MATHEMATICS AND STATISTICS OF EXPERIMENTATION
SHE Level  1 
SCQF Credit Points  20.00 
ECTS Credit Points  10.00 
Module Code  M1G308810 
Module Leader  Leonard Scott 
School  School of Computing, Engineering and Built Environment 
Subject  Computing 
Trimester 

Summary of Content
This first year module is designed to provide a basic introduction to the theory and application of mathematical and statistical methods within the chemical sciences. Practical statistical work will be carried out using an appropriate statistical package.
Syllabus
Mathematics: Algebra: Linear equations in one and two unknowns. Quadratic equations. Changing the subject of a formula. Indices. Logarithms. Exponential and logarithmic functions, and their application. Statistics: Descriptive statistics: Types of variable. The frequency distribution and its diagrammatic representations. Measures of location and dispersion. The shape of distributions. The Normal distribution. Analysing data from simple experiments. Probability: Definition and simple examples. Tables of the Standard Normal Distribution. Sampling theory: Definitions (random samples, population). Sampling distribution of the sample mean. The standard error of the sample mean. Estimation and confidence intervals: Estimating the mean of a Normal population, with a confidence interval. Estimating the difference between the means of two Normal populations, with a confidence interval, for independent and paired samples. Hypothesis testing: One and twosample tests of means, for independent and paired samples. Regression and correlation: Descriptive methods. The method of least squares. Coefficient of determination. Correlation coeficient. Introduction to calculus: Basic differentiation, basic integration with application to elementary rates of change problems.
Learning Outcomes
At the end of this module the student should be able to:1 graph mathematical functions, manipulate mathematical expressions, and apply mathematical models to practical problems; 2 collate experimental data and present them graphically, and calculate appropriate summary statistics;3 use tables of the Standard Normal Distribution to calculate probabilities. 4 use statistical methods to analyse and interpret data arising from simple experiments;5 apply simple linear regression analysis;6 use a statistical package to aid the analysis, interpretation and presentation of experimental data.
Teaching / Learning Strategy
Lectures and computer software driven lessons will explain the theoretical development of statistical and mathematical concepts. Practical statistical work will be carried out using Minitab. Computer delivered tests will be used to drive the learning. The assessment will be continuous, consisting of assignments and computer software drive laboratory work. A final examination will be taken.
Indicative Reading
Catch Up Maths & Stats, 2nd Edition 2013; M Harris, G Taylor & J Taylor, Scion Publishing, ISBN 19048422909. Gardiner WP  1997, Statistics for the Biosciences: Data Analysis using Minitab Software  Prentice Hall ISBN 0134475828. Murdoch J and Barnes JA  1998, Statistical Tables for students of science, engineering, psychology, business, management, 4 th Edition  Macmillan. ISBN 0333558596. Maths for Science, S Jordan, S Ross, P Murphy, OUP Oxford 2012, ISBN 0199644969. Core Maths for the Biosciences, MB Reed, OUP Oxford 2011. ISBN 0199216347.
Transferrable Skills
Computer skills Analytical thinking Independent Learning skills
Module Structure
Activity  Total Hours 

Assessment (FT)  20.00 
Lectures (FT)  24.00 
Independent Learning (FT)  120.00 
Practicals (FT)  36.00 
Assessment Methods
Component  Duration  Weighting  Threshold  Description 

Coursework  n/a  20.00  35%  Laboratory Work 
Exam (Exams Office)  2.00  40.00  35%  Final examination 
Coursework  n/a  20.00  35%  Class Test 1 
Coursework  n/a  20.00  35%  Class Test 2 