Question: This sheet provides some information from a random sample of 100 solar installation projects by SD Solar during 2020. The company would like to find
This sheet provides some information from a random sample of 100 solar installation projects by SD Solar during 2020. The company would like to find a model to estimate Electricity Generation based on 1) number of panels installed, 2) energy score (categorical variable), 3) house size, and 4) pre-installation consumption. a) Create a multiple regression model to estimate electricity generation based on the above mentioned factors. b) Check the necessary conditions for residuals and multicollinearity. Is there any non-significant factor. c) Now remove all the non-significant variables and create another model with only significant factors. Is this model a better model, why? A sample of solar installation projects by SD SOLAR in the past year. Number House Size (SQFT) Energy Score (1-10) Pre-install Consumption (KW) Number of Panels installed Electricity Generation (KW) Cost of Installation ($) 1 1746 L8 441 16 544 17140 2 1093 L8 517 19 494 16100 3 1755 L9 349 14 420 12350 4 1745 L10 313 13 338 12300 5 1784 L10 400 15 465 15480 6 1721 L7 406 15 435 15170 7 1957 L10 481 19 570 18300 8 1254 L10 235 11 286 12280 9 1964 L7 481 17 425 15410 10 1634 L9 647 23 621 26220 11 1431 L7 515 18 576 14940 12 1733 L7 284 11 341 10080 13 1483 L8 279 10 300 9140 14 1438 L8 497 19 608 18850 15 1478 L8 457 18 450 18710 16 2103 L9 481 18 594 17370 17 1833 L8 781 28 700 28680 18 1684 L9 509 19 570 17220 19 1828 L8 334 12 372 10700 20 1974 L10 552 22 682 21610 21 1372 L8 410 17 527 18790 22 2160 L10 851 29 812 33700 23 1666 L8 317 12 360 10410 24 1866 L9 383 13 390 10500 25 1114 L7 392 14 434 16500 26 1729 L9 763 26 806 29460 27 1763 L7 601 22 638 23100 28 1561 L9 633 23 621 20040 29 1177 L9 428 17 425 15320 30 2093 L7 671 24 672 25950 31 1491 L9 306 12 420 11870 32 1498 L9 449 16 528 13300 33 1855 L8 558 21 672 17730 34 1437 L10 446 16 480 14760 35 1566 L10 407 15 465 16140 36 2005 L8 383 16 464 19030 37 1116 L9 357 13 338 11590 38 2068 L7 483 17 561 19450 39 1638 L8 202 9 279 8660 40 1686 L8 493 18 576 17930 41 1963 L7 521 21 714 18360 42 1512 L7 421 15 495 15210 43 1744 L10 500 19 475 21610 44 1650 L7 193 9 297 9790 45 1270 L10 375 13 338 15550 46 1738 L7 330 12 396 13000 47 1599 L7 650 25 825 23700 48 1235 L10 448 16 544 18130 49 1479 L10 261 11 352 11000 50 2008 L10 431 18 630 16440 51 1984 L9 430 16 528 16000 52 1835 L7 721 27 864 27680 53 1548 L10 227 10 260 8930 54 2016 L8 571 22 660 25370 55 1285 L8 416 17 544 17040 56 1990 L9 775 29 754 26370 57 2067 L7 625 23 644 20290 58 2265 L7 727 26 728 24290 59 1599 L8 461 16 480 16480 60 1773 L8 487 17 578 15470 61 1690 L8 587 23 575 18770 62 2064 L10 484 18 540 15340 63 1674 L7 582 22 638 17600 64 1866 L8 704 25 675 29230 65 2026 L10 400 16 528 14560 66 2083 L10 642 25 700 21580 67 1389 L8 206 8 272 7790 68 1881 L10 484 17 442 19420 69 1696 L10 659 23 598 21850 70 2041 L9 323 13 338 13560 71 1452 L10 191 9 297 8540 72 1482 L9 385 15 390 17760 73 1715 L9 656 25 750 23900 74 2148 L7 379 14 476 12300 75 2009 L10 394 16 512 13810 76 1659 L9 404 17 595 15640 77 1214 L10 564 22 638 23370 78 1472 L10 501 19 532 20050 79 1624 L10 649 23 713 27240 80 1638 L7 514 19 532 15680 81 1552 L9 505 20 540 19360 82 1608 L9 254 9 315 8700 83 2369 L7 744 27 729 23390 84 1118 L9 423 16 544 15300 85 1055 L9 556 20 520 18800 86 1712 L7 386 16 544 18310 87 1962 L10 325 13 377 12760 88 1551 L8 254 11 319 12650 89 1980 L10 383 16 432 18530 90 2184 L9 425 15 465 16130 91 2100 L9 510 19 646 17860 92 2300 L7 842 29 899 28800 93 1480 L10 430 15 495 14990 94 1045 L8 318 12 420 11550 95 1493 L7 449 17 578 14680 96 2077 L10 523 20 680 19260 97 1716 L8 378 16 464 14080 98 1962 L10 430 16 544 15520 99 1092 L7 580 20 520 16680 100 1836 L10 362 15 405 12710
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