Business Analytics Lab Worksheet 01
CME Group Foundation Business Analytics Lab
Mark Gruhlke
Summer 2017
Star Schema
Task 2
mydata = read.csv(file="data/Scoring.csv")
head(mydata)#Extracting the status Column
Status = mydata$Status
#Calling the status Column
Status [1] good good bad good good good good good good bad good good good good bad good good good bad good bad good bad good good good good good bad good bad good good good good good good bad good bad good good
[43] bad bad good good good bad good good good good good good bad good good good good good good bad good good good good good good good good good good good good good good good good good good good bad good good
[85] good good good bad bad good bad good good good good bad good good good good good good bad bad good bad bad good bad good good bad bad good bad bad good good good good good bad good good good bad
[127] good good good good bad good good good good good bad good good bad good good good good bad bad good good good good bad good bad bad good good good good good good good good good good bad good bad good
[169] good good good good bad good good bad good good good bad good good good good good good bad good bad good good good good bad good good good good good good good good good good good good bad good good good
[211] good bad bad good bad good good good good good good good good good good good good good bad good good bad good good good good good good bad bad good good good bad bad good good bad good good good good
[253] good good good good good bad good good good good good good good good bad good good good good good bad good good bad good good good good good good good good good good good good good good good good good bad
[295] bad good good good bad good good bad good bad bad bad good good bad bad good good bad good good bad good good bad good bad good good good good good bad good good good bad bad good good good good
[337] bad good good good good good good good bad bad bad good good good bad good good bad good bad good good bad good good bad good bad good good good good good good good good bad good good good good bad
[379] good good good bad good good good good good bad good good bad bad bad good good bad good bad bad good bad good bad good good good good good bad good good good good good bad bad good good good bad
[421] good bad good bad bad good bad good good bad good good good bad good good bad good good good good bad good good good bad good good good bad bad good good good good good good good bad good bad good
[463] good good bad good good good good good good good good good good good good good good good bad bad bad good good good good good bad good good good good bad bad bad good bad bad good bad good bad good
[505] good good good bad good bad good good bad good good good good good good good good bad good good good good good good good good good bad bad good good good good good good good good good good good good bad
