To give students the ability to collect, evaluate and draw conclusions from information collected, using statistical techniques.
Analysis of Variation, Experimental Design and Optimisation: Randomisation, blocking, balance design, Latin Squares, One-way Anova, Two-way Anova, one at a time design, steepest ascent Chi Squared Distribution and Goodness-of-fit: Contingency tables and chi-squared analysis. Non Parametric Statistics: Sign tests and Wilcoxon test signed rank test. Linear Regression and Correlation: simple and mutliple linear regression with application to drug potency and shelf-life estimation. Probability and Probability Distributions: probability of simple, two and multiple events, conditional probability, permutations, combinations, binomial, possion and normal distribution. Review of Measures of Central Tendency and Variation: sampling, frequency distributions, graphical representation, modes, median, arithmetic mean, variance, standard deviation. Test of Hypotheses: z-tests, t-tests, f-tests, test for proportions, one-sample and two-sample tests,confidence intervals, type I and type II errors.
Introduction to R for statistical analysis.
The primary teaching/learning vehicle will consist of formal lectures in which techniques and issues will be introduced. Tutorials will be made available based on need.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Formal Examination | 70 |
| Other Assessment(s) | 30 |