Introductory description

As the business management landscape undergoes constant evolution, the utilization of advanced statistical tools proves instrumental in facilitating progress from foundational enhancements to achieving excellence. This module covers statistical methods essential for effective data modelling. It describes the application of statistical techniques for comprehensive data analysis and interpretation. Additionally, it explores some of the tools for investigating processes, aiming either to solve specific problems or to gain insights that can shape future development and enhancement strategies.

This module describes the general context in which statistical techniques are appropriate, and when they are not. It indicates the basis of statistics as a means of modelling the system under consideration, and describes some of the tools for investigating processes, either to solve specific problems or to gain insights to support future development and improvement.

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.

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.

• Statistical models and "real world" systems.
• Statistical Distributions (Continuous and Discrete).
• Exploratory Data Analysis.
• Sampling and Inference.
• Analysis of Variance (ANOVA).
• Regression (linear and non-linear) and Correlation.
• Time-series Analysis.

Learning outcomes

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

• Collaboratively apply statistical tools and methods tailored to specific scenarios, address challenges and provide insightful recommendations for decision making.
• Evaluate the assumptions and assess the appropriateness of different statistical models for diverse scenarios.
• Apply advanced statistical tools and techniques to solve problems in real-world systems.
• Critically interpret the quantitative data analysis results, present them in a meaningful way for decision making and provide practical recommendations.
• Demonstrate advanced practical skills in implementing statistical analyses using applicable software packages and illustrate the outcomes through clear visualizations.

Indicative reading list

Please refer to the Talis Aspire link.

View reading list on Talis Aspire

International

Topics are of high international demand

Subject specific skills

Students are expected to gain/improve on the following:

• Data management and statistical analysis.
• Clear mathematical communication.
• Advanced quantitative reasoning.
• Statistical modelling.
• Use of software to support decision making.

Transferable skills

Problem-solving; critical thinking; communication; Teamwork;