Module 3: Statistics for Logistics
Course Description:
This module covers basic statistics and forecasting techniques that support analysis and planning of complex logistics and transportation systems. It also gives a comprehensive overview of multi-criteria optimization models and methods that can be used in decision making under conflicting objectives.
Contents - Measurement and describing data
Measurement levels, exploratory data analysis (EDA) techniques, measures of central tendancy and measures of dispersion.
- Basic probability distributions
Basic discrete and continuous distributions, choosing appropriate statistical distributions for data and determining techniques for the problem to be solved.
- Parametric and Non-parametric statistics
The links between data and parametric and non-parametric statistical techniques used in hypothesis testing, ANOVA and regression analysis
- Elementary forecasting techniques
Moving averages and smoothing methods; components of time series and time series decomposition, Holt’s and winters’ exponential smoothing, new-product forecasting
- Multi-criteria Decision Making
Relationship with vector type optimization, ordinal and cardinal approaches, normalization methods, utility functions for aggregation, CBA: surplus approach vs. resource cost approach, basic assumptions and application