Free Statistics

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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 computationMon, 28 Nov 2011 11:52:53 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/28/t1322499180o4ntfovgxdkpwx0.htm/, Retrieved Fri, 29 Mar 2024 14:02:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147865, Retrieved Fri, 29 Mar 2024 14:02:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact75
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-12 13:32:37] [76963dc1903f0f612b6153510a3818cf]
- R  D  [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-17 12:14:40] [76963dc1903f0f612b6153510a3818cf]
-         [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2008-12-22 18:19:51] [1ce0d16c8f4225c977b42c8fa93bc163]
- RMP       [Univariate Data Series] [Identifying Integ...] [2009-11-22 12:08:06] [b98453cac15ba1066b407e146608df68]
- RMPD        [Central Tendency] [] [2011-11-28 16:37:14] [d6b4d011b409693eac2700c83288e3e7]
- RMPD          [Multiple Regression] [] [2011-11-28 16:46:36] [d6b4d011b409693eac2700c83288e3e7]
- R  D            [Multiple Regression] [] [2011-11-28 16:52:22] [d6b4d011b409693eac2700c83288e3e7]
-                     [Multiple Regression] [] [2011-11-28 16:52:53] [e232377fd09030116200e3da7df6eeaf] [Current]
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Dataseries X:
9 676
8 642
9 402
9 610
9 294
9 448
10 319
9 548
9 801
9 596
8 923
9 746
9 829
9 125
9 782
9 441
9 162
9 915
10 444
10 209
9 985
9 842
9 429
10 132
9 849
9 172
10 313
9 819
9 955
10 048
10 082
10 541
10 208
10 233
9 439
9 963
10 158
9 225
10 474
9 757
10 490
10 281
10 444
10 640
10 695
10 786
9 832
9 747
10 411
9 511
10 402
9 701
10 540
10 112
10 915
11 183
10 384
10 834
9 886
10 216
10 943
9 867
10 203
10 837
10 573
10 647
11 502
10 656
10 866
10 835
9 945
10 331




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147865&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147865&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147865&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 time4 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Monthyly[t] = + 9.40343409932416 -0.000985975897309706births[t] + 0.0179399820177391t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Monthyly[t] =  +  9.40343409932416 -0.000985975897309706births[t] +  0.0179399820177391t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147865&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Monthyly[t] =  +  9.40343409932416 -0.000985975897309706births[t] +  0.0179399820177391t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147865&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147865&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
Monthyly[t] = + 9.40343409932416 -0.000985975897309706births[t] + 0.0179399820177391t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.403434099324160.13672168.778100
births-0.0009859758973097060.000185-5.32761e-061e-06
t0.01793998201773910.0024257.397600

\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) & 9.40343409932416 & 0.136721 & 68.7781 & 0 & 0 \tabularnewline
births & -0.000985975897309706 & 0.000185 & -5.3276 & 1e-06 & 1e-06 \tabularnewline
t & 0.0179399820177391 & 0.002425 & 7.3976 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147865&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]9.40343409932416[/C][C]0.136721[/C][C]68.7781[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]births[/C][C]-0.000985975897309706[/C][C]0.000185[/C][C]-5.3276[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]t[/C][C]0.0179399820177391[/C][C]0.002425[/C][C]7.3976[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147865&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147865&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)9.403434099324160.13672168.778100
births-0.0009859758973097060.000185-5.32761e-061e-06
t0.01793998201773910.0024257.397600







Multiple Linear Regression - Regression Statistics
Multiple R0.721700373651017
R-squared0.520851429328018
Adjusted R-squared0.506963064960714
F-TEST (value)37.5027192225899
F-TEST (DF numerator)2
F-TEST (DF denominator)69
p-value9.4672047978861e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.424796762935144
Sum Squared Residuals12.4512079962122

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.721700373651017 \tabularnewline
R-squared & 0.520851429328018 \tabularnewline
Adjusted R-squared & 0.506963064960714 \tabularnewline
F-TEST (value) & 37.5027192225899 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 69 \tabularnewline
p-value & 9.4672047978861e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.424796762935144 \tabularnewline
Sum Squared Residuals & 12.4512079962122 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147865&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.721700373651017[/C][/ROW]
