Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
102750 2.75 45.498 NA NA NA 95276 2.73 46.1773 0.06455399 NA NA 112053 2.82 46.1937 0.06363636 0.06455399 NA 98841 2.83 46.1272 0.06512702 0.06363636 0.06455399 123102 2.9 46.4199 0.06490826 0.06512702 0.06363636 118152 3.05 46.4535 0.06605923 0.06490826 0.06512702 101752 3.15 46.648 0.06900452 0.06605923 0.06490826 148219 3.26 46.5669 0.07110609 0.06900452 0.06605923 124966 3.38 46.9866 0.07228381 0.07110609 0.06900452 134741 3.54 47.2997 0.07477876 0.07228381 0.07110609 132168 3.81 47.548 0.07763158 0.07477876 0.07228381 100950 5.27 47.4375 0.08300654 0.07763158 0.07477876 96418 6.71 47.1083 0.11406926 0.08300654 0.07763158 86891 9.09 46.9634 0.14399142 0.11406926 0.08300654 89796 11.08 46.9733 0.19258475 0.14399142 0.11406926 119663 11.91 46.83 0.23179916 0.19258475 0.14399142 130539 11.81 47.1848 0.248125 0.23179916 0.19258475 120851 11.81 47.1292 0.24300412 0.248125 0.23179916 145422 12.09 47.1505 0.24102041 0.24300412 0.248125 150583 11.95 46.6882 0.24473684 0.24102041 0.24300412 127054 11.67 46.7161 0.239 0.24473684 0.24102041 137473 11.6 46.536 0.23063241 0.239 0.24473684 127094 11.71 45.0062 0.22700587 0.23063241 0.239 132080 11.62 43.4204 0.22737864 0.22700587 0.23063241 188311 11.64 42.8246 0.2238921 0.22737864 0.22700587 107487 11.66 41.8301 0.22341651 0.2238921 0.22737864 84669 11.67 41.3862 0.22209524 0.22341651 0.2238921 149184 11.69 41.4258 0.22144213 0.22209524 0.22341651 121026 11.58 41.3326 0.22098299 0.22144213 0.22209524 81073 11.4 41.6042 0.21766917 0.22098299 0.22144213 132947 11.44 42.0025 0.21268657 0.21766917 0.22098299 141294 11.38 42.4426 0.21107011 0.21268657 0.21766917 155077 11.31 42.9708 0.20957643 0.21107011 0.21268657 145154 11.45 43.1611 0.20714286 0.20957643 0.21107011 127094 11.73 43.2561 0.20856102 0.20714286 0.20957643 151414 12.11 43.7944 0.21211573 0.20856102 0.20714286 167858 12.23 44.4309 0.2181982 0.21211573 0.20856102 127070 12.39 44.8644 0.21996403 0.2181982 0.21211573 154692 12.34 44.916 0.22204301 0.21996403 0.2181982 170905 12.42 45.1733 0.22075134 0.22204301 0.21996403 127751 12.37 45.3729 0.22139037 0.22075134 0.22204301 173795 12.37 45.3841 0.21893805 0.22139037 0.22075134 190181 12.39 45.6491 0.21778169 0.21893805 0.22139037 198417 12.43 45.9698 0.21698774 0.21778169 0.21893805 183018 12.48 46.1015 0.21655052 0.21698774 0.21778169 171608 12.45 46.1172 0.21666667 0.21655052 0.21698774 188087 12.58 46.7939 0.21502591 0.21666667 0.21655052 197042 12.59 47.2798 0.21689655 0.21502591 0.21666667 208788 12.54 47.023 0.21632302 0.21689655 0.21502591 178111 13.01 47.7335 0.21435897 0.21632302 0.21689655 236455 13.31 48.3415 0.22013536 0.21435897 0.21632302 233219 13.45 48.7789 0.22369748 0.22013536 0.21435897 188106 13.28 49.2046 0.22416667 0.22369748 0.22013536 238876 13.38 49.5627 0.22023217 0.22416667 0.22369748 205148 13.36 49.6389 0.22042834 0.22023217 0.22416667 214727 13.4 49.6517 0.21901639 0.22042834 0.22023217 213428 13.49 49.8872 0.21895425 0.21901639 0.22042834 195128 13.47 49.9859 0.21970684 0.21895425 0.21901639 206047 13.62 50.0357 0.21866883 0.21970684 0.21895425 201773 13.57 50.1135 0.22003231 0.21866883 0.21970684 192772 13.59 49.4201 0.21851852 0.22003231 0.21866883 198230 13.48 49.6618 0.21744 0.21851852 0.22003231 181172 13.47 50.6053 0.21430843 0.21744 0.21851852 189079 13.47 51.6639 0.21246057 0.21430843 0.21744 179073 13.36 51.8472 0.21079812 0.21246057 0.21430843 197421 13.37 52.2056 0.20713178 0.21079812 0.21246057 195244 13.4 52.1834 0.20506135 0.20713178 0.21079812 219826 13.41 52.3807 0.20395738 0.20506135 0.20713178 211793 13.37 52.5124 0.20318182 0.20395738 0.20506135 203394 13.42 52.9384 0.20105263 0.20318182 0.20395738 209578 13.41 53.3363 0.2 0.20105263 0.20318182 214769 13.46 53.6296 0.19896142 0.2 0.20105263 226177 13.64 53.2837 0.19881832 0.19896142 0.2 191449 13.93 53.5675 0.19970717 0.19881832 0.19896142 200989 14.46 53.7364 0.2015919 0.19970717 0.19881832 216707 14.92 53.1571 0.20716332 0.2015919 0.19970717 192882 16.27 53.5566 0.21133144 0.20716332 0.2015919 199736 17.36 53.5534 0.22755245 0.21133144 0.20716332 202349 19.07 53.4808 0.24011065 0.22755245 0.21133144 204137 21.1 53.1195 0.26087551 0.24011065 0.22755245 215588 22.39 53.1786 0.28590786 0.26087551 0.24011065 229454 23.13 53.4617 0.30013405 0.28590786 0.26087551 175048 23.27 53.409 0.30757979 0.30013405 0.28590786 212799 24.57 53.4536 0.30658762 0.30757979 0.30013405 181727 26.32 53.7071 0.32033898 0.30658762 0.30757979 211607 28.57 53.7262 0.33830334 0.32033898 0.30658762 185853 30.44 53.5481 0.36210393 0.33830334 0.32033898 158277 31.4 52.4571 0.38002497 0.36210393 0.33830334 180695 31.84 51.1904 0.38765432 0.38002497 0.36210393 175959 31.86 50.5575 0.38924205 0.38765432 0.38002497 139550 32.3 50.166 0.38524788 0.38924205 0.38765432 155810 32.93 50.353 0.39056832 0.38524788 0.38924205 138305 32.73 51.1727 0.39531813 0.39056832 0.38524788 147014 33.1 51.8129 0.38964286 0.39531813 0.39056832 135994 33.23 52.7175 0.39033019 0.38964286 0.39531813 166455 33.94 53.0142 0.38865497 0.39033019 0.38964286 177737 34.27 52.7119 0.39327926 0.38865497 0.39033019 167021 35.96 52.4633 0.39390805 0.39327926 0.38865497 132134 36.25 52.7501 0.40910125 0.39390805 0.39327926 169834 36.92 52.5233 0.40960452 0.40910125 0.39390805 130599 36.16 52.8211 0.41436588 0.40960452 0.40910125 156836 36.59 53.0699 0.40267261 0.41436588 0.40960452 119749 35.05 53.4044 0.40386313 0.40267261 0.41436588 148996 34.53 53.3959 0.38264192 0.40386313 0.40267261 147491 34.07 53.0761 0.37410618 0.38264192 0.40386313 147216 33.65 52.6972 0.36555794 0.37410618 0.38264192 153455 33.84 52.0996 0.36027837 0.36555794 0.37410618 112004 33.99 51.5219 0.36115261 0.36027837 0.36555794 158512 35.41 50.4933 0.36159574 0.36115261 0.36027837 104139 35.53 51.4979 0.37550371 0.36159574 0.36115261 102536 34.71 51.1159 0.3755814 0.37550371 0.36159574 93017 33.2 50.6623 0.36730159 0.3755814 0.37550371 91988 32.25 50.3505 0.34984194 0.36730159 0.3755814 123616 32.92 50.1943 0.33663883 0.34984194 0.36730159 134498 33.27 50.0395 0.33938144 0.33663883 0.34984194 149812 32.91 49.6075 0.34123077 0.33938144 0.33663883 110334 32.39 49.4584 0.33684749 0.34123077 0.33938144 136639 32.44 49.011 0.3308478 0.33684749 0.34123077 102712 32.84 48.8232 0.33034623 0.3308478 0.33684749 112951 32.44 48.4682 0.33510204 0.33034623 0.3308478 107897 32.5 49.3992 0.33237705 0.33510204 0.33034623 73242 31.12 49.089 0.33231084 0.33237705 0.33510204 72800 30.28 49.4906 0.31787538 0.33231084 0.33237705 78767 28.76 50.0805 0.3092952 0.31787538 0.33231084 114791 28.59 50.4295 0.29168357 0.3092952 0.31787538 109351 28.83 50.7333 0.28820565 0.29168357 0.3092952 122520 28.93 51.5016 0.28974874 0.28820565 0.29168357 137338 29.31 52.0679 0.28958959 0.28974874 0.28820565 132061 29.27 52.8472 0.29251497 0.28958959 0.28974874 130607 29.36 53.2874 0.29066534 0.29251497 0.28958959 118570 29.05 53.4759 0.29069307 0.29066534 0.29251497 