APPLIED STATISTICS AND DATA ANALYSIS
Applying knowledge and understanding: understanding of statistical methods as useful instruments for research in economics and social sciences, ability to use descriptive and inferential statistics in order to summarize information, to analyze and interpret relationships between variables and to test hypotheses, ability to use at least one statistical software in order to develop simple data analysis.
Making judgements: making judgements on the appropriate statistical models and methods to be used for analyzing a specific dataset and on the interpretation of the experimental results.
Communication skills: communication skills in order to present a statistical analysis, including both the methodology and the final conclusions, in a consistent and convincing way.
Learning skills: learning skills based on the prerequisites that are required for understanding autonomously a report with a statistical analysis and for learning more advanced statistical procedures.
1) Introduction to statistics and data analysis;
2) Explorative data analysis;
3) A review of inference concepts;
4) Linear regression with a single predictor;
5) Towards multiple linear regression and logistic regression;
6) Predictive and classification methods;
7) Unsupervised methods (principal component analysis, cluster analysis).
1) J. Maindonald, W.J. Braun: Data Analysis and Graphics Using R – An Example-Based Approach (Third Edition); Cambridge University Press, 2010.
2) J. Ledolter, R.V. Hogg: Applied Statistics for Engineers and Physical Scientist (Third Edition); Prentice Hall, 2009.
3) J.P. Marques de Sá: Applied Statistics Using SPSS, STATISTICA, MATLAB and R; Springer, 2007.