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 computationMon, 05 Nov 2012 07:27:09 -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/2012/Nov/05/t1352118498ujqlgxxd949qgdc.htm/, Retrieved Fri, 03 Feb 2023 09:55:06 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186011, Retrieved Fri, 03 Feb 2023 09:55:06 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact106
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [ws7/2] [2011-11-24 10:28:55] [8ae0a4da1b3ee81f40dbba5e42914d07]
-    D  [Multiple Regression] [ws7 Multiple Regr...] [2011-11-24 12:34:36] [75512e061a94450f738c2449abbaac12]
-    D    [Multiple Regression] [WS7 - 1] [2012-11-05 12:22:22] [fe055a25191a04e375a94ef97fddf389]
- R  D        [Multiple Regression] [WS7 - 1] [2012-11-05 12:27:09] [bb6586829c319b85526f2aafa1a31f73] [Current]
Feedback Forum

Post a new message
Dataseries X:
72772	26073	22274
45104	18103	14819
44525	15100	15136
41169	14738	13704
31118	22259	19638
28435	10277	7551
22162	6225	8019
20202	7663	6509
17773	6618	6634
17094	9945	11166
15153	7590	7508
11218	4293	4275
10796	4656	4944
9594	5145	5441
9309	2001	1689
8556	1779	1522
8041	1609	1416
7639	2191	1594
6884	1617	1909
6642	2554	2599
6321	2198	1262
6216	1578	1199
5865	3446	4404
5799	1380	1166
5695	1249	1122
5644	1223	886
5446	834	778
5395	3754	4436
5363	2283	1890
5338	3028	3107
5160	1100	1038
5091	457	300
5057	1201	988
5039	2192	2008
4880	1508	1522
4735	1393	1336
4693	952	976
4653	1032	798
4586	1279	869
4398	1370	1260
3974	649	578
3858	1900	2359
3826	666	736
3819	1313	1690
3556	1353	1201
3372	1500	813
3193	877	778
3126	874	687
3104	1133	1270
2967	754	671
2848	695	1559
2748	609	489
2649	696	773
2625	756	629
2572	670	637
2548	301	277
2477	630	776
2442	798	1651
2392	436	377
2372	388	222
2346	864	1068
2251	497	399
2230	449	547
2225	919	668
2220	536	451
2205	673	724
2193	837	853
2116	534	434
2102	845	730
2099	626	612
2096	871	558
2064	740	859
2036	391	311
1920	435	318
1813	424	312
1776	338	343
1752	744	710
1738	368	273
1729	393	259
1685	938	1274
1684	804	625
1549	456	245
1533	267	235
1528	338	250
1489	NA	NA
1456	635	303
1393	402	250
1370	462	403
1357	525	441
1292	1069	1507
1278	487	388
1256	756	530
1219	360	107
1217	481	868
1187	NA	NA
1187	480	753
1182	68	62
1157	262	346
1156	520	478
1155	298	292




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186011&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186011&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186011&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 time8 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
zondag[t] = + 136.517245101424 -0.01724708490382weekdag[t] + 0.932853142070083zaterdag[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
zondag[t] =  +  136.517245101424 -0.01724708490382weekdag[t] +  0.932853142070083zaterdag[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186011&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]zondag[t] =  +  136.517245101424 -0.01724708490382weekdag[t] +  0.932853142070083zaterdag[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186011&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186011&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
zondag[t] = + 136.517245101424 -0.01724708490382weekdag[t] + 0.932853142070083zaterdag[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)136.51724510142466.7489392.04520.0435970.021799
weekdag-0.017247084903820.017717-0.97350.3327880.166394
zaterdag0.9328531420700830.04323221.577900

\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) & 136.517245101424 & 66.748939 & 2.0452 & 0.043597 & 0.021799 \tabularnewline
weekdag & -0.01724708490382 & 0.017717 & -0.9735 & 0.332788 & 0.166394 \tabularnewline
zaterdag & 0.932853142070083 & 0.043232 & 21.5779 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186011&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]136.517245101424[/C][C]66.748939[/C][C]2.0452[/C][C]0.043597[/C][C]0.021799[/C][/ROW]
[ROW][C]weekdag[/C][C]-0.01724708490382[/C][C]0.017717[/C][C]-0.9735[/C][C]0.332788[/C][C]0.166394[/C][/ROW]
[ROW][C]zaterdag[/C][C]0.932853142070083[/C][C]0.043232[/C][C]21.5779[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186011&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186011&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)136.51724510142466.7489392.04520.0435970.021799
weekdag-0.017247084903820.017717-0.97350.3327880.166394
zaterdag0.9328531420700830.04323221.577900







Multiple Linear Regression - Regression Statistics
Multiple R0.990648729993195
R-squared0.981384906237129
Adjusted R-squared0.980993009526332
F-TEST (value)2504.19276099712
F-TEST (DF numerator)2
F-TEST (DF denominator)95
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation556.463791154703
Sum Squared Residuals29416935.3322951

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.990648729993195 \tabularnewline
R-squared & 0.981384906237129 \tabularnewline
Adjusted R-squared & 0.980993009526332 \tabularnewline
F-TEST (value) & 2504.19276099712 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 95 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 556.463791154703 \tabularnewline
Sum Squared Residuals & 29416935.3322951 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186011&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.990648729993195[/C][/ROW]
[ROW][C]R-squared[/C][C]0.981384906237129[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.980993009526332[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2504.19276099712[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]95[/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]556.463791154703[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]29416935.3322951[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186011&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186011&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 R0.990648729993195
R-squared0.981384906237129
Adjusted R-squared0.980993009526332
F-TEST (value)2504.19276099712
F-TEST (DF numerator)2
F-TEST (DF denominator)95
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation556.463791154703
Sum Squared Residuals29416935.3322951







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12227423203.6923556739-929.692355673907
21481916246.0451584942-1427.04515849423
31513613454.67323501711681.32676498291
41370413174.8616145249529.138385475062
51963820364.2005464023-726.200546402326
675519233.02812691554-1682.02812691554
780195561.298158849232457.70184115077
865096936.5452635575-427.545263557497
966346003.60689932564630.393100674361
10111669118.92007364252047.0799263575
1175086955.52751586577552.472484134231
1242753947.77798555724327.222014442763
1349444293.68194595809650.318054041911
1454414770.57812848475670.421871515249
1516891842.603269014-153.603269014
1615221648.49692640702-126.496926407018
1714161498.79414098057-82.7941409805711
