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STAT8008 - Time Series & M-V Analysis

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Title:Time Series & M-V Analysis
Long Title:Time Series and Multivariate Analysis
Module Code:STAT8008
 
Credits: 5
NFQ Level:Advanced
Field of Study: Statistics
Valid From: Semester 1 - 2014/15 ( September 2014 )
Module Delivered in 2 programme(s)
Module Coordinator: AINE NI SHE
Module Author: Sean Lacey
Module Description: This module will provide the learner with the necessary tools to develop and critically evaluate multivariate regression models and time series models. In this module, the forecasting function of these models is presented and evaluated, enabling the learner to create short and medium term forecasting models.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Describe the assumptions underlying multivariate regression and time series models and interpret the coefficients of these models.
LO2 Perform residuals analysis and tests of fit along with procedures for estimating more parsimonious multivariate regression models.
LO3 Apply the theoretical principles that govern a time series.
LO4 Apply regression and time series model for prediction, and understand and give an account of the paradigm under which the forecasts are being made, along with their reliability.
LO5 Apply SPSS and R to generate and analyse multivariate regression and time series models and perform diagnostic analysis and forecasts for these models.
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
Multivariate Regression
Assumptions, Multiple Correlation, Interpreting Coefficients, Analysis of Residuals, Confidence Intervals of Estimates, F-Test, Dummy Variables, Stepwise Regression
Time series analysis
Decomposition (trend, periodicity, seasonality, white noise), Smoothing Techniques, Serial Correlation, Correlograms, Autoregressive (AR), Moving Average (MA) and mixed (ARMA) models, Vector AutoRegression, Stationarity, Diagnostic Checking
Forecasting
Forecast Error, Confidence Intervals, MAD, MAPE, MPE,RMSE, Thiel U Statistic
Software analysis
R, Minitab, Excel, SPSS
Assessment Breakdown%
Course Work100.00%
Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Short Answer Questions Theory test 1,2 20.0 Week 5
Practical/Skills Evaluation Practical assessment 5 30.0 Week 9
Practical/Skills Evaluation Solve and analyse problems in the laboratory setting. 1,2,3,4,5 50.0 Sem End
No End of Module Formal Examination
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.

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 Lecture 2.0 Every Week 2.00
Lab Computer practical 2.0 Every Week 2.00
Independent & Directed Learning (Non-contact) Work based on texts and class material 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 Lecture 1.5 Every Week 1.50
Lab Computer practical 1.5 Every Week 1.50
Independent & Directed Learning (Non-contact) Work based on texts and class material 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
  • Douglas C. Montgomery, Cheryl L. Jennings, Murat Kulahci 2011, Introduction to Time Series Analysis and Forecasting, John Wiley & Sons [ISBN: 9781118211502]
  • Bruce L. Bowerman, Richard T. O'Connell, Anne B. Koehler 2005, Forecasting, time series, and regression: An Applied Approach, Thomson Brooks/Cole Belmont, CA [ISBN: 978-0534409777]
This module does not have any article/paper resources
Other Resources
 

Module Delivered in

Programme Code Programme Semester Delivery
CR_BBISY_8 Bachelor of Business (Honours) in Information Systems 8 Elective
CR_SDAAN_8 Higher Diploma in Science in Data Science & Analytics 2 Elective

Cork Institute of Technology
Rossa Avenue, Bishopstown, Cork

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