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a. Test at 5% level of significance whether sex is indeed associated with whether or not the beneficiary is smoker. Test Procedure: p-value: Decision: Conclusion: At alpha = 0.05, there is sex is associated with whether or not the beneficiary is a smoker. There is a b. What can you say about the relationship between medical charges and age? = relationship between medical charges and age. linearly associated. C. Test at 5% level of significance if medical charges is linearly associated with age. Ho: P = 0 p= 0; (in words) The medical charges and age are not (in symbols) 0; (in words) The medical charges and age are linearly Ha: P + 0: (in symbols) associated. p-value: Decision: (reject / fail to reject Ho) Conclusion: At alpha= 0.05, there is medical charges is linearly associated with age. Ho: P = 0 (in symbols) d. Check if the data on medical charges and age support the researcher's claim. Create a model to do this and interpret its components. Assume all the necessary requirements for a regression analysis were met. Model: = Interpret the following values: Regression constant: Regression coefficient: B. = The medical charges R-squared: on age. (reject / fail to reject Ho) Ha: P = 0;(in symbols) dependent on age. (sufficient / no sufficient) evidence that Test Procedure: p-value: Decision: Int pretation: e. At 5% level of significance, test whether the medical charges are indeed linearly dependent on age. p = 0; (in words) The medical charges is linearly dependent (sufficient / no sufficient) evidence that where ß. - age (increase/decrease) by = 0. Conclusion: At alpha= 0.05, there is medical charges is linearly dependent on age. for every 1yr. increase in age. (in words) The medical charges is not linearly (reject or fail to reject Ho) (sufficient/no sufficient) evidence that

a. Test at 5% level of significance whether sex is indeed associated with whether or not the beneficiary is smoker. Test Procedure: p-value: Decision: Conclusion: At alpha = 0.05, there is sex is associated with whether or not the beneficiary is a smoker. There is a b. What can you say about the relationship between medical charges and age? = relationship between medical charges and age. linearly associated. C. Test at 5% level of significance if medical charges is linearly associated with age. Ho: P = 0 p= 0; (in words) The medical charges and age are not (in symbols) 0; (in words) The medical charges and age are linearly Ha: P + 0: (in symbols) associated. p-value: Decision: (reject / fail to reject Ho) Conclusion: At alpha= 0.05, there is medical charges is linearly associated with age. Ho: P = 0 (in symbols) d. Check if the data on medical charges and age support the researcher's claim. Create a model to do this and interpret its components. Assume all the necessary requirements for a regression analysis were met. Model: = Interpret the following values: Regression constant: Regression coefficient: B. = The medical charges R-squared: on age. (reject / fail to reject Ho) Ha: P = 0;(in symbols) dependent on age. (sufficient / no sufficient) evidence that Test Procedure: p-value: Decision: Int pretation: e. At 5% level of significance, test whether the medical charges are indeed linearly dependent on age. p = 0; (in words) The medical charges is linearly dependent (sufficient / no sufficient) evidence that where ß. - age (increase/decrease) by = 0. Conclusion: At alpha= 0.05, there is medical charges is linearly dependent on age. for every 1yr. increase in age. (in words) The medical charges is not linearly (reject or fail to reject Ho) (sufficient/no sufficient) evidence that

