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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 09:58:24 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t1258650000cdltq7kxxadwcxo.htm/, Retrieved Fri, 29 Mar 2024 13:14:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57826, Retrieved Fri, 29 Mar 2024 13:14:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact130
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 14:03:14] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [Multiple Regressi...] [2009-11-19 16:58:24] [b58cdc967a53abb3723a2bc8f9332128] [Current]
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Dataseries X:
4	7.2	102.9	271244
4.1	7.4	97.4	269907
4	8.8	111.4	271296
3.8	9.3	87.4	270157
4.7	9.3	96.8	271322
4.3	8.7	114.1	267179
3.9	8.2	110.3	264101
4	8.3	103.9	265518
4.3	8.5	101.6	269419
4.8	8.6	94.6	268714
4.4	8.5	95.9	272482
4.3	8.2	104.7	268351
4.7	8.1	102.8	268175
4.7	7.9	98.1	270674
4.9	8.6	113.9	272764
5	8.7	80.9	272599
4.2	8.7	95.7	270333
4.3	8.5	113.2	270846
4.8	8.4	105.9	270491
4.8	8.5	108.8	269160
4.8	8.7	102.3	274027
4.2	8.7	99	273784
4.6	8.6	100.7	276663
4.8	8.5	115.5	274525
4.5	8.3	100.7	271344
4.4	8	109.9	271115
4.3	8.2	114.6	270798
3.9	8.1	85.4	273911
3.7	8.1	100.5	273985
4	8	114.8	271917
4.1	7.9	116.5	273338
3.7	7.9	112.9	270601
3.8	8	102	273547
3.8	8	106	275363
3.8	7.9	105.3	281229
3.3	8	118.8	277793
3.3	7.7	106.1	279913
3.3	7.2	109.3	282500
3.2	7.5	117.2	280041
3.4	7.3	92.5	282166
4.2	7	104.2	290304
4.9	7	112.5	283519
5.1	7	122.4	287816
5.5	7.2	113.3	285226
5.6	7.3	100	287595
6.4	7.1	110.7	289741
6.1	6.8	112.8	289148
7.1	6.4	109.8	288301
7.8	6.1	117.3	290155
7.9	6.5	109.1	289648
7.4	7.7	115.9	288225
7.5	7.9	96	289351
6.8	7.5	99.8	294735
5.2	6.9	116.8	305333
4.7	6.6	115.7	309030
4.1	6.9	99.4	310215
3.9	7.7	94.3	321935
2.6	8	91	325734
2.7	8	93.2	320846
1.8	7.7	103.1	323023
1	7.3	94.1	319753
0.3	7.4	91.8	321753
1.3	8.1	102.7	320757
1	8.3	82.6	324479
1.1	8.2	89.1	324641




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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57826&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57826&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57826&T=0

As an alternative you can also use a QR Code:  

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

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







Multiple Linear Regression - Estimated Regression Equation
Cons.index[t] = + 19.1792040203402 -1.12771759422269Werkl.graad[t] + 0.0611701113554312Industr.prod.[t] -4.5112205089636e-05BrutoSchuld[t] -0.135530992464506M1[t] -0.169606970781555M2[t] + 0.117225185486256M3[t] + 1.76991643625086M4[t] + 1.10643031597644M5[t] -0.190436397986985M6[t] -0.374667261299632M7[t] + 0.0442239049331357M8[t] + 1.11890712542945M9[t] + 1.0920282953553M10[t] + 0.899403442297472M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Cons.index[t] =  +  19.1792040203402 -1.12771759422269Werkl.graad[t] +  0.0611701113554312Industr.prod.[t] -4.5112205089636e-05BrutoSchuld[t] -0.135530992464506M1[t] -0.169606970781555M2[t] +  0.117225185486256M3[t] +  1.76991643625086M4[t] +  1.10643031597644M5[t] -0.190436397986985M6[t] -0.374667261299632M7[t] +  0.0442239049331357M8[t] +  1.11890712542945M9[t] +  1.0920282953553M10[t] +  0.899403442297472M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57826&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Cons.index[t] =  +  19.1792040203402 -1.12771759422269Werkl.graad[t] +  0.0611701113554312Industr.prod.[t] -4.5112205089636e-05BrutoSchuld[t] -0.135530992464506M1[t] -0.169606970781555M2[t] +  0.117225185486256M3[t] +  1.76991643625086M4[t] +  1.10643031597644M5[t] -0.190436397986985M6[t] -0.374667261299632M7[t] +  0.0442239049331357M8[t] +  1.11890712542945M9[t] +  1.0920282953553M10[t] +  0.899403442297472M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57826&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57826&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Cons.index[t] = + 19.1792040203402 -1.12771759422269Werkl.graad[t] + 0.0611701113554312Industr.prod.[t] -4.5112205089636e-05BrutoSchuld[t] -0.135530992464506M1[t] -0.169606970781555M2[t] + 0.117225185486256M3[t] + 1.76991643625086M4[t] + 1.10643031597644M5[t] -0.190436397986985M6[t] -0.374667261299632M7[t] + 0.0442239049331357M8[t] + 1.11890712542945M9[t] + 1.0920282953553M10[t] + 0.899403442297472M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)19.17920402034029.1612932.09350.0413970.020698
Werkl.graad-1.127717594222690.354742-3.1790.0025360.001268
Industr.prod.0.06117011135543120.0374551.63310.108720.05436
BrutoSchuld-4.5112205089636e-051.2e-05-3.62830.0006690.000335
M1-0.1355309924645060.827324-0.16380.8705340.435267
M2-0.1696069707815550.852666-0.19890.8431370.421568
M30.1172251854862560.7723180.15180.8799680.439984
M41.769916436250861.0706111.65320.104560.05228
M51.106430315976440.849331.30270.1986420.099321
M6-0.1904363979869850.793594-0.240.8113370.405668
M7-0.3746672612996320.790863-0.47370.6377440.318872
M80.04422390493313570.8006730.05520.9561730.478086
M91.118907125429450.8547591.3090.1965080.098254
M101.09202829535530.8458861.2910.2026450.101323
M110.8994034422974720.8321781.08080.2849790.14249

