470-6403/04 – Basic Methods of Statistical Data Analysis in Practice (SMIP)
| Gurantor department | Department of Applied Mathematics | Credits | 10 |
| Subject guarantor | prof. Ing. Radim Briš, CSc. | Subject version guarantor | prof. Ing. Radim Briš, CSc. |
| Study level | postgraduate | Requirement | Choice-compulsory type B |
| Year | | Semester | winter + summer |
| | Study language | English |
| Year of introduction | 2019/2020 | Year of cancellation | |
| Intended for the faculties | FS, USP, FAST, HGF, FMT, FEI | Intended for study types | Doctoral |
Subject aims expressed by acquired skills and competences
The objective of the course is to develop sufficient knowledge of statistical tools and procedures in applied engineering fields.
Teaching methods
Lectures
Project work
Summary
The course will emphasize methods of applied statistics and data analysis. Theoretical considerations will be included to the extent that knowledge of theory is necessary for a sound understanding of methods and contributes to the development of data analysis skills and the ability to interpret results of statistical analysis.
The objective of the course is to develop sufficient knowledge of statistical tools and procedures, understanding of the underlying theory on which the procedures are based, and facility in the application of statistical tools to enable the student to incorporate sound statistical methodology into other areas of his or her own work.
Compulsory literature:
Recommended literature:
Briš R., Probability and Statistics for Engineers, 2011, electronics script, Project CZ.1.07/2.2.00/15.0132. Dostupné z http://homel.vsb.cz/~bri10/Teaching/Prob%20&%20Stat.pdf
Way of continuous check of knowledge in the course of semester
Lectures. Consultancy to semestral project. Oral exam.
E-learning
http://www.am.vsb.cz/bris/Teaching/SMIP_PhD.rar
Other requirements
There are not defined other requirements for student.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
Lectures:
Exploratory data analysis, types of variables, summarization of distributions. Probability theory.
Random variable and probability distribution, expected
value operator and moments of probability distribution, joint and conditional
distributions.
Probability models for discrete and continuous random variables.
Sampling distributions of the mean, distribution of sample proportion.
Point and interval estimation, hypothesis testing, pure significance tests, p-values Two sample tests, paired difference tests.
One factor analysis of variance, ANOVA table, multiple comparisons, post hoc
analysis.
Simple linear regression model.
Multiple regression models.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks