Course: Applied non spatial statistical analysis
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