Mathematical Statistics I

Course guarantor:

Lecturer:

Tutor:

Supervisor:

Department of Mathematics

Synopsis:

Random sample. Idea of statistical inference. Random variables and their distribution. Normal distribution. Central limit theorem. Multiple distribution. Independence. Correlation. Theory of estimation. point and interval estimate. Hypotheses testing. Test statistic and statistical decision. P-value. Simple linear regression parameters estimation, hypotheses testing, prediction intervals, regression diagnostic. Simulation independent realizations of random variables.

Requirements:

Basic calculus.

Syllabus of lectures:

Random sample. Idea of statistical inference. Random variables and their distribution. Normal distribution. Central limit theorem. Multiple distribution. Independence. Correlation. Theory of estimation. point and interval estimate. Hypotheses testing. Test statistic and statistical decision. P-value. Simple linear regression parameters estimation, hypotheses testing, prediction intervals, regression diagnostic. Simulation independent realizations of random variables

Syllabus of tutorials:

Study Objective:

The goal is to get a basic knowledge of inferential statistics for postgraduate students who did not encounter the subject in their studies or who have some rudiment knowledge only.

Study materials:

Jay Devore: Probability and Statistics for Engineering and the Sciences

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans: