Advanced analytics for mobility and transport

• Formally define problems and identify the relevant analysis
• Choose, collect, format, and clean data according to recommended standards
• Describe data by means of descriptive statistics, ANOVA, and test for significance
• Explain the theoretical basis for linear regression and “ordinary least squares”
• Perform residual analysis, apply data transformations, and evaluate model fit
• Fit multiple regression models with stepwise regression
• Formulate decision models with logistic regression
• Evaluate case studies in forecasting or causal analysis
• Develop skills in relevant statistical analysis software, especially “R”.

The course introduces linear regression and then continues to advanced methods. Learning objectives cover both methods and problem solving tips. A variety of data sets from mobility and transport are explored to demonstrate complex models, but also to demonstrate what can go wrong, and how to avoid false interpretations. The course ends with an emphasis on real world data and the possibility of multiple interpretations of that data.

62668, Students should have previously passed a course covering descriptive statistics, distribution fitting, and hypothesis testing. This is a firm requirement.

Lectures, exercises, and some group work.

Research group: Production, Transportation and Planning
Obligatorisk B.Eng. in Mobility, transport and logistics: 3. semester