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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 computationWed, 03 Dec 2008 08:49:48 -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/03/t12283195615q5fsohn0wsy64d.htm/, Retrieved Thu, 09 May 2024 08:03:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28750, Retrieved Thu, 09 May 2024 08:03:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsHypothese 1 en 2 Paper: multiple regression
Estimated Impact326

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] [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] [0cdfeda4aa2f9e551c2e529c44a404df] [Current]
-               [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 
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Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28750&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28750&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28750&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 time3 seconds
R Server'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
Omzet_Industriële_Sector[t] = + 16230758.1876265 -1476066.76604186Dummy_1_tijdenscrisis[t] -1233878.07618445Dummy_2_voorcrisis[t] -1410306.05586093M1[t] -1731072.65853649M2[t] + 432468.291996321M3[t] -1133834.15747087M4[t] -1226877.66896657M5[t] + 438903.296803129M6[t] -3247736.55266406M7[t] -3007666.40213125M8[t] -110686.051598434M9[t] + 229381.098934378M10[t] -700586.35053281M11[t] + 88834.649467188t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Omzet_Industriële_Sector[t] =  +  16230758.1876265 -1476066.76604186Dummy_1_tijdenscrisis[t] -1233878.07618445Dummy_2_voorcrisis[t] -1410306.05586093M1[t] -1731072.65853649M2[t] +  432468.291996321M3[t] -1133834.15747087M4[t] -1226877.66896657M5[t] +  438903.296803129M6[t] -3247736.55266406M7[t] -3007666.40213125M8[t] -110686.051598434M9[t] +  229381.098934378M10[t] -700586.35053281M11[t] +  88834.649467188t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28750&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Omzet_Industriële_Sector[t] =  +  16230758.1876265 -1476066.76604186Dummy_1_tijdenscrisis[t] -1233878.07618445Dummy_2_voorcrisis[t] -1410306.05586093M1[t] -1731072.65853649M2[t] +  432468.291996321M3[t] -1133834.15747087M4[t] -1226877.66896657M5[t] +  438903.296803129M6[t] -3247736.55266406M7[t] -3007666.40213125M8[t] -110686.051598434M9[t] +  229381.098934378M10[t] -700586.35053281M11[t] +  88834.649467188t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28750&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28750&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
Omzet_Industriële_Sector[t] = + 16230758.1876265 -1476066.76604186Dummy_1_tijdenscrisis[t] -1233878.07618445Dummy_2_voorcrisis[t] -1410306.05586093M1[t] -1731072.65853649M2[t] + 432468.291996321M3[t] -1133834.15747087M4[t] -1226877.66896657M5[t] + 438903.296803129M6[t] -3247736.55266406M7[t] -3007666.40213125M8[t] -110686.051598434M9[t] + 229381.098934378M10[t] -700586.35053281M11[t] + 88834.649467188t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)16230758.1876265433828.07426737.412900
Dummy_1_tijdenscrisis-1476066.76604186301162.440975-4.90121.3e-056e-06
Dummy_2_voorcrisis-1233878.07618445278799.166525-4.42576.1e-053e-05
M1-1410306.05586093505209.318392-2.79150.0076720.003836
M2-1731072.65853649508072.68847-3.40710.0013930.000696
M3432468.291996321507354.0214520.85240.3985060.199253
M4-1133834.15747087506709.391175-2.23760.030240.01512
M5-1226877.66896657506589.691049-2.42180.0195340.009767
M6438903.296803129502000.0028230.87430.3865920.193296
M7-3247736.55266406501583.178989-6.47500
M8-3007666.40213125501241.883487-6.000400
M9-110686.051598434500976.270682-0.22090.8261380.413069
M10229381.098934378500786.4609980.4580.6491270.324563
M11-700586.35053281500672.540647-1.39930.1685820.084291
t88834.6494671886166.75389514.405400

\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) & 16230758.1876265 & 433828.074267 & 37.4129 & 0 & 0 \tabularnewline
