Courses taught

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1. Categorical Data Analysis  (331-3704) [-C-]

Semester E Course (2 hours Theory and 1 hour Lab/Tutorial Exercises)

3 Teaching Points and 5 ECTS Points

Course Contents: Contingency tables, odds ratio, risk ratio, goodness-of-fit tests, log-linear models, Bayes analysis, repeatde measures, matched pairs. Computer applications with GLIM and SAS.

Prerequisite(s): Probabilities I, Probabilities II, Statistics I, Statistics II and Applied Linear Algebra I.

Textbooks used:

 

2. Sampling Theory (331-3253) [-E3-]

Semester E Course (2  hours Theory and 1 hours Laboratories)

3 Teaching Points and 5 ECTS Points

Course Contents: Sampling techniques, simple random sampling, stratified sampling, cluster sampling, systematic sampling, ratio sampling, estimation of standard error, questionnaire design, regression estimation. The students will carry out a survey that best suits their needs and interests.

 Prerequisite(s): Probabilities I, Applied Linear Algebra I and Statistics I.

Textbooks used:

 

3. Simulation Techniques with C++ and Matlab  (-CA- Postgraduate)

Semester A Course (3  hours Theory)

3 Credit Points and 6 ECTS Points

Course Contents: The C++ programming language. Introduction to Matlab. Simulation techniques, random number generators, Monte Carlo integration, random number control, Box-Muller method, Poisson process, Markov Chains. Simulation software, statistical analysis of simulation results.

Prerequisite(s):

Textbooks used:

4. Categorical Data Analysis (Postgraduate course)

5. Regression Analysis

6. Statistical Packages

7.Exploratory and Data Analysis (Postgraduate course)