[ROW][C]R-squared[/C][C]0.520851429328018[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.506963064960714[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]37.5027192225899[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]69[/C][/ROW]
[ROW][C]p-value[/C][C]9.4672047978861e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.424796762935144[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]12.4512079962122[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147865&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147865&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 R0.721700373651017
R-squared0.520851429328018
Adjusted R-squared0.506963064960714
F-TEST (value)37.5027192225899
F-TEST (DF numerator)2
F-TEST (DF denominator)69
p-value9.4672047978861e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.424796762935144
Sum Squared Residuals12.4512079962122







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
198.754854374760540.245145625239459
288.80631753728681-0.80631753728681
399.06089173465888-0.0608917346588774
498.87374873003620.126251269963802
599.2032570956038-0.203257095603804
699.06935678943585-0.0693567894358487
7109.214487662206540.78551233779346
899.00663916374036-0.00663916374035642
998.775127243738740.22487275626126
1098.995192284704970.00480771529503127
1188.69071814830243-0.690718148302434
1298.883175864143990.116824135856009
1398.819279846685020.180720153314975
1499.5313468604088-0.531346860408797
1598.901500677894060.0984993221059409
1699.25565844089441-0.255658440894408
1799.54868569826155-0.548685698261555
1898.824185829605090.175814170394914
19109.30652045925570.693479540744304
20109.556164777141220.443835222858784
2198.808987462846620.191012537153376
2298.967921998179650.0320780018203493
2399.3930700257863-0.393070025786298
24109.703844849305020.29615515069498
2599.0148401129517-0.0148401129517002
2699.70028577744811-0.70028577744811
27109.579203157945180.420796842054819
2899.09823933592421-0.0982393359242087
2998.982086595907830.0179134040921721
30109.894306716785470.10569328321453
31109.878723518294680.121276481705321
32109.444100563447260.555899436552736
33109.790370519269140.209629480730865
34109.783661103854130.216338896145869
3599.59849005102607-0.598490051026071
3699.09977866285352-0.0997786628535241
37109.911429242205580.0885707577944234
3899.86330883910357-0.863308839103566
39109.635740822691190.364259177308812
4099.37464962577028-0.37464962577028
41109.655845172369710.344154827630289
42109.879854116925180.120145883074822
43109.737080027681430.262919972318565
44109.561768733826470.438231266173528
45109.525480041492180.474519958507822
46109.453696216854730.546303783145267
4799.42628130759623-0.426281307596226
4899.52802924088529-0.52802924088529
49109.877257124399090.122742875600909
5099.79659951668586-0.796599516685859
51109.922010871510360.0779891284896438
5299.64514406023249-0.645144060232493
53109.821826161717090.178173838282905
541010.2617638277834-0.261763827783388
55109.487965164261430.512034835738567
561110.22763950310990.772360496890122
571010.0473983297684-0.0473983297683657
58109.621649157996740.378350842003263
5999.58831839335437-0.588318393354372
601010.2668622265696-0.266862226569614
61109.56799773124320.432002268756803
6299.66087188145647-0.660871881456473
631010.3334998592879-0.333499859287857
64109.726331122411240.273668877588757
651010.0045687413187-0.00456874131874433
66109.949546506935570.0504534930644347
671110.11045299406320.889547005936788
68109.976552687895260.0234473121047438
69109.787437731477960.212562268522043
70109.83594296631230.164057033687703
7199.74542559962597-0.745425599625968
721010.3687547825919-0.368754782591867

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9 & 8.75485437476054 & 0.245145625239459 \tabularnewline
2 & 8 & 8.80631753728681 & -0.80631753728681 \tabularnewline
3 & 9 & 9.06089173465888 & -0.0608917346588774 \tabularnewline
4 & 9 & 8.8737487300362 & 0.126251269963802 \tabularnewline
5 & 9 & 9.2032570956038 & -0.203257095603804 \tabularnewline
6 & 9 & 9.06935678943585 & -0.0693567894358487 \tabularnewline
7 & 10 & 9.21448766220654 & 0.78551233779346 \tabularnewline
8 & 9 & 9.00663916374036 & -0.00663916374035642 \tabularnewline
9 & 9 & 8.77512724373874 & 0.22487275626126 \tabularnewline
10 & 9 & 8.99519228470497 & 0.00480771529503127 \tabularnewline
11 & 8 & 8.69071814830243 & -0.690718148302434 \tabularnewline
12 & 9 & 8.88317586414399 & 0.116824135856009 \tabularnewline
13 & 9 & 8.81927984668502 & 0.180720153314975 \tabularnewline
14 & 9 & 9.5313468604088 & -0.531346860408797 \tabularnewline
15 & 9 & 8.90150067789406 & 0.0984993221059409 \tabularnewline
16 & 9 & 9.25565844089441 & -0.255658440894408 \tabularnewline
17 & 9 & 9.54868569826155 & -0.548685698261555 \tabularnewline
18 & 9 & 8.82418582960509 & 0.175814170394914 \tabularnewline
19 & 10 & 9.3065204592557 & 0.693479540744304 \tabularnewline
20 & 10 & 9.55616477714122 & 0.443835222858784 \tabularnewline
21 & 9 & 8.80898746284662 & 0.191012537153376 \tabularnewline
