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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 26 Aug 2023 11:00:23 +0200
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2023/Aug/26/t1693040609r46dcrt19vbshpl.htm/, Retrieved Wed, 27 May 2026 05:02:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319940, Retrieved Wed, 27 May 2026 05:02:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact341

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Vraag 4] [2023-08-26 09:00:23] [be7b2b75e23a5e4204aac38a719ba7c9] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0 1 3247039 1823.522005 0.641 1 0 1
0 1 3168033 2980.447411 0.669 1 0 1
0 1 3089027 3861.353383 0.694 1 0 1
0 1 3026939 5422.770252 0.724 1 0 1
0 1 2947314 8228.318168 0.767 1 0 1
0 1 2900401 10526.28013 0.782 1 0 1
0.0042 2 27181094 6704.650988 0.608 0 0 3
0.0044 2 29411415 7060.095524 0.644 0 0 3
0 2 31183660 8093.530893 0.684 0 0 3
0.0016 2 32831096 10176.67563 0.709 0 0 3
0.0016 2 34860715 12162.26435 0.74 0 0 3
0.0021 2 37565847 13404.41178 0.753 0 0 3
0 3 12968345 2825.645746 0.3154 1 0 3
0 3 14682284 2728.585592 0.387 1 0 3
0 3 16440924 3076.632312 0.442 1 0 3
0 3 18865716 3949.493773 0.502 1 0 3
0 3 21759420 6201.401882 0.543 1 0 3
0 3 25096150 6770.668417 0.577 1 0 3
0.003 4 33655151 8869.030241 0.738 0 0 6
0.0033 4 35419682 10754.19842 0.771 0 0 6
0.0055 4 37057452 11810.06136 0.78 0 0 6
0.0054 4 38728696 12430.71372 0.795 0 0 6
0.0038 4 40382389 17711.44179 0.818 0 0 6
0.0058 4 42096739 19579.00648 0.822 0 0 6
0.003 5 3442810 1350.108251 0.612 1 0 2
0.0005 5 3168215 1734.655302 0.647 1 0 2
0 5 3069588 2318.238073 0.68 1 0 2
0.0032 5 3000612 4031.947204 0.727 1 0 2
0.0021 5 2908220 7260.968756 0.737 1 0 2
0.0047 5 2881922 7649.491409 0.749 1 0 2
0.0399 6 17495000 18187.74639 0.886 0 0 4
0.0629 6 18311000 21962.52308 0.898 0 1 4
0.0541 6 19153000 26316.94788 0.907 0 0 4
0.0477 6 20127400 31311.63694 0.92 0 0 4
0.0348 6 21249200 37409.16307 0.929 0 0 4
0.0296 6 22742475 42733.93625 0.938 0 0 4
0.0012 7 7382000 4334.433664 0.612 1 0 2
0.0038 7 7763000 2471.585879 0.64 1 0 2
0.0038 7 8048600 3534.329148 0.674 1 0 2
0.0059 7 8306500 5551.448842 0.719 1 0 2
0.0086 7 8763400 13799.78614 0.745 1 0 2
0.0142 7 9295784 16180.88715 0.757 1 0 2
0.0012 8 266134 18790.19964 0.7642 0 0 5
0.0027 8 283790 21557.12912 0.776 0 0 5
0.0022 8 297890 26149.41714 0.784 0 0 5
0.0016 8 322526 27596.02551 0.79 0 0 5
0.0016 8 348676 29857.27156 0.807 0 0 5
0.0021 8 372039 29937.10217 0.806 0 0 5
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0.0016 9 829848 37399.84481 0.796 0 0 2
0.0005 9 1114590 40557.02431 0.8 0 0 2
0.0026 9 1300217 41714.84361 0.846 0 0 2
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0 10 155727053 2756.443467 0.597 0 0 2
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0.0005 11 265942 10245.5033 0.752 0 0 5
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0 11 277319 16520.94123 0.795 0 0 5
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0.0139 12 10216000 5090.870996 0.661 1 0 1
0.0142 12 10160000 4181.114215 0.683 1 0 1
0.0137 12 9979610 6003.689136 0.715 1 0 1
0.0172 12 9730146 8795.014046 0.774 1 0 1
0.0113 12 9527985 14405.07815 0.803 1 0 1
0.0079 12 9464495 17794.17415 0.805 1 0 1
0.0073 13 10045158 20349.59192 0.857 0 0 1
0.0038 13 10156637 22886.1257 0.873 0 0 1
0.0027 13 10251250 27983.99612 0.885 0 0 1
0.0027 13 10421137 32249.49946 0.899 0 0 1
0.0021 13 10709973 38133.50146 0.905 0 0 1
0.0063 13 11128246 42354.63149 0.915 0 0 1
0 14 194317 4212.857165 0.662 0 0 5
0 14 213676 4524.593863 0.677 0 0 5
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0 14 276089 6786.503036 0.7 0 0 5
0 14 306165 7630.706929 0.706 0 0 5
0 14 336701 8051.145743 0.709 0 0 5
0 15 5331803 1001.087611 0.377 1 0 3
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0 15 8696916 1768.744657 0.489 1 0 3
0 15 9729160 1890.14073 0.512 1 0 3
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0 16 9599855 4986.880533 0.662 0 0 6
0 16 10239004 5899.641861 0.689 0 0 6
0 17 4239154 963.76 0.6506 1 0 1
0 17 3780378 2353.142821 0.672 1 0 1
0 17 3766706 4529.325724 0.694 1 0 1
0 17 3781287 6172.578892 0.719 1 0 1
0 17 3763599 9006.428506 0.739 1 0 1
0 17 3648200 10181.95024 0.766 1 0 1
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0 18 1728340 8077.682429 0.584 0 0 3
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0.0011 18 1946351 12947.48722 0.683 0 0 3
0 18 2089315 14200.74841 0.712 0 0 3
0.0145 19 154564278 6898.855909 0.656 0 0 6
0.0098 19 164913306 8268.667378 0.684 0 0 6
0.0115 19 175287587 9012.955425 0.698 0 0 6
0.0134 19 184738458 10401.84334 0.716 0 0 6
0.015 19 192979029 13259.7294 0.736 0 0 6
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0.0018 22 5661139 715.0820612 0.294 0 0 3
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0.0011 22 9319710 755.8289272 0.418 0 0 3
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0.0236 24 28519597 20446.6282 0.863 0 0 5
0.0126 24 29671900 23904.84893 0.867 0 0 5
0.0131 24 30769700 29185.35506 0.887 0 0 5
0.0177 24 31995000 33754.86956 0.899 0 0 5
0.0134 24 33245773 40277.61931 0.908 0 0 5
0.0174 24 34750545 42145.09786 0.922 0 0 5
0 25 3095807 2530694046 0.3 0 0 3
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0 26 13671033 5621.10987 0.734 0 0 6
0.0005 26 14497826 8028.911405 0.759 0 0 6
0.0038 26 15262754 9571.978941 0.783 0 0 6
0.0011 26 15973778 11831.31005 0.806 0 0 6
0 26 16661942 16551.30715 0.819 0 0 6
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0.0629 27 1164970000 1268.27162 0.559 1 0 2
0.0716 27 1217550000 2070.771609 0.594 1 0 2
0.077 27 1262645000 2933.314817 0.635 1 0 2
0.1196 27 1296075000 4455.205022 0.685 1 1 2
0.1024 27 1324655000 7635.072611 0.722 1 0 2
0.0739 27 1350695000 11351.06206 0.748 1 0 2
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0.0018 31 3257466 5657.044646 0.691 0 0 5
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0.0011 32 1081563 34823.65424 0.852 0 0 2
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0.0091 33 5171370 19794.3714 0.837 0 0 1
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0.0066 33 5339616 28657.54079 0.898 0 0 1
0.007 33 5404523 32920.47703 0.909 0 0 1
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0.0132 33 5591572 44803.96224 0.928 0 0 1
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0.0016 35 8562622 6499.853498 0.668 0 0 5
0.0027 35 9102998 7208.898739 0.695 0 0 5
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0.0044 38 69905988 5856.82851 0.628 0 0 3
0.0005 38 75381899 6731.070564 0.658 0 0 3
0.0021 38 80953881 8940.441663 0.675 0 0 3
0.0016 38 87813257 10004.39071 0.694 0 0 3
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0.0027 41 1415594 6824.020302 0.78 1 0 1
0.0022 41 1396985 9419.756951 0.814 1 0 1
0.0027 41 1362550 14424.5942 0.841 1 0 1
0.0016 41 1337090 22664.03933 0.859 1 0 1
0.0005 41 1322696 26022.46885 0.868 1 0 1
0.0042 42 51647768 351.6378224 0.2104 1 0 3
0.0093 42 59155148 464.8293016 0.283 1 0 3
0.0076 42 66537331 490.0363908 0.325 1 0 3
0.0086 42 74624405 580.9214739 0.394 1 0 3
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0.0016 43 873596 7676.778694 0.738 0 0 4
0.0012 44 65075486 2601.247891 0.606 0 0 2
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0.0011 44 96866642 6099.121248 0.696 0 0 2
0.0048 45 5041992 17263.39249 0.823 0 0 1
0.0049 45 5124573 20053.31122 0.858 0 0 1
0.0022 45 5176209 26748.63105 0.891 0 0 1
0.0038 45 5228172 31129.29964 0.904 0 0 1
0.0021 45 5313399 39969.3876 0.908 0 0 1
0.0005 45 5413971 40620.17607 0.918 0 0 1
0.0447 46 58851217 18941.40506 0.83 0 0 1
0.0426 46 59753100 21326.82672 0.849 0 0 1
0.035 46 60912500 26090.10166 0.86 0 0 1
0.0375 46 62704895 29044.39611 0.878 0 0 1
0.0359 46 64374989 35095.26023 0.886 0 0 1
0.0422 46 65659789 37679.13326 0.899 0 0 1
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0.0016 51 103930 11640.87688 0.749 0 0 5
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0.0148 52 10608800 16125.77175 0.796 0 0 1
0.0197 52 10805808 19515.78932 0.835 0 1 1
0.0032 52 10955141 25446.38326 0.857 0 0 1
0.0011 52 11077841 30856.01183 0.854 0 0 1
0.0069 52 11045011 25284.46408 0.868 0 0 1
0 53 9708544 3612.942598 0.515 0 0 5
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0.0011 53 14006366 6515.045726 0.613 0 0 5
