This module will develop the learner's ability to analyse and understand data through the use of sampling theory and inferential statistics.
Discrete probability distributions and continuous distributions: Binomial, Poisson, Geometric, Normal distribution, standard normal, uniform and exponnetial
Regression and Correlation: Bivariate distributions, scatter diagrams, regression lines, least square regression line, the calculation and interpretation of the correlation coefficient and the coefficient of determination.
Sampling: Methods of sampling and sampling design. The central limit theorem, confidence intervals and their application to sampling.
Hypothesis Testing: Null and alternative hypotheses, type I and type II errors, levels of significance, one and two tail tests. Tests for population parameters and a difference in population parameters.
Chi-Square distribution: Its application to contingency tables, tests for independence and goodness-of-fit tests.
Analysis of Variance: Experimental Design, ANOVA tables, randomized block design, one-factor and two-factor ANOVA tests.
This module will be taught using 2-hour weekly lectures and 1-hour tutorial sessions.
The lectures will provide theoretical material which will be underpinned by many examples to demonstrate the use of this material. The practical sessions will provide students with supervised practice time using appropriate exercises.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Formal Examination | 90 |
| Other Assessment(s) | 10 |