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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 11:20:57 -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/t1258741467yxip2wanczox1pb.htm/, Retrieved Fri, 29 Mar 2024 11:49:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58392, Retrieved Fri, 29 Mar 2024 11:49:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact135
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [WS7] [2009-11-18 17:01:04] [8b1aef4e7013bd33fbc2a5833375c5f5]
-   PD        [Multiple Regression] [WS7(1)] [2009-11-20 18:20:57] [5edea6bc5a9a9483633d9320282a2734] [Current]
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Dataseries X:
8,1	10,9
7,7	10
7,5	9,2
7,6	9,2
7,8	9,5
7,8	9,6
7,8	9,5
7,5	9,1
7,5	8,9
7,1	9
7,5	10,1
7,5	10,3
7,6	10,2
7,7	9,6
7,7	9,2
7,9	9,3
8,1	9,4
8,2	9,4
8,2	9,2
8,2	9
7,9	9
7,3	9
6,9	9,8
6,6	10
6,7	9,8
6,9	9,3
7	9
7,1	9
7,2	9,1
7,1	9,1
6,9	9,1
7	9,2
6,8	8,8
6,4	8,3
6,7	8,4
6,6	8,1
6,4	7,7
6,3	7,9
6,2	7,9
6,5	8
6,8	7,9
6,8	7,6
6,4	7,1
6,1	6,8
5,8	6,5
6,1	6,9
7,2	8,2
7,3	8,7
6,9	8,3
6,1	7,9
5,8	7,5
6,2	7,8
7,1	8,3
7,7	8,4
7,9	8,2
7,7	7,7
7,4	7,2
7,5	7,3
8	8,1
8,1	8,5




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=58392&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=58392&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58392&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
Werkl_Vrouwen[t] = + 2.70950552586520 + 0.835106106989052Werkl_Mannen[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Werkl_Vrouwen[t] =  +  2.70950552586520 +  0.835106106989052Werkl_Mannen[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58392&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Werkl_Vrouwen[t] =  +  2.70950552586520 +  0.835106106989052Werkl_Mannen[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58392&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58392&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
Werkl_Vrouwen[t] = + 2.70950552586520 + 0.835106106989052Werkl_Mannen[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.709505525865201.1110252.43870.0178210.00891
Werkl_Mannen0.8351061069890520.1542425.41431e-061e-06

\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) & 2.70950552586520 & 1.111025 & 2.4387 & 0.017821 & 0.00891 \tabularnewline
Werkl_Mannen & 0.835106106989052 & 0.154242 & 5.4143 & 1e-06 & 1e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58392&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]2.70950552586520[/C][C]1.111025[/C][C]2.4387[/C][C]0.017821[/C][C]0.00891[/C][/ROW]
[ROW][C]Werkl_Mannen[/C][C]0.835106106989052[/C][C]0.154242[/C][C]5.4143[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58392&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58392&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)2.709505525865201.1110252.43870.0178210.00891
Werkl_Mannen0.8351061069890520.1542425.41431e-061e-06







Multiple Linear Regression - Regression Statistics
Multiple R0.579423898585697
R-squared0.335732054252248
Adjusted R-squared0.324279158635908
F-TEST (value)29.3141634656339
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value1.2302241262363e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.78189229948904
Sum Squared Residuals35.458622944015

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.579423898585697 \tabularnewline
R-squared & 0.335732054252248 \tabularnewline
Adjusted R-squared & 0.324279158635908 \tabularnewline
F-TEST (value) & 29.3141634656339 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 1.2302241262363e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.78189229948904 \tabularnewline
Sum Squared Residuals & 35.458622944015 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58392&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.579423898585697[/C][/ROW]
[ROW][C]R-squared[/C][C]0.335732054252248[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.324279158635908[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]29.3141634656339[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]1.2302241262363e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.78189229948904[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]35.458622944015[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58392&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58392&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 R0.579423898585697
