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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 16 Nov 2012 08:11:54 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/16/t13530715621v8ndtgry45hskw.htm/, Retrieved Sat, 27 Apr 2024 07:25:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=189913, Retrieved Sat, 27 Apr 2024 07:25:01 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact102

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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Dataset

Dataseries X:
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Boxplots 
7	6,4	7,7	19,1	18,5	22,4	5,7	5,2	6,4
7	6,3	7,9	18,1	16,3	18,6	5,9	5,2	6,7
7	6,2	7,9	17	16,3	18,6	5,9	5,2	6,8
7,2	6,5	8	17,1	16,3	18,6	6,1	5,5	6,9
7,3	6,8	7,9	17,4	16,8	16,2	6,3	5,8	6,9
7,1	6,8	7,6	16,8	16,8	16,2	6,2	5,8	6,7
6,8	6,4	7,1	15,3	16,8	16,2	5,9	5,5	6,4
6,4	6,1	6,8	14,3	14,8	13,8	5,7	5,3	6,2
6,1	5,8	6,5	13,4	14,8	13,8	5,4	5,1	5,9
6,5	6,1	6,9	15,3	14,8	13,8	5,6	5,2	6,1
7,7	7,2	8,2	22,1	21,4	24,1	6,2	5,8	6,7
7,9	7,3	8,7	23,7	21,4	24,1	6,3	5,8	6,8
7,5	6,9	8,3	22,2	21,4	24,1	6	5,5	6,6
6,9	6,1	7,9	19,5	16,1	19,9	5,6	5	6,4
6,6	5,8	7,5	16,6	16,1	19,9	5,5	4,9	6,4
6,9	6,2	7,8	17,3	16,1	19,9	5,9	5,3	6,7
7,7	7,1	8,3	19,8	19,6	22,3	6,5	6,1	7,1
8	7,7	8,4	21,2	19,6	22,3	6,8	6,5	7,1
8	8	8,2	21,5	19,6	22,3	6,8	6,8	6,8
7,7	7,8	7,6	20,6	18,9	20,9	6,5	6,7	6,2
7,3	7,4	7,2	19,1	18,9	20,9	6,2	6,4	5,9
7,4	7,4	7,5	19,6	18,9	20,9	6,2	6,3	6,2
8,1	7,7	8,7	23,4	24,3	23,5	6,6	6,2	7,1
8,3	7,7	9	24,3	24,3	23,5	6,7	6,1	7,4
8,1	7,8	8,6	24,1	24,3	23,5	6,5	6,2	7
7,9	8	7,9	22,8	22,9	23,1	6,4	6,4	6,5
7,9	8,1	7,8	22,5	22,9	23,1	6,5	6,6	6,3
8,3	8,4	8,2	23,8	22,9	23,1	6,8	7	6,6
8,6	8,4	8,9	24,9	24	25,7	7,1	7	7,2
8,7	8,4	9	25,2	24	25,7	7,2	7	7,4
8,5	8,3	8,8	24,3	24	25,7	7,1	6,9	7,4
8,3	8,2	8,4	22,8	22,1	19,7	7	6,8	7,2
8	8	8	20,7	22,1	19,7	6,9	6,7	7,1
8	8	8,1	19,8	22,1	19,7	6,9	6,7	7,2
8,8	8,6	9	22,5	22,1	23,1	7,4	7,1	7,6
8,7	8,4	9,2	22,6	22,1	23,1	7,3	7	7,7
8,5	8,2	8,8	22,5	22,1	23,1	7	6,8	7,3
8,1	7,9	8,4	21,8	21,6	20,7	6,8	6,5	7,1
7,8	7,6	8	21,2	21,6	20,7	6,5	6,2	6,8
7,7	7,6	7,7	20,6	21,6	20,7	6,4	6,3	6,5
7,5	7,7	7,2	19,9	19,4	18	6,3	6,4	6,1
7,2	7,5	6,8	18,7	19,4	18	6	6,3	5,7
6,9	7,1	6,6	17,6	19,4	18	5,9	6,1	5,6
6,6	6,6	6,6	16,4	15,9	16,9	5,7	5,7	5,7
6,5	6,4	6,6	15,9	15,9	16,9	5,7	5,6	5,8
6,6	6,5	6,9	16,8	15,9	16,9	5,7	5,6	5,9
7,7	7,4	7,9	22,8	21,8	24,4	6,2	6,2	6,3
8	7,7	8,3	24	21,8	24,4	6,4	6,3	6,5
7,7	7,6	7,8	22,2	21,8	24,4	6,2	6,2	6,3
7,2	7,2	7,3	17,9	17,6	15,5	6,2	6	6,3
7	7	7,1	16	17,6	15,5	6,1	5,9	6,3
7	7	7	16	17,6	15,5	6,1	6	6,3
7,3	7,3	7,2	18,5	19	18,4	6,2	6,1	6,3
7,3	7,3	7,2	19,3	19	18,4	6,1	6,1	6,2
7,1	7,1	7,1	18,5	19	18,4	6,1	6	6,2
7	7	7,1	17	16,3	16,2	6,2	6	6,3
7	6,8	7,1	15,9	16,3	16,2	6,2	5,9	6,4
7	6,8	7,2	15,8	16,3	16,2	6,2	5,9	6,6
7,7	7,4	8	19,2	19,7	21,1	6,6	6,3	7,1
7,9	7,6	8,3	20,9	19,7	21,1	6,7	6,3	7,1
7,7	7,6	7,9	20,7	19,7	21,1	6,4	6,2	6,7

