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STAT8011 - Regression Analysis

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Title: Regression Analysis
Long Title: Regression Analysis
Module Code:STAT8011
 
Credits: 5
NFQ Level:Advanced
Field of Study: Statistics
Valid From: Semester 1 - 2018/19 ( September 2018 )
Module Delivered in 1 programme(s)
Module Coordinator: David Goulding
Module Author: Catherine Palmer
Module Description: In this module the learner will study statistical techniques, with particular emphasis on large data sets. Statistical analytical software such as R will be used in the labs.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Explore data sets and select appropriate statistical methods for data science problems.
LO2 Apply the concepts of Design of Experiments and analyse associated sets of data.
LO3 Analyse data sets with continuous response variables and multiple predictors (both categorical and continuous) using ANOVA, multiple regression and ANCOVA.
LO4 Analyse data sets with binary response variables using logistic regression.
LO5 Interpret the results of statistical analyses performed by a software package or presented in research papers.
Pre-requisite learning
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named CIT module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
Co-requisites
No Co Requisites listed
 

Module Content & Assessment

Indicative Content
Data Analysis Protocol
Consolidate prior knowledge of graphical and numerical descriptive statistics to explore categorical and continuous data sets. Outliers, missing values, testing of assumptions and transformation of variables. Model fitting and model interpretation. Model diagnostics.
Design of Experiments
Observational (vs) experimental data. The fundamentals of experimental design. Analysis of variance. Factorial design.
Multiple Regression
Assumptions, collinearity, interpreting coefficients, model fitting, model diagnostics, confidence intervals of coefficients, Analysis of covariance (ANCOVA).
Logistic Regression
Overview of different types of generalised linear models and their uses with a focus on logistic regression for binary data.
Software analysis
SPSS, R, Excel
Assessment Breakdown%
Course Work100.00%
Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Short Answer Questions Theory Assessment - Design of Experiments and ANOVA 1,2,3,5 25.0 Week 7
Short Answer Questions Theory Assessment - multiple regression and logistic regression 1,3,4,5 25.0 Week 11
Project Analyse (large) data set(s) and report results. 1,3,5 50.0 Sem End
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

The institute reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Module Content delivery 2.0 Every Week 2.00
Lab Labs 2.0 Every Week 2.00
Independent Learning Study, practice and completion of worksheets 3.0 Every Week 3.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Module Content delivery 1.5 Every Week 1.50
Lab Lab 1.5 Every Week 1.50
Independent Learning Study, practice and completion of worksheets 4.0 Every Week 4.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 3.00
 

Module Resources

Recommended Book Resources
  • Michael J. Crawley 2012, The R Book, Wiley-Blackwell [ISBN: 978-0470973929]
  • Julian J. Faraway 2014, Linear Models with R, Chapman and Hall [ISBN: 978143988733]
Supplementary Book Resources
  • Annette J. Dobson 2002, An Introduction to Generalized Linear Models, Second Edition, Chapman and Hall [ISBN: 978-1584881650]
  • Trevor Hastie, Robert Tibshirani, Jerome H. Friedman 2009, The Elements of Statistical Learning, Second Ed., Springer [ISBN: 978-038784857]
This module does not have any article/paper resources
Other Resources
 

Module Delivered in

Programme Code Programme Semester Delivery
CR_SDAAN_8 Higher Diploma in Science in Data Science & Analytics 2 Mandatory

Cork Institute of Technology
Rossa Avenue, Bishopstown, Cork

Tel: 021-4326100     Fax: 021-4545343
Email: help@cit.edu.ie