Obtaining data samples from the dataset HW 3 Dataset 2 (which will be sent in an email with that subject line), utilize any or all of the various regression model-fit assessment visual plots and/or graphs, results of statistical hypothesis tests, and numerical regression model accuracy measures, as well as any other applicable diagnostic tools available in R that we have discussed in the course thus far, to help identify and validate a linear regression model that appears to best fit the given data. These will likely include for example direct X-Y variable graphs and residuals vs. fitted value plots implemented in R. You will likely need to try a number of different response and/or predictor variable transformations, for example, using the diagnostic tools to decide which regression models appear consistent with the data and which do not. You should identify one or perhaps two candidate regression models that you believe are most consistent with the given data. Please try to include screenshots of the graphical plots you use as well as quote any relevant R code output. You can or even should include the R code itself as well if you feel it helps to support your argument. Please explain your reasoning as to why the model(s) you propose may be the right one(s) in plain natural language writing.
Y 6.904355 5 6.30117 6 7.073385 7 7.475314 8 7.596583 9 7.552108 10 8.551552 11 7.922484 12 9.139111 13 8.246758 14 9.068535 15 9.6885 16 7.971996 17 8.641349 18 8.822217 19 9.30944 20 8.946918 21 7.853857 22 8.050755 23 10.016164 24 9.284432 25 8.625539 26 9.505715 27 10.271746 28 10.682188 29 9.58716 30 9.73934 31 9.04989 32 10.223064 33 9.732724 34 10.338421 35 10.519457 36 10.739388 37 9.970709 38 10.579601 39 9.519255 40 10.034915 41 10.049885 42 9.315296 43 10.586441 44 10.716324 45 10.476754 46 11.079377 47 10.37905 48 10.0995 49 11.040455 50 10.457955 51 11.624538 52 10.724861 53 11.305792 54 11.175629 55 10.658784 56 11.873966 57 11.442336 58 11.199955 59 11.326964 60 11.561392 61 11.299185 62 9.789724 63 11.460208 64 11.165157 65 11.471925 66 11.700297 67 12.138884 68 11.104567 69 12.148262 70 11.693284 71 12.072585 72 12.041283 73 11.968569 74 11.113417 75 11.616373 76 11.99937 77 11.236656 78 11.467481 79 12.054552 80 12.172988 81 12.045322 11.394793 83 11.311743 4 12.641656 85 12.037655 11.976036 87 11.894225 88 11.380108 89 12.305618 90 11.913149 91 11.952199 92 12.531872 93 12.497476 94 12.803812 95 11.890609 96 12.474596 97 12.86549 98 11.634845 99 11.779944 100 11.664372 101 11.520339 102 12.309449 103 12.615384 104 12.908403 105 12.849254 106 11.844053 107 13.288503 108 12.049309 109 12.453718 110 12.207932 111 12.375823 112 12.548872 113 12.531977 114 12.477318 115 12.561217 116 12.28163 117 12.289261 118 11.727697 119 12.383817 120 12.335256 121 13.958988 122 11.943311 123 12.709191 124 11.909815 125 11.937346 126 12.750725 127 12.205741 128 12.718714 129 12.520939 130 12.443559 131 11.753265 132 12.168324 133 12.885438 134 13.09436 135 12.578871 136 12.839993 137 13.415952 138 13.588876 139 12.334728 140 12.83886 141 13.512403 142 12.690824 143 12.913392 144 12.910414 145 12.523374 146 12.758523 147 12.979702 148 12.800958 149 13.577964 150 12.794063 151 12.831177 152 13.409307 153 12.545721 154 Y 6.904355 5 6.30117 6 7.073385 7 7.475314 8 7.596583 9 7.552108 10 8.551552 11 7.922484 12 9.139111 13 8.246758 14 9.068535 15 9.6885 16 7.971996 17 8.641349 18 8.822217 19 9.30944 20 8.946918 21 7.853857 22 8.050755 23 10.016164 24 9.284432 25 8.625539 26 9.505715 27 10.271746 28 10.682188 29 9.58716 30 9.73934 31 9.04989 32 10.223064 33 9.732724 34 10.338421 35 10.519457 36 10.739388 37 9.970709 38 10.579601 39 9.519255 40 10.034915 41 10.049885 42 9.315296 43 10.586441 44 10.716324 45 10.476754 46 11.079377 47 10.37905 48 10.0995 49 11.040455 50 10.457955 51 11.624538 52 10.724861 53 11.305792 54 11.175629 55 10.658784 56 11.873966 57 11.442336 58 11.199955 59 11.326964 60 11.561392 61 11.299185 62 9.789724 63 11.460208 64 11.165157 65 11.471925 66 11.700297 67 12.138884 68 11.104567 69 12.148262 70 11.693284 71 12.072585 72 12.041283 73 11.968569 74 11.113417 75 11.616373 76 11.99937 77 11.236656 78 11.467481 79 12.054552 80 12.172988 81 12.045322 11.394793 83 11.311743 4 12.641656 85 12.037655 11.976036 87 11.894225 88 11.380108 89 12.305618 90 11.913149 91 11.952199 92 12.531872 93 12.497476 94 12.803812 95 11.890609 96 12.474596 97 12.86549 98 11.634845 99 11.779944 100 11.664372 101 11.520339 102 12.309449 103 12.615384 104 12.908403 105 12.849254 106 11.844053 107 13.288503 108 12.049309 109 12.453718 110 12.207932 111 12.375823 112 12.548872 113 12.531977 114 12.477318 115 12.561217 116 12.28163 117 12.289261 118 11.727697 119 12.383817 120 12.335256 121 13.958988 122 11.943311 123 12.709191 124 11.909815 125 11.937346 126 12.750725 127 12.205741 128 12.718714 129 12.520939 130 12.443559 131 11.753265 132 12.168324 133 12.885438 134 13.09436 135 12.578871 136 12.839993 137 13.415952 138 13.588876 139 12.334728 140 12.83886 141 13.512403 142 12.690824 143 12.913392 144 12.910414 145 12.523374 146 12.758523 147 12.979702 148 12.800958 149 13.577964 150 12.794063 151 12.831177 152 13.409307 153 12.545721 154
