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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 computationThu, 19 Nov 2009 11:30:59 -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/19/t1258655672fx4vs250r56uqt8.htm/, Retrieved Fri, 29 Mar 2024 05:32:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57883, Retrieved Fri, 29 Mar 2024 05:32:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact153
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] [Workshop7] [2009-11-19 18:30:59] [307139c5e328127f586f26d5bcc435d8] [Current]
-    D        [Multiple Regression] [workshop7] [2009-11-20 12:37:03] [34b80aeb109c116fd63bf2eb7493a276]
-   P           [Multiple Regression] [workshop7] [2009-11-20 13:01:05] [34b80aeb109c116fd63bf2eb7493a276]
-    D            [Multiple Regression] [Model 2 Seizonali...] [2009-12-05 14:50:04] [34b80aeb109c116fd63bf2eb7493a276]
-               [Multiple Regression] [Workshop 7] [2009-11-20 16:29:41] [78762f311bef5a0e45c439762ada383c]
-    D          [Multiple Regression] [Toetsen van hypot...] [2009-12-05 13:35:34] [34b80aeb109c116fd63bf2eb7493a276]
-    D          [Multiple Regression] [Model 1 zonder se...] [2009-12-05 14:33:00] [34b80aeb109c116fd63bf2eb7493a276]
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Dataseries X:
5.4	2.7
5.4	2.5
5.6	2.2
5.7	2.9
5.8	3.1
5.8	3
5.8	2.8
5.9	2.5
6.1	1.9
6.4	1.9
6.4	1.8
6.3	2
6.2	2.6
6.2	2.5
6.3	2.5
6.4	1.6
6.5	1.4
6.6	0.8
6.6	1.1
6.6	1.3
6.8	1.2
7	1.3
7.2	1.1
7.3	1.3
7.5	1.2
7.6	1.6
7.6	1.7
7.7	1.5
7.7	0.9
7.7	1.5
7.7	1.4
7.6	1.6
7.7	1.7
7.9	1.4
7.9	1.8
7.9	1.7
7.8	1.4
7.6	1.2
7.4	1
7	1.7
7	2.4
7.2	2
7.5	2.1
7.8	2
7.8	1.8
7.7	2.7
7.6	2.3
7.6	1.9
7.5	2
7.5	2.3
7.6	2.8
7.6	2.4
7.9	2.3
7.6	2.7
7.5	2.7
7.5	2.9
7.6	3
7.7	2.2
7.8	2.3
7.9	2.8
7.9	2.8




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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57883&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=57883&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57883&T=0

As an alternative you can also use a QR Code:  

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

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







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 7.70546056652312 -0.309228385849713X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  7.70546056652312 -0.309228385849713X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57883&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  7.70546056652312 -0.309228385849713X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57883&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57883&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 7.70546056652312 -0.309228385849713X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.705460566523120.32417323.769600
X-0.3092283858497130.155277-1.99150.0510660.025533

\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) & 7.70546056652312 & 0.324173 & 23.7696 & 0 & 0 \tabularnewline
X & -0.309228385849713 & 0.155277 & -1.9915 & 0.051066 & 0.025533 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57883&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]7.70546056652312[/C][C]0.324173[/C][C]23.7696[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-0.309228385849713[/C][C]0.155277[/C][C]-1.9915[/C][C]0.051066[/C][C]0.025533[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57883&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57883&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)7.705460566523120.32417323.769600
X-0.3092283858497130.155277-1.99150.0510660.025533







Multiple Linear Regression - Regression Statistics
Multiple R0.250968372521009
R-squared0.0629851240058438
Adjusted R-squared0.0471035159381463
F-TEST (value)3.96591602924345
F-TEST (DF numerator)1
F-TEST (DF denominator)59
p-value0.0510662732824697
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.74579163111317
Sum Squared Residuals32.8161042652681

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.250968372521009 \tabularnewline
R-squared & 0.0629851240058438 \tabularnewline
Adjusted R-squared & 0.0471035159381463 \tabularnewline
F-TEST (value) & 3.96591602924345 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 0.0510662732824697 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.74579163111317 \tabularnewline
Sum Squared Residuals & 32.8161042652681 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57883&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.250968372521009[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0629851240058438[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0471035159381463[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.96591602924345[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]59[/C][/ROW]
[ROW][C]p-value[/C][C]0.0510662732824697[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.74579163111317[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]32.8161042652681[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57883&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57883&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 R0.250968372521009
R-squared0.0629851240058438
Adjusted R-squared0.0471035159381463
F-TEST (value)3.96591602924345
F-TEST (DF numerator)1
F-TEST (DF denominator)59
p-value0.0510662732824697
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.74579163111317
Sum Squared Residuals32.8161042652681







