Quantitative Methods

Module aims

  • To introduce students to key concepts underpinning quantitative techniques used in transport analysis. 
  • To show how fundamental concepts of probability can be used to represent travel behaviour and how statistical techniques can be used to summarise and analyse travel related data. 

Learning outcomes

On successfully completing this course unit, students will be able to: 

  • Understand concepts and techniques for the acquisition, processing, description and presentation of quantitative information, including elementary descriptive and inferential statistics.
  • Have a foundation in essential quantitative methods that will be required in other core course units and for study project research and report writing.

Module syllabus

Sources and examples of transport data; survey methods; presentation and interpretation of data; systematic and non-systematic variations in data, models for systematic variation; approaches to decision making and design; optimisation; probability and random processes; sampling; standard probability distributions; estimation of population parameters; hypothesis testing; categorical models; continuous models; model specification and fitting; model calibration and validation; students will be introduced to relevant software packages.   

No.

Topic

Staff

01

Presentation and interpretation of data

DJG

02

Presentation and interpretation of data

DJG

Tutorial

Data editing and descriptive statistics

DJG

03

Sources and examples of transport data, survey methods

HT

04

Sources and examples of transport data, survey methods

HT

Tutorial

Workshop on survey methods

HT

05

Probability and random processes

BGH

06

Probability and random processes

BGH

Tutorial

Exercise on probability

BGH

07

Standard probability distributions

BGH

08

Standard probability distributions

BGH

Tutorial

Workshop on distributions

BGH

09

Sampling and estimation

BGH

10

Sampling and estimation

BGH

Tutorial

Theoretical and empirical distributions

BGH

11

Hypothesis testing: philosophy, simple examples

BGH

12

Hypothesis testing: philosophy, simple examples

BGH

Tutorial

Hypothesis testing workshop

BGH

13

Decision making and optimisation

BGH

14

Decision making and optimisation

BGH

Tutorial

Optimisation workshop

BGH

15

Introduction to linear regression

DJG

16

Introduction to linear regression

DJG

Tutorial

Introduction to regression with Excel and SPSS

DJG

17

Further regression analysis

DJG

18

Further regression analysis

DJG

Tutorial

Further regression analysis workshop

DJG

Teaching methods

There will be 18 hours of lectures delivered over 9 weeks in two hour blocks. Tutorials are scheduled after each two-hour lecture to review and practice problems and to set coursework. If you have questions about specific problems, please be prepared to ask them at the tutorial.

Assessments

Information will be provided separately.

Reading list

Core

Module leaders

Professor Daniel Graham