22 & 9 & 8.96792199817965 & 0.0320780018203493 \tabularnewline
23 & 9 & 9.3930700257863 & -0.393070025786298 \tabularnewline
24 & 10 & 9.70384484930502 & 0.29615515069498 \tabularnewline
25 & 9 & 9.0148401129517 & -0.0148401129517002 \tabularnewline
26 & 9 & 9.70028577744811 & -0.70028577744811 \tabularnewline
27 & 10 & 9.57920315794518 & 0.420796842054819 \tabularnewline
28 & 9 & 9.09823933592421 & -0.0982393359242087 \tabularnewline
29 & 9 & 8.98208659590783 & 0.0179134040921721 \tabularnewline
30 & 10 & 9.89430671678547 & 0.10569328321453 \tabularnewline
31 & 10 & 9.87872351829468 & 0.121276481705321 \tabularnewline
32 & 10 & 9.44410056344726 & 0.555899436552736 \tabularnewline
33 & 10 & 9.79037051926914 & 0.209629480730865 \tabularnewline
34 & 10 & 9.78366110385413 & 0.216338896145869 \tabularnewline
35 & 9 & 9.59849005102607 & -0.598490051026071 \tabularnewline
36 & 9 & 9.09977866285352 & -0.0997786628535241 \tabularnewline
37 & 10 & 9.91142924220558 & 0.0885707577944234 \tabularnewline
38 & 9 & 9.86330883910357 & -0.863308839103566 \tabularnewline
39 & 10 & 9.63574082269119 & 0.364259177308812 \tabularnewline
40 & 9 & 9.37464962577028 & -0.37464962577028 \tabularnewline
41 & 10 & 9.65584517236971 & 0.344154827630289 \tabularnewline
42 & 10 & 9.87985411692518 & 0.120145883074822 \tabularnewline
43 & 10 & 9.73708002768143 & 0.262919972318565 \tabularnewline
44 & 10 & 9.56176873382647 & 0.438231266173528 \tabularnewline
45 & 10 & 9.52548004149218 & 0.474519958507822 \tabularnewline
46 & 10 & 9.45369621685473 & 0.546303783145267 \tabularnewline
47 & 9 & 9.42628130759623 & -0.426281307596226 \tabularnewline
48 & 9 & 9.52802924088529 & -0.52802924088529 \tabularnewline
49 & 10 & 9.87725712439909 & 0.122742875600909 \tabularnewline
50 & 9 & 9.79659951668586 & -0.796599516685859 \tabularnewline
51 & 10 & 9.92201087151036 & 0.0779891284896438 \tabularnewline
52 & 9 & 9.64514406023249 & -0.645144060232493 \tabularnewline
53 & 10 & 9.82182616171709 & 0.178173838282905 \tabularnewline
54 & 10 & 10.2617638277834 & -0.261763827783388 \tabularnewline
55 & 10 & 9.48796516426143 & 0.512034835738567 \tabularnewline
56 & 11 & 10.2276395031099 & 0.772360496890122 \tabularnewline
57 & 10 & 10.0473983297684 & -0.0473983297683657 \tabularnewline
58 & 10 & 9.62164915799674 & 0.378350842003263 \tabularnewline
59 & 9 & 9.58831839335437 & -0.588318393354372 \tabularnewline
60 & 10 & 10.2668622265696 & -0.266862226569614 \tabularnewline
61 & 10 & 9.5679977312432 & 0.432002268756803 \tabularnewline
62 & 9 & 9.66087188145647 & -0.660871881456473 \tabularnewline
63 & 10 & 10.3334998592879 & -0.333499859287857 \tabularnewline
64 & 10 & 9.72633112241124 & 0.273668877588757 \tabularnewline
65 & 10 & 10.0045687413187 & -0.00456874131874433 \tabularnewline
66 & 10 & 9.94954650693557 & 0.0504534930644347 \tabularnewline
67 & 11 & 10.1104529940632 & 0.889547005936788 \tabularnewline
68 & 10 & 9.97655268789526 & 0.0234473121047438 \tabularnewline
69 & 10 & 9.78743773147796 & 0.212562268522043 \tabularnewline
70 & 10 & 9.8359429663123 & 0.164057033687703 \tabularnewline
71 & 9 & 9.74542559962597 & -0.745425599625968 \tabularnewline
72 & 10 & 10.3687547825919 & -0.368754782591867 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147865&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]9[/C][C]8.75485437476054[/C][C]0.245145625239459[/C][/ROW]
[ROW][C]2[/C][C]8[/C][C]8.80631753728681[/C][C]-0.80631753728681[/C][/ROW]
[ROW][C]3[/C][C]9[/C][C]9.06089173465888[/C][C]-0.0608917346588774[/C][/ROW]
[ROW][C]4[/C][C]9[/C][C]8.8737487300362[/C][C]0.126251269963802[/C][/ROW]
[ROW][C]5[/C][C]9[/C][C]9.2032570956038[/C][C]-0.203257095603804[/C][/ROW]
[ROW][C]6[/C][C]9[/C][C]9.06935678943585[/C][C]-0.0693567894358487[/C][/ROW]
[ROW][C]7[/C][C]10[/C][C]9.21448766220654[/C][C]0.78551233779346[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]9.00663916374036[/C][C]-0.00663916374035642[/C][/ROW]
[ROW][C]9[/C][C]9[/C][C]8.77512724373874[/C][C]0.22487275626126[/C][/ROW]
[ROW][C]10[/C][C]9[/C][C]8.99519228470497[/C][C]0.00480771529503127[/C][/ROW]
[ROW][C]11[/C][C]8[/C][C]8.69071814830243[/C][C]-0.690718148302434[/C][/ROW]
[ROW][C]12[/C][C]9[/C][C]8.88317586414399[/C][C]0.116824135856009[/C][/ROW]
[ROW][C]13[/C][C]9[/C][C]8.81927984668502[/C][C]0.180720153314975[/C][/ROW]
[ROW][C]14[/C][C]9[/C][C]9.5313468604088[/C][C]-0.531346860408797[/C][/ROW]
[ROW][C]15[/C][C]9[/C][C]8.90150067789406[/C][C]0.0984993221059409[/C][/ROW]
[ROW][C]16[/C][C]9[/C][C]9.25565844089441[/C][C]-0.255658440894408[/C][/ROW]
[ROW][C]17[/C][C]9[/C][C]9.54868569826155[/C][C]-0.548685698261555[/C][/ROW]
[ROW][C]18[/C][C]9[/C][C]8.82418582960509[/C][C]0.175814170394914[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]9.3065204592557[/C][C]0.693479540744304[/C][/ROW]
[ROW][C]20[/C][C]10[/C][C]9.55616477714122[/C][C]0.443835222858784[/C][/ROW]
[ROW][C]21[/C][C]9[/C][C]8.80898746284662[/C][C]0.191012537153376[/C][/ROW]
[ROW][C]22[/C][C]9[/C][C]8.96792199817965[/C][C]0.0320780018203493[/C][/ROW]
