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).
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Date posted: September 2024 No Banner module data is available
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Description The aim of this module is to give a thorough grounding in probability, descriptive and inferential statistics, with an emphasis on the acquisition of both skills and understanding. Students will learn how probability and statistics can be used as a tool for solving problems and as a language for communicating information. Students will participate in the following learning activities: a) Lectures which are designed to introduce learners to the mathematical principles and problem solving techniques that underpin this module. b) Tutorials for which problem sheets based on lecture content will be distributed for the students to attempt in advance. c) Reading the textbooks recommended. | |||||||||||||||||||||||||||||||||||||||||||
Learning Outcomes 1. Apply the rules of probability, assign probabilities to events, obtain expectations of discrete and continuous random variables. 2. Test statistical hypotheses and compute confidence intervals. 3. Demonstrate an understanding of concepts by use of examples or counterexamples. 4. Discuss the assumptions and limitations of conclusions drawn from sample data or graphical/numerical summaries of data | |||||||||||||||||||||||||||||||||||||||||||
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
SetsDefinitions, set operations; set identities. Russell's paradox.Organising and Describing DataMeasures of central tendency and variability; graphical summaries.ProbabilityRandom experiments; axioms of probability; independent events; conditional probability; Bayes' theoremRandom VariablesDiscrete and continuous random variables; characteristics of random variables; probability distributions and densities.Probability Density FunctionsBasic combinatorics, the binomial, Poisson, Pascal and normal distributions.SamplingRandom samples; sampling distribution; the central limit theorem; point estimation.Hypothesis TestingConfidence intervals; hypothesis tests.Simple Linear RegressionLeast squares; regression model; correlation. | |||||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List
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Other Resources None | |||||||||||||||||||||||||||||||||||||||||||