Dummy_1_tijdenscrisis & -1476066.76604186 & 301162.440975 & -4.9012 & 1.3e-05 & 6e-06 \tabularnewline
Dummy_2_voorcrisis & -1233878.07618445 & 278799.166525 & -4.4257 & 6.1e-05 & 3e-05 \tabularnewline
M1 & -1410306.05586093 & 505209.318392 & -2.7915 & 0.007672 & 0.003836 \tabularnewline
M2 & -1731072.65853649 & 508072.68847 & -3.4071 & 0.001393 & 0.000696 \tabularnewline
M3 & 432468.291996321 & 507354.021452 & 0.8524 & 0.398506 & 0.199253 \tabularnewline
M4 & -1133834.15747087 & 506709.391175 & -2.2376 & 0.03024 & 0.01512 \tabularnewline
M5 & -1226877.66896657 & 506589.691049 & -2.4218 & 0.019534 & 0.009767 \tabularnewline
M6 & 438903.296803129 & 502000.002823 & 0.8743 & 0.386592 & 0.193296 \tabularnewline
M7 & -3247736.55266406 & 501583.178989 & -6.475 & 0 & 0 \tabularnewline
M8 & -3007666.40213125 & 501241.883487 & -6.0004 & 0 & 0 \tabularnewline
M9 & -110686.051598434 & 500976.270682 & -0.2209 & 0.826138 & 0.413069 \tabularnewline
M10 & 229381.098934378 & 500786.460998 & 0.458 & 0.649127 & 0.324563 \tabularnewline
M11 & -700586.35053281 & 500672.540647 & -1.3993 & 0.168582 & 0.084291 \tabularnewline
t & 88834.649467188 & 6166.753895 & 14.4054 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28750&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]16230758.1876265[/C][C]433828.074267[/C][C]37.4129[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Dummy_1_tijdenscrisis[/C][C]-1476066.76604186[/C][C]301162.440975[/C][C]-4.9012[/C][C]1.3e-05[/C][C]6e-06[/C][/ROW]
[ROW][C]Dummy_2_voorcrisis[/C][C]-1233878.07618445[/C][C]278799.166525[/C][C]-4.4257[/C][C]6.1e-05[/C][C]3e-05[/C][/ROW]
[ROW][C]M1[/C][C]-1410306.05586093[/C][C]505209.318392[/C][C]-2.7915[/C][C]0.007672[/C][C]0.003836[/C][/ROW]
[ROW][C]M2[/C][C]-1731072.65853649[/C][C]508072.68847[/C][C]-3.4071[/C][C]0.001393[/C][C]0.000696[/C][/ROW]
[ROW][C]M3[/C][C]432468.291996321[/C][C]507354.021452[/C][C]0.8524[/C][C]0.398506[/C][C]0.199253[/C][/ROW]
[ROW][C]M4[/C][C]-1133834.15747087[/C][C]506709.391175[/C][C]-2.2376[/C][C]0.03024[/C][C]0.01512[/C][/ROW]
[ROW][C]M5[/C][C]-1226877.66896657[/C][C]506589.691049[/C][C]-2.4218[/C][C]0.019534[/C][C]0.009767[/C][/ROW]
[ROW][C]M6[/C][C]438903.296803129[/C][C]502000.002823[/C][C]0.8743[/C][C]0.386592[/C][C]0.193296[/C][/ROW]
[ROW][C]M7[/C][C]-3247736.55266406[/C][C]501583.178989[/C][C]-6.475[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M8[/C][C]-3007666.40213125[/C][C]501241.883487[/C][C]-6.0004[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]-110686.051598434[/C][C]500976.270682[/C][C]-0.2209[/C][C]0.826138[/C][C]0.413069[/C][/ROW]
[ROW][C]M10[/C][C]229381.098934378[/C][C]500786.460998[/C][C]0.458[/C][C]0.649127[/C][C]0.324563[/C][/ROW]
[ROW][C]M11[/C][C]-700586.35053281[/C][C]500672.540647[/C][C]-1.3993[/C][C]0.168582[/C][C]0.084291[/C][/ROW]
[ROW][C]t[/C][C]88834.649467188[/C][C]6166.753895[/C][C]14.4054[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28750&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28750&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)16230758.1876265433828.07426737.412900
Dummy_1_tijdenscrisis-1476066.76604186301162.440975-4.90121.3e-056e-06
Dummy_2_voorcrisis-1233878.07618445278799.166525-4.42576.1e-053e-05
M1-1410306.05586093505209.318392-2.79150.0076720.003836
M2-1731072.65853649508072.68847-3.40710.0013930.000696
M3432468.291996321507354.0214520.85240.3985060.199253
M4-1133834.15747087506709.391175-2.23760.030240.01512
M5-1226877.66896657506589.691049-2.42180.0195340.009767
M6438903.296803129502000.0028230.87430.3865920.193296
M7-3247736.55266406501583.178989-6.47500
M8-3007666.40213125501241.883487-6.000400
M9-110686.051598434500976.270682-0.22090.8261380.413069
M10229381.098934378500786.4609980.4580.6491270.324563
M11-700586.35053281500672.540647-1.39930.1685820.084291
t88834.6494671886166.75389514.405400






Multiple Linear Regression - Regression Statistics
Multiple R0.952288281719405
R-squared0.906852971500098
Adjusted R-squared0.877873895966795
F-TEST (value)31.2933713312537
F-TEST (DF numerator)14
F-TEST (DF denominator)45
