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

• apply estimation and testing methods  in order to make data-based decisions
• apply checks for method’s assumptions
• comprehend and interpret correctly the statistical significance
• comprehend the notion of uncertainty which is always contained in statistical inference critique data-based claims and evaluate data-based decisions
• use statistical software to summarize data numerically and visually, and to perform data analysis

Course description:

1) Statistical packages (how to use).

2) The principles of statistical inference and 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) Simple linear regression analysis

Teaching aids: Textbooks

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