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Module Descriptor School of Computer Science and Statistics

Module CodeST7002
Module NameIntroduction to Multiple Linear Regression
Module Short Title
Semester TaughtHilary
Contact Hours

Lecture hours:    21

Lab hours:         3

Total hours:        24

Module PersonnelProf M. O’Regan and demonstrators
Learning Outcomes

Students will have the ability

  • to carry out an initial examination of the data
  • to use a regression package (MINITAB) to apply multiple regression to simple data sets
  • to interpret the results of the model
  • to construct and exploit derived variables, such as logs, products and indicator variables
  • to see such modelling as the basis for more advanced statistical analysis
Learning Aims

Multiple linear regression – and its many variants – is the most widely used tool in applied statistics. This course will build on simple linear regression, introduced in the Base Module. The aim is to become familiar with its use, to further develop experience and confidence in use and role of statistical modelling. As the class is diverse in terms of research area and quantitative skills, students are encouraged to conduct small analyses of data in their own research fields.

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
  • The multiple linear regression model and its analysis including:
    • Confidence intervals and statistical significance tests on model parameters
    • Issues in the interpretation of the multiple parameters
    • Analysis of variance in regression: F-tests, r-squared
    • Indicator variables and interaction terms
  • Model validation: residuals, residual plots, normal plots, diagnostics
  • Introduction to logistic regression
Recommended Reading List

The reading list will be available at a later date. 

Module Prerequisites

Base Module ST7001.

Assessment Details

Exam 100%

Module Website
Academic Year of Data2017-18