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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 computationThu, 26 Nov 2009 11:21:27 -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/26/t1259259850ry8qlxqc77speev.htm/, Retrieved Sun, 28 Apr 2024 07:48:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=60229, Retrieved Sun, 28 Apr 2024 07:48:14 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact125

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
F    D    [Multiple Regression] [Multiple Regression] [2009-11-19 15:45:41] [976efdaed7598845c859b86bc2e467ce]
-    D        [Multiple Regression] [] [2009-11-26 18:21:27] [429631dabc57c2ce83a6344a979b9063] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-0.7	-0.4	-2.9	-0.8	1	1.4
-0.7	-0.3	-0.7	-2.9	-0.8	1
1.5	1.4	-0.7	-0.7	-2.9	-0.8
3	2.6	1.5	-0.7	-0.7	-2.9
3.2	2.8	3	1.5	-0.7	-0.7
3.1	2.6	3.2	3	1.5	-0.7
3.9	3.4	3.1	3.2	3	1.5
1	1.7	3.9	3.1	3.2	3
1.3	1.2	1	3.9	3.1	3.2
0.8	0	1.3	1	3.9	3.1
1.2	0	0.8	1.3	1	3.9
2.9	1.6	1.2	0.8	1.3	1
3.9	2.5	2.9	1.2	0.8	1.3
4.5	3.2	3.9	2.9	1.2	0.8
4.5	3.4	4.5	3.9	2.9	1.2
3.3	2.3	4.5	4.5	3.9	2.9
2	1.9	3.3	4.5	4.5	3.9
1.5	1.7	2	3.3	4.5	4.5
1	1.9	1.5	2	3.3	4.5
2.1	3.3	1	1.5	2	3.3
3	3.8	2.1	1	1.5	2
4	4.4	3	2.1	1	1.5
5.1	4.5	4	3	2.1	1
4.5	3.5	5.1	4	3	2.1
4.2	3	4.5	5.1	4	3
3.3	2.8	4.2	4.5	5.1	4
2.7	2.9	3.3	4.2	4.5	5.1
1.8	2.6	2.7	3.3	4.2	4.5
1.4	2.1	1.8	2.7	3.3	4.2
0.5	1.5	1.4	1.8	2.7	3.3
-0.4	1.1	0.5	1.4	1.8	2.7
0.8	1.5	-0.4	0.5	1.4	1.8
0.7	1.7	0.8	-0.4	0.5	1.4
1.9	2.3	0.7	0.8	-0.4	0.5
2	2.3	1.9	0.7	0.8	-0.4
1.1	1.9	2	1.9	0.7	0.8
0.9	2	1.1	2	1.9	0.7
0.4	1.6	0.9	1.1	2	1.9
0.7	1.2	0.4	0.9	1.1	2
2.1	1.9	0.7	0.4	0.9	1.1
2.8	2.1	2.1	0.7	0.4	0.9
3.9	2.4	2.8	2.1	0.7	0.4
3.5	2.9	3.9	2.8	2.1	0.7
2	2.5	3.5	3.9	2.8	2.1
2	2.3	2	3.5	3.9	2.8
1.5	2.5	2	2	3.5	3.9
2.5	2.6	1.5	2	2	3.5
3.1	2.4	2.5	1.5	2	2
2.7	2.5	3.1	2.5	1.5	2
2.8	2.1	2.7	3.1	2.5	1.5
2.5	2.2	2.8	2.7	3.1	2.5
3	2.7	2.5	2.8	2.7	3.1
3.2	3	3	2.5	2.8	2.7
2.8	3.2	3.2	3	2.5	2.8
2.4	3	2.8	3.2	3	2.5
2	2.7	2.4	2.8	3.2	3
1.8	2.5	2	2.4	2.8	3.2
1.1	1.6	1.8	2	2.4	2.8
-1.5	0.1	1.1	1.8	2	2.4
-3.7	-1.9	-1.5	1.1	1.8	2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60229&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]4 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=60229&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60229&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
bbp[t] = + 0.283583481329755 + 0.87157532309714dnst[t] + 0.388041478512539y1[t] + 0.000169689182079754y2[t] -0.0877779250738779y3[t] -0.0561761314020608y4[t] + 0.163466937282331M1[t] -0.0834875894043308M2[t] -0.00580475650251201M3[t] -0.00489251410928679M4[t] -0.163817702920595M5[t] -0.156310262467637M6[t] -0.476683426511671M7[t] -0.745738408049973M8[t] -0.343987974397176M9[t] -0.211691278468082M10[t] -0.0699298934146428M11[t] -0.0136047722817271t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
