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Curricular information is subject to change
At the end of the module students should be able to demonstrate an understanding of:
Probability – definitions, combinatorics, Bayes Theorem
Random Variables - moment generating functions, conditional expectation, bivariate random variables, conditional and marginal distributions
Distributions – discrete distributions: geometric, binomial;, negative binomial, hypergeometric, Poisson, uniform, normal and continuous distributions: normal, lognormal, exponential, gamma, chi-square, t, F, beta and uniform and compound distributions
The Central Limit Theorem - definition, continuity correction and applications
Statistical Inference – random samples and sampling distributions, interval estimation
Theory of Estimation – methods of moments and maximum likelihood, properties of estimators
Hypothesis Testing – simple and composite hypothesis, likelihood ratio test, contingency tables
Linear Regression – correlation, least squares estimates, inference from linear models, model checking, analysis of variance
Probability
Random Variables
Distributions
The Central Limit Theorem
Statistical Inference
Theory of Estimation
Hypothesis Testing
Linear Regression
Student Effort Type | Hours |
---|---|
Lectures | 42 |
Tutorial | 10 |
Autonomous Student Learning | 74 |
Total | 126 |
Not applicable to this module.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Continuous Assessment: Continuous assessment | Throughout the Trimester | n/a | Other | No | 20 |
Examination: Exam | 3 hour End of Trimester Exam | No | Other | No | 80 |
Resit In | Terminal Exam |
---|---|
Spring | Yes - 3 Hour |
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
Assignments individually marked and returned. Group feedback given in tutorials.
Name | Role |
---|---|
Ms Courtney Clarke | Tutor |