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation791572.744768455
Sum Squared Residuals28196433461711.9

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.952288281719405 \tabularnewline
R-squared & 0.906852971500098 \tabularnewline
Adjusted R-squared & 0.877873895966795 \tabularnewline
F-TEST (value) & 31.2933713312537 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 45 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 791572.744768455 \tabularnewline
Sum Squared Residuals & 28196433461711.9 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28750&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.952288281719405[/C][/ROW]
[ROW][C]R-squared[/C][C]0.906852971500098[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.877873895966795[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]31.2933713312537[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]45[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]791572.744768455[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]28196433461711.9[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28750&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28750&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 R0.952288281719405
R-squared0.906852971500098
Adjusted R-squared0.877873895966795
F-TEST (value)31.2933713312537
F-TEST (DF numerator)14
F-TEST (DF denominator)45
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation791572.744768455
Sum Squared Residuals28196433461711.9






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11492938814909286.781232820101.2187672456
21471782514677354.828024440470.1719756243
31582628116929730.4280244-1103449.42802438
41630131015452262.6280244849047.371975625
51503301715448053.7659959-415036.765995863
61699846117202669.3812327-204208.38123275
71406646313604864.1812328461598.81876725
81332893713933768.9812328-604831.981232751
91731971816919583.9812328400134.018767249
101758642717348485.7812327237941.218767251
111588703716507352.9812328-620315.981232752
121793567917296773.9812328638905.018767249
131586948915975302.574839-105813.574839006
141589251115743370.6216306149140.378369364
151755655817995746.2216306-439188.221630632
161679164316518278.4216306273364.578369364
171595368916514069.5596021-560380.559602116
181814491417034807.09865461110106.90134545
191439088113437001.8986546953879.101345446
201388570913765906.6986546119802.301345446
211733257216751721.6986546580850.301345446
221715259617180623.4986546-28027.4986545544
231600387716339490.6986546-335613.698654554
241684146717128911.6986546-287444.698654552
251478339815807440.2922608-1024042.29226081
261466784815575508.3390524-907660.339052439
271771436217827883.9390524-113521.939052438
281628208816350416.1390524-68328.1390524391
291501486616104018.5871665-1089152.58716651
301772258217858634.2024034-136052.202403404
311387650914260829.0024034-384320.002403405
321549549014589733.8024034905756.197596597
331779952117575548.8024034223972.197596596
341792007918004450.6024034-84371.6024034042
351724802217163317.802403484704.1975965965
361881378217952738.8024034861043.197596598
371624968816631267.3960097-381579.396009660
381782335917875402.2088431-52043.2088431474
392042443820127777.8088431296660.191156854
401781421918650310.0088431-836091.008843148
411969996018646101.14681461053858.85318537
421977632820400716.7620515-624388.762051518
431567983316802911.5620515-1123078.56205152
441711926717131816.3620515-12549.3620515181
452009261320117631.3620515-25018.3620515182
462086368820546533.1620515317154.837948482
472092520319705400.36205151219802.63794848
482103259320494821.3620515537771.637948483
492066468419173349.95565781491334.04434223
501971151118941418.0024494770092.997550597
512255329321193793.60244941359499.39755060
521949833319716325.8024494-217992.802449403
532072282819712116.94042091010711.05957912
542132127521466732.5556578-145457.555657774
551796084817868927.355657891920.6443422264
561778965518197832.1556578-408177.155657773
572000370921183647.1556578-1179938.15565777
582116985221612548.9556578-442696.955657774
