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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 09:17:50 -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/t1228148380udsk4bghfo6q4o4.htm/, Retrieved Sun, 05 May 2024 10:45:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26966, Retrieved Sun, 05 May 2024 10:45:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsblog
Estimated Impact265

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] [12d343c4448a5f9e527bb31caeac580b]
-   PD    [Multiple Regression] [blog] [2008-12-01 16:17:50] [0cdfeda4aa2f9e551c2e529c44a404df] [Current]
-   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	0
15014866,2	0
17722582,4	1
13876509,4	0
15495489,6	0
17799521,1	0
17920079,1	0
17248022,4	0
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 time5 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 & 5 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26966&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]5 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=26966&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26966&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 time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Multiple Linear Regression - Estimated Regression Equation
x[t] = + 16924267.42 + 3270915.6y[t] -1733304.22000000M1[t] -1670022.94M2[t] + 582352.98M3[t] -895115.22M4[t] -947761.86M5[t] -94104.8399999996M6[t] -3037726.88M7[t] -2708822.26M8[t] + 276992.8M9[t] + 705894.68M10[t] -135237.780000001M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
x[t] =  +  16924267.42 +  3270915.6y[t] -1733304.22000000M1[t] -1670022.94M2[t] +  582352.98M3[t] -895115.22M4[t] -947761.86M5[t] -94104.8399999996M6[t] -3037726.88M7[t] -2708822.26M8[t] +  276992.8M9[t] +  705894.68M10[t] -135237.780000001M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26966&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]x[t] =  +  16924267.42 +  3270915.6y[t] -1733304.22000000M1[t] -1670022.94M2[t] +  582352.98M3[t] -895115.22M4[t] -947761.86M5[t] -94104.8399999996M6[t] -3037726.88M7[t] -2708822.26M8[t] +  276992.8M9[t] +  705894.68M10[t] -135237.780000001M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26966&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26966&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] = + 16924267.42 + 3270915.6y[t] -1733304.22000000M1[t] -1670022.94M2[t] + 582352.98M3[t] -895115.22M4[t] -947761.86M5[t] -94104.8399999996M6[t] -3037726.88M7[t] -2708822.26M8[t] + 276992.8M9[t] + 705894.68M10[t] -135237.780000001M11[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)16924267.42484445.0071934.935400
y3270915.6269136.11510512.153400
M1-1733304.22000000648165.596922-2.67420.0102720.005136
M2-1670022.94648165.596922-2.57650.0131810.00659
M3582352.98648165.5969220.89850.3735190.186759
M4-895115.22648165.596922-1.3810.1738110.086906
M5-947761.86648165.596922-1.46220.1503350.075168
M6-94104.8399999996645926.676253-0.14570.8847890.442395
M7-3037726.88648165.596922-4.68672.4e-051.2e-05
M8-2708822.26648165.596922-4.17920.0001266.3e-05
M9276992.8648165.5969220.42730.6710770.335538
M10705894.68648165.5969221.08910.2816780.140839
M11-135237.780000001648165.596922-0.20860.8356250.417813

\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) & 16924267.42 & 484445.00719 & 34.9354 & 0 & 0 \tabularnewline
y & 3270915.6 & 269136.115105 & 12.1534 & 0 & 0 \tabularnewline
M1 & -1733304.22000000 & 648165.596922 & -2.6742 & 0.010272 & 0.005136 \tabularnewline
M2 & -1670022.94 & 648165.596922 & -2.5765 & 0.013181 & 0.00659 \tabularnewline
M3 & 582352.98 & 648165.596922 & 0.8985 & 0.373519 & 0.186759 \tabularnewline
M4 & -895115.22 & 648165.596922 & -1.381 & 0.173811 & 0.086906 \tabularnewline
M5 & -947761.86 & 648165.596922 & -1.4622 & 0.150335 & 0.075168 \tabularnewline
M6 & -94104.8399999996 & 645926.676253 & -0.1457 & 0.884789 & 0.442395 \tabularnewline
