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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 05:04:16 -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/t12587187312ms9diw7g59561h.htm/, Retrieved Fri, 29 Mar 2024 07:09:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58062, Retrieved Fri, 29 Mar 2024 07:09:16 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsWs7 link 5
Estimated Impact119
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]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:14:11] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [Ws7 link 5] [2009-11-20 12:04:16] [88e98f4c87ea17c4967db8279bda8533] [Current]
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Dataseries X:
1.4	8.2	1,2	1,4
1.2	8.0	1	1,2
1.0	7.5	1,7	1
1.7	6.8	7.5	1,7
2.4	6.5	6.8	7.5
2.0	6.6	6.5	6.8
2.1	7.6	6.6	6.5
2.0	8.0	7.6	6.6
1.8	8.1	8.0	7.6
2.7	7.7	8.1	8.0
2.3	7.5	7.7	8.1
1.9	7.6	7.5	7.7
2.0	7.8	7.6	7.5
2.3	7.8	7.8	7.6
2.8	7.8	7.8	7.8
2.4	7.5	7.8	7.8
2.3	7.5	7.5	7.8
2.7	7.1	7.5	7.5
2.7	7.5	7.1	7.5
2.9	7.5	7.5	7.1
3.0	7.6	7.5	7.5
2.2	7.7	7.6	7.5
2.3	7.7	7.7	7.6
2.8	7.9	7.7	7.7
2.8	8.1	7.9	7.7
2.8	8.2	8.1	7.9
2.2	8.2	8.2	8.1
2.6	8.2	8.2	8.2
2.8	7.9	8.2	8.2
2.5	7.3	7.9	8.2
2.4	6.9	7.3	7.9
2.3	6.6	6.9	7.3
1.9	6.7	6.6	6.9
1.7	6.9	6.7	6.6
2.0	7.0	6.9	6.7
2.1	7.1	7.0	6.9
1.7	7.2	7.1	7.0
1.8	7.1	7.2	7.1
1.8	6.9	7.1	7.2
1.8	7.0	6.9	7.1
1.3	6.8	7.0	6.9
1.3	6.4	6.8	7.0
1.3	6.7	6.4	6.8
1.2	6.6	6.7	6.4
1.4	6.4	6.6	6.7
2.2	6.3	6.4	6.6
2.9	6.2	6.3	6.4
3.1	6.5	6.2	6.3
3.5	6.8	6.5	6.2
3.6	6.8	6.8	6.5
4.4	6.4	6.8	6.8
4.1	6.1	6.4	6.8
5.1	5.8	6.1	6.4
5.8	6.1	5.8	6.1
5.9	7.2	6.1	5.8
5.4	7.3	7.2	6.1
5.5	6.9	7.3	7.2
4.8	6.1	6.9	7.3
3.2	5.8	6.1	6.9
2.7	6.2	5.8	6.1
2.1	7.1	6.2	5.8
1.9	7.7	7.1	6.2
0.6	7.9	7.7	7.1
0.7	7.7	7.9	7.7




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=58062&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=58062&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58062&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
Y[t] = + 3.30821434872672 -0.272802800787046X[t] + 0.0702404974155654Y1[t] -0.00157170726815757Y2[t] + 0.00350368217653026M1[t] + 0.00247072088343351M2[t] -0.205170993272856M3[t] -0.266929670970975M4[t] + 0.327279121223635M5[t] + 0.349232147671393M6[t] + 0.495317702851394M7[t] + 0.328181865572133M8[t] + 0.252601193983415M9[t] + 0.183724384734404M10[t] -0.0281642113791766M11[t] + 0.0185621130818993t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  3.30821434872672 -0.272802800787046X[t] +  0.0702404974155654Y1[t] -0.00157170726815757Y2[t] +  0.00350368217653026M1[t] +  0.00247072088343351M2[t] -0.205170993272856M3[t] -0.266929670970975M4[t] +  0.327279121223635M5[t] +  0.349232147671393M6[t] +  0.495317702851394M7[t] +  0.328181865572133M8[t] +  0.252601193983415M9[t] +  0.183724384734404M10[t] -0.0281642113791766M11[t] +  0.0185621130818993t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58062&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  3.30821434872672 -0.272802800787046X[t] +  0.0702404974155654Y1[t] -0.00157170726815757Y2[t] +  0.00350368217653026M1[t] +  0.00247072088343351M2[t] -0.205170993272856M3[t] -0.266929670970975M4[t] +  0.327279121223635M5[t] +  0.349232147671393M6[t] +  0.495317702851394M7[t] +  0.328181865572133M8[t] +  0.252601193983415M9[t] +  0.183724384734404M10[t] -0.0281642113791766M11[t] +  0.0185621130818993t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58062&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58062&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
Y[t] = + 3.30821434872672 -0.272802800787046X[t] + 0.0702404974155654Y1[t] -0.00157170726815757Y2[t] + 0.00350368217653026M1[t] + 0.00247072088343351M2[t] -0.205170993272856M3[t] -0.266929670970975M4[t] + 0.327279121223635M5[t] + 0.349232147671393M6[t] + 0.495317702851394M7[t] + 0.328181865572133M8[t] + 0.252601193983415M9[t] + 0.183724384734404M10[t] -0.0281642113791766M11[t] + 0.0185621130818993t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.308214348726722.5194531.31310.1954030.097701
X-0.2728028007870460.340819-0.80040.4274040.213702
Y10.07024049741556540.2322440.30240.7636220.381811
Y2-0.001571707268157570.21132-0.00740.9940970.497048
M10.003503682176530260.7583240.00460.9963330.498166
M20.002470720883433510.7615270.00320.9974250.498712
M3-0.2051709932728560.747499-0.27450.7848960.392448
M4-0.2669296709709750.762942-0.34990.7279680.363984
M50.3272791212236350.7779990.42070.6758750.337938
M60.3492321476713930.7854830.44460.6586010.329301
M70.4953177028513940.766220.64640.5210720.260536
M80.3281818655721330.7746180.42370.6736990.33685
M90.2526011939834150.7663350.32960.7431190.37156
M100.1837243847344040.7685760.2390.8120880.406044
M11-0.02816421137917660.770074-0.03660.9709770.485488
t0.01856211308189930.0109411.69650.0962680.048134

\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) & 3.30821434872672 & 2.519453 & 1.3131 & 0.195403 & 0.097701 \tabularnewline
X & -0.272802800787046 & 0.340819 & -0.8004 & 0.427404 & 0.213702 \tabularnewline
Y1 & 0.0702404974155654 & 0.232244 & 0.3024 & 0.763622 & 0.381811 \tabularnewline
Y2 & -0.00157170726815757 & 0.21132 & -0.0074 & 0.994097 & 0.497048 \tabularnewline
M1 & 0.00350368217653026 & 0.758324 & 0.0046 & 0.996333 & 0.498166 \tabularnewline
M2 & 0.00247072088343351 & 0.761527 & 0.0032 & 0.997425 & 0.498712 \tabularnewline
M3 & -0.205170993272856 & 0.747499 & -0.2745 & 0.784896 & 0.392448 \tabularnewline
M4 & -0.266929670970975 & 0.762942 & -0.3499 & 0.727968 & 0.363984 \tabularnewline
M5 & 0.327279121223635 & 0.777999 & 0.4207 & 0.675875 & 0.337938 \tabularnewline
M6 & 0.349232147671393 & 0.785483 & 0.4446 & 0.658601 & 0.329301 \tabularnewline
M7 & 0.495317702851394 & 0.76622 & 0.6464 & 0.521072 & 0.260536 \tabularnewline
M8 & 0.328181865572133 & 0.774618 & 0.4237 & 0.673699 & 0.33685 \tabularnewline
M9 & 0.252601193983415 & 0.766335 & 0.3296 & 0.743119 & 0.37156 \tabularnewline
M10 & 0.183724384734404 & 0.768576 & 0.239 & 0.812088 & 0.406044 \tabularnewline
M11 & -0.0281642113791766 & 0.770074 & -0.0366 & 0.970977 & 0.485488 \tabularnewline
t & 0.0185621130818993 & 0.010941 & 1.6965 & 0.096268 & 0.048134 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58062&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]3.30821434872672[/C][C]2.519453[/C][C]1.3131[/C][C]0.195403[/C][C]0.097701[/C][/ROW]
[ROW][C]X[/C][C]-0.272802800787046[/C][C]0.340819[/C][C]-0.8004[/C][C]0.427404[/C][C]0.213702[/C][/ROW]
[ROW][C]Y1[/C][C]0.0702404974155654[/C][C]0.232244[/C][C]0.3024[/C][C]0.763622[/C][C]0.381811[/C][/ROW]
[ROW][C]Y2[/C][C]-0.00157170726815757[/C][C]0.21132[/C][C]-0.0074[/C][C]0.994097[/C][C]0.497048[/C][/ROW]
[ROW][C]M1[/C][C]0.00350368217653026[/C][C]0.758324[/C][C]0.0046[/C][C]0.996333[/C][C]0.498166[/C][/ROW]
[ROW][C]M2[/C][C]0.00247072088343351[/C][C]0.761527[/C][C]0.0032[/C][C]0.997425[/C][C]0.498712[/C][/ROW]
[ROW][C]M3[/C][C]-0.205170993272856[/C][C]0.747499[/C][C]-0.2745[/C][C]0.784896[/C][C]0.392448[/C][/ROW]
[ROW][C]M4[/C][C]-0.266929670970975[/C][C]0.762942[/C][C]-0.3499[/C][C]0.727968[/C][C]0.363984[/C][/ROW]
[ROW][C]M5[/C][C]0.327279121223635[/C][C]0.777999[/C][C]0.4207[/C][C]0.675875[/C][C]0.337938[/C][/ROW]
[ROW][C]M6[/C][C]0.349232147671393[/C][C]0.785483[/C][C]0.4446[/C][C]0.658601[/C][C]0.329301[/C][/ROW]
[ROW][C]M7[/C][C]0.495317702851394[/C][C]0.76622[/C][C]0.6464[/C][C]0.521072[/C][C]0.260536[/C][/ROW]
[ROW][C]M8[/C][C]0.328181865572133[/C][C]0.774618[/C][C]0.4237[/C][C]0.673699[/C][C]0.33685[/C][/ROW]
[ROW][C]M9[/C][C]0.252601193983415[/C][C]0.766335[/C][C]0.3296[/C][C]0.743119[/C][C]0.37156[/C][/ROW]
[ROW][C]M10[/C][C]0.183724384734404[/C][C]0.768576[/C][C]0.239[/C][C]0.812088[/C][C]0.406044[/C][/ROW]
[ROW][C]M11[/C][C]-0.0281642113791766[/C][C]0.770074[/C][C]-0.0366[/C][C]0.970977[/C][C]0.485488[/C][/ROW]
[ROW][C]t[/C][C]0.0185621130818993[/C][C]0.010941[/C][C]1.6965[/C][C]0.096268[/C][C]0.048134[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58062&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58062&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)3.308214348726722.5194531.31310.1954030.097701
X-0.2728028007870460.340819-0.80040.4274040.213702
Y10.07024049741556540.2322440.30240.7636220.381811
Y2-0.001571707268157570.21132-0.00740.9940970.497048
M10.003503682176530260.7583240.00460.9963330.498166
M20.002470720883433510.7615270.00320.9974250.498712
M3-0.2051709932728560.747499-0.27450.7848960.392448
M4-0.2669296709709750.762942-0.34990.7279680.363984
M50.3272791212236350.7779990.42070.6758750.337938
M60.3492321476713930.7854830.44460.6586010.329301
M70.4953177028513940.766220.64640.5210720.260536
M80.3281818655721330.7746180.42370.6736990.33685
M90.2526011939834150.7663350.32960.7431190.37156
M100.1837243847344040.7685760.2390.8120880.406044
M11-0.02816421137917660.770074-0.03660.9709770.485488
t0.01856211308189930.0109411.69650.0962680.048134







Multiple Linear Regression - Regression Statistics
Multiple R0.454844093786271
R-squared0.206883149652254
Adjusted R-squared-0.0409658660814167
F-TEST (value)0.834714429023849
F-TEST (DF numerator)15
F-TEST (DF denominator)48
p-value0.635944893191229
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.20872056478554
Sum Squared Residuals70.1282593793028

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.454844093786271 \tabularnewline
R-squared & 0.206883149652254 \tabularnewline
Adjusted R-squared & -0.0409658660814167 \tabularnewline
F-TEST (value) & 0.834714429023849 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 48 \tabularnewline
p-value & 0.635944893191229 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.20872056478554 \tabularnewline
Sum Squared Residuals & 70.1282593793028 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58062&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.454844093786271[/C][/ROW]
[ROW][C]R-squared[/C][C]0.206883149652254[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0409658660814167[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.834714429023849[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]48[/C][/ROW]
[ROW][C]p-value[/C][C]0.635944893191229[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.20872056478554[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]70.1282593793028[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58062&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58062&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 R0.454844093786271
R-squared0.206883149652254
Adjusted R-squared-0.0409658660814167
F-TEST (value)0.834714429023849
F-TEST (DF numerator)15
F-TEST (DF denominator)48
p-value0.635944893191229
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.20872056478554
Sum Squared Residuals70.1282593793028







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.41.175385384254630.224614615745371
21.21.23374133817136-0.0337413381713568
311.23054582713502-0.230545827135018
41.71.7846059129923-0.0846059129923012
52.42.42093340815871-0.0209334081587133
622.41419631347271-0.414196313472706
72.12.31353674286956-0.213536742869565
822.12592522504613-0.125925225046134
91.82.06815087815868-0.268150878158679
102.72.133352669140680.566647330859321
112.31.966333376573370.333666623426635
121.91.97236000437989-0.0723600043798864
1321.947203630676090.0527963693239052
142.31.978623711221190.321376288778806
152.81.789229768693171.01077023130683
162.41.827874044313070.572125955686933
172.32.41957280036491-0.119572800364906
182.72.569680572389830.130319427610171
192.72.597110921370690.102889078629315
202.92.477262079046810.422737920953188
2132.392334557554030.607665442445973
222.22.32176363104977-0.121763631049766
232.32.135304027032830.164695972967174
242.82.127312620609680.672687379390324
252.82.108865955193810.69113404480619
262.82.112848584933390.68715141506661
272.21.930478692146920.269521307853076
282.61.887124956803890.712875043196111
292.82.581736702316510.218263297683488
302.52.76486137309373-0.264861373093727
312.42.99695737540155-0.596957375401555
322.32.90307131683497-0.603071316834975
331.92.79832901193205-0.898329011932045
341.72.70094931752953-1.00094931752953
3522.49423348317544-0.494233483175439
362.12.52038923584574-0.420389235845735
371.72.5220416300402-0.822041630040201
381.82.57371794092245-0.773717940922449
391.82.43201767953710-0.632017679537096
401.82.34764990608587-0.547649906085874
411.33.02231976271498-1.72231976271498
421.33.15775075234953-1.85775075234953
431.33.21277572286272-1.91277572286272
441.23.11318311087600-1.91318311087600
451.43.10322955060458-1.70322955060458
462.23.06630420575988-0.866304205759877
472.92.893548294518980.00645170548102447
483.12.851566899729200.248433100270803
493.52.8130211747030.686978825297002
503.62.851150963536020.748849036463978
514.42.770720970596001.62927902940400
524.12.781269047249671.31873095275033
535.13.455437326444891.64456267355511
545.83.393510988694212.40648901130579
555.93.279619237495482.62038076250452
565.43.180558268196082.21944173180392
575.53.237956001750672.26204399824933
