470-8542/02 – Special Methods of Data Analysis (SMAD)
| Gurantor department | Department of Applied Mathematics | Credits | 5 |
| Subject guarantor | prof. Ing. Radim Briš, CSc. | Subject version guarantor | prof. Ing. Radim Briš, CSc. |
| Study level | undergraduate or graduate | Requirement | Compulsory |
| Year | 2 | Semester | winter |
| | Study language | English |
| Year of introduction | 2015/2016 | Year of cancellation | |
| Intended for the faculties | FEI, FS, HGF | Intended for study types | Follow-up Master, Master, Bachelor |
Subject aims expressed by acquired skills and competences
This subject could be considered a multidisciplinary subject in between statistics and informatics. Its aim is to expand basic knowledge of statistical methods acquired by students within the scope of the subject 541-0181 / 01 - Statistics and / or 548-0093 / 01 - Quantitative methods in geography, especially about advanced statistical methods used in technical practice combined with special computer-based procedures.
After passing the subject students should be able to effectively evaluate their own data and choose a suitable method for the creating a data model. They should be able to verify usability of the data model and they should be know how to interpret results in connection with the practical focus of the task.
Teaching methods
Lectures
Project work
Summary
Computer-based data processing requires their users to be able to analyze complex problems. This subject is a combination of lectures and computer-based practical, whereby theory is firmly placed into practice. In contrast to classical mathematical statistics, emphasis is placed not on particular methods but on their appropriate combinations, enabling the assessment of data quality, the selection of a suitable statistical model, its verification and the interpretation of the results with respect to the goal of data analysis. The learning is centered around focusing more on conceptual understanding of key concepts, and statistical thinking, and less on formulas and calculations, which can now be left to PCs. Statistical skills enable students to intelligently collect, analyze and interpret data relevant to their decision-making.
Compulsory literature:
Recommended literature:
Additional study materials
Way of continuous check of knowledge in the course of semester
4 tests during the semester - required minimum 15 points (maximum 33 points).
Students will receive credit if they meet the required minimum.
The course ends with an examination, in which students can get a maximum of 67 points. It is not possible to take an examination if student don’t have a credit. Total evaluation of the course is the sum of credit points and exam points. Students will pass exam if they meet the compulsory gain at least 51 points in total.
E-learning
Other requirements
No additional requirements are imposed on the student.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
Introduction to probability theory
Random Variable
Random Vector
Probability models for discrete random variable
Probability models for continous random variable
Statistical survey and exploratory analysis
Sample characteristics, Introduction to estimation theory
Hypothesis testing – principle
One-sample and two-samples parametric tests of hypothesis
Goodness of Fit tests
Tests for comparing more than two variances, ANOVA (one factor, two factors), Kruskal-Wallis test
Analysis of Independence
Introduction to Regression Analysis
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction