Modifikace regresní funkce

but.committeeprof. 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.defenceThe student didn't attend.cs
but.jazykangličtina (English)
but.programMathematical Engineeringcs
but.resultpráce nebyla úspěšně obhájenacs
dc.contributor.advisorŽák, Liboren
dc.contributor.authorPopoola, Seyi Jamesen
dc.contributor.refereeHübnerová, Zuzanaen
dc.date.accessioned2022-06-16T06:54:17Z
dc.date.available2022-06-16T06:54:17Z
dc.date.created2022cs
dc.description.abstractThe 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.abstractThe 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.markFcs
dc.identifier.citationPOPOOLA, S. Modifikace regresní funkce [online]. Brno: Vysoké učení technické v Brně. Fakulta strojního inženýrství. 2022.cs
dc.identifier.other137283cs
dc.identifier.urihttp://hdl.handle.net/11012/206142
dc.language.isoencs
dc.publisherVysoké učení technické v Brně. Fakulta strojního inženýrstvícs
dc.rightsStandardní licenční smlouva - přístup k plnému textu bez omezenícs
dc.subjectRegression Analysisen
dc.subjectModified Information Criterionen
dc.subjectThe Linear Regression Analysisen
dc.subjectRegression Lineen
dc.subjectChange-point Analysisen
dc.subjectMethod for Detecting Change-Pointen
dc.subjectDescription of Italy Covid - 19 Dataen
dc.subjectThe Linear Regression Analysisen
dc.subjectThe Change-Point Test.en
dc.subjectRegression Analysiscs
dc.subjectModified Information Criterioncs
dc.subjectThe Linear Regression Analysiscs
dc.subjectRegression Linecs
dc.subjectChange-point Analysiscs
dc.subjectMethod for Detecting Change-Pointcs
dc.subjectDescription of Italy Covid - 19 Datacs
dc.subjectThe Linear Regression Analysiscs
dc.subjectThe Change-Point Test.cs
dc.titleModifikace regresní funkceen
dc.title.alternativeModification of Regression Functioncs
dc.typeTextcs
dc.type.drivermasterThesisen
dc.type.evskpdiplomová prácecs
dcterms.dateAccepted2022-06-15cs
dcterms.modified2022-06-15-12:31:39cs
eprints.affiliatedInstitution.facultyFakulta strojního inženýrstvícs
sync.item.dbid137283en
sync.item.dbtypeZPen
sync.item.insts2022.06.16 08:54:17en
sync.item.modts2022.06.16 08:16:53en
thesis.disciplinebez specializacecs
thesis.grantorVysoké učení technické v Brně. Fakulta strojního inženýrství. Ústav matematikycs
thesis.levelInženýrskýcs
thesis.nameIng.cs
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