## Free Statistics

of Irreproducible Research!

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, 10 Nov 2012 16:11:43 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/10/t1352583173rpvexxa9q7xt55x.htm/, Retrieved Sat, 10 Dec 2022 05:34:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=187427, Retrieved Sat, 10 Dec 2022 05:34:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact52
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [HPC Retail Sales] [2008-03-08 13:40:54] [1c0f2c85e8a48e42648374b3bcceca26]
-  MPD    [Multiple Regression] [] [2012-11-10 21:11:43] [7338cd26db379c04f0557b08db763c32] [Current]
- RMP       [Exponential Smoothing] [] [2012-11-10 22:13:06] [391561951b5d7f721cfaa4f5575ab127]
- R P         [Exponential Smoothing] [] [2012-11-10 22:22:04] [391561951b5d7f721cfaa4f5575ab127]
- R P         [Exponential Smoothing] [] [2012-11-10 22:23:12] [391561951b5d7f721cfaa4f5575ab127]
-               [Exponential Smoothing] [] [2012-12-18 08:54:52] [391561951b5d7f721cfaa4f5575ab127]
- R         [Multiple Regression] [] [2012-12-20 16:40:22] [74be16979710d4c4e7c6647856088456]
Feedback Forum

Post a new message
Dataseries X:
617
614
647
580
614
636
388
356
639
753
611
639
630
586
695
552
619
681
421
307
754
690
644
643
608
651
691
627
634
731
475
337
803
722
590
724
627
696
825
677
656
785
412
352
839
729
696
641
695
638
762
635
721
854
418
367
824
687
601
676
740
691
683
594
729
731
386
331
706
715
657
653
642
643
718
654
632
731
392
344
792
852
649
629
685
617
715
715
629
916
531
357
917
828
708
858
775
785
1006
789
734
906
532
387
991
841
892
782
811
792
978
773
796
946
594
438
1023
868
791
760
779
852
1001
734
996
869
599
426
1138
1091
830
909

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 9 seconds R Server 'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187427&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187427&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187427&T=0

As an alternative you can also use a 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 Output view raw output of R engine Computing time 9 seconds R Server 'George Udny Yule' @ yule.wessa.net

 Multiple Linear Regression - Estimated Regression Equation Aantal_Faillissementen[t] = + 576.704545454545 -5.91824494949483M1[t] -11.9008838383838M2[t] + 91.2073863636363M3[t] -37.229797979798M4[t] -0.121527777777779M5[t] + 91.1685606060606M6[t] -241.541351010101M7[t] -347.705808080808M8[t] + 143.402462121212M9[t] + 82.3289141414141M10[t] -20.2900883838384M11[t] + 1.98263888888889t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Aantal_Faillissementen[t] =  +  576.704545454545 -5.91824494949483M1[t] -11.9008838383838M2[t] +  91.2073863636363M3[t] -37.229797979798M4[t] -0.121527777777779M5[t] +  91.1685606060606M6[t] -241.541351010101M7[t] -347.705808080808M8[t] +  143.402462121212M9[t] +  82.3289141414141M10[t] -20.2900883838384M11[t] +  1.98263888888889t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187427&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Aantal_Faillissementen[t] =  +  576.704545454545 -5.91824494949483M1[t] -11.9008838383838M2[t] +  91.2073863636363M3[t] -37.229797979798M4[t] -0.121527777777779M5[t] +  91.1685606060606M6[t] -241.541351010101M7[t] -347.705808080808M8[t] +  143.402462121212M9[t] +  82.3289141414141M10[t] -20.2900883838384M11[t] +  1.98263888888889t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187427&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187427&T=1

As an alternative you can also use a QR Code:

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

 Multiple Linear Regression - Estimated Regression Equation Aantal_Faillissementen[t] = + 576.704545454545 -5.91824494949483M1[t] -11.9008838383838M2[t] + 91.2073863636363M3[t] -37.229797979798M4[t] -0.121527777777779M5[t] + 91.1685606060606M6[t] -241.541351010101M7[t] -347.705808080808M8[t] + 143.402462121212M9[t] + 82.3289141414141M10[t] -20.2900883838384M11[t] + 1.98263888888889t + e[t]

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 576.704545454545 22.395765 25.7506 0 0 M1 -5.91824494949483 27.827114 -0.2127 0.831941 0.415971 M2 -11.9008838383838 27.818688 -0.4278 0.669569 0.334784 M3 91.2073863636363 27.811062 3.2795 0.001364 0.000682 M4 -37.229797979798 27.804237 -1.339 0.183124 0.091562 M5 -0.121527777777779 27.798213 -0.0044 0.996519 0.49826 M6 91.1685606060606 27.792992 3.2803 0.001361 0.00068 M7 -241.541351010101 27.788573 -8.6921 0 0 M8 -347.705808080808 27.784957 -12.5142 0 0 M9 143.402462121212 27.782145 5.1617 1e-06 0 M10 82.3289141414141 27.780135 2.9636 0.003673 0.001837 M11 -20.2900883838384 27.77893 -0.7304 0.466573 0.233287 t 1.98263888888889 0.149425 13.2685 0 0

\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) & 576.704545454545 & 22.395765 & 25.7506 & 0 & 0 \tabularnewline
M1 & -5.91824494949483 & 27.827114 & -0.2127 & 0.831941 & 0.415971 \tabularnewline
M2 & -11.9008838383838 & 27.818688 & -0.4278 & 0.669569 & 0.334784 \tabularnewline
M3 & 91.2073863636363 & 27.811062 & 3.2795 & 0.001364 & 0.000682 \tabularnewline
M4 & -37.229797979798 & 27.804237 & -1.339 & 0.183124 & 0.091562 \tabularnewline
M5 & -0.121527777777779 & 27.798213 & -0.0044 & 0.996519 & 0.49826 \tabularnewline
M6 & 91.1685606060606 & 27.792992 & 3.2803 & 0.001361 & 0.00068 \tabularnewline
M7 & -241.541351010101 & 27.788573 & -8.6921 & 0 & 0 \tabularnewline
M8 & -347.705808080808 & 27.784957 & -12.5142 & 0 & 0 \tabularnewline
M9 & 143.402462121212 & 27.782145 & 5.1617 & 1e-06 & 0 \tabularnewline
M10 & 82.3289141414141 & 27.780135 & 2.9636 & 0.003673 & 0.001837 \tabularnewline
M11 & -20.2900883838384 & 27.77893 & -0.7304 & 0.466573 & 0.233287 \tabularnewline
t & 1.98263888888889 & 0.149425 & 13.2685 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187427&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]576.704545454545[/C][C]22.395765[/C][C]25.7506[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-5.91824494949483[/C][C]27.827114[/C][C]-0.2127[/C][C]0.831941[/C][C]0.415971[/C][/ROW]
[ROW][C]M2[/C][C]-11.9008838383838[/C][C]27.818688[/C][C]-0.4278[/C][C]0.669569[/C][C]0.334784[/C][/ROW]
[ROW][C]M3[/C][C]91.2073863636363[/C][C]27.811062[/C][C]3.2795[/C][C]0.001364[/C][C]0.000682[/C][/ROW]
[ROW][C]M4[/C][C]-37.229797979798[/C][C]27.804237[/C][C]-1.339[/C][C]0.183124[/C][C]0.091562[/C][/ROW]
[ROW][C]M5[/C][C]-0.121527777777779[/C][C]27.798213[/C][C]-0.0044[/C][C]0.996519[/C][C]0.49826[/C][/ROW]
[ROW][C]M6[/C][C]91.1685606060606[/C][C]27.792992[/C][C]3.2803[/C][C]0.001361[/C][C]0.00068[/C][/ROW]
[ROW][C]M7[/C][C]-241.541351010101[/C][C]27.788573[/C][C]-8.6921[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M8[/C][C]-347.705808080808[/C][C]27.784957[/C][C]-12.5142[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]143.402462121212[/C][C]27.782145[/C][C]5.1617[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]M10[/C][C]82.3289141414141[/C][C]27.780135[/C][C]2.9636[/C][C]0.003673[/C][C]0.001837[/C][/ROW]
[ROW][C]M11[/C][C]-20.2900883838384[/C][C]27.77893[/C][C]-0.7304[/C][C]0.466573[/C][C]0.233287[/C][/ROW]
[ROW][C]t[/C][C]1.98263888888889[/C][C]0.149425[/C][C]13.2685[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187427&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187427&T=2

As an alternative you can also use a QR Code:

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

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 576.704545454545 22.395765 25.7506 0 0 M1 -5.91824494949483 27.827114 -0.2127 0.831941 0.415971 M2 -11.9008838383838 27.818688 -0.4278 0.669569 0.334784 M3 91.2073863636363 27.811062 3.2795 0.001364 0.000682 M4 -37.229797979798 27.804237 -1.339 0.183124 0.091562 M5 -0.121527777777779 27.798213 -0.0044 0.996519 0.49826 M6 91.1685606060606 27.792992 3.2803 0.001361 0.00068 M7 -241.541351010101 27.788573 -8.6921 0 0 M8 -347.705808080808 27.784957 -12.5142 0 0 M9 143.402462121212 27.782145 5.1617 1e-06 0 M10 82.3289141414141 27.780135 2.9636 0.003673 0.001837 M11 -20.2900883838384 27.77893 -0.7304 0.466573 0.233287 t 1.98263888888889 0.149425 13.2685 0 0

 Multiple Linear Regression - Regression Statistics Multiple R 0.928510769134262 R-squared 0.862132248398299 Adjusted R-squared 0.848229617984682 F-TEST (value) 62.0121676797135 F-TEST (DF numerator) 12 F-TEST (DF denominator) 119 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 65.1464225614041 Sum Squared Residuals 505042.708333333

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.928510769134262 \tabularnewline
R-squared & 0.862132248398299 \tabularnewline
F-TEST (value) & 62.0121676797135 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 119 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 65.1464225614041 \tabularnewline
Sum Squared Residuals & 505042.708333333 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187427&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.928510769134262[/C][/ROW]
[ROW][C]R-squared[/C][C]0.862132248398299[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]62.0121676797135[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]119[/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]65.1464225614041[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]505042.708333333[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187427&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187427&T=3

As an alternative you can also use a QR Code:

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

 Multiple Linear Regression - Regression Statistics Multiple R 0.928510769134262 R-squared 0.862132248398299 Adjusted R-squared 0.848229617984682 F-TEST (value) 62.0121676797135 F-TEST (DF numerator) 12 F-TEST (DF denominator) 119 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 65.1464225614041 Sum Squared Residuals 505042.708333333

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 617 572.768939393938 44.2310606060617 2 614 568.768939393939 45.2310606060606 3 647 673.859848484848 -26.8598484848486 4 580 547.405303030303 32.594696969697 5 614 586.496212121212 27.5037878787878 6 636 679.768939393939 -43.7689393939394 7 388 349.041666666667 38.9583333333333 8 356 244.859848484849 111.140151515151 9 639 737.950757575758 -98.9507575757577 10 753 678.859848484849 74.1401515151514 11 611 578.223484848485 32.7765151515151 12 639 600.496212121212 38.5037878787879 13 630 596.560606060606 33.4393939393939 14 586 592.560606060606 -6.56060606060607 15 695 697.651515151515 -2.65151515151515 16 552 571.19696969697 -19.1969696969697 17 619 610.287878787879 8.7121212121212 18 681 703.560606060606 -22.5606060606061 19 421 372.833333333333 48.1666666666667 20 307 268.651515151515 38.3484848484849 21 754 761.742424242424 -7.74242424242425 22 690 702.651515151515 -12.6515151515152 23 644 602.015151515152 41.9848484848485 24 643 624.287878787879 18.7121212121212 25 608 620.352272727273 -12.3522727272729 26 651 616.352272727273 34.6477272727273 27 691 721.443181818182 -30.4431818181818 28 627 594.988636363636 32.0113636363636 29 634 634.079545454545 -0.0795454545454694 30 731 727.352272727273 3.64772727272724 31 475 396.625 78.375 32 337 292.443181818182 44.5568181818182 33 803 785.534090909091 17.4659090909091 34 722 726.443181818182 -4.44318181818185 35 590 625.806818181818 -35.8068181818182 36 724 648.079545454545 75.9204545454545 37 627 644.143939393939 -17.1439393939395 38 696 640.143939393939 55.8560606060606 39 825 745.234848484848 79.7651515151515 40 677 618.780303030303 58.219696969697 41 656 657.871212121212 -1.87121212121214 42 785 751.143939393939 33.8560606060606 43 412 420.416666666667 -8.41666666666667 44 352 316.234848484848 35.7651515151515 45 839 809.325757575758 29.6742424242424 46 729 750.234848484848 -21.2348484848485 47 696 649.598484848485 46.4015151515151 48 641 671.871212121212 -30.8712121212121 49 695 667.935606060606 27.0643939393938 50 638 663.935606060606 -25.9356060606061 51 762 769.026515151515 -7.02651515151516 52 635 642.57196969697 -7.57196969696972 53 721 681.662878787879 39.3371212121212 54 854 774.935606060606 79.0643939393939 55 418 444.208333333333 -26.2083333333333 56 367 340.026515151515 26.9734848484849 57 824 833.117424242424 -9.11742424242422 58 687 774.026515151515 -87.0265151515152 59 601 673.390151515152 -72.3901515151515 60 676 695.662878787879 -19.6628787878788 61 740 691.727272727273 48.2727272727271 62 691 687.727272727273 3.27272727272728 63 683 792.818181818182 -109.818181818182 64 594 666.363636363636 -72.3636363636364 65 729 705.454545454545 23.5454545454545 66 731 798.727272727273 -67.7272727272727 67 386 468 -82 68 331 363.818181818182 -32.8181818181818 69 706 856.909090909091 -150.909090909091 70 715 797.818181818182 -82.8181818181819 71 657 697.181818181818 -40.1818181818182 72 653 719.454545454545 -66.4545454545455 73 642 715.518939393939 -73.5189393939395 74 643 711.518939393939 -68.5189393939394 75 718 816.609848484848 -98.6098484848485 76 654 690.155303030303 -36.155303030303 77 632 729.246212121212 -97.2462121212121 78 731 822.518939393939 -91.5189393939394 79 392 491.791666666667 -99.7916666666667 80 344 387.609848484848 -43.6098484848485 81 792 880.700757575758 -88.7007575757576 82 852 821.609848484848 30.3901515151515 83 649 720.973484848485 -71.9734848484849 84 629 743.246212121212 -114.246212121212 85 685 739.310606060606 -54.3106060606062 86 617 735.310606060606 -118.310606060606 87 715 840.401515151515 -125.401515151515 88 715 713.94696969697 1.05303030303029 89 629 753.037878787879 -124.037878787879 90 916 846.310606060606 69.6893939393939 91 531 515.583333333333 15.4166666666667 92 357 411.401515151515 -54.4015151515151 93 917 904.492424242424 12.5075757575758 94 828 845.401515151515 -17.4015151515151 95 708 744.765151515152 -36.7651515151515 96 858 767.037878787879 90.9621212121212 97 775 763.102272727273 11.8977272727272 98 785 759.102272727273 25.8977272727273 99 1006 864.193181818182 141.806818181818 100 789 737.738636363636 51.2613636363637 101 734 776.829545454545 -42.8295454545454 102 906 870.102272727273 35.8977272727273 103 532 539.375 -7.37499999999999 104 387 435.193181818182 -48.1931818181818 105 991 928.284090909091 62.7159090909091 106 841 869.193181818182 -28.1931818181818 107 892 768.556818181818 123.443181818182 108 782 790.829545454545 -8.82954545454546 109 811 786.893939393939 24.1060606060605 110 792 782.893939393939 9.10606060606062 111 978 887.984848484848 90.0151515151515 112 773 761.530303030303 11.469696969697 113 796 800.621212121212 -4.62121212121212 114 946 893.893939393939 52.1060606060606 115 594 563.166666666667 30.8333333333333 116 438 458.984848484848 -20.9848484848485 117 1023 952.075757575758 70.9242424242424 118 868 892.984848484848 -24.9848484848485 119 791 792.348484848485 -1.34848484848482 120 760 814.621212121212 -54.6212121212121 121 779 810.685606060606 -31.6856060606062 122 852 806.685606060606 45.314393939394 123 1001 911.776515151515 89.2234848484849 124 734 785.32196969697 -51.3219696969696 125 996 824.412878787879 171.587121212121 126 869 917.685606060606 -48.685606060606 127 599 586.958333333333 12.0416666666666 128 426 482.776515151515 -56.7765151515151 129 1138 975.867424242424 162.132575757576 130 1091 916.776515151515 174.223484848485 131 830 816.140151515151 13.8598484848486 132 909 838.412878787879 70.5871212121213