[547] bad bad good good good good good good bad good bad bad good good good good bad good bad good good good good bad good bad bad good bad bad good bad bad bad good good good good good bad good good
[589] good good good bad good good good good bad good good good good bad good good bad bad good good bad good good good good good good good good good bad good good bad good bad good good good bad good good
[631] bad good good good good bad good good good good bad good good bad good good bad good bad good bad bad bad good good bad good good good good good good good good good good good good good bad bad good
[673] good good good bad good good good bad good bad good good good good bad good good good good good good bad good good bad bad bad good good good good good good bad good bad good good good bad bad bad
[715] good good good good good bad good bad good good good bad good good bad bad bad bad bad bad good good good bad good good good good bad bad good bad good good good good good bad good good good bad
[757] good good good bad bad good good good bad bad good bad bad good good good good good bad good good good good good bad good bad good good good good good good bad good good good good bad bad good good
[799] good good good good good good bad good good bad bad good good bad bad good good bad good good bad bad bad bad good good good bad good good good bad good bad good good good good good good bad good
[841] good good good good good good bad good good good good good good good good bad good good good good good bad good good good good good bad good bad bad good good bad good bad good bad bad good good bad
[883] good bad good good bad good bad good bad good good good good good good bad good good good good good good good good good good good good bad good good good good good good good good good bad good good good
[925] good good good good good good good bad good good good good bad good bad bad good good good bad good good bad bad bad good good good good good good good bad good good bad good bad good bad good bad
[967] good good bad good good bad good good good good good good good bad good bad good good good bad good good good good good good good good bad good good bad good bad
[ reached getOption("max.print") -- omitted 3446 entries ]
Levels: bad good#Extracting the Savings Column and Assets Column
Savings = mydata$Savings
Assets = mydata$Assets
#Calling the Savings Column
Savings [1] 4.2000000 4.9800000 1.9800000 7.9333333 7.0838710 12.8307692 1.8750000 2.7000000 0.8500000 -0.4000000 2.7130435 3.3784615 3.9600000 5.5440000 0.6750000 1.4933333 4.7200000 4.6000000 3.7090909
[20] 4.8960000 2.4400000 15.2181818 0.2000000 4.1684211 4.3555556 3.6000000 6.3000000 3.5200000 0.4000000 3.1418182 6.9300000 6.5600000 2.7000000 4.4181818 2.5371429 7.8240000 2.4000000 1.6484211
[39] 4.0000000 1.6984615 4.1333333 12.3157895 3.2160000 -0.1600000 4.4347826 4.3714286 5.1360000 6.4000000 4.3200000 2.8125000 1.0000000 9.8000000 4.5200000 2.1600000 0.7200000 3.9000000 3.3000000
[58] 3.8000000 18.7500000 5.7377049 2.6181818 2.4500000 2.7600000 2.7720000 2.2666667 3.1800000 6.2500000 8.8235294 2.4900000 7.1200000 2.8200000 12.4000000 13.3200000 0.6171429 1.3200000 9.8400000