R-squared0.335732054252248
Adjusted R-squared0.324279158635908
F-TEST (value)29.3141634656339
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value1.2302241262363e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.78189229948904
Sum Squared Residuals35.458622944015







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110.99.473864992476511.42613500752349
2109.13982254968090.860177450319103
39.28.97280132828310.227198671716909
49.29.0563119389820.143688061018004
59.59.22333316037980.276666839620194
69.69.22333316037980.376666839620194
79.59.22333316037980.276666839620194
89.18.97280132828310.127198671716909
98.98.9728013282831-0.0728013282830901
1098.638758885487470.361241114512530
1110.18.97280132828311.12719867171691
1210.38.97280132828311.32719867171691
1310.29.0563119389821.14368806101800
149.69.13982254968090.460177450319099
159.29.13982254968090.0601774503190984
169.39.30684377107871-0.00684377107871056
179.49.47386499247652-0.0738649924765206
189.49.55737560317543-0.157375603175426
199.29.55737560317543-0.357375603175427
2099.55737560317543-0.557375603175426
2199.30684377107871-0.306843771078711
2298.805780106885280.19421989311472
239.88.471737664089661.32826233591034
24108.221205831992941.77879416800706
259.88.304716442691851.49528355730815
269.38.471737664089660.82826233591034
2798.555248274788560.444751725211435
2898.638758885487470.361241114512530
299.18.722269496186370.377730503813624
309.18.638758885487470.46124111451253
319.18.471737664089660.62826233591034
329.28.555248274788560.644751725211434
338.88.388227053390750.411772946609246
348.38.054184610595130.245815389404866
358.48.304716442691850.095283557308151
368.18.22120583199294-0.121205831992944
377.78.05418461059513-0.354184610595134
387.97.97067399989623-0.0706739998962283
397.97.887163389197320.0128366108026765
4088.13769522129404-0.137695221294039
417.98.38822705339075-0.488227053390754
427.68.38822705339075-0.788227053390755
437.18.05418461059513-0.954184610595134
446.87.80365277849842-1.00365277849842
456.57.5531209464017-1.05312094640170
466.97.80365277849842-0.903652778498418
478.28.72226949618637-0.522269496186376
488.78.80578010688528-0.105780106885281
498.38.47173766408966-0.171737664089659
507.97.803652778498420.0963472215015821
517.57.5531209464017-0.0531209464017029
527.87.88716338919732-0.087163389197324
538.38.63875888548747-0.338758885487469
548.49.1398225496809-0.7398225496809
558.29.30684377107871-1.10684377107871
567.79.1398225496809-1.4398225496809
577.28.88929071758418-1.68929071758419
587.38.9728013282831-1.67280132828309
598.19.39035438177762-1.29035438177762
608.59.47386499247652-0.973864992476521

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 10.9 & 9.47386499247651 & 1.42613500752349 \tabularnewline
2 & 10 & 9.1398225496809 & 0.860177450319103 \tabularnewline
3 & 9.2 & 8.9728013282831 & 0.227198671716909 \tabularnewline
4 & 9.2 & 9.056311938982 & 0.143688061018004 \tabularnewline
5 & 9.5 & 9.2233331603798 & 0.276666839620194 \tabularnewline
6 & 9.6 & 9.2233331603798 & 0.376666839620194 \tabularnewline
7 & 9.5 & 9.2233331603798 & 0.276666839620194 \tabularnewline
8 & 9.1 & 8.9728013282831 & 0.127198671716909 \tabularnewline
9 & 8.9 & 8.9728013282831 & -0.0728013282830901 \tabularnewline
10 & 9 & 8.63875888548747 & 0.361241114512530 \tabularnewline
11 & 10.1 & 8.9728013282831 & 1.12719867171691 \tabularnewline
12 & 10.3 & 8.9728013282831 & 1.32719867171691 \tabularnewline
13 & 10.2 & 9.056311938982 & 1.14368806101800 \tabularnewline
14 & 9.6 & 9.1398225496809 & 0.460177450319099 \tabularnewline
15 & 9.2 & 9.1398225496809 & 0.0601774503190984 \tabularnewline
16 & 9.3 & 9.30684377107871 & -0.00684377107871056 \tabularnewline
17 & 9.4 & 9.47386499247652 & -0.0738649924765206 \tabularnewline
18 & 9.4 & 9.55737560317543 & -0.157375603175426 \tabularnewline
19 & 9.2 & 9.55737560317543 & -0.357375603175427 \tabularnewline
20 & 9 & 9.55737560317543 & -0.557375603175426 \tabularnewline
21 & 9 & 9.30684377107871 & -0.306843771078711 \tabularnewline
22 & 9 & 8.80578010688528 & 0.19421989311472 \tabularnewline
