Syllabus
6.3.1. Classification of multivariate methods. Statistics in MS Excel (analytic module - t-test, contingency tables, regression models).
2. Contingency tables, row and column profiles. Correspondence analysis.
3.4.3. Contingency tables, Pearson chi-square test, contingency coefficients,sign schema. Odds, odds ratio,logit, loglinear model, two dimensional models, interactions. Overall tests and residuals. Information criteria (AIC, BIC).24.4.4. Linear regression and multilevel models in SPSS.
Annotation
The course is focused on categorical data analysis (loglinear models, correspondence analysis) and regression models. Exam consists of 3 homework and presentation of statistical technique (every part is evaluated separately 0-100 %, every part has the same weight).
Grading: 0-50 % 4 (failed), 51 % - 69 % 3 (good), 70-84 % 2 (very good) and 85 % and more 1 (excelent)