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Course detail

Applied Statistics and Design of Experiments

Course unit code: FSI-XAP-K
Academic year: 2016/2017
Type of course unit: compulsory
Level of course unit: Master's (2nd cycle)
Year of study: 1
Semester: winter
Number of ECTS credits:
Learning outcomes of the course unit:
Populations, samples, binomial and Poisson distributions, distribution of averages, distribution of a continuous probability, confidence intervals, testing of hypotheses, regression analysis, design of experiments.
Mode of delivery:
20 % face-to-face, 80 % distance learning
Prerequisites:
The knowledge of probability theory and basic statistics is assumed.
Co-requisites:
Not applicable.
Recommended optional programme components:
Not applicable.
Course contents (annotation):
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.
Recommended or required reading:
J. Anděl: Matematická statistika, SNTL/ALFA, Praha 1978
Montgomery, D. C. - Renger, G.: Applied Statistics and Probability for Engineers. New York : John Wiley & Sons, 2003.
Hahn, G. J. - Shapiro, S. S.: Statistical Models in Engineering.New York : John Wiley & Sons, 1994.
Planned learning activities and teaching methods:
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.
Language of instruction:
Czech
Work placements:
Not applicable.
Course curriculum:
Not applicable.
Aims:
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.
Specification of controlled education, way of implementation and compensation for absences:
Missed lessons may be compensated for via a written test.

Type of course unit:

Tuition: 13 hours, optionally
Teacher / Lecturer: doc. RNDr. Bohumil Maroš, CSc.
Syllabus: 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
Controlled Self-study: 26 hours, compulsory
Teacher / Lecturer: doc. RNDr. Zdeněk Karpíšek, CSc.
Syllabus: 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.

The study programmes with the given course