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
15.46.8705439247289-1.47054392472889
25.46.93238960189883-1.53238960189883
35.67.02515811765375-1.42515811765375
45.76.80869824755895-1.10869824755895
55.86.746852570389-0.946852570389005
65.86.77777540897398-0.977775408973976
75.86.83962108614392-1.03962108614392
85.96.93238960189883-1.03238960189883
96.17.11792663340866-1.01792663340866
106.47.11792663340866-0.717926633408661
116.47.14884947199363-0.748849471993632
126.37.08700379482369-0.78700379482369
136.26.90146676331386-0.701466763313861
146.26.93238960189883-0.732389601898833
156.36.93238960189883-0.632389601898833
166.47.21069514916358-0.810695149163575
176.57.27254082633352-0.772540826333518
186.67.45807785784335-0.858077857843347
196.67.36530934208843-0.765309342088433
206.67.30346366491849-0.70346366491849
216.87.33438650350346-0.534386503503461
2277.30346366491849-0.303463664918490
237.27.36530934208843-0.165309342088432
247.37.30346366491849-0.00346366491848968
257.57.334386503503460.165613496496539
267.67.210695149163580.389304850836424
277.67.17977231057860.420227689421396
287.77.241617987748550.458382012251453
297.77.427155019258380.272844980741625
307.77.241617987748550.458382012251453
317.77.272540826333520.427459173666482
327.67.210695149163580.389304850836424
337.77.17977231057860.520227689421396
347.97.272540826333520.627459173666482
357.97.148849471993630.751150528006368
367.97.17977231057860.720227689421396
377.87.272540826333520.527459173666482
387.67.334386503503460.265613496496539
397.47.396232180673400.0037678193265967
4077.1797723105786-0.179772310578604
4176.963312440483800.0366875595161955
427.27.087003794823690.112996205176310
437.57.056080956238720.443919043761281
447.87.087003794823690.71299620517631
457.87.148849471993630.651150528006367
467.76.870543924728890.82945607527111
477.66.994235279068780.605764720931224
487.67.117926633408660.482073366591338
497.57.087003794823690.41299620517631
507.56.994235279068780.505764720931224
517.66.839621086143920.760378913856081
527.66.963312440483800.636687559516195
537.96.994235279068780.905764720931225
547.66.870543924728890.72945607527111
557.56.870543924728890.62945607527111
567.56.808698247558950.691301752441053
577.66.777775408973980.822224591026024
587.77.025158117653750.674841882346253
597.86.994235279068780.805764720931224
607.96.839621086143921.06037891385608
617.96.839621086143921.06037891385608

