Upon completion of this course, the student is expected to be able to:

• translate a research question into a statistical hypothesis or/and  into a regression model
• apply estimation and testing methods  in order to make data-based decisions
• model and investigate relationships between two or more variables within a regression framework
• apply checks for method’s assumptions
• comprehend and interpret correctly the statistical significance
•  interpret results correctly, effectively, and in context without relying on statistical jargon
• comprehend the notion of uncertainty which is always contained in statistical inference critique data-based claims and evaluate data-based decisions
• complete a research project that employs simple statistical inference
• use statistical software to summarize data numerically and visually, and to perform data analysis
• comply to ethical issues.

Course description:

1)   Statistical packages (how to use).

2) Brief overview of (a) the principles of statistical inference and (b) inference about means, proportions and variances (confidence intervals and hypothesis tests for a population mean, proportion or variance and for comparing two population means, proportions or variances; Analysis of variance and multiple comparisons tests ; Goodness-of-fit test; Chi-Square test of independence).

3) How to apply checks for method’s assumptions (tests for Normality, tests for comparing variances, normal probability plots, residuals plots, etc.).

4) Non-parametric tests (Sign test, Mann-Whitney test, Wilcoxon test, Kruskal-Wallis test, Friedman test, etc.).

5) Regression analysis (simple linear regression and correlation; multiple regression; logistic regression).

6) Diagnostic tools for checking the regression assumptions (residuals plots, etc.); data transformations.

Teaching aids: e-books

Examination: Laboratory autonomous exercises/practicals, Group and small autonomous assignments and final autonomous assignment.