[77] 0.0000000 3.7028571 2.9333333 0.3600000 5.8909091 0.4235294 13.6800000 7.5000000 0.6240000 7.3000000 2.7349229 0.5769231 5.1600000 2.2105263 2.8187919 14.9760000 8.5950000 7.2000000 4.5000000
[96] 13.3920000 2.8421053 2.1120000 3.6000000 0.4160000 3.1090909 3.8000000 5.6492308 2.5846154 14.4000000 3.5200000 11.7500000 2.8500000 7.6114286 6.6947368 2.2720000 8.2835821 1.5120000 13.8268657
[115] 0.6600000 7.4250000 3.8347826 2.4757895 13.7142857 5.0000000 3.8666667 1.8333333 6.8307692 15.0545455 9.1764706 2.5714286 4.8387097 1.8461538 2.2320000 4.1052632 0.6428571 4.8461538 7.5000000
[134] 11.5714286 7.7076923 4.6666667 2.8695652 2.4545455 2.9040000 5.4750000 1.2600000 1.6421053 5.0040000 20.2353333 3.0461538 -0.1270588 16.2857143 2.2000000 4.6000000 14.4000000 2.3040000 8.4545455
[153] 0.0000000 7.2857143 0.7840000 4.0400000 1.3600000 12.8571429 1.7760000 2.3225806 3.4036364 8.6160000 16.0200000 3.7800000 17.5483871 1.6000000 3.0000000 2.5600000 0.5076923 6.7200000 3.9111111
[172] 1.4823529 2.7428571 9.0000000 2.1378579 7.9000000 5.2000000 20.6250000 2.6000000 1.6000000 4.4336842 0.4285714 5.3520000 2.5000000 3.1714286 5.4562909 2.2500000 2.1000000 2.2971429 5.2800000
[191] 6.4800000 5.1000000 1.6800000 0.9900000 2.1000000 0.6315789 1.6822430 9.6413793 1.7500000 1.8285714 2.7840000 8.9294118 8.0914286 6.0000000 -1.1280000 5.9600000 1.8000000 3.7800000 1.5300000
[210] 3.7080000 3.3000000 1.0080000 2.5440000 4.7500000 5.6000000 1.2545455 3.0240000 3.7200000 4.4727273 6.0000000 8.1473684 3.3722628 3.5040000 7.0121951 6.2280000 7.0000000 2.9100000 4.7666667
[229] 3.0600000 2.2000000 0.0000000 4.7400000 2.8695652 3.6000000 2.5714286 3.1800000 2.9647059 12.1000000 6.6171429 2.6222222 3.8400000 3.6000000 11.4600000 6.9529412 1.2631579 9.9000000 14.2500000
[248] 1.3333333 4.8311688 4.7500000 4.4571429 4.5500000 3.1309091 4.5000000 3.6000000 4.2923077 15.1404000 2.0160000 5.7085714 6.3000000 11.2400000 2.6891566 4.9200000 9.2880000 7.0129870 6.4285714
[267] 1.6800000 8.4960000 5.8200000 1.7684211 3.4800000 2.8200000 4.2000000 2.5440000 4.5333333 -0.3000000 4.6153846 11.3400000 1.9200000 3.4690909 6.1600000 3.7800000 0.5294118 13.9636364 6.8000000
[286] 3.5000000 5.0181818 6.5400000 6.5700000 5.9142857 2.1600000 0.1200000 3.1456311 1.5000000 4.5257143 0.8640000 8.2200000 0.6720000 0.3157895 5.4857143 1.8514286 -0.9913043 -0.2000000 2.1600000
[305] 1.5230769 7.4400000 2.0057143 -0.1440000 0.7000000 0.9415385 10.8461539 1.4400000 3.9085714 2.3571429 3.1200000 0.8000000 14.5600000 2.4500000 1.2218182 6.1714286 3.0000000 7.7700000 7.1092437
[324] 2.0347826 2.6896552 14.3684211 -0.9000000 12.7800000 4.0727273 0.3888889 4.9578947 -1.6013592 7.1250000 13.2857143 3.3458824 2.7716129 3.0000000 4.0800000 2.9200000 3.1090909 5.6400000 -0.6250000
[343] 5.6290909 5.5457143 -1.8162162 -1.4117647 -7.2000000 7.6800000 1.4400000 2.6040000 5.7303371 -1.8000000 8.8800000 2.0250000 1.4400000 3.0580645 4.0909091 14.9600000 3.6000000 6.4285714 4.3333333