592042283920771416.1556578-348577.155657773
601981056221560837.1556578-1750275.15565777

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14929388 & 14909286.7812328 & 20101.2187672456 \tabularnewline
2 & 14717825 & 14677354.8280244 & 40470.1719756243 \tabularnewline
3 & 15826281 & 16929730.4280244 & -1103449.42802438 \tabularnewline
4 & 16301310 & 15452262.6280244 & 849047.371975625 \tabularnewline
5 & 15033017 & 15448053.7659959 & -415036.765995863 \tabularnewline
6 & 16998461 & 17202669.3812327 & -204208.38123275 \tabularnewline
7 & 14066463 & 13604864.1812328 & 461598.81876725 \tabularnewline
8 & 13328937 & 13933768.9812328 & -604831.981232751 \tabularnewline
9 & 17319718 & 16919583.9812328 & 400134.018767249 \tabularnewline
10 & 17586427 & 17348485.7812327 & 237941.218767251 \tabularnewline
11 & 15887037 & 16507352.9812328 & -620315.981232752 \tabularnewline
12 & 17935679 & 17296773.9812328 & 638905.018767249 \tabularnewline
13 & 15869489 & 15975302.574839 & -105813.574839006 \tabularnewline
14 & 15892511 & 15743370.6216306 & 149140.378369364 \tabularnewline
15 & 17556558 & 17995746.2216306 & -439188.221630632 \tabularnewline
16 & 16791643 & 16518278.4216306 & 273364.578369364 \tabularnewline
17 & 15953689 & 16514069.5596021 & -560380.559602116 \tabularnewline
18 & 18144914 & 17034807.0986546 & 1110106.90134545 \tabularnewline
19 & 14390881 & 13437001.8986546 & 953879.101345446 \tabularnewline
20 & 13885709 & 13765906.6986546 & 119802.301345446 \tabularnewline
21 & 17332572 & 16751721.6986546 & 580850.301345446 \tabularnewline
22 & 17152596 & 17180623.4986546 & -28027.4986545544 \tabularnewline
23 & 16003877 & 16339490.6986546 & -335613.698654554 \tabularnewline
24 & 16841467 & 17128911.6986546 & -287444.698654552 \tabularnewline
25 & 14783398 & 15807440.2922608 & -1024042.29226081 \tabularnewline
26 & 14667848 & 15575508.3390524 & -907660.339052439 \tabularnewline
27 & 17714362 & 17827883.9390524 & -113521.939052438 \tabularnewline
28 & 16282088 & 16350416.1390524 & -68328.1390524391 \tabularnewline
29 & 15014866 & 16104018.5871665 & -1089152.58716651 \tabularnewline
30 & 17722582 & 17858634.2024034 & -136052.202403404 \tabularnewline
31 & 13876509 & 14260829.0024034 & -384320.002403405 \tabularnewline
32 & 15495490 & 14589733.8024034 & 905756.197596597 \tabularnewline
33 & 17799521 & 17575548.8024034 & 223972.197596596 \tabularnewline
34 & 17920079 & 18004450.6024034 & -84371.6024034042 \tabularnewline
35 & 17248022 & 17163317.8024034 & 84704.1975965965 \tabularnewline
36 & 18813782 & 17952738.8024034 & 861043.197596598 \tabularnewline
37 & 16249688 & 16631267.3960097 & -381579.396009660 \tabularnewline
38 & 17823359 & 17875402.2088431 & -52043.2088431474 \tabularnewline
39 & 20424438 & 20127777.8088431 & 296660.191156854 \tabularnewline
40 & 17814219 & 18650310.0088431 & -836091.008843148 \tabularnewline
41 & 19699960 & 18646101.1468146 & 1053858.85318537 \tabularnewline
42 & 19776328 & 20400716.7620515 & -624388.762051518 \tabularnewline
43 & 15679833 & 16802911.5620515 & -1123078.56205152 \tabularnewline
44 & 17119267 & 17131816.3620515 & -12549.3620515181 \tabularnewline
45 & 20092613 & 20117631.3620515 & -25018.3620515182 \tabularnewline
46 & 20863688 & 20546533.1620515 & 317154.837948482 \tabularnewline
47 & 20925203 & 19705400.3620515 & 1219802.63794848 \tabularnewline
48 & 21032593 & 20494821.3620515 & 537771.637948483 \tabularnewline
49 & 20664684 & 19173349.9556578 & 1491334.04434223 \tabularnewline
50 & 19711511 & 18941418.0024494 & 770092.997550597 \tabularnewline
51 & 22553293 & 21193793.6024494 & 1359499.39755060 \tabularnewline
52 & 19498333 & 19716325.8024494 & -217992.802449403 \tabularnewline
53 & 20722828 & 19712116.9404209 & 1010711.05957912 \tabularnewline
54 & 21321275 & 21466732.5556578 & -145457.555657774 \tabularnewline
55 & 17960848 & 17868927.3556578 & 91920.6443422264 \tabularnewline
56 & 17789655 & 18197832.1556578 & -408177.155657773 \tabularnewline
57 & 20003709 & 21183647.1556578 & -1179938.15565777 \tabularnewline
