People Innovation Excellence

CATEGORICAL DATA ANALYSIS (2 Credits)

Learning Outcomes:

On successful completion of this course, students will be able to:Recognise data as being categorical data and summarise data as categorical data where appropriate; Explain the need for, the structure, and the usefulness of generalized linear model; Explain the need for, the structure, and the usefulness of logistic regression; Explain the need for, the structure, and the usefulness of contingency tables; Apply the method which are appropriate with data; Interpret the results of the method for categorical data.

Topics:

  1. Introduction;
  2. Contingency Tables;
  3. Generalized Linear Model;
  4. Logistic Regression;
  5. Building andApplying Logistic Regression Model;
  6. Multi-Category Logit Models;
  7. Log-Linear Models for ContingencyTables;
  8. Model for Matched Pairs;
  9. Modelling Correlated;
  10. Random Effects: Generalized Linear Mixed Models.

 

 


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