Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
1 119.992 74.997 0.00784 0.00007 0.0313 0.02211 21.033 0.414783 -4.813031 0.266482 0.284654 1 122.4 113.819 0.00968 0.00008 0.04518 0.01929 19.085 0.458359 -4.075192 0.33559 0.368674 1 116.682 111.555 0.0105 0.00009 0.03858 0.01309 20.651 0.429895 -4.443179 0.311173 0.332634 1 116.676 111.366 0.00997 0.00009 0.04005 0.01353 20.644 0.434969 -4.117501 0.334147 0.368975 1 116.014 110.655 0.01284 0.00011 0.04825 0.01767 19.649 0.417356 -3.747787 0.234513 0.410335 1 120.552 113.787 0.00968 0.00008 0.03526 0.01222 21.378 0.415564 -4.242867 0.299111 0.357775 1 120.267 114.82 0.00333 0.00003 0.00937 0.00607 24.886 0.59604 -5.634322 0.257682 0.211756 1 107.332 104.315 0.0029 0.00003 0.00946 0.00344 26.892 0.63742 -6.167603 0.183721 0.163755 1 95.73 91.754 0.00551 0.00006 0.01277 0.0107 21.812 0.615551 -5.498678 0.327769 0.231571 1 95.056 91.226 0.00532 0.00006 0.01725 0.01022 21.862 0.547037 -5.011879 0.325996 0.271362 1 88.333 84.072 0.00505 0.00006 0.01342 0.01166 21.118 0.611137 -5.24977 0.391002 0.24974 1 91.904 86.292 0.0054 0.00006 0.01641 0.01141 21.414 0.58339 -4.960234 0.363566 0.275931 1 136.926 131.276 0.00293 0.00002 0.00717 0.00581 25.703 0.4606 -6.547148 0.152813 0.138512 1 139.173 76.556 0.0039 0.00003 0.00932 0.01041 24.889 0.430166 -5.660217 0.254989 0.199889 1 152.845 75.836 0.00294 0.00002 0.00972 0.00609 24.922 0.474791 -6.105098 0.203653 0.1701 1 142.167 83.159 0.00369 0.00003 0.00888 0.00839 25.175 0.565924 -5.340115 0.210185 0.234589 1 144.188 82.764 0.00544 0.00004 0.012 0.01859 22.333 0.56738 -5.44004 0.239764 0.218164 1 168.778 75.603 0.00718 0.00004 0.01893 0.02919 20.376 0.631099 -2.93107 0.434326 0.430788 1 153.046 68.623 0.00742 0.00005 0.03572 0.0316 17.28 0.665318 -3.949079 0.35787 0.377429 1 156.405 142.822 0.00768 0.00005 0.02374 0.03365 17.153 0.649554 -4.554466 0.340176 0.322111 1 153.848 65.782 0.0084 0.00005 0.02383 0.03871 17.536 0.660125 -4.095442 0.262564 0.365391 1 153.88 78.128 0.0048 0.00003 0.02591 0.01849 19.493 0.629017 -5.18696 0.237622 0.259765 1 167.93 79.068 0.00442 0.00003 0.0254 0.0128 22.468 0.61906 -4.330956 0.262384 0.285695 1 173.917 86.18 0.00476 0.00003 0.0247 0.0184 20.422 0.537264 -5.248776 0.210279 0.253556 1 163.656 76.779 0.00742 0.00005 0.00948 0.01778 23.831 0.397937 -5.557447 0.22089 0.215961 1 104.4 77.968 0.00633 0.00006 0.02245 0.02887 22.066 0.522746 -5.571843 0.236853 0.219514 1 171.041 75.501 0.00455 0.00003 0.01169 0.01095 25.908 0.418622 -6.18359 0.226278 0.147403 1 146.845 81.737 0.00496 0.00003 0.01144 0.01328 25.119 0.358773 -6.27169 0.196102 0.162999 1 155.358 80.055 0.0031 0.00002 0.01012 0.00677 25.97 0.470478 -7.120925 0.279789 0.108514 1 162.568 77.63 0.00502 0.00003 0.01057 0.0117 25.678 0.427785 -6.635729 0.209866 0.135242 0 197.076 192.055 0.00289 