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0 56 8549200 1378.692051 0.45 0 0 5
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0 56 9705029 1526.169509 0.481 0 0 5
0 56 10289210 1614.025896 0.496 0 0 5
0 57 5247836 2121.708345 0.534 0 0 5
0 57 5867849 2380.936624 0.554 0 0 5
0 57 6524283 2638.272292 0.574 0 0 5
0 57 7204153 3078.01306 0.59 0 0 5
0 57 7872658 3923.597021 0.597 0 0 5
0 57 8505646 4213.02445 0.614 0 0 5
0.0018 58 5800500 20347.73007 0.811 0 0 2
0 58 6435500 23958.62616 0.827 0 0 2
0.0011 58 6665000 26962.55949 0.862 0 0 2
0 58 6783500 33016.75237 0.896 0 0 2
0.0005 58 6957800 44800.17398 0.911 0 0 2
0 58 7150100 51306.33284 0.93 0 0 2
0.0236 59 10369341 8243.472188 0.746 1 0 1
0.0213 59 10311238 9338.510532 0.769 1 0 1
0.0213 59 10210971 11876.08117 0.795 1 0 1
0.0113 59 10107146 16251.38085 0.818 1 0 1
0.0198 59 10038188 20678.67991 0.83 1 0 1
0.0179 59 9920362 23094.47333 0.835 1 0 1
0.006 60 3558430 15110.90948 0.801 0 0 1
0.0011 60 3637510 20474.73557 0.857 0 0 1
0.0016 60 3805174 30173.20484 0.89 0 0 1
0.0021 60 4070262 38683.01724 0.908 0 0 1
0.0043 60 4489544 44280.69146 0.902 0 0 1
0.0021 60 4599533 46374.23951 0.934 0 0 1
0 61 261057 21520.22588 0.831 0 0 1
0.0005 61 268916 24421.83151 0.86 0 0 1
0 61 281205 29453.42665 0.885 0 0 1
0.0011 61 292074 35332.64437 0.89 0 0 1
0 61 317414 42721.16286 0.909 0 0 1
0 61 320716 40696.0378 0.933 0 0 1
0.0006 62 906021106 1227.157753 0.467 0 0 2
0.0005 62 978893217 1595.71234 0.493 0 0 2
0.0011 62 1053050912 1977.645046 0.526 0 0 2
0.0027 62 1126135777 2549.265918 0.564 0 0 2
0.0043 62 1197146906 3637.64307 0.6 0 0 2
0.0016 62 1263065852 4916.485846 0.636 0 0 2
0.0042 63 187766086 3476.054747 0.577 0 0 2
0.006 63 199914831 4741.78854 0.606 0 0 2
0.0038 63 211540429 4601.849337 0.629 0 0 2
0.0043 63 223614649 5647.24128 0.646 0 0 2
0.0016 63 236159276 7486.01947 0.675 0 0 2
0.0037 63 248883232 9421.586953 0.691 0 0 2
0 64 18458187 3550.700491 0.573 0 0 2
0 64 20845893 5242.189637 0.607 0 0 2
0 64 23565413 9651.690102 0.628 0 0 2
0 64 26316609 9239.309726 0.643 0 0 2
0 64 29111417 11736.73816 0.659 0 0 2
0 64 32776571 14895.39198 0.672 0 0 2
0.0036 65 58260738 8679.432858 0.647 0 0 2
0.0055 65 61583089 9553.681342 0.67 0 0 2
0.0066 65 66131854 10411.54924 0.691 0 0 2
0.0021 65 69617100 13205.62327 0.741 0 0 2
0.0134 65 72845542 16511.03298 0.781 0 0 2
0.0079 65 76453574 16932.17721 0.796 0 0 2
0.0006 66 5123000 16793.39109 0.828 0 0 2
0.0005 66 5692000 20765.62907 0.853 0 0 2
0.0022 66 6289000 24935.34976 0.868 0 0 2
0.0005 66 6809000 25228.08697 0.88 0 0 2
0 66 7308800 27399.44764 0.893 0 0 2
0.0011 66 7910500 31708.08711 0.902 0 0 2
0.0429 67 56797087 20052.20651 0.806 0 0 1
0.0383 67 56860281 22963.16029 0.83 0 0 1
0.0344 67 56942108 27022.86161 0.852 0 0 1
0.029 67 57685327 29457.89097 0.868 0 0 1
0.0284 67 58826731 35402.91685 0.874 0 0 1
0.0296 67 59539717 36237.11043 0.878 0 0 1
0 68 13163019 2029.043053 0.388 0 0 3
0 68 14995249 2247.873782 0.394 0 0 3
0 68 16686561 2336.356921 0.407 0 0 3
0 68 17997738 2317.614362 0.43 0 0 3
0 68 19497986 2567.190743 0.454 0 0 3
0.0021 68 21418603 2762.341426 0.486 0 0 3
0.0067 69 2465362 5232.396507 0.66 0 0 5
0.006 69 2561993 6206.229836 0.662 0 0 5
0.0055 69 2656864 6288.501328 0.691 0 0 5
0.0139 69 2728777 7235.120884 0.708 0 0 5
0.0129 69 2790122 8230.784445 0.721 0 0 5
0.0137 69 2840992 8167.223362 0.732 0 0 5
0.0157 70 124229000 21387.48758 0.845 0 0 2
0.0197 70 125757000 24507.06309 0.855 0 0 2
0.0426 70 126843000 26838.86865 0.869 0 0 2
0.0263 70 127761000 30361.81497 0.881 0 0 2
0.0354 70 128063000 34798.7659 0.895 0 0 2
0.0385 70 127629000 37191.38595 0.907 0 0 2
0 71 13325583 2365.105085 0.422 1 0 2
0 71 15889449 2645.62365 0.443 1 0 2
0 71 17874725 3086.354821 0.469 1 0 2
0 71 20017068 3490.716252 0.485 1 0 2
0 71 22356391 4061.241117 0.505 1 0 2
0 71 24909969 3863.289475 0.462 1 0 2
0 72 3968198 4653.176088 0.686 0 0 2
0 72 4716373 5066.959891 0.702 0 0 2
0 72 5103130 5734.639122 0.726 0 0 2
0 72 5535595 7245.386276 0.736 0 0 2
0 72 6489822 9332.542462 0.726 0 0 2
0.0016 72 7992573 9129.389779 0.735 0 0 2
0 73 359090 1212.194964 0.5356 0 0 3
0 73 398773 1954.752575 0.57 0 0 3
0 73 435079 3039.919351 0.592 0 0 3
0 73 467664 3801.929908 0.625 0 0 3
0 73 491723 5710.16342 0.636 0 0 3
0 73 513979 6097.221554 0.652 0 0 3
0 74 12408931 1815.595266 0.422 0 0 3
0.0016 74 13812472 1799.902923 0.431 0 0 3
0.0016 74 15274234 2063.823523 0.464 0 0 3
0.0016 74 16959081 2457.351516 0.492 0 0 3
0 74 18907008 2812.645055 0.526 0 0 3
0 74 21082383 3076.858139 0.553 0 0 3
0.0127 75 16439095 7471.688544 0.666 1 0 2
0.0093 75 15577894 6274.410381 0.685 1 0 2
0.0076 75 14883626 7887.892984 0.737 1 0 2
0.0113 75 15012985 12705.43385 0.759 1 0 2
0.015 75 15674000 18513.92541 0.781 1 0 2
0.0148 75 16792089 22391.31823 0.797 1 0 2
0.0085 76 24963953 1533.847389 0.455 0 0 3
0.0077 76 28147734 1655.849353 0.451 0 0 3
0.0071 76 31450483 1689.951468 0.48 0 0 3
0.0155 76 35074931 1861.732531 0.523 0 0 3
0.0102 76 39148416 2243.288394 0.559 0 0 3
0.0164 76 43646629 2650.443555 0.585 0 0 3
0 77 4515400 1831.53856 0.568 1 0 2
0.0005 77 4628400 1328.807047 0.594 1 0 2
0 77 4898400 1644.311975 0.615 1 0 2
0.0016 77 5104700 2071.417492 0.631 1 0 2
0 77 5318700 2681.122187 0.649 1 0 2
0 77 5607200 2922.702873 0.669 1 0 2
0.003 78 4470000 4530.4 0.709 1 0 1
0.0022 78 4494000 8795.05811 0.75 1 0 1
0.0049 78 15274234 10754.93402 0.778 1 0 1
0.0038 78 4439000 14070.29807 0.803 1 0 1
0.007 78 4434508 20250.7087 0.816 1 0 1
0.0121 78 4267558 21156.64422 0.828 1 0 1
0 79 4502363 1214.90971 0.441 1 0 2
0 79 4957180 1572.325142 0.466 1 0 2
0 79 5329304 1969.701704 0.497 1 0 2
0 79 5664605 2548.398911 0.529 1 0 2
0 79 6052190 3585.167178 0.569 1 0 2
0 79 6415169 4882.41856 0.598 1 0 2
0 80 1667121 1037.097978 0.487 0 0 3
0 80 1787273 1258.037724 0.467 0 0 3
0 80 1868699 1411.780904 0.46 0 0 3
0 80 1933728 1646.846469 0.48 0 0 3
0 80 1999930 2139.870679 0.505 0 0 3
0 80 2089928 2665.44626 0.516 0 0 3
0.0006 81 2614338 2231.1 0.68 1 0 1
0.0033 81 2457222 5796.503311 0.728 1 0 1
0.0044 81 2367550 8019.319446 0.788 1 0 1
0.0032 81 2263122 12214.41323 0.821 1 0 1
0.0032 81 2177322 19432.17933 0.824 1 0 1
0 81 2034319 21252.79231 0.844 1 0 1
0 82 2022729 2010.78 0.3846 0 0 3
0 82 2191179 1392.74 0.387 0 0 3
0 82 2884522 1031.521619 0.373 0 0 3
0 82 3176414 780.448997 0.399 0 0 3
0 82 3662993 1006.154722 0.42 0 0 3
0 82 4181563 1219.823365 0.432 0 0 3
0 83 4651004 8643.8 0.709 0 0 3
0 83 5035884 13529.77265 0.727 0 0 3
0 83 5355751 17375.64648 0.743 0 0 3
0 83 5704759 20392.47615 0.757 0 0 3
0 83 6053078 27843.12564 0.741 0 0 3
0 83 6198258 25416.08445 0.693 0 0 3
0.0006 84 3700114 3406.26 0.712 1 0 1
0.0049 84 3601613 6379.414074 0.756 1 0 1
0.0038 84 3499536 8457.656179 0.798 1 0 1
0.0038 84 3377075 13031.46174 0.831 1 0 1
0.0054 84 3198231 20743.98803 0.831 1 0 1
0.0026 84 2987773 24658.08091 0.855 1 0 1
0 85 392175 33785.32433 0.817 0 0 1
0 85 414225 41334.75262 0.855 0 0 1
0 85 436300 55340.09732 0.872 0 0 1
0 85 458095 64074.50515 0.89 0 0 1
0 85 488650 86693.89512 0.892 0 0 1
0 85 530946 91622.17742 0.904 0 0 1
0 86 12301336 1009.371303 0.4268 0 0 3
0 86 13902688 1028.940596 0.456 0 0 3
0 86 15766806 1144.578853 0.472 0 0 3
0 86 17802997 1180.242607 0.5 0 0 3
0 86 19996469 1463.094812 0.507 0 0 3
0 86 22346573 1396.203885 0.517 0 0 3
0 87 9729717 496.8073653 0.385 0 0 3
0 87 10109789 640.9537997 0.399 0 0 3
0 87 11376172 686.3825764 0.375 0 0 3
0 87 12676038 721.9696787 0.417 0 0 3
0 87 14271234 910.9173989 0.455 0 0 3
0 87 16097305 1058.912262 0.474 0 0 3
0 88 236190 3639.9 0.553 0 0 2
0 88 259327 6103.005252 0.606 0 0 2
0 88 280384 7742.577293 0.635 0 0 2
0 88 310423 9458.807576 0.66 0 0 2
0 88 345054 12243.32426 0.688 0 0 2
0 88 386203 12843.89778 0.712 0 0 2
0.0018 89 19012724 8156.44291 0.695 0 0 2
0 89 21023321 11624.96753 0.725 0 0 2
0 89 23185608 12927.84405 0.734 0 0 2
0.0011 89 25174109 15507.31565 0.761 0 0 2
0.0016 89 27111069 19873.17089 0.781 0 0 2
0.0047 89 29170456 23006.92553 0.799 0 0 2
0 90 8868263 896.8105358 0.271 0 0 3
0 90 9856810 1014.494245 0.308 0 0 3
0 90 10967690 1159.551873 0.352 0 0 3
0 90 12391906 1472.481882 0.39 0 0 3
0 90 14138216 1737.714752 0.408 0 0 3
0 90 16006670 1838.190576 0.421 0 0 3
0 91 367618 10583.29915 0.763 0 0 1
0 91 379905 13675.08574 0.783 0 0 1
0 91 390087 19425.79571 0.809 0 0 1