23 & 9.8 & 8.47173766408966 & 1.32826233591034 \tabularnewline
24 & 10 & 8.22120583199294 & 1.77879416800706 \tabularnewline
25 & 9.8 & 8.30471644269185 & 1.49528355730815 \tabularnewline
26 & 9.3 & 8.47173766408966 & 0.82826233591034 \tabularnewline
27 & 9 & 8.55524827478856 & 0.444751725211435 \tabularnewline
28 & 9 & 8.63875888548747 & 0.361241114512530 \tabularnewline
29 & 9.1 & 8.72226949618637 & 0.377730503813624 \tabularnewline
30 & 9.1 & 8.63875888548747 & 0.46124111451253 \tabularnewline
31 & 9.1 & 8.47173766408966 & 0.62826233591034 \tabularnewline
32 & 9.2 & 8.55524827478856 & 0.644751725211434 \tabularnewline
33 & 8.8 & 8.38822705339075 & 0.411772946609246 \tabularnewline
34 & 8.3 & 8.05418461059513 & 0.245815389404866 \tabularnewline
35 & 8.4 & 8.30471644269185 & 0.095283557308151 \tabularnewline
36 & 8.1 & 8.22120583199294 & -0.121205831992944 \tabularnewline
37 & 7.7 & 8.05418461059513 & -0.354184610595134 \tabularnewline
38 & 7.9 & 7.97067399989623 & -0.0706739998962283 \tabularnewline
39 & 7.9 & 7.88716338919732 & 0.0128366108026765 \tabularnewline
40 & 8 & 8.13769522129404 & -0.137695221294039 \tabularnewline
41 & 7.9 & 8.38822705339075 & -0.488227053390754 \tabularnewline
42 & 7.6 & 8.38822705339075 & -0.788227053390755 \tabularnewline
43 & 7.1 & 8.05418461059513 & -0.954184610595134 \tabularnewline
44 & 6.8 & 7.80365277849842 & -1.00365277849842 \tabularnewline
45 & 6.5 & 7.5531209464017 & -1.05312094640170 \tabularnewline
46 & 6.9 & 7.80365277849842 & -0.903652778498418 \tabularnewline
47 & 8.2 & 8.72226949618637 & -0.522269496186376 \tabularnewline
48 & 8.7 & 8.80578010688528 & -0.105780106885281 \tabularnewline
49 & 8.3 & 8.47173766408966 & -0.171737664089659 \tabularnewline
50 & 7.9 & 7.80365277849842 & 0.0963472215015821 \tabularnewline
51 & 7.5 & 7.5531209464017 & -0.0531209464017029 \tabularnewline
52 & 7.8 & 7.88716338919732 & -0.087163389197324 \tabularnewline
53 & 8.3 & 8.63875888548747 & -0.338758885487469 \tabularnewline
54 & 8.4 & 9.1398225496809 & -0.7398225496809 \tabularnewline
55 & 8.2 & 9.30684377107871 & -1.10684377107871 \tabularnewline
56 & 7.7 & 9.1398225496809 & -1.4398225496809 \tabularnewline
57 & 7.2 & 8.88929071758418 & -1.68929071758419 \tabularnewline
58 & 7.3 & 8.9728013282831 & -1.67280132828309 \tabularnewline
59 & 8.1 & 9.39035438177762 & -1.29035438177762 \tabularnewline
60 & 8.5 & 9.47386499247652 & -0.973864992476521 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58392&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]10.9[/C][C]9.47386499247651[/C][C]1.42613500752349[/C][/ROW]
[ROW][C]2[/C][C]10[/C][C]9.1398225496809[/C][C]0.860177450319103[/C][/ROW]
[ROW][C]3[/C][C]9.2[/C][C]8.9728013282831[/C][C]0.227198671716909[/C][/ROW]
[ROW][C]4[/C][C]9.2[/C][C]9.056311938982[/C][C]0.143688061018004[/C][/ROW]
[ROW][C]5[/C][C]9.5[/C][C]9.2233331603798[/C][C]0.276666839620194[/C][/ROW]
[ROW][C]6[/C][C]9.6[/C][C]9.2233331603798[/C][C]0.376666839620194[/C][/ROW]
[ROW][C]7[/C][C]9.5[/C][C]9.2233331603798[/C][C]0.276666839620194[/C][/ROW]
[ROW][C]8[/C][C]9.1[/C][C]8.9728013282831[/C][C]0.127198671716909[/C][/ROW]
[ROW][C]9[/C][C]8.9[/C][C]8.9728013282831[/C][C]-0.0728013282830901[/C][/ROW]
[ROW][C]10[/C][C]9[/C][C]8.63875888548747[/C][C]0.361241114512530[/C][/ROW]
[ROW][C]11[/C][C]10.1[/C][C]8.9728013282831[/C][C]1.12719867171691[/C][/ROW]
[ROW][C]12[/C][C]10.3[/C][C]8.9728013282831[/C][C]1.32719867171691[/C][/ROW]
[ROW][C]13[/C][C]10.2[/C][C]9.056311938982[/C][C]1.14368806101800[/C][/ROW]
[ROW][C]14[/C][C]9.6[/C][C]9.1398225496809[/C][C]0.460177450319099[/C][/ROW]
[ROW][C]15[/C][C]9.2[/C][C]9.1398225496809[/C][C]0.0601774503190984[/C][/ROW]
[ROW][C]16[/C][C]9.3[/C][C]9.30684377107871[/C][C]-0.00684377107871056[/C][/ROW]
[ROW][C]17[/C][C]9.4[/C][C]9.47386499247652[/C][C]-0.0738649924765206[/C][/ROW]
[ROW][C]18[/C][C]9.4[/C][C]9.55737560317543[/C][C]-0.157375603175426[/C][/ROW]
[ROW][C]19[/C][C]9.2[/C][C]9.55737560317543[/C][C]-0.357375603175427[/C][/ROW]
[ROW][C]20[/C][C]9[/C][C]9.55737560317543[/C][C]-0.557375603175426[/C][/ROW]
[ROW][C]21[/C][C]9[/C][C]9.30684377107871[/C][C]-0.306843771078711[/C][/ROW]
[ROW][C]22[/C][C]9[/C][C]8.80578010688528[/C][C]0.19421989311472[/C][/ROW]