584.83.377630176520151.42236982347985
593.23.21058081869939-0.0105808186993931
602.73.12837123943551-0.428371239435507
612.12.93348222513227-0.833482225132268
621.92.84991746121559-0.949917461215588
630.62.64700706189179-2.04700706189179
640.72.67147613255520-1.97147613255520

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.4 & 1.17538538425463 & 0.224614615745371 \tabularnewline
2 & 1.2 & 1.23374133817136 & -0.0337413381713568 \tabularnewline
3 & 1 & 1.23054582713502 & -0.230545827135018 \tabularnewline
4 & 1.7 & 1.7846059129923 & -0.0846059129923012 \tabularnewline
5 & 2.4 & 2.42093340815871 & -0.0209334081587133 \tabularnewline
6 & 2 & 2.41419631347271 & -0.414196313472706 \tabularnewline
7 & 2.1 & 2.31353674286956 & -0.213536742869565 \tabularnewline
8 & 2 & 2.12592522504613 & -0.125925225046134 \tabularnewline
9 & 1.8 & 2.06815087815868 & -0.268150878158679 \tabularnewline
10 & 2.7 & 2.13335266914068 & 0.566647330859321 \tabularnewline
11 & 2.3 & 1.96633337657337 & 0.333666623426635 \tabularnewline
12 & 1.9 & 1.97236000437989 & -0.0723600043798864 \tabularnewline
13 & 2 & 1.94720363067609 & 0.0527963693239052 \tabularnewline
14 & 2.3 & 1.97862371122119 & 0.321376288778806 \tabularnewline
15 & 2.8 & 1.78922976869317 & 1.01077023130683 \tabularnewline
16 & 2.4 & 1.82787404431307 & 0.572125955686933 \tabularnewline
17 & 2.3 & 2.41957280036491 & -0.119572800364906 \tabularnewline
18 & 2.7 & 2.56968057238983 & 0.130319427610171 \tabularnewline
19 & 2.7 & 2.59711092137069 & 0.102889078629315 \tabularnewline
20 & 2.9 & 2.47726207904681 & 0.422737920953188 \tabularnewline
21 & 3 & 2.39233455755403 & 0.607665442445973 \tabularnewline
22 & 2.2 & 2.32176363104977 & -0.121763631049766 \tabularnewline
23 & 2.3 & 2.13530402703283 & 0.164695972967174 \tabularnewline
24 & 2.8 & 2.12731262060968 & 0.672687379390324 \tabularnewline
25 & 2.8 & 2.10886595519381 & 0.69113404480619 \tabularnewline
26 & 2.8 & 2.11284858493339 & 0.68715141506661 \tabularnewline
27 & 2.2 & 1.93047869214692 & 0.269521307853076 \tabularnewline
28 & 2.6 & 1.88712495680389 & 0.712875043196111 \tabularnewline
29 & 2.8 & 2.58173670231651 & 0.218263297683488 \tabularnewline
30 & 2.5 & 2.76486137309373 & -0.264861373093727 \tabularnewline
31 & 2.4 & 2.99695737540155 & -0.596957375401555 \tabularnewline
32 & 2.3 & 2.90307131683497 & -0.603071316834975 \tabularnewline
33 & 1.9 & 2.79832901193205 & -0.898329011932045 \tabularnewline
34 & 1.7 & 2.70094931752953 & -1.00094931752953 \tabularnewline
35 & 2 & 2.49423348317544 & -0.494233483175439 \tabularnewline
36 & 2.1 & 2.52038923584574 & -0.420389235845735 \tabularnewline
37 & 1.7 & 2.5220416300402 & -0.822041630040201 \tabularnewline
38 & 1.8 & 2.57371794092245 & -0.773717940922449 \tabularnewline
39 & 1.8 & 2.43201767953710 & -0.632017679537096 \tabularnewline
40 & 1.8 & 2.34764990608587 & -0.547649906085874 \tabularnewline
41 & 1.3 & 3.02231976271498 & -1.72231976271498 \tabularnewline
42 & 1.3 & 3.15775075234953 & -1.85775075234953 \tabularnewline
43 & 1.3 & 3.21277572286272 & -1.91277572286272 \tabularnewline
44 & 1.2 & 3.11318311087600 & -1.91318311087600 \tabularnewline
45 & 1.4 & 3.10322955060458 & -1.70322955060458 \tabularnewline
46 & 2.2 & 3.06630420575988 & -0.866304205759877 \tabularnewline
47 & 2.9 & 2.89354829451898 & 0.00645170548102447 \tabularnewline
48 & 3.1 & 2.85156689972920 & 0.248433100270803 \tabularnewline
49 & 3.5 & 2.813021174703 & 0.686978825297002 \tabularnewline
50 & 3.6 & 2.85115096353602 & 0.748849036463978 \tabularnewline
51 & 4.4 & 2.77072097059600 & 1.62927902940400 \tabularnewline
52 & 4.1 & 2.78126904724967 & 1.31873095275033 \tabularnewline
53 & 5.1 & 3.45543732644489 & 1.64456267355511 \tabularnewline
54 & 5.8 & 3.39351098869421 & 2.40648901130579 \tabularnewline
55 & 5.9 & 3.27961923749548 & 2.62038076250452 \tabularnewline
56 & 5.4 & 3.18055826819608 & 2.21944173180392 \tabularnewline
57 & 5.5 & 3.23795600175067 & 2.26204399824933 \tabularnewline
58 & 4.8 & 3.37763017652015 & 1.42236982347985 \tabularnewline
59 & 3.2 & 3.21058081869939 & -0.0105808186993931 \tabularnewline
60 & 2.7 & 3.12837123943551 & -0.428371239435507 \tabularnewline
61 & 2.1 & 2.93348222513227 & -0.833482225132268 \tabularnewline
62 & 1.9 & 2.84991746121559 & -0.949917461215588 \tabularnewline
63 & 0.6 & 2.64700706189179 & -2.04700706189179 \tabularnewline
