General learning objectives:
- Students can explain the role of Inferential Statistical in the process of data analysis;
- Students are able to construct confidence intervals for proportions and means;
- Students are able to demonstrate the principles of hypothesis testing and can perform by hand and via R different types of tests for means;
- Students are able to investigate the relationship between two variables by modelling this relationship via regression analysis, choosing a measure for the strength and giving a descriptive interpretation of that relationship.
More specific learning objectives:
Upon completion of this course, you are able to:
- explain the role of Inferential Statistical in the process of data analysis and knows what a Sampling Distribution Model is and know what the Central Limit Theorem tells us;
- construct and interpret confidence intervals for proportions and means;
- demonstrate the principles of hypothesis testing and can perform by hand and via statistical software tests for means in a one sample, two samples and related samples situation;
- apply and interpret the principles of hypotheses testing and can explain the risk of making errors when testing hypotheses (type I versus type II);
- can explain the similarity between a confidence interval and a statistical test, by interpreting the outcome of a confidence interval in terms of a test;
- select and interpret different measures for the strength of the relationship between two variables given the measurement level of the variables and perform a test for a correlation (by hand and via R);
- model, execute and interpret a linear relationship between two variables via (simple) regression on a descriptive level (by hand and via a statistical program);
- analyse, evaluate and interpret the statistical and practical implications of the outcomes of a regression analysis, plus analysing and interpreting residuals, confidence intervals and prediction intervals for the expected outcomes for specific values of the independent variable(s).