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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 01 Dec 2008 08:44:12 -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/2008/Dec/01/t122814631110pf5rdjgsuv7rj.htm/, Retrieved Sun, 05 May 2024 10:02:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26951, Retrieved Sun, 05 May 2024 10:02:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsblog
Estimated Impact263

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [blog] [2008-12-01 15:44:12] [0cdfeda4aa2f9e551c2e529c44a404df] [Current]
-   PD    [Multiple Regression] [blog] [2008-12-01 16:17:50] [12d343c4448a5f9e527bb31caeac580b]
-   PD      [Multiple Regression] [dioxine] [2008-12-01 16:30:23] [7a664918911e34206ce9d0436dd7c1c8]
-    D        [Multiple Regression] [Hypothese 1 en 2 ...] [2008-12-03 15:49:48] [12d343c4448a5f9e527bb31caeac580b]
-               [Multiple Regression] [paper - omzet en ...] [2008-12-03 20:34:00] [7a664918911e34206ce9d0436dd7c1c8]
- RM D          [Multiple Regression] [] [2009-12-07 20:08:54] [1f74ef2f756548f1f3a7b6136ea56d7f]
- RM D          [Multiple Regression] [] [2009-12-07 20:21:10] [1f74ef2f756548f1f3a7b6136ea56d7f]
- RMPD          [Univariate Data Series] [Paper: 1 univaria...] [2009-12-11 14:47:17] [0f0e461427f61416e46aeda5f4901bed]
- RMPD          [Univariate Data Series] [Paper: 1 univaria...] [2009-12-11 14:49:38] [0f0e461427f61416e46aeda5f4901bed]
-  MPD          [Multiple Regression] [Paper: 2 Multiple...] [2009-12-11 14:53:22] [0f0e461427f61416e46aeda5f4901bed]
- RMPD          [(Partial) Autocorrelation Function] [paper:3 ACF (d,D=0)] [2009-12-11 14:59:19] [0f0e461427f61416e46aeda5f4901bed]
-                 [(Partial) Autocorrelation Function] [paper:4 ACF (d=1,...] [2009-12-11 15:01:14] [0f0e461427f61416e46aeda5f4901bed]
- RM              [Variance Reduction Matrix] [paper:5 VRM] [2009-12-11 15:03:16] [0f0e461427f61416e46aeda5f4901bed]
- RM              [Spectral Analysis] [paper6: Spectruma...] [2009-12-11 15:05:17] [0f0e461427f61416e46aeda5f4901bed]
- RM              [Spectral Analysis] [paper6: Spectruma...] [2009-12-11 15:05:17] [0f0e461427f61416e46aeda5f4901bed]
- RM              [Spectral Analysis] [paper:7 Spectruma...] [2009-12-11 15:07:09] [0f0e461427f61416e46aeda5f4901bed]
-                   [Spectral Analysis] [paper:8 Spectruma...] [2009-12-11 15:42:28] [0f0e461427f61416e46aeda5f4901bed]
-                 [(Partial) Autocorrelation Function] [paper:8 ACF (d=1,...] [2009-12-11 15:39:41] [0f0e461427f61416e46aeda5f4901bed]
- RM              [ARIMA Backward Selection] [paper: 9 Backward...] [2009-12-11 15:54:31] [0f0e461427f61416e46aeda5f4901bed]
- RM D          [Multiple Regression] [] [2009-12-12 21:02:49] [9b30bff5dd5a100f8196daf92e735633]
- RM D          [Multiple Regression] [] [2009-12-12 21:45:38] [9b30bff5dd5a100f8196daf92e735633]
-  MPD          [Multiple Regression] [mutiple regression ] [2009-12-14 19:31:04] [ba905ddf7cdf9ecb063c35348c4dab2e]
- RMPD          [Univariate Data Series] [Paper Datareeks] [2009-12-15 11:21:03] [83058a88a37d754675a5cd22dab372fc]
-   PD            [Univariate Data Series] [paper run sequenc...] [2010-12-14 13:20:41] [d87a19cd5db53e12ea62bda70b3bb267]
-   PD            [Univariate Data Series] [paper run sequenc...] [2010-12-14 13:20:41] [d87a19cd5db53e12ea62bda70b3bb267]
-  MPD          [Multiple Regression] [Multiple regression] [2009-12-16 16:36:16] [fa44bc1b850de3469c0e3e9a5981c418]
- RMPD          [Univariate Data Series] [] [2009-12-16 19:28:18] [09f192433169b2c787c4a71fde86e883]
-  M D          [Multiple Regression] [Multiple Regression] [2009-12-18 15:13:02] [976efdaed7598845c859b86bc2e467ce]
- RM D          [Multiple Regression] [] [2009-12-18 16:07:40] [4409a44d89cea4fe559b38f99bc8a66c]
- RMPD          [Univariate Data Series] [] [2009-12-18 16:38:33] [4409a44d89cea4fe559b38f99bc8a66c]
-  M D          [Multiple Regression] [Paper Multiple Re...] [2009-12-29 19:11:43] [f15cf5036ae52d4243ad71d4fb151dbe]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
14929387,5	0
14717825,3	0
15826281,2	0
16301309,6	0
15033016,9	0
16998460,6	0
14066462,7	0
13328937,3	0
17319718,2	0
17586426,8	0
15887037,4	0
17935679,1	0
15869489	0
15892510,9	0
17556558,1	0
16791643	0
15953688,5	0
18144913,6	0
14390881	0
13885708,7	0
17332571,5	0
17152595,8	0
16003877,1	0
16841467,1	0
14783398,1	0
14667847,5	0
17714362,2	0
16282088	1
15014866,2	1
17722582,4	1
13876509,4	1
15495489,6	1
17799521,1	1
17920079,1	1
17248022,4	1
18813782,4	1
16249688,3	1
17823358,5	1
20424438,3	1
17814218,7	1
19699959,6	1
19776328,1	1
15679833,1	1
