Quantitative Business Analysis

Description

Overview: The analysis of data and development of empirical models plays a vital role in business analysis and operational research. This module will provide a basis for students to learn a range of widely used methods ranging from effective presentation of data to development of sophisticated statistical models. Quantitative Business Analysis runs over one semester but in two parts. The first part provides an introduction to the basic theory and application of statistical modelling. Topics covered included data analysis, probability theory, distributions and moments, estimation and hypothesis testing. The second part focuses mainly on two areas - regression modelling and multivariate analysis. While key background theory will be presented, the emphasis is on the generation and interpretation of output from commercially available software. Throughout, there is an emphasis on the use of statistical analysis to help support decision-making and the management of business and industrial problems. Cases are used to illustrate topical issues. Syllabus: The class is taught in two parts. The first half is concerned with developing a solid grounding in the fundamental aspects of statistical reasoning and methods. Internet based material will be used as a substitute for formal lectures allowing contact time with between students and lecturer to be used for exercises, case studies and providing additional support. Specifically the following topics will be covered: Visualising data Summarising data Calculus of probability Probability distributions Estimation Hypothesis Testing Goodness-of-fit Tests The second half builds on the material covered in the first half, first through the development of advanced statistical models and secondly through analysing complex multi-variate data sets. This class will be taught through exploring case studies to motivate statistical model during supervised computer lab sessions. Specifically the following topics will be covered: Simple linear regression modelling Multiple regression modelling Logistic regression Learning Outcomes: 1. subject specific knowledge and skills • To display and interpret data using appropriate visual displays. • Select, construct and interpret summary statistics. • Understand probabilistic reasoning and compute probabilities for simple problems. • Use graphical methods to identify appropriate models and estimate parameters. • Apply and interpret formal statistical estimation procedures and goodness-of-fit tests. • To develop and validate appropriate simple and multiple linear regression models • Understand the basic principles of classification methods • Understand the basic principles of ANalysis Of VAriance (ANOVA). • Use SPSS to perform appropriate statistical analyses 2. cognitive abilities and non-subject specific skills • Develop ability to construct a numerical argument • Critical thinking with respect to quantitative analysis • An ability to express problems in forms conducive for the software support available Assessment: This module will be assessed using 2 exams, both will take place within the Sem 1 exam diet. Each is equally weighted at 50%. One will be a written exam and the other an in-person, computer based exam. Reassessment The same type of assessment and same weighting as the original submission will apply for the resit for this module. Students are required to retake the failed component(s). Existing marks for passed components will be retained, and the new resit mark will be combined to calculate the overall result. If it is not possible to retake a failed component, e.g. if the assessment was group work, the student will be set an alternative individual assessment.