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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, 17 Dec 2011 11:04:09 -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/2011/Dec/17/t13241378746c4ejnpuf681esh.htm/, Retrieved Fri, 29 Mar 2024 09:12:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=156431, Retrieved Fri, 29 Mar 2024 09:12:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact126
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
-  M D  [Multiple Regression] [Workshop 8, Multi...] [2010-11-29 10:32:18] [d946de7cca328fbcf207448a112523ab]
-         [Multiple Regression] [Workshop 8, Multi...] [2010-11-29 20:25:47] [3635fb7041b1998c5a1332cf9de22bce]
-    D      [Multiple Regression] [Workshop 8, Multi...] [2010-11-29 20:46:24] [3635fb7041b1998c5a1332cf9de22bce]
- R PD        [Multiple Regression] [Paper, MR poging 2] [2010-12-19 20:57:32] [3635fb7041b1998c5a1332cf9de22bce]
-   PD            [Multiple Regression] [paper] [2011-12-17 16:04:09] [6e647d331a8f33aa61a2d78ef323178e] [Current]
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Dataseries X:
589	248.85	65453
559	249.68	65715
623	251.13	66261
617	251.24	66332
603	253.24	66229
558	254.66	66579
609	255.85	66817
583	256.93	67373
570	258.99	68078
543	258.30	69137
598	260.53	69816
569	260.65	70252
552	260.98	70389
514	262.09	70572
569	263.18	70780
529	262.62	70912
515	263.18	71594
481	264.91	72587
536	265.20	73677
498	266.14	74712
446	268.15	75208
503	270.62	75657
470	272.65	76011
458	274.50	76748
437	274.37	76537
502	277.85	76622
482	280.15	76404
474	280.67	76219
457	281.42	76875
522	283.23	77374
513	283.34	77743
515	284.09	78030
506	285.47	77805
576	287.27	77905
556	287.96	78158
559	289.05	78616
541	289.84	79740
606	292.68	80312
600	294.61	80921
588	296.22	81078
570	296.70	81394
626	300.82	81787
601	303.57	82252
588	304.32	82854
573	304.52	83498
622	306.69	83811
570	308.73	84531
547	308.30	85330
512	309.67	86247
554	311.68	86386
517	312.62	86918
506	315.18	87184
479	320.19	87843
527	325.96	88204
508	330.45	87675
532	329.16	85964
532	327.53	84387
588	326.87	84530
566	326.52	85497
573	326.65	85968
545	329.25	86030
597	333.11	86963
555	334.51	87324
548	336.21	87770
524	339.91	88534
572	344.53	88888




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 5 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156431&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156431&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156431&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Werkloosheid[t] = + 592.380127455885 + 2.86224278480316CPI[t] -0.0112122750615483BBP[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Werkloosheid[t] =  +  592.380127455885 +  2.86224278480316CPI[t] -0.0112122750615483BBP[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156431&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Werkloosheid[t] =  +  592.380127455885 +  2.86224278480316CPI[t] -0.0112122750615483BBP[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156431&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156431&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
Werkloosheid[t] = + 592.380127455885 + 2.86224278480316CPI[t] -0.0112122750615483BBP[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)592.38012745588556.71291810.445200
CPI2.862242784803160.7224713.96170.0001929.6e-05
BBP-0.01121227506154830.002775-4.040.0001487.4e-05

\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) & 592.380127455885 & 56.712918 & 10.4452 & 0 & 0 \tabularnewline
CPI & 2.86224278480316 & 0.722471 & 3.9617 & 0.000192 & 9.6e-05 \tabularnewline
BBP & -0.0112122750615483 & 0.002775 & -4.04 & 0.000148 & 7.4e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156431&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]592.380127455885[/C][C]56.712918[/C][C]10.4452[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]CPI[/C][C]2.86224278480316[/C][C]0.722471[/C][C]3.9617[/C][C]0.000192[/C][C]9.6e-05[/C][/ROW]
[ROW][C]BBP[/C][C]-0.0112122750615483[/C][C]0.002775[/C][C]-4.04[/C][C]0.000148[/C][C]7.4e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156431&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156431&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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)592.38012745588556.71291810.445200
CPI2.862242784803160.7224713.96170.0001929.6e-05
BBP-0.01121227506154830.002775-4.040.0001487.4e-05







Multiple Linear Regression - Regression Statistics
Multiple R0.454216298588512
R-squared0.206312445903448
Adjusted R-squared0.181116015614668
F-TEST (value)8.18816171730976
F-TEST (DF numerator)2
F-TEST (DF denominator)63
p-value0.000690178552545828
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation42.1368551576271
Sum Squared Residuals111857.417442215

