Modifikace regresní funkce
| but.committee | prof. RNDr. Josef Šlapal, CSc. (předseda) doc. Ing. Luděk Nechvátal, Ph.D. (místopředseda) doc. RNDr. Jiří Tomáš, Dr. (člen) doc. Ing. Jiří Šremr, Ph.D. (člen) prof. Mgr. Pavel Řehák, Ph.D. (člen) prof. Bruno Rubino (člen) | cs |
| but.defence | The student didn't attend. | cs |
| but.jazyk | angličtina (English) | |
| but.program | Mathematical Engineering | cs |
| but.result | práce nebyla úspěšně obhájena | cs |
| dc.contributor.advisor | Žák, Libor | en |
| dc.contributor.author | Popoola, Seyi James | en |
| dc.contributor.referee | Hübnerová, Zuzana | en |
| dc.date.created | 2022 | cs |
| dc.description.abstract | The regression analysis is a modelling technique that establishes, mathematically, the relationship between entities of a particular subject. Although the modelling is done in such a way that one variable is seen as a subject of the other(s), regression does not imply causation. The modeling has assumptions such as linearity, normality, little or no multicollinearity, homoscedasticity as conditions for optimal relationship establishment. The simplest of the regression technique is the linear regression which also is the most commonly used. It involves the use of a straight line model to define the best pattern of relationship. This best pattern is assessed by the measure of goodness of fit which describes the amount of variation in the response variable explained by the stimuli (or stimulus). Change-point regression is a type of linear regression that takes into account a change in course of the movement of the relationship under study. This type of change in course is taken into account by modelling the regression in segments to account for the entire relationship observable in the data at hand. Several information criterions are used for detecting this change in course, the Schwartz Information Criterion (SIC), the Bayesian Information Criterion (BIC), amongst others. The detection method adopted for this work is the Modified Information Criterion (MIC) which tests a null hypothesis of no change point against an alternative that states presence of change-point. The data upon which this methodology is applied is the Italy COVID-19 data. The data was subjected to a linear regression and evaluated after which it was subjected to this change point test and the test shows the presence of a change in course. The sections which the test divides the data into were modelled individually and their regression lines were obtained. The two sections were plotted on a graph with their regression lines intercepting at the crest of the plot. | en |
| dc.description.abstract | The regression analysis is a modelling technique that establishes, mathematically, the relationship between entities of a particular subject. Although the modelling is done in such a way that one variable is seen as a subject of the other(s), regression does not imply causation. The modeling has assumptions such as linearity, normality, little or no multicollinearity, homoscedasticity as conditions for optimal relationship establishment. The simplest of the regression technique is the linear regression which also is the most commonly used. It involves the use of a straight line model to define the best pattern of relationship. This best pattern is assessed by the measure of goodness of fit which describes the amount of variation in the response variable explained by the stimuli (or stimulus). Change-point regression is a type of linear regression that takes into account a change in course of the movement of the relationship under study. This type of change in course is taken into account by modelling the regression in segments to account for the entire relationship observable in the data at hand. Several information criterions are used for detecting this change in course, the Schwartz Information Criterion (SIC), the Bayesian Information Criterion (BIC), amongst others. The detection method adopted for this work is the Modified Information Criterion (MIC) which tests a null hypothesis of no change point against an alternative that states presence of change-point. The data upon which this methodology is applied is the Italy COVID-19 data. The data was subjected to a linear regression and evaluated after which it was subjected to this change point test and the test shows the presence of a change in course. The sections which the test divides the data into were modelled individually and their regression lines were obtained. The two sections were plotted on a graph with their regression lines intercepting at the crest of the plot. | cs |
| dc.description.mark | E | cs |
| dc.identifier.citation | POPOOLA, S. Modifikace regresní funkce [online]. Brno: Vysoké učení technické v Brně. Fakulta strojního inženýrství. 2022. | cs |
| dc.identifier.other | 137283 | cs |
| dc.identifier.uri | http://hdl.handle.net/11012/206142 | |
| dc.language.iso | en | cs |
| dc.publisher | Vysoké učení technické v Brně. Fakulta strojního inženýrství | cs |
| dc.rights | Standardní licenční smlouva - přístup k plnému textu bez omezení | cs |
| dc.subject | Regression Analysis | en |
| dc.subject | Modified Information Criterion | en |
| dc.subject | The Linear Regression Analysis | en |
| dc.subject | Regression Line | en |
| dc.subject | Change-point Analysis | en |
| dc.subject | Method for Detecting Change-Point | en |
| dc.subject | Description of Italy Covid - 19 Data | en |
| dc.subject | The Linear Regression Analysis | en |
| dc.subject | The Change-Point Test. | en |
| dc.subject | Regression Analysis | cs |
| dc.subject | Modified Information Criterion | cs |
| dc.subject | The Linear Regression Analysis | cs |
| dc.subject | Regression Line | cs |
| dc.subject | Change-point Analysis | cs |
| dc.subject | Method for Detecting Change-Point | cs |
| dc.subject | Description of Italy Covid - 19 Data | cs |
| dc.subject | The Linear Regression Analysis | cs |
| dc.subject | The Change-Point Test. | cs |
| dc.title | Modifikace regresní funkce | en |
| dc.title.alternative | Modification of Regression Function | cs |
| dc.type | Text | cs |
| dc.type.driver | masterThesis | en |
| dc.type.evskp | diplomová práce | cs |
| dcterms.dateAccepted | 2022-06-15 | cs |
| dcterms.modified | 2023-06-14-15:13:45 | cs |
| eprints.affiliatedInstitution.faculty | Fakulta strojního inženýrství | cs |
| sync.item.dbid | 137283 | en |
| sync.item.dbtype | ZP | en |
| sync.item.insts | 2025.03.27 10:37:12 | en |
| sync.item.modts | 2025.01.15 21:14:01 | en |
| thesis.discipline | bez specializace | cs |
| thesis.grantor | Vysoké učení technické v Brně. Fakulta strojního inženýrství. Ústav matematiky | cs |
| thesis.level | Inženýrský | cs |
| thesis.name | Ing. | cs |