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Using the obtained database, a multiple regression prediction model was further used to develop the prediction model.
A multiple regression prediction model was built to calculate an overall condition index utilizing those measured distress data.
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.
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Simple and multiple regression, testing, estimation, prediction, and confidence procedures, modeling, regression diagnostics and plots, polynomial regression, colinearity and confounding, model selection, geometry of least squares.
In the first stage, only acceleration amplitude of tool vibrations in axial, radial and tangential directions were used to develop multiple regression models for prediction of surface roughness.
It was concluded that the random forest regression model is a superior choice over multiple regression models for prediction of surface roughness during machining of AISI 4340 steel (69 HRC).
In multiple regression analysis, the prediction performance tends to improve as the number of the input parameters increases.
The multiple regression models for prediction of GDR included as independent variables each surrogate index of insulin sensitivity, BMI, race, gender, and interaction between index and race.
Two models were developed using sequential multiple regression to determine prediction of, social and emotional loneliness from those IVs having significant bivariate association with either DVs.
As with multiple regression and GWAS predictions, ridge regression can account for the presence of confounding variables, such as age, gender, and population structure.
The multiple regression models for the prediction of mechanical properties are developed.
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