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.454216298588512 \tabularnewline
R-squared & 0.206312445903448 \tabularnewline
Adjusted R-squared & 0.181116015614668 \tabularnewline
F-TEST (value) & 8.18816171730976 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 63 \tabularnewline
p-value & 0.000690178552545828 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 42.1368551576271 \tabularnewline
Sum Squared Residuals & 111857.417442215 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156431&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.454216298588512[/C][/ROW]
[ROW][C]R-squared[/C][C]0.206312445903448[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.181116015614668[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.18816171730976[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]63[/C][/ROW]
[ROW][C]p-value[/C][C]0.000690178552545828[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]42.1368551576271[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]111857.417442215[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156431&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156431&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 R0.454216298588512
R-squared0.206312445903448
Adjusted R-squared0.181116015614668
F-TEST (value)8.18816171730976
F-TEST (DF numerator)2
F-TEST (DF denominator)63
p-value0.000690178552545828
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation42.1368551576271
Sum Squared Residuals111857.417442215







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1589570.77220485063418.227795149366
2559570.210250295894-11.2102502958943
3623568.23860015025454.7613998497464
4617567.75737532721249.2426246727879
5603574.63672522815828.3632747718421
6558574.776813711037-16.7768137110365
7609575.51436116030433.4856388396962
8583572.3715584336710.6284415663296
9570570.363124651973-0.363124651973359
10543556.51437784028-13.5143778402796
11598555.28404448359942.7159555164008
12569550.73896169094118.2610383090594
13552550.1474201264941.85257987350637
14514551.272663281362-37.2726632813617
15569552.06035470399516.9396452960048
16529548.977478436381-19.977478436381
17515542.933562803895-27.9335628038949
18481536.751453685487-55.751453685487
19536525.36012427599210.6398757240078
20498516.445927805005-18.4459278050047
21446516.637747371931-70.6377473719311
22503518.67317554776-15.6731755477598
23470520.514383029122-50.5143830291221
24458517.546085460647-59.5460854606469
25437519.539783936609-82.5397839366092
26502528.547345447493-26.5473454474927
27482537.574779815957-55.5747798159573
28474541.137416950442-67.1374169504415
29457535.928846598668-78.9288465986682
30522535.514580783449-13.5145807834494
31513531.692097992066-18.6920979920663
32515530.620857138004-15.6208571380043
33506537.093514069881-31.0935140698812
34576541.12432357637234.8756764236281
35556540.26256550731415.7374344926856
36559538.24718816456120.7528118354392
37541527.90576279537513.094237204625
38606529.6211109690176.3788890309896
39600528.31696403119871.6830359688024
40588531.16484773006856.8351522699323
41570528.99564534732441.0043546526761
42626536.38166152152489.6183384784756
43601539.03912127611361.9608787238868
44588534.43601377766353.5639862223365
45573527.78775719498745.212242805013
46622530.48938194374591.5106180562547
47570528.25551918042941.7444808195709
48547518.06614700878728.9338529912134
49512511.7057633925270.294236607472774
50554515.90036515642638.0996348435737
51517512.6259430413984.37405695860235
52506516.970819404122-10.9708194041219
53479523.921766490425-44.9217664904254
54527536.389276061521-9.38927606152069
55508555.172039672846-47.172039672846
56532570.663949110759-38.6639491107591
57532583.680251143591-51.6802511435914
58588580.187815571827.81218442818003
59566568.343760612622-2.34376061262162
60573563.4348706206579.5651293793432
61545570.181540807329-25.1815408073291
62597570.76874532424526.2312546757552
63555570.72825392575-15.7282539257502
64548570.593391982465-22.5933919824651
65524572.617512139214-48.617512139214
66572581.871928433216-9.87192843321641

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 589 & 570.772204850634 & 18.227795149366 \tabularnewline
2 & 559 & 570.210250295894 & -11.2102502958943 \tabularnewline
3 & 623 & 568.238600150254 & 54.7613998497464 \tabularnewline
4 & 617 & 567.757375327212 & 49.2426246727879 \tabularnewline
5 & 603 & 574.636725228158 & 28.3632747718421 \tabularnewline
6 & 558 & 574.776813711037 & -16.7768137110365 \tabularnewline
7 & 609 & 575.514361160304 & 33.4856388396962 \tabularnewline
8 & 583 & 572.37155843367 & 10.6284415663296 \tabularnewline
9 & 570 & 570.363124651973 & -0.363124651973359 \tabularnewline
10 & 543 & 556.51437784028 & -13.5143778402796 \tabularnewline
