Introductory description

The module is designed to provide a foundation in the analysis and presentation of quantitative data and covers the basic elements of statistics that are essential for business analysis.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

1. Introduction to R
2. Visualising Data in R
3. Likelihood
4. The General Linear Model: t-tests
5. Confidence Intervals
6. The General Linear Model: ANOVA and Regression
7. The General Linear Model: Repeated Measures
8. The Generalised Linear Model: Logistic Regression
9. The Generalised Linear Model: Poisson Regression

Learning outcomes

By the end of the module, students should be able to:

• Demonstrate understanding of null hypothesis significance testing and contrast this with the estimation approach.
• Conduct reproducible statistical analysis using the general and generalised linear model.
• Construct 4* publication quality visualisations of data.
• Plan an analysis and think critically about data.

Indicative reading list

Wickham, H., & Grolemund, G. (2016). R for data science: Import, tidy, transform, visualize, and model data. Sebastopol, Canada: O'Reilly. Retrieved from http://r4ds.had.co.nz/
Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. London: Sage.
Dalgaard, P. (2008). Introductory statistics with R (2nd ed.). New York: Springer.
Cumming, G. (2012). Understanding the new statistics: Effect sizes, confidence intervals, and meta-analysis. New York: Routledge.

Subject specific skills

Be able to conduct reproducible statistical analysis using the general and generalised linear model.
Be able to construct 4* publication quality visualisations of data.

Transferable skills

Written communication.
Problem solving.