Predictive Modelling - MAST5955

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2022 to 2023
Canterbury
Autumn Term 5 15 (7.5) Oscar Rodriguez de Rivera Ortega checkmark-circle

Overview

This module will develop the ideas introduced in Introduction to Data Analytics by introducing new statistical models. These include the commonly used Analysis of Variance (ANOVA), Analysis of Covariance (ANCOVA), simple and multiple linear regression. These models are suitable for continuous responses and their practical use will be the main focus. The module will conclude by considering more complicated models for binary data (logistic regression) and data observed over time. All methods will be taught using a suitable computer package.

Syllabus: Introduction: What is a statistical model; what is prediction; ANOVA and ANCOVA; Linear regression (simple and multiple): parameter estimation, diagnostics, variable selection, model interpretation and prediction; Logistic regression: particularities, estimation, analysis of deviance, applications (e.g. text analysis); Prediction: prediction (interpolation and extrapolation) using linear regression and logistic regression; Introduction to time series analysis.

Details

Contact hours

30 contact hours
120 hours of private study
Total number of study hours: 150

Method of assessment

100% coursework

Indicative reading

Dietz, D.M., Barr, C.D., and Cetinkaya-Rundel, M. (2015) OpenIntro Statistics, 3rd Edition. https://drive.google.com/file/d/0B-DHaDEbiOGkc1RycUtIcUtIelE/view.
Horton, N.J., Pruim, R and Kaplan, D.T. (2015) A Student's Guide to R. https://cran.r-project.org/doc/contrib/Horton+Pruim+Kaplan_MOSAIC-StudentGuide.pdf
Darlington, R.B., Hayes, A.F. (2016) Regression Analysis and Linear Models: Concepts, Applications, and Implementation (Methodology in the Social Sciences). Guilford Press.
Faraway, J.J. (2004) Linear Models with R. Chapman and Hall/CRC

See the library reading list for this module (Canterbury)

Learning outcomes

The intended subject specific learning outcomes.
On successfully completing the module students will be able to:
1 demonstrate knowledge and critical understanding of the underlying concepts and principles of statistical modelling;
2 demonstrate the capability to use a range of established techniques and a reasonable level of skill and manipulation in the following areas: measures of relationship, ANOVA and ANCOVA, linear regression, logistic regression and time series analysis;
3 apply the concepts and principles of statistical modelling in well-defined contexts beyond those in which they were first studied, showing the ability to evaluate critically the appropriateness of different tools and techniques;
4 make appropriate use of R and IT tools to analyse data and report results.

The intended generic learning outcomes.
On successfully completing the module students will be able to:
1 make effective use of IT facilities for solving problems;
2 communicate straightforward arguments and conclusions reasonably accurately and clearly;
3 manage their own learning and development;
4 communicate technical and non-technical material competently.
5 demonstrate critical thinking skills.

Notes

  1. Credit level 5. Intermediate level module usually taken in Stage 2 of an undergraduate degree.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
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