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 5.4 & 6.8705439247289 & -1.47054392472889 \tabularnewline
2 & 5.4 & 6.93238960189883 & -1.53238960189883 \tabularnewline
3 & 5.6 & 7.02515811765375 & -1.42515811765375 \tabularnewline
4 & 5.7 & 6.80869824755895 & -1.10869824755895 \tabularnewline
5 & 5.8 & 6.746852570389 & -0.946852570389005 \tabularnewline
6 & 5.8 & 6.77777540897398 & -0.977775408973976 \tabularnewline
7 & 5.8 & 6.83962108614392 & -1.03962108614392 \tabularnewline
8 & 5.9 & 6.93238960189883 & -1.03238960189883 \tabularnewline
9 & 6.1 & 7.11792663340866 & -1.01792663340866 \tabularnewline
10 & 6.4 & 7.11792663340866 & -0.717926633408661 \tabularnewline
11 & 6.4 & 7.14884947199363 & -0.748849471993632 \tabularnewline
12 & 6.3 & 7.08700379482369 & -0.78700379482369 \tabularnewline
13 & 6.2 & 6.90146676331386 & -0.701466763313861 \tabularnewline
14 & 6.2 & 6.93238960189883 & -0.732389601898833 \tabularnewline
15 & 6.3 & 6.93238960189883 & -0.632389601898833 \tabularnewline
16 & 6.4 & 7.21069514916358 & -0.810695149163575 \tabularnewline
17 & 6.5 & 7.27254082633352 & -0.772540826333518 \tabularnewline
18 & 6.6 & 7.45807785784335 & -0.858077857843347 \tabularnewline
19 & 6.6 & 7.36530934208843 & -0.765309342088433 \tabularnewline
20 & 6.6 & 7.30346366491849 & -0.70346366491849 \tabularnewline
21 & 6.8 & 7.33438650350346 & -0.534386503503461 \tabularnewline
22 & 7 & 7.30346366491849 & -0.303463664918490 \tabularnewline
23 & 7.2 & 7.36530934208843 & -0.165309342088432 \tabularnewline
24 & 7.3 & 7.30346366491849 & -0.00346366491848968 \tabularnewline
25 & 7.5 & 7.33438650350346 & 0.165613496496539 \tabularnewline
26 & 7.6 & 7.21069514916358 & 0.389304850836424 \tabularnewline
27 & 7.6 & 7.1797723105786 & 0.420227689421396 \tabularnewline
28 & 7.7 & 7.24161798774855 & 0.458382012251453 \tabularnewline
29 & 7.7 & 7.42715501925838 & 0.272844980741625 \tabularnewline
30 & 7.7 & 7.24161798774855 & 0.458382012251453 \tabularnewline
31 & 7.7 & 7.27254082633352 & 0.427459173666482 \tabularnewline
32 & 7.6 & 7.21069514916358 & 0.389304850836424 \tabularnewline
33 & 7.7 & 7.1797723105786 & 0.520227689421396 \tabularnewline
34 & 7.9 & 7.27254082633352 & 0.627459173666482 \tabularnewline
35 & 7.9 & 7.14884947199363 & 0.751150528006368 \tabularnewline
36 & 7.9 & 7.1797723105786 & 0.720227689421396 \tabularnewline
37 & 7.8 & 7.27254082633352 & 0.527459173666482 \tabularnewline
38 & 7.6 & 7.33438650350346 & 0.265613496496539 \tabularnewline
39 & 7.4 & 7.39623218067340 & 0.0037678193265967 \tabularnewline
40 & 7 & 7.1797723105786 & -0.179772310578604 \tabularnewline
41 & 7 & 6.96331244048380 & 0.0366875595161955 \tabularnewline
42 & 7.2 & 7.08700379482369 & 0.112996205176310 \tabularnewline
43 & 7.5 & 7.05608095623872 & 0.443919043761281 \tabularnewline
44 & 7.8 & 7.08700379482369 & 0.71299620517631 \tabularnewline
45 & 7.8 & 7.14884947199363 & 0.651150528006367 \tabularnewline
46 & 7.7 & 6.87054392472889 & 0.82945607527111 \tabularnewline
47 & 7.6 & 6.99423527906878 & 0.605764720931224 \tabularnewline
48 & 7.6 & 7.11792663340866 & 0.482073366591338 \tabularnewline
49 & 7.5 & 7.08700379482369 & 0.41299620517631 \tabularnewline
50 & 7.5 & 6.99423527906878 & 0.505764720931224 \tabularnewline
51 & 7.6 & 6.83962108614392 & 0.760378913856081 \tabularnewline
52 & 7.6 & 6.96331244048380 & 0.636687559516195 \tabularnewline
53 & 7.9 & 6.99423527906878 & 0.905764720931225 \tabularnewline
54 & 7.6 & 6.87054392472889 & 0.72945607527111 \tabularnewline
55 & 7.5 & 6.87054392472889 & 0.62945607527111 \tabularnewline
56 & 7.5 & 6.80869824755895 & 0.691301752441053 \tabularnewline
57 & 7.6 & 6.77777540897398 & 0.822224591026024 \tabularnewline
58 & 7.7 & 7.02515811765375 & 0.674841882346253 \tabularnewline
59 & 7.8 & 6.99423527906878 & 0.805764720931224 \tabularnewline
60 & 7.9 & 6.83962108614392 & 1.06037891385608 \tabularnewline
61 & 7.9 & 6.83962108614392 & 1.06037891385608 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57883&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]5.4[/C][C]6.8705439247289[/C][C]-1.47054392472889[/C][/ROW]
[ROW][C]2[/C][C]5.4[/C][C]6.93238960189883[/C][C]-1.53238960189883[/C][/ROW]
[ROW][C]3[/C][C]5.6[/C][C]7.02515811765375[/C][C]-1.42515811765375[/C][/ROW]
[ROW][C]4[/C][C]5.7[/C][C]6.80869824755895[/C][C]-1.10869824755895[/C][/ROW]
[ROW][C]5[/C][C]5.8[/C][C]6.746852570389[/C][C]-0.946852570389005[/C][/ROW]
[ROW][C]6[/C][C]5.8[/C][C]6.77777540897398[/C][C]-0.977775408973976[/C][/ROW]
[ROW][C]7[/C][C]5.8[/C][C]6.83962108614392[/C][C]-1.03962108614392[/C][/ROW]
[ROW][C]8[/C][C]5.9[/C][C]6.93238960189883[/C][C]-1.03238960189883[/C][/ROW]
[ROW][C]9[/C][C]6.1[/C][C]7.11792663340866[/C][C]-1.01792663340866[/C][/ROW]
[ROW][C]10[/C][C]6.4[/C][C]7.11792663340866[/C][C]-0.717926633408661[/C][/ROW]
[ROW][C]11[/C][C]6.4[/C][C]7.14884947199363[/C][C]-0.748849471993632[/C][/ROW]
[ROW][C]12[/C][C]6.3[/C][C]7.08700379482369[/C][C]-0.78700379482369[/C][/ROW]
[ROW][C]13[/C][C]6.2[/C][C]6.90146676331386[/C][C]-0.701466763313861[/C][/ROW]
[ROW][C]14[/C][C]6.2[/C][C]6.93238960189883[/C][C]-0.732389601898833[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]6.93238960189883[/C][C]-0.632389601898833[/C][/ROW]
[ROW][C]16[/C][C]6.4[/C][C]7.21069514916358[/C][C]-0.810695149163575[/C][/ROW]
[ROW][C]17[/C][C]6.5[/C][C]7.27254082633352[/C][C]-0.772540826333518[/C][/ROW]
[ROW][C]18[/C][C]6.6[/C][C]7.45807785784335[/C][C]-0.858077857843347[/C][/ROW]
[ROW][C]19[/C][C]6.6[/C][C]7.36530934208843[/C][C]-0.765309342088433[/C][/ROW]
[ROW][C]20[/C][C]6.6[/C][C]7.30346366491849[/C][C]-0.70346366491849[/C][/ROW]
[ROW][C]21[/C][C]6.8[/C][C]7.33438650350346[/C][C]-0.534386503503461[/C][/ROW]