64 & 0.7 & 2.67147613255520 & -1.97147613255520 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58062&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]1.4[/C][C]1.17538538425463[/C][C]0.224614615745371[/C][/ROW]
[ROW][C]2[/C][C]1.2[/C][C]1.23374133817136[/C][C]-0.0337413381713568[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]1.23054582713502[/C][C]-0.230545827135018[/C][/ROW]
[ROW][C]4[/C][C]1.7[/C][C]1.7846059129923[/C][C]-0.0846059129923012[/C][/ROW]
[ROW][C]5[/C][C]2.4[/C][C]2.42093340815871[/C][C]-0.0209334081587133[/C][/ROW]
[ROW][C]6[/C][C]2[/C][C]2.41419631347271[/C][C]-0.414196313472706[/C][/ROW]
[ROW][C]7[/C][C]2.1[/C][C]2.31353674286956[/C][C]-0.213536742869565[/C][/ROW]
[ROW][C]8[/C][C]2[/C][C]2.12592522504613[/C][C]-0.125925225046134[/C][/ROW]
[ROW][C]9[/C][C]1.8[/C][C]2.06815087815868[/C][C]-0.268150878158679[/C][/ROW]
[ROW][C]10[/C][C]2.7[/C][C]2.13335266914068[/C][C]0.566647330859321[/C][/ROW]
[ROW][C]11[/C][C]2.3[/C][C]1.96633337657337[/C][C]0.333666623426635[/C][/ROW]
[ROW][C]12[/C][C]1.9[/C][C]1.97236000437989[/C][C]-0.0723600043798864[/C][/ROW]
[ROW][C]13[/C][C]2[/C][C]1.94720363067609[/C][C]0.0527963693239052[/C][/ROW]
[ROW][C]14[/C][C]2.3[/C][C]1.97862371122119[/C][C]0.321376288778806[/C][/ROW]
[ROW][C]15[/C][C]2.8[/C][C]1.78922976869317[/C][C]1.01077023130683[/C][/ROW]
[ROW][C]16[/C][C]2.4[/C][C]1.82787404431307[/C][C]0.572125955686933[/C][/ROW]
[ROW][C]17[/C][C]2.3[/C][C]2.41957280036491[/C][C]-0.119572800364906[/C][/ROW]
[ROW][C]18[/C][C]2.7[/C][C]2.56968057238983[/C][C]0.130319427610171[/C][/ROW]
[ROW][C]19[/C][C]2.7[/C][C]2.59711092137069[/C][C]0.102889078629315[/C][/ROW]
[ROW][C]20[/C][C]2.9[/C][C]2.47726207904681[/C][C]0.422737920953188[/C][/ROW]
[ROW][C]21[/C][C]3[/C][C]2.39233455755403[/C][C]0.607665442445973[/C][/ROW]
[ROW][C]22[/C][C]2.2[/C][C]2.32176363104977[/C][C]-0.121763631049766[/C][/ROW]
[ROW][C]23[/C][C]2.3[/C][C]2.13530402703283[/C][C]0.164695972967174[/C][/ROW]
[ROW][C]24[/C][C]2.8[/C][C]2.12731262060968[/C][C]0.672687379390324[/C][/ROW]
[ROW][C]25[/C][C]2.8[/C][C]2.10886595519381[/C][C]0.69113404480619[/C][/ROW]
[ROW][C]26[/C][C]2.8[/C][C]2.11284858493339[/C][C]0.68715141506661[/C][/ROW]
[ROW][C]27[/C][C]2.2[/C][C]1.93047869214692[/C][C]0.269521307853076[/C][/ROW]
[ROW][C]28[/C][C]2.6[/C][C]1.88712495680389[/C][C]0.712875043196111[/C][/ROW]
[ROW][C]29[/C][C]2.8[/C][C]2.58173670231651[/C][C]0.218263297683488[/C][/ROW]
[ROW][C]30[/C][C]2.5[/C][C]2.76486137309373[/C][C]-0.264861373093727[/C][/ROW]
[ROW][C]31[/C][C]2.4[/C][C]2.99695737540155[/C][C]-0.596957375401555[/C][/ROW]
[ROW][C]32[/C][C]2.3[/C][C]2.90307131683497[/C][C]-0.603071316834975[/C][/ROW]
[ROW][C]33[/C][C]1.9[/C][C]2.79832901193205[/C][C]-0.898329011932045[/C][/ROW]
[ROW][C]34[/C][C]1.7[/C][C]2.70094931752953[/C][C]-1.00094931752953[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]2.49423348317544[/C][C]-0.494233483175439[/C][/ROW]
[ROW][C]36[/C][C]2.1[/C][C]2.52038923584574[/C][C]-0.420389235845735[/C][/ROW]
[ROW][C]37[/C][C]1.7[/C][C]2.5220416300402[/C][C]-0.822041630040201[/C][/ROW]
[ROW][C]38[/C][C]1.8[/C][C]2.57371794092245[/C][C]-0.773717940922449[/C][/ROW]
[ROW][C]39[/C][C]1.8[/C][C]2.43201767953710[/C][C]-0.632017679537096[/C][/ROW]
[ROW][C]40[/C][C]1.8[/C][C]2.34764990608587[/C][C]-0.547649906085874[/C][/ROW]
[ROW][C]41[/C][C]1.3[/C][C]3.02231976271498[/C][C]-1.72231976271498[/C][/ROW]
[ROW][C]42[/C][C]1.3[/C][C]3.15775075234953[/C][C]-1.85775075234953[/C][/ROW]
[ROW][C]43[/C][C]1.3[/C][C]3.21277572286272[/C][C]-1.91277572286272[/C][/ROW]
[ROW][C]44[/C][C]1.2[/C][C]3.11318311087600[/C][C]-1.91318311087600[/C][/ROW]
[ROW][C]45[/C][C]1.4[/C][C]3.10322955060458[/C][C]-1.70322955060458[/C][/ROW]
[ROW][C]46[/C][C]2.2[/C][C]3.06630420575988[/C][C]-0.866304205759877[/C][/ROW]
[ROW][C]47[/C][C]2.9[/C][C]2.89354829451898[/C][C]0.00645170548102447[/C][/ROW]
[ROW][C]48[/C][C]3.1[/C][C]2.85156689972920[/C][C]0.248433100270803[/C][/ROW]
[ROW][C]49[/C][C]3.5[/C][C]2.813021174703[/C][C]0.686978825297002[/C][/ROW]
[ROW][C]50[/C][C]3.6[/C][C]2.85115096353602[/C][C]0.748849036463978[/C][/ROW]
[ROW][C]51[/C][C]4.4[/C][C]2.77072097059600[/C][C]1.62927902940400[/C][/ROW]
[ROW][C]52[/C][C]4.1[/C][C]2.78126904724967[/C][C]1.31873095275033[/C][/ROW]
[ROW][C]53[/C][C]5.1[/C][C]3.45543732644489[/C][C]1.64456267355511[/C][/ROW]