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 617 & 572.768939393938 & 44.2310606060617 \tabularnewline
2 & 614 & 568.768939393939 & 45.2310606060606 \tabularnewline
3 & 647 & 673.859848484848 & -26.8598484848486 \tabularnewline
4 & 580 & 547.405303030303 & 32.594696969697 \tabularnewline
5 & 614 & 586.496212121212 & 27.5037878787878 \tabularnewline
6 & 636 & 679.768939393939 & -43.7689393939394 \tabularnewline
7 & 388 & 349.041666666667 & 38.9583333333333 \tabularnewline
8 & 356 & 244.859848484849 & 111.140151515151 \tabularnewline
9 & 639 & 737.950757575758 & -98.9507575757577 \tabularnewline
10 & 753 & 678.859848484849 & 74.1401515151514 \tabularnewline
11 & 611 & 578.223484848485 & 32.7765151515151 \tabularnewline
12 & 639 & 600.496212121212 & 38.5037878787879 \tabularnewline
13 & 630 & 596.560606060606 & 33.4393939393939 \tabularnewline
14 & 586 & 592.560606060606 & -6.56060606060607 \tabularnewline
15 & 695 & 697.651515151515 & -2.65151515151515 \tabularnewline
16 & 552 & 571.19696969697 & -19.1969696969697 \tabularnewline
17 & 619 & 610.287878787879 & 8.7121212121212 \tabularnewline
18 & 681 & 703.560606060606 & -22.5606060606061 \tabularnewline
19 & 421 & 372.833333333333 & 48.1666666666667 \tabularnewline
20 & 307 & 268.651515151515 & 38.3484848484849 \tabularnewline
21 & 754 & 761.742424242424 & -7.74242424242425 \tabularnewline
22 & 690 & 702.651515151515 & -12.6515151515152 \tabularnewline
23 & 644 & 602.015151515152 & 41.9848484848485 \tabularnewline
24 & 643 & 624.287878787879 & 18.7121212121212 \tabularnewline
25 & 608 & 620.352272727273 & -12.3522727272729 \tabularnewline
26 & 651 & 616.352272727273 & 34.6477272727273 \tabularnewline
27 & 691 & 721.443181818182 & -30.4431818181818 \tabularnewline
28 & 627 & 594.988636363636 & 32.0113636363636 \tabularnewline
29 & 634 & 634.079545454545 & -0.0795454545454694 \tabularnewline
30 & 731 & 727.352272727273 & 3.64772727272724 \tabularnewline
31 & 475 & 396.625 & 78.375 \tabularnewline
32 & 337 & 292.443181818182 & 44.5568181818182 \tabularnewline
33 & 803 & 785.534090909091 & 17.4659090909091 \tabularnewline
34 & 722 & 726.443181818182 & -4.44318181818185 \tabularnewline
35 & 590 & 625.806818181818 & -35.8068181818182 \tabularnewline
36 & 724 & 648.079545454545 & 75.9204545454545 \tabularnewline
37 & 627 & 644.143939393939 & -17.1439393939395 \tabularnewline
38 & 696 & 640.143939393939 & 55.8560606060606 \tabularnewline
39 & 825 & 745.234848484848 & 79.7651515151515 \tabularnewline
40 & 677 & 618.780303030303 & 58.219696969697 \tabularnewline
41 & 656 & 657.871212121212 & -1.87121212121214 \tabularnewline
42 & 785 & 751.143939393939 & 33.8560606060606 \tabularnewline
43 & 412 & 420.416666666667 & -8.41666666666667 \tabularnewline
44 & 352 & 316.234848484848 & 35.7651515151515 \tabularnewline
45 & 839 & 809.325757575758 & 29.6742424242424 \tabularnewline
46 & 729 & 750.234848484848 & -21.2348484848485 \tabularnewline
47 & 696 & 649.598484848485 & 46.4015151515151 \tabularnewline
48 & 641 & 671.871212121212 & -30.8712121212121 \tabularnewline
49 & 695 & 667.935606060606 & 27.0643939393938 \tabularnewline
50 & 638 & 663.935606060606 & -25.9356060606061 \tabularnewline
51 & 762 & 769.026515151515 & -7.02651515151516 \tabularnewline
52 & 635 & 642.57196969697 & -7.57196969696972 \tabularnewline
53 & 721 & 681.662878787879 & 39.3371212121212 \tabularnewline
54 & 854 & 774.935606060606 & 79.0643939393939 \tabularnewline
55 & 418 & 444.208333333333 & -26.2083333333333 \tabularnewline
56 & 367 & 340.026515151515 & 26.9734848484849 \tabularnewline
57 & 824 & 833.117424242424 & -9.11742424242422 \tabularnewline
58 & 687 & 774.026515151515 & -87.0265151515152 \tabularnewline
59 & 601 & 673.390151515152 & -72.3901515151515 \tabularnewline
60 & 676 & 695.662878787879 & -19.6628787878788 \tabularnewline
61 & 740 & 691.727272727273 & 48.2727272727271 \tabularnewline
62 & 691 & 687.727272727273 & 3.27272727272728 \tabularnewline
63 & 683 & 792.818181818182 & -109.818181818182 \tabularnewline
64 & 594 & 666.363636363636 & -72.3636363636364 \tabularnewline
65 & 729 & 705.454545454545 & 23.5454545454545 \tabularnewline
66 & 731 & 798.727272727273 & -67.7272727272727 \tabularnewline
67 & 386 & 468 & -82 \tabularnewline
68 & 331 & 363.818181818182 & -32.8181818181818 \tabularnewline
69 & 706 & 856.909090909091 & -150.909090909091 \tabularnewline
70 & 715 & 797.818181818182 & -82.8181818181819 \tabularnewline
71 & 657 & 697.181818181818 & -40.1818181818182 \tabularnewline
72 & 653 & 719.454545454545 & -66.4545454545455 \tabularnewline
73 & 642 & 715.518939393939 & -73.5189393939395 \tabularnewline
74 & 643 & 711.518939393939 & -68.5189393939394 \tabularnewline
75 & 718 & 816.609848484848 & -98.6098484848485 \tabularnewline
76 & 654 & 690.155303030303 & -36.155303030303 \tabularnewline
77 & 632 & 729.246212121212 & -97.2462121212121 \tabularnewline
78 & 731 & 822.518939393939 & -91.5189393939394 \tabularnewline
79 & 392 & 491.791666666667 & -99.7916666666667 \tabularnewline
80 & 344 & 387.609848484848 & -43.6098484848485 \tabularnewline
81 & 792 & 880.700757575758 & -88.7007575757576 \tabularnewline
82 & 852 & 821.609848484848 & 30.3901515151515 \tabularnewline
83 & 649 & 720.973484848485 & -71.9734848484849 \tabularnewline
84 & 629 & 743.246212121212 & -114.246212121212 \tabularnewline
85 & 685 & 739.310606060606 & -54.3106060606062 \tabularnewline
86 & 617 & 735.310606060606 & -118.310606060606 \tabularnewline
87 & 715 & 840.401515151515 & -125.401515151515 \tabularnewline
88 & 715 & 713.94696969697 & 1.05303030303029 \tabularnewline
89 & 629 & 753.037878787879 & -124.037878787879 \tabularnewline
90 & 916 & 846.310606060606 & 69.6893939393939 \tabularnewline
91 & 531 & 515.583333333333 & 15.4166666666667 \tabularnewline
92 & 357 & 411.401515151515 & -54.4015151515151 \tabularnewline
93 & 917 & 904.492424242424 & 12.5075757575758 \tabularnewline
94 & 828 & 845.401515151515 & -17.4015151515151 \tabularnewline
95 & 708 & 744.765151515152 & -36.7651515151515 \tabularnewline
96 & 858 & 767.037878787879 & 90.9621212121212 \tabularnewline
97 & 775 & 763.102272727273 & 11.8977272727272 \tabularnewline
98 & 785 & 759.102272727273 & 25.8977272727273 \tabularnewline
99 & 1006 & 864.193181818182 & 141.806818181818 \tabularnewline
100 & 789 & 737.738636363636 & 51.2613636363637 \tabularnewline
101 & 734 & 776.829545454545 & -42.8295454545454 \tabularnewline
102 & 906 & 870.102272727273 & 35.8977272727273 \tabularnewline
103 & 532 & 539.375 & -7.37499999999999 \tabularnewline
104 & 387 & 435.193181818182 & -48.1931818181818 \tabularnewline
105 & 991 & 928.284090909091 & 62.7159090909091 \tabularnewline
106 & 841 & 869.193181818182 & -28.1931818181818 \tabularnewline
107 & 892 & 768.556818181818 & 123.443181818182 \tabularnewline
108 & 782 & 790.829545454545 & -8.82954545454546 \tabularnewline
109 & 811 & 786.893939393939 & 24.1060606060605 \tabularnewline
110 & 792 & 782.893939393939 & 9.10606060606062 \tabularnewline
111 & 978 & 887.984848484848 & 90.0151515151515 \tabularnewline
112 & 773 & 761.530303030303 & 11.469696969697 \tabularnewline
113 & 796 & 800.621212121212 & -4.62121212121212 \tabularnewline
114 & 946 & 893.893939393939 & 52.1060606060606 \tabularnewline
115 & 594 & 563.166666666667 & 30.8333333333333 \tabularnewline
116 & 438 & 458.984848484848 & -20.9848484848485 \tabularnewline
117 & 1023 & 952.075757575758 & 70.9242424242424 \tabularnewline
118 & 868 & 892.984848484848 & -24.9848484848485 \tabularnewline
119 & 791 & 792.348484848485 & -1.34848484848482 \tabularnewline
120 & 760 & 814.621212121212 & -54.6212121212121 \tabularnewline
121 & 779 & 810.685606060606 & -31.6856060606062 \tabularnewline
122 & 852 & 806.685606060606 & 45.314393939394 \tabularnewline
123 & 1001 & 911.776515151515 & 89.2234848484849 \tabularnewline
124 & 734 & 785.32196969697 & -51.3219696969696 \tabularnewline
125 & 996 & 824.412878787879 & 171.587121212121 \tabularnewline
126 & 869 & 917.685606060606 & -48.685606060606 \tabularnewline
127 & 599 & 586.958333333333 & 12.0416666666666 \tabularnewline
128 & 426 & 482.776515151515 & -56.7765151515151 \tabularnewline
129 & 1138 & 975.867424242424 & 162.132575757576 \tabularnewline
130 & 1091 & 916.776515151515 & 174.223484848485 \tabularnewline
131 & 830 & 816.140151515151 & 13.8598484848486 \tabularnewline
132 & 909 & 838.412878787879 & 70.5871212121213 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187427&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]617[/C][C]572.768939393938[/C][C]44.2310606060617[/C][/ROW]
[ROW][C]2[/C][C]614[/C][C]568.768939393939[/C][C]45.2310606060606[/C][/ROW]
[ROW][C]3[/C][C]647[/C][C]673.859848484848[/C][C]-26.8598484848486[/C][/ROW]
[ROW][C]4[/C][C]580[/C][C]547.405303030303[/C][C]32.594696969697[/C][/ROW]
[ROW][C]5[/C][C]614[/C][C]586.496212121212[/C][C]27.5037878787878[/C][/ROW]
[ROW][C]6[/C][C]636[/C][C]679.768939393939[/C][C]-43.7689393939394[/C][/ROW]
[ROW][C]7[/C][C]388[/C][C]349.041666666667[/C][C]38.9583333333333[/C][/ROW]
[ROW][C]8[/C][C]356[/C][C]244.859848484849[/C][C]111.140151515151[/C][/ROW]
[ROW][C]9[/C][C]639[/C][C]737.950757575758[/C][C]-98.9507575757577[/C][/ROW]
[ROW][C]10[/C][C]753[/C][C]678.859848484849[/C][C]74.1401515151514[/C][/ROW]
[ROW][C]11[/C][C]611[/C][C]578.223484848485[/C][C]32.7765151515151[/C][/ROW]
[ROW][C]12[/C][C]639[/C][C]600.496212121212[/C][C]38.5037878787879[/C][/ROW]