95873 29 53.7593 0.28705534 0.29069307 0.29066534 103116 27.65 54.8216 0.28627838 0.28705534 0.29069307 98619 27.64 55.0698 0.27134446 0.28627838 0.28705534 104178 27.8 55.3384 0.26992187 0.27134446 0.28627838 123468 27.84 55.6911 0.27095517 0.26992187 0.27134446 99651 27.85 55.9506 0.2700291 0.27095517 0.26992187 120264 27.76 56.1549 0.26934236 0.2700291 0.27095517 122795 28.05 56.3326 0.26769527 0.26934236 0.2700291 108524 27.66 56.3847 0.26945245 0.26769527 0.26934236 105760 27.39 56.2832 0.264689 0.26945245 0.26769527 117191 27.56 56.1943 0.26085714 0.264689 0.26945245 122882 27.55 56.4108 0.2617284 0.26085714 0.264689 93275 27.3 56.4759 0.26163343 0.2617284 0.26085714 99842 27.38 56.3801 0.25925926 0.26163343 0.2617284 83803 26.91 56.5796 0.25952607 0.25925926 0.26163343 61132 26.05 56.6645 0.25386792 0.25952607 0.25925926 118563 26.52 56.5122 0.24483083 0.25386792 0.25952607 106993 26.79 56.5982 0.24808232 0.24483083 0.25386792 118108 26.52 56.6317 0.24967381 0.24808232 0.24483083 99017 25.91 56.2637 0.2464684 0.24967381 0.24808232 99852 25.76 56.496 0.2403525 0.2464684 0.24967381 112720 25.42 56.7412 0.23851852 0.2403525 0.2464684 113636 25.65 56.508 0.23471837 0.23851852 0.2403525 118220 25.69 56.6984 0.23597056 0.23471837 0.23851852 128854 26.04 57.2954 0.23568807 0.23597056 0.23471837 123898 25.8 57.5555 0.23824337 0.23568807 0.23597056 100823 23.13 57.1707 0.23540146 0.23824337 0.23568807 115107 18.1 56.7784 0.2116194 0.23540146 0.23824337 90624 12.78 56.8228 0.16636029 0.2116194 0.23540146 132001 12.24 56.938 0.11767956 0.16636029 0.2116194 157969 12.04 56.7427 0.11239669 0.11767956 0.16636029 169333 11.03 57.0569 0.10995434 0.11239669 0.11767956 144907 10.09 56.9807 0.10073059 0.10995434 0.11239669 169346 11.08 57.0954 0.09197812 0.10073059 0.10995434 144666 11.79 57.3542 0.10054446 0.09197812 0.10073059 158829 12.23 57.623 0.1068903 0.10054446 0.09197812 127286 12.4 58.1006 0.11077899 0.1068903 0.10054446 120578 13.86 57.9173 0.11221719 0.11077899 0.1068903 129293 15.47 58.663 0.12464029 0.11221719 0.11077899 122371 15.87 58.7602 0.13862007 0.12464029 0.11221719 115176 16.57 59.1416 0.14157003 0.13862007 0.12464029 142168 16.92 59.517 0.14702751 0.14157003 0.13862007 153260 17.31 59.7996 0.14960212 0.14702751 0.14157003 173906 17.77 60.2152 0.15251101 0.14960212 0.14702751 178446 18.07 60.7146 0.15615114 0.15251101 0.14960212 155962 17.49 60.8781 0.15795455 0.15615114 0.15251101 168257 17.21 61.7569 0.15208696 0.15795455 0.15615114 149456 17.12 62.091 0.14926279 0.15208696 0.15795455 136105 16.46 62.394 0.14835355 0.14926279 0.15208696 141507 22.4 62.4207 0.14263432 0.14835355 0.14926279 152084 15.2 62.6908 0.19360415 0.14263432 0.14835355 145138 14.24 62.8421 0.13103448 0.19360415 0.14263432 146548 14.21 63.1885 0.12223176 0.13103448 0.19360415 173098 14.69 63.1203 0.12134927 0.12223176 0.13103448 165471 14.68 63.2843 0.12502128 0.12134927 0.12223176 152271 14.02 63.3155 0.12440678 0.12502128 0.12134927 163201 13.38 63.5859 0.11831224 0.12440678 0.12502128 157823 13.08 63.405 0.11243697 0.11831224 0.12440678 166167 11.92 63.7184 0.10918197 0.11243697 0.11831224 154253 11.52 63.8175 0.09916805 0.10918197 0.11243697 170299 12.34 64.1273 0.0957606 0.09916805 0.10918197 166388 13.91 64.3162 0.10240664 0.0957606 0.09916805 141051 14.84 64.026 0.11486375 0.10240664 0.0957606 160254 15.54 64.166 0.12203947 0.11486375 0.10240664 164995 17.33 64.222 0.1270646 0.12203947 0.11486375 195971 17.97 63.7707 0.14077985 