Module Identifier MMM7010  
Academic Year 2007/2008  
Co-ordinator Dr John A Lane  
Semester Semester 1  
Other staff Professor John Gough, Mrs Glenda Roberts, Dr John A Lane, Dr Rolf Gohm  
Course delivery Practical   1 Hours. (computing practical classes)  
  Other   4 Hours. (example classes)  
  Lecture   1 Hours. (2 x 1-hour lectures per week)  
Assessment TypeAssessment Length/DetailsProportion
Semester Exam2 Hours (written examination)  70%
Semester Assessment2 Hours (open book, in class test)  30%
Supplementary Exam2 Hours [If open book test passed (50% 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
1. identify common types of data; summarise and interpret them in business contexts.
2. calculate probabilities and conditional probabilities in a variety of situations.
3. select an appropriate probability distribution for common types of data; find relevant probabilities using tables, calculator or computer package.
4. calculate the mean, variance and standard deviation of multiples and sums of independent random variables.
5. calculate confidence intervals for single random samples and paired data.
6. formulate, carry out and interpret tests of hypotheses in common business contexts.
7. use a computer package to carry out simple data analyses, including the construction of control charts, and interpret the output.
8. use a computer package to estimate a linear relationship between two or more variables, interpret the fitted model and use it for prediction.

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.


To introduce students to basic methods for summarising and interpreting data. To provide an understanding of, and working facility in, probability and statistical inference. To illustrate the uses of probability and statistics in solving business problems.


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
J Curwin and R Slater (2000) Improve your maths, a refresher course Thomson Learning 1861525516
J Curwin and R Slater (2001) Quantitative Methods for Business Decisions 5th edition. Thomson Learning 1861525311
M C Fleming and J G Nellis (2000) Principles of Applied Statistics 2nd edition. Thomas Learning 1861525869
Swift, Louise. (2005.) Quantitative methods for business, management, and finance /Louise Swift and Sally Piff. Palgrave Macmillan 1403935289
** Supplementary Text
Curwin, J. (Aug. 2007) Quantitative Methods:Short Course Thomson Learning EMEA, Limited 1844809056TRADECLOTH
N A Weiss (1997) Introductory Statistics 4th edition. Addison Wesley 0201545675
P Newbold (1995) Statistics for Business and Economics 4th edition. Prentice Hall 0131855549
Ryan, Barbara (May 2006) Minitab Student Version 14 for Windows + Minitab Handbook Thomson Delmar Learning 0534643779MIXEDMEDIA
** Recommended Background
Moore, DS, McCabe,GP, Duckworth,WM and Sclove, SL (2003) The Practice of Business Statistics W H Freeman 0716797739


This module is at CQFW Level 7