Graphical representation: Histogram, Cumulative Frequency curve, Ogive Lorenz curve.
Measure of Location
Raw Data: mean, median, mode.
Tabulated discrete data: mean, median, mode.
Tabulated continuous data: mean, median, mode.
Other measure of Location.
Relationships between averages.
Measure of Dispersion
Concept; Basic relationships in Probability.
Probability Trees; Bayes Theorem.
Discrete Probability Distribution: Poisson distribution; Binomial distribution.
Normal distribution; Standard Normal Distribution; Normal Approximation.
Using Statistical Inference
Statistical Inference; Sampling Distribution.
Estimation- Inference about a population.
Confidence Interval for the population mean/ population percentage.
The t- distribution.
Significance testing using Confidence intervals.
Correlation and Simple Regression Analysis
Measuring Linear Association.
Prediction from the regression line.
Linear Programming & Matrices
Formulate a problem in Linear Programming terms.
Solve two-variable problems using inequalities and graphical methods.
Maximization and Minimization problems (Max Profit; Min Cost).
Matrix Manipulation; Addition; Multiplication; Inverse of a Matrix.
Solving simultaneous equations using Matrices (Cramer’s rule).
Leontief Input-Output analysis.
Use of Calculus
Differentiate different functions.
Apply differentiation to economic models.
Find maxima and minima of one variable function.
Differentiate function of more than one variable.
Find maxima and minima subject to constraints.
Minimum Intended Learning Outcomes (MIMLOs)
Upon successful completion of this module, the learner should be able to:
Apply Financial Mathematics concepts with application to present value and depreciation, sinking funds, annuities and mortgages.
Calculate different measures of central tendency and dispersion, different measures of central tendency and dispersion.
Discuss the reasons why quantitative methods are important in business decision-making.
Apply the concept of Probability in assessing the risk factor in the decision making process, and in Statistical Inference.
Solve business problems in Linear Programming terms using inequalities and graphical methods
3, 4, 5
Where the combined marks of the assessment and examination do not reach the pass mark the learner will be required to repeat the element of assessment that they failed. Reassessment materials will be published on Moodle after the Examination Board and will be aligned to the MIMLOs and learners will be capped at 40% unless there are personal mitigating circumstances.
Aims & Objectives
Gain knowledge and mathematical skills that will help to analyse, evaluate and find solutions to business problems.
Develop simple mathematical models which attempt to describe a business problem by a number of equations or mathematical procedures.
Understand the importance of quantitative methods in business decision-making.
Understand and apply the concept of Probability in assessing the risk factor into decision making process, and in Statistical Inference.
Gain knowledge in Matrix, Linear Programming, Simple Game theory and Calculus.