[362] 0.1200000 5.6000000 2.3400000 2.6511628 7.5420000 4.8000000 -0.4800000 5.9250000 15.0000000 7.9800000 3.0923077 3.7384615 2.6400000 1.5545455 -4.0806000 2.7600000 5.5200000 4.1280000 2.6800000
[381] 5.1120000 1.9200000 10.2000000 13.5000000 1.6615385 4.3058824 5.2800000 4.8000000 8.4720000 6.6000000 5.4705882 -0.6720000 2.7428571 -1.1868132 4.5600000 6.7200000 3.5000000 7.6363636 1.8000000
[400] 3.7500000 3.7777778 4.8500000 2.8486957 4.9285714 3.1285714 3.4971429 2.0800000 2.2560000 4.4210526 7.9462500 4.5428571 4.0320000 3.0514286 0.2278481 0.0900000 -5.8800000 1.3043478 5.3000000
[419] 5.5200000 3.9927273 4.2666667 -0.5672727 2.0571429 1.2705882 2.1857143 4.4800000 2.0088106 2.3595506 6.7090909 2.7600000 7.4541176 7.8000000 8.1000000 7.8923077 1.3642105 7.0720000 1.3440000
[438] 4.0000000 9.9840000 -0.1714286 12.8571429 1.9869767 2.3294118 4.7454545 1.7837838 0.3582090 5.2363636 0.4266667 1.0344828 8.3454545 2.8285714 3.6000000 7.3000000 6.2400000 6.0857143 1.6363636
[457] 3.2275862 7.4000000 5.5555556 5.4800000 1.2681638 3.0000000 3.0428571 0.4444444 3.4800000 1.6363636 4.5818182 2.4000000 0.2571429 2.8588235 1.0000000 1.1500000 6.5000000 8.9760000 12.5250000
[476] 5.2500000 -0.5866667 12.1714286 3.1600000 1.9800000 3.7800000 -0.7200000 0.7285714 1.8461538 2.9160000 2.3400000 5.5466667 2.2800000 2.3555556 4.0714286 5.8200000 4.2162162 1.6695652 2.5894737
[495] 3.6000000 8.4000000 2.3333333 1.5000000 1.4000000 0.3600000 4.1600000 4.4800000 1.8720000 4.0800000 5.9294118 5.5875000 0.7200000 5.7000000 1.6363636 5.7402062 1.9090909 3.6000000 12.1371429
[514] 2.2000000 7.2461538 2.4272727 6.9750000 1.8200000 2.3040000 5.6000000 30.4200000 4.0000000 20.8615385 10.7500000 5.3333333 1.6800000 2.6742857 6.2769231 3.2400000 7.1563636 2.9880000 0.2076923
[533] 3.4560000 2.8800000 1.6524590 3.6000000 6.1764706 7.8400000 5.7240000 3.0500000 10.6666667 7.0400000 3.4800000 -0.5625000 0.4698947 0.4137931 3.2231405 7.4000000 7.3200000 2.6400000 2.9600000
[552] 0.3469880 -1.0285714 6.8363636 -0.9062500 -0.1800000 6.6705882 2.4705882 3.4941176 6.5142857 1.1200000 2.0640000 1.3200000 9.9000000 0.2571429 5.0800000 2.8581818 2.6619718 6.6470588 1.8800000
[571] 2.5405091 1.1840000 2.6500000 8.6117647 2.9200000 7.9000000 3.3600000 0.1551724 3.4800000 2.8500000 1.9636364 1.7454545 3.3517241 10.5000000 2.8800000 7.7400000 8.7000000 1.4769231 6.6428571
[590] 2.9454545 1.5750000 0.5454545 1.7280000 1.9800000 1.3636364 3.3600000 4.1632653 3.1428571 6.7320000 4.0145455 0.1200000 2.3333333 20.2800000 2.3791304 8.2285714 13.3200000 3.0000000 0.0000000
[609] 0.8000000 5.1000000 13.5000000 3.0000000 3.6955200 0.6776471 4.5176471 2.4000000 1.8947368 6.5167883 0.6776471 3.1418182 3.6000000 -0.1500000 7.2000000 2.4000000 2.2666667 6.6461538 3.9600000
[628] 2.7818182 2.1600000 10.7368421 17.2444444 2.3261538 9.0000000 0.3818182 8.2000000 1.5000000 2.4000000 3.1090909 3.6428571 0.9692308 0.1125000 -1.3333333 3.7846154 2.6142857 -0.2571429 2.9280000
