Coefficients Standardi zed Coefficien Unstandardized Coefficients ts Collinearity Statistics Model Std. Error Beta Sig. 000 Tolerance VIF (Constant) Percentage of beds that are for-profit hospital State population 356.998 43.206 8.263 -667.738 219.309 -438 -3.045 .007 .864 1.158 1.395E-02 .004 .567 3.947 .001 .864 1.158 a. Dependent Variable: Survival Size In a regression analysis the following SPSS output table is produced. The purpose of this analysis is to build a 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 5,000 means 5,000 thousand). Which of the following statements is(are) correct? [There may be more than one correct 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 population of 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 6,000 thousand 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.
Coefficients Standardi zed Coefficien Unstandardized Coefficients ts Collinearity Statistics Model Std. Error Beta Sig. 000 Tolerance VIF (Constant) Percentage of beds that are for-profit hospital State population 356.998 43.206 8.263 -667.738 219.309 -438 -3.045 .007 .864 1.158 1.395E-02 .004 .567 3.947 .001 .864 1.158 a. Dependent Variable: Survival Size In a regression analysis the following SPSS output table is produced. The purpose of this analysis is to build a 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 5,000 means 5,000 thousand). Which of the following statements is(are) correct? [There may be more than one correct 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 population of 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 6,000 thousand 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.
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter10: Statistics
Section: Chapter Questions
Problem 25SGR
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Attached is the question and answer choices. There may be multiple correct answers.
![Coefficients
Standardi
zed
Unstandardized
Coefficien
Coefficients
ts
Collinearity Statistics
Model
Std. Error
Beta
t
Sig.
Tolerance
VIF
1
(Constant)
356.998
43.206
8.263
.000
Percentage of beds that
are for-profit hospital
-667.738
219.309
-438
-3.045
.007
.864
1.158
State population
1.395E-02
.004
.567
3.947
.001
.864
1.158
a. Dependent Variable: Survival Size
In a regression analysis the following SPSS output table is produced. The purpose of this analysis is to build
a 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 5,00O
means 5,000 thousand). Which of the following statements is(are) correct? [There may be more than one
correct 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 population
of 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 6,000
thousand 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.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F25a33f2a-68f2-433d-bc18-d9cec2c33332%2F2c933e1a-b0ed-420b-8b5d-ef9f7efde7c0%2F39uqv5j_processed.png&w=3840&q=75)
Transcribed Image Text:Coefficients
Standardi
zed
Unstandardized
Coefficien
Coefficients
ts
Collinearity Statistics
Model
Std. Error
Beta
t
Sig.
Tolerance
VIF
1
(Constant)
356.998
43.206
8.263
.000
Percentage of beds that
are for-profit hospital
-667.738
219.309
-438
-3.045
.007
.864
1.158
State population
1.395E-02
.004
.567
3.947
.001
.864
1.158
a. Dependent Variable: Survival Size
In a regression analysis the following SPSS output table is produced. The purpose of this analysis is to build
a 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 5,00O
means 5,000 thousand). Which of the following statements is(are) correct? [There may be more than one
correct 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 population
of 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 6,000
thousand 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.
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