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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 10:13:36 -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/t1258737311omo16ikesyoeyu0.htm/, Retrieved Thu, 28 Mar 2024 10:21:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58341, Retrieved Thu, 28 Mar 2024 10:21:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact114
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2009-11-20 17:13:36] [71596e6a53ccce532e52aaf6113616ef] [Current]
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Dataseries X:
3	0
3.21	0
3.37	0
3.51	0
3.75	0
4.11	0
4.25	0
4.25	0
4.5	0
4.7	0
4.75	0
4.75	0
4.75	0
4.75	0
4.75	0
4.75	0
4.58	0
4.5	0
4.5	0
4.49	0
4.03	0
3.75	0
3.39	0
3.25	0
3.25	0
3.25	0
3.25	0
3.25	0
3.25	0
3.25	0
3.25	0
3.25	0
3.25	0
3.25	0
3.25	0
2.85	0
2.75	0
2.75	0
2.55	0
2.5	0
2.5	0
2.1	0
2	0
2	0
2	0
2	0
2	0
2	0
2	0
2	0
2	0
2	0
2	0
2	0
2	0
2	0
2	0
2	0
2	0
2	0
2	0
2	0
2	0
2	0
2	0
2	0
2	0
2	0
2	0
2	0
2	0
2.21	0
2.25	0
2.25	0
2.45	0
2.5	0
2.5	0
2.64	0
2.75	0
2.93	0
3	0
3.17	0
3.25	0
3.39	0
3.5	0
3.5	0
3.65	0
3.75	0
3.75	0
3.9	0
4	0
4	0
4	0
4	0
4	0
4	0
4	0
4	0
4	0
4	0
4	0
4	0
4.18	0
4.25	0
4.25	0
3.97	1
3.42	1
2.75	1
2.31	1
2	1
1.66	1
1.31	1
1.09	1
1	1
1	1
1	1
1	1
1	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58341&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58341&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58341&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Rente[t] = + 3.17110895987953 -1.33998063891578Crisis[t] -0.056110895987954M1[t] -0.066110895987953M2[t] -0.0691108959879531M3[t] -0.0801108959879526M4[t] -0.0951108959879533M5[t] -0.0871108959879531M6[t] -0.0441108959879533M7[t] -0.0201108959879536M8[t] -0.0341108959879521M9[t] + 0.0808871679036255M10[t] + 0.0955555555555557M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Rente[t] =  +  3.17110895987953 -1.33998063891578Crisis[t] -0.056110895987954M1[t] -0.066110895987953M2[t] -0.0691108959879531M3[t] -0.0801108959879526M4[t] -0.0951108959879533M5[t] -0.0871108959879531M6[t] -0.0441108959879533M7[t] -0.0201108959879536M8[t] -0.0341108959879521M9[t] +  0.0808871679036255M10[t] +  0.0955555555555557M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58341&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Rente[t] =  +  3.17110895987953 -1.33998063891578Crisis[t] -0.056110895987954M1[t] -0.066110895987953M2[t] -0.0691108959879531M3[t] -0.0801108959879526M4[t] -0.0951108959879533M5[t] -0.0871108959879531M6[t] -0.0441108959879533M7[t] -0.0201108959879536M8[t] -0.0341108959879521M9[t] +  0.0808871679036255M10[t] +  0.0955555555555557M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58341&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58341&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
Rente[t] = + 3.17110895987953 -1.33998063891578Crisis[t] -0.056110895987954M1[t] -0.066110895987953M2[t] -0.0691108959879531M3[t] -0.0801108959879526M4[t] -0.0951108959879533M5[t] -0.0871108959879531M6[t] -0.0441108959879533M7[t] -0.0201108959879536M8[t] -0.0341108959879521M9[t] + 0.0808871679036255M10[t] + 0.0955555555555557M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.171108959879530.3329549.524200
Crisis-1.339980638915780.293417-4.56681.4e-057e-06
M1-0.0561108959879540.456752-0.12280.9024630.451231
M2-0.0661108959879530.456752-0.14470.8851930.442596
M3-0.06911089598795310.456752-0.15130.8800220.440011
M4-0.08011089598795260.456752-0.17540.8611090.430554
M5-0.09511089598795330.456752-0.20820.835450.417725
M6-0.08711089598795310.456752-0.19070.8491150.424557
M7-0.04411089598795330.456752-0.09660.9232480.461624
M8-0.02011089598795360.456752-0.0440.9649640.482482
M9-0.03411089598795210.456752-0.07470.940610.470305
M100.08088716790362550.4574840.17680.8599990.43
M110.09555555555555570.4686050.20390.8388150.419407

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 3.17110895987953 & 0.332954 & 9.5242 & 0 & 0 \tabularnewline
Crisis & -1.33998063891578 & 0.293417 & -4.5668 & 1.4e-05 & 7e-06 \tabularnewline
M1 & -0.056110895987954 & 0.456752 & -0.1228 & 0.902463 & 0.451231 \tabularnewline
M2 & -0.066110895987953 & 0.456752 & -0.1447 & 0.885193 & 0.442596 \tabularnewline
M3 & -0.0691108959879531 & 0.456752 & -0.1513 & 0.880022 & 0.440011 \tabularnewline
M4 & -0.0801108959879526 & 0.456752 & -0.1754 & 0.861109 & 0.430554 \tabularnewline
M5 & -0.0951108959879533 & 0.456752 & -0.2082 & 0.83545 & 0.417725 \tabularnewline
M6 & -0.0871108959879531 & 0.456752 & -0.1907 & 0.849115 & 0.424557 \tabularnewline
M7 & -0.0441108959879533 & 0.456752 & -0.0966 & 0.923248 & 0.461624 \tabularnewline
M8 & -0.0201108959879536 & 0.456752 & -0.044 & 0.964964 & 0.482482 \tabularnewline
M9 & -0.0341108959879521 & 0.456752 & -0.0747 & 0.94061 & 0.470305 \tabularnewline
M10 & 0.0808871679036255 & 0.457484 & 0.1768 & 0.859999 & 0.43 \tabularnewline
M11 & 0.0955555555555557 & 0.468605 & 0.2039 & 0.838815 & 0.419407 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58341&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]3.17110895987953[/C][C]0.332954[/C][C]9.5242[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Crisis[/C][C]-1.33998063891578[/C][C]0.293417[/C][C]-4.5668[/C][C]1.4e-05[/C][C]7e-06[/C][/ROW]
[ROW][C]M1[/C][C]-0.056110895987954[/C][C]0.456752[/C][C]-0.1228[/C][C]0.902463[/C][C]0.451231[/C][/ROW]
[ROW][C]M2[/C][C]-0.066110895987953[/C][C]0.456752[/C][C]-0.1447[/C][C]0.885193[/C][C]0.442596[/C][/ROW]
[ROW][C]M3[/C][C]-0.0691108959879531[/C][C]0.456752[/C][C]-0.1513[/C][C]0.880022[/C][C]0.440011[/C][/ROW]
[ROW][C]M4[/C][C]-0.0801108959879526[/C][C]0.456752[/C][C]-0.1754[/C][C]0.861109[/C][C]0.430554[/C][/ROW]
[ROW][C]M5[/C][C]-0.0951108959879533[/C][C]0.456752[/C][C]-0.2082[/C][C]0.83545[/C][C]0.417725[/C][/ROW]
[ROW][C]M6[/C][C]-0.0871108959879531[/C][C]0.456752[/C][C]-0.1907[/C][C]0.849115[/C][C]0.424557[/C][/ROW]
[ROW][C]M7[/C][C]-0.0441108959879533[/C][C]0.456752[/C][C]-0.0966[/C][C]0.923248[/C][C]0.461624[/C][/ROW]
[ROW][C]M8[/C][C]-0.0201108959879536[/C][C]0.456752[/C][C]-0.044[/C][C]0.964964[/C][C]0.482482[/C][/ROW]
[ROW][C]M9[/C][C]-0.0341108959879521[/C][C]0.456752[/C][C]-0.0747[/C][C]0.94061[/C][C]0.470305[/C][/ROW]
[ROW][C]M10[/C][C]0.0808871679036255[/C][C]0.457484[/C][C]0.1768[/C][C]0.859999[/C][C]0.43[/C][/ROW]
[ROW][C]M11[/C][C]0.0955555555555557[/C][C]0.468605[/C][C]0.2039[/C][C]0.838815[/C][C]0.419407[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58341&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58341&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.171108959879530.3329549.524200
Crisis-1.339980638915780.293417-4.56681.4e-057e-06
M1-0.0561108959879540.456752-0.12280.9024630.451231
M2-0.0661108959879530.456752-0.14470.8851930.442596
M3-0.06911089598795310.456752-0.15130.8800220.440011
M4-0.08011089598795260.456752-0.17540.8611090.430554
M5-0.09511089598795330.456752-0.20820.835450.417725
M6-0.08711089598795310.456752-0.19070.8491150.424557
M7-0.04411089598795330.456752-0.09660.9232480.461624
M8-0.02011089598795360.456752-0.0440.9649640.482482
M9-0.03411089598795210.456752-0.07470.940610.470305
M100.08088716790362550.4574840.17680.8599990.43
M110.09555555555555570.4686050.20390.8388150.419407







Multiple Linear Regression - Regression Statistics
Multiple R0.408922480909892
R-squared0.167217595393501
Adjusted R-squared0.0720424634384724
F-TEST (value)1.75694629425377
F-TEST (DF numerator)12
F-TEST (DF denominator)105
p-value0.0651354955774184
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.994062319041879
Sum Squared Residuals103.756788884586

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.408922480909892 \tabularnewline