[ROW][C]54[/C][C]5.8[/C][C]3.39351098869421[/C][C]2.40648901130579[/C][/ROW]
[ROW][C]55[/C][C]5.9[/C][C]3.27961923749548[/C][C]2.62038076250452[/C][/ROW]
[ROW][C]56[/C][C]5.4[/C][C]3.18055826819608[/C][C]2.21944173180392[/C][/ROW]
[ROW][C]57[/C][C]5.5[/C][C]3.23795600175067[/C][C]2.26204399824933[/C][/ROW]
[ROW][C]58[/C][C]4.8[/C][C]3.37763017652015[/C][C]1.42236982347985[/C][/ROW]
[ROW][C]59[/C][C]3.2[/C][C]3.21058081869939[/C][C]-0.0105808186993931[/C][/ROW]
[ROW][C]60[/C][C]2.7[/C][C]3.12837123943551[/C][C]-0.428371239435507[/C][/ROW]
[ROW][C]61[/C][C]2.1[/C][C]2.93348222513227[/C][C]-0.833482225132268[/C][/ROW]
[ROW][C]62[/C][C]1.9[/C][C]2.84991746121559[/C][C]-0.949917461215588[/C][/ROW]
[ROW][C]63[/C][C]0.6[/C][C]2.64700706189179[/C][C]-2.04700706189179[/C][/ROW]
[ROW][C]64[/C][C]0.7[/C][C]2.67147613255520[/C][C]-1.97147613255520[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58062&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58062&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
11.41.175385384254630.224614615745371
21.21.23374133817136-0.0337413381713568
311.23054582713502-0.230545827135018
41.71.7846059129923-0.0846059129923012
52.42.42093340815871-0.0209334081587133
622.41419631347271-0.414196313472706
72.12.31353674286956-0.213536742869565
822.12592522504613-0.125925225046134
91.82.06815087815868-0.268150878158679
102.72.133352669140680.566647330859321
112.31.966333376573370.333666623426635
121.91.97236000437989-0.0723600043798864
1321.947203630676090.0527963693239052
142.31.978623711221190.321376288778806
152.81.789229768693171.01077023130683
162.41.827874044313070.572125955686933
172.32.41957280036491-0.119572800364906
182.72.569680572389830.130319427610171
192.72.597110921370690.102889078629315
202.92.477262079046810.422737920953188
2132.392334557554030.607665442445973
222.22.32176363104977-0.121763631049766
232.32.135304027032830.164695972967174
242.82.127312620609680.672687379390324
252.82.108865955193810.69113404480619
262.82.112848584933390.68715141506661
272.21.930478692146920.269521307853076
282.61.887124956803890.712875043196111
292.82.581736702316510.218263297683488
302.52.76486137309373-0.264861373093727
312.42.99695737540155-0.596957375401555
322.32.90307131683497-0.603071316834975
331.92.79832901193205-0.898329011932045
341.72.70094931752953-1.00094931752953
3522.49423348317544-0.494233483175439
362.12.52038923584574-0.420389235845735
371.72.5220416300402-0.822041630040201
381.82.57371794092245-0.773717940922449
391.82.43201767953710-0.632017679537096
401.82.34764990608587-0.547649906085874
411.33.02231976271498-1.72231976271498
421.33.15775075234953-1.85775075234953
431.33.21277572286272-1.91277572286272
441.23.11318311087600-1.91318311087600
451.43.10322955060458-1.70322955060458
462.23.06630420575988-0.866304205759877
472.92.893548294518980.00645170548102447
483.12.851566899729200.248433100270803
493.52.8130211747030.686978825297002
503.62.851150963536020.748849036463978
514.42.770720970596001.62927902940400
524.12.781269047249671.31873095275033
535.13.455437326444891.64456267355511
545.83.393510988694212.40648901130579
555.93.279619237495482.62038076250452
565.43.180558268196082.21944173180392
575.53.237956001750672.26204399824933
584.83.377630176520151.42236982347985
593.23.21058081869939-0.0105808186993931
602.73.12837123943551-0.428371239435507
612.12.93348222513227-0.833482225132268
621.92.84991746121559-0.949917461215588
630.62.64700706189179-2.04700706189179
640.72.67147613255520-1.97147613255520







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.02665770727315890.05331541454631780.97334229272684
200.007460718893745250.01492143778749050.992539281106255
210.002299848616661210.004599697233322420.997700151383339
220.002087340519995880.004174681039991770.997912659480004
230.0005531626227991510.001106325245598300.9994468373772
240.0002243775899328220.0004487551798656440.999775622410067
255.8722781171827e-050.0001174455623436540.999941277218828
261.58471115452228e-053.16942230904455e-050.999984152888455
278.42400272603909e-061.68480054520782e-050.999991575997274
283.23245383325265e-066.4649076665053e-060.999996767546167
291.51974072916483e-063.03948145832966e-060.999998480259271
307.67921468425358e-071.53584293685072e-060.999999232078532
316.8165858847117e-071.36331717694234e-060.999999318341412