17119266,5	1
20092613	1
20863688,3	1
20925203,1	1
21032593	1
20664684,3	1
19711511,4	1
22553293,4	1
19498332,9	1
20722827,8	1
21321275	1
17960847,7	1
17789654,9	1
20003708,5	1
21169851,7	1
20422839,4	1
19810562,3	1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 6 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26951&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26951&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26951&T=0

As an alternative you can also use a QR Code:  
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 time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Multiple Linear Regression - Estimated Regression Equation
x[t] = + 15453593.0116667 -187164.427777778y[t] -1341566.15125000M1[t] -1376771.60527778M2[t] + 777117.580694444M3[t] -761404.467777779M4[t] -912537.841805555M5[t] + 496815.564166667M6[t] -3199476.32986111M7[t] -2969058.44388889M8[t] -81730.1179166674M9[t] + 248685.028055555M10[t] -690934.165972223M11[t] + 98486.7340277778t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
x[t] =  +  15453593.0116667 -187164.427777778y[t] -1341566.15125000M1[t] -1376771.60527778M2[t] +  777117.580694444M3[t] -761404.467777779M4[t] -912537.841805555M5[t] +  496815.564166667M6[t] -3199476.32986111M7[t] -2969058.44388889M8[t] -81730.1179166674M9[t] +  248685.028055555M10[t] -690934.165972223M11[t] +  98486.7340277778t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26951&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]x[t] =  +  15453593.0116667 -187164.427777778y[t] -1341566.15125000M1[t] -1376771.60527778M2[t] +  777117.580694444M3[t] -761404.467777779M4[t] -912537.841805555M5[t] +  496815.564166667M6[t] -3199476.32986111M7[t] -2969058.44388889M8[t] -81730.1179166674M9[t] +  248685.028055555M10[t] -690934.165972223M11[t] +  98486.7340277778t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26951&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26951&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
x[t] = + 15453593.0116667 -187164.427777778y[t] -1341566.15125000M1[t] -1376771.60527778M2[t] + 777117.580694444M3[t] -761404.467777779M4[t] -912537.841805555M5[t] + 496815.564166667M6[t] -3199476.32986111M7[t] -2969058.44388889M8[t] -81730.1179166674M9[t] + 248685.028055555M10[t] -690934.165972223M11[t] + 98486.7340277778t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)15453593.0116667576154.59944626.82200
y-187164.427777778554405.021808-0.33760.7372040.368602
M1-1341566.15125000672370.86634-1.99530.0519550.025978
M2-1376771.60527778670654.415434-2.05290.0457980.022899
M3777117.580694444669316.3548431.16110.2516070.125803
M4-761404.467777779677494.127493-1.12390.2669060.133453
M5-912537.841805555674652.674481-1.35260.1827940.091397
M6496815.564166667672180.366020.73910.4635950.231797
M7-3199476.32986111670081.288069-4.77481.9e-059e-06
M8-2969058.44388889668358.957192-4.44235.6e-052.8e-05
M9-81730.1179166674667016.291839-0.12250.9030120.451506
M10248685.028055555666055.5880530.37340.7105880.355294
M11-690934.165972223665478.500071-1.03830.304580.15229
t98486.734027777816004.2944296.153800

\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) & 15453593.0116667 & 576154.599446 & 26.822 & 0 & 0 \tabularnewline
y & -187164.427777778 & 554405.021808 & -0.3376 & 0.737204 & 0.368602 \tabularnewline
M1 & -1341566.15125000 & 672370.86634 & -1.9953 & 0.051955 & 0.025978 \tabularnewline
M2 & -1376771.60527778 & 670654.415434 & -2.0529 & 0.045798 & 0.022899 \tabularnewline
M3 & 777117.580694444 & 669316.354843 & 1.1611 & 0.251607 & 0.125803 \tabularnewline
M4 & -761404.467777779 & 677494.127493 & -1.1239 & 0.266906 & 0.133453 \tabularnewline
M5 & -912537.841805555 & 674652.674481 & -1.3526 & 0.182794 & 0.091397 \tabularnewline
M6 & 496815.564166667 & 672180.36602 & 0.7391 & 0.463595 & 0.231797 \tabularnewline
M7 & -3199476.32986111 & 670081.288069 & -4.7748 & 1.9e-05 & 9e-06 \tabularnewline
M8 & -2969058.44388889 & 668358.957192 & -4.4423 & 5.6e-05 & 2.8e-05 \tabularnewline
M9 & -81730.1179166674 & 667016.291839 & -0.1225 & 0.903012 & 0.451506 \tabularnewline
M10 & 248685.028055555 & 666055.588053 & 0.3734 & 0.710588 & 0.355294 \tabularnewline
M11 & -690934.165972223 & 665478.500071 & -1.0383 & 0.30458 & 0.15229 \tabularnewline
t & 98486.7340277778 & 16004.294429 & 6.1538 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26951&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]15453593.0116667[/C][C]576154.599446[/C][C]26.822[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]y[/C][C]-187164.427777778[/C][C]554405.021808[/C][C]-0.3376[/C][C]0.737204[/C][C]0.368602[/C][/ROW]