0 91 401268 21401.10282 0.829 0 0 1
0 91 409379 26193.01756 0.849 0 0 1
0 91 420028 29424.05837 0.875 0 0 1
0.0012 92 25791494 2705.00321 0.499 0 0 3
0.0033 92 27460603 3219.942322 0.53 0 0 3
0.0044 92 28849621 3553.994247 0.572 0 0 3
0.0016 92 30179285 4530.82369 0.602 0 0 3
0.0005 92 31596855 5866.564216 0.635 0 0 3
0.0005 92 33333789 6916.435024 0.662 0 0 3
0 93 2141445 1884.718484 0.428 0 0 3
0 93 2397245 2182.484304 0.442 0 0 3
0 93 2709359 2180.644244 0.461 0 0 3
0.0005 93 3042823 2431.841423 9.476 0 0 3
0 93 3407541 3255.497599 0.499 0 0 3
0 93 3830239 3526.34393 0.516 0 0 3
0 94 1084441 5479.873433 0.652 0 0 3
0 94 1133996 6863.113023 0.673 0 0 3
0 94 1186873 8780.069579 0.704 0 0 3
0 94 1221003 10664.05114 0.734 0 0 3
0 94 1244121 14270.20454 0.767 0 0 3
0 94 1255882 17408.18892 0.788 0 0 3
0.0006 95 88828310 6613.371927 0.677 0 0 5
0.0055 95 95687452 8677.717168 0.702 0 0 5
0.0038 95 101719673 10799.05835 0.724 0 0 5
0.0038 95 106995583 11506.78254 0.742 0 0 5
0.0064 95 113661809 14551.04561 0.757 0 0 5
0.0042 95 120828307 16658.0818 0.772 0 0 5
0.0018 96 3706000 1467.592 0.591 1 0 1
0.0016 96 3667748 1824.269765 0.597 1 0 1
0 96 3639592 1839.712736 0.64 1 0 1
0.0005 96 3603945 2647.756644 0.666 1 0 1
0.0011 96 3570108 3723.402213 0.684 1 0 1
0.0005 96 3559519 4226.959198 0.697 1 0 1
0.0006 97 2243502 2822.423466 0.561 1 0 2
0 97 2316567 3195.32814 0.589 1 0 2
0.0005 97 2397436 3689.703496 0.637 1 0 2
0.0054 97 2496832 4921.366946 0.683 1 0 2
0.0038 97 2628131 7278.375529 0.72 1 0 2
0.0016 97 2814226 9969.140218 0.743 1 0 2
0.0006 98 14071231 242.0012139 0.253 1 0 3
0.0016 98 16248232 341.0832111 0.298 1 0 3
0 98 18067687 445.1502639 0.343 1 0 3
0 98 20312705 606.7713799 0.388 1 0 3
0 98 22846758 823.7864484 0.412 1 0 3
0 98 25676606 1012.437187 0.435 1 0 3
0 99 41711465 538.5246547 0.404 0 0 2
0 99 43793310 723.2821899 0.431 0 0 2
0 99 46095462 1035.524118 0.468 0 0 2
0 99 48073707 1742.632258 0.509 0 0 2
0 99 49479752 2989.784895 0.549 0 0 2
0 99 50986514 4225.119352 0.574 0 0 2
0.0024 100 1513721 4283.087381 0.581 0 0 3
0 100 1706489 4432.684185 0.558 0 0 3
0 100 1899257 4896.969082 0.557 0 0 3
0 100 2009228 6250.797544 0.575 0 0 3
0 100 2106375 7884.762889 0.617 0 0 3
0 100 2263934 9132.342914 0.645 0 0 3
0.0193 101 15184166 20503.51148 0.866 0 0 1
0.0317 101 15530498 24218.8446 0.876 0 0 1
0.0213 101 15925513 31591.92861 0.886 0 0 1
0.0188 101 16281779 35428.39431 0.906 0 0 1
0.0204 101 16445593 45843.89223 0.921 0 0 1
0.0222 101 16754962 46707.26971 0.928 0 0 1
0 102 19773772 859.192148 0.42 0 0 2
0 102 21903379 1032.518879 0.446 0 0 2
0 102 23740911 1220.424872 0.469 0 0 2
0 102 25309449 1422.473508 0.502 0 0 2
0 102 26475859 1777.233566 0.548 0 0 2
0 102 27649925 2142.098495 0.569 0 0 2
0 103 4331277 1960.458677 0.534 0 0 5
0 103 4700779 2276.096714 0.57 0 0 5
0 103 5026796 2730.256684 0.592 0 0 5
0 103 5309703 3150.697993 0.614 0 0 5
0 103 5594506 3931.054301 0.633 0 0 5
0 103 5877108 4535.28474 0.657 0 0 5
0.0085 104 3531700 14908.45047 0.854 0 0 4
0.0033 104 3732000 18467.09166 0.869 0 0 4
0.0071 104 3857700 21509.41191 0.886 0 0 4
0.0086 104 4087500 25085.5015 0.894 0 0 4
0.0145 104 4259800 29860.37025 0.905 0 0 4
0.0185 104 4408100 32986.03121 0.915 0 0 4
0 105 8549424 548.7747647 0.235 0 0 3
0 105 9819964 581.3993611 0.252 0 0 3
0 105 11352973 597.1910827 0.274 0 0 3
0 105 13127012 653.6088958 0.303 0 0 3
0 105 15228525 784.1224604 0.336 0 0 3
0.0011 105 17731634 878.762877 0.351 0 0 3
0.0067 106 100221563 2290.110365 0.4027 0 0 3
0.0033 106 110732904 2256.164707 0.4334 0 0 3
0.0011 106 122352009 2431.567438 0.462 0 0 3
0.0027 106 135393616 3425.934594 0.485 0 0 3
0 106 150347390 4413.840495 0.512 0 0 3
0.0005 106 167297284 5385.879532 0.53 0 0 3
0.0079 107 4286401 20591.61601 0.888 0 0 1
0.0115 107 4381336 26785.21028 0.917 0 0 1
0.0087 107 4490967 36950.49466 0.934 0 0 1
0.0113 107 4591910 42501.62478 0.938 0 0 1
0.0048 107 4768212 61757.24557 0.942 0 0 1
0.0021 107 5018573 65447.49582 0.961 0 0 1
0.0006 108 18652889 536.4052659 0.338 0 0 3
0 108 21202118 719.1315456 0.398 0 0 3
0 108 24039274 832.084437 0.43 0 0 3
0 108 27568436 1027.032301 0.47 0 0 3
0.0016 108 31663896 1382.487 0.492 0 0 3
0 108 36306796 1638.068089 0.508 0 0 3
0.026 109 52150266 5882.241782 0.661 1 0 1
0.0213 109 51057189 3410.956522 0.671 1 0 1
0.0251 109 49175848 3802.540243 0.706 1 0 1
0.0247 109 47451600 6042.184784 0.733 1 0 1
0.0198 109 46258200 8395.812545 0.743 1 0 1
0.0106 109 45593300 8475.471746 0.746 1 0 1
0.0018 110 21449000 1772.466119 0.5606 1 0 2
0.0038 110 23225000 1660.755705 0.595 1 0 2
0.0055 110 24650400 1984.47012 0.621 1 0 2
0.0054 110 25864350 2503.279375 0.653 1 0 2
0.0032 110 27302800 3632.740121 0.683 1 0 2
0.0121 110 29774500 4853.649706 0.703 1 0 2
0 111 1983277 25151.46145 0.6524 0 0 2
0 111 2236666 28888.6726 0.704 0 0 2
0 111 2267991 34848.67962 0.742 0 0 2
0 111 2444751 35843.88055 0.782 0 0 2
0 111 2759014 43170.76341 0.804 0 0 2
0 111 3464644 43822.81584 0.822 0 0 2
0.0024 112 7840709 21259.90972 0.82 0 0 1
0.0044 112 7959017 24523.88346 0.838 0 0 1
0.0082 112 8011566 29375.4048 0.849 0 0 1
0.0021 112 8171966 33755.14293 0.884 0 0 1
0 112 8321496 41316.22518 0.899 0 0 1
0.0005 112 8429991 46457.34578 0.906 0 0 1
0 113 113747135 2235.333287 0.433 0 0 2
0 113 125938339 2549.151037 0.45 0 0 2
0 113 138523285 2770.166499 0.487 0 0 2
0 113 150780300 3282.315892 0.515 0 0 2
0 113 163644603 4104.907382 0.535 0 0 2
0 113 177911533 4447.90271 0.56 0 0 2
0 114 2576018 6093.477941 0.694 0 0 5
0 114 2796344 7007.858141 0.719 0 0 5
0 114 3030347 8421.50997 0.735 0 0 5
0.0016 114 3269541 9785.957928 0.755 0 0 5
0 114 3516268 14518.60512 0.771 0 0 5
0 114 3772938 18781.0507 0.785 0 0 5
0 115 4535520 1922.853312 0.432 0 0 4
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0 115 5572222 2175.685453 0.472 0 0 4
0 115 6161517 2240.966339 0.503 0 0 4
0 115 6787187 2730.023437 0.53 0 0 4
0 115 7430836 3289.822855 0.543 0 0 4
0 116 4432736 5433.919544 0.613 0 0 6
0 116 4870694 6445.358712 0.624 0 0 6
0.0011 116 5302700 6352.789954 0.646 0 0 6
0 116 5703740 6917.880004 0.664 0 0 6
0 116 6047117 8721.418217 0.68 0 0 6
0 116 6379219 10074.38878 0.702 0 0 6
0 117 22737056 3541.696649 0.648 0 0 6
0 117 24441074 4673.595535 0.678 0 0 6
0 117 25914879 5202.233095 0.693 0 0 6
0 117 27273194 6240.731544 0.712 0 0 6
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0 117 30158966 11145.5119 0.748 0 0 6
0.0218 118 38363667 6183.616381 0.748 1 0 1
0.0169 118 38624370 8253.363678 0.785 1 0 1
0.0098 118 38258629 10651.21145 0.802 1 0 1
0.0118 118 38182222 13345.87962 0.824 1 0 1
0.0086 118 38125759 18310.44321 0.836 1 0 1
0.0095 118 38063164 23833.2097 0.86 1 0 1
0.0024 119 9952494 13163.22256 0.767 0 0 1
0.0011 119 10063945 14899.31943 0.785 0 0 1
0.0027 119 10289898 18883.91146 0.797 0 0 1
0.0027 119 10483861 21483.3193 0.814 0 0 1
0.0011 119 10558177 26631.56396 0.829 0 0 1
0.0005 119 10514844 26454.1026 0.845 0 0 1
0 120 495517 52872.96 0.796 0 0 2
0.0005 120 522304 69808.84 0.81 0 0 2
0 120 592267 85860.61144 0.826 0 0 2
0 120 758855 100426.5461 0.835 0 0 2
0.0011 120 1389342 115012.4503 0.844 0 0 2
0.0011 120 2109568 127610.2088 0.855 0 0 2
0.0212 121 22794284 4497.469274 0.695 1 0 1
0.0295 121 22619004 5776.405547 0.709 1 0 1
0.0218 121 22442971 5878.005537 0.746 1 0 1
0.0091 121 21451748 9131.091692 0.795 1 0 1
0.0096 121 20537875 16726.62759 0.795 1 0 1
0.0042 121 20058035 18931.55078 0.807 1 0 1
0.0822 122 148689000 6854.085483 0.701 1 0 2
0.0984 122 148160042 5515.985165 0.72 1 0 2
0.0945 122 146596557 6825.411391 0.746 1 0 2
0.0745 122 144067054 10231.42645 0.774 1 0 2
0.0836 122 142742350 20163.58765 0.798 1 0 2
0.0591 122 143201676 25784.56716 0.815 1 0 2
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0 125 107898 5988.121672 0.692 0 0 5
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0 125 109165 10081.1108 0.718 0 0 5
0 125 109328 10193.68288 0.721 0 0 5
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0 126 369469 1759.830668 0.45 0 0 4
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0 126 504477 1848.299337 0.529 0 0 4
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0 127 165570 2390.72858 0.628 0 0 4
0 127 171283 2913.473449 0.647 0 0 4
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0 127 178781 4447.004781 0.685 0 0 4
0 127 183526 5502.633675 0.697 0 0 4