[ROW][C]23[/C][C]9[/C][C]9.3930700257863[/C][C]-0.393070025786298[/C][/ROW]
[ROW][C]24[/C][C]10[/C][C]9.70384484930502[/C][C]0.29615515069498[/C][/ROW]
[ROW][C]25[/C][C]9[/C][C]9.0148401129517[/C][C]-0.0148401129517002[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]9.70028577744811[/C][C]-0.70028577744811[/C][/ROW]
[ROW][C]27[/C][C]10[/C][C]9.57920315794518[/C][C]0.420796842054819[/C][/ROW]
[ROW][C]28[/C][C]9[/C][C]9.09823933592421[/C][C]-0.0982393359242087[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]8.98208659590783[/C][C]0.0179134040921721[/C][/ROW]
[ROW][C]30[/C][C]10[/C][C]9.89430671678547[/C][C]0.10569328321453[/C][/ROW]
[ROW][C]31[/C][C]10[/C][C]9.87872351829468[/C][C]0.121276481705321[/C][/ROW]
[ROW][C]32[/C][C]10[/C][C]9.44410056344726[/C][C]0.555899436552736[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]9.79037051926914[/C][C]0.209629480730865[/C][/ROW]
[ROW][C]34[/C][C]10[/C][C]9.78366110385413[/C][C]0.216338896145869[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]9.59849005102607[/C][C]-0.598490051026071[/C][/ROW]
[ROW][C]36[/C][C]9[/C][C]9.09977866285352[/C][C]-0.0997786628535241[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]9.91142924220558[/C][C]0.0885707577944234[/C][/ROW]
[ROW][C]38[/C][C]9[/C][C]9.86330883910357[/C][C]-0.863308839103566[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]9.63574082269119[/C][C]0.364259177308812[/C][/ROW]
[ROW][C]40[/C][C]9[/C][C]9.37464962577028[/C][C]-0.37464962577028[/C][/ROW]
[ROW][C]41[/C][C]10[/C][C]9.65584517236971[/C][C]0.344154827630289[/C][/ROW]
[ROW][C]42[/C][C]10[/C][C]9.87985411692518[/C][C]0.120145883074822[/C][/ROW]
[ROW][C]43[/C][C]10[/C][C]9.73708002768143[/C][C]0.262919972318565[/C][/ROW]
[ROW][C]44[/C][C]10[/C][C]9.56176873382647[/C][C]0.438231266173528[/C][/ROW]
[ROW][C]45[/C][C]10[/C][C]9.52548004149218[/C][C]0.474519958507822[/C][/ROW]
[ROW][C]46[/C][C]10[/C][C]9.45369621685473[/C][C]0.546303783145267[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]9.42628130759623[/C][C]-0.426281307596226[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]9.52802924088529[/C][C]-0.52802924088529[/C][/ROW]
[ROW][C]49[/C][C]10[/C][C]9.87725712439909[/C][C]0.122742875600909[/C][/ROW]
[ROW][C]50[/C][C]9[/C][C]9.79659951668586[/C][C]-0.796599516685859[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]9.92201087151036[/C][C]0.0779891284896438[/C][/ROW]
[ROW][C]52[/C][C]9[/C][C]9.64514406023249[/C][C]-0.645144060232493[/C][/ROW]
[ROW][C]53[/C][C]10[/C][C]9.82182616171709[/C][C]0.178173838282905[/C][/ROW]
[ROW][C]54[/C][C]10[/C][C]10.2617638277834[/C][C]-0.261763827783388[/C][/ROW]
[ROW][C]55[/C][C]10[/C][C]9.48796516426143[/C][C]0.512034835738567[/C][/ROW]
[ROW][C]56[/C][C]11[/C][C]10.2276395031099[/C][C]0.772360496890122[/C][/ROW]
[ROW][C]57[/C][C]10[/C][C]10.0473983297684[/C][C]-0.0473983297683657[/C][/ROW]
[ROW][C]58[/C][C]10[/C][C]9.62164915799674[/C][C]0.378350842003263[/C][/ROW]
[ROW][C]59[/C][C]9[/C][C]9.58831839335437[/C][C]-0.588318393354372[/C][/ROW]
[ROW][C]60[/C][C]10[/C][C]10.2668622265696[/C][C]-0.266862226569614[/C][/ROW]
[ROW][C]61[/C][C]10[/C][C]9.5679977312432[/C][C]0.432002268756803[/C][/ROW]
[ROW][C]62[/C][C]9[/C][C]9.66087188145647[/C][C]-0.660871881456473[/C][/ROW]
[ROW][C]63[/C][C]10[/C][C]10.3334998592879[/C][C]-0.333499859287857[/C][/ROW]
[ROW][C]64[/C][C]10[/C][C]9.72633112241124[/C][C]0.273668877588757[/C][/ROW]
[ROW][C]65[/C][C]10[/C][C]10.0045687413187[/C][C]-0.00456874131874433[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]9.94954650693557[/C][C]0.0504534930644347[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]10.1104529940632[/C][C]0.889547005936788[/C][/ROW]
[ROW][C]68[/C][C]10[/C][C]9.97655268789526[/C][C]0.0234473121047438[/C][/ROW]
[ROW][C]69[/C][C]10[/C][C]9.78743773147796[/C][C]0.212562268522043[/C][/ROW]
[ROW][C]70[/C][C]10[/C][C]9.8359429663123[/C][C]0.164057033687703[/C][/ROW]
[ROW][C]71[/C][C]9[/C][C]9.74542559962597[/C][C]-0.745425599625968[/C][/ROW]
[ROW][C]72[/C][C]10[/C][C]10.3687547825919[/C][C]-0.368754782591867[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147865&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147865&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
198.754854374760540.245145625239459
288.80631753728681-0.80631753728681
399.06089173465888-0.0608917346588774
498.87374873003620.126251269963802
599.2032570956038-0.203257095603804
699.06935678943585-0.0693567894358487
7109.214487662206540.78551233779346
899.00663916374036-0.00663916374035642
998.775127243738740.22487275626126
1098.995192284704970.00480771529503127
1188.69071814830243-0.690718148302434
1298.883175864143990.116824135856009
1398.819279846685020.180720153314975
1499.5313468604088-0.531346860408797
1598.901500677894060.0984993221059409
1699.25565844089441-0.255658440894408
1799.54868569826155-0.548685698261555
1898.824185829605090.175814170394914
19109.30652045925570.693479540744304