1815942048.64799779669-454.647997796695
1919091526.21184335085382.788156649149
2025992404.46903201724194.530967982756
2112622077.90962769442-815.90962769442
2211991501.35162352587-302.35162352587
2344043249.975019714031154.02498028597
2411661323.83873580089-157.838735800886
2511221203.4286710197-81.428671019703
268861180.05409065598-294.054090655976
27778820.58914120167-42.5891412016696
2844363545.39991737641890.600082623593
2918902173.72485210824-283.724852108237
3031072869.13162007304237.868379926956
3110381073.6607432748-35.6607432748043
32300475.026221782104-175.026221782104
339881169.65536036898-181.655360368976
3420082094.4232716887-86.4232716886972
3515221459.0940090124762.9059909875322
3613361354.31672498546-18.3167249854622
37976943.65286689851632.347133101484
387981018.97100166028-220.971001660275
398691250.54128244014-381.541282440142
4012601338.67337033044-78.6733703304376
41578673.399018897128-95.3990188971276
4223591842.39896147564516.601038524356
43736691.81009087808444.1899091219157
4416901295.48680339175394.513196608245
4512011337.33691240426-136.336912404263
468131477.63978791087-664.639787910868
47778899.55950859899-121.55950859899
48687897.916503861336-210.916503861336
4912701139.90490352537130.095096474629
50671788.716413312633-117.716413312633
511559735.730481034053823.269518965947
52489657.229819306408-168.229819306408
53773740.09550407198332.904495928017
54629796.48062263388-167.48062263388
55637717.169347915755-80.169347915755
56277373.360468529586-96.360468529586
57776681.49369529881594.5063047011854
581651838.816671138222812.183328861778
59377501.986187954043-124.986187954043
60222457.554178832756-235.554178832756
611068902.040698665614165.959301334386
62399561.322068591757-162.322068591757
63547516.90730655537330.0926934446269
64668955.434518752831-287.434518752831
65451598.238000764509-147.238000764509
66724726.297587501667-2.29758750166726
67853879.492467820007-26.4924678200067
68434598.165991310366-164.165991310366
69730888.524777682815-158.524777682815
70612684.281680824178-72.2816808241782
71558912.88244188606-354.88244188606
72859791.23058699180167.7694130081986
73311466.147758786649-155.147758786649
74318509.193958886576-191.193958886576
75312500.778012408514-188.778012408514
76343421.190784331928-78.1907843319283
77710800.343090050074-90.3430900500735
78273449.831767820376-176.831767820376
79259473.308320136262-214.308320136262
801274982.472154300226291.527845699774
81625857.487080347738-232.487080347738
82245535.182543369365-290.182543369365
83235359.149252876581-124.149252876581
84250425.468061388076-175.468061388076
85NANA-400.767234695965
86303540.499018942577-237.499018942577
87250390.86689041957-140.86689041957
88403564.860850473734-161.860850473734
8944145.454020278608395.545979721392
9015071687.77495078247-180.774950782473
91388678.091881867209-290.091881867209
92530874.320179748898-344.320179748898
93107-196.770095890814303.770095890814
94868678.81446351423189.18553648577
95NANA-117.565204405875
96753767.969891090067-14.9698910900666
9762185.663248829052-123.663248829052
98346448.587098374397-102.587098374397
99478NANA
100292NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 22274 & 23203.6923556739 & -929.692355673907 \tabularnewline
2 & 14819 & 16246.0451584942 & -1427.04515849423 \tabularnewline
3 & 15136 & 13454.6732350171 & 1681.32676498291 \tabularnewline
4 & 13704 & 13174.8616145249 & 529.138385475062 \tabularnewline
5 & 19638 & 20364.2005464023 & -726.200546402326 \tabularnewline
6 & 7551 & 9233.02812691554 & -1682.02812691554 \tabularnewline
7 & 8019 & 5561.29815884923 & 2457.70184115077 \tabularnewline
8 & 6509 & 6936.5452635575 & -427.545263557497 \tabularnewline
9 & 6634 & 6003.60689932564 & 630.393100674361 \tabularnewline
10 & 11166 & 9118.9200736425 & 2047.0799263575 \tabularnewline
11 & 7508 & 6955.52751586577 & 552.472484134231 \tabularnewline
12 & 4275 & 3947.77798555724 & 327.222014442763 \tabularnewline
13 & 4944 & 4293.68194595809 & 650.318054041911 \tabularnewline
14 & 5441 & 4770.57812848475 & 670.421871515249 \tabularnewline
15 & 1689 & 1842.603269014 & -153.603269014 \tabularnewline
16 & 1522 & 1648.49692640702 & -126.496926407018 \tabularnewline
17 & 1416 & 1498.79414098057 & -82.7941409805711 \tabularnewline
18 & 1594 & 2048.64799779669 & -454.647997796695 \tabularnewline
19 & 1909 & 1526.21184335085 & 382.788156649149 \tabularnewline
20 & 2599 & 2404.46903201724 & 194.530967982756 \tabularnewline
21 & 1262 & 2077.90962769442 & -815.90962769442 \tabularnewline
22 & 1199 & 1501.35162352587 & -302.35162352587 \tabularnewline
23 & 4404 & 3249.97501971403 & 1154.02498028597 \tabularnewline
24 & 1166 & 1323.83873580089 & -157.838735800886 \tabularnewline
25 & 1122 & 1203.4286710197 & -81.428671019703 \tabularnewline
26 & 886 & 1180.05409065598 & -294.054090655976 \tabularnewline
27 & 778 & 820.58914120167 & -42.5891412016696 \tabularnewline
28 & 4436 & 3545.39991737641 & 890.600082623593 \tabularnewline
29 & 1890 & 2173.72485210824 & -283.724852108237 \tabularnewline
30 & 3107 & 2869.13162007304 & 237.868379926956 \tabularnewline
31 & 1038 & 1073.6607432748 & -35.6607432748043 \tabularnewline
32 & 300 & 475.026221782104 & -175.026221782104 \tabularnewline
33 & 988 & 1169.65536036898 & -181.655360368976 \tabularnewline
34 & 2008 & 2094.4232716887 & -86.4232716886972 \tabularnewline
35 & 1522 & 1459.09400901247 & 62.9059909875322 \tabularnewline
36 & 1336 & 1354.31672498546 & -18.3167249854622 \tabularnewline
37 & 976 & 943.652866898516 & 32.347133101484 \tabularnewline
38 & 798 & 1018.97100166028 & -220.971001660275 \tabularnewline
39 & 869 & 1250.54128244014 & -381.541282440142 \tabularnewline
40 & 1260 & 1338.67337033044 & -78.6733703304376 \tabularnewline
41 & 578 & 673.399018897128 & -95.3990188971276 \tabularnewline
42 & 2359 & 1842.39896147564 & 516.601038524356 \tabularnewline
43 & 736 & 691.810090878084 & 44.1899091219157 \tabularnewline
44 & 1690 & 1295.48680339175 & 394.513196608245 \tabularnewline
45 & 1201 & 1337.33691240426 & -136.336912404263 \tabularnewline
46 & 813 & 1477.63978791087 & -664.639787910868 \tabularnewline
47 & 778 & 899.55950859899 & -121.55950859899 \tabularnewline
48 & 687 & 897.916503861336 & -210.916503861336 \tabularnewline
49 & 1270 & 1139.90490352537 & 130.095096474629 \tabularnewline
50 & 671 & 788.716413312633 & -117.716413312633 \tabularnewline
51 & 1559 & 735.730481034053 & 823.269518965947 \tabularnewline
52 & 489 & 657.229819306408 & -168.229819306408 \tabularnewline
53 & 773 & 740.095504071983 & 32.904495928017 \tabularnewline
54 & 629 & 796.48062263388 & -167.48062263388 \tabularnewline
55 & 637 & 717.169347915755 & -80.169347915755 \tabularnewline
56 & 277 & 373.360468529586 & -96.360468529586 \tabularnewline
57 & 776 & 681.493695298815 & 94.5063047011854 \tabularnewline
58 & 1651 & 838.816671138222 & 812.183328861778 \tabularnewline
