Overview and aims
The aim of this module is to provide students with an introduction to the statistical principles and statistical methods required for the analysis of large datasets. The module extends knowledge of data presentation gained in the Business Information and Decision Making module to include comparative graphical analysis.
The Statistical Approaches to Data Analysis module introduces hypothesis testing and its applications for initial exploration and visualisation of data and for predictive modelling. Statistical decision making is informed by an understanding of the uncertainty built into common statistical models.
This module includes practical computer exercises to familiarise students with the concepts taught. Emphasis is placed on the appropriate selection, implementation and interpretation of statistical techniques.
Statistical techniques are presented in the context of data analytics, market research, financial projection and quality control in product output.
Statistical Approaches to Data Analysis MBA module content
- Statistical inference: population and sample as used in an industrial context (e.g. product sampling, market research)
- Descriptive statistics as an exploratory data analysis tool
- Exploration, visualisation and communication of data. Identification and extraction of key features in visual representations of data
- Confidence, uncertainty and error bars
- Principles, strengths and limitations of hypothesis testing
- Selection of appropriate statistical tests based on the nature of available data and interpretation of the results of tests
- Correlation, regression and forecasting
After studying this Statistical Approaches to Data Analysis MBA module, you should be able to:
- recognise different types of data and select appropriate visual formats for display
- critically appraise and apply relevant techniques for the summary and graphical presentation of large datasets, and interpret the results of your analysis
- demonstrate an understanding of the application of statistical hypothesis testing, and the use of statistical significance in this context
- critically evaluate and present analysis results and statistical procedures using appropriate software packages
This module will help you gain the skills and qualities to:
- Use statistical software packages for data analysis and the presentation of results
- appreciate the fact that simple methods of statistical inference depend on assumptions that are not always met, and take corrective action where necessary
- recognise different types of data and use appropriate methods of display and analysis
- statistically evaluate experimental results and characterise the level of uncertainty involved in forecasts