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Based on the statistical analysis results, multiple prediction equations were developed for estimating fracture gradient from formation overburden pressure and pore pressure.
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The multiple regression analysis provided the prediction equations of the soft tissue profile changes from the related dento-skeletal and other factors.
To derive forced expiratory volume in one second (FEV1) and forced vital capacity (FVC) prediction equations we used multiple linear regression with values obtained from lifelong nonsmokers who did not report a history of chronic lung disease for men (n = 277) and women (n = 418) separately.
Haan and Allen (1972), Decoursy and Deal (1974) have also demonstrated the use of multiple regression analysis for development of hydrological prediction equations involving geomorphic parameters.
Most importantly, using multiple logistic regression analysis, they created mortality prediction equations of burned children with concomitant inhalation injury using three serum cytokine values.
The adult prediction equations used to predict TGV were developed using a healthy adult population [ 25].
The prediction equations provided straightforward procedures to predict the effects of a single fence and multiple-fence arrays.
Develop a gender specific database of trunk muscle cross-sectional areas across multiple levels of the thoracic and lumbar spine and develop prediction equations for the physiological cross-sectional area as a function of gender and anthropometry.
Several prediction equations have been developed to predict body adiposity.
Data from 120 pools were used in least-squares, multiple-regression analysis with 19 different independent variables to develop prediction equations for pool depth, length, and exit-slope gradient.
We used prediction equations derived from NHANES to estimate percent-predicted FEV1 [25].
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