Session 1: Linear and logistic regression

This session will cover the basics of linear and logistic regression. See below for a [#Topics list of topics]. Please make sure to do the readings and let me know in advance if there is terminology that you would like to be clarified. The goal of this session is to go through the basic steps of building linear or logistic regression model, understanding the output, and validating how good this model is.

I've also posted some [#assignments assignments] below. There is only one way to learn how to use the methods we will talk about and that is to apply them yourself to a data set that you understand. The tutorial is intended to get you to the level where you can do that. Make sure to read the assigned readings and, if you have time, you could familiarize yourself with the scripts attached below. They use data sets from the R package languageR.

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Materials

Reading

Baa08

Section 4.3.2 (pp. 91 - 105)

Functional relations: linear regression

Sections 6 - 6.2.4 (pp. 181-212)

Regression Modeling (Introduction and Ordinary Least Squares Regression)

Collinearity, Model criticism, and Validation

Section 6.3 (pp. 214-234)

Generalized Linear Models

Section 6.6 (pp. 258-259)

General considerations

Optional reading:

G&H07

Chapter 3 (pp. 29-49)

Linear regression: the basics

Chapter 4 (pp. 53-74)

Linear regression: before and after fitting the model

G&H07

Chapter 5 (pp. 79-105)

Logistic regression

Q&A

to assume one direction, you could do a one-tailed test (which is less conservative).

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Assignments

Baa08

Section 4.7 (p. 126)

Exercises 3 and 7*

Section 6.7 (p. 260)

Exercise 1, 8

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Possible topics

DenmarkMiniCourseSession1 (last edited 2008-11-24 21:03:23 by 77)

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