[ROW][C]22[/C][C]7[/C][C]7.30346366491849[/C][C]-0.303463664918490[/C][/ROW]
[ROW][C]23[/C][C]7.2[/C][C]7.36530934208843[/C][C]-0.165309342088432[/C][/ROW]
[ROW][C]24[/C][C]7.3[/C][C]7.30346366491849[/C][C]-0.00346366491848968[/C][/ROW]
[ROW][C]25[/C][C]7.5[/C][C]7.33438650350346[/C][C]0.165613496496539[/C][/ROW]
[ROW][C]26[/C][C]7.6[/C][C]7.21069514916358[/C][C]0.389304850836424[/C][/ROW]
[ROW][C]27[/C][C]7.6[/C][C]7.1797723105786[/C][C]0.420227689421396[/C][/ROW]
[ROW][C]28[/C][C]7.7[/C][C]7.24161798774855[/C][C]0.458382012251453[/C][/ROW]
[ROW][C]29[/C][C]7.7[/C][C]7.42715501925838[/C][C]0.272844980741625[/C][/ROW]
[ROW][C]30[/C][C]7.7[/C][C]7.24161798774855[/C][C]0.458382012251453[/C][/ROW]
[ROW][C]31[/C][C]7.7[/C][C]7.27254082633352[/C][C]0.427459173666482[/C][/ROW]
[ROW][C]32[/C][C]7.6[/C][C]7.21069514916358[/C][C]0.389304850836424[/C][/ROW]
[ROW][C]33[/C][C]7.7[/C][C]7.1797723105786[/C][C]0.520227689421396[/C][/ROW]
[ROW][C]34[/C][C]7.9[/C][C]7.27254082633352[/C][C]0.627459173666482[/C][/ROW]
[ROW][C]35[/C][C]7.9[/C][C]7.14884947199363[/C][C]0.751150528006368[/C][/ROW]
[ROW][C]36[/C][C]7.9[/C][C]7.1797723105786[/C][C]0.720227689421396[/C][/ROW]
[ROW][C]37[/C][C]7.8[/C][C]7.27254082633352[/C][C]0.527459173666482[/C][/ROW]
[ROW][C]38[/C][C]7.6[/C][C]7.33438650350346[/C][C]0.265613496496539[/C][/ROW]
[ROW][C]39[/C][C]7.4[/C][C]7.39623218067340[/C][C]0.0037678193265967[/C][/ROW]
[ROW][C]40[/C][C]7[/C][C]7.1797723105786[/C][C]-0.179772310578604[/C][/ROW]
[ROW][C]41[/C][C]7[/C][C]6.96331244048380[/C][C]0.0366875595161955[/C][/ROW]
[ROW][C]42[/C][C]7.2[/C][C]7.08700379482369[/C][C]0.112996205176310[/C][/ROW]
[ROW][C]43[/C][C]7.5[/C][C]7.05608095623872[/C][C]0.443919043761281[/C][/ROW]
[ROW][C]44[/C][C]7.8[/C][C]7.08700379482369[/C][C]0.71299620517631[/C][/ROW]
[ROW][C]45[/C][C]7.8[/C][C]7.14884947199363[/C][C]0.651150528006367[/C][/ROW]
[ROW][C]46[/C][C]7.7[/C][C]6.87054392472889[/C][C]0.82945607527111[/C][/ROW]
[ROW][C]47[/C][C]7.6[/C][C]6.99423527906878[/C][C]0.605764720931224[/C][/ROW]
[ROW][C]48[/C][C]7.6[/C][C]7.11792663340866[/C][C]0.482073366591338[/C][/ROW]
[ROW][C]49[/C][C]7.5[/C][C]7.08700379482369[/C][C]0.41299620517631[/C][/ROW]
[ROW][C]50[/C][C]7.5[/C][C]6.99423527906878[/C][C]0.505764720931224[/C][/ROW]
[ROW][C]51[/C][C]7.6[/C][C]6.83962108614392[/C][C]0.760378913856081[/C][/ROW]
[ROW][C]52[/C][C]7.6[/C][C]6.96331244048380[/C][C]0.636687559516195[/C][/ROW]
[ROW][C]53[/C][C]7.9[/C][C]6.99423527906878[/C][C]0.905764720931225[/C][/ROW]
[ROW][C]54[/C][C]7.6[/C][C]6.87054392472889[/C][C]0.72945607527111[/C][/ROW]
[ROW][C]55[/C][C]7.5[/C][C]6.87054392472889[/C][C]0.62945607527111[/C][/ROW]
[ROW][C]56[/C][C]7.5[/C][C]6.80869824755895[/C][C]0.691301752441053[/C][/ROW]
[ROW][C]57[/C][C]7.6[/C][C]6.77777540897398[/C][C]0.822224591026024[/C][/ROW]
[ROW][C]58[/C][C]7.7[/C][C]7.02515811765375[/C][C]0.674841882346253[/C][/ROW]
[ROW][C]59[/C][C]7.8[/C][C]6.99423527906878[/C][C]0.805764720931224[/C][/ROW]
[ROW][C]60[/C][C]7.9[/C][C]6.83962108614392[/C][C]1.06037891385608[/C][/ROW]
[ROW][C]61[/C][C]7.9[/C][C]6.83962108614392[/C][C]1.06037891385608[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57883&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57883&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
15.46.8705439247289-1.47054392472889
25.46.93238960189883-1.53238960189883
35.67.02515811765375-1.42515811765375
45.76.80869824755895-1.10869824755895
55.86.746852570389-0.946852570389005
65.86.77777540897398-0.977775408973976
75.86.83962108614392-1.03962108614392
85.96.93238960189883-1.03238960189883
96.17.11792663340866-1.01792663340866
106.47.11792663340866-0.717926633408661
116.47.14884947199363-0.748849471993632
126.37.08700379482369-0.78700379482369
136.26.90146676331386-0.701466763313861
146.26.93238960189883-0.732389601898833
156.36.93238960189883-0.632389601898833
166.47.21069514916358-0.810695149163575
176.57.27254082633352-0.772540826333518
186.67.45807785784335-0.858077857843347
196.67.36530934208843-0.765309342088433
206.67.30346366491849-0.70346366491849
216.87.33438650350346-0.534386503503461
2277.30346366491849-0.303463664918490
237.27.36530934208843-0.165309342088432
247.37.30346366491849-0.00346366491848968
257.57.334386503503460.165613496496539
267.67.210695149163580.389304850836424
277.67.17977231057860.420227689421396
287.77.241617987748550.458382012251453
297.77.427155019258380.272844980741625
307.77.241617987748550.458382012251453
317.77.272540826333520.427459173666482
327.67.210695149163580.389304850836424
337.77.17977231057860.520227689421396
347.97.272540826333520.627459173666482
357.97.148849471993630.751150528006368
367.97.17977231057860.720227689421396
377.87.272540826333520.527459173666482
387.67.334386503503460.265613496496539
397.47.396232180673400.0037678193265967
4077.1797723105786-0.179772310578604
4176.963312440483800.0366875595161955
427.27.087003794823690.112996205176310
437.57.056080956238720.443919043761281
447.87.087003794823690.71299620517631
457.87.148849471993630.651150528006367
467.76.870543924728890.82945607527111
477.66.994235279068780.605764720931224
487.67.117926633408660.482073366591338
497.57.087003794823690.41299620517631
507.56.994235279068780.505764720931224
517.66.839621086143920.760378913856081
527.66.963312440483800.636687559516195
537.96.994235279068780.905764720931225
547.66.870543924728890.72945607527111
557.56.870543924728890.62945607527111
567.56.808698247558950.691301752441053
577.66.777775408973980.822224591026024
587.77.025158117653750.674841882346253
597.86.994235279068780.805764720931224
607.96.839621086143921.06037891385608
617.96.839621086143921.06037891385608







