Significance of Statistical Analysis Technique
Statistical analysis techniques are crucial for environmental sciences. These methods are used to analyze data and determine the statistical significance of associations between environmental factors and specific outcomes. For example, statistical analysis can explore the relationship between temperature and stroke occurrence or the connection between particulate matter and land use. Researchers like Yu et al. employ these techniques to understand complex environmental relationships.
Synonyms: Analysis method, Data analysis technique, Statistical method, Data processing, Analytical technique, Analysis procedure, Regression, Anova, T-test, Chi-square, Time series analysis
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The concept of Statistical Analysis Technique in scientific sources
Statistical Analysis Technique involves methods for data analysis, determining statistical significance, and exploring associations between variables like temperature and stroke occurrence, or PDC and land-use type.
From: Sustainability Journal (MDPI)
(1) A mathematical approach used to examine data trends, which is considered limited in revealing the intricate web of factors influencing complex hydrological processes, leading to uncertainty.[1] (2) The statistical analysis technique employed principally includes variance analysis, correlation analysis, and regression analysis to decipher the complicated relationship between various influencing factors.[2] (3) Statistical analysis techniques and computational intelligence-based approaches are the predominant operational frameworks used in short-term electricity price forecasting methodologies.[3] (4) These are methods used, including Pearson’s correlation test and linear regression, to establish relationships between variables.[4] (5) Yu et al. utilized a statistical analysis technique to explore the associations between the PDC and the land-use type of the land surrounding the station.[5]
From: International Journal of Environmental Research and Public Health (MDPI)
(1) The graph theoretical techniques reduce the number of variables under review, which permitted the application of traditional and otherwise unscalable statistical analysis techniques.[6] (2) Structural equation modelling, a multivariate technique, that is used to analyze structural relationships and tests the underlying factors and hypotheses.[7] (3) Results varied based on study population, sociodemographic characteristics, type of assay used, sampling strategies, statistical analysis techniques, occupational hazards and timing of economic closures.[8] (4) Methods used to analyze data and determine the statistical significance of associations between temperature and stroke occurrence.[9]