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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 13:10:02 -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/t12586614488z4icofjcxxmgfq.htm/, Retrieved Fri, 29 Mar 2024 15:20:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57929, Retrieved Fri, 29 Mar 2024 15:20:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact158
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]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:14:11] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [Model 4] [2009-11-19 20:10:02] [b58cdc967a53abb3723a2bc8f9332128] [Current]
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Dataseries X:
7.2	102.9
7.4	97.4
8.8	111.4
9.3	87.4
9.3	96.8
8.7	114.1
8.2	110.3
8.3	103.9
8.5	101.6
8.6	94.6
8.5	95.9
8.2	104.7
8.1	102.8
7.9	98.1
8.6	113.9
8.7	80.9
8.7	95.7
8.5	113.2
8.4	105.9
8.5	108.8
8.7	102.3
8.7	99
8.6	100.7
8.5	115.5
8.3	100.7
8	109.9
8.2	114.6
8.1	85.4
8.1	100.5
8	114.8
7.9	116.5
7.9	112.9
8	102
8	106
7.9	105.3
8	118.8
7.7	106.1
7.2	109.3
7.5	117.2
7.3	92.5
7	104.2
7	112.5
7	122.4
7.2	113.3
7.3	100
7.1	110.7
6.8	112.8
6.4	109.8
6.1	117.3
6.5	109.1
7.7	115.9
7.9	96
7.5	99.8
6.9	116.8
6.6	115.7
6.9	99.4
7.7	94.3
8	91
8	93.2
7.7	103.1
7.3	94.1
7.4	91.8
8.1	102.7
8.3	82.6
8.2	89.1




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=57929&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=57929&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57929&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
Werkl.graad[t] = + 13.0943338250401 -0.0408074251652485Industr.prod.[t] -0.686310695654252M1[t] -0.769705127263402M2[t] + 0.412105088011278M3[t] -0.474479148358612M4[t] -0.167840781384215M5[t] + 0.0808139189278146M6[t] -0.101027132222573M7[t] -0.203219555927245M8[t] -0.211116295816997M9[t] -0.139082822411200M10[t] -0.182161181323630M11[t] -0.0230558398694423t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Werkl.graad[t] =  +  13.0943338250401 -0.0408074251652485Industr.prod.[t] -0.686310695654252M1[t] -0.769705127263402M2[t] +  0.412105088011278M3[t] -0.474479148358612M4[t] -0.167840781384215M5[t] +  0.0808139189278146M6[t] -0.101027132222573M7[t] -0.203219555927245M8[t] -0.211116295816997M9[t] -0.139082822411200M10[t] -0.182161181323630M11[t] -0.0230558398694423t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57929&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Werkl.graad[t] =  +  13.0943338250401 -0.0408074251652485Industr.prod.[t] -0.686310695654252M1[t] -0.769705127263402M2[t] +  0.412105088011278M3[t] -0.474479148358612M4[t] -0.167840781384215M5[t] +  0.0808139189278146M6[t] -0.101027132222573M7[t] -0.203219555927245M8[t] -0.211116295816997M9[t] -0.139082822411200M10[t] -0.182161181323630M11[t] -0.0230558398694423t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57929&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57929&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
Werkl.graad[t] = + 13.0943338250401 -0.0408074251652485Industr.prod.[t] -0.686310695654252M1[t] -0.769705127263402M2[t] + 0.412105088011278M3[t] -0.474479148358612M4[t] -0.167840781384215M5[t] + 0.0808139189278146M6[t] -0.101027132222573M7[t] -0.203219555927245M8[t] -0.211116295816997M9[t] -0.139082822411200M10[t] -0.182161181323630M11[t] -0.0230558398694423t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)13.09433382504011.13490411.537800
Industr.prod.-0.04080742516524850.010068-4.05310.0001738.6e-05
M1-0.6863106956542520.285862-2.40080.0200430.010021
M2-0.7697051272634020.289174-2.66170.0103710.005186
M30.4121050880112780.2791781.47610.1460550.073028
M4-0.4744791483586120.361374-1.3130.1950640.097532
M5-0.1678407813842150.306095-0.54830.5858580.292929
M60.08081391892781460.2937060.27520.7843110.392155
M7-0.1010271322225730.29337-0.34440.7319860.365993
M8-0.2032195559272450.292018-0.69590.4896410.244821
M9-0.2111162958169970.308692-0.68390.497130.248565
M10-0.1390828224112000.307882-0.45170.6533710.326686
M11-0.1821611813236300.303703-0.59980.5512950.275647
t-0.02305583986944230.00305-7.559800

\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) & 13.0943338250401 & 1.134904 & 11.5378 & 0 & 0 \tabularnewline
Industr.prod. & -0.0408074251652485 & 0.010068 & -4.0531 & 0.000173 & 8.6e-05 \tabularnewline