20109.556164777141220.443835222858784
2198.808987462846620.191012537153376
2298.967921998179650.0320780018203493
2399.3930700257863-0.393070025786298
24109.703844849305020.29615515069498
2599.0148401129517-0.0148401129517002
2699.70028577744811-0.70028577744811
27109.579203157945180.420796842054819
2899.09823933592421-0.0982393359242087
2998.982086595907830.0179134040921721
30109.894306716785470.10569328321453
31109.878723518294680.121276481705321
32109.444100563447260.555899436552736
33109.790370519269140.209629480730865
34109.783661103854130.216338896145869
3599.59849005102607-0.598490051026071
3699.09977866285352-0.0997786628535241
37109.911429242205580.0885707577944234
3899.86330883910357-0.863308839103566
39109.635740822691190.364259177308812
4099.37464962577028-0.37464962577028
41109.655845172369710.344154827630289
42109.879854116925180.120145883074822
43109.737080027681430.262919972318565
44109.561768733826470.438231266173528
45109.525480041492180.474519958507822
46109.453696216854730.546303783145267
4799.42628130759623-0.426281307596226
4899.52802924088529-0.52802924088529
49109.877257124399090.122742875600909
5099.79659951668586-0.796599516685859
51109.922010871510360.0779891284896438
5299.64514406023249-0.645144060232493
53109.821826161717090.178173838282905
541010.2617638277834-0.261763827783388
55109.487965164261430.512034835738567
561110.22763950310990.772360496890122
571010.0473983297684-0.0473983297683657
58109.621649157996740.378350842003263
5999.58831839335437-0.588318393354372
601010.2668622265696-0.266862226569614
61109.56799773124320.432002268756803
6299.66087188145647-0.660871881456473
631010.3334998592879-0.333499859287857
64109.726331122411240.273668877588757
651010.0045687413187-0.00456874131874433
66109.949546506935570.0504534930644347
671110.11045299406320.889547005936788
68109.976552687895260.0234473121047438
69109.787437731477960.212562268522043
70109.83594296631230.164057033687703
7199.74542559962597-0.745425599625968
721010.3687547825919-0.368754782591867







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.687179755832950.6256404883341010.312820244167051
70.7897430559193660.4205138881612680.210256944080634
80.7170714961158930.5658570077682140.282928503884107
90.604198153674850.79160369265030.39580184632515
100.5189363366647810.9621273266704380.481063663335219
110.6052874322835250.7894251354329490.394712567716475
120.5148629824720570.9702740350558870.485137017527943
130.4374739118871130.8749478237742260.562526088112887
140.6118054509486850.7763890981026310.388194549051315
150.5304376500788650.939124699842270.469562349921135
160.4614432246304740.9228864492609480.538556775369526
170.4566599620444960.9133199240889920.543340037955504
180.395216457432070.790432914864140.60478354256793
190.5590782338314240.8818435323371530.440921766168576
200.5492671428306260.9014657143387470.450732857169374
210.4731766680470460.9463533360940920.526823331952954
220.399739410118270.799478820236540.60026058988173
230.4075062305932910.8150124611865810.592493769406709
240.360495326967930.7209906539358610.639504673032069
250.2956661498155450.591332299631090.704333850184455
260.4166736655110610.8333473310221220.583326334488939
270.4099391631779590.8198783263559180.590060836822041
280.3494479384077550.6988958768155090.650552061592245
290.2851485127309140.5702970254618270.714851487269086
300.2296114574656850.459222914931370.770388542534315
310.1806760260417330.3613520520834660.819323973958267
320.1963255441952380.3926510883904750.803674455804762
330.1563206284492250.3126412568984490.843679371550775
340.1232017510104040.2464035020208070.876798248989596
350.1799376667029920.3598753334059850.820062333297008
360.1425152585879570.2850305171759140.857484741412043
370.1070486159933510.2140972319867020.892951384006649
380.2535638239656410.5071276479312820.746436176034359
390.2288313176924160.4576626353848330.771168682307584
400.2253925895723780.4507851791447560.774607410427622
410.1979866573172870.3959733146345740.802013342682713
420.1537416477873610.3074832955747220.846258352212639
430.1236700794137940.2473401588275880.876329920586206
440.1164628286299130.2329256572598260.883537171370087
450.1186949539691010.2373899079382020.881305046030899
460.1479148324480190.2958296648960390.852085167551981
470.1451409945622390.2902819891244790.854859005437761
480.159125631176660.3182512623533210.84087436882334
490.1223567868145990.2447135736291970.877643213185401
500.2257532176695150.4515064353390310.774246782330484
510.1726574482840020.3453148965680040.827342551715998
520.2636585234477780.5273170468955560.736341476552222
530.2062574502798780.4125149005597560.793742549720122
540.1951174106244390.3902348212488780.804882589375561
550.1830248411265080.3660496822530150.816975158873492
560.2747084769172870.5494169538345740.725291523082713
570.2067956472455270.4135912944910550.793204352754473
580.1965530407644140.3931060815288290.803446959235586
590.2321945338585250.4643890677170510.767805466141475