11 & 598 & 555.284044483599 & 42.7159555164008 \tabularnewline
12 & 569 & 550.738961690941 & 18.2610383090594 \tabularnewline
13 & 552 & 550.147420126494 & 1.85257987350637 \tabularnewline
14 & 514 & 551.272663281362 & -37.2726632813617 \tabularnewline
15 & 569 & 552.060354703995 & 16.9396452960048 \tabularnewline
16 & 529 & 548.977478436381 & -19.977478436381 \tabularnewline
17 & 515 & 542.933562803895 & -27.9335628038949 \tabularnewline
18 & 481 & 536.751453685487 & -55.751453685487 \tabularnewline
19 & 536 & 525.360124275992 & 10.6398757240078 \tabularnewline
20 & 498 & 516.445927805005 & -18.4459278050047 \tabularnewline
21 & 446 & 516.637747371931 & -70.6377473719311 \tabularnewline
22 & 503 & 518.67317554776 & -15.6731755477598 \tabularnewline
23 & 470 & 520.514383029122 & -50.5143830291221 \tabularnewline
24 & 458 & 517.546085460647 & -59.5460854606469 \tabularnewline
25 & 437 & 519.539783936609 & -82.5397839366092 \tabularnewline
26 & 502 & 528.547345447493 & -26.5473454474927 \tabularnewline
27 & 482 & 537.574779815957 & -55.5747798159573 \tabularnewline
28 & 474 & 541.137416950442 & -67.1374169504415 \tabularnewline
29 & 457 & 535.928846598668 & -78.9288465986682 \tabularnewline
30 & 522 & 535.514580783449 & -13.5145807834494 \tabularnewline
31 & 513 & 531.692097992066 & -18.6920979920663 \tabularnewline
32 & 515 & 530.620857138004 & -15.6208571380043 \tabularnewline
33 & 506 & 537.093514069881 & -31.0935140698812 \tabularnewline
34 & 576 & 541.124323576372 & 34.8756764236281 \tabularnewline
35 & 556 & 540.262565507314 & 15.7374344926856 \tabularnewline
36 & 559 & 538.247188164561 & 20.7528118354392 \tabularnewline
37 & 541 & 527.905762795375 & 13.094237204625 \tabularnewline
38 & 606 & 529.62111096901 & 76.3788890309896 \tabularnewline
39 & 600 & 528.316964031198 & 71.6830359688024 \tabularnewline
40 & 588 & 531.164847730068 & 56.8351522699323 \tabularnewline
41 & 570 & 528.995645347324 & 41.0043546526761 \tabularnewline
42 & 626 & 536.381661521524 & 89.6183384784756 \tabularnewline
43 & 601 & 539.039121276113 & 61.9608787238868 \tabularnewline
44 & 588 & 534.436013777663 & 53.5639862223365 \tabularnewline
45 & 573 & 527.787757194987 & 45.212242805013 \tabularnewline
46 & 622 & 530.489381943745 & 91.5106180562547 \tabularnewline
47 & 570 & 528.255519180429 & 41.7444808195709 \tabularnewline
48 & 547 & 518.066147008787 & 28.9338529912134 \tabularnewline
49 & 512 & 511.705763392527 & 0.294236607472774 \tabularnewline
50 & 554 & 515.900365156426 & 38.0996348435737 \tabularnewline
51 & 517 & 512.625943041398 & 4.37405695860235 \tabularnewline
52 & 506 & 516.970819404122 & -10.9708194041219 \tabularnewline
53 & 479 & 523.921766490425 & -44.9217664904254 \tabularnewline
54 & 527 & 536.389276061521 & -9.38927606152069 \tabularnewline
55 & 508 & 555.172039672846 & -47.172039672846 \tabularnewline
56 & 532 & 570.663949110759 & -38.6639491107591 \tabularnewline
57 & 532 & 583.680251143591 & -51.6802511435914 \tabularnewline
58 & 588 & 580.18781557182 & 7.81218442818003 \tabularnewline
59 & 566 & 568.343760612622 & -2.34376061262162 \tabularnewline
60 & 573 & 563.434870620657 & 9.5651293793432 \tabularnewline
61 & 545 & 570.181540807329 & -25.1815408073291 \tabularnewline
62 & 597 & 570.768745324245 & 26.2312546757552 \tabularnewline
63 & 555 & 570.72825392575 & -15.7282539257502 \tabularnewline
64 & 548 & 570.593391982465 & -22.5933919824651 \tabularnewline
65 & 524 & 572.617512139214 & -48.617512139214 \tabularnewline
66 & 572 & 581.871928433216 & -9.87192843321641 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156431&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]589[/C][C]570.772204850634[/C][C]18.227795149366[/C][/ROW]
[ROW][C]2[/C][C]559[/C][C]570.210250295894[/C][C]-11.2102502958943[/C][/ROW]
[ROW][C]3[/C][C]623[/C][C]568.238600150254[/C][C]54.7613998497464[/C][/ROW]
[ROW][C]4[/C][C]617[/C][C]567.757375327212[/C][C]49.2426246727879[/C][/ROW]
[ROW][C]5[/C][C]603[/C][C]574.636725228158[/C][C]28.3632747718421[/C][/ROW]
[ROW][C]6[/C][C]558[/C][C]574.776813711037[/C][C]-16.7768137110365[/C][/ROW]
[ROW][C]7[/C][C]609[/C][C]575.514361160304[/C][C]33.4856388396962[/C][/ROW]
[ROW][C]8[/C][C]583[/C][C]572.37155843367[/C][C]10.6284415663296[/C][/ROW]
[ROW][C]9[/C][C]570[/C][C]570.363124651973[/C][C]-0.363124651973359[/C][/ROW]
[ROW][C]10[/C][C]543[/C][C]556.51437784028[/C][C]-13.5143778402796[/C][/ROW]
[ROW][C]11[/C][C]598[/C][C]555.284044483599[/C][C]42.7159555164008[/C][/ROW]
[ROW][C]12[/C][C]569[/C][C]550.738961690941[/C][C]18.2610383090594[/C][/ROW]
[ROW][C]13[/C][C]552[/C][C]550.147420126494[/C][C]1.85257987350637[/C][/ROW]
[ROW][C]14[/C][C]514[/C][C]551.272663281362[/C][C]-37.2726632813617[/C][/ROW]
[ROW][C]15[/C][C]569[/C][C]552.060354703995[/C][C]16.9396452960048[/C][/ROW]
