This course intends to revise statistical material required for regression analysis and introduce students to linear regression models, estimation, hypothesis testing, prediction and inferencing. 

Lectures: 10 x 2 hours.

An additional 2-hour revision lecture outwith normal teaching hours.

9 x 1-hour tutorials  

6 x 2-hour computer labs

None

1. Practical skills assessment; Individual; 800 words; 30%; ILOs 1-5.
2. Degree exam: in-person; Individual; 120 minutes; 70%; ILOs 1-4.

The main aims of this course are to build upon the foundation provided by the statistics component in second year by providing an introduction to the fundamental theoretical concepts and applications of statistics as they relate to the linear regression model. To this end, we will discuss random variables; probability distributions of random variables (e.g. normal, chi-square, t and F); the multi-variate regression model; desirable properties of an estimator (e.g. unbiasedness, consistency, and efficiency); the Gauss-Markov conditions; and inferences in multiple regression model (e.g. model specification, collinearity and heteroscedasticity).

By the end of this course, students should be able to:

1. critically analyse linear regression models

2. identify and assess the properties of ordinary least squares estimators under different model assumptions

3. estimate linear regression models and make inference

4. communicate clearly and effectively the results of econometric analysis

5. use statistical software to perform econometric analysis on empirical data