This module builds on the concepts introduced in Quantitative Techniques I. In Statistics the emphasis is on making inferences from data using confidence intervals and hypothesis testing. The concepts of risk and uncertainty are developed in portfolio analysis and decision theory.
Basic Probability:
Mutually exclusive events, independent events, conditional probability, posterior probability and Bayes’ Theorem.
Probability Distributions:
Discrete and continuous distributions. The mean, variance and standard deviation of a probability distribution. The binomial, Poisson and normal distributions.
Sampling:
Methods of sampling and sampling design. Confidence intervals and their application to sampling.
Hypothesis Testing:
Tests for population parameters and a differences in population parameters, using normal and t-distributions.
Chi-Square distribution:
Hypothesis testing for categorical data, tests for independence and goodness-of-fit.
Decision Analysis:
Risk and uncertainty, payoff tables, value of perfect and imperfect information, decision trees.
Portfolio Analysis:
Risk and return of securities. Covariance and correlation between securities. Analysis of a two-stock portfolio.
Stock control:
Economic Order Quantity, quantity discounts, gradual replenishment.
Linear Programming:
The formulation of linear programming problems. Solution to two-variable linear programming problems including shadow prices, sensitivity analysis, alternative optima and degeneracy.
The module is taught through a combination of lecture hours, tutorials, laboratory sessions, and online resources.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Formal Examination | 60 |
| Other Assessment(s) | 40 |