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STAT10060

Academic Year 2024/2025

Statistical Modelling (STAT10060)

Subject:
Statistics & Actuarial Science
College:
Science
School:
Mathematics & Statistics
Level:
1 (Introductory)
Credits:
5
Module Coordinator:
Professor Brendan Murphy
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This course is an introduction to some of the basic statistical methods and tools used for analyzing data and make decisions. Core concepts will be presented using practical data examples, focusing on applications and critical evaluation. The module will introduce these methods also using the statistical software R.
Main topics covered:
- Introduction and basics of inference.
- Comparing two populations: Confidence intervals and hypothesis testing for the difference between population means and population proportions - Independent and paired samples - Sample size calculations.
- Analysis of categorical data: One and two-way frequency tables - Hypothesis testing - Test for association.
- Simple linear regression: Correlation - Regression line - Checking model adequacy - Using the model for inference and prediction.
- Analysis of variance: One-way ANOVA - Multiple comparisons - Randomized block experiments - Two-way ANOVA.

About this Module

Learning Outcomes:

On completion of this module, students should have acquired the following skills:
- Perform basic inference for comparing two populations
- Being able to analyze and perform inference on categorical data in the form of frequency tables
- Fit, assess and use the simple linear regression model
- Conduct analysis of variance for comparing multiple populations and investigate effect of factors
- Use the statistical software R to implement the methods and being able to interpret the relevant output
- Have a general understanding of all the statistical methods introduced and being able to use them according to the context and purpose of analysis

Student Effort Hours:
Student Effort Type Hours
Autonomous Student Learning

90
Lectures

24
Tutorial

5
Computer Aided Lab

5
Total

124

Approaches to Teaching and Learning:
Lectures, tutorials, computer labs, enquiry and problem-based learning.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Incompatibles:
ECON20040 - Statistics for Economists, MATH10250 - Intro Calculus for Engineers , STAT20080 - Regression & ANOVA, STAT20090 - Stat Infer & Good of Fit


 

Assessment Strategy  
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (In-person): End of semester examination End of trimester
Duration:
2 hr(s)
 Standard conversion grade scale 40% No

60

 No
Assignment(Including Essay): Written and coding assignments Week 6, Week 9 Standard conversion grade scale 40% No

25

 No
Quizzes/Short Exercises: Online quizzes Week 4, Week 7, Week 10 Standard conversion grade scale 40% No

15

 No

Carry forward of passed components
No
 

Resit In Terminal Exam
Autumn Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Dr Codrin Andrei Lecturer / Co-Lecturer
Kate Finucane Tutor
Ms Hannah Kane Tutor
Brian O'Sullivan Tutor
Dr Fabian Ofurum Tutor
Yinshen Xu Tutor
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