Module Identifier MMM7010  
Academic Year 2001/2002  
Co-ordinator Dr J Lane  
Semester Semester 1 (Taught over 2 semesters)  
Course delivery Lecture   2 per week  
  Other   4 Hours Example Classes  
  Practical   4 Hours Computing  
  Other   4 Hours Example classes  
Assessment Course work   2 hours open book, in class test   30%  
  Exam   2 Hours   70%  

General 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. This 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 are introduced and applied to a variety of relevant examples.
The module will make substantial use of the MINITAB statistical package for some of the calculations.


Learning outcomes

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


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, equally likely events, 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 including Quality Control. 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.
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
** Supplementary Text
P Newbold. (1995) Statistics for Business and Economics. 4th edition. Prentice Hall ISBN 0-13-185554-9
N A Weiss. (1997) Introductory Statistics. 4th edition. Addison Wesley ISBN 0-201-545-675