Mathematic Statistics 2

Course guarantor:

Lecturer:

Tutor:

Supervisor:

Department of Mathematics

Synopsis:

Multivariate normal distribution. Principal component analysis. Linear regression. Nonlinear regression. Bayes theorem. Bayesian parameters estimates. Bayesian inference in linear model. Time series and their frequency domain description. Kalman-Bucy filtr.

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Requirements:

Syllabus of lectures:

1. Dependence and independence of random variables.

2. Contingency tables.

3. Linear model.

4. Analysis of variance.

5. Correlation analysis.

6. Analysis of principal components.

7. Logistic regression.

8. Time series analysis in time domain. Box-Jenkins metodology.

9. Time series in frequency domain - Fourier analysis.

Syllabus of tutorials:

Study Objective:

Some advanced methods of mathematical statistics.

Study materials:

Jay L. Devore: Probability and Statistics for Engineering and the Sciences

Duxbury

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans: