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

ILO

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

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

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.