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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 09:53:53 -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/t1258649699wvkfakivvidp26w.htm/, Retrieved Fri, 29 Mar 2024 08:35:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57822, Retrieved Fri, 29 Mar 2024 08:35:02 +0000
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Original text written by user:Multiple lineair regression software (2)
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact152
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 14:03:14] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [shw7: Multiple li...] [2009-11-19 16:53:53] [7a39e26d7a09dd77604df90cb29f8d39] [Current]
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Dataseries X:
0.7461	0.527
0.7775	0.472
0.7790	0
0.7744	0.052
0.7905	0.313
0.7719	0.364
0.7811	0.363
0.7557	-0.155
0.7637	0.052
0.7595	0.568
0.7471	0.668
0.7615	1.378
0.7487	0.252
0.7389	-0.402
0.7337	-0.05
0.7510	0.555
0.7382	0.05
0.7159	0.15
0.7542	0.45
0.7636	0.299
0.7433	0.199
0.7658	0.496
0.7627	0.444
0.7480	-0.393
0.7692	-0.444
0.7850	0.198
0.7913	0.494
0.7720	0.133
0.7880	0.388
0.8070	0.484
0.8268	0.278
0.8244	0.369
0.8487	0.165
0.8572	0.155
0.8214	0.087
0.8827	0.414
0.9216	0.36
0.8865	0.975
0.8816	0.27
0.8884	0.359
0.9466	0.169
0.9180	0.381
0.9337	0.154
0.9559	0.486
0.9626	0.925
0.9434	0.728
0.8639	-0.014
0.7996	0.046
0.6680	-0.819
0.6572	-1.674
0.6928	-0.788
0.6438	0.279
0.6454	0.396
0.6873	-0.141
0.7265	-0.019
0.7912	0.099
0.8114	0.742
0.8281	0.005
0.8393	0.448




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57822&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]6 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=57822&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57822&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 time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 0.772647651071932 + 0.0700411042991505X[t] -0.000190631685312515M1[t] + 0.00240989211865488M2[t] + 0.00406895727169555M3[t] -0.0260309794167778M4[t] -0.00934246972346833M5[t] -0.00996982849640152M6[t] + 0.0146382701539164M7[t] + 0.0301313224239747M8[t] + 0.0241132248770421M9[t] + 0.0308083018096797M10[t] + 0.0113569242639656M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  0.772647651071932 +  0.0700411042991505X[t] -0.000190631685312515M1[t] +  0.00240989211865488M2[t] +  0.00406895727169555M3[t] -0.0260309794167778M4[t] -0.00934246972346833M5[t] -0.00996982849640152M6[t] +  0.0146382701539164M7[t] +  0.0301313224239747M8[t] +  0.0241132248770421M9[t] +  0.0308083018096797M10[t] +  0.0113569242639656M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57822&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  0.772647651071932 +  0.0700411042991505X[t] -0.000190631685312515M1[t] +  0.00240989211865488M2[t] +  0.00406895727169555M3[t] -0.0260309794167778M4[t] -0.00934246972346833M5[t] -0.00996982849640152M6[t] +  0.0146382701539164M7[t] +  0.0301313224239747M8[t] +  0.0241132248770421M9[t] +  0.0308083018096797M10[t] +  0.0113569242639656M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57822&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57822&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
Y[t] = + 0.772647651071932 + 0.0700411042991505X[t] -0.000190631685312515M1[t] + 0.00240989211865488M2[t] + 0.00406895727169555M3[t] -0.0260309794167778M4[t] -0.00934246972346833M5[t] -0.00996982849640152M6[t] + 0.0146382701539164M7[t] + 0.0301313224239747M8[t] + 0.0241132248770421M9[t] + 0.0308083018096797M10[t] + 0.0113569242639656M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.7726476510719320.03919119.715100
X0.07004110429915050.0238392.93810.0051470.002573
M1-0.0001906316853125150.052114-0.00370.9970970.498549
M20.002409892118654880.0523920.0460.9635120.481756
M30.004068957271695550.0520720.07810.9380550.469028
M4-0.02603097941677780.051335-0.50710.6145230.307262
M5-0.009342469723468330.051348-0.18190.8564260.428213
M6-0.009969828496401520.051366-0.19410.8469580.423479
M70.01463827015391640.0513690.2850.7769530.388476
M80.03013132242397470.0514060.58610.5606420.280321
M90.02411322487704210.0513120.46990.6406210.32031
