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MATH 5358: Regression analysis
Spring 2018

Table of Contents


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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    
Week2 1/22 R tutorial    
Week2 1/24 R markdown    
Week3 1/29 Simple linear regression   HW 1 - Due 2/5
Week3 1/31 Inferences about the slope and the intercept    
Week4 2/5 Confidence and prediction intervals   HW 2 - Due 2/12
Week4 2/7 Analysis of variance    
Week5 2/12 Dummy variable regression   HW 3 - Due 2/19
Week5 2/14 Regression diagnostics    
Week6 2/19 Transformation of variables   HW 4 - Due 2/26
Week6 2/21 Weighted least squares    
Week7 2/26 Polynomial regression    
Week7 2/28 Midterm 1    
Week8 3/5 Multiple linear regression   HW 5 - Due 3/12
Week8 3/7 Analysis of covariance    
Week9 3/12 Spring vacation    
Week9 3/14 Spring vacation    
Week10 3/19 Regression diagnostics for multiple regression   HW 6 - Due 3/26
Week10 3/21 Transformations    
Week11 3/26 Multicollinearity   HW 7 - Due 4/2
Week11 3/28 Variable selection    
Week12 4/2 Assessing the predictive ability of regression models    
Week12 4/4 Midterm 2    
Week13 4/9 Autocorrelation   HW 8 - Due 4/16
Week13 4/11 Generalized least squares estimation    
Week14 4/16 Logistic function and odds   HW 9 - Due 4/23
Week14 4/18 Logistic regression with a single predictor    
Week15 4/23 Binary logistic regression   Final HW - Due 5/7
Week15 4/25 Random effects model    
Week16 4/30 Mixed effect models    
Week16 5/2 Review    

Resources

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