bbp[t] =  +  0.283583481329755 +  0.87157532309714dnst[t] +  0.388041478512539y1[t] +  0.000169689182079754y2[t] -0.0877779250738779y3[t] -0.0561761314020608y4[t] +  0.163466937282331M1[t] -0.0834875894043308M2[t] -0.00580475650251201M3[t] -0.00489251410928679M4[t] -0.163817702920595M5[t] -0.156310262467637M6[t] -0.476683426511671M7[t] -0.745738408049973M8[t] -0.343987974397176M9[t] -0.211691278468082M10[t] -0.0699298934146428M11[t] -0.0136047722817271t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60229&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]bbp[t] =  +  0.283583481329755 +  0.87157532309714dnst[t] +  0.388041478512539y1[t] +  0.000169689182079754y2[t] -0.0877779250738779y3[t] -0.0561761314020608y4[t] +  0.163466937282331M1[t] -0.0834875894043308M2[t] -0.00580475650251201M3[t] -0.00489251410928679M4[t] -0.163817702920595M5[t] -0.156310262467637M6[t] -0.476683426511671M7[t] -0.745738408049973M8[t] -0.343987974397176M9[t] -0.211691278468082M10[t] -0.0699298934146428M11[t] -0.0136047722817271t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60229&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60229&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
bbp[t] = + 0.283583481329755 + 0.87157532309714dnst[t] + 0.388041478512539y1[t] + 0.000169689182079754y2[t] -0.0877779250738779y3[t] -0.0561761314020608y4[t] + 0.163466937282331M1[t] -0.0834875894043308M2[t] -0.00580475650251201M3[t] -0.00489251410928679M4[t] -0.163817702920595M5[t] -0.156310262467637M6[t] -0.476683426511671M7[t] -0.745738408049973M8[t] -0.343987974397176M9[t] -0.211691278468082M10[t] -0.0699298934146428M11[t] -0.0136047722817271t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.2835834813297550.3711190.76410.4490620.224531
dnst0.871575323097140.1295726.726600
y10.3880414785125390.1355892.86190.0065390.00327
y20.0001696891820797540.1446070.00120.9990690.499535
y3-0.08777792507387790.148348-0.59170.5572210.27861
y4-0.05617613140206080.108466-0.51790.6072370.303618
M10.1634669372823310.4308140.37940.7062750.353137
M2-0.08348758940433080.428772-0.19470.8465560.423278
M3-0.005804756502512010.435481-0.01330.9894280.494714
M4-0.004892514109286790.434877-0.01130.9910770.495538
M5-0.1638177029205950.432803-0.37850.7069630.353481
M6-0.1563102624676370.429149-0.36420.7175110.358756
M7-0.4766834265116710.438455-1.08720.2831530.141577
M8-0.7457384080499730.440422-1.69320.0978160.048908
M9-0.3439879743971760.448933-0.76620.4478220.223911
M10-0.2116912784680820.439268-0.48190.6323660.316183
M11-0.06992989341464280.428518-0.16320.8711510.435576
t-0.01360477228172710.005232-2.60020.0128050.006403

\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) & 0.283583481329755 & 0.371119 & 0.7641 & 0.449062 & 0.224531 \tabularnewline
dnst & 0.87157532309714 & 0.129572 & 6.7266 & 0 & 0 \tabularnewline
y1 & 0.388041478512539 & 0.135589 & 2.8619 & 0.006539 & 0.00327 \tabularnewline
y2 & 0.000169689182079754 & 0.144607 & 0.0012 & 0.999069 & 0.499535 \tabularnewline
y3 & -0.0877779250738779 & 0.148348 & -0.5917 & 0.557221 & 0.27861 \tabularnewline
y4 & -0.0561761314020608 & 0.108466 & -0.5179 & 0.607237 & 0.303618 \tabularnewline
M1 & 0.163466937282331 & 0.430814 & 0.3794 & 0.706275 & 0.353137 \tabularnewline
M2 & -0.0834875894043308 & 0.428772 & -0.1947 & 0.846556 & 0.423278 \tabularnewline