0.1270646 0.12203947 182635 17.27 63.8022 0.14515347 0.14077985 0.1270646 189829 16.93 63.236 0.13916197 0.14515347 0.14077985 209476 15.95 63.8059 0.13609325 0.13916197 0.14515347 189848 16.14 63.576 0.12800963 0.13609325 0.13916197 183746 16.61 63.5346 0.12912 0.12800963 0.13609325 192682 17.08 63.7465 0.13224522 0.12912 0.12800963 169677 17.72 64.1419 0.13566322 0.13224522 0.12912 201823 18.85 63.7117 0.14052339 0.13566322 0.13224522 172643 18.79 64.3504 0.14795918 0.14052339 0.13566322 202931 17.75 64.6721 0.14679687 0.14795918 0.14052339 175863 16.02 64.5975 0.13791764 0.14679687 0.14795918 222061 14.61 64.7028 0.12428239 0.13791764 0.14679687 199797 13.83 64.9174 0.1130805 0.12428239 0.13791764 214638 13.92 64.8436 0.10646651 0.1130805 0.12428239 200106 19.57 65.043 0.10674847 0.10646651 0.1130805 166077 25.63 65.1372 0.14870821 0.10674847 0.10646651 160586 30.08 64.6442 0.19314243 0.14870821 0.10674847 158330 29.51 63.8853 0.22531835 0.19314243 0.14870821 141749 25.75 63.4658 0.22055306 0.22531835 0.19314243 170795 22.98 63.1915 0.19245142 0.22055306 0.22531835 153286 18.39 62.7585 0.17072808 0.19245142 0.22055306 163426 16.75 62.4265 0.13642433 0.17072808 0.19245142 172562 16.39 62.5503 0.12407407 0.13642433 0.17072808 197474 16.57 63.1756 0.12122781 0.12407407 0.13642433 189822 16.4 63.742 0.12219764 0.12122781 0.12407407 188511 16.15 63.8029 0.12058824 0.12219764 0.12122781 207437 16.8 63.8503 0.11857562 0.12058824 0.12219764 192128 17.14 64.4151 0.12298682 0.11857562 0.12058824 175716 17.97 64.2992 0.12492711 0.12298682 0.11857562 159108 18.06 64.2209 0.13078603 0.12492711 0.12298682 175801 16.6 63.9602 0.13105951 0.13078603 0.12492711 186723 14.87 63.596 0.12037708 0.13105951 0.13078603 154970 14.42 64.0409 0.1076756 0.12037708 0.13105951 172446 14.48 64.5973 0.1040404 0.1076756 0.12037708 185965 15.5 65.0756 0.10394831 0.1040404 0.1076756 195525 16.74 65.2831 0.11111111 0.10394831 0.1040404 193156 18.27 65.2957 0.1198282 0.11111111 0.10394831 212705 18.2 65.8801 0.13031384 0.1198282 0.11111111 201357 18.03 65.5581 0.12953737 0.13031384 0.1198282 189971 17.86 65.715 0.12796309 0.12953737 0.13031384 216523 18.22 66.2013 0.12639774 0.12796309 0.12953737 193233 17.63 66.4879 0.12849083 0.12639774 0.12796309 191996 16.22 66.5431 0.12415493 0.12849083 0.12639774 211974 15.5 66.8264 0.11430585 0.12415493 0.12849083 175907 15.71 67.1172 0.10869565 0.11430585 0.12415493 206109 16.49 67.0479 0.10978337 0.10869565 0.11430585 220275 16.69 67.2498 0.11483287 0.10978337 0.10869565 211342 16.71 67.0325 0.11590278 0.11483287 0.10978337 222528 16.07 67.1532 0.11588072 0.11590278 0.11483287 229523 14.96 67.3586 0.11128809 0.11588072 0.11590278 204153 14.51 67.2888 0.10360111 0.11128809 0.11588072 206735 14.37 67.6092 0.10020718 0.10360111 0.11128809 223416 14.59 68.1214 0.09903515 0.10020718 0.10360111 228292 13.72 68.4089 0.10013727 0.09903515 0.10020718 203121 12.2 68.7737 0.09410151 0.10013727 0.09903515 205957 11.64 69.0299 0.08367627 0.09410151 0.10013727 176918 12.09 69.0418 0.07961696 0.08367627 0.09410151 219839 11.76 69.7582 0.08241309 0.07961696 0.08367627 217213 12.85 70.125 0.0798913 0.08241309 0.07961696 216618 14.05 70.4978 0.08717775 0.0798913 0.08241309 248057 15.18 70.948 0.09525424 0.08717775 0.0798913 245642 16.09 71.0595 0.10256757 0.09525424 0.08717775 242485 15.97 71.4749 0.10842318 0.10256757 0.09525424 260423 15 71.7333 0.10718121 0.10842318 0.10256757 221030 14.8 72.3479 0.10040161 0.10718121 