MATLAB: An Introduction with Applications
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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a. Test at 5% level of significance whether sex is indeed associated with whether or not the beneficiary is smoker. Test Procedure: p-value: Decision: p-value: Decision: Conclusion: At alpha = 0.05, there is sex is associated with whether or not the beneficiary is a smoker. b. What can you say about the relationship between medical charges and age? There is a relationship between medical charges and age. c. Test at 5% level of significance if medical charges is linearly associated with age. 0 Ho: P (in symbols) p = 0; (in words) The medical charges and age are not linearly associated. p = 0;(in symbols) Ha: p = 0 (in words) The medical charges and age are linearly associated. Conclusion: At alpha = 0.05, there is medical charges is linearly associated with age. Model: = Interpret the following values: Regression constant: Regression coefficient: ß, = The medical charges R-squared: Interpretation: Ho: P = 0 (reject / fail to reject Ho) d. Check if the data on medical charges and age support the researcher's claim. Create a model to do this and interpret its components. Assume all the necessary requirements for a regression analysis were met. (in symbols) (reject / fail to reject Ho) on age. Ha: P = 0;(in symbols). dependent on age. Test Procedure: p-value: Decision: (sufficient / no sufficient) evidence that e. At 5% level of significance, test whether the medical charges are indeed linearly dependent on age. p= 0; (in words) The medical charges is linearly dependent (sufficient / no sufficient) evidence that where ß. - age (increase/decrease) by Conclusion: At alpha = 0.05, there is medical charges is linearly dependent on age. for every 1yr. increase in age. = 0; (in words) The medical charges is not linearly (reject or fail to reject Ho) (sufficient/no sufficient) evidence that
Transcribed Image Text:a. Test at 5% level of significance whether sex is indeed associated with whether or not the beneficiary is smoker. Test Procedure: p-value: Decision: p-value: Decision: Conclusion: At alpha = 0.05, there is sex is associated with whether or not the beneficiary is a smoker. b. What can you say about the relationship between medical charges and age? There is a relationship between medical charges and age. c. Test at 5% level of significance if medical charges is linearly associated with age. 0 Ho: P (in symbols) p = 0; (in words) The medical charges and age are not linearly associated. p = 0;(in symbols) Ha: p = 0 (in words) The medical charges and age are linearly associated. Conclusion: At alpha = 0.05, there is medical charges is linearly associated with age. Model: = Interpret the following values: Regression constant: Regression coefficient: ß, = The medical charges R-squared: Interpretation: Ho: P = 0 (reject / fail to reject Ho) d. Check if the data on medical charges and age support the researcher's claim. Create a model to do this and interpret its components. Assume all the necessary requirements for a regression analysis were met. (in symbols) (reject / fail to reject Ho) on age. Ha: P = 0;(in symbols). dependent on age. Test Procedure: p-value: Decision: (sufficient / no sufficient) evidence that e. At 5% level of significance, test whether the medical charges are indeed linearly dependent on age. p= 0; (in words) The medical charges is linearly dependent (sufficient / no sufficient) evidence that where ß. - age (increase/decrease) by Conclusion: At alpha = 0.05, there is medical charges is linearly dependent on age. for every 1yr. increase in age. = 0; (in words) The medical charges is not linearly (reject or fail to reject Ho) (sufficient/no sufficient) evidence that
CORRELATION ANALYSIS, SIMPLE REGRESSION ANALYSIS & PATH ANALYSIS IN R Problem on Healthcare Insurance: Solve & explain. To help better understand how private health care insurance works, a study was conducted on the factors that affect an individual's medical costs billed by insurance. A random sample of beneficiaries from an insurance company, & data on age, sex, whether the beneficiary is a smoker or not & individual's medical costs billed by insurance were obtained. It was believed that age is a major factor in an individual's medical costs billed. Since there are available data on sex & whether the beneficiary is a smoker or not, the researcher is also interested in determining if these 2 variables are associated. Expected Frequencies of Sex vs Smoker Chi-Square Test p-value = 0.005324 Test of Independence between Sex & Smoker Doornik-Hansen Test Residuals: Min 1Q -8059 -6671 Frequency table: Correlation Coefficients bet. Medical Charges & Age Pearson's r = 0.2990082 Spearman's rs = 0.5343921 Ha: not equal to 0 p-value < 2.2e-16 Ha: not equal to 0 p-value < 2.2e-16 Regression of Medical Charges on Age Coefficients: no sex yes female 547 115 male 517 159 Call: 1m (formula = charges age, data = exer6) Median -5939 Estimate (Intercept) 3165.9 257.7 age Fisher's Exact Test p-value = 0.005491 smoker DH: 2272.361 p-value: <0.0001 3Q 5440 Std. Error 937.1 22.5 Max 47829 t value 3.378 11.453 Kendall's IT = 0.4753024 Ha: not equal to 0 p-value < 2.2e-16 Pr (>|t|) 0.000751 < 2e-16 *** Signif. codes: 0 1*** 1 0.001 '**' 0.01 '*' 0.05 '.' 0.1'' 1 Residual standard error: 11560 on 1336 degrees of freedom Multiple R-squared: 0.08941, Adjusted R-squared: F-statistic: 131.2 on 1 and 1336 DF, p-value: < 2.2e-16 0.08872
Transcribed Image Text:CORRELATION ANALYSIS, SIMPLE REGRESSION ANALYSIS & PATH ANALYSIS IN R Problem on Healthcare Insurance: Solve & explain. To help better understand how private health care insurance works, a study was conducted on the factors that affect an individual's medical costs billed by insurance. A random sample of beneficiaries from an insurance company, & data on age, sex, whether the beneficiary is a smoker or not & individual's medical costs billed by insurance were obtained. It was believed that age is a major factor in an individual's medical costs billed. Since there are available data on sex & whether the beneficiary is a smoker or not, the researcher is also interested in determining if these 2 variables are associated. Expected Frequencies of Sex vs Smoker Chi-Square Test p-value = 0.005324 Test of Independence between Sex & Smoker Doornik-Hansen Test Residuals: Min 1Q -8059 -6671 Frequency table: Correlation Coefficients bet. Medical Charges & Age Pearson's r = 0.2990082 Spearman's rs = 0.5343921 Ha: not equal to 0 p-value < 2.2e-16 Ha: not equal to 0 p-value < 2.2e-16 Regression of Medical Charges on Age Coefficients: no sex yes female 547 115 male 517 159 Call: 1m (formula = charges age, data = exer6) Median -5939 Estimate (Intercept) 3165.9 257.7 age Fisher's Exact Test p-value = 0.005491 smoker DH: 2272.361 p-value: <0.0001 3Q 5440 Std. Error 937.1 22.5 Max 47829 t value 3.378 11.453 Kendall's IT = 0.4753024 Ha: not equal to 0 p-value < 2.2e-16 Pr (>|t|) 0.000751 < 2e-16 *** Signif. codes: 0 1*** 1 0.001 '**' 0.01 '*' 0.05 '.' 0.1'' 1 Residual standard error: 11560 on 1336 degrees of freedom Multiple R-squared: 0.08941, Adjusted R-squared: F-statistic: 131.2 on 1 and 1336 DF, p-value: < 2.2e-16 0.08872
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Follow-up question on problem on Health Insurance:

a) Check if the data on medical charges and age support the researcher’s claim. Create a model to do this and interpret its components. Assume all the necessary requirements for a regression analysis were met. 

Model: _______ = ___________________ 

Interpret the following values:

Regression constant: __________________________________________

Regression coefficient: β1 = _______ where β1 – age

The medical charges ________(increase/decrease) by ______for every 1yr. increase in age.

R-squared: _______

Interpretation: _____________________________________________________________

b) At 5% level of significance, test whether the medical charges are indeed linearly dependent on age.

Ho:  (in symbols) _________; (in words) The medical charges is linearly dependent

      on age.

Ha: (in symbols) ________; (in words) The medical charges is not linearly dependent on age.

Test Procedure:  ______________

p-value:              ______________

Decision:           ______________ (reject or fail to reject Ho)

 

Conclusion: At alpha = 0.05, there is ______________ (sufficient/no sufficient) evidence that medical charges is linearly dependent on age.

 

 

 

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