\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) & 19.1792040203402 & 9.161293 & 2.0935 & 0.041397 & 0.020698 \tabularnewline
Werkl.graad & -1.12771759422269 & 0.354742 & -3.179 & 0.002536 & 0.001268 \tabularnewline
Industr.prod. & 0.0611701113554312 & 0.037455 & 1.6331 & 0.10872 & 0.05436 \tabularnewline
BrutoSchuld & -4.5112205089636e-05 & 1.2e-05 & -3.6283 & 0.000669 & 0.000335 \tabularnewline
M1 & -0.135530992464506 & 0.827324 & -0.1638 & 0.870534 & 0.435267 \tabularnewline
M2 & -0.169606970781555 & 0.852666 & -0.1989 & 0.843137 & 0.421568 \tabularnewline
M3 & 0.117225185486256 & 0.772318 & 0.1518 & 0.879968 & 0.439984 \tabularnewline
M4 & 1.76991643625086 & 1.070611 & 1.6532 & 0.10456 & 0.05228 \tabularnewline
M5 & 1.10643031597644 & 0.84933 & 1.3027 & 0.198642 & 0.099321 \tabularnewline
M6 & -0.190436397986985 & 0.793594 & -0.24 & 0.811337 & 0.405668 \tabularnewline
M7 & -0.374667261299632 & 0.790863 & -0.4737 & 0.637744 & 0.318872 \tabularnewline
M8 & 0.0442239049331357 & 0.800673 & 0.0552 & 0.956173 & 0.478086 \tabularnewline
M9 & 1.11890712542945 & 0.854759 & 1.309 & 0.196508 & 0.098254 \tabularnewline
M10 & 1.0920282953553 & 0.845886 & 1.291 & 0.202645 & 0.101323 \tabularnewline
M11 & 0.899403442297472 & 0.832178 & 1.0808 & 0.284979 & 0.14249 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57826&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]19.1792040203402[/C][C]9.161293[/C][C]2.0935[/C][C]0.041397[/C][C]0.020698[/C][/ROW]
[ROW][C]Werkl.graad[/C][C]-1.12771759422269[/C][C]0.354742[/C][C]-3.179[/C][C]0.002536[/C][C]0.001268[/C][/ROW]
[ROW][C]Industr.prod.[/C][C]0.0611701113554312[/C][C]0.037455[/C][C]1.6331[/C][C]0.10872[/C][C]0.05436[/C][/ROW]
[ROW][C]BrutoSchuld[/C][C]-4.5112205089636e-05[/C][C]1.2e-05[/C][C]-3.6283[/C][C]0.000669[/C][C]0.000335[/C][/ROW]
[ROW][C]M1[/C][C]-0.135530992464506[/C][C]0.827324[/C][C]-0.1638[/C][C]0.870534[/C][C]0.435267[/C][/ROW]
[ROW][C]M2[/C][C]-0.169606970781555[/C][C]0.852666[/C][C]-0.1989[/C][C]0.843137[/C][C]0.421568[/C][/ROW]
[ROW][C]M3[/C][C]0.117225185486256[/C][C]0.772318[/C][C]0.1518[/C][C]0.879968[/C][C]0.439984[/C][/ROW]
[ROW][C]M4[/C][C]1.76991643625086[/C][C]1.070611[/C][C]1.6532[/C][C]0.10456[/C][C]0.05228[/C][/ROW]
[ROW][C]M5[/C][C]1.10643031597644[/C][C]0.84933[/C][C]1.3027[/C][C]0.198642[/C][C]0.099321[/C][/ROW]
[ROW][C]M6[/C][C]-0.190436397986985[/C][C]0.793594[/C][C]-0.24[/C][C]0.811337[/C][C]0.405668[/C][/ROW]
[ROW][C]M7[/C][C]-0.374667261299632[/C][C]0.790863[/C][C]-0.4737[/C][C]0.637744[/C][C]0.318872[/C][/ROW]
[ROW][C]M8[/C][C]0.0442239049331357[/C][C]0.800673[/C][C]0.0552[/C][C]0.956173[/C][C]0.478086[/C][/ROW]
[ROW][C]M9[/C][C]1.11890712542945[/C][C]0.854759[/C][C]1.309[/C][C]0.196508[/C][C]0.098254[/C][/ROW]
[ROW][C]M10[/C][C]1.0920282953553[/C][C]0.845886[/C][C]1.291[/C][C]0.202645[/C][C]0.101323[/C][/ROW]
[ROW][C]M11[/C][C]0.899403442297472[/C][C]0.832178[/C][C]1.0808[/C][C]0.284979[/C][C]0.14249[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57826&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57826&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)19.17920402034029.1612932.09350.0413970.020698
Werkl.graad-1.127717594222690.354742-3.1790.0025360.001268
Industr.prod.0.06117011135543120.0374551.63310.108720.05436
BrutoSchuld-4.5112205089636e-051.2e-05-3.62830.0006690.000335
M1-0.1355309924645060.827324-0.16380.8705340.435267
M2-0.1696069707815550.852666-0.19890.8431370.421568
M30.1172251854862560.7723180.15180.8799680.439984
M41.769916436250861.0706111.65320.104560.05228
M51.106430315976440.849331.30270.1986420.099321
M6-0.1904363979869850.793594-0.240.8113370.405668
M7-0.3746672612996320.790863-0.47370.6377440.318872
M80.04422390493313570.8006730.05520.9561730.478086
M91.118907125429450.8547591.3090.1965080.098254
M101.09202829535530.8458861.2910.2026450.101323
M110.8994034422974720.8321781.08080.2849790.14249