[ROW][C]16[/C][C]529[/C][C]548.977478436381[/C][C]-19.977478436381[/C][/ROW]
[ROW][C]17[/C][C]515[/C][C]542.933562803895[/C][C]-27.9335628038949[/C][/ROW]
[ROW][C]18[/C][C]481[/C][C]536.751453685487[/C][C]-55.751453685487[/C][/ROW]
[ROW][C]19[/C][C]536[/C][C]525.360124275992[/C][C]10.6398757240078[/C][/ROW]
[ROW][C]20[/C][C]498[/C][C]516.445927805005[/C][C]-18.4459278050047[/C][/ROW]
[ROW][C]21[/C][C]446[/C][C]516.637747371931[/C][C]-70.6377473719311[/C][/ROW]
[ROW][C]22[/C][C]503[/C][C]518.67317554776[/C][C]-15.6731755477598[/C][/ROW]
[ROW][C]23[/C][C]470[/C][C]520.514383029122[/C][C]-50.5143830291221[/C][/ROW]
[ROW][C]24[/C][C]458[/C][C]517.546085460647[/C][C]-59.5460854606469[/C][/ROW]
[ROW][C]25[/C][C]437[/C][C]519.539783936609[/C][C]-82.5397839366092[/C][/ROW]
[ROW][C]26[/C][C]502[/C][C]528.547345447493[/C][C]-26.5473454474927[/C][/ROW]
[ROW][C]27[/C][C]482[/C][C]537.574779815957[/C][C]-55.5747798159573[/C][/ROW]
[ROW][C]28[/C][C]474[/C][C]541.137416950442[/C][C]-67.1374169504415[/C][/ROW]
[ROW][C]29[/C][C]457[/C][C]535.928846598668[/C][C]-78.9288465986682[/C][/ROW]
[ROW][C]30[/C][C]522[/C][C]535.514580783449[/C][C]-13.5145807834494[/C][/ROW]
[ROW][C]31[/C][C]513[/C][C]531.692097992066[/C][C]-18.6920979920663[/C][/ROW]
[ROW][C]32[/C][C]515[/C][C]530.620857138004[/C][C]-15.6208571380043[/C][/ROW]
[ROW][C]33[/C][C]506[/C][C]537.093514069881[/C][C]-31.0935140698812[/C][/ROW]
[ROW][C]34[/C][C]576[/C][C]541.124323576372[/C][C]34.8756764236281[/C][/ROW]
[ROW][C]35[/C][C]556[/C][C]540.262565507314[/C][C]15.7374344926856[/C][/ROW]
[ROW][C]36[/C][C]559[/C][C]538.247188164561[/C][C]20.7528118354392[/C][/ROW]
[ROW][C]37[/C][C]541[/C][C]527.905762795375[/C][C]13.094237204625[/C][/ROW]
[ROW][C]38[/C][C]606[/C][C]529.62111096901[/C][C]76.3788890309896[/C][/ROW]
[ROW][C]39[/C][C]600[/C][C]528.316964031198[/C][C]71.6830359688024[/C][/ROW]
[ROW][C]40[/C][C]588[/C][C]531.164847730068[/C][C]56.8351522699323[/C][/ROW]
[ROW][C]41[/C][C]570[/C][C]528.995645347324[/C][C]41.0043546526761[/C][/ROW]
[ROW][C]42[/C][C]626[/C][C]536.381661521524[/C][C]89.6183384784756[/C][/ROW]
[ROW][C]43[/C][C]601[/C][C]539.039121276113[/C][C]61.9608787238868[/C][/ROW]
[ROW][C]44[/C][C]588[/C][C]534.436013777663[/C][C]53.5639862223365[/C][/ROW]
[ROW][C]45[/C][C]573[/C][C]527.787757194987[/C][C]45.212242805013[/C][/ROW]
[ROW][C]46[/C][C]622[/C][C]530.489381943745[/C][C]91.5106180562547[/C][/ROW]
[ROW][C]47[/C][C]570[/C][C]528.255519180429[/C][C]41.7444808195709[/C][/ROW]
[ROW][C]48[/C][C]547[/C][C]518.066147008787[/C][C]28.9338529912134[/C][/ROW]
[ROW][C]49[/C][C]512[/C][C]511.705763392527[/C][C]0.294236607472774[/C][/ROW]
[ROW][C]50[/C][C]554[/C][C]515.900365156426[/C][C]38.0996348435737[/C][/ROW]
[ROW][C]51[/C][C]517[/C][C]512.625943041398[/C][C]4.37405695860235[/C][/ROW]
[ROW][C]52[/C][C]506[/C][C]516.970819404122[/C][C]-10.9708194041219[/C][/ROW]
[ROW][C]53[/C][C]479[/C][C]523.921766490425[/C][C]-44.9217664904254[/C][/ROW]
[ROW][C]54[/C][C]527[/C][C]536.389276061521[/C][C]-9.38927606152069[/C][/ROW]
[ROW][C]55[/C][C]508[/C][C]555.172039672846[/C][C]-47.172039672846[/C][/ROW]
[ROW][C]56[/C][C]532[/C][C]570.663949110759[/C][C]-38.6639491107591[/C][/ROW]
[ROW][C]57[/C][C]532[/C][C]583.680251143591[/C][C]-51.6802511435914[/C][/ROW]
[ROW][C]58[/C][C]588[/C][C]580.18781557182[/C][C]7.81218442818003[/C][/ROW]
[ROW][C]59[/C][C]566[/C][C]568.343760612622[/C][C]-2.34376061262162[/C][/ROW]
[ROW][C]60[/C][C]573[/C][C]563.434870620657[/C][C]9.5651293793432[/C][/ROW]
[ROW][C]61[/C][C]545[/C][C]570.181540807329[/C][C]-25.1815408073291[/C][/ROW]
[ROW][C]62[/C][C]597[/C][C]570.768745324245[/C][C]26.2312546757552[/C][/ROW]
[ROW][C]63[/C][C]555[/C][C]570.72825392575[/C][C]-15.7282539257502[/C][/ROW]
[ROW][C]64[/C][C]548[/C][C]570.593391982465[/C][C]-22.5933919824651[/C][/ROW]
[ROW][C]65[/C][C]524[/C][C]572.617512139214[/C][C]-48.617512139214[/C][/ROW]
[ROW][C]66[/C][C]572[/C][C]581.871928433216[/C][C]-9.87192843321641[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156431&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156431&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 IndexActualsInterpolationForecastResidualsPrediction Error
1589570.77220485063418.227795149366
2559570.210250295894-11.2102502958943
3623568.23860015025454.7613998497464
4617567.75737532721249.2426246727879
5603574.63672522815828.3632747718421
6558574.776813711037-16.7768137110365
7609575.51436116030433.4856388396962
8583572.3715584336710.6284415663296
9570570.363124651973-0.363124651973359
10543556.51437784028-13.5143778402796
11598555.28404448359942.7159555164008
12569550.73896169094118.2610383090594
13552550.1474201264941.85257987350637
14514551.272663281362-37.2726632813617
15569552.06035470399516.9396452960048
16529548.977478436381-19.977478436381
