Course syllabus
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. This course edition will be 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).
List of topics and potential exam questions: 2023_Topics and hence potential exam questions.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 non-compulsory computer lab at 15.15-17.00 on 2 November which will NOT be via zoom. 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 may occur).
(Here are the videos of the 2020 lectures. These are to be considered as useful material in case you miss a lecture, but we discourage you to rely on these videos as a substitute to attendance. Three years have passed and a few things have changed and we don't want you to get confused!)
| week | Topics | slides/notes | code and data files |
MiniAnalysis |
|---|---|---|---|---|
| 44 |
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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lab0.pdf (you must go through this file before the Thursday lab)
Also, check Check "lecture 1" in Jörnsten's notes found in Course PM |
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| 44 |
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). (optional: TooBigToFail_2013.pdf)
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| 44 |
Thursday 2 November Computer lab at 15.15-17.00 in MVF24 and MVF25: this lab is an intro to R and Rstudio. It is not structured with presentation of concepts. We are there to help if you have questions regarding exercises.
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| 45 |
Tuesday: Proof of the unbiasedness; variance of the estimators; some residuals-based diagnostics; box-cox transformations; leverage values;
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Box-Cox transf. see section 12.4 in Rawling's book. Check "lecture 3" in Jörnsten's notes. |
also see again Lecture2.R |
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| 45 |
Thursday: deletion-based diagnostics; MSE; unbiasedness of yhat; expectation and variance of residuals; proof variability decomposition (SSEr, SStor, SSRegr); Rsquared; 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. |
Friday 10 Nov *: present work for MiniAnalysis1-2023.pdf (this event is typically 60-70 minutes long)
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| 46 |
Tuesday: more on pvalues; unbiasedness of MSE; confidence intervals for parameters and for E(Y0). Prediction intervals for Ypred0. |
(see also the relevant bits in lectures 4-5 of Jörsten's notes) |
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| 46 |
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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| 47 |
Tue: models with categorical and numerical covariates. problems with p-values and large datasets; |
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| 47 |
Thu: Multicollinearity; Variance Inflation Factor; Partial F test; greedy variables selection: backward search. |
Check "lecture 6" in Jörnsten's notes
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Friday 24 Nov at 13.15*: present minianalysis 2 (this event is typically 60-70 minutes long)
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| 48 |
Tue: Bias/variance tradoff and the pMSE. Training and testing, PMSE with regsubsets.
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PROJECT no minianalysis this week |
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| 48 |
Thu: (reprise the end of slides 9) Mallow's Cp; interactions; adj-Rsquared; |
regsubsets-olsrr-categorical.R
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no minianalysis this week
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| 49 |
Tue: Kullback-Leibler; AIC, BIC; K-fold CV; LOOCV; hat-matrix; residuals; |
(optional if you are interested) AkaikeEasyIntro.pdf See also "lecture 14" in Jörnsten's notes. |
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| 49 |
Thu: standardised residuals, studentised residuals; Cook's distance, DFBETAs; intro GLMs; |
See also "lecture 14" in Jörnsten's notes. (Additional support: Agresti's book chapters 4 and 14.4) |
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Friday 8 Dec at 13.15*: present minianalysis 3
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| 50 |
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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completion of slides_12.pdf then slides_13.pdf |
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| 50 |
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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Computer labs and software
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 instructions.
No previous knowledge of R is required. You are encouraged to attend the lab on Thursday 3´2 November to experiment with some basic analyses. No further computer lab will be given.
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 |
|---|---|---|