Multiple Linear Regression - Regression Statistics
Multiple R0.70097736397713
R-squared0.491369264808326
Adjusted R-squared0.348952658954657
F-TEST (value)3.45022451464123
F-TEST (DF numerator)14
F-TEST (DF denominator)50
p-value0.000612196827674438
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.23968636711867
Sum Squared Residuals76.841114440994

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.70097736397713 \tabularnewline
R-squared & 0.491369264808326 \tabularnewline
Adjusted R-squared & 0.348952658954657 \tabularnewline
F-TEST (value) & 3.45022451464123 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 50 \tabularnewline
p-value & 0.000612196827674438 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.23968636711867 \tabularnewline
Sum Squared Residuals & 76.841114440994 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57826&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.70097736397713[/C][/ROW]
[ROW][C]R-squared[/C][C]0.491369264808326[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.348952658954657[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.45022451464123[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]50[/C][/ROW]
[ROW][C]p-value[/C][C]0.000612196827674438[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.23968636711867[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]76.841114440994[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57826&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57826&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.70097736397713
R-squared0.491369264808326
Adjusted R-squared0.348952658954657
F-TEST (value)3.45022451464123
F-TEST (DF numerator)14
F-TEST (DF denominator)50
p-value0.000612196827674438
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.23968636711867
Sum Squared Residuals76.841114440994







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
144.98209585061306-0.982095850613056
24.14.44635575920136-0.346355759201357
343.948103989663930.0518960103360667
43.83.620236572383940.179763427616060
54.73.479193779921141.22080622007886
64.34.104100414626660.195899585373342
73.94.39013729254062-0.490137292540619
844.24084399206434-0.240843992064341
94.34.77330972554396-0.473309725543958
104.84.237272461147710.562727538852286
114.44.066957723496470.333042276503533
124.34.230525058618880.069474941381117
134.74.09948236209710.600517637902899
144.73.890714978735060.809285021264939
154.94.260348069825470.639651930174534
1653.789097400278361.21090259972164
174.24.133153184797430.0668468152025671
184.34.109164377187610.190835622812386
194.83.607178293209411.19282170679059
204.84.150735367924960.649264632075036
214.84.382708243595180.417291756404821
224.24.164930311884890.035069688115112
234.64.05918836910050.5408116308995
244.84.274324228767320.52567577123268
254.53.60252103147710.8974789685229
264.44.47985605086235-0.0798560508623518
274.34.84294478066957-0.542944780669568
283.94.68180624483381-0.781806244833815
293.74.93865050284977-1.23865050284977
3044.72258018081665-0.722580180816648
314.14.69100582279813-0.591005822798131
323.75.01315669348168-1.31315669348168
333.85.17541338458746-1.37541338458746
343.85.31129123549225-1.51129123549225
353.84.92399086885209-1.12399086885209
363.34.89261770711866-1.59261770711866
373.34.22290370391695-0.922903703916952
383.34.83172560448174-1.53172560448174
393.25.37441727450607-2.17441727450607
403.45.64588685782058-2.24588685782058
414.25.66928319365205-1.46928319365205
424.95.18621471547189-0.286214715471888
435.15.41372080930785-0.313720809307846
445.55.167261054543810.332738945456192
455.65.208739220733270.39126077926673
466.45.965113308884410.434886691115585
476.16.26601250555795-0.166012505557954
487.15.672395804594181.42760419540582
497.86.250317897326031.54968210267397
507.95.286431856185822.61356814381418
517.44.700154324445892.69984567555411
527.54.859220497461942.64077950253806
536.84.636383725824632.16361627417537
545.24.577940311897190.622059688102808
554.74.497957782143990.202042217856006
564.13.528002891985210.571997108014792
573.92.859829425540141.04017057445986
582.62.121392682590730.478607317409269
592.72.283850532992990.416149467007008
601.82.23013720090096-0.430137200900958
6112.14267915456976-1.14267915456976
620.31.76491575053367-1.46491575053367
631.31.97403156088908-0.674031560889081
6412.00375242722136-1.00375242722136
651.11.84333561295498-0.74333561295498

