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Module DescriptorSchool of Computer Science and Statistics

Module CodeST2002
Module NameST2002 INTRODUCTION TO REGRESSION
Module Short Title
ECTS5
Semester Taught
Contact HoursLectures/Tutorials/Statistical Laboratories
Module PersonnelLecturer - Arnab Bhattacharya
Learning Outcomes

Regression is probably the most widely used tool in statistics. When students have successfully completed this module they should:

  • Understand the concepts involved in simple and multiple linear regression analysis
  • Understand how to use R software for regression
  • Understand how to diagnose performance 
  • Understand how to create better models
Learning Aims

To introduce students to the statistical ideas and techniques involved in regression analysis. Regression is probably the most widely used tool in Statistics. When students have successfully completed this module, they should:    

  • Understand the concepts involved in simple and multiple linear regression analysis;
  • Understand how to use R software for regression;
  • Understand how to diagnose performance;
  • Understand how to create better models.  
Module Content

Specific topics addressed in this module include:

  • Review of simple linear regression model: assumptions, model fitting, estimation of coefficients and their standard errors, statistical tests; residual diagnostics including plots, identification of outliers, leverage points.  
  • Confidence intervals and statistical significance tests on model parameters;  
  • Issues in the interpretation of the multiple parameters;  
  • Transformation of variables;  
  • Prediction intervals;  
  • Analysis of variance in regression: F-tests, r-squared Model validation: residuals, residual plots, normal plots, diagnostics  
Recommended Reading List
  1. M.H. Kutner, C.J. Nachtsheim, and J. Neter, Applied Linear Regression Models, McGraw-Hill/Irwin; 4 edition (2004). Note this book started life as -Applied Linear Statistical Models' by J. Neter and W. Wasserman and went through various editions/variations with added authors - all versions in the library will contain useful material  
  2. E. Mullins, Statistics for the quality control chemistry laboratory, Royal Society of Chemistry, 2003.  
  3. M. Stuart, "Introduction to Statistical Analysis for Business and Industry, a problem solving approach", Hodder Arnold Publishers, 2003.  
  4. N.R Draper and H Smith - Applied Regression Analysis (Wiley Series in Probability and Statistics) - interested students who are comfortable with a lot of algebra may find this quite helpful.  
  5. M. Mendenhall, T. Sincich, Regression Analysis, Pearson; 7th edition (2011).
Module PrerequisitesJF Introductory Statistics
Assessment Details

One 2-hour examination (80%) and one mid-term assignment (20%). In supplemental examination the performance is based on a single 2-hour examination. 

Module Website
Academic Year of Data2015/16