BM0118 Data Analysis

Synopsis of Module

This module aims to develop confidence in handling numerical information by consideration of a range of basic techniques for acquisition, handling, analysis and interpretation of data. An introduction to the role of mathematical models as decision making tools within a business environment will also be provided. By exposure to a variety of methods for handling and modelling data (including the use of spreadsheets), students will gain an understanding of the practical role of business modelling within a modern company.

Formal teaching will take the form of a mixture of lectures, seminars and workshops. Directed learning will mainly consist of additional reading and conclusion of exercises.

Assessment takes the form of two class-based tasks plus a three-hour summative examination

Aims of Module

The module aims to:

•	establish basic skills and confidence in handling numeric data.

•	expose the student to a wide range of introductory statistical techniques for use in the analysis and interpretation of business data.

•	demonstrate the role of modelling as an aid to decision making.

•	introduce and evaluate a selection of models and techniques to assist in the solution of business problems.

•	identify the role of IT as an aid to data analysis and development of business models.

Learning Outcomes

At the end of the module, the student will be able to:

•	carry out numerical calculations

•	identify common sources of data

•	classify data by type

•	recognise and select data collection techniques

•	demonstrate the ways in which data may be presented

•	differentiate between and select statistical tools

•	calculate statistics consistent with data type

•	interpret results of calculations

•	apply modelling techniques

•	explain the relevance of techniques applied

•	use a spreadsheet to perform statistical calculations and present data

•	create and design simple spreadsheet models

Outline Syllabus

DATA COLLECTION

Sources of information and types of data

Primary data collection methods

Sampling and survey design

PRESENTATION OF DATA

Tabulation

Graphical representation

SUMMARISING DATA

Measures of central tendency (mean, median, mode)

Measures of dispersion (standard deviation, range, inter-quartile range)

LINEAR MODELS

Identifying relationships between two variables - correlation

Describing relationships between two variables - linear equations

Modelling relationships between two variables - linear regression

Modelling relationships between more than two variables - multiple regression

PROBABILITY

Basic probability theory

Expected values and confidence intervals

The Normal distribution

FINANCIAL MODELS

Simple and compound interest

Investment appraisal (NPV and IRR)

INTRODUCTION TO LINEAR PROGRAMMING

Simultaneous equations

Graphical linear programming as an application of linear models

INDEX NUMBERS

Construction of an index

Simple and chain base

TIME BASED MODELS

Trend forecasting (using linear regression)

Decomposition (using moving average and seasonality)

Summative Assessment

Assessment A - computer based assignment, weighting 15%

Assessment B - computer based assignment, weighting 15%

Assessment C - closed book examination, weighting 70%