MVE190 / MSG500, MSG501 Statistical learning with regression models Autumn 25
This page contains the program of the course: lectures, exercise sessions and computer labs. Other information, such as learning outcomes, teachers, literature and examination, are in a separate course PM. The course is in-person for what concerns the lectures (no Zoom, no video-recorded lectures). For compulsory mini-analyses presentations it will be possible to connect via zoom or be in the room (see below).
2025_Topics and hence potential exam questions.pdf
Minutes of the mid-course meeting for Statistical Learning 2025.pdf
Program
The schedule of the course is in TimeEdit.
Lectures will be "in-person" in University rooms and will not be recorded.
We will have a single in-person non-compulsory computer lab at 15.15-17.00 on 6 November. Bring your own laptop. This lab is NOT structured with presentations of concepts. The lab is useful to get you started with the R/Rstudio software if you have no previous experience. If you already have some experience with R you probably won't need it.
Notice, there are some activities for which attendance is mandatory either in room or via zoom. These are the Fridays "mini-analyses". See the course PM for details and what to do in case you are unable to attend.
Lectures (below is a plan based on last year. Deviations
| week | Topics | slides/notes | code and data files |
MiniAnalysis |
|---|---|---|---|---|
| 45 |
Tuesday: General intro to the course and mention of several topics: bias in linear regression, least squares; parameters interpretation in simple linear regression;
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Also, check Check "lecture 1" in Jörnsten's notes found in Course PM
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no mini analysis this week |
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| 45 |
Thursday: least squares estimates and relation to correlation; interpretation of coefficients with transformed variables. the 5 basic assumptions and residual plots.
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Check "lecture 2" in Jörnsten's notes (linked in the previous lecture). |
no mini analysis this week |
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| 45 |
Thursday 6 November Computer lab: BRING YOUR LAPTOP with R/RStudio in MVF24 and MVF25: the lab is not structured with presentation of concepts. We are there to help you go through some basic exercise for those that are new to R.
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lab1.pdf |
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no mini analysis this week |
| 46 |
Tuesday: Proof of the unbiasedness of OLS parameter estimators; variance of the estimators; some residuals-based diagnostics; box-cox transformations;
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Box-Cox transf. see section 12.4 in Rawling's book. Check "lecture 3" in Jörnsten's notes. We did not manage to introduce the leverage (-->next lecture)
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also see again Lecture2.R |
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| 46 |
Thursday: leverage values; deletion-based diagnostics; MSE and proof of unbiasedness of MSE; unbiasedness of yhat; expectation and variance of residuals; variability decomposition (SSEr, SStor, SSRegr); Rsquared (coeff of determination); t-test construction Optional exercises for self-study from Rawling's book Links to an external site.: exercise 1.1, exercise 1.4, relevant bits of exercise 1.9, ex. 1.10, ex. 1.16,
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Check "lecture 4" in Jörnsten's notes. The R-squared is also known as coefficient of determination (page 220 and onward in the book by Rawling et al) |
Friday 14 Nov *: present work for mini-analysis 1.
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| 47 |
Tuesday: completing t-tests; pvalues; confidence intervals for parameters and for E(Y0). Prediction intervals for Ypred0. |
we finish slides_4.pdf, then we cover (see also the relevant bits in lectures 4-5 of Jörsten's notes) |
no mini analysis this week |
|
| 47 |
Thursday: The Simpson's paradox and notation for multiple lin. regression. Properties of the estimators in multiple regression and sampling distributions. t-test and categorical covariates (not everything); Topics also found in chapter 9 ("Class variables") in Rawlings et al Links to an external site. up to sect. 9.3.
Optional exercises for self-study from Rawling's book Links to an external site.: exercise 3.5(part a and d), ex. 3.10, ex. 3.11, ex. 3.12 (we never do regression without intercept...but if you are interested...), ex 3.13
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no mini analysis this week |
|
| 48 |
Tue: models with categorical and numerical covariates. |
(we stopped at slide 35) |
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PROJECT (final version) project25.pdf |
| 48 |
Thu: problems with p-values and large datasets; Multicollinearity; Variance Inflation Factor; Partial F test; |
(we restart from slide 35 from slides_7) for Partial F: section 2 in Lect 6 of notes; multicollinearity: pages 100--103 in ISLR and pages 372-373 in Rawlings' et al. |
Friday 28 Nov at 13.15*: present minianalysis 2 DEADLINE 9.00am zoom: https://chalmers.zoom.us/j/61066694734 passcode: 653917 |
|
| 49 |
Tue: greedy variables selection: backward search. Bias/variance tradoff and the pMSE. Training and testing, PMSE with regsubsets.
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[we stopped at slide 34] [ISLR: sections 2.2.1-2.2.2, 5.1.1, 6.1.1-6.1.2] [Rawling's et al: sec 7.3, 7.4] |
no minianalysis this week |
|
| 49 |
Thu: more on pMSE with categorical covariates; interactions |
[we restart from page 35 of slides_9.pdf] |
regsubsets-olsrr-categorical.R
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no minianalysis this week
|
| 50 |
Tue: ; adj-Rsquared; Kullback-Leibler; AIC, BIC; K-fold CV; |
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| 50 |
Thu: LOOCV; hat-matrix; residuals; standardised residuals, studentised residuals; Cook's distance, DFBETAs; intro GLMs; |
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Friday 12 Dec at 13.15*: present minianalysis 3
|
| 51 |
Tue: the exponential family; Newton-Raphson; Poisson regression; confidence intervals for GLMs; asymptotic properties of the MLE; CI for predictions; Wald test; deviance; likelihood ratio test;
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no mini analysis this week |
| 51 |
Thu: Poisson + offset term. Negative binomial regression also with offset. Quick tour through GLM diagnostics (diagnostics can be skipped for the exam)
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no mini analysis this week |
Computer lab and software
You are encouraged to attend the lab on Thursday 6 November to experiment with some basic analyses. No further computer lab will be given.
Software: We will use the statistical package R to analyze data, powered via the Rstudio interface. You will need to install both on your computer, see the page Installing R and Rstudio.
No previous knowledge of R is required.
Some useful resources:
- R: there are lots (too many!) resources online to learn about R: here is just a possible one to get started, from Uni. Copenhagen http://r.sund.ku.dk/index.html (Links to an external site.)
If you are familiar to MATLAB or Python, the following may be useful:
- a MATLAB/R cheat sheet is at http://mathesaurus.sourceforge.net/octave-r.html (Links to an external site.)
- a MATLAB/Python/R cheat sheet: http://mathesaurus.sourceforge.net/matlab-python-xref.pdf (Links to an external site.)
Course summary:
| Date | Details | Due |
|---|---|---|