59 & 377 & 501.986187954043 & -124.986187954043 \tabularnewline
60 & 222 & 457.554178832756 & -235.554178832756 \tabularnewline
61 & 1068 & 902.040698665614 & 165.959301334386 \tabularnewline
62 & 399 & 561.322068591757 & -162.322068591757 \tabularnewline
63 & 547 & 516.907306555373 & 30.0926934446269 \tabularnewline
64 & 668 & 955.434518752831 & -287.434518752831 \tabularnewline
65 & 451 & 598.238000764509 & -147.238000764509 \tabularnewline
66 & 724 & 726.297587501667 & -2.29758750166726 \tabularnewline
67 & 853 & 879.492467820007 & -26.4924678200067 \tabularnewline
68 & 434 & 598.165991310366 & -164.165991310366 \tabularnewline
69 & 730 & 888.524777682815 & -158.524777682815 \tabularnewline
70 & 612 & 684.281680824178 & -72.2816808241782 \tabularnewline
71 & 558 & 912.88244188606 & -354.88244188606 \tabularnewline
72 & 859 & 791.230586991801 & 67.7694130081986 \tabularnewline
73 & 311 & 466.147758786649 & -155.147758786649 \tabularnewline
74 & 318 & 509.193958886576 & -191.193958886576 \tabularnewline
75 & 312 & 500.778012408514 & -188.778012408514 \tabularnewline
76 & 343 & 421.190784331928 & -78.1907843319283 \tabularnewline
77 & 710 & 800.343090050074 & -90.3430900500735 \tabularnewline
78 & 273 & 449.831767820376 & -176.831767820376 \tabularnewline
79 & 259 & 473.308320136262 & -214.308320136262 \tabularnewline
80 & 1274 & 982.472154300226 & 291.527845699774 \tabularnewline
81 & 625 & 857.487080347738 & -232.487080347738 \tabularnewline
82 & 245 & 535.182543369365 & -290.182543369365 \tabularnewline
83 & 235 & 359.149252876581 & -124.149252876581 \tabularnewline
84 & 250 & 425.468061388076 & -175.468061388076 \tabularnewline
85 & NA & NA & -400.767234695965 \tabularnewline
86 & 303 & 540.499018942577 & -237.499018942577 \tabularnewline
87 & 250 & 390.86689041957 & -140.86689041957 \tabularnewline
88 & 403 & 564.860850473734 & -161.860850473734 \tabularnewline
89 & 441 & 45.454020278608 & 395.545979721392 \tabularnewline
90 & 1507 & 1687.77495078247 & -180.774950782473 \tabularnewline
91 & 388 & 678.091881867209 & -290.091881867209 \tabularnewline
92 & 530 & 874.320179748898 & -344.320179748898 \tabularnewline
93 & 107 & -196.770095890814 & 303.770095890814 \tabularnewline
94 & 868 & 678.81446351423 & 189.18553648577 \tabularnewline
95 & NA & NA & -117.565204405875 \tabularnewline
96 & 753 & 767.969891090067 & -14.9698910900666 \tabularnewline
97 & 62 & 185.663248829052 & -123.663248829052 \tabularnewline
98 & 346 & 448.587098374397 & -102.587098374397 \tabularnewline
99 & 478 & NA & NA \tabularnewline
100 & 292 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186011&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]22274[/C][C]23203.6923556739[/C][C]-929.692355673907[/C][/ROW]
[ROW][C]2[/C][C]14819[/C][C]16246.0451584942[/C][C]-1427.04515849423[/C][/ROW]
[ROW][C]3[/C][C]15136[/C][C]13454.6732350171[/C][C]1681.32676498291[/C][/ROW]
[ROW][C]4[/C][C]13704[/C][C]13174.8616145249[/C][C]529.138385475062[/C][/ROW]
[ROW][C]5[/C][C]19638[/C][C]20364.2005464023[/C][C]-726.200546402326[/C][/ROW]
[ROW][C]6[/C][C]7551[/C][C]9233.02812691554[/C][C]-1682.02812691554[/C][/ROW]
[ROW][C]7[/C][C]8019[/C][C]5561.29815884923[/C][C]2457.70184115077[/C][/ROW]
[ROW][C]8[/C][C]6509[/C][C]6936.5452635575[/C][C]-427.545263557497[/C][/ROW]
[ROW][C]9[/C][C]6634[/C][C]6003.60689932564[/C][C]630.393100674361[/C][/ROW]
[ROW][C]10[/C][C]11166[/C][C]9118.9200736425[/C][C]2047.0799263575[/C][/ROW]
[ROW][C]11[/C][C]7508[/C][C]6955.52751586577[/C][C]552.472484134231[/C][/ROW]
[ROW][C]12[/C][C]4275[/C][C]3947.77798555724[/C][C]327.222014442763[/C][/ROW]
[ROW][C]13[/C][C]4944[/C][C]4293.68194595809[/C][C]650.318054041911[/C][/ROW]
[ROW][C]14[/C][C]5441[/C][C]4770.57812848475[/C][C]670.421871515249[/C][/ROW]
[ROW][C]15[/C][C]1689[/C][C]1842.603269014[/C][C]-153.603269014[/C][/ROW]
[ROW][C]16[/C][C]1522[/C][C]1648.49692640702[/C][C]-126.496926407018[/C][/ROW]
[ROW][C]17[/C][C]1416[/C][C]1498.79414098057[/C][C]-82.7941409805711[/C][/ROW]
[ROW][C]18[/C][C]1594[/C][C]2048.64799779669[/C][C]-454.647997796695[/C][/ROW]
[ROW][C]19[/C][C]1909[/C][C]1526.21184335085[/C][C]382.788156649149[/C][/ROW]
[ROW][C]20[/C][C]2599[/C][C]2404.46903201724[/C][C]194.530967982756[/C][/ROW]
[ROW][C]21[/C][C]1262[/C][C]2077.90962769442[/C][C]-815.90962769442[/C][/ROW]
[ROW][C]22[/C][C]1199[/C][C]1501.35162352587[/C][C]-302.35162352587[/C][/ROW]
[ROW][C]23[/C][C]4404[/C][C]3249.97501971403[/C][C]1154.02498028597[/C][/ROW]
[ROW][C]24[/C][C]1166[/C][C]1323.83873580089[/C][C]-157.838735800886[/C][/ROW]
[ROW][C]25[/C][C]1122[/C][C]1203.4286710197[/C][C]-81.428671019703[/C][/ROW]
[ROW][C]26[/C][C]886[/C][C]1180.05409065598[/C][C]-294.054090655976[/C][/ROW]
[ROW][C]27[/C][C]778[/C][C]820.58914120167[/C][C]-42.5891412016696[/C][/ROW]
[ROW][C]28[/C][C]4436[/C][C]3545.39991737641[/C][C]890.600082623593[/C][/ROW]
[ROW][C]29[/C][C]1890[/C][C]2173.72485210824[/C][C]-283.724852108237[/C][/ROW]
[ROW][C]30[/C][C]3107[/C][C]2869.13162007304[/C][C]237.868379926956[/C][/ROW]
[ROW][C]31[/C][C]1038[/C][C]1073.6607432748[/C][C]-35.6607432748043[/C][/ROW]
[ROW][C]32[/C][C]300[/C][C]475.026221782104[/C][C]-175.026221782104[/C][/ROW]
[ROW][C]33[/C][C]988[/C][C]1169.65536036898[/C][C]-181.655360368976[/C][/ROW]
[ROW][C]34[/C][C]2008[/C][C]2094.4232716887[/C][C]-86.4232716886972[/C][/ROW]
[ROW][C]35[/C][C]1522[/C][C]1459.09400901247[/C][C]62.9059909875322[/C][/ROW]
[ROW][C]36[/C][C]1336[/C][C]1354.31672498546[/C][C]-18.3167249854622[/C][/ROW]
[ROW][C]37[/C][C]976[/C][C]943.652866898516[/C][C]32.347133101484[/C][/ROW]
[ROW][C]38[/C][C]798[/C][C]1018.97100166028[/C][C]-220.971001660275[/C][/ROW]
[ROW][C]39[/C][C]869[/C][C]1250.54128244014[/C][C]-381.541282440142[/C][/ROW]
[ROW][C]40[/C][C]1260[/C][C]1338.67337033044[/C][C]-78.6733703304376[/C][/ROW]
[ROW][C]41[/C][C]578[/C][C]673.399018897128[/C][C]-95.3990188971276[/C][/ROW]
[ROW][C]42[/C][C]2359[/C][C]1842.39896147564[/C][C]516.601038524356[/C][/ROW]
[ROW][C]43[/C][C]736[/C][C]691.810090878084[/C][C]44.1899091219157[/C][/ROW]
[ROW][C]44[/C][C]1690[/C][C]1295.48680339175[/C][C]394.513196608245[/C][/ROW]
[ROW][C]45[/C][C]1201[/C][C]1337.33691240426[/C][C]-136.336912404263[/C][/ROW]
[ROW][C]46[/C][C]813[/C][C]1477.63978791087[/C][C]-664.639787910868[/C][/ROW]
[ROW][C]47[/C][C]778[/C][C]899.55950859899[/C][C]-121.55950859899[/C][/ROW]
[ROW][C]48[/C][C]687[/C][C]897.916503861336[/C][C]-210.916503861336[/C][/ROW]
[ROW][C]49[/C][C]1270[/C][C]1139.90490352537[/C][C]130.095096474629[/C][/ROW]
[ROW][C]50[/C][C]671[/C][C]788.716413312633[/C][C]-117.716413312633[/C][/ROW]
[ROW][C]51[/C][C]1559[/C][C]735.730481034053[/C][C]823.269518965947[/C][/ROW]
[ROW][C]52[/C][C]489[/C][C]657.229819306408[/C][C]-168.229819306408[/C][/ROW]
[ROW][C]53[/C][C]773[/C][C]740.095504071983[/C][C]32.904495928017[/C][/ROW]
[ROW][C]54[/C][C]629[/C][C]796.48062263388[/C][C]-167.48062263388[/C][/ROW]