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 8 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189913&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189913&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189913&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
Totaal[t] = + 0.0981473390861909 + 0.419630839440572Mannen[t] + 0.338435874594549Vrouwen[t] + 0.0113186213520091TotaalJongerdan25jaar[t] + 0.0067365270872766MannenJongerdan25jaar[t] + 0.00647230505605263VrouwenJongerdan25jaar[t] + 0.200738507619287TotaalOuderdan25[t] -0.0201043486615964MannenOuderdan25[t] + 0.013167842822975`VrouwenOuderdan25\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Totaal[t] =  +  0.0981473390861909 +  0.419630839440572Mannen[t] +  0.338435874594549Vrouwen[t] +  0.0113186213520091TotaalJongerdan25jaar[t] +  0.0067365270872766MannenJongerdan25jaar[t] +  0.00647230505605263VrouwenJongerdan25jaar[t] +  0.200738507619287TotaalOuderdan25[t] -0.0201043486615964MannenOuderdan25[t] +  0.013167842822975`VrouwenOuderdan25\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189913&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Totaal[t] =  +  0.0981473390861909 +  0.419630839440572Mannen[t] +  0.338435874594549Vrouwen[t] +  0.0113186213520091TotaalJongerdan25jaar[t] +  0.0067365270872766MannenJongerdan25jaar[t] +  0.00647230505605263VrouwenJongerdan25jaar[t] +  0.200738507619287TotaalOuderdan25[t] -0.0201043486615964MannenOuderdan25[t] +  0.013167842822975`VrouwenOuderdan25\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189913&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189913&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Totaal[t] = + 0.0981473390861909 + 0.419630839440572Mannen[t] + 0.338435874594549Vrouwen[t] + 0.0113186213520091TotaalJongerdan25jaar[t] + 0.0067365270872766MannenJongerdan25jaar[t] + 0.00647230505605263VrouwenJongerdan25jaar[t] + 0.200738507619287TotaalOuderdan25[t] -0.0201043486615964MannenOuderdan25[t] + 0.013167842822975`VrouwenOuderdan25\r`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.09814733908619090.0803521.22150.2274210.11371
Mannen0.4196308394405720.0703035.968900
Vrouwen0.3384358745945490.058775.758700
TotaalJongerdan25jaar0.01131862135200910.0117540.9630.340010.170005
MannenJongerdan25jaar0.00673652708727660.0073460.9170.3633860.181693
VrouwenJongerdan25jaar0.006472305056052630.0054361.19070.2391710.119586
TotaalOuderdan250.2007385076192870.1633621.22880.224680.11234
MannenOuderdan25-0.02010434866159640.105651-0.19030.8498220.424911
`VrouwenOuderdan25\r`0.0131678428229750.0877070.15010.8812390.44062

\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) & 0.0981473390861909 & 0.080352 & 1.2215 & 0.227421 & 0.11371 \tabularnewline
Mannen & 0.419630839440572 & 0.070303 & 5.9689 & 0 & 0 \tabularnewline
Vrouwen & 0.338435874594549 & 0.05877 & 5.7587 & 0 & 0 \tabularnewline
TotaalJongerdan25jaar & 0.0113186213520091 & 0.011754 & 0.963 & 0.34001 & 0.170005 \tabularnewline
MannenJongerdan25jaar & 0.0067365270872766 & 0.007346 & 0.917 & 0.363386 & 0.181693 \tabularnewline
VrouwenJongerdan25jaar & 0.00647230505605263 & 0.005436 & 1.1907 & 0.239171 & 0.119586 \tabularnewline
TotaalOuderdan25 & 0.200738507619287 & 0.163362 & 1.2288 & 0.22468 & 0.11234 \tabularnewline
MannenOuderdan25 & -0.0201043486615964 & 0.105651 & -0.1903 & 0.849822 & 0.424911 \tabularnewline
`VrouwenOuderdan25\r` & 0.013167842822975 & 0.087707 & 0.1501 & 0.881239 & 0.44062 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189913&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]0.0981473390861909[/C][C]0.080352[/C][C]1.2215[/C][C]0.227421[/C][C]0.11371[/C][/ROW]
[ROW][C]Mannen[/C][C]0.419630839440572[/C][C]0.070303[/C][C]5.9689[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Vrouwen[/C][C]0.338435874594549[/C][C]0.05877[/C][C]5.7587[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]TotaalJongerdan25jaar[/C][C]0.0113186213520091[/C][C]0.011754[/C][C]0.963[/C][C]0.34001[/C][C]0.170005[/C][/ROW]
[ROW][C]MannenJongerdan25jaar[/C][C]0.0067365270872766[/C][C]0.007346[/C][C]0.917[/C][C]0.363386[/C][C]0.181693[/C][/ROW]
[ROW][C]VrouwenJongerdan25jaar[/C][C]0.00647230505605263[/C][C]0.005436[/C][C]1.1907[/C][C]0.239171[/C][C]0.119586[/C][/ROW]
[ROW][C]TotaalOuderdan25[/C][C]0.200738507619287[/C][C]0.163362[/C][C]1.2288[/C][C]0.22468[/C][C]0.11234[/C][/ROW]
[ROW][C]MannenOuderdan25[/C][C]-0.0201043486615964[/C][C]0.105651[/C][C]-0.1903[/C][C]0.849822[/C][C]0.424911[/C][/ROW]
[ROW][C]`VrouwenOuderdan25\r`[/C][C]0.013167842822975[/C][C]0.087707[/C][C]0.1501[/C][C]0.881239[/C][C]0.44062[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189913&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189913&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.09814733908619090.0803521.22150.2274210.11371
Mannen0.4196308394405720.0703035.968900
Vrouwen0.3384358745945490.058775.758700
TotaalJongerdan25jaar0.01131862135200910.0117540.9630.340010.170005
MannenJongerdan25jaar0.00673652708727660.0073460.9170.3633860.181693
VrouwenJongerdan25jaar0.006472305056052630.0054361.19070.2391710.119586
TotaalOuderdan250.2007385076192870.1633621.22880.224680.11234
MannenOuderdan25-0.02010434866159640.105651-0.19030.8498220.424911
`VrouwenOuderdan25\r`0.0131678428229750.0877070.15010.8812390.44062






