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FINN1037: Quantitative Methods 2

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Type Tied
Level 1
Credits 10
Availability Available in 2024/2025
Module Cap None.
Location Durham
Department Finance

Prerequisites

  • None

Corequisites

  • None

Excluded Combinations 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 statistics, data fundamentals, organisation and visualisation
  • Measures of central tendency and dispersion
  • Basic probability concepts and distributions
  • Sampling and the Central Limit Theorem
  • Confidence intervals and sample size determination
  • Hypothesis testing in finance
  • Introduction to regression analysis and its applications in finance

Learning Outcomes

Subject-specific Knowledge:

  • 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 principles of regression analysis and its potential applications in modelling and predicting financial relationships

Subject-specific Skills:

  • 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.

Key Skills:

  • 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 written in-person exam and an assignment.

Teaching Methods and Learning Hours

ActivityNumberFrequencyDurationTotalMonitored
Workshops10Weekly2 hrs20Yes
Preparation and Reading80 
Total100 

Summative Assessment

Component: Written AssignmentComponent Weighting: 40%
ElementLength / DurationElement WeightingResit Opportunity
Assignment1250 words100same
Component: ExamComponent Weighting: 60%
ElementLength / DurationElement WeightingResit Opportunity
Written in-person exam2 hours100same

Formative Assessment

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

More information

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