Course syllabus
MVE155 / MSG200 Statistical inference (7.5 hp) 2022
Course is offered by the department of Mathematical Sciences
Teacher
Course purpose
"Statistical Inference" is a second course in mathematical statistics suitable for students with different backgrounds. A main prerequisite is an introductory course in probability and statistics. The course gives a deeper understanding of some traditional topics in mathematical statistics such as methods based on likelihood, aspects of experimental design, non-parametric testing, analysis of variance, introduction to Bayesian inference, chi-squared tests, multiple regression.
Install Rstudio
Step 1: install R from http://ftp.acc.umu.se/mirror/CRAN/
Step 2: install RStudio from https://rstudio.com/products/rstudio/download/
No previous knowledge of programming is required.
Schedule
Zoom link: https://chalmers.zoom.us/j/67125508615 Passcode 971816
Recorded Zoom sessions can be found under the option "Modules".
| Date and place | Description of sessions | Chapters in Compendium |
|
Mon 17/01, 13.15-15.00, Vasa B |
Introductory lecture | Chapter 1 |
|
Tue 18/01, 13.15-15.00, KC |
Lecture 1: Random sampling Slides1.pdf | Chapters 2-3 |
|
Wed 19/01, 13.15-15.00, HC3 |
Exercise 1: 3.7.1, 3.7.2, R session Simple random sampling.R | |
|
Fri 21/01, 13.15-15.00, KA |
Lecture 2: Stratified samples Slides2.pdf. Parametric models Slides3.pdf |
Chapters 3-4 |
| Mon 24/01, 13.15-15.00, KC | Exercise 2: 3.7.6, 3.7.7, 3.7.8, 4.8.1, R solution of 3.7.7 | |
| Tue 25/01, 13.15-15.00, KC |
Lecture 3: Maximum likelihood Slides4.pdf. Exact confidence intervals |
Chapter 4 |
| Wed 26/01, 13.15-15.00, KA | Exercise 3: 4.8.3, 4.8.5, 4.8.6, 4.8.7 | |
| Fri 28/01, 13.15-15.00, KA | Lecture 4: Hypothesis testing Slides5.pdf, Slides6.pdf. R session Multinomial and Chi2 .R | Chapter 5 |
| Mon 31/01, 13.15-15.00, KA | Exercise 4: Power of the test 5.8.1, Likelihood ratio test 5.8.3, Testing with confidence intervals 5.8.6, 5.8.7 | |
| Tue 01/02, 13.15-15.00, KC |
Lecture 5: Bayesian inference Slides7.pdf. |
Chapter 6 |
| Wed 02/02, 13.15-15.00, KA | Exercise 5: R session Bayes.R, 6.5.2, 6.5.3 | |
| Fri 04/02, 13.15-15.00, KA | Lecture 6: Bayesian inference Slides8.pdf. | Chapter 6 |
| Mon 07/02, 13.15-15.00, Pascal | Exercise 6: 5.8.2, 6.5.4, 6.5.5, 6.5.6, R session Summarising Data.R | |
| Fri 11/02, 13.15-15.00, KB |
Lecture 7: Empirical distribution, QQ-plot Slides9.pdf. |
Chapter 7 |
| Mon 14/02, 13.15-15.00, Pascal |
Exercise 7: 7.7.1, 7.7.2, 7.7.3, 7.7.4, 7.7.5, 7.7.6. R session ExercisesCh7.R |
|
| Tue 15/02, 13.15-15.00, Pascal | Lecture 8: Comparing two populations Slides10.pdf, Slides11.pdf | Chapter 8 |
| Wed 16/02, 13.15-15.00, Pascal | Exercise 8: 8.6.2, 8.6.5, 8.6.6, 8.6.9 | |
| Fri 18/02, 13.15-15.00, KA | Lecture 9: ANOVA1 Slides11.pdf, Slides12.pdf | Chapter 8-9 |
| Mon 21/02, 13.15-15.00, Pascal | Exercise 9: R session t-test and ANOVA.R . An example of a final exam | |
| Tue 22/02, 13.15-15.00, Pascal | Lecture 10: ANOVA2 Slides13.pdf | Chapter 9 |
| Wed 23/02, 13.15-15.00, Pascal | Exercise 10: 9.8.1, 9.8.2, 9.8.3, 9.8.4 | |
| Fri 25/02, 13.15-15.00, KA |
Lecture 11: Nonparametric tests Slides14.pdf. R session Nonparametric tests.R |
Chapter 9-10 |
| Mon 28/02, 13.15-15.00, Pascal | Exercise 11: 8.6.3, 8.6.7, 9.8.5, 9.8.6, 9.8.7 | |
| Tue 01/03, 13.15-15.00, Pascal |
Lecture 12: Categorical data Slides15.pdf |
Chapter 10 |
| Wed 02/03, 13.15-15.00, Pascal |
Exercise 12: 10.5.3, 10.5.6, 10.5.7, 10.5.9. McNemar's test |
|
| Fri 04/03, 13.15-15.00, KA | Lecture 13: Simple linear regression Slides16.pdf | Chapter 11 |
| Mon 07/03, 13.15-15.00, Pascal |
Exercise 13: 11.6.4, 11.6.5. R session Regression.R |
|
| Tue 08/03, 13.15-15.00, Pascal | Lecture 14: Multiple regression Slides17.pdf | Chapter 11 |
| Wed 09/03, 13.15-15.00, Pascal | Exercise 14: 11.6.3, 11.6.6, 11.6.7, 14.1.1, 14.1.14, 14.1.21, from old exam | Chapter 14 |
| Fri 11/03, 13.15-15.00, KA |
Course summary. List of statistical tests Answering students' questions. |
Chapters 1-14 |
| 15/03, 14.00-18.00 | Exam 1 (register before 25.02.2022) | |
| 09/06, 8.30-12.30 | Exam 2 (register before 19.05.2022) | |
| 16/08, 14.00-18.00 | Exam 3 (register before ) |
Course literature
The course is build around the Compendium - click and download. The compendium may undergo minor updates - on the first page you will see when it was last updated.
Recommended additional textbook: Mathematical statistics and data analysis, 3rd edition (2nd edition is also OK), by John Rice (Cremona).
Learning objectives and syllabus
Learning objectives:
- summarize multiple sample data in a meaningful and informative way,
- recognize several basic types of statistical problems corresponding to various sampling designs,
- estimate relevant parameters and perform appropriate statistical tests for multiple sample data sets.
Link to the syllabus on Studieportalen: Study plan
Examination form
The grading of the course is based on a written examination. Preparing for the final exam, check Section 12.1 of the Compendium to see the list of the topics that may be addressed by the final exam questions.
You are allowed to use your own course summary (four A4 pages) during the final exam. Importantly, this summary should not be produced by copying and pasting of different parts of the compendium.
Several old exams with solutions are given in the module "Old exams".
Maximal number of points for the final exam is 30. Passing limits
- CTH students: 12 points for '3', 18 points for '4', 24 points for '5'
- GU students: 12 points for 'G', 20 points for 'VG'