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4 & 4.98209585061306 & -0.982095850613056 \tabularnewline
2 & 4.1 & 4.44635575920136 & -0.346355759201357 \tabularnewline
3 & 4 & 3.94810398966393 & 0.0518960103360667 \tabularnewline
4 & 3.8 & 3.62023657238394 & 0.179763427616060 \tabularnewline
5 & 4.7 & 3.47919377992114 & 1.22080622007886 \tabularnewline
6 & 4.3 & 4.10410041462666 & 0.195899585373342 \tabularnewline
7 & 3.9 & 4.39013729254062 & -0.490137292540619 \tabularnewline
8 & 4 & 4.24084399206434 & -0.240843992064341 \tabularnewline
9 & 4.3 & 4.77330972554396 & -0.473309725543958 \tabularnewline
10 & 4.8 & 4.23727246114771 & 0.562727538852286 \tabularnewline
11 & 4.4 & 4.06695772349647 & 0.333042276503533 \tabularnewline
12 & 4.3 & 4.23052505861888 & 0.069474941381117 \tabularnewline
13 & 4.7 & 4.0994823620971 & 0.600517637902899 \tabularnewline
14 & 4.7 & 3.89071497873506 & 0.809285021264939 \tabularnewline
15 & 4.9 & 4.26034806982547 & 0.639651930174534 \tabularnewline
16 & 5 & 3.78909740027836 & 1.21090259972164 \tabularnewline
17 & 4.2 & 4.13315318479743 & 0.0668468152025671 \tabularnewline
18 & 4.3 & 4.10916437718761 & 0.190835622812386 \tabularnewline
19 & 4.8 & 3.60717829320941 & 1.19282170679059 \tabularnewline
20 & 4.8 & 4.15073536792496 & 0.649264632075036 \tabularnewline
21 & 4.8 & 4.38270824359518 & 0.417291756404821 \tabularnewline
22 & 4.2 & 4.16493031188489 & 0.035069688115112 \tabularnewline
23 & 4.6 & 4.0591883691005 & 0.5408116308995 \tabularnewline
24 & 4.8 & 4.27432422876732 & 0.52567577123268 \tabularnewline
25 & 4.5 & 3.6025210314771 & 0.8974789685229 \tabularnewline
26 & 4.4 & 4.47985605086235 & -0.0798560508623518 \tabularnewline
27 & 4.3 & 4.84294478066957 & -0.542944780669568 \tabularnewline
28 & 3.9 & 4.68180624483381 & -0.781806244833815 \tabularnewline
29 & 3.7 & 4.93865050284977 & -1.23865050284977 \tabularnewline
30 & 4 & 4.72258018081665 & -0.722580180816648 \tabularnewline
31 & 4.1 & 4.69100582279813 & -0.591005822798131 \tabularnewline
32 & 3.7 & 5.01315669348168 & -1.31315669348168 \tabularnewline
33 & 3.8 & 5.17541338458746 & -1.37541338458746 \tabularnewline
34 & 3.8 & 5.31129123549225 & -1.51129123549225 \tabularnewline
35 & 3.8 & 4.92399086885209 & -1.12399086885209 \tabularnewline
36 & 3.3 & 4.89261770711866 & -1.59261770711866 \tabularnewline
37 & 3.3 & 4.22290370391695 & -0.922903703916952 \tabularnewline
38 & 3.3 & 4.83172560448174 & -1.53172560448174 \tabularnewline
39 & 3.2 & 5.37441727450607 & -2.17441727450607 \tabularnewline
40 & 3.4 & 5.64588685782058 & -2.24588685782058 \tabularnewline
41 & 4.2 & 5.66928319365205 & -1.46928319365205 \tabularnewline
42 & 4.9 & 5.18621471547189 & -0.286214715471888 \tabularnewline
43 & 5.1 & 5.41372080930785 & -0.313720809307846 \tabularnewline
44 & 5.5 & 5.16726105454381 & 0.332738945456192 \tabularnewline
45 & 5.6 & 5.20873922073327 & 0.39126077926673 \tabularnewline
46 & 6.4 & 5.96511330888441 & 0.434886691115585 \tabularnewline
47 & 6.1 & 6.26601250555795 & -0.166012505557954 \tabularnewline
48 & 7.1 & 5.67239580459418 & 1.42760419540582 \tabularnewline
49 & 7.8 & 6.25031789732603 & 1.54968210267397 \tabularnewline
50 & 7.9 & 5.28643185618582 & 2.61356814381418 \tabularnewline
51 & 7.4 & 4.70015432444589 & 2.69984567555411 \tabularnewline
52 & 7.5 & 4.85922049746194 & 2.64077950253806 \tabularnewline
53 & 6.8 & 4.63638372582463 & 2.16361627417537 \tabularnewline
54 & 5.2 & 4.57794031189719 & 0.622059688102808 \tabularnewline
55 & 4.7 & 4.49795778214399 & 0.202042217856006 \tabularnewline
56 & 4.1 & 3.52800289198521 & 0.571997108014792 \tabularnewline
57 & 3.9 & 2.85982942554014 & 1.04017057445986 \tabularnewline
58 & 2.6 & 2.12139268259073 & 0.478607317409269 \tabularnewline
59 & 2.7 & 2.28385053299299 & 0.416149467007008 \tabularnewline
60 & 1.8 & 2.23013720090096 & -0.430137200900958 \tabularnewline
61 & 1 & 2.14267915456976 & -1.14267915456976 \tabularnewline
62 & 0.3 & 1.76491575053367 & -1.46491575053367 \tabularnewline