17515542.933562803895-27.9335628038949
18481536.751453685487-55.751453685487
19536525.36012427599210.6398757240078
20498516.445927805005-18.4459278050047
21446516.637747371931-70.6377473719311
22503518.67317554776-15.6731755477598
23470520.514383029122-50.5143830291221
24458517.546085460647-59.5460854606469
25437519.539783936609-82.5397839366092
26502528.547345447493-26.5473454474927
27482537.574779815957-55.5747798159573
28474541.137416950442-67.1374169504415
29457535.928846598668-78.9288465986682
30522535.514580783449-13.5145807834494
31513531.692097992066-18.6920979920663
32515530.620857138004-15.6208571380043
33506537.093514069881-31.0935140698812
34576541.12432357637234.8756764236281
35556540.26256550731415.7374344926856
36559538.24718816456120.7528118354392
37541527.90576279537513.094237204625
38606529.6211109690176.3788890309896
39600528.31696403119871.6830359688024
40588531.16484773006856.8351522699323
41570528.99564534732441.0043546526761
42626536.38166152152489.6183384784756
43601539.03912127611361.9608787238868
44588534.43601377766353.5639862223365
45573527.78775719498745.212242805013
46622530.48938194374591.5106180562547
47570528.25551918042941.7444808195709
48547518.06614700878728.9338529912134
49512511.7057633925270.294236607472774
50554515.90036515642638.0996348435737
51517512.6259430413984.37405695860235
52506516.970819404122-10.9708194041219
53479523.921766490425-44.9217664904254
54527536.389276061521-9.38927606152069
55508555.172039672846-47.172039672846
56532570.663949110759-38.6639491107591
57532583.680251143591-51.6802511435914
58588580.187815571827.81218442818003
59566568.343760612622-2.34376061262162
60573563.4348706206579.5651293793432
61545570.181540807329-25.1815408073291
62597570.76874532424526.2312546757552
63555570.72825392575-15.7282539257502
64548570.593391982465-22.5933919824651
65524572.617512139214-48.617512139214
66572581.871928433216-9.87192843321641







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.2821151564650170.5642303129300340.717884843534983
70.1867482714830470.3734965429660950.813251728516953
80.1553495529949080.3106991059898170.844650447005092
90.1264046631008420.2528093262016830.873595336899158
100.09701519181791960.1940303836358390.90298480818208
110.1110213086261090.2220426172522170.888978691373891
120.06904558262021920.1380911652404380.930954417379781
130.04477898105944330.08955796211888650.955221018940557
140.05821826978363850.1164365395672770.941781730216361
150.04388562383006790.08777124766013570.956114376169932
160.03114613107550110.06229226215100230.968853868924499
170.02200396845658590.04400793691317190.977996031543414
180.02226869355903830.04453738711807660.977731306440962
190.0228906609518620.04578132190372390.977109339048138
200.01311647779088390.02623295558176780.986883522209116
210.01807311188547460.03614622377094910.981926888114525
220.01294965795550820.02589931591101640.987050342044492
230.008974529876331310.01794905975266260.991025470123669
240.007372019377926790.01474403875585360.992627980622073
250.01291411947139220.02582823894278430.987085880528608
260.01217504113142230.02435008226284470.987824958868578
270.01049878526363980.02099757052727960.98950121473636
280.01285954599600920.02571909199201850.987140454003991
290.03251072532767120.06502145065534240.967489274672329
300.06070260918434770.1214052183686950.939297390815652
310.09277086546402340.1855417309280470.907229134535977
320.1450987097299620.2901974194599230.854901290270038
330.2801435891883420.5602871783766840.719856410811658
340.4342659107324880.8685318214649760.565734089267512
350.5330401538838840.9339196922322330.466959846116116
360.6539480220175440.6921039559649110.346051977982456
370.8398148319879870.3203703360240260.160185168012013
380.9218349219286510.1563301561426980.0781650780713488
390.9416019471496190.1167961057007620.0583980528503808
400.9364689723882210.1270620552235590.0635310276117795
410.9389063302566290.1221873394867430.0610936697433715
420.9406840522849160.1186318954301690.0593159477150844
430.9143863366840540.1712273266318920.0856136633159462
440.878624843813460.242750312373080.12137515618654
450.8325839478657620.3348321042684760.167416052134238
460.9292156268210070.1415687463579870.0707843731789934
470.9190564084185460.1618871831629090.0809435915814544
480.8967995019431610.2064009961136780.103200498056839
490.8535074783981780.2929850432036440.146492521601822
500.8871516211277390.2256967577445210.112848378872261
510.8609995983267690.2780008033464610.139000401673231
520.8305375192652520.3389249614694960.169462480734748
530.8643743334861660.2712513330276680.135625666513834
540.8388512880369910.3222974239260190.161148711963009
550.9090276489524750.1819447020950510.0909723510475253
560.9150704639608560.1698590720782880.0849295360391439