R-squared & 0.167217595393501 \tabularnewline
Adjusted R-squared & 0.0720424634384724 \tabularnewline
F-TEST (value) & 1.75694629425377 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 105 \tabularnewline
p-value & 0.0651354955774184 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.994062319041879 \tabularnewline
Sum Squared Residuals & 103.756788884586 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58341&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.408922480909892[/C][/ROW]
[ROW][C]R-squared[/C][C]0.167217595393501[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0720424634384724[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.75694629425377[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]105[/C][/ROW]
[ROW][C]p-value[/C][C]0.0651354955774184[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.994062319041879[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]103.756788884586[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58341&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58341&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 R0.408922480909892
R-squared0.167217595393501
Adjusted R-squared0.0720424634384724
F-TEST (value)1.75694629425377
F-TEST (DF numerator)12
F-TEST (DF denominator)105
p-value0.0651354955774184
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.994062319041879
Sum Squared Residuals103.756788884586







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
133.11499806389159-0.114998063891585
23.213.104998063891580.105001936108422
33.373.101998063891580.268001936108422
43.513.090998063891580.419001936108422
53.753.075998063891580.674001936108424
64.113.083998063891581.02600193610842
74.253.126998063891581.12300193610842
84.253.150998063891581.09900193610842
94.53.136998063891581.36300193610842
104.73.251996127783151.44800387221685
114.753.266664515435091.48333548456491
124.753.171108959879531.57889104012047
134.753.114998063891581.63500193610842
144.753.104998063891581.64500193610842
154.753.101998063891581.64800193610842
164.753.090998063891581.65900193610842
174.583.075998063891581.50400193610842
184.53.083998063891581.41600193610842
194.53.126998063891581.37300193610842
204.493.150998063891581.33900193610842
214.033.136998063891580.893001936108422
223.753.251996127783160.498003872216843
233.393.266664515435090.123335484564914
243.253.171108959879530.0788910401204689
253.253.114998063891580.135001936108423
263.253.104998063891580.145001936108422
273.253.101998063891580.148001936108421
283.253.090998063891580.159001936108422
293.253.075998063891580.174001936108422
303.253.083998063891580.166001936108422
313.253.126998063891580.123001936108422
323.253.150998063891580.0990019361084223
333.253.136998063891580.113001936108422
343.253.25199612778316-0.00199612778315666
353.253.26666451543509-0.0166645154350859
362.853.17110895987953-0.321108959879531
372.753.11499806389158-0.364998063891577
382.753.10499806389158-0.354998063891578
392.553.10199806389158-0.551998063891579
402.53.09099806389158-0.590998063891578
412.53.07599806389158-0.575998063891578
422.13.08399806389158-0.983998063891578
4323.12699806389158-1.12699806389158
4423.15099806389158-1.15099806389158
4523.13699806389158-1.13699806389158
4623.25199612778316-1.25199612778316
4723.26666451543509-1.26666451543509
4823.17110895987953-1.17110895987953
4923.11499806389158-1.11499806389158
5023.10499806389158-1.10499806389158
5123.10199806389158-1.10199806389158
5223.09099806389158-1.09099806389158
5323.07599806389158-1.07599806389158
5423.08399806389158-1.08399806389158
5523.12699806389158-1.12699806389158
5623.15099806389158-1.15099806389158
5723.13699806389158-1.13699806389158
5823.25199612778316-1.25199612778316
5923.26666451543509-1.26666451543509
6023.17110895987953-1.17110895987953
6123.11499806389158-1.11499806389158
6223.10499806389158-1.10499806389158
6323.10199806389158-1.10199806389158
6423.09099806389158-1.09099806389158
6523.07599806389158-1.07599806389158
6623.08399806389158-1.08399806389158
6723.12699806389158-1.12699806389158
6823.15099806389158-1.15099806389158
6923.13699806389158-1.13699806389158
7023.25199612778316-1.25199612778316
7123.26666451543509-1.26666451543509
722.213.17110895987953-0.961108959879532
732.253.11499806389158-0.864998063891577
742.253.10499806389158-0.854998063891578
752.453.10199806389158-0.651998063891578
762.53.09099806389158-0.590998063891578
772.53.07599806389158-0.575998063891578
782.643.08399806389158-0.443998063891578
792.753.12699806389158-0.376998063891578
802.933.15099806389158-0.220998063891578
8133.13699806389158-0.136998063891578
823.173.25199612778316-0.0819961277831567
833.253.26666451543509-0.0166645154350859
843.393.171108959879530.218891040120469
853.53.114998063891580.385001936108423
863.53.104998063891580.395001936108422
873.653.101998063891580.548001936108421
883.753.090998063891580.659001936108422
893.753.075998063891580.674001936108422
903.93.083998063891580.816001936108422
9143.126998063891580.873001936108422
9243.150998063891580.849001936108422
9343.136998063891580.863001936108422
9443.251996127783160.748003872216843
9543.266664515435090.733335484564914
9643.171108959879530.828891040120469
9743.114998063891580.885001936108423
9843.104998063891580.895001936108422
9943.101998063891580.898001936108421
10043.090998063891580.909001936108422
10143.075998063891580.924001936108423
10243.083998063891580.916001936108422
1034.183.126998063891581.05300193610842
1044.253.150998063891581.09900193610842
1054.253.136998063891581.11300193610842
1063.971.912015488867382.05798451113262
1073.421.926683876519311.49331612348069
1082.751.831128320963750.918871679036248
1092.311.775017424975800.534982575024202
11021.76501742497580.234982575024201
1111.661.7620174249758-0.102017424975799
1121.311.7510174249758-0.441017424975799
1131.091.7360174249758-0.646017424975798
11411.74401742497580-0.744017424975798
11511.7870174249758-0.787017424975799
11611.8110174249758-0.811017424975798
11711.7970174249758-0.797017424975799
11811.91201548886738-0.912015488867377

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3 & 3.11499806389159 & -0.114998063891585 \tabularnewline
2 & 3.21 & 3.10499806389158 & 0.105001936108422 \tabularnewline
3 & 3.37 & 3.10199806389158 & 0.268001936108422 \tabularnewline
4 & 3.51 & 3.09099806389158 & 0.419001936108422 \tabularnewline
5 & 3.75 & 3.07599806389158 & 0.674001936108424 \tabularnewline
6 & 4.11 & 3.08399806389158 & 1.02600193610842 \tabularnewline
7 & 4.25 & 3.12699806389158 & 1.12300193610842 \tabularnewline
8 & 4.25 & 3.15099806389158 & 1.09900193610842 \tabularnewline
9 & 4.5 & 3.13699806389158 & 1.36300193610842 \tabularnewline
10 & 4.7 & 3.25199612778315 & 1.44800387221685 \tabularnewline
11 & 4.75 & 3.26666451543509 & 1.48333548456491 \tabularnewline
12 & 4.75 & 3.17110895987953 & 1.57889104012047 \tabularnewline
13 & 4.75 & 3.11499806389158 & 1.63500193610842 \tabularnewline
14 & 4.75 & 3.10499806389158 & 1.64500193610842 \tabularnewline
15 & 4.75 & 3.10199806389158 & 1.64800193610842 \tabularnewline
16 & 4.75 & 3.09099806389158 & 1.65900193610842 \tabularnewline
17 & 4.58 & 3.07599806389158 & 1.50400193610842 \tabularnewline
18 & 4.5 & 3.08399806389158 & 1.41600193610842 \tabularnewline
19 & 4.5 & 3.12699806389158 & 1.37300193610842 \tabularnewline
20 & 4.49 & 3.15099806389158 & 1.33900193610842 \tabularnewline
21 & 4.03 & 3.13699806389158 & 0.893001936108422 \tabularnewline
22 & 3.75 & 3.25199612778316 & 0.498003872216843 \tabularnewline
23 & 3.39 & 3.26666451543509 & 0.123335484564914 \tabularnewline