M3 & -0.00580475650251201 & 0.435481 & -0.0133 & 0.989428 & 0.494714 \tabularnewline
M4 & -0.00489251410928679 & 0.434877 & -0.0113 & 0.991077 & 0.495538 \tabularnewline
M5 & -0.163817702920595 & 0.432803 & -0.3785 & 0.706963 & 0.353481 \tabularnewline
M6 & -0.156310262467637 & 0.429149 & -0.3642 & 0.717511 & 0.358756 \tabularnewline
M7 & -0.476683426511671 & 0.438455 & -1.0872 & 0.283153 & 0.141577 \tabularnewline
M8 & -0.745738408049973 & 0.440422 & -1.6932 & 0.097816 & 0.048908 \tabularnewline
M9 & -0.343987974397176 & 0.448933 & -0.7662 & 0.447822 & 0.223911 \tabularnewline
M10 & -0.211691278468082 & 0.439268 & -0.4819 & 0.632366 & 0.316183 \tabularnewline
M11 & -0.0699298934146428 & 0.428518 & -0.1632 & 0.871151 & 0.435576 \tabularnewline
t & -0.0136047722817271 & 0.005232 & -2.6002 & 0.012805 & 0.006403 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60229&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]0.283583481329755[/C][C]0.371119[/C][C]0.7641[/C][C]0.449062[/C][C]0.224531[/C][/ROW]
[ROW][C]dnst[/C][C]0.87157532309714[/C][C]0.129572[/C][C]6.7266[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]y1[/C][C]0.388041478512539[/C][C]0.135589[/C][C]2.8619[/C][C]0.006539[/C][C]0.00327[/C][/ROW]
[ROW][C]y2[/C][C]0.000169689182079754[/C][C]0.144607[/C][C]0.0012[/C][C]0.999069[/C][C]0.499535[/C][/ROW]
[ROW][C]y3[/C][C]-0.0877779250738779[/C][C]0.148348[/C][C]-0.5917[/C][C]0.557221[/C][C]0.27861[/C][/ROW]
[ROW][C]y4[/C][C]-0.0561761314020608[/C][C]0.108466[/C][C]-0.5179[/C][C]0.607237[/C][C]0.303618[/C][/ROW]
[ROW][C]M1[/C][C]0.163466937282331[/C][C]0.430814[/C][C]0.3794[/C][C]0.706275[/C][C]0.353137[/C][/ROW]
[ROW][C]M2[/C][C]-0.0834875894043308[/C][C]0.428772[/C][C]-0.1947[/C][C]0.846556[/C][C]0.423278[/C][/ROW]
[ROW][C]M3[/C][C]-0.00580475650251201[/C][C]0.435481[/C][C]-0.0133[/C][C]0.989428[/C][C]0.494714[/C][/ROW]
[ROW][C]M4[/C][C]-0.00489251410928679[/C][C]0.434877[/C][C]-0.0113[/C][C]0.991077[/C][C]0.495538[/C][/ROW]
[ROW][C]M5[/C][C]-0.163817702920595[/C][C]0.432803[/C][C]-0.3785[/C][C]0.706963[/C][C]0.353481[/C][/ROW]
[ROW][C]M6[/C][C]-0.156310262467637[/C][C]0.429149[/C][C]-0.3642[/C][C]0.717511[/C][C]0.358756[/C][/ROW]
[ROW][C]M7[/C][C]-0.476683426511671[/C][C]0.438455[/C][C]-1.0872[/C][C]0.283153[/C][C]0.141577[/C][/ROW]
[ROW][C]M8[/C][C]-0.745738408049973[/C][C]0.440422[/C][C]-1.6932[/C][C]0.097816[/C][C]0.048908[/C][/ROW]
[ROW][C]M9[/C][C]-0.343987974397176[/C][C]0.448933[/C][C]-0.7662[/C][C]0.447822[/C][C]0.223911[/C][/ROW]
[ROW][C]M10[/C][C]-0.211691278468082[/C][C]0.439268[/C][C]-0.4819[/C][C]0.632366[/C][C]0.316183[/C][/ROW]
[ROW][C]M11[/C][C]-0.0699298934146428[/C][C]0.428518[/C][C]-0.1632[/C][C]0.871151[/C][C]0.435576[/C][/ROW]
[ROW][C]t[/C][C]-0.0136047722817271[/C][C]0.005232[/C][C]-2.6002[/C][C]0.012805[/C][C]0.006403[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60229&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60229&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)0.2835834813297550.3711190.76410.4490620.224531
dnst0.871575323097140.1295726.726600
