|
Lecturer(s)
|
-
Ponížil Petr, prof. RNDr. Ph.D.
|
|
Course content
|
- Pseudo-random number generators for uniform and normal distribution. - Random variable behaviour. - Description statistics. - Statistical hypothesis - formulation and testing. - Dependencies between quantities (correlation and regression analysis, least squares method, Fourier analysis).
|
|
Learning activities and teaching methods
|
Methods for working with texts (Textbook, book), Individual work of students
- Preparation for examination
- 50 hours per semester
|
| prerequisite |
|---|
| Knowledge |
|---|
| Knowledge of mathematics and physics. |
| Knowledge of mathematics and physics. |
| learning outcomes |
|---|
| test statistical hypothesis |
| test statistical hypothesis |
| explain linear regression models |
| explain linear regression models |
| explain non-linear regression models |
| explain non-linear regression models |
| define one and two factor ANOVA |
| define one and two factor ANOVA |
| design appropriate non-parametric tests |
| design appropriate non-parametric tests |
| Skills |
|---|
| use basic and more advanced statistical methods in the processing of experimental data |
| use basic and more advanced statistical methods in the processing of experimental data |
| test statistical hypotheses |
| test statistical hypotheses |
| calculate the parameters of the regression models and test them |
| calculate the parameters of the regression models and test them |
| analyze one and two factor ANOVA |
| analyze one and two factor ANOVA |
| test the data using non-parametric tests |
| test the data using non-parametric tests |
| teaching methods |
|---|
| Knowledge |
|---|
| Individual work of students |
| Individual work of students |
| Methods for working with texts (Textbook, book) |
| Methods for working with texts (Textbook, book) |
| Skills |
|---|
| Individual work of students |
| Individual work of students |
| Practice exercises |
| Practice exercises |
| assessment methods |
|---|
| Knowledge |
|---|
| Oral examination |
| Oral examination |
|
Recommended literature
|
-
DAS, N.C. Experimental Designs in Data Science with Least Resources. Shroff Publishers, 2018. ISBN 978-9352136889.
-
Devore, Jay L. Probability and statistics for engineering and the sciences. 6th ed. Belmont, CA : Thomson-Brooks/Cole, 2004. ISBN 534399339.
-
FREEDMAN, D., PISANI, R., PURVES, R. Statistics. W.W. Norton & Company, 2007. ISBN 0393930432.
-
Hogg, Robert V. Introduction to mathematical statistics. 6th ed. Upper Saddle River, NJ ; London : Pearson Prentice Hall, 2005. ISBN 130085073.
-
Jiří Neubauer, Marek Sedlačík, Oldřich Kříž. Základy statisticky. Aplikace v technických a ekonomických oborech. Praha, 2012. ISBN 978-80-247-4273-1.
-
MELOUN, M., MILITKÝ, J. Statistické zpracování experimentálních dat. Praha: Plus, 1995.
-
MERRIN, J. Introduction to Error Analysis: The Science of Measurements, Uncertainties, and Data Analysis. CreateSpace Independent Publishing Platform, 2017. ISBN 978-1975906658.
-
MONTGOMERY, D. C., RUNGER, G. C. Applied statistics and Probability for Engineers. New York : Wiley, 1994. ISBN 0471540412.
-
NATRELLA, M.G. Experimental Statistics. Mineola, New York: Dover Publications, 2005. ISBN 9780486154558.
-
Orvis, W.J. Excel pro vědce a inženýry. Computer Press, 1996.
-
RASCH, D., SCHOTT, D. Mathematical Statistics. Hoboken: Wiley, 2018. ISBN 978-1-119-38528-8.
-
Rogers, L. and D. Willoughby. Numbers: Data and Statistics for Non-specialists.. London: Harper Collins, 2013. ISBN 978-0007507153.
-
ROSS, S.M. Introductory Statistics. 4th Ed. Amsterdam: Elsevier/AP, 2017. ISBN 978-0-12-804317-2.
-
UTTS, J.M., HECKARD, R.F. Mind on Statistics. 5th Ed. Stamford: Cengage Learning, 2015. ISBN 978-1-285-46318.
|