preliminary subject to changes

Course Description

An introductory course to applied linear regression analysis.

Topics discussed include, but are not limited to, least-squares regression, analysis of variance, model diagnostics, dichotomous, polytomous, and missing data.

Course Objectives

The course will enable students to analyse data using regression models, diagnose their regression models, and interpret and report the regression results. Students will learn how to use the statistical computing software R.

Course Materials


The course will follow the textbook: Fox, John. Applied regression analysis and generalized linear models, 3rd ed., Sage Publications, 2015.

Available from bookdepository and amazon.

The required readings from this textbook are listed below.

Further recommendations:

Additionally, the following text may be used as a reference: Wooldridge, J. M. “Introductory Econometrics: A Modern Approach, 6th ed., Cengage Learning, (2016).

Available from amazon and bookdepository. Relevant chapters are listed below as [IEMA].

A brief introduction to the software R and RStudio con be found here and a slightly longer version online here.

Course Requirements:

Students must read the corresponding chapters of the textbook before each session. Students need access to a computer with the statistical computing software R. The software is available free of charge from As a graphical frontend RStudio is recommended.

Instructor Information:

Prof. Dr. Dennis A. V. Dittrich

You can always reach me via email. For meetings in my office appointments can be arranged through my webpage at:

Updated information, links to the literature, additional materials, etc. can be found on my webpage as well.

Grading Guidelines:

Grading ComponentWeight
Grading ComponentWeight
Problem Sets35%
Final Examination65%


A typical 3 credit course requires 150 hours of your time. The table below identifies how I expect those 150 hours will be allocated. While you do not receive direct marks for reading, reading will affect your class participation (your ability to participate in class discussions and activities) and your final exam mark.

Class Time (3 hours / week)45 hours
Reading (2 hours / week)30 hours
Problem Sets (2 hour / week)30 hours
Preparation and Review (3 hours / week)45 hours

Weekly Topics and Reading Assignments

Session 1: 7.9.

  • Introduction to course and to regression (Ch. 1, 2)

Session 2: 14.9.

  • Examining and transforming data (Ch. 3, 4)

Session 3: 28.9.

  • Linear least-squares regression (Ch. 5)

    Further recommendations:
    IEMA Chapters 2 and 3

Session 4: 19.10.

  • Statistical inference for regression (Ch. 6)

    Further recommendations:
    IEMA Chapter 4

Session 5: 26.10.

  • Dummy-variable regression and analysis of variance (Ch. 7, 8)

    Further recommendations:
    IEMA Chapter 7

Session 6: 02.11.

Session 7: 09.11.

  • Diagnostics: Unusual and influential data (Ch. 11)

    Further recommendations:
    IEMA Chapter 9

Session 8: 16.11.

  • Diagnostics: Nonlinearity and other ills (Ch. 12)

    Further recommendations:
    IEMA Chapter 8

Session 9: 30.11.

  • Diagnostics: Collinearity and model selection (Ch. 13, 22)

Session 10: 07.12.

  • Logit and probit models for dichotomous data (Ch. 14)

    Further recommendations:
    IEMA Chapter 17

Session 11: 14.12.

  • Logit and probit models for polytomous data (Ch. 14)

Session 12: 18.12.

  • Generalized linear models (Ch. 15)

Session 13: 21.12.

  • Generalized linear models (Ch. 15)

Session 14: 4.01.

  • Missing data (Ch. 20)

Session 15: 11.01.

Final exam (oral)

Topics and reading assignments are subject to changes.