y10.3880414785125390.1355892.86190.0065390.00327
y20.0001696891820797540.1446070.00120.9990690.499535
y3-0.08777792507387790.148348-0.59170.5572210.27861
y4-0.05617613140206080.108466-0.51790.6072370.303618
M10.1634669372823310.4308140.37940.7062750.353137
M2-0.08348758940433080.428772-0.19470.8465560.423278
M3-0.005804756502512010.435481-0.01330.9894280.494714
M4-0.004892514109286790.434877-0.01130.9910770.495538
M5-0.1638177029205950.432803-0.37850.7069630.353481
M6-0.1563102624676370.429149-0.36420.7175110.358756
M7-0.4766834265116710.438455-1.08720.2831530.141577
M8-0.7457384080499730.440422-1.69320.0978160.048908
M9-0.3439879743971760.448933-0.76620.4478220.223911
M10-0.2116912784680820.439268-0.48190.6323660.316183
M11-0.06992989341464280.428518-0.16320.8711510.435576
t-0.01360477228172710.005232-2.60020.0128050.006403






Multiple Linear Regression - Regression Statistics
Multiple R0.936070244926371
R-squared0.876227503436516
Adjusted R-squared0.826129111970344
F-TEST (value)17.4901324731787
F-TEST (DF numerator)17
F-TEST (DF denominator)42
p-value7.8381745538536e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.663394132954228
Sum Squared Residuals18.4838545767999

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.936070244926371 \tabularnewline
R-squared & 0.876227503436516 \tabularnewline
Adjusted R-squared & 0.826129111970344 \tabularnewline
F-TEST (value) & 17.4901324731787 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 42 \tabularnewline
p-value & 7.8381745538536e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.663394132954228 \tabularnewline
Sum Squared Residuals & 18.4838545767999 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60229&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.936070244926371[/C][/ROW]
[ROW][C]R-squared[/C][C]0.876227503436516[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.826129111970344[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.4901324731787[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]42[/C][/ROW]
[ROW][C]p-value[/C][C]7.8381745538536e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.663394132954228[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]18.4838545767999[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60229&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60229&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 R0.936070244926371
R-squared0.876227503436516
Adjusted R-squared0.826129111970344
F-TEST (value)17.4901324731787
F-TEST (DF numerator)17
F-TEST (DF denominator)42
p-value7.8381745538536e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.663394132954228
Sum Squared Residuals18.4838545767999






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-0.7-1.207065030977280.507065030977279
2-0.7-0.346661174496943-0.353338825503057
31.51.484918930767720.0150810692322801
433.29666648210517-0.29666648210517
53.23.75729963051641-0.557299630516412
63.13.46163862838131-0.36163862838131
73.93.530896363823070.369103636176928
811.97515499251186-0.975154992511862
91.30.7998710222206370.500128977779363
100.8-0.07601182384173130.876011823841731
111.20.06779003402093211.13220996597907
122.91.810344822467061.08965517753294
133.93.431399290515560.468600709484437
144.54.162249563508730.337750436491273
154.54.461944339851430.0380556601485679
163.33.30734341960794-0.00734341960794171
1722.21169066861462-0.211690668614617
181.51.492915044240390.00708495575961385
1911.24434434742973-0.244344347429733
202.12.16930712237691-0.0693071223769058
2133.53691916042892-0.536919160428917
2244.59995729493411-0.599957294934111
235.15.13499798691171-0.0349979869117128
244.54.6059692233846-0.105969223384603
254.23.949069054493670.250930945506331
263.33.244948584859520.0550514151404846
272.73.03776895087547-0.337768950875472
281.82.59066527304983-0.790665273049833
291.41.72886147822480-0.328861478224804
300.51.14767491417504-0.647674914175043
31-0.40.228467453684035-0.628467453684035
320.80.03071746646911110.769282533530889
330.71.16014583153810-0.460145831538099
341.91.892741079039340.00725892096066377
3522.43175550528109-0.431755505281089
361.12.11982470686981-1.01982470686981
370.91.90790814548861-1.00790814548861
380.41.14476855112512-0.744768551125121
390.70.739544324839955-0.0395443248399549
402.11.521396223358800.578603776641196
412.82.121614492374730.678385507625273
423.92.650611045467661.24938895453234
433.53.039643244957670.460356755042335
4422.11323229707945-0.113232297079453
4521.609053790826750.390946209173255
461.51.87512367080771-0.375123670807705
472.52.050554416804500.449445583195496
483.12.404785304342580.695214695657419
492.72.91868854047944-0.218688540479442
502.82.094694475003580.705305524996419
512.52.175823453665420.324176546334578
5232.483928601878250.516071398121749
533.22.780533730269440.419466269730559
542.83.04716036773560-0.247160367735605
552.42.356648590105490.043351409894506
5621.611588121562670.38841187843733
571.81.694010194985600.105989805014398
581.11.008189779060580.0918102209394215
59-1.5-0.385097943018238-1.11490205698176
60-3.7-3.04092405706406-0.659075942935939

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -0.7 & -1.20706503097728 & 0.507065030977279 \tabularnewline
2 & -0.7 & -0.346661174496943 & -0.353338825503057 \tabularnewline
3 & 1.5 & 1.48491893076772 & 0.0150810692322801 \tabularnewline
4 & 3 & 3.29666648210517 & -0.29666648210517 \tabularnewline
5 & 3.2 & 3.75729963051641 & -0.557299630516412 \tabularnewline
6 & 3.1 & 3.46163862838131 & -0.36163862838131 \tabularnewline
7 & 3.9 & 3.53089636382307 & 0.369103636176928 \tabularnewline
8 & 1 & 1.97515499251186 & -0.975154992511862 \tabularnewline
9 & 1.3 & 0.799871022220637 & 0.500128977779363 \tabularnewline
10 & 0.8 & -0.0760118238417313 & 0.876011823841731 \tabularnewline
11 & 1.2 & 0.0677900340209321 & 1.13220996597907 \tabularnewline
12 & 2.9 & 1.81034482246706 & 1.08965517753294 \tabularnewline
13 & 3.9 & 3.43139929051556 & 0.468600709484437 \tabularnewline
14 & 4.5 & 4.16224956350873 & 0.337750436491273 \tabularnewline
15 & 4.5 & 4.46194433985143 & 0.0380556601485679 \tabularnewline
16 & 3.3 & 3.30734341960794 & -0.00734341960794171 \tabularnewline
17 & 2 & 2.21169066861462 & -0.211690668614617 \tabularnewline
18 & 1.5 & 1.49291504424039 & 0.00708495575961385 \tabularnewline
19 & 1 & 1.24434434742973 & -0.244344347429733 \tabularnewline
20 & 2.1 & 2.16930712237691 & -0.0693071223769058 \tabularnewline
21 & 3 & 3.53691916042892 & -0.536919160428917 \tabularnewline
22 & 4 & 4.59995729493411 & -0.599957294934111 \tabularnewline
23 & 5.1 & 5.13499798691171 & -0.0349979869117128 \tabularnewline
24 & 4.5 & 4.6059692233846 & -0.105969223384603 \tabularnewline
25 & 4.2 & 3.94906905449367 & 0.250930945506331 \tabularnewline
26 & 3.3 & 3.24494858485952 & 0.0550514151404846 \tabularnewline
27 & 2.7 & 3.03776895087547 & -0.337768950875472 \tabularnewline
28 & 1.8 & 2.59066527304983 & -0.790665273049833 \tabularnewline
29 & 1.4 & 1.72886147822480 & -0.328861478224804 \tabularnewline
30 & 0.5 & 1.14767491417504 & -0.647674914175043 \tabularnewline
31 & -0.4 & 0.228467453684035 & -0.628467453684035 \tabularnewline
32 & 0.8 & 0.0307174664691111 & 0.769282533530889 \tabularnewline
33 & 0.7 & 1.16014583153810 & -0.460145831538099 \tabularnewline
34 & 1.9 & 1.89274107903934 & 0.00725892096066377 \tabularnewline
35 & 2 & 2.43175550528109 & -0.431755505281089 \tabularnewline
36 & 1.1 & 2.11982470686981 & -1.01982470686981 \tabularnewline
37 & 0.9 & 1.90790814548861 & -1.00790814548861 \tabularnewline
38 & 0.4 & 1.14476855112512 & -0.744768551125121 \tabularnewline
39 & 0.7 & 0.739544324839955 & -0.0395443248399549 \tabularnewline
40 & 2.1 & 1.52139622335880 & 0.578603776641196 \tabularnewline
41 & 2.8 & 2.12161449237473 & 0.678385507625273 \tabularnewline
42 & 3.9 & 2.65061104546766 & 1.24938895453234 \tabularnewline
43 & 3.5 & 3.03964324495767 & 0.460356755042335 \tabularnewline
44 & 2 & 2.11323229707945 & -0.113232297079453 \tabularnewline
45 & 2 & 1.60905379082675 & 0.390946209173255 \tabularnewline
46 & 1.5 & 1.87512367080771 & -0.375123670807705 \tabularnewline
47 & 2.5 & 2.05055441680450 & 0.449445583195496 \tabularnewline
48 & 3.1 & 2.40478530434258 & 0.695214695657419 \tabularnewline
49 & 2.7 & 2.91868854047944 & -0.218688540479442 \tabularnewline
50 & 2.8 & 2.09469447500358 & 0.705305524996419 \tabularnewline
51 & 2.5 & 2.17582345366542 & 0.324176546334578 \tabularnewline
52 & 3 & 2.48392860187825 & 0.516071398121749 \tabularnewline
53 & 3.2 & 2.78053373026944 & 0.419466269730559 \tabularnewline
54 & 2.8 & 3.04716036773560 & -0.247160367735605 \tabularnewline
55 & 2.4 & 2.35664859010549 & 0.043351409894506 \tabularnewline
56 & 2 & 1.61158812156267 & 0.38841187843733 \tabularnewline
57 & 1.8 & 1.69401019498560 & 0.105989805014398 \tabularnewline
58 & 1.1 & 1.00818977906058 & 0.0918102209394215 \tabularnewline
59 & -1.5 & -0.385097943018238 & -1.11490205698176 \tabularnewline
60 & -3.7 & -3.04092405706406 & -0.659075942935939 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60229&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]-0.7[/C][C]-1.20706503097728[/C][C]0.507065030977279[/C][/ROW]
[ROW][C]2[/C][C]-0.7[/C][C]-0.346661174496943[/C][C]-0.353338825503057[/C][/ROW]