58 & 21169852 & 21612548.9556578 & -442696.955657774 \tabularnewline
59 & 20422839 & 20771416.1556578 & -348577.155657773 \tabularnewline
60 & 19810562 & 21560837.1556578 & -1750275.15565777 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28750&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]14929388[/C][C]14909286.7812328[/C][C]20101.2187672456[/C][/ROW]
[ROW][C]2[/C][C]14717825[/C][C]14677354.8280244[/C][C]40470.1719756243[/C][/ROW]
[ROW][C]3[/C][C]15826281[/C][C]16929730.4280244[/C][C]-1103449.42802438[/C][/ROW]
[ROW][C]4[/C][C]16301310[/C][C]15452262.6280244[/C][C]849047.371975625[/C][/ROW]
[ROW][C]5[/C][C]15033017[/C][C]15448053.7659959[/C][C]-415036.765995863[/C][/ROW]
[ROW][C]6[/C][C]16998461[/C][C]17202669.3812327[/C][C]-204208.38123275[/C][/ROW]
[ROW][C]7[/C][C]14066463[/C][C]13604864.1812328[/C][C]461598.81876725[/C][/ROW]
[ROW][C]8[/C][C]13328937[/C][C]13933768.9812328[/C][C]-604831.981232751[/C][/ROW]
[ROW][C]9[/C][C]17319718[/C][C]16919583.9812328[/C][C]400134.018767249[/C][/ROW]
[ROW][C]10[/C][C]17586427[/C][C]17348485.7812327[/C][C]237941.218767251[/C][/ROW]
[ROW][C]11[/C][C]15887037[/C][C]16507352.9812328[/C][C]-620315.981232752[/C][/ROW]
[ROW][C]12[/C][C]17935679[/C][C]17296773.9812328[/C][C]638905.018767249[/C][/ROW]
[ROW][C]13[/C][C]15869489[/C][C]15975302.574839[/C][C]-105813.574839006[/C][/ROW]
[ROW][C]14[/C][C]15892511[/C][C]15743370.6216306[/C][C]149140.378369364[/C][/ROW]
[ROW][C]15[/C][C]17556558[/C][C]17995746.2216306[/C][C]-439188.221630632[/C][/ROW]
[ROW][C]16[/C][C]16791643[/C][C]16518278.4216306[/C][C]273364.578369364[/C][/ROW]
[ROW][C]17[/C][C]15953689[/C][C]16514069.5596021[/C][C]-560380.559602116[/C][/ROW]
[ROW][C]18[/C][C]18144914[/C][C]17034807.0986546[/C][C]1110106.90134545[/C][/ROW]
[ROW][C]19[/C][C]14390881[/C][C]13437001.8986546[/C][C]953879.101345446[/C][/ROW]
[ROW][C]20[/C][C]13885709[/C][C]13765906.6986546[/C][C]119802.301345446[/C][/ROW]
[ROW][C]21[/C][C]17332572[/C][C]16751721.6986546[/C][C]580850.301345446[/C][/ROW]
[ROW][C]22[/C][C]17152596[/C][C]17180623.4986546[/C][C]-28027.4986545544[/C][/ROW]
[ROW][C]23[/C][C]16003877[/C][C]16339490.6986546[/C][C]-335613.698654554[/C][/ROW]
[ROW][C]24[/C][C]16841467[/C][C]17128911.6986546[/C][C]-287444.698654552[/C][/ROW]
[ROW][C]25[/C][C]14783398[/C][C]15807440.2922608[/C][C]-1024042.29226081[/C][/ROW]
[ROW][C]26[/C][C]14667848[/C][C]15575508.3390524[/C][C]-907660.339052439[/C][/ROW]
[ROW][C]27[/C][C]17714362[/C][C]17827883.9390524[/C][C]-113521.939052438[/C][/ROW]
[ROW][C]28[/C][C]16282088[/C][C]16350416.1390524[/C][C]-68328.1390524391[/C][/ROW]
[ROW][C]29[/C][C]15014866[/C][C]16104018.5871665[/C][C]-1089152.58716651[/C][/ROW]
[ROW][C]30[/C][C]17722582[/C][C]17858634.2024034[/C][C]-136052.202403404[/C][/ROW]
[ROW][C]31[/C][C]13876509[/C][C]14260829.0024034[/C][C]-384320.002403405[/C][/ROW]
[ROW][C]32[/C][C]15495490[/C][C]14589733.8024034[/C][C]905756.197596597[/C][/ROW]
[ROW][C]33[/C][C]17799521[/C][C]17575548.8024034[/C][C]223972.197596596[/C][/ROW]
[ROW][C]34[/C][C]17920079[/C][C]18004450.6024034[/C][C]-84371.6024034042[/C][/ROW]
[ROW][C]35[/C][C]17248022[/C][C]17163317.8024034[/C][C]84704.1975965965[/C][/ROW]
[ROW][C]36[/C][C]18813782[/C][C]17952738.8024034[/C][C]861043.197596598[/C][/ROW]
[ROW][C]37[/C][C]16249688[/C][C]16631267.3960097[/C][C]-381579.396009660[/C][/ROW]
[ROW][C]38[/C][C]17823359[/C][C]17875402.2088431[/C][C]-52043.2088431474[/C][/ROW]
[ROW][C]39[/C][C]20424438[/C][C]20127777.8088431[/C][C]296660.191156854[/C][/ROW]
[ROW][C]40[/C][C]17814219[/C][C]18650310.0088431[/C][C]-836091.008843148[/C][/ROW]
[ROW][C]41[/C][C]19699960[/C][C]18646101.1468146[/C][C]1053858.85318537[/C][/ROW]
[ROW][C]42[/C][C]19776328[/C][C]20400716.7620515[/C][C]-624388.762051518[/C][/ROW]
[ROW][C]43[/C][C]15679833[/C][C]16802911.5620515[/C][C]-1123078.56205152[/C][/ROW]
[ROW][C]44[/C][C]17119267[/C][C]17131816.3620515[/C][C]-12549.3620515181[/C][/ROW]
[ROW][C]45[/C][C]20092613[/C][C]20117631.3620515[/C][C]-25018.3620515182[/C][/ROW]
[ROW][C]46[/C][C]20863688[/C][C]20546533.1620515[/C][C]317154.837948482[/C][/ROW]