[ROW][C]23[/C][C]9.8[/C][C]8.47173766408966[/C][C]1.32826233591034[/C][/ROW]
[ROW][C]24[/C][C]10[/C][C]8.22120583199294[/C][C]1.77879416800706[/C][/ROW]
[ROW][C]25[/C][C]9.8[/C][C]8.30471644269185[/C][C]1.49528355730815[/C][/ROW]
[ROW][C]26[/C][C]9.3[/C][C]8.47173766408966[/C][C]0.82826233591034[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]8.55524827478856[/C][C]0.444751725211435[/C][/ROW]
[ROW][C]28[/C][C]9[/C][C]8.63875888548747[/C][C]0.361241114512530[/C][/ROW]
[ROW][C]29[/C][C]9.1[/C][C]8.72226949618637[/C][C]0.377730503813624[/C][/ROW]
[ROW][C]30[/C][C]9.1[/C][C]8.63875888548747[/C][C]0.46124111451253[/C][/ROW]
[ROW][C]31[/C][C]9.1[/C][C]8.47173766408966[/C][C]0.62826233591034[/C][/ROW]
[ROW][C]32[/C][C]9.2[/C][C]8.55524827478856[/C][C]0.644751725211434[/C][/ROW]
[ROW][C]33[/C][C]8.8[/C][C]8.38822705339075[/C][C]0.411772946609246[/C][/ROW]
[ROW][C]34[/C][C]8.3[/C][C]8.05418461059513[/C][C]0.245815389404866[/C][/ROW]
[ROW][C]35[/C][C]8.4[/C][C]8.30471644269185[/C][C]0.095283557308151[/C][/ROW]
[ROW][C]36[/C][C]8.1[/C][C]8.22120583199294[/C][C]-0.121205831992944[/C][/ROW]
[ROW][C]37[/C][C]7.7[/C][C]8.05418461059513[/C][C]-0.354184610595134[/C][/ROW]
[ROW][C]38[/C][C]7.9[/C][C]7.97067399989623[/C][C]-0.0706739998962283[/C][/ROW]
[ROW][C]39[/C][C]7.9[/C][C]7.88716338919732[/C][C]0.0128366108026765[/C][/ROW]
[ROW][C]40[/C][C]8[/C][C]8.13769522129404[/C][C]-0.137695221294039[/C][/ROW]
[ROW][C]41[/C][C]7.9[/C][C]8.38822705339075[/C][C]-0.488227053390754[/C][/ROW]
[ROW][C]42[/C][C]7.6[/C][C]8.38822705339075[/C][C]-0.788227053390755[/C][/ROW]
[ROW][C]43[/C][C]7.1[/C][C]8.05418461059513[/C][C]-0.954184610595134[/C][/ROW]
[ROW][C]44[/C][C]6.8[/C][C]7.80365277849842[/C][C]-1.00365277849842[/C][/ROW]
[ROW][C]45[/C][C]6.5[/C][C]7.5531209464017[/C][C]-1.05312094640170[/C][/ROW]
[ROW][C]46[/C][C]6.9[/C][C]7.80365277849842[/C][C]-0.903652778498418[/C][/ROW]
[ROW][C]47[/C][C]8.2[/C][C]8.72226949618637[/C][C]-0.522269496186376[/C][/ROW]
[ROW][C]48[/C][C]8.7[/C][C]8.80578010688528[/C][C]-0.105780106885281[/C][/ROW]
[ROW][C]49[/C][C]8.3[/C][C]8.47173766408966[/C][C]-0.171737664089659[/C][/ROW]
[ROW][C]50[/C][C]7.9[/C][C]7.80365277849842[/C][C]0.0963472215015821[/C][/ROW]
[ROW][C]51[/C][C]7.5[/C][C]7.5531209464017[/C][C]-0.0531209464017029[/C][/ROW]
[ROW][C]52[/C][C]7.8[/C][C]7.88716338919732[/C][C]-0.087163389197324[/C][/ROW]
[ROW][C]53[/C][C]8.3[/C][C]8.63875888548747[/C][C]-0.338758885487469[/C][/ROW]
[ROW][C]54[/C][C]8.4[/C][C]9.1398225496809[/C][C]-0.7398225496809[/C][/ROW]
[ROW][C]55[/C][C]8.2[/C][C]9.30684377107871[/C][C]-1.10684377107871[/C][/ROW]
[ROW][C]56[/C][C]7.7[/C][C]9.1398225496809[/C][C]-1.4398225496809[/C][/ROW]
[ROW][C]57[/C][C]7.2[/C][C]8.88929071758418[/C][C]-1.68929071758419[/C][/ROW]
[ROW][C]58[/C][C]7.3[/C][C]8.9728013282831[/C][C]-1.67280132828309[/C][/ROW]
[ROW][C]59[/C][C]8.1[/C][C]9.39035438177762[/C][C]-1.29035438177762[/C][/ROW]
[ROW][C]60[/C][C]8.5[/C][C]9.47386499247652[/C][C]-0.973864992476521[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58392&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58392&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
110.99.473864992476511.42613500752349
2109.13982254968090.860177450319103
39.28.97280132828310.227198671716909
49.29.0563119389820.143688061018004
59.59.22333316037980.276666839620194
69.69.22333316037980.376666839620194
79.59.22333316037980.276666839620194
89.18.97280132828310.127198671716909
98.98.9728013282831-0.0728013282830901
1098.638758885487470.361241114512530
1110.18.97280132828311.12719867171691
1210.38.97280132828311.32719867171691
1310.29.0563119389821.14368806101800
149.69.13982254968090.460177450319099
159.29.13982254968090.0601774503190984
169.39.30684377107871-0.00684377107871056
179.49.47386499247652-0.0738649924765206
189.49.55737560317543-0.157375603175426
199.29.55737560317543-0.357375603175427
2099.55737560317543-0.557375603175426
2199.30684377107871-0.306843771078711
2298.805780106885280.19421989311472
239.88.471737664089661.32826233591034
24108.221205831992941.77879416800706
259.88.304716442691851.49528355730815
269.38.471737664089660.82826233591034
2798.555248274788560.444751725211435
2898.638758885487470.361241114512530
299.18.722269496186370.377730503813624
309.18.638758885487470.46124111451253