M7 & -3037726.88 & 648165.596922 & -4.6867 & 2.4e-05 & 1.2e-05 \tabularnewline
M8 & -2708822.26 & 648165.596922 & -4.1792 & 0.000126 & 6.3e-05 \tabularnewline
M9 & 276992.8 & 648165.596922 & 0.4273 & 0.671077 & 0.335538 \tabularnewline
M10 & 705894.68 & 648165.596922 & 1.0891 & 0.281678 & 0.140839 \tabularnewline
M11 & -135237.780000001 & 648165.596922 & -0.2086 & 0.835625 & 0.417813 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26966&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]16924267.42[/C][C]484445.00719[/C][C]34.9354[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]y[/C][C]3270915.6[/C][C]269136.115105[/C][C]12.1534[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-1733304.22000000[/C][C]648165.596922[/C][C]-2.6742[/C][C]0.010272[/C][C]0.005136[/C][/ROW]
[ROW][C]M2[/C][C]-1670022.94[/C][C]648165.596922[/C][C]-2.5765[/C][C]0.013181[/C][C]0.00659[/C][/ROW]
[ROW][C]M3[/C][C]582352.98[/C][C]648165.596922[/C][C]0.8985[/C][C]0.373519[/C][C]0.186759[/C][/ROW]
[ROW][C]M4[/C][C]-895115.22[/C][C]648165.596922[/C][C]-1.381[/C][C]0.173811[/C][C]0.086906[/C][/ROW]
[ROW][C]M5[/C][C]-947761.86[/C][C]648165.596922[/C][C]-1.4622[/C][C]0.150335[/C][C]0.075168[/C][/ROW]
[ROW][C]M6[/C][C]-94104.8399999996[/C][C]645926.676253[/C][C]-0.1457[/C][C]0.884789[/C][C]0.442395[/C][/ROW]
[ROW][C]M7[/C][C]-3037726.88[/C][C]648165.596922[/C][C]-4.6867[/C][C]2.4e-05[/C][C]1.2e-05[/C][/ROW]
[ROW][C]M8[/C][C]-2708822.26[/C][C]648165.596922[/C][C]-4.1792[/C][C]0.000126[/C][C]6.3e-05[/C][/ROW]
[ROW][C]M9[/C][C]276992.8[/C][C]648165.596922[/C][C]0.4273[/C][C]0.671077[/C][C]0.335538[/C][/ROW]
[ROW][C]M10[/C][C]705894.68[/C][C]648165.596922[/C][C]1.0891[/C][C]0.281678[/C][C]0.140839[/C][/ROW]
[ROW][C]M11[/C][C]-135237.780000001[/C][C]648165.596922[/C][C]-0.2086[/C][C]0.835625[/C][C]0.417813[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26966&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26966&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)16924267.42484445.0071934.935400
y3270915.6269136.11510512.153400
M1-1733304.22000000648165.596922-2.67420.0102720.005136
M2-1670022.94648165.596922-2.57650.0131810.00659
M3582352.98648165.5969220.89850.3735190.186759
M4-895115.22648165.596922-1.3810.1738110.086906
M5-947761.86648165.596922-1.46220.1503350.075168
M6-94104.8399999996645926.676253-0.14570.8847890.442395
M7-3037726.88648165.596922-4.68672.4e-051.2e-05
M8-2708822.26648165.596922-4.17920.0001266.3e-05
M9276992.8648165.5969220.42730.6710770.335538
M10705894.68648165.5969221.08910.2816780.140839
M11-135237.780000001648165.596922-0.20860.8356250.417813






Multiple Linear Regression - Regression Statistics
Multiple R0.915451082204775
R-squared0.838050683909894
Adjusted R-squared0.796701922354973
F-TEST (value)20.2678545232066
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value1.22124532708767e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1021299.74921083
Sum Squared Residuals49023499353690.8

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.915451082204775 \tabularnewline
R-squared & 0.838050683909894 \tabularnewline
Adjusted R-squared & 0.796701922354973 \tabularnewline
F-TEST (value) & 20.2678545232066 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 1.22124532708767e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1021299.74921083 \tabularnewline
Sum Squared Residuals & 49023499353690.8 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26966&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.915451082204775[/C][/ROW]
[ROW][C]R-squared[/C][C]0.838050683909894[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.796701922354973[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]20.2678545232066[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]1.22124532708767e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1021299.74921083[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]49023499353690.8[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26966&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26966&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 R0.915451082204775
