Syllabus
1. Multiple regression analysis, dummy variables. Problems- heteroskedasticity, multicolinearity.
2. Covariance and correlation. Bivariate, multivariate and partial correlation. Spurious correlation. Correlation matrix.
3. Factor analysis. Basics, number of factors, tests, Extraction and rotation.
4. Factor analysis. Typologies, control of scales. Realibilty of scales. Factor scores.
5. Cluster analysis, basics, clustering of cases. Measurement of distances. Hierarchical clustering.
6. Clustrer membership. Typologies. K-means cluster. Clustering of variables.
7. Discrimination analysis.
8. Time series. Descriptives in time series. Types of time series.
9. Moving averages. Trend functions. Seasonal models of time series.
10. Missing values, results, handling and replacing. Data weighting.
11. Open ended questions and data analysis. Coding, duplicities. Frequency and contingency table.
12. Random and non-random samples and influence on statistical techniques. Small samples, samples from small populations.
Annotation
The course is follow up of Statistics I and Statistics II. This course is focused on advanced statistical techniques and data analysis in SPSS.