319.18.471737664089660.62826233591034
329.28.555248274788560.644751725211434
338.88.388227053390750.411772946609246
348.38.054184610595130.245815389404866
358.48.304716442691850.095283557308151
368.18.22120583199294-0.121205831992944
377.78.05418461059513-0.354184610595134
387.97.97067399989623-0.0706739998962283
397.97.887163389197320.0128366108026765
4088.13769522129404-0.137695221294039
417.98.38822705339075-0.488227053390754
427.68.38822705339075-0.788227053390755
437.18.05418461059513-0.954184610595134
446.87.80365277849842-1.00365277849842
456.57.5531209464017-1.05312094640170
466.97.80365277849842-0.903652778498418
478.28.72226949618637-0.522269496186376
488.78.80578010688528-0.105780106885281
498.38.47173766408966-0.171737664089659
507.97.803652778498420.0963472215015821
517.57.5531209464017-0.0531209464017029
527.87.88716338919732-0.087163389197324
538.38.63875888548747-0.338758885487469
548.49.1398225496809-0.7398225496809
558.29.30684377107871-1.10684377107871
567.79.1398225496809-1.4398225496809
577.28.88929071758418-1.68929071758419
587.38.9728013282831-1.67280132828309
598.19.39035438177762-1.29035438177762
608.59.47386499247652-0.973864992476521







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.1044538689823270.2089077379646530.895546131017674
60.05483459348994610.1096691869798920.945165406510054
70.03239777966417570.06479555932835140.967602220335824
80.01209586237956750.02419172475913510.987904137620432
90.004423281040885110.008846562081770230.995576718959115
100.01229109698441290.02458219396882570.987708903015587
110.03126764155795270.06253528311590540.968732358442047
120.07672672496325720.1534534499265140.923273275036743
130.08766328818893570.1753265763778710.912336711811064
140.05988219759863280.1197643951972660.940117802401367
150.05228540305601930.1045708061120390.94771459694398
160.05173736931496080.1034747386299220.94826263068504
170.0491721882539890.0983443765079780.95082781174601
180.04161566595276250.0832313319055250.958384334047238
190.03643914532702260.07287829065404530.963560854672977
200.03398808836022100.06797617672044210.96601191163978
210.02760735316533580.05521470633067160.972392646834664
220.02112249627904030.04224499255808060.97887750372096
230.02670749596022350.05341499192044690.973292504039777
240.05443304530035030.1088660906007010.94556695469965
250.09050722899221210.1810144579844240.909492771007788
260.1061620898825500.2123241797651010.89383791011745
270.1199714394401280.2399428788802550.880028560559872
280.1308990412172870.2617980824345750.869100958782713
290.1433579818145240.2867159636290480.856642018185476
300.1700401514140010.3400803028280030.829959848585999
310.2294940562955170.4589881125910340.770505943704483
320.3457061271018620.6914122542037240.654293872898138
330.4561833453633320.9123666907266630.543816654636668
340.5587924852655380.8824150294689250.441207514734462
350.6307377031608120.7385245936783770.369262296839188
360.681693156361340.6366136872773210.318306843638661
370.7246738249097880.5506523501804250.275326175090212
380.725672151414970.548655697170060.27432784858503
390.7219261497105350.5561477005789290.278073850289465
400.7185051866597420.5629896266805160.281494813340258
410.709731017260390.5805379654792190.290268982739609
420.7156253776598370.5687492446803260.284374622340163
430.7470784269133220.5058431461733560.252921573086678
440.7904035593768530.4191928812462940.209596440623147
450.8813773096493610.2372453807012770.118622690350639
460.9270484717581790.1459030564836430.0729515282418215
470.899013155671510.2019736886569810.100986844328491
480.9239411480840550.1521177038318910.0760588519159453
490.9085804925008350.182839014998330.091419507499165
500.8650368822870670.2699262354258650.134963117712933
510.7856186311049730.4287627377900540.214381368895027
520.7204735856873820.5590528286252370.279526414312618
530.950609249963710.0987815000725780.049390750036289
540.9983631949400280.003273610119944830.00163680505997241
550.995785471441920.008429057116159630.00421452855807982

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.104453868982327 & 0.208907737964653 & 0.895546131017674 \tabularnewline
6 & 0.0548345934899461 & 0.109669186979892 & 0.945165406510054 \tabularnewline