0 127 189194 5748.263641 0.711 0 0 4
0 128 17378833 32584.08977 0.727 0 0 2
0.0016 128 19131578 32819.84943 0.743 0 0 2
0 128 20764312 34139.65786 0.765 0 0 2
0 128 23228890 38293.23543 0.791 0 0 2
0.0005 128 25940770 44841.92932 0.835 0 0 2
0 128 29086357 50573.39065 0.854 0 0 2
0 129 8029725 1544.487138 0.369 0 0 3
0 129 8974077 1635.13476 0.38 0 0 3
0 129 9884052 1899.700913 0.415 0 0 3
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0 129 12203957 2619.976311 0.476 0 0 3
0 129 13703513 2789.243388 0.499 0 0 3
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0 130 76417 11588.2181 0.718 0 0 3
0 130 81131 14626.46037 0.715 0 0 3
0 130 82475 14161.5154 0.741 0 0 3
0 130 86956 19274.84812 0.77 0 0 3
0 130 88303 23034.64782 0.793 0 0 3
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0 131 4564297 723.4657472 0.334 0 0 3
0 131 5439695 911.4476839 0.373 0 0 3
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0 131 6766103 1428.535497 0.413 0 0 3
0 132 3230698 25279.62536 0.782 0 0 2
0 132 3670704 34433.67294 0.819 0 0 2
0 132 4027887 40983.9929 0.845 0 0 2
0.0011 132 4166664 50918.82248 0.883 0 0 2
0.0011 132 4839396 63447.48174 0.92 0 0 2
0.0016 132 5312437 77690.78939 0.93 0 0 2
0.0024 133 1996498 8630.1 0.788 1 0 1
0.0033 133 1988628 14268.84991 0.825 1 0 1
0.0027 133 1988925 18047.49626 0.857 1 0 1
0.0048 133 1997012 22785.02008 0.878 1 0 1
0.0038 133 2021316 29624.19018 0.877 1 0 1
0.0042 133 2057159 28900.87803 0.894 1 0 1
0.0036 134 5305016 7135.439137 0.754 1 0 1
0.0055 134 5373361 9358.727962 0.764 1 0 1
0.0066 134 5388720 11354.83338 0.785 1 0 1
0.0075 134 5372280 15193.61838 0.822 1 0 1
0.0027 134 5379233 23691.64388 0.842 1 0 1
0.0053 134 5407579 26647.42196 0.853 1 0 1
0 135 21701476 1295.326927 0.375 0 0 3
0 135 24786190 1500.412635 0.402 0 0 3
0 135 27250535 1812.470805 0.429 0 0 3
0.0011 135 30186341 2237.188411 0.463 0 0 3
0 135 32955496 3187.310976 0.485 0 0 3
0 135 12763776 4172.538735 0.499 0 0 3
0.02 136 39157685 14802.99111 0.806 0 0 1
0.0109 136 39889852 16912.42931 0.825 0 0 1
0.0197 136 40567864 21530.47468 0.837 0 0 1
0.0204 136 42921895 26189.45552 0.856 0 0 1
0.0145 136 45954106 33463.6541 0.873 0 0 1
0.0185 136 46773055 31988.25387 0.889 0 0 1
0 137 17740637 2632.134361 0.655 0 0 2
0.0005 137 18372120 3414.982947 0.685 0 0 2
0 137 18781938 4392.675558 0.711 0 0 2
0 137 19372538 5299.684029 0.738 0 0 2
0 137 19945832 7419.359138 0.757 0 0 2
0 137 20425000 10163.85986 0.768 0 0 2
0 138 422763 7031.483199 0.6076 0 0 6
0 138 450036 6995.184768 0.642 0 0 6
0 138 472390 7745.052716 0.671 0 0 6
0 138 493630 9965.641005 0.691 0 0 6
0 138 19945832 12869.77541 0.711 0 0 6
0 138 537077 15410.8696 0.719 0 0 6
0 139 906034 3668.72473 0.513 0 0 3
0 139 981764 4229.213802 0.471 0 0 3
0 139 1061468 4628.369374 0.465 0 0 3
0 139 1095053 5544.022446 0.517 0 0 3
0 139 1158897 6901.297505 0.561 0 0 3
0 139 1248158 7667.509597 0.586 0 0 3
0 140 5502976 1583.905974 0.534 1 0 2
0 140 5849540 777.3802177 0.55 1 0 2
0 140 6216205 939.4592372 0.587 1 0 2
0.0016 140 6712841 1407.90493 0.618 1 0 2
0.0005 140 7309728 1911.427564 0.642 1 0 2
0.0016 140 7995062 2366.880977 0.647 1 0 2
0 141 27219619 966.0707967 0.372 0 0 3
0 141 30811854 1032.671846 0.395 0 0 3
0 141 34178042 1174.301211 0.434 0 0 3
0 141 38249984 1495.394346 0.477 0 0 3
0 141 43270144 1908.73738 0.506 0 0 3
0 141 49082997 2268.866465 0.533 0 0 3
0.0024 142 57837878 5214.465293 0.619 0 0 2
0.0027 142 60151472 7277.539903 0.649 0 0 2
0.0082 142 62958021 7283.51129 0.683 0 0 2
0.0054 142 65002231 9604.710478 0.714 0 0 2
0.0027 142 66545760 12255.21202 0.731 0 0 2
0.0063 142 67843979 14714.45022 0.748 0 0 2
0 143 3973327 803.3261555 0.417 0 0 3
0 143 4398238 903.823658 0.425 0 0 3
0 143 4970367 970.2085595 0.434 0 0 3
0.0005 143 5534598 990.5722639 0.44 0 0 3
0 143 6161796 1110.404433 0.466 0 0 3
0 143 6859482 1342.26891 0.5 0 0 3
0.0012 144 95496 2595.233932 0.671 0 0 4
0 144 96369 3156.509687 0.673 0 0 4
0 144 98082 3647.468753 0.693 0 0 4
0 144 100406 4227.460214 0.702 0 0 4
0 144 103005 4568.495013 0.717 0 0 4
0 144 104951 5223.00442 0.724 0 0 4
0.0012 145 1237487 8347.753046 0.69 0 0 6
0.0016 145 1258364 10210.31594 0.716 0 0 6
0.0005 145 1267984 14524.0912 0.746 0 0 6
0.0021 145 1290535 21572.51352 0.773 0 0 6
0.0032 145 1315372 30693.869 0.774 0 0 6
0.0005 145 1341588 31830.51804 0.785 0 0 6
0 146 6349089 834.1909624 0.2682 0 0 3
0 146 7241134 763.6752348 0.299 0 0 3
0 146 8342559 787.2251187 0.323 0 0 3
0 146 9710043 1366.996011 0.348 0 0 3
0 146 11133861 1668.577985 0.391 0 0 3
0 146 12705135 1999.034948 0.405 0 0 3
0.0133 147 10319123 11787.89671 0.768 1 0 1
0.0082 147 10315241 14580.27406 0.796 1 0 1
0.0071 147 10255063 16188.22719 0.825 1 0 1
0.008 147 10197101 20806.60891 0.854 1 0 1
0.0113 147 11133861 27844.79533 0.865 1 0 1
0.0074 147 10510785 29047.24644 0.885 1 0 1
0.0006 148 8603225 4111.240242 0.616 0 0 3
0 148 9256037 4802.371599 0.653 0 0 3
0 148 9699197 6003.312008 0.682 0 0 3
0.0016 148 10017601 7400.072075 0.707 0 0 3
0.0032 148 10407336 9609.707561 0.719 0 0 3
0.0016 148 10886668 10595.87525 0.732 0 0 3
0.0091 149 55748875 6721.171291 0.615 0 0 2
0.0055 149 59423208 8015.980827 0.655 0 0 2
0.0104 149 63240121 9582.276419 0.685 0 0 2
0.0075 149 67007855 10864.43918 0.71 0 0 2
0.0059 149 70440032 16048.92248 0.76 0 0 2
0.0069 149 74569867 20639.85717 0.787 0 0 2
0 150 3154855 7404.971779 0.717 0 0 6
0.0011 150 3248035 8958.114964 0.742 0 0 6
0 150 3321245 10204.90524 0.754 0 0 6
0 150 3324096 10421.03573 0.766 0 0 6
0 150 3350824 14705.55175 0.79 0 0 6
0 150 3396777 18817.74196 0.802 0 0 6
0 151 20799075 10970.09123 0.66 0 0 6
0 151 22650102 11122.52151 0.672 0 0 6
0.0011 151 24488340 11424.20449 0.7 0 0 6
0.0005 151 26327225 11891.62467 0.753 0 0 6
0.0016 151 28141701 17192.91545 0.774 0 0 6
0.0021 151 29893080 18004.46532 0.766 0 0 6
0.0151 152 57580402 17666.00471 0.844 0 0 1
0.0328 152 58166950 21745.61019 0.867 0 0 1
0.0311 152 58892514 26245.85621 0.886 0 0 1
0.0525 152 59987905 31732.9523 0.896 0 0 1
0.075 152 61806995 36257.51245 0.898 0 1 1
0.076 152 63700300 37705.59422 0.92 0 0 1
0 153 2086639 70093.36651 0.771 0 0 2
0 153 2571020 75543.49559 0.798 0 0 2
0.0016 153 3154925 81351.36414 0.818 0 0 2
0 153 4087931 84604.3423 0.838 0 0 2
0 153 6894278 68508.70682 0.846 0 0 2
0.0005 153 8900453 60513.61971 0.862 0 0 2
0.1336 154 256514000 25492.95165 0.879 0 1 5
0.1099 154 269394000 30068.23092 0.885 0 0 5
0.1158 154 282162411 36449.85512 0.895 0 0 5
0.118 154 292805298 41921.80976 0.91 0 0 5
0.1206 154 304093966 48401.42734 0.918 0 0 5
0.1319 154 313993272 51450.95911 0.922 0 0 5
0 155 71130448 1095.578929 0.54 1 0 2
0.0011 155 76372719 1562.407479 0.579 1 0 2
0 155 80285562 2030.797288 0.612 1 0 2
0.0011 155 83527678 2757.24085 0.64 1 0 2
0 155 86707801 3852.182615 0.67 1 0 2
0.0026 155 90451881 4909.945061 0.689 1 0 2
0.0012 156 8452275 1492.426961 0.416 0 0 3
0 156 9394304 1556.583483 0.432 0 0 3
0 156 10531221 1666.884356 0.469 0 0 3
0 156 11731746 2051.607433 0.517 0 0 3
0 156 13082517 2768.423449 0.569 0 0 3
0 156 14699937 3574.05852 0.586 0 0 3
0 157 10682868 1805.895689 0.466 0 0 3
0 157 11518262 2221.150183 0.44 0 0 3
0.0033 157 12222251 2269.972781 0.428 0 0 3
0.0048 157 12777511 1707.489061 0.439 0 0 3
0 157 13558469 1293.881491 0.505 0 0 3
0 157 14710826 2257.937635 0.532 0 0 3
0.0073 158 39360225 6124.628782 0.647 0 0 3
0.0038 158 42898520 6861.530512 0.63 0 0 3
0.006 158 45728315 7561.0928 0.613 0 0 3
0.0011 158 48247395 8940.66793 0.633 0 0 3
0.0075 158 50412129 11518.10832 0.664 0 0 3
0.0106 158 52998213 12440.26362 0.696 0 0 3
0.0339 159 43747962 10042.27077 0.789 0 0 2
0.029 159 45524681 14428.27252 0.817 0 0 2
0.0328 159 47008111 18083.0841 0.847 0 0 2
0.0359 159 48082519 22947.22316 0.874 0 0 2
0.0332 159 49054708 28655.98352 0.89 0 0 2
0.0222 159 50199853 32097.164 0.9 0 0 2
0.0097 160 8668067 20502.32228 0.863 0 0 1
0.0137 160 8840998 23600.16552 0.897 0 0 1
0.0087 160 8872109 29275.83802 0.896 0 0 1
0.0048 160 8993531 33540.01951 0.901 0 0 1
0.0075 160 9219637 41853.69618 0.908 0 0 1
0.0111 160 9519374 44724.97434 0.932 0 0 1
0.0109 161 6875364 28226.59936 0.854 0 0 1
0.0093 161 7071850 30535.45414 0.889 0 0 1
0.0044 161 7184250 35748.87697 0.901 0 0 1
0.0054 161 7389625 39184.01884 0.917 0 0 1
0.0054 161 7647675 52584.73495 0.935 0 0 1
0.0079 161 7996861 57849.58399 0.943 0 0 1

