Sentence examples for multivariate regression method from inspiring English sources
To test the hypotheses, independent t-test, one way ANOVA and multivariate regression method were used.
The correlation models between the purification effects of total nitrogen, total phosphorus and COD and their influencing factors are established using the multivariate regression method.
The regression models were built by using the linear multivariate regression method Partial Least Squares (PLS), and the nonlinear multivariate regression methods Support Vector Machines (SVM) and Locally Weighted Regression (LWR).
The Marzano statistical temperature and humidity 2 (STH2) and statistical temperature and refractivity 2 (STN2) models are developed using the statistical multivariate regression method.
Partial least squares (PLS) is a multivariate regression method that projects the input output data down into a latent space, extracting a number of principal factors with an orthogonal structure, while capturing most of the variance in the original data.
To understand the regulation of this core response module we used a multivariate regression method to develop a model of the regulatory influences of the module.
We consider data-based multivariate regression methods as alternative solution to the problem.
The atom balances were determined using all of the gaseous decomposition products based on quantitation by multivariate regression methods.
Partial least square regression (PLS) is one of the popular multivariate regression methods in a a wide range of fields.The reason of that PLS have been designed to confront the situation that many correlated predictor variables and few samples situation.
A comparison between support vector regression (SVR) and Artificial Neural Networks (ANNs) multivariate regression methods is established showing the underlying algorithm for each and making a comparison between them to indicate the inherent advantages and limitations.
Three multivariate regression methods (principal component regression, PCR; partial least square regression, PLSR; and support vector machine regression, SVMR) were used to develop models to predict root density, and these models were compared to determine the one with the most accurate predictions.
