# Statistics4MR-2022-23

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## Plan

Нажми "развернуть", чтобы просмотреть содержимое - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 🡣

### Module 1

(1 lecture + 1 class per group X 7 weeks)

```   Bootstrap + Jacknife + CV
Sampling
Welch test
Mann-Whitney test
Sample size calculation
Multiple comparison corrections
```

### Module 2

(1 lecture + 1 class per group X 8 weeks)

```   CUPED
Difference in Difference estimator
Matching
Contingency tables, Chi-squared tests
ANOVA, ANCOVA
Partial correlation + problem solving
```

### Module 3

(1 lecture + 1 class per group X 11 weeks)

```   Discriminant analysis
Logit
PCA
Factor analysis
Cluster analysis, Dendrogramms
Conjoint Analysis
Multidimensional scaling
```

UoL grade = 0.7 * UoL exam + 0.3 * UoL coursework

Fall grade = 0.4 * HA + 0.6 December Exam

Final grade = 0.5 * Fall grade + 0.2 HA + 0.3 * Final exam

## Log-book

Week 1

Naive bootstrap, t-statistic bootstrap

Sources of wisdom:

Week 2

Variance estimation: jackknife, bootstrap. Bootstrap in bootstrap.

Week 3

Examples of bootstrap failures, problem solving

Week 4

Welch test, Mann-Whitney test

Week 5

Sample size calculation exercises, MDE

Week 6

Multiple comparisons: FWER.

Week 8

Multiple comparisons: FDR.

Week 9

PCA+SVD: maximum variance approach

Week 10

PCA+SVD: average R2 maximization

Week 11

PCA+SVD: problem solving