# Untitled

**Source**: https://teaching.scss.tcd.ie/wp-json/wp/v2/module/355
**Parent**: https://teaching.scss.tcd.ie/module/stu22005-applied-probability-ii/

{"id":355,"date":"2020-07-17T14:48:24","date\_gmt":"2020-07-17T14:48:24","guid":{"rendered":"https:\/\/teaching.scss.tcd.ie\/?post\_type=module&p=355"},"modified":"2026-01-19T10:40:33","modified\_gmt":"2026-01-19T10:40:33","slug":"stu22005-applied-probability-ii","status":"publish","type":"module","link":"https:\/\/teaching.scss.tcd.ie\/module\/stu22005-applied-probability-ii\/","title":{"rendered":"STU22005 – Applied Probability II"},"content":{"rendered":"\n

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| **Module Code<\/strong><\/td> STU22005<\/td><\/tr>|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **Module Name<\/strong> <\/td> Applied Probability II<\/a><\/td><\/tr>|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **ECTS Weighting [**[1]<\/strong><\/a><\/strong><\/td> 5 ECTS <\/td><\/tr>|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **Semester Taught<\/strong><\/td> Semester 2<\/td><\/tr>|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **Module Coordinator\/s  <\/strong><\/td> Professor Caroline Brophy<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nModule Learning Outcomes <\/h2>\n\n\n\n On successful completion of this module, students will be able to:<\/p>\n\n\n\n  \n1. Derive and implement confidence intervals and hypothesis tests for means and variances;<\/li>\n\n\n\n- Conduct and explain the outputs of hypothesis testing in regression analysis;<\/li>\n\n\n\n- Define and compute maximum likelihood estimates;<\/li>\n\n\n\n- Implement a bootstrap analysis to construct confidence intervals and perform hypothesis tests.<\/li>\n<\/ol>\n\n\n\nModule Content<\/h2>\n\n\n\n This module will cover a range of topics, including:<\/p>\n\n\n\n          \n- Recap of probability distributions;<\/li>\n\n\n\n- Derivation of the confidence interval and tests of hypothesis for normal data;<\/li>\n\n\n\n- The Central Limit Theorem and what it says about confidence intervals and tests of hypothesis;<\/li>\n\n\n\n- Hypothesis testing for regression analysis;<\/li>\n\n\n\n- The difference between a confidence interval and a prediction interval;<\/li>\n\n\n\n- The bootstrap approach to confidence intervals and tests of hypothesis;<\/li>\n\n\n\n- Introduction to maximum likelihood estimation and computation;<\/li>\n\n\n\n- Graphical assessments of normality;<\/li>\n\n\n\n- Introduction to multivariate distributions.<\/li>\n<\/ul>\n\n\n\nTeaching and Learning Methods <\/h2>\n\n\n\n Lectures and laboratories. <\/p>\n\n\n\nAssessment Details <\/h2>\n\n\n\n                             |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |                            | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |                            | **Assessment Component<\/strong><\/td> **Brief Description<\/strong> <\/td> **Learning Outcomes Addressed<\/strong><\/td> **% of Total<\/strong><\/td> **Week Set<\/strong><\/td> **Week Due<\/strong><\/td><\/tr>|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Examination <\/td> In-person 1.5 hour exam<\/td> All<\/td> 85%<\/td> N\/A<\/td> N\/A<\/td><\/tr>|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Continuous Assessment<\/td> Three live online quizzes run during the semester<\/td> All <\/td> 15%<\/td> Details on Blackboard<\/td> Details on Blackboard<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nReassessment Details<\/h2>\n\n\n\n Examination (In-person 1.5 hours, 100%).<\/p>\n\n\n\n <\/p>\n\n\n\nContact Hours and Indicative Student Workload<\/h2>\n\n\n\n  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **Contact Hours (scheduled hours per student over full module), broken down by<\/strong>: <\/td> **39 hours<\/strong><\/td><\/tr>|  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Lecture and in-class discussion \/ question and answer sessions <\/td> 32 hours<\/td><\/tr>|  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Laboratory \/ Tutorial<\/td> 7 hours<\/td><\/tr>|  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | | **Independent Study (outside scheduled contact hours), broken down by:<\/strong><\/td> **86 hours<\/strong><\/td><\/tr>|  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | | Preparation for classes and review of material (including preparation for examination, if applicable)<\/td> 81 hours<\/td><\/tr>|  |  |  |  | | --- | --- | --- | --- | | Completion of assessments (including examination, if applicable)<\/td> 5 hours<\/td><\/tr>|  |  | | --- | --- | | **Total Hours<\/strong><\/td> **125 hours<\/strong><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nRecommended Reading List<\/h2>\n\n\n\n \n- George Casella, Roger L. Berger, Statistical Inference, 2nd Edition.<\/li>\n\n\n\n- Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining, Introduction to Linear Regression Analysis, 5th Edition.<\/li>\n\n\n\n- Blitzstein JK, Hwang J. Introduction to Probability, Second Edition. CRC Press; 2019. doi:10.1201\/9780429428357 (available in electronic form from the TCD library, and also has been made freely available by Harvard here:\u00a0[https:\/\/projects.iq.harvard.edu<\/a>).<\/li>\n\n\n\n- Bain, L. J., & Engelhardt, M. (1992). Introduction to Probability and Mathematical Statistics. Brooks\/Cole Cengage Learning.<\/li>\n\n\n\n- John A. Rice, Mathematical Statistics and Data Analysis, 3rd Edition<\/li>\n\n\n\n- Quinn, G. P., & Keough, M. J. (2002).\u00a0Experimental design and data analysis for biologists. Cambridge university press.<\/li>\n<\/ul>\n\n\n\nModule Pre-requisites <\/h2>\n\n\n\n ****Prerequisite modules:<\/strong> <\/strong>STU11002 and STU22004. Alternatively, STU12501, STU12502 and STU23501.<\/p>\n\n\n\nModule Co-requisites <\/h2>\n\n\n\n N\/A<\/p>\n\n\n\nModule Website<\/h2>\n\n\n\n [Blackboard<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" (Semester 2, 5 credits) This module will develop several important ideas in statistical analysis making use of some of the ideas introduced in STU22004. 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