IB94X-15 Business Statistics
Introductory description
This module will introduce students to the R data analysis package and to a range of statistical analysis tools. Students will get hands on experience with analysing real datasets in R, with extensive support and guidance from teaching staff. They will learn how to identify the correct statistical tools to use to answer different business related questions, and how to interpret the results.
Module aims
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. It is also designed to introduce students to the R statistical programming language. Much of the material is required knowledge for other core and optional modules.
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.
Introduction to R
Visualising Data in R
Likelihood
The General Linear Model: t-tests
Confidence Intervals
The General Linear Model: ANOVA and Regression
The General Linear Model: Repeated Measures
The Generalised Linear Model: Logistic Regression
The Generalised Linear Model: Poisson Regression
Learning outcomes
By the end of the module, students should be able to:
- Demonstrate comprehensive understanding of null hypothesis significance testing and contrast this with the estimation approach
- Plan an analysis of, 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
Conduct reproducible statistical analysis using the general and generalised linear model
Construct 4* publication quality visualisations of data
Transferable skills
Written communication.
Problem solving.
Study time
| Type | Required |
|---|---|
| Other activity | 27 hours (36%) |
| Private study | 49 hours (64%) |
| Total | 76 hours |
Private study description
Self study to include pre-reading for lectures and preparation for lab sessions
Other activity description
This module will be split as two hours face-to-face workshops and one online lecture hour per week. The lecture hour may be live, or may be prerecorded, or as asynchronous tasks with either online or face-to-face support
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Assessment group A3
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
Assessment component |
|||
| Individual Assignment | 80% | 59 hours | Yes (extension) |
Reassessment component is the same |
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Assessment component |
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| Mid-term assignment | 20% | 15 hours | Yes (extension) |
Reassessment component is the same |
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Feedback on assessment
A mixture of hand-picked general comments plus a bespoke comment per question.
Post-requisite modules
If you pass this module, you can take:
- IB9EO-15 Pricing Analytics
- IB9V6-15 Discrete Event Simulation
Courses
This module is Core for:
- Year 1 of TIBS-N1N3 Postgraduate Taught Business Analytics