[ROW][C]55[/C][C]637[/C][C]717.169347915755[/C][C]-80.169347915755[/C][/ROW]
[ROW][C]56[/C][C]277[/C][C]373.360468529586[/C][C]-96.360468529586[/C][/ROW]
[ROW][C]57[/C][C]776[/C][C]681.493695298815[/C][C]94.5063047011854[/C][/ROW]
[ROW][C]58[/C][C]1651[/C][C]838.816671138222[/C][C]812.183328861778[/C][/ROW]
[ROW][C]59[/C][C]377[/C][C]501.986187954043[/C][C]-124.986187954043[/C][/ROW]
[ROW][C]60[/C][C]222[/C][C]457.554178832756[/C][C]-235.554178832756[/C][/ROW]
[ROW][C]61[/C][C]1068[/C][C]902.040698665614[/C][C]165.959301334386[/C][/ROW]
[ROW][C]62[/C][C]399[/C][C]561.322068591757[/C][C]-162.322068591757[/C][/ROW]
[ROW][C]63[/C][C]547[/C][C]516.907306555373[/C][C]30.0926934446269[/C][/ROW]
[ROW][C]64[/C][C]668[/C][C]955.434518752831[/C][C]-287.434518752831[/C][/ROW]
[ROW][C]65[/C][C]451[/C][C]598.238000764509[/C][C]-147.238000764509[/C][/ROW]
[ROW][C]66[/C][C]724[/C][C]726.297587501667[/C][C]-2.29758750166726[/C][/ROW]
[ROW][C]67[/C][C]853[/C][C]879.492467820007[/C][C]-26.4924678200067[/C][/ROW]
[ROW][C]68[/C][C]434[/C][C]598.165991310366[/C][C]-164.165991310366[/C][/ROW]
[ROW][C]69[/C][C]730[/C][C]888.524777682815[/C][C]-158.524777682815[/C][/ROW]
[ROW][C]70[/C][C]612[/C][C]684.281680824178[/C][C]-72.2816808241782[/C][/ROW]
[ROW][C]71[/C][C]558[/C][C]912.88244188606[/C][C]-354.88244188606[/C][/ROW]
[ROW][C]72[/C][C]859[/C][C]791.230586991801[/C][C]67.7694130081986[/C][/ROW]
[ROW][C]73[/C][C]311[/C][C]466.147758786649[/C][C]-155.147758786649[/C][/ROW]
[ROW][C]74[/C][C]318[/C][C]509.193958886576[/C][C]-191.193958886576[/C][/ROW]
[ROW][C]75[/C][C]312[/C][C]500.778012408514[/C][C]-188.778012408514[/C][/ROW]
[ROW][C]76[/C][C]343[/C][C]421.190784331928[/C][C]-78.1907843319283[/C][/ROW]
[ROW][C]77[/C][C]710[/C][C]800.343090050074[/C][C]-90.3430900500735[/C][/ROW]
[ROW][C]78[/C][C]273[/C][C]449.831767820376[/C][C]-176.831767820376[/C][/ROW]
[ROW][C]79[/C][C]259[/C][C]473.308320136262[/C][C]-214.308320136262[/C][/ROW]
[ROW][C]80[/C][C]1274[/C][C]982.472154300226[/C][C]291.527845699774[/C][/ROW]
[ROW][C]81[/C][C]625[/C][C]857.487080347738[/C][C]-232.487080347738[/C][/ROW]
[ROW][C]82[/C][C]245[/C][C]535.182543369365[/C][C]-290.182543369365[/C][/ROW]
[ROW][C]83[/C][C]235[/C][C]359.149252876581[/C][C]-124.149252876581[/C][/ROW]
[ROW][C]84[/C][C]250[/C][C]425.468061388076[/C][C]-175.468061388076[/C][/ROW]
[ROW][C]85[/C][C]NA[/C][C]NA[/C][C]-400.767234695965[/C][/ROW]
[ROW][C]86[/C][C]303[/C][C]540.499018942577[/C][C]-237.499018942577[/C][/ROW]
[ROW][C]87[/C][C]250[/C][C]390.86689041957[/C][C]-140.86689041957[/C][/ROW]
[ROW][C]88[/C][C]403[/C][C]564.860850473734[/C][C]-161.860850473734[/C][/ROW]
[ROW][C]89[/C][C]441[/C][C]45.454020278608[/C][C]395.545979721392[/C][/ROW]
[ROW][C]90[/C][C]1507[/C][C]1687.77495078247[/C][C]-180.774950782473[/C][/ROW]
[ROW][C]91[/C][C]388[/C][C]678.091881867209[/C][C]-290.091881867209[/C][/ROW]
[ROW][C]92[/C][C]530[/C][C]874.320179748898[/C][C]-344.320179748898[/C][/ROW]
[ROW][C]93[/C][C]107[/C][C]-196.770095890814[/C][C]303.770095890814[/C][/ROW]
[ROW][C]94[/C][C]868[/C][C]678.81446351423[/C][C]189.18553648577[/C][/ROW]
[ROW][C]95[/C][C]NA[/C][C]NA[/C][C]-117.565204405875[/C][/ROW]
[ROW][C]96[/C][C]753[/C][C]767.969891090067[/C][C]-14.9698910900666[/C][/ROW]
[ROW][C]97[/C][C]62[/C][C]185.663248829052[/C][C]-123.663248829052[/C][/ROW]
[ROW][C]98[/C][C]346[/C][C]448.587098374397[/C][C]-102.587098374397[/C][/ROW]
[ROW][C]99[/C][C]478[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]100[/C][C]292[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186011&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186011&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
12227423203.6923556739-929.692355673907
21481916246.0451584942-1427.04515849423
31513613454.67323501711681.32676498291
41370413174.8616145249529.138385475062
51963820364.2005464023-726.200546402326
675519233.02812691554-1682.02812691554
780195561.298158849232457.70184115077
865096936.5452635575-427.545263557497
966346003.60689932564630.393100674361
10111669118.92007364252047.0799263575
1175086955.52751586577552.472484134231
1242753947.77798555724327.222014442763
1349444293.68194595809650.318054041911
1454414770.57812848475670.421871515249
1516891842.603269014-153.603269014
1615221648.49692640702-126.496926407018
1714161498.79414098057-82.7941409805711
1815942048.64799779669-454.647997796695
1919091526.21184335085382.788156649149
2025992404.46903201724194.530967982756
2112622077.90962769442-815.90962769442
2211991501.35162352587-302.35162352587
2344043249.975019714031154.02498028597
2411661323.83873580089-157.838735800886
2511221203.4286710197-81.428671019703
268861180.05409065598-294.054090655976
27778820.58914120167-42.5891412016696
2844363545.39991737641890.600082623593
2918902173.72485210824-283.724852108237
3031072869.13162007304237.868379926956
3110381073.6607432748-35.6607432748043
32300475.026221782104-175.026221782104
339881169.65536036898-181.655360368976
3420082094.4232716887-86.4232716886972
3515221459.0940090124762.9059909875322
3613361354.31672498546-18.3167249854622
37976943.65286689851632.347133101484
387981018.97100166028-220.971001660275
398691250.54128244014-381.541282440142
4012601338.67337033044-78.6733703304376
41578673.399018897128-95.3990188971276
4223591842.39896147564516.601038524356
43736691.81009087808444.1899091219157
4416901295.48680339175394.513196608245
4512011337.33691240426-136.336912404263
468131477.63978791087-664.639787910868
47778899.55950859899-121.55950859899
48687897.916503861336-210.916503861336
4912701139.90490352537130.095096474629
50671788.716413312633-117.716413312633
511559735.730481034053823.269518965947
52489657.229819306408-168.229819306408
53773740.09550407198332.904495928017
54629796.48062263388-167.48062263388
55637717.169347915755-80.169347915755
56277373.360468529586-96.360468529586
57776681.49369529881594.5063047011854
581651838.816671138222812.183328861778
59377501.986187954043-124.986187954043
60222457.554178832756-235.554178832756
611068902.040698665614165.959301334386
62399561.322068591757-162.322068591757
63547516.90730655537330.0926934446269
64668955.434518752831-287.434518752831
65451598.238000764509-147.238000764509
66724726.297587501667-2.29758750166726
67853879.492467820007-26.4924678200067
68434598.165991310366-164.165991310366
69730888.524777682815-158.524777682815
70612684.281680824178-72.2816808241782
71558912.88244188606-354.88244188606
72859791.23058699180167.7694130081986
73311466.147758786649-155.147758786649
74318509.193958886576-191.193958886576
75312500.778012408514-188.778012408514
76343421.190784331928-78.1907843319283
77710800.343090050074-90.3430900500735
78273449.831767820376-176.831767820376
79259473.308320136262-214.308320136262
801274982.472154300226291.527845699774
81625857.487080347738-232.487080347738
82245535.182543369365-290.182543369365