0.10842318 229157 15.31 72.8018 0.09899666 0.10040161 0.10718121 220858 14.7 73.5563 0.10227121 0.09899666 0.10040161 212270 15.06 73.6891 0.09819639 0.10227121 0.09899666 195944 15.53 73.5889 0.1001996 0.09819639 0.10227121 239741 15.78 73.6895 0.10291584 0.1001996 0.09819639 212013 16.76 73.676 0.10422721 0.10291584 0.1001996 240514 17.4 73.8858 0.11033575 0.10422721 0.10291584 241982 16.78 74.1391 0.11432326 0.11033575 0.10422721 245447 15.51 73.8447 0.11003279 0.11432326 0.11033575 240839 15.22 74.7803 0.10170492 0.11003279 0.11432326 244875 15.44 75.0755 0.09954218 0.10170492 0.11003279 226375 15.25 74.9925 0.10078329 0.09954218 0.10170492 231567 15.1 75.1822 0.09921926 0.10078329 0.09954218 235746 15.82 75.4725 0.09830729 0.09921926 0.10078329 238990 16.43 74.9823 0.10306189 0.09830729 0.09921926 198120 16.1 76.153 0.10641192 0.10306189 0.09830729 201663 17.31 76.0724 0.10393802 0.10641192 0.10306189 238198 19.27 76.7608 0.11117534 0.10393802 0.10641192 261641 18.9 77.3269 0.12328855 0.11117534 0.10393802 253014 17.96 77.9694 0.12068966 0.12328855 0.11117534 275225 18.16 77.8351 0.11461391 0.12068966 0.12328855 250957 18.65 78.3005 0.11566879 0.11461391 0.12068966 260375 19.97 78.8378 0.11856325 0.11566879 0.11461391 250694 21.41 78.7843 0.1265526 0.11856325 0.11566879 216953 21.38 79.4683 0.13524953 0.1265526 0.11856325 247816 21.63 79.9829 0.13480454 0.13524953 0.1265526 224135 21.86 80.0837 0.13638083 0.13480454 0.13524953 211073 20.48 81.0483 0.13739786 0.13638083 0.13480454 245623 18.76 81.6195 0.1283208 0.13739786 0.13638083 250947 17.13 81.6408 0.11725 0.1283208 0.13739786 278223 17.06 82.1311 0.10692884 0.11725 0.1283208 254232 16.85 82.5332 0.1065584 0.10692884 0.11725 266293 16.41 83.1538 0.10511541 0.1065584 0.10692884 280897 16.95 84.0293 0.10224299 0.10511541 0.1065584 274565 16.73 84.7873 0.10541045 0.10224299 0.10511541 280555 17.71 85.5125 0.10378412 0.10541045 0.10224299 252757 17.25 86.2601 0.10959158 0.10378412 0.10541045 250131 16.05 86.5262 0.10681115 0.10959158 0.10378412 271208 14.31 86.9662 0.09950403 0.10681115 0.10959158 230593 13.02 87.0687 0.08855198 0.09950403 0.10681115 263407 11.88 87.1414 0.08042001 0.08855198 0.09950403 289968 11.77 87.4497 0.07324291 0.08042001 0.08855198 282846 11.8 88.0124 0.07243077 0.07324291 0.08042001 271314 11.12 87.4571 0.07248157 0.07243077 0.07324291 289718 10.78 87.1484 0.06822086 0.07248157 0.07243077 300227 10.55 88.936 0.06605392 0.06822086 0.07248157 259951 10.99 88.778 0.06456548 0.06605392 0.06822086 263149 11.66 89.4857 0.06717604 0.06456548 0.06605392 267953 10.79 89.4358 0.07109756 0.06717604 0.06456548 252378 9.38 89.7761 0.06579268 0.07109756 0.06717604 280356 9.21 90.1893 0.05723002 0.06579268 0.07109756 234298 9.48 90.6683 0.056056 0.05723002 0.06579268 271574 10.5 90.831 0.05762918 0.056056 0.05723002 262378 12.88 91.0632 0.06363636 0.05762918 0.056056 289457 14.6 91.7311 0.07749699 0.06363636 0.05762918 278274 14.52 91.5818 0.08784597 0.07749699 0.06363636 288932 16.11 92.1587 0.08736462 0.08784597 0.07749699 283813 17.88 92.5363 0.09664067 0.08736462 0.08784597 267600 19.69 92.1699 0.1070018 0.09664067 0.08736462 267574 20.76 93.3786 0.11727219 0.1070018 0.09664067 254862 21.05 93.824 0.12342449 0.11727219 0.1070018 248974 22.79 94.5441 0.12507427 0.12342449 0.11727219 256840 23.31 94.5458 0.13541295 0.12507427 0.12342449 250914 25.14 94.8185 0.13809242 0.13541295 0.12507427 279334 26.41 95.1983 0.14805654 0.13809242 0.13541295 286549 