24 & 3.25 & 3.17110895987953 & 0.0788910401204689 \tabularnewline
25 & 3.25 & 3.11499806389158 & 0.135001936108423 \tabularnewline
26 & 3.25 & 3.10499806389158 & 0.145001936108422 \tabularnewline
27 & 3.25 & 3.10199806389158 & 0.148001936108421 \tabularnewline
28 & 3.25 & 3.09099806389158 & 0.159001936108422 \tabularnewline
29 & 3.25 & 3.07599806389158 & 0.174001936108422 \tabularnewline
30 & 3.25 & 3.08399806389158 & 0.166001936108422 \tabularnewline
31 & 3.25 & 3.12699806389158 & 0.123001936108422 \tabularnewline
32 & 3.25 & 3.15099806389158 & 0.0990019361084223 \tabularnewline
33 & 3.25 & 3.13699806389158 & 0.113001936108422 \tabularnewline
34 & 3.25 & 3.25199612778316 & -0.00199612778315666 \tabularnewline
35 & 3.25 & 3.26666451543509 & -0.0166645154350859 \tabularnewline
36 & 2.85 & 3.17110895987953 & -0.321108959879531 \tabularnewline
37 & 2.75 & 3.11499806389158 & -0.364998063891577 \tabularnewline
38 & 2.75 & 3.10499806389158 & -0.354998063891578 \tabularnewline
39 & 2.55 & 3.10199806389158 & -0.551998063891579 \tabularnewline
40 & 2.5 & 3.09099806389158 & -0.590998063891578 \tabularnewline
41 & 2.5 & 3.07599806389158 & -0.575998063891578 \tabularnewline
42 & 2.1 & 3.08399806389158 & -0.983998063891578 \tabularnewline
43 & 2 & 3.12699806389158 & -1.12699806389158 \tabularnewline
44 & 2 & 3.15099806389158 & -1.15099806389158 \tabularnewline
45 & 2 & 3.13699806389158 & -1.13699806389158 \tabularnewline
46 & 2 & 3.25199612778316 & -1.25199612778316 \tabularnewline
47 & 2 & 3.26666451543509 & -1.26666451543509 \tabularnewline
48 & 2 & 3.17110895987953 & -1.17110895987953 \tabularnewline
49 & 2 & 3.11499806389158 & -1.11499806389158 \tabularnewline
50 & 2 & 3.10499806389158 & -1.10499806389158 \tabularnewline
51 & 2 & 3.10199806389158 & -1.10199806389158 \tabularnewline
52 & 2 & 3.09099806389158 & -1.09099806389158 \tabularnewline
53 & 2 & 3.07599806389158 & -1.07599806389158 \tabularnewline
54 & 2 & 3.08399806389158 & -1.08399806389158 \tabularnewline
55 & 2 & 3.12699806389158 & -1.12699806389158 \tabularnewline
56 & 2 & 3.15099806389158 & -1.15099806389158 \tabularnewline
57 & 2 & 3.13699806389158 & -1.13699806389158 \tabularnewline
58 & 2 & 3.25199612778316 & -1.25199612778316 \tabularnewline
59 & 2 & 3.26666451543509 & -1.26666451543509 \tabularnewline
60 & 2 & 3.17110895987953 & -1.17110895987953 \tabularnewline
61 & 2 & 3.11499806389158 & -1.11499806389158 \tabularnewline
62 & 2 & 3.10499806389158 & -1.10499806389158 \tabularnewline
63 & 2 & 3.10199806389158 & -1.10199806389158 \tabularnewline
64 & 2 & 3.09099806389158 & -1.09099806389158 \tabularnewline
65 & 2 & 3.07599806389158 & -1.07599806389158 \tabularnewline
66 & 2 & 3.08399806389158 & -1.08399806389158 \tabularnewline
67 & 2 & 3.12699806389158 & -1.12699806389158 \tabularnewline
68 & 2 & 3.15099806389158 & -1.15099806389158 \tabularnewline
69 & 2 & 3.13699806389158 & -1.13699806389158 \tabularnewline
70 & 2 & 3.25199612778316 & -1.25199612778316 \tabularnewline
71 & 2 & 3.26666451543509 & -1.26666451543509 \tabularnewline
72 & 2.21 & 3.17110895987953 & -0.961108959879532 \tabularnewline
73 & 2.25 & 3.11499806389158 & -0.864998063891577 \tabularnewline
74 & 2.25 & 3.10499806389158 & -0.854998063891578 \tabularnewline
75 & 2.45 & 3.10199806389158 & -0.651998063891578 \tabularnewline
76 & 2.5 & 3.09099806389158 & -0.590998063891578 \tabularnewline
77 & 2.5 & 3.07599806389158 & -0.575998063891578 \tabularnewline
78 & 2.64 & 3.08399806389158 & -0.443998063891578 \tabularnewline
79 & 2.75 & 3.12699806389158 & -0.376998063891578 \tabularnewline
80 & 2.93 & 3.15099806389158 & -0.220998063891578 \tabularnewline
81 & 3 & 3.13699806389158 & -0.136998063891578 \tabularnewline
82 & 3.17 & 3.25199612778316 & -0.0819961277831567 \tabularnewline
83 & 3.25 & 3.26666451543509 & -0.0166645154350859 \tabularnewline
84 & 3.39 & 3.17110895987953 & 0.218891040120469 \tabularnewline
85 & 3.5 & 3.11499806389158 & 0.385001936108423 \tabularnewline
86 & 3.5 & 3.10499806389158 & 0.395001936108422 \tabularnewline
87 & 3.65 & 3.10199806389158 & 0.548001936108421 \tabularnewline
88 & 3.75 & 3.09099806389158 & 0.659001936108422 \tabularnewline
89 & 3.75 & 3.07599806389158 & 0.674001936108422 \tabularnewline
90 & 3.9 & 3.08399806389158 & 0.816001936108422 \tabularnewline
91 & 4 & 3.12699806389158 & 0.873001936108422 \tabularnewline
92 & 4 & 3.15099806389158 & 0.849001936108422 \tabularnewline
93 & 4 & 3.13699806389158 & 0.863001936108422 \tabularnewline
94 & 4 & 3.25199612778316 & 0.748003872216843 \tabularnewline
95 & 4 & 3.26666451543509 & 0.733335484564914 \tabularnewline
96 & 4 & 3.17110895987953 & 0.828891040120469 \tabularnewline
97 & 4 & 3.11499806389158 & 0.885001936108423 \tabularnewline
98 & 4 & 3.10499806389158 & 0.895001936108422 \tabularnewline
99 & 4 & 3.10199806389158 & 0.898001936108421 \tabularnewline
100 & 4 & 3.09099806389158 & 0.909001936108422 \tabularnewline
101 & 4 & 3.07599806389158 & 0.924001936108423 \tabularnewline
102 & 4 & 3.08399806389158 & 0.916001936108422 \tabularnewline
103 & 4.18 & 3.12699806389158 & 1.05300193610842 \tabularnewline
104 & 4.25 & 3.15099806389158 & 1.09900193610842 \tabularnewline
105 & 4.25 & 3.13699806389158 & 1.11300193610842 \tabularnewline
106 & 3.97 & 1.91201548886738 & 2.05798451113262 \tabularnewline
107 & 3.42 & 1.92668387651931 & 1.49331612348069 \tabularnewline
108 & 2.75 & 1.83112832096375 & 0.918871679036248 \tabularnewline
109 & 2.31 & 1.77501742497580 & 0.534982575024202 \tabularnewline
110 & 2 & 1.7650174249758 & 0.234982575024201 \tabularnewline
111 & 1.66 & 1.7620174249758 & -0.102017424975799 \tabularnewline
112 & 1.31 & 1.7510174249758 & -0.441017424975799 \tabularnewline
113 & 1.09 & 1.7360174249758 & -0.646017424975798 \tabularnewline
114 & 1 & 1.74401742497580 & -0.744017424975798 \tabularnewline
115 & 1 & 1.7870174249758 & -0.787017424975799 \tabularnewline
116 & 1 & 1.8110174249758 & -0.811017424975798 \tabularnewline
117 & 1 & 1.7970174249758 & -0.797017424975799 \tabularnewline
118 & 1 & 1.91201548886738 & -0.912015488867377 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58341&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]3[/C][C]3.11499806389159[/C][C]-0.114998063891585[/C][/ROW]
[ROW][C]2[/C][C]3.21[/C][C]3.10499806389158[/C][C]0.105001936108422[/C][/ROW]
[ROW][C]3[/C][C]3.37[/C][C]3.10199806389158[/C][C]0.268001936108422[/C][/ROW]
[ROW][C]4[/C][C]3.51[/C][C]3.09099806389158[/C][C]0.419001936108422[/C][/ROW]
[ROW][C]5[/C][C]3.75[/C][C]3.07599806389158[/C][C]0.674001936108424[/C][/ROW]
[ROW][C]6[/C][C]4.11[/C][C]3.08399806389158[/C][C]1.02600193610842[/C][/ROW]
[ROW][C]7[/C][C]4.25[/C][C]3.12699806389158[/C][C]1.12300193610842[/C][/ROW]
[ROW][C]8[/C][C]4.25[/C][C]3.15099806389158[/C][C]1.09900193610842[/C][/ROW]
[ROW][C]9[/C][C]4.5[/C][C]3.13699806389158[/C][C]1.36300193610842[/C][/ROW]
[ROW][C]10[/C][C]4.7[/C][C]3.25199612778315[/C][C]1.44800387221685[/C][/ROW]
[ROW][C]11[/C][C]4.75[/C][C]3.26666451543509[/C][C]1.48333548456491[/C][/ROW]
[ROW][C]12[/C][C]4.75[/C][C]3.17110895987953[/C][C]1.57889104012047[/C][/ROW]
[ROW][C]13[/C][C]4.75[/C][C]3.11499806389158[/C][C]1.63500193610842[/C][/ROW]
[ROW][C]14[/C][C]4.75[/C][C]3.10499806389158[/C][C]1.64500193610842[/C][/ROW]
[ROW][C]15[/C][C]4.75[/C][C]3.10199806389158[/C][C]1.64800193610842[/C][/ROW]
[ROW][C]16[/C][C]4.75[/C][C]3.09099806389158[/C][C]1.65900193610842[/C][/ROW]
[ROW][C]17[/C][C]4.58[/C][C]3.07599806389158[/C][C]1.50400193610842[/C][/ROW]
[ROW][C]18[/C][C]4.5[/C][C]3.08399806389158[/C][C]1.41600193610842[/C][/ROW]
[ROW][C]19[/C][C]4.5[/C][C]3.12699806389158[/C][C]1.37300193610842[/C][/ROW]
