Module Specifications.
Current Academic Year 2024 - 2025
All Module information is indicative, and this portal is an interim interface pending the full upgrade of Coursebuilder and subsequent integration to the new DCU Student Information System (DCU Key).
As such, this is a point in time view of data which will be refreshed periodically. Some fields/data may not yet be available pending the completion of the full Coursebuilder upgrade and integration project. We will post status updates as they become available. Thank you for your patience and understanding.
Date posted: September 2024
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Repeat examination |
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Description MS226 aims to provide students with an introduction to the basics of statistics, including the use of common discrete and continuous distributions, central limit theorem, sampling techniques as well as estimation and hypothesis testing techniques. Practical examples will be provided throughout using R. | |||||||||||||||||||||||||||||||||||||||||||
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Learning Outcomes 1. Define and apply common discrete and continuous distributions. Extend the theory to joint distributions and conditional distributions. 2. State the Central Limit Theorem. Define a random sample and sampling distributions. Apply basic statistical tests to random samples from a Normal distribution. 3. Estimate parameters using the method of moments and maximum likelihood estimation. Understand the properties of estimators. 4. Perform basic hypothesis tests and tests for goodness of fit. 5. Calculate confidence intervals for common distributions. | |||||||||||||||||||||||||||||||||||||||||||
All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml |
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Indicative Content and Learning Activities
Probability DistributionsDiscrete: Uniform, binomial, Poisson, geometric, negative binomial, hypergeometric. Continuous: Uniform, normal, log normal, exponential, gamma, chi-square, t, F, beta. Application of distributions using R. Use of generating functions to determine the moments and cumulants of random variables.Joint Distributions and Conditional DistributionsExplain joint distributions, marginal distributions and conditional distributions. Calculate the expected value, correlation and covariance of jointly distributed random variables. Extend to linear combinations of random variables. Calculate conditional expectations.Sampling DistributionsState the Central Limit Theorem and understand its fundamental importance in statistics. Understand the use of samples in statistical inference for a population. Define the sampling distributions for the sample mean (normal and t distributions) and sample variance. Ratio of sample variances from Normal distributions and the F-statistic.EstimationUse of method of moments and MLE for parameter estimation. Consideration and use of efficiency, consistency, bias, mean square error, asymptotic distribution of MLEs. Bootstrapping and the use of empirical distributions. Implementation of methods using R.Confidence IntervalsDefine confidence intervals for common distributions. Calculate confidence intervals for two sample situations and paired data. Calculation of confidence intervals in R.Hypothesis testingTheory – null and alternative hypothesis, error types, LRT, level of significance. Critical value approach and probability value approach. Application of hypothesis testing for one and two sample situations for common distributions. Goodness of fit test. Contingency tables. Use of R to perform hypothesis tests and interpretation of R output. | |||||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List | |||||||||||||||||||||||||||||||||||||||||||
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Other Resources None | |||||||||||||||||||||||||||||||||||||||||||