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What would I need to type in in R programming to get similar results to the one

What would I need to type in in R programming to get similar results to the one

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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What would I need to type in in R programming to get similar results to the one listed in the example results 

Hours (y) = monthly labor hours required Xray(x₁) = monthly X-ray exposures BedDays (x₂) = monthly occupied bed days Length(13) = average lengh of patients' stay (in days) The main objective of the regression analysis is to help the navy evaluate the performance of its hospitals in terms of how many labor hours are used relative to how many labor hours are needed. The navy selected hospitals 1 through 17 from hospitals that it thought were efficiently run hospitals to evaluate the efficiency of questionable hospitals. Consider relating y to x₁, I2, and 23 by using the model. y = Bo + B₁1+ B₂x2 + 3x3 + € Print and submit R code and output of your regression analysis. Results should look similar to figure 1. Coefficients: Estimate Std. Error t value Pr (>|t|) (Intercept) 1523.38924 786.89772 0.0749. 1.936 2.637 0.0205 * 9.305 4.12e-07 *** -320.95083 153.19222 -2.095 0.0563. 0.05299 0.97848 0.02009 0.10515 xray BedDays Length signif. codes: 0 ***** 0.001 **** 0.01 **' 0.05.¹0.1¹ 1 Residual standard error: 614.8 on 13 degrees of freedom Multiple R-squared: 0.9901, Adjusted R-squared: 0.9878 F-statistic: 432 on 3 and 13 DF, p-value: 2.894e-13
Transcribed Image Text:Hours (y) = monthly labor hours required Xray(x₁) = monthly X-ray exposures BedDays (x₂) = monthly occupied bed days Length(13) = average lengh of patients' stay (in days) The main objective of the regression analysis is to help the navy evaluate the performance of its hospitals in terms of how many labor hours are used relative to how many labor hours are needed. The navy selected hospitals 1 through 17 from hospitals that it thought were efficiently run hospitals to evaluate the efficiency of questionable hospitals. Consider relating y to x₁, I2, and 23 by using the model. y = Bo + B₁1+ B₂x2 + 3x3 + € Print and submit R code and output of your regression analysis. Results should look similar to figure 1. Coefficients: Estimate Std. Error t value Pr (>|t|) (Intercept) 1523.38924 786.89772 0.0749. 1.936 2.637 0.0205 * 9.305 4.12e-07 *** -320.95083 153.19222 -2.095 0.0563. 0.05299 0.97848 0.02009 0.10515 xray BedDays Length signif. codes: 0 ***** 0.001 **** 0.01 **' 0.05.¹0.1¹ 1 Residual standard error: 614.8 on 13 degrees of freedom Multiple R-squared: 0.9901, Adjusted R-squared: 0.9878 F-statistic: 432 on 3 and 13 DF, p-value: 2.894e-13
1 2 WN 3 4 5 6 7 ∞ 8 9 10 11 12 13 14 15 16 17 18 19 A B Xray BedDays 472.92 2463 2048 1339.75 3940 620.25 6505 568.33 1497.60 1365.83 5723 11520 5779 1687.00 5969 1639.92 8461 2872.33 20106 3655.08 13313 2912.00 10771 3921.00 15543 3865.67 36194 7684.10 34703 12446.33 39204 14098.40 86533 15524.00 D Length Hours 4.45 566.52 6.92 696.82 4.28 1033.15 3.90 1603.62 5.50 1611.37 4.60 1613.27 5.62 1854.17 2160.55 2305.58 3503.93 5.15 6.18 6.15 5.88 4.88 5.50 7.00 10343.81 10.78 11732.17 3571.89 3741.40 4026.52 7.05 15414.94 6.35 18854.45
Transcribed Image Text:1 2 WN 3 4 5 6 7 ∞ 8 9 10 11 12 13 14 15 16 17 18 19 A B Xray BedDays 472.92 2463 2048 1339.75 3940 620.25 6505 568.33 1497.60 1365.83 5723 11520 5779 1687.00 5969 1639.92 8461 2872.33 20106 3655.08 13313 2912.00 10771 3921.00 15543 3865.67 36194 7684.10 34703 12446.33 39204 14098.40 86533 15524.00 D Length Hours 4.45 566.52 6.92 696.82 4.28 1033.15 3.90 1603.62 5.50 1611.37 4.60 1613.27 5.62 1854.17 2160.55 2305.58 3503.93 5.15 6.18 6.15 5.88 4.88 5.50 7.00 10343.81 10.78 11732.17 3571.89 3741.40 4026.52 7.05 15414.94 6.35 18854.45
Expert Solution
Step 1: Given Information:

Consider the given data:

XrayBedDaysLengthHours
2463472.924.45566.52
20481339.756.92696.82
3940620.254.281033.15
6505568.333.91603.62
57231497.65.51611.37
115201365.834.61613.27
577916875.621854.17
59691639.925.152160.55
84612872.336.182305.58
201063655.086.153503.93
1331329125.883571.89
1077139214.883741.4
155433865.675.54026.52
361947684.1710343.81
3470312446.3310.7811732.81
3920414098.47.0515414.94
86533155246.3518854.45
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