Objectives
After this course attendees are able to:
1.      Explain the basic principles behind statistical modelling (Central Limit Theorem).
2.      Choose appropriate bivariate data-analysis techniques* (given a particular research question), correctly apply these techniques (by formulating statistical hypotheses, checking the statistical assumptions and deriving the test statistic) and interpret their results in meaningful ways.
3.      Estimate a multivariate regression model, check its assumptions (normality, linearity, homoscedasticity) and interpret its outcomes.

* The following data-analysis techniques will be treated: descriptive data analysis (mean, median, variance, standard deviation), univariate (one sample t-test, proportion test) and bivariate parametric tests (paired/independent samples t-test, ANOVA, Pearson correlation) and a non-parametric test (chi-square).

Course description
In this course attendees will actively work on solving concrete statistical problems in the domains of transport, infrastructure and logistics using various bivariate and multivariate data-analysis techniques. The course will treat the basic principles behind statistical modelling so that attendees really understand what the results of statistical tests mean.

Assignment
Attendees have to apply several data-analysis techniques to their own data (or a given dataset) and report the results in a brief research report.  

Program
Day 1 –   Basic principles behind statistical modelling, descriptive statistics and bivariate data-analysis techniques (Maarten Kroesen)
Day 2 –   Multiple regression (Eric Molin)

Course material
Slides and online materials

Methodology
The working method consists of oral lectures combined with (short) in-class assignments using SPSS. To this end, students should bring a laptop with SPSS installed on it.

Prerequisite
None.

Max. 25 participants!
(first come, first served)