Attached is the question and answer choices. There may be multiple correct answers. CoefficientsStandardizedUnstandardizedCoefficienCoefficientstsCollinearity StatisticsModelStd. ErrorBetatSig.ToleranceVIF1(Constant)356.99843.2068.263.000Percentage of beds thatare for-profit hospital-667.738219.309-438-3.045.007.8641.158State population1.395E-02.004.5673.947.001.8641.158a. Dependent Variable: Survival SizeIn a regression analysis the following SPSS output table is produced. The purpose of this analysis is to builda prediction model for predicting the survival size by using percentage of beds that are for-profit hospital(in the recorded data 0.34 means 34%) and the state population in thousand (in the recorded data 5,00Omeans 5,000 thousand). Which of the following statements is(are) correct? [There may be more than onecorrect answer.]a)Both variables are insignificant factors for predicting the survival size.b)lf both predictor variables are significant, the predicted average survival size for a state with a populationof 6,000 thousand and 10% of the beds that are for-profit hospital would be 373.91.c)lf both predictor variables are significant, the predicted survival size for a state with a population of 6,000thousand and 10% of the beds that are for-profit hospital would be 16.93.d)There is strong collinearity since the VIF is less than 10.e)As the percentage of beds that are for-profit hospital increases the survival size decreases.

Introduction: The given output is for a regression model that predicts the survival size, based on the percentage of beds for-profit hospital (in proportion), and the state population (thousand).Explanation: The regression equation, based on the given output, is: (Survival size) = 356.998 – 667.738 (Percentage of beds that are for-profit hospital) + 0.01395 (State population). Based on the p-values given in the column “Sig.”, the respective p-values for Percentage of beds that are for-profit hospital and State population are 0.007 and 0.001, which are very small compared to most commonly used significance levels, such as 0.10, 0.05, 0.01, etc. Hence, the factors are significant in predicting survival size. For a state with population 6,000 thousand, percentage of beds that are for-profit hospital as 10%, the survival size is: (Survival size) = 356.998 – 667.738 (Percentage of beds that are for-profit hospital) + 0.01395 (State population). = 356.998 – (667.738) ∙ (0.10) + (0.01395) ∙ (6,000) ≈ 373.92. Thus, the predicted average survival size would be 373.92, approximately considered to be 373.91. The VIF of less than 10 implies little to no multicollinearity. The negative value of the coefficient of Percentage of beds that are for-profit hospital, –667.738 implies that as the percentage increases, the survival size decreases with it. Thus, the correct options are, b, and e.