This module will introduce a number of common methods for analysing survey data, focusing on regression analysis. The module will cover in depth linear and logistic regression analyses, before introducing further regression techniques such as ordinal and multinomial logistic regression.
Pre-requisite: STAT2009. Any equivalent modules can be accepted with permission from the Programme coordinator
Pre-requisite: STAT2009
Having successfully completed this module you will be able to:
The module is split into three parts:
Part 1: Linear regression: simple linear regression, least squares estimation, interpreting regression coefficients, checking the regression assumptions, hypothesis testing, confidence intervals, multiple linear regression, model selection, handling of categorical explanatory variables, interactions, variable transformations.
Part 2: Logistic regression: modelling dichotomous outcome variables, probabilities and odds, the logistic regression model, how good is the model, model interpretation, model selection, diagnostic methods, grouped data.
Part 3: Further regression analysis: further regression methods will be introduced, including ordinal and multinomial regression methods.
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| Type | Hours |
|---|---|
| Independent Study | 120 |
| Teaching | 30 |
| Total study time | 150 |
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Assignment | 50% |
| Assignment | 50% |
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Assignment | 100% |
Repeat type: Internal & External