Multiple regression (an extension of simple linear regression) is used to predict the value of a dependent variable (also known as an outcome variable) based on the value of two or more independent variables (also known as predictor variables). For example, you could use multiple regression to...
Structural equation modeling (SEM), which is rarely used in green space research, may serve as an alternative, capturing multiple regression equations simultaneously (Hair. et al., 2014). The present research addressed these research gaps by examining residential green space–mental health pathways ...
As a result, the measurement data were removed using 95% confidence intervals for the estimation errors so that the multiple regression equation was identified using only about 30 of the more than 200 measurement data. In this study, we evaluated the estimation model of neuropsychological ...
These modalities contain complementary information, and it is of interest to analyze both modalities across multiple slices jointly. To do this, we augment our model of the phenotypic readouts (Equation (1)) to include a separate likelihood for each modality, allowing for straightforward multimodal ...
The square terms of l-asparagine, starch and yeast extract have a higher effect value than the linear terms, indicating that these variables have more influence on the enzyme production; any alteration in these levels could influence the production in a significant manner. The regression equation ...
Evolving toxicity models using multigene symbolic regression and multiple objectives. Int. J. Mach. Learn. Comput. 1 (1), 30 [19] Kaya, A., 2009. Residual and fully softened strength evaluation of soils using artificial neural networks. Geotech. Geol. Eng. 27 (2), 281-288 [20] Kaya...
Is the power regression equation accurate. It looks like Beta^X. Shouldn’t it be X^Beta in figure 4. Additionally, can we use a linear equation (LN or Log) or multiple regression (LN or Log) equation either changing dependent and/or independent variables and change the coefficients for ...
2.1. Multivariate adaptive regression splines (MARS) MARS method is based on a statistical approach for fitting the data of dependent and independent variables by a series of splines of different gradients (Friedman, 1991). The endpoints of the splines (knots) mark the end of one set of data...
The long short-term memory (LSTM), artificial neural network (ANN), least square support vector machine (LSSVM), ensemble tree (ET), decision tree (DT), Gaussian process regression (GPR), support vector machine (SVM), and multilinear regression (MLR) models are employed using 162 data ...
and statistical techniques to compute the glacier mass balance. The mass balance is calculated using a regression equation developed between AAR and in-situ measurements of mass balance. This regression model is based on the hypothesis that a strong relationship exists between the net mass balance ...