Introductory description

This module will enable you to better understand and use quantitative methods in business practice.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Data presentation and description
Probability and frequency distributions
Theory of sampling; Central limit theorem
Confidence intervals; z tests; t-tests
The correlation coefficient
Simple and Multiple regression
Interactions in regression
Non-parametric statistics
Logistic regression

Learning outcomes

By the end of the module, students should be able to:

• - Demonstrate the ability to think quantitatively about data and determine what data can tell us, and what it cannot.
• - Describe a data set in a way that highlights what is important, by drawing the client directly to relevant comparisons.
• - Choose which statistical method is most appropriate for each kind of data, and each kind of research question

Indicative reading list

Wisniewski, M. (2009). Quantitative methods for decision makers. FT Prentice Hall. (Background)
Judd, C. M., & McClelland, G. H. (1989). Data analysis: A model comparison approach. San Diego, CA: Harcourt, Brace, Jovanovich.
Pampel, F. C. (2000). Logistic Regression: A primer. Sage.

Subject specific skills

• Conduct basic inferential statistical tests, such as t-tests and Chi-square tests.
• Conduct and interpret a multiple regression analysis.
• Conduct and interpret a logistic regression analysis.

Transferable skills

Develop facility with spreadsheets and statistical analysis packages.