0.00001 0.0068 0.00339 26.775 0.422229 -7.3483 0.177551 0.085569 0 199.228 192.091 0.00241 0.00001 0.00641 0.00167 30.94 0.432439 -7.682587 0.173319 0.068501 0 198.383 193.104 0.00212 0.00001 0.00825 0.00119 30.775 0.465946 -7.067931 0.175181 0.09632 0 202.266 197.079 0.0018 0.000009 0.00606 0.00072 32.684 0.368535 -7.695734 0.17854 0.056141 0 203.184 196.16 0.00178 0.000009 0.0061 0.00065 33.047 0.340068 -7.964984 0.163519 0.044539 0 201.464 195.708 0.00198 0.00001 0.0076 0.00135 31.732 0.344252 -7.777685 0.170183 0.05761 1 177.876 168.013 0.00411 0.00002 0.01347 0.00586 23.216 0.360148 -6.149653 0.218037 0.165827 1 176.17 163.564 0.00369 0.00002 0.0116 0.0034 24.951 0.341435 -6.006414 0.196371 0.173218 1 180.198 175.456 0.00284 0.00002 0.00885 0.00231 26.738 0.403884 -6.452058 0.212294 0.141929 1 187.733 173.015 0.00316 0.00002 0.01003 0.00265 26.31 0.396793 -6.006647 0.266892 0.160691 1 186.163 177.584 0.00298 0.00002 0.00941 0.00231 26.822 0.32648 -6.647379 0.201095 0.130554 1 184.055 166.977 0.00258 0.00001 0.00901 0.00257 26.453 0.306443 -7.044105 0.063412 0.11573 0 237.226 225.227 0.00298 0.00001 0.01024 0.0074 22.736 0.305062 -7.31055 0.098648 0.095032 0 241.404 232.483 0.00281 0.00001 0.01038 0.00675 23.145 0.457702 -6.793547 0.158266 0.117399 0 243.439 232.435 0.0021 0.000009 0.00898 0.00454 25.368 0.438296 -7.057869 0.091608 0.09147 0 242.852 227.911 0.00225 0.000009 0.00879 0.00476 25.032 0.431285 -6.99582 0.102083 0.102706 0 245.51 231.848 0.00235 0.00001 0.00977 0.00476 24.602 0.467489 -7.156076 0.127642 0.097336 0 252.455 182.786 0.00185 0.000007 0.0073 0.00432 26.805 0.610367 -7.31951 0.200873 0.086398 0 122.188 115.765 0.00524 0.00004 0.00776 0.00839 23.162 0.579597 -6.439398 0.266392 0.133867 0 122.964 114.676 0.00428 0.00003 0.00802 0.00462 24.971 0.538688 -6.482096 0.264967 0.128872 0 124.445 117.495 0.00431 0.00003 0.01024 0.00479 25.135 0.553134 -6.650471 0.254498 0.103561 0 126.344 112.773 0.00448 0.00004 0.00959 0.00474 25.03 0.507504 -6.689151 0.291954 0.105993 0 128.001 122.08 0.00436 0.00003 0.01072 0.00481 24.692 0.459766 -7.072419 0.220434 0.119308 0 129.336 118.604 0.0049 0.00004 0.01219 0.00484 25.429 0.420383 -6.836811 0.269866 0.147491 1 108.807 102.874 0.00761 0.00007 0.01609 0.01036 21.028 0.536009 -4.649573 0.205558 0.3167 1 109.86 104.437 0.00874 0.00008 0.01992 0.0118 20.767 0.558586 -4.333543 0.221727 0.344834 1 110.417 103.37 0.00784 0.00007 0.02302 0.00969 21.422 0.541781 -4.438453 0.238298 0.335041 1 117.274 110.402 0.00752 0.00006 0.01459 0.00681 22.817 0.530529 -4.60826 0.290024 0.314464 1 116.879 108.153 0.00788 0.00007 0.01625 0.00786 22.603 0.540049 -4.476755 0.262633 0.326197 1 114.847 104.68 0.00867 0.00008 0.01974 0.01143 21.66 0.547975 -4.609161 0.221711 0.316395 0 209.144 109.379 0.00282 0.00001 0.01258 0.00871 25.554 0.341788 -7.040508 0.066994 0.101516 0 223.365 98.664 0.00264 0.00001 0.01296 0.00301 26.138 0.447979 -7.293801 0.086372 0.098555 0 222.236 205.495 0.00266 0.00001 0.01108 0.0034 25.856 0.364867 -6.966321 0.095882 0.103224 0 228.832 223.634 0.00296 0.00001 0.01075 0.00351 25.964 0.25657 -7.24562 0.018689 0.093534 0 229.401 221.156 0.00205 0.000009 0.00957 0.003 26.415 0.27685 -7.496264 0.056844 0.073581 0 228.969 113.201 0.00238 0.00001 0.0116 0.0042 24.547 0.305429 -7.314237 0.006274 0.091546 1 140.341 67.021 0.00817 0.00006 0.0181 0.02183 19.56 0.460139 -5.409423 0.22685 0.226156 1 136.969 66.004 0.00923 0.00007 0.01759 0.02659 19.979 0.498133 -5.324574 0.20566 0.226247 1 143.533 65.809 0.01101 0.00008 0.02422 0.04882 20.338 0.513237 -5.86975 0.151814 0.18558 1 148.09 67.343 0.00762 0.00005 0.02494 0.02431 21.718 0.487407 -6.261141 0.120956 0.141958 1 142.729 65.476 0.00831 0.00006 0.01906 0.02599 20.264 0.489345 -5.720868 0.15883 0.180828 1 136.358 65.75 0.00971 0.00007 0.02466 0.03361 18.57 0.543299 -5.207985 0.224852 0.242981 1 120.08 111.208 0.00405 0.00003 0.00925 0.00442 25.742 0.495954 -5.79182 0.329066 0.18818 1 112.014 107.024 0.00533 0.00005 0.01375 0.00623 24.178 0.509127 -5.389129 0.306636 0.225461 1 110.793 107.316 0.00494 0.00004 0.01325 0.00479 25.438 0.437031 -5.31336 0.201861 0.244512 1 110.707 105.007 0.00516 0.00005 0.01219 0.00472 25.197 0.463514 -5.477592 0.315074 0.228624 1 112.876 106.981 0.005 0.00004 0.02231 0.00905 23.37 0.489538 -5.775966 0.341169 0.193918 1 110.568 106.821 0.00462 0.00004 0.01199 0.0042 25.82 0.429484 -5.391029 0.250572 0.232744 1 95.385 90.264 0.00608 0.00006 0.01886 0.01062 21.875 0.644954 -5.115212 0.249494 0.260015 1 100.77 85.545 0.01038 0.0001 0.01783 0.0222 19.2 0.594387 -4.913885 0.265699 0.277948 1 96.106 84.51 0.00694 0.00007 0.02451 0.01823 19.055 0.544805 -4.441519 0.155097 0.327978 1 95.605 87.549 0.00702 0.00007 0.01841 0.01825 19.659 0.576084 -5.132032 0.210458 0.260633 1 100.96 95.628 0.00606 0.00006 0.01421 0.01237 20.536 0.55461 -5.022288 0.146948 0.264666 1 98.804 87.804 0.00432 0.00004 0.01343 0.00882 22.244 0.576644 -6.025367 0.078202 0.177275 1 176.858 75.344 0.00747 0.00004 0.03022 0.0547 13.893 0.556494 -5.288912 0.343073 0.242119 1 180.978 155.495 0.00406 0.00002 0.02493 0.02782 16.176 0.583574 -5.657899 0.315903 0.200423 1 178.222 141.047 0.00321 0.00002 0.02415 0.03151 15.924 0.598714 -6.366916 0.335753 0.144614 1 176.281 125.61 0.0052 0.00003 0.04159 0.04824 13.922 0.602874 -5.515071 0.299549 0.220968 1 173.898 74.677 0.00448 0.00003 0.04254 0.04214 14.739 0.599371 -5.783272 0.299793 0.194052 1 179.711 144.878 0.00709 0.00004 0.02768 0.07223 11.866 0.590951 -4.379411 0.375531 0.332086 1 166.605 78.032 0.00742 0.00004 0.04282 0.08725 11.744 0.65341 -4.508984 0.389232 0.301952 1 151.955 147.226 0.00419 0.00003 0.04962 