Statistická analýza výsledků geologického průzkumu

Abstract

The 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.The 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.