[647] 3.0260870 3.3646154 1.5384615 10.8000000 1.2240000 2.7500000 0.1125000 9.0600000 8.7000000 9.8313253 6.3473684 5.5862069 1.3800000 2.9217391 7.2000000 9.2142857 0.3000000 4.5000000 7.9000000
[666] 12.4000000 4.1071429 7.5000000 3.2400000 -0.4861091 0.9000000 3.6750000 5.4276923 7.0451613 -0.2173913 3.7107692 3.4457143 16.4000000 2.9090909 0.9000000 7.7142857 14.8800000 6.1565217 4.8648649
[685] 1.0800000 1.3440000 2.1818182 18.9000000 3.5625000 0.2817391 3.7894737 3.1680000 8.4600000 7.0200000 7.7777778 3.4500000 3.2533333 7.0666667 0.6338028 0.4400000 6.0000000 6.5505882 -0.4258065
[704] 0.7200000 3.3000000 3.5733333 5.1840000 2.1000000 3.5368421 5.0769231 4.8800000 1.2672000 -1.4786730 2.3142857 -0.1476923 1.2857143 0.6857143 2.3400000 6.9000000 5.8125000 2.3680000 3.3517241
[723] 3.7894737 5.3866667 4.2545455 -0.3750000 0.7363636 6.1333333 3.3000000 1.8000000 3.7371429 2.5285714 11.0571429 6.6240000 2.3400000 1.1320755 4.2500000 1.9569231 3.3912000 2.9008000 7.7760000
[742] 4.0444444 2.4000000 4.8947368 0.9000000 -0.5760000 3.7333333 2.0914286 5.2500000 6.7200000 0.1200000 1.2396694 23.1000000 3.3692308 -1.6000000 5.1330363 6.1333333 2.8000000 6.5000000 2.8235294
[761] 3.0600000 3.2727273 2.2500000 3.2000000 0.5450000 3.4560000 2.2500000 0.3428571 0.8000000 2.8000000 9.1764706 20.4857143 4.8240000 6.1714286 3.2727273 9.5142857 0.6222222 11.7000000 -0.6600000
[780] 1.1700000 -0.3375000 3.8000000 1.0028571 2.4428571 1.6740000 12.7358491 4.5000000 2.7230769 3.4285714 1.6800000 11.5000000 2.2950000 0.2400000 2.7600000 1.9200000 2.3333333 6.7800000 4.1400000
[799] 5.4171429 4.5333333 1.1040000 4.2070588 5.3739130 3.7136842 18.7200000 2.9557895 -0.4200000 5.6914286 4.7154000 8.3200000 13.6666667 3.6809816 10.0884956 1.6500000 5.8500000 6.0000000 2.9400000
[818] 9.0000000 4.7076923 3.4285714 3.6545455 2.7600000 -2.7000000 4.3200000 3.0600000 -1.4608696 1.4040000 5.3333333 3.5345455 -0.0800000 1.8991304 4.1142857 2.9760000 9.0000000 1.9920000 3.4200000
[837] 30.2000000 6.9176471 0.9000000 4.0500000 10.5000000 3.4560000 5.9563636 6.1600000 1.9200000 3.8964706 0.2880000 -0.9000000 1.5300000 3.9000000 7.0819672 4.6200000 2.0600000 7.5789474 0.9913043
[856] 4.1538462 4.4526316 2.1666667 5.3142857 0.5454545 2.8333333 2.1853659 -1.2960000 -1.0800000 3.0000000 6.0000000 2.1767442 2.1000000 3.6981818 4.7040000 9.4615385 2.9333333 5.2800000 -1.9200000
[875] 4.3405714 6.6514286 5.1958763 2.5875000 3.5345455 0.7800000 1.2800000 2.0000000 3.0000000 3.1200000 -1.0588235 6.4500000 -0.3450000 3.6000000 3.5200000 3.3600000 5.0400000 3.7800000 5.0666667
[894] 2.5600000 1.8897638 5.3076923 3.7166667 2.2702703 0.3085714 1.0200000 7.6909091 3.0000000 7.5085714 2.3750000 4.2000000 2.8421053 3.1200000 2.5548387 4.0800000 4.3200000 1.5840000 3.1680000
[913] 4.5942857 1.6581818 3.7800000 7.6153846 26.0000000 16.2576923 2.7000000 2.4000000 1.8782609 3.8571429 1.0800000 8.0000000 1.5000000 3.7120000 2.2702703 7.4880000 2.2560000 5.3742857 2.2758621