[ROW][C]13[/C][C]630[/C][C]596.560606060606[/C][C]33.4393939393939[/C][/ROW]
[ROW][C]14[/C][C]586[/C][C]592.560606060606[/C][C]-6.56060606060607[/C][/ROW]
[ROW][C]15[/C][C]695[/C][C]697.651515151515[/C][C]-2.65151515151515[/C][/ROW]
[ROW][C]16[/C][C]552[/C][C]571.19696969697[/C][C]-19.1969696969697[/C][/ROW]
[ROW][C]17[/C][C]619[/C][C]610.287878787879[/C][C]8.7121212121212[/C][/ROW]
[ROW][C]18[/C][C]681[/C][C]703.560606060606[/C][C]-22.5606060606061[/C][/ROW]
[ROW][C]19[/C][C]421[/C][C]372.833333333333[/C][C]48.1666666666667[/C][/ROW]
[ROW][C]20[/C][C]307[/C][C]268.651515151515[/C][C]38.3484848484849[/C][/ROW]
[ROW][C]21[/C][C]754[/C][C]761.742424242424[/C][C]-7.74242424242425[/C][/ROW]
[ROW][C]22[/C][C]690[/C][C]702.651515151515[/C][C]-12.6515151515152[/C][/ROW]
[ROW][C]23[/C][C]644[/C][C]602.015151515152[/C][C]41.9848484848485[/C][/ROW]
[ROW][C]24[/C][C]643[/C][C]624.287878787879[/C][C]18.7121212121212[/C][/ROW]
[ROW][C]25[/C][C]608[/C][C]620.352272727273[/C][C]-12.3522727272729[/C][/ROW]
[ROW][C]26[/C][C]651[/C][C]616.352272727273[/C][C]34.6477272727273[/C][/ROW]
[ROW][C]27[/C][C]691[/C][C]721.443181818182[/C][C]-30.4431818181818[/C][/ROW]
[ROW][C]28[/C][C]627[/C][C]594.988636363636[/C][C]32.0113636363636[/C][/ROW]
[ROW][C]29[/C][C]634[/C][C]634.079545454545[/C][C]-0.0795454545454694[/C][/ROW]
[ROW][C]30[/C][C]731[/C][C]727.352272727273[/C][C]3.64772727272724[/C][/ROW]
[ROW][C]31[/C][C]475[/C][C]396.625[/C][C]78.375[/C][/ROW]
[ROW][C]32[/C][C]337[/C][C]292.443181818182[/C][C]44.5568181818182[/C][/ROW]
[ROW][C]33[/C][C]803[/C][C]785.534090909091[/C][C]17.4659090909091[/C][/ROW]
[ROW][C]34[/C][C]722[/C][C]726.443181818182[/C][C]-4.44318181818185[/C][/ROW]
[ROW][C]35[/C][C]590[/C][C]625.806818181818[/C][C]-35.8068181818182[/C][/ROW]
[ROW][C]36[/C][C]724[/C][C]648.079545454545[/C][C]75.9204545454545[/C][/ROW]
[ROW][C]37[/C][C]627[/C][C]644.143939393939[/C][C]-17.1439393939395[/C][/ROW]
[ROW][C]38[/C][C]696[/C][C]640.143939393939[/C][C]55.8560606060606[/C][/ROW]
[ROW][C]39[/C][C]825[/C][C]745.234848484848[/C][C]79.7651515151515[/C][/ROW]
[ROW][C]40[/C][C]677[/C][C]618.780303030303[/C][C]58.219696969697[/C][/ROW]
[ROW][C]41[/C][C]656[/C][C]657.871212121212[/C][C]-1.87121212121214[/C][/ROW]
[ROW][C]42[/C][C]785[/C][C]751.143939393939[/C][C]33.8560606060606[/C][/ROW]
[ROW][C]43[/C][C]412[/C][C]420.416666666667[/C][C]-8.41666666666667[/C][/ROW]
[ROW][C]44[/C][C]352[/C][C]316.234848484848[/C][C]35.7651515151515[/C][/ROW]
[ROW][C]45[/C][C]839[/C][C]809.325757575758[/C][C]29.6742424242424[/C][/ROW]
[ROW][C]46[/C][C]729[/C][C]750.234848484848[/C][C]-21.2348484848485[/C][/ROW]
[ROW][C]47[/C][C]696[/C][C]649.598484848485[/C][C]46.4015151515151[/C][/ROW]
[ROW][C]48[/C][C]641[/C][C]671.871212121212[/C][C]-30.8712121212121[/C][/ROW]
[ROW][C]49[/C][C]695[/C][C]667.935606060606[/C][C]27.0643939393938[/C][/ROW]
[ROW][C]50[/C][C]638[/C][C]663.935606060606[/C][C]-25.9356060606061[/C][/ROW]
[ROW][C]51[/C][C]762[/C][C]769.026515151515[/C][C]-7.02651515151516[/C][/ROW]
[ROW][C]52[/C][C]635[/C][C]642.57196969697[/C][C]-7.57196969696972[/C][/ROW]
[ROW][C]53[/C][C]721[/C][C]681.662878787879[/C][C]39.3371212121212[/C][/ROW]
[ROW][C]54[/C][C]854[/C][C]774.935606060606[/C][C]79.0643939393939[/C][/ROW]
[ROW][C]55[/C][C]418[/C][C]444.208333333333[/C][C]-26.2083333333333[/C][/ROW]
[ROW][C]56[/C][C]367[/C][C]340.026515151515[/C][C]26.9734848484849[/C][/ROW]
[ROW][C]57[/C][C]824[/C][C]833.117424242424[/C][C]-9.11742424242422[/C][/ROW]
[ROW][C]58[/C][C]687[/C][C]774.026515151515[/C][C]-87.0265151515152[/C][/ROW]
[ROW][C]59[/C][C]601[/C][C]673.390151515152[/C][C]-72.3901515151515[/C][/ROW]
[ROW][C]60[/C][C]676[/C][C]695.662878787879[/C][C]-19.6628787878788[/C][/ROW]
[ROW][C]61[/C][C]740[/C][C]691.727272727273[/C][C]48.2727272727271[/C][/ROW]
[ROW][C]62[/C][C]691[/C][C]687.727272727273[/C][C]3.27272727272728[/C][/ROW]
[ROW][C]63[/C][C]683[/C][C]792.818181818182[/C][C]-109.818181818182[/C][/ROW]
[ROW][C]64[/C][C]594[/C][C]666.363636363636[/C][C]-72.3636363636364[/C][/ROW]
[ROW][C]65[/C][C]729[/C][C]705.454545454545[/C][C]23.5454545454545[/C][/ROW]
[ROW][C]66[/C][C]731[/C][C]798.727272727273[/C][C]-67.7272727272727[/C][/ROW]
[ROW][C]67[/C][C]386[/C][C]468[/C][C]-82[/C][/ROW]
[ROW][C]68[/C][C]331[/C][C]363.818181818182[/C][C]-32.8181818181818[/C][/ROW]
[ROW][C]69[/C][C]706[/C][C]856.909090909091[/C][C]-150.909090909091[/C][/ROW]
[ROW][C]70[/C][C]715[/C][C]797.818181818182[/C][C]-82.8181818181819[/C][/ROW]
[ROW][C]71[/C][C]657[/C][C]697.181818181818[/C][C]-40.1818181818182[/C][/ROW]
[ROW][C]72[/C][C]653[/C][C]719.454545454545[/C][C]-66.4545454545455[/C][/ROW]
[ROW][C]73[/C][C]642[/C][C]715.518939393939[/C][C]-73.5189393939395[/C][/ROW]
[ROW][C]74[/C][C]643[/C][C]711.518939393939[/C][C]-68.5189393939394[/C][/ROW]
[ROW][C]75[/C][C]718[/C][C]816.609848484848[/C][C]-98.6098484848485[/C][/ROW]
[ROW][C]76[/C][C]654[/C][C]690.155303030303[/C][C]-36.155303030303[/C][/ROW]
[ROW][C]77[/C][C]632[/C][C]729.246212121212[/C][C]-97.2462121212121[/C][/ROW]
[ROW][C]78[/C][C]731[/C][C]822.518939393939[/C][C]-91.5189393939394[/C][/ROW]
[ROW][C]79[/C][C]392[/C][C]491.791666666667[/C][C]-99.7916666666667[/C][/ROW]
[ROW][C]80[/C][C]344[/C][C]387.609848484848[/C][C]-43.6098484848485[/C][/ROW]
[ROW][C]81[/C][C]792[/C][C]880.700757575758[/C][C]-88.7007575757576[/C][/ROW]
[ROW][C]82[/C][C]852[/C][C]821.609848484848[/C][C]30.3901515151515[/C][/ROW]
[ROW][C]83[/C][C]649[/C][C]720.973484848485[/C][C]-71.9734848484849[/C][/ROW]
[ROW][C]84[/C][C]629[/C][C]743.246212121212[/C][C]-114.246212121212[/C][/ROW]
[ROW][C]85[/C][C]685[/C][C]739.310606060606[/C][C]-54.3106060606062[/C][/ROW]
[ROW][C]86[/C][C]617[/C][C]735.310606060606[/C][C]-118.310606060606[/C][/ROW]
[ROW][C]87[/C][C]715[/C][C]840.401515151515[/C][C]-125.401515151515[/C][/ROW]
[ROW][C]88[/C][C]715[/C][C]713.94696969697[/C][C]1.05303030303029[/C][/ROW]
[ROW][C]89[/C][C]629[/C][C]753.037878787879[/C][C]-124.037878787879[/C][/ROW]
[ROW][C]90[/C][C]916[/C][C]846.310606060606[/C][C]69.6893939393939[/C][/ROW]
[ROW][C]91[/C][C]531[/C][C]515.583333333333[/C][C]15.4166666666667[/C][/ROW]
[ROW][C]92[/C][C]357[/C][C]411.401515151515[/C][C]-54.4015151515151[/C][/ROW]
[ROW][C]93[/C][C]917[/C][C]904.492424242424[/C][C]12.5075757575758[/C][/ROW]
[ROW][C]94[/C][C]828[/C][C]845.401515151515[/C][C]-17.4015151515151[/C][/ROW]
[ROW][C]95[/C][C]708[/C][C]744.765151515152[/C][C]-36.7651515151515[/C][/ROW]
[ROW][C]96[/C][C]858[/C][C]767.037878787879[/C][C]90.9621212121212[/C][/ROW]
[ROW][C]97[/C][C]775[/C][C]763.102272727273[/C][C]11.8977272727272[/C][/ROW]
[ROW][C]98[/C][C]785[/C][C]759.102272727273[/C][C]25.8977272727273[/C][/ROW]
[ROW][C]99[/C][C]1006[/C][C]864.193181818182[/C][C]141.806818181818[/C][/ROW]
[ROW][C]100[/C][C]789[/C][C]737.738636363636[/C][C]51.2613636363637[/C][/ROW]
[ROW][C]101[/C][C]734[/C][C]776.829545454545[/C][C]-42.8295454545454[/C][/ROW]
[ROW][C]102[/C][C]906[/C][C]870.102272727273[/C][C]35.8977272727273[/C][/ROW]
[ROW][C]103[/C][C]532[/C][C]539.375[/C][C]-7.37499999999999[/C][/ROW]
[ROW][C]104[/C][C]387[/C][C]435.193181818182[/C][C]-48.1931818181818[/C][/ROW]
[ROW][C]105[/C][C]991[/C][C]928.284090909091[/C][C]62.7159090909091[/C][/ROW]
[ROW][C]106[/C][C]841[/C][C]869.193181818182[/C][C]-28.1931818181818[/C][/ROW]
[ROW][C]107[/C][C]892[/C][C]768.556818181818[/C][C]123.443181818182[/C][/ROW]
[ROW][C]108[/C][C]782[/C][C]790.829545454545[/C][C]-8.82954545454546[/C][/ROW]
[ROW][C]109[/C][C]811[/C][C]786.893939393939[/C][C]24.1060606060605[/C][/ROW]
[ROW][C]110[/C][C]792[/C][C]782.893939393939[/C][C]9.10606060606062[/C][/ROW]
[ROW][C]111[/C][C]978[/C][C]887.984848484848[/C][C]90.0151515151515[/C][/ROW]
[ROW][C]112[/C][C]773[/C][C]761.530303030303[/C][C]11.469696969697[/C][/ROW]
[ROW][C]113[/C][C]796[/C][C]800.621212121212[/C][C]-4.62121212121212[/C][/ROW]
[ROW][C]114[/C][C]946[/C][C]893.893939393939[/C][C]52.1060606060606[/C][/ROW]
[ROW][C]115[/C][C]594[/C][C]563.166666666667[/C][C]30.8333333333333[/C][/ROW]
[ROW][C]116[/C][C]438[/C][C]458.984848484848[/C][C]-20.9848484848485[/C][/ROW]
[ROW][C]117[/C][C]1023[/C][C]952.075757575758[/C][C]70.9242424242424[/C][/ROW]
[ROW][C]118[/C][C]868[/C][C]892.984848484848[/C][C]-24.9848484848485[/C][/ROW]
[ROW][C]119[/C][C]791[/C][C]792.348484848485[/C][C]-1.34848484848482[/C][/ROW]
[ROW][C]120[/C][C]760[/C][C]814.621212121212[/C][C]-54.6212121212121[/C][/ROW]
[ROW][C]121[/C][C]779[/C][C]810.685606060606[/C][C]-31.6856060606062[/C][/ROW]
[ROW][C]122[/C][C]852[/C][C]806.685606060606[/C][C]45.314393939394[/C][/ROW]
[ROW][C]123[/C][C]1001[/C][C]911.776515151515[/C][C]89.2234848484849[/C][/ROW]
[ROW][C]124[/C][C]734[/C][C]785.32196969697[/C][C]-51.3219696969696[/C][/ROW]
[ROW][C]125[/C][C]996[/C][C]824.412878787879[/C][C]171.587121212121[/C][/ROW]
[ROW][C]126[/C][C]869[/C][C]917.685606060606[/C][C]-48.685606060606[/C][/ROW]
[ROW][C]127[/C][C]599[/C][C]586.958333333333[/C][C]12.0416666666666[/C][/ROW]
[ROW][C]128[/C][C]426[/C][C]482.776515151515[/C][C]-56.7765151515151[/C][/ROW]
[ROW][C]129[/C][C]1138[/C][C]975.867424242424[/C][C]162.132575757576[/C][/ROW]
[ROW][C]130[/C][C]1091[/C][C]916.776515151515[/C][C]174.223484848485[/C][/ROW]
[ROW][C]131[/C][C]830[/C][C]816.140151515151[/C][C]13.8598484848486[/C][/ROW]
[ROW][C]132[/C][C]909[/C][C]838.412878787879[/C][C]70.5871212121213[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187427&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187427&T=4

