Question: A hospital would like to develop a regression model to predict the total hospital bill for a patient based on the age of the patient

A hospital would like to develop a regression model to predict the total hospital bill for a patient based on the age of the patient (x1), his or her length of stay (x2), and the number of days in the hospital's intensive care unit(ICU) (x3). Data for these variables can be found below. Complete parts a through e below.

a) Construct a regression model using all three independent variables. (Round to the nearest whole number as needed.)

b) Interpret the meaning of the regression coefficients.

c) Predict the average hospital bill for a 69-year-old person hospitalized for ten days with no days spent in the ICU. (Round to the nearest dollar as needed.)

d) Construct a 95% confidence interval for the average hospital bill for the patient described in part c. (Round to the nearest dollar as needed.)

e) Construct a 95% prediction interval for the average hospital bill for the patient described in part c, that is, a 69-year-old person hospitalized for ten days with no days spent in the ICU. (Round to the nearest dollar as needed.)

Below is the excel table given.

Thank you!! (:

A hospital would like to develop a regression model to predict the

total hospital bill for a patient based on the age of the

patient (x1), his or her length of stay (x2), and the number

of days in the hospital's intensive care unit(ICU) (x3). Data for these

\f\f\fsert Format Tools Data Window Help BONE Q4data ut Formulas Data Review View ? Tell me 24 Clear Reapply Flash Fill Stocks Geography Sort Filter Advanced Text to Columns Ex Remove D E F G H M N Observation 50 ANOVA of SS MS F ignificance F Regression 3 1.99E+09 6.62E+08 32.10275 2.39E-11 Residual 46 '9.49E+08 20627117 Total 49 2.94E+09 Coefficientsandard Erre t Stat P-value Lower 95% Upper 95% ower 95.0%/pper 95.0% Intercept -1180.77 1801.019 -0.65561 0.515341 -4806.03 2444.498 -4806.03 2444.498 Age 133.0478 43.33861 3.06996 0.003586 45.8117 220.2839 45.8117 220.2839 Days 1287.829 277.3178 4.643872 2.87E-05 729.6167 1846.04 729.6167 1846.04 ICU 703.5848 922.1324 0.762998 0.44936 -1152.57 2559.741 -1152.57 2559.741 RESIDUAL OUTPUT + Observationlicted Costs Residuals 1 2901.066 -1471.07 2 4896.783 -2927.78 3 6622.1 -3892,1 4 6275.01 -3443.01 5 4763.735 -1599.73 6 4721.085 -1101.09 7 5120.229 -1182.23 8 8536.822 -4255.82 9 4535.985 182.0149 10 5029.83 -150.83 11 8532.518 -3403.52 12 5918.516 -86.5156 13 10223.79 -3963.79

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