Multiple linear regression (MLR) is a statistical technique that uses several explanatory variables to predict the outcome of a response variable.
In addition, the Multiple-Linear-Regression (MLR) method is applied on the extracted training sample points to estimate the illumination surface. Furthermore, the estimated illumination surface is used to normalize the non-uniform light of the image to binarize the image using Otsu’s global ...
A correlation is established between the corresponding pixels in the target SLC-off image and two auxiliary fill images in parallel using the multiple linear regression (MLR) model in two successive steps. In the first step, almost half the gap locations have been recovered using the MLR model,...
Sign in to download full-size image Figure 13.13. Plot of the standardized residual versus the age variable. 13.4.4 Multicollinearity Problems In a multiple regression situation, it is not uncommon to have independent variables that are interrelated to a certain extent especially when survey data ar...
Non-linear model for stress–strain curve of concrete; compressive behavior. Full size image Figure 5 Non-linear model for the stress–strain curve of concrete under tension. Full size image To select the most consistent mesh size for our specimens in ABAQUS, we compared the experimental values...
Full size image From Fig.3, when the MLR model tested the input data and crop yield, it can be seen that the linear relationship was preserved between the variable input datasets and the crop yield. Thus, farmers and entrepreneurs could use this data set to predict cherry coffee productivity...
In this study, daily average PM2.5 forecasting models were developed and applied in the Northern Xinjiang, China, through combining the back propagation artificial neural network (BPANN) and multiple linear regression (MLR) with another BPANN model. The meteorological (daily average precipitation, pre...
Full size image Again, the performance of MAGPIE was superior to other machine learning and deep learning methods on this benchmark. MAGPIE outcompeted other tools with the best AUC of 0.97 and AUPRC of 0.88 (Fig. 4B, Additional file 1: Table S6-S8). Furthermore, MAGPIE computed the path...
Full size image 4 Conclusion We have presented a novel method for computing high-quality pairwise structural RNA alignments. We approach the original problem using a flexible graph-based model, which naturally deals with pseudoknots. We find solutions in our model by means of an integer linear pr...
3.3 Multiple regression The results of the multiple regression analysis with either d2 and Stroop main scores as dependent variables are presented in Figs. 2 and 3 and Tables 4 and 5. Sign in to download hi-res image Fig. 2. Multiple regression model with KL as the dependent variable. Onl...