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Curricular information is subject to change
The student will be able to construct a statistical model, fit the model to data and judge when a model is satisfactory. The student will be familiar with a number of statistical model types. The student will be able to apply statistical models to real world applications and assess the adequacy of the applied models.
Indicative Module Content:Introduction to modeling
Model selection
Non-Parametric regression and smoothing
Survival models
Markov chains
Poisson processes
Finite mixture models
Student Effort Type | Hours |
---|---|
Specified Learning Activities | 24 |
Autonomous Student Learning | 72 |
Online Learning | 24 |
Total | 120 |
Knowledge of probability theory and basic statistics. Good knowledge in calculus (integrals, differentials) and linear algebra (vectors, matrices, eigenvalues).
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Multiple Choice Questionnaire: Online quiz in brightspace. | Week 12 | n/a | Standard conversion grade scale 40% | No | 20 |
Continuous Assessment: Online assessments involving statistical modeling mini-projects | Throughout the Trimester | n/a | Other | No | 80 |
Resit In | Terminal Exam |
---|---|
Autumn | Yes - 2 Hour |
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
After the deadline for the assessments, an outline assessment solution will be provided. Feedback will be given to students along with their marks for their assessments.
Name | Role |
---|---|
Dr Niamh Cahill | Lecturer / Co-Lecturer |
Professor Brendan Murphy | Lecturer / Co-Lecturer |
Mr Brian Buckley | Tutor |