M1 & -0.686310695654252 & 0.285862 & -2.4008 & 0.020043 & 0.010021 \tabularnewline
M2 & -0.769705127263402 & 0.289174 & -2.6617 & 0.010371 & 0.005186 \tabularnewline
M3 & 0.412105088011278 & 0.279178 & 1.4761 & 0.146055 & 0.073028 \tabularnewline
M4 & -0.474479148358612 & 0.361374 & -1.313 & 0.195064 & 0.097532 \tabularnewline
M5 & -0.167840781384215 & 0.306095 & -0.5483 & 0.585858 & 0.292929 \tabularnewline
M6 & 0.0808139189278146 & 0.293706 & 0.2752 & 0.784311 & 0.392155 \tabularnewline
M7 & -0.101027132222573 & 0.29337 & -0.3444 & 0.731986 & 0.365993 \tabularnewline
M8 & -0.203219555927245 & 0.292018 & -0.6959 & 0.489641 & 0.244821 \tabularnewline
M9 & -0.211116295816997 & 0.308692 & -0.6839 & 0.49713 & 0.248565 \tabularnewline
M10 & -0.139082822411200 & 0.307882 & -0.4517 & 0.653371 & 0.326686 \tabularnewline
M11 & -0.182161181323630 & 0.303703 & -0.5998 & 0.551295 & 0.275647 \tabularnewline
t & -0.0230558398694423 & 0.00305 & -7.5598 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57929&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]13.0943338250401[/C][C]1.134904[/C][C]11.5378[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Industr.prod.[/C][C]-0.0408074251652485[/C][C]0.010068[/C][C]-4.0531[/C][C]0.000173[/C][C]8.6e-05[/C][/ROW]
[ROW][C]M1[/C][C]-0.686310695654252[/C][C]0.285862[/C][C]-2.4008[/C][C]0.020043[/C][C]0.010021[/C][/ROW]
[ROW][C]M2[/C][C]-0.769705127263402[/C][C]0.289174[/C][C]-2.6617[/C][C]0.010371[/C][C]0.005186[/C][/ROW]
[ROW][C]M3[/C][C]0.412105088011278[/C][C]0.279178[/C][C]1.4761[/C][C]0.146055[/C][C]0.073028[/C][/ROW]
[ROW][C]M4[/C][C]-0.474479148358612[/C][C]0.361374[/C][C]-1.313[/C][C]0.195064[/C][C]0.097532[/C][/ROW]
[ROW][C]M5[/C][C]-0.167840781384215[/C][C]0.306095[/C][C]-0.5483[/C][C]0.585858[/C][C]0.292929[/C][/ROW]
[ROW][C]M6[/C][C]0.0808139189278146[/C][C]0.293706[/C][C]0.2752[/C][C]0.784311[/C][C]0.392155[/C][/ROW]
[ROW][C]M7[/C][C]-0.101027132222573[/C][C]0.29337[/C][C]-0.3444[/C][C]0.731986[/C][C]0.365993[/C][/ROW]
[ROW][C]M8[/C][C]-0.203219555927245[/C][C]0.292018[/C][C]-0.6959[/C][C]0.489641[/C][C]0.244821[/C][/ROW]
[ROW][C]M9[/C][C]-0.211116295816997[/C][C]0.308692[/C][C]-0.6839[/C][C]0.49713[/C][C]0.248565[/C][/ROW]
[ROW][C]M10[/C][C]-0.139082822411200[/C][C]0.307882[/C][C]-0.4517[/C][C]0.653371[/C][C]0.326686[/C][/ROW]
[ROW][C]M11[/C][C]-0.182161181323630[/C][C]0.303703[/C][C]-0.5998[/C][C]0.551295[/C][C]0.275647[/C][/ROW]
[ROW][C]t[/C][C]-0.0230558398694423[/C][C]0.00305[/C][C]-7.5598[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57929&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57929&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)13.09433382504011.13490411.537800
Industr.prod.-0.04080742516524850.010068-4.05310.0001738.6e-05
M1-0.6863106956542520.285862-2.40080.0200430.010021
M2-0.7697051272634020.289174-2.66170.0103710.005186
M30.4121050880112780.2791781.47610.1460550.073028
M4-0.4744791483586120.361374-1.3130.1950640.097532
M5-0.1678407813842150.306095-0.54830.5858580.292929
M60.08081391892781460.2937060.27520.7843110.392155
M7-0.1010271322225730.29337-0.34440.7319860.365993
M8-0.2032195559272450.292018-0.69590.4896410.244821
M9-0.2111162958169970.308692-0.68390.497130.248565
M10-0.1390828224112000.307882-0.45170.6533710.326686
M11-0.1821611813236300.303703-0.59980.5512950.275647
t-0.02305583986944230.00305-7.559800







Multiple Linear Regression - Regression Statistics
Multiple R0.810885941911432
R-squared0.65753601078959
Adjusted R-squared0.570241268441839
F-TEST (value)7.53236670508973
F-TEST (DF numerator)13
F-TEST (DF denominator)51
p-value5.48792649102126e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.459290198692366
Sum Squared Residuals10.7583218173585

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.810885941911432 \tabularnewline
R-squared & 0.65753601078959 \tabularnewline
Adjusted R-squared & 0.570241268441839 \tabularnewline
F-TEST (value) & 7.53236670508973 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 51 \tabularnewline
p-value & 5.48792649102126e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.459290198692366 \tabularnewline
Sum Squared Residuals & 10.7583218173585 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57929&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.810885941911432[/C][/ROW]
[ROW][C]R-squared[/C][C]0.65753601078959[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.570241268441839[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.53236670508973[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]51[/C][/ROW]
[ROW][C]p-value[/C][C]5.48792649102126e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.459290198692366[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]10.7583218173585[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57929&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57929&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 R0.810885941911432
R-squared0.65753601078959
Adjusted R-squared0.570241268441839
F-TEST (value)7.53236670508973
F-TEST (DF numerator)13
F-TEST (DF denominator)51
p-value5.48792649102126e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.459290198692366
Sum Squared Residuals10.7583218173585







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
17.28.18588324001229-0.98588324001229
27.48.30387380694256-0.903873806942562
38.88.89132423003432-0.091324230034321
49.38.961062357760950.338937642239047
59.38.861055088312570.438944911687429
68.78.380685493396360.319314506603638
78.28.33085681800448-0.130856818004476
88.38.46677607548795-0.166776075487950
98.58.52968057360883-0.0296805736088283
108.68.86431018330192-0.264310183301923
118.58.74512633180523-0.245126331805227
128.28.54512633180523-0.345126331805228
138.17.91329390409550.186706095904494
147.97.99863853089358-0.0986385308935782
158.68.512635588687890.087364411312107
168.78.94964054290176-0.249640542901761
178.78.629273177561040.0707268224389616
188.58.140742097611780.359257902388224
198.48.233739410298260.16626058970174
208.57.990149613744920.509850386255075
218.78.224445297559850.475554702440153
228.78.408087434141520.291912565858479
238.68.272580612578730.327419387421273
248.57.827736061587240.672263938412764
258.37.722319418509220.577680581490781
2687.240440835510340.75955916448966
278.28.20740031263891-0.00740031263891179
288.18.48933705122483-0.389337051224835
298.18.15672745833454-0.0567274583345375
3087.798780138914070.201219861085929
317.97.524510625113320.375489374886682
327.97.54616909213410.353830907865902
3387.960017446676110.0399825533238874
3487.845765379551470.154234620448527
357.97.808196378385280.0918036216147247
3687.41640148010860.583598519891392
377.77.225289244183570.47471075581643
387.26.988255212176180.211744787823819
397.57.82463092877596-0.324630928775957
407.37.92293425411826-0.622934254118262
4177.72906990678981-0.72906990678981
4277.61596713836083-0.615967138360834
4377.00707673820504-0.007076738205044
447.27.25317604363469-0.0531760436346909
457.37.7649622185733-0.464962218573302
467.17.3773004028415-0.277300402841498
476.87.2254706112126-0.425470611212604
486.47.50699822816254-1.10699822816254
496.16.49157600389948-0.391576003899479
506.56.71974661877592-0.219746618775923
517.77.601010503057470.0989894969425285
527.97.503438187606580.396561812393416
537.57.6319524990836-0.131952499083595
546.97.16382513171696-0.263825131716957
556.67.0038164083789-0.403816408378902
566.97.54372917499834-0.643729174998336
577.77.72089446358191-0.0208944635819103
5887.904536600163580.095463399836415
5987.748626066018170.251373933981834
607.77.50373789833640.196262101663606
617.37.161638189299940.138361810700064
627.47.149044995701410.250955004298586
638.17.862998436805440.237001563194555
648.37.77358760638760.526412393612394
658.27.791921869918450.408078130081553

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 7.2 & 8.18588324001229 & -0.98588324001229 \tabularnewline
2 & 7.4 & 8.30387380694256 & -0.903873806942562 \tabularnewline
3 & 8.8 & 8.89132423003432 & -0.091324230034321 \tabularnewline
4 & 9.3 & 8.96106235776095 & 0.338937642239047 \tabularnewline
5 & 9.3 & 8.86105508831257 & 0.438944911687429 \tabularnewline
6 & 8.7 & 8.38068549339636 & 0.319314506603638 \tabularnewline
7 & 8.2 & 8.33085681800448 & -0.130856818004476 \tabularnewline
8 & 8.3 & 8.46677607548795 & -0.166776075487950 \tabularnewline
9 & 8.5 & 8.52968057360883 & -0.0296805736088283 \tabularnewline
10 & 8.6 & 8.86431018330192 & -0.264310183301923 \tabularnewline
