639-3022/01 – Applied Statistics (AST)
| Gurantor department | Department of Quality Management | Credits | 4 |
| Subject guarantor | Ing. Mgr. Petra Halfarová, Ph.D. | Subject version guarantor | Ing. Mgr. Petra Halfarová, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Compulsory |
| Year | 2 | Semester | winter |
| | Study language | Czech |
| Year of introduction | 2023/2024 | Year of cancellation | |
| Intended for the faculties | FMT | Intended for study types | Master, Follow-up Master |
Subject aims expressed by acquired skills and competences
Knowledge of basic methods of mathematical statistics
Analysis of real data
Ability to correctly process experimental data
Proficiency with Excel in mathematical statistics
Teaching methods
Lectures
Tutorials
Summary
Předmět se věnuje vysvětlení pojmů a principů z oblasti matematické statistiky. Je tedy věnován prostor aparátu k výkladu odhadu parametrů základního souboru, testování hypotéz, modelování technologických procesů pomocí regresních modelů a jejich hodnocení v korelační analýze. Vícerozměrná regresní analýza je probírána za předpokladu platnosti požadovaných podmínek. Korelační analýza uvádí způsoby měření míry závislosti pro různé varianty zadání hodnocených proměnných.
Compulsory literature:
Recommended literature:
Way of continuous check of knowledge in the course of semester
One test during the semester, where its score is counted towards the cumulative credit score.
One term paper, where its score counts towards the cumulative credit score.
The examination is in written form.
E-learning
LMS
Other requirements
80% attendance at seminars, submission of assigned programs.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1. Introduction to statistics - explanation of its applicability to metallurgists. Graphical representation of a data set, assessment of data type. General principles of testing.
2. Verification of homogeneity of the data set using graphs. Outliers - their display, detection (box plot) and resolution.
3. Verification of data independence using graphs. Effect of data dependency on the quality of file processing.
4. Verification of data normality: normal distribution, Gaussian curve and its parameters, empirical histogram. Reasons for the required normality and procedure if it is not met.
5. Numerical characteristics of position, variability, skewness and peakedness. Concept of robustness of numerical characteristics.
6. Theoretical distributions of Student's, Fisher's and Pearson's: graphs of distributions. Examples of these distributions, working with tables of quantiles and critical values.
7. Point and interval estimation. Concepts of 'confidence level' and 'significance level'.
8. Analysis of two sets of statistics: testing the significance of the difference between sample means and sample variances; two-sample t-test, F test.
9. Evaluation of the degree of dependence (correlation) of two variables: Pearson correlation coefficient, Spearman's rank correlation coefficient.
10. Regression analysis - simple (pairwise) linear regression. Estimation of regression coefficients using the least squares method. Evaluation of significance and quality of the regression function. Simple non-linear regression models (power, exponential, logarithmic, quadratic and polynomial).
11. Regression analysis - multiple linear regression. Evaluation of model significance and regression coefficients. Application of multiple regression.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
Předmět neobsahuje žádné hodnocení.