Sentence examples for linear regression prediction from inspiring English sources

Weir constant linear regression prediction equations (R2⩾0⋅993), capable of predicting discharge characteristics in combination with discharge models at any intermediate weir angle, were fitted.

The r2 values of the matrix-based linear regression prediction models ranged from 0.9933 to 0.9986.

The application result based on prospecting bores in MaYi coalmine confirms that the widely applied one-dimension linear regression prediction method of CMC in engineering in China can't fully reflect the storage law of CMC.

Comparing to the efficiency results of stand-alone GP, MGGP, and conventional multi linear regression prediction models as benchmarks, the proposed Pareto-optimal MA-MGGP model put forward a parsimonious solution, which has a noteworthy importance of being applied in practice.

Then a linear regression prediction model is built between the latent components (by projecting the data points from the high dimension space onto the latent components to obtain new coordinates of the data points in the new space) and the dependent variables.

Objective: This current study develop prediction model of independent and dependent variable that induce to loss of grip strength using non-linear neural network and linear multiple regression prediction approach for both hands.

Three alternative methods are presented: Prediction of latent variables measured as summed scores using linear regression models, prediction of individual item responses using logistic regression models and propensity scores to control for differences in item responses, and prediction of latent variables using Item Response Theory models with covariates.

Pursuant to this protocol, a multiple linear regression (MLR) prediction model built by correlating microwave moisture values to the moisture determined by Karl Fischer titration was chosen and rated using conventional criteria such as coefficient of determination (R2) and root mean square error of calibration (RMSEC).

By the use of multiple linear regression analysis, prediction models for jugular TPTD parameters were developed.

Through a fully conditional specification, applying linear regression as prediction method for variables at scale level, and two-way interaction for categorical variables, we generated M=5 complete imputed datasets with 10 iterations per dataset.

Under the assumption of MNAR, we developed linear multilevel regression prediction models with only significant variables that best explained the variance of the complicated grief and depression response variables in our population.