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Thanks for a great läsperiod! I had fun - hope you guys did too. The last demos and lecture notes are posted below.
New due date for the final project reports is January 8!
Office hours next week, Mon-Tue 13.15-15.00 in my office

Exam: Dec 17 8.30-13.30 in MVF32. Open book, open notes.
Written component: 8.30-11.30. The format will be discussion problems (example: I give you a partial analysis result, some diagnostic plots etc, and ask you to comment on the fit and propose the next step of analysis).
Presentations: 11.30-13.30. You will present your project (as much as you have done so far). The presentation will consist of 6 slides (hard copy) where I suggest; Slide 1: Data description; Slide 2: Problem setup - define the question(s) you intend to solve and list the selected methods you have used and why; Slide 3: "Issues" with the data - e.g. missing values, outliers, small data set, etc; Slide 4-5: Main results - key results; Slide 6: Open questions, future work, unresolved issues - here is where you get a chance to get feedback from me and the other students.
Email me if you have any questions regarding the exam. Both the written component and the presentation are compulsory.
Examiner and lecturer
Rebecka Jörnsten (jornsten@chalmers.se)

Course literature
Main text book
Draper and Smith, Applied Regression Analysis

Other texts
Lecture notes

Syllabus
(pdf file)

Preliminary Course Plan

Lectures
 Week Chapter Contents 44 0, 1, 2:1-6 Introduction. Simple linear regression, diagnostics. 45 4, 5:1-4, 6:1-2, 8:1-3 Multiple regression, diagnostics and testing. 46 14, 23, 9:1 + notes Dummy variables, ANCOVA, Model selection. 47 11, 15 + notes Model Selection criteria 48 26 + notes Bootstrap. Cross-validation. 49 16:1-4, 17 Regularized regression 50 9:2-3, 9:5, 18 Weighted Least Squares, Non-linear models, Generalized linear models. In-class presentations 51 - Dec 17 Final Exam

Lecture Notes
 Week Notes and handouts 44 Lecture 1 Lecture 2 45 Lecture 3 Lecture 4 Demo from class. 46 Lecture 5 . Pollution demo from class. Please note you have to unblock several commands to run this yourself, e.g. to read the data into R. Read the demo file before running it. The pollution data. 47 Model selection code, The code that runs model selection several times. cars2.txt The cars data. carsdata1.q Demo part 1 carsdata2.q The interaction demo code.. 48 Cross validation 49 CART and GLM wine2.txt The wine data. wiine.q Demo part 1, CARTwine CART on the wine data. SAheart_data.txt The heart disease data set. sbpstart.q Demo part 1, ModSelsbp.q Model selection , ModSelsbprepeat.q Repeated Model selection on random splits , CARTsbpclass CART and GLM on the heart disease data. CARTpoll CART on the pollution data. 50 Regularized regression, I Demo Cross-validation. This is CV run K times on random splits of the data, B-fold CV., Updated code for model selection on the heart disease data, now including lasso.

Labs and Exercises
 Week Labs and data links 44 Lab 1 R-tutorial. No report due. 45 Lab 2 . New due date Nov 17. 46 47 Lab 3 . Due date Nov 27. 48 49 50
Examination
To pass this course you must hand in satisfactory lab reports, complete and present a data analysis project, and pass a final exam (details - see Syllabus ).

The exam takes place on Dec 17. Room: To be announced.
The exam is open-book and open notes.
Bring ID and receipt for your student union fee

You will be notified the result of your exam by email from LADOK (This is done automatically as soon as the exams have been marked an the results are registered..)
The exams will then be kept at the students' office in the Mathematical Sciences building.
Check that the number of points and your grade given on the exam and registered in LADOK coincide.
Complaints of the marking should be written and handed in at the office. There is a form you can use, ask the person in the office.).

The following link will tell you all about the examination room rules at Chalmers: Examination room instructions

Computer labs
Please see the Syllabus for instructions on how to write a lab report.