Business Analytics Lab Worksheet 01
Ally Ungashick
July 17, 2017
Task 1
mydata = read.csv("data/creditrisk.csv")
head(mydata)## Loan.Purpose Checking Savings Months.Customer Months.Employed Gender
## 1 Small Appliance 0 739 13 12 M
## 2 Furniture 0 1230 25 0 M
## 3 New Car 0 389 19 119 M
## 4 Furniture 638 347 13 14 M
## 5 Education 963 4754 40 45 M
## 6 Furniture 2827 0 11 13 M
## Marital.Status Age Housing Years Job Credit.Risk
## 1 Single 23 Own 3 Unskilled Low
## 2 Divorced 32 Own 1 Skilled High
## 3 Single 38 Own 4 Management High
## 4 Single 36 Own 2 Unskilled High
## 5 Single 31 Rent 3 Skilled Low
## 6 Married 25 Own 1 Skilled Low
Task 2
mydata = read.csv(file="data/creditrisk.csv")
head(mydata)## Loan.Purpose Checking Savings Months.Customer Months.Employed Gender
## 1 Small Appliance 0 739 13 12 M
## 2 Furniture 0 1230 25 0 M
## 3 New Car 0 389 19 119 M
## 4 Furniture 638 347 13 14 M
## 5 Education 963 4754 40 45 M
## 6 Furniture 2827 0 11 13 M
## Marital.Status Age Housing Years Job Credit.Risk
## 1 Single 23 Own 3 Unskilled Low
## 2 Divorced 32 Own 1 Skilled High
## 3 Single 38 Own 4 Management High
## 4 Single 36 Own 2 Unskilled High
## 5 Single 31 Rent 3 Skilled Low
## 6 Married 25 Own 1 Skilled Lowchecking = mydata$Checking
checking## [1] 0 0 0 638 963 2827 0 0 6509 966 0
## [12] 0 322 0 396 0 652 708 207 287 0 101
## [23] 0 0 0 141 0 2484 237 0 335 3565 0
## [34] 16647 0 0 0 940 0 0 218 0 16935 664
## [45] 150 0 216 0 0 0 265 4256 870 162 0
## [56] 0 0 461 0 0 0 580 0 0 0 0
## [67] 758 399 513 0 0 565 0 0 0 166 9783
## [78] 674 0 15328 0 713 0 0 0 0 0 303
## [89] 900 0 1257 0 273 522 0 0 0 0 514
## [100] 457 5133 0 644 305 9621 0 0 0 0 0
## [111] 6851 13496 509 0 19155 0 0 374 0 828 0
## [122] 829 0 0 939 0 889 876 893 12760 0 0
## [133] 959 0 0 0 0 698 0 0 0 12974 0
## [144] 317 0 0 0 192 0 0 0 0 0 942
## [155] 0 3329 0 0 0 0 0 0 339 0 0
## [166] 0 105 0 216 113 109 0 0 8176 0 468
## [177] 7885 0 0 0 0 0 0 0 0 0 734
## [188] 0 0 172 644 0 617 0 586 0 0 0
## [199] 0 0 522 585 5588 0 352 0 2715 560 895
## [210] 305 0 0 0 8948 0 0 0 0 0 483
## [221] 0 0 0 663 624 0 0 152 0 0 498
## [232] 0 156 1336 0 0 0 2641 0 0 0 0
## [243] 0 887 0 0 0 0 18408 497 0 946 986
## [254] 8122 0 778 645 0 682 19812 0 0 859 0
## [265] 0 0 0 0 0 795 0 0 0 0 852
## [276] 0 0 425 0 0 0 11072 0 219 8060 0
## [287] 0 0 0 1613 757 0 0 977 197 0 0
## [298] 0 0 0 256 296 0 0 0 298 0 8636
## [309] 0 0 19766 0 0 0 0 4089 0 271 949
## [320] 0 911 0 0 0 0 271 0 0 0 0
## [331] 4802 177 0 0 996 705 0 0 5960 0 759
## [342] 0 651 257 955 0 8249 0 956 382 0 842
## [353] 3111 0 0 2846 231 0 17366 0 332 242 0
## [364] 929 0 0 0 0 0 0 0 646 538 0
## [375] 0 0 0 135 2472 0 10417 211 16630 0 642
## [386] 0 296 898 478 315 122 0 0 0 670 444
## [397] 3880 819 0 0 0 0 0 0 0 0 0
## [408] 161 0 0 789 765 0 0 983 0 0 798
## [419] 0 193 497 0 0 0 0savings = mydata$Savings
savings## [1] 739 1230 389 347 4754 0 229 533 493 0 989
