Module FINN1037: Quantitative Methods 2

Department: Finance

FINN1037: Quantitative Methods 2

Prerequisites

• None

Corequisites

• None

Excluded Combination of Modules

• None

Aims

• To introduce students to the essential analytical and statistical techniques necessary in their degrees, to support other year 1 modules and to provide a foundation for further study.

Content

• Introduction to probability and uncertainty.
• Probability Density Functions and Probability Mass Functions.
• Descriptive statistics
• Bayesian Statistics
• Fitting probability functions and the method of maximum likelihood.
• Hypothesis testing and the Neyman—Pearson/Frequentist approach.
• Linear regression models and the least squares estimator.

Learning Outcomes

• By the end of the module students should:
• Have knowledge of the foundational statistical techniques that underpin those studied in further core and optional modules on the degree.
• Understand that statistical techniques are underpinned by assumptions and be able to design statistical experiments using data.
• Have knowledge of the variety of use cases for different statistical techniques and be confident to adapt and apply them in a number of contexts.
• Have sufficient knowledge to critically evaluate statements made regarding data and understand confidence, significance and statistical power.
• Understand the difference in the intuition between Bayesian and Frequentist analysis and critically evaluate evidence from statistical techniques applying these approaches.

• Will have acquired an array of mathematical and statistical skills widely used in finance and business.
• Be prepared for successful study of second year core modules in finance, economics and business.

• Written communication - through formative and summative assessment.
• Adaptive problem solving and critical thinking when applying statistical techniques to data and the ability to critically evaluate statistical evidence.
• Be able to learn new techniques based on those studies in this module.
• Have appropriate skills in designing statistical experiments using data.
• Computer literacy through the use of appropriate statistical software.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• Workshops will deliver essential material in an efficient way to a large audience and will identify key reading and exercises.
• Students are arranged in subgroups in each workshop and the classes are divided into a whole class and subgroup component.
• Formative assessment is by means of exercises undertaken in the workshops.
• Summative assessment is by means of a multiple-choice timed test in term and an assignment. 

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

A range of exercises will be completed throughout the module in workshops.

■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University