[ROW][C]20[/C][C]4.49[/C][C]3.15099806389158[/C][C]1.33900193610842[/C][/ROW]
[ROW][C]21[/C][C]4.03[/C][C]3.13699806389158[/C][C]0.893001936108422[/C][/ROW]
[ROW][C]22[/C][C]3.75[/C][C]3.25199612778316[/C][C]0.498003872216843[/C][/ROW]
[ROW][C]23[/C][C]3.39[/C][C]3.26666451543509[/C][C]0.123335484564914[/C][/ROW]
[ROW][C]24[/C][C]3.25[/C][C]3.17110895987953[/C][C]0.0788910401204689[/C][/ROW]
[ROW][C]25[/C][C]3.25[/C][C]3.11499806389158[/C][C]0.135001936108423[/C][/ROW]
[ROW][C]26[/C][C]3.25[/C][C]3.10499806389158[/C][C]0.145001936108422[/C][/ROW]
[ROW][C]27[/C][C]3.25[/C][C]3.10199806389158[/C][C]0.148001936108421[/C][/ROW]
[ROW][C]28[/C][C]3.25[/C][C]3.09099806389158[/C][C]0.159001936108422[/C][/ROW]
[ROW][C]29[/C][C]3.25[/C][C]3.07599806389158[/C][C]0.174001936108422[/C][/ROW]
[ROW][C]30[/C][C]3.25[/C][C]3.08399806389158[/C][C]0.166001936108422[/C][/ROW]
[ROW][C]31[/C][C]3.25[/C][C]3.12699806389158[/C][C]0.123001936108422[/C][/ROW]
[ROW][C]32[/C][C]3.25[/C][C]3.15099806389158[/C][C]0.0990019361084223[/C][/ROW]
[ROW][C]33[/C][C]3.25[/C][C]3.13699806389158[/C][C]0.113001936108422[/C][/ROW]
[ROW][C]34[/C][C]3.25[/C][C]3.25199612778316[/C][C]-0.00199612778315666[/C][/ROW]
[ROW][C]35[/C][C]3.25[/C][C]3.26666451543509[/C][C]-0.0166645154350859[/C][/ROW]
[ROW][C]36[/C][C]2.85[/C][C]3.17110895987953[/C][C]-0.321108959879531[/C][/ROW]
[ROW][C]37[/C][C]2.75[/C][C]3.11499806389158[/C][C]-0.364998063891577[/C][/ROW]
[ROW][C]38[/C][C]2.75[/C][C]3.10499806389158[/C][C]-0.354998063891578[/C][/ROW]
[ROW][C]39[/C][C]2.55[/C][C]3.10199806389158[/C][C]-0.551998063891579[/C][/ROW]
[ROW][C]40[/C][C]2.5[/C][C]3.09099806389158[/C][C]-0.590998063891578[/C][/ROW]
[ROW][C]41[/C][C]2.5[/C][C]3.07599806389158[/C][C]-0.575998063891578[/C][/ROW]
[ROW][C]42[/C][C]2.1[/C][C]3.08399806389158[/C][C]-0.983998063891578[/C][/ROW]
[ROW][C]43[/C][C]2[/C][C]3.12699806389158[/C][C]-1.12699806389158[/C][/ROW]
[ROW][C]44[/C][C]2[/C][C]3.15099806389158[/C][C]-1.15099806389158[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]3.13699806389158[/C][C]-1.13699806389158[/C][/ROW]
[ROW][C]46[/C][C]2[/C][C]3.25199612778316[/C][C]-1.25199612778316[/C][/ROW]
[ROW][C]47[/C][C]2[/C][C]3.26666451543509[/C][C]-1.26666451543509[/C][/ROW]
[ROW][C]48[/C][C]2[/C][C]3.17110895987953[/C][C]-1.17110895987953[/C][/ROW]
[ROW][C]49[/C][C]2[/C][C]3.11499806389158[/C][C]-1.11499806389158[/C][/ROW]
[ROW][C]50[/C][C]2[/C][C]3.10499806389158[/C][C]-1.10499806389158[/C][/ROW]
[ROW][C]51[/C][C]2[/C][C]3.10199806389158[/C][C]-1.10199806389158[/C][/ROW]
[ROW][C]52[/C][C]2[/C][C]3.09099806389158[/C][C]-1.09099806389158[/C][/ROW]
[ROW][C]53[/C][C]2[/C][C]3.07599806389158[/C][C]-1.07599806389158[/C][/ROW]
[ROW][C]54[/C][C]2[/C][C]3.08399806389158[/C][C]-1.08399806389158[/C][/ROW]
[ROW][C]55[/C][C]2[/C][C]3.12699806389158[/C][C]-1.12699806389158[/C][/ROW]
[ROW][C]56[/C][C]2[/C][C]3.15099806389158[/C][C]-1.15099806389158[/C][/ROW]
[ROW][C]57[/C][C]2[/C][C]3.13699806389158[/C][C]-1.13699806389158[/C][/ROW]
[ROW][C]58[/C][C]2[/C][C]3.25199612778316[/C][C]-1.25199612778316[/C][/ROW]
[ROW][C]59[/C][C]2[/C][C]3.26666451543509[/C][C]-1.26666451543509[/C][/ROW]
[ROW][C]60[/C][C]2[/C][C]3.17110895987953[/C][C]-1.17110895987953[/C][/ROW]
[ROW][C]61[/C][C]2[/C][C]3.11499806389158[/C][C]-1.11499806389158[/C][/ROW]
[ROW][C]62[/C][C]2[/C][C]3.10499806389158[/C][C]-1.10499806389158[/C][/ROW]
[ROW][C]63[/C][C]2[/C][C]3.10199806389158[/C][C]-1.10199806389158[/C][/ROW]
[ROW][C]64[/C][C]2[/C][C]3.09099806389158[/C][C]-1.09099806389158[/C][/ROW]
[ROW][C]65[/C][C]2[/C][C]3.07599806389158[/C][C]-1.07599806389158[/C][/ROW]
[ROW][C]66[/C][C]2[/C][C]3.08399806389158[/C][C]-1.08399806389158[/C][/ROW]
[ROW][C]67[/C][C]2[/C][C]3.12699806389158[/C][C]-1.12699806389158[/C][/ROW]
[ROW][C]68[/C][C]2[/C][C]3.15099806389158[/C][C]-1.15099806389158[/C][/ROW]
[ROW][C]69[/C][C]2[/C][C]3.13699806389158[/C][C]-1.13699806389158[/C][/ROW]
[ROW][C]70[/C][C]2[/C][C]3.25199612778316[/C][C]-1.25199612778316[/C][/ROW]
[ROW][C]71[/C][C]2[/C][C]3.26666451543509[/C][C]-1.26666451543509[/C][/ROW]
[ROW][C]72[/C][C]2.21[/C][C]3.17110895987953[/C][C]-0.961108959879532[/C][/ROW]
[ROW][C]73[/C][C]2.25[/C][C]3.11499806389158[/C][C]-0.864998063891577[/C][/ROW]
[ROW][C]74[/C][C]2.25[/C][C]3.10499806389158[/C][C]-0.854998063891578[/C][/ROW]
[ROW][C]75[/C][C]2.45[/C][C]3.10199806389158[/C][C]-0.651998063891578[/C][/ROW]
[ROW][C]76[/C][C]2.5[/C][C]3.09099806389158[/C][C]-0.590998063891578[/C][/ROW]
[ROW][C]77[/C][C]2.5[/C][C]3.07599806389158[/C][C]-0.575998063891578[/C][/ROW]
[ROW][C]78[/C][C]2.64[/C][C]3.08399806389158[/C][C]-0.443998063891578[/C][/ROW]
[ROW][C]79[/C][C]2.75[/C][C]3.12699806389158[/C][C]-0.376998063891578[/C][/ROW]
[ROW][C]80[/C][C]2.93[/C][C]3.15099806389158[/C][C]-0.220998063891578[/C][/ROW]
[ROW][C]81[/C][C]3[/C][C]3.13699806389158[/C][C]-0.136998063891578[/C][/ROW]
[ROW][C]82[/C][C]3.17[/C][C]3.25199612778316[/C][C]-0.0819961277831567[/C][/ROW]
[ROW][C]83[/C][C]3.25[/C][C]3.26666451543509[/C][C]-0.0166645154350859[/C][/ROW]
[ROW][C]84[/C][C]3.39[/C][C]3.17110895987953[/C][C]0.218891040120469[/C][/ROW]
[ROW][C]85[/C][C]3.5[/C][C]3.11499806389158[/C][C]0.385001936108423[/C][/ROW]
[ROW][C]86[/C][C]3.5[/C][C]3.10499806389158[/C][C]0.395001936108422[/C][/ROW]
[ROW][C]87[/C][C]3.65[/C][C]3.10199806389158[/C][C]0.548001936108421[/C][/ROW]
[ROW][C]88[/C][C]3.75[/C][C]3.09099806389158[/C][C]0.659001936108422[/C][/ROW]
[ROW][C]89[/C][C]3.75[/C][C]3.07599806389158[/C][C]0.674001936108422[/C][/ROW]
[ROW][C]90[/C][C]3.9[/C][C]3.08399806389158[/C][C]0.816001936108422[/C][/ROW]
[ROW][C]91[/C][C]4[/C][C]3.12699806389158[/C][C]0.873001936108422[/C][/ROW]
[ROW][C]92[/C][C]4[/C][C]3.15099806389158[/C][C]0.849001936108422[/C][/ROW]
[ROW][C]93[/C][C]4[/C][C]3.13699806389158[/C][C]0.863001936108422[/C][/ROW]
[ROW][C]94[/C][C]4[/C][C]3.25199612778316[/C][C]0.748003872216843[/C][/ROW]
[ROW][C]95[/C][C]4[/C][C]3.26666451543509[/C][C]0.733335484564914[/C][/ROW]
[ROW][C]96[/C][C]4[/C][C]3.17110895987953[/C][C]0.828891040120469[/C][/ROW]
[ROW][C]97[/C][C]4[/C][C]3.11499806389158[/C][C]0.885001936108423[/C][/ROW]
[ROW][C]98[/C][C]4[/C][C]3.10499806389158[/C][C]0.895001936108422[/C][/ROW]
[ROW][C]99[/C][C]4[/C][C]3.10199806389158[/C][C]0.898001936108421[/C][/ROW]
[ROW][C]100[/C][C]4[/C][C]3.09099806389158[/C][C]0.909001936108422[/C][/ROW]
[ROW][C]101[/C][C]4[/C][C]3.07599806389158[/C][C]0.924001936108423[/C][/ROW]
[ROW][C]102[/C][C]4[/C][C]3.08399806389158[/C][C]0.916001936108422[/C][/ROW]
[ROW][C]103[/C][C]4.18[/C][C]3.12699806389158[/C][C]1.05300193610842[/C][/ROW]
[ROW][C]104[/C][C]4.25[/C][C]3.15099806389158[/C][C]1.09900193610842[/C][/ROW]
[ROW][C]105[/C][C]4.25[/C][C]3.13699806389158[/C][C]1.11300193610842[/C][/ROW]
[ROW][C]106[/C][C]3.97[/C][C]1.91201548886738[/C][C]2.05798451113262[/C][/ROW]
[ROW][C]107[/C][C]3.42[/C][C]1.92668387651931[/C][C]1.49331612348069[/C][/ROW]
[ROW][C]108[/C][C]2.75[/C][C]1.83112832096375[/C][C]0.918871679036248[/C][/ROW]
[ROW][C]109[/C][C]2.31[/C][C]1.77501742497580[/C][C]0.534982575024202[/C][/ROW]
[ROW][C]110[/C][C]2[/C][C]1.7650174249758[/C][C]0.234982575024201[/C][/ROW]
[ROW][C]111[/C][C]1.66[/C][C]1.7620174249758[/C][C]-0.102017424975799[/C][/ROW]
[ROW][C]112[/C][C]1.31[/C][C]1.7510174249758[/C][C]-0.441017424975799[/C][/ROW]
[ROW][C]113[/C][C]1.09[/C][C]1.7360174249758[/C][C]-0.646017424975798[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]1.74401742497580[/C][C]-0.744017424975798[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]1.7870174249758[/C][C]-0.787017424975799[/C][/ROW]