M100.03080830180967970.0512990.60060.5510820.275541
M110.01135692426396560.0513010.22140.8257790.41289

\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) & 0.772647651071932 & 0.039191 & 19.7151 & 0 & 0 \tabularnewline
X & 0.0700411042991505 & 0.023839 & 2.9381 & 0.005147 & 0.002573 \tabularnewline
M1 & -0.000190631685312515 & 0.052114 & -0.0037 & 0.997097 & 0.498549 \tabularnewline
M2 & 0.00240989211865488 & 0.052392 & 0.046 & 0.963512 & 0.481756 \tabularnewline
M3 & 0.00406895727169555 & 0.052072 & 0.0781 & 0.938055 & 0.469028 \tabularnewline
M4 & -0.0260309794167778 & 0.051335 & -0.5071 & 0.614523 & 0.307262 \tabularnewline
M5 & -0.00934246972346833 & 0.051348 & -0.1819 & 0.856426 & 0.428213 \tabularnewline
M6 & -0.00996982849640152 & 0.051366 & -0.1941 & 0.846958 & 0.423479 \tabularnewline
M7 & 0.0146382701539164 & 0.051369 & 0.285 & 0.776953 & 0.388476 \tabularnewline
M8 & 0.0301313224239747 & 0.051406 & 0.5861 & 0.560642 & 0.280321 \tabularnewline
M9 & 0.0241132248770421 & 0.051312 & 0.4699 & 0.640621 & 0.32031 \tabularnewline
M10 & 0.0308083018096797 & 0.051299 & 0.6006 & 0.551082 & 0.275541 \tabularnewline
M11 & 0.0113569242639656 & 0.051301 & 0.2214 & 0.825779 & 0.41289 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57822&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]0.772647651071932[/C][C]0.039191[/C][C]19.7151[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]0.0700411042991505[/C][C]0.023839[/C][C]2.9381[/C][C]0.005147[/C][C]0.002573[/C][/ROW]
[ROW][C]M1[/C][C]-0.000190631685312515[/C][C]0.052114[/C][C]-0.0037[/C][C]0.997097[/C][C]0.498549[/C][/ROW]
[ROW][C]M2[/C][C]0.00240989211865488[/C][C]0.052392[/C][C]0.046[/C][C]0.963512[/C][C]0.481756[/C][/ROW]
[ROW][C]M3[/C][C]0.00406895727169555[/C][C]0.052072[/C][C]0.0781[/C][C]0.938055[/C][C]0.469028[/C][/ROW]
[ROW][C]M4[/C][C]-0.0260309794167778[/C][C]0.051335[/C][C]-0.5071[/C][C]0.614523[/C][C]0.307262[/C][/ROW]
[ROW][C]M5[/C][C]-0.00934246972346833[/C][C]0.051348[/C][C]-0.1819[/C][C]0.856426[/C][C]0.428213[/C][/ROW]
[ROW][C]M6[/C][C]-0.00996982849640152[/C][C]0.051366[/C][C]-0.1941[/C][C]0.846958[/C][C]0.423479[/C][/ROW]
[ROW][C]M7[/C][C]0.0146382701539164[/C][C]0.051369[/C][C]0.285[/C][C]0.776953[/C][C]0.388476[/C][/ROW]
[ROW][C]M8[/C][C]0.0301313224239747[/C][C]0.051406[/C][C]0.5861[/C][C]0.560642[/C][C]0.280321[/C][/ROW]
[ROW][C]M9[/C][C]0.0241132248770421[/C][C]0.051312[/C][C]0.4699[/C][C]0.640621[/C][C]0.32031[/C][/ROW]
[ROW][C]M10[/C][C]0.0308083018096797[/C][C]0.051299[/C][C]0.6006[/C][C]0.551082[/C][C]0.275541[/C][/ROW]
[ROW][C]M11[/C][C]0.0113569242639656[/C][C]0.051301[/C][C]0.2214[/C][C]0.825779[/C][C]0.41289[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57822&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57822&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)0.7726476510719320.03919119.715100
X0.07004110429915050.0238392.93810.0051470.002573
M1-0.0001906316853125150.052114-0.00370.9970970.498549
M20.002409892118654880.0523920.0460.9635120.481756
M30.004068957271695550.0520720.07810.9380550.469028
M4-0.02603097941677780.051335-0.50710.6145230.307262
M5-0.009342469723468330.051348-0.18190.8564260.428213
M6-0.009969828496401520.051366-0.19410.8469580.423479
M70.01463827015391640.0513690.2850.7769530.388476
M80.03013132242397470.0514060.58610.5606420.280321
M90.02411322487704210.0513120.46990.6406210.32031
M100.03080830180967970.0512990.60060.5510820.275541
M110.01135692426396560.0513010.22140.8257790.41289







Multiple Linear Regression - Regression Statistics
Multiple R0.478529492155792
R-squared0.228990474862880
Adjusted R-squared0.0278575552618919
F-TEST (value)1.13850321129508
F-TEST (DF numerator)12
F-TEST (DF denominator)46
p-value0.354332486664713
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0764656629349064
Sum Squared Residuals0.268961889971437

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.478529492155792 \tabularnewline
R-squared & 0.228990474862880 \tabularnewline
Adjusted R-squared & 0.0278575552618919 \tabularnewline
F-TEST (value) & 1.13850321129508 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 46 \tabularnewline
p-value & 0.354332486664713 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0764656629349064 \tabularnewline
