470-2401/02 – Statistics I (STA1)
Gurantor department | Department of Applied Mathematics | Credits | 6 |
Subject guarantor | prof. Ing. Radim Briš, CSc. | Subject version guarantor | prof. Ing. Radim Briš, CSc. |
Study level | undergraduate or graduate | Requirement | Choice-compulsory |
Year | 2 | Semester | summer |
| | Study language | English |
Year of introduction | 2015/2016 | Year of cancellation | 2020/2021 |
Intended for the faculties | FEI | Intended for study types | Bachelor |
Subject aims expressed by acquired skills and competences
Studens learn basics of mathematical theory of statistics and gain hands-on experince with data analysis using software environment R.
Teaching methods
Lectures
Tutorials
Project work
Summary
The course is an introduction to mathematical statistics. Students learn mathematical basis of statistics and gain hands-on experince with data analysis using software environment R.
Compulsory literature:
Recommended literature:
Way of continuous check of knowledge in the course of semester
*10 short tests in the course of semester per 2 points (max.)
*Semestral project (with max. 20 points)
E-learning
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,
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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