63 & 1.3 & 1.97403156088908 & -0.674031560889081 \tabularnewline
64 & 1 & 2.00375242722136 & -1.00375242722136 \tabularnewline
65 & 1.1 & 1.84333561295498 & -0.74333561295498 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57826&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]4[/C][C]4.98209585061306[/C][C]-0.982095850613056[/C][/ROW]
[ROW][C]2[/C][C]4.1[/C][C]4.44635575920136[/C][C]-0.346355759201357[/C][/ROW]
[ROW][C]3[/C][C]4[/C][C]3.94810398966393[/C][C]0.0518960103360667[/C][/ROW]
[ROW][C]4[/C][C]3.8[/C][C]3.62023657238394[/C][C]0.179763427616060[/C][/ROW]
[ROW][C]5[/C][C]4.7[/C][C]3.47919377992114[/C][C]1.22080622007886[/C][/ROW]
[ROW][C]6[/C][C]4.3[/C][C]4.10410041462666[/C][C]0.195899585373342[/C][/ROW]
[ROW][C]7[/C][C]3.9[/C][C]4.39013729254062[/C][C]-0.490137292540619[/C][/ROW]
[ROW][C]8[/C][C]4[/C][C]4.24084399206434[/C][C]-0.240843992064341[/C][/ROW]
[ROW][C]9[/C][C]4.3[/C][C]4.77330972554396[/C][C]-0.473309725543958[/C][/ROW]
[ROW][C]10[/C][C]4.8[/C][C]4.23727246114771[/C][C]0.562727538852286[/C][/ROW]
[ROW][C]11[/C][C]4.4[/C][C]4.06695772349647[/C][C]0.333042276503533[/C][/ROW]
[ROW][C]12[/C][C]4.3[/C][C]4.23052505861888[/C][C]0.069474941381117[/C][/ROW]
[ROW][C]13[/C][C]4.7[/C][C]4.0994823620971[/C][C]0.600517637902899[/C][/ROW]
[ROW][C]14[/C][C]4.7[/C][C]3.89071497873506[/C][C]0.809285021264939[/C][/ROW]
[ROW][C]15[/C][C]4.9[/C][C]4.26034806982547[/C][C]0.639651930174534[/C][/ROW]
[ROW][C]16[/C][C]5[/C][C]3.78909740027836[/C][C]1.21090259972164[/C][/ROW]
[ROW][C]17[/C][C]4.2[/C][C]4.13315318479743[/C][C]0.0668468152025671[/C][/ROW]
[ROW][C]18[/C][C]4.3[/C][C]4.10916437718761[/C][C]0.190835622812386[/C][/ROW]
[ROW][C]19[/C][C]4.8[/C][C]3.60717829320941[/C][C]1.19282170679059[/C][/ROW]
[ROW][C]20[/C][C]4.8[/C][C]4.15073536792496[/C][C]0.649264632075036[/C][/ROW]
[ROW][C]21[/C][C]4.8[/C][C]4.38270824359518[/C][C]0.417291756404821[/C][/ROW]
[ROW][C]22[/C][C]4.2[/C][C]4.16493031188489[/C][C]0.035069688115112[/C][/ROW]
[ROW][C]23[/C][C]4.6[/C][C]4.0591883691005[/C][C]0.5408116308995[/C][/ROW]
[ROW][C]24[/C][C]4.8[/C][C]4.27432422876732[/C][C]0.52567577123268[/C][/ROW]
[ROW][C]25[/C][C]4.5[/C][C]3.6025210314771[/C][C]0.8974789685229[/C][/ROW]
[ROW][C]26[/C][C]4.4[/C][C]4.47985605086235[/C][C]-0.0798560508623518[/C][/ROW]
[ROW][C]27[/C][C]4.3[/C][C]4.84294478066957[/C][C]-0.542944780669568[/C][/ROW]
[ROW][C]28[/C][C]3.9[/C][C]4.68180624483381[/C][C]-0.781806244833815[/C][/ROW]
[ROW][C]29[/C][C]3.7[/C][C]4.93865050284977[/C][C]-1.23865050284977[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]4.72258018081665[/C][C]-0.722580180816648[/C][/ROW]
[ROW][C]31[/C][C]4.1[/C][C]4.69100582279813[/C][C]-0.591005822798131[/C][/ROW]
[ROW][C]32[/C][C]3.7[/C][C]5.01315669348168[/C][C]-1.31315669348168[/C][/ROW]
[ROW][C]33[/C][C]3.8[/C][C]5.17541338458746[/C][C]-1.37541338458746[/C][/ROW]
[ROW][C]34[/C][C]3.8[/C][C]5.31129123549225[/C][C]-1.51129123549225[/C][/ROW]
[ROW][C]35[/C][C]3.8[/C][C]4.92399086885209[/C][C]-1.12399086885209[/C][/ROW]
[ROW][C]36[/C][C]3.3[/C][C]4.89261770711866[/C][C]-1.59261770711866[/C][/ROW]
[ROW][C]37[/C][C]3.3[/C][C]4.22290370391695[/C][C]-0.922903703916952[/C][/ROW]
[ROW][C]38[/C][C]3.3[/C][C]4.83172560448174[/C][C]-1.53172560448174[/C][/ROW]
[ROW][C]39[/C][C]3.2[/C][C]5.37441727450607[/C][C]-2.17441727450607[/C][/ROW]
[ROW][C]40[/C][C]3.4[/C][C]5.64588685782058[/C][C]-2.24588685782058[/C][/ROW]
[ROW][C]41[/C][C]4.2[/C][C]5.66928319365205[/C][C]-1.46928319365205[/C][/ROW]
[ROW][C]42[/C][C]4.9[/C][C]5.18621471547189[/C][C]-0.286214715471888[/C][/ROW]
[ROW][C]43[/C][C]5.1[/C][C]5.41372080930785[/C][C]-0.313720809307846[/C][/ROW]
[ROW][C]44[/C][C]5.5[/C][C]5.16726105454381[/C][C]0.332738945456192[/C][/ROW]
[ROW][C]45[/C][C]5.6[/C][C]5.20873922073327[/C][C]0.39126077926673[/C][/ROW]
[ROW][C]46[/C][C]6.4[/C][C]5.96511330888441[/C][C]0.434886691115585[/C][/ROW]
[ROW][C]47[/C][C]6.1[/C][C]6.26601250555795[/C][C]-0.166012505557954[/C][/ROW]
[ROW][C]48[/C][C]7.1[/C][C]5.67239580459418[/C][C]1.42760419540582[/C][/ROW]