[ROW][C]M1[/C][C]-1341566.15125000[/C][C]672370.86634[/C][C]-1.9953[/C][C]0.051955[/C][C]0.025978[/C][/ROW]
[ROW][C]M2[/C][C]-1376771.60527778[/C][C]670654.415434[/C][C]-2.0529[/C][C]0.045798[/C][C]0.022899[/C][/ROW]
[ROW][C]M3[/C][C]777117.580694444[/C][C]669316.354843[/C][C]1.1611[/C][C]0.251607[/C][C]0.125803[/C][/ROW]
[ROW][C]M4[/C][C]-761404.467777779[/C][C]677494.127493[/C][C]-1.1239[/C][C]0.266906[/C][C]0.133453[/C][/ROW]
[ROW][C]M5[/C][C]-912537.841805555[/C][C]674652.674481[/C][C]-1.3526[/C][C]0.182794[/C][C]0.091397[/C][/ROW]
[ROW][C]M6[/C][C]496815.564166667[/C][C]672180.36602[/C][C]0.7391[/C][C]0.463595[/C][C]0.231797[/C][/ROW]
[ROW][C]M7[/C][C]-3199476.32986111[/C][C]670081.288069[/C][C]-4.7748[/C][C]1.9e-05[/C][C]9e-06[/C][/ROW]
[ROW][C]M8[/C][C]-2969058.44388889[/C][C]668358.957192[/C][C]-4.4423[/C][C]5.6e-05[/C][C]2.8e-05[/C][/ROW]
[ROW][C]M9[/C][C]-81730.1179166674[/C][C]667016.291839[/C][C]-0.1225[/C][C]0.903012[/C][C]0.451506[/C][/ROW]
[ROW][C]M10[/C][C]248685.028055555[/C][C]666055.588053[/C][C]0.3734[/C][C]0.710588[/C][C]0.355294[/C][/ROW]
[ROW][C]M11[/C][C]-690934.165972223[/C][C]665478.500071[/C][C]-1.0383[/C][C]0.30458[/C][C]0.15229[/C][/ROW]
[ROW][C]t[/C][C]98486.7340277778[/C][C]16004.294429[/C][C]6.1538[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26951&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26951&T=2

As an alternative you can also use a QR Code:  
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)15453593.0116667576154.59944626.82200
y-187164.427777778554405.021808-0.33760.7372040.368602
M1-1341566.15125000672370.86634-1.99530.0519550.025978
M2-1376771.60527778670654.415434-2.05290.0457980.022899
M3777117.580694444669316.3548431.16110.2516070.125803
M4-761404.467777779677494.127493-1.12390.2669060.133453
M5-912537.841805555674652.674481-1.35260.1827940.091397
M6496815.564166667672180.366020.73910.4635950.231797
M7-3199476.32986111670081.288069-4.77481.9e-059e-06
M8-2969058.44388889668358.957192-4.44235.6e-052.8e-05
M9-81730.1179166674667016.291839-0.12250.9030120.451506
M10248685.028055555666055.5880530.37340.7105880.355294
M11-690934.165972223665478.500071-1.03830.304580.15229
t98486.734027777816004.2944296.153800






Multiple Linear Regression - Regression Statistics
Multiple R0.912059686768993
R-squared0.831852872229153
Adjusted R-squared0.784333031772175
F-TEST (value)17.5053801576262
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value1.29785071578681e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1051909.56908919
Sum Squared Residuals50899632110904.9

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.912059686768993 \tabularnewline
R-squared & 0.831852872229153 \tabularnewline
Adjusted R-squared & 0.784333031772175 \tabularnewline
F-TEST (value) & 17.5053801576262 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 46 \tabularnewline
p-value & 1.29785071578681e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1051909.56908919 \tabularnewline
Sum Squared Residuals & 50899632110904.9 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26951&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.912059686768993[/C][/ROW]
[ROW][C]R-squared[/C][C]0.831852872229153[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.784333031772175[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.5053801576262[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]46[/C][/ROW]
[ROW][C]p-value[/C][C]1.29785071578681e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1051909.56908919[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]50899632110904.9[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26951&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26951&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.912059686768993
R-squared0.831852872229153
Adjusted R-squared0.784333031772175
F-TEST (value)17.5053801576262
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value1.29785071578681e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1051909.56908919
Sum Squared Residuals50899632110904.9






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
114929387.514210513.5944445718873.905555543
214717825.314273794.8744444444030.425555557
315826281.216526170.7944444-699889.594444444
416301309.615086135.481215174.12
515033016.915033488.84-471.939999998604
616998460.616541328.98457131.620000001
714066462.712943523.821122938.88
813328937.313272428.4456508.860000001
917319718.216258243.51061474.70000000
1017586426.816687145.38899281.42
1115887037.415846012.9241024.480000001
1217935679.116635433.821300245.28000000
131586948915392354.4027778477134.597222224
1415892510.915455635.6827778436875.217222221
1517556558.117708011.6027778-151453.502777776
161679164316267976.2883333523666.711666667
1715953688.516215329.6483333-261641.148333334
1818144913.617723169.7883333421743.811666667
191439088114125364.6283333265516.371666667
2013885708.714454269.2483333-568560.548333334
2117332571.517440084.3083333-107512.808333333
2217152595.817868986.1883333-716390.388333333
2316003877.117027853.7283333-1023976.62833333
2416841467.117817274.6283333-975807.528333332
2514783398.116574195.2111111-1790797.11111111
2614667847.516637476.4911111-1969628.99111111
2717714362.218889852.4111111-1175490.21111111
281628208817262652.6688889-980564.668888888
2915014866.217210006.0288889-2195139.82888889
3017722582.418717846.1688889-995263.76888889
3113876509.415120041.0088889-1243531.60888889
3215495489.615448945.628888946543.971111111
3317799521.118434760.6888889-635239.588888887
3417920079.118863662.5688889-943583.468888888
3517248022.418022530.1088889-774507.70888889
3618813782.418811951.00888891831.39111110945
3716249688.317568871.5916667-1319183.29166666
3817823358.517632152.8716667191205.628333334
3920424438.319884528.7916667539909.508333334
4017814218.718444493.4772222-630274.777222223
4119699959.618391846.83722221308112.76277778
4219776328.119899686.9772222-123358.877222221
4315679833.116301881.8172222-622048.717222223
4417119266.516630786.4372222488480.062777778
452009261319616601.4972222476011.502777778
4620863688.320045503.3772222818184.922777778
4720925203.119204370.91722221720832.18277778
482103259319993791.81722221038801.18277778
4920664684.318750712.41913971.90000000
5019711511.418813993.68897517.719999999
5122553293.421066369.61486923.80000000
5219498332.919626334.2855556-128001.385555557
5320722827.819573687.64555561149140.15444444
542132127521081527.7855556239747.214444443
5517960847.717483722.6255556477125.074444444
5617789654.917812627.2455556-22972.3455555567
5720003708.520798442.3055556-794733.805555556
5821169851.721227344.1855556-57492.4855555578
5920422839.420386211.725555636627.6744444429
6019810562.321175632.6255556-1365070.32555556

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14929387.5 & 14210513.5944445 & 718873.905555543 \tabularnewline
2 & 14717825.3 & 14273794.8744444 & 444030.425555557 \tabularnewline
3 & 15826281.2 & 16526170.7944444 & -699889.594444444 \tabularnewline
4 & 16301309.6 & 15086135.48 & 1215174.12 \tabularnewline
5 & 15033016.9 & 15033488.84 & -471.939999998604 \tabularnewline
6 & 16998460.6 & 16541328.98 & 457131.620000001 \tabularnewline
7 & 14066462.7 & 12943523.82 & 1122938.88 \tabularnewline
8 & 13328937.3 & 13272428.44 & 56508.860000001 \tabularnewline
9 & 17319718.2 & 16258243.5 & 1061474.70000000 \tabularnewline
10 & 17586426.8 & 16687145.38 & 899281.42 \tabularnewline
11 & 15887037.4 & 15846012.92 & 41024.480000001 \tabularnewline
12 & 17935679.1 & 16635433.82 & 1300245.28000000 \tabularnewline
13 & 15869489 & 15392354.4027778 & 477134.597222224 \tabularnewline
14 & 15892510.9 & 15455635.6827778 & 436875.217222221 \tabularnewline
15 & 17556558.1 & 17708011.6027778 & -151453.502777776 \tabularnewline
16 & 16791643 & 16267976.2883333 & 523666.711666667 \tabularnewline
17 & 15953688.5 & 16215329.6483333 & -261641.148333334 \tabularnewline
18 & 18144913.6 & 17723169.7883333 & 421743.811666667 \tabularnewline
19 & 14390881 & 14125364.6283333 & 265516.371666667 \tabularnewline
20 & 13885708.7 & 14454269.2483333 & -568560.548333334 \tabularnewline
21 & 17332571.5 & 17440084.3083333 & -107512.808333333 \tabularnewline
22 & 17152595.8 & 17868986.1883333 & -716390.388333333 \tabularnewline
23 & 16003877.1 & 17027853.7283333 & -1023976.62833333 \tabularnewline
24 & 16841467.1 & 17817274.6283333 & -975807.528333332 \tabularnewline
25 & 14783398.1 & 16574195.2111111 & -1790797.11111111 \tabularnewline
26 & 14667847.5 & 16637476.4911111 & -1969628.99111111 \tabularnewline
27 & 17714362.2 & 18889852.4111111 & -1175490.21111111 \tabularnewline
28 & 16282088 & 17262652.6688889 & -980564.668888888 \tabularnewline
29 & 15014866.2 & 17210006.0288889 & -2195139.82888889 \tabularnewline
30 & 17722582.4 & 18717846.1688889 & -995263.76888889 \tabularnewline
31 & 13876509.4 & 15120041.0088889 & -1243531.60888889 \tabularnewline
32 & 15495489.6 & 15448945.6288889 & 46543.971111111 \tabularnewline
33 & 17799521.1 & 18434760.6888889 & -635239.588888887 \tabularnewline
34 & 17920079.1 & 18863662.5688889 & -943583.468888888 \tabularnewline
35 & 17248022.4 & 18022530.1088889 & -774507.70888889 \tabularnewline