570.9569355343137620.08612893137247640.0430644656862382
580.9109272431087550.1781455137824890.0890727568912447
590.8301914905007640.3396170189984720.169808509499236
600.7254057089503810.5491885820992370.274594291049619

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.282115156465017 & 0.564230312930034 & 0.717884843534983 \tabularnewline
7 & 0.186748271483047 & 0.373496542966095 & 0.813251728516953 \tabularnewline
8 & 0.155349552994908 & 0.310699105989817 & 0.844650447005092 \tabularnewline
9 & 0.126404663100842 & 0.252809326201683 & 0.873595336899158 \tabularnewline
10 & 0.0970151918179196 & 0.194030383635839 & 0.90298480818208 \tabularnewline
11 & 0.111021308626109 & 0.222042617252217 & 0.888978691373891 \tabularnewline
12 & 0.0690455826202192 & 0.138091165240438 & 0.930954417379781 \tabularnewline
13 & 0.0447789810594433 & 0.0895579621188865 & 0.955221018940557 \tabularnewline
14 & 0.0582182697836385 & 0.116436539567277 & 0.941781730216361 \tabularnewline
15 & 0.0438856238300679 & 0.0877712476601357 & 0.956114376169932 \tabularnewline
16 & 0.0311461310755011 & 0.0622922621510023 & 0.968853868924499 \tabularnewline
17 & 0.0220039684565859 & 0.0440079369131719 & 0.977996031543414 \tabularnewline
18 & 0.0222686935590383 & 0.0445373871180766 & 0.977731306440962 \tabularnewline
19 & 0.022890660951862 & 0.0457813219037239 & 0.977109339048138 \tabularnewline
20 & 0.0131164777908839 & 0.0262329555817678 & 0.986883522209116 \tabularnewline
21 & 0.0180731118854746 & 0.0361462237709491 & 0.981926888114525 \tabularnewline
22 & 0.0129496579555082 & 0.0258993159110164 & 0.987050342044492 \tabularnewline
23 & 0.00897452987633131 & 0.0179490597526626 & 0.991025470123669 \tabularnewline
24 & 0.00737201937792679 & 0.0147440387558536 & 0.992627980622073 \tabularnewline
25 & 0.0129141194713922 & 0.0258282389427843 & 0.987085880528608 \tabularnewline
26 & 0.0121750411314223 & 0.0243500822628447 & 0.987824958868578 \tabularnewline
27 & 0.0104987852636398 & 0.0209975705272796 & 0.98950121473636 \tabularnewline
28 & 0.0128595459960092 & 0.0257190919920185 & 0.987140454003991 \tabularnewline
29 & 0.0325107253276712 & 0.0650214506553424 & 0.967489274672329 \tabularnewline
30 & 0.0607026091843477 & 0.121405218368695 & 0.939297390815652 \tabularnewline
31 & 0.0927708654640234 & 0.185541730928047 & 0.907229134535977 \tabularnewline
32 & 0.145098709729962 & 0.290197419459923 & 0.854901290270038 \tabularnewline
33 & 0.280143589188342 & 0.560287178376684 & 0.719856410811658 \tabularnewline
34 & 0.434265910732488 & 0.868531821464976 & 0.565734089267512 \tabularnewline
35 & 0.533040153883884 & 0.933919692232233 & 0.466959846116116 \tabularnewline
36 & 0.653948022017544 & 0.692103955964911 & 0.346051977982456 \tabularnewline
37 & 0.839814831987987 & 0.320370336024026 & 0.160185168012013 \tabularnewline
38 & 0.921834921928651 & 0.156330156142698 & 0.0781650780713488 \tabularnewline
39 & 0.941601947149619 & 0.116796105700762 & 0.0583980528503808 \tabularnewline
40 & 0.936468972388221 & 0.127062055223559 & 0.0635310276117795 \tabularnewline
41 & 0.938906330256629 & 0.122187339486743 & 0.0610936697433715 \tabularnewline
42 & 0.940684052284916 & 0.118631895430169 & 0.0593159477150844 \tabularnewline
43 & 0.914386336684054 & 0.171227326631892 & 0.0856136633159462 \tabularnewline
44 & 0.87862484381346 & 0.24275031237308 & 0.12137515618654 \tabularnewline
45 & 0.832583947865762 & 0.334832104268476 & 0.167416052134238 \tabularnewline
46 & 0.929215626821007 & 0.141568746357987 & 0.0707843731789934 \tabularnewline
47 & 0.919056408418546 & 0.161887183162909 & 0.0809435915814544 \tabularnewline
48 & 0.896799501943161 & 0.206400996113678 & 0.103200498056839 \tabularnewline
49 & 0.853507478398178 & 0.292985043203644 & 0.146492521601822 \tabularnewline
50 & 0.887151621127739 & 0.225696757744521 & 0.112848378872261 \tabularnewline
51 & 0.860999598326769 & 0.278000803346461 & 0.139000401673231 \tabularnewline
52 & 0.830537519265252 & 0.338924961469496 & 0.169462480734748 \tabularnewline
53 & 0.864374333486166 & 0.271251333027668 & 0.135625666513834 \tabularnewline
54 & 0.838851288036991 & 0.322297423926019 & 0.161148711963009 \tabularnewline
55 & 0.909027648952475 & 0.181944702095051 & 0.0909723510475253 \tabularnewline
56 & 0.915070463960856 & 0.169859072078288 & 0.0849295360391439 \tabularnewline
57 & 0.956935534313762 & 0.0861289313724764 & 0.0430644656862382 \tabularnewline
58 & 0.910927243108755 & 0.178145513782489 & 0.0890727568912447 \tabularnewline
59 & 0.830191490500764 & 0.339617018998472 & 0.169808509499236 \tabularnewline
60 & 0.725405708950381 & 0.549188582099237 & 0.274594291049619 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156431&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]6[/C][C]0.282115156465017[/C][C]0.564230312930034[/C][C]0.717884843534983[/C][/ROW]