Sum Squared Residuals & 0.268961889971437 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57822&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.478529492155792[/C][/ROW]
[ROW][C]R-squared[/C][C]0.228990474862880[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0278575552618919[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.13850321129508[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]46[/C][/ROW]
[ROW][C]p-value[/C][C]0.354332486664713[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0764656629349064[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.268961889971437[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57822&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57822&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 R0.478529492155792
R-squared0.228990474862880
Adjusted R-squared0.0278575552618919
F-TEST (value)1.13850321129508
F-TEST (DF numerator)12
F-TEST (DF denominator)46
p-value0.354332486664713
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0764656629349064
Sum Squared Residuals0.268961889971437







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
10.74610.80936868135227-0.0632686813522695
20.77750.808116944419786-0.0306169444197858
30.7790.7767166083436270.00228339165637259
40.77440.750258809078710.02414119092129
50.79050.7852280469940980.00527195300590231
60.77190.788172784540421-0.0162727845404211
70.78110.81271084208644-0.0316108420864399
80.75570.791922602329538-0.0362226023295382
90.76370.80040301337253-0.0367030133725297
100.75950.84323930012353-0.0837393001235291
110.74710.83079203300773-0.08369203300773
120.76150.869164292796161-0.107664292796161
130.74870.790107377670005-0.0414073776700053
140.73890.746901019262328-0.00800101926232827
150.73370.77321455312867-0.0395145531286699
160.7510.785489484541183-0.0344894845411826
170.73820.766807236563421-0.0286072365634211
180.71590.773183988220403-0.057283988220403
190.75420.818804418160466-0.064604418160466
200.76360.823721263681353-0.0601212636813526
210.74330.810699055704505-0.0673990557045049
220.76580.83819634061399-0.0723963406139903
230.76270.81510282564472-0.0524028256447202
240.7480.7451214970823660.00287850291763425
250.76920.7413587690777970.0278412309222034
260.7850.788925681841819-0.00392568184181853
270.79130.811316913867408-0.0200169138674078
280.7720.7559321385269410.0160678614730589
290.7880.790481129816534-0.00248112981653392
300.8070.796577717056320.0104222829436808
310.82680.8067573482210120.0200426517789878
320.82440.828624140982293-0.00422414098229306
330.84870.8083176581583340.0403823418416663
340.85720.814312324047980.04288767595202
350.82140.7900981514099240.0313018485900764
360.88270.801644668251780.0810553317482199
370.92160.7976718169343140.123928183065686
380.88650.8433476198822580.0431523801177415
390.88160.7956277065043980.085972293495602
400.88840.7717614280985490.116638571901451
410.94660.775142127975020.17145787202498
420.9180.7893634833135070.128636516686493
430.93370.7980722512879170.135627748712083
440.95590.8368189501852940.119081049814706
450.96260.8615488974256880.101051102574312
460.94340.8544458768113930.0889541231886068
470.86390.783023999875710.0808760001242906
480.79960.7758695418696930.0237304581303072
490.6680.715093354965615-0.0470933549656151
500.65720.657808734593809-0.00060873459380887
510.69280.721524218155897-0.0287242181558969
520.64380.766158139754617-0.122358139754617
530.64540.791041458650927-0.145641458650927
540.68730.75280202686935-0.0655020268693501
550.72650.785955140244164-0.0594551402441644
560.79120.809713042821522-0.0185130428215224
570.81140.848731375338944-0.0373313753389436
580.82810.8038061584031070.0242938415968925
590.83930.8153829900619170.0239170099380832

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0.7461 & 0.80936868135227 & -0.0632686813522695 \tabularnewline
2 & 0.7775 & 0.808116944419786 & -0.0306169444197858 \tabularnewline
3 & 0.779 & 0.776716608343627 & 0.00228339165637259 \tabularnewline
4 & 0.7744 & 0.75025880907871 & 0.02414119092129 \tabularnewline
5 & 0.7905 & 0.785228046994098 & 0.00527195300590231 \tabularnewline
6 & 0.7719 & 0.788172784540421 & -0.0162727845404211 \tabularnewline
