| Description |
The course will cover basic probability theory, including conditional probabilities & Bayes theorem, permutations & combinations, random variables and expected values, distributions (including binomial, Poisson, normal, geometric, hypergeometric, Chi-square, F & Students t), the central limit theorem, estimation, confidence intervals, hypothesis testing, (including p values & confidence limits), Type I & Type II error, Students t tests and non-parametric alternatives. The course will also cover contingency tables & goodness of fit tests, regression & correlation, and simple one-way ANOVA. The course will make extensive use of PC-based software (e.g., SPSS, Matlab TM or R). Calculus and linear algebra are not required , but are recommended. |
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