Statistická analýza výsledků geologického průzkumu

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but.committeedoc. Ing. Luděk Nechvátal, Ph.D. (předseda) prof. RNDr. Miloslav Druckmüller, CSc. (místopředseda) prof. Mgr. Pavel Řehák, Ph.D. (člen) doc. RNDr. Jiří Tomáš, Dr. (člen) doc. Mgr. et Mgr. Aleš Návrat, Ph.D. (člen) Mariapia Palombaro (člen) Gennaro Ciampa (člen) Matteo Colangeli (člen) Carmela Scalone (člen)cs
but.defenceThe student presented his master's thesis on the topic: Statistical analysis of geological survey results. After the presentation, the reviews of the supervisor and opponent were read. Student was able to partially react to the oppononet's comments.cs
but.jazykangličtina (English)
but.programApplied and Interdisciplinary Mathematicscs
but.resultpráce byla úspěšně obhájenacs
dc.contributor.advisorHübnerová, Zuzanaen
dc.contributor.authorMansoor, Mohsinen
dc.contributor.refereeHrabec, Pavelen
dc.date.created2024cs
dc.description.abstractThe statistical analysis of data from geological surveys is the main focus of this thesis, which looks at how CO2 concentrations change over time. The study examines the complex relationships that exist between different environmental factors and CO2 levels using multiple linear regression models. The models incorporate cosinusoidal and sinusoidal components to capture daily averages, thereby accounting for data variations throughout the year. In-depth residual analysis and autocorrelation studies are used in the research to assess the model’s performance and guarantee that the models are reliable and accurate in predicting CO2 levels. This study offers a solid basis for future research and improves the knowledge of soil behavior.en
dc.description.abstractThe statistical analysis of data from geological surveys is the main focus of this thesis, which looks at how CO2 concentrations change over time. The study examines the complex relationships that exist between different environmental factors and CO2 levels using multiple linear regression models. The models incorporate cosinusoidal and sinusoidal components to capture daily averages, thereby accounting for data variations throughout the year. In-depth residual analysis and autocorrelation studies are used in the research to assess the model’s performance and guarantee that the models are reliable and accurate in predicting CO2 levels. This study offers a solid basis for future research and improves the knowledge of soil behavior.cs
dc.description.markEcs
dc.identifier.citationMANSOOR, M. Statistická analýza výsledků geologického průzkumu [online]. Brno: Vysoké učení technické v Brně. Fakulta strojního inženýrství. 2024.cs
dc.identifier.other162485cs
dc.identifier.urihttp://hdl.handle.net/11012/249623
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.subjectLeast Squares Estimationen
dc.subjectHypothesis Testingen
dc.subjectResidual Analysisen
dc.subjectRegression Analysiscs
dc.subjectLeast Squares Estimationcs
dc.subjectHypothesis Testingcs
dc.subjectResidual Analysiscs
dc.titleStatistická analýza výsledků geologického průzkumuen
dc.title.alternativeStatistical analysis of results of geological surveycs
dc.typeTextcs
dc.type.drivermasterThesisen
dc.type.evskpdiplomová prácecs
dcterms.dateAccepted2024-10-04cs
dcterms.modified2024-10-10-07:52:17cs
eprints.affiliatedInstitution.facultyFakulta strojního inženýrstvícs
sync.item.dbid162485en
sync.item.dbtypeZPen
sync.item.insts2025.03.27 10:46:41en
sync.item.modts2025.01.15 16:54:49en
thesis.disciplinebez specializacecs
thesis.grantorVysoké učení technické v Brně. Fakulta strojního inženýrství. Ústav matematikycs
thesis.levelInženýrskýcs
thesis.nameIng.cs
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