Applied Statistics and Design of Experiments (FSI-XAP)

Academic year 2018/2019
Supervisor: doc. RNDr. Zdeněk Karpíšek, CSc.  
Supervising institute: ÚM all courses guaranted by this institute
Teaching language: Czech
Aims of the course unit:
We want to show the importance of statistics in engineering and we have taken two specific measures to accomplish this goal. First, to explain that statistics is an integral part of engineer's work. Second, we try to present a practical example of each topic as soon as possible.
Learning outcomes and competences:
Populations, samples, binomial and Poisson distributions, distribution of averages, distribution of a continuous probability, confidence intervals, testing of hypotheses, regression analysis, design of experiments.
Prerequisites:
The knowledge of probability theory and basic statistics is assumed.
Course contents:
Students sometimes use statistics to describe the results of an experiment or an investigation. This process is referred to as data analysis or descriptive statistics. Technicians also use another way; if the entire population of interest is not accessible to them for some reason, they often observe only a portion of the population (a sample) and use statistics to answer questions about the whole population. This process is called inferential statistics. Statistical inference is the main focus of the course.
Teaching methods and criteria:
The course is taught through lectures explaining the basic principles and theory of the discipline. Exercises are focused on practical topics presented in lectures.
Assesment methods and criteria linked to learning outcomes:
Exam has a written and an oral part.
Controlled participation in lessons:
Missed lessons may be compensated for via a written test.
Type of course unit:
    Lecture  13 × 2 hrs. optionally                  
    Computer-assisted exercise  13 × 1 hrs. compulsory                  
Course curriculum:
    Lecture 1. Collection of observations.
2. Common and special causes of variation.
3. Normal distribution in engineering subjects.
4. Distributions of averages.
5. Basic assumptions for different types of control charts.
6. Confidence intervals.
7. Hypothesis testing.
8. Outliers.
9. Correlation.
10. Linear regression model.
11. Factorial experiment, orthogonal designs.
12. Full and fractioanal design.
13. Process optimization with design experiment
    Computer-assisted exercise 1. Random generator of software MATHCAD (STATISTICA).
2. Examples of common and special causes.
3. Normal distribution in engineering subjects.
4. Probability density functions and probability distributions.
5. Computation of distributions of averages.
6. Basic assumptions for different types of control charts.
7. Confidence intervals for different sizes of samples.
8. Hypothesis testing.
9. Grubbs and Dixon tests.
10. Linear regression model.
11. Factorial experiment.
12. Orthogonal designs, full and fractioanal design.
13. Process optimization with design experiment.
Literature - fundamental:
1. Anděl, J.: Statistické metody. 2. vyd. Praha: MATFYZPRESS, 2003.
2. Meloun, M. - Militký, J.: Statistické zpracování experimentálních dat. Praha: PLUS, 1994.
Literature - recommended:
1. Montgomery, D. C. - Renger, G.: Applied Statistics and Probability for Engineers. New York : John Wiley & Sons, 2003.
2. Hahn, G. J. - Shapiro, S. S.: Statistical Models in Engineering.New York : John Wiley & Sons, 1994.
The study programmes with the given course:
Programme Study form Branch Spec. Final classification   Course-unit credits     Obligation     Level     Year     Semester  
M2I-P full-time study M-KSB Quality, Reliability and Safety -- Cr,Ex 4 Compulsory 2 1 W