7 & 0.7811 & 0.81271084208644 & -0.0316108420864399 \tabularnewline
8 & 0.7557 & 0.791922602329538 & -0.0362226023295382 \tabularnewline
9 & 0.7637 & 0.80040301337253 & -0.0367030133725297 \tabularnewline
10 & 0.7595 & 0.84323930012353 & -0.0837393001235291 \tabularnewline
11 & 0.7471 & 0.83079203300773 & -0.08369203300773 \tabularnewline
12 & 0.7615 & 0.869164292796161 & -0.107664292796161 \tabularnewline
13 & 0.7487 & 0.790107377670005 & -0.0414073776700053 \tabularnewline
14 & 0.7389 & 0.746901019262328 & -0.00800101926232827 \tabularnewline
15 & 0.7337 & 0.77321455312867 & -0.0395145531286699 \tabularnewline
16 & 0.751 & 0.785489484541183 & -0.0344894845411826 \tabularnewline
17 & 0.7382 & 0.766807236563421 & -0.0286072365634211 \tabularnewline
18 & 0.7159 & 0.773183988220403 & -0.057283988220403 \tabularnewline
19 & 0.7542 & 0.818804418160466 & -0.064604418160466 \tabularnewline
20 & 0.7636 & 0.823721263681353 & -0.0601212636813526 \tabularnewline
21 & 0.7433 & 0.810699055704505 & -0.0673990557045049 \tabularnewline
22 & 0.7658 & 0.83819634061399 & -0.0723963406139903 \tabularnewline
23 & 0.7627 & 0.81510282564472 & -0.0524028256447202 \tabularnewline
24 & 0.748 & 0.745121497082366 & 0.00287850291763425 \tabularnewline
25 & 0.7692 & 0.741358769077797 & 0.0278412309222034 \tabularnewline
26 & 0.785 & 0.788925681841819 & -0.00392568184181853 \tabularnewline
27 & 0.7913 & 0.811316913867408 & -0.0200169138674078 \tabularnewline
28 & 0.772 & 0.755932138526941 & 0.0160678614730589 \tabularnewline
29 & 0.788 & 0.790481129816534 & -0.00248112981653392 \tabularnewline
30 & 0.807 & 0.79657771705632 & 0.0104222829436808 \tabularnewline
31 & 0.8268 & 0.806757348221012 & 0.0200426517789878 \tabularnewline
32 & 0.8244 & 0.828624140982293 & -0.00422414098229306 \tabularnewline
33 & 0.8487 & 0.808317658158334 & 0.0403823418416663 \tabularnewline
34 & 0.8572 & 0.81431232404798 & 0.04288767595202 \tabularnewline
35 & 0.8214 & 0.790098151409924 & 0.0313018485900764 \tabularnewline
36 & 0.8827 & 0.80164466825178 & 0.0810553317482199 \tabularnewline
37 & 0.9216 & 0.797671816934314 & 0.123928183065686 \tabularnewline
38 & 0.8865 & 0.843347619882258 & 0.0431523801177415 \tabularnewline
39 & 0.8816 & 0.795627706504398 & 0.085972293495602 \tabularnewline
40 & 0.8884 & 0.771761428098549 & 0.116638571901451 \tabularnewline
41 & 0.9466 & 0.77514212797502 & 0.17145787202498 \tabularnewline
42 & 0.918 & 0.789363483313507 & 0.128636516686493 \tabularnewline
43 & 0.9337 & 0.798072251287917 & 0.135627748712083 \tabularnewline
44 & 0.9559 & 0.836818950185294 & 0.119081049814706 \tabularnewline
45 & 0.9626 & 0.861548897425688 & 0.101051102574312 \tabularnewline
46 & 0.9434 & 0.854445876811393 & 0.0889541231886068 \tabularnewline
47 & 0.8639 & 0.78302399987571 & 0.0808760001242906 \tabularnewline
48 & 0.7996 & 0.775869541869693 & 0.0237304581303072 \tabularnewline
49 & 0.668 & 0.715093354965615 & -0.0470933549656151 \tabularnewline
50 & 0.6572 & 0.657808734593809 & -0.00060873459380887 \tabularnewline
51 & 0.6928 & 0.721524218155897 & -0.0287242181558969 \tabularnewline
52 & 0.6438 & 0.766158139754617 & -0.122358139754617 \tabularnewline
53 & 0.6454 & 0.791041458650927 & -0.145641458650927 \tabularnewline
54 & 0.6873 & 0.75280202686935 & -0.0655020268693501 \tabularnewline
55 & 0.7265 & 0.785955140244164 & -0.0594551402441644 \tabularnewline
56 & 0.7912 & 0.809713042821522 & -0.0185130428215224 \tabularnewline
57 & 0.8114 & 0.848731375338944 & -0.0373313753389436 \tabularnewline
58 & 0.8281 & 0.803806158403107 & 0.0242938415968925 \tabularnewline
59 & 0.8393 & 0.815382990061917 & 0.0239170099380832 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57822&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]0.7461[/C][C]0.80936868135227[/C][C]-0.0632686813522695[/C][/ROW]
[ROW][C]2[/C][C]0.7775[/C][C]0.808116944419786[/C][C]-0.0306169444197858[/C][/ROW]
[ROW][C]3[/C][C]0.779[/C][C]0.776716608343627[/C][C]0.00228339165637259[/C][/ROW]
[ROW][C]4[/C][C]0.7744[/C][C]0.75025880907871[/C][C]0.02414119092129[/C][/ROW]
[ROW][C]5[/C][C]0.7905[/C][C]0.785228046994098[/C][C]0.00527195300590231[/C][/ROW]
[ROW][C]6[/C][C]0.7719[/C][C]0.788172784540421[/C][C]-0.0162727845404211[/C][/ROW]