[ROW][C]7[/C][C]0.186748271483047[/C][C]0.373496542966095[/C][C]0.813251728516953[/C][/ROW]
[ROW][C]8[/C][C]0.155349552994908[/C][C]0.310699105989817[/C][C]0.844650447005092[/C][/ROW]
[ROW][C]9[/C][C]0.126404663100842[/C][C]0.252809326201683[/C][C]0.873595336899158[/C][/ROW]
[ROW][C]10[/C][C]0.0970151918179196[/C][C]0.194030383635839[/C][C]0.90298480818208[/C][/ROW]
[ROW][C]11[/C][C]0.111021308626109[/C][C]0.222042617252217[/C][C]0.888978691373891[/C][/ROW]
[ROW][C]12[/C][C]0.0690455826202192[/C][C]0.138091165240438[/C][C]0.930954417379781[/C][/ROW]
[ROW][C]13[/C][C]0.0447789810594433[/C][C]0.0895579621188865[/C][C]0.955221018940557[/C][/ROW]
[ROW][C]14[/C][C]0.0582182697836385[/C][C]0.116436539567277[/C][C]0.941781730216361[/C][/ROW]
[ROW][C]15[/C][C]0.0438856238300679[/C][C]0.0877712476601357[/C][C]0.956114376169932[/C][/ROW]
[ROW][C]16[/C][C]0.0311461310755011[/C][C]0.0622922621510023[/C][C]0.968853868924499[/C][/ROW]
[ROW][C]17[/C][C]0.0220039684565859[/C][C]0.0440079369131719[/C][C]0.977996031543414[/C][/ROW]
[ROW][C]18[/C][C]0.0222686935590383[/C][C]0.0445373871180766[/C][C]0.977731306440962[/C][/ROW]
[ROW][C]19[/C][C]0.022890660951862[/C][C]0.0457813219037239[/C][C]0.977109339048138[/C][/ROW]
[ROW][C]20[/C][C]0.0131164777908839[/C][C]0.0262329555817678[/C][C]0.986883522209116[/C][/ROW]
[ROW][C]21[/C][C]0.0180731118854746[/C][C]0.0361462237709491[/C][C]0.981926888114525[/C][/ROW]
[ROW][C]22[/C][C]0.0129496579555082[/C][C]0.0258993159110164[/C][C]0.987050342044492[/C][/ROW]
[ROW][C]23[/C][C]0.00897452987633131[/C][C]0.0179490597526626[/C][C]0.991025470123669[/C][/ROW]
[ROW][C]24[/C][C]0.00737201937792679[/C][C]0.0147440387558536[/C][C]0.992627980622073[/C][/ROW]
[ROW][C]25[/C][C]0.0129141194713922[/C][C]0.0258282389427843[/C][C]0.987085880528608[/C][/ROW]
[ROW][C]26[/C][C]0.0121750411314223[/C][C]0.0243500822628447[/C][C]0.987824958868578[/C][/ROW]
[ROW][C]27[/C][C]0.0104987852636398[/C][C]0.0209975705272796[/C][C]0.98950121473636[/C][/ROW]
[ROW][C]28[/C][C]0.0128595459960092[/C][C]0.0257190919920185[/C][C]0.987140454003991[/C][/ROW]
[ROW][C]29[/C][C]0.0325107253276712[/C][C]0.0650214506553424[/C][C]0.967489274672329[/C][/ROW]
[ROW][C]30[/C][C]0.0607026091843477[/C][C]0.121405218368695[/C][C]0.939297390815652[/C][/ROW]
[ROW][C]31[/C][C]0.0927708654640234[/C][C]0.185541730928047[/C][C]0.907229134535977[/C][/ROW]
[ROW][C]32[/C][C]0.145098709729962[/C][C]0.290197419459923[/C][C]0.854901290270038[/C][/ROW]
[ROW][C]33[/C][C]0.280143589188342[/C][C]0.560287178376684[/C][C]0.719856410811658[/C][/ROW]
[ROW][C]34[/C][C]0.434265910732488[/C][C]0.868531821464976[/C][C]0.565734089267512[/C][/ROW]
[ROW][C]35[/C][C]0.533040153883884[/C][C]0.933919692232233[/C][C]0.466959846116116[/C][/ROW]
[ROW][C]36[/C][C]0.653948022017544[/C][C]0.692103955964911[/C][C]0.346051977982456[/C][/ROW]
[ROW][C]37[/C][C]0.839814831987987[/C][C]0.320370336024026[/C][C]0.160185168012013[/C][/ROW]
[ROW][C]38[/C][C]0.921834921928651[/C][C]0.156330156142698[/C][C]0.0781650780713488[/C][/ROW]
[ROW][C]39[/C][C]0.941601947149619[/C][C]0.116796105700762[/C][C]0.0583980528503808[/C][/ROW]
[ROW][C]40[/C][C]0.936468972388221[/C][C]0.127062055223559[/C][C]0.0635310276117795[/C][/ROW]
[ROW][C]41[/C][C]0.938906330256629[/C][C]0.122187339486743[/C][C]0.0610936697433715[/C][/ROW]
[ROW][C]42[/C][C]0.940684052284916[/C][C]0.118631895430169[/C][C]0.0593159477150844[/C][/ROW]
[ROW][C]43[/C][C]0.914386336684054[/C][C]0.171227326631892[/C][C]0.0856136633159462[/C][/ROW]
[ROW][C]44[/C][C]0.87862484381346[/C][C]0.24275031237308[/C][C]0.12137515618654[/C][/ROW]
[ROW][C]45[/C][C]0.832583947865762[/C][C]0.334832104268476[/C][C]0.167416052134238[/C][/ROW]
[ROW][C]46[/C][C]0.929215626821007[/C][C]0.141568746357987[/C][C]0.0707843731789934[/C][/ROW]
[ROW][C]47[/C][C]0.919056408418546[/C][C]0.161887183162909[/C][C]0.0809435915814544[/C][/ROW]
[ROW][C]48[/C][C]0.896799501943161[/C][C]0.206400996113678[/C][C]0.103200498056839[/C][/ROW]
[ROW][C]49[/C][C]0.853507478398178[/C][C]0.292985043203644[/C][C]0.146492521601822[/C][/ROW]
[ROW][C]50[/C][C]0.887151621127739[/C][C]0.225696757744521[/C][C]0.112848378872261[/C][/ROW]
[ROW][C]51[/C][C]0.860999598326769[/C][C]0.278000803346461[/C][C]0.139000401673231[/C][/ROW]
[ROW][C]52[/C][C]0.830537519265252[/C][C]0.338924961469496[/C][C]0.169462480734748[/C][/ROW]
[ROW][C]53[/C][C]0.864374333486166[/C][C]0.271251333027668[/C][C]0.135625666513834[/C][/ROW]
[ROW][C]54[/C][C]0.838851288036991[/C][C]0.322297423926019[/C][C]0.161148711963009[/C][/ROW]
[ROW][C]55[/C][C]0.909027648952475[/C][C]0.181944702095051[/C][C]0.0909723510475253[/C][/ROW]
[ROW][C]56[/C][C]0.915070463960856[/C][C]0.169859072078288[/C][C]0.0849295360391439[/C][/ROW]
