Statistics4MR-2022-23

Plan

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

 [развернуть] 

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

Grades

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

Internal grades:

Fall grade = 0.4 * HA + 0.6 December Exam

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

Actual grades

Log-book

Lecture recordings: youtube

lecture recordings: ya-folder

Seminar recordings

Lecture handwritten notes

HA1: deadline: 2022-12-06, 21:00.

Week 1

Naive bootstrap, t-statistic bootstrap

Sources of wisdom:

Efron, Leisurely Look at bootstrap, jackknife and cv

Tim Hesterberg, What Teachers Should Know about the Bootstrap?

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