600.1875686483442750.3751372966885490.812431351655726
610.1714961593006040.3429923186012070.828503840699396
620.3026345867956810.6052691735913620.697365413204319
630.4302263993840720.8604527987681450.569773600615928
640.3163165108977850.632633021795570.683683489102215
650.3547990849784430.7095981699568860.645200915021557
660.5319731352378740.9360537295242520.468026864762126

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.68717975583295 & 0.625640488334101 & 0.312820244167051 \tabularnewline
7 & 0.789743055919366 & 0.420513888161268 & 0.210256944080634 \tabularnewline
8 & 0.717071496115893 & 0.565857007768214 & 0.282928503884107 \tabularnewline
9 & 0.60419815367485 & 0.7916036926503 & 0.39580184632515 \tabularnewline
10 & 0.518936336664781 & 0.962127326670438 & 0.481063663335219 \tabularnewline
11 & 0.605287432283525 & 0.789425135432949 & 0.394712567716475 \tabularnewline
12 & 0.514862982472057 & 0.970274035055887 & 0.485137017527943 \tabularnewline
13 & 0.437473911887113 & 0.874947823774226 & 0.562526088112887 \tabularnewline
14 & 0.611805450948685 & 0.776389098102631 & 0.388194549051315 \tabularnewline
15 & 0.530437650078865 & 0.93912469984227 & 0.469562349921135 \tabularnewline
16 & 0.461443224630474 & 0.922886449260948 & 0.538556775369526 \tabularnewline
17 & 0.456659962044496 & 0.913319924088992 & 0.543340037955504 \tabularnewline
18 & 0.39521645743207 & 0.79043291486414 & 0.60478354256793 \tabularnewline
19 & 0.559078233831424 & 0.881843532337153 & 0.440921766168576 \tabularnewline
20 & 0.549267142830626 & 0.901465714338747 & 0.450732857169374 \tabularnewline
21 & 0.473176668047046 & 0.946353336094092 & 0.526823331952954 \tabularnewline
22 & 0.39973941011827 & 0.79947882023654 & 0.60026058988173 \tabularnewline
23 & 0.407506230593291 & 0.815012461186581 & 0.592493769406709 \tabularnewline
24 & 0.36049532696793 & 0.720990653935861 & 0.639504673032069 \tabularnewline
25 & 0.295666149815545 & 0.59133229963109 & 0.704333850184455 \tabularnewline
26 & 0.416673665511061 & 0.833347331022122 & 0.583326334488939 \tabularnewline
27 & 0.409939163177959 & 0.819878326355918 & 0.590060836822041 \tabularnewline
28 & 0.349447938407755 & 0.698895876815509 & 0.650552061592245 \tabularnewline
29 & 0.285148512730914 & 0.570297025461827 & 0.714851487269086 \tabularnewline
30 & 0.229611457465685 & 0.45922291493137 & 0.770388542534315 \tabularnewline
31 & 0.180676026041733 & 0.361352052083466 & 0.819323973958267 \tabularnewline
32 & 0.196325544195238 & 0.392651088390475 & 0.803674455804762 \tabularnewline
33 & 0.156320628449225 & 0.312641256898449 & 0.843679371550775 \tabularnewline
34 & 0.123201751010404 & 0.246403502020807 & 0.876798248989596 \tabularnewline
35 & 0.179937666702992 & 0.359875333405985 & 0.820062333297008 \tabularnewline
36 & 0.142515258587957 & 0.285030517175914 & 0.857484741412043 \tabularnewline
37 & 0.107048615993351 & 0.214097231986702 & 0.892951384006649 \tabularnewline
38 & 0.253563823965641 & 0.507127647931282 & 0.746436176034359 \tabularnewline
39 & 0.228831317692416 & 0.457662635384833 & 0.771168682307584 \tabularnewline
40 & 0.225392589572378 & 0.450785179144756 & 0.774607410427622 \tabularnewline
41 & 0.197986657317287 & 0.395973314634574 & 0.802013342682713 \tabularnewline
42 & 0.153741647787361 & 0.307483295574722 & 0.846258352212639 \tabularnewline
43 & 0.123670079413794 & 0.247340158827588 & 0.876329920586206 \tabularnewline
44 & 0.116462828629913 & 0.232925657259826 & 0.883537171370087 \tabularnewline
45 & 0.118694953969101 & 0.237389907938202 & 0.881305046030899 \tabularnewline
46 & 0.147914832448019 & 0.295829664896039 & 0.852085167551981 \tabularnewline
47 & 0.145140994562239 & 0.290281989124479 & 0.854859005437761 \tabularnewline
48 & 0.15912563117666 & 0.318251262353321 & 0.84087436882334 \tabularnewline
49 & 0.122356786814599 & 0.244713573629197 & 0.877643213185401 \tabularnewline
50 & 0.225753217669515 & 0.451506435339031 & 0.774246782330484 \tabularnewline
51 & 0.172657448284002 & 0.345314896568004 & 0.827342551715998 \tabularnewline
52 & 0.263658523447778 & 0.527317046895556 & 0.736341476552222 \tabularnewline
53 & 0.206257450279878 & 0.412514900559756 & 0.793742549720122 \tabularnewline
54 & 0.195117410624439 & 0.390234821248878 & 0.804882589375561 \tabularnewline
55 & 0.183024841126508 & 0.366049682253015 & 0.816975158873492 \tabularnewline
56 & 0.274708476917287 & 0.549416953834574 & 0.725291523082713 \tabularnewline
57 & 0.206795647245527 & 0.413591294491055 & 0.793204352754473 \tabularnewline
58 & 0.196553040764414 & 0.393106081528829 & 0.803446959235586 \tabularnewline
59 & 0.232194533858525 & 0.464389067717051 & 0.767805466141475 \tabularnewline
60 & 0.187568648344275 & 0.375137296688549 & 0.812431351655726 \tabularnewline
61 & 0.171496159300604 & 0.342992318601207 & 0.828503840699396 \tabularnewline
62 & 0.302634586795681 & 0.605269173591362 & 0.697365413204319 \tabularnewline
63 & 0.430226399384072 & 0.860452798768145 & 0.569773600615928 \tabularnewline
