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Multiple regression and correlation analysis were used to analyze the data.
Multiple regression and Student's t-tests were used to determine statistical significance of non-survival data.
Data were analyzed using multiple regression and principal components analyses.
Introduction to multiple regression, and introduction to the interpretation of regression results.
R2 values were 0.997, 0.819, and 0.420 in ANN, multiple regression, and linear regression, respectively.
Global multiple regression and regression tree models are compared with a simple average model.
Fittings of multiple regression and logistic regression for complex survey design have been proposed.
Multiple regression and neural network-based models are compared using statistical methods.
Validation of baseline and best performing linear multiple regression and linear mixed models was done using cross-validation.
The coefficients of correlation for the nonlinear multiple regression and ANFIS models were 0.87 and 0.91, respectively.
Ordinary multiple regression and its generalized form (GLM) are very popular and are often used for modeling species distributions.
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