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.02509644735547470.05019289471094940.974903552644525
60.008088075162444730.01617615032488950.991911924837555
70.003622818242827410.007245636485654820.996377181757173
80.005751338185515050.01150267637103010.994248661814485
90.01437881689600790.02875763379201570.985621183103992
100.02887029079067090.05774058158134170.97112970920933
110.02583614209045820.05167228418091650.974163857909542
120.02227638196094240.04455276392188470.977723618039058
130.03938168612193760.07876337224387530.960618313878062
140.06680086416618930.1336017283323790.93319913583381
150.1488756683085480.2977513366170960.851124331691452
160.1763969265840690.3527938531681390.82360307341593
170.2032641538082270.4065283076164540.796735846191773
180.2160577739582750.4321155479165490.783942226041725
190.2672480082475530.5344960164951070.732751991752447
200.410929280924710.821858561849420.58907071907529
210.5739230013192330.8521539973615340.426076998680767
220.77285244329040.45429511341920.2271475567096
230.8755095361593040.2489809276813920.124490463840696
240.9542129499234420.0915741001531170.0457870500765585
250.9824213268036770.03515734639264630.0175786731963232
260.997491702604080.005016594791839890.00250829739591994
270.9994936705156970.001012658968606150.000506329484303077
280.9997996226692840.0004007546614329920.000200377330716496
290.9997079608626450.0005840782747108980.000292039137355449
300.9998093152563340.0003813694873329470.000190684743666474
310.9998172347861960.0003655304276083740.000182765213804187
320.9998157823999260.0003684352001482940.000184217600074147
330.9998530657939840.0002938684120328140.000146934206016407
340.9999033033225280.0001933933549434829.6696677471741e-05
350.9999622887057247.54225885522416e-053.77112942761208e-05
360.9999815164372073.69671255865273e-051.84835627932637e-05
370.9999816273056863.6745388627857e-051.83726943139285e-05
380.999963967569057.20648618987994e-053.60324309493997e-05
390.9999128323454870.0001743353090266988.7167654513349e-05
400.9999644670914537.10658170942894e-053.55329085471447e-05
410.9999986474732452.70505351066890e-061.35252675533445e-06
420.999999834770463.30459081437200e-071.65229540718600e-07
430.9999997769183934.46163214019253e-072.23081607009627e-07
440.999999623225367.53549279051757e-073.76774639525879e-07
450.999999295294891.40941022172089e-067.04705110860446e-07
460.9999987212585122.55748297697487e-061.27874148848743e-06
470.9999961782879397.64342412227562e-063.82171206113781e-06
480.999986341599042.7316801918806e-051.3658400959403e-05
490.9999716263013245.67473973513371e-052.83736986756686e-05
500.9999576179407498.47641185018995e-054.23820592509497e-05
510.9998688158006770.0002623683986461180.000131184199323059
520.9996475903700660.0007048192598684670.000352409629934233
530.9990331391453770.001933721709246130.000966860854623063
540.9966462458150130.006707508369973780.00335375418498689
550.9927479367836410.01450412643271720.00725206321635859
560.987681598895540.02463680220891970.0123184011044599

