TMA4267 Linear Statistical Models

TMA4267 Linear Statistical Models

(Spring 2023)

Messages:

10.08.23: Solutions for the re-sit exam are here.
09.06.23: I have finished grading the TMA4267 exam. The grade frequency:
A: 37%
B: 24%
C: 28%
D: 5%
E: 3%
F: 3%
24.05.23: Solutions for the today's exam are here.
10.05.23: Permitted examination support material: C:
– Tabeller og formler i statistikk, Tapir forlag,
– K.Rottman. Matematisk formelsamling,
– Stamped yellow A4 sheet with your own handwritten notes,
– Calculator: HP30S, Citizen SR-270X, Citizen SR-270X College or Casio fx-82ES PLUS.
21.04.23: There will be no lecture on Tuesday 25.04.23. Work with the obligatory project.
11.04.23: Dear students! The Faculty of Information Technology and Electrical Engineering (IE) is sending out a questionnaire-based student evaluation for courses taught at IE. Please help us to improve our teaching by answering this survey in the course TMA4267 Linear Statistical Models https://nettskjema.no/a/333937 The survey closes on May 5. (You answer anonymously, but need to log in with Feide for safety reasons.)
28.03.23: There will be no lecture on Friday, March 31.
22.03.23: There will be no lecture on Friday, March 24.
15.02.23: Compulsory assignments are supposed to be submitted on Blackboard.
13.02.23: Compulsory assignments are mandatory. 1-st and 2-nd have to be done individually, 3-rd can be done in groups of two or three.
28.01.23: There are still no volunteers for the reference group. Please be more active.
21.01.23: We have to establish a reference group. Request for volunteers to send a message to the lecturer.
14.01.23: Sections "Progress" and "Plan" added below.
12.01.23: Deadlines for deliverig assignments:
Compulsory assignment 1: February 19th
Compulsory assignment 2: March 19th
Compulsory assignment 3: April 30th.
04.01.23: First lecture is 10.01.2023.
04.01.23: Exercises start in week 3.
02.01.23: Lecture records from the previous year are posted below.
24.12.22: Welcome to the home page of TMA4267 Linear Statistical Models.

Teaching material

From the book Applied multivariate statistical analysis, 4th edition (2015) by Härdle and Simar (HS)
From the book Regression: models, methods and applications (2013) by Fahrmeir, Kneib, Lang and Marx (FKLM)
Note: Design of experiments by Tyssedal (T)
Note: Multiple hypothesis testing by Halle, Bakke and Langaas (HBL)

Statistical software: R

Here are some links concerning R
https://www.r-project.org/about.html
https://www.r-project.org
https://rstudio.com
https://link.springer.com/book/10.1007/978-0-387-79054-1

Lecturer   
Nikolai Ushakov http://www.math.ntnu.no/~ushakov

Teaching assistant   
Håkon Gryvill (hakon.gryvill@ntnu.no)

Reference group   
Celine Natalie Badin Olsson (cnolsson@stud.ntnu.no)
Marcus Nilsen Hjørund (marcushj@stud.ntnu.no)
Max Karl Stafne Pfisterer (mkpfiste@stud.ntnu.no)

Lectures:
Tuesday 14:15-16:00 K5
Friday 12:15-14:00 EL2

Exercises:
Thursday 14:15-16:00 KJL2

Week 3: Recommended exercise 1. Solutions.
Week 4: Recommended exercise 2. Solutions.
Week 5: Recommended exercise 3. Solutions.
Week 6: Recommended exercise 4. Solutions.
Week 7: Compulsory assignment 1.
Week 8: Recommended exercise 5. Solutions.
Week 9: Recommended exercise 6. Solutions.
Week 11: Compulsory assignment 2.
Week 12: Recommended exercise 7. Solutions.
Week 13: Recommended exercise 8. Solutions.
Week 15: Recommended exercise 9-10. Solutions.
Compulsory assignment 3.

Progress:

