Week 0 · Prerequisites
What to refresh before the first lectures, and the additional reading list
The programme assumes undergraduate statistics. The prerequisites sheet lists the concepts each of the first three weeks builds on and points to pre-recorded lectures on Ilias and to Khan Academy units for anyone who needs to close a gap. A second sheet, distributed to the 2024/25 cohort, names the two handbooks the lectures follow and a few additions.
Materials
| Material | WS 2024/25 | WS 2023/24 |
|---|---|---|
| 📖 Prerequisites, as a web page | Week 00. Prerequisites | rendered from the same source |
| 📄 Prerequisites, PDF from Ilias | mk68-00-part-1-prerequisites.pdf · 2 pages · added 14 Oct 2024 | mk68-00-part-1-prerequisites.pdf · 2 pages · added 4 Dec 2023 |
| 📄 Additional literature, PDF from Ilias | mk68-additional-literature.pdf · 2 pages · added 23 Oct 2024 | not distributed |
| 💻 Source | mk68-00-part-1-prerequisites.qmd |
mk68-00-part-1-prerequisites.qmd |
What the prerequisites sheet covers
Before Week 1. Mean, median, mode, maximum, minimum, quantiles, quartiles and percentiles; box plot and histogram; distribution, skew and kurtosis; powers and the logarithm transformation. Four pre-recorded Ilias lectures (introduction to statistics and scale levels; basic measures; understanding and visualising data I and II) and Khan Academy units on summarising and displaying quantitative data and on logarithms.
Before Week 2. Mean, variance and standard deviation; probability; the normal distribution and confidence intervals. Three pre-recorded Ilias lectures (probability; statistical distributions; confidence intervals) and Khan Academy units on variance, standard deviation and basic probability.
Before Week 3. Optional only: the Khan Academy module on modelling data distributions, in particular percentiles, z-scores, and the normal distribution with the empirical rule.
What the additional literature sheet covers
Two key handbooks, with the chapters the lectures draw on, and three additions:
- 📖 OpenIntro Statistics (Diez, Çetinkaya-Rundel and Barr, 2019): sampling principles and strategies, experiments, summarising data, defining probability and the law of large numbers, random variables, continuous distributions, the normal distribution, inference for numerical data, and the introduction to linear regression.
- 📖 Mastering ’Metrics: the path from cause to effect (Angrist and Pischke, 2015): Chapter 1, Randomized Trials, and Chapter 2, Regression.
- 📖 Fundamentals of Data Visualization (Wilke, 2019), Part I chapters 6 to 9 on visualising distributions.
- 📖 Our World in Data on the Gini coefficient and on the history of global economic inequality.
The full list with links is on the reading list page.