[ROW][C]7[/C][C]0.7811[/C][C]0.81271084208644[/C][C]-0.0316108420864399[/C][/ROW]
[ROW][C]8[/C][C]0.7557[/C][C]0.791922602329538[/C][C]-0.0362226023295382[/C][/ROW]
[ROW][C]9[/C][C]0.7637[/C][C]0.80040301337253[/C][C]-0.0367030133725297[/C][/ROW]
[ROW][C]10[/C][C]0.7595[/C][C]0.84323930012353[/C][C]-0.0837393001235291[/C][/ROW]
[ROW][C]11[/C][C]0.7471[/C][C]0.83079203300773[/C][C]-0.08369203300773[/C][/ROW]
[ROW][C]12[/C][C]0.7615[/C][C]0.869164292796161[/C][C]-0.107664292796161[/C][/ROW]
[ROW][C]13[/C][C]0.7487[/C][C]0.790107377670005[/C][C]-0.0414073776700053[/C][/ROW]
[ROW][C]14[/C][C]0.7389[/C][C]0.746901019262328[/C][C]-0.00800101926232827[/C][/ROW]
[ROW][C]15[/C][C]0.7337[/C][C]0.77321455312867[/C][C]-0.0395145531286699[/C][/ROW]
[ROW][C]16[/C][C]0.751[/C][C]0.785489484541183[/C][C]-0.0344894845411826[/C][/ROW]
[ROW][C]17[/C][C]0.7382[/C][C]0.766807236563421[/C][C]-0.0286072365634211[/C][/ROW]
[ROW][C]18[/C][C]0.7159[/C][C]0.773183988220403[/C][C]-0.057283988220403[/C][/ROW]
[ROW][C]19[/C][C]0.7542[/C][C]0.818804418160466[/C][C]-0.064604418160466[/C][/ROW]
[ROW][C]20[/C][C]0.7636[/C][C]0.823721263681353[/C][C]-0.0601212636813526[/C][/ROW]
[ROW][C]21[/C][C]0.7433[/C][C]0.810699055704505[/C][C]-0.0673990557045049[/C][/ROW]
[ROW][C]22[/C][C]0.7658[/C][C]0.83819634061399[/C][C]-0.0723963406139903[/C][/ROW]
[ROW][C]23[/C][C]0.7627[/C][C]0.81510282564472[/C][C]-0.0524028256447202[/C][/ROW]
[ROW][C]24[/C][C]0.748[/C][C]0.745121497082366[/C][C]0.00287850291763425[/C][/ROW]
[ROW][C]25[/C][C]0.7692[/C][C]0.741358769077797[/C][C]0.0278412309222034[/C][/ROW]
[ROW][C]26[/C][C]0.785[/C][C]0.788925681841819[/C][C]-0.00392568184181853[/C][/ROW]
[ROW][C]27[/C][C]0.7913[/C][C]0.811316913867408[/C][C]-0.0200169138674078[/C][/ROW]
[ROW][C]28[/C][C]0.772[/C][C]0.755932138526941[/C][C]0.0160678614730589[/C][/ROW]
[ROW][C]29[/C][C]0.788[/C][C]0.790481129816534[/C][C]-0.00248112981653392[/C][/ROW]
[ROW][C]30[/C][C]0.807[/C][C]0.79657771705632[/C][C]0.0104222829436808[/C][/ROW]
[ROW][C]31[/C][C]0.8268[/C][C]0.806757348221012[/C][C]0.0200426517789878[/C][/ROW]
[ROW][C]32[/C][C]0.8244[/C][C]0.828624140982293[/C][C]-0.00422414098229306[/C][/ROW]
[ROW][C]33[/C][C]0.8487[/C][C]0.808317658158334[/C][C]0.0403823418416663[/C][/ROW]
[ROW][C]34[/C][C]0.8572[/C][C]0.81431232404798[/C][C]0.04288767595202[/C][/ROW]
[ROW][C]35[/C][C]0.8214[/C][C]0.790098151409924[/C][C]0.0313018485900764[/C][/ROW]
[ROW][C]36[/C][C]0.8827[/C][C]0.80164466825178[/C][C]0.0810553317482199[/C][/ROW]
[ROW][C]37[/C][C]0.9216[/C][C]0.797671816934314[/C][C]0.123928183065686[/C][/ROW]
[ROW][C]38[/C][C]0.8865[/C][C]0.843347619882258[/C][C]0.0431523801177415[/C][/ROW]
[ROW][C]39[/C][C]0.8816[/C][C]0.795627706504398[/C][C]0.085972293495602[/C][/ROW]
[ROW][C]40[/C][C]0.8884[/C][C]0.771761428098549[/C][C]0.116638571901451[/C][/ROW]
[ROW][C]41[/C][C]0.9466[/C][C]0.77514212797502[/C][C]0.17145787202498[/C][/ROW]
[ROW][C]42[/C][C]0.918[/C][C]0.789363483313507[/C][C]0.128636516686493[/C][/ROW]
[ROW][C]43[/C][C]0.9337[/C][C]0.798072251287917[/C][C]0.135627748712083[/C][/ROW]
[ROW][C]44[/C][C]0.9559[/C][C]0.836818950185294[/C][C]0.119081049814706[/C][/ROW]
[ROW][C]45[/C][C]0.9626[/C][C]0.861548897425688[/C][C]0.101051102574312[/C][/ROW]
[ROW][C]46[/C][C]0.9434[/C][C]0.854445876811393[/C][C]0.0889541231886068[/C][/ROW]
[ROW][C]47[/C][C]0.8639[/C][C]0.78302399987571[/C][C]0.0808760001242906[/C][/ROW]
[ROW][C]48[/C][C]0.7996[/C][C]0.775869541869693[/C][C]0.0237304581303072[/C][/ROW]
[ROW][C]49[/C][C]0.668[/C][C]0.715093354965615[/C][C]-0.0470933549656151[/C][/ROW]
[ROW][C]50[/C][C]0.6572[/C][C]0.657808734593809[/C][C]-0.00060873459380887[/C][/ROW]
[ROW][C]51[/C][C]0.6928[/C][C]0.721524218155897[/C][C]-0.0287242181558969[/C][/ROW]
[ROW][C]52[/C][C]0.6438[/C][C]0.766158139754617[/C][C]-0.122358139754617[/C][/ROW]
[ROW][C]53[/C][C]0.6454[/C][C]0.791041458650927[/C][C]-0.145641458650927[/C][/ROW]
[ROW][C]54[/C][C]0.6873[/C][C]0.75280202686935[/C][C]-0.0655020268693501[/C][/ROW]
[ROW][C]55[/C][C]0.7265[/C][C]0.785955140244164[/C][C]-0.0594551402441644[/C][/ROW]
[ROW][C]56[/C][C]0.7912[/C][C]0.809713042821522[/C][C]-0.0185130428215224[/C][/ROW]
[ROW][C]57[/C][C]0.8114[/C][C]0.848731375338944[/C][C]-0.0373313753389436[/C][/ROW]
