Statistical Modelling (Part II, Michaelmas 2022)
General information
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This course consists of 16 lectures and 8 practical sessions. It complements the Part II Principles of Statistics, but takes a more applied perspective.
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Prerequisites: Part IB Statistics.
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Location: MR5.
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Please email me or leave a comment below if you find any mistakes or have any questions.
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Lectures will be recorded and the recordings can be found on Moodle.
Lectures
Practicals
| Number | Date | Topic | Optional Reading |
|---|---|---|---|
| P1 | Basic R; Solution | CRAN Intro to R 1,2,5,8 | |
| P2 | Writing functions, linear models; Code; Solution | CRAN Intro to R 6, 10 | |
| P3 | Linear models; Code | CRAN Intro to R 11.1–3 | |
| P4 | Model selection; Code; Solution | ||
| P5 | ANOVA and ANCOVA; Code; Solution | 2019 notes 1.2.5 | |
| P6 | Binomial GLMs; Code; Solution | ||
| P7 | Binomial and Poisson GLMs; Code; Solution | ||
| P8 | Contigency tables and Gamma GLMs; Code; Solution | Agresti 4.7 |
Example sheets
Readings
Theory for LM and GLM
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A. Agresti. Foundations of Linear and Generalized Linear Models. Wiley 2015.
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G. James, D. Witten, T. Hastie, R. Tibshirani. An Introduction to Statistical Learning (with Applications in R). Springer 2013.
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B. Efron. Exponential Families in Theory and Practice. CUP 2022.
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Prof Richard Weber’s notes for IB Statistics.
R and statistical computing
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W. N. Venables, D. M. Smith and the R Core Team. An Introduction to R.
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H. Wickham. Advanced R (for anyone who wants to really understand R as a programming language).