Gwybodaeth Modiwlau
Module Identifier
MMM7010
Module Title
RESEARCH SKILLS FOR MANAGERS
Academic Year
2012/2013
Co-ordinator
Semester
Intended for use in future years
Course Delivery
| Delivery Type | Delivery length / details |
|---|---|
| Practical | 1 hour (computing practical classes) |
| Other | 4 hours (example classes) |
| Lecture | 2 hour (1 x 2 hour lectures per week) |
Assessment
| Assessment Type | Assessment length / details | Proportion |
|---|---|---|
| Semester Assessment | 2 Hours (open book, in class test) | 30% |
| Semester Exam | 2 Hours (written examination) | 70% |
| Supplementary Exam | 2 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
- To assess the research studies carried out by others
- To define and commission effective research studies
- To prepare convincing research proposals
- To demonstrate understanding of methods of conducting research in a business context
- To evaluate research reports and assess the reliability and validity of research findings
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.
The module will make substantial use of a statistical package for some of the calculations.
Aims
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.
Content
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.
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 List
Recommended TextBrynam, A. & Bell, E. Business Research Methods Oxford University Press Primo search Cooper, R. & Schindler, P. Business Research Methods McGraw-Hill Primo search Curwin, J., and Slater, R. (2000) Improve Your Maths, A Refesher Course Thomson Learning Primo search Curwin, J., and Slater, R. (2001) Quantitative Methods for Business Decisions 5 Edition Thomson Learning Primo search Fleming, M. C., and Nellis, J. G. (2000) Principles of Applied Statistics 2 Edition Thomson Learning Primo search Gill, J. & Johnson, P. Research Methods for Managers Paul Chapman Publishing Primo search Saunders, M.N., Lewis, P. & Thomhill, A. Research Methods for Business Students Prentice Hall Primo search Swift, L., and Piff, S. (2005) Quantitative Methods for Business, Management and Finance Palgrave Macmillan Primo search
Notes
This module is at CQFW Level 7