Latest news
Old exams we will discuss next week: Dec 2012 Dec 2011, April 2011

No class this Friday (11/12). Instead, I will office hours January 11th and 12th, 10am-3pm, before the exam and project deadline.

Lab 3 is posted. Prediction and report due December 17th instead of this week. So no MiniAnalysis 3.
Project report is due January 14th (day of the final).
The schedule for the course can be found via the link to webTimeEdit top of the page.

Teachers
Course coordinator: Rebecka Jornsten
Office Hours: Tue, 10-11, Thur 15-16 in MVH3029.
Course litterature

J.O. Rawlings, S.G. Pantula, D.A. Dickey. Applied Regression Analysis.
Lecture notes
You will use the statistical package R for the lab and project work.

Programme
Syllabus
(pdf file)

Preliminary Course Outline
Week Chapter
Topic
45
1:1-7,9, 11, 12:1-4
Basics, Simple linear model, Diagnostics, Matrix formulation
46
2,3,4,(6), 11, 12, 13.1
Multiple regression. Diagnostics and testing
47
9+notes
ANOVA,ANCOVA, Model selection
48
7,13+notes
Model selection
49
notes, 7
Bootstrap, Cross-validation
50
13+notes
Regularized regression
51
8,10,12,15
WLS, NLM, GLM
In-class presentations, Old Exams

Lecture notes, Labs and MiniAnalyses
Week Material
Labs and MiniAnalysis
45
Lecture 1 , Lecture 1 R code , The television data
Demo1 , Demo 1b , The chocolate data

MiniAnalysis1
Due Nov 6.

46
Lecture 4 , Lecture 4 R code

For those curious about Sweave, here is the Rnw-file for Lecture 4.

Lab1
Due Nov 13, Housing data.

Lab report: Here's a LaTeX template but you can use any editor you want. Check the pdf-file for information about report writing.

47
Lecture 7 , Lecture 7 R code , Cars data.
MiniAnalysis2
Due Nov 20.

The file contains code from Lecture 7, with additional commenting. The task is to analyze the Housing data from Lab 1 with this code. Which model is selected? Does it matter if you use 90 or 45 random observations to train on? Does backward model selection result in a different model that selection via prediction MSE?

48
Lecture 10 , Lecture 10 R code Lab2

myCV.R cross-validation function, mySel.R model selection function, SplitSel.R Function that repeats model selection many times the Crime data

Due Nov 27

49
Lecture 13 , Lecture 13 R code Project proposal due Dec 1
MiniAnalysis3 , Wine data
Due Dec 4
50
Lecture 16 , Lecture 16 R code

Lab3 , PSA training data , PSA test data
Due Dec 17
51

Assignments

Labs and MiniAnalysis 10% (mandatory!), Project 30% and Final 60%
Examination

Written final + Final Project
Old Exams

I will post old exams here and we will spend the last week of classes going through them.
Examination procedures
In Chalmers Student Portal you can read about when exams are given and what rules apply on exams at Chalmers.
At the link Scedule you can find when exams are given for courses at University of Gothenburg.
At the exam, you should be able to show valid identification.
Before the exam, it is important that you report that you want to take the examination. If you study at Chalmers, you will do this by the Chalmers Student Portal, and if you study at University of Gothenburg, so sign up via GU's Student Portal.

You can see your results in Ladok by logging on to the Student portal.

At the annual examination:
When it is practical a separate review is arranged. The date of the review will be announced here on the course website. Anyone who can not participate in the review may thereafter retrieve and review their exam on Mathematical sciences study expedition, Monday through Friday, from 9:00 to 13:00. Check that you have the right grades and score. Any complaints about the marking must be submitted in writing at the office, where there is a form to fill out.

At re-examination:
Exams are reviewed and picked up at the Mathematical sciences study expedition, Monday through Friday, from 9:00 to 13:00. Any complaints about the marking must be submitted in writing at the office, where there is a form to fill out.