325.42688907420529e-071.08537781484106e-060.999999457311093
332.41476903059539e-074.82953806119078e-070.999999758523097
341.11068516516172e-072.22137033032345e-070.999999888931484
352.68833244954545e-085.37666489909089e-080.999999973116676
368.20177681400673e-091.64035536280135e-080.999999991798223
377.44822461663e-091.489644923326e-080.999999992551775
386.5938855544015e-091.3187771108803e-080.999999993406114
394.89932015414293e-099.79864030828586e-090.99999999510068
407.04951851799092e-081.40990370359818e-070.999999929504815
411.82837706763651e-073.65675413527302e-070.999999817162293
422.23198220597419e-064.46396441194839e-060.999997768017794
432.38140371191107e-054.76280742382214e-050.99997618596288
443.03107815266728e-056.06215630533456e-050.999969689218473
450.2338797248026560.4677594496053120.766120275197344

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.0266577072731589 & 0.0533154145463178 & 0.97334229272684 \tabularnewline
20 & 0.00746071889374525 & 0.0149214377874905 & 0.992539281106255 \tabularnewline
21 & 0.00229984861666121 & 0.00459969723332242 & 0.997700151383339 \tabularnewline
22 & 0.00208734051999588 & 0.00417468103999177 & 0.997912659480004 \tabularnewline
23 & 0.000553162622799151 & 0.00110632524559830 & 0.9994468373772 \tabularnewline
24 & 0.000224377589932822 & 0.000448755179865644 & 0.999775622410067 \tabularnewline
25 & 5.8722781171827e-05 & 0.000117445562343654 & 0.999941277218828 \tabularnewline
26 & 1.58471115452228e-05 & 3.16942230904455e-05 & 0.999984152888455 \tabularnewline
27 & 8.42400272603909e-06 & 1.68480054520782e-05 & 0.999991575997274 \tabularnewline
28 & 3.23245383325265e-06 & 6.4649076665053e-06 & 0.999996767546167 \tabularnewline
29 & 1.51974072916483e-06 & 3.03948145832966e-06 & 0.999998480259271 \tabularnewline
30 & 7.67921468425358e-07 & 1.53584293685072e-06 & 0.999999232078532 \tabularnewline
31 & 6.8165858847117e-07 & 1.36331717694234e-06 & 0.999999318341412 \tabularnewline
32 & 5.42688907420529e-07 & 1.08537781484106e-06 & 0.999999457311093 \tabularnewline
33 & 2.41476903059539e-07 & 4.82953806119078e-07 & 0.999999758523097 \tabularnewline
34 & 1.11068516516172e-07 & 2.22137033032345e-07 & 0.999999888931484 \tabularnewline
35 & 2.68833244954545e-08 & 5.37666489909089e-08 & 0.999999973116676 \tabularnewline
36 & 8.20177681400673e-09 & 1.64035536280135e-08 & 0.999999991798223 \tabularnewline
37 & 7.44822461663e-09 & 1.489644923326e-08 & 0.999999992551775 \tabularnewline
38 & 6.5938855544015e-09 & 1.3187771108803e-08 & 0.999999993406114 \tabularnewline
39 & 4.89932015414293e-09 & 9.79864030828586e-09 & 0.99999999510068 \tabularnewline
40 & 7.04951851799092e-08 & 1.40990370359818e-07 & 0.999999929504815 \tabularnewline
41 & 1.82837706763651e-07 & 3.65675413527302e-07 & 0.999999817162293 \tabularnewline
42 & 2.23198220597419e-06 & 4.46396441194839e-06 & 0.999997768017794 \tabularnewline
43 & 2.38140371191107e-05 & 4.76280742382214e-05 & 0.99997618596288 \tabularnewline
44 & 3.03107815266728e-05 & 6.06215630533456e-05 & 0.999969689218473 \tabularnewline
45 & 0.233879724802656 & 0.467759449605312 & 0.766120275197344 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58062&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]19[/C][C]0.0266577072731589[/C][C]0.0533154145463178[/C][C]0.97334229272684[/C][/ROW]
[ROW][C]20[/C][C]0.00746071889374525[/C][C]0.0149214377874905[/C][C]0.992539281106255[/C][/ROW]
[ROW][C]21[/C][C]0.00229984861666121[/C][C]0.00459969723332242[/C][C]0.997700151383339[/C][/ROW]
[ROW][C]22[/C][C]0.00208734051999588[/C][C]0.00417468103999177[/C][C]0.997912659480004[/C][/ROW]
[ROW][C]23[/C][C]0.000553162622799151[/C][C]0.00110632524559830[/C][C]0.9994468373772[/C][/ROW]
[ROW][C]24[/C][C]0.000224377589932822[/C][C]0.000448755179865644[/C][C]0.999775622410067[/C][/ROW]
[ROW][C]25[/C][C]5.8722781171827e-05[/C][C]0.000117445562343654[/C][C]0.999941277218828[/C][/ROW]
[ROW][C]26[/C][C]1.58471115452228e-05[/C][C]3.16942230904455e-05[/C][C]0.999984152888455[/C][/ROW]
[ROW][C]27[/C][C]8.42400272603909e-06[/C][C]1.68480054520782e-05[/C][C]0.999991575997274[/C][/ROW]
[ROW][C]28[/C][C]3.23245383325265e-06[/C][C]6.4649076665053e-06[/C][C]0.999996767546167[/C][/ROW]
[ROW][C]29[/C][C]1.51974072916483e-06[/C][C]3.03948145832966e-06[/C][C]0.999998480259271[/C][/ROW]
[ROW][C]30[/C][C]7.67921468425358e-07[/C][C]1.53584293685072e-06[/C][C]0.999999232078532[/C][/ROW]
