IT528 PG Logistic regression model

Anonymous
timer Asked: Feb 1st, 2019
account_balance_wallet $40

Question Description

  1. Using the Loans.csv file, build a logistic regression model to predict the “Good Risk” dependent variable (use family=binomial() in the glm function in R). In this column, ‘1’ indicates that making the loan is a good risk for the lender; ‘0’ indicates that making the loan is a bad risk. Make sure that you do not use the Applicant ID as an independent variable! You will need to load the MASS package in R by issuing library(MASS), before using the glm function to build your model. Show the creation of the model in your Word document.
  2. In your Word document, document your logistic model’s output, and specifically explain which independent variables have the most predictive power and which have the least. Make sure you identify how you know, and explain why it matters.
  3. Apply your logistic regression model to the data in Applications.csv to generate predictions of “Good Risk” for each loan applicant. If your glm model is stored in an R object called ‘LoanModel’, for example, and your Applications.csv data is in a frame called ‘Appl’, then you would issue a command that looks like this: LoanPredictions <- predict(LoanModel, Appl, type=“response”). Document the application of your model to the Applications data in your Word document.
  4. In your Word document, interpret your predictions for the Applications.csv data. Specifically address the following:
    1. How many loans do you predict to be a good risk for the lender?
    2. How many are predicted to be a bad risk?
    3. What are your highest and lowest post-probability percentages for predictions?
    4. How many loans have at least a 75% post-probability percentage and what does that mean for the lender?
    5. How many loans have less than a 25% post-probability percentage and what does that mean for the lender?
    6. Suppose that the lender is willing to accept a little higher risk and has decided they will make loans to applicants who have post-probability percentages between 40% and 65%. List two things the lender could do to mitigate risk when lending to this group, and explain how these will help.
  5. Make sure that you cite at least five supporting sources beyond the textbook in support of your writing and explanations. Cite correctly in APA format.