R-squared0.838050683909894
Adjusted R-squared0.796701922354973
F-TEST (value)20.2678545232066
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value1.22124532708767e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1021299.74921083
Sum Squared Residuals49023499353690.8






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
114929387.515190963.2-261575.700000014
214717825.315254244.48-536419.179999997
315826281.217506620.4-1680339.2
416301309.616029152.2272157.4
515033016.915976505.56-943488.66
616998460.616830162.58168298.020000001
714066462.713886540.54179922.160000000
813328937.314215445.16-886507.859999999
917319718.217201260.22118457.979999999
1017586426.817630162.1-43735.3000000006
1115887037.416789029.64-901992.24
1217935679.116924267.421011411.68000000
131586948915190963.2678525.800000003
1415892510.915254244.48638266.42
1517556558.117506620.449937.7000000019
161679164316029152.2762490.8
1715953688.515976505.56-22817.0600000004
1818144913.616830162.581314751.02
191439088113886540.54504340.46
2013885708.714215445.16-329736.46
2117332571.517201260.22131311.280000000
2217152595.817630162.1-477566.3
2316003877.116789029.64-785152.54
2416841467.116924267.42-82800.3199999987
2514783398.115190963.2-407565.099999997
2614667847.515254244.48-586396.98
2717714362.217506620.4207741.800000000
281628208816029152.2252935.800000001
2915014866.215976505.56-961639.36
3017722582.420101078.18-2378495.78000000
3113876509.413886540.54-10031.1399999990
3215495489.614215445.161280044.44
3317799521.117201260.22598260.880000001
3417920079.117630162.1289917.000000001
3517248022.416789029.64458992.759999999
3618813782.420195183.02-1381400.62000000
3716249688.318461878.8-2212190.50000000
3817823358.518525160.08-701801.580000001
3920424438.320777536-353097.699999999
4017814218.719300067.8-1485849.1
4119699959.619247421.16452538.440000001
4219776328.120101078.18-324750.079999999
4315679833.117157456.14-1477623.04
4417119266.517486360.76-367094.26
452009261320472175.82-379562.82
4620863688.320901077.7-37389.3999999999
4720925203.120059945.24865257.860000002
482103259320195183.02837409.98
4920664684.318461878.82202805.50000000
5019711511.418525160.081186351.32000000
5122553293.4207775361775757.4
5219498332.919300067.8198265.099999999
5320722827.819247421.161475406.64
542132127520101078.181220196.82
5517960847.717157456.14803391.559999999
5617789654.917486360.76303294.139999999
5720003708.520472175.82-468467.32
5821169851.720901077.7268773.999999999
5920422839.420059945.24362894.159999999
6019810562.320195183.02-384620.719999999

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14929387.5 & 15190963.2 & -261575.700000014 \tabularnewline
2 & 14717825.3 & 15254244.48 & -536419.179999997 \tabularnewline
3 & 15826281.2 & 17506620.4 & -1680339.2 \tabularnewline
4 & 16301309.6 & 16029152.2 & 272157.4 \tabularnewline
5 & 15033016.9 & 15976505.56 & -943488.66 \tabularnewline
6 & 16998460.6 & 16830162.58 & 168298.020000001 \tabularnewline
7 & 14066462.7 & 13886540.54 & 179922.160000000 \tabularnewline
8 & 13328937.3 & 14215445.16 & -886507.859999999 \tabularnewline
9 & 17319718.2 & 17201260.22 & 118457.979999999 \tabularnewline
10 & 17586426.8 & 17630162.1 & -43735.3000000006 \tabularnewline
11 & 15887037.4 & 16789029.64 & -901992.24 \tabularnewline
12 & 17935679.1 & 16924267.42 & 1011411.68000000 \tabularnewline
13 & 15869489 & 15190963.2 & 678525.800000003 \tabularnewline
14 & 15892510.9 & 15254244.48 & 638266.42 \tabularnewline
15 & 17556558.1 & 17506620.4 & 49937.7000000019 \tabularnewline
16 & 16791643 & 16029152.2 & 762490.8 \tabularnewline
17 & 15953688.5 & 15976505.56 & -22817.0600000004 \tabularnewline