[ROW][C]58[/C][C]0.8281[/C][C]0.803806158403107[/C][C]0.0242938415968925[/C][/ROW]
[ROW][C]59[/C][C]0.8393[/C][C]0.815382990061917[/C][C]0.0239170099380832[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57822&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57822&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
10.74610.80936868135227-0.0632686813522695
20.77750.808116944419786-0.0306169444197858
30.7790.7767166083436270.00228339165637259
40.77440.750258809078710.02414119092129
50.79050.7852280469940980.00527195300590231
60.77190.788172784540421-0.0162727845404211
70.78110.81271084208644-0.0316108420864399
80.75570.791922602329538-0.0362226023295382
90.76370.80040301337253-0.0367030133725297
100.75950.84323930012353-0.0837393001235291
110.74710.83079203300773-0.08369203300773
120.76150.869164292796161-0.107664292796161
130.74870.790107377670005-0.0414073776700053
140.73890.746901019262328-0.00800101926232827
150.73370.77321455312867-0.0395145531286699
160.7510.785489484541183-0.0344894845411826
170.73820.766807236563421-0.0286072365634211
180.71590.773183988220403-0.057283988220403
190.75420.818804418160466-0.064604418160466
200.76360.823721263681353-0.0601212636813526
210.74330.810699055704505-0.0673990557045049
220.76580.83819634061399-0.0723963406139903
230.76270.81510282564472-0.0524028256447202
240.7480.7451214970823660.00287850291763425
250.76920.7413587690777970.0278412309222034
260.7850.788925681841819-0.00392568184181853
270.79130.811316913867408-0.0200169138674078
280.7720.7559321385269410.0160678614730589
290.7880.790481129816534-0.00248112981653392
300.8070.796577717056320.0104222829436808
310.82680.8067573482210120.0200426517789878
320.82440.828624140982293-0.00422414098229306
330.84870.8083176581583340.0403823418416663
340.85720.814312324047980.04288767595202
350.82140.7900981514099240.0313018485900764
360.88270.801644668251780.0810553317482199
370.92160.7976718169343140.123928183065686
380.88650.8433476198822580.0431523801177415
390.88160.7956277065043980.085972293495602
400.88840.7717614280985490.116638571901451
410.94660.775142127975020.17145787202498
420.9180.7893634833135070.128636516686493
430.93370.7980722512879170.135627748712083
440.95590.8368189501852940.119081049814706
450.96260.8615488974256880.101051102574312
460.94340.8544458768113930.0889541231886068
470.86390.783023999875710.0808760001242906
480.79960.7758695418696930.0237304581303072
490.6680.715093354965615-0.0470933549656151
500.65720.657808734593809-0.00060873459380887
510.69280.721524218155897-0.0287242181558969
520.64380.766158139754617-0.122358139754617
530.64540.791041458650927-0.145641458650927
540.68730.75280202686935-0.0655020268693501
550.72650.785955140244164-0.0594551402441644
560.79120.809713042821522-0.0185130428215224
570.81140.848731375338944-0.0373313753389436
580.82810.8038061584031070.0242938415968925
590.83930.8153829900619170.0239170099380832







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.03359738801116200.06719477602232400.966402611988838
170.01832221052140370.03664442104280750.981677789478596
180.01109598838459230.02219197676918460.988904011615408
190.004766798467461940.009533596934923880.995233201532538
200.001514473425148720.003028946850297440.998485526574851
210.0005721826501648490.001144365300329700.999427817349835
220.0002060833501865390.0004121667003730790.999793916649814
238.30191698789731e-050.0001660383397579460.99991698083012
242.56769367998686e-055.13538735997372e-050.9999743230632
251.12358654741466e-052.24717309482932e-050.999988764134526
264.21186873817185e-068.4237374763437e-060.999995788131262
272.03887384487026e-064.07774768974053e-060.999997961126155
285.16425638641425e-071.03285127728285e-060.999999483574361
291.52754195348215e-073.0550839069643e-070.999999847245805
302.9254657857638e-075.8509315715276e-070.999999707453421
314.17141620585397e-078.34283241170794e-070.99999958285838
324.94532215011616e-079.8906443002323e-070.999999505467785
332.32009284874381e-064.64018569748761e-060.999997679907151
346.70097181141657e-061.34019436228331e-050.999993299028189
355.17694808816737e-061.03538961763347e-050.999994823051912
362.11031267868055e-054.22062535736111e-050.999978896873213
