STP80120 – Advanced Linear Models 2

Module CodeSTP80120
Module NameAdvanced Linear Models 2
ECTS Weighting5 ECTS
Semester taughtSemester 2
Module Coordinator/s  Prof Caroline Brophy

Module Learning Outcomes

  1. Fit and interpret simple and multiple linear regression models, and understand their underlying theory, using statistical software as appropriate
  2. Diagnose and resolve diagnostic issues with regression models, using statistical software as appropriate.;
  3. Identify data situations when generalized linear models are required beyond regular regression models, and fit and interpret a range of GLMs, using statistical software as appropriate;

Module Content

This module will cover simple linear regression and multiple regression, with underlying theory; Regression model diagnostics and how to resolve problems; Generalized Linear Models (GLMs), to include an overview of GLMs, logistic regression, Poisson regression and ordinal regression. An emphasis will be placed on learning how to implement these techniques using R or other statistical software. Multiple choice quizzes will be used as study aids to help students evaluate their knowledge throughout the semester.

Teaching and learning Methods

Online module consisting of 4 weekly sessions. Live tutorial sessions each week. All learning takes place through Blackboard.

Assessment Details

Assessment ComponentBrief DescriptionLearning Outcomes Addressed% of totalWeek setWeek Due
Final Exam 2 Hour Take Home examAll100%NANA

Reassessment Details

Examination 100% take home 5hr exam

Contact Hours and Indicative Student Workload

Contact Hours (lectures, labs, tutorials, meetings, etc.)30
Independent study (outside scheduled contact hours), broken down by:50
Preparation for classes and review of material (including preparation for examination, if applicable20
completion of assessments (including examination, if applicable)25
Total Hours125

Recommended Reading List

Material will be provided when needed.

Module Pre-requisites

N/A

Module Co-requisites

N/A

Module Website

Blackboard