83235359.149252876581-124.149252876581
84250425.468061388076-175.468061388076
85NANA-400.767234695965
86303540.499018942577-237.499018942577
87250390.86689041957-140.86689041957
88403564.860850473734-161.860850473734
8944145.454020278608395.545979721392
9015071687.77495078247-180.774950782473
91388678.091881867209-290.091881867209
92530874.320179748898-344.320179748898
93107-196.770095890814303.770095890814
94868678.81446351423189.18553648577
95NANA-117.565204405875
96753767.969891090067-14.9698910900666
9762185.663248829052-123.663248829052
98346448.587098374397-102.587098374397
99478NANA
100292NANA







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.9999999979058364.18832781277266e-092.09416390638633e-09
718.24074931462229e-164.12037465731114e-16
816.93299534810091e-183.46649767405046e-18
914.09011788926512e-172.04505894463256e-17
1011.44801722683887e-197.24008613419433e-20
1113.73406209947122e-191.86703104973561e-19
1217.8234047951211e-193.91170239756055e-19
1313.08929570405549e-181.54464785202774e-18
1411.41780106663249e-177.08900533316244e-18
1516.86938945168114e-183.43469472584057e-18
1617.37159741430109e-183.68579870715055e-18
1711.20637062164343e-176.03185310821717e-18
1814.94858287303833e-182.47429143651917e-18
1916.20791552638991e-183.10395776319496e-18
2012.32417564133118e-171.16208782066559e-17
2111.70291735234375e-198.51458676171875e-20
2212.8429543712071e-191.42147718560355e-19
2312.67311752235699e-201.3365587611785e-20
2418.01947429851128e-204.00973714925564e-20
2512.78615146312717e-191.39307573156359e-19
2615.91806478815695e-192.95903239407848e-19
2711.96241755037319e-189.81208775186593e-19
2818.80631355193432e-194.40315677596716e-19
2911.05956326300789e-185.29781631503947e-19
3014.21089194052819e-182.10544597026409e-18
3111.48730193704784e-177.43650968523918e-18
3214.72735044964463e-172.36367522482231e-17
3311.34001548897048e-166.70007744485242e-17
3413.37492125747102e-161.68746062873551e-16
350.9999999999999991.18134566563151e-155.90672832815753e-16
360.9999999999999983.93928394961795e-151.96964197480898e-15
370.9999999999999941.23861398577143e-146.19306992885717e-15
380.9999999999999853.03027907143328e-141.51513953571664e-14
390.9999999999999872.68127513819574e-141.34063756909787e-14
400.9999999999999656.98205420948541e-143.4910271047427e-14
410.9999999999998962.08732193791393e-131.04366096895697e-13
420.9999999999998692.62188798514858e-131.31094399257429e-13
430.9999999999995958.09695829451726e-134.04847914725863e-13
440.9999999999995249.51432283033958e-134.75716141516979e-13
450.9999999999987082.58340280488182e-121.29170140244091e-12
460.9999999999999481.03938740240475e-135.19693701202374e-14
470.9999999999998662.67935577703458e-131.33967788851729e-13
480.9999999999997734.53845221120395e-132.26922610560197e-13
490.9999999999992671.46515025383121e-127.32575126915604e-13
500.9999999999981893.62256883284575e-121.81128441642287e-12
510.9999999999999852.93825308548433e-141.46912654274217e-14
520.9999999999999559.0281022277447e-144.51405111387235e-14
530.9999999999998473.06360690376837e-131.53180345188418e-13
540.9999999999995948.12293449450333e-134.06146724725166e-13
550.9999999999986642.67215027857417e-121.33607513928708e-12
560.9999999999956958.61083324306762e-124.30541662153381e-12
570.9999999999882642.34728014035973e-111.17364007017987e-11
580.9999999999999941.295624653905e-146.47812326952501e-15
590.9999999999999764.87533934289007e-142.43766967144503e-14
600.9999999999999091.82611054978859e-139.13055274894294e-14
610.9999999999998572.86933001456628e-131.43466500728314e-13
620.9999999999994491.10178272725634e-125.50891363628169e-13
630.9999999999990431.91425913627921e-129.57129568139607e-13
640.9999999999981563.68827276156224e-121.84413638078112e-12
650.9999999999930581.38844927564582e-116.9422463782291e-12
660.999999999980463.90803099624808e-111.95401549812404e-11
670.9999999999332631.3347445998679e-106.6737229993395e-11
680.9999999997620574.75886468623e-102.379432343115e-10
690.9999999991889911.62201867438572e-098.11009337192862e-10
700.9999999974790755.04185011972298e-092.52092505986149e-09
710.9999999970644185.87116343251131e-092.93558171625565e-09
720.9999999926579421.46841160374488e-087.34205801872441e-09
730.9999999771270884.5745823363732e-082.2872911681866e-08
740.9999999237693251.52461350195316e-077.62306750976581e-08
750.9999997506005524.9879889532953e-072.49399447664765e-07
760.9999994362116671.12757666572876e-065.63788332864379e-07
770.999998198824183.60235163997302e-061.80117581998651e-06
780.9999949326175611.01347648773721e-055.06738243868603e-06
790.9999855033663642.89932672717975e-051.44966336358988e-05
800.9999912925652591.74148694828517e-058.70743474142583e-06
810.9999726365334415.47269331185955e-052.73634665592978e-05
820.9999189599502740.0001620800994529258.10400497264626e-05
830.999844080862610.0003118382747803450.000155919137390172
840.9997434581663590.0005130836672811480.000256541833640574
850.9994420342665670.001115931466865170.000557965733432584
860.9983700674282350.003259865143530630.00162993257176531
870.9956041102259830.008791779548033140.00439588977401657
880.9884533668852790.02309326622944130.0115466331147207
890.9867527869836390.02649442603272240.0132472130163612
900.9647005793555140.07059884128897270.0352994206444863
910.9489017963038840.1021964073922330.0510982036961164
920.9935371930963260.0129256138073470.00646280690367349
930.9581293865378710.08374122692425790.041870613462129
940.9144658859746170.1710682280507660.0855341140253828

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.999999997905836 & 4.18832781277266e-09 & 2.09416390638633e-09 \tabularnewline
7 & 1 & 8.24074931462229e-16 & 4.12037465731114e-16 \tabularnewline
8 & 1 & 6.93299534810091e-18 & 3.46649767405046e-18 \tabularnewline
9 & 1 & 4.09011788926512e-17 & 2.04505894463256e-17 \tabularnewline
10 & 1 & 1.44801722683887e-19 & 7.24008613419433e-20 \tabularnewline
11 & 1 & 3.73406209947122e-19 & 1.86703104973561e-19 \tabularnewline
12 & 1 & 7.8234047951211e-19 & 3.91170239756055e-19 \tabularnewline
13 & 1 & 3.08929570405549e-18 & 1.54464785202774e-18 \tabularnewline
14 & 1 & 1.41780106663249e-17 & 7.08900533316244e-18 \tabularnewline
15 & 1 & 6.86938945168114e-18 & 3.43469472584057e-18 \tabularnewline
16 & 1 & 7.37159741430109e-18 & 3.68579870715055e-18 \tabularnewline
17 & 1 & 1.20637062164343e-17 & 6.03185310821717e-18 \tabularnewline
18 & 1 & 4.94858287303833e-18 & 2.47429143651917e-18 \tabularnewline
19 & 1 & 6.20791552638991e-18 & 3.10395776319496e-18 \tabularnewline
20 & 1 & 2.32417564133118e-17 & 1.16208782066559e-17 \tabularnewline
21 & 1 & 1.70291735234375e-19 & 8.51458676171875e-20 \tabularnewline
22 & 1 & 2.8429543712071e-19 & 1.42147718560355e-19 \tabularnewline
23 & 1 & 2.67311752235699e-20 & 1.3365587611785e-20 \tabularnewline
24 & 1 & 8.01947429851128e-20 & 4.00973714925564e-20 \tabularnewline
25 & 1 & 2.78615146312717e-19 & 1.39307573156359e-19 \tabularnewline
26 & 1 & 5.91806478815695e-19 & 2.95903239407848e-19 \tabularnewline
27 & 1 & 1.96241755037319e-18 & 9.81208775186593e-19 \tabularnewline
28 & 1 & 8.80631355193432e-19 & 4.40315677596716e-19 \tabularnewline
29 & 1 & 1.05956326300789e-18 & 5.29781631503947e-19 \tabularnewline
30 & 1 & 4.21089194052819e-18 & 2.10544597026409e-18 \tabularnewline
31 & 1 & 1.48730193704784e-17 & 7.43650968523918e-18 \tabularnewline
32 & 1 & 4.72735044964463e-17 & 2.36367522482231e-17 \tabularnewline
33 & 1 & 1.34001548897048e-16 & 6.70007744485242e-17 \tabularnewline
34 & 1 & 3.37492125747102e-16 & 1.68746062873551e-16 \tabularnewline
35 & 0.999999999999999 & 1.18134566563151e-15 & 5.90672832815753e-16 \tabularnewline
36 & 0.999999999999998 & 3.93928394961795e-15 & 1.96964197480898e-15 \tabularnewline
37 & 0.999999999999994 & 1.23861398577143e-14 & 6.19306992885717e-15 \tabularnewline
38 & 0.999999999999985 & 3.03027907143328e-14 & 1.51513953571664e-14 \tabularnewline
39 & 0.999999999999987 & 2.68127513819574e-14 & 1.34063756909787e-14 \tabularnewline
40 & 0.999999999999965 & 6.98205420948541e-14 & 3.4910271047427e-14 \tabularnewline
41 & 0.999999999999896 & 2.08732193791393e-13 & 1.04366096895697e-13 \tabularnewline
42 & 0.999999999999869 & 2.62188798514858e-13 & 1.31094399257429e-13 \tabularnewline
43 & 0.999999999999595 & 8.09695829451726e-13 & 4.04847914725863e-13 \tabularnewline
44 & 0.999999999999524 & 9.51432283033958e-13 & 4.75716141516979e-13 \tabularnewline
45 & 0.999999999998708 & 2.58340280488182e-12 & 1.29170140244091e-12 \tabularnewline
46 & 0.999999999999948 & 1.03938740240475e-13 & 5.19693701202374e-14 \tabularnewline
47 & 0.999999999999866 & 2.67935577703458e-13 & 1.33967788851729e-13 \tabularnewline
48 & 0.999999999999773 & 4.53845221120395e-13 & 2.26922610560197e-13 \tabularnewline
49 & 0.999999999999267 & 1.46515025383121e-12 & 7.32575126915604e-13 \tabularnewline
50 & 0.999999999998189 & 3.62256883284575e-12 & 1.81128441642287e-12 \tabularnewline
51 & 0.999999999999985 & 2.93825308548433e-14 & 1.46912654274217e-14 \tabularnewline
52 & 0.999999999999955 & 9.0281022277447e-14 & 4.51405111387235e-14 \tabularnewline
53 & 0.999999999999847 & 3.06360690376837e-13 & 1.53180345188418e-13 \tabularnewline
54 & 0.999999999999594 & 8.12293449450333e-13 & 4.06146724725166e-13 \tabularnewline
55 & 0.999999999998664 & 2.67215027857417e-12 & 1.33607513928708e-12 \tabularnewline
56 & 0.999999999995695 & 8.61083324306762e-12 & 4.30541662153381e-12 \tabularnewline
57 & 0.999999999988264 & 2.34728014035973e-11 & 1.17364007017987e-11 \tabularnewline
58 & 0.999999999999994 & 1.295624653905e-14 & 6.47812326952501e-15 \tabularnewline
59 & 0.999999999999976 & 4.87533934289007e-14 & 2.43766967144503e-14 \tabularnewline
60 & 0.999999999999909 & 1.82611054978859e-13 & 9.13055274894294e-14 \tabularnewline
61 & 0.999999999999857 & 2.86933001456628e-13 & 1.43466500728314e-13 \tabularnewline
62 & 0.999999999999449 & 1.10178272725634e-12 & 5.50891363628169e-13 \tabularnewline
63 & 0.999999999999043 & 1.91425913627921e-12 & 9.57129568139607e-13 \tabularnewline
64 & 0.999999999998156 & 3.68827276156224e-12 & 1.84413638078112e-12 \tabularnewline
65 & 0.999999999993058 & 1.38844927564582e-11 & 6.9422463782291e-12 \tabularnewline
66 & 0.99999999998046 & 3.90803099624808e-11 & 1.95401549812404e-11 \tabularnewline
67 & 0.999999999933263 & 1.3347445998679e-10 & 6.6737229993395e-11 \tabularnewline
68 & 0.999999999762057 & 4.75886468623e-10 & 2.379432343115e-10 \tabularnewline
69 & 0.999999999188991 & 1.62201867438572e-09 & 8.11009337192862e-10 \tabularnewline
70 & 0.999999997479075 & 5.04185011972298e-09 & 2.52092505986149e-09 \tabularnewline
71 & 0.999999997064418 & 5.87116343251131e-09 & 2.93558171625565e-09 \tabularnewline
72 & 0.999999992657942 & 1.46841160374488e-08 & 7.34205801872441e-09 \tabularnewline
73 & 0.999999977127088 & 4.5745823363732e-08 & 2.2872911681866e-08 \tabularnewline
74 & 0.999999923769325 & 1.52461350195316e-07 & 7.62306750976581e-08 \tabularnewline
75 & 0.999999750600552 & 4.9879889532953e-07 & 2.49399447664765e-07 \tabularnewline
76 & 0.999999436211667 & 1.12757666572876e-06 & 5.63788332864379e-07 \tabularnewline
77 & 0.99999819882418 & 3.60235163997302e-06 & 1.80117581998651e-06 \tabularnewline
78 & 0.999994932617561 & 1.01347648773721e-05 & 5.06738243868603e-06 \tabularnewline
79 & 0.999985503366364 & 2.89932672717975e-05 & 1.44966336358988e-05 \tabularnewline
80 & 0.999991292565259 & 1.74148694828517e-05 & 8.70743474142583e-06 \tabularnewline
81 & 0.999972636533441 & 5.47269331185955e-05 & 2.73634665592978e-05 \tabularnewline
82 & 0.999918959950274 & 0.000162080099452925 & 8.10400497264626e-05 \tabularnewline
83 & 0.99984408086261 & 0.000311838274780345 & 0.000155919137390172 \tabularnewline
84 & 0.999743458166359 & 0.000513083667281148 & 0.000256541833640574 \tabularnewline
85 & 0.999442034266567 & 0.00111593146686517 & 0.000557965733432584 \tabularnewline
86 & 0.998370067428235 & 0.00325986514353063 & 0.00162993257176531 \tabularnewline
87 & 0.995604110225983 & 0.00879177954803314 & 0.00439588977401657 \tabularnewline
88 & 0.988453366885279 & 0.0230932662294413 & 0.0115466331147207 \tabularnewline
89 & 0.986752786983639 & 0.0264944260327224 & 0.0132472130163612 \tabularnewline
90 & 0.964700579355514 & 0.0705988412889727 & 0.0352994206444863 \tabularnewline
91 & 0.948901796303884 & 0.102196407392233 & 0.0510982036961164 \tabularnewline
92 & 0.993537193096326 & 0.012925613807347 & 0.00646280690367349 \tabularnewline
93 & 0.958129386537871 & 0.0837412269242579 & 0.041870613462129 \tabularnewline
94 & 0.914465885974617 & 0.171068228050766 & 0.0855341140253828 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186011&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]0.999999997905836[/C][C]4.18832781277266e-09[/C][C]2.09416390638633e-09[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]8.24074931462229e-16[/C][C]4.12037465731114e-16[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]6.93299534810091e-18[/C][C]3.46649767405046e-18[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]4.09011788926512e-17[/C][C]2.04505894463256e-17[/C][/ROW]
[ROW][C]10[/C][C]1[/C][C]1.44801722683887e-19[/C][C]7.24008613419433e-20[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]3.73406209947122e-19[/C][C]1.86703104973561e-19[/C][/ROW]
[ROW][C]12[/C][C]1[/C][C]7.8234047951211e-19[/C][C]3.91170239756055e-19[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]3.08929570405549e-18[/C][C]1.54464785202774e-18[/C][/ROW]
