Applied Statistical Modelling

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

• Check the model and interpret the results
• Report writing
• Statistical computing
• Use these models to describe the relationship between a response and a set of explanatory variables
• Data handling and manipulation
• Use of statistical software (SPSS/Stata) effectively
• Summarise data with an appropriate statistical model
• Carry out and interpret statistical analyses such as hypothesis tests, linear regression and logistic regression
• Problem analysis and problem solving
• Gain an understanding of some of the basic theory behind statistical modelling.

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.

Teaching and learning methods

Teaching will be delivered through lectures, workshop activities, and in-class exercises. A range of resources will also be provided for further self-directed study.

Resources & Reading list

Textbooks

Agresti, A. (2013). Categorical Data Analysis. Wiley.

Field, A.. Discovering Statistics Using SPSS. London: Sage.

Mehmetoglu, M. and Jakobsen, G. (2017). Applied Statistics using Stata. A Guide for the Social Sciences.. Springer.

Fox, J. (2016). Applied Regression Analysis and Generalised Linear Models. Sage.

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat Information

Repeat type: Internal & External