11 & 8.5 & 8.74512633180523 & -0.245126331805227 \tabularnewline
12 & 8.2 & 8.54512633180523 & -0.345126331805228 \tabularnewline
13 & 8.1 & 7.9132939040955 & 0.186706095904494 \tabularnewline
14 & 7.9 & 7.99863853089358 & -0.0986385308935782 \tabularnewline
15 & 8.6 & 8.51263558868789 & 0.087364411312107 \tabularnewline
16 & 8.7 & 8.94964054290176 & -0.249640542901761 \tabularnewline
17 & 8.7 & 8.62927317756104 & 0.0707268224389616 \tabularnewline
18 & 8.5 & 8.14074209761178 & 0.359257902388224 \tabularnewline
19 & 8.4 & 8.23373941029826 & 0.16626058970174 \tabularnewline
20 & 8.5 & 7.99014961374492 & 0.509850386255075 \tabularnewline
21 & 8.7 & 8.22444529755985 & 0.475554702440153 \tabularnewline
22 & 8.7 & 8.40808743414152 & 0.291912565858479 \tabularnewline
23 & 8.6 & 8.27258061257873 & 0.327419387421273 \tabularnewline
24 & 8.5 & 7.82773606158724 & 0.672263938412764 \tabularnewline
25 & 8.3 & 7.72231941850922 & 0.577680581490781 \tabularnewline
26 & 8 & 7.24044083551034 & 0.75955916448966 \tabularnewline
27 & 8.2 & 8.20740031263891 & -0.00740031263891179 \tabularnewline
28 & 8.1 & 8.48933705122483 & -0.389337051224835 \tabularnewline
29 & 8.1 & 8.15672745833454 & -0.0567274583345375 \tabularnewline
30 & 8 & 7.79878013891407 & 0.201219861085929 \tabularnewline
31 & 7.9 & 7.52451062511332 & 0.375489374886682 \tabularnewline
32 & 7.9 & 7.5461690921341 & 0.353830907865902 \tabularnewline
33 & 8 & 7.96001744667611 & 0.0399825533238874 \tabularnewline
34 & 8 & 7.84576537955147 & 0.154234620448527 \tabularnewline
35 & 7.9 & 7.80819637838528 & 0.0918036216147247 \tabularnewline
36 & 8 & 7.4164014801086 & 0.583598519891392 \tabularnewline
37 & 7.7 & 7.22528924418357 & 0.47471075581643 \tabularnewline
38 & 7.2 & 6.98825521217618 & 0.211744787823819 \tabularnewline
39 & 7.5 & 7.82463092877596 & -0.324630928775957 \tabularnewline
40 & 7.3 & 7.92293425411826 & -0.622934254118262 \tabularnewline
41 & 7 & 7.72906990678981 & -0.72906990678981 \tabularnewline
42 & 7 & 7.61596713836083 & -0.615967138360834 \tabularnewline
43 & 7 & 7.00707673820504 & -0.007076738205044 \tabularnewline
44 & 7.2 & 7.25317604363469 & -0.0531760436346909 \tabularnewline
45 & 7.3 & 7.7649622185733 & -0.464962218573302 \tabularnewline
46 & 7.1 & 7.3773004028415 & -0.277300402841498 \tabularnewline
47 & 6.8 & 7.2254706112126 & -0.425470611212604 \tabularnewline
48 & 6.4 & 7.50699822816254 & -1.10699822816254 \tabularnewline
49 & 6.1 & 6.49157600389948 & -0.391576003899479 \tabularnewline
50 & 6.5 & 6.71974661877592 & -0.219746618775923 \tabularnewline
51 & 7.7 & 7.60101050305747 & 0.0989894969425285 \tabularnewline
52 & 7.9 & 7.50343818760658 & 0.396561812393416 \tabularnewline
53 & 7.5 & 7.6319524990836 & -0.131952499083595 \tabularnewline
54 & 6.9 & 7.16382513171696 & -0.263825131716957 \tabularnewline
55 & 6.6 & 7.0038164083789 & -0.403816408378902 \tabularnewline
56 & 6.9 & 7.54372917499834 & -0.643729174998336 \tabularnewline
57 & 7.7 & 7.72089446358191 & -0.0208944635819103 \tabularnewline
58 & 8 & 7.90453660016358 & 0.095463399836415 \tabularnewline
59 & 8 & 7.74862606601817 & 0.251373933981834 \tabularnewline
60 & 7.7 & 7.5037378983364 & 0.196262101663606 \tabularnewline
61 & 7.3 & 7.16163818929994 & 0.138361810700064 \tabularnewline
62 & 7.4 & 7.14904499570141 & 0.250955004298586 \tabularnewline
63 & 8.1 & 7.86299843680544 & 0.237001563194555 \tabularnewline
64 & 8.3 & 7.7735876063876 & 0.526412393612394 \tabularnewline
65 & 8.2 & 7.79192186991845 & 0.408078130081553 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57929&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]7.2[/C][C]8.18588324001229[/C][C]-0.98588324001229[/C][/ROW]
[ROW][C]2[/C][C]7.4[/C][C]8.30387380694256[/C][C]-0.903873806942562[/C][/ROW]
[ROW][C]3[/C][C]8.8[/C][C]8.89132423003432[/C][C]-0.091324230034321[/C][/ROW]
[ROW][C]4[/C][C]9.3[/C][C]8.96106235776095[/C][C]0.338937642239047[/C][/ROW]
[ROW][C]5[/C][C]9.3[/C][C]8.86105508831257[/C][C]0.438944911687429[/C][/ROW]
[ROW][C]6[/C][C]8.7[/C][C]8.38068549339636[/C][C]0.319314506603638[/C][/ROW]
[ROW][C]7[/C][C]8.2[/C][C]8.33085681800448[/C][C]-0.130856818004476[/C][/ROW]
[ROW][C]8[/C][C]8.3[/C][C]8.46677607548795[/C][C]-0.166776075487950[/C][/ROW]