  • Lecture 1 (10.01.2023): Introduction to multivariate distributions (HS 4.1).
  • Lecture 2 (13.01.2023): Multivariate expectations and moments (HS 4.2 using HS 2.1-2.3)
  • Lecture 3 (17.01.2023): Transformations (HS 4.3, 4.4), PCA (HS 11.1-11.3).
  • Lecture 4 (20.01.2023): Charactestic functions (HS 4.2). Multivariate normal distribution (HS 4.4, 5.1).
  • Lecture 5 (24.01.2023): Multivariate normal distribution (HS 4.4, 5.1).
  • Lecture 6 (27.01.2023): Multivariate normal distribution (HS 4.4, 5.1).
  • Lecture 7 (31.01.2023): Multivariate normal distribution (HS 4.4, 5.1).
  • Lecture 8 (03.02.2023): Estimation in the multivariate normal distribution (HS 3.3, 4.5).
  • Lecture 9 (07.02.2023): Quadratic forms and idempotent matrices (FKLM Appendix B, Th. B2, B8).
  • Lecture 10 (10.02.2023) Multiple linear regression: model, parameter estimation (FKLM 3.1, 3.2).
  • Lecture 11 (14.02.2023) Properties of estimators, fitted values, residuals (FKLM 3.2).
  • Lecture 12 (17.02.2023) Multiple linear regression: inference about coefficients. (FKLM 3.3).
  • Lecture 13 (21.02.2023) ANOVA decomposition and coefficient of determination (FKLM 3.2, 3.3).
  • Lecture 14 (24.02.2023) F-test for regression coefficients (a special case). General F-test for regression coefficients (FKLM 3.2, 3.3).
  • Lecture 15 (28.02.2023) General F-test for regression coefficients (FKLM 3.2, 3.3).
  • Lecture 16 (03.03.2023) General F-test for regression coefficients (FKLM 3.2, 3.3).
  • Lecture 17 (07.03.2023) Transformation of data. Model analysis and model selection (FKLM 3.4).
  • Lecture 18 (10.03.2023) Model analysis and model selection (FKLM 3.4).
  • Lecture 19 (14.03.2023) Model selection (FKLM 3.4). Multiple hypothesis testing (HBL).
  • Lecture 20 (17.03.2023) ANOVA (HS 8.1.1).
  • Lecture 21 (21.03.2023) Design of experiment (DOE): two-level factorial design (T).
  • Lecture 22 (28.03.2023) Design of experiment (DOE): two-level factorial design (T).
  • Lecture 23 (14.04.2023) Design of experiment (DOE): two-level factorial design (T).
  • Lecture 24 (18.04.2023) Design of experiment (DOE): two-level factorial design (T). Repetition.
  • Lecture 25 (21.04.2023) Repetition.

    Plan:

  • No lectures.

    Lecture records (2022)
    Lecture 1 Part 1
    Lecture 1 Part 2
    Lecture 2 Part 1
    Lecture 2 Part 2
    Lecture 3 Part 1
    Lecture 3 Part 2
    Lecture 4 Part 1
    Lecture 4 Part 2
    Lecture 5 Part 1
    Lecture 5 Part 2
    Lecture 6 Part 1
    Lecture 6 Part 2
    Lecture 7 Part 1
    Lecture 7 Part 2
    Lecture 8 Part 1
    Lecture 8 Part 2
    Lecture 9 Part 1
    Lecture 9 Part 2
    Lecture 10 Part 1
    Lecture 10 Part 2
    Lecture 11 Part 1
    Lecture 11 Part 2
    Lecture 12 Part 1
    Lecture 12 Part 2
    Lecture 13 Part 1
    Lecture 13 Part 2
    Lecture 14 Part 1
    Lecture 14 Part 2
    Lecture 15 Part 1
    Lecture 15 Part 2
    Lecture 16 Part 1
    Lecture 16 Part 2
    Lecture 17 Part 1
    Lecture 17 Part 2
    Lecture 18 Part 1
    Lecture 18 Part 2
    Lecture 19 Part 1
    Lecture 19 Part 2
    Lecture 20 Part 1
    Lecture 20 Part 2
    Lecture 21 Part 1
    Lecture 21 Part 2
    Lecture 22 Part 1
    Lecture 22 Part 2
    Lecture 23 Part 1
    Lecture 23 Part 2
    Lecture 24 Part 1
    Lecture 24 Part 2
    Lecture 25 Part 1
    Lecture 25 Part 2
    Pensum - Syllabus

    HS: 2.1-2.5 (pp. 53-68), 3.3 (pp. 89-93), 4.1-4.5 (pp. 118-143), 5.1 (pp.183-188), 8.1.1 (pp. 255-259), 11.1 (pp. 320-324).
    FKLM: 3.1 (pp. 73-86), 3.2 (pp. 104-125), 3.3 (pp. 125-139), 3.4 (pp. 139-150), 3.5 (pp. 169, 171-174), B2 (Theorem B.2(8)), B3 (Theorem B.8).
    HBL: 3 (pp. 3-4), 5 (pp. 4-6).
    T: pp. 1-7, 9-13, 15-16, 20-21, 24-25.
    Exam

    4 hour written exam
    Permitted examination support material: C:
    – Tabeller og formler i statistikk, Tapir forlag,
    – K.Rottman. Matematisk formelsamling,
    – Stamped yellow A4 sheet with your own handwritten notes,
    – Calculator: HP30S, Citizen SR-270X, Citizen SR-270X College or Casio fx-82ES PLUS.
    Table of previous exams:
     
    Exam Problems Solutions
    June 2019 pdf pdf
    May 2018 pdf pdf
    May 2017 pdf pdf
    June 2016 pdf pdf
    May 2015 pdf pdf
    May 2014 pdf pdf
    August 2014 pdf pdf