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time5 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319940&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]5 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319940&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319940&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R ServerBig Analytics Cloud Computing Center






Multiple Linear Regression - Estimated Regression Equation
Medailleaandeel[t] = -0.00177767 + 3.4482e-05groep[t] + 4.58338e-11`POPt-4`[t] + 1.14998e-13`BBPpct-4`[t] + 0.00384316HDI[t] + 0.00514291Communisme[t] + 0.054197Hosting[t] -0.000313296Werelddeel[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Medailleaandeel[t] =  -0.00177767 +  3.4482e-05groep[t] +  4.58338e-11`POPt-4`[t] +  1.14998e-13`BBPpct-4`[t] +  0.00384316HDI[t] +  0.00514291Communisme[t] +  0.054197Hosting[t] -0.000313296Werelddeel[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319940&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Medailleaandeel[t] =  -0.00177767 +  3.4482e-05groep[t] +  4.58338e-11`POPt-4`[t] +  1.14998e-13`BBPpct-4`[t] +  0.00384316HDI[t] +  0.00514291Communisme[t] +  0.054197Hosting[t] -0.000313296Werelddeel[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319940&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319940&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
Medailleaandeel[t] = -0.00177767 + 3.4482e-05groep[t] + 4.58338e-11`POPt-4`[t] + 1.14998e-13`BBPpct-4`[t] + 0.00384316HDI[t] + 0.00514291Communisme[t] + 0.054197Hosting[t] -0.000313296Werelddeel[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.001778 0.001685-1.0550e+00 0.2916 0.1458
groep+3.448e-05 9.537e-06+3.6150e+00 0.0003155 0.0001577
`POPt-4`+4.583e-11 3.246e-12+1.4120e+01 2.945e-41 1.472e-41
`BBPpct-4`+1.15e-13 1.668e-12+6.8960e-02 0.945 0.4725
HDI+0.003843 0.0009818+3.9150e+00 9.697e-05 4.849e-05
Communisme+0.005143 0.001159+4.4370e+00 1.017e-05 5.086e-06
Hosting+0.0542 0.005557+9.7530e+00 1.719e-21 8.594e-22
Werelddeel-0.0003133 0.0003155-9.9300e-01 0.321 0.1605