0.01658 19.664 0.501037 -6.411497 0.207156 0.13412 1 148.272 142.299 0.00459 0.00003 0.02521 0.01914 18.78 0.454444 -5.952058 0.08784 0.186489 1 152.125 76.596 0.00382 0.00003 0.03794 0.01211 20.969 0.447456 -6.152551 0.17352 0.160809 1 157.821 68.401 0.00358 0.00002 0.02321 0.0085 22.219 0.50238 -6.251425 0.188056 0.160812 1 157.447 149.605 0.00369 0.00002 0.01909 0.01018 21.693 0.447285 -6.247076 0.180528 0.164916 1 159.116 144.811 0.00342 0.00002 0.02024 0.00852 22.663 0.366329 -6.41744 0.194627 0.151709 1 125.036 116.187 0.0128 0.0001 0.02174 0.08151 15.338 0.629574 -4.020042 0.265315 0.340623 1 125.791 96.206 0.01378 0.00011 0.0263 0.10323 15.433 0.57101 -5.159169 0.202146 0.260375 1 126.512 99.77 0.01936 0.00015 0.03963 0.16744 12.435 0.638545 -3.760348 0.242861 0.378483 1 125.641 116.346 0.03316 0.00026 0.04791 0.31482 8.867 0.671299 -3.700544 0.260481 0.370961 1 128.451 75.632 0.01551 0.00012 0.03672 0.11843 15.06 0.639808 -4.20273 0.310163 0.356881 1 139.224 66.157 0.03011 0.00022 0.05005 0.2593 10.489 0.596362 -3.269487 0.270641 0.444774 1 150.258 75.349 0.00248 0.00002 0.00659 0.00495 26.759 0.296888 -6.878393 0.089267 0.113942 1 154.003 128.621 0.00183 0.00001 0.00582 0.00243 28.409 0.263654 -7.111576 0.14478 0.093193 1 149.689 133.608 0.00257 0.00002 0.00818 0.00578 27.421 0.365488 -6.997403 0.210279 0.112878 1 155.078 144.148 0.00168 0.00001 0.00632 0.00233 29.746 0.334171 -6.981201 0.18455 0.106802 1 151.884 133.751 0.00258 0.00002 0.00788 0.00659 26.833 0.393563 -6.600023 0.249172 0.105306 1 151.989 132.857 0.00174 0.00001 0.00576 0.00238 29.928 0.311369 -6.739151 0.160686 0.11513 1 193.03 80.297 0.00766 0.00004 0.01815 0.00947 21.934 0.497554 -5.845099 0.278679 0.185668 1 200.714 89.686 0.00621 0.00003 0.01439 0.00704 23.239 0.436084 -5.25832 0.256454 0.23252 1 208.519 199.02 0.00609 0.00003 0.01058 0.0083 22.407 0.338097 -6.471427 0.184378 0.13639 1 204.664 189.621 0.00841 0.00004 0.01483 0.01316 21.305 0.498877 -4.876336 0.212054 0.268144 1 210.141 185.258 0.00534 0.00003 0.01017 0.0062 23.671 0.441097 -5.96304 0.250283 0.177807 1 206.327 92.02 0.00495 0.00002 0.01284 0.01048 21.864 0.331508 -6.729713 0.181701 0.115515 1 151.872 69.085 0.00856 0.00006 0.00832 0.06051 23.693 0.407701 -4.673241 0.261549 0.274407 1 158.219 71.948 0.00476 0.00003 0.00747 0.01554 26.356 0.450798 -6.051233 0.27328 0.170106 1 170.756 79.032 0.00555 0.00003 0.00971 0.01802 25.69 0.486738 -4.597834 0.372114 0.28278 1 178.285 82.063 0.00462 0.00003 0.00744 0.00856 25.02 0.470422 -4.913137 0.393056 0.251972 1 217.116 93.978 0.00404 0.00002 0.00631 0.00681 24.581 0.462516 -5.517173 0.389295 0.220657 1 128.94 88.251 0.00581 0.00005 0.01117 0.0235 24.743 0.487756 -6.186128 0.279933 0.152428 1 176.824 83.961 0.0046 0.00003 0.0063 0.01161 27.166 0.400088 -4.711007 0.281618 0.234809 1 138.19 83.34 