Course content and objectives:
The present course offers an introduction to regression modeling methods with applications. The presentation begins with simple and multiple linear regression models for which fitting, parametric and model inference as well as prediction will be explained.
A special attention will be paid to the diagnostic strategies which are key components of good model fitting. Further topics include transformations and weightings to correct model inadequacies, the multicollinearity issue and shrinkage regression methods, variable selection and model building techniques. Later in the course, some general strategies for regression modeling will be presented with a particular focus on the generalized linear models (GLM) using the examples with binary and count response variables.
To illustrate the influence of electronic computation on regression analysis theory and practice, a number of aspects of computer usage is integrated into the course based on the statistical software package R.
The overall goal of the course is twofold: to acquaint students with the statistical methodology of the regression modeling
and to develop advanced practical skills that are necessary for applying regression analysis to a realworld data analysis problem.
The course is lectured and examined in English.
Recommended prerequisites:
 SF 1901 or equivalent course of the type 'a first course in probability and statistics'.
 Multivariate normal distribution.
 Basic differential and integral calculus, basic linear algebra.
Guest lecturers from If :
Course literature and supplementary reading :
 D. Montgomery, E. Peck, G. Vining: Introduction to Linear Regression Analysis.
WileyInterscience, 5th Edition (2012). ISBN10: 9780470542811. 645 pages. Acronym below: MPV.
The textbook MPV can be bought at THS Kårbokhandel, Drottning Kristinas väg 1519.
There is a number of other books that cover the topics of the course. Here are some recommendations
 G. James, D. Witten, T. Hastie, R. Tibshirani: An introduction to Statistical Learning.Web page for the book by the publisher Springer .
 A. J. Izenman: Modern Multivariate Statistical Techniques. Regression, Classification, and Manifold Learning.Web page for the book by the publisher Springer .
 J.O Rawlings, S.G Pantula, D.A Dickey: Applied Regression Analysis  A Research Tool, Springer, 2ed Edition. Freely available as ebook link .
Preliminary plan of lectures and exercises sessions.
 Lecturers (in alphabetic order) AH=Alexandre Chotard, TK=Timo Koski, (guest lecturers from KTH), TP=Tatjana Pavlenko, FR= Felix Rios. Guest lecturers from If: Guest(If). The addresses of the lecture halls and guiding instructions are found by clicking on the Hall links below
 Problems to be solved during the exercise sessions and recommended exercises to be solved on your own are found here.
Day 
Date 
Time 
Hall 
Topic 
Lecturer 
1. Wed 
18/01 
1315 
E1

Lecture 1: Introduction (the course work and computer projects). Introduction to regression modeling. Simple linear regression: model fitting and inference. Chapter 2 in MPV.

TP 
2.Fri 
20/01 
1012 
M1

Lecture 2: Simple linear regression: inference and prediction. Chapter 2 in MPV.

TP 
3. Mon

23/01

810 
D1 
Exercise 1: Simple regression. Problem solving at the board and applications with R.

FR

4. Thu

26/01 
1517 
F2 
Lecture 3: Multiple linear regression: matrix notations, model fitting and properties of the estimates.
Chapter 3 in MPV.

TP

5. Fri

27/01 
1012 
F2 
Lecture 4: Multiple linear regression: inference and prediction. Chapter 3 in MPV. Project I handout. 
TP

6. Mon

30/01 
0810 
E1 
Exercise 2: Multiple regression. Problem solving at the board and applications with R. 
FR

7. Tue

31/01 
1012 
M1 
Lecture 5: Model adequacy checking. Residual analysis. Chapter 4 in MPV. 
TP

8. Thu

2/02 
1012 
F2 
Lecture 6: Model adequacy checking (cont.). Transformations to correct model model inadequacies.
Chapters 45 in MPV.

TP

9. Fri

3/02 
0810 
M1 
Exercise 3: Model adequacy checking, theoretical exercises and applications with R.

FR

10. Mon

6/02 
0810 
E1

Lecture 7: Methods for detecting influential observations: leverage and measures of influence. Chapter 6 in MPV.

TP

11. Tue

07/02 
1517 
E1 
Lecture 8: Multicollinearity: sources and effects. Chapter 9 in MPV.

TP

12. Fri

10/02 
1012 
D1 
Exercise 4: Diagnostic for leverage, influence and multicollinearity.
Chapter 6 and 9 in MPV. Model diagnostics with R.
 FR

13. Mon
 13/02 
0810 
E1 
Lecture 9: Methods for dealing with multicollinearity. Model respecification: ridge and PCA regression. Chapter 9 in MPV.

TP

14. Tue

14/02 
1517 
F2 
Lecture 10: Variable selection and model building. Chapter 10 in MPV.

TP

15. Wed

15/02 
1315 
M1

Exercise 5: Multicollinearity (Ridge regression, principal component regression (PCR)), Ch. 10:
Variable selection and model building with R. 
FR

16. Fri

17/02 
1315 
E1 
Lecture 11: Variable selection and model building (cont.). Chapter 10 in MPV. Bootstrapping in regression.
Chapter 15.4 in MPV.

TP

17. Mon

20/02 
0810 
D1

Lecture 12: Relation to other methods of multivariate statistical analysis:
Regression and Classification, CART.

AC

18. Wed

22/02 
1315 
F2

Lecture 13: Models with a binary response variable. Introduction to logistic regression.

TK

19. Fri

24/02 
1012 
M1

Lecture 14: Generalized Linear Models (GLM) and exponential families.
GLM modelling of binary response variables using logitlink functions. Project II handout.

Guest (If)

20. Mon

27/02 
1315 
M1

Exercise 6: GLMmodeling of Poisson regression. Hypotheses testing and model validation: Likelihood
ratio test, Deviance and Wald test.

Guest (If)

21. Wed

1/03 
Obs! 10.0012.00 
4V3Ora

Exercise 7: GLMmodeling with R.

Guest (If)

22. Fre

3/03 
1315 
D1

Lecture 15: Discussion on the project II results. If presentation.

Guest (If)

23. Mon

6/03 
0810 
Q1

Lecture 16: Repetition/Reserve.

TP

Tue

14/03 
0813 
L21 m.m. 
Exam 
TP

Fri

8/06 
0813 
TBA m.m. 
Reexam 
TP