Applicant IDNumber of Missed/Late Lines of Credit Credit Payments ScoreMonthly Income Age at First Age Credit in YearsMarital Status 250162 13 5 511 3014 27 31 2 337157 22 4 495 2012 25 34 1 696961 7 5 641 3382 27 38 1 102576 6 6 748 3865 22 33 1 399338 6 7 799 3774 21 44 2 916894 28 3 519 3004 25 27 3 332229 9 7 693 3966 23 38 1 591594 22 3 515 2158 24 39 1 988822 5 6 811 4562 26 38 1 990531 0 6 709 4780 19 38 2 302120 18 3 491 1797 24 38 2 851836 3 8 789 4758 20 39 1 465514 0 6 772 4894 23 34 1 203291 8 7 810 4329 21 39 1 183488 25 4 491 1913 21 39 3 528534 28 5 499 2075 25 28 3 260650 0 8 709 4744 21 37 1 963949 8 5 641 3250 24 39 1 455615 10 3 541 2491 17 35 2 432768 14 7 560 2744 20 30 1 673501 25 5 497 2159 19 34 1 334354 1 8 711 4699 19 43 1 450082 22 3 515 2243 24 30 1 799506 16 4 548 2363 22 44 1 839577 4 8 795 4357 25 36 1 630035 5 6 619 3402 26 43 2 174765 5 6 629 3985 22 40 1 480448 3 7 808 4657 24 38 1 605712 4 6 813 4343 24 43 2 510435 27 3 549 2322 20 28 1 587635 4 7 836 4940 21 39 2 616259 7 7 709 3807 23 34 2 471782 12 5 622 3434 27 33 2 793010 12 3 532 3208 27 34 2 597727 10 6 804 4734 23 40 1 373615 1 6 833 4687 23 33 2 906102 4 7 796 5132 22 44 3 800324 2 7 735 3925 21 36 1 164313 12 4 646 3087 18 35 1 533675 1 7 803 4961 22 44 1 958620 11 6 608 3058 18 33 1 807605 28 4 507 2128 19 30 1 775431 4 7 711 3705 23 37 3 547205 0 8 790 5013 22 35 2 888942 10 6 701 3817 27 27 2 394878 12 4 532 2662 16 36 3 207967 492165 492918 882604 468202 240088 890107 668821 566858 851205 452357 568492 752701 870175 889868 280600 383243 247490 189360 347183 337498 895487 245112 834030 231762 505225 525546 561397 378241 333250 109378 566994 849787 688110 900707 521465 19 28 6 12 3 5 29 3 4 14 6 6 8 20 7 3 17 12 6 11 12 13 20 3 23 1 4 15 3 4 19 10 26 6 12 16 5 3 5 6 8 6 5 6 5 4 7 6 8 4 7 7 5 4 8 3 5 4 5 8 3 6 8 6 8 7 4 3 4 6 7 3 516 487 626 619 785 660 474 826 731 523 806 565 694 476 787 832 543 600 789 528 530 620 517 803 482 751 770 582 704 765 509 473 505 615 621 522 2036 2133 3828 3491 3798 4439 1670 4318 4792 2989 4382 3178 3910 2036 4177 4345 2673 2990 3564 2995 2943 3393 2189 4451 1849 4775 4587 2866 4619 4535 1982 2354 2218 3350 3604 2343 24 23 23 21 25 17 24 18 21 27 20 19 22 24 24 20 20 19 19 19 21 16 25 21 23 26 27 26 24 27 23 26 21 23 26 25 40 28 35 29 42 43 44 42 40 31 31 41 34 37 33 42 40 35 43 35 44 36 36 43 44 37 35 26 40 41 35 42 26 41 32 36 2 3 1 2 1 2 3 1 2 3 2 1 3 3 2 2 2 1 3 3 3 3 1 2 2 1 2 3 2 2 2 2 1 2 1 2
Applicant IDNumber of Missed/Late Lines of Credit Credit Payments ScoreMonthly Income Age at First Age Credit in YearsMarital Status Good Risk 701445 18 4 543 2562 20 32 1 0 838181 0 8 707 4731 16 40 1 1 611138 11 4 538 2410 20 36 1 0 467118 13 6 543 2816 24 35 3 0 870643 12 4 537 2517 23 36 3 0 456293 4 8 800 5142 20 41 1 1 331236 5 8 720 4098 18 43 1 1 164077 21 4 498 2155 14 32 1 0 162443 6 6 658 3730 19 33 1 1 525891 6 8 715 4138 23 42 1 1 561710 24 4 475 1983 16 35 3 0 824683 23 4 499 2044 17 37 3 0 723682 32 4 484 1834 20 33 2 0 325387 18 4 538 3188 22 29 2 0 278317 15 6 570 2724 19 33 1 0 546865 6 8 751 4082 22 39 1 1 612359 23 4 488 1992 19 35 3 0 687886 1 8 761 4616 25 40 1 1 163628 21 4 513 2155 21 37 1 0 542030 17 4 506 2391 25 35 3 0 968465 17 3 498 2263 21 36 3 0 185087 25 4 492 1988 21 31 3 0 846310 19 4 488 2126 25 35 1 0 796712 11 6 599 2989 18 35 1 0 387895 35 4 492 2088 23 28 2 0 717829 3 8 747 4497 24 40 1 1 902524 22 4 491 1969 19 29 3 0 618661 9 6 648 3457 25 34 1 1 321583 29 6 660 3013 15 31 1 1 934822 25 4 511 2245 18 32 3 0 410612 9 8 718 3831 22 32 1 1 775575 3 8 783 4957 19 37 1 1 473082 15 4 543 2902 22 33 1 0 935118 17 4 519 2236 21 38 2 0 572285 18 7 750 3728 17 31 1 1 757212 9 8 710 3503 23 40 1 1 962531 7 8 798 4322 18 33 1 1 602238 14 4 517 2954 19 33 2 0 331585 16 6 580 2946 21 29 1 0 369630 10 6 630 3533 21 32 1 1 110768 9 8 703 3878 19 35 1 1 429113 18 4 496 2133 25 39 3 0 554931 8 8 751 3958 23 32 1 1 309668 19 4 550 2673 25 31 1 0 453650 2 8 802 5000 21 36 1 1 865972 16 4 538 2544 14 36 1 0 507056 925450 291930 223894 136436 458095 470710 597930 211836 647049 461292 752010 882066 900953 261328 573037 865960 778043 177827 639855 209113 168761 314497 888333 228907 220247 526949 405204 730404 724996 135506 768029 994100 263373 576878 508792 959244 969700 389118 421484 979143 319310 893109 107844 496810 305773 410024 15 11 8 15 7 8 7 6 23 15 25 12 23 9 10 22 18 16 15 10 14 17 7 24 32 2 21 27 18 27 26 22 20 15 12 7 9 25 19 14 29 12 5 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Tutor Answer

writercollins
School: Purdue University

Attached.

Running head: R STUDIO, LOGISTIC REGRESSION, GLM,

R studio, logistic regression, glm,
Name
Institution
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1

R studio, logistic regression, glm
Question 1

The library to read windows excel files and MASS were loaded. The data was the imported into
Rstudio with default parameters, with read_xlxs function (Gandrud, 2016)..

Quality checked the data to be sure it was usable in the model

The model was then created using loan dataset, with the good risk as the dependent variable
while all other columns were independent variables, except for applicant ID which was not used
in the model and binomial family was used to direct a logical model created (Studio, 2012)..

Question 2
The output of the model as well as its statistical summary using Anova (analysis of variance) are
show...

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Anonymous
Goes above and beyond expectations !

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