[ROW][C]9[/C][C]8.5[/C][C]8.52968057360883[/C][C]-0.0296805736088283[/C][/ROW]
[ROW][C]10[/C][C]8.6[/C][C]8.86431018330192[/C][C]-0.264310183301923[/C][/ROW]
[ROW][C]11[/C][C]8.5[/C][C]8.74512633180523[/C][C]-0.245126331805227[/C][/ROW]
[ROW][C]12[/C][C]8.2[/C][C]8.54512633180523[/C][C]-0.345126331805228[/C][/ROW]
[ROW][C]13[/C][C]8.1[/C][C]7.9132939040955[/C][C]0.186706095904494[/C][/ROW]
[ROW][C]14[/C][C]7.9[/C][C]7.99863853089358[/C][C]-0.0986385308935782[/C][/ROW]
[ROW][C]15[/C][C]8.6[/C][C]8.51263558868789[/C][C]0.087364411312107[/C][/ROW]
[ROW][C]16[/C][C]8.7[/C][C]8.94964054290176[/C][C]-0.249640542901761[/C][/ROW]
[ROW][C]17[/C][C]8.7[/C][C]8.62927317756104[/C][C]0.0707268224389616[/C][/ROW]
[ROW][C]18[/C][C]8.5[/C][C]8.14074209761178[/C][C]0.359257902388224[/C][/ROW]
[ROW][C]19[/C][C]8.4[/C][C]8.23373941029826[/C][C]0.16626058970174[/C][/ROW]
[ROW][C]20[/C][C]8.5[/C][C]7.99014961374492[/C][C]0.509850386255075[/C][/ROW]
[ROW][C]21[/C][C]8.7[/C][C]8.22444529755985[/C][C]0.475554702440153[/C][/ROW]
[ROW][C]22[/C][C]8.7[/C][C]8.40808743414152[/C][C]0.291912565858479[/C][/ROW]
[ROW][C]23[/C][C]8.6[/C][C]8.27258061257873[/C][C]0.327419387421273[/C][/ROW]
[ROW][C]24[/C][C]8.5[/C][C]7.82773606158724[/C][C]0.672263938412764[/C][/ROW]
[ROW][C]25[/C][C]8.3[/C][C]7.72231941850922[/C][C]0.577680581490781[/C][/ROW]
[ROW][C]26[/C][C]8[/C][C]7.24044083551034[/C][C]0.75955916448966[/C][/ROW]
[ROW][C]27[/C][C]8.2[/C][C]8.20740031263891[/C][C]-0.00740031263891179[/C][/ROW]
[ROW][C]28[/C][C]8.1[/C][C]8.48933705122483[/C][C]-0.389337051224835[/C][/ROW]
[ROW][C]29[/C][C]8.1[/C][C]8.15672745833454[/C][C]-0.0567274583345375[/C][/ROW]
[ROW][C]30[/C][C]8[/C][C]7.79878013891407[/C][C]0.201219861085929[/C][/ROW]
[ROW][C]31[/C][C]7.9[/C][C]7.52451062511332[/C][C]0.375489374886682[/C][/ROW]
[ROW][C]32[/C][C]7.9[/C][C]7.5461690921341[/C][C]0.353830907865902[/C][/ROW]
[ROW][C]33[/C][C]8[/C][C]7.96001744667611[/C][C]0.0399825533238874[/C][/ROW]
[ROW][C]34[/C][C]8[/C][C]7.84576537955147[/C][C]0.154234620448527[/C][/ROW]
[ROW][C]35[/C][C]7.9[/C][C]7.80819637838528[/C][C]0.0918036216147247[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]7.4164014801086[/C][C]0.583598519891392[/C][/ROW]
[ROW][C]37[/C][C]7.7[/C][C]7.22528924418357[/C][C]0.47471075581643[/C][/ROW]
[ROW][C]38[/C][C]7.2[/C][C]6.98825521217618[/C][C]0.211744787823819[/C][/ROW]
[ROW][C]39[/C][C]7.5[/C][C]7.82463092877596[/C][C]-0.324630928775957[/C][/ROW]
[ROW][C]40[/C][C]7.3[/C][C]7.92293425411826[/C][C]-0.622934254118262[/C][/ROW]
[ROW][C]41[/C][C]7[/C][C]7.72906990678981[/C][C]-0.72906990678981[/C][/ROW]
[ROW][C]42[/C][C]7[/C][C]7.61596713836083[/C][C]-0.615967138360834[/C][/ROW]
[ROW][C]43[/C][C]7[/C][C]7.00707673820504[/C][C]-0.007076738205044[/C][/ROW]
[ROW][C]44[/C][C]7.2[/C][C]7.25317604363469[/C][C]-0.0531760436346909[/C][/ROW]
[ROW][C]45[/C][C]7.3[/C][C]7.7649622185733[/C][C]-0.464962218573302[/C][/ROW]
[ROW][C]46[/C][C]7.1[/C][C]7.3773004028415[/C][C]-0.277300402841498[/C][/ROW]
[ROW][C]47[/C][C]6.8[/C][C]7.2254706112126[/C][C]-0.425470611212604[/C][/ROW]
[ROW][C]48[/C][C]6.4[/C][C]7.50699822816254[/C][C]-1.10699822816254[/C][/ROW]
[ROW][C]49[/C][C]6.1[/C][C]6.49157600389948[/C][C]-0.391576003899479[/C][/ROW]
[ROW][C]50[/C][C]6.5[/C][C]6.71974661877592[/C][C]-0.219746618775923[/C][/ROW]
[ROW][C]51[/C][C]7.7[/C][C]7.60101050305747[/C][C]0.0989894969425285[/C][/ROW]
[ROW][C]52[/C][C]7.9[/C][C]7.50343818760658[/C][C]0.396561812393416[/C][/ROW]
[ROW][C]53[/C][C]7.5[/C][C]7.6319524990836[/C][C]-0.131952499083595[/C][/ROW]
[ROW][C]54[/C][C]6.9[/C][C]7.16382513171696[/C][C]-0.263825131716957[/C][/ROW]
[ROW][C]55[/C][C]6.6[/C][C]7.0038164083789[/C][C]-0.403816408378902[/C][/ROW]
[ROW][C]56[/C][C]6.9[/C][C]7.54372917499834[/C][C]-0.643729174998336[/C][/ROW]
[ROW][C]57[/C][C]7.7[/C][C]7.72089446358191[/C][C]-0.0208944635819103[/C][/ROW]
[ROW][C]58[/C][C]8[/C][C]7.90453660016358[/C][C]0.095463399836415[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]7.74862606601817[/C][C]0.251373933981834[/C][/ROW]
[ROW][C]60[/C][C]7.7[/C][C]7.5037378983364[/C][C]0.196262101663606[/C][/ROW]
[ROW][C]61[/C][C]7.3[/C][C]7.16163818929994[/C][C]0.138361810700064[/C][/ROW]
[ROW][C]62[/C][C]7.4[/C][C]7.14904499570141[/C][C]0.250955004298586[/C][/ROW]
