This module will provide an overview of statistical methods for linear and logistic regression.
Pre-requisite for: STAT6087, STAT6089, STAT6090, STAT6102 and STAT6103
One of the pre-requisites for STAT6091
Aims and Objectives
Having successfully completed this module you will be able to:
- On successful completion of this module you will understand and be able to apply the different techniques involved in fitting regression models.
- On successful completion of this module you will be able to use a statistical computing package to apply the different regression analysis techniques and understand and interpret the outputs.
- On successful completion of this module you will be able to apply regression methods to typical data sets arising in official statistics.
- On successful completion of this module you will be able to carry out an in-depth analysis of a dataset and undertake good statistical reporting.
Linear regression covering;
- Basic (ordinary least squares) regression model
- Residual analysis
- Model building and selection for multiple regression model
- Assessing model fit
- Handling categorical variables, outliers, interactions, transformations
- Spline regression, polynomial regression, Weighted Least Squares
Introduction to logistic regression covering;
- Basic model
- Interpreting the parameters
- Assessing model fit
- Model building and selection
Learning and Teaching
Teaching and learning methods
Depending on feasibility, teaching may be delivered face to face intensively over a week, or online using a mixture of synchronous and asynchronous online methods, which may include lectures, discussion boards, workshop activities, exercises, and videos. A range of resources will also be provided for further self-directed study..
|Total study time||100|
Resources & Reading list
Laboratory space and equipment required. Computing Lab in SPSS.
Hosmer, W. H. , Lemeshow, S. and Sturdivant, T. (2013). Applied Logistic Regression (3rd edition). New York: Wiley.
Field, A. (2013 / 2017). Discovering Statistics using IBM SPSS Statistics. London: Sage.
Agresti, A. (2013). An Introduction to Categorical Data Analysis. New York: Wiley.
There will be opportunities to evaluate your progress through formative assessment, with summative assessment based on one online assignment.
This is how we’ll formally assess what you have learned in this module.
This is how we’ll assess you if you don’t meet the criteria to pass this module.
Repeat type: Internal & External