Knee replacement surgeries at Basin General Hospital are scheduled each week. The time to conduct such...

 

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Knee replacement surgeries at Basin General Hospital are scheduled each week. The time to conduct such a surgery, Y, is considerable and varied. Due to the demand for these surgeries, hospital administrators want to determine what, if anything, explains the long surgery time. Considerations include equipment prep time (X1), the patient case severity on a scale of 1 to 10, 10 being most severe (X2), and patient gender (X3). Hospital staff believes these are the three key contributors, but want to validate this. The data are shown below. Set up a spreadsheet and use Excel to determine the following: a. If the hospital wants to use a simple linear regression model (one X, one Y) based on either X1 or X2, which one of these independent variables do you recommend they use? Why? b. Generate a prediction equation to estimate the coefficients (b's) for: Y = bo+b₁X1 + b₂x2 For each additional patient scheduled, X2, what is the change in surgery hours? c. Set up a binary variable (X3) for patient gender (i.e., Male = 0, Female = 1) in combination with the model from part b; that is, Y = bo + b₁X1 + b₂X2 + b₂X3. Is Gender a significant predictor of Surgery Time? How do you know? d. Add a column of quadratic terms for X₁² (i.e., create a new column for X1*X1) to the regression model in part b, that is: Y = b + b₁X1 + b₂X2 + X₁². Is the squared term (X₁²) significant in the prediction knee surgery hours? How do you know? e. What is the "best" prediction model you can recommend to the hospital from parts a through above. Explain your rationale? Patient 1 2 A WI 10 11 12 13 14 15 0.100 Y, Surgery Hours 7.7 7.8 11.4 7.3 8.8 10.4 73 77 82 7.3 9.6 6.3 6.4 X1, Equipment Prep hours 3.5 4.4 3.2 3.8 4.5 3.3 3.9 3.7 3.6 4.5 3.4 4.0 4.2 X2, Severity 3.4 4.6 6.3 4.1 5.8 5.1 4.2 3.4 4.5 4.2 4.4 2.9 2.9 X3, Gender F M ΣυΣΣΙ M F M M F F M F TTT M Knee replacement surgeries at Basin General Hospital are scheduled each week. The time to conduct such a surgery, Y, is considerable and varied. Due to the demand for these surgeries, hospital administrators want to determine what, if anything, explains the long surgery time. Considerations include equipment prep time (X1), the patient case severity on a scale of 1 to 10, 10 being most severe (X2), and patient gender (X3). Hospital staff believes these are the three key contributors, but want to validate this. The data are shown below. Set up a spreadsheet and use Excel to determine the following: a. If the hospital wants to use a simple linear regression model (one X, one Y) based on either X1 or X2, which one of these independent variables do you recommend they use? Why? b. Generate a prediction equation to estimate the coefficients (b's) for: Y = bo+b₁X1 + b₂x2 For each additional patient scheduled, X2, what is the change in surgery hours? c. Set up a binary variable (X3) for patient gender (i.e., Male = 0, Female = 1) in combination with the model from part b; that is, Y = bo + b₁X1 + b₂X2 + b₂X3. Is Gender a significant predictor of Surgery Time? How do you know? d. Add a column of quadratic terms for X₁² (i.e., create a new column for X1*X1) to the regression model in part b, that is: Y = b + b₁X1 + b₂X2 + X₁². Is the squared term (X₁²) significant in the prediction knee surgery hours? How do you know? e. What is the "best" prediction model you can recommend to the hospital from parts a through above. Explain your rationale? Patient 1 2 A WI 10 11 12 13 14 15 0.100 Y, Surgery Hours 7.7 7.8 11.4 7.3 8.8 10.4 73 77 82 7.3 9.6 6.3 6.4 X1, Equipment Prep hours 3.5 4.4 3.2 3.8 4.5 3.3 3.9 3.7 3.6 4.5 3.4 4.0 4.2 X2, Severity 3.4 4.6 6.3 4.1 5.8 5.1 4.2 3.4 4.5 4.2 4.4 2.9 2.9 X3, Gender F M ΣυΣΣΙ M F M M F F M F TTT M

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Solution Here are the steps to solve this problem a Of the two independent variables X1 equipment pr... View the full answer