Module Identifier MBM6010  
Academic Year 2000/2001  
Co-ordinator Dr J A Lane  
Semester Semester 1  
Course delivery Lecture   2 per week  
  Practical   4 Hours Computing plus 4 x 1 hr examples classes  
Assessment Exam   2 Hours   70%  
  In-course assessment   1 Hours In class test (after about 2/3 of the course is complete)   30%  

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, so called 'stochastic' situations. This requires material on properties of standard 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 contexts.
The module will make substantial use of the MINITAB statistical package for some of the calcuations.

Learning outcomes
On completion of this course, a student should be able tod:

1. Summarising Data. Types of data. Frequency tables, pie and barcharts; descriptive statistics, hostograms and stem and leaf plots. Comparing data sets. X-Y 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. Applictions. Central Limit Theorem, approximation of the Binomial and Poisson distributions bythe 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
Curwin, J & Slater, R. (1991) Quantitative Methods for Business Decisions. 5th. Thomson Learning ISBN 1-861525-311
Murdoch & Barnes. Statistical Tables. 4th. Macmillan ISBN 0333-558596
Newbold, P. (1995) Statistics for Business & Economics. 4th. Prentice Hall ISBN 0-13-185554-9
** Should Be Purchased
Fleming, M C and Nellis, J G. (2000) Principles of Applied Statistics. 2nd. Thomson Learning ISBN 1-86152-586-9
Weiss, N A. (1997) Introductory Statistics. 4th. Addison Wesley ISBN 0-201-545-675
Curwin, J and Slater, R. (2000) Improve your Maths, a refresher course. Thomson Learning ISBN 1-86152-551-6