Multiple Linear Regression - Regression Statistics
Multiple R0.998484860757407
R-squared0.996972017161738
Adjusted R-squared0.996506173648159
F-TEST (value)2140.14360638539
F-TEST (DF numerator)8
F-TEST (DF denominator)52
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0381559370428426
Sum Squared Residuals0.075705527644103

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.998484860757407 \tabularnewline
R-squared & 0.996972017161738 \tabularnewline
Adjusted R-squared & 0.996506173648159 \tabularnewline
F-TEST (value) & 2140.14360638539 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 52 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0381559370428426 \tabularnewline
Sum Squared Residuals & 0.075705527644103 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189913&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.998484860757407[/C][/ROW]
[ROW][C]R-squared[/C][C]0.996972017161738[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.996506173648159[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2140.14360638539[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]52[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0381559370428426[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.075705527644103[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189913&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189913&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.998484860757407
R-squared0.996972017161738
Adjusted R-squared0.996506173648159
F-TEST (value)2140.14360638539
F-TEST (DF numerator)8
F-TEST (DF denominator)52
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0381559370428426
Sum Squared Residuals0.075705527644103






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
176.999473072534130.000526927465873532
277.01856147772271-0.0185614777227082
376.965464694573740.0345353054262614
47.27.161762577208240.0382374227917585
57.37.279154956321050.0208450436789482
67.17.14812560180496-0.0481256018049578
76.86.735936796169240.0640632038307588
86.46.42943117394148-0.0294311739414822
96.16.13167336511378-0.0316733651137793
106.56.455213182574880.0447868174251197
117.77.661147390047850.0388526099521477
127.97.91182884049663-0.0118288404966244
137.57.53480040660266-0.0348004066026613
146.96.897397035482460.00260296451753997
156.66.585246015995870.0147539840041261
166.96.93875616552684-0.038756165526842
177.77.662676551412130.0373234485878663
1888.01632452524989-0.0163245252498923
1988.06794053112339-0.067940531123385
207.77.690877460108750.00912253989124606
217.37.31253224193249-0.0125322419324929
227.47.42568310269991-0.0256831026999138
238.18.14806830105476-0.0480683010547619
248.38.285820461124920.0141795388750849
258.18.14272019744154-0.0427201974415444
267.97.93232834350552-0.0323283435055183
277.97.95047166604953-0.0504716660495318
288.38.282579641145180.0174203588548253
298.68.62429566776988-0.0242956677698827
308.78.684242260961460.0157577390385356
318.58.54634182698592-0.0463418269859197
328.38.279696244913520.0203037550864776
3388.0172464221703-0.0172464221703036
3488.04222003469525-0.0422200346952477
358.88.748751591809880.0512484081901171
368.78.71689782936241-0.016897829362409
378.58.434997629818620.0650023701813834
388.18.11015923203408-0.0101592320340816
397.87.783963857018680.0160361429813154
407.77.649607283354130.0503927166458667
417.57.454782389053880.0452176109461193
427.27.158421271156720.0415787288432811
436.96.893061511662460.00693848833753681
446.66.60817718817572-0.0081771881757196
456.56.52191892876006-0.0219189287600578
466.66.67691631858159-0.0769163185815859
477.77.642793256261910.0572067437380935
4887.95841003877660.0415899612233982
497.77.686084663879360.01391533612064
507.27.21846825995911-0.0184682599591067
5177.0272861206875-0.0272861206874959
5276.991432098361880.0085679016381185
537.37.259569316973490.0404306830265051
547.37.247233579010880.052766420989124
557.17.12241936144786-0.0224193614478589
5677.05244128626105-0.0524412862610515
5776.959391854034180.0406081459658158
5876.994737147923030.00526285207696667
597.77.689203735725810.0107962642741911
607.97.91397617305263-0.0139761730526321
617.77.71285984439559-0.0128598443955942