36 & 18813782.4 & 18811951.0088889 & 1831.39111110945 \tabularnewline
37 & 16249688.3 & 17568871.5916667 & -1319183.29166666 \tabularnewline
38 & 17823358.5 & 17632152.8716667 & 191205.628333334 \tabularnewline
39 & 20424438.3 & 19884528.7916667 & 539909.508333334 \tabularnewline
40 & 17814218.7 & 18444493.4772222 & -630274.777222223 \tabularnewline
41 & 19699959.6 & 18391846.8372222 & 1308112.76277778 \tabularnewline
42 & 19776328.1 & 19899686.9772222 & -123358.877222221 \tabularnewline
43 & 15679833.1 & 16301881.8172222 & -622048.717222223 \tabularnewline
44 & 17119266.5 & 16630786.4372222 & 488480.062777778 \tabularnewline
45 & 20092613 & 19616601.4972222 & 476011.502777778 \tabularnewline
46 & 20863688.3 & 20045503.3772222 & 818184.922777778 \tabularnewline
47 & 20925203.1 & 19204370.9172222 & 1720832.18277778 \tabularnewline
48 & 21032593 & 19993791.8172222 & 1038801.18277778 \tabularnewline
49 & 20664684.3 & 18750712.4 & 1913971.90000000 \tabularnewline
50 & 19711511.4 & 18813993.68 & 897517.719999999 \tabularnewline
51 & 22553293.4 & 21066369.6 & 1486923.80000000 \tabularnewline
52 & 19498332.9 & 19626334.2855556 & -128001.385555557 \tabularnewline
53 & 20722827.8 & 19573687.6455556 & 1149140.15444444 \tabularnewline
54 & 21321275 & 21081527.7855556 & 239747.214444443 \tabularnewline
55 & 17960847.7 & 17483722.6255556 & 477125.074444444 \tabularnewline
56 & 17789654.9 & 17812627.2455556 & -22972.3455555567 \tabularnewline
57 & 20003708.5 & 20798442.3055556 & -794733.805555556 \tabularnewline
58 & 21169851.7 & 21227344.1855556 & -57492.4855555578 \tabularnewline
59 & 20422839.4 & 20386211.7255556 & 36627.6744444429 \tabularnewline
60 & 19810562.3 & 21175632.6255556 & -1365070.32555556 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26951&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]14929387.5[/C][C]14210513.5944445[/C][C]718873.905555543[/C][/ROW]
[ROW][C]2[/C][C]14717825.3[/C][C]14273794.8744444[/C][C]444030.425555557[/C][/ROW]
[ROW][C]3[/C][C]15826281.2[/C][C]16526170.7944444[/C][C]-699889.594444444[/C][/ROW]
[ROW][C]4[/C][C]16301309.6[/C][C]15086135.48[/C][C]1215174.12[/C][/ROW]
[ROW][C]5[/C][C]15033016.9[/C][C]15033488.84[/C][C]-471.939999998604[/C][/ROW]
[ROW][C]6[/C][C]16998460.6[/C][C]16541328.98[/C][C]457131.620000001[/C][/ROW]
[ROW][C]7[/C][C]14066462.7[/C][C]12943523.82[/C][C]1122938.88[/C][/ROW]
[ROW][C]8[/C][C]13328937.3[/C][C]13272428.44[/C][C]56508.860000001[/C][/ROW]
[ROW][C]9[/C][C]17319718.2[/C][C]16258243.5[/C][C]1061474.70000000[/C][/ROW]
[ROW][C]10[/C][C]17586426.8[/C][C]16687145.38[/C][C]899281.42[/C][/ROW]
[ROW][C]11[/C][C]15887037.4[/C][C]15846012.92[/C][C]41024.480000001[/C][/ROW]
[ROW][C]12[/C][C]17935679.1[/C][C]16635433.82[/C][C]1300245.28000000[/C][/ROW]
[ROW][C]13[/C][C]15869489[/C][C]15392354.4027778[/C][C]477134.597222224[/C][/ROW]
[ROW][C]14[/C][C]15892510.9[/C][C]15455635.6827778[/C][C]436875.217222221[/C][/ROW]
[ROW][C]15[/C][C]17556558.1[/C][C]17708011.6027778[/C][C]-151453.502777776[/C][/ROW]
[ROW][C]16[/C][C]16791643[/C][C]16267976.2883333[/C][C]523666.711666667[/C][/ROW]
[ROW][C]17[/C][C]15953688.5[/C][C]16215329.6483333[/C][C]-261641.148333334[/C][/ROW]
[ROW][C]18[/C][C]18144913.6[/C][C]17723169.7883333[/C][C]421743.811666667[/C][/ROW]
[ROW][C]19[/C][C]14390881[/C][C]14125364.6283333[/C][C]265516.371666667[/C][/ROW]
[ROW][C]20[/C][C]13885708.7[/C][C]14454269.2483333[/C][C]-568560.548333334[/C][/ROW]
[ROW][C]21[/C][C]17332571.5[/C][C]17440084.3083333[/C][C]-107512.808333333[/C][/ROW]
[ROW][C]22[/C][C]17152595.8[/C][C]17868986.1883333[/C][C]-716390.388333333[/C][/ROW]
[ROW][C]23[/C][C]16003877.1[/C][C]17027853.7283333[/C][C]-1023976.62833333[/C][/ROW]
[ROW][C]24[/C][C]16841467.1[/C][C]17817274.6283333[/C][C]-975807.528333332[/C][/ROW]
[ROW][C]25[/C][C]14783398.1[/C][C]16574195.2111111[/C][C]-1790797.11111111[/C][/ROW]
[ROW][C]26[/C][C]14667847.5[/C][C]16637476.4911111[/C][C]-1969628.99111111[/C][/ROW]
[ROW][C]27[/C][C]17714362.2[/C][C]18889852.4111111[/C][C]-1175490.21111111[/C][/ROW]
[ROW][C]28[/C][C]16282088[/C][C]17262652.6688889[/C][C]-980564.668888888[/C][/ROW]
[ROW][C]29[/C][C]15014866.2[/C][C]17210006.0288889[/C][C]-2195139.82888889[/C][/ROW]
[ROW][C]30[/C][C]17722582.4[/C][C]18717846.1688889[/C][C]-995263.76888889[/C][/ROW]
[ROW][C]31[/C][C]13876509.4[/C][C]15120041.0088889[/C][C]-1243531.60888889[/C][/ROW]
[ROW][C]32[/C][C]15495489.6[/C][C]15448945.6288889[/C][C]46543.971111111[/C][/ROW]
[ROW][C]33[/C][C]17799521.1[/C][C]18434760.6888889[/C][C]-635239.588888887[/C][/ROW]
[ROW][C]34[/C][C]17920079.1[/C][C]18863662.5688889[/C][C]-943583.468888888[/C][/ROW]
[ROW][C]35[/C][C]17248022.4[/C][C]18022530.1088889[/C][C]-774507.70888889[/C][/ROW]
[ROW][C]36[/C][C]18813782.4[/C][C]18811951.0088889[/C][C]1831.39111110945[/C][/ROW]
[ROW][C]37[/C][C]16249688.3[/C][C]17568871.5916667[/C][C]-1319183.29166666[/C][/ROW]
[ROW][C]38[/C][C]17823358.5[/C][C]17632152.8716667[/C][C]191205.628333334[/C][/ROW]
[ROW][C]39[/C][C]20424438.3[/C][C]19884528.7916667[/C][C]539909.508333334[/C][/ROW]
[ROW][C]40[/C][C]17814218.7[/C][C]18444493.4772222[/C][C]-630274.777222223[/C][/ROW]
[ROW][C]41[/C][C]19699959.6[/C][C]18391846.8372222[/C][C]1308112.76277778[/C][/ROW]
[ROW][C]42[/C][C]19776328.1[/C][C]19899686.9772222[/C][C]-123358.877222221[/C][/ROW]
[ROW][C]43[/C][C]15679833.1[/C][C]16301881.8172222[/C][C]-622048.717222223[/C][/ROW]
[ROW][C]44[/C][C]17119266.5[/C][C]16630786.4372222[/C][C]488480.062777778[/C][/ROW]
[ROW][C]45[/C][C]20092613[/C][C]19616601.4972222[/C][C]476011.502777778[/C][/ROW]
[ROW][C]46[/C][C]20863688.3[/C][C]20045503.3772222[/C][C]818184.922777778[/C][/ROW]
[ROW][C]47[/C][C]20925203.1[/C][C]19204370.9172222[/C][C]1720832.18277778[/C][/ROW]
[ROW][C]48[/C][C]21032593[/C][C]19993791.8172222[/C][C]1038801.18277778[/C][/ROW]
[ROW][C]49[/C][C]20664684.3[/C][C]18750712.4[/C][C]1913971.90000000[/C][/ROW]
[ROW][C]50[/C][C]19711511.4[/C][C]18813993.68[/C][C]897517.719999999[/C][/ROW]
[ROW][C]51[/C][C]22553293.4[/C][C]21066369.6[/C][C]1486923.80000000[/C][/ROW]
[ROW][C]52[/C][C]19498332.9[/C][C]19626334.2855556[/C][C]-128001.385555557[/C][/ROW]
[ROW][C]53[/C][C]20722827.8[/C][C]19573687.6455556[/C][C]1149140.15444444[/C][/ROW]
[ROW][C]54[/C][C]21321275[/C][C]21081527.7855556[/C][C]239747.214444443[/C][/ROW]
[ROW][C]55[/C][C]17960847.7[/C][C]17483722.6255556[/C][C]477125.074444444[/C][/ROW]
[ROW][C]56[/C][C]17789654.9[/C][C]17812627.2455556[/C][C]-22972.3455555567[/C][/ROW]
[ROW][C]57[/C][C]20003708.5[/C][C]20798442.3055556[/C][C]-794733.805555556[/C][/ROW]
[ROW][C]58[/C][C]21169851.7[/C][C]21227344.1855556[/C][C]-57492.4855555578[/C][/ROW]
[ROW][C]59[/C][C]20422839.4[/C][C]20386211.7255556[/C][C]36627.6744444429[/C][/ROW]
[ROW][C]60[/C][C]19810562.3[/C][C]21175632.6255556[/C][C]-1365070.32555556[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26951&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26951&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
114929387.514210513.5944445718873.905555543
214717825.314273794.8744444444030.425555557
315826281.216526170.7944444-699889.594444444
416301309.615086135.481215174.12
515033016.915033488.84-471.939999998604
616998460.616541328.98457131.620000001
714066462.712943523.821122938.88
813328937.313272428.4456508.860000001
917319718.216258243.51061474.70000000
1017586426.816687145.38899281.42
1115887037.415846012.9241024.480000001
1217935679.116635433.821300245.28000000
131586948915392354.4027778477134.597222224
1415892510.915455635.6827778436875.217222221
1517556558.117708011.6027778-151453.502777776
161679164316267976.2883333523666.711666667
1715953688.516215329.6483333-261641.148333334
1818144913.617723169.7883333421743.811666667
191439088114125364.6283333265516.371666667
2013885708.714454269.2483333-568560.548333334
2117332571.517440084.3083333-107512.808333333
2217152595.817868986.1883333-716390.388333333
2316003877.117027853.7283333-1023976.62833333
2416841467.117817274.6283333-975807.528333332
2514783398.116574195.2111111-1790797.11111111
2614667847.516637476.4911111-1969628.99111111
2717714362.218889852.4111111-1175490.21111111
281628208817262652.6688889-980564.668888888
2915014866.217210006.0288889-2195139.82888889
3017722582.418717846.1688889-995263.76888889
3113876509.415120041.0088889-1243531.60888889
3215495489.615448945.628888946543.971111111
3317799521.118434760.6888889-635239.588888887
3417920079.118863662.5688889-943583.468888888
3517248022.418022530.1088889-774507.70888889
3618813782.418811951.00888891831.39111110945
3716249688.317568871.5916667-1319183.29166666
3817823358.517632152.8716667191205.628333334
3920424438.319884528.7916667539909.508333334
4017814218.718444493.4772222-630274.777222223
4119699959.618391846.83722221308112.76277778
4219776328.119899686.9772222-123358.877222221
4315679833.116301881.8172222-622048.717222223
4417119266.516630786.4372222488480.062777778
452009261319616601.4972222476011.502777778
4620863688.320045503.3772222818184.922777778
4720925203.119204370.91722221720832.18277778
482103259319993791.81722221038801.18277778
4920664684.318750712.41913971.90000000
5019711511.418813993.68897517.719999999
5122553293.421066369.61486923.80000000
5219498332.919626334.2855556-128001.385555557
5320722827.819573687.64555561149140.15444444
