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Seminar for Statistics
 
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Regression

Lecturer: Prof. Nicolai Meinshausen
Lectures: Wed 10-12 HG E 41
Fri 13-15 HG E 41
Fri 13-15 HG E 19
see "Schedule" section below
Assistants: Christopher Nowzohour
   

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:

Schedule / Exercises:

Wednesdays there will be lectures every week (exceptions will be announced). Fridays will alternate between lectures and exercise sessions. The first exercise session is on February 21 and will be an introduction to the statistical programming language R. All exercises take place in computer rooms, where you can solve the R questions and ask questions. You can also bring your own laptop if you prefer. Please check this course website regularly for announcements regarding the schedule.

Text:

Additional material:

An Introduction to R

R reference card by Tom Short

R-Scripts, Outputs, and Slides:

admissions.R

admission.pdf

admission_fit.pdf

admission_output.pdf

brainsize.R

brainsize.txt

father.R

father.pdf

qqexamples.R

qqexamples.pdf

qqplots.R

qqplots.pdf

qqplot_examples_smallsample.pdf

qqplots_FALSE.pdf

qqplots_TRUE.pdf

tukeyplots.R

tuk1.pdf

tuk2.pdf

tuk3.pdf

xplots.pdf

xplots_FALSE.pdf

xplots_TRUE.pdf

Slides from April 11 (Summary of Linear Regression)

appliedExample.R

applications.pdf

Exam

Here you can find a copy of the lecture notes with everything that was covered during the course marked in blue (but without the additional material covered during the lectures).

Alternative texts:

 

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© 2016 Mathematics Department | Imprint | Disclaimer | 28 May 2014
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