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 7 & 6.99947307253413 & 0.000526927465873532 \tabularnewline
2 & 7 & 7.01856147772271 & -0.0185614777227082 \tabularnewline
3 & 7 & 6.96546469457374 & 0.0345353054262614 \tabularnewline
4 & 7.2 & 7.16176257720824 & 0.0382374227917585 \tabularnewline
5 & 7.3 & 7.27915495632105 & 0.0208450436789482 \tabularnewline
6 & 7.1 & 7.14812560180496 & -0.0481256018049578 \tabularnewline
7 & 6.8 & 6.73593679616924 & 0.0640632038307588 \tabularnewline
8 & 6.4 & 6.42943117394148 & -0.0294311739414822 \tabularnewline
9 & 6.1 & 6.13167336511378 & -0.0316733651137793 \tabularnewline
10 & 6.5 & 6.45521318257488 & 0.0447868174251197 \tabularnewline
11 & 7.7 & 7.66114739004785 & 0.0388526099521477 \tabularnewline
12 & 7.9 & 7.91182884049663 & -0.0118288404966244 \tabularnewline
13 & 7.5 & 7.53480040660266 & -0.0348004066026613 \tabularnewline
14 & 6.9 & 6.89739703548246 & 0.00260296451753997 \tabularnewline
15 & 6.6 & 6.58524601599587 & 0.0147539840041261 \tabularnewline
16 & 6.9 & 6.93875616552684 & -0.038756165526842 \tabularnewline
17 & 7.7 & 7.66267655141213 & 0.0373234485878663 \tabularnewline
18 & 8 & 8.01632452524989 & -0.0163245252498923 \tabularnewline
19 & 8 & 8.06794053112339 & -0.067940531123385 \tabularnewline
20 & 7.7 & 7.69087746010875 & 0.00912253989124606 \tabularnewline
21 & 7.3 & 7.31253224193249 & -0.0125322419324929 \tabularnewline
22 & 7.4 & 7.42568310269991 & -0.0256831026999138 \tabularnewline
23 & 8.1 & 8.14806830105476 & -0.0480683010547619 \tabularnewline
24 & 8.3 & 8.28582046112492 & 0.0141795388750849 \tabularnewline
25 & 8.1 & 8.14272019744154 & -0.0427201974415444 \tabularnewline
26 & 7.9 & 7.93232834350552 & -0.0323283435055183 \tabularnewline
27 & 7.9 & 7.95047166604953 & -0.0504716660495318 \tabularnewline
28 & 8.3 & 8.28257964114518 & 0.0174203588548253 \tabularnewline
29 & 8.6 & 8.62429566776988 & -0.0242956677698827 \tabularnewline
30 & 8.7 & 8.68424226096146 & 0.0157577390385356 \tabularnewline
31 & 8.5 & 8.54634182698592 & -0.0463418269859197 \tabularnewline
32 & 8.3 & 8.27969624491352 & 0.0203037550864776 \tabularnewline
33 & 8 & 8.0172464221703 & -0.0172464221703036 \tabularnewline
34 & 8 & 8.04222003469525 & -0.0422200346952477 \tabularnewline
35 & 8.8 & 8.74875159180988 & 0.0512484081901171 \tabularnewline
36 & 8.7 & 8.71689782936241 & -0.016897829362409 \tabularnewline
37 & 8.5 & 8.43499762981862 & 0.0650023701813834 \tabularnewline
38 & 8.1 & 8.11015923203408 & -0.0101592320340816 \tabularnewline
39 & 7.8 & 7.78396385701868 & 0.0160361429813154 \tabularnewline
40 & 7.7 & 7.64960728335413 & 0.0503927166458667 \tabularnewline
41 & 7.5 & 7.45478238905388 & 0.0452176109461193 \tabularnewline
42 & 7.2 & 7.15842127115672 & 0.0415787288432811 \tabularnewline
43 & 6.9 & 6.89306151166246 & 0.00693848833753681 \tabularnewline
44 & 6.6 & 6.60817718817572 & -0.0081771881757196 \tabularnewline
45 & 6.5 & 6.52191892876006 & -0.0219189287600578 \tabularnewline
46 & 6.6 & 6.67691631858159 & -0.0769163185815859 \tabularnewline
47 & 7.7 & 7.64279325626191 & 0.0572067437380935 \tabularnewline
48 & 8 & 7.9584100387766 & 0.0415899612233982 \tabularnewline
49 & 7.7 & 7.68608466387936 & 0.01391533612064 \tabularnewline
50 & 7.2 & 7.21846825995911 & -0.0184682599591067 \tabularnewline
51 & 7 & 7.0272861206875 & -0.0272861206874959 \tabularnewline
52 & 7 & 6.99143209836188 & 0.0085679016381185 \tabularnewline
53 & 7.3 & 7.25956931697349 & 0.0404306830265051 \tabularnewline
54 & 7.3 & 7.24723357901088 & 0.052766420989124 \tabularnewline
55 & 7.1 & 7.12241936144786 & -0.0224193614478589 \tabularnewline
56 & 7 & 7.05244128626105 & -0.0524412862610515 \tabularnewline
57 & 7 & 6.95939185403418 & 0.0406081459658158 \tabularnewline
58 & 7 & 6.99473714792303 & 0.00526285207696667 \tabularnewline
59 & 7.7 & 7.68920373572581 & 0.0107962642741911 \tabularnewline
60 & 7.9 & 7.91397617305263 & -0.0139761730526321 \tabularnewline
61 & 7.7 & 7.71285984439559 & -0.0128598443955942 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189913&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[/C][C]6.99947307253413[/C][C]0.000526927465873532[/C][/ROW]
[ROW][C]2[/C][C]7[/C][C]7.01856147772271[/C][C]-0.0185614777227082[/C][/ROW]
[ROW][C]3[/C][C]7[/C][C]6.96546469457374[/C][C]0.0345353054262614[/C][/ROW]
[ROW][C]4[/C][C]7.2[/C][C]7.16176257720824[/C][C]0.0382374227917585[/C][/ROW]
[ROW][C]5[/C][C]7.3[/C][C]7.27915495632105[/C][C]0.0208450436789482[/C][/ROW]
[ROW][C]6[/C][C]7.1[/C][C]7.14812560180496[/C][C]-0.0481256018049578[/C][/ROW]
[ROW][C]7[/C][C]6.8[/C][C]6.73593679616924[/C][C]0.0640632038307588[/C][/ROW]
[ROW][C]8[/C][C]6.4[/C][C]6.42943117394148[/C][C]-0.0294311739414822[/C][/ROW]
[ROW][C]9[/C][C]6.1[/C][C]6.13167336511378[/C][C]-0.0316733651137793[/C][/ROW]
[ROW][C]10[/C][C]6.5[/C][C]6.45521318257488[/C][C]0.0447868174251197[/C][/ROW]
[ROW][C]11[/C][C]7.7[/C][C]7.66114739004785[/C][C]0.0388526099521477[/C][/ROW]
[ROW][C]12[/C][C]7.9[/C][C]7.91182884049663[/C][C]-0.0118288404966244[/C][/ROW]
[ROW][C]13[/C][C]7.5[/C][C]7.53480040660266[/C][C]-0.0348004066026613[/C][/ROW]
[ROW][C]14[/C][C]6.9[/C][C]6.89739703548246[/C][C]0.00260296451753997[/C][/ROW]
[ROW][C]15[/C][C]6.6[/C][C]6.58524601599587[/C][C]0.0147539840041261[/C][/ROW]
[ROW][C]16[/C][C]6.9[/C][C]6.93875616552684[/C][C]-0.038756165526842[/C][/ROW]
[ROW][C]17[/C][C]7.7[/C][C]7.66267655141213[/C][C]0.0373234485878663[/C][/ROW]
[ROW][C]18[/C][C]8[/C][C]8.01632452524989[/C][C]-0.0163245252498923[/C][/ROW]
[ROW][C]19[/C][C]8[/C][C]8.06794053112339[/C][C]-0.067940531123385[/C][/ROW]
[ROW][C]20[/C][C]7.7[/C][C]7.69087746010875[/C][C]0.00912253989124606[/C][/ROW]
[ROW][C]21[/C][C]7.3[/C][C]7.31253224193249[/C][C]-0.0125322419324929[/C][/ROW]
