1) What is the probability of a stroke over the next 10 years for John Smith, a 68-year-old smoker who has blood pressure of 175? What action might the physician recommend for John to reduce the risk of stroke? 2) Is there any multicollinearity problem in the above multiple regression model? How do you know? SUMMARY OUTPUTRegression StatisticsMultiple RR SquareAdjusted R SquareStandard ErrorObservationsANOVARegressionResidualTotalInterceptAgePressureSmoker (Yes=1)0.9252030310.8560006490.8290007716.141529119df2031619SSMSF3587.455921 1195.819 31.70387603.4940788 37.718384190.95Coefficients Standard Error t Stat P-value-107.53650381.222244070.2983520846.82635185114.71362875 -7.30863 1.76E-060.159044683 7.68491 9.3E-070.045408505 6.570401 6.44E-062.908212167 2.347267 0.032109Significance F5.76336E-07Lower 95%Upper 95%Lower 95.0%Upper 95.0%-138.7280033 -76.3450042 -138.7280033 -76.345004230.885084402 1.559403737 0.885084402 1.5594037370.202090353 0.394613815 0.202090353 0.3946138150.661217468 12.99148623 0.661217468 12.99148623

Given regression outputWith respect to the output the regression equation is Risk of…

1) What is the probability of a stroke over the next 10 years for John Smith, a 68-year-old smoker who has blood pressure of 175? What action might the physician recommend for John to reduce the risk of stroke? 2) Is there any multicollinearity problem in the above multiple regression model? How do you know?

1) What is the probability of a stroke over the next 10 years for John Smith, a 68-year-old smoker who has blood pressure of 175? What action might the physician recommend for John to reduce the risk of stroke? 2) Is there any multicollinearity problem in the above multiple regression model? How do you know?

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Question

2) Is there any multicollinearity problem in the above multiple regression model? How do you know?

SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
ANOVA
Regression
Residual
Total
Intercept
Age
Pressure
Smoker (Yes=1)
0.925203031
0.856000649
0.829000771
6.141529119
df
20
3
16
19
SS
MS
F
3587.455921 1195.819 31.70387
603.4940788 37.71838
4190.95
Coefficients Standard Error t Stat P-value
-107.5365038
1.22224407
0.298352084
6.826351851
14.71362875 -7.30863 1.76E-06
0.159044683 7.68491 9.3E-07
0.045408505 6.570401 6.44E-06
2.908212167 2.347267 0.032109
Significance F
5.76336E-07
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
-138.7280033 -76.3450042 -138.7280033 -76.34500423
0.885084402 1.559403737 0.885084402 1.559403737
0.202090353 0.394613815 0.202090353 0.394613815
0.661217468 12.99148623 0.661217468 12.99148623

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