18 & 18144913.6 & 16830162.58 & 1314751.02 \tabularnewline
19 & 14390881 & 13886540.54 & 504340.46 \tabularnewline
20 & 13885708.7 & 14215445.16 & -329736.46 \tabularnewline
21 & 17332571.5 & 17201260.22 & 131311.280000000 \tabularnewline
22 & 17152595.8 & 17630162.1 & -477566.3 \tabularnewline
23 & 16003877.1 & 16789029.64 & -785152.54 \tabularnewline
24 & 16841467.1 & 16924267.42 & -82800.3199999987 \tabularnewline
25 & 14783398.1 & 15190963.2 & -407565.099999997 \tabularnewline
26 & 14667847.5 & 15254244.48 & -586396.98 \tabularnewline
27 & 17714362.2 & 17506620.4 & 207741.800000000 \tabularnewline
28 & 16282088 & 16029152.2 & 252935.800000001 \tabularnewline
29 & 15014866.2 & 15976505.56 & -961639.36 \tabularnewline
30 & 17722582.4 & 20101078.18 & -2378495.78000000 \tabularnewline
31 & 13876509.4 & 13886540.54 & -10031.1399999990 \tabularnewline
32 & 15495489.6 & 14215445.16 & 1280044.44 \tabularnewline
33 & 17799521.1 & 17201260.22 & 598260.880000001 \tabularnewline
34 & 17920079.1 & 17630162.1 & 289917.000000001 \tabularnewline
35 & 17248022.4 & 16789029.64 & 458992.759999999 \tabularnewline
36 & 18813782.4 & 20195183.02 & -1381400.62000000 \tabularnewline
37 & 16249688.3 & 18461878.8 & -2212190.50000000 \tabularnewline
38 & 17823358.5 & 18525160.08 & -701801.580000001 \tabularnewline
39 & 20424438.3 & 20777536 & -353097.699999999 \tabularnewline
40 & 17814218.7 & 19300067.8 & -1485849.1 \tabularnewline
41 & 19699959.6 & 19247421.16 & 452538.440000001 \tabularnewline
42 & 19776328.1 & 20101078.18 & -324750.079999999 \tabularnewline
43 & 15679833.1 & 17157456.14 & -1477623.04 \tabularnewline
44 & 17119266.5 & 17486360.76 & -367094.26 \tabularnewline
45 & 20092613 & 20472175.82 & -379562.82 \tabularnewline
46 & 20863688.3 & 20901077.7 & -37389.3999999999 \tabularnewline
47 & 20925203.1 & 20059945.24 & 865257.860000002 \tabularnewline
48 & 21032593 & 20195183.02 & 837409.98 \tabularnewline
49 & 20664684.3 & 18461878.8 & 2202805.50000000 \tabularnewline
50 & 19711511.4 & 18525160.08 & 1186351.32000000 \tabularnewline
51 & 22553293.4 & 20777536 & 1775757.4 \tabularnewline
52 & 19498332.9 & 19300067.8 & 198265.099999999 \tabularnewline
53 & 20722827.8 & 19247421.16 & 1475406.64 \tabularnewline
54 & 21321275 & 20101078.18 & 1220196.82 \tabularnewline
55 & 17960847.7 & 17157456.14 & 803391.559999999 \tabularnewline
56 & 17789654.9 & 17486360.76 & 303294.139999999 \tabularnewline
57 & 20003708.5 & 20472175.82 & -468467.32 \tabularnewline
58 & 21169851.7 & 20901077.7 & 268773.999999999 \tabularnewline
59 & 20422839.4 & 20059945.24 & 362894.159999999 \tabularnewline
60 & 19810562.3 & 20195183.02 & -384620.719999999 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26966&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]15190963.2[/C][C]-261575.700000014[/C][/ROW]
[ROW][C]2[/C][C]14717825.3[/C][C]15254244.48[/C][C]-536419.179999997[/C][/ROW]
[ROW][C]3[/C][C]15826281.2[/C][C]17506620.4[/C][C]-1680339.2[/C][/ROW]
[ROW][C]4[/C][C]16301309.6[/C][C]16029152.2[/C][C]272157.4[/C][/ROW]
[ROW][C]5[/C][C]15033016.9[/C][C]15976505.56[/C][C]-943488.66[/C][/ROW]
[ROW][C]6[/C][C]16998460.6[/C][C]16830162.58[/C][C]168298.020000001[/C][/ROW]
[ROW][C]7[/C][C]14066462.7[/C][C]13886540.54[/C][C]179922.160000000[/C][/ROW]
[ROW][C]8[/C][C]13328937.3[/C][C]14215445.16[/C][C]-886507.859999999[/C][/ROW]
[ROW][C]9[/C][C]17319718.2[/C][C]17201260.22[/C][C]118457.979999999[/C][/ROW]
[ROW][C]10[/C][C]17586426.8[/C][C]17630162.1[/C][C]-43735.3000000006[/C][/ROW]
[ROW][C]11[/C][C]15887037.4[/C][C]16789029.64[/C][C]-901992.24[/C][/ROW]
[ROW][C]12[/C][C]17935679.1[/C][C]16924267.42[/C][C]1011411.68000000[/C][/ROW]
[ROW][C]13[/C][C]15869489[/C][C]15190963.2[/C][C]678525.800000003[/C][/ROW]
[ROW][C]14[/C][C]15892510.9[/C][C]15254244.48[/C][C]638266.42[/C][/ROW]
[ROW][C]15[/C][C]17556558.1[/C][C]17506620.4[/C][C]49937.7000000019[/C][/ROW]