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -0.001778 &  0.001685 & -1.0550e+00 &  0.2916 &  0.1458 \tabularnewline
groep & +3.448e-05 &  9.537e-06 & +3.6150e+00 &  0.0003155 &  0.0001577 \tabularnewline
`POPt-4` & +4.583e-11 &  3.246e-12 & +1.4120e+01 &  2.945e-41 &  1.472e-41 \tabularnewline
`BBPpct-4` & +1.15e-13 &  1.668e-12 & +6.8960e-02 &  0.945 &  0.4725 \tabularnewline
HDI & +0.003843 &  0.0009818 & +3.9150e+00 &  9.697e-05 &  4.849e-05 \tabularnewline
Communisme & +0.005143 &  0.001159 & +4.4370e+00 &  1.017e-05 &  5.086e-06 \tabularnewline
Hosting & +0.0542 &  0.005557 & +9.7530e+00 &  1.719e-21 &  8.594e-22 \tabularnewline
Werelddeel & -0.0003133 &  0.0003155 & -9.9300e-01 &  0.321 &  0.1605 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319940&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]-0.001778[/C][C] 0.001685[/C][C]-1.0550e+00[/C][C] 0.2916[/C][C] 0.1458[/C][/ROW]
[ROW][C]groep[/C][C]+3.448e-05[/C][C] 9.537e-06[/C][C]+3.6150e+00[/C][C] 0.0003155[/C][C] 0.0001577[/C][/ROW]
[ROW][C]`POPt-4`[/C][C]+4.583e-11[/C][C] 3.246e-12[/C][C]+1.4120e+01[/C][C] 2.945e-41[/C][C] 1.472e-41[/C][/ROW]
[ROW][C]`BBPpct-4`[/C][C]+1.15e-13[/C][C] 1.668e-12[/C][C]+6.8960e-02[/C][C] 0.945[/C][C] 0.4725[/C][/ROW]
[ROW][C]HDI[/C][C]+0.003843[/C][C] 0.0009818[/C][C]+3.9150e+00[/C][C] 9.697e-05[/C][C] 4.849e-05[/C][/ROW]
[ROW][C]Communisme[/C][C]+0.005143[/C][C] 0.001159[/C][C]+4.4370e+00[/C][C] 1.017e-05[/C][C] 5.086e-06[/C][/ROW]
[ROW][C]Hosting[/C][C]+0.0542[/C][C] 0.005557[/C][C]+9.7530e+00[/C][C] 1.719e-21[/C][C] 8.594e-22[/C][/ROW]
[ROW][C]Werelddeel[/C][C]-0.0003133[/C][C] 0.0003155[/C][C]-9.9300e-01[/C][C] 0.321[/C][C] 0.1605[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319940&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319940&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.001778 0.001685-1.0550e+00 0.2916 0.1458
groep+3.448e-05 9.537e-06+3.6150e+00 0.0003155 0.0001577
`POPt-4`+4.583e-11 3.246e-12+1.4120e+01 2.945e-41 1.472e-41
`BBPpct-4`+1.15e-13 1.668e-12+6.8960e-02 0.945 0.4725
HDI+0.003843 0.0009818+3.9150e+00 9.697e-05 4.849e-05
Communisme+0.005143 0.001159+4.4370e+00 1.017e-05 5.086e-06
Hosting+0.0542 0.005557+9.7530e+00 1.719e-21 8.594e-22
Werelddeel-0.0003133 0.0003155-9.9300e-01 0.321 0.1605