7 & 0.0323977796641757 & 0.0647955593283514 & 0.967602220335824 \tabularnewline
8 & 0.0120958623795675 & 0.0241917247591351 & 0.987904137620432 \tabularnewline
9 & 0.00442328104088511 & 0.00884656208177023 & 0.995576718959115 \tabularnewline
10 & 0.0122910969844129 & 0.0245821939688257 & 0.987708903015587 \tabularnewline
11 & 0.0312676415579527 & 0.0625352831159054 & 0.968732358442047 \tabularnewline
12 & 0.0767267249632572 & 0.153453449926514 & 0.923273275036743 \tabularnewline
13 & 0.0876632881889357 & 0.175326576377871 & 0.912336711811064 \tabularnewline
14 & 0.0598821975986328 & 0.119764395197266 & 0.940117802401367 \tabularnewline
15 & 0.0522854030560193 & 0.104570806112039 & 0.94771459694398 \tabularnewline
16 & 0.0517373693149608 & 0.103474738629922 & 0.94826263068504 \tabularnewline
17 & 0.049172188253989 & 0.098344376507978 & 0.95082781174601 \tabularnewline
18 & 0.0416156659527625 & 0.083231331905525 & 0.958384334047238 \tabularnewline
19 & 0.0364391453270226 & 0.0728782906540453 & 0.963560854672977 \tabularnewline
20 & 0.0339880883602210 & 0.0679761767204421 & 0.96601191163978 \tabularnewline
21 & 0.0276073531653358 & 0.0552147063306716 & 0.972392646834664 \tabularnewline
22 & 0.0211224962790403 & 0.0422449925580806 & 0.97887750372096 \tabularnewline
23 & 0.0267074959602235 & 0.0534149919204469 & 0.973292504039777 \tabularnewline
24 & 0.0544330453003503 & 0.108866090600701 & 0.94556695469965 \tabularnewline
25 & 0.0905072289922121 & 0.181014457984424 & 0.909492771007788 \tabularnewline
26 & 0.106162089882550 & 0.212324179765101 & 0.89383791011745 \tabularnewline
27 & 0.119971439440128 & 0.239942878880255 & 0.880028560559872 \tabularnewline
28 & 0.130899041217287 & 0.261798082434575 & 0.869100958782713 \tabularnewline
29 & 0.143357981814524 & 0.286715963629048 & 0.856642018185476 \tabularnewline
30 & 0.170040151414001 & 0.340080302828003 & 0.829959848585999 \tabularnewline
31 & 0.229494056295517 & 0.458988112591034 & 0.770505943704483 \tabularnewline
32 & 0.345706127101862 & 0.691412254203724 & 0.654293872898138 \tabularnewline
33 & 0.456183345363332 & 0.912366690726663 & 0.543816654636668 \tabularnewline
34 & 0.558792485265538 & 0.882415029468925 & 0.441207514734462 \tabularnewline
35 & 0.630737703160812 & 0.738524593678377 & 0.369262296839188 \tabularnewline
36 & 0.68169315636134 & 0.636613687277321 & 0.318306843638661 \tabularnewline
37 & 0.724673824909788 & 0.550652350180425 & 0.275326175090212 \tabularnewline
38 & 0.72567215141497 & 0.54865569717006 & 0.27432784858503 \tabularnewline
39 & 0.721926149710535 & 0.556147700578929 & 0.278073850289465 \tabularnewline
40 & 0.718505186659742 & 0.562989626680516 & 0.281494813340258 \tabularnewline
41 & 0.70973101726039 & 0.580537965479219 & 0.290268982739609 \tabularnewline
42 & 0.715625377659837 & 0.568749244680326 & 0.284374622340163 \tabularnewline
43 & 0.747078426913322 & 0.505843146173356 & 0.252921573086678 \tabularnewline
44 & 0.790403559376853 & 0.419192881246294 & 0.209596440623147 \tabularnewline
45 & 0.881377309649361 & 0.237245380701277 & 0.118622690350639 \tabularnewline
46 & 0.927048471758179 & 0.145903056483643 & 0.0729515282418215 \tabularnewline
47 & 0.89901315567151 & 0.201973688656981 & 0.100986844328491 \tabularnewline
48 & 0.923941148084055 & 0.152117703831891 & 0.0760588519159453 \tabularnewline
49 & 0.908580492500835 & 0.18283901499833 & 0.091419507499165 \tabularnewline
50 & 0.865036882287067 & 0.269926235425865 & 0.134963117712933 \tabularnewline
51 & 0.785618631104973 & 0.428762737790054 & 0.214381368895027 \tabularnewline
52 & 0.720473585687382 & 0.559052828625237 & 0.279526414312618 \tabularnewline
53 & 0.95060924996371 & 0.098781500072578 & 0.049390750036289 \tabularnewline
54 & 0.998363194940028 & 0.00327361011994483 & 0.00163680505997241 \tabularnewline
55 & 0.99578547144192 & 0.00842905711615963 & 0.00421452855807982 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58392&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]5[/C][C]0.104453868982327[/C][C]0.208907737964653[/C][C]0.895546131017674[/C][/ROW]
[ROW][C]6[/C][C]0.0548345934899461[/C][C]0.109669186979892[/C][C]0.945165406510054[/C][/ROW]
[ROW][C]7[/C][C]0.0323977796641757[/C][C]0.0647955593283514[/C][C]0.967602220335824[/C][/ROW]