[ROW][C]22[/C][C]7.4[/C][C]7.42568310269991[/C][C]-0.0256831026999138[/C][/ROW]
[ROW][C]23[/C][C]8.1[/C][C]8.14806830105476[/C][C]-0.0480683010547619[/C][/ROW]
[ROW][C]24[/C][C]8.3[/C][C]8.28582046112492[/C][C]0.0141795388750849[/C][/ROW]
[ROW][C]25[/C][C]8.1[/C][C]8.14272019744154[/C][C]-0.0427201974415444[/C][/ROW]
[ROW][C]26[/C][C]7.9[/C][C]7.93232834350552[/C][C]-0.0323283435055183[/C][/ROW]
[ROW][C]27[/C][C]7.9[/C][C]7.95047166604953[/C][C]-0.0504716660495318[/C][/ROW]
[ROW][C]28[/C][C]8.3[/C][C]8.28257964114518[/C][C]0.0174203588548253[/C][/ROW]
[ROW][C]29[/C][C]8.6[/C][C]8.62429566776988[/C][C]-0.0242956677698827[/C][/ROW]
[ROW][C]30[/C][C]8.7[/C][C]8.68424226096146[/C][C]0.0157577390385356[/C][/ROW]
[ROW][C]31[/C][C]8.5[/C][C]8.54634182698592[/C][C]-0.0463418269859197[/C][/ROW]
[ROW][C]32[/C][C]8.3[/C][C]8.27969624491352[/C][C]0.0203037550864776[/C][/ROW]
[ROW][C]33[/C][C]8[/C][C]8.0172464221703[/C][C]-0.0172464221703036[/C][/ROW]
[ROW][C]34[/C][C]8[/C][C]8.04222003469525[/C][C]-0.0422200346952477[/C][/ROW]
[ROW][C]35[/C][C]8.8[/C][C]8.74875159180988[/C][C]0.0512484081901171[/C][/ROW]
[ROW][C]36[/C][C]8.7[/C][C]8.71689782936241[/C][C]-0.016897829362409[/C][/ROW]
[ROW][C]37[/C][C]8.5[/C][C]8.43499762981862[/C][C]0.0650023701813834[/C][/ROW]
[ROW][C]38[/C][C]8.1[/C][C]8.11015923203408[/C][C]-0.0101592320340816[/C][/ROW]
[ROW][C]39[/C][C]7.8[/C][C]7.78396385701868[/C][C]0.0160361429813154[/C][/ROW]
[ROW][C]40[/C][C]7.7[/C][C]7.64960728335413[/C][C]0.0503927166458667[/C][/ROW]
[ROW][C]41[/C][C]7.5[/C][C]7.45478238905388[/C][C]0.0452176109461193[/C][/ROW]
[ROW][C]42[/C][C]7.2[/C][C]7.15842127115672[/C][C]0.0415787288432811[/C][/ROW]
[ROW][C]43[/C][C]6.9[/C][C]6.89306151166246[/C][C]0.00693848833753681[/C][/ROW]
[ROW][C]44[/C][C]6.6[/C][C]6.60817718817572[/C][C]-0.0081771881757196[/C][/ROW]
[ROW][C]45[/C][C]6.5[/C][C]6.52191892876006[/C][C]-0.0219189287600578[/C][/ROW]
[ROW][C]46[/C][C]6.6[/C][C]6.67691631858159[/C][C]-0.0769163185815859[/C][/ROW]
[ROW][C]47[/C][C]7.7[/C][C]7.64279325626191[/C][C]0.0572067437380935[/C][/ROW]
[ROW][C]48[/C][C]8[/C][C]7.9584100387766[/C][C]0.0415899612233982[/C][/ROW]
[ROW][C]49[/C][C]7.7[/C][C]7.68608466387936[/C][C]0.01391533612064[/C][/ROW]
[ROW][C]50[/C][C]7.2[/C][C]7.21846825995911[/C][C]-0.0184682599591067[/C][/ROW]
[ROW][C]51[/C][C]7[/C][C]7.0272861206875[/C][C]-0.0272861206874959[/C][/ROW]
[ROW][C]52[/C][C]7[/C][C]6.99143209836188[/C][C]0.0085679016381185[/C][/ROW]
[ROW][C]53[/C][C]7.3[/C][C]7.25956931697349[/C][C]0.0404306830265051[/C][/ROW]
[ROW][C]54[/C][C]7.3[/C][C]7.24723357901088[/C][C]0.052766420989124[/C][/ROW]
[ROW][C]55[/C][C]7.1[/C][C]7.12241936144786[/C][C]-0.0224193614478589[/C][/ROW]
[ROW][C]56[/C][C]7[/C][C]7.05244128626105[/C][C]-0.0524412862610515[/C][/ROW]
[ROW][C]57[/C][C]7[/C][C]6.95939185403418[/C][C]0.0406081459658158[/C][/ROW]
[ROW][C]58[/C][C]7[/C][C]6.99473714792303[/C][C]0.00526285207696667[/C][/ROW]
[ROW][C]59[/C][C]7.7[/C][C]7.68920373572581[/C][C]0.0107962642741911[/C][/ROW]
[ROW][C]60[/C][C]7.9[/C][C]7.91397617305263[/C][C]-0.0139761730526321[/C][/ROW]
[ROW][C]61[/C][C]7.7[/C][C]7.71285984439559[/C][C]-0.0128598443955942[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189913&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189913&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
176.999473072534130.000526927465873532
277.01856147772271-0.0185614777227082
376.965464694573740.0345353054262614
47.27.161762577208240.0382374227917585
57.37.279154956321050.0208450436789482
67.17.14812560180496-0.0481256018049578
76.86.735936796169240.0640632038307588
86.46.42943117394148-0.0294311739414822
96.16.13167336511378-0.0316733651137793
106.56.455213182574880.0447868174251197
117.77.661147390047850.0388526099521477
127.97.91182884049663-0.0118288404966244
137.57.53480040660266-0.0348004066026613
146.96.897397035482460.00260296451753997
156.66.585246015995870.0147539840041261
166.96.93875616552684-0.038756165526842
177.77.662676551412130.0373234485878663
1888.01632452524989-0.0163245252498923
1988.06794053112339-0.067940531123385
207.77.690877460108750.00912253989124606
217.37.31253224193249-0.0125322419324929
227.47.42568310269991-0.0256831026999138
238.18.14806830105476-0.0480683010547619
248.38.285820461124920.0141795388750849
258.18.14272019744154-0.0427201974415444
267.97.93232834350552-0.0323283435055183
277.97.95047166604953-0.0504716660495318
288.38.282579641145180.0174203588548253
298.68.62429566776988-0.0242956677698827
308.78.684242260961460.0157577390385356
318.58.54634182698592-0.0463418269859197
328.38.279696244913520.0203037550864776
3388.0172464221703-0.0172464221703036
3488.04222003469525-0.0422200346952477
358.88.748751591809880.0512484081901171
368.78.71689782936241-0.016897829362409
378.58.434997629818620.0650023701813834
388.18.11015923203408-0.0101592320340816
397.87.783963857018680.0160361429813154
407.77.649607283354130.0503927166458667
417.57.454782389053880.0452176109461193
427.27.158421271156720.0415787288432811
436.96.893061511662460.00693848833753681
446.66.60817718817572-0.0081771881757196
456.56.52191892876006-0.0219189287600578
466.66.67691631858159-0.0769163185815859
477.77.642793256261910.0572067437380935
4887.95841003877660.0415899612233982
497.77.686084663879360.01391533612064
507.27.21846825995911-0.0184682599591067
5177.0272861206875-0.0272861206874959
5276.991432098361880.0085679016381185
537.37.259569316973490.0404306830265051
547.37.247233579010880.052766420989124
557.17.12241936144786-0.0224193614478589
5677.05244128626105-0.0524412862610515
5776.959391854034180.0406081459658158
5876.994737147923030.00526285207696667
597.77.689203735725810.0107962642741911
607.97.91397617305263-0.0139761730526321
617.77.71285984439559-0.0128598443955942