[ROW][C]63[/C][C]8.1[/C][C]7.86299843680544[/C][C]0.237001563194555[/C][/ROW]
[ROW][C]64[/C][C]8.3[/C][C]7.7735876063876[/C][C]0.526412393612394[/C][/ROW]
[ROW][C]65[/C][C]8.2[/C][C]7.79192186991845[/C][C]0.408078130081553[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57929&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57929&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
17.28.18588324001229-0.98588324001229
27.48.30387380694256-0.903873806942562
38.88.89132423003432-0.091324230034321
49.38.961062357760950.338937642239047
59.38.861055088312570.438944911687429
68.78.380685493396360.319314506603638
78.28.33085681800448-0.130856818004476
88.38.46677607548795-0.166776075487950
98.58.52968057360883-0.0296805736088283
108.68.86431018330192-0.264310183301923
118.58.74512633180523-0.245126331805227
128.28.54512633180523-0.345126331805228
138.17.91329390409550.186706095904494
147.97.99863853089358-0.0986385308935782
158.68.512635588687890.087364411312107
168.78.94964054290176-0.249640542901761
178.78.629273177561040.0707268224389616
188.58.140742097611780.359257902388224
198.48.233739410298260.16626058970174
208.57.990149613744920.509850386255075
218.78.224445297559850.475554702440153
228.78.408087434141520.291912565858479
238.68.272580612578730.327419387421273
248.57.827736061587240.672263938412764
258.37.722319418509220.577680581490781
2687.240440835510340.75955916448966
278.28.20740031263891-0.00740031263891179
288.18.48933705122483-0.389337051224835
298.18.15672745833454-0.0567274583345375
3087.798780138914070.201219861085929
317.97.524510625113320.375489374886682
327.97.54616909213410.353830907865902
3387.960017446676110.0399825533238874
3487.845765379551470.154234620448527
357.97.808196378385280.0918036216147247
3687.41640148010860.583598519891392
377.77.225289244183570.47471075581643
387.26.988255212176180.211744787823819
397.57.82463092877596-0.324630928775957
407.37.92293425411826-0.622934254118262
4177.72906990678981-0.72906990678981
4277.61596713836083-0.615967138360834
4377.00707673820504-0.007076738205044
447.27.25317604363469-0.0531760436346909
457.37.7649622185733-0.464962218573302
467.17.3773004028415-0.277300402841498
476.87.2254706112126-0.425470611212604
486.47.50699822816254-1.10699822816254
496.16.49157600389948-0.391576003899479
506.56.71974661877592-0.219746618775923
517.77.601010503057470.0989894969425285
527.97.503438187606580.396561812393416
537.57.6319524990836-0.131952499083595
546.97.16382513171696-0.263825131716957
556.67.0038164083789-0.403816408378902
566.97.54372917499834-0.643729174998336
577.77.72089446358191-0.0208944635819103
5887.904536600163580.095463399836415
5987.748626066018170.251373933981834
607.77.50373789833640.196262101663606
617.37.161638189299940.138361810700064
627.47.149044995701410.250955004298586
638.17.862998436805440.237001563194555
648.37.77358760638760.526412393612394
658.27.791921869918450.408078130081553







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.6993210693385070.6013578613229870.300678930661493
180.5544805314463470.8910389371073070.445519468553653
190.4847485793893210.9694971587786420.515251420610679
200.3616428820589730.7232857641179450.638357117941027
210.2591676457430010.5183352914860020.740832354256999
220.1740238355135080.3480476710270160.825976164486492
230.1101608994696690.2203217989393380.889839100530331
240.07793795711723960.1558759142344790.92206204288276
250.09449373761468680.1889874752293740.905506262385313
260.06983127327371050.1396625465474210.93016872672629
270.0944572354020110.1889144708040220.905542764597989
280.2310473725012270.4620947450024540.768952627498773
290.3060312165720790.6120624331441590.69396878342792
300.2790934092228850.558186818445770.720906590777115
310.2369966113210630.4739932226421260.763003388678937
320.2410738653347260.4821477306694510.758926134665274
330.200783072423270.401566144846540.79921692757673
340.1789529744630810.3579059489261630.821047025536919
350.1475583036697420.2951166073394840.852441696330258
360.3235079756319000.6470159512638010.6764920243681
370.4849822881471080.9699645762942160.515017711852892
380.5950624533648560.8098750932702880.404937546635144
390.5762992753983370.8474014492033260.423700724601663
400.6480995483388940.7038009033222120.351900451661106