[ROW][C]49[/C][C]7.8[/C][C]6.25031789732603[/C][C]1.54968210267397[/C][/ROW]
[ROW][C]50[/C][C]7.9[/C][C]5.28643185618582[/C][C]2.61356814381418[/C][/ROW]
[ROW][C]51[/C][C]7.4[/C][C]4.70015432444589[/C][C]2.69984567555411[/C][/ROW]
[ROW][C]52[/C][C]7.5[/C][C]4.85922049746194[/C][C]2.64077950253806[/C][/ROW]
[ROW][C]53[/C][C]6.8[/C][C]4.63638372582463[/C][C]2.16361627417537[/C][/ROW]
[ROW][C]54[/C][C]5.2[/C][C]4.57794031189719[/C][C]0.622059688102808[/C][/ROW]
[ROW][C]55[/C][C]4.7[/C][C]4.49795778214399[/C][C]0.202042217856006[/C][/ROW]
[ROW][C]56[/C][C]4.1[/C][C]3.52800289198521[/C][C]0.571997108014792[/C][/ROW]
[ROW][C]57[/C][C]3.9[/C][C]2.85982942554014[/C][C]1.04017057445986[/C][/ROW]
[ROW][C]58[/C][C]2.6[/C][C]2.12139268259073[/C][C]0.478607317409269[/C][/ROW]
[ROW][C]59[/C][C]2.7[/C][C]2.28385053299299[/C][C]0.416149467007008[/C][/ROW]
[ROW][C]60[/C][C]1.8[/C][C]2.23013720090096[/C][C]-0.430137200900958[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]2.14267915456976[/C][C]-1.14267915456976[/C][/ROW]
[ROW][C]62[/C][C]0.3[/C][C]1.76491575053367[/C][C]-1.46491575053367[/C][/ROW]
[ROW][C]63[/C][C]1.3[/C][C]1.97403156088908[/C][C]-0.674031560889081[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]2.00375242722136[/C][C]-1.00375242722136[/C][/ROW]
[ROW][C]65[/C][C]1.1[/C][C]1.84333561295498[/C][C]-0.74333561295498[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57826&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57826&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
144.98209585061306-0.982095850613056
24.14.44635575920136-0.346355759201357
343.948103989663930.0518960103360667
43.83.620236572383940.179763427616060
54.73.479193779921141.22080622007886
64.34.104100414626660.195899585373342
73.94.39013729254062-0.490137292540619
844.24084399206434-0.240843992064341
94.34.77330972554396-0.473309725543958
104.84.237272461147710.562727538852286
114.44.066957723496470.333042276503533
124.34.230525058618880.069474941381117
134.74.09948236209710.600517637902899
144.73.890714978735060.809285021264939
154.94.260348069825470.639651930174534
1653.789097400278361.21090259972164
174.24.133153184797430.0668468152025671
184.34.109164377187610.190835622812386
194.83.607178293209411.19282170679059
204.84.150735367924960.649264632075036
214.84.382708243595180.417291756404821
224.24.164930311884890.035069688115112
234.64.05918836910050.5408116308995
244.84.274324228767320.52567577123268
254.53.60252103147710.8974789685229
264.44.47985605086235-0.0798560508623518
274.34.84294478066957-0.542944780669568
283.94.68180624483381-0.781806244833815
293.74.93865050284977-1.23865050284977
3044.72258018081665-0.722580180816648
314.14.69100582279813-0.591005822798131
323.75.01315669348168-1.31315669348168
333.85.17541338458746-1.37541338458746
343.85.31129123549225-1.51129123549225
353.84.92399086885209-1.12399086885209
363.34.89261770711866-1.59261770711866
373.34.22290370391695-0.922903703916952
383.34.83172560448174-1.53172560448174
393.25.37441727450607-2.17441727450607
403.45.64588685782058-2.24588685782058
414.25.66928319365205-1.46928319365205
424.95.18621471547189-0.286214715471888
435.15.41372080930785-0.313720809307846
445.55.167261054543810.332738945456192
455.65.208739220733270.39126077926673
466.45.965113308884410.434886691115585
476.16.26601250555795-0.166012505557954
487.15.672395804594181.42760419540582
497.86.250317897326031.54968210267397
507.95.286431856185822.61356814381418
517.44.700154324445892.69984567555411
527.54.859220497461942.64077950253806
536.84.636383725824632.16361627417537
545.24.577940311897190.622059688102808
554.74.497957782143990.202042217856006
564.13.528002891985210.571997108014792
573.92.859829425540141.04017057445986
582.62.121392682590730.478607317409269
592.72.283850532992990.416149467007008
601.82.23013720090096-0.430137200900958
6112.14267915456976-1.14267915456976
620.31.76491575053367-1.46491575053367
631.31.97403156088908-0.674031560889081
6412.00375242722136-1.00375242722136
651.11.84333561295498-0.74333561295498