## [12] 3305 578 821 228 129 732 683 0 12348 17545 3871
## [23] 0 485 10723 245 0 0 236 485 1708 0 407
## [34] 895 150 490 162 715 323 128 0 109 189 537
## [45] 6520 138 0 660 724 897 947 0 917 595 789
## [56] 0 746 140 659 717 667 0 763 1366 552 14643
## [67] 2665 0 442 8357 0 863 322 800 656 922 885
## [78] 2886 626 0 904 784 806 3281 759 680 104 899
## [89] 1732 706 0 576 904 194 710 5564 192 637 405
## [100] 318 698 369 0 492 308 127 565 12632 116 178
## [111] 901 650 241 609 131 544 10853 0 409 391 322
## [122] 583 12242 479 496 466 1583 1533 0 4873 0 717
## [133] 7876 4449 0 104 897 4033 945 836 325 19568 803
## [144] 10980 265 609 1851 199 500 509 270 457 260 3036
## [155] 643 0 6345 922 909 775 979 948 2790 309 762
## [166] 970 320 861 262 692 540 470 192 12230 772 14186
## [177] 6330 18716 886 750 3870 3273 406 461 340 6490 348
## [188] 506 14717 0 1571 0 411 544 0 835 823 5180
## [199] 408 821 385 2223 0 605 7525 3529 1435 887 243
## [210] 4553 418 771 463 110 10099 13428 208 552 3105 415
## [221] 1238 238 127 0 785 718 493 757 9125 364 598
## [232] 374 0 0 508 956 636 0 1519 922 180 701
## [243] 296 519 800 736 11838 364 212 888 999 0 578
## [254] 136 734 861 855 4486 2017 0 500 859 3305 1218
## [265] 9016 11587 8944 807 867 16804 347 836 142 169 3613
## [276] 403 836 0 11481 3285 164 891 0 841 607 486
## [287] 108 0 113 0 208 603 343 463 0 299 490
## [298] 6628 859 750 954 591 13970 857 5857 3326 726 214
## [309] 207 713 2141 483 127 367 813 0 102 759 0
## [320] 503 823 693 973 648 523 7090 596 904 541 154
## [331] 0 0 337 716 837 0 7710 531 129 941 596
## [342] 987 0 460 0 798 0 959 1482 883 12721 0
## [353] 0 302 538 0 702 2688 0 425 214 0 272
## [364] 124 17124 612 862 146 14190 396 519 0 344 204
## [375] 148 435 914 0 0 412 19811 822 0 3369 0
## [386] 707 818 177 4071 466 460 991 17653 497 4014 921
## [397] 0 0 607 15800 369 4973 0 761 471 674 547
## [408] 524 815 0 989 10406 957 770 950 160 276 137
## [419] 579 2684 0 0 0 712 912meanChecking = mean(checking)
meanChecking## [1] 1048.014meanSavings = mean(savings)
meanSavings## [1] 1812.562spreadChecking = sd(checking)
spreadChecking## [1] 3147.183spreadSavings = sd(savings)
spreadSavings## [1] 3597.285snr_Checking = meanChecking/spreadChecking
snr_Checking## [1] 0.3330006snr_Savings = meanSavings/spreadSavings
snr_Savings## [1] 0.5038695Of the checking and savings, which has the higher SNR? Why do you think that is? Savings has the higher SNR of the two. I would suspect this is because, compared to checking at 1048.014, savings stands at 1812.562. But also, savings has greater units of data, while checking has more 0’s than savings.
Task 3