24.41 95.8921 0.15426402 0.14805654 0.13809242 302266 24.28 96.0691 0.14249854 0.15426402 0.14805654 298205 26.78 96.1568 0.14157434 0.14249854 0.15426402 300843 27.73 96.0239 0.15533643 0.14157434 0.14249854 312955 26.59 95.7182 0.16047454 0.15533643 0.14157434 275962 29.03 96.1105 0.15387731 0.16047454 0.15533643 299561 28.57 95.8225 0.16712723 0.15387731 0.16047454 260975 28.34 95.8391 0.1641954 0.16712723 0.15387731 274836 26.4 95.5791 0.16278001 0.1641954 0.16712723 284112 23.19 94.9499 0.15172414 0.16278001 0.1641954 247331 23.85 94.369 0.13243861 0.15172414 0.16278001 298120 22.75 94.1259 0.13566553 0.13243861 0.15172414 306008 21.66 93.9061 0.12911464 0.13566553 0.13243861 306813 22.65 93.2803 0.12244206 0.12911464 0.13566553 288550 23.09 92.7057 0.12746201 0.12244206 0.12911464 301636 22.33 92.1721 0.1297191 0.12746201 0.12244206 293215 22.14 92.0023 0.12580282 0.1297191 0.12746201 270713 23.02 91.6795 0.12473239 0.12580282 0.1297191 311803 19.88 91.2682 0.12910824 0.12473239 0.12580282 281316 17 90.7894 0.11187394 0.12910824 0.12473239 281450 15.46 90.8311 0.09582864 0.11187394 0.12910824 295494 16.29 91.3471 0.08749293 0.09582864 0.11187394 246411 16.58 91.3672 0.09198193 0.08749293 0.09582864 267037 19.27 92.1054 0.09325084 0.09198193 0.08749293 296134 22.53 92.479 0.10777405 0.09325084 0.09198193 296505 23.75 92.8824 0.1253059 0.10777405 0.09325084 270677 23.35 93.7637 0.13209121 0.1253059 0.10777405 290855 23.73 93.5461 0.12979433 0.13209121 0.1253059 296068 24.58 93.5765 0.13176013 0.12979433 0.13209121 272653 25.49 93.7116 0.13602656 0.13176013 0.12979433 315720 26.25 93.4006 0.14082873 0.13602656 0.13176013 286298 24.19 93.8758 0.14478764 0.14082873 0.13602656 284170 24.15 93.4191 0.13342526 0.14478764 0.14082873 273338 27.76 93.9571 0.13349917 0.13342526 0.14478764 250262 30.37 94.2558 0.15277931 0.13349917 0.13342526 294768 30.39 94.0416 0.16586565 0.15277931 0.13349917 318088 26.01 93.3666 0.16498371 0.16586565 0.15277931 319111 24.05 93.3852 0.14151251 0.16498371 0.16586565 312982 25.5 93.5219 0.13106267 0.14151251 0.16498371 335511 26.75 93.9144 0.13881328 0.13106267 0.14151251 319674 27.56 93.7371 0.14545949 0.13881328 0.13106267 316796 26.43 94.3262 0.14929577 0.14545949 0.13881328 329992 26.28 94.4442 0.14271058 0.14929577 0.14545949 291352 26.54 95.2224 0.14205405 0.14271058 0.14929577 314131 27.17 95.1545 0.14384824 0.14205405 0.14271058 309876 28.57 95.3434 0.14742268 0.14384824 0.14205405 288494 29.17 95.9228 0.15426566 0.14742268 0.14384824 329991 30.66 95.4538 0.15665951 0.15426566 0.14742268 311663 31 95.8653 0.16360726 0.15665951 0.15426566 317854 33.14 96.6472 0.16489362 0.16360726 0.15665951 344729 33.74 95.8588 0.17525119 0.16489362 0.16360726 324108 33.38 96.5901 0.17785978 0.17525119 0.16489362 333756 36.54 96.6687 0.17624076 0.17785978 0.17525119 297013 37.52 96.745 0.19282322 0.17624076 0.17785978 313249 41.84 97.6604 0.19757767 0.19282322 0.17624076 329660 41.19 97.8427 0.21917234 0.19757767 0.19282322 320586 36.46 98.5495 0.21565445 0.21917234 0.19757767 325786 35.27 99.002 0.19159222 0.21565445 0.21917234 293425 36.93 99.6741 0.18495018 0.19159222 0.21565445 324180 41.28 99.5181 0.19254432 0.18495018 0.19159222 315528 44.78 99.6518 0.21355406 0.19254432 0.18495018 319982 43.04 99.8158 0.23011305 0.21355406 0.19254432 327865 44.41 100.2232 0.22139918 0.23011305 0.21355406 312106 49.07 99.8997 0.22832905 0.22139918 0.23011305 329039 52.85 100.1025 0.2511259 0.22832905 0.22139918 