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.0250964473554747 & 0.0501928947109494 & 0.974903552644525 \tabularnewline
6 & 0.00808807516244473 & 0.0161761503248895 & 0.991911924837555 \tabularnewline
7 & 0.00362281824282741 & 0.00724563648565482 & 0.996377181757173 \tabularnewline
8 & 0.00575133818551505 & 0.0115026763710301 & 0.994248661814485 \tabularnewline
9 & 0.0143788168960079 & 0.0287576337920157 & 0.985621183103992 \tabularnewline
10 & 0.0288702907906709 & 0.0577405815813417 & 0.97112970920933 \tabularnewline
11 & 0.0258361420904582 & 0.0516722841809165 & 0.974163857909542 \tabularnewline
12 & 0.0222763819609424 & 0.0445527639218847 & 0.977723618039058 \tabularnewline
13 & 0.0393816861219376 & 0.0787633722438753 & 0.960618313878062 \tabularnewline
14 & 0.0668008641661893 & 0.133601728332379 & 0.93319913583381 \tabularnewline
15 & 0.148875668308548 & 0.297751336617096 & 0.851124331691452 \tabularnewline
16 & 0.176396926584069 & 0.352793853168139 & 0.82360307341593 \tabularnewline
17 & 0.203264153808227 & 0.406528307616454 & 0.796735846191773 \tabularnewline
18 & 0.216057773958275 & 0.432115547916549 & 0.783942226041725 \tabularnewline
19 & 0.267248008247553 & 0.534496016495107 & 0.732751991752447 \tabularnewline
20 & 0.41092928092471 & 0.82185856184942 & 0.58907071907529 \tabularnewline
21 & 0.573923001319233 & 0.852153997361534 & 0.426076998680767 \tabularnewline
22 & 0.7728524432904 & 0.4542951134192 & 0.2271475567096 \tabularnewline
23 & 0.875509536159304 & 0.248980927681392 & 0.124490463840696 \tabularnewline
24 & 0.954212949923442 & 0.091574100153117 & 0.0457870500765585 \tabularnewline
25 & 0.982421326803677 & 0.0351573463926463 & 0.0175786731963232 \tabularnewline
26 & 0.99749170260408 & 0.00501659479183989 & 0.00250829739591994 \tabularnewline
27 & 0.999493670515697 & 0.00101265896860615 & 0.000506329484303077 \tabularnewline
28 & 0.999799622669284 & 0.000400754661432992 & 0.000200377330716496 \tabularnewline
29 & 0.999707960862645 & 0.000584078274710898 & 0.000292039137355449 \tabularnewline
30 & 0.999809315256334 & 0.000381369487332947 & 0.000190684743666474 \tabularnewline
31 & 0.999817234786196 & 0.000365530427608374 & 0.000182765213804187 \tabularnewline
32 & 0.999815782399926 & 0.000368435200148294 & 0.000184217600074147 \tabularnewline
33 & 0.999853065793984 & 0.000293868412032814 & 0.000146934206016407 \tabularnewline
34 & 0.999903303322528 & 0.000193393354943482 & 9.6696677471741e-05 \tabularnewline
35 & 0.999962288705724 & 7.54225885522416e-05 & 3.77112942761208e-05 \tabularnewline
36 & 0.999981516437207 & 3.69671255865273e-05 & 1.84835627932637e-05 \tabularnewline
37 & 0.999981627305686 & 3.6745388627857e-05 & 1.83726943139285e-05 \tabularnewline
38 & 0.99996396756905 & 7.20648618987994e-05 & 3.60324309493997e-05 \tabularnewline
39 & 0.999912832345487 & 0.000174335309026698 & 8.7167654513349e-05 \tabularnewline
40 & 0.999964467091453 & 7.10658170942894e-05 & 3.55329085471447e-05 \tabularnewline
41 & 0.999998647473245 & 2.70505351066890e-06 & 1.35252675533445e-06 \tabularnewline
42 & 0.99999983477046 & 3.30459081437200e-07 & 1.65229540718600e-07 \tabularnewline
43 & 0.999999776918393 & 4.46163214019253e-07 & 2.23081607009627e-07 \tabularnewline
44 & 0.99999962322536 & 7.53549279051757e-07 & 3.76774639525879e-07 \tabularnewline
45 & 0.99999929529489 & 1.40941022172089e-06 & 7.04705110860446e-07 \tabularnewline
46 & 0.999998721258512 & 2.55748297697487e-06 & 1.27874148848743e-06 \tabularnewline
47 & 0.999996178287939 & 7.64342412227562e-06 & 3.82171206113781e-06 \tabularnewline
48 & 0.99998634159904 & 2.7316801918806e-05 & 1.3658400959403e-05 \tabularnewline
49 & 0.999971626301324 & 5.67473973513371e-05 & 2.83736986756686e-05 \tabularnewline
50 & 0.999957617940749 & 8.47641185018995e-05 & 4.23820592509497e-05 \tabularnewline
51 & 0.999868815800677 & 0.000262368398646118 & 0.000131184199323059 \tabularnewline
52 & 0.999647590370066 & 0.000704819259868467 & 0.000352409629934233 \tabularnewline
53 & 0.999033139145377 & 0.00193372170924613 & 0.000966860854623063 \tabularnewline
54 & 0.996646245815013 & 0.00670750836997378 & 0.00335375418498689 \tabularnewline
55 & 0.992747936783641 & 0.0145041264327172 & 0.00725206321635859 \tabularnewline
56 & 0.98768159889554 & 0.0246368022089197 & 0.0123184011044599 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57883&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]0.0250964473554747[/C][C]0.0501928947109494[/C][C]0.974903552644525[/C][/ROW]
