450-2005/01 – Statistical Data Processing (MZD)
Gurantor department | Department of Cybernetics and Biomedical Engineering | Credits | 4 |
Subject guarantor | doc. Ing. Radovan Hájovský, Ph.D. | Subject version guarantor | doc. Ing. Radovan Hájovský, Ph.D. |
Study level | undergraduate or graduate | Requirement | Compulsory |
Year | 2 | Semester | winter |
| | Study language | Czech |
Year of introduction | 2010/2011 | Year of cancellation | 2021/2022 |
Intended for the faculties | FEI | Intended for study types | Bachelor |
Subject aims expressed by acquired skills and competences
The purpose of the course is to explain the basic notions and method for statistic analysis. Methods of search analysis of one dimensional data will studied.
Teaching methods
Lectures
Individual consultations
Tutorials
Experimental work in labs
Summary
Course deals with tools and methods of statistical data processing. Description of program system MATLAB/Statistics Toolbox is implement.
Compulsory literature:
Literature MATLAB - Statistic ToolBox
Recommended literature:
Literature MATLAB - Statistic Tool Box
Way of continuous check of knowledge in the course of semester
Continuous assessment:
Continuous monitoring is done through participation in student computer labs
Terms of the credit:
Students can reach 40 points for computer practice essays. The minimum number of points for the credit is 10 For graduation a student must receive credit and pass the final exam. Final exam has two parts: written with a profit of 5-40 points and the oral gain 5-20 points. The completion of this course the student must pass both parts of the test.
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:
Probability theory.
Chance quantity.Definition, properties od distributive and frequency function.
Numeral characteristics, central moment, dispersion, skew, median etc.
Measurement errors. Accuracy of devices.
Search analysis.Order statistics. Statistic graphs - box plot, histogram, diagram of dispersion, etc.
Probability distribution of discrete stochastic quantity. Poissonovo, binomic distribution.
Probability distribution of continuous stochastic quantity.Uniform, exponencial, Normal distribution.
Pearson, student, Fisher, Snedecker distribution.
Minimum size of selection, independence of elements, homogeneity.
Statistic analysis of one-dimensional data. Parameter estimation of situation and dispersion.
Interval parameter estimation.
Robust parameter estimation.
Hypothesis testing.
Linear regression.
Computer labs:
MATLAB, Statistic software.
Work with data in MATLAB.
Tasks from probability theory.
Frequency and distributive function.
Chance quantity and numeral characteristics. Setting of Task.
Error estimation.
Order statistics. Graf analysis(histogram, boq plot)
Discrete probability distribution.Demo.
Continuous probability distribution. Demo.
Point estimations, interval estimations.
Testing of hypothesis.
Linear regression.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction