DA II General learning objectives:
Upon completion of this course, students are able to:
- analyse, evaluate and interpret the statistical and practical implications of the outcomes of a regression analysis, plus analyse and interprete residuals, confidence intervals and prediction intervals for the expected outcomes for specific values of the independent variable(s);
- construct a test for the difference between means if the assumptions for a parametric test are not fulfilled (Wilcoxon rank sum test and the Wilcoxon signed rank test;
- perform a multiple regression for an additive linear model with categorial independent variables and can interpret and evaluate the outcomes of that analysis;
- identify and investigate the assumptions for an additive model and can interpret and evaluate the outcomes of that analysis (multicollinearity and residual analysis);
- conduct statistical inferences for differences between two and more means by using the two sample t-test, ANOVA and the linear model;
- conduct and interpret a multivariate analysis with an interaction effect between two variables.
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This course assumes prior knowledge of Research Methodology and Data Analysis I. The basic principles of inferential statistics have been introduced in Data Analysis I and applied to situations in which one group is analysed and two groups are compared (both via confidence intervals and tests). To measure the relationship between variables, different measures of strength are introduced and the course ended with an introduction to simple regression. In Data Analysis II, the assumptions of the different statistical procedures will be discussed in more detail and some non- parametric alternatives will be given. This is then deepened by applying these statistical techniques to analyse, evaluate and interpret relationships between more than two variables through multivariate techniques such as ANOVA and regression
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