410.7198655263630020.5602689472739970.280134473636998
420.6691171408590710.6617657182818580.330882859140929
430.7434893882514780.5130212234970450.256510611748522
440.9342926388384790.1314147223230420.0657073611615212
450.896944509337730.2061109813245380.103055490662269
460.82538291197560.34923417604880.1746170880244
470.7604672110928070.4790655778143870.239532788907193
480.950754652045440.09849069590912250.0492453479545612

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.699321069338507 & 0.601357861322987 & 0.300678930661493 \tabularnewline
18 & 0.554480531446347 & 0.891038937107307 & 0.445519468553653 \tabularnewline
19 & 0.484748579389321 & 0.969497158778642 & 0.515251420610679 \tabularnewline
20 & 0.361642882058973 & 0.723285764117945 & 0.638357117941027 \tabularnewline
21 & 0.259167645743001 & 0.518335291486002 & 0.740832354256999 \tabularnewline
22 & 0.174023835513508 & 0.348047671027016 & 0.825976164486492 \tabularnewline
23 & 0.110160899469669 & 0.220321798939338 & 0.889839100530331 \tabularnewline
24 & 0.0779379571172396 & 0.155875914234479 & 0.92206204288276 \tabularnewline
25 & 0.0944937376146868 & 0.188987475229374 & 0.905506262385313 \tabularnewline
26 & 0.0698312732737105 & 0.139662546547421 & 0.93016872672629 \tabularnewline
27 & 0.094457235402011 & 0.188914470804022 & 0.905542764597989 \tabularnewline
28 & 0.231047372501227 & 0.462094745002454 & 0.768952627498773 \tabularnewline
29 & 0.306031216572079 & 0.612062433144159 & 0.69396878342792 \tabularnewline
30 & 0.279093409222885 & 0.55818681844577 & 0.720906590777115 \tabularnewline
31 & 0.236996611321063 & 0.473993222642126 & 0.763003388678937 \tabularnewline
32 & 0.241073865334726 & 0.482147730669451 & 0.758926134665274 \tabularnewline
33 & 0.20078307242327 & 0.40156614484654 & 0.79921692757673 \tabularnewline
34 & 0.178952974463081 & 0.357905948926163 & 0.821047025536919 \tabularnewline
35 & 0.147558303669742 & 0.295116607339484 & 0.852441696330258 \tabularnewline
36 & 0.323507975631900 & 0.647015951263801 & 0.6764920243681 \tabularnewline
37 & 0.484982288147108 & 0.969964576294216 & 0.515017711852892 \tabularnewline
38 & 0.595062453364856 & 0.809875093270288 & 0.404937546635144 \tabularnewline
39 & 0.576299275398337 & 0.847401449203326 & 0.423700724601663 \tabularnewline
40 & 0.648099548338894 & 0.703800903322212 & 0.351900451661106 \tabularnewline
41 & 0.719865526363002 & 0.560268947273997 & 0.280134473636998 \tabularnewline
42 & 0.669117140859071 & 0.661765718281858 & 0.330882859140929 \tabularnewline
43 & 0.743489388251478 & 0.513021223497045 & 0.256510611748522 \tabularnewline
44 & 0.934292638838479 & 0.131414722323042 & 0.0657073611615212 \tabularnewline
45 & 0.89694450933773 & 0.206110981324538 & 0.103055490662269 \tabularnewline
46 & 0.8253829119756 & 0.3492341760488 & 0.1746170880244 \tabularnewline
47 & 0.760467211092807 & 0.479065577814387 & 0.239532788907193 \tabularnewline
48 & 0.95075465204544 & 0.0984906959091225 & 0.0492453479545612 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57929&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]17[/C][C]0.699321069338507[/C][C]0.601357861322987[/C][C]0.300678930661493[/C][/ROW]
[ROW][C]18[/C][C]0.554480531446347[/C][C]0.891038937107307[/C][C]0.445519468553653[/C][/ROW]
[ROW][C]19[/C][C]0.484748579389321[/C][C]0.969497158778642[/C][C]0.515251420610679[/C][/ROW]
[ROW][C]20[/C][C]0.361642882058973[/C][C]0.723285764117945[/C][C]0.638357117941027[/C][/ROW]
[ROW][C]21[/C][C]0.259167645743001[/C][C]0.518335291486002[/C][C]0.740832354256999[/C][/ROW]
[ROW][C]22[/C][C]0.174023835513508[/C][C]0.348047671027016[/C][C]0.825976164486492[/C][/ROW]
[ROW][C]23[/C][C]0.110160899469669[/C][C]0.220321798939338[/C][C]0.889839100530331[/C][/ROW]
[ROW][C]24[/C][C]0.0779379571172396[/C][C]0.155875914234479[/C][C]0.92206204288276[/C][/ROW]
[ROW][C]25[/C][C]0.0944937376146868[/C][C]0.188987475229374[/C][C]0.905506262385313[/C][/ROW]
[ROW][C]26[/C][C]0.0698312732737105[/C][C]0.139662546547421[/C][C]0.93016872672629[/C][/ROW]
[ROW][C]27[/C][C]0.094457235402011[/C][C]0.188914470804022[/C][C]0.905542764597989[/C][/ROW]
[ROW][C]28[/C][C]0.231047372501227[/C][C]0.462094745002454[/C][C]0.768952627498773[/C][/ROW]
