Regression

 Lecturer: Prof. Nicolai Meinshausen Lectures: Wed 10-12 HG E 33.1Fri 13-15 HG E 33.1see "Schedule" section below Assistants: Jana Jankova

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.

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 26 and will be an introduction to the statistical programming language R. In the exercise sessions, you can solve the R questions and ask questions. You need to bring your own laptop for solving the R questions. Please check this course website regularly for announcements regarding the schedule.

Text:

R reference card by Tom Short

R-Scripts, Outputs, and Slides:

boston.R

brainsize.R

brainsize.txt

brainsize_sim.R

boston_confidence2.R

janfeb.R

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.)
• T. Hastie, R. Tibshirani, and J. Friedman (2009), "The Elements of Statistical Learning", 2nd edition, Springer.

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