To give students a conceptual introduction to the field of statistics and its applications. To enable students to apply statistical methodologies in their own organisations. To provide students with a full understanding of how statistical inference provides sound evidence for business decisions.

Data and Statistics - various types of data, qualitative and quantitative data, sources of data. Graphical presentation of data - bar charts, pie charts, histograms, ogive curves, box plots. Measures of location and spread - mean, median, mode, range, standard deviation and variance. Introduction to probability - discrete and continuous distributions e.g. Binomial, Poisson and Normal. Sampling and Sampling Distributions - populations and samples, various sampling methods. Point and Interval estimation for means, variances and proportions in one and two sample applications. Hypothesis testing - One and two tailed tests, type I and type II errors, p - values. Analysis of qualitative data - contingency tables, goodness - of - fit tests. Correlation and Linear Regression - scatter plots, method of least squares, use of residuals to validate model. Analysis of Variance. Multiple Regression - multicollinearity, dummy variables, model assumptions, variable selection procedures. Applications of statistics - forecasting, quality control, index numbers, decision analysis. Non- parametric Statistics - sign test Wilcoxon signed - rank, Mann - Whitney and Krusaal - Wallis tests. Spearman-s test for linear correlation. The course will be underpinned by extensive use of Case studies Statistical software packages Student organisation based assignments.

Analyse data sets using representative and reduction techniques. Distinguish between qualitative and quantitative data. Identify the various sources of data e.g. library, surveys, questionnaires etc. Analyse qualitative data. Apply multiple regression techniques. Apply appropriate statistical methods in their own organisations. Understand estimation and hypothesis testing medthodologies. Apply statistcal software and interpret the resulting analysis.

Discuss the role of probability as a measure of occurrence. Demonstrate variation and its causes.

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The module will be taught on a 'learn-apply-learn-apply-' philosophy.There are in term assignments where students are expected to apply statistical methologies within their own organisations and a final examination. Graduate attributes will be developed in the following ways: Knowledgeable: Students will gain a deep knowledge of data analysis for business decisions through practical examples, coursework and in-class groupwork; Proactive: Students are expected to be proactive in their own learning through independent study and self-directed learning; Responsible: Students will develop a sense of responsibility through class discussions on various ethical issues involved in decisions; Collaborative: Students will be required to participate in class discussions and group work; Articulate: Students will become articulate in expressing advice through the use of in-class discussions, presentations and written coursework