0.00704 0.00005 0.02567 0.01968 18.305 0.538016 -5.418787 0.160267 0.229892 1 182.018 79.187 0.00842 0.00005 0.0158 0.01813 18.784 0.589956 -5.44514 0.142466 0.215558 1 156.239 79.82 0.00694 0.00004 0.0142 0.0202 19.196 0.618663 -5.944191 0.143359 0.181988 1 145.174 80.637 0.00733 0.00005 0.01495 0.01874 18.857 0.637518 -5.594275 0.12795 0.222716 1 138.145 81.114 0.00544 0.00004 0.01805 0.01794 18.178 0.623209 -5.540351 0.087165 0.214075 1 166.888 79.512 0.00638 0.00004 0.01859 0.01796 18.33 0.585169 -5.825257 0.115697 0.196535 1 119.031 109.216 0.0044 0.00004 0.0057 0.01724 26.842 0.457541 -6.890021 0.152941 0.112856 1 120.078 105.667 0.0027 0.00002 0.00588 0.00487 26.369 0.491345 -5.892061 0.195976 0.183572 1 120.289 100.209 0.00492 0.00004 0.0082 0.0161 23.949 0.46716 -6.135296 0.20363 0.169923 1 120.256 104.773 0.00407 0.00003 0.00815 0.01015 26.017 0.468621 -6.112667 0.217013 0.170633 1 119.056 86.795 0.00346 0.00003 0.00701 0.00903 23.389 0.470972 -5.436135 0.254909 0.232209 1 118.747 109.836 0.00331 0.00003 0.00621 0.00504 25.619 0.482296 -6.448134 0.178713 0.141422 1 106.516 93.105 0.00589 0.00006 0.03112 0.03031 17.06 0.637814 -5.301321 0.320385 0.24308 1 110.453 105.554 0.00494 0.00004 0.02592 0.02529 17.707 0.653427 -5.333619 0.322044 0.228319 1 113.4 107.816 0.00451 0.00004 0.02973 0.02278 19.013 0.6479 -4.378916 0.300067 0.259451 1 113.166 100.673 0.00502 0.00004 0.03347 0.0369 16.747 0.625362 -4.654894 0.304107 0.274387 1 112.239 104.095 0.00472 0.00004 0.0353 0.02629 17.366 0.640945 -5.634576 0.306014 0.209191 1 116.15 109.815 0.00381 0.00003 0.01812 0.01827 18.801 0.624811 -5.866357 0.23307 0.184985 1 170.368 79.543 0.00571 0.00003 0.01964 0.02485 18.54 0.677131 -4.796845 0.397749 0.277227 1 208.083 91.802 0.00757 0.00004 0.04003 0.04238 15.648 0.606344 -5.410336 0.288917 0.231723 1 198.458 148.691 0.00376 0.00002 0.02076 0.01728 18.702 0.606273 -5.585259 0.310746 0.209863 1 202.805 86.232 0.0037 0.00002 0.01177 0.0201 18.687 0.536102 -5.898673 0.213353 0.189032 1 202.544 164.168 0.00254 0.00001 0.01558 0.01049 20.68 0.49748 -6.132663 0.220617 0.159777 1 223.361 87.638 0.00352 0.00002 0.01478 0.01493 20.366 0.566849 -5.456811 0.345238 0.232861 1 169.774 151.451 0.01568 0.00009 0.05426 0.0753 12.359 0.56161 -3.297668 0.414758 0.457533 1 183.52 161.34 0.01466 0.00008 0.04101 0.06057 14.367 0.478024 -4.276605 0.355736 0.336085 1 188.62 165.982 0.01719 0.00009 0.0458 0.08069 12.298 0.55287 -3.377325 0.335357 0.418646 1 202.632 177.258 0.01627 0.00008 0.04265 0.07889 14.989 0.427627 -4.892495 0.262281 0.270173 1 186.695 149.442 0.01872 0.0001 0.03714 0.10952 12.529 0.507826 -4.484303 0.340256 0.301487 1 192.818 168.793 0.03107 0.00016 0.0794 0.21713 8.441 0.625866 -2.434031 0.450493 0.527367 1 198.116 174.478 0.02714 0.00014 0.05556 0.16265 9.449 0.584164 -2.839756 