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
  • A (September start)-B (January start)

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 two-sample 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