[ROW][C]29[/C][C]0.306031216572079[/C][C]0.612062433144159[/C][C]0.69396878342792[/C][/ROW]
[ROW][C]30[/C][C]0.279093409222885[/C][C]0.55818681844577[/C][C]0.720906590777115[/C][/ROW]
[ROW][C]31[/C][C]0.236996611321063[/C][C]0.473993222642126[/C][C]0.763003388678937[/C][/ROW]
[ROW][C]32[/C][C]0.241073865334726[/C][C]0.482147730669451[/C][C]0.758926134665274[/C][/ROW]
[ROW][C]33[/C][C]0.20078307242327[/C][C]0.40156614484654[/C][C]0.79921692757673[/C][/ROW]
[ROW][C]34[/C][C]0.178952974463081[/C][C]0.357905948926163[/C][C]0.821047025536919[/C][/ROW]
[ROW][C]35[/C][C]0.147558303669742[/C][C]0.295116607339484[/C][C]0.852441696330258[/C][/ROW]
[ROW][C]36[/C][C]0.323507975631900[/C][C]0.647015951263801[/C][C]0.6764920243681[/C][/ROW]
[ROW][C]37[/C][C]0.484982288147108[/C][C]0.969964576294216[/C][C]0.515017711852892[/C][/ROW]
[ROW][C]38[/C][C]0.595062453364856[/C][C]0.809875093270288[/C][C]0.404937546635144[/C][/ROW]
[ROW][C]39[/C][C]0.576299275398337[/C][C]0.847401449203326[/C][C]0.423700724601663[/C][/ROW]
[ROW][C]40[/C][C]0.648099548338894[/C][C]0.703800903322212[/C][C]0.351900451661106[/C][/ROW]
[ROW][C]41[/C][C]0.719865526363002[/C][C]0.560268947273997[/C][C]0.280134473636998[/C][/ROW]
[ROW][C]42[/C][C]0.669117140859071[/C][C]0.661765718281858[/C][C]0.330882859140929[/C][/ROW]
[ROW][C]43[/C][C]0.743489388251478[/C][C]0.513021223497045[/C][C]0.256510611748522[/C][/ROW]
[ROW][C]44[/C][C]0.934292638838479[/C][C]0.131414722323042[/C][C]0.0657073611615212[/C][/ROW]
[ROW][C]45[/C][C]0.89694450933773[/C][C]0.206110981324538[/C][C]0.103055490662269[/C][/ROW]
[ROW][C]46[/C][C]0.8253829119756[/C][C]0.3492341760488[/C][C]0.1746170880244[/C][/ROW]
[ROW][C]47[/C][C]0.760467211092807[/C][C]0.479065577814387[/C][C]0.239532788907193[/C][/ROW]
[ROW][C]48[/C][C]0.95075465204544[/C][C]0.0984906959091225[/C][C]0.0492453479545612[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57929&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57929&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
170.6993210693385070.6013578613229870.300678930661493
180.5544805314463470.8910389371073070.445519468553653
190.4847485793893210.9694971587786420.515251420610679
200.3616428820589730.7232857641179450.638357117941027
210.2591676457430010.5183352914860020.740832354256999
220.1740238355135080.3480476710270160.825976164486492
230.1101608994696690.2203217989393380.889839100530331
240.07793795711723960.1558759142344790.92206204288276
250.09449373761468680.1889874752293740.905506262385313
260.06983127327371050.1396625465474210.93016872672629
270.0944572354020110.1889144708040220.905542764597989
280.2310473725012270.4620947450024540.768952627498773
290.3060312165720790.6120624331441590.69396878342792
300.2790934092228850.558186818445770.720906590777115
310.2369966113210630.4739932226421260.763003388678937
320.2410738653347260.4821477306694510.758926134665274
330.200783072423270.401566144846540.79921692757673
340.1789529744630810.3579059489261630.821047025536919
350.1475583036697420.2951166073394840.852441696330258
360.3235079756319000.6470159512638010.6764920243681
370.4849822881471080.9699645762942160.515017711852892
380.5950624533648560.8098750932702880.404937546635144
390.5762992753983370.8474014492033260.423700724601663
400.6480995483388940.7038009033222120.351900451661106
410.7198655263630020.5602689472739970.280134473636998
420.6691171408590710.6617657182818580.330882859140929
430.7434893882514780.5130212234970450.256510611748522
440.9342926388384790.1314147223230420.0657073611615212
450.896944509337730.2061109813245380.103055490662269
460.82538291197560.34923417604880.1746170880244
470.7604672110928070.4790655778143870.239532788907193
480.950754652045440.09849069590912250.0492453479545612







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.03125OK

\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 & 0 & 0 & OK \tabularnewline
10% type I error level & 1 & 0.03125 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57929&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.03125[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57929&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57929&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.03125OK



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')
}