Multiple Linear Regression - Regression Statistics
Multiple R 0.5462
R-squared 0.2983
Adjusted R-squared 0.2932
F-TEST (value) 58.17
F-TEST (DF numerator)7
F-TEST (DF denominator)958
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 0.01339
Sum Squared Residuals 0.1717

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5462 \tabularnewline
R-squared &  0.2983 \tabularnewline
Adjusted R-squared &  0.2932 \tabularnewline
F-TEST (value) &  58.17 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 958 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.01339 \tabularnewline
Sum Squared Residuals &  0.1717 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319940&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5462[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.2983[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.2932[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 58.17[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]958[/C][/ROW]
[ROW][C]p-value[/C][C] 0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.01339[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 0.1717[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319940&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319940&T=3

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R 0.5462
R-squared 0.2983
Adjusted R-squared 0.2932
F-TEST (value) 58.17
F-TEST (DF numerator)7
F-TEST (DF denominator)958
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 0.01339
Sum Squared Residuals 0.1717






Menu of Residual Diagnostics
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319940&T=4

[TABLE]
[ROW][C]Menu of Residual Diagnostics[/C][/ROW]
[ROW][C]Description[/C][C]Link[/C][/ROW]
[ROW][C]Histogram[/C][C]Compute[/C][/ROW]
[ROW][C]Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]QQ Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Kernel Density Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness/Kurtosis Test[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness-Kurtosis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Harrell-Davis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]Blocked Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C](Partial) Autocorrelation Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Spectral Analysis[/C][C]Compute[/C][/ROW]
[ROW][C]Tukey lambda PPCC Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Box-Cox Normality Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Summary Statistics[/C][C]Compute[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319940&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319940&T=4

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Menu of Residual Diagnostics
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute






Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 36.768, df1 = 2, df2 = 956, p-value = 4.133e-16
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 39.283, df1 = 14, df2 = 944, p-value < 2.2e-16
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.0012584, df1 = 2, df2 = 956, p-value = 0.9987


\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline

> reset_test_fitted
	RESET test
data:  mylm
RESET = 36.768, df1 = 2, df2 = 956, p-value = 4.133e-16

 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline

> reset_test_regressors
	RESET test
data:  mylm
RESET = 39.283, df1 = 14, df2 = 944, p-value < 2.2e-16

 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline

> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.0012584, df1 = 2, df2 = 956, p-value = 0.9987

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319940&T=5

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]
> reset_test_fitted
	RESET test
data:  mylm
RESET = 36.768, df1 = 2, df2 = 956, p-value = 4.133e-16

[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]
> reset_test_regressors
	RESET test
data:  mylm
RESET = 39.283, df1 = 14, df2 = 944, p-value < 2.2e-16

[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.0012584, df1 = 2, df2 = 956, p-value = 0.9987

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319940&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319940&T=5

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 36.768, df1 = 2, df2 = 956, p-value = 4.133e-16
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 39.283, df1 = 14, df2 = 944, p-value < 2.2e-16
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.0012584, df1 = 2, df2 = 956, p-value = 0.9987






Variance Inflation Factors (Multicollinearity)
> vif
     groep   `POPt-4` `BBPpct-4`        HDI Communisme    Hosting Werelddeel 
  1.059110   1.037791   1.017639   1.018491   1.257145   1.027425   1.252484 


\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline

> vif
     groep   `POPt-4` `BBPpct-4`        HDI Communisme    Hosting Werelddeel 
  1.059110   1.037791   1.017639   1.018491   1.257145   1.027425   1.252484 

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319940&T=6

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
     groep   `POPt-4` `BBPpct-4`        HDI Communisme    Hosting Werelddeel 
  1.059110   1.037791   1.017639   1.018491   1.257145   1.027425   1.252484 

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319940&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319940&T=6

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Variance Inflation Factors (Multicollinearity)
> vif
     groep   `POPt-4` `BBPpct-4`        HDI Communisme    Hosting Werelddeel 
  1.059110   1.037791   1.017639   1.018491   1.257145   1.027425   1.252484


