The module aims to provide the student with an introduction to quantitative analysis and develop analytical skills of the student. 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. The module focuses on utilising appropriate quantitative analysis in the context of business and focuses on the practical uses of the techniques.
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, and the difference between a census and sampling. Construction of frequency tables, N way tabulations of data.
Presenting Data and Visualisations
Techniques for presenting data, for example, bar charts, histograms, pie charts, frequency polygons, and Ogives.
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.
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, trends, seasonal variation, and forecasting.
Probability
Understanding the general rules of probability.
Probability Distributions
Understanding, calculating, and interpreting discrete and continuous distributions.
Sampling
Understanding methods of sampling and sampling design. The central limit theorem, standard error, sampling distribution, point estimates, confidence intervals, and their application to sampling. Determination of sample size.
Hypothesis Testing
Understanding hypothesis testing, level of significance, P values, Type I, and II error. Testing for a population mean(s) and proportion(s).
Relationship Analysis
Assessment of the relationship between variables, testing for the independence of variables. Interpretation of results.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Formal Examination | 60 |
| Other Assessment(s) | 40 |