As an alternative you can also use a QR Code:

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

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 617 572.768939393938 44.2310606060617 2 614 568.768939393939 45.2310606060606 3 647 673.859848484848 -26.8598484848486 4 580 547.405303030303 32.594696969697 5 614 586.496212121212 27.5037878787878 6 636 679.768939393939 -43.7689393939394 7 388 349.041666666667 38.9583333333333 8 356 244.859848484849 111.140151515151 9 639 737.950757575758 -98.9507575757577 10 753 678.859848484849 74.1401515151514 11 611 578.223484848485 32.7765151515151 12 639 600.496212121212 38.5037878787879 13 630 596.560606060606 33.4393939393939 14 586 592.560606060606 -6.56060606060607 15 695 697.651515151515 -2.65151515151515 16 552 571.19696969697 -19.1969696969697 17 619 610.287878787879 8.7121212121212 18 681 703.560606060606 -22.5606060606061 19 421 372.833333333333 48.1666666666667 20 307 268.651515151515 38.3484848484849 21 754 761.742424242424 -7.74242424242425 22 690 702.651515151515 -12.6515151515152 23 644 602.015151515152 41.9848484848485 24 643 624.287878787879 18.7121212121212 25 608 620.352272727273 -12.3522727272729 26 651 616.352272727273 34.6477272727273 27 691 721.443181818182 -30.4431818181818 28 627 594.988636363636 32.0113636363636 29 634 634.079545454545 -0.0795454545454694 30 731 727.352272727273 3.64772727272724 31 475 396.625 78.375 32 337 292.443181818182 44.5568181818182 33 803 785.534090909091 17.4659090909091 34 722 726.443181818182 -4.44318181818185 35 590 625.806818181818 -35.8068181818182 36 724 648.079545454545 75.9204545454545 37 627 644.143939393939 -17.1439393939395 38 696 640.143939393939 55.8560606060606 39 825 745.234848484848 79.7651515151515 40 677 618.780303030303 58.219696969697 41 656 657.871212121212 -1.87121212121214 42 785 751.143939393939 33.8560606060606 43 412 420.416666666667 -8.41666666666667 44 352 316.234848484848 35.7651515151515 45 839 809.325757575758 29.6742424242424 46 729 750.234848484848 -21.2348484848485 47 696 649.598484848485 46.4015151515151 48 641 671.871212121212 -30.8712121212121 49 695 667.935606060606 27.0643939393938 50 638 663.935606060606 -25.9356060606061 51 762 769.026515151515 -7.02651515151516 52 635 642.57196969697 -7.57196969696972 53 721 681.662878787879 39.3371212121212 54 854 774.935606060606 79.0643939393939 55 418 444.208333333333 -26.2083333333333 56 367 340.026515151515 26.9734848484849 57 824 833.117424242424 -9.11742424242422 58 687 774.026515151515 -87.0265151515152 59 601 673.390151515152 -72.3901515151515 60 676 695.662878787879 -19.6628787878788 61 740 691.727272727273 48.2727272727271 62 691 687.727272727273 3.27272727272728 63 683 792.818181818182 -109.818181818182 64 594 666.363636363636 -72.3636363636364 65 729 705.454545454545 23.5454545454545 66 731 798.727272727273 -67.7272727272727 67 386 468 -82 68 331 363.818181818182 -32.8181818181818 69 706 856.909090909091 -150.909090909091 70 715 797.818181818182 -82.8181818181819 71 657 697.181818181818 -40.1818181818182 72 653 719.454545454545 -66.4545454545455 73 642 715.518939393939 -73.5189393939395 74 643 711.518939393939 -68.5189393939394 75 718 816.609848484848 -98.6098484848485 76 654 690.155303030303 -36.155303030303 77 632 729.246212121212 -97.2462121212121 78 731 822.518939393939 -91.5189393939394 79 392 491.791666666667 -99.7916666666667 80 344 387.609848484848 -43.6098484848485 81 792 880.700757575758 -88.7007575757576 82 852 821.609848484848 30.3901515151515 83 649 720.973484848485 -71.9734848484849 84 629 743.246212121212 -114.246212121212 85 685 739.310606060606 -54.3106060606062 86 617 735.310606060606 -118.310606060606 87 715 840.401515151515 -125.401515151515 88 715 713.94696969697 1.05303030303029 89 629 753.037878787879 -124.037878787879 90 916 846.310606060606 69.6893939393939 91 531 515.583333333333 15.4166666666667 92 357 411.401515151515 -54.4015151515151 93 917 904.492424242424 12.5075757575758 94 828 845.401515151515 -17.4015151515151 95 708 744.765151515152 -36.7651515151515 96 858 767.037878787879 90.9621212121212 97 775 763.102272727273 11.8977272727272 98 785 759.102272727273 25.8977272727273 99 1006 864.193181818182 141.806818181818 100 789 737.738636363636 51.2613636363637 101 734 776.829545454545 -42.8295454545454 102 906 870.102272727273 35.8977272727273 103 532 539.375 -7.37499999999999 104 387 435.193181818182 -48.1931818181818 105 991 928.284090909091 62.7159090909091 106 841 869.193181818182 -28.1931818181818 107 892 768.556818181818 123.443181818182 108 782 790.829545454545 -8.82954545454546 109 811 786.893939393939 24.1060606060605 110 792 782.893939393939 9.10606060606062 111 978 887.984848484848 90.0151515151515 112 773 761.530303030303 11.469696969697 113 796 800.621212121212 -4.62121212121212 114 946 893.893939393939 52.1060606060606 115 594 563.166666666667 30.8333333333333 116 438 458.984848484848 -20.9848484848485 117 1023 952.075757575758 70.9242424242424 118 868 892.984848484848 -24.9848484848485 119 791 792.348484848485 -1.34848484848482 120 760 814.621212121212 -54.6212121212121 121 779 810.685606060606 -31.6856060606062 122 852 806.685606060606 45.314393939394 123 1001 911.776515151515 89.2234848484849 124 734 785.32196969697 -51.3219696969696 125 996 824.412878787879 171.587121212121 126 869 917.685606060606 -48.685606060606 127 599 586.958333333333 12.0416666666666 128 426 482.776515151515 -56.7765151515151 129 1138 975.867424242424 162.132575757576 130 1091 916.776515151515 174.223484848485 131 830 816.140151515151 13.8598484848486 132 909 838.412878787879 70.5871212121213

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.071711075203567 0.143422150407134 0.928288924796433 17 0.0223780376273161 0.0447560752546323 0.977621962372684 18 0.0132598548749277 0.0265197097498555 0.986740145125072 19 0.00517248046014443 0.0103449609202889 0.994827519539856 20 0.00619593477216386 0.0123918695443277 0.993804065227836 21 0.0277010269880361 0.0554020539760722 0.972298973011964 22 0.0308915127045479 0.0617830254090957 0.969108487295452 23 0.0171700906056775 0.034340181211355 0.982829909394322 24 0.00848477998423394 0.0169695599684679 0.991515220015766 25 0.00473606765023691 0.00947213530047382 0.995263932349763 26 0.0031265563417052 0.0062531126834104 0.996873443658295 27 0.00142611619925217 0.00285223239850434 0.998573883800748 28 0.000998688272563235 0.00199737654512647 0.999001311727437 29 0.000442658361110094 0.000885316722220188 0.99955734163889 30 0.000351198745026709 0.000702397490053419 0.999648801254973 31 0.000291731624287799 0.000583463248575599 0.999708268375712 32 0.000170013787647376 0.000340027575294753 0.999829986212353 33 0.000297138273120955 0.00059427654624191 0.999702861726879 34 0.000177509067545023 0.000355018135090046 0.999822490932455 35 0.000221228889782546 0.000442457779565093 0.999778771110217 36 0.0002443082875649 0.0004886165751298 0.999755691712435 37 0.000146538307793399 0.000293076615586798 0.999853461692207 38 0.000121798461765963 0.000243596923531926 0.999878201538234 39 0.000579742738514257 0.00115948547702851 0.999420257261486 40 0.000497724484384117 0.000995448968768235 0.999502275515616 41 0.000292428533282766 0.000584857066565532 0.999707571466717 42 0.00025396412979647 0.00050792825959294 0.999746035870203 43 0.000334350425767924 0.000668700851535848 0.999665649574232 44 0.00028912155575627 0.000578243111512541 0.999710878444244 45 0.000289161871377808 0.000578323742755616 0.999710838128622 46 0.000220819193088419 0.000441638386176839 0.999779180806912 47 0.000191195503818053 0.000382391007636106 0.999808804496182 48 0.000276602138147124 0.000553204276294248 0.999723397861853 49 0.000196997937003093 0.000393995874006186 0.999803002062997 50 0.000195129528673718 0.000390259057347436 0.999804870471326 51 0.000116486719084525 0.00023297343816905 0.999883513280916 52 8.27206675721817e-05 0.000165441335144363 0.999917279332428 53 8.12612739009483e-05 0.000162522547801897 0.999918738726099 54 0.000307012720816881 0.000614025441633761 0.999692987279183 55 0.00036924218696701 0.000738484373934019 0.999630757813033 56 0.000490212831544525 0.000980425663089051 0.999509787168456 57 0.000325981402077948 0.000651962804155896 0.999674018597922 58 0.000569432563653484 0.00113886512730697 0.999430567436347 59 0.000797056001969669 0.00159411200393934 0.99920294399803 60 0.000670989538210521 0.00134197907642104 0.999329010461789 61 0.00115197585337299 0.00230395170674598 0.998848024146627 62 0.00106968204265761 0.00213936408531523 0.998930317957342 63 0.00204634373954159 0.00409268747908318 0.997953656260458 64 0.00217577017259607 0.00435154034519214 0.997824229827404 65 0.002740708273071 0.00548141654614201 0.997259291726929 66 0.00239671666330886 0.00479343332661773 0.997603283336691 67 0.00290492044998503 0.00580984089997006 0.997095079550015 68 0.00366897010056429 0.00733794020112858 0.996331029899436 69 0.00941747116873525 0.0188349423374705 0.990582528831265 70 0.00740224771258473 0.0148044954251695 0.992597752287415 71 0.00529935358678152 0.010598707173563 0.994700646413218 72 0.00423398968740772 0.00846797937481544 0.995766010312592 73 0.0033996341590631 0.00679926831812621 0.996600365840937 74 0.00253110177734202 0.00506220355468403 0.997468898222658 75 0.00239466246405624 0.00478932492811249 0.997605337535944 76 0.00160434766447569 0.00320869532895138 0.998395652335524 77 0.00147321639025794 0.00294643278051589 0.998526783609742 78 0.00119746165057126 0.00239492330114252 0.998802538349429 79 0.00108138768431128 0.00216277536862256 0.998918612315689 80 0.000880277776359411 0.00176055555271882 0.999119722223641 81 0.000986997010159008 0.00197399402031802 0.999013002989841 82 0.0017639409174184 0.00352788183483679 0.998236059082582 83 0.00121396152220431 0.00242792304440862 0.998786038477796 84 0.00148057172031507 0.00296114344063014 0.998519428279685 85 0.000931172001127554 0.00186234400225511 0.999068827998872 86 0.00139790941254217 0.00279581882508435 0.998602090587458 87 0.00952732021101461 0.0190546404220292 0.990472679788985 88 0.00841708557499279 0.0168341711499856 0.991582914425007 89 0.0223497337172571 0.0446994674345142 0.977650266282743 90 0.0515998850298651 0.10319977005973 0.948400114970135 91 0.0489212084526303 0.0978424169052606 0.95107879154737 92 0.0361999008967657 0.0723998017935315 0.963800099103234 93 0.0491589161664736 0.0983178323329472 0.950841083833526 94 0.0418393330429266 0.0836786660858532 0.958160666957073 95 0.0400135623536229 0.0800271247072459 0.959986437646377 96 0.0938560139371247 0.187712027874249 0.906143986062875 97 0.080137980217483 0.160275960434966 0.919862019782517 98 0.0704199598255832 0.140839919651166 0.929580040174417 99 0.168040662972089 0.336081325944177 0.831959337027911 100 0.203848250804151 0.407696501608302 0.796151749195849 101 0.224649864817257 0.449299729634515 0.775350135182743 102 0.213228671503486 0.426457343006972 0.786771328496514 103 0.16531988609803 0.330639772196059 0.83468011390197 104 0.125999665462518 0.251999330925037 0.874000334537482 105 0.119368892693504 0.238737785387009 0.880631107306496 106 0.115269310668752 0.230538621337505 0.884730689331248 107 0.269069882817378 0.538139765634756 0.730930117182622 108 0.208607131227944 0.417214262455888 0.791392868772056 109 0.20347329557196 0.406946591143919 0.79652670442804 110 0.146047540641995 0.29209508128399 0.853952459358005 111 0.124577981454143 0.249155962908285 0.875422018545857 112 0.135697053590972 0.271394107181945 0.864302946409028 113 0.176490139600068 0.352980279200136 0.823509860399932 114 0.32304496664998 0.64608993329996 0.67695503335002 115 0.31524688800345 0.6304937760069 0.68475311199655 116 0.479009463437732 0.958018926875465 0.520990536562268

