Question 3a: What input variables are significant based on a level of significan

Question

Question 3a: What input variables are significant based on a level of significance of 0.05? (select all that apply)

Question 3b: For a given month, the average daily patient load is 137.3, number of X-ray exposure is 24895, the occupied bed-days is 3912, and the average length of patient’s day is 5.15 days. What is the predicted monthly labor-hours for nurse based on the multiple linear regression?

Question 3c: Suppose you had decided to use a level of significance of 0.10 instead of 0.05. Would this have impacted the multiple linear regression model that you obtained?

Question 3d: Assume that Hershey Medical Center currently uses a nursing scheduling software that also provides predictions of monthly nurse labor-hours. The software provides an R2­ = 0.947. Which model provides a better prediction?

a. Monthly nurse labor-hours Question 3: You are interested to develop a linear regression model that predicts the monthly nurse labor-hours required at Hershey Medical Center. You have collected information on from different measuring criteria as listed below: 1. Monthly nurse labor-hours 2. Average daily patient load 3. Monthly X-ray exposures 4. Monthly occupied bed-days 5. Average length of a patient's stay, in days. Based on the information that you were collected, a multiple linear regression is performed using Excel, and the summary output is shown below: SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.995646082 0.99131112 0.988414827 601.1542611 17 ANOVA MS Significance Regression Residual Total 4 494765381.5 123691345 342.2689115 2.98975E-12 12 4336637.348 361386.446 16 499102018.9 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% 1602.73006 -0.05083 0.04733 1.01147 340.19214 828.84086 0.05091 0.02059 0.10898 158.98688 1.9337 0.9985 2.2990 9.2808 2.1397 0.0771 0.3377 0.0403 0.0000 0.0536 203.1590 3408.6192 0.0601 0.0922 1.2489 6.2105 203.1590 3408.6192 Intercept daily patient load monthly X-ray exposure monthly occupied bed-days average length of patient's stay, in da 0.1618 0.0025 0.7740 -686.5948 0.0601 0.0922 1.2489 6.2105-686.5948 0.1618 0.0025 0.7740 Use this information and answer question 3a to 3d

Explanation / Answer

Q3a) Monthly X-ray exposure and Monthly occupied bed-days are significanrt at 5%. Option C and D are Correct

Q3b) y = 1602.73006-0.05083*137.3+0.04733*24895+1.01147*3912-340.19214*5.15

y = 4978.913

Q3c) Yes, additional factor(s) would be included. Option B is Correct

Q3d) The regression model provides a better prediction. Option A is Correct

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