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ; par6 = 12 ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 &lt;- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning &lt;- ''
par6 &lt;- as.numeric(par6)
if(is.na(par6)) {
par6 &lt;- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 &lt;- as.numeric(par1)
if(is.na(par1)) {
par1 &lt;- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 &lt;- 0
par4 &lt;- as.numeric(par4)
if (!is.numeric(par4)) par4 &lt;- 0
if (par5=='') par5 &lt;- 0
par5 &lt;- as.numeric(par5)
if (!is.numeric(par5)) par5 &lt;- 0
x &lt;- na.omit(t(y))
k &lt;- length(x[1,])
n &lt;- length(x[,1])
x1 &lt;- cbind(x[,par1], x[,1:k!=par1])
mycolnames &lt;- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) &lt;- mycolnames #colnames(x)[par1]
x &lt;- x1
if (par3 == 'First Differences'){
(n &lt;- n -1)
x2 &lt;- 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] &lt;- x[i+1,j] - x[i,j]
}
}
x &lt;- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n &lt;- n - par6)
x2 &lt;- 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] &lt;- x[i+par6,j] - x[i,j]
}
}
x &lt;- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n &lt;- n -1)
x2 &lt;- 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] &lt;- x[i+1,j] - x[i,j]
}
}
x &lt;- x2
(n &lt;- n - par6)
x2 &lt;- 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] &lt;- x[i+par6,j] - x[i,j]
}
}
x &lt;- x2
}
if(par4 &gt; 0) {
x2 &lt;- 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] &lt;- x[i+par4-j,par1]
}
}
x &lt;- cbind(x[(par4+1):n,], x2)
n &lt;- n - par4
}
if(par5 &gt; 0) {
x2 &lt;- 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] &lt;- x[i+par5*par6-j*par6,par1]
}
}
x &lt;- cbind(x[(par5*par6+1):n,], x2)
n &lt;- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 &lt;- 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] &lt;- 1
}
x &lt;- cbind(x, x2)
}
if (par2 == 'Include Monthly Dummies'){
x2 &lt;- 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] &lt;- 1
}
x &lt;- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 &lt;- 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] &lt;- 1
}
x &lt;- cbind(x, x2)
}
(k &lt;- length(x[n,]))
if (par3 == 'Linear Trend'){
x &lt;- cbind(x, c(1:n))
colnames(x)[k+1] &lt;- 't'
}
print(x)
(k &lt;- length(x[n,]))
head(x)
df &lt;- as.data.frame(x)
(mylm &lt;- lm(df))
(mysum &lt;- summary(mylm))
if (n &gt; n25) {
kp3 &lt;- k + 3
nmkm3 &lt;- n - k - 3
gqarr &lt;- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests &lt;- 0
numsignificant1 &lt;- 0
numsignificant5 &lt;- 0
numsignificant10 &lt;- 0
for (mypoint in kp3:nmkm3) {
j &lt;- 0
numgqtests &lt;- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j &lt;- j + 1
gqarr[mypoint-kp3+1,j] &lt;- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] &lt; 0.01) numsignificant1 &lt;- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] &lt; 0.05) numsignificant5 &lt;- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] &lt; 0.10) numsignificant10 &lt;- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid &lt;- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit&lt;-seq(min(sresid),max(sresid),length=40)
yfit&lt;-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$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 &lt;- as.ts(mysum$resid))
bitmap(file='test5.png')
dum &lt;- cbind(lag(myerror,k=1),myerror)
dum
dum1 &lt;- dum[2:length(myerror),]
dum1
z &lt;- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$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$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar &lt;- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n &gt; n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a&lt;-table.start()
a&lt;-table.row.start(a)
a&lt;-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a&lt;-table.row.end(a)
myeq &lt;- colnames(x)[1]
myeq &lt;- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] &gt; 0) myeq &lt;- paste(myeq, '+', '')
myeq &lt;- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq &lt;- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq &lt;- paste(myeq, '[t]', sep='')
}
}
myeq &lt;- paste(myeq, ' + e[t]')
a&lt;-table.row.start(a)
a&lt;-table.element(a, myeq)
a&lt;-table.row.end(a)
a&lt;-table.row.start(a)
a&lt;-table.element(a, mywarning)
a&lt;-table.row.end(a)
a&lt;-table.end(a)
table.save(a,file='mytable1.tab')
a&lt;-table.start()
a&lt;-table.row.start(a)
a&lt;-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE)
a&lt;-table.row.end(a)
a&lt;-table.row.start(a)
a&lt;-table.element(a,'Variable',header=TRUE)
a&lt;-table.element(a,'Parameter',header=TRUE)
a&lt;-table.element(a,'S.D.',header=TRUE)
a&lt;-table.element(a,'T-STAT&lt;br /&gt;H0: parameter = 0',header=TRUE)
a&lt;-table.element(a,'2-tail p-value',header=TRUE)
a&lt;-table.element(a,'1-tail p-value',header=TRUE)
a&lt;-table.row.end(a)
for (i in 1:k){
a&lt;-table.row.start(a)
a&lt;-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a&lt;-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a&lt;-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a&lt;-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a&lt;-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a&lt;-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a&lt;-table.row.end(a)
}
a&lt;-table.end(a)
table.save(a,file='mytable2.tab')
a&lt;-table.start()
a&lt;-table.row.start(a)
a&lt;-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a&lt;-table.row.end(a)
a&lt;-table.row.start(a)
a&lt;-table.element(a, 'Multiple R',1,TRUE)
a&lt;-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a&lt;-table.row.end(a)
a&lt;-table.row.start(a)
a&lt;-table.element(a, 'R-squared',1,TRUE)
a&lt;-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a&lt;-table.row.end(a)
a&lt;-table.row.start(a)
a&lt;-table.element(a, 'Adjusted R-squared',1,TRUE)
a&lt;-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a&lt;-table.row.end(a)
a&lt;-table.row.start(a)
a&lt;-table.element(a, 'F-TEST (value)',1,TRUE)
a&lt;-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a&lt;-table.row.end(a)
a&lt;-table.row.start(a)
a&lt;-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a&lt;-table.element(a, signif(mysum$fstatistic[2],6))
a&lt;-table.row.end(a)
a&lt;-table.row.start(a)
a&lt;-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a&lt;-table.element(a, signif(mysum$fstatistic[3],6))
a&lt;-table.row.end(a)
a&lt;-table.row.start(a)
a&lt;-table.element(a, 'p-value',1,TRUE)
a&lt;-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a&lt;-table.row.end(a)
a&lt;-table.row.start(a)
a&lt;-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a&lt;-table.row.end(a)
a&lt;-table.row.start(a)
a&lt;-table.element(a, 'Residual Standard Deviation',1,TRUE)
a&lt;-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a&lt;-table.row.end(a)
a&lt;-table.row.start(a)
a&lt;-table.element(a, 'Sum Squared Residuals',1,TRUE)
a&lt;-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a&lt;-table.row.end(a)
a&lt;-table.end(a)
table.save(a,file='mytable3.tab')
myr &lt;- as.numeric(mysum$resid)
myr
a &lt;-table.start()
a &lt;- table.row.start(a)
a &lt;- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a &lt;- table.row.end(a)
a &lt;- table.row.start(a)
a &lt;- table.element(a,'Description',1,TRUE)
a &lt;- table.element(a,'Link',1,TRUE)
a &lt;- table.row.end(a)
a &lt;- table.row.start(a)
a &lt;-table.element(a,'Histogram',1,header=TRUE)
a &lt;- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&amp;data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a &lt;- table.row.end(a)
a &lt;- table.row.start(a)
a &lt;-table.element(a,'Central Tendency',1,header=TRUE)
a &lt;- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&amp;data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a &lt;- table.row.end(a)
a &lt;- table.row.start(a)
a &lt;-table.element(a,'QQ Plot',1,header=TRUE)
a &lt;- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&amp;data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a &lt;- table.row.end(a)
a &lt;- table.row.start(a)
a &lt;-table.element(a,'Kernel Density Plot',1,header=TRUE)
a &lt;- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&amp;data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a &lt;- table.row.end(a)
a &lt;- table.row.start(a)
a &lt;-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a &lt;- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&amp;data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a &lt;- table.row.end(a)
a &lt;- table.row.start(a)
a &lt;-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a &lt;- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&amp;data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a &lt;- table.row.end(a)
a &lt;- table.row.start(a)
a &lt;-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a &lt;- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&amp;data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a &lt;- table.row.end(a)
a &lt;- table.row.start(a)
a &lt;-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a &lt;- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&amp;data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a &lt;- table.row.end(a)
a &lt;- table.row.start(a)
a &lt;-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a &lt;- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&amp;data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a &lt;- table.row.end(a)
a &lt;- table.row.start(a)
a &lt;-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a &lt;- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&amp;data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a &lt;- table.row.end(a)
a &lt;- table.row.start(a)
a &lt;-table.element(a,'Spectral Analysis',1,header=TRUE)
a &lt;- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&amp;data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a &lt;- table.row.end(a)
a &lt;- table.row.start(a)
a &lt;-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a &lt;- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&amp;data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a &lt;- table.row.end(a)
a &lt;- table.row.start(a)
a &lt;-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a &lt;- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&amp;data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a &lt;- table.row.end(a)
a &lt;- table.row.start(a)
a &lt;- table.element(a,'Summary Statistics',1,header=TRUE)
a &lt;- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&amp;data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a &lt;- table.row.end(a)
a&lt;-table.end(a)
table.save(a,file='mytable7.tab')
if(n &lt; 200) {
a&lt;-table.start()
a&lt;-table.row.start(a)
a&lt;-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a&lt;-table.row.end(a)
a&lt;-table.row.start(a)
a&lt;-table.element(a, 'Time or Index', 1, TRUE)
a&lt;-table.element(a, 'Actuals', 1, TRUE)
a&lt;-table.element(a, 'Interpolation&lt;br /&gt;Forecast', 1, TRUE)
a&lt;-table.element(a, 'Residuals&lt;br /&gt;Prediction Error', 1, TRUE)
a&lt;-table.row.end(a)
for (i in 1:n) {
a&lt;-table.row.start(a)
a&lt;-table.element(a,i, 1, TRUE)
a&lt;-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a&lt;-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a&lt;-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a&lt;-table.row.end(a)
}
a&lt;-table.end(a)
table.save(a,file='mytable4.tab')
if (n &gt; n25) {
a&lt;-table.start()
a&lt;-table.row.start(a)
a&lt;-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a&lt;-table.row.end(a)
a&lt;-table.row.start(a)
a&lt;-table.element(a,'p-values',header=TRUE)
a&lt;-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a&lt;-table.row.end(a)
a&lt;-table.row.start(a)
a&lt;-table.element(a,'breakpoint index',header=TRUE)
a&lt;-table.element(a,'greater',header=TRUE)
a&lt;-table.element(a,'2-sided',header=TRUE)
a&lt;-table.element(a,'less',header=TRUE)
a&lt;-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a&lt;-table.row.start(a)
a&lt;-table.element(a,mypoint,header=TRUE)
a&lt;-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a&lt;-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a&lt;-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a&lt;-table.row.end(a)
}
a&lt;-table.end(a)
table.save(a,file='mytable5.tab')
a&lt;-table.start()
a&lt;-table.row.start(a)
a&lt;-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a&lt;-table.row.end(a)
a&lt;-table.row.start(a)
a&lt;-table.element(a,'Description',header=TRUE)
a&lt;-table.element(a,'# significant tests',header=TRUE)
a&lt;-table.element(a,'% significant tests',header=TRUE)
a&lt;-table.element(a,'OK/NOK',header=TRUE)
a&lt;-table.row.end(a)
a&lt;-table.row.start(a)
a&lt;-table.element(a,'1% type I error level',header=TRUE)
a&lt;-table.element(a,signif(numsignificant1,6))
a&lt;-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests &lt; 0.01) dum &lt;- 'OK' else dum &lt;- 'NOK'
a&lt;-table.element(a,dum)
a&lt;-table.row.end(a)
a&lt;-table.row.start(a)
a&lt;-table.element(a,'5% type I error level',header=TRUE)
a&lt;-table.element(a,signif(numsignificant5,6))
a&lt;-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests &lt; 0.05) dum &lt;- 'OK' else dum &lt;- 'NOK'
a&lt;-table.element(a,dum)
a&lt;-table.row.end(a)
a&lt;-table.row.start(a)
a&lt;-table.element(a,'10% type I error level',header=TRUE)
a&lt;-table.element(a,signif(numsignificant10,6))
a&lt;-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests &lt; 0.1) dum &lt;- 'OK' else dum &lt;- 'NOK'
a&lt;-table.element(a,dum)
a&lt;-table.row.end(a)
a&lt;-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a&lt;-table.start()
a&lt;-table.row.start(a)
a&lt;-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a&lt;-table.row.end(a)
a&lt;-table.row.start(a)
reset_test_fitted &lt;- resettest(mylm,power=2:3,type='fitted')
a&lt;-table.element(a,paste('&lt;pre&gt;',RC.texteval('reset_test_fitted'),'&lt;/pre&gt;',sep=''))
a&lt;-table.row.end(a)
a&lt;-table.row.start(a)
a&lt;-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a&lt;-table.row.end(a)
a&lt;-table.row.start(a)
reset_test_regressors &lt;- resettest(mylm,power=2:3,type='regressor')
a&lt;-table.element(a,paste('&lt;pre&gt;',RC.texteval('reset_test_regressors'),'&lt;/pre&gt;',sep=''))
a&lt;-table.row.end(a)
a&lt;-table.row.start(a)
a&lt;-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a&lt;-table.row.end(a)
a&lt;-table.row.start(a)
reset_test_principal_components &lt;- resettest(mylm,power=2:3,type='princomp')
a&lt;-table.element(a,paste('&lt;pre&gt;',RC.texteval('reset_test_principal_components'),'&lt;/pre&gt;',sep=''))
a&lt;-table.row.end(a)
a&lt;-table.end(a)
table.save(a,file='mytable8.tab')
a&lt;-table.start()
a&lt;-table.row.start(a)
a&lt;-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a&lt;-table.row.end(a)
a&lt;-table.row.start(a)
vif &lt;- vif(mylm)
a&lt;-table.element(a,paste('&lt;pre&gt;',RC.texteval('vif'),'&lt;/pre&gt;',sep=''))
a&lt;-table.row.end(a)
a&lt;-table.end(a)
table.save(a,file='mytable9.tab')