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.8983613366239350.2032773267521310.101638663376065
130.913885136115140.1722297277697210.0861148638848605
140.8667048761811940.2665902476376120.133295123818806
150.7969120875136910.4061758249726180.203087912486309
160.7252935389992070.5494129220015860.274706461000793
170.6990600504032710.6018798991934570.300939949596729
180.6010004620024920.7979990759950170.398999537997508
190.5800577897895060.8398844204209880.419942210210494
200.7528818533858690.4942362932282610.247118146614131
210.6786334472818020.6427331054363970.321366552718198
220.6106744644331170.7786510711337660.389325535566883
230.6031995719951640.7936008560096730.396800428004836
240.5623371037482650.8753257925034710.437662896251735
250.4939000292819710.9878000585639420.506099970718029
260.4472013765776080.8944027531552150.552798623422392
270.5440753135575950.9118493728848110.455924686442405
280.5562407087673620.8875185824652750.443759291232638
290.5377760236666140.9244479526667730.462223976333386
300.4587293157068030.9174586314136060.541270684293197
310.5993417169563970.8013165660872050.400658283043603
320.5382256737681760.9235486524636470.461774326231824
330.511833318337320.976333363325360.48816668166268
340.6750702957022690.6498594085954620.324929704297731
350.68226453329570.63547093340860.3177354667043
360.728771635034070.5424567299318590.27122836496593
370.766287672401490.4674246551970210.23371232759851
380.7645913037306690.4708173925386610.235408696269331
390.696813655991040.606372688017920.30318634400896
400.6806523842997480.6386952314005040.319347615700252
410.6260766814616340.7478466370767320.373923318538366
420.5640236688031190.8719526623937620.435976331196881
430.6054628566976360.7890742866047290.394537143302364
440.5026990415462050.9946019169075890.497300958453795
450.3974541508396970.7949083016793950.602545849160303
460.4324670978745010.8649341957490010.567532902125499
470.3683268660039610.7366537320079220.631673133996039
480.2887607761843840.5775215523687680.711239223815616
490.1715302581824930.3430605163649860.828469741817507