277589 57.42 98.2644 0.26909369 0.2511259 0.22832905 300884 56.21 99.4949 0.288833 0.26909369 0.2511259 314028 52.16 100.5129 0.28217871 0.288833 0.26909369 314259 49.79 101.1118 0.26396761 0.28217871 0.288833 303472 51.8 101.2313 0.25299797 0.26396761 0.28217871 290744 53.86 101.2755 0.26122037 0.25299797 0.26396761 313340 52.32 101.4651 0.2710619 0.26122037 0.25299797 294281 56.65 101.9012 0.26186186 0.2710619 0.26122037 325796 62.04 101.7589 0.28114144 0.26186186 0.2710619 329839 62.12 102.1304 0.30637037 0.28114144 0.26186186 322588 64.93 102.0989 0.30616067 0.30637037 0.28114144 336528 66.13 102.4526 0.31906634 0.30616067 0.30637037 316381 62.4 102.2753 0.32432565 0.31906634 0.30616067 308602 55.47 102.2299 0.30754066 0.32432565 0.31906634 299010 52.22 102.1419 0.27487611 0.30754066 0.32432565 293645 53.84 103.2191 0.25915633 0.27487611 0.30754066 320108 52.23 102.7129 0.26679881 0.25915633 0.27487611 252869 50.71 103.7659 0.25805336 0.26679881 0.25915633 324248 53 103.9538 0.24918919 0.25805336 0.26679881 304775 57.28 104.7077 0.25803311 0.24918919 0.25805336 320208 59.36 104.7507 0.27711659 0.25803311 0.24918919 321260 60.95 104.7581 0.28552189 0.27711659 0.25803311 310320 65.56 104.7111 0.29246641 0.28552189 0.27711659 319197 68.21 104.9122 0.31473836 0.29246641 0.28552189 297503 68.51 105.2764 0.32809043 0.31473836 0.29246641 316184 72.49 104.772 0.32858513 0.32809043 0.31473836 303411 79.65 105.3295 0.34700814 0.32858513 0.32809043 300841 82.76 105.3213 0.37892483 0.34700814 0.32858513
Names of X columns:
barrels_purchased unit_price US_IND_PROD defl_price1 defl_price2 defl_price3
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
No Linear Trend
Linear Trend
First Differences
Seasonal Differences (s)
First and Seasonal Differences (s)
Degree of Predetermination (lagged endogenous variables)
Degree of Seasonal Predetermination
Seasonality
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
library(lattice) library(lmtest) library(car) library(MASS) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test mywarning <- '' par6 <- as.numeric(par6) if(is.na(par6)) { par6 <- 12 mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.' } par1 <- as.numeric(par1) if(is.na(par1)) { par1 <- 1 mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.' } if (par4=='') par4 <- 0 par4 <- as.numeric(par4) if (!is.numeric(par4)) par4 <- 0 if (par5=='') par5 <- 0 par5 <- as.numeric(par5) if (!is.numeric(par5)) par5 <- 0 x <- na.omit(t(y)) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ (n <- n -1) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par3 == 'Seasonal Differences (s)'){ (n <- n - par6) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+par6,j] - x[i,j] } } x <- x2 } if (par3 == 'First and Seasonal Differences (s)'){ (n <- n -1) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 (n <- n - par6) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+par6,j] - x[i,j] } } x <- x2 } if(par4 > 0) { x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep=''))) for (i in 1:(n-par4)) { for (j in 1:par4) { x2[i,j] <- x[i+par4-j,par1] } } x <- cbind(x[(par4+1):n,], x2) n <- n - par4 } if(par5 > 0) { x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep=''))) for (i in 1:(n-par5*par6)) { for (j in 1:par5) { x2[i,j] <- x[i+par5*par6-j*par6,par1] } } x <- cbind(x[(par5*par6+1):n,], x2) n <- n - par5*par6 } if (par2 == 'Include Seasonal Dummies'){ x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep =''))) for (i in 1:(par6-1)){ x2[seq(i,n,par6),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } (k <- length(x[n,])) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } print(x) (k <- length(x[n,])) head(x) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') sresid <- studres(mylm) hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals') xfit<-seq(min(sresid),max(sresid),length=40) yfit<-dnorm(xfit) lines(xfit, yfit) grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqPlot(mylm, main='QQ Plot') grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) print(z) plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, mywarning) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+')) a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' ')) a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+')) a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' ')) a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' ')) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[2],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[3],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' ')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') myr <- as.numeric(mysum$resid) myr a <-table.start() a <- table.row.start(a) a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE) a <- table.row.end(a) a <- table.row.start(a) a <- table.element(a,'Description',1,TRUE) a <- table.element(a,'Link',1,TRUE) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Histogram',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Central Tendency',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'QQ Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Kernel Density Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Spectral Analysis',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <- table.element(a,'Summary Statistics',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a<-table.end(a) table.save(a,file='mytable7.tab') if(n < 200) { a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' ')) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' ')) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,signif(numsignificant1,6)) a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' ')) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,signif(numsignificant5,6)) a<-table.element(a,signif(numsignificant5/numgqtests,6)) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,signif(numsignificant10,6)) a<-table.element(a,signif(numsignificant10/numgqtests,6)) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') } } a<-table.start() a<-table.row.start(a) a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) reset_test_fitted <- resettest(mylm,power=2:3,type='fitted') a<-table.element(a,paste('<pre>',RC.texteval('reset_test_fitted'),'</pre>',sep='')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) reset_test_regressors <- resettest(mylm,power=2:3,type='regressor') a<-table.element(a,paste('<pre>',RC.texteval('reset_test_regressors'),'</pre>',sep='')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp') a<-table.element(a,paste('<pre>',RC.texteval('reset_test_principal_components'),'</pre>',sep='')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable8.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) vif <- vif(mylm) a<-table.element(a,paste('<pre>',RC.texteval('vif'),'</pre>',sep='')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable9.tab')
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation