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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 computationFri, 20 Nov 2009 06:37:34 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258724586iu4hpjost572j7u.htm/, Retrieved Fri, 29 Mar 2024 11:58:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58153, Retrieved Fri, 29 Mar 2024 11:58:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact110
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]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [ws7] [2009-11-20 13:37:34] [557d56ec4b06cd0135c259898de8ce95] [Current]
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Dataseries X:
10284,5	1,038351422	1,4
12792	0,933031106	1,3
12823,61538	0,932783124	1,3
13845,66667	0,953755367	1,2
15335,63636	1,009865664	1,1
11188,5	0,979532493	1,4
13633,25	0,98651077	1,2
12298,46667	0,964661281	1,5
15353,63636	0,946761816	1,1
12696,15385	0,959068881	1,3
12213,93333	0,985710058	1,5
13683,72727	0,92582159	1,1
11214,14286	1,036865325	1,4
13950,23077	0,944443576	1,3
11179,13333	0,944901812	1,5
11801,875	0,989151445	1,6
11188,82353	1,054361624	1,7
16456,27273	1,033478919	1,1
11110,0625	1,001368875	1,6
16530,69231	1,019812646	1,3
10038,41176	0,993902155	1,7
11681,25	0,961444482	1,6
11148,88235	0,957449711	1,7
8631	0,93308639	1,9
9386,444444	1,045170549	1,8
9764,736842	0,953166261	1,9
12043,75	0,966782226	1,6
12948,06667	0,972992606	1,5
10987,125	1,013607482	1,6
11648,3125	0,984839518	1,6
10633,35294	0,973220775	1,7
10219,3	0,957284573	2
9037,6	0,972067159	2
10296,31579	0,986878944	1,9
11705,41176	0,954654488	1,7
10681,94444	0,978986976	1,8
9362,947368	1,003056035	1,9
11306,35294	0,970081156	1,7
10984,45	0,991376354	2
10062,61905	1,022609041	2,1
8118,583333	1,089901216	2,4
8867,48	1,060373568	2,5
8346,72	0,985952627	2,5
8529,307692	1,037512164	2,6
10697,18182	1,025335152	2,2
8591,84	1,006376649	2,5
8695,607143	1,018762056	2,8
8125,571429	1,01601847	2,8
7009,758621	1,112410461	2,9
7883,466667	1,037903689	3
7527,645161	1,045436015	3,1
6763,758621	1,09935434	2,9
6682,333333	1,101920787	2,7
7855,681818	1,080574973	2,2
6738,88	1,024388761	2,5
7895,434783	1,024282249	2,3
6361,884615	0,993865289	2,6
6935,956522	0,984935203	2,3
8344,454545	1,005791114	2,2
9107,944444	0,94742834	1,8




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58153&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58153&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58153&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Invoer/inflatie[t] = + 14821.9835564809 + 3533.54666618665`Uitvoer/inflatie`[t] -4143.3985152365Inflatie[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Invoer/inflatie[t] =  +  14821.9835564809 +  3533.54666618665`Uitvoer/inflatie`[t] -4143.3985152365Inflatie[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58153&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Invoer/inflatie[t] =  +  14821.9835564809 +  3533.54666618665`Uitvoer/inflatie`[t] -4143.3985152365Inflatie[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58153&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58153&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
Invoer/inflatie[t] = + 14821.9835564809 + 3533.54666618665`Uitvoer/inflatie`[t] -4143.3985152365Inflatie[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)14821.98355648093900.877483.79970.0003540.000177
`Uitvoer/inflatie`3533.546666186654263.0232160.82890.4106280.205314
Inflatie-4143.3985152365350.717647-11.814100

\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) & 14821.9835564809 & 3900.87748 & 3.7997 & 0.000354 & 0.000177 \tabularnewline
`Uitvoer/inflatie` & 3533.54666618665 & 4263.023216 & 0.8289 & 0.410628 & 0.205314 \tabularnewline
Inflatie & -4143.3985152365 & 350.717647 & -11.8141 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58153&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]14821.9835564809[/C][C]3900.87748[/C][C]3.7997[/C][C]0.000354[/C][C]0.000177[/C][/ROW]
[ROW][C]`Uitvoer/inflatie`[/C][C]3533.54666618665[/C][C]4263.023216[/C][C]0.8289[/C][C]0.410628[/C][C]0.205314[/C][/ROW]
[ROW][C]Inflatie[/C][C]-4143.3985152365[/C][C]350.717647[/C][C]-11.8141[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58153&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58153&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)14821.98355648093900.877483.79970.0003540.000177
`Uitvoer/inflatie`3533.546666186654263.0232160.82890.4106280.205314
Inflatie-4143.3985152365350.717647-11.814100







Multiple Linear Regression - Regression Statistics
Multiple R0.882273496726411
R-squared0.778406523025849
Adjusted R-squared0.770631313307458
F-TEST (value)100.113894186626
F-TEST (DF numerator)2
F-TEST (DF denominator)57
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1184.69780364966
Sum Squared Residuals80000006.5004223

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.882273496726411 \tabularnewline
R-squared & 0.778406523025849 \tabularnewline
Adjusted R-squared & 0.770631313307458 \tabularnewline
F-TEST (value) & 100.113894186626 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 57 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1184.69780364966 \tabularnewline
Sum Squared Residuals & 80000006.5004223 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58153&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.882273496726411[/C][/ROW]
[ROW][C]R-squared[/C][C]0.778406523025849[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.770631313307458[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]100.113894186626[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]57[/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]1184.69780364966[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]80000006.5004223[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58153&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58153&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 R0.882273496726411
R-squared0.778406523025849
Adjusted R-squared0.770631313307458
F-TEST (value)100.113894186626
F-TEST (DF numerator)2
F-TEST (DF denominator)57
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1184.69780364966
Sum Squared Residuals80000006.5004223







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110284.512690.2888406881-2405.78884068811
21279212732.474440728259.5255592717623
312823.6153812731.598184758992.0171952411318
413845.6666713220.0444356176625.622234382379
515335.6363613832.65264004441502.98371995564
611188.512482.4494102115-1293.94941021150
713633.2513335.7871806879297.462819312128
812298.4666712015.5614371031282.905232896909
915353.6363613609.67224832041743.96411167959
1012696.1538512824.4801337744-128.326283774408
1112213.9333312089.9382728987123.995057101252
1213683.7272713535.6789825489148.048287451079
1311214.1428612685.0376475881-1470.89478758814
1413950.2307712772.80093604971177.42983395031
1511179.1333311945.7404312925-766.607101292522
1611801.87511687.758722936114.116277063995
1711188.8235311503.8420820192-315.018552019241
1816456.2727313916.09117852742540.18155147257
1911110.062511730.9295819819-620.867081981874
2016530.6923113039.12106208183491.57124791821
2110038.4117611290.2057268949-1251.79396689487
2211681.2511589.854876197291.3951238028015
2311148.8823511161.3993149243-12.5169649243203
24863110246.6306801802-1615.63068018024
259386.44444411057.0251380707-1670.58069407067
269764.73684210317.5838414097-552.846999409744
2712043.7511608.7160437134435.033956286644
2812948.0666712045.0005627818903.066107218241
2910987.12511774.1752709455-787.050270945493
3011648.312511672.5223276603-24.2098276603152
3110633.3529411217.1271055437-583.774165543736
3210219.39917.796237524301.503762475989
339037.69970.03119500193-932.431195001927
3410296.3157910436.7091800326-140.393390032600
3511705.4117611151.5222640114553.889495988579
3610681.9444410823.1623943402-141.217954340199
379362.94736810493.8716860042-1130.92431800425
3811306.3529411206.0331152932100.319824706810
3910984.4510038.2611366209946.188863379075
4010062.619059734.28344212218328.335607877823
418118.5833338729.04392818293-610.460595182927
428867.488210.36675450854657.113245491456
438346.727947.39688654352399.32311345648
448529.3076927715.24506509635814.062626903653
4510697.181829329.576431034231367.60538896577
468591.848019.56612139174572.273878608257
478695.6071436820.310980435011875.29616256499
488125.5714296810.616391271311314.95503772869
497009.7586216736.8821381928272.876482807193
507883.4666676059.269130860231824.19753613977
517527.6451615671.545104762511856.10005623749
526763.7586216690.7477253599373.0108956400724
536682.3333337528.49608864802-846.162755648021
547855.6818189524.76891636953-1669.08709836953
556738.888083.21275970032-1344.33275970032
567895.4347838911.51609762512-1016.08131462512
576361.8846157561.01679545063-1199.13218045063
586935.9565228772.48147440752-1836.52495240752
598344.4545459260.5166607155-916.062115715505
609107.94444410711.648481313-1603.704037313

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 10284.5 & 12690.2888406881 & -2405.78884068811 \tabularnewline
2 & 12792 & 12732.4744407282 & 59.5255592717623 \tabularnewline
3 & 12823.61538 & 12731.5981847589 & 92.0171952411318 \tabularnewline
4 & 13845.66667 & 13220.0444356176 & 625.622234382379 \tabularnewline
5 & 15335.63636 & 13832.6526400444 & 1502.98371995564 \tabularnewline
6 & 11188.5 & 12482.4494102115 & -1293.94941021150 \tabularnewline
7 & 13633.25 & 13335.7871806879 & 297.462819312128 \tabularnewline
8 & 12298.46667 & 12015.5614371031 & 282.905232896909 \tabularnewline
9 & 15353.63636 & 13609.6722483204 & 1743.96411167959 \tabularnewline
10 & 12696.15385 & 12824.4801337744 & -128.326283774408 \tabularnewline
11 & 12213.93333 & 12089.9382728987 & 123.995057101252 \tabularnewline
12 & 13683.72727 & 13535.6789825489 & 148.048287451079 \tabularnewline
13 & 11214.14286 & 12685.0376475881 & -1470.89478758814 \tabularnewline
14 & 13950.23077 & 12772.8009360497 & 1177.42983395031 \tabularnewline
15 & 11179.13333 & 11945.7404312925 & -766.607101292522 \tabularnewline
16 & 11801.875 & 11687.758722936 & 114.116277063995 \tabularnewline
17 & 11188.82353 & 11503.8420820192 & -315.018552019241 \tabularnewline
18 & 16456.27273 & 13916.0911785274 & 2540.18155147257 \tabularnewline
19 & 11110.0625 & 11730.9295819819 & -620.867081981874 \tabularnewline
20 & 16530.69231 & 13039.1210620818 & 3491.57124791821 \tabularnewline
21 & 10038.41176 & 11290.2057268949 & -1251.79396689487 \tabularnewline
22 & 11681.25 & 11589.8548761972 & 91.3951238028015 \tabularnewline
23 & 11148.88235 & 11161.3993149243 & -12.5169649243203 \tabularnewline
24 & 8631 & 10246.6306801802 & -1615.63068018024 \tabularnewline
25 & 9386.444444 & 11057.0251380707 & -1670.58069407067 \tabularnewline
26 & 9764.736842 & 10317.5838414097 & -552.846999409744 \tabularnewline
27 & 12043.75 & 11608.7160437134 & 435.033956286644 \tabularnewline
28 & 12948.06667 & 12045.0005627818 & 903.066107218241 \tabularnewline
29 & 10987.125 & 11774.1752709455 & -787.050270945493 \tabularnewline
30 & 11648.3125 & 11672.5223276603 & -24.2098276603152 \tabularnewline
31 & 10633.35294 & 11217.1271055437 & -583.774165543736 \tabularnewline
32 & 10219.3 & 9917.796237524 & 301.503762475989 \tabularnewline
33 & 9037.6 & 9970.03119500193 & -932.431195001927 \tabularnewline
34 & 10296.31579 & 10436.7091800326 & -140.393390032600 \tabularnewline
35 & 11705.41176 & 11151.5222640114 & 553.889495988579 \tabularnewline
36 & 10681.94444 & 10823.1623943402 & -141.217954340199 \tabularnewline
37 & 9362.947368 & 10493.8716860042 & -1130.92431800425 \tabularnewline
38 & 11306.35294 & 11206.0331152932 & 100.319824706810 \tabularnewline
39 & 10984.45 & 10038.2611366209 & 946.188863379075 \tabularnewline
40 & 10062.61905 & 9734.28344212218 & 328.335607877823 \tabularnewline
41 & 8118.583333 & 8729.04392818293 & -610.460595182927 \tabularnewline
42 & 8867.48 & 8210.36675450854 & 657.113245491456 \tabularnewline
43 & 8346.72 & 7947.39688654352 & 399.32311345648 \tabularnewline
44 & 8529.307692 & 7715.24506509635 & 814.062626903653 \tabularnewline
45 & 10697.18182 & 9329.57643103423 & 1367.60538896577 \tabularnewline
46 & 8591.84 & 8019.56612139174 & 572.273878608257 \tabularnewline
47 & 8695.607143 & 6820.31098043501 & 1875.29616256499 \tabularnewline
48 & 8125.571429 & 6810.61639127131 & 1314.95503772869 \tabularnewline
49 & 7009.758621 & 6736.8821381928 & 272.876482807193 \tabularnewline
50 & 7883.466667 & 6059.26913086023 & 1824.19753613977 \tabularnewline
51 & 7527.645161 & 5671.54510476251 & 1856.10005623749 \tabularnewline
52 & 6763.758621 & 6690.74772535993 & 73.0108956400724 \tabularnewline
53 & 6682.333333 & 7528.49608864802 & -846.162755648021 \tabularnewline
54 & 7855.681818 & 9524.76891636953 & -1669.08709836953 \tabularnewline
55 & 6738.88 & 8083.21275970032 & -1344.33275970032 \tabularnewline
56 & 7895.434783 & 8911.51609762512 & -1016.08131462512 \tabularnewline
57 & 6361.884615 & 7561.01679545063 & -1199.13218045063 \tabularnewline
58 & 6935.956522 & 8772.48147440752 & -1836.52495240752 \tabularnewline
59 & 8344.454545 & 9260.5166607155 & -916.062115715505 \tabularnewline
60 & 9107.944444 & 10711.648481313 & -1603.704037313 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58153&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]10284.5[/C][C]12690.2888406881[/C][C]-2405.78884068811[/C][/ROW]
[ROW][C]2[/C][C]12792[/C][C]12732.4744407282[/C][C]59.5255592717623[/C][/ROW]
[ROW][C]3[/C][C]12823.61538[/C][C]12731.5981847589[/C][C]92.0171952411318[/C][/ROW]
[ROW][C]4[/C][C]13845.66667[/C][C]13220.0444356176[/C][C]625.622234382379[/C][/ROW]
[ROW][C]5[/C][C]15335.63636[/C][C]13832.6526400444[/C][C]1502.98371995564[/C][/ROW]
[ROW][C]6[/C][C]11188.5[/C][C]12482.4494102115[/C][C]-1293.94941021150[/C][/ROW]
[ROW][C]7[/C][C]13633.25[/C][C]13335.7871806879[/C][C]297.462819312128[/C][/ROW]
[ROW][C]8[/C][C]12298.46667[/C][C]12015.5614371031[/C][C]282.905232896909[/C][/ROW]
[ROW][C]9[/C][C]15353.63636[/C][C]13609.6722483204[/C][C]1743.96411167959[/C][/ROW]
[ROW][C]10[/C][C]12696.15385[/C][C]12824.4801337744[/C][C]-128.326283774408[/C][/ROW]
[ROW][C]11[/C][C]12213.93333[/C][C]12089.9382728987[/C][C]123.995057101252[/C][/ROW]
[ROW][C]12[/C][C]13683.72727[/C][C]13535.6789825489[/C][C]148.048287451079[/C][/ROW]
[ROW][C]13[/C][C]11214.14286[/C][C]12685.0376475881[/C][C]-1470.89478758814[/C][/ROW]
[ROW][C]14[/C][C]13950.23077[/C][C]12772.8009360497[/C][C]1177.42983395031[/C][/ROW]
[ROW][C]15[/C][C]11179.13333[/C][C]11945.7404312925[/C][C]-766.607101292522[/C][/ROW]
[ROW][C]16[/C][C]11801.875[/C][C]11687.758722936[/C][C]114.116277063995[/C][/ROW]
[ROW][C]17[/C][C]11188.82353[/C][C]11503.8420820192[/C][C]-315.018552019241[/C][/ROW]
[ROW][C]18[/C][C]16456.27273[/C][C]13916.0911785274[/C][C]2540.18155147257[/C][/ROW]
[ROW][C]19[/C][C]11110.0625[/C][C]11730.9295819819[/C][C]-620.867081981874[/C][/ROW]
[ROW][C]20[/C][C]16530.69231[/C][C]13039.1210620818[/C][C]3491.57124791821[/C][/ROW]
[ROW][C]21[/C][C]10038.41176[/C][C]11290.2057268949[/C][C]-1251.79396689487[/C][/ROW]
[ROW][C]22[/C][C]11681.25[/C][C]11589.8548761972[/C][C]91.3951238028015[/C][/ROW]
[ROW][C]23[/C][C]11148.88235[/C][C]11161.3993149243[/C][C]-12.5169649243203[/C][/ROW]
[ROW][C]24[/C][C]8631[/C][C]10246.6306801802[/C][C]-1615.63068018024[/C][/ROW]
[ROW][C]25[/C][C]9386.444444[/C][C]11057.0251380707[/C][C]-1670.58069407067[/C][/ROW]
[ROW][C]26[/C][C]9764.736842[/C][C]10317.5838414097[/C][C]-552.846999409744[/C][/ROW]
[ROW][C]27[/C][C]12043.75[/C][C]11608.7160437134[/C][C]435.033956286644[/C][/ROW]
[ROW][C]28[/C][C]12948.06667[/C][C]12045.0005627818[/C][C]903.066107218241[/C][/ROW]
[ROW][C]29[/C][C]10987.125[/C][C]11774.1752709455[/C][C]-787.050270945493[/C][/ROW]
[ROW][C]30[/C][C]11648.3125[/C][C]11672.5223276603[/C][C]-24.2098276603152[/C][/ROW]
[ROW][C]31[/C][C]10633.35294[/C][C]11217.1271055437[/C][C]-583.774165543736[/C][/ROW]
[ROW][C]32[/C][C]10219.3[/C][C]9917.796237524[/C][C]301.503762475989[/C][/ROW]
[ROW][C]33[/C][C]9037.6[/C][C]9970.03119500193[/C][C]-932.431195001927[/C][/ROW]
[ROW][C]34[/C][C]10296.31579[/C][C]10436.7091800326[/C][C]-140.393390032600[/C][/ROW]
[ROW][C]35[/C][C]11705.41176[/C][C]11151.5222640114[/C][C]553.889495988579[/C][/ROW]
[ROW][C]36[/C][C]10681.94444[/C][C]10823.1623943402[/C][C]-141.217954340199[/C][/ROW]
[ROW][C]37[/C][C]9362.947368[/C][C]10493.8716860042[/C][C]-1130.92431800425[/C][/ROW]
[ROW][C]38[/C][C]11306.35294[/C][C]11206.0331152932[/C][C]100.319824706810[/C][/ROW]
[ROW][C]39[/C][C]10984.45[/C][C]10038.2611366209[/C][C]946.188863379075[/C][/ROW]
[ROW][C]40[/C][C]10062.61905[/C][C]9734.28344212218[/C][C]328.335607877823[/C][/ROW]
[ROW][C]41[/C][C]8118.583333[/C][C]8729.04392818293[/C][C]-610.460595182927[/C][/ROW]
[ROW][C]42[/C][C]8867.48[/C][C]8210.36675450854[/C][C]657.113245491456[/C][/ROW]
[ROW][C]43[/C][C]8346.72[/C][C]7947.39688654352[/C][C]399.32311345648[/C][/ROW]
[ROW][C]44[/C][C]8529.307692[/C][C]7715.24506509635[/C][C]814.062626903653[/C][/ROW]
[ROW][C]45[/C][C]10697.18182[/C][C]9329.57643103423[/C][C]1367.60538896577[/C][/ROW]
[ROW][C]46[/C][C]8591.84[/C][C]8019.56612139174[/C][C]572.273878608257[/C][/ROW]
[ROW][C]47[/C][C]8695.607143[/C][C]6820.31098043501[/C][C]1875.29616256499[/C][/ROW]
[ROW][C]48[/C][C]8125.571429[/C][C]6810.61639127131[/C][C]1314.95503772869[/C][/ROW]
[ROW][C]49[/C][C]7009.758621[/C][C]6736.8821381928[/C][C]272.876482807193[/C][/ROW]
[ROW][C]50[/C][C]7883.466667[/C][C]6059.26913086023[/C][C]1824.19753613977[/C][/ROW]
[ROW][C]51[/C][C]7527.645161[/C][C]5671.54510476251[/C][C]1856.10005623749[/C][/ROW]
[ROW][C]52[/C][C]6763.758621[/C][C]6690.74772535993[/C][C]73.0108956400724[/C][/ROW]
[ROW][C]53[/C][C]6682.333333[/C][C]7528.49608864802[/C][C]-846.162755648021[/C][/ROW]
[ROW][C]54[/C][C]7855.681818[/C][C]9524.76891636953[/C][C]-1669.08709836953[/C][/ROW]
[ROW][C]55[/C][C]6738.88[/C][C]8083.21275970032[/C][C]-1344.33275970032[/C][/ROW]
[ROW][C]56[/C][C]7895.434783[/C][C]8911.51609762512[/C][C]-1016.08131462512[/C][/ROW]
[ROW][C]57[/C][C]6361.884615[/C][C]7561.01679545063[/C][C]-1199.13218045063[/C][/ROW]
[ROW][C]58[/C][C]6935.956522[/C][C]8772.48147440752[/C][C]-1836.52495240752[/C][/ROW]
[ROW][C]59[/C][C]8344.454545[/C][C]9260.5166607155[/C][C]-916.062115715505[/C][/ROW]
[ROW][C]60[/C][C]9107.944444[/C][C]10711.648481313[/C][C]-1603.704037313[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58153&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58153&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
110284.512690.2888406881-2405.78884068811
21279212732.474440728259.5255592717623
312823.6153812731.598184758992.0171952411318
413845.6666713220.0444356176625.622234382379
515335.6363613832.65264004441502.98371995564
611188.512482.4494102115-1293.94941021150
713633.2513335.7871806879297.462819312128
812298.4666712015.5614371031282.905232896909
915353.6363613609.67224832041743.96411167959
1012696.1538512824.4801337744-128.326283774408
1112213.9333312089.9382728987123.995057101252
1213683.7272713535.6789825489148.048287451079
1311214.1428612685.0376475881-1470.89478758814
1413950.2307712772.80093604971177.42983395031
1511179.1333311945.7404312925-766.607101292522
1611801.87511687.758722936114.116277063995
1711188.8235311503.8420820192-315.018552019241
1816456.2727313916.09117852742540.18155147257
1911110.062511730.9295819819-620.867081981874
2016530.6923113039.12106208183491.57124791821
2110038.4117611290.2057268949-1251.79396689487
2211681.2511589.854876197291.3951238028015
2311148.8823511161.3993149243-12.5169649243203
24863110246.6306801802-1615.63068018024
259386.44444411057.0251380707-1670.58069407067
269764.73684210317.5838414097-552.846999409744
2712043.7511608.7160437134435.033956286644
2812948.0666712045.0005627818903.066107218241
2910987.12511774.1752709455-787.050270945493
3011648.312511672.5223276603-24.2098276603152
3110633.3529411217.1271055437-583.774165543736
3210219.39917.796237524301.503762475989
339037.69970.03119500193-932.431195001927
3410296.3157910436.7091800326-140.393390032600
3511705.4117611151.5222640114553.889495988579
3610681.9444410823.1623943402-141.217954340199
379362.94736810493.8716860042-1130.92431800425
3811306.3529411206.0331152932100.319824706810
3910984.4510038.2611366209946.188863379075
4010062.619059734.28344212218328.335607877823
418118.5833338729.04392818293-610.460595182927
428867.488210.36675450854657.113245491456
438346.727947.39688654352399.32311345648
448529.3076927715.24506509635814.062626903653
4510697.181829329.576431034231367.60538896577
468591.848019.56612139174572.273878608257
478695.6071436820.310980435011875.29616256499
488125.5714296810.616391271311314.95503772869
497009.7586216736.8821381928272.876482807193
507883.4666676059.269130860231824.19753613977
517527.6451615671.545104762511856.10005623749
526763.7586216690.7477253599373.0108956400724
536682.3333337528.49608864802-846.162755648021
547855.6818189524.76891636953-1669.08709836953
556738.888083.21275970032-1344.33275970032
567895.4347838911.51609762512-1016.08131462512
576361.8846157561.01679545063-1199.13218045063
586935.9565228772.48147440752-1836.52495240752
598344.4545459260.5166607155-916.062115715505
609107.94444410711.648481313-1603.704037313







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.01475781602403610.02951563204807210.985242183975964
70.003704921739942610.007409843479885230.996295078260057
80.2924123230968340.5848246461936670.707587676903166
90.2026050225639280.4052100451278560.797394977436072
100.1248479720661840.2496959441323680.875152027933816
110.1808243106826440.3616486213652870.819175689317356
120.1987839313550040.3975678627100080.801216068644996
130.1528227191224030.3056454382448050.847177280877597
140.1410395074778840.2820790149557680.858960492522116
150.09645880245760540.1929176049152110.903541197542395
160.1125412746751120.2250825493502240.887458725324888
170.1298603463200530.2597206926401060.870139653679947
180.3144998650848080.6289997301696160.685500134915192
190.2438794392173590.4877588784347180.756120560782641
200.8786384929918190.2427230140163620.121361507008181
210.840190796810060.3196184063798810.159809203189941
220.8146192986730890.3707614026538220.185380701326911
230.787547797682070.4249044046358610.212452202317930
240.7908109723383740.4183780553232520.209189027661626
250.756889815022370.486220369955260.24311018497763
260.7293570818321160.5412858363357680.270642918167884
270.7092911269259030.5814177461481940.290708873074097
280.7556998705665990.4886002588668020.244300129433401
290.6961842704524850.607631459095030.303815729547515
300.6571903859677480.6856192280645040.342809614032252
310.5838300199583690.8323399600832630.416169980041631
320.6007142059899830.7985715880200330.399285794010017
330.5463390490953680.9073219018092640.453660950904632
340.4920294708581860.9840589417163730.507970529141814
350.4992030395302280.9984060790604550.500796960469772
360.4464621392580820.8929242785161640.553537860741918
370.3732979062366270.7465958124732530.626702093763373
380.3659240802033140.7318481604066280.634075919796686
390.5449698074103840.9100603851792310.455030192589616
400.6150193757190770.7699612485618460.384980624280923
410.5450051250344920.9099897499310160.454994874965508
420.5839094677038740.8321810645922520.416090532296126
430.5287509536037850.942498092792430.471249046396215
440.5112590128640170.9774819742719670.488740987135983
450.8891697190512450.221660561897510.110830280948755
460.8735725233135010.2528549533729980.126427476686499
470.9206405393721970.1587189212556060.079359460627803
480.9138617658933730.1722764682132550.0861382341066273
490.8577711464517460.2844577070965080.142228853548254
500.8858935885728090.2282128228543830.114106411427191
510.9843966812814420.0312066374371170.0156033187185585
520.9885369575296280.02292608494074390.0114630424703719
530.9707338003969870.0585323992060250.0292661996030125
540.9646641837915460.07067163241690740.0353358162084537

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.0147578160240361 & 0.0295156320480721 & 0.985242183975964 \tabularnewline
7 & 0.00370492173994261 & 0.00740984347988523 & 0.996295078260057 \tabularnewline
8 & 0.292412323096834 & 0.584824646193667 & 0.707587676903166 \tabularnewline
9 & 0.202605022563928 & 0.405210045127856 & 0.797394977436072 \tabularnewline
10 & 0.124847972066184 & 0.249695944132368 & 0.875152027933816 \tabularnewline
11 & 0.180824310682644 & 0.361648621365287 & 0.819175689317356 \tabularnewline
12 & 0.198783931355004 & 0.397567862710008 & 0.801216068644996 \tabularnewline
13 & 0.152822719122403 & 0.305645438244805 & 0.847177280877597 \tabularnewline
14 & 0.141039507477884 & 0.282079014955768 & 0.858960492522116 \tabularnewline
15 & 0.0964588024576054 & 0.192917604915211 & 0.903541197542395 \tabularnewline
16 & 0.112541274675112 & 0.225082549350224 & 0.887458725324888 \tabularnewline
17 & 0.129860346320053 & 0.259720692640106 & 0.870139653679947 \tabularnewline
18 & 0.314499865084808 & 0.628999730169616 & 0.685500134915192 \tabularnewline
19 & 0.243879439217359 & 0.487758878434718 & 0.756120560782641 \tabularnewline
20 & 0.878638492991819 & 0.242723014016362 & 0.121361507008181 \tabularnewline
21 & 0.84019079681006 & 0.319618406379881 & 0.159809203189941 \tabularnewline
22 & 0.814619298673089 & 0.370761402653822 & 0.185380701326911 \tabularnewline
23 & 0.78754779768207 & 0.424904404635861 & 0.212452202317930 \tabularnewline
24 & 0.790810972338374 & 0.418378055323252 & 0.209189027661626 \tabularnewline
25 & 0.75688981502237 & 0.48622036995526 & 0.24311018497763 \tabularnewline
26 & 0.729357081832116 & 0.541285836335768 & 0.270642918167884 \tabularnewline
27 & 0.709291126925903 & 0.581417746148194 & 0.290708873074097 \tabularnewline
28 & 0.755699870566599 & 0.488600258866802 & 0.244300129433401 \tabularnewline
29 & 0.696184270452485 & 0.60763145909503 & 0.303815729547515 \tabularnewline
30 & 0.657190385967748 & 0.685619228064504 & 0.342809614032252 \tabularnewline
31 & 0.583830019958369 & 0.832339960083263 & 0.416169980041631 \tabularnewline
32 & 0.600714205989983 & 0.798571588020033 & 0.399285794010017 \tabularnewline
33 & 0.546339049095368 & 0.907321901809264 & 0.453660950904632 \tabularnewline
34 & 0.492029470858186 & 0.984058941716373 & 0.507970529141814 \tabularnewline
35 & 0.499203039530228 & 0.998406079060455 & 0.500796960469772 \tabularnewline
36 & 0.446462139258082 & 0.892924278516164 & 0.553537860741918 \tabularnewline
37 & 0.373297906236627 & 0.746595812473253 & 0.626702093763373 \tabularnewline
38 & 0.365924080203314 & 0.731848160406628 & 0.634075919796686 \tabularnewline
39 & 0.544969807410384 & 0.910060385179231 & 0.455030192589616 \tabularnewline
40 & 0.615019375719077 & 0.769961248561846 & 0.384980624280923 \tabularnewline
41 & 0.545005125034492 & 0.909989749931016 & 0.454994874965508 \tabularnewline
42 & 0.583909467703874 & 0.832181064592252 & 0.416090532296126 \tabularnewline
43 & 0.528750953603785 & 0.94249809279243 & 0.471249046396215 \tabularnewline
44 & 0.511259012864017 & 0.977481974271967 & 0.488740987135983 \tabularnewline
45 & 0.889169719051245 & 0.22166056189751 & 0.110830280948755 \tabularnewline
46 & 0.873572523313501 & 0.252854953372998 & 0.126427476686499 \tabularnewline
47 & 0.920640539372197 & 0.158718921255606 & 0.079359460627803 \tabularnewline
48 & 0.913861765893373 & 0.172276468213255 & 0.0861382341066273 \tabularnewline
49 & 0.857771146451746 & 0.284457707096508 & 0.142228853548254 \tabularnewline
50 & 0.885893588572809 & 0.228212822854383 & 0.114106411427191 \tabularnewline
51 & 0.984396681281442 & 0.031206637437117 & 0.0156033187185585 \tabularnewline
52 & 0.988536957529628 & 0.0229260849407439 & 0.0114630424703719 \tabularnewline
53 & 0.970733800396987 & 0.058532399206025 & 0.0292661996030125 \tabularnewline
54 & 0.964664183791546 & 0.0706716324169074 & 0.0353358162084537 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58153&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]0.0147578160240361[/C][C]0.0295156320480721[/C][C]0.985242183975964[/C][/ROW]
[ROW][C]7[/C][C]0.00370492173994261[/C][C]0.00740984347988523[/C][C]0.996295078260057[/C][/ROW]
[ROW][C]8[/C][C]0.292412323096834[/C][C]0.584824646193667[/C][C]0.707587676903166[/C][/ROW]
[ROW][C]9[/C][C]0.202605022563928[/C][C]0.405210045127856[/C][C]0.797394977436072[/C][/ROW]
[ROW][C]10[/C][C]0.124847972066184[/C][C]0.249695944132368[/C][C]0.875152027933816[/C][/ROW]
[ROW][C]11[/C][C]0.180824310682644[/C][C]0.361648621365287[/C][C]0.819175689317356[/C][/ROW]
[ROW][C]12[/C][C]0.198783931355004[/C][C]0.397567862710008[/C][C]0.801216068644996[/C][/ROW]
[ROW][C]13[/C][C]0.152822719122403[/C][C]0.305645438244805[/C][C]0.847177280877597[/C][/ROW]
[ROW][C]14[/C][C]0.141039507477884[/C][C]0.282079014955768[/C][C]0.858960492522116[/C][/ROW]
[ROW][C]15[/C][C]0.0964588024576054[/C][C]0.192917604915211[/C][C]0.903541197542395[/C][/ROW]
[ROW][C]16[/C][C]0.112541274675112[/C][C]0.225082549350224[/C][C]0.887458725324888[/C][/ROW]
[ROW][C]17[/C][C]0.129860346320053[/C][C]0.259720692640106[/C][C]0.870139653679947[/C][/ROW]
[ROW][C]18[/C][C]0.314499865084808[/C][C]0.628999730169616[/C][C]0.685500134915192[/C][/ROW]
[ROW][C]19[/C][C]0.243879439217359[/C][C]0.487758878434718[/C][C]0.756120560782641[/C][/ROW]
[ROW][C]20[/C][C]0.878638492991819[/C][C]0.242723014016362[/C][C]0.121361507008181[/C][/ROW]
[ROW][C]21[/C][C]0.84019079681006[/C][C]0.319618406379881[/C][C]0.159809203189941[/C][/ROW]
[ROW][C]22[/C][C]0.814619298673089[/C][C]0.370761402653822[/C][C]0.185380701326911[/C][/ROW]
[ROW][C]23[/C][C]0.78754779768207[/C][C]0.424904404635861[/C][C]0.212452202317930[/C][/ROW]
[ROW][C]24[/C][C]0.790810972338374[/C][C]0.418378055323252[/C][C]0.209189027661626[/C][/ROW]
[ROW][C]25[/C][C]0.75688981502237[/C][C]0.48622036995526[/C][C]0.24311018497763[/C][/ROW]
[ROW][C]26[/C][C]0.729357081832116[/C][C]0.541285836335768[/C][C]0.270642918167884[/C][/ROW]
[ROW][C]27[/C][C]0.709291126925903[/C][C]0.581417746148194[/C][C]0.290708873074097[/C][/ROW]
[ROW][C]28[/C][C]0.755699870566599[/C][C]0.488600258866802[/C][C]0.244300129433401[/C][/ROW]
[ROW][C]29[/C][C]0.696184270452485[/C][C]0.60763145909503[/C][C]0.303815729547515[/C][/ROW]
[ROW][C]30[/C][C]0.657190385967748[/C][C]0.685619228064504[/C][C]0.342809614032252[/C][/ROW]
[ROW][C]31[/C][C]0.583830019958369[/C][C]0.832339960083263[/C][C]0.416169980041631[/C][/ROW]
[ROW][C]32[/C][C]0.600714205989983[/C][C]0.798571588020033[/C][C]0.399285794010017[/C][/ROW]
[ROW][C]33[/C][C]0.546339049095368[/C][C]0.907321901809264[/C][C]0.453660950904632[/C][/ROW]
[ROW][C]34[/C][C]0.492029470858186[/C][C]0.984058941716373[/C][C]0.507970529141814[/C][/ROW]
[ROW][C]35[/C][C]0.499203039530228[/C][C]0.998406079060455[/C][C]0.500796960469772[/C][/ROW]
[ROW][C]36[/C][C]0.446462139258082[/C][C]0.892924278516164[/C][C]0.553537860741918[/C][/ROW]
[ROW][C]37[/C][C]0.373297906236627[/C][C]0.746595812473253[/C][C]0.626702093763373[/C][/ROW]
[ROW][C]38[/C][C]0.365924080203314[/C][C]0.731848160406628[/C][C]0.634075919796686[/C][/ROW]
[ROW][C]39[/C][C]0.544969807410384[/C][C]0.910060385179231[/C][C]0.455030192589616[/C][/ROW]
[ROW][C]40[/C][C]0.615019375719077[/C][C]0.769961248561846[/C][C]0.384980624280923[/C][/ROW]
[ROW][C]41[/C][C]0.545005125034492[/C][C]0.909989749931016[/C][C]0.454994874965508[/C][/ROW]
[ROW][C]42[/C][C]0.583909467703874[/C][C]0.832181064592252[/C][C]0.416090532296126[/C][/ROW]
[ROW][C]43[/C][C]0.528750953603785[/C][C]0.94249809279243[/C][C]0.471249046396215[/C][/ROW]
[ROW][C]44[/C][C]0.511259012864017[/C][C]0.977481974271967[/C][C]0.488740987135983[/C][/ROW]
[ROW][C]45[/C][C]0.889169719051245[/C][C]0.22166056189751[/C][C]0.110830280948755[/C][/ROW]
[ROW][C]46[/C][C]0.873572523313501[/C][C]0.252854953372998[/C][C]0.126427476686499[/C][/ROW]
[ROW][C]47[/C][C]0.920640539372197[/C][C]0.158718921255606[/C][C]0.079359460627803[/C][/ROW]
[ROW][C]48[/C][C]0.913861765893373[/C][C]0.172276468213255[/C][C]0.0861382341066273[/C][/ROW]
[ROW][C]49[/C][C]0.857771146451746[/C][C]0.284457707096508[/C][C]0.142228853548254[/C][/ROW]
[ROW][C]50[/C][C]0.885893588572809[/C][C]0.228212822854383[/C][C]0.114106411427191[/C][/ROW]
[ROW][C]51[/C][C]0.984396681281442[/C][C]0.031206637437117[/C][C]0.0156033187185585[/C][/ROW]
[ROW][C]52[/C][C]0.988536957529628[/C][C]0.0229260849407439[/C][C]0.0114630424703719[/C][/ROW]
[ROW][C]53[/C][C]0.970733800396987[/C][C]0.058532399206025[/C][C]0.0292661996030125[/C][/ROW]
[ROW][C]54[/C][C]0.964664183791546[/C][C]0.0706716324169074[/C][C]0.0353358162084537[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58153&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58153&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
60.01475781602403610.02951563204807210.985242183975964
70.003704921739942610.007409843479885230.996295078260057
80.2924123230968340.5848246461936670.707587676903166
90.2026050225639280.4052100451278560.797394977436072
100.1248479720661840.2496959441323680.875152027933816
110.1808243106826440.3616486213652870.819175689317356
120.1987839313550040.3975678627100080.801216068644996
130.1528227191224030.3056454382448050.847177280877597
140.1410395074778840.2820790149557680.858960492522116
150.09645880245760540.1929176049152110.903541197542395
160.1125412746751120.2250825493502240.887458725324888
170.1298603463200530.2597206926401060.870139653679947
180.3144998650848080.6289997301696160.685500134915192
190.2438794392173590.4877588784347180.756120560782641
200.8786384929918190.2427230140163620.121361507008181
210.840190796810060.3196184063798810.159809203189941
220.8146192986730890.3707614026538220.185380701326911
230.787547797682070.4249044046358610.212452202317930
240.7908109723383740.4183780553232520.209189027661626
250.756889815022370.486220369955260.24311018497763
260.7293570818321160.5412858363357680.270642918167884
270.7092911269259030.5814177461481940.290708873074097
280.7556998705665990.4886002588668020.244300129433401
290.6961842704524850.607631459095030.303815729547515
300.6571903859677480.6856192280645040.342809614032252
310.5838300199583690.8323399600832630.416169980041631
320.6007142059899830.7985715880200330.399285794010017
330.5463390490953680.9073219018092640.453660950904632
340.4920294708581860.9840589417163730.507970529141814
350.4992030395302280.9984060790604550.500796960469772
360.4464621392580820.8929242785161640.553537860741918
370.3732979062366270.7465958124732530.626702093763373
380.3659240802033140.7318481604066280.634075919796686
390.5449698074103840.9100603851792310.455030192589616
400.6150193757190770.7699612485618460.384980624280923
410.5450051250344920.9099897499310160.454994874965508
420.5839094677038740.8321810645922520.416090532296126
430.5287509536037850.942498092792430.471249046396215
440.5112590128640170.9774819742719670.488740987135983
450.8891697190512450.221660561897510.110830280948755
460.8735725233135010.2528549533729980.126427476686499
470.9206405393721970.1587189212556060.079359460627803
480.9138617658933730.1722764682132550.0861382341066273
490.8577711464517460.2844577070965080.142228853548254
500.8858935885728090.2282128228543830.114106411427191
510.9843966812814420.0312066374371170.0156033187185585
520.9885369575296280.02292608494074390.0114630424703719
530.9707338003969870.0585323992060250.0292661996030125
540.9646641837915460.07067163241690740.0353358162084537







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.0204081632653061NOK
5% type I error level40.0816326530612245NOK
10% type I error level60.122448979591837NOK

\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 & 1 & 0.0204081632653061 & NOK \tabularnewline
5% type I error level & 4 & 0.0816326530612245 & NOK \tabularnewline
10% type I error level & 6 & 0.122448979591837 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58153&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]1[/C][C]0.0204081632653061[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]4[/C][C]0.0816326530612245[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]6[/C][C]0.122448979591837[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58153&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58153&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 level10.0204081632653061NOK
5% type I error level40.0816326530612245NOK
10% type I error level60.122448979591837NOK



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')
}