All economics students, on both single and joint honours programmes, take this course. It is optional for students outside of economics. The module is designed to prepare students for the econometrics modules taken in second and third year. It also complements the economics modules taken in by students in first and second year. It provides an introduction to the topic of statistics, with reference to economics examples. It then covers more advanced topics leading up to regression analysis.
The course content is as follows: describing data; probability; discrete random variables; continuous random variables; sampling; estimation; hypothesis testing; simple regression and multiple regression.
One of the pre-requisites for MATH2040, MATH3085, ECON1002, ECON1004, ECON2001, ECON2002, ECON2003, ECON2004, ECON2026 and ECON3016
Pre-requisite for ECON2036
Pre-requisites: ECON1008 OR ECON1005
Aims and Objectives
Having successfully completed this module you will be able to:
- Use Excel to make basic statistical calculations and critically evaluate the basis for these calculations;
- Identify the appropriate regression model to apply to an economics dataset
- Identify common problems which may affect regression analyses
- Identify the statistical concepts in questions about economic models
- Use the basic concepts of probability and Bayes Theorem
- Use graphical and numerical methods to calculate and illustrate descriptive statistics
- Manipulate the probability models that are most widely used in economics, and apply them correctly and carry out the appropriate statistical analysis
1. Describing data
3. Discrete Random Variables
4. Continuous Random Variables
7. Hypothesis Testing
8. Simple Regression
9. Multiple Regression
Learning and Teaching
Teaching and learning methods
The course is based on lectures and tutorials, and students may also use interactive online resources listed on Blackboard..
|Total study time||150|
Resources & Reading list
University of Southampton e books collection.
Statistics for Business and Economics. Pearson.
The assessment in this module is by coursework in form of a problem set (10% of the mark), participation and performance in online quizzes in class (10%) and an end of semester examination (80%).
Summative assessment description
Referral assessment description
Repeat assessment description
Repeat type: Internal & External