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Seminar for Statistics
 
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Regression

Lecturer: Prof. Marloes Maathuis
Lectures: Wed 13-15 HG D 5.2
Fri 13-15 HG D 5.2
Assistants: Patric Müller
Johannes Lederer
   

First Lecture: 22. Februar 2012.

Exercises: Have a look here.

Exam: Have a look here. (This is not a complete list of questions, but should give you a better idea of what type of questions you can expect).

Outline:

In regression analysis, we examine the relationship between a random response variable and several other explanatory variables. In this class, we consider the theory of linear regression with one or more explanatory variables. Moreover, we also study robust methods, generalized linear models, and nonparametric methods. Several numerical examples will illustrate the theory. The main goals for this class are:

Text:

Slides:

Week 1:
Role of statistical models

(Nonparametric) Regression

Examining Data


Weeks 2-4:

Transformations

Linear Regression


Weeks 5-8:

Dummy variable Regression


Weeks 9-13:

Model diagnostics and unusual and influential data

Model building

Robust regression

Logistic regression

R Code:

Week 1:
Introduction

(Nonparametric) Regression

Examining Data


Weeks 2-4:

Transformations

Linear Regression

Ozone Data Set


Weeks 5-8:

Testing and p-values

Categorical predictors

Confidence intervals and bands
fruitfly code


Weeks 9-13:

Diagnostics

Model building

Chicago insurance_redlining

Logistic regression

Coxph

Additional material:

R reference card by Tom Short: http://cran.r-project.org/doc/contrib/Short-refcard.pdf

Cox Proportional-Hazards Regression for Survival Data by John Fox: http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-cox-regression.pdf

Alternative texts:

 

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© 2016 Mathematics Department | Imprint | Disclaimer | 12 June 2012
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