Delivery Type	Delivery length / details
Lecture	18 Hours. (18 x 1 hour lectures)
Seminars / Tutorials	6 Hours. (6 x 1 hour tutorials)
Practical	2 Hours. (2 x 1 hour computer practicals)

Assessment Type	Assessment length / details	Proportion
Semester Assessment	continuous assessment	20%
Semester Exam	2 Hours (written examination)	80%
Supplementary Exam	2 Hours Written exam	100%

Learning Outcomes

On completion of this module, a student should be able to:

* Summarise and present a data set;

* Calculate various summary measures for grouped and ungrouped data;

* Construct and interpret statistical diagrams;

* Fit a straight line to suitable data;

* Calculate and interpret correlations;

* Describe and illustrate basic probability concepts;

* Calculate and interpret expectations;

* Solve simple linear programming problems;

* Use and interpret the output from statistical software.

Content

1. STATISTICAL DIAGRAMS: pie charts; simple multiple and stacked barcharts; histograms; cumulative polygons; stem and leaf diagrams
2. SUMMARY MEASURES: minimum, maximum, median, quartiles, percentiles; five number summaries and box-and-whisker plots; mean and mode; variance and standard deviation; calculations from grouped data.
3. SCATTERPLOTS, REGRESSION AND CORRELATION : scatterplots; the idea of a line of best fit; importance of the mean point; least squares regression; the existence of two regression lines for bivariate data; correlation and its measurement; the (product moment) correlation coefficient; Spearman'r rank correlation.
4. PROBABILITY: definition and properties; unions and intersections; mutually exclusive events; the addition law; independent events; the multiplication law; equally likely outcomes; conditional probability; binomial probabilities.
5. LINEAR PROGRAMMING: equations of straight lines; formulating simple linear programming problems; feasible regions; the objective line; deducing the optimum.

The computer package MINITAB: introduction, producing and interpreting diagrams and tables, producing and interpreting summary measures; regression and correlation.

Aims

To introduce students to quantitative methods and to appreciate their importance in business.

Brief description

This module introduces statistical methods and the application of formal decision models in a business context, together with the use of statistical computer software. The software package MINITAB is used in practical classes.

Course Delivery

Assessment

Learning Outcomes

Content

Aims

Brief description

Reading List

Notes