[ROW][C]14[/C][C]1[/C][C]1.41780106663249e-17[/C][C]7.08900533316244e-18[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]6.86938945168114e-18[/C][C]3.43469472584057e-18[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]7.37159741430109e-18[/C][C]3.68579870715055e-18[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]1.20637062164343e-17[/C][C]6.03185310821717e-18[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]4.94858287303833e-18[/C][C]2.47429143651917e-18[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]6.20791552638991e-18[/C][C]3.10395776319496e-18[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]2.32417564133118e-17[/C][C]1.16208782066559e-17[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]1.70291735234375e-19[/C][C]8.51458676171875e-20[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]2.8429543712071e-19[/C][C]1.42147718560355e-19[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]2.67311752235699e-20[/C][C]1.3365587611785e-20[/C][/ROW]
[ROW][C]24[/C][C]1[/C][C]8.01947429851128e-20[/C][C]4.00973714925564e-20[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]2.78615146312717e-19[/C][C]1.39307573156359e-19[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]5.91806478815695e-19[/C][C]2.95903239407848e-19[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]1.96241755037319e-18[/C][C]9.81208775186593e-19[/C][/ROW]
[ROW][C]28[/C][C]1[/C][C]8.80631355193432e-19[/C][C]4.40315677596716e-19[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]1.05956326300789e-18[/C][C]5.29781631503947e-19[/C][/ROW]
[ROW][C]30[/C][C]1[/C][C]4.21089194052819e-18[/C][C]2.10544597026409e-18[/C][/ROW]
[ROW][C]31[/C][C]1[/C][C]1.48730193704784e-17[/C][C]7.43650968523918e-18[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]4.72735044964463e-17[/C][C]2.36367522482231e-17[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]1.34001548897048e-16[/C][C]6.70007744485242e-17[/C][/ROW]
[ROW][C]34[/C][C]1[/C][C]3.37492125747102e-16[/C][C]1.68746062873551e-16[/C][/ROW]
[ROW][C]35[/C][C]0.999999999999999[/C][C]1.18134566563151e-15[/C][C]5.90672832815753e-16[/C][/ROW]
[ROW][C]36[/C][C]0.999999999999998[/C][C]3.93928394961795e-15[/C][C]1.96964197480898e-15[/C][/ROW]
[ROW][C]37[/C][C]0.999999999999994[/C][C]1.23861398577143e-14[/C][C]6.19306992885717e-15[/C][/ROW]
[ROW][C]38[/C][C]0.999999999999985[/C][C]3.03027907143328e-14[/C][C]1.51513953571664e-14[/C][/ROW]
[ROW][C]39[/C][C]0.999999999999987[/C][C]2.68127513819574e-14[/C][C]1.34063756909787e-14[/C][/ROW]
[ROW][C]40[/C][C]0.999999999999965[/C][C]6.98205420948541e-14[/C][C]3.4910271047427e-14[/C][/ROW]
[ROW][C]41[/C][C]0.999999999999896[/C][C]2.08732193791393e-13[/C][C]1.04366096895697e-13[/C][/ROW]
[ROW][C]42[/C][C]0.999999999999869[/C][C]2.62188798514858e-13[/C][C]1.31094399257429e-13[/C][/ROW]
[ROW][C]43[/C][C]0.999999999999595[/C][C]8.09695829451726e-13[/C][C]4.04847914725863e-13[/C][/ROW]
[ROW][C]44[/C][C]0.999999999999524[/C][C]9.51432283033958e-13[/C][C]4.75716141516979e-13[/C][/ROW]
[ROW][C]45[/C][C]0.999999999998708[/C][C]2.58340280488182e-12[/C][C]1.29170140244091e-12[/C][/ROW]
[ROW][C]46[/C][C]0.999999999999948[/C][C]1.03938740240475e-13[/C][C]5.19693701202374e-14[/C][/ROW]
[ROW][C]47[/C][C]0.999999999999866[/C][C]2.67935577703458e-13[/C][C]1.33967788851729e-13[/C][/ROW]
[ROW][C]48[/C][C]0.999999999999773[/C][C]4.53845221120395e-13[/C][C]2.26922610560197e-13[/C][/ROW]
[ROW][C]49[/C][C]0.999999999999267[/C][C]1.46515025383121e-12[/C][C]7.32575126915604e-13[/C][/ROW]
[ROW][C]50[/C][C]0.999999999998189[/C][C]3.62256883284575e-12[/C][C]1.81128441642287e-12[/C][/ROW]
[ROW][C]51[/C][C]0.999999999999985[/C][C]2.93825308548433e-14[/C][C]1.46912654274217e-14[/C][/ROW]
[ROW][C]52[/C][C]0.999999999999955[/C][C]9.0281022277447e-14[/C][C]4.51405111387235e-14[/C][/ROW]
[ROW][C]53[/C][C]0.999999999999847[/C][C]3.06360690376837e-13[/C][C]1.53180345188418e-13[/C][/ROW]
[ROW][C]54[/C][C]0.999999999999594[/C][C]8.12293449450333e-13[/C][C]4.06146724725166e-13[/C][/ROW]
[ROW][C]55[/C][C]0.999999999998664[/C][C]2.67215027857417e-12[/C][C]1.33607513928708e-12[/C][/ROW]
[ROW][C]56[/C][C]0.999999999995695[/C][C]8.61083324306762e-12[/C][C]4.30541662153381e-12[/C][/ROW]
[ROW][C]57[/C][C]0.999999999988264[/C][C]2.34728014035973e-11[/C][C]1.17364007017987e-11[/C][/ROW]
[ROW][C]58[/C][C]0.999999999999994[/C][C]1.295624653905e-14[/C][C]6.47812326952501e-15[/C][/ROW]
[ROW][C]59[/C][C]0.999999999999976[/C][C]4.87533934289007e-14[/C][C]2.43766967144503e-14[/C][/ROW]
[ROW][C]60[/C][C]0.999999999999909[/C][C]1.82611054978859e-13[/C][C]9.13055274894294e-14[/C][/ROW]
[ROW][C]61[/C][C]0.999999999999857[/C][C]2.86933001456628e-13[/C][C]1.43466500728314e-13[/C][/ROW]
[ROW][C]62[/C][C]0.999999999999449[/C][C]1.10178272725634e-12[/C][C]5.50891363628169e-13[/C][/ROW]
[ROW][C]63[/C][C]0.999999999999043[/C][C]1.91425913627921e-12[/C][C]9.57129568139607e-13[/C][/ROW]
[ROW][C]64[/C][C]0.999999999998156[/C][C]3.68827276156224e-12[/C][C]1.84413638078112e-12[/C][/ROW]
[ROW][C]65[/C][C]0.999999999993058[/C][C]1.38844927564582e-11[/C][C]6.9422463782291e-12[/C][/ROW]
[ROW][C]66[/C][C]0.99999999998046[/C][C]3.90803099624808e-11[/C][C]1.95401549812404e-11[/C][/ROW]
[ROW][C]67[/C][C]0.999999999933263[/C][C]1.3347445998679e-10[/C][C]6.6737229993395e-11[/C][/ROW]
[ROW][C]68[/C][C]0.999999999762057[/C][C]4.75886468623e-10[/C][C]2.379432343115e-10[/C][/ROW]
[ROW][C]69[/C][C]0.999999999188991[/C][C]1.62201867438572e-09[/C][C]8.11009337192862e-10[/C][/ROW]
[ROW][C]70[/C][C]0.999999997479075[/C][C]5.04185011972298e-09[/C][C]2.52092505986149e-09[/C][/ROW]
[ROW][C]71[/C][C]0.999999997064418[/C][C]5.87116343251131e-09[/C][C]2.93558171625565e-09[/C][/ROW]
[ROW][C]72[/C][C]0.999999992657942[/C][C]1.46841160374488e-08[/C][C]7.34205801872441e-09[/C][/ROW]
[ROW][C]73[/C][C]0.999999977127088[/C][C]4.5745823363732e-08[/C][C]2.2872911681866e-08[/C][/ROW]
[ROW][C]74[/C][C]0.999999923769325[/C][C]1.52461350195316e-07[/C][C]7.62306750976581e-08[/C][/ROW]
[ROW][C]75[/C][C]0.999999750600552[/C][C]4.9879889532953e-07[/C][C]2.49399447664765e-07[/C][/ROW]
[ROW][C]76[/C][C]0.999999436211667[/C][C]1.12757666572876e-06[/C][C]5.63788332864379e-07[/C][/ROW]
[ROW][C]77[/C][C]0.99999819882418[/C][C]3.60235163997302e-06[/C][C]1.80117581998651e-06[/C][/ROW]
[ROW][C]78[/C][C]0.999994932617561[/C][C]1.01347648773721e-05[/C][C]5.06738243868603e-06[/C][/ROW]
[ROW][C]79[/C][C]0.999985503366364[/C][C]2.89932672717975e-05[/C][C]1.44966336358988e-05[/C][/ROW]
[ROW][C]80[/C][C]0.999991292565259[/C][C]1.74148694828517e-05[/C][C]8.70743474142583e-06[/C][/ROW]
[ROW][C]81[/C][C]0.999972636533441[/C][C]5.47269331185955e-05[/C][C]2.73634665592978e-05[/C][/ROW]
[ROW][C]82[/C][C]0.999918959950274[/C][C]0.000162080099452925[/C][C]8.10400497264626e-05[/C][/ROW]
[ROW][C]83[/C][C]0.99984408086261[/C][C]0.000311838274780345[/C][C]0.000155919137390172[/C][/ROW]
[ROW][C]84[/C][C]0.999743458166359[/C][C]0.000513083667281148[/C][C]0.000256541833640574[/C][/ROW]
[ROW][C]85[/C][C]0.999442034266567[/C][C]0.00111593146686517[/C][C]0.000557965733432584[/C][/ROW]
[ROW][C]86[/C][C]0.998370067428235[/C][C]0.00325986514353063[/C][C]0.00162993257176531[/C][/ROW]
[ROW][C]87[/C][C]0.995604110225983[/C][C]0.00879177954803314[/C][C]0.00439588977401657[/C][/ROW]
[ROW][C]88[/C][C]0.988453366885279[/C][C]0.0230932662294413[/C][C]0.0115466331147207[/C][/ROW]
[ROW][C]89[/C][C]0.986752786983639[/C][C]0.0264944260327224[/C][C]0.0132472130163612[/C][/ROW]
[ROW][C]90[/C][C]0.964700579355514[/C][C]0.0705988412889727[/C][C]0.0352994206444863[/C][/ROW]
[ROW][C]91[/C][C]0.948901796303884[/C][C]0.102196407392233[/C][C]0.0510982036961164[/C][/ROW]
[ROW][C]92[/C][C]0.993537193096326[/C][C]0.012925613807347[/C][C]0.00646280690367349[/C][/ROW]
[ROW][C]93[/C][C]0.958129386537871[/C][C]0.0837412269242579[/C][C]0.041870613462129[/C][/ROW]
[ROW][C]94[/C][C]0.914465885974617[/C][C]0.171068228050766[/C][C]0.0855341140253828[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186011&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186011&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
60.9999999979058364.18832781277266e-092.09416390638633e-09
718.24074931462229e-164.12037465731114e-16
816.93299534810091e-183.46649767405046e-18
914.09011788926512e-172.04505894463256e-17
1011.44801722683887e-197.24008613419433e-20
1113.73406209947122e-191.86703104973561e-19
1217.8234047951211e-193.91170239756055e-19
1313.08929570405549e-181.54464785202774e-18
1411.41780106663249e-177.08900533316244e-18
1516.86938945168114e-183.43469472584057e-18
1617.37159741430109e-183.68579870715055e-18
1711.20637062164343e-176.03185310821717e-18
1814.94858287303833e-182.47429143651917e-18
1916.20791552638991e-183.10395776319496e-18
2012.32417564133118e-171.16208782066559e-17
2111.70291735234375e-198.51458676171875e-20
2212.8429543712071e-191.42147718560355e-19
2312.67311752235699e-201.3365587611785e-20
2418.01947429851128e-204.00973714925564e-20
2512.78615146312717e-191.39307573156359e-19
2615.91806478815695e-192.95903239407848e-19
2711.96241755037319e-189.81208775186593e-19
2818.80631355193432e-194.40315677596716e-19
2911.05956326300789e-185.29781631503947e-19
3014.21089194052819e-182.10544597026409e-18
3111.48730193704784e-177.43650968523918e-18
3214.72735044964463e-172.36367522482231e-17
3311.34001548897048e-166.70007744485242e-17
3413.37492125747102e-161.68746062873551e-16
350.9999999999999991.18134566563151e-155.90672832815753e-16
360.9999999999999983.93928394961795e-151.96964197480898e-15
370.9999999999999941.23861398577143e-146.19306992885717e-15
380.9999999999999853.03027907143328e-141.51513953571664e-14
390.9999999999999872.68127513819574e-141.34063756909787e-14
400.9999999999999656.98205420948541e-143.4910271047427e-14
410.9999999999998962.08732193791393e-131.04366096895697e-13
420.9999999999998692.62188798514858e-131.31094399257429e-13
430.9999999999995958.09695829451726e-134.04847914725863e-13
440.9999999999995249.51432283033958e-134.75716141516979e-13
450.9999999999987082.58340280488182e-121.29170140244091e-12
460.9999999999999481.03938740240475e-135.19693701202374e-14
470.9999999999998662.67935577703458e-131.33967788851729e-13
480.9999999999997734.53845221120395e-132.26922610560197e-13
490.9999999999992671.46515025383121e-127.32575126915604e-13
500.9999999999981893.62256883284575e-121.81128441642287e-12
510.9999999999999852.93825308548433e-141.46912654274217e-14
520.9999999999999559.0281022277447e-144.51405111387235e-14
530.9999999999998473.06360690376837e-131.53180345188418e-13
540.9999999999995948.12293449450333e-134.06146724725166e-13
550.9999999999986642.67215027857417e-121.33607513928708e-12
560.9999999999956958.61083324306762e-124.30541662153381e-12
570.9999999999882642.34728014035973e-111.17364007017987e-11
580.9999999999999941.295624653905e-146.47812326952501e-15
590.9999999999999764.87533934289007e-142.43766967144503e-14
600.9999999999999091.82611054978859e-139.13055274894294e-14
610.9999999999998572.86933001456628e-131.43466500728314e-13
620.9999999999994491.10178272725634e-125.50891363628169e-13
630.9999999999990431.91425913627921e-129.57129568139607e-13
640.9999999999981563.68827276156224e-121.84413638078112e-12
650.9999999999930581.38844927564582e-116.9422463782291e-12
660.999999999980463.90803099624808e-111.95401549812404e-11
670.9999999999332631.3347445998679e-106.6737229993395e-11
680.9999999997620574.75886468623e-102.379432343115e-10
690.9999999991889911.62201867438572e-098.11009337192862e-10
700.9999999974790755.04185011972298e-092.52092505986149e-09
710.9999999970644185.87116343251131e-092.93558171625565e-09
720.9999999926579421.46841160374488e-087.34205801872441e-09
730.9999999771270884.5745823363732e-082.2872911681866e-08
740.9999999237693251.52461350195316e-077.62306750976581e-08
750.9999997506005524.9879889532953e-072.49399447664765e-07
760.9999994362116671.12757666572876e-065.63788332864379e-07
770.999998198824183.60235163997302e-061.80117581998651e-06
780.9999949326175611.01347648773721e-055.06738243868603e-06
790.9999855033663642.89932672717975e-051.44966336358988e-05
800.9999912925652591.74148694828517e-058.70743474142583e-06
810.9999726365334415.47269331185955e-052.73634665592978e-05
820.9999189599502740.0001620800994529258.10400497264626e-05
830.999844080862610.0003118382747803450.000155919137390172
840.9997434581663590.0005130836672811480.000256541833640574
850.9994420342665670.001115931466865170.000557965733432584
860.9983700674282350.003259865143530630.00162993257176531
870.9956041102259830.008791779548033140.00439588977401657
880.9884533668852790.02309326622944130.0115466331147207
890.9867527869836390.02649442603272240.0132472130163612
900.9647005793555140.07059884128897270.0352994206444863
910.9489017963038840.1021964073922330.0510982036961164
920.9935371930963260.0129256138073470.00646280690367349
930.9581293865378710.08374122692425790.041870613462129
940.9144658859746170.1710682280507660.0855341140253828







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level820.921348314606742NOK
5% type I error level850.955056179775281NOK
10% type I error level870.97752808988764NOK

\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 & 82 & 0.921348314606742 & NOK \tabularnewline
5% type I error level & 85 & 0.955056179775281 & NOK \tabularnewline
10% type I error level & 87 & 0.97752808988764 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186011&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]82[/C][C]0.921348314606742[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]85[/C][C]0.955056179775281[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]87[/C][C]0.97752808988764[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186011&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186011&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 level820.921348314606742NOK
5% type I error level850.955056179775281NOK
10% type I error level870.97752808988764NOK



Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; 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')
}