0.356224 0.454721 1 121.345 98.25 0.00684 0.00006 0.01399 0.04179 21.52 0.566867 -4.865194 0.246404 0.168581 1 119.1 88.833 0.00692 0.00006 0.01405 0.04611 21.824 0.65168 -4.239028 0.175691 0.247455 1 117.87 95.654 0.00647 0.00005 0.01804 0.02631 22.431 0.6283 -3.583722 0.207914 0.206256 1 122.336 94.794 0.00727 0.00006 0.01289 0.03191 22.953 0.611679 -5.4351 0.230532 0.220546 1 117.963 100.757 0.01813 0.00015 0.02161 0.10748 19.075 0.630547 -3.444478 0.303214 0.261305 1 126.144 97.543 0.00975 0.00008 0.01581 0.03828 21.534 0.635015 -5.070096 0.280091 0.249703 1 127.93 112.173 0.00605 0.00005 0.0165 0.02663 19.651 0.654945 -5.498456 0.234196 0.216638 1 114.238 77.022 0.00581 0.00005 0.01994 0.02073 20.437 0.653139 -5.185987 0.259229 0.244948 1 115.322 107.802 0.00619 0.00005 0.01722 0.0281 19.388 0.577802 -5.283009 0.226528 0.238281 1 114.554 91.121 0.00651 0.00006 0.0194 0.02707 18.954 0.685151 -5.529833 0.24275 0.22052 1 112.15 97.527 0.00519 0.00005 0.01033 0.01435 21.219 0.557045 -5.617124 0.184896 0.212386 1 102.273 85.902 0.00907 0.00009 0.01553 0.03882 18.447 0.671378 -2.929379 0.396746 0.367233 0 236.2 102.137 0.00277 0.00001 0.01426 0.0062 24.078 0.469928 -6.816086 0.17227 0.119652 0 237.323 229.256 0.00303 0.00001 0.00747 0.00533 24.679 0.384868 -7.018057 0.176316 0.091604 0 260.105 237.303 0.00339 0.00001 0.0123 0.0091 21.083 0.440988 -7.517934 0.160414 0.075587 0 197.569 90.794 0.00803 0.00004 0.01272 0.01337 19.269 0.372222 -5.736781 0.164529 0.202879 0 240.301 219.783 0.00517 0.00002 0.01191 0.00965 21.02 0.371837 -7.169701 0.073298 0.100881 0 244.99 239.17 0.00451 0.00002 0.01121 0.01049 21.528 0.522812 -7.3045 0.171088 0.09622 0 112.547 105.715 0.00355 0.00003 0.00786 0.00435 26.436 0.413295 -6.323531 0.218885 0.160376 0 110.739 100.139 0.00356 0.00003 0.0095 0.0043 26.55 0.36909 -6.085567 0.192375 0.174152 0 113.715 96.913 0.00349 0.00003 0.00905 0.00478 26.547 0.380253 -5.943501 0.19215 0.179677 0 117.004 99.923 0.00353 0.00003 0.01062 0.0059 25.445 0.387482 -6.012559 0.229298 0.163118 0 115.38 108.634 0.00332 0.00003 0.00933 0.00401 26.005 0.405991 -5.966779 0.197938 0.184067 0 116.388 108.97 0.00346 0.00003 0.01021 0.00415 26.143 0.361232 -6.016891 0.109256 0.174429 1 151.737 129.859 0.00314 0.00002 0.00886 0.0057 24.151 0.39661 -6.486822 0.197919 0.132703 1 148.79 138.99 0.00309 0.00002 0.00956 0.00488 24.412 0.402591 -6.311987 0.182459 0.160306 1 148.143 135.041 0.00392 0.00003 0.00876 0.0054 23.683 0.398499 -5.711205 0.240875 0.19273 1 150.44 144.736 0.00396 0.00003 0.01574 0.00611 23.133 0.352396 -6.261446 0.183218 0.144105 1 148.462 141.998 0.00397 0.00003 0.01103 0.00639 22.866 0.408598 -5.704053 0.216204 0.19771 1 149.818 144.786 0.00336 0.00002 0.01341 0.00595 23.008 0.329577 -6.27717 0.109397 0.156368 0 117.226 106.656 0.00417 0.00004 0.01223 0.00955 23.079 0.603515 -5.61907 0.191576 0.215724 0 116.848 99.503 0.00531 0.00005 0.01144 0.01179 22.085 0.663842 -5.198864 0.206768 0.252404 0 116.286 96.983 0.00314 0.00003 0.0099 0.00737 24.199 0.598515 -5.592584 0.133917 0.214346 0 116.556 86.228 0.00496 0.00004 0.00972 0.01397 23.958 0.566424 -6.431119 0.15331 0.120605 0 116.342 94.246 0.00267 0.00002 0.00789 0.0068 25.023 0.528485 -6.359018 0.116636 0.138868 0 114.563 86.647 0.00327 0.00003 0.00721 0.00703 24.775 0.555303 -6.710219 0.149694 0.121777 0 201.774 78.228 0.00694 0.00003 0.01582 0.04441 19.368 0.508479 -6.934474 0.15989 0.112838 0 174.188 94.261 0.00459 0.00003 0.02498 0.02764 19.517 0.448439 -6.538586 0.121952 0.13305 0 209.516 89.488 0.00564 0.00003 0.01657 0.0181 19.147 0.431674 -6.195325 0.129303 0.168895 0 174.688 74.287 0.0136 0.00008 0.01365 0.10715 17.883 0.407567 -6.787197 0.158453 0.131728 0 198.764 74.904 0.0074 0.00004 0.01321 0.07223 19.02 0.451221 -6.744577 0.207454 0.123306 0 214.289 77.973 0.00567 0.00003 0.01161 0.04398 21.209 0.462803 -5.724056 0.190667 0.148569
Names of X columns:
status MDVP:Fo(Hz) MDVP:Flo(Hz) MDVP:Jitter(%) MDVP:Jitter(Abs) Shimmer:APQ5 NHR HNR RPDE spread1 spread2 PPE
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal Dummies
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
Linear Trend
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) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test mywarning <- '' 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 (par5=='') par5 <- 0 par5 <- as.numeric(par5) 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=12)'){ (n <- n - 12) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+12,j] - x[i,j] } } x <- x2 } if (par3 == 'First and Seasonal Differences (s=12)'){ (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 - 12) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+12,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*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep=''))) for (i in 1:(n-par5*12)) { for (j in 1:par5) { x2[i,j] <- x[i+par5*12-j*12,par1] } } x <- cbind(x[(par5*12+1):n,], x2) n <- n - par5*12 } 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' } 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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) 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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, mywarning) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$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$sigma,6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' ')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') if(n < 200) { a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(x[i]-mysum$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') } }
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