[932] -0.3900000 8.0616333 2.9364706 1.9285714 0.0000000 -2.8000000 8.0000000 5.4109091 7.2000000 0.2470588 -0.5142857 0.1107692 2.5699482 5.1428571 8.2568807 0.9375000 -1.6744186 -0.5400000 11.6000000
[951] 2.1600000 3.0000000 0.9120000 6.5454545 4.7040000 2.5200000 5.4000000 2.8595745 -8.1600000 8.5371429 4.6200000 1.8500000 3.1058824 1.7431579 6.8347826 0.5714286 3.3600000 6.3529412 2.6000000
[970] 3.7800000 3.0000000 0.9600000 4.0800000 8.4600000 2.0625000 11.2000000 13.0285714 10.6666667 1.9200000 -1.2500000 4.2201290 1.2600000 2.2105263 5.5333333 1.4100000 1.3764706 4.0800000 1.4500000
[989] 7.8000000 2.4000000 1.8000000 6.0000000 4.8413793 2.4000000 9.6120000 2.8200000 5.1264000 3.2470588 2.0100000 1.5000000
[ reached getOption("max.print") -- omitted 3446 entries ]Assets [1] 0 0 3000 2500 0 3500 10000 0 15000 0 4000 3000 5000 3500 0 4162 16500 5000 750 0 0 10000 2000 0 3500 4000 4000 4000 5000 5000
[31] 2500 1000 3000 0 8500 13500 3000 0 14000 0 4000 50000 7000 3000 0 15900 3500 0 15000 4000 4000 0 2100 500 0 0 5000 4000 5000 0
[61] 0 0 3000 3000 3000 4000 3000 2500 7500 0 5500 9000 9000 3000 0 4000 0 30000 3000 4000 0 4500 2500 9000 6000 0 10000 0 0 0
[91] 0 6000 25000 0 3000 0 4000 15000 4000 12000 0 6000 0 0 4500 0 7000 5000 0 0 9000 12500 4000 3000 1500 10000 3500 10000 4000 5500
[121] 0 4000 0 0 200000 0 12000 0 0 3000 0 4000 24000 0 0 5000 0 3500 2000 0 6700 150000 50000 6000 0 5200 10000 0 6000 4000
[151] 0 11000 0 0 12000 0 4000 0 4000 15000 3500 16000 0 0 86000 5000 6000 0 4900 5000 0 7000 0 5000 3500 25300 6550 6500 15000 2000
[181] 0 8000 4000 5000 3500 2500 0 0 0 3000 5000 6000 7500 0 8000 5500 0 0 4000 0 12000 8000 4500 4000 9500 0 3000 2000 10000 3000
[211] 4000 0 0 0 2000 2500 3000 100000 3000 4000 7000 6300 4000 9000 9000 1434 5000 9500 0 0 30000 0 0 0 4000 0 9000 7000 0 0
[241] 4500 2500 3500 6000 0 4000 0 1500 7000 3000 0 0 2500 23000 5000 4000 3500 0 6000 0 4500 12000 6000 5000 5000 0 16000 8500 15000 16000
[271] 3500 0 7000 5500 0 0 0 4000 0 2500 9000 40000 6000 35000 14000 0 6000 6500 5000 13500 6000 75000 0 3000 12500 12000 4000 4000 0 4000
[301] 0 3000 4500 0 0 0 3000 2000 0 15000 9000 22000 0 0 8000 0 3000 22000 0 3500 0 12000 0 3000 3000 3500 3000 10000 6000 6500
[331] 13500 3000 0 0 14000 2500 5000 6000 9500 5000 3000 3500 7000 4600 0 4000 0 4000 14500 4000 1000 4000 4000 0 2000 2200 3000 10000 6100 2000
[361] 0 4500 6000 1150 4000 0 4000 7000 0 3500 3000 2000 3500 2500 5000 4000 1000 2000 0 0 4000 0 1800 2700 4000 0 0 0 32000 0
[391] 4000 100000 9000 10000 3500 3000 100000 0 0 4500 5000 12000 0 0 2600 20000 0 1500 0 6000 4500 0 20000 0 0 5000 0 0 0 15000
[421] 0 0 0 0 0 7000 0 3000 0 10000 0 0 1800 2500 0 4300 0 2500 0 5000 2000 0 4000 15000 0 0 0 0 3000 2500
[451] 0 0 3000 3000 2000 2000 5000 8000 2500 7000 4000 3000 5000 6000 0 4500 6000 3000 7000 3000 3000 0 5500 3000 0 0 5000 1500 16000 8000
[481] 0 5000 0 10000 3500 0 5000 0 3500 4500 3500 18000 2500 6000 0 8500 0 7500 10000 4000 0 0 0 0 12000 7000 0 3000 11500 0
[511] 4000 3000 0 0 10000 0 5000 3000 0 4000 0 0 0 10000 6000 6000 2000 4000 0 10000 0 4000 0 0 3000 4000 6000 3000 0 2500
[541] 4500 3500 3500 4000 8500 3500 0 2500 6000 0 8000 10000 30000 1500 2500 2000 8000 0 5000 4000 20000 3000 0 0 0 2100 21500 15000 2000 0
[571] 4000 0 3000 4000 0 2000 4000 5500 0 0 10000 4500 4000 15500 13000 3000 4000 3000 8000 5000 0 3500 7000 4000 3000 0 0 3500 5000 7000
[601] 0 5000 8000 5500 0 4000 0 4000 10000 0 11000 30000 4000 0 4000 500 3500 20500 0 0 0 5000 4000 0 2500 6000 0 0 4030 12000
[631] 0 0 6000 0 0 3000 0 0 4000 0 0 27000 5000 0 4100 90000 2700 0 0 7000 0 4500 0 6500 0 12000 0 6000 9000 15000
[661] 4000 14000 16000 0 97000 0 0 14000 0 3572 0 4000 10000 7500 4000 11700 0 0 0 0 0 4000 5000 0 0 2000 0 0 0 20000
[691] 0 4000 4500 5000 2500 32000 3000 0 0 0 3500 6000 0 0 0 20000 7500 0 4000 6000 7000 0 1750 0 4000 18000 2000 0 0 11000
[721] 4000 0 3500 2500 3500 5000 16000 8000 0 0 0 0 42 7700 5000 0 6000 0 9000 3500 0 4000 1600 0 15000 0 0 0 0 15000
[751] 4000 3500 18000 2500 4000 0 0 3000 5000 7000 30000 2500 0 2250 4800 9000 3500 3500 0 0 13000 0 1500 0 0 9800 5200 4000 16000 0
[781] 2500 0 2000 6000 9500 8000 0 0 30000 0 8000 0 3000 7750 0 0 5000 0 3000 4000 0 5000 1500 10000 2000 12000 0 0 4500 0
[811] 20000 4000 0 4000 5000 9000 12000 14000 0 16000 0 2000 10000 7000 0 0 16000 0 0 2500 0 0 4700 4100 30000 16500 22000 8000 0 0
[841] 10000 0 30000 0 15000 10000 7000 5000 4000 0 8000 5050 7000 1000 0 0 3000 6000 2000 4750 9000 5500 0 3500 3000 8000 5000 12000 14000 0
[871] 100000 5000 0 0 8000 40000 3000 0 2000 2500 0 0 5000 13000 9000 3200 0 3500 0 0 7000 0 500 0 3000 3500 4000 0 0 0
[901] 0 3000 12000 6500 10000 0 0 6000 0 0 0 3500 3500 3000 0 3000 0 25000 0 0 1500 0 0 3000 0 6500 6000 4000 0 2500
[931] 0 0 4000 1200 6000 2500 3500 3500 0 2500 0 2500 0 0 0 0 15000 5000 10000 7500 0 4500 0 6500 3000 2000 0 2500 3600 0
[961] 70000 0 4000 0 0 7000 9000 0 0 0 0 1500 5000 0 0 9000 0 8000 6000 0 5000 2000 3000 5500 2500 0 3000 0 12000 5000
[991] 5000 0 10000 3000 0 4000 2500 0 2000 7000
[ reached getOption("max.print") -- omitted 3446 entries ]#Find the average of the savings, assets, and debt columns.
meanSavings = mean(Savings)
meanAssets = mean(Assets)
Debt = mydata$Debt
meanDebt = mean(Debt)
#Call mean savings, assets, and debt.
meanSavings[1] 3.860083meanAssets[1] 5354.949meanDebt[1] 342.2571#Find the standard deviation of savings, assets, and debt columns
sdSavings = sd(Savings)
sdAssets = sd(Assets)
sdDebt = sd(Debt)
sdSavings[1] 3.726292sdAssets[1] 11534.33sdDebt[1] 1244.695#Find the snr of the savings, assets, and debt
snr_Savings = meanSavings/sdSavings
snr_Assets = meanAssets/sdAssets
snr_Debt = meanDebt/sdDebt
#Call snr_Saving
snr_Savings[1] 1.035905snr_Assets[1] 0.4642619snr_Debt[1] 0.2749728Of the Checking and Savings, which has a higher SNR? Why do you think that is? Savings because in a traditional checking accounts money is moving in and out more often than in a savings account.
Task 3
Watson
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