370.0002293668676702940.0004587337353405890.99977063313233
380.0003576522117563620.0007153044235127240.999642347788244
390.0003222879813710400.0006445759627420790.99967771201863
400.001343175537417780.002686351074835560.998656824462582
410.1267939392279390.2535878784558780.873206060772061
420.1534477929116370.3068955858232750.846552207088363
430.3333802673504080.6667605347008150.666619732649592

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.0335973880111620 & 0.0671947760223240 & 0.966402611988838 \tabularnewline
17 & 0.0183222105214037 & 0.0366444210428075 & 0.981677789478596 \tabularnewline
18 & 0.0110959883845923 & 0.0221919767691846 & 0.988904011615408 \tabularnewline
19 & 0.00476679846746194 & 0.00953359693492388 & 0.995233201532538 \tabularnewline
20 & 0.00151447342514872 & 0.00302894685029744 & 0.998485526574851 \tabularnewline
21 & 0.000572182650164849 & 0.00114436530032970 & 0.999427817349835 \tabularnewline
22 & 0.000206083350186539 & 0.000412166700373079 & 0.999793916649814 \tabularnewline
23 & 8.30191698789731e-05 & 0.000166038339757946 & 0.99991698083012 \tabularnewline
24 & 2.56769367998686e-05 & 5.13538735997372e-05 & 0.9999743230632 \tabularnewline
25 & 1.12358654741466e-05 & 2.24717309482932e-05 & 0.999988764134526 \tabularnewline
26 & 4.21186873817185e-06 & 8.4237374763437e-06 & 0.999995788131262 \tabularnewline
27 & 2.03887384487026e-06 & 4.07774768974053e-06 & 0.999997961126155 \tabularnewline
28 & 5.16425638641425e-07 & 1.03285127728285e-06 & 0.999999483574361 \tabularnewline
29 & 1.52754195348215e-07 & 3.0550839069643e-07 & 0.999999847245805 \tabularnewline
30 & 2.9254657857638e-07 & 5.8509315715276e-07 & 0.999999707453421 \tabularnewline
31 & 4.17141620585397e-07 & 8.34283241170794e-07 & 0.99999958285838 \tabularnewline
32 & 4.94532215011616e-07 & 9.8906443002323e-07 & 0.999999505467785 \tabularnewline
33 & 2.32009284874381e-06 & 4.64018569748761e-06 & 0.999997679907151 \tabularnewline
34 & 6.70097181141657e-06 & 1.34019436228331e-05 & 0.999993299028189 \tabularnewline
35 & 5.17694808816737e-06 & 1.03538961763347e-05 & 0.999994823051912 \tabularnewline
36 & 2.11031267868055e-05 & 4.22062535736111e-05 & 0.999978896873213 \tabularnewline
37 & 0.000229366867670294 & 0.000458733735340589 & 0.99977063313233 \tabularnewline
38 & 0.000357652211756362 & 0.000715304423512724 & 0.999642347788244 \tabularnewline
39 & 0.000322287981371040 & 0.000644575962742079 & 0.99967771201863 \tabularnewline
40 & 0.00134317553741778 & 0.00268635107483556 & 0.998656824462582 \tabularnewline
41 & 0.126793939227939 & 0.253587878455878 & 0.873206060772061 \tabularnewline
42 & 0.153447792911637 & 0.306895585823275 & 0.846552207088363 \tabularnewline
43 & 0.333380267350408 & 0.666760534700815 & 0.666619732649592 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57822&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]16[/C][C]0.0335973880111620[/C][C]0.0671947760223240[/C][C]0.966402611988838[/C][/ROW]
[ROW][C]17[/C][C]0.0183222105214037[/C][C]0.0366444210428075[/C][C]0.981677789478596[/C][/ROW]
[ROW][C]18[/C][C]0.0110959883845923[/C][C]0.0221919767691846[/C][C]0.988904011615408[/C][/ROW]
[ROW][C]19[/C][C]0.00476679846746194[/C][C]0.00953359693492388[/C][C]0.995233201532538[/C][/ROW]
[ROW][C]20[/C][C]0.00151447342514872[/C][C]0.00302894685029744[/C][C]0.998485526574851[/C][/ROW]
[ROW][C]21[/C][C]0.000572182650164849[/C][C]0.00114436530032970[/C][C]0.999427817349835[/C][/ROW]
[ROW][C]22[/C][C]0.000206083350186539[/C][C]0.000412166700373079[/C][C]0.999793916649814[/C][/ROW]
[ROW][C]23[/C][C]8.30191698789731e-05[/C][C]0.000166038339757946[/C][C]0.99991698083012[/C][/ROW]
[ROW][C]24[/C][C]2.56769367998686e-05[/C][C]5.13538735997372e-05[/C][C]0.9999743230632[/C][/ROW]
[ROW][C]25[/C][C]1.12358654741466e-05[/C][C]2.24717309482932e-05[/C][C]0.999988764134526[/C][/ROW]
[ROW][C]26[/C][C]4.21186873817185e-06[/C][C]8.4237374763437e-06[/C][C]0.999995788131262[/C][/ROW]
[ROW][C]27[/C][C]2.03887384487026e-06[/C][C]4.07774768974053e-06[/C][C]0.999997961126155[/C][/ROW]
[ROW][C]28[/C][C]5.16425638641425e-07[/C][C]1.03285127728285e-06[/C][C]0.999999483574361[/C][/ROW]
[ROW][C]29[/C][C]1.52754195348215e-07[/C][C]3.0550839069643e-07[/C][C]0.999999847245805[/C][/ROW]
