Магистърски програми - Каталог 2019/2020 | Когнитивна наука (на английски език) | COGM008

COGM008 Статистика

Анотация:

The course is an introduction to theory and application of statistical methods in the cognitive science.

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Към курсовете на програмата График Виртуално обучение
Когнитивна наука (на английски език)

Преподавател(и):

проф. Димитър Атанасов  д-р
гл. ас. Иван Ванков  д-р

Описание на курса:

Компетенции:

Students graduating the course are supposed to have the following competences:

1) basic understanding of probability theory and statistics

2) ability to understand and critically appraise the methods and statistical analyses in the cognitive science research

3) conduct their own statistical analyses using state-of-the art tools, such as R and JASP
Предварителни изисквания:
Elementary mathematical and software skills.

Форми на провеждане:
Редовен

Учебни форми:
Лекция

Език, на който се води курса:
Английски

Теми, които се разглеждат в курса:

  1. Introduction. Course overview. What is statistics for? Kinds of statistics. Descriptive statistics. Statistical inference.
  2. Probability theory. Samples and populations. Measures of central tendency. Measures of variance. Random variables. Probability distributions. Sampling error.
  3. Z scores. The normal distribution. The central limit theorem. The logic of null hypothesis significance testing.
  4. The logic of null hypothesis significance testing. P values and statistical significance. Statistical errors. Statistical power.
  5. Introduction to R. Exploring data using R.
  6. Correlation.
  7. Computing correlations in R.
  8. Student t test.
  9. Running t-test analyses in R.
  10. Analysis of variance.
  11. Running ANOVA analyses in R.
  12. Practice in data analysis.
  13. The replication crisis.
  14. Regression analyses.
  15. Regression analyses in R.
  16. Bayesian statistics. Bayes factors. Bayesian parameter estimation.
  17. Bayesian statistics in R.
  18. Paper presentations.
  19. Advanced topics in statistics. The language as a fixed effect fallacy. Linear mixed models. Bootstrapping.
  20. Midterm Test
  21. Assignment - analyze a data set using R.

Литература по темите:

Agresti, A. (2012). Statistics: The Art and Science of Learning from Data, Pearson, ISBN: 9780321755940

Kruschke, J. (2015). Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan. Academic Press / Elsevier, ISBN: 9780124058880

Poldrack, R. (2018). Statistical Thinking for the 21st Century. http://web.stanford.edu/group/poldracklab/statsthinking21/