[ROW][C]116[/C][C]1[/C][C]1.8110174249758[/C][C]-0.811017424975798[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]1.7970174249758[/C][C]-0.797017424975799[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]1.91201548886738[/C][C]-0.912015488867377[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58341&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58341&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
133.11499806389159-0.114998063891585
23.213.104998063891580.105001936108422
33.373.101998063891580.268001936108422
43.513.090998063891580.419001936108422
53.753.075998063891580.674001936108424
64.113.083998063891581.02600193610842
74.253.126998063891581.12300193610842
84.253.150998063891581.09900193610842
94.53.136998063891581.36300193610842
104.73.251996127783151.44800387221685
114.753.266664515435091.48333548456491
124.753.171108959879531.57889104012047
134.753.114998063891581.63500193610842
144.753.104998063891581.64500193610842
154.753.101998063891581.64800193610842
164.753.090998063891581.65900193610842
174.583.075998063891581.50400193610842
184.53.083998063891581.41600193610842
194.53.126998063891581.37300193610842
204.493.150998063891581.33900193610842
214.033.136998063891580.893001936108422
223.753.251996127783160.498003872216843
233.393.266664515435090.123335484564914
243.253.171108959879530.0788910401204689
253.253.114998063891580.135001936108423
263.253.104998063891580.145001936108422
273.253.101998063891580.148001936108421
283.253.090998063891580.159001936108422
293.253.075998063891580.174001936108422
303.253.083998063891580.166001936108422
313.253.126998063891580.123001936108422
323.253.150998063891580.0990019361084223
333.253.136998063891580.113001936108422
343.253.25199612778316-0.00199612778315666
353.253.26666451543509-0.0166645154350859
362.853.17110895987953-0.321108959879531
372.753.11499806389158-0.364998063891577
382.753.10499806389158-0.354998063891578
392.553.10199806389158-0.551998063891579
402.53.09099806389158-0.590998063891578
412.53.07599806389158-0.575998063891578
422.13.08399806389158-0.983998063891578
4323.12699806389158-1.12699806389158
4423.15099806389158-1.15099806389158
4523.13699806389158-1.13699806389158
4623.25199612778316-1.25199612778316
4723.26666451543509-1.26666451543509
4823.17110895987953-1.17110895987953
4923.11499806389158-1.11499806389158
5023.10499806389158-1.10499806389158
5123.10199806389158-1.10199806389158
5223.09099806389158-1.09099806389158
5323.07599806389158-1.07599806389158
5423.08399806389158-1.08399806389158
5523.12699806389158-1.12699806389158
5623.15099806389158-1.15099806389158
5723.13699806389158-1.13699806389158
5823.25199612778316-1.25199612778316
5923.26666451543509-1.26666451543509
6023.17110895987953-1.17110895987953
6123.11499806389158-1.11499806389158
6223.10499806389158-1.10499806389158
6323.10199806389158-1.10199806389158
6423.09099806389158-1.09099806389158
6523.07599806389158-1.07599806389158
6623.08399806389158-1.08399806389158
6723.12699806389158-1.12699806389158
6823.15099806389158-1.15099806389158
6923.13699806389158-1.13699806389158
7023.25199612778316-1.25199612778316
7123.26666451543509-1.26666451543509
722.213.17110895987953-0.961108959879532
732.253.11499806389158-0.864998063891577
742.253.10499806389158-0.854998063891578
752.453.10199806389158-0.651998063891578
762.53.09099806389158-0.590998063891578
772.53.07599806389158-0.575998063891578
782.643.08399806389158-0.443998063891578
792.753.12699806389158-0.376998063891578
802.933.15099806389158-0.220998063891578
8133.13699806389158-0.136998063891578
823.173.25199612778316-0.0819961277831567
833.253.26666451543509-0.0166645154350859
843.393.171108959879530.218891040120469
853.53.114998063891580.385001936108423
863.53.104998063891580.395001936108422
873.653.101998063891580.548001936108421
883.753.090998063891580.659001936108422
893.753.075998063891580.674001936108422
903.93.083998063891580.816001936108422
9143.126998063891580.873001936108422
9243.150998063891580.849001936108422
9343.136998063891580.863001936108422
9443.251996127783160.748003872216843
9543.266664515435090.733335484564914
9643.171108959879530.828891040120469
9743.114998063891580.885001936108423
9843.104998063891580.895001936108422
9943.101998063891580.898001936108421
10043.090998063891580.909001936108422
10143.075998063891580.924001936108423
10243.083998063891580.916001936108422
1034.183.126998063891581.05300193610842
1044.253.150998063891581.09900193610842
1054.253.136998063891581.11300193610842
1063.971.912015488867382.05798451113262
1073.421.926683876519311.49331612348069
1082.751.831128320963750.918871679036248
1092.311.775017424975800.534982575024202
11021.76501742497580.234982575024201
1111.661.7620174249758-0.102017424975799
1121.311.7510174249758-0.441017424975799
1131.091.7360174249758-0.646017424975798
11411.74401742497580-0.744017424975798
11511.7870174249758-0.787017424975799
11611.8110174249758-0.811017424975798
11711.7970174249758-0.797017424975799
11811.91201548886738-0.912015488867377







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.8318570117231390.3362859765537220.168142988276861
170.7693736249091460.4612527501817090.230626375090854
180.6808626388335050.6382747223329910.319137361166495
190.5867412915142860.8265174169714280.413258708485714
200.4961208641077790.9922417282155570.503879135892221
210.4124695744599280.8249391489198570.587530425540072
220.3751999690352150.750399938070430.624800030964785
230.3937278581802740.7874557163605470.606272141819726
240.4293459410055160.8586918820110320.570654058994484
250.3692027796352060.7384055592704110.630797220364794
260.3240122803796370.6480245607592730.675987719620363
270.2905863320614420.5811726641228850.709413667938558
280.2668938963504340.5337877927008680.733106103649566
290.2483694311201560.4967388622403120.751630568879844
300.244618450617610.489236901235220.75538154938239
310.2467334534579350.493466906915870.753266546542065
320.2465290241554550.4930580483109110.753470975844545
330.2354889763619380.4709779527238760.764511023638062
340.2199969534027260.4399939068054520.780003046597274
350.1943154034322110.3886308068644210.80568459656779
360.1914398314082720.3828796628165430.808560168591728
370.1755660555725310.3511321111450620.824433944427469
380.1646235362518730.3292470725037460.835376463748127
390.1717915224686370.3435830449372740.828208477531363
400.1851411800774160.3702823601548320.814858819922584
410.1982489059784280.3964978119568560.801751094021572
420.2648130279744360.5296260559488710.735186972025564
430.3534168886608560.7068337773217130.646583111339144
440.438723830454280.877447660908560.56127616954572
450.5083942538268030.9832114923463940.491605746173197
460.5730767065486350.853846586902730.426923293451365
470.6242272338833910.7515455322332170.375772766116609
480.6529183974050710.6941632051898580.347081602594929
490.6676392441283760.6647215117432480.332360755871624
500.6823348729645540.6353302540708910.317665127035446
510.6949362176704520.6101275646590950.305063782329548
520.7061756240010410.5876487519979180.293824375998959
530.7155964958560970.5688070082878060.284403504143903
540.7233645248582350.5532709502835310.276635475141766
550.7334177810003340.5331644379993310.266582218999666
560.744280615914930.5114387681701410.255719384085070
570.7523983372134510.4952033255730970.247601662786549
580.7694497094531010.4611005810937970.230550290546899
590.789438278998460.4211234420030790.210561721001539
600.8010740778501390.3978518442997230.198925922149861
610.8087268183788470.3825463632423070.191273181621154
620.8146744134623170.3706511730753660.185325586537683
630.8210006342420010.3579987315159980.178999365757999
640.826060310175910.3478793796481810.173939689824091
650.8294834317145530.3410331365708940.170516568285447
660.8348928003884470.3302143992231070.165107199611553
670.8456051138418580.3087897723162830.154394886158142
680.8598313341545640.2803373316908720.140168665845436
690.8744297533402930.2511404933194150.125570246659707
700.9087089431549270.1825821136901450.0912910568450727
710.946617637485090.1067647250298200.0533823625149102
720.961610707791970.07677858441606020.0383892922080301
730.97115865088970.05768269822060010.0288413491103001
740.9780288123110850.043942375377830.021971187688915
750.9799469669367280.04010606612654420.0200530330632721
760.9802876685492040.03942466290159220.0197123314507961
770.9799996695645150.04000066087097020.0200003304354851
780.9781015531064650.04379689378706950.0218984468935347
790.975951585315280.04809682936944050.0240484146847203
800.9711862449867070.05762751002658690.0288137550132935
810.964521286659110.070957426681780.03547871334089
820.9680126337163550.06397473256728960.0319873662836448
830.978386055547190.04322788890561930.0216139444528096
840.9789089423006710.0421821153986570.0210910576993285
850.9745400924658580.05091981506828340.0254599075341417
860.9668093992516460.06638120149670750.0331906007483537
870.9524189446718360.0951621106563280.047581055328164
880.9308581091207720.1382837817584560.0691418908792278
890.9015506531114760.1968986937770480.0984493468885239
900.8664200374507040.2671599250985930.133579962549297
910.823417856806610.3531642863867810.176582143193391
920.7696603029269440.4606793941461130.230339697073057
930.7059853873291690.5880292253416620.294014612670831
940.6678915617579220.6642168764841550.332108438242078
950.7492813013023930.5014373973952130.250718698697607
960.7614662017912390.4770675964175230.238533798208761
970.7373365195089890.5253269609820210.262663480491011
980.686954368033810.6260912639323810.313045631966190
990.6028060581407620.7943878837184750.397193941859238
1000.4855379792664520.9710759585329040.514462020733548
1010.3526134401279390.7052268802558780.647386559872061
1020.2228771042029210.4457542084058420.777122895797079

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.831857011723139 & 0.336285976553722 & 0.168142988276861 \tabularnewline
17 & 0.769373624909146 & 0.461252750181709 & 0.230626375090854 \tabularnewline
18 & 0.680862638833505 & 0.638274722332991 & 0.319137361166495 \tabularnewline
19 & 0.586741291514286 & 0.826517416971428 & 0.413258708485714 \tabularnewline
20 & 0.496120864107779 & 0.992241728215557 & 0.503879135892221 \tabularnewline
21 & 0.412469574459928 & 0.824939148919857 & 0.587530425540072 \tabularnewline
22 & 0.375199969035215 & 0.75039993807043 & 0.624800030964785 \tabularnewline
23 & 0.393727858180274 & 0.787455716360547 & 0.606272141819726 \tabularnewline
24 & 0.429345941005516 & 0.858691882011032 & 0.570654058994484 \tabularnewline
25 & 0.369202779635206 & 0.738405559270411 & 0.630797220364794 \tabularnewline
26 & 0.324012280379637 & 0.648024560759273 & 0.675987719620363 \tabularnewline
27 & 0.290586332061442 & 0.581172664122885 & 0.709413667938558 \tabularnewline
28 & 0.266893896350434 & 0.533787792700868 & 0.733106103649566 \tabularnewline
29 & 0.248369431120156 & 0.496738862240312 & 0.751630568879844 \tabularnewline
30 & 0.24461845061761 & 0.48923690123522 & 0.75538154938239 \tabularnewline
31 & 0.246733453457935 & 0.49346690691587 & 0.753266546542065 \tabularnewline
32 & 0.246529024155455 & 0.493058048310911 & 0.753470975844545 \tabularnewline
33 & 0.235488976361938 & 0.470977952723876 & 0.764511023638062 \tabularnewline
34 & 0.219996953402726 & 0.439993906805452 & 0.780003046597274 \tabularnewline
35 & 0.194315403432211 & 0.388630806864421 & 0.80568459656779 \tabularnewline
36 & 0.191439831408272 & 0.382879662816543 & 0.808560168591728 \tabularnewline
37 & 0.175566055572531 & 0.351132111145062 & 0.824433944427469 \tabularnewline
38 & 0.164623536251873 & 0.329247072503746 & 0.835376463748127 \tabularnewline
39 & 0.171791522468637 & 0.343583044937274 & 0.828208477531363 \tabularnewline
40 & 0.185141180077416 & 0.370282360154832 & 0.814858819922584 \tabularnewline
41 & 0.198248905978428 & 0.396497811956856 & 0.801751094021572 \tabularnewline
42 & 0.264813027974436 & 0.529626055948871 & 0.735186972025564 \tabularnewline
43 & 0.353416888660856 & 0.706833777321713 & 0.646583111339144 \tabularnewline
44 & 0.43872383045428 & 0.87744766090856 & 0.56127616954572 \tabularnewline
45 & 0.508394253826803 & 0.983211492346394 & 0.491605746173197 \tabularnewline
46 & 0.573076706548635 & 0.85384658690273 & 0.426923293451365 \tabularnewline
47 & 0.624227233883391 & 0.751545532233217 & 0.375772766116609 \tabularnewline
48 & 0.652918397405071 & 0.694163205189858 & 0.347081602594929 \tabularnewline
49 & 0.667639244128376 & 0.664721511743248 & 0.332360755871624 \tabularnewline
50 & 0.682334872964554 & 0.635330254070891 & 0.317665127035446 \tabularnewline
51 & 0.694936217670452 & 0.610127564659095 & 0.305063782329548 \tabularnewline
52 & 0.706175624001041 & 0.587648751997918 & 0.293824375998959 \tabularnewline
53 & 0.715596495856097 & 0.568807008287806 & 0.284403504143903 \tabularnewline
54 & 0.723364524858235 & 0.553270950283531 & 0.276635475141766 \tabularnewline
55 & 0.733417781000334 & 0.533164437999331 & 0.266582218999666 \tabularnewline
56 & 0.74428061591493 & 0.511438768170141 & 0.255719384085070 \tabularnewline
57 & 0.752398337213451 & 0.495203325573097 & 0.247601662786549 \tabularnewline
58 & 0.769449709453101 & 0.461100581093797 & 0.230550290546899 \tabularnewline
59 & 0.78943827899846 & 0.421123442003079 & 0.210561721001539 \tabularnewline
60 & 0.801074077850139 & 0.397851844299723 & 0.198925922149861 \tabularnewline
61 & 0.808726818378847 & 0.382546363242307 & 0.191273181621154 \tabularnewline
62 & 0.814674413462317 & 0.370651173075366 & 0.185325586537683 \tabularnewline
63 & 0.821000634242001 & 0.357998731515998 & 0.178999365757999 \tabularnewline
64 & 0.82606031017591 & 0.347879379648181 & 0.173939689824091 \tabularnewline
65 & 0.829483431714553 & 0.341033136570894 & 0.170516568285447 \tabularnewline
66 & 0.834892800388447 & 0.330214399223107 & 0.165107199611553 \tabularnewline
67 & 0.845605113841858 & 0.308789772316283 & 0.154394886158142 \tabularnewline
68 & 0.859831334154564 & 0.280337331690872 & 0.140168665845436 \tabularnewline
69 & 0.874429753340293 & 0.251140493319415 & 0.125570246659707 \tabularnewline
70 & 0.908708943154927 & 0.182582113690145 & 0.0912910568450727 \tabularnewline
71 & 0.94661763748509 & 0.106764725029820 & 0.0533823625149102 \tabularnewline
72 & 0.96161070779197 & 0.0767785844160602 & 0.0383892922080301 \tabularnewline
73 & 0.9711586508897 & 0.0576826982206001 & 0.0288413491103001 \tabularnewline
74 & 0.978028812311085 & 0.04394237537783 & 0.021971187688915 \tabularnewline
75 & 0.979946966936728 & 0.0401060661265442 & 0.0200530330632721 \tabularnewline
76 & 0.980287668549204 & 0.0394246629015922 & 0.0197123314507961 \tabularnewline
77 & 0.979999669564515 & 0.0400006608709702 & 0.0200003304354851 \tabularnewline
78 & 0.978101553106465 & 0.0437968937870695 & 0.0218984468935347 \tabularnewline
79 & 0.97595158531528 & 0.0480968293694405 & 0.0240484146847203 \tabularnewline
80 & 0.971186244986707 & 0.0576275100265869 & 0.0288137550132935 \tabularnewline
81 & 0.96452128665911 & 0.07095742668178 & 0.03547871334089 \tabularnewline
82 & 0.968012633716355 & 0.0639747325672896 & 0.0319873662836448 \tabularnewline
83 & 0.97838605554719 & 0.0432278889056193 & 0.0216139444528096 \tabularnewline
84 & 0.978908942300671 & 0.042182115398657 & 0.0210910576993285 \tabularnewline
85 & 0.974540092465858 & 0.0509198150682834 & 0.0254599075341417 \tabularnewline