[ROW][C]8[/C][C]0.0120958623795675[/C][C]0.0241917247591351[/C][C]0.987904137620432[/C][/ROW]
[ROW][C]9[/C][C]0.00442328104088511[/C][C]0.00884656208177023[/C][C]0.995576718959115[/C][/ROW]
[ROW][C]10[/C][C]0.0122910969844129[/C][C]0.0245821939688257[/C][C]0.987708903015587[/C][/ROW]
[ROW][C]11[/C][C]0.0312676415579527[/C][C]0.0625352831159054[/C][C]0.968732358442047[/C][/ROW]
[ROW][C]12[/C][C]0.0767267249632572[/C][C]0.153453449926514[/C][C]0.923273275036743[/C][/ROW]
[ROW][C]13[/C][C]0.0876632881889357[/C][C]0.175326576377871[/C][C]0.912336711811064[/C][/ROW]
[ROW][C]14[/C][C]0.0598821975986328[/C][C]0.119764395197266[/C][C]0.940117802401367[/C][/ROW]
[ROW][C]15[/C][C]0.0522854030560193[/C][C]0.104570806112039[/C][C]0.94771459694398[/C][/ROW]
[ROW][C]16[/C][C]0.0517373693149608[/C][C]0.103474738629922[/C][C]0.94826263068504[/C][/ROW]
[ROW][C]17[/C][C]0.049172188253989[/C][C]0.098344376507978[/C][C]0.95082781174601[/C][/ROW]
[ROW][C]18[/C][C]0.0416156659527625[/C][C]0.083231331905525[/C][C]0.958384334047238[/C][/ROW]
[ROW][C]19[/C][C]0.0364391453270226[/C][C]0.0728782906540453[/C][C]0.963560854672977[/C][/ROW]
[ROW][C]20[/C][C]0.0339880883602210[/C][C]0.0679761767204421[/C][C]0.96601191163978[/C][/ROW]
[ROW][C]21[/C][C]0.0276073531653358[/C][C]0.0552147063306716[/C][C]0.972392646834664[/C][/ROW]
[ROW][C]22[/C][C]0.0211224962790403[/C][C]0.0422449925580806[/C][C]0.97887750372096[/C][/ROW]
[ROW][C]23[/C][C]0.0267074959602235[/C][C]0.0534149919204469[/C][C]0.973292504039777[/C][/ROW]
[ROW][C]24[/C][C]0.0544330453003503[/C][C]0.108866090600701[/C][C]0.94556695469965[/C][/ROW]
[ROW][C]25[/C][C]0.0905072289922121[/C][C]0.181014457984424[/C][C]0.909492771007788[/C][/ROW]
[ROW][C]26[/C][C]0.106162089882550[/C][C]0.212324179765101[/C][C]0.89383791011745[/C][/ROW]
[ROW][C]27[/C][C]0.119971439440128[/C][C]0.239942878880255[/C][C]0.880028560559872[/C][/ROW]
[ROW][C]28[/C][C]0.130899041217287[/C][C]0.261798082434575[/C][C]0.869100958782713[/C][/ROW]
[ROW][C]29[/C][C]0.143357981814524[/C][C]0.286715963629048[/C][C]0.856642018185476[/C][/ROW]
[ROW][C]30[/C][C]0.170040151414001[/C][C]0.340080302828003[/C][C]0.829959848585999[/C][/ROW]
[ROW][C]31[/C][C]0.229494056295517[/C][C]0.458988112591034[/C][C]0.770505943704483[/C][/ROW]
[ROW][C]32[/C][C]0.345706127101862[/C][C]0.691412254203724[/C][C]0.654293872898138[/C][/ROW]
[ROW][C]33[/C][C]0.456183345363332[/C][C]0.912366690726663[/C][C]0.543816654636668[/C][/ROW]
[ROW][C]34[/C][C]0.558792485265538[/C][C]0.882415029468925[/C][C]0.441207514734462[/C][/ROW]
[ROW][C]35[/C][C]0.630737703160812[/C][C]0.738524593678377[/C][C]0.369262296839188[/C][/ROW]
[ROW][C]36[/C][C]0.68169315636134[/C][C]0.636613687277321[/C][C]0.318306843638661[/C][/ROW]
[ROW][C]37[/C][C]0.724673824909788[/C][C]0.550652350180425[/C][C]0.275326175090212[/C][/ROW]
[ROW][C]38[/C][C]0.72567215141497[/C][C]0.54865569717006[/C][C]0.27432784858503[/C][/ROW]
[ROW][C]39[/C][C]0.721926149710535[/C][C]0.556147700578929[/C][C]0.278073850289465[/C][/ROW]
[ROW][C]40[/C][C]0.718505186659742[/C][C]0.562989626680516[/C][C]0.281494813340258[/C][/ROW]
[ROW][C]41[/C][C]0.70973101726039[/C][C]0.580537965479219[/C][C]0.290268982739609[/C][/ROW]
[ROW][C]42[/C][C]0.715625377659837[/C][C]0.568749244680326[/C][C]0.284374622340163[/C][/ROW]
[ROW][C]43[/C][C]0.747078426913322[/C][C]0.505843146173356[/C][C]0.252921573086678[/C][/ROW]
[ROW][C]44[/C][C]0.790403559376853[/C][C]0.419192881246294[/C][C]0.209596440623147[/C][/ROW]
[ROW][C]45[/C][C]0.881377309649361[/C][C]0.237245380701277[/C][C]0.118622690350639[/C][/ROW]
[ROW][C]46[/C][C]0.927048471758179[/C][C]0.145903056483643[/C][C]0.0729515282418215[/C][/ROW]
[ROW][C]47[/C][C]0.89901315567151[/C][C]0.201973688656981[/C][C]0.100986844328491[/C][/ROW]
[ROW][C]48[/C][C]0.923941148084055[/C][C]0.152117703831891[/C][C]0.0760588519159453[/C][/ROW]
[ROW][C]49[/C][C]0.908580492500835[/C][C]0.18283901499833[/C][C]0.091419507499165[/C][/ROW]
[ROW][C]50[/C][C]0.865036882287067[/C][C]0.269926235425865[/C][C]0.134963117712933[/C][/ROW]
[ROW][C]51[/C][C]0.785618631104973[/C][C]0.428762737790054[/C][C]0.214381368895027[/C][/ROW]
[ROW][C]52[/C][C]0.720473585687382[/C][C]0.559052828625237[/C][C]0.279526414312618[/C][/ROW]
