See attached file.
(Table 1) presents data concerning the need for labor in 16 U.S. Navy hospitals. Here, y = monthly labor hours required; x1 = monthly X-ray exposures; x2 = monthly occupied bed days (a hospital has one occupied bed day if one bed is occupied for an entire day); and x3 = average length of patients stay (in days). (Fig 1) gives the Excel output of a regression analysis of the data using the model
Excel Output of a Regression Analysis of the Hospital Labor Needs Data Using the Model y = b0 + b1 x1 + b2 x2 + b3 x3 + e days (a hospital has one occupied bed day if one bed is occupied for an entire day); and x3 = average length of patients' stay (in days (Fig 1) gives the Excel output of a regression analysis of the data using the model
Note that the variables x1, x2, and x3 are denoted as XRay, BedDays, and LengthStay on the output.
a. Find (on the output) and report the values of b1, b2, and b3, the least squares point estimates of b1, b2, and b3. Interpret b1, b2, and b3.
b. Consider a questionable hospital for which XRay = 56,194, BedDays = 14,077.88, and LengthStay = 6.89. A point prediction of the labor hours corresponding to this combination of values of the independent variables is given on the Excel add-in output.
Report this point prediction and show (within rounding) how it has been calculated.
c. If the actual number of labor hours used by the questionable hospital was y = 17,207.31, how does this y value compare with the point prediction?
The solution provides step by step method for the calculation of regression analysis. Formula for the calculation and interpretations of the results are also included.