[ROW][C]31[/C][C]6.8165858847117e-07[/C][C]1.36331717694234e-06[/C][C]0.999999318341412[/C][/ROW]
[ROW][C]32[/C][C]5.42688907420529e-07[/C][C]1.08537781484106e-06[/C][C]0.999999457311093[/C][/ROW]
[ROW][C]33[/C][C]2.41476903059539e-07[/C][C]4.82953806119078e-07[/C][C]0.999999758523097[/C][/ROW]
[ROW][C]34[/C][C]1.11068516516172e-07[/C][C]2.22137033032345e-07[/C][C]0.999999888931484[/C][/ROW]
[ROW][C]35[/C][C]2.68833244954545e-08[/C][C]5.37666489909089e-08[/C][C]0.999999973116676[/C][/ROW]
[ROW][C]36[/C][C]8.20177681400673e-09[/C][C]1.64035536280135e-08[/C][C]0.999999991798223[/C][/ROW]
[ROW][C]37[/C][C]7.44822461663e-09[/C][C]1.489644923326e-08[/C][C]0.999999992551775[/C][/ROW]
[ROW][C]38[/C][C]6.5938855544015e-09[/C][C]1.3187771108803e-08[/C][C]0.999999993406114[/C][/ROW]
[ROW][C]39[/C][C]4.89932015414293e-09[/C][C]9.79864030828586e-09[/C][C]0.99999999510068[/C][/ROW]
[ROW][C]40[/C][C]7.04951851799092e-08[/C][C]1.40990370359818e-07[/C][C]0.999999929504815[/C][/ROW]
[ROW][C]41[/C][C]1.82837706763651e-07[/C][C]3.65675413527302e-07[/C][C]0.999999817162293[/C][/ROW]
[ROW][C]42[/C][C]2.23198220597419e-06[/C][C]4.46396441194839e-06[/C][C]0.999997768017794[/C][/ROW]
[ROW][C]43[/C][C]2.38140371191107e-05[/C][C]4.76280742382214e-05[/C][C]0.99997618596288[/C][/ROW]
[ROW][C]44[/C][C]3.03107815266728e-05[/C][C]6.06215630533456e-05[/C][C]0.999969689218473[/C][/ROW]
[ROW][C]45[/C][C]0.233879724802656[/C][C]0.467759449605312[/C][C]0.766120275197344[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58062&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58062&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
190.02665770727315890.05331541454631780.97334229272684
200.007460718893745250.01492143778749050.992539281106255
210.002299848616661210.004599697233322420.997700151383339
220.002087340519995880.004174681039991770.997912659480004
230.0005531626227991510.001106325245598300.9994468373772
240.0002243775899328220.0004487551798656440.999775622410067
255.8722781171827e-050.0001174455623436540.999941277218828
261.58471115452228e-053.16942230904455e-050.999984152888455
278.42400272603909e-061.68480054520782e-050.999991575997274
283.23245383325265e-066.4649076665053e-060.999996767546167
291.51974072916483e-063.03948145832966e-060.999998480259271
307.67921468425358e-071.53584293685072e-060.999999232078532
316.8165858847117e-071.36331717694234e-060.999999318341412
325.42688907420529e-071.08537781484106e-060.999999457311093
332.41476903059539e-074.82953806119078e-070.999999758523097
341.11068516516172e-072.22137033032345e-070.999999888931484
352.68833244954545e-085.37666489909089e-080.999999973116676
368.20177681400673e-091.64035536280135e-080.999999991798223
377.44822461663e-091.489644923326e-080.999999992551775
386.5938855544015e-091.3187771108803e-080.999999993406114
394.89932015414293e-099.79864030828586e-090.99999999510068
407.04951851799092e-081.40990370359818e-070.999999929504815
411.82837706763651e-073.65675413527302e-070.999999817162293
422.23198220597419e-064.46396441194839e-060.999997768017794
432.38140371191107e-054.76280742382214e-050.99997618596288
443.03107815266728e-056.06215630533456e-050.999969689218473
450.2338797248026560.4677594496053120.766120275197344







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level240.888888888888889NOK
5% type I error level250.925925925925926NOK
10% type I error level260.962962962962963NOK

\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 & 24 & 0.888888888888889 & NOK \tabularnewline
5% type I error level & 25 & 0.925925925925926 & NOK \tabularnewline
10% type I error level & 26 & 0.962962962962963 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58062&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]24[/C][C]0.888888888888889[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]25[/C][C]0.925925925925926[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]26[/C][C]0.962962962962963[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58062&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58062&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 level240.888888888888889NOK
5% type I error level250.925925925925926NOK
10% type I error level260.962962962962963NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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')
}