[ROW][C]53[/C][C]0.95060924996371[/C][C]0.098781500072578[/C][C]0.049390750036289[/C][/ROW]
[ROW][C]54[/C][C]0.998363194940028[/C][C]0.00327361011994483[/C][C]0.00163680505997241[/C][/ROW]
[ROW][C]55[/C][C]0.99578547144192[/C][C]0.00842905711615963[/C][C]0.00421452855807982[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58392&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58392&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
50.1044538689823270.2089077379646530.895546131017674
60.05483459348994610.1096691869798920.945165406510054
70.03239777966417570.06479555932835140.967602220335824
80.01209586237956750.02419172475913510.987904137620432
90.004423281040885110.008846562081770230.995576718959115
100.01229109698441290.02458219396882570.987708903015587
110.03126764155795270.06253528311590540.968732358442047
120.07672672496325720.1534534499265140.923273275036743
130.08766328818893570.1753265763778710.912336711811064
140.05988219759863280.1197643951972660.940117802401367
150.05228540305601930.1045708061120390.94771459694398
160.05173736931496080.1034747386299220.94826263068504
170.0491721882539890.0983443765079780.95082781174601
180.04161566595276250.0832313319055250.958384334047238
190.03643914532702260.07287829065404530.963560854672977
200.03398808836022100.06797617672044210.96601191163978
210.02760735316533580.05521470633067160.972392646834664
220.02112249627904030.04224499255808060.97887750372096
230.02670749596022350.05341499192044690.973292504039777
240.05443304530035030.1088660906007010.94556695469965
250.09050722899221210.1810144579844240.909492771007788
260.1061620898825500.2123241797651010.89383791011745
270.1199714394401280.2399428788802550.880028560559872
280.1308990412172870.2617980824345750.869100958782713
290.1433579818145240.2867159636290480.856642018185476
300.1700401514140010.3400803028280030.829959848585999
310.2294940562955170.4589881125910340.770505943704483
320.3457061271018620.6914122542037240.654293872898138
330.4561833453633320.9123666907266630.543816654636668
340.5587924852655380.8824150294689250.441207514734462
350.6307377031608120.7385245936783770.369262296839188
360.681693156361340.6366136872773210.318306843638661
370.7246738249097880.5506523501804250.275326175090212
380.725672151414970.548655697170060.27432784858503
390.7219261497105350.5561477005789290.278073850289465
400.7185051866597420.5629896266805160.281494813340258
410.709731017260390.5805379654792190.290268982739609
420.7156253776598370.5687492446803260.284374622340163
430.7470784269133220.5058431461733560.252921573086678
440.7904035593768530.4191928812462940.209596440623147
450.8813773096493610.2372453807012770.118622690350639
460.9270484717581790.1459030564836430.0729515282418215
470.899013155671510.2019736886569810.100986844328491
480.9239411480840550.1521177038318910.0760588519159453
490.9085804925008350.182839014998330.091419507499165
500.8650368822870670.2699262354258650.134963117712933
510.7856186311049730.4287627377900540.214381368895027
520.7204735856873820.5590528286252370.279526414312618
530.950609249963710.0987815000725780.049390750036289
540.9983631949400280.003273610119944830.00163680505997241
550.995785471441920.008429057116159630.00421452855807982







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level30.0588235294117647NOK
5% type I error level60.117647058823529NOK
10% type I error level150.294117647058824NOK

\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 & 3 & 0.0588235294117647 & NOK \tabularnewline
5% type I error level & 6 & 0.117647058823529 & NOK \tabularnewline
10% type I error level & 15 & 0.294117647058824 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58392&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]3[/C][C]0.0588235294117647[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]6[/C][C]0.117647058823529[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]15[/C][C]0.294117647058824[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58392&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58392&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 level30.0588235294117647NOK
5% type I error level60.117647058823529NOK
10% type I error level150.294117647058824NOK



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}