[ROW][C]47[/C][C]20925203[/C][C]19705400.3620515[/C][C]1219802.63794848[/C][/ROW]
[ROW][C]48[/C][C]21032593[/C][C]20494821.3620515[/C][C]537771.637948483[/C][/ROW]
[ROW][C]49[/C][C]20664684[/C][C]19173349.9556578[/C][C]1491334.04434223[/C][/ROW]
[ROW][C]50[/C][C]19711511[/C][C]18941418.0024494[/C][C]770092.997550597[/C][/ROW]
[ROW][C]51[/C][C]22553293[/C][C]21193793.6024494[/C][C]1359499.39755060[/C][/ROW]
[ROW][C]52[/C][C]19498333[/C][C]19716325.8024494[/C][C]-217992.802449403[/C][/ROW]
[ROW][C]53[/C][C]20722828[/C][C]19712116.9404209[/C][C]1010711.05957912[/C][/ROW]
[ROW][C]54[/C][C]21321275[/C][C]21466732.5556578[/C][C]-145457.555657774[/C][/ROW]
[ROW][C]55[/C][C]17960848[/C][C]17868927.3556578[/C][C]91920.6443422264[/C][/ROW]
[ROW][C]56[/C][C]17789655[/C][C]18197832.1556578[/C][C]-408177.155657773[/C][/ROW]
[ROW][C]57[/C][C]20003709[/C][C]21183647.1556578[/C][C]-1179938.15565777[/C][/ROW]
[ROW][C]58[/C][C]21169852[/C][C]21612548.9556578[/C][C]-442696.955657774[/C][/ROW]
[ROW][C]59[/C][C]20422839[/C][C]20771416.1556578[/C][C]-348577.155657773[/C][/ROW]
[ROW][C]60[/C][C]19810562[/C][C]21560837.1556578[/C][C]-1750275.15565777[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28750&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28750&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
11492938814909286.781232820101.2187672456
21471782514677354.828024440470.1719756243
31582628116929730.4280244-1103449.42802438
41630131015452262.6280244849047.371975625
51503301715448053.7659959-415036.765995863
61699846117202669.3812327-204208.38123275
71406646313604864.1812328461598.81876725
81332893713933768.9812328-604831.981232751
91731971816919583.9812328400134.018767249
101758642717348485.7812327237941.218767251
111588703716507352.9812328-620315.981232752
121793567917296773.9812328638905.018767249
131586948915975302.574839-105813.574839006
141589251115743370.6216306149140.378369364
151755655817995746.2216306-439188.221630632
161679164316518278.4216306273364.578369364
171595368916514069.5596021-560380.559602116
181814491417034807.09865461110106.90134545
191439088113437001.8986546953879.101345446
201388570913765906.6986546119802.301345446
211733257216751721.6986546580850.301345446
221715259617180623.4986546-28027.4986545544
231600387716339490.6986546-335613.698654554
241684146717128911.6986546-287444.698654552
251478339815807440.2922608-1024042.29226081
261466784815575508.3390524-907660.339052439
271771436217827883.9390524-113521.939052438
281628208816350416.1390524-68328.1390524391
291501486616104018.5871665-1089152.58716651
301772258217858634.2024034-136052.202403404
311387650914260829.0024034-384320.002403405
321549549014589733.8024034905756.197596597
331779952117575548.8024034223972.197596596
341792007918004450.6024034-84371.6024034042
351724802217163317.802403484704.1975965965
361881378217952738.8024034861043.197596598
371624968816631267.3960097-381579.396009660
381782335917875402.2088431-52043.2088431474
392042443820127777.8088431296660.191156854
401781421918650310.0088431-836091.008843148
411969996018646101.14681461053858.85318537
421977632820400716.7620515-624388.762051518
431567983316802911.5620515-1123078.56205152
441711926717131816.3620515-12549.3620515181
452009261320117631.3620515-25018.3620515182
462086368820546533.1620515317154.837948482
472092520319705400.36205151219802.63794848
482103259320494821.3620515537771.637948483
492066468419173349.95565781491334.04434223
501971151118941418.0024494770092.997550597
512255329321193793.60244941359499.39755060
521949833319716325.8024494-217992.802449403
532072282819712116.94042091010711.05957912
542132127521466732.5556578-145457.555657774
551796084817868927.355657891920.6443422264
561778965518197832.1556578-408177.155657773
572000370921183647.1556578-1179938.15565777
582116985221612548.9556578-442696.955657774
592042283920771416.1556578-348577.155657773
601981056221560837.1556578-1750275.15565777






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.09872558597273750.1974511719454750.901274414027262
190.07265901367505570.1453180273501110.927340986324944
200.02840429699850160.05680859399700310.971595703001498
210.02210472574146010.04420945148292010.97789527425854
220.0257243433296380.0514486866592760.974275656670362
230.01107086829782000.02214173659563990.98892913170218
240.02900452310487020.05800904620974040.97099547689513
250.05091550674122620.1018310134824520.949084493258774
260.05544434709854720.1108886941970940.944555652901453
270.05237780612059620.1047556122411920.947622193879404
280.03462132737386810.06924265474773620.965378672626132
290.05359820109771740.1071964021954350.946401798902283
300.02999361646544020.05998723293088040.97000638353456
310.01754867727579030.03509735455158060.98245132272421
320.05848726052579270.1169745210515850.941512739474207
330.03948763208542820.07897526417085640.960512367914572
340.02153793122296240.04307586244592470.978462068777038
350.01565138162110300.03130276324220590.984348618378897
360.05357298194862140.1071459638972430.946427018051379
370.02927710102467530.05855420204935060.970722898975325
380.02266620716004080.04533241432008160.97733379283996
390.02976989737499980.05953979474999950.970230102625
400.04495364481634630.08990728963269250.955046355183654
410.05024237025477390.1004847405095480.949757629745226
420.05468835930639280.1093767186127860.945311640693607

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.0987255859727375 & 0.197451171945475 & 0.901274414027262 \tabularnewline
19 & 0.0726590136750557 & 0.145318027350111 & 0.927340986324944 \tabularnewline
20 & 0.0284042969985016 & 0.0568085939970031 & 0.971595703001498 \tabularnewline
21 & 0.0221047257414601 & 0.0442094514829201 & 0.97789527425854 \tabularnewline
22 & 0.025724343329638 & 0.051448686659276 & 0.974275656670362 \tabularnewline
23 & 0.0110708682978200 & 0.0221417365956399 & 0.98892913170218 \tabularnewline
24 & 0.0290045231048702 & 0.0580090462097404 & 0.97099547689513 \tabularnewline
25 & 0.0509155067412262 & 0.101831013482452 & 0.949084493258774 \tabularnewline
26 & 0.0554443470985472 & 0.110888694197094 & 0.944555652901453 \tabularnewline
27 & 0.0523778061205962 & 0.104755612241192 & 0.947622193879404 \tabularnewline
28 & 0.0346213273738681 & 0.0692426547477362 & 0.965378672626132 \tabularnewline
29 & 0.0535982010977174 & 0.107196402195435 & 0.946401798902283 \tabularnewline
30 & 0.0299936164654402 & 0.0599872329308804 & 0.97000638353456 \tabularnewline
31 & 0.0175486772757903 & 0.0350973545515806 & 0.98245132272421 \tabularnewline
32 & 0.0584872605257927 & 0.116974521051585 & 0.941512739474207 \tabularnewline
33 & 0.0394876320854282 & 0.0789752641708564 & 0.960512367914572 \tabularnewline
34 & 0.0215379312229624 & 0.0430758624459247 & 0.978462068777038 \tabularnewline
35 & 0.0156513816211030 & 0.0313027632422059 & 0.984348618378897 \tabularnewline
36 & 0.0535729819486214 & 0.107145963897243 & 0.946427018051379 \tabularnewline
37 & 0.0292771010246753 & 0.0585542020493506 & 0.970722898975325 \tabularnewline
38 & 0.0226662071600408 & 0.0453324143200816 & 0.97733379283996 \tabularnewline
39 & 0.0297698973749998 & 0.0595397947499995 & 0.970230102625 \tabularnewline
40 & 0.0449536448163463 & 0.0899072896326925 & 0.955046355183654 \tabularnewline
41 & 0.0502423702547739 & 0.100484740509548 & 0.949757629745226 \tabularnewline
42 & 0.0546883593063928 & 0.109376718612786 & 0.945311640693607 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28750&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]18[/C][C]0.0987255859727375[/C][C]0.197451171945475[/C][C]0.901274414027262[/C][/ROW]
[ROW][C]19[/C][C]0.0726590136750557[/C][C]0.145318027350111[/C][C]0.927340986324944[/C][/ROW]
[ROW][C]20[/C][C]0.0284042969985016[/C][C]0.0568085939970031[/C][C]0.971595703001498[/C][/ROW]
[ROW][C]21[/C][C]0.0221047257414601[/C][C]0.0442094514829201[/C][C]0.97789527425854[/C][/ROW]
[ROW][C]22[/C][C]0.025724343329638[/C][C]0.051448686659276[/C][C]0.974275656670362[/C][/ROW]
[ROW][C]23[/C][C]0.0110708682978200[/C][C]0.0221417365956399[/C][C]0.98892913170218[/C][/ROW]