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.071711075203567 & 0.143422150407134 & 0.928288924796433 \tabularnewline
17 & 0.0223780376273161 & 0.0447560752546323 & 0.977621962372684 \tabularnewline
18 & 0.0132598548749277 & 0.0265197097498555 & 0.986740145125072 \tabularnewline
19 & 0.00517248046014443 & 0.0103449609202889 & 0.994827519539856 \tabularnewline
20 & 0.00619593477216386 & 0.0123918695443277 & 0.993804065227836 \tabularnewline
21 & 0.0277010269880361 & 0.0554020539760722 & 0.972298973011964 \tabularnewline
22 & 0.0308915127045479 & 0.0617830254090957 & 0.969108487295452 \tabularnewline
23 & 0.0171700906056775 & 0.034340181211355 & 0.982829909394322 \tabularnewline
24 & 0.00848477998423394 & 0.0169695599684679 & 0.991515220015766 \tabularnewline
25 & 0.00473606765023691 & 0.00947213530047382 & 0.995263932349763 \tabularnewline
26 & 0.0031265563417052 & 0.0062531126834104 & 0.996873443658295 \tabularnewline
27 & 0.00142611619925217 & 0.00285223239850434 & 0.998573883800748 \tabularnewline
28 & 0.000998688272563235 & 0.00199737654512647 & 0.999001311727437 \tabularnewline
29 & 0.000442658361110094 & 0.000885316722220188 & 0.99955734163889 \tabularnewline
30 & 0.000351198745026709 & 0.000702397490053419 & 0.999648801254973 \tabularnewline
31 & 0.000291731624287799 & 0.000583463248575599 & 0.999708268375712 \tabularnewline
32 & 0.000170013787647376 & 0.000340027575294753 & 0.999829986212353 \tabularnewline
33 & 0.000297138273120955 & 0.00059427654624191 & 0.999702861726879 \tabularnewline
34 & 0.000177509067545023 & 0.000355018135090046 & 0.999822490932455 \tabularnewline
35 & 0.000221228889782546 & 0.000442457779565093 & 0.999778771110217 \tabularnewline
36 & 0.0002443082875649 & 0.0004886165751298 & 0.999755691712435 \tabularnewline
37 & 0.000146538307793399 & 0.000293076615586798 & 0.999853461692207 \tabularnewline
38 & 0.000121798461765963 & 0.000243596923531926 & 0.999878201538234 \tabularnewline
39 & 0.000579742738514257 & 0.00115948547702851 & 0.999420257261486 \tabularnewline
40 & 0.000497724484384117 & 0.000995448968768235 & 0.999502275515616 \tabularnewline
41 & 0.000292428533282766 & 0.000584857066565532 & 0.999707571466717 \tabularnewline
42 & 0.00025396412979647 & 0.00050792825959294 & 0.999746035870203 \tabularnewline
43 & 0.000334350425767924 & 0.000668700851535848 & 0.999665649574232 \tabularnewline
44 & 0.00028912155575627 & 0.000578243111512541 & 0.999710878444244 \tabularnewline
45 & 0.000289161871377808 & 0.000578323742755616 & 0.999710838128622 \tabularnewline
46 & 0.000220819193088419 & 0.000441638386176839 & 0.999779180806912 \tabularnewline
47 & 0.000191195503818053 & 0.000382391007636106 & 0.999808804496182 \tabularnewline
48 & 0.000276602138147124 & 0.000553204276294248 & 0.999723397861853 \tabularnewline
49 & 0.000196997937003093 & 0.000393995874006186 & 0.999803002062997 \tabularnewline
50 & 0.000195129528673718 & 0.000390259057347436 & 0.999804870471326 \tabularnewline
51 & 0.000116486719084525 & 0.00023297343816905 & 0.999883513280916 \tabularnewline
52 & 8.27206675721817e-05 & 0.000165441335144363 & 0.999917279332428 \tabularnewline
53 & 8.12612739009483e-05 & 0.000162522547801897 & 0.999918738726099 \tabularnewline
54 & 0.000307012720816881 & 0.000614025441633761 & 0.999692987279183 \tabularnewline
55 & 0.00036924218696701 & 0.000738484373934019 & 0.999630757813033 \tabularnewline
56 & 0.000490212831544525 & 0.000980425663089051 & 0.999509787168456 \tabularnewline
57 & 0.000325981402077948 & 0.000651962804155896 & 0.999674018597922 \tabularnewline
58 & 0.000569432563653484 & 0.00113886512730697 & 0.999430567436347 \tabularnewline
59 & 0.000797056001969669 & 0.00159411200393934 & 0.99920294399803 \tabularnewline
60 & 0.000670989538210521 & 0.00134197907642104 & 0.999329010461789 \tabularnewline
61 & 0.00115197585337299 & 0.00230395170674598 & 0.998848024146627 \tabularnewline
62 & 0.00106968204265761 & 0.00213936408531523 & 0.998930317957342 \tabularnewline
63 & 0.00204634373954159 & 0.00409268747908318 & 0.997953656260458 \tabularnewline
64 & 0.00217577017259607 & 0.00435154034519214 & 0.997824229827404 \tabularnewline
65 & 0.002740708273071 & 0.00548141654614201 & 0.997259291726929 \tabularnewline
66 & 0.00239671666330886 & 0.00479343332661773 & 0.997603283336691 \tabularnewline
67 & 0.00290492044998503 & 0.00580984089997006 & 0.997095079550015 \tabularnewline
68 & 0.00366897010056429 & 0.00733794020112858 & 0.996331029899436 \tabularnewline
69 & 0.00941747116873525 & 0.0188349423374705 & 0.990582528831265 \tabularnewline
70 & 0.00740224771258473 & 0.0148044954251695 & 0.992597752287415 \tabularnewline
71 & 0.00529935358678152 & 0.010598707173563 & 0.994700646413218 \tabularnewline
72 & 0.00423398968740772 & 0.00846797937481544 & 0.995766010312592 \tabularnewline
73 & 0.0033996341590631 & 0.00679926831812621 & 0.996600365840937 \tabularnewline
74 & 0.00253110177734202 & 0.00506220355468403 & 0.997468898222658 \tabularnewline
75 & 0.00239466246405624 & 0.00478932492811249 & 0.997605337535944 \tabularnewline
76 & 0.00160434766447569 & 0.00320869532895138 & 0.998395652335524 \tabularnewline
77 & 0.00147321639025794 & 0.00294643278051589 & 0.998526783609742 \tabularnewline
78 & 0.00119746165057126 & 0.00239492330114252 & 0.998802538349429 \tabularnewline
79 & 0.00108138768431128 & 0.00216277536862256 & 0.998918612315689 \tabularnewline
80 & 0.000880277776359411 & 0.00176055555271882 & 0.999119722223641 \tabularnewline
81 & 0.000986997010159008 & 0.00197399402031802 & 0.999013002989841 \tabularnewline
82 & 0.0017639409174184 & 0.00352788183483679 & 0.998236059082582 \tabularnewline
83 & 0.00121396152220431 & 0.00242792304440862 & 0.998786038477796 \tabularnewline
84 & 0.00148057172031507 & 0.00296114344063014 & 0.998519428279685 \tabularnewline
85 & 0.000931172001127554 & 0.00186234400225511 & 0.999068827998872 \tabularnewline
86 & 0.00139790941254217 & 0.00279581882508435 & 0.998602090587458 \tabularnewline
87 & 0.00952732021101461 & 0.0190546404220292 & 0.990472679788985 \tabularnewline
88 & 0.00841708557499279 & 0.0168341711499856 & 0.991582914425007 \tabularnewline
89 & 0.0223497337172571 & 0.0446994674345142 & 0.977650266282743 \tabularnewline
90 & 0.0515998850298651 & 0.10319977005973 & 0.948400114970135 \tabularnewline
91 & 0.0489212084526303 & 0.0978424169052606 & 0.95107879154737 \tabularnewline
92 & 0.0361999008967657 & 0.0723998017935315 & 0.963800099103234 \tabularnewline
93 & 0.0491589161664736 & 0.0983178323329472 & 0.950841083833526 \tabularnewline
94 & 0.0418393330429266 & 0.0836786660858532 & 0.958160666957073 \tabularnewline
95 & 0.0400135623536229 & 0.0800271247072459 & 0.959986437646377 \tabularnewline
96 & 0.0938560139371247 & 0.187712027874249 & 0.906143986062875 \tabularnewline
97 & 0.080137980217483 & 0.160275960434966 & 0.919862019782517 \tabularnewline
98 & 0.0704199598255832 & 0.140839919651166 & 0.929580040174417 \tabularnewline
99 & 0.168040662972089 & 0.336081325944177 & 0.831959337027911 \tabularnewline
100 & 0.203848250804151 & 0.407696501608302 & 0.796151749195849 \tabularnewline
101 & 0.224649864817257 & 0.449299729634515 & 0.775350135182743 \tabularnewline
102 & 0.213228671503486 & 0.426457343006972 & 0.786771328496514 \tabularnewline
103 & 0.16531988609803 & 0.330639772196059 & 0.83468011390197 \tabularnewline
104 & 0.125999665462518 & 0.251999330925037 & 0.874000334537482 \tabularnewline
105 & 0.119368892693504 & 0.238737785387009 & 0.880631107306496 \tabularnewline
106 & 0.115269310668752 & 0.230538621337505 & 0.884730689331248 \tabularnewline
107 & 0.269069882817378 & 0.538139765634756 & 0.730930117182622 \tabularnewline
108 & 0.208607131227944 & 0.417214262455888 & 0.791392868772056 \tabularnewline
109 & 0.20347329557196 & 0.406946591143919 & 0.79652670442804 \tabularnewline
110 & 0.146047540641995 & 0.29209508128399 & 0.853952459358005 \tabularnewline
111 & 0.124577981454143 & 0.249155962908285 & 0.875422018545857 \tabularnewline
112 & 0.135697053590972 & 0.271394107181945 & 0.864302946409028 \tabularnewline
113 & 0.176490139600068 & 0.352980279200136 & 0.823509860399932 \tabularnewline
114 & 0.32304496664998 & 0.64608993329996 & 0.67695503335002 \tabularnewline
115 & 0.31524688800345 & 0.6304937760069 & 0.68475311199655 \tabularnewline
116 & 0.479009463437732 & 0.958018926875465 & 0.520990536562268 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187427&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]16[/C][C]0.071711075203567[/C][C]0.143422150407134[/C][C]0.928288924796433[/C][/ROW]
[ROW][C]17[/C][C]0.0223780376273161[/C][C]0.0447560752546323[/C][C]0.977621962372684[/C][/ROW]
[ROW][C]18[/C][C]0.0132598548749277[/C][C]0.0265197097498555[/C][C]0.986740145125072[/C][/ROW]
[ROW][C]19[/C][C]0.00517248046014443[/C][C]0.0103449609202889[/C][C]0.994827519539856[/C][/ROW]
[ROW][C]20[/C][C]0.00619593477216386[/C][C]0.0123918695443277[/C][C]0.993804065227836[/C][/ROW]
[ROW][C]21[/C][C]0.0277010269880361[/C][C]0.0554020539760722[/C][C]0.972298973011964[/C][/ROW]
[ROW][C]22[/C][C]0.0308915127045479[/C][C]0.0617830254090957[/C][C]0.969108487295452[/C][/ROW]
