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Lecture Tue 13.15-15 in MVF32 and Thur 13.15-15 in MVH11.
Office hours Fri 13.15-15.00 in my office
Thursday lecture = review lecture, and we'll go over the old final.
Friday lecture = in-class presentations. Please prepare 4-6 slides (just print them out on paper. State the problem and describe the data. Show figures and preliminary results. Any complications/challenges? Analysis plan - be specific to get the best feedback.
Final is Dec 16. Project due date after the break - specific date TBA.

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 43 0, 1, 2:1-6 Introduction. Simple linear regression, diagnostics. 44 4, 5:1-4, 6:1-2, 8:1-3 Multiple regression, diagnostics and testing. 45 14, 23, 9:1 + notes Dummy variables, ANCOVA, Model selection. 46 11, 15 + notes Model Selection criteria 47 26 + notes Bootstrap. Cross-validation. 48 16:1-4, 17 Regularized regression 49 9:2-3, 9:5, 18 Weighted Least Squares, Non-linear models, Generalized linear models. In-class presentations 50 - Dec 16 Final Exam

Lecture Notes
 Week Notes and handouts 43 Lecture 1 , Lecture 2 , Demo 1 44 Lecture 3 , Lecture 4 45 Lecture 5 , ldldemo.r Run demo - read the comments in the file, ldldata The data file Stepwise backward model selection to try on the LDL data: add this line of code to the ldldemo.r file: print(step(mm1b)). This eliminates one variable at a time until a criterion AIC is minimized, then it stops. We will get to AIC next lecture, but for now think about it as something similar to doing a backward F-test. 46 Lecture 7 - recap , Lecture 7 - model selection. 47 Lecture 9a - indicator variables, Lecture 9b - crossvalidation 48 Lecture 11 - CART , CART demo on cars data , cars data for CART demo 49 Lecture 13 - regularized regression , demo code , the cars data

Labs and Exercises
 Week Labs and data links 43 Lab 1 R-tutorial. No report due. 44 Lab 2 Least Squares part 1. Report due Nov 16. 45 46 47 Lab 3 Least Squares part 2. Report due Dec 7. 48 49
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 16. 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.