86 & 0.966809399251646 & 0.0663812014967075 & 0.0331906007483537 \tabularnewline
87 & 0.952418944671836 & 0.095162110656328 & 0.047581055328164 \tabularnewline
88 & 0.930858109120772 & 0.138283781758456 & 0.0691418908792278 \tabularnewline
89 & 0.901550653111476 & 0.196898693777048 & 0.0984493468885239 \tabularnewline
90 & 0.866420037450704 & 0.267159925098593 & 0.133579962549297 \tabularnewline
91 & 0.82341785680661 & 0.353164286386781 & 0.176582143193391 \tabularnewline
92 & 0.769660302926944 & 0.460679394146113 & 0.230339697073057 \tabularnewline
93 & 0.705985387329169 & 0.588029225341662 & 0.294014612670831 \tabularnewline
94 & 0.667891561757922 & 0.664216876484155 & 0.332108438242078 \tabularnewline
95 & 0.749281301302393 & 0.501437397395213 & 0.250718698697607 \tabularnewline
96 & 0.761466201791239 & 0.477067596417523 & 0.238533798208761 \tabularnewline
97 & 0.737336519508989 & 0.525326960982021 & 0.262663480491011 \tabularnewline
98 & 0.68695436803381 & 0.626091263932381 & 0.313045631966190 \tabularnewline
99 & 0.602806058140762 & 0.794387883718475 & 0.397193941859238 \tabularnewline
100 & 0.485537979266452 & 0.971075958532904 & 0.514462020733548 \tabularnewline
101 & 0.352613440127939 & 0.705226880255878 & 0.647386559872061 \tabularnewline
102 & 0.222877104202921 & 0.445754208405842 & 0.777122895797079 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58341&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]16[/C][C]0.831857011723139[/C][C]0.336285976553722[/C][C]0.168142988276861[/C][/ROW]
[ROW][C]17[/C][C]0.769373624909146[/C][C]0.461252750181709[/C][C]0.230626375090854[/C][/ROW]
[ROW][C]18[/C][C]0.680862638833505[/C][C]0.638274722332991[/C][C]0.319137361166495[/C][/ROW]
[ROW][C]19[/C][C]0.586741291514286[/C][C]0.826517416971428[/C][C]0.413258708485714[/C][/ROW]
[ROW][C]20[/C][C]0.496120864107779[/C][C]0.992241728215557[/C][C]0.503879135892221[/C][/ROW]
[ROW][C]21[/C][C]0.412469574459928[/C][C]0.824939148919857[/C][C]0.587530425540072[/C][/ROW]
[ROW][C]22[/C][C]0.375199969035215[/C][C]0.75039993807043[/C][C]0.624800030964785[/C][/ROW]
[ROW][C]23[/C][C]0.393727858180274[/C][C]0.787455716360547[/C][C]0.606272141819726[/C][/ROW]
[ROW][C]24[/C][C]0.429345941005516[/C][C]0.858691882011032[/C][C]0.570654058994484[/C][/ROW]
[ROW][C]25[/C][C]0.369202779635206[/C][C]0.738405559270411[/C][C]0.630797220364794[/C][/ROW]
[ROW][C]26[/C][C]0.324012280379637[/C][C]0.648024560759273[/C][C]0.675987719620363[/C][/ROW]
[ROW][C]27[/C][C]0.290586332061442[/C][C]0.581172664122885[/C][C]0.709413667938558[/C][/ROW]
[ROW][C]28[/C][C]0.266893896350434[/C][C]0.533787792700868[/C][C]0.733106103649566[/C][/ROW]
[ROW][C]29[/C][C]0.248369431120156[/C][C]0.496738862240312[/C][C]0.751630568879844[/C][/ROW]
[ROW][C]30[/C][C]0.24461845061761[/C][C]0.48923690123522[/C][C]0.75538154938239[/C][/ROW]
[ROW][C]31[/C][C]0.246733453457935[/C][C]0.49346690691587[/C][C]0.753266546542065[/C][/ROW]
[ROW][C]32[/C][C]0.246529024155455[/C][C]0.493058048310911[/C][C]0.753470975844545[/C][/ROW]
[ROW][C]33[/C][C]0.235488976361938[/C][C]0.470977952723876[/C][C]0.764511023638062[/C][/ROW]
[ROW][C]34[/C][C]0.219996953402726[/C][C]0.439993906805452[/C][C]0.780003046597274[/C][/ROW]
[ROW][C]35[/C][C]0.194315403432211[/C][C]0.388630806864421[/C][C]0.80568459656779[/C][/ROW]
[ROW][C]36[/C][C]0.191439831408272[/C][C]0.382879662816543[/C][C]0.808560168591728[/C][/ROW]
[ROW][C]37[/C][C]0.175566055572531[/C][C]0.351132111145062[/C][C]0.824433944427469[/C][/ROW]
[ROW][C]38[/C][C]0.164623536251873[/C][C]0.329247072503746[/C][C]0.835376463748127[/C][/ROW]
[ROW][C]39[/C][C]0.171791522468637[/C][C]0.343583044937274[/C][C]0.828208477531363[/C][/ROW]
[ROW][C]40[/C][C]0.185141180077416[/C][C]0.370282360154832[/C][C]0.814858819922584[/C][/ROW]
[ROW][C]41[/C][C]0.198248905978428[/C][C]0.396497811956856[/C][C]0.801751094021572[/C][/ROW]
[ROW][C]42[/C][C]0.264813027974436[/C][C]0.529626055948871[/C][C]0.735186972025564[/C][/ROW]
[ROW][C]43[/C][C]0.353416888660856[/C][C]0.706833777321713[/C][C]0.646583111339144[/C][/ROW]
[ROW][C]44[/C][C]0.43872383045428[/C][C]0.87744766090856[/C][C]0.56127616954572[/C][/ROW]
[ROW][C]45[/C][C]0.508394253826803[/C][C]0.983211492346394[/C][C]0.491605746173197[/C][/ROW]
[ROW][C]46[/C][C]0.573076706548635[/C][C]0.85384658690273[/C][C]0.426923293451365[/C][/ROW]
[ROW][C]47[/C][C]0.624227233883391[/C][C]0.751545532233217[/C][C]0.375772766116609[/C][/ROW]
[ROW][C]48[/C][C]0.652918397405071[/C][C]0.694163205189858[/C][C]0.347081602594929[/C][/ROW]
[ROW][C]49[/C][C]0.667639244128376[/C][C]0.664721511743248[/C][C]0.332360755871624[/C][/ROW]
[ROW][C]50[/C][C]0.682334872964554[/C][C]0.635330254070891[/C][C]0.317665127035446[/C][/ROW]
[ROW][C]51[/C][C]0.694936217670452[/C][C]0.610127564659095[/C][C]0.305063782329548[/C][/ROW]
[ROW][C]52[/C][C]0.706175624001041[/C][C]0.587648751997918[/C][C]0.293824375998959[/C][/ROW]
[ROW][C]53[/C][C]0.715596495856097[/C][C]0.568807008287806[/C][C]0.284403504143903[/C][/ROW]
[ROW][C]54[/C][C]0.723364524858235[/C][C]0.553270950283531[/C][C]0.276635475141766[/C][/ROW]
[ROW][C]55[/C][C]0.733417781000334[/C][C]0.533164437999331[/C][C]0.266582218999666[/C][/ROW]
[ROW][C]56[/C][C]0.74428061591493[/C][C]0.511438768170141[/C][C]0.255719384085070[/C][/ROW]
[ROW][C]57[/C][C]0.752398337213451[/C][C]0.495203325573097[/C][C]0.247601662786549[/C][/ROW]
[ROW][C]58[/C][C]0.769449709453101[/C][C]0.461100581093797[/C][C]0.230550290546899[/C][/ROW]
[ROW][C]59[/C][C]0.78943827899846[/C][C]0.421123442003079[/C][C]0.210561721001539[/C][/ROW]
[ROW][C]60[/C][C]0.801074077850139[/C][C]0.397851844299723[/C][C]0.198925922149861[/C][/ROW]
[ROW][C]61[/C][C]0.808726818378847[/C][C]0.382546363242307[/C][C]0.191273181621154[/C][/ROW]
[ROW][C]62[/C][C]0.814674413462317[/C][C]0.370651173075366[/C][C]0.185325586537683[/C][/ROW]
[ROW][C]63[/C][C]0.821000634242001[/C][C]0.357998731515998[/C][C]0.178999365757999[/C][/ROW]
[ROW][C]64[/C][C]0.82606031017591[/C][C]0.347879379648181[/C][C]0.173939689824091[/C][/ROW]
[ROW][C]65[/C][C]0.829483431714553[/C][C]0.341033136570894[/C][C]0.170516568285447[/C][/ROW]
[ROW][C]66[/C][C]0.834892800388447[/C][C]0.330214399223107[/C][C]0.165107199611553[/C][/ROW]
[ROW][C]67[/C][C]0.845605113841858[/C][C]0.308789772316283[/C][C]0.154394886158142[/C][/ROW]
[ROW][C]68[/C][C]0.859831334154564[/C][C]0.280337331690872[/C][C]0.140168665845436[/C][/ROW]
[ROW][C]69[/C][C]0.874429753340293[/C][C]0.251140493319415[/C][C]0.125570246659707[/C][/ROW]
[ROW][C]70[/C][C]0.908708943154927[/C][C]0.182582113690145[/C][C]0.0912910568450727[/C][/ROW]
[ROW][C]71[/C][C]0.94661763748509[/C][C]0.106764725029820[/C][C]0.0533823625149102[/C][/ROW]
[ROW][C]72[/C][C]0.96161070779197[/C][C]0.0767785844160602[/C][C]0.0383892922080301[/C][/ROW]
[ROW][C]73[/C][C]0.9711586508897[/C][C]0.0576826982206001[/C][C]0.0288413491103001[/C][/ROW]
[ROW][C]74[/C][C]0.978028812311085[/C][C]0.04394237537783[/C][C]0.021971187688915[/C][/ROW]
[ROW][C]75[/C][C]0.979946966936728[/C][C]0.0401060661265442[/C][C]0.0200530330632721[/C][/ROW]
[ROW][C]76[/C][C]0.980287668549204[/C][C]0.0394246629015922[/C][C]0.0197123314507961[/C][/ROW]
[ROW][C]77[/C][C]0.979999669564515[/C][C]0.0400006608709702[/C][C]0.0200003304354851[/C][/ROW]
[ROW][C]78[/C][C]0.978101553106465[/C][C]0.0437968937870695[/C][C]0.0218984468935347[/C][/ROW]
[ROW][C]79[/C][C]0.97595158531528[/C][C]0.0480968293694405[/C][C]0.0240484146847203[/C][/ROW]
[ROW][C]80[/C][C]0.971186244986707[/C][C]0.0576275100265869[/C][C]0.0288137550132935[/C][/ROW]
[ROW][C]81[/C][C]0.96452128665911[/C][C]0.07095742668178[/C][C]0.03547871334089[/C][/ROW]
[ROW][C]82[/C][C]0.968012633716355[/C][C]0.0639747325672896[/C][C]0.0319873662836448[/C][/ROW]
[ROW][C]83[/C][C]0.97838605554719[/C][C]0.0432278889056193[/C][C]0.0216139444528096[/C][/ROW]
[ROW][C]84[/C][C]0.978908942300671[/C][C]0.042182115398657[/C][C]0.0210910576993285[/C][/ROW]
[ROW][C]85[/C][C]0.974540092465858[/C][C]0.0509198150682834[/C][C]0.0254599075341417[/C][/ROW]
[ROW][C]86[/C][C]0.966809399251646[/C][C]0.0663812014967075[/C][C]0.0331906007483537[/C][/ROW]
[ROW][C]87[/C][C]0.952418944671836[/C][C]0.095162110656328[/C][C]0.047581055328164[/C][/ROW]
[ROW][C]88[/C][C]0.930858109120772[/C][C]0.138283781758456[/C][C]0.0691418908792278[/C][/ROW]
[ROW][C]89[/C][C]0.901550653111476[/C][C]0.196898693777048[/C][C]0.0984493468885239[/C][/ROW]
[ROW][C]90[/C][C]0.866420037450704[/C][C]0.267159925098593[/C][C]0.133579962549297[/C][/ROW]
[ROW][C]91[/C][C]0.82341785680661[/C][C]0.353164286386781[/C][C]0.176582143193391[/C][/ROW]
[ROW][C]92[/C][C]0.769660302926944[/C][C]0.460679394146113[/C][C]0.230339697073057[/C][/ROW]
[ROW][C]93[/C][C]0.705985387329169[/C][C]0.588029225341662[/C][C]0.294014612670831[/C][/ROW]
[ROW][C]94[/C][C]0.667891561757922[/C][C]0.664216876484155[/C][C]0.332108438242078[/C][/ROW]
[ROW][C]95[/C][C]0.749281301302393[/C][C]0.501437397395213[/C][C]0.250718698697607[/C][/ROW]
[ROW][C]96[/C][C]0.761466201791239[/C][C]0.477067596417523[/C][C]0.238533798208761[/C][/ROW]
[ROW][C]97[/C][C]0.737336519508989[/C][C]0.525326960982021[/C][C]0.262663480491011[/C][/ROW]
[ROW][C]98[/C][C]0.68695436803381[/C][C]0.626091263932381[/C][C]0.313045631966190[/C][/ROW]
[ROW][C]99[/C][C]0.602806058140762[/C][C]0.794387883718475[/C][C]0.397193941859238[/C][/ROW]
[ROW][C]100[/C][C]0.485537979266452[/C][C]0.971075958532904[/C][C]0.514462020733548[/C][/ROW]
[ROW][C]101[/C][C]0.352613440127939[/C][C]0.705226880255878[/C][C]0.647386559872061[/C][/ROW]
[ROW][C]102[/C][C]0.222877104202921[/C][C]0.445754208405842[/C][C]0.777122895797079[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58341&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58341&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
160.8318570117231390.3362859765537220.168142988276861
170.7693736249091460.4612527501817090.230626375090854
180.6808626388335050.6382747223329910.319137361166495
190.5867412915142860.8265174169714280.413258708485714
200.4961208641077790.9922417282155570.503879135892221
210.4124695744599280.8249391489198570.587530425540072
220.3751999690352150.750399938070430.624800030964785
230.3937278581802740.7874557163605470.606272141819726
240.4293459410055160.8586918820110320.570654058994484
250.3692027796352060.7384055592704110.630797220364794
260.3240122803796370.6480245607592730.675987719620363
270.2905863320614420.5811726641228850.709413667938558
280.2668938963504340.5337877927008680.733106103649566
290.2483694311201560.4967388622403120.751630568879844
300.244618450617610.489236901235220.75538154938239
310.2467334534579350.493466906915870.753266546542065
320.2465290241554550.4930580483109110.753470975844545
330.2354889763619380.4709779527238760.764511023638062
340.2199969534027260.4399939068054520.780003046597274
350.1943154034322110.3886308068644210.80568459656779
360.1914398314082720.3828796628165430.808560168591728
370.1755660555725310.3511321111450620.824433944427469
380.1646235362518730.3292470725037460.835376463748127
390.1717915224686370.3435830449372740.828208477531363
400.1851411800774160.3702823601548320.814858819922584
410.1982489059784280.3964978119568560.801751094021572
420.2648130279744360.5296260559488710.735186972025564
430.3534168886608560.7068337773217130.646583111339144
440.438723830454280.877447660908560.56127616954572
450.5083942538268030.9832114923463940.491605746173197
460.5730767065486350.853846586902730.426923293451365
470.6242272338833910.7515455322332170.375772766116609
480.6529183974050710.6941632051898580.347081602594929
490.6676392441283760.6647215117432480.332360755871624
500.6823348729645540.6353302540708910.317665127035446
510.6949362176704520.6101275646590950.305063782329548
520.7061756240010410.5876487519979180.293824375998959
530.7155964958560970.5688070082878060.284403504143903
540.7233645248582350.5532709502835310.276635475141766
550.7334177810003340.5331644379993310.266582218999666
560.744280615914930.5114387681701410.255719384085070
570.7523983372134510.4952033255730970.247601662786549
580.7694497094531010.4611005810937970.230550290546899
590.789438278998460.4211234420030790.210561721001539
600.8010740778501390.3978518442997230.198925922149861
610.8087268183788470.3825463632423070.191273181621154
620.8146744134623170.3706511730753660.185325586537683
630.8210006342420010.3579987315159980.178999365757999
640.826060310175910.3478793796481810.173939689824091
650.8294834317145530.3410331365708940.170516568285447
660.8348928003884470.3302143992231070.165107199611553
670.8456051138418580.3087897723162830.154394886158142
680.8598313341545640.2803373316908720.140168665845436
690.8744297533402930.2511404933194150.125570246659707
700.9087089431549270.1825821136901450.0912910568450727
710.946617637485090.1067647250298200.0533823625149102
720.961610707791970.07677858441606020.0383892922080301
730.97115865088970.05768269822060010.0288413491103001
740.9780288123110850.043942375377830.021971187688915
750.9799469669367280.04010606612654420.0200530330632721
760.9802876685492040.03942466290159220.0197123314507961
770.9799996695645150.04000066087097020.0200003304354851
780.9781015531064650.04379689378706950.0218984468935347
790.975951585315280.04809682936944050.0240484146847203
800.9711862449867070.05762751002658690.0288137550132935
810.964521286659110.070957426681780.03547871334089
820.9680126337163550.06397473256728960.0319873662836448
830.978386055547190.04322788890561930.0216139444528096
840.9789089423006710.0421821153986570.0210910576993285
850.9745400924658580.05091981506828340.0254599075341417
860.9668093992516460.06638120149670750.0331906007483537
870.9524189446718360.0951621106563280.047581055328164
880.9308581091207720.1382837817584560.0691418908792278
890.9015506531114760.1968986937770480.0984493468885239
900.8664200374507040.2671599250985930.133579962549297
910.823417856806610.3531642863867810.176582143193391
920.7696603029269440.4606793941461130.230339697073057
930.7059853873291690.5880292253416620.294014612670831
940.6678915617579220.6642168764841550.332108438242078
950.7492813013023930.5014373973952130.250718698697607
960.7614662017912390.4770675964175230.238533798208761
970.7373365195089890.5253269609820210.262663480491011
980.686954368033810.6260912639323810.313045631966190
990.6028060581407620.7943878837184750.397193941859238
1000.4855379792664520.9710759585329040.514462020733548
1010.3526134401279390.7052268802558780.647386559872061
1020.2228771042029210.4457542084058420.777122895797079







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level80.0919540229885057NOK
10% type I error level160.183908045977011NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 8 & 0.0919540229885057 & NOK \tabularnewline
10% type I error level & 16 & 0.183908045977011 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58341&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]8[/C][C]0.0919540229885057[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.183908045977011[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58341&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58341&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level80.0919540229885057NOK
10% type I error level160.183908045977011NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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')
}