[ROW][C]24[/C][C]0.0290045231048702[/C][C]0.0580090462097404[/C][C]0.97099547689513[/C][/ROW]
[ROW][C]25[/C][C]0.0509155067412262[/C][C]0.101831013482452[/C][C]0.949084493258774[/C][/ROW]
[ROW][C]26[/C][C]0.0554443470985472[/C][C]0.110888694197094[/C][C]0.944555652901453[/C][/ROW]
[ROW][C]27[/C][C]0.0523778061205962[/C][C]0.104755612241192[/C][C]0.947622193879404[/C][/ROW]
[ROW][C]28[/C][C]0.0346213273738681[/C][C]0.0692426547477362[/C][C]0.965378672626132[/C][/ROW]
[ROW][C]29[/C][C]0.0535982010977174[/C][C]0.107196402195435[/C][C]0.946401798902283[/C][/ROW]
[ROW][C]30[/C][C]0.0299936164654402[/C][C]0.0599872329308804[/C][C]0.97000638353456[/C][/ROW]
[ROW][C]31[/C][C]0.0175486772757903[/C][C]0.0350973545515806[/C][C]0.98245132272421[/C][/ROW]
[ROW][C]32[/C][C]0.0584872605257927[/C][C]0.116974521051585[/C][C]0.941512739474207[/C][/ROW]
[ROW][C]33[/C][C]0.0394876320854282[/C][C]0.0789752641708564[/C][C]0.960512367914572[/C][/ROW]
[ROW][C]34[/C][C]0.0215379312229624[/C][C]0.0430758624459247[/C][C]0.978462068777038[/C][/ROW]
[ROW][C]35[/C][C]0.0156513816211030[/C][C]0.0313027632422059[/C][C]0.984348618378897[/C][/ROW]
[ROW][C]36[/C][C]0.0535729819486214[/C][C]0.107145963897243[/C][C]0.946427018051379[/C][/ROW]
[ROW][C]37[/C][C]0.0292771010246753[/C][C]0.0585542020493506[/C][C]0.970722898975325[/C][/ROW]
[ROW][C]38[/C][C]0.0226662071600408[/C][C]0.0453324143200816[/C][C]0.97733379283996[/C][/ROW]
[ROW][C]39[/C][C]0.0297698973749998[/C][C]0.0595397947499995[/C][C]0.970230102625[/C][/ROW]
[ROW][C]40[/C][C]0.0449536448163463[/C][C]0.0899072896326925[/C][C]0.955046355183654[/C][/ROW]
[ROW][C]41[/C][C]0.0502423702547739[/C][C]0.100484740509548[/C][C]0.949757629745226[/C][/ROW]
[ROW][C]42[/C][C]0.0546883593063928[/C][C]0.109376718612786[/C][C]0.945311640693607[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28750&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28750&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
180.09872558597273750.1974511719454750.901274414027262
190.07265901367505570.1453180273501110.927340986324944
200.02840429699850160.05680859399700310.971595703001498
210.02210472574146010.04420945148292010.97789527425854
220.0257243433296380.0514486866592760.974275656670362
230.01107086829782000.02214173659563990.98892913170218
240.02900452310487020.05800904620974040.97099547689513
250.05091550674122620.1018310134824520.949084493258774
260.05544434709854720.1108886941970940.944555652901453
270.05237780612059620.1047556122411920.947622193879404
280.03462132737386810.06924265474773620.965378672626132
290.05359820109771740.1071964021954350.946401798902283
300.02999361646544020.05998723293088040.97000638353456
310.01754867727579030.03509735455158060.98245132272421
320.05848726052579270.1169745210515850.941512739474207
330.03948763208542820.07897526417085640.960512367914572
340.02153793122296240.04307586244592470.978462068777038
350.01565138162110300.03130276324220590.984348618378897
360.05357298194862140.1071459638972430.946427018051379
370.02927710102467530.05855420204935060.970722898975325
380.02266620716004080.04533241432008160.97733379283996
390.02976989737499980.05953979474999950.970230102625
400.04495364481634630.08990728963269250.955046355183654
410.05024237025477390.1004847405095480.949757629745226
420.05468835930639280.1093767186127860.945311640693607






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level60.24NOK
10% type I error level150.6NOK

\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 & 6 & 0.24 & NOK \tabularnewline
10% type I error level & 15 & 0.6 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28750&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]6[/C][C]0.24[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]15[/C][C]0.6[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28750&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28750&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 level60.24NOK
10% type I error level150.6NOK


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')
}