# Module Descriptor School of Computer Science and Statistics

 Module Code ST2002 Module Name ST2002 INTRODUCTION TO REGRESSION Module Short Title ECTS 5 Semester Taught Contact Hours Lectures/Tutorials/Statistical Laboratories Module Personnel Lecturer - 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 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   E. Mullins, Statistics for the quality control chemistry laboratory, Royal Society of Chemistry, 2003.   M. Stuart, "Introduction to Statistical Analysis for Business and Industry, a problem solving approach", Hodder Arnold Publishers, 2003.   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.   M. Mendenhall, T. Sincich, Regression Analysis, Pearson; 7th edition (2011). Module Prerequisites JF 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 Data 2015/16