## Regression

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

• That you learn to perform a regression analysis and interpret the results correctly. We will use the statistical software R to get hands-on experience with this.
• That you learn to interpret and critique regression analyses done by others. This is important because regression analysis is one of the most widely used statistical methods.

### 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 building

Robust regression

### R Code:

Week 1:
Introduction

Examining Data

Weeks 2-4:

Linear Regression

Ozone Data Set

Weeks 5-8:

Categorical predictors

Weeks 9-13:

Diagnostics

Model building

Logistic regression

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:

• John Fox (1997), "Applied Regression Analysis, Linear Models, and Related Methods", Sage Publications. (Intuitive examples, not very mathematical.)
• Sanford Weisberg (2005), "Applied Linear Regression", 3rd edition, Wiley. (Similar as the one by Fox but shorter.)
• Paul D. Allison (1999), "Multiple linear regression, a primer", Thousand Oaks. (Brief, good for interpretations, not very mathematical.)
• Peter Dalgaard (2002), "Introductory Statistics with R", Springer. (Introduction based on the software R.)

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