[ROW][C]16[/C][C]16791643[/C][C]16029152.2[/C][C]762490.8[/C][/ROW]
[ROW][C]17[/C][C]15953688.5[/C][C]15976505.56[/C][C]-22817.0600000004[/C][/ROW]
[ROW][C]18[/C][C]18144913.6[/C][C]16830162.58[/C][C]1314751.02[/C][/ROW]
[ROW][C]19[/C][C]14390881[/C][C]13886540.54[/C][C]504340.46[/C][/ROW]
[ROW][C]20[/C][C]13885708.7[/C][C]14215445.16[/C][C]-329736.46[/C][/ROW]
[ROW][C]21[/C][C]17332571.5[/C][C]17201260.22[/C][C]131311.280000000[/C][/ROW]
[ROW][C]22[/C][C]17152595.8[/C][C]17630162.1[/C][C]-477566.3[/C][/ROW]
[ROW][C]23[/C][C]16003877.1[/C][C]16789029.64[/C][C]-785152.54[/C][/ROW]
[ROW][C]24[/C][C]16841467.1[/C][C]16924267.42[/C][C]-82800.3199999987[/C][/ROW]
[ROW][C]25[/C][C]14783398.1[/C][C]15190963.2[/C][C]-407565.099999997[/C][/ROW]
[ROW][C]26[/C][C]14667847.5[/C][C]15254244.48[/C][C]-586396.98[/C][/ROW]
[ROW][C]27[/C][C]17714362.2[/C][C]17506620.4[/C][C]207741.800000000[/C][/ROW]
[ROW][C]28[/C][C]16282088[/C][C]16029152.2[/C][C]252935.800000001[/C][/ROW]
[ROW][C]29[/C][C]15014866.2[/C][C]15976505.56[/C][C]-961639.36[/C][/ROW]
[ROW][C]30[/C][C]17722582.4[/C][C]20101078.18[/C][C]-2378495.78000000[/C][/ROW]
[ROW][C]31[/C][C]13876509.4[/C][C]13886540.54[/C][C]-10031.1399999990[/C][/ROW]
[ROW][C]32[/C][C]15495489.6[/C][C]14215445.16[/C][C]1280044.44[/C][/ROW]
[ROW][C]33[/C][C]17799521.1[/C][C]17201260.22[/C][C]598260.880000001[/C][/ROW]
[ROW][C]34[/C][C]17920079.1[/C][C]17630162.1[/C][C]289917.000000001[/C][/ROW]
[ROW][C]35[/C][C]17248022.4[/C][C]16789029.64[/C][C]458992.759999999[/C][/ROW]
[ROW][C]36[/C][C]18813782.4[/C][C]20195183.02[/C][C]-1381400.62000000[/C][/ROW]
[ROW][C]37[/C][C]16249688.3[/C][C]18461878.8[/C][C]-2212190.50000000[/C][/ROW]
[ROW][C]38[/C][C]17823358.5[/C][C]18525160.08[/C][C]-701801.580000001[/C][/ROW]
[ROW][C]39[/C][C]20424438.3[/C][C]20777536[/C][C]-353097.699999999[/C][/ROW]
[ROW][C]40[/C][C]17814218.7[/C][C]19300067.8[/C][C]-1485849.1[/C][/ROW]
[ROW][C]41[/C][C]19699959.6[/C][C]19247421.16[/C][C]452538.440000001[/C][/ROW]
[ROW][C]42[/C][C]19776328.1[/C][C]20101078.18[/C][C]-324750.079999999[/C][/ROW]
[ROW][C]43[/C][C]15679833.1[/C][C]17157456.14[/C][C]-1477623.04[/C][/ROW]
[ROW][C]44[/C][C]17119266.5[/C][C]17486360.76[/C][C]-367094.26[/C][/ROW]
[ROW][C]45[/C][C]20092613[/C][C]20472175.82[/C][C]-379562.82[/C][/ROW]
[ROW][C]46[/C][C]20863688.3[/C][C]20901077.7[/C][C]-37389.3999999999[/C][/ROW]
[ROW][C]47[/C][C]20925203.1[/C][C]20059945.24[/C][C]865257.860000002[/C][/ROW]
[ROW][C]48[/C][C]21032593[/C][C]20195183.02[/C][C]837409.98[/C][/ROW]
[ROW][C]49[/C][C]20664684.3[/C][C]18461878.8[/C][C]2202805.50000000[/C][/ROW]
[ROW][C]50[/C][C]19711511.4[/C][C]18525160.08[/C][C]1186351.32000000[/C][/ROW]
[ROW][C]51[/C][C]22553293.4[/C][C]20777536[/C][C]1775757.4[/C][/ROW]
[ROW][C]52[/C][C]19498332.9[/C][C]19300067.8[/C][C]198265.099999999[/C][/ROW]
[ROW][C]53[/C][C]20722827.8[/C][C]19247421.16[/C][C]1475406.64[/C][/ROW]
[ROW][C]54[/C][C]21321275[/C][C]20101078.18[/C][C]1220196.82[/C][/ROW]
[ROW][C]55[/C][C]17960847.7[/C][C]17157456.14[/C][C]803391.559999999[/C][/ROW]
[ROW][C]56[/C][C]17789654.9[/C][C]17486360.76[/C][C]303294.139999999[/C][/ROW]
[ROW][C]57[/C][C]20003708.5[/C][C]20472175.82[/C][C]-468467.32[/C][/ROW]
[ROW][C]58[/C][C]21169851.7[/C][C]20901077.7[/C][C]268773.999999999[/C][/ROW]
[ROW][C]59[/C][C]20422839.4[/C][C]20059945.24[/C][C]362894.159999999[/C][/ROW]
[ROW][C]60[/C][C]19810562.3[/C][C]20195183.02[/C][C]-384620.719999999[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26966&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26966&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
114929387.515190963.2-261575.700000014
214717825.315254244.48-536419.179999997
315826281.217506620.4-1680339.2
416301309.616029152.2272157.4
515033016.915976505.56-943488.66
616998460.616830162.58168298.020000001
714066462.713886540.54179922.160000000
813328937.314215445.16-886507.859999999
917319718.217201260.22118457.979999999
1017586426.817630162.1-43735.3000000006
1115887037.416789029.64-901992.24
1217935679.116924267.421011411.68000000
131586948915190963.2678525.800000003
1415892510.915254244.48638266.42
1517556558.117506620.449937.7000000019
161679164316029152.2762490.8
1715953688.515976505.56-22817.0600000004
1818144913.616830162.581314751.02
191439088113886540.54504340.46
2013885708.714215445.16-329736.46
2117332571.517201260.22131311.280000000
2217152595.817630162.1-477566.3
2316003877.116789029.64-785152.54
2416841467.116924267.42-82800.3199999987
2514783398.115190963.2-407565.099999997
2614667847.515254244.48-586396.98
2717714362.217506620.4207741.800000000
281628208816029152.2252935.800000001
2915014866.215976505.56-961639.36
3017722582.420101078.18-2378495.78000000
3113876509.413886540.54-10031.1399999990
3215495489.614215445.161280044.44
3317799521.117201260.22598260.880000001
3417920079.117630162.1289917.000000001
3517248022.416789029.64458992.759999999
3618813782.420195183.02-1381400.62000000
3716249688.318461878.8-2212190.50000000
3817823358.518525160.08-701801.580000001
3920424438.320777536-353097.699999999
4017814218.719300067.8-1485849.1
4119699959.619247421.16452538.440000001
4219776328.120101078.18-324750.079999999
4315679833.117157456.14-1477623.04
4417119266.517486360.76-367094.26
452009261320472175.82-379562.82
4620863688.320901077.7-37389.3999999999
4720925203.120059945.24865257.860000002
482103259320195183.02837409.98
4920664684.318461878.82202805.50000000
5019711511.418525160.081186351.32000000
5122553293.4207775361775757.4
5219498332.919300067.8198265.099999999
5320722827.819247421.161475406.64
542132127520101078.181220196.82
5517960847.717157456.14803391.559999999
5617789654.917486360.76303294.139999999
5720003708.520472175.82-468467.32
5821169851.720901077.7268773.999999999
5920422839.420059945.24362894.159999999
6019810562.320195183.02-384620.719999999






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.4623650095101810.9247300190203620.537634990489819
170.3480067458600950.696013491720190.651993254139905
180.3133023549761890.6266047099523780.686697645023811
190.2054722005406170.4109444010812340.794527799459383
200.1343216309414820.2686432618829630.865678369058518
210.07540627267952140.1508125453590430.924593727320479
220.04338978124941110.08677956249882220.956610218750589
230.02491598932567260.04983197865134520.975084010674327
240.0201406504173220.0402813008346440.979859349582678
250.01218303929424850.02436607858849690.987816960705752
260.007779592257852630.01555918451570530.992220407742147
270.006778856270255190.01355771254051040.993221143729745
280.003403282299029070.006806564598058140.99659671770097
290.003140284960164810.006280569920329610.996859715039835
300.004378356881158550.00875671376231710.995621643118841
310.002184953505984240.004369907011968480.997815046494016
320.007087474518438950.01417494903687790.992912525481561
330.00424545033383580.00849090066767160.995754549666164
340.002284604718119960.004569209436239920.99771539528188
350.002156486665770980.004312973331541970.99784351333423
360.002219198319845430.004438396639690850.997780801680155
370.03459665136641920.06919330273283830.96540334863358
380.07922002492667550.1584400498533510.920779975073324
390.2072860777024610.4145721554049230.792713922297539
400.2470466978638390.4940933957276780.752953302136161
410.3457687656386370.6915375312772740.654231234361363
420.3871463110726560.7742926221453120.612853688927344
430.7885069827058970.4229860345882070.211493017294103
440.7101409081950310.5797181836099380.289859091804969

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.462365009510181 & 0.924730019020362 & 0.537634990489819 \tabularnewline
17 & 0.348006745860095 & 0.69601349172019 & 0.651993254139905 \tabularnewline
18 & 0.313302354976189 & 0.626604709952378 & 0.686697645023811 \tabularnewline
19 & 0.205472200540617 & 0.410944401081234 & 0.794527799459383 \tabularnewline
20 & 0.134321630941482 & 0.268643261882963 & 0.865678369058518 \tabularnewline
21 & 0.0754062726795214 & 0.150812545359043 & 0.924593727320479 \tabularnewline
22 & 0.0433897812494111 & 0.0867795624988222 & 0.956610218750589 \tabularnewline
23 & 0.0249159893256726 & 0.0498319786513452 & 0.975084010674327 \tabularnewline
24 & 0.020140650417322 & 0.040281300834644 & 0.979859349582678 \tabularnewline
25 & 0.0121830392942485 & 0.0243660785884969 & 0.987816960705752 \tabularnewline
26 & 0.00777959225785263 & 0.0155591845157053 & 0.992220407742147 \tabularnewline
27 & 0.00677885627025519 & 0.0135577125405104 & 0.993221143729745 \tabularnewline
28 & 0.00340328229902907 & 0.00680656459805814 & 0.99659671770097 \tabularnewline
29 & 0.00314028496016481 & 0.00628056992032961 & 0.996859715039835 \tabularnewline
30 & 0.00437835688115855 & 0.0087567137623171 & 0.995621643118841 \tabularnewline
31 & 0.00218495350598424 & 0.00436990701196848 & 0.997815046494016 \tabularnewline
32 & 0.00708747451843895 & 0.0141749490368779 & 0.992912525481561 \tabularnewline
33 & 0.0042454503338358 & 0.0084909006676716 & 0.995754549666164 \tabularnewline
34 & 0.00228460471811996 & 0.00456920943623992 & 0.99771539528188 \tabularnewline
35 & 0.00215648666577098 & 0.00431297333154197 & 0.99784351333423 \tabularnewline
36 & 0.00221919831984543 & 0.00443839663969085 & 0.997780801680155 \tabularnewline
37 & 0.0345966513664192 & 0.0691933027328383 & 0.96540334863358 \tabularnewline
38 & 0.0792200249266755 & 0.158440049853351 & 0.920779975073324 \tabularnewline
39 & 0.207286077702461 & 0.414572155404923 & 0.792713922297539 \tabularnewline
40 & 0.247046697863839 & 0.494093395727678 & 0.752953302136161 \tabularnewline
41 & 0.345768765638637 & 0.691537531277274 & 0.654231234361363 \tabularnewline
42 & 0.387146311072656 & 0.774292622145312 & 0.612853688927344 \tabularnewline
43 & 0.788506982705897 & 0.422986034588207 & 0.211493017294103 \tabularnewline
44 & 0.710140908195031 & 0.579718183609938 & 0.289859091804969 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26966&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]0.462365009510181[/C][C]0.924730019020362[/C][C]0.537634990489819[/C][/ROW]
[ROW][C]17[/C][C]0.348006745860095[/C][C]0.69601349172019[/C][C]0.651993254139905[/C][/ROW]
[ROW][C]18[/C][C]0.313302354976189[/C][C]0.626604709952378[/C][C]0.686697645023811[/C][/ROW]
[ROW][C]19[/C][C]0.205472200540617[/C][C]0.410944401081234[/C][C]0.794527799459383[/C][/ROW]
[ROW][C]20[/C][C]0.134321630941482[/C][C]0.268643261882963[/C][C]0.865678369058518[/C][/ROW]
[ROW][C]21[/C][C]0.0754062726795214[/C][C]0.150812545359043[/C][C]0.924593727320479[/C][/ROW]
[ROW][C]22[/C][C]0.0433897812494111[/C][C]0.0867795624988222[/C][C]0.956610218750589[/C][/ROW]
[ROW][C]23[/C][C]0.0249159893256726[/C][C]0.0498319786513452[/C][C]0.975084010674327[/C][/ROW]
[ROW][C]24[/C][C]0.020140650417322[/C][C]0.040281300834644[/C][C]0.979859349582678[/C][/ROW]
[ROW][C]25[/C][C]0.0121830392942485[/C][C]0.0243660785884969[/C][C]0.987816960705752[/C][/ROW]
[ROW][C]26[/C][C]0.00777959225785263[/C][C]0.0155591845157053[/C][C]0.992220407742147[/C][/ROW]
[ROW][C]27[/C][C]0.00677885627025519[/C][C]0.0135577125405104[/C][C]0.993221143729745[/C][/ROW]
[ROW][C]28[/C][C]0.00340328229902907[/C][C]0.00680656459805814[/C][C]0.99659671770097[/C][/ROW]
[ROW][C]29[/C][C]0.00314028496016481[/C][C]0.00628056992032961[/C][C]0.996859715039835[/C][/ROW]
[ROW][C]30[/C][C]0.00437835688115855[/C][C]0.0087567137623171[/C][C]0.995621643118841[/C][/ROW]
[ROW][C]31[/C][C]0.00218495350598424[/C][C]0.00436990701196848[/C][C]0.997815046494016[/C][/ROW]
[ROW][C]32[/C][C]0.00708747451843895[/C][C]0.0141749490368779[/C][C]0.992912525481561[/C][/ROW]
[ROW][C]33[/C][C]0.0042454503338358[/C][C]0.0084909006676716[/C][C]0.995754549666164[/C][/ROW]
