457-0516/01 – Statistics I (STA1)
| Gurantor department | Department of Applied Mathematics | Credits | 4 |
| Subject guarantor | prof. Ing. Radim Briš, CSc. | Subject version guarantor | prof. Ing. Radim Briš, CSc. |
| Study level | undergraduate or graduate | Requirement | Compulsory |
| Year | 1 | Semester | summer |
| | Study language | Czech |
| Year of introduction | 1999/2000 | Year of cancellation | 2009/2010 |
| Intended for the faculties | FEI | Intended for study types | Master |
Subject aims expressed by acquired skills and competences
This is an introductory course in statistics. The course will emphasize methods of applied statistics and data analysis.
Teaching methods
Lectures
Tutorials
Project work
Summary
This is an introductory course in statistics. 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:
Way of continuous check of knowledge in the course of semester
*10 short tests in the course of semester with max. 2 points
*semestral project with max. 20 points
E-learning
Other requirements
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
Lectures:
Exploratory data analysis, types of variables. Exploratory analysis of single discrete and continuous 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.
Central Limit Theorem.
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, least squares estimation of parameters and properties of the estimates.
Multiple regression models.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
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