Programme And Module Handbook
 
Course Details in 2019/20 Session


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Module Title LC Quantitative Methods in Accounting and Finance
SchoolBirmingham Business School
Department Birmingham Business School
Module Code 07 24733
Module Lead Robert Fleming
Level Certificate Level
Credits 20
Semester Full Term
Pre-requisites
Co-requisites
Restrictions As a compulsory module: BSc. Accounting and Finance
Contact Hours Lecture-0 hours
Seminar-0 hours
Tutorial-0 hours
Project supervision-0 hours
Demonstration-0 hours
Practical Classes and workshops-0 hours
Supervised time in studio/workshop-0 hours
Fieldwork-0 hours
External Visits-0 hours
Work based learning-0 hours
Guided independent study-0 hours
Placement-0 hours
Year Abroad-0 hours
Exclusions
Description This module enables students to use a range of mathematical and statistical methods and appreciate their uses in both academic and applied contexts related to accounting and finance.

The first half of the module is designed to develop numeracy skills, and provide a solid foundation in mathematics.
Spreadsheet techniques will be introduced where relevant. It will include:
Ratio and percentage applications in finance and accountancy.
Algebraic equations and functions, together with techniques for solving and manipulating them. These will be used for model building and include linear, quadratic and exponent functions.
The concepts and techniques of compound interest and discounting, fundamental to financial mathematics.
The analysis and interpretation of gradients of curves, and maxima and minima of functions using differential calculus are explored.

The second half of the module introduces statistical methods as a series of techniques for describing and summarising data, representing uncertainty through probability and probability models, and for statistical inference. The methods include:
Frequency tables and descriptive statistics; random variables, mean, variance and covariance; probability concepts and measures; discrete and continuous probability distributions.
Sampling and sampling error; alternative sampling techniques; statistical estimation.
Fundamental principles of hypothesis testing; contingency tables, correlation and simple regression.
Learning Outcomes

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

  • formulate, manipulate and solve simple equations and represent relationships using linear and non-linear models.
  • understand logarithms, and solve exponent and quadratic functions
  • use compound interest and discounting techniques to calculate future values and present values.
  • understand gradients of curves; use differentiation to calculate the gradients of standard functions; find maximum and minimum points on curves.
  • calculate and interpret measures of central tendency and dispersion using spreadsheet functions as appropriate
  • understand basic probability theory, and apply discrete and continuous probability distributions
  • apply sampling theory to estimation, and to parametric and nonparametric hypothesis testing.
  • explore the association between two variables through the use of variance, covariance and correlation analysis.
Assessment 24733-01 : Class test : Class Test (15%)
24733-02 : Coursework : Coursework (25%)
24733-03 : Final Exam : Exam (Centrally Timetabled) - Written Unseen (60%)
Assessment Methods & Exceptions A written examination of 2 hours. (60%). Coursework assignment, 1,000 words (40%). Reassessment:
A written examination of 2 hours. (100%).
Other
Reading List