The module introduces students to the methods of statistical analysis, i.e. to how data are used in business, social science or pure science. Beginning from an elementary level (assuming no background in statistics), the course shows how economic data can be described and analyzed. The elements of probability and random variables are introduced in the context of economic applications. The probability theory enables an introduction to elementary statistical inference: parameter estimation, confidence intervals and hypothesis tests. With these foundations, students are then introduced to the linear regression model that forms a starting point for econometrics.
The aims of the module are:
- To ensure that students from a wide range of educational backgrounds have a broad understanding of basic statistical skills
- To give students the ability to present data clearly and unambiguously to an audience with no specialist knowledge of statistics
- To give students an understanding and ability to calculate basic statistical measures
- To provide students with the rudiments of probability theory
- To provide knowledge of various models of probability distributions and the requirements underlying the models
- To give students the ability to discuss the meaning of the calculations they perform
On successful completion of the module a student will demonstrate the ability to:
- absorb large quantities of information rapidly over a short period of time
- the ability to apply appropriate and effective study skills and strategies
- identify the accuracy of data and acknowledge points where errors are present in their calculations
- clearly present data in tables and diagrammatically
- use relevant presentational techniques to compare different datasets
- calculate basic statistical measures of data
- perform linear regression and calculate the (product-moment) coefficient of correlation
- use the basics of probability theory and demonstrate set-theoretic results using Venn diagrams
- analyse experiments to be able to provide a probability tree
- understand the requirements imposed by the discrete models of data introduced in the course and be able to apply these requirements in an informed manner
- use statistical tables to calculate probabilities
- use approximations of distributions and know when these approximations are good
- an ability to work well under examination conditions
- an ability to absorb and retain concepts
- effective time management skills
- the application of appropriate study strategies
- an ability to clearly communicate knowledge without immediate recourse to source material
1. Introduction to Statistics. Week 3. (Triola Chapter 1)
2. Representation of Data. Week 4-11. (Chapter 1, Newbold Chapter 2-3, Triola Chapter 2 & 3)
3. Probability. Week 16-17. (Chapter 3, Newbold Chapter 4, Triola Chapter 4)
4. Discrete Probability Distribution. Week 18-20. (Chapter 4 & 5, Newbold Chapter 5, Triola Chapter 5)
5. Continuous Probability Distribution. Week 20-22. (Chapter 6, Newbold 6, Triola Chapter 6)
6. Normal Distribution. Week 22-23. (Chapter 7 & 8, Newbold Chapter 6, Triola Chapter Chapter 6)
7. Sampling and Estimation. Week 23. (Chapter 9, Newbold Chapter 7, Triola Chapter 7)
8. Correlation and Regression. Week 24-25. (Chapter 2, Newbold Chapter 12, Triola Chapter 10)
Learning & Teaching Methods
Students are required to attend a one-hour lecture and a two-hour class per week.
40 per cent Coursework Mark, 60 per cent Exam Mark
Exam Duration and Period
2:00 hour exam during Summer Examination period.
- Crawford, J. & J. Chamber (2001) A Concise Course in Advanced Level Statistics with Worked Examples, 4th ed. Nelson Thornes.
Newbold, P., W. L. Carlson & B. Thorn (2006) Statistics for Business & Economics, 6th ed. Pearson Education.
Triola, M. F. (2007) Elementary Statistics Using Excel, 3rd ed. Pearson Education.