

Welcome to the Regression Analysis course! This course introduces regression models, which relates an outcome to a set of predictors of interest. These models are the most important statistical technique that a data scientist needs to master.

You will learn the theory behind regression models and, through real-world examples, learn to fit, diagnose, and utilize regression models to examine the relationship between predictors and a continuous or discrete outcome, using the statistical software R. You will learn modern approaches on model selection and novel uses of regression models.

Before beginning the class make sure that you have the following:

  • A basic understanding of linear algebra and calculus.
  • A basic understanding of statistical inference.
  • Basic knowledge of the R programming language. This is not required, but a big plus.

Course Syllabus

Course Schedule

The instructor reserves the right to adjust this schedule in any way that serves the educational needs of the students enrolled in this course.

Week Date Topics Reading Notes
Week1 1/17 Introduction MAR 1.1-3 Lecture 1
Week2 1/22 R tutorial   Lab 1, Lab 2
Week2 1/24 R markdown   Lab 3
Week3 1/29 Simple linear regression MAR 2.1 Lecture 2, HW 1 - Due
Week3 1/31 Inference for regression, Analysis of variance MAR 2.2-5, 7 Lab 4, Lab 5
Week4 2/5 Dummy variable regression MAR 2.6 Lecture 3, HW 2 - Due 2/12
Week4 2/7 Regression diagnostics MAR 3.1-3.2.5 Lab 6
Week5 2/12 Transformation of variables MAR 3.2.6-3.3.3 HW 3 - Due 2/19
Week5 2/14 Transformation of variables, weighted least squares   Lecture 4
Week6 2/19 Weighted least squares MAR 4 Lab 7, HW 4 - Due 2/26
Week6 2/21 Weighted least squares / Polynomial regression MAR 5.1 Lecture 5
Week7 2/26 Multiple linear regression MAR 5.2 Lab 8
Week7 2/28 Midterm 1   Midterm I
Week8 3/5 Multiple linear regression MAR 5.2 HW 5 - Due 3/19
Week8 3/7 Analysis of covariance MAR 5.3  
Week9 3/12 Spring vacation    
Week9 3/14 Spring vacation    
Week10 3/19 Regression diagnostics for multiple regression MAR 6.1 Lecture 6, HW 6 - Due 3/26
Week10 3/21 Transformations MAR 6.2-6 Lab 9
Week11 3/26 Multicollinearity   HW 7 - Due 4/2
Week11 3/28 Variable selection MAR 7.1-2 Lecture 7
Week12 4/2 Assessing the predictive ability of regression models MAR 7.3-4 Lab 10
Week12 4/4 Midterm 2   Midterm II (SOLN)
Week13 4/9 Logistic function and odds MAR 8.1 HW 8 - Due 4/16
Week13 4/11 Logistic regression with a single predictor MAR 8.2 Lecture 8
Week14 4/16 Binary logistic regression   HW 9 - Due 4/23
Week14 4/18 Binary logistic regression   Lab 11
Week15 4/23 Correlated error MAR 9.1 Lecture 9, Final HW - Due 5/4
Week15 4/25 Autocorrelation MAR 9.2 Lab 12
Week16 4/30 Presenatation - Fianl project    
Week16 5/2 Presenatation - Fianl project   Final project

Resources

R software

Version control