Course detail
Applied Statistics and Design of Experiments
FSI-XAPAcad. year: 2024/2025
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.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Department
Entry knowledge
Rules for evaluation and completion of the course
Missed lessons may be compensated for via a written test.
Aims
Populations, samples, binomial and Poisson distributions, distribution of averages, distribution of a continuous probability, confidence intervals, testing of hypotheses, regression analysis, design of experiments.
Study aids
Prerequisites and corequisites
Basic literature
Meloun, M. - Militký, J.: Statistické zpracování experimentálních dat. Praha: PLUS, 1994. (CS)
Recommended reading
Montgomery, D. C. - Renger, G.: Applied Statistics and Probability for Engineers. New York : John Wiley & Sons, 2003.
Elearning
Classification of course in study plans
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
2. Normal distribution in engineering subjects, Distributions of averages.
3. Basic assumptions for different types of control charts.
4. Confidence intervals.
5. Hypothesis testing I.
6. Hypothesis testing II.
7. Correlation.
8. Linear regression model.
9. Introduction to the Design of Experiment
10. Factorial experiment, orthogonal designs.
11. Full and fractioanal design.
12. Response surfaces
13. Process optimization with design experiment
Computer-assisted exercise
Teacher / Lecturer
Syllabus
2. Normal distribution in engineering subjects, Distributions of averages.
3. Basic assumptions for different types of control charts.
4. Confidence intervals.
5. Hypothesis testing I.
6. Hypothesis testing II.
7. Correlation.
8. Linear regression model.
9. Introduction to the Design of Experiment
10. Factorial experiment, orthogonal designs.
11. Full and fractioanal design.
12. Response surfaces
13. Process optimization with design experiment
Elearning