This is a postgraduate advanced module in applied statistical modelling designed to equip students with highly sought after employability skills in data analysis. The module will cover a wide range of statistical models including a revision of introductory statistics, linear regression, logistic regression, multinomial logistic regression, log-linear models, models for rates (Poisson regression), and ordinal logistic regression. Some theory behind the methods will be covered, although the emphasis is on the practical application of these methods using statistical software. In this respect, students will be introduced to the statistical software of their choice Stata, SPSS or R.
Aims and Objectives
Having successfully completed this module you will be able to:
- Statistical computing
- Problem analysis and problem solving
- Gain an understanding of some of the basic theory behind statistical modelling.
- Use of statistical software (SPSS/R/Stata) effectively
- Report writing
- Carry out and interpret statistical analyses such as hypothesis tests, linear regression and logistic regression
- Summarise data with an appropriate statistical model
- Use these models to describe the relationship between a response and a set of explanatory variables
- Data handling and manipulation
- Check the model and interpret the results
Normal distribution, sampling distributions and the central limit theorem, confidence intervals, hypothesis tests for means and proportions, chi-squared test of independence, two sample t-tests, correlation, simple linear regression, multiple linear regression, model selection and diagnostics, logistic regression, multinomial logistic regression, log-linear models, models for rates (Poisson regression), ordinal logistic regression.
Learning and Teaching
Teaching and learning methods
Teaching will be delivered by a mixture of synchronous and asynchronous online methods, which may include lectures, quizzes, discussion boards, workshop activities, exercises, and videos. A range of resources will also be provided for further self-directed study. Face-to-face teaching opportunities will be explored depending on circumstances and feasbility.
|Total study time||200|
Resources & Reading list
Agresti, A. (2013). Categorical Data Analysis. Wiley.
Fox, J. (2016). Applied Regression Analysis and Generalised Linear Models. Sage.
Mehmetoglu, M. and Jakobsen, G. (2017). Applied Statistics using Stata. A Guide for the Social Sciences.. Springer.
Field, A.. Discovering Statistics Using SPSS. London: Sage.
Summative assessment description
Referral assessment description
Repeat type: Internal & External