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.07920899147564190.1584179829512840.920791008524358
190.03201988192303870.06403976384607750.967980118076961
200.01391944683967910.02783889367935830.98608055316032
210.004406577846753540.008813155693507070.995593422153247
220.002924121556110890.005848243112221780.99707587844389
230.001015662105228940.002031324210457880.998984337894771
240.0004047879670674480.0008095759341348970.999595212032933
250.0002404459885305770.0004808919770611550.99975955401147
268.05596697315464e-050.0001611193394630930.999919440330268
272.33890915395731e-054.67781830791462e-050.99997661090846
287.44302671943692e-061.48860534388738e-050.99999255697328
292.42187628770629e-064.84375257541257e-060.999997578123712
306.1859377294455e-071.2371875458891e-060.999999381406227
311.93170057115475e-073.86340114230951e-070.999999806829943
325.6337905387488e-081.12675810774976e-070.999999943662095
332.12725187478656e-084.25450374957312e-080.999999978727481
344.79914517265863e-099.59829034531725e-090.999999995200855
351.25261912380757e-092.50523824761513e-090.99999999874738
362.42476266505483e-094.84952533010966e-090.999999997575237
374.56092156675827e-099.12184313351654e-090.999999995439078
383.51276315100169e-097.02552630200337e-090.999999996487237
391.07481618862785e-082.1496323772557e-080.999999989251838
404.78535660465935e-089.5707132093187e-080.999999952146434
413.16233249703565e-066.3246649940713e-060.999996837667503
423.76368488209598e-067.52736976419196e-060.999996236315118
439.97195822912086e-061.99439164582417e-050.99999002804177
442.74746389234141e-055.49492778468281e-050.999972525361077
450.0003888238934895530.0007776477869791060.99961117610651
460.02788240099555980.05576480199111970.97211759900444
470.785288497969290.4294230040614180.214711502030709