[ROW][C]6[/C][C]0.00808807516244473[/C][C]0.0161761503248895[/C][C]0.991911924837555[/C][/ROW]
[ROW][C]7[/C][C]0.00362281824282741[/C][C]0.00724563648565482[/C][C]0.996377181757173[/C][/ROW]
[ROW][C]8[/C][C]0.00575133818551505[/C][C]0.0115026763710301[/C][C]0.994248661814485[/C][/ROW]
[ROW][C]9[/C][C]0.0143788168960079[/C][C]0.0287576337920157[/C][C]0.985621183103992[/C][/ROW]
[ROW][C]10[/C][C]0.0288702907906709[/C][C]0.0577405815813417[/C][C]0.97112970920933[/C][/ROW]
[ROW][C]11[/C][C]0.0258361420904582[/C][C]0.0516722841809165[/C][C]0.974163857909542[/C][/ROW]
[ROW][C]12[/C][C]0.0222763819609424[/C][C]0.0445527639218847[/C][C]0.977723618039058[/C][/ROW]
[ROW][C]13[/C][C]0.0393816861219376[/C][C]0.0787633722438753[/C][C]0.960618313878062[/C][/ROW]
[ROW][C]14[/C][C]0.0668008641661893[/C][C]0.133601728332379[/C][C]0.93319913583381[/C][/ROW]
[ROW][C]15[/C][C]0.148875668308548[/C][C]0.297751336617096[/C][C]0.851124331691452[/C][/ROW]
[ROW][C]16[/C][C]0.176396926584069[/C][C]0.352793853168139[/C][C]0.82360307341593[/C][/ROW]
[ROW][C]17[/C][C]0.203264153808227[/C][C]0.406528307616454[/C][C]0.796735846191773[/C][/ROW]
[ROW][C]18[/C][C]0.216057773958275[/C][C]0.432115547916549[/C][C]0.783942226041725[/C][/ROW]
[ROW][C]19[/C][C]0.267248008247553[/C][C]0.534496016495107[/C][C]0.732751991752447[/C][/ROW]
[ROW][C]20[/C][C]0.41092928092471[/C][C]0.82185856184942[/C][C]0.58907071907529[/C][/ROW]
[ROW][C]21[/C][C]0.573923001319233[/C][C]0.852153997361534[/C][C]0.426076998680767[/C][/ROW]
[ROW][C]22[/C][C]0.7728524432904[/C][C]0.4542951134192[/C][C]0.2271475567096[/C][/ROW]
[ROW][C]23[/C][C]0.875509536159304[/C][C]0.248980927681392[/C][C]0.124490463840696[/C][/ROW]
[ROW][C]24[/C][C]0.954212949923442[/C][C]0.091574100153117[/C][C]0.0457870500765585[/C][/ROW]
[ROW][C]25[/C][C]0.982421326803677[/C][C]0.0351573463926463[/C][C]0.0175786731963232[/C][/ROW]
[ROW][C]26[/C][C]0.99749170260408[/C][C]0.00501659479183989[/C][C]0.00250829739591994[/C][/ROW]
[ROW][C]27[/C][C]0.999493670515697[/C][C]0.00101265896860615[/C][C]0.000506329484303077[/C][/ROW]
[ROW][C]28[/C][C]0.999799622669284[/C][C]0.000400754661432992[/C][C]0.000200377330716496[/C][/ROW]
[ROW][C]29[/C][C]0.999707960862645[/C][C]0.000584078274710898[/C][C]0.000292039137355449[/C][/ROW]
[ROW][C]30[/C][C]0.999809315256334[/C][C]0.000381369487332947[/C][C]0.000190684743666474[/C][/ROW]
[ROW][C]31[/C][C]0.999817234786196[/C][C]0.000365530427608374[/C][C]0.000182765213804187[/C][/ROW]
[ROW][C]32[/C][C]0.999815782399926[/C][C]0.000368435200148294[/C][C]0.000184217600074147[/C][/ROW]
[ROW][C]33[/C][C]0.999853065793984[/C][C]0.000293868412032814[/C][C]0.000146934206016407[/C][/ROW]
[ROW][C]34[/C][C]0.999903303322528[/C][C]0.000193393354943482[/C][C]9.6696677471741e-05[/C][/ROW]
[ROW][C]35[/C][C]0.999962288705724[/C][C]7.54225885522416e-05[/C][C]3.77112942761208e-05[/C][/ROW]
[ROW][C]36[/C][C]0.999981516437207[/C][C]3.69671255865273e-05[/C][C]1.84835627932637e-05[/C][/ROW]
[ROW][C]37[/C][C]0.999981627305686[/C][C]3.6745388627857e-05[/C][C]1.83726943139285e-05[/C][/ROW]
[ROW][C]38[/C][C]0.99996396756905[/C][C]7.20648618987994e-05[/C][C]3.60324309493997e-05[/C][/ROW]
[ROW][C]39[/C][C]0.999912832345487[/C][C]0.000174335309026698[/C][C]8.7167654513349e-05[/C][/ROW]
[ROW][C]40[/C][C]0.999964467091453[/C][C]7.10658170942894e-05[/C][C]3.55329085471447e-05[/C][/ROW]
[ROW][C]41[/C][C]0.999998647473245[/C][C]2.70505351066890e-06[/C][C]1.35252675533445e-06[/C][/ROW]
[ROW][C]42[/C][C]0.99999983477046[/C][C]3.30459081437200e-07[/C][C]1.65229540718600e-07[/C][/ROW]
[ROW][C]43[/C][C]0.999999776918393[/C][C]4.46163214019253e-07[/C][C]2.23081607009627e-07[/C][/ROW]
[ROW][C]44[/C][C]0.99999962322536[/C][C]7.53549279051757e-07[/C][C]3.76774639525879e-07[/C][/ROW]
[ROW][C]45[/C][C]0.99999929529489[/C][C]1.40941022172089e-06[/C][C]7.04705110860446e-07[/C][/ROW]
[ROW][C]46[/C][C]0.999998721258512[/C][C]2.55748297697487e-06[/C][C]1.27874148848743e-06[/C][/ROW]
[ROW][C]47[/C][C]0.999996178287939[/C][C]7.64342412227562e-06[/C][C]3.82171206113781e-06[/C][/ROW]
[ROW][C]48[/C][C]0.99998634159904[/C][C]2.7316801918806e-05[/C][C]1.3658400959403e-05[/C][/ROW]
[ROW][C]49[/C][C]0.999971626301324[/C][C]5.67473973513371e-05[/C][C]2.83736986756686e-05[/C][/ROW]
[ROW][C]50[/C][C]0.999957617940749[/C][C]8.47641185018995e-05[/C][C]4.23820592509497e-05[/C][/ROW]
[ROW][C]51[/C][C]0.999868815800677[/C][C]0.000262368398646118[/C][C]0.000131184199323059[/C][/ROW]
[ROW][C]52[/C][C]0.999647590370066[/C][C]0.000704819259868467[/C][C]0.000352409629934233[/C][/ROW]