[ROW][C]23[/C][C]0.0171700906056775[/C][C]0.034340181211355[/C][C]0.982829909394322[/C][/ROW]
[ROW][C]24[/C][C]0.00848477998423394[/C][C]0.0169695599684679[/C][C]0.991515220015766[/C][/ROW]
[ROW][C]25[/C][C]0.00473606765023691[/C][C]0.00947213530047382[/C][C]0.995263932349763[/C][/ROW]
[ROW][C]26[/C][C]0.0031265563417052[/C][C]0.0062531126834104[/C][C]0.996873443658295[/C][/ROW]
[ROW][C]27[/C][C]0.00142611619925217[/C][C]0.00285223239850434[/C][C]0.998573883800748[/C][/ROW]
[ROW][C]28[/C][C]0.000998688272563235[/C][C]0.00199737654512647[/C][C]0.999001311727437[/C][/ROW]
[ROW][C]29[/C][C]0.000442658361110094[/C][C]0.000885316722220188[/C][C]0.99955734163889[/C][/ROW]
[ROW][C]30[/C][C]0.000351198745026709[/C][C]0.000702397490053419[/C][C]0.999648801254973[/C][/ROW]
[ROW][C]31[/C][C]0.000291731624287799[/C][C]0.000583463248575599[/C][C]0.999708268375712[/C][/ROW]
[ROW][C]32[/C][C]0.000170013787647376[/C][C]0.000340027575294753[/C][C]0.999829986212353[/C][/ROW]
[ROW][C]33[/C][C]0.000297138273120955[/C][C]0.00059427654624191[/C][C]0.999702861726879[/C][/ROW]
[ROW][C]34[/C][C]0.000177509067545023[/C][C]0.000355018135090046[/C][C]0.999822490932455[/C][/ROW]
[ROW][C]35[/C][C]0.000221228889782546[/C][C]0.000442457779565093[/C][C]0.999778771110217[/C][/ROW]
[ROW][C]36[/C][C]0.0002443082875649[/C][C]0.0004886165751298[/C][C]0.999755691712435[/C][/ROW]
[ROW][C]37[/C][C]0.000146538307793399[/C][C]0.000293076615586798[/C][C]0.999853461692207[/C][/ROW]
[ROW][C]38[/C][C]0.000121798461765963[/C][C]0.000243596923531926[/C][C]0.999878201538234[/C][/ROW]
[ROW][C]39[/C][C]0.000579742738514257[/C][C]0.00115948547702851[/C][C]0.999420257261486[/C][/ROW]
[ROW][C]40[/C][C]0.000497724484384117[/C][C]0.000995448968768235[/C][C]0.999502275515616[/C][/ROW]
[ROW][C]41[/C][C]0.000292428533282766[/C][C]0.000584857066565532[/C][C]0.999707571466717[/C][/ROW]
[ROW][C]42[/C][C]0.00025396412979647[/C][C]0.00050792825959294[/C][C]0.999746035870203[/C][/ROW]
[ROW][C]43[/C][C]0.000334350425767924[/C][C]0.000668700851535848[/C][C]0.999665649574232[/C][/ROW]
[ROW][C]44[/C][C]0.00028912155575627[/C][C]0.000578243111512541[/C][C]0.999710878444244[/C][/ROW]
[ROW][C]45[/C][C]0.000289161871377808[/C][C]0.000578323742755616[/C][C]0.999710838128622[/C][/ROW]
[ROW][C]46[/C][C]0.000220819193088419[/C][C]0.000441638386176839[/C][C]0.999779180806912[/C][/ROW]
[ROW][C]47[/C][C]0.000191195503818053[/C][C]0.000382391007636106[/C][C]0.999808804496182[/C][/ROW]
[ROW][C]48[/C][C]0.000276602138147124[/C][C]0.000553204276294248[/C][C]0.999723397861853[/C][/ROW]
[ROW][C]49[/C][C]0.000196997937003093[/C][C]0.000393995874006186[/C][C]0.999803002062997[/C][/ROW]
[ROW][C]50[/C][C]0.000195129528673718[/C][C]0.000390259057347436[/C][C]0.999804870471326[/C][/ROW]
[ROW][C]51[/C][C]0.000116486719084525[/C][C]0.00023297343816905[/C][C]0.999883513280916[/C][/ROW]
[ROW][C]52[/C][C]8.27206675721817e-05[/C][C]0.000165441335144363[/C][C]0.999917279332428[/C][/ROW]
[ROW][C]53[/C][C]8.12612739009483e-05[/C][C]0.000162522547801897[/C][C]0.999918738726099[/C][/ROW]
[ROW][C]54[/C][C]0.000307012720816881[/C][C]0.000614025441633761[/C][C]0.999692987279183[/C][/ROW]
[ROW][C]55[/C][C]0.00036924218696701[/C][C]0.000738484373934019[/C][C]0.999630757813033[/C][/ROW]
[ROW][C]56[/C][C]0.000490212831544525[/C][C]0.000980425663089051[/C][C]0.999509787168456[/C][/ROW]
[ROW][C]57[/C][C]0.000325981402077948[/C][C]0.000651962804155896[/C][C]0.999674018597922[/C][/ROW]
[ROW][C]58[/C][C]0.000569432563653484[/C][C]0.00113886512730697[/C][C]0.999430567436347[/C][/ROW]
[ROW][C]59[/C][C]0.000797056001969669[/C][C]0.00159411200393934[/C][C]0.99920294399803[/C][/ROW]
[ROW][C]60[/C][C]0.000670989538210521[/C][C]0.00134197907642104[/C][C]0.999329010461789[/C][/ROW]
[ROW][C]61[/C][C]0.00115197585337299[/C][C]0.00230395170674598[/C][C]0.998848024146627[/C][/ROW]
[ROW][C]62[/C][C]0.00106968204265761[/C][C]0.00213936408531523[/C][C]0.998930317957342[/C][/ROW]
[ROW][C]63[/C][C]0.00204634373954159[/C][C]0.00409268747908318[/C][C]0.997953656260458[/C][/ROW]
[ROW][C]64[/C][C]0.00217577017259607[/C][C]0.00435154034519214[/C][C]0.997824229827404[/C][/ROW]
[ROW][C]65[/C][C]0.002740708273071[/C][C]0.00548141654614201[/C][C]0.997259291726929[/C][/ROW]
[ROW][C]66[/C][C]0.00239671666330886[/C][C]0.00479343332661773[/C][C]0.997603283336691[/C][/ROW]
[ROW][C]67[/C][C]0.00290492044998503[/C][C]0.00580984089997006[/C][C]0.997095079550015[/C][/ROW]
[ROW][C]68[/C][C]0.00366897010056429[/C][C]0.00733794020112858[/C][C]0.996331029899436[/C][/ROW]
[ROW][C]69[/C][C]0.00941747116873525[/C][C]0.0188349423374705[/C][C]0.990582528831265[/C][/ROW]
[ROW][C]70[/C][C]0.00740224771258473[/C][C]0.0148044954251695[/C][C]0.992597752287415[/C][/ROW]
[ROW][C]71[/C][C]0.00529935358678152[/C][C]0.010598707173563[/C][C]0.994700646413218[/C][/ROW]
[ROW][C]72[/C][C]0.00423398968740772[/C][C]0.00846797937481544[/C][C]0.995766010312592[/C][/ROW]
[ROW][C]73[/C][C]0.0033996341590631[/C][C]0.00679926831812621[/C][C]0.996600365840937[/C][/ROW]
[ROW][C]74[/C][C]0.00253110177734202[/C][C]0.00506220355468403[/C][C]0.997468898222658[/C][/ROW]
[ROW][C]75[/C][C]0.00239466246405624[/C][C]0.00478932492811249[/C][C]0.997605337535944[/C][/ROW]
[ROW][C]76[/C][C]0.00160434766447569[/C][C]0.00320869532895138[/C][C]0.998395652335524[/C][/ROW]
[ROW][C]77[/C][C]0.00147321639025794[/C][C]0.00294643278051589[/C][C]0.998526783609742[/C][/ROW]
[ROW][C]78[/C][C]0.00119746165057126[/C][C]0.00239492330114252[/C][C]0.998802538349429[/C][/ROW]
[ROW][C]79[/C][C]0.00108138768431128[/C][C]0.00216277536862256[/C][C]0.998918612315689[/C][/ROW]
[ROW][C]80[/C][C]0.000880277776359411[/C][C]0.00176055555271882[/C][C]0.999119722223641[/C][/ROW]
[ROW][C]81[/C][C]0.000986997010159008[/C][C]0.00197399402031802[/C][C]0.999013002989841[/C][/ROW]
[ROW][C]82[/C][C]0.0017639409174184[/C][C]0.00352788183483679[/C][C]0.998236059082582[/C][/ROW]
[ROW][C]83[/C][C]0.00121396152220431[/C][C]0.00242792304440862[/C][C]0.998786038477796[/C][/ROW]
[ROW][C]84[/C][C]0.00148057172031507[/C][C]0.00296114344063014[/C][C]0.998519428279685[/C][/ROW]
[ROW][C]85[/C][C]0.000931172001127554[/C][C]0.00186234400225511[/C][C]0.999068827998872[/C][/ROW]
[ROW][C]86[/C][C]0.00139790941254217[/C][C]0.00279581882508435[/C][C]0.998602090587458[/C][/ROW]
[ROW][C]87[/C][C]0.00952732021101461[/C][C]0.0190546404220292[/C][C]0.990472679788985[/C][/ROW]
[ROW][C]88[/C][C]0.00841708557499279[/C][C]0.0168341711499856[/C][C]0.991582914425007[/C][/ROW]
[ROW][C]89[/C][C]0.0223497337172571[/C][C]0.0446994674345142[/C][C]0.977650266282743[/C][/ROW]
[ROW][C]90[/C][C]0.0515998850298651[/C][C]0.10319977005973[/C][C]0.948400114970135[/C][/ROW]
[ROW][C]91[/C][C]0.0489212084526303[/C][C]0.0978424169052606[/C][C]0.95107879154737[/C][/ROW]
[ROW][C]92[/C][C]0.0361999008967657[/C][C]0.0723998017935315[/C][C]0.963800099103234[/C][/ROW]
[ROW][C]93[/C][C]0.0491589161664736[/C][C]0.0983178323329472[/C][C]0.950841083833526[/C][/ROW]
[ROW][C]94[/C][C]0.0418393330429266[/C][C]0.0836786660858532[/C][C]0.958160666957073[/C][/ROW]
[ROW][C]95[/C][C]0.0400135623536229[/C][C]0.0800271247072459[/C][C]0.959986437646377[/C][/ROW]
[ROW][C]96[/C][C]0.0938560139371247[/C][C]0.187712027874249[/C][C]0.906143986062875[/C][/ROW]
[ROW][C]97[/C][C]0.080137980217483[/C][C]0.160275960434966[/C][C]0.919862019782517[/C][/ROW]
[ROW][C]98[/C][C]0.0704199598255832[/C][C]0.140839919651166[/C][C]0.929580040174417[/C][/ROW]
[ROW][C]99[/C][C]0.168040662972089[/C][C]0.336081325944177[/C][C]0.831959337027911[/C][/ROW]
[ROW][C]100[/C][C]0.203848250804151[/C][C]0.407696501608302[/C][C]0.796151749195849[/C][/ROW]
[ROW][C]101[/C][C]0.224649864817257[/C][C]0.449299729634515[/C][C]0.775350135182743[/C][/ROW]
[ROW][C]102[/C][C]0.213228671503486[/C][C]0.426457343006972[/C][C]0.786771328496514[/C][/ROW]
[ROW][C]103[/C][C]0.16531988609803[/C][C]0.330639772196059[/C][C]0.83468011390197[/C][/ROW]
[ROW][C]104[/C][C]0.125999665462518[/C][C]0.251999330925037[/C][C]0.874000334537482[/C][/ROW]
[ROW][C]105[/C][C]0.119368892693504[/C][C]0.238737785387009[/C][C]0.880631107306496[/C][/ROW]
[ROW][C]106[/C][C]0.115269310668752[/C][C]0.230538621337505[/C][C]0.884730689331248[/C][/ROW]
[ROW][C]107[/C][C]0.269069882817378[/C][C]0.538139765634756[/C][C]0.730930117182622[/C][/ROW]
[ROW][C]108[/C][C]0.208607131227944[/C][C]0.417214262455888[/C][C]0.791392868772056[/C][/ROW]
[ROW][C]109[/C][C]0.20347329557196[/C][C]0.406946591143919[/C][C]0.79652670442804[/C][/ROW]
[ROW][C]110[/C][C]0.146047540641995[/C][C]0.29209508128399[/C][C]0.853952459358005[/C][/ROW]
[ROW][C]111[/C][C]0.124577981454143[/C][C]0.249155962908285[/C][C]0.875422018545857[/C][/ROW]
[ROW][C]112[/C][C]0.135697053590972[/C][C]0.271394107181945[/C][C]0.864302946409028[/C][/ROW]
[ROW][C]113[/C][C]0.176490139600068[/C][C]0.352980279200136[/C][C]0.823509860399932[/C][/ROW]
[ROW][C]114[/C][C]0.32304496664998[/C][C]0.64608993329996[/C][C]0.67695503335002[/C][/ROW]
[ROW][C]115[/C][C]0.31524688800345[/C][C]0.6304937760069[/C][C]0.68475311199655[/C][/ROW]
[ROW][C]116[/C][C]0.479009463437732[/C][C]0.958018926875465[/C][C]0.520990536562268[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187427&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187427&T=5