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.898361336623935 & 0.203277326752131 & 0.101638663376065 \tabularnewline
13 & 0.91388513611514 & 0.172229727769721 & 0.0861148638848605 \tabularnewline
14 & 0.866704876181194 & 0.266590247637612 & 0.133295123818806 \tabularnewline
15 & 0.796912087513691 & 0.406175824972618 & 0.203087912486309 \tabularnewline
16 & 0.725293538999207 & 0.549412922001586 & 0.274706461000793 \tabularnewline
17 & 0.699060050403271 & 0.601879899193457 & 0.300939949596729 \tabularnewline
18 & 0.601000462002492 & 0.797999075995017 & 0.398999537997508 \tabularnewline
19 & 0.580057789789506 & 0.839884420420988 & 0.419942210210494 \tabularnewline
20 & 0.752881853385869 & 0.494236293228261 & 0.247118146614131 \tabularnewline
21 & 0.678633447281802 & 0.642733105436397 & 0.321366552718198 \tabularnewline
22 & 0.610674464433117 & 0.778651071133766 & 0.389325535566883 \tabularnewline
23 & 0.603199571995164 & 0.793600856009673 & 0.396800428004836 \tabularnewline
24 & 0.562337103748265 & 0.875325792503471 & 0.437662896251735 \tabularnewline
25 & 0.493900029281971 & 0.987800058563942 & 0.506099970718029 \tabularnewline
26 & 0.447201376577608 & 0.894402753155215 & 0.552798623422392 \tabularnewline
27 & 0.544075313557595 & 0.911849372884811 & 0.455924686442405 \tabularnewline
28 & 0.556240708767362 & 0.887518582465275 & 0.443759291232638 \tabularnewline
29 & 0.537776023666614 & 0.924447952666773 & 0.462223976333386 \tabularnewline
30 & 0.458729315706803 & 0.917458631413606 & 0.541270684293197 \tabularnewline
31 & 0.599341716956397 & 0.801316566087205 & 0.400658283043603 \tabularnewline
32 & 0.538225673768176 & 0.923548652463647 & 0.461774326231824 \tabularnewline
33 & 0.51183331833732 & 0.97633336332536 & 0.48816668166268 \tabularnewline
34 & 0.675070295702269 & 0.649859408595462 & 0.324929704297731 \tabularnewline
35 & 0.6822645332957 & 0.6354709334086 & 0.3177354667043 \tabularnewline
36 & 0.72877163503407 & 0.542456729931859 & 0.27122836496593 \tabularnewline
37 & 0.76628767240149 & 0.467424655197021 & 0.23371232759851 \tabularnewline
38 & 0.764591303730669 & 0.470817392538661 & 0.235408696269331 \tabularnewline
39 & 0.69681365599104 & 0.60637268801792 & 0.30318634400896 \tabularnewline
40 & 0.680652384299748 & 0.638695231400504 & 0.319347615700252 \tabularnewline
41 & 0.626076681461634 & 0.747846637076732 & 0.373923318538366 \tabularnewline
42 & 0.564023668803119 & 0.871952662393762 & 0.435976331196881 \tabularnewline
43 & 0.605462856697636 & 0.789074286604729 & 0.394537143302364 \tabularnewline
44 & 0.502699041546205 & 0.994601916907589 & 0.497300958453795 \tabularnewline
45 & 0.397454150839697 & 0.794908301679395 & 0.602545849160303 \tabularnewline
46 & 0.432467097874501 & 0.864934195749001 & 0.567532902125499 \tabularnewline
47 & 0.368326866003961 & 0.736653732007922 & 0.631673133996039 \tabularnewline
48 & 0.288760776184384 & 0.577521552368768 & 0.711239223815616 \tabularnewline
49 & 0.171530258182493 & 0.343060516364986 & 0.828469741817507 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189913&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]12[/C][C]0.898361336623935[/C][C]0.203277326752131[/C][C]0.101638663376065[/C][/ROW]
[ROW][C]13[/C][C]0.91388513611514[/C][C]0.172229727769721[/C][C]0.0861148638848605[/C][/ROW]
[ROW][C]14[/C][C]0.866704876181194[/C][C]0.266590247637612[/C][C]0.133295123818806[/C][/ROW]
[ROW][C]15[/C][C]0.796912087513691[/C][C]0.406175824972618[/C][C]0.203087912486309[/C][/ROW]
[ROW][C]16[/C][C]0.725293538999207[/C][C]0.549412922001586[/C][C]0.274706461000793[/C][/ROW]
[ROW][C]17[/C][C]0.699060050403271[/C][C]0.601879899193457[/C][C]0.300939949596729[/C][/ROW]
[ROW][C]18[/C][C]0.601000462002492[/C][C]0.797999075995017[/C][C]0.398999537997508[/C][/ROW]
[ROW][C]19[/C][C]0.580057789789506[/C][C]0.839884420420988[/C][C]0.419942210210494[/C][/ROW]
[ROW][C]20[/C][C]0.752881853385869[/C][C]0.494236293228261[/C][C]0.247118146614131[/C][/ROW]
[ROW][C]21[/C][C]0.678633447281802[/C][C]0.642733105436397[/C][C]0.321366552718198[/C][/ROW]
[ROW][C]22[/C][C]0.610674464433117[/C][C]0.778651071133766[/C][C]0.389325535566883[/C][/ROW]
[ROW][C]23[/C][C]0.603199571995164[/C][C]0.793600856009673[/C][C]0.396800428004836[/C][/ROW]
[ROW][C]24[/C][C]0.562337103748265[/C][C]0.875325792503471[/C][C]0.437662896251735[/C][/ROW]
[ROW][C]25[/C][C]0.493900029281971[/C][C]0.987800058563942[/C][C]0.506099970718029[/C][/ROW]
[ROW][C]26[/C][C]0.447201376577608[/C][C]0.894402753155215[/C][C]0.552798623422392[/C][/ROW]
[ROW][C]27[/C][C]0.544075313557595[/C][C]0.911849372884811[/C][C]0.455924686442405[/C][/ROW]
[ROW][C]28[/C][C]0.556240708767362[/C][C]0.887518582465275[/C][C]0.443759291232638[/C][/ROW]
