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Module Overview

Quantitative Methods & Statistics

The module aims to provide the student with an introduction to quantitative analysis and immerses them in some of the most fundamental areas of quantitative analysis. The module gives the students a detailed understanding of both the role and purpose of quantitative techniques in effective management and in the process of managerial decision making.

Module Code

MATH 1104

ECTS Credits

10

*Curricular information is subject to change

Organising Data

Important statistical terms, the general nature of statistics and its role in business, uses and abuses of statistics. Knowledge of data sources, primary and secondary data, the difference between a census and sampling. Construction of frequency tables, 3 way tabulations of data.

Presenting Data

Techniques for presenting data, for example bar charts, histograms, pie charts, frequency polygons, Ogives and Lorenz curves.

Measures of Central Tendency

Understanding, calculating and interpreting measures of central tendency including the mean, median and mode. Understanding the merits of each measure and selecting the most appropriate measure(s).

Measures of Dispersion

Understanding, calculating and interpreting measures of dispersion including the range, quartiles, percentiles, variance, standard deviation and coefficient of variation. Understanding the merits of each measure and selecting the most appropriate measure(s). Understanding, calculating and interpreting skewness.

Normal Distribution

Description of the normal distribution graphically and with parameters.  The conditions which give rise to a normal distribution.  Z-scores and reading Tables. Finding areas under the normal curve.

Index Numbers

Understanding the concept of index numbers, price relatives, simple and weighted index numbers, Laspeyre, Paasche, Fisher and Marshall-Edgeworth index numbers.  Change of base.  Inflation / Deflation.

Correlation and Regression

Scatter diagrams. Understanding, calculating and interpreting Pearson’s correlation coefficient, Spearman's rank correlation coefficient and the least square regression line.

Time Series Analysis

Understanding time series models. Understanding, calculating and interpreting moving averages, trend, seasonal variation and forecasting.

 

 

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Total Contact Hours                                    48 hours

Tutorials                                                      24 hours

Total Self-Directed Learning Hours           72 hours

Module Content & Assessment
Assessment Breakdown %
Formal Examination60
Other Assessment(s)40