64 & 0.316316510897785 & 0.63263302179557 & 0.683683489102215 \tabularnewline
65 & 0.354799084978443 & 0.709598169956886 & 0.645200915021557 \tabularnewline
66 & 0.531973135237874 & 0.936053729524252 & 0.468026864762126 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147865&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]0.68717975583295[/C][C]0.625640488334101[/C][C]0.312820244167051[/C][/ROW]
[ROW][C]7[/C][C]0.789743055919366[/C][C]0.420513888161268[/C][C]0.210256944080634[/C][/ROW]
[ROW][C]8[/C][C]0.717071496115893[/C][C]0.565857007768214[/C][C]0.282928503884107[/C][/ROW]
[ROW][C]9[/C][C]0.60419815367485[/C][C]0.7916036926503[/C][C]0.39580184632515[/C][/ROW]
[ROW][C]10[/C][C]0.518936336664781[/C][C]0.962127326670438[/C][C]0.481063663335219[/C][/ROW]
[ROW][C]11[/C][C]0.605287432283525[/C][C]0.789425135432949[/C][C]0.394712567716475[/C][/ROW]
[ROW][C]12[/C][C]0.514862982472057[/C][C]0.970274035055887[/C][C]0.485137017527943[/C][/ROW]
[ROW][C]13[/C][C]0.437473911887113[/C][C]0.874947823774226[/C][C]0.562526088112887[/C][/ROW]
[ROW][C]14[/C][C]0.611805450948685[/C][C]0.776389098102631[/C][C]0.388194549051315[/C][/ROW]
[ROW][C]15[/C][C]0.530437650078865[/C][C]0.93912469984227[/C][C]0.469562349921135[/C][/ROW]
[ROW][C]16[/C][C]0.461443224630474[/C][C]0.922886449260948[/C][C]0.538556775369526[/C][/ROW]
[ROW][C]17[/C][C]0.456659962044496[/C][C]0.913319924088992[/C][C]0.543340037955504[/C][/ROW]
[ROW][C]18[/C][C]0.39521645743207[/C][C]0.79043291486414[/C][C]0.60478354256793[/C][/ROW]
[ROW][C]19[/C][C]0.559078233831424[/C][C]0.881843532337153[/C][C]0.440921766168576[/C][/ROW]
[ROW][C]20[/C][C]0.549267142830626[/C][C]0.901465714338747[/C][C]0.450732857169374[/C][/ROW]
[ROW][C]21[/C][C]0.473176668047046[/C][C]0.946353336094092[/C][C]0.526823331952954[/C][/ROW]
[ROW][C]22[/C][C]0.39973941011827[/C][C]0.79947882023654[/C][C]0.60026058988173[/C][/ROW]
[ROW][C]23[/C][C]0.407506230593291[/C][C]0.815012461186581[/C][C]0.592493769406709[/C][/ROW]
[ROW][C]24[/C][C]0.36049532696793[/C][C]0.720990653935861[/C][C]0.639504673032069[/C][/ROW]
[ROW][C]25[/C][C]0.295666149815545[/C][C]0.59133229963109[/C][C]0.704333850184455[/C][/ROW]
[ROW][C]26[/C][C]0.416673665511061[/C][C]0.833347331022122[/C][C]0.583326334488939[/C][/ROW]
[ROW][C]27[/C][C]0.409939163177959[/C][C]0.819878326355918[/C][C]0.590060836822041[/C][/ROW]
[ROW][C]28[/C][C]0.349447938407755[/C][C]0.698895876815509[/C][C]0.650552061592245[/C][/ROW]
[ROW][C]29[/C][C]0.285148512730914[/C][C]0.570297025461827[/C][C]0.714851487269086[/C][/ROW]
[ROW][C]30[/C][C]0.229611457465685[/C][C]0.45922291493137[/C][C]0.770388542534315[/C][/ROW]
[ROW][C]31[/C][C]0.180676026041733[/C][C]0.361352052083466[/C][C]0.819323973958267[/C][/ROW]
[ROW][C]32[/C][C]0.196325544195238[/C][C]0.392651088390475[/C][C]0.803674455804762[/C][/ROW]
[ROW][C]33[/C][C]0.156320628449225[/C][C]0.312641256898449[/C][C]0.843679371550775[/C][/ROW]
[ROW][C]34[/C][C]0.123201751010404[/C][C]0.246403502020807[/C][C]0.876798248989596[/C][/ROW]
[ROW][C]35[/C][C]0.179937666702992[/C][C]0.359875333405985[/C][C]0.820062333297008[/C][/ROW]
[ROW][C]36[/C][C]0.142515258587957[/C][C]0.285030517175914[/C][C]0.857484741412043[/C][/ROW]
[ROW][C]37[/C][C]0.107048615993351[/C][C]0.214097231986702[/C][C]0.892951384006649[/C][/ROW]
[ROW][C]38[/C][C]0.253563823965641[/C][C]0.507127647931282[/C][C]0.746436176034359[/C][/ROW]
[ROW][C]39[/C][C]0.228831317692416[/C][C]0.457662635384833[/C][C]0.771168682307584[/C][/ROW]
[ROW][C]40[/C][C]0.225392589572378[/C][C]0.450785179144756[/C][C]0.774607410427622[/C][/ROW]
[ROW][C]41[/C][C]0.197986657317287[/C][C]0.395973314634574[/C][C]0.802013342682713[/C][/ROW]
[ROW][C]42[/C][C]0.153741647787361[/C][C]0.307483295574722[/C][C]0.846258352212639[/C][/ROW]
[ROW][C]43[/C][C]0.123670079413794[/C][C]0.247340158827588[/C][C]0.876329920586206[/C][/ROW]
[ROW][C]44[/C][C]0.116462828629913[/C][C]0.232925657259826[/C][C]0.883537171370087[/C][/ROW]
[ROW][C]45[/C][C]0.118694953969101[/C][C]0.237389907938202[/C][C]0.881305046030899[/C][/ROW]
[ROW][C]46[/C][C]0.147914832448019[/C][C]0.295829664896039[/C][C]0.852085167551981[/C][/ROW]
[ROW][C]47[/C][C]0.145140994562239[/C][C]0.290281989124479[/C][C]0.854859005437761[/C][/ROW]
[ROW][C]48[/C][C]0.15912563117666[/C][C]0.318251262353321[/C][C]0.84087436882334[/C][/ROW]
[ROW][C]49[/C][C]0.122356786814599[/C][C]0.244713573629197[/C][C]0.877643213185401[/C][/ROW]
[ROW][C]50[/C][C]0.225753217669515[/C][C]0.451506435339031[/C][C]0.774246782330484[/C][/ROW]
[ROW][C]51[/C][C]0.172657448284002[/C][C]0.345314896568004[/C][C]0.827342551715998[/C][/ROW]
[ROW][C]52[/C][C]0.263658523447778[/C][C]0.527317046895556[/C][C]0.736341476552222[/C][/ROW]
[ROW][C]53[/C][C]0.206257450279878[/C][C]0.412514900559756[/C][C]0.793742549720122[/C][/ROW]
[ROW][C]54[/C][C]0.195117410624439[/C][C]0.390234821248878[/C][C]0.804882589375561[/C][/ROW]
[ROW][C]55[/C][C]0.183024841126508[/C][C]0.366049682253015[/C][C]0.816975158873492[/C][/ROW]