[ROW][C]30[/C][C]2.9254657857638e-07[/C][C]5.8509315715276e-07[/C][C]0.999999707453421[/C][/ROW]
[ROW][C]31[/C][C]4.17141620585397e-07[/C][C]8.34283241170794e-07[/C][C]0.99999958285838[/C][/ROW]
[ROW][C]32[/C][C]4.94532215011616e-07[/C][C]9.8906443002323e-07[/C][C]0.999999505467785[/C][/ROW]
[ROW][C]33[/C][C]2.32009284874381e-06[/C][C]4.64018569748761e-06[/C][C]0.999997679907151[/C][/ROW]
[ROW][C]34[/C][C]6.70097181141657e-06[/C][C]1.34019436228331e-05[/C][C]0.999993299028189[/C][/ROW]
[ROW][C]35[/C][C]5.17694808816737e-06[/C][C]1.03538961763347e-05[/C][C]0.999994823051912[/C][/ROW]
[ROW][C]36[/C][C]2.11031267868055e-05[/C][C]4.22062535736111e-05[/C][C]0.999978896873213[/C][/ROW]
[ROW][C]37[/C][C]0.000229366867670294[/C][C]0.000458733735340589[/C][C]0.99977063313233[/C][/ROW]
[ROW][C]38[/C][C]0.000357652211756362[/C][C]0.000715304423512724[/C][C]0.999642347788244[/C][/ROW]
[ROW][C]39[/C][C]0.000322287981371040[/C][C]0.000644575962742079[/C][C]0.99967771201863[/C][/ROW]
[ROW][C]40[/C][C]0.00134317553741778[/C][C]0.00268635107483556[/C][C]0.998656824462582[/C][/ROW]
[ROW][C]41[/C][C]0.126793939227939[/C][C]0.253587878455878[/C][C]0.873206060772061[/C][/ROW]
[ROW][C]42[/C][C]0.153447792911637[/C][C]0.306895585823275[/C][C]0.846552207088363[/C][/ROW]
[ROW][C]43[/C][C]0.333380267350408[/C][C]0.666760534700815[/C][C]0.666619732649592[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57822&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57822&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
160.03359738801116200.06719477602232400.966402611988838
170.01832221052140370.03664442104280750.981677789478596
180.01109598838459230.02219197676918460.988904011615408
190.004766798467461940.009533596934923880.995233201532538
200.001514473425148720.003028946850297440.998485526574851
210.0005721826501648490.001144365300329700.999427817349835
220.0002060833501865390.0004121667003730790.999793916649814
238.30191698789731e-050.0001660383397579460.99991698083012
242.56769367998686e-055.13538735997372e-050.9999743230632
251.12358654741466e-052.24717309482932e-050.999988764134526
264.21186873817185e-068.4237374763437e-060.999995788131262
272.03887384487026e-064.07774768974053e-060.999997961126155
285.16425638641425e-071.03285127728285e-060.999999483574361
291.52754195348215e-073.0550839069643e-070.999999847245805
302.9254657857638e-075.8509315715276e-070.999999707453421
314.17141620585397e-078.34283241170794e-070.99999958285838
324.94532215011616e-079.8906443002323e-070.999999505467785
332.32009284874381e-064.64018569748761e-060.999997679907151
346.70097181141657e-061.34019436228331e-050.999993299028189
355.17694808816737e-061.03538961763347e-050.999994823051912
362.11031267868055e-054.22062535736111e-050.999978896873213
370.0002293668676702940.0004587337353405890.99977063313233
380.0003576522117563620.0007153044235127240.999642347788244
390.0003222879813710400.0006445759627420790.99967771201863
400.001343175537417780.002686351074835560.998656824462582
410.1267939392279390.2535878784558780.873206060772061
420.1534477929116370.3068955858232750.846552207088363
430.3333802673504080.6667605347008150.666619732649592







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level220.785714285714286NOK
5% type I error level240.857142857142857NOK
10% type I error level250.892857142857143NOK

\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 & 22 & 0.785714285714286 & NOK \tabularnewline
5% type I error level & 24 & 0.857142857142857 & NOK \tabularnewline
10% type I error level & 25 & 0.892857142857143 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57822&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]22[/C][C]0.785714285714286[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]24[/C][C]0.857142857142857[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]25[/C][C]0.892857142857143[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57822&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57822&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 level220.785714285714286NOK
5% type I error level240.857142857142857NOK
10% type I error level250.892857142857143NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}