Question

what is the relationship between days delinquent and credit scores? Use simple Regression. determine and interpret the coefficient of determination.

credit scores 400 425 520 $40 510 355 535 565 575 640 621 650 647 630 624 648 620 679 682 665 680 695 705 712 730 720 795 days del 91 90 70 88 85 65 75 55 50 36 43 40 29 35 37 45 38 36 35 25 22 15 18 13 1 8 15 1 760 745 800

Answer 1

Solution:- Table 1:- х Y X^2 Y^2 X*Y (X-X)^2 (Y-Y)^2 î (Y-9)^2 91 400 8281 160000 36400 2450.25 55585.92111 456.105051 3147.776749 90 425 8100 180625 38250 2352.25 44422.58778 459.7345786 1206.490951 70 520 4900 270400 36400 812.25 13401.92111 532.3251304 151.908839 88 540 7744 291600 47520 2162.25 9171.254444 466.9936338 5329.929508 85 510 7225 260100 43350 1892.25 15817.25444 477.8822165 1031.552014 65 555 4225 308025 36075 552.25 6523.254444 550.4727683 20.49582662 75 535 5625 286225 40125 1122.25 10153.92111 514.1774924 433.5768212 55 565 3025 319225 31075 182.25 5007.921111 586.7680442 473.847749 50 575 2500 330625 28750 72.25 3692.587778 604.9156822 894.9480392 36 640 1296 409600 23040 30.25 17.92111111 655.7290684 247.4035929 43 621 1849 385641 26703 2.25 218.0544444 630.3223753 86.90668092 40 650 1600 422500 26000 2.25 202.5877778 641.2109581 77.2472584 29 647 841 418609 18763 156.25 126.1877778 681.1357615 1165.250215 35 630 1225 396900 22050 42.25 33.25444444 659.358596 861.9271588 37 624 1369 389376 23088 20.25 138.4544444 652.0995408 789.5841941 44 648 1936 419904 28512 6.25 149.6544444 626.6928477 453.9947394 45 620 2025 384400 27900 12.25 248.5877778 623.0633201 9.383930069 40 679 1600 461041 27160 2.25 1869.121111 641.2109581 1428.011691 38 682 1444 465124 25916 12.25 2137.521111 648.4700132 1124.260013 36 665 1296 442225 23940 30.25 854.5877778 655.7290684 85.95017263 35 680 1225 462400 23800 42.25 1956.587778 659.358596 426.0675593 25 695 625 483025 17375 272.25 3508.587778 695.6538719 0.42754844 22 705 484 497025 15510 380.25 4793.254444 706.5424547 2.379166349 15 712 225 506944 10680 702.25 5811.521111 731.9491478 397.9684969 18 730 324 532900 13140 552.25 8879.921111 721.060565 79.91349801 13 720 169 518400 9360 812.25 7095.254444 739.208203 368.9550606 1 795 1 632025 795 1640.25 25355.25444 782.762534 149.7555737 8 760 64 577600 6080 1122.25 15433.92111 757.3558409 6.991577372 15 745 225 555025 11175 702.25 11931.92111 731.9491478 170.3247439 1 800 1 640000 800 1640.25 26972.58778 782.762534 297.1302335 Total = 1245 19073 71449 12407489 719732 19781.5 281511.3667 19073 20920.3596 Mean= 41.5 635.7666667 a= 786.3920616 b= -3.629527589 R^2= 0.92568556

and a = 5-bă Now, from Table to awe get a = 786.39206/6 & 6 = 3.629527589. Hence ŷ - 786.3920616-3.629527589(x). Now, Couetticident of determination is denoted 64 R2 & is evaluated as, R2 - 1 - SSE SST cohere; 20920.3596 7 from SSE = E(4-4) SST= 2(992 = 281511.3667) Table. Now, Ras 0.926 92.6 % Hence, 92.6% of the response variable variation explained by the linear model, or we can Say that 92,6% of the data fit the linear regressim model.