As an alternative you can also use a QR Code:

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

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.071711075203567 0.143422150407134 0.928288924796433 17 0.0223780376273161 0.0447560752546323 0.977621962372684 18 0.0132598548749277 0.0265197097498555 0.986740145125072 19 0.00517248046014443 0.0103449609202889 0.994827519539856 20 0.00619593477216386 0.0123918695443277 0.993804065227836 21 0.0277010269880361 0.0554020539760722 0.972298973011964 22 0.0308915127045479 0.0617830254090957 0.969108487295452 23 0.0171700906056775 0.034340181211355 0.982829909394322 24 0.00848477998423394 0.0169695599684679 0.991515220015766 25 0.00473606765023691 0.00947213530047382 0.995263932349763 26 0.0031265563417052 0.0062531126834104 0.996873443658295 27 0.00142611619925217 0.00285223239850434 0.998573883800748 28 0.000998688272563235 0.00199737654512647 0.999001311727437 29 0.000442658361110094 0.000885316722220188 0.99955734163889 30 0.000351198745026709 0.000702397490053419 0.999648801254973 31 0.000291731624287799 0.000583463248575599 0.999708268375712 32 0.000170013787647376 0.000340027575294753 0.999829986212353 33 0.000297138273120955 0.00059427654624191 0.999702861726879 34 0.000177509067545023 0.000355018135090046 0.999822490932455 35 0.000221228889782546 0.000442457779565093 0.999778771110217 36 0.0002443082875649 0.0004886165751298 0.999755691712435 37 0.000146538307793399 0.000293076615586798 0.999853461692207 38 0.000121798461765963 0.000243596923531926 0.999878201538234 39 0.000579742738514257 0.00115948547702851 0.999420257261486 40 0.000497724484384117 0.000995448968768235 0.999502275515616 41 0.000292428533282766 0.000584857066565532 0.999707571466717 42 0.00025396412979647 0.00050792825959294 0.999746035870203 43 0.000334350425767924 0.000668700851535848 0.999665649574232 44 0.00028912155575627 0.000578243111512541 0.999710878444244 45 0.000289161871377808 0.000578323742755616 0.999710838128622 46 0.000220819193088419 0.000441638386176839 0.999779180806912 47 0.000191195503818053 0.000382391007636106 0.999808804496182 48 0.000276602138147124 0.000553204276294248 0.999723397861853 49 0.000196997937003093 0.000393995874006186 0.999803002062997 50 0.000195129528673718 0.000390259057347436 0.999804870471326 51 0.000116486719084525 0.00023297343816905 0.999883513280916 52 8.27206675721817e-05 0.000165441335144363 0.999917279332428 53 8.12612739009483e-05 0.000162522547801897 0.999918738726099 54 0.000307012720816881 0.000614025441633761 0.999692987279183 55 0.00036924218696701 0.000738484373934019 0.999630757813033 56 0.000490212831544525 0.000980425663089051 0.999509787168456 57 0.000325981402077948 0.000651962804155896 0.999674018597922 58 0.000569432563653484 0.00113886512730697 0.999430567436347 59 0.000797056001969669 0.00159411200393934 0.99920294399803 60 0.000670989538210521 0.00134197907642104 0.999329010461789 61 0.00115197585337299 0.00230395170674598 0.998848024146627 62 0.00106968204265761 0.00213936408531523 0.998930317957342 63 0.00204634373954159 0.00409268747908318 0.997953656260458 64 0.00217577017259607 0.00435154034519214 0.997824229827404 65 0.002740708273071 0.00548141654614201 0.997259291726929 66 0.00239671666330886 0.00479343332661773 0.997603283336691 67 0.00290492044998503 0.00580984089997006 0.997095079550015 68 0.00366897010056429 0.00733794020112858 0.996331029899436 69 0.00941747116873525 0.0188349423374705 0.990582528831265 70 0.00740224771258473 0.0148044954251695 0.992597752287415 71 0.00529935358678152 0.010598707173563 0.994700646413218 72 0.00423398968740772 0.00846797937481544 0.995766010312592 73 0.0033996341590631 0.00679926831812621 0.996600365840937 74 0.00253110177734202 0.00506220355468403 0.997468898222658 75 0.00239466246405624 0.00478932492811249 0.997605337535944 76 0.00160434766447569 0.00320869532895138 0.998395652335524 77 0.00147321639025794 0.00294643278051589 0.998526783609742 78 0.00119746165057126 0.00239492330114252 0.998802538349429 79 0.00108138768431128 0.00216277536862256 0.998918612315689 80 0.000880277776359411 0.00176055555271882 0.999119722223641 81 0.000986997010159008 0.00197399402031802 0.999013002989841 82 0.0017639409174184 0.00352788183483679 0.998236059082582 83 0.00121396152220431 0.00242792304440862 0.998786038477796 84 0.00148057172031507 0.00296114344063014 0.998519428279685 85 0.000931172001127554 0.00186234400225511 0.999068827998872 86 0.00139790941254217 0.00279581882508435 0.998602090587458 87 0.00952732021101461 0.0190546404220292 0.990472679788985 88 0.00841708557499279 0.0168341711499856 0.991582914425007 89 0.0223497337172571 0.0446994674345142 0.977650266282743 90 0.0515998850298651 0.10319977005973 0.948400114970135 91 0.0489212084526303 0.0978424169052606 0.95107879154737 92 0.0361999008967657 0.0723998017935315 0.963800099103234 93 0.0491589161664736 0.0983178323329472 0.950841083833526 94 0.0418393330429266 0.0836786660858532 0.958160666957073 95 0.0400135623536229 0.0800271247072459 0.959986437646377 96 0.0938560139371247 0.187712027874249 0.906143986062875 97 0.080137980217483 0.160275960434966 0.919862019782517 98 0.0704199598255832 0.140839919651166 0.929580040174417 99 0.168040662972089 0.336081325944177 0.831959337027911 100 0.203848250804151 0.407696501608302 0.796151749195849 101 0.224649864817257 0.449299729634515 0.775350135182743 102 0.213228671503486 0.426457343006972 0.786771328496514 103 0.16531988609803 0.330639772196059 0.83468011390197 104 0.125999665462518 0.251999330925037 0.874000334537482 105 0.119368892693504 0.238737785387009 0.880631107306496 106 0.115269310668752 0.230538621337505 0.884730689331248 107 0.269069882817378 0.538139765634756 0.730930117182622 108 0.208607131227944 0.417214262455888 0.791392868772056 109 0.20347329557196 0.406946591143919 0.79652670442804 110 0.146047540641995 0.29209508128399 0.853952459358005 111 0.124577981454143 0.249155962908285 0.875422018545857 112 0.135697053590972 0.271394107181945 0.864302946409028 113 0.176490139600068 0.352980279200136 0.823509860399932 114 0.32304496664998 0.64608993329996 0.67695503335002 115 0.31524688800345 0.6304937760069 0.68475311199655 116 0.479009463437732 0.958018926875465 0.520990536562268

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 59 0.584158415841584 NOK 5% type I error level 71 0.702970297029703 NOK 10% type I error level 78 0.772277227722772 NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 59 & 0.584158415841584 & NOK \tabularnewline
5% type I error level & 71 & 0.702970297029703 & NOK \tabularnewline
10% type I error level & 78 & 0.772277227722772 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187427&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]59[/C][C]0.584158415841584[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]71[/C][C]0.702970297029703[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]78[/C][C]0.772277227722772[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187427&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187427&T=6

As an alternative you can also use a QR Code:

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

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 59 0.584158415841584 NOK 5% type I error level 71 0.702970297029703 NOK 10% type I error level 78 0.772277227722772 NOK

library(lattice)library(lmtest)n25 <- 25 #minimum number of obs. for Goldfeld-Quandt testpar1 <- as.numeric(par1)x <- 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 <- x1if (par3 == 'First Differences'){x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))for (i in 1:n-1) {for (j in 1:k) {x2[i,j] <- x[i+1,j] - x[i,j]}}x <- x2}if (par2 == 'Include Monthly Dummies'){x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))for (i in 1:11){x2[seq(i,n,12),i] <- 1}x <- cbind(x, x2)}if (par2 == 'Include Quarterly Dummies'){x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))for (i in 1:3){x2[seq(i,n,4),i] <- 1}x <- cbind(x, x2)}k <- length(x[1,])if (par3 == 'Linear Trend'){x <- cbind(x, c(1:n))colnames(x)[k+1] <- 't'}xk <- length(x[1,])df <- as.data.frame(x)(mylm <- lm(df))(mysum <- summary(mylm))if (n > n25) {kp3 <- k + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$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)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)zplot(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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')if (rownames(mysum$coefficients)[i] != '(Intercept)') {myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')}}myeq <- paste(myeq, ' + e[t]')a<-table.row.start(a)a<-table.element(a, myeq)a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable1.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Variable',header=TRUE)a<-table.element(a,'Parameter',header=TRUE)a<-table.element(a,'S.D.',header=TRUE)a<-table.element(a,'T-STATH0: parameter = 0',header=TRUE)a<-table.element(a,'2-tail p-value',header=TRUE)a<-table.element(a,'1-tail p-value',header=TRUE)a<-table.row.end(a)for (i in 1:k){a<-table.row.start(a)a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)a<-table.element(a,mysum$coefficients[i,1])a<-table.element(a, round(mysum$coefficients[i,2],6))a<-table.element(a, round(mysum$coefficients[i,3],4))a<-table.element(a, round(mysum$coefficients[i,4],6))a<-table.element(a, round(mysum$coefficients[i,4]/2,6))a<-table.row.end(a)}a<-table.end(a)table.save(a,file='mytable2.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Multiple R',1,TRUE)a<-table.element(a, sqrt(mysum$r.squared))a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'R-squared',1,TRUE)a<-table.element(a, mysum$r.squared)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Adjusted R-squared',1,TRUE)a<-table.element(a, mysum$adj.r.squared)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'F-TEST (value)',1,TRUE)a<-table.element(a, mysum$fstatistic[1])a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)a<-table.element(a, mysum$fstatistic[2])a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)a<-table.element(a, mysum$fstatistic[3])a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'p-value',1,TRUE)a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))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, mysum$sigma)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Sum Squared Residuals',1,TRUE)a<-table.element(a, sum(myerror*myerror))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable3.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,x[i])a<-table.element(a,x[i]-mysum$resid[i])a<-table.element(a,mysum\$resid[i])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,gqarr[mypoint-kp3+1,1])a<-table.element(a,gqarr[mypoint-kp3+1,2])a<-table.element(a,gqarr[mypoint-kp3+1,3])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,numsignificant1)a<-table.element(a,numsignificant1/numgqtests)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,numsignificant5)a<-table.element(a,numsignificant5/numgqtests)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,numsignificant10)a<-table.element(a,numsignificant10/numgqtests)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')}