Applied Statistics

Module Title - Applied Statistics
Number of credits - 20

Module description

This module focuses on data analysis using multiple regression models. A unifying theme is the transferability of statistical ideas. Topics covered include linear regression models, general linear models, prediction problems, sensitivity analysis, analysis of incomplete data, robust regression, and multiple comparisons.

To emphasise the importance of properly designed studies in statistics, basics of experimental design will be introduced. Randomization, blocking and confounding, factorial designs, fractional factorial designs, incomplete block designs will also be discussed.

In the later part, generalized linear models for continuous and discrete data will be introduced. Logistic regression, log-linear models, conditional and quasi likelihoods will be discussed.

Every data analysis topic discussed in the module will be complemented by computing sessions with R statistical package.

By the end of the module you will be able to:

  • understand statistical association and analyse data by building appropriate models;
  • appreciate the use of statistics in various subject areas;
  • analyse discrete, continuous and multi-factor data;
  • appreciate statistical aspects in model building;
  • implement various data analysis methods using some standard statistical software.

Teaching and assessment:

  • Assessment: Coursework (20%), Exam  (70%), Coursework (10%)
  • Semester 1 and 2.
  • 44 hours of lectures, 6 hours of computer practicals, 10 hours of examples classes