[ROW][C]53[/C][C]0.999033139145377[/C][C]0.00193372170924613[/C][C]0.000966860854623063[/C][/ROW]
[ROW][C]54[/C][C]0.996646245815013[/C][C]0.00670750836997378[/C][C]0.00335375418498689[/C][/ROW]
[ROW][C]55[/C][C]0.992747936783641[/C][C]0.0145041264327172[/C][C]0.00725206321635859[/C][/ROW]
[ROW][C]56[/C][C]0.98768159889554[/C][C]0.0246368022089197[/C][C]0.0123184011044599[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57883&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57883&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
50.02509644735547470.05019289471094940.974903552644525
60.008088075162444730.01617615032488950.991911924837555
70.003622818242827410.007245636485654820.996377181757173
80.005751338185515050.01150267637103010.994248661814485
90.01437881689600790.02875763379201570.985621183103992
100.02887029079067090.05774058158134170.97112970920933
110.02583614209045820.05167228418091650.974163857909542
120.02227638196094240.04455276392188470.977723618039058
130.03938168612193760.07876337224387530.960618313878062
140.06680086416618930.1336017283323790.93319913583381
150.1488756683085480.2977513366170960.851124331691452
160.1763969265840690.3527938531681390.82360307341593
170.2032641538082270.4065283076164540.796735846191773
180.2160577739582750.4321155479165490.783942226041725
190.2672480082475530.5344960164951070.732751991752447
200.410929280924710.821858561849420.58907071907529
210.5739230013192330.8521539973615340.426076998680767
220.77285244329040.45429511341920.2271475567096
230.8755095361593040.2489809276813920.124490463840696
240.9542129499234420.0915741001531170.0457870500765585
250.9824213268036770.03515734639264630.0175786731963232
260.997491702604080.005016594791839890.00250829739591994
270.9994936705156970.001012658968606150.000506329484303077
280.9997996226692840.0004007546614329920.000200377330716496
290.9997079608626450.0005840782747108980.000292039137355449
300.9998093152563340.0003813694873329470.000190684743666474
310.9998172347861960.0003655304276083740.000182765213804187
320.9998157823999260.0003684352001482940.000184217600074147
330.9998530657939840.0002938684120328140.000146934206016407
340.9999033033225280.0001933933549434829.6696677471741e-05
350.9999622887057247.54225885522416e-053.77112942761208e-05
360.9999815164372073.69671255865273e-051.84835627932637e-05
370.9999816273056863.6745388627857e-051.83726943139285e-05
380.999963967569057.20648618987994e-053.60324309493997e-05
390.9999128323454870.0001743353090266988.7167654513349e-05
400.9999644670914537.10658170942894e-053.55329085471447e-05
410.9999986474732452.70505351066890e-061.35252675533445e-06
420.999999834770463.30459081437200e-071.65229540718600e-07
430.9999997769183934.46163214019253e-072.23081607009627e-07
440.999999623225367.53549279051757e-073.76774639525879e-07
450.999999295294891.40941022172089e-067.04705110860446e-07
460.9999987212585122.55748297697487e-061.27874148848743e-06
470.9999961782879397.64342412227562e-063.82171206113781e-06
480.999986341599042.7316801918806e-051.3658400959403e-05
490.9999716263013245.67473973513371e-052.83736986756686e-05
500.9999576179407498.47641185018995e-054.23820592509497e-05
510.9998688158006770.0002623683986461180.000131184199323059
520.9996475903700660.0007048192598684670.000352409629934233
530.9990331391453770.001933721709246130.000966860854623063
540.9966462458150130.006707508369973780.00335375418498689
550.9927479367836410.01450412643271720.00725206321635859
560.987681598895540.02463680220891970.0123184011044599







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level300.576923076923077NOK
5% type I error level370.711538461538462NOK
10% type I error level420.807692307692308NOK

\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 & 30 & 0.576923076923077 & NOK \tabularnewline
5% type I error level & 37 & 0.711538461538462 & NOK \tabularnewline
10% type I error level & 42 & 0.807692307692308 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57883&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]30[/C][C]0.576923076923077[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]37[/C][C]0.711538461538462[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]42[/C][C]0.807692307692308[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57883&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57883&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 level300.576923076923077NOK
5% type I error level370.711538461538462NOK
10% type I error level420.807692307692308NOK



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