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).

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 extensively treat the basic principles behind statistical modelling so that attendees really understand what the results of statistical tests mean.

Program:

• Day 1   Probability density functions, basic principles behind statistical modelling, descriptive statistics and bivariate data-analysis techniques (Maarten Kroesen)
• Day 2   Continuation of bivariate data-analysis techniques (Maarten Kroesen)
• Day 3    Multiple regression (Eric Molin)

More detailed information on the course in the attached program below.

Lecturers: Dr. Maarten Kroesen and Dr. Eric Molin

Dates: 7, 10 & 15 October 2019

Time: 10.00 – 16.00 h

Location: TU Delft

Room:
7 October: TPM – Classroom H
10 October: ARCH/BK – Instructiezaal Q
15 October: ARCH/BK – Instructiezaal Q

ECTS: 1 (attendance only) – 3 (attendance & passing assignment)

Participation: free for TRAIL/Beta/OML members and PhD students of participating faculties

Registration: HERE

TRAIL OML Course Data Analysis and Statistics – Fall 2019