ST201      Half Unit
Statistical Models and Data Analysis

This information is for the 2017/18 session.

Teacher responsible

Dr Nicholas Cron (COL2.04)


This course is available on the BSc in Accounting and Finance and BSc in Management. This course is available as an outside option to students on other programmes where regulations permit and to General Course students.

Also available to students who have studied statistics and mathematics to the level of MA107/ST107 Quantitative Methods or ST108 Statistical Methods for the Social Sciences.

Course content

A second course in statistics with an emphasis on problems of practical importance and statistical analysis using computers. Principles of modelling: data preparation, mathematical and statistical models, linear and non-linear models. Simple linear regression. Multiple regression: assumptions, transformations, diagnostics, model selection. Logistic regression: odds ratios and likelihood. The course will conclude with a brief introduction to time series.


20 hours of lectures and 16 hours of computer workshops in the LT. 4 hours of lectures in the ST.

Students will be given their assessed project to start on in week 6 which is due in at the end of LT.

Formative coursework

Moodle quizzes and a quantitative research project.

Indicative reading

Baddeley & Barrowclough (2009), Running Regressions: A Practical Guide to Quantitative Research in

Dougherty (2011) Introduction to Econometrics;

Economics, Finance and Development Studies;

Fox (2016) Applied Regression Analysis and Generalized Linear Models;

Diaz et al (2014), OpenIntro Statistics.


Exam (80%, duration: 2 hours) in the main exam period.
Coursework (20%) in the LT.

Student performance results

(2014/15 - 2016/17 combined)

Classification % of students
First 63
2:1 24.7
2:2 11.9
Third 0
Fail 0.5

Key facts

Department: Statistics

Total students 2016/17: 108

Average class size 2016/17: 29

Capped 2016/17: No

Lecture capture used 2016/17: Yes (LT)

Value: Half Unit

Guidelines for interpreting course guide information

PDAM skills

  • Self-management
  • Problem solving
  • Application of information skills
  • Application of numeracy skills
  • Specialist skills