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.0792089914756419 & 0.158417982951284 & 0.920791008524358 \tabularnewline
19 & 0.0320198819230387 & 0.0640397638460775 & 0.967980118076961 \tabularnewline
20 & 0.0139194468396791 & 0.0278388936793583 & 0.98608055316032 \tabularnewline
21 & 0.00440657784675354 & 0.00881315569350707 & 0.995593422153247 \tabularnewline
22 & 0.00292412155611089 & 0.00584824311222178 & 0.99707587844389 \tabularnewline
23 & 0.00101566210522894 & 0.00203132421045788 & 0.998984337894771 \tabularnewline
24 & 0.000404787967067448 & 0.000809575934134897 & 0.999595212032933 \tabularnewline
25 & 0.000240445988530577 & 0.000480891977061155 & 0.99975955401147 \tabularnewline
26 & 8.05596697315464e-05 & 0.000161119339463093 & 0.999919440330268 \tabularnewline
27 & 2.33890915395731e-05 & 4.67781830791462e-05 & 0.99997661090846 \tabularnewline
28 & 7.44302671943692e-06 & 1.48860534388738e-05 & 0.99999255697328 \tabularnewline
29 & 2.42187628770629e-06 & 4.84375257541257e-06 & 0.999997578123712 \tabularnewline
30 & 6.1859377294455e-07 & 1.2371875458891e-06 & 0.999999381406227 \tabularnewline
31 & 1.93170057115475e-07 & 3.86340114230951e-07 & 0.999999806829943 \tabularnewline
32 & 5.6337905387488e-08 & 1.12675810774976e-07 & 0.999999943662095 \tabularnewline
33 & 2.12725187478656e-08 & 4.25450374957312e-08 & 0.999999978727481 \tabularnewline
34 & 4.79914517265863e-09 & 9.59829034531725e-09 & 0.999999995200855 \tabularnewline
35 & 1.25261912380757e-09 & 2.50523824761513e-09 & 0.99999999874738 \tabularnewline
36 & 2.42476266505483e-09 & 4.84952533010966e-09 & 0.999999997575237 \tabularnewline
37 & 4.56092156675827e-09 & 9.12184313351654e-09 & 0.999999995439078 \tabularnewline
38 & 3.51276315100169e-09 & 7.02552630200337e-09 & 0.999999996487237 \tabularnewline
39 & 1.07481618862785e-08 & 2.1496323772557e-08 & 0.999999989251838 \tabularnewline
40 & 4.78535660465935e-08 & 9.5707132093187e-08 & 0.999999952146434 \tabularnewline
41 & 3.16233249703565e-06 & 6.3246649940713e-06 & 0.999996837667503 \tabularnewline
42 & 3.76368488209598e-06 & 7.52736976419196e-06 & 0.999996236315118 \tabularnewline
43 & 9.97195822912086e-06 & 1.99439164582417e-05 & 0.99999002804177 \tabularnewline
44 & 2.74746389234141e-05 & 5.49492778468281e-05 & 0.999972525361077 \tabularnewline
45 & 0.000388823893489553 & 0.000777647786979106 & 0.99961117610651 \tabularnewline
46 & 0.0278824009955598 & 0.0557648019911197 & 0.97211759900444 \tabularnewline
47 & 0.78528849796929 & 0.429423004061418 & 0.214711502030709 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57826&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]0.0792089914756419[/C][C]0.158417982951284[/C][C]0.920791008524358[/C][/ROW]
[ROW][C]19[/C][C]0.0320198819230387[/C][C]0.0640397638460775[/C][C]0.967980118076961[/C][/ROW]
[ROW][C]20[/C][C]0.0139194468396791[/C][C]0.0278388936793583[/C][C]0.98608055316032[/C][/ROW]
[ROW][C]21[/C][C]0.00440657784675354[/C][C]0.00881315569350707[/C][C]0.995593422153247[/C][/ROW]
[ROW][C]22[/C][C]0.00292412155611089[/C][C]0.00584824311222178[/C][C]0.99707587844389[/C][/ROW]
[ROW][C]23[/C][C]0.00101566210522894[/C][C]0.00203132421045788[/C][C]0.998984337894771[/C][/ROW]
[ROW][C]24[/C][C]0.000404787967067448[/C][C]0.000809575934134897[/C][C]0.999595212032933[/C][/ROW]
[ROW][C]25[/C][C]0.000240445988530577[/C][C]0.000480891977061155[/C][C]0.99975955401147[/C][/ROW]
[ROW][C]26[/C][C]8.05596697315464e-05[/C][C]0.000161119339463093[/C][C]0.999919440330268[/C][/ROW]
[ROW][C]27[/C][C]2.33890915395731e-05[/C][C]4.67781830791462e-05[/C][C]0.99997661090846[/C][/ROW]
[ROW][C]28[/C][C]7.44302671943692e-06[/C][C]1.48860534388738e-05[/C][C]0.99999255697328[/C][/ROW]
[ROW][C]29[/C][C]2.42187628770629e-06[/C][C]4.84375257541257e-06[/C][C]0.999997578123712[/C][/ROW]
[ROW][C]30[/C][C]6.1859377294455e-07[/C][C]1.2371875458891e-06[/C][C]0.999999381406227[/C][/ROW]
[ROW][C]31[/C][C]1.93170057115475e-07[/C][C]3.86340114230951e-07[/C][C]0.999999806829943[/C][/ROW]
[ROW][C]32[/C][C]5.6337905387488e-08[/C][C]1.12675810774976e-07[/C][C]0.999999943662095[/C][/ROW]
[ROW][C]33[/C][C]2.12725187478656e-08[/C][C]4.25450374957312e-08[/C][C]0.999999978727481[/C][/ROW]
[ROW][C]34[/C][C]4.79914517265863e-09[/C][C]9.59829034531725e-09[/C][C]0.999999995200855[/C][/ROW]
[ROW][C]35[/C][C]1.25261912380757e-09[/C][C]2.50523824761513e-09[/C][C]0.99999999874738[/C][/ROW]
[ROW][C]36[/C][C]2.42476266505483e-09[/C][C]4.84952533010966e-09[/C][C]0.999999997575237[/C][/ROW]
[ROW][C]37[/C][C]4.56092156675827e-09[/C][C]9.12184313351654e-09[/C][C]0.999999995439078[/C][/ROW]
[ROW][C]38[/C][C]3.51276315100169e-09[/C][C]7.02552630200337e-09[/C][C]0.999999996487237[/C][/ROW]
[ROW][C]39[/C][C]1.07481618862785e-08[/C][C]2.1496323772557e-08[/C][C]0.999999989251838[/C][/ROW]
[ROW][C]40[/C][C]4.78535660465935e-08[/C][C]9.5707132093187e-08[/C][C]0.999999952146434[/C][/ROW]
[ROW][C]41[/C][C]3.16233249703565e-06[/C][C]6.3246649940713e-06[/C][C]0.999996837667503[/C][/ROW]
[ROW][C]42[/C][C]3.76368488209598e-06[/C][C]7.52736976419196e-06[/C][C]0.999996236315118[/C][/ROW]
[ROW][C]43[/C][C]9.97195822912086e-06[/C][C]1.99439164582417e-05[/C][C]0.99999002804177[/C][/ROW]
[ROW][C]44[/C][C]2.74746389234141e-05[/C][C]5.49492778468281e-05[/C][C]0.999972525361077[/C][/ROW]
[ROW][C]45[/C][C]0.000388823893489553[/C][C]0.000777647786979106[/C][C]0.99961117610651[/C][/ROW]
[ROW][C]46[/C][C]0.0278824009955598[/C][C]0.0557648019911197[/C][C]0.97211759900444[/C][/ROW]
[ROW][C]47[/C][C]0.78528849796929[/C][C]0.429423004061418[/C][C]0.214711502030709[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57826&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57826&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.07920899147564190.1584179829512840.920791008524358
190.03201988192303870.06403976384607750.967980118076961
200.01391944683967910.02783889367935830.98608055316032
210.004406577846753540.008813155693507070.995593422153247
220.002924121556110890.005848243112221780.99707587844389
230.001015662105228940.002031324210457880.998984337894771
240.0004047879670674480.0008095759341348970.999595212032933
250.0002404459885305770.0004808919770611550.99975955401147
268.05596697315464e-050.0001611193394630930.999919440330268
272.33890915395731e-054.67781830791462e-050.99997661090846
287.44302671943692e-061.48860534388738e-050.99999255697328
292.42187628770629e-064.84375257541257e-060.999997578123712
306.1859377294455e-071.2371875458891e-060.999999381406227
311.93170057115475e-073.86340114230951e-070.999999806829943
325.6337905387488e-081.12675810774976e-070.999999943662095
332.12725187478656e-084.25450374957312e-080.999999978727481
344.79914517265863e-099.59829034531725e-090.999999995200855
351.25261912380757e-092.50523824761513e-090.99999999874738
362.42476266505483e-094.84952533010966e-090.999999997575237
374.56092156675827e-099.12184313351654e-090.999999995439078
383.51276315100169e-097.02552630200337e-090.999999996487237
391.07481618862785e-082.1496323772557e-080.999999989251838
404.78535660465935e-089.5707132093187e-080.999999952146434
413.16233249703565e-066.3246649940713e-060.999996837667503
423.76368488209598e-067.52736976419196e-060.999996236315118
439.97195822912086e-061.99439164582417e-050.99999002804177
442.74746389234141e-055.49492778468281e-050.999972525361077
450.0003888238934895530.0007776477869791060.99961117610651
460.02788240099555980.05576480199111970.97211759900444
470.785288497969290.4294230040614180.214711502030709







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level250.833333333333333NOK
5% type I error level260.866666666666667NOK
10% type I error level280.933333333333333NOK

\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 & 25 & 0.833333333333333 & NOK \tabularnewline
5% type I error level & 26 & 0.866666666666667 & NOK \tabularnewline
10% type I error level & 28 & 0.933333333333333 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57826&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]25[/C][C]0.833333333333333[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]26[/C][C]0.866666666666667[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]28[/C][C]0.933333333333333[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57826&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57826&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level250.833333333333333NOK
5% type I error level260.866666666666667NOK
10% type I error level280.933333333333333NOK



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')
}