Module Identifier MBM6010  
Academic Year 2003/2004  
Co-ordinator Dr John A Lane  
Semester Semester 1  
Course delivery Lecture   2 per week plus tutorials and examples classes as required.  
  Practical   4 hours Computing  
  Other   4 hours Example Classes  
Assessment TypeAssessment Length/DetailsProportion
Semester Exam2 Hours  70%
Semester Assessment2 Hours Open book, in class test  30%
Supplementary Exam2 Hours If open book test passed (40% or more), mark is carried forward with weighting 30% and supplementary exam will contribute 70%. If open book test failed, supplementary exam will be 100%.100%

Learning outcomes

On completion of this module, a student should be able to

Brief description

The first part of the course deals with the dual but distinct problems of summarising and interpreting data and providing mathematical models for situations where there is inherent uncertainty. The latter requires material on properties of standard probability distributions. The concepts and rules are generously illustrated with examples from business or administrative contexts. The remaining part of the course is concerned with statistical inference. Here the basic ideas and elements are introduced and applied to a variety of contexts including applications to quality control.
The module will make substantial use of a statistical package for some of the calculations.



  1. Summarising Data. Types of data. Frequency tables, pie and barcharts; descriptive statistics, histograms, stem and leaf, box and whisker plots. Comparing data sets. X-Y scatter plots, correlation.
  2. Probability. Elementary rules, symmetric situations, combinatorics, sampling with and without replacement. Applications.
  3. Conditional Probability and Tree Diagrams. The chain rule, Bayes Rule. Applications. Expected value; decision making.
  4. Probability Distributions. Binomial and Poisson, applications in modelling, 'rare event' model for the Poisson. Mean, variance and standard deviation, basic properties. Normal distribution, density function, use of Statistical Tables. Applications. Central Limit Theorem, approximation of the Binomial and Poisson distributions by the Normal distribution.
  5. Confidence intervals. Single Normal random sample, distribution of the sample mean, confidence levels, confidence interval for the mean, with variance both known and unknown. Matched pairs. Large sample interval for the Binomial and the Poisson.
  6. Hypothesis Testing. Examples for Normal, Binomial and Poisson data. Simple and composite hypotheses, critical (rejection) region, type I and II errors, P-value, significance level, power function, formulation of problems. Control charts and quality control.
  7. Regression. Linear regression of y on x. Least squares estimates, the correlation coefficient, the fitted line, tests on slope and intercept, prediction.

Reading Lists

** Recommended Text
L Swift (2001) Quantitative Methods for Business, Management and Finance Palgrave ISBN 0-333-92076-7
M C Fleming and J G Nellis (2000) Principles of Applied Statistics 2nd edition. Thomas Learning ISBN 1-86152-586-9
J Curwin and R Slater (2001) Quantitative Methods for Business Decisions 5th edition. Thomson Learning ISBN 1-861525-311
J Curwin and R Slater (2000) Improve your maths, a refresher course Thomson Learning ISBN 1-86152-551-6
** Recommended Background
Moore, DS, McCabe,GP, Duckworth,WM and Sclove, SL (2003) The Practice of Business Statistics W H Freeman ISBN 0716797739
Barbara Ryan, Brian L. Joiner (2001) Minitab Handbook 4th. Duxbury 0534370934
** Supplementary Text
Newbold, P (1984) Statistics for Business & Economics Prentice Hall
Weiss, N A (1997) Introductory Statistics 4th. Addison Wesley ISBN 0-201-545-675


This module is at CQFW Level 7