542132127521081527.7855556239747.214444443
5517960847.717483722.6255556477125.074444444
5617789654.917812627.2455556-22972.3455555567
5720003708.520798442.3055556-794733.805555556
5821169851.721227344.1855556-57492.4855555578
5920422839.420386211.725555636627.6744444429
6019810562.321175632.6255556-1365070.32555556






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.05811324812841610.1162264962568320.941886751871584
180.01900119919300940.03800239838601870.98099880080699
190.01756099079393340.03512198158786690.982439009206067
200.007413774198993480.01482754839798700.992586225801007
210.01019407259040300.02038814518080610.989805927409597
220.02105951684958680.04211903369917350.978940483150413
230.01237157762269310.02474315524538620.987628422377307
240.04588581657043750.0917716331408750.954114183429563
250.05865088955346010.1173017791069200.94134911044654
260.05460602452222750.1092120490444550.945393975477772
270.03813429482731460.07626858965462920.961865705172685
280.02048514554714400.04097029109428800.979514854452856
290.03735277628830560.07470555257661130.962647223711694
300.02399480041237210.04798960082474410.976005199587628
310.0143404038624910.0286808077249820.985659596137509
320.04071173879778180.08142347759556360.959288261202218
330.02334066927952510.04668133855905010.976659330720475
340.01602140138496400.03204280276992790.983978598615036
350.02145517622816330.04291035245632660.978544823771837
360.01574791412489360.03149582824978720.984252085875106
370.1152668293986210.2305336587972430.884733170601379
380.2011435859531220.4022871719062450.798856414046877
390.3773884219429600.7547768438859190.62261157805704
400.3393154837038820.6786309674077650.660684516296118
410.446041892772250.89208378554450.55395810722775
420.4050196621198110.8100393242396230.594980337880189
430.7316204539266860.5367590921466280.268379546073314

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.0581132481284161 & 0.116226496256832 & 0.941886751871584 \tabularnewline
18 & 0.0190011991930094 & 0.0380023983860187 & 0.98099880080699 \tabularnewline
19 & 0.0175609907939334 & 0.0351219815878669 & 0.982439009206067 \tabularnewline
20 & 0.00741377419899348 & 0.0148275483979870 & 0.992586225801007 \tabularnewline
21 & 0.0101940725904030 & 0.0203881451808061 & 0.989805927409597 \tabularnewline
22 & 0.0210595168495868 & 0.0421190336991735 & 0.978940483150413 \tabularnewline
23 & 0.0123715776226931 & 0.0247431552453862 & 0.987628422377307 \tabularnewline
24 & 0.0458858165704375 & 0.091771633140875 & 0.954114183429563 \tabularnewline
25 & 0.0586508895534601 & 0.117301779106920 & 0.94134911044654 \tabularnewline
26 & 0.0546060245222275 & 0.109212049044455 & 0.945393975477772 \tabularnewline
27 & 0.0381342948273146 & 0.0762685896546292 & 0.961865705172685 \tabularnewline
28 & 0.0204851455471440 & 0.0409702910942880 & 0.979514854452856 \tabularnewline
29 & 0.0373527762883056 & 0.0747055525766113 & 0.962647223711694 \tabularnewline
30 & 0.0239948004123721 & 0.0479896008247441 & 0.976005199587628 \tabularnewline
31 & 0.014340403862491 & 0.028680807724982 & 0.985659596137509 \tabularnewline
32 & 0.0407117387977818 & 0.0814234775955636 & 0.959288261202218 \tabularnewline
33 & 0.0233406692795251 & 0.0466813385590501 & 0.976659330720475 \tabularnewline
34 & 0.0160214013849640 & 0.0320428027699279 & 0.983978598615036 \tabularnewline
35 & 0.0214551762281633 & 0.0429103524563266 & 0.978544823771837 \tabularnewline
36 & 0.0157479141248936 & 0.0314958282497872 & 0.984252085875106 \tabularnewline
37 & 0.115266829398621 & 0.230533658797243 & 0.884733170601379 \tabularnewline
38 & 0.201143585953122 & 0.402287171906245 & 0.798856414046877 \tabularnewline
39 & 0.377388421942960 & 0.754776843885919 & 0.62261157805704 \tabularnewline
40 & 0.339315483703882 & 0.678630967407765 & 0.660684516296118 \tabularnewline
41 & 0.44604189277225 & 0.8920837855445 & 0.55395810722775 \tabularnewline
42 & 0.405019662119811 & 0.810039324239623 & 0.594980337880189 \tabularnewline
43 & 0.731620453926686 & 0.536759092146628 & 0.268379546073314 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26951&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]17[/C][C]0.0581132481284161[/C][C]0.116226496256832[/C][C]0.941886751871584[/C][/ROW]
[ROW][C]18[/C][C]0.0190011991930094[/C][C]0.0380023983860187[/C][C]0.98099880080699[/C][/ROW]
[ROW][C]19[/C][C]0.0175609907939334[/C][C]0.0351219815878669[/C][C]0.982439009206067[/C][/ROW]
[ROW][C]20[/C][C]0.00741377419899348[/C][C]0.0148275483979870[/C][C]0.992586225801007[/C][/ROW]
[ROW][C]21[/C][C]0.0101940725904030[/C][C]0.0203881451808061[/C][C]0.989805927409597[/C][/ROW]
[ROW][C]22[/C][C]0.0210595168495868[/C][C]0.0421190336991735[/C][C]0.978940483150413[/C][/ROW]
[ROW][C]23[/C][C]0.0123715776226931[/C][C]0.0247431552453862[/C][C]0.987628422377307[/C][/ROW]
[ROW][C]24[/C][C]0.0458858165704375[/C][C]0.091771633140875[/C][C]0.954114183429563[/C][/ROW]
[ROW][C]25[/C][C]0.0586508895534601[/C][C]0.117301779106920[/C][C]0.94134911044654[/C][/ROW]
[ROW][C]26[/C][C]0.0546060245222275[/C][C]0.109212049044455[/C][C]0.945393975477772[/C][/ROW]
[ROW][C]27[/C][C]0.0381342948273146[/C][C]0.0762685896546292[/C][C]0.961865705172685[/C][/ROW]
[ROW][C]28[/C][C]0.0204851455471440[/C][C]0.0409702910942880[/C][C]0.979514854452856[/C][/ROW]
[ROW][C]29[/C][C]0.0373527762883056[/C][C]0.0747055525766113[/C][C]0.962647223711694[/C][/ROW]
[ROW][C]30[/C][C]0.0239948004123721[/C][C]0.0479896008247441[/C][C]0.976005199587628[/C][/ROW]
[ROW][C]31[/C][C]0.014340403862491[/C][C]0.028680807724982[/C][C]0.985659596137509[/C][/ROW]
[ROW][C]32[/C][C]0.0407117387977818[/C][C]0.0814234775955636[/C][C]0.959288261202218[/C][/ROW]
[ROW][C]33[/C][C]0.0233406692795251[/C][C]0.0466813385590501[/C][C]0.976659330720475[/C][/ROW]
[ROW][C]34[/C][C]0.0160214013849640[/C][C]0.0320428027699279[/C][C]0.983978598615036[/C][/ROW]
[ROW][C]35[/C][C]0.0214551762281633[/C][C]0.0429103524563266[/C][C]0.978544823771837[/C][/ROW]
[ROW][C]36[/C][C]0.0157479141248936[/C][C]0.0314958282497872[/C][C]0.984252085875106[/C][/ROW]
[ROW][C]37[/C][C]0.115266829398621[/C][C]0.230533658797243[/C][C]0.884733170601379[/C][/ROW]
[ROW][C]38[/C][C]0.201143585953122[/C][C]0.402287171906245[/C][C]0.798856414046877[/C][/ROW]
[ROW][C]39[/C][C]0.377388421942960[/C][C]0.754776843885919[/C][C]0.62261157805704[/C][/ROW]
[ROW][C]40[/C][C]0.339315483703882[/C][C]0.678630967407765[/C][C]0.660684516296118[/C][/ROW]
[ROW][C]41[/C][C]0.44604189277225[/C][C]0.8920837855445[/C][C]0.55395810722775[/C][/ROW]
[ROW][C]42[/C][C]0.405019662119811[/C][C]0.810039324239623[/C][C]0.594980337880189[/C][/ROW]
[ROW][C]43[/C][C]0.731620453926686[/C][C]0.536759092146628[/C][C]0.268379546073314[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26951&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26951&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.05811324812841610.1162264962568320.941886751871584
180.01900119919300940.03800239838601870.98099880080699
190.01756099079393340.03512198158786690.982439009206067
200.007413774198993480.01482754839798700.992586225801007
210.01019407259040300.02038814518080610.989805927409597
220.02105951684958680.04211903369917350.978940483150413
230.01237157762269310.02474315524538620.987628422377307
240.04588581657043750.0917716331408750.954114183429563
250.05865088955346010.1173017791069200.94134911044654
260.05460602452222750.1092120490444550.945393975477772
270.03813429482731460.07626858965462920.961865705172685
280.02048514554714400.04097029109428800.979514854452856
290.03735277628830560.07470555257661130.962647223711694
300.02399480041237210.04798960082474410.976005199587628
310.0143404038624910.0286808077249820.985659596137509
320.04071173879778180.08142347759556360.959288261202218
330.02334066927952510.04668133855905010.976659330720475
340.01602140138496400.03204280276992790.983978598615036
350.02145517622816330.04291035245632660.978544823771837
360.01574791412489360.03149582824978720.984252085875106
370.1152668293986210.2305336587972430.884733170601379
380.2011435859531220.4022871719062450.798856414046877
390.3773884219429600.7547768438859190.62261157805704
400.3393154837038820.6786309674077650.660684516296118
410.446041892772250.89208378554450.55395810722775
420.4050196621198110.8100393242396230.594980337880189
430.7316204539266860.5367590921466280.268379546073314






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level130.481481481481481NOK
10% type I error level170.62962962962963NOK

\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 & 13 & 0.481481481481481 & NOK \tabularnewline
10% type I error level & 17 & 0.62962962962963 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26951&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]13[/C][C]0.481481481481481[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]17[/C][C]0.62962962962963[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26951&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26951&T=6

As an alternative you can also use a QR Code:  
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 level130.481481481481481NOK
10% type I error level170.62962962962963NOK


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}