[ROW][C]56[/C][C]0.274708476917287[/C][C]0.549416953834574[/C][C]0.725291523082713[/C][/ROW]
[ROW][C]57[/C][C]0.206795647245527[/C][C]0.413591294491055[/C][C]0.793204352754473[/C][/ROW]
[ROW][C]58[/C][C]0.196553040764414[/C][C]0.393106081528829[/C][C]0.803446959235586[/C][/ROW]
[ROW][C]59[/C][C]0.232194533858525[/C][C]0.464389067717051[/C][C]0.767805466141475[/C][/ROW]
[ROW][C]60[/C][C]0.187568648344275[/C][C]0.375137296688549[/C][C]0.812431351655726[/C][/ROW]
[ROW][C]61[/C][C]0.171496159300604[/C][C]0.342992318601207[/C][C]0.828503840699396[/C][/ROW]
[ROW][C]62[/C][C]0.302634586795681[/C][C]0.605269173591362[/C][C]0.697365413204319[/C][/ROW]
[ROW][C]63[/C][C]0.430226399384072[/C][C]0.860452798768145[/C][C]0.569773600615928[/C][/ROW]
[ROW][C]64[/C][C]0.316316510897785[/C][C]0.63263302179557[/C][C]0.683683489102215[/C][/ROW]
[ROW][C]65[/C][C]0.354799084978443[/C][C]0.709598169956886[/C][C]0.645200915021557[/C][/ROW]
[ROW][C]66[/C][C]0.531973135237874[/C][C]0.936053729524252[/C][C]0.468026864762126[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147865&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147865&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
60.687179755832950.6256404883341010.312820244167051
70.7897430559193660.4205138881612680.210256944080634
80.7170714961158930.5658570077682140.282928503884107
90.604198153674850.79160369265030.39580184632515
100.5189363366647810.9621273266704380.481063663335219
110.6052874322835250.7894251354329490.394712567716475
120.5148629824720570.9702740350558870.485137017527943
130.4374739118871130.8749478237742260.562526088112887
140.6118054509486850.7763890981026310.388194549051315
150.5304376500788650.939124699842270.469562349921135
160.4614432246304740.9228864492609480.538556775369526
170.4566599620444960.9133199240889920.543340037955504
180.395216457432070.790432914864140.60478354256793
190.5590782338314240.8818435323371530.440921766168576
200.5492671428306260.9014657143387470.450732857169374
210.4731766680470460.9463533360940920.526823331952954
220.399739410118270.799478820236540.60026058988173
230.4075062305932910.8150124611865810.592493769406709
240.360495326967930.7209906539358610.639504673032069
250.2956661498155450.591332299631090.704333850184455
260.4166736655110610.8333473310221220.583326334488939
270.4099391631779590.8198783263559180.590060836822041
280.3494479384077550.6988958768155090.650552061592245
290.2851485127309140.5702970254618270.714851487269086
300.2296114574656850.459222914931370.770388542534315
310.1806760260417330.3613520520834660.819323973958267
320.1963255441952380.3926510883904750.803674455804762
330.1563206284492250.3126412568984490.843679371550775
340.1232017510104040.2464035020208070.876798248989596
350.1799376667029920.3598753334059850.820062333297008
360.1425152585879570.2850305171759140.857484741412043
370.1070486159933510.2140972319867020.892951384006649
380.2535638239656410.5071276479312820.746436176034359
390.2288313176924160.4576626353848330.771168682307584
400.2253925895723780.4507851791447560.774607410427622
410.1979866573172870.3959733146345740.802013342682713
420.1537416477873610.3074832955747220.846258352212639
430.1236700794137940.2473401588275880.876329920586206
440.1164628286299130.2329256572598260.883537171370087
450.1186949539691010.2373899079382020.881305046030899
460.1479148324480190.2958296648960390.852085167551981
470.1451409945622390.2902819891244790.854859005437761
480.159125631176660.3182512623533210.84087436882334
490.1223567868145990.2447135736291970.877643213185401
500.2257532176695150.4515064353390310.774246782330484
510.1726574482840020.3453148965680040.827342551715998
520.2636585234477780.5273170468955560.736341476552222
530.2062574502798780.4125149005597560.793742549720122
540.1951174106244390.3902348212488780.804882589375561
550.1830248411265080.3660496822530150.816975158873492
560.2747084769172870.5494169538345740.725291523082713
570.2067956472455270.4135912944910550.793204352754473
580.1965530407644140.3931060815288290.803446959235586
590.2321945338585250.4643890677170510.767805466141475
600.1875686483442750.3751372966885490.812431351655726
610.1714961593006040.3429923186012070.828503840699396
620.3026345867956810.6052691735913620.697365413204319
630.4302263993840720.8604527987681450.569773600615928
640.3163165108977850.632633021795570.683683489102215
650.3547990849784430.7095981699568860.645200915021557
660.5319731352378740.9360537295242520.468026864762126







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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147865&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147865&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147865&T=6

As an alternative you can also use a QR Code:  

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

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



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal 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')
}