[ROW][C]29[/C][C]0.537776023666614[/C][C]0.924447952666773[/C][C]0.462223976333386[/C][/ROW]
[ROW][C]30[/C][C]0.458729315706803[/C][C]0.917458631413606[/C][C]0.541270684293197[/C][/ROW]
[ROW][C]31[/C][C]0.599341716956397[/C][C]0.801316566087205[/C][C]0.400658283043603[/C][/ROW]
[ROW][C]32[/C][C]0.538225673768176[/C][C]0.923548652463647[/C][C]0.461774326231824[/C][/ROW]
[ROW][C]33[/C][C]0.51183331833732[/C][C]0.97633336332536[/C][C]0.48816668166268[/C][/ROW]
[ROW][C]34[/C][C]0.675070295702269[/C][C]0.649859408595462[/C][C]0.324929704297731[/C][/ROW]
[ROW][C]35[/C][C]0.6822645332957[/C][C]0.6354709334086[/C][C]0.3177354667043[/C][/ROW]
[ROW][C]36[/C][C]0.72877163503407[/C][C]0.542456729931859[/C][C]0.27122836496593[/C][/ROW]
[ROW][C]37[/C][C]0.76628767240149[/C][C]0.467424655197021[/C][C]0.23371232759851[/C][/ROW]
[ROW][C]38[/C][C]0.764591303730669[/C][C]0.470817392538661[/C][C]0.235408696269331[/C][/ROW]
[ROW][C]39[/C][C]0.69681365599104[/C][C]0.60637268801792[/C][C]0.30318634400896[/C][/ROW]
[ROW][C]40[/C][C]0.680652384299748[/C][C]0.638695231400504[/C][C]0.319347615700252[/C][/ROW]
[ROW][C]41[/C][C]0.626076681461634[/C][C]0.747846637076732[/C][C]0.373923318538366[/C][/ROW]
[ROW][C]42[/C][C]0.564023668803119[/C][C]0.871952662393762[/C][C]0.435976331196881[/C][/ROW]
[ROW][C]43[/C][C]0.605462856697636[/C][C]0.789074286604729[/C][C]0.394537143302364[/C][/ROW]
[ROW][C]44[/C][C]0.502699041546205[/C][C]0.994601916907589[/C][C]0.497300958453795[/C][/ROW]
[ROW][C]45[/C][C]0.397454150839697[/C][C]0.794908301679395[/C][C]0.602545849160303[/C][/ROW]
[ROW][C]46[/C][C]0.432467097874501[/C][C]0.864934195749001[/C][C]0.567532902125499[/C][/ROW]
[ROW][C]47[/C][C]0.368326866003961[/C][C]0.736653732007922[/C][C]0.631673133996039[/C][/ROW]
[ROW][C]48[/C][C]0.288760776184384[/C][C]0.577521552368768[/C][C]0.711239223815616[/C][/ROW]
[ROW][C]49[/C][C]0.171530258182493[/C][C]0.343060516364986[/C][C]0.828469741817507[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189913&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189913&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.8983613366239350.2032773267521310.101638663376065
130.913885136115140.1722297277697210.0861148638848605
140.8667048761811940.2665902476376120.133295123818806
150.7969120875136910.4061758249726180.203087912486309
160.7252935389992070.5494129220015860.274706461000793
170.6990600504032710.6018798991934570.300939949596729
180.6010004620024920.7979990759950170.398999537997508
190.5800577897895060.8398844204209880.419942210210494
200.7528818533858690.4942362932282610.247118146614131
210.6786334472818020.6427331054363970.321366552718198
220.6106744644331170.7786510711337660.389325535566883
230.6031995719951640.7936008560096730.396800428004836
240.5623371037482650.8753257925034710.437662896251735
250.4939000292819710.9878000585639420.506099970718029
260.4472013765776080.8944027531552150.552798623422392
270.5440753135575950.9118493728848110.455924686442405
280.5562407087673620.8875185824652750.443759291232638
290.5377760236666140.9244479526667730.462223976333386
300.4587293157068030.9174586314136060.541270684293197
310.5993417169563970.8013165660872050.400658283043603
320.5382256737681760.9235486524636470.461774326231824
330.511833318337320.976333363325360.48816668166268
340.6750702957022690.6498594085954620.324929704297731
350.68226453329570.63547093340860.3177354667043
360.728771635034070.5424567299318590.27122836496593
370.766287672401490.4674246551970210.23371232759851
380.7645913037306690.4708173925386610.235408696269331
390.696813655991040.606372688017920.30318634400896
400.6806523842997480.6386952314005040.319347615700252
410.6260766814616340.7478466370767320.373923318538366
420.5640236688031190.8719526623937620.435976331196881
430.6054628566976360.7890742866047290.394537143302364
440.5026990415462050.9946019169075890.497300958453795
450.3974541508396970.7949083016793950.602545849160303
460.4324670978745010.8649341957490010.567532902125499
470.3683268660039610.7366537320079220.631673133996039
480.2887607761843840.5775215523687680.711239223815616
490.1715302581824930.3430605163649860.828469741817507






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 level00OK

\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 & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189913&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189913&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189913&T=6

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


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

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


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal 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')
}