Regression Model Sample Clauses

Regression Model. Y = b0 + b1X1 + b2X2 + …..+ bnXn + Έ Whereby: Y = Dependent Variable b0 = Constant (Intercept) b1, b2, bn = Beta coefficients (slopes) for independent variables X1, X2, Xn respectively X1, X2, Xn = Independent Variables Έ = Standard error term The study included all variables in the regression model by using an OLS estimation technique. The final model looks as the follows.
Sample 1
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Regression Model. Yi = x 0 + β1*HMO (0/1) + β2*Age + β3*Female (0/1) + β4*Smoker ()/1) + β5*Private Insurance (0/1) + β6*Race + β7*Marital Status + β8*Region of the U.S. + β9*Education level + β10*Health Status (self-perceived) + β11*Income (%FDL) + ε In the two-part model, Yi first entails any ER expenditures: Pr(y > 0jx) = F(xδ) where x represents the explanatory variables and δ the corresponding parameters to be estimated. In the second part, the model is conditional E(y | y > 0, x) = g(xγ) ) where x, again, represents the explanatory variables γ represents the parameters to be estimated and g stand for the transformation function. [36] x 0 is the intercept, β1- β11 represent the coefficients of each explanatory variable, and ε is the error term. Perceived health status is a proxy for comorbidities, which are often present in asthma patients. The average person with asthma has 2.3 other chronic conditions. Income as a percentage of FPL corrects for the difference in single person income and family income and is a proxy for socio-economic status.
Sample 1

Related to Regression Model

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