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As such, this is a point in time view of data which will be refreshed periodically.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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Description This module firstly introduces students to data summarisation and presentation, including numerical measures of location and spread for both ungrouped and grouped data, and graphical methods. Secondly, it reviews the properties of the Normal distribution and calculations of probabilities involving normally distributed random variables and means of large random samples. Thirdly, it looks at concepts in hypothesis testing including Type I and Type II errors and tests for a single population mean and differences between two population means. Fourthly, students will learn how to perform power and sample size calculations and estimate confidence intervals. Fifthly, this module focuses on methods for analysing complex parametric data, basic correlation metrics and linear regression analysis. Sixthly, students will learn about non-parametric tests and then examine elementary aspects of Bayes' theorem, conditionality and odds ratios. The module will conduct work through a simple freely available graphical interface called R for data exploration and calculations. | |||||||||||||||||||||||||||||||||||||||||||
Learning Outcomes 1. Summarise and explore data numerically and graphically using computational tools 2. Identify and test in an appropriate manner sources of variation in observational and experimental data 3. Perform probability calculations for normally distributed variables 4. Determine p-values, false discovery rates and power in tests 5. Identify and perform some one and two-sample statistical inference procedures for parametric models 6. Calculate and interpret correlation and create simple linear regression model | |||||||||||||||||||||||||||||||||||||||||||
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
Data sampling summarisation and visualisation R How to use R for data exploration and calculations variables Random variables and the Normal distribution T-tests T-tests between categories Power Power calculations ANOVA What to do with data that has more than two categories or more than two factors that may interact Correlation coefficients Regression Regression models Rank-based tests Non-parametric rank-based tests Bayes' theorem Bayes' theorem and odds ratios | |||||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List
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Other Resources None | |||||||||||||||||||||||||||||||||||||||||||