MVE591 Mathematical statistics
Quick links
This page provides detailed weekly plan of the course. Other information, including the assessment, literature and the structure is provided on a separate Course plan (Kurs-PM) page.
Purpose and learning objectives
The course MVE591, Mathematical Statistics gives introduction to probability and statistical theory as well as to modern computational statistical methods provided by Python language. It is intended for 2nd year Engineering students with general mathematical knowledge, no other prerequisites are assumed.
On successful completion of the course the student should be able to
- identify and describe problems arising in technical studies for which the treatment requires use of fundamental concepts and methods from Probability theory and Mathematical Statistics;
- summarise data by deriving summary statistics and use graphical methods for their representation;
- formulate probabillistic models for real-world situations and analyse them with the help of probability tools, e.g., conditional probabulities, random variables, distributions;
- understand the random sampling method to gather representative statistics from a population and master various estimation approached for its main characteristics: the mean and variance;
- be able to check statistical hypotheses on the values of the mean and the variance for one and two samples;
- study linear dependence between two and multiple variates using the tools of regression analysis, analysis of variance and multiple regression.
- Use Python language to achieve the above goals
Lecturer
Examinator: Prof. Sergei Zuyev
Lecturer: Prof. Sergei Zuyev
Teaching Assistants:
Isac Boström isac.bostrom@chalmers.se
Vilgot Jansson vilgotj@chalmers.se
David Lund dlund@chalmers.se
Samuel Winquist samwin@chalmers.se
Student Representatives:
TKMAS linusdalek@gmail.com Linus Albinsson Dalek
TKMAS filip.ambjorn@telia.com Filip Ambjörn
TKMAS Nils@bredin.se Nils Bredin
TKMAS eliasg407@gmail.com Elias Grönlund
TKMAS perhaglund1@gmail.com Per Haglund
TKMAS carl.hoerfelt@gmail.com Carl Hörfelt
TKMAS linus@skeppsprojekt.se Linus Olofsson
Minutes of the Student representatives-Staff meetings
Minutes of 2024-04-22 meeting
Minutes of 2024-05-23 meeting
Programme
The teaching is organised in the form of lectures and assisted computer labs. The course timetable still follows TimeEdit. There are four alternative lab sessions assigned to the course each week, but the students are supposed to participate only at one of these. The booking is done via the VLE system, see Course plan (kurs-PM) page for what is VLE.
Topics covered:
- Probability:
- Probability, events, basic combinatorics.
- Random variables, expectation and variance.
- Main distributions: Binomial, Poisson, Exponential, Normal.
- Central Limit Theorem and Poisson Theorem and their applications.
- Statistics:
- Descriptive statistics. Sample mean and variance.
- Estimates: point and interval, sampling.
- Hypotheses testing: one and two-sample tests.
- Regression and correlation, multiple regression.
Weekly plan
| Week | Day | Content | |
|---|---|---|---|
| 12 | Tuesday | Two Lectures (lecture notes will be posted on VLE, see Course plan for what is VLE) | |
| Thursday | VLE Study 1 | ||
| 13 | Tuesday | VLE Study 2 | |
| 15 | Thursday | VLE Study 3 | |
| 16 | Thursday | VLE Study 4 | |
| 17 | Wednesday | 9-11am Test 1 | |
| Friday | Lecture | ||
| 18 | Monday OR Friday | VLE Study 5 | |
| 19 | Monday | Lecture | |
| Monday | VLE Study 6 | ||
| 20 | Monday | Lecture | |
| Thursday | VLE Study 7 | ||
| 21 | Thursday 23/05 | 2-4pm Test 2 | |
| 22 | Wednesday 29/05 | 9-11am Exam | |
| 34 | Monday 26th of August |
1-3pm Resit examination SB-D080 |
|
| 44 | Wednesday 30th of October | 1-3pm 2nd resit examination Sal A |