[ROW][C]57[/C][C]0.956935534313762[/C][C]0.0861289313724764[/C][C]0.0430644656862382[/C][/ROW]
[ROW][C]58[/C][C]0.910927243108755[/C][C]0.178145513782489[/C][C]0.0890727568912447[/C][/ROW]
[ROW][C]59[/C][C]0.830191490500764[/C][C]0.339617018998472[/C][C]0.169808509499236[/C][/ROW]
[ROW][C]60[/C][C]0.725405708950381[/C][C]0.549188582099237[/C][C]0.274594291049619[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156431&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156431&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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.2821151564650170.5642303129300340.717884843534983
70.1867482714830470.3734965429660950.813251728516953
80.1553495529949080.3106991059898170.844650447005092
90.1264046631008420.2528093262016830.873595336899158
100.09701519181791960.1940303836358390.90298480818208
110.1110213086261090.2220426172522170.888978691373891
120.06904558262021920.1380911652404380.930954417379781
130.04477898105944330.08955796211888650.955221018940557
140.05821826978363850.1164365395672770.941781730216361
150.04388562383006790.08777124766013570.956114376169932
160.03114613107550110.06229226215100230.968853868924499
170.02200396845658590.04400793691317190.977996031543414
180.02226869355903830.04453738711807660.977731306440962
190.0228906609518620.04578132190372390.977109339048138
200.01311647779088390.02623295558176780.986883522209116
210.01807311188547460.03614622377094910.981926888114525
220.01294965795550820.02589931591101640.987050342044492
230.008974529876331310.01794905975266260.991025470123669
240.007372019377926790.01474403875585360.992627980622073
250.01291411947139220.02582823894278430.987085880528608
260.01217504113142230.02435008226284470.987824958868578
270.01049878526363980.02099757052727960.98950121473636
280.01285954599600920.02571909199201850.987140454003991
290.03251072532767120.06502145065534240.967489274672329
300.06070260918434770.1214052183686950.939297390815652
310.09277086546402340.1855417309280470.907229134535977
320.1450987097299620.2901974194599230.854901290270038
330.2801435891883420.5602871783766840.719856410811658
340.4342659107324880.8685318214649760.565734089267512
350.5330401538838840.9339196922322330.466959846116116
360.6539480220175440.6921039559649110.346051977982456
370.8398148319879870.3203703360240260.160185168012013
380.9218349219286510.1563301561426980.0781650780713488
390.9416019471496190.1167961057007620.0583980528503808
400.9364689723882210.1270620552235590.0635310276117795
410.9389063302566290.1221873394867430.0610936697433715
420.9406840522849160.1186318954301690.0593159477150844
430.9143863366840540.1712273266318920.0856136633159462
440.878624843813460.242750312373080.12137515618654
450.8325839478657620.3348321042684760.167416052134238
460.9292156268210070.1415687463579870.0707843731789934
470.9190564084185460.1618871831629090.0809435915814544
480.8967995019431610.2064009961136780.103200498056839
490.8535074783981780.2929850432036440.146492521601822
500.8871516211277390.2256967577445210.112848378872261
510.8609995983267690.2780008033464610.139000401673231
520.8305375192652520.3389249614694960.169462480734748
530.8643743334861660.2712513330276680.135625666513834
540.8388512880369910.3222974239260190.161148711963009
550.9090276489524750.1819447020950510.0909723510475253
560.9150704639608560.1698590720782880.0849295360391439
570.9569355343137620.08612893137247640.0430644656862382
580.9109272431087550.1781455137824890.0890727568912447
590.8301914905007640.3396170189984720.169808509499236
600.7254057089503810.5491885820992370.274594291049619







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level120.218181818181818NOK
10% type I error level170.309090909090909NOK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 12 & 0.218181818181818 & NOK \tabularnewline
10% type I error level & 17 & 0.309090909090909 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156431&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]12[/C][C]0.218181818181818[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]17[/C][C]0.309090909090909[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156431&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156431&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 testsOK/NOK
1% type I error level00OK
5% type I error level120.218181818181818NOK
10% type I error level170.309090909090909NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- 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 <- x1
if (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'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,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$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$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')
}