[ROW][C]34[/C][C]0.00228460471811996[/C][C]0.00456920943623992[/C][C]0.99771539528188[/C][/ROW]
[ROW][C]35[/C][C]0.00215648666577098[/C][C]0.00431297333154197[/C][C]0.99784351333423[/C][/ROW]
[ROW][C]36[/C][C]0.00221919831984543[/C][C]0.00443839663969085[/C][C]0.997780801680155[/C][/ROW]
[ROW][C]37[/C][C]0.0345966513664192[/C][C]0.0691933027328383[/C][C]0.96540334863358[/C][/ROW]
[ROW][C]38[/C][C]0.0792200249266755[/C][C]0.158440049853351[/C][C]0.920779975073324[/C][/ROW]
[ROW][C]39[/C][C]0.207286077702461[/C][C]0.414572155404923[/C][C]0.792713922297539[/C][/ROW]
[ROW][C]40[/C][C]0.247046697863839[/C][C]0.494093395727678[/C][C]0.752953302136161[/C][/ROW]
[ROW][C]41[/C][C]0.345768765638637[/C][C]0.691537531277274[/C][C]0.654231234361363[/C][/ROW]
[ROW][C]42[/C][C]0.387146311072656[/C][C]0.774292622145312[/C][C]0.612853688927344[/C][/ROW]
[ROW][C]43[/C][C]0.788506982705897[/C][C]0.422986034588207[/C][C]0.211493017294103[/C][/ROW]
[ROW][C]44[/C][C]0.710140908195031[/C][C]0.579718183609938[/C][C]0.289859091804969[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26966&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26966&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
160.4623650095101810.9247300190203620.537634990489819
170.3480067458600950.696013491720190.651993254139905
180.3133023549761890.6266047099523780.686697645023811
190.2054722005406170.4109444010812340.794527799459383
200.1343216309414820.2686432618829630.865678369058518
210.07540627267952140.1508125453590430.924593727320479
220.04338978124941110.08677956249882220.956610218750589
230.02491598932567260.04983197865134520.975084010674327
240.0201406504173220.0402813008346440.979859349582678
250.01218303929424850.02436607858849690.987816960705752
260.007779592257852630.01555918451570530.992220407742147
270.006778856270255190.01355771254051040.993221143729745
280.003403282299029070.006806564598058140.99659671770097
290.003140284960164810.006280569920329610.996859715039835
300.004378356881158550.00875671376231710.995621643118841
310.002184953505984240.004369907011968480.997815046494016
320.007087474518438950.01417494903687790.992912525481561
330.00424545033383580.00849090066767160.995754549666164
340.002284604718119960.004569209436239920.99771539528188
350.002156486665770980.004312973331541970.99784351333423
360.002219198319845430.004438396639690850.997780801680155
370.03459665136641920.06919330273283830.96540334863358
380.07922002492667550.1584400498533510.920779975073324
390.2072860777024610.4145721554049230.792713922297539
400.2470466978638390.4940933957276780.752953302136161
410.3457687656386370.6915375312772740.654231234361363
420.3871463110726560.7742926221453120.612853688927344
430.7885069827058970.4229860345882070.211493017294103
440.7101409081950310.5797181836099380.289859091804969






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level80.275862068965517NOK
5% type I error level140.482758620689655NOK
10% type I error level160.551724137931034NOK

\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 & 8 & 0.275862068965517 & NOK \tabularnewline
5% type I error level & 14 & 0.482758620689655 & NOK \tabularnewline
10% type I error level & 16 & 0.551724137931034 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26966&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]8[/C][C]0.275862068965517[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]14[/C][C]0.482758620689655[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.551724137931034[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26966&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26966&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 level80.275862068965517NOK
5% type I error level140.482758620689655NOK
10% type I error level160.551724137931034NOK


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 = 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')
}