In R programming, use direct X-Y variable plots and residuals vs. fitted value plots

implemented in R, as well as perhaps other types of plots and/or regression diagnostic

tests and/or goodness-of-fit measures and the like, to identify an appropriate linear

regression model (possibly requiring transformations of the variables, for example) that

seems reasonably consistent with the data. Please explain why you believe your model

may be a valid one.

1 144.2345992 4.370958447

2 19.2532018 2.435301829

3 17.4881601 3.363128411

4 95.3070282 3.632862605

5 21.5609028 3.404268323

6 19.0417205 2.893875484

7 73.7288413 4.511521997

8 17.1872164 2.905340962

9 166.088409 5.018423714

10 20.022695 2.937285901

11 73.1361943 4.304869654

12 208.6549137 5.286645393

13 3.9291444 1.611139299

14 11.8118429 2.721211233

15 7.6608973 2.866678664

16 31.3364994 3.635950398

17 11.6980475 2.715747079

18 5.4438693 0.343544579

19 0.8855689 0.559533071

20 80.5389932 4.320113346

21 7.0044725 2.693361406

22 1.6216979 1.218691566

23 18.0011175 2.828082644

24 41.1142646 4.214674699

25 133.5241225 4.895193461

26 10.54236 2.569530868

27 11.4260383 2.742730617

28 1.2516942 1.236836915

29 17.2484689 3.460097355

30 11.5856046 2.360005124

31 42.0668669 3.455450123

32 31.7660675 3.704837337

33 56.550552 4.035103522

34 19.1541317 2.391073625

35 68.3665038 3.504955123

36 2.0843029 1.282991321

37 8.6441363 2.215540992

38 15.6401544 2.149092406

39 1.4203844 0.58579235

40 20.2851251 3.036122607

41 23.6401125 3.2059986

42 8.9809278 2.638942701

43 34.3231905 3.758163236

44 9.5694213 2.273295173

45 4.1569621 1.631718956

46 54.028465 3.432818026

47 7.0154187 2.188606824

48 68.5468015 4.444101262

49 18.4854712 2.568553797

50 22.8160372 3.655647883

51 27.1554029 3.321925265

52 4.2223365 2.216161059

53 174.0447229 4.57572752

54 33.3172891 3.642899306

55 17.3890028 3.089760647

56 14.2595176 3.276550747

57 39.4647473 3.679288816

58 14.7271363 3.089832887

59 0.7711779 0.006909917

60 50.8418877 3.284882954

61 12.743259 2.632765357

62 14.1446912 3.185230565

63 38.9947557 3.581823727

64 67.9225183 4.399736827

65 13.0359994 2.272707941

66 151.2227017 4.302542632

67 17.1072417 3.33584812

68 71.2241002 4.038506099

69 52.6242793 3.920728568

70 64.6288531 3.720878163

71 6.3090934 1.956881061

72 27.8859547 2.909813613

73 15.6581294 3.623518162

74 18.0150245 2.046476642

75 17.9853974 2.457171185

76 33.3017019 3.580996498

77 20.982215 3.768178738

78 44.0476472 3.463767589

79 10.5467804 2.114223703

80 6.666142 1.900219101

81 98.3404026 4.51270701

82 19.4115248 3.257921438

83 26.3862646 3.088440229

84 20.6234882 2.879103462

85 5.2911807 1.805671105

86 18.9893552 3.611996898

87 22.9480867 2.782860154

88 22.0727012 2.817243294

89 33.6224175 3.933346329

90 20.5832786 3.821773111

91 89.5319609 4.392116376

92 10.4990218 2.523826077

93 43.6696963 3.650348561

94 42.2713361 4.391110456

95 4.0944234 1.889211121

96 14.6156788 2.139207413

97 7.9260022 1.868261319

98 6.2590585 1.540786

99 53.9251393 3.079982553

100 41.166098 3.65320434