answer following in r studio with data provided

#* 1) The following data reflect information taken from 17 Naval hospitals at various sites around the world. A breif description of the variables is as follows: #* y = monthly labor-hours #* x1 = average daily patient load #* x2 = monthly X-ray exposures #* x3 = monthly occupied bed-days #* x4 = eligible population in the area/1000 #* x5 = average length of patient's stay, in days #* x6 = meals consumed (snacks and additional dishes don't count as a whole meal)

#import the hospitals.csv as a data frame

#* a) Use backwards-elimination as we discussed to determine the best #* predictions for y

#* b) Which model seems to predict the best? Round the numbers in the equation #* to the nearest 3 decimal places

#* c) Use that equation to predict monthly labor-hours if average daily patient #* load was 450, the monthly x-ray exposures was 2500, the monthly occupied beds #* was 5000, eligible population was 38,000, the average length of patients' #* stay was 1000 days, and there were 1,500 meals consumed.

x1 x2 x3 x4 x5 x6 V 975.76 8225.35 446 44.73 8223.23 9220.61 4339.818 79.02 8362.55 6080.68 35.14 8358.26 5020.31 2776.798 243.78 5957.69 776.87 25.18 5959.49 6266.39 2211.333 386.51 147.62 3441.24 60.73 153.81 8033.8 1406.665 328.05 6932.33 5513.4 63.51 6932.54 2282.66 2341.573 503.84 5425.07 1574.97 33.27 5415.32 8151.86 2417.948 539.98 3809.17 3071.92 20.71 3817.75 5620.28 2520.693 569.23 1267.02 3943.22 80.36 1275.14 4283.1 1396.555 -9.91 4389.68 3484.59 2.75 4396.96 4643.56 1524.16 712.02 8257.06 9292.88 93.95 8264.32 346.07 3536.255 578.01 9187.42 7534.56 44.02 9181. 11 4326.34 3703.865 329.39 3620.15 9426.6 38.33 3615.96 4476.87 1450.958 854.75 9839.31 3155.59 24.23 9844.41 3942.09 4601.948 387.13 2158.06 5884.69 28.73 2162.73 3703.34 1451.195 718.6 622.07 3485.87 55.32 622.58 8334.85 2093.988 916.63 4494.86 4468.76 4.77 4496.2 6801.57 3254.925 899.55 3095.3 1409.08 79.72 3100.31 6203.87 2798.125 900. 11 6221.88 7531.28 34.47 6231 3722.33 3502.6 889.19 290.82 713.38 24.33 289.55 3586.83 1767.895 736.15 8430.96 697.29 5.02 8429.35 3594.5 3764.51 119.75 5852.34 7958.83 27.58 5855.07 1981.94 2139.915 672.57 4886.91 8042.21 28.77 4882.11 3349.44 2837.898 303.04 3162.7 3156.92 56 3170.71 4435.27 1512.195 585.79 4662.01 939.88 54.14 4657.05 2889.13 2691.673 702.29 7229.03 8633.38 60.07 7220.97 3307.75 3476.408 837.74 9635.14 415.45 87.24 9630.06 1626.95 4341.025 263.85 6851.8 6788.94 93.74 6850.65 1438.28 2445.48 795.3 5762.59 2675.89 14.72 5754.13 1342.73 3261.518 891.37 9798.1 2758.12 7.28 9797.13 8823.39 4711.895 419.57 9715.51 5445.29 47.54 9707.8 7873.59 3351.128