BM0249 Experimental design and statistical treatment of experimental data

Code BM0249
Name Experimental design and statistical treatment of experimental data
Status Compulsory/Courses of Limited Choice
Level and type Doctoral Studies, Academic
Field of study Aviation Transport
Faculty Faculty of Civil and Mechanical Engineering
Academic staff Mārtiņš Kleinhofs, Vitālijs Pavelko
Credit points 8.0
Parts 1
Annotation The study module envisages an in-depth understanding of the planning of scientific experiments from the evaluation of initiation parameters, moment methods, maximum reliability methods, evaluation distribution functions to the development of a general linear model. Study module the task is to develop students' competence in conducting scientific experiments, to develop the ability to analyze the obtained experimental results..
Contents
Content Full- and part-time intramural studies Part time extramural studies
Contact hours Independent work Contact hours Independent work
Random variable sample. 2 6 0 0
The method of sections. Application of the distribution function with location and scale parameters (DLSP). 2 6 0 0
The method of moments. Application for the DLSP. 2 6 0 0
Sufficient statistics. Maximum-likelihood method (MLM). Censored data. 4 6 0 0
Least square method. 4 6 0 0
Estimation of location and scale parameters based on linear combination of order statistics. 2 6 0 0
Unbiased estimation (UE). Example of UE of reliability for exponential distribution. 2 6 0 0
Bayes solutions. A prior probability and a posterior probability. Loss function. 4 8 0 0
Rao-Cramer-Freshe inequality. Information matrix. 4 8 0 0
MLM estimate distribution. Information matrix. 2 8 0 0
Statistical hypothesis testing. Two types of errors. The level of significance of the test. Power of test. 4 8 0 0
Tests of the normal distribution mean. Connection with confidence interval. 4 8 0 0
P-bound for random variable. 2 8 0 0
Required sample size for required power of test. Application for normal distribution. 4 8 0 0
Parameter comparison. Hypothesis testing concerning means of two normal distributions. 2 8 0 0
Distribution-free or nonparametric test of equality of the two population means. 4 6 0 0
One-way analysis of variance. 4 8 0 0
Two-factor analysis of variance. 4 8 0 0
Regression analysis. 4 8 0 0
General linear model. 4 8 0 0
Total: 64 144 0 0
Goals and objectives
of the course in terms
of competences and skills 		The aim of the study course is to master the basic concepts and methods of scientific experiment planning, to acquire practical skills in conducting experiments and conducting them. The task of the study module is to understand and be able to use, to process the obtained experimental results by applying mathematical statistical methods to solve the aircraft fatigue problem.
Learning outcomes
and assessment 		A student understands mathematical statistical methods for distribution function parameter estimation. - Test.
A student understands the mathematical methods for statistical hypothesis testing. - Test.
A student is able to make one-way analysis of variance. - Laboratory work, test.
A student is able to make two-factor analysis of variance. - Laboratory work, test.
A student is able to make regression analysis. - Laboratory work, test.
A student is able to make data analysis using general linear model. - Laboratory work, test.
A student understands and is able to use mathematical statistical methods for airplane reliability problem solution. - Exam.
Evaluation criteria of study results
Laboratory works - 40%
Presentations - 40%
Exam - 20%
 
Course prerequisites Mathematics, theory of probability and mathematics statistics.
Course planning
Part CP Hours Tests
Lectures Practical Lab. Test Exam Work
1 8.0 32.0 0.0 32.0 *
[Extended course information PDF]