[ROW][C]3[/C][C]1.5[/C][C]1.48491893076772[/C][C]0.0150810692322801[/C][/ROW]
[ROW][C]4[/C][C]3[/C][C]3.29666648210517[/C][C]-0.29666648210517[/C][/ROW]
[ROW][C]5[/C][C]3.2[/C][C]3.75729963051641[/C][C]-0.557299630516412[/C][/ROW]
[ROW][C]6[/C][C]3.1[/C][C]3.46163862838131[/C][C]-0.36163862838131[/C][/ROW]
[ROW][C]7[/C][C]3.9[/C][C]3.53089636382307[/C][C]0.369103636176928[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]1.97515499251186[/C][C]-0.975154992511862[/C][/ROW]
[ROW][C]9[/C][C]1.3[/C][C]0.799871022220637[/C][C]0.500128977779363[/C][/ROW]
[ROW][C]10[/C][C]0.8[/C][C]-0.0760118238417313[/C][C]0.876011823841731[/C][/ROW]
[ROW][C]11[/C][C]1.2[/C][C]0.0677900340209321[/C][C]1.13220996597907[/C][/ROW]
[ROW][C]12[/C][C]2.9[/C][C]1.81034482246706[/C][C]1.08965517753294[/C][/ROW]
[ROW][C]13[/C][C]3.9[/C][C]3.43139929051556[/C][C]0.468600709484437[/C][/ROW]
[ROW][C]14[/C][C]4.5[/C][C]4.16224956350873[/C][C]0.337750436491273[/C][/ROW]
[ROW][C]15[/C][C]4.5[/C][C]4.46194433985143[/C][C]0.0380556601485679[/C][/ROW]
[ROW][C]16[/C][C]3.3[/C][C]3.30734341960794[/C][C]-0.00734341960794171[/C][/ROW]
[ROW][C]17[/C][C]2[/C][C]2.21169066861462[/C][C]-0.211690668614617[/C][/ROW]
[ROW][C]18[/C][C]1.5[/C][C]1.49291504424039[/C][C]0.00708495575961385[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]1.24434434742973[/C][C]-0.244344347429733[/C][/ROW]
[ROW][C]20[/C][C]2.1[/C][C]2.16930712237691[/C][C]-0.0693071223769058[/C][/ROW]
[ROW][C]21[/C][C]3[/C][C]3.53691916042892[/C][C]-0.536919160428917[/C][/ROW]
[ROW][C]22[/C][C]4[/C][C]4.59995729493411[/C][C]-0.599957294934111[/C][/ROW]
[ROW][C]23[/C][C]5.1[/C][C]5.13499798691171[/C][C]-0.0349979869117128[/C][/ROW]
[ROW][C]24[/C][C]4.5[/C][C]4.6059692233846[/C][C]-0.105969223384603[/C][/ROW]
[ROW][C]25[/C][C]4.2[/C][C]3.94906905449367[/C][C]0.250930945506331[/C][/ROW]
[ROW][C]26[/C][C]3.3[/C][C]3.24494858485952[/C][C]0.0550514151404846[/C][/ROW]
[ROW][C]27[/C][C]2.7[/C][C]3.03776895087547[/C][C]-0.337768950875472[/C][/ROW]
[ROW][C]28[/C][C]1.8[/C][C]2.59066527304983[/C][C]-0.790665273049833[/C][/ROW]
[ROW][C]29[/C][C]1.4[/C][C]1.72886147822480[/C][C]-0.328861478224804[/C][/ROW]
[ROW][C]30[/C][C]0.5[/C][C]1.14767491417504[/C][C]-0.647674914175043[/C][/ROW]
[ROW][C]31[/C][C]-0.4[/C][C]0.228467453684035[/C][C]-0.628467453684035[/C][/ROW]
[ROW][C]32[/C][C]0.8[/C][C]0.0307174664691111[/C][C]0.769282533530889[/C][/ROW]
[ROW][C]33[/C][C]0.7[/C][C]1.16014583153810[/C][C]-0.460145831538099[/C][/ROW]
[ROW][C]34[/C][C]1.9[/C][C]1.89274107903934[/C][C]0.00725892096066377[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]2.43175550528109[/C][C]-0.431755505281089[/C][/ROW]
[ROW][C]36[/C][C]1.1[/C][C]2.11982470686981[/C][C]-1.01982470686981[/C][/ROW]
[ROW][C]37[/C][C]0.9[/C][C]1.90790814548861[/C][C]-1.00790814548861[/C][/ROW]
[ROW][C]38[/C][C]0.4[/C][C]1.14476855112512[/C][C]-0.744768551125121[/C][/ROW]
[ROW][C]39[/C][C]0.7[/C][C]0.739544324839955[/C][C]-0.0395443248399549[/C][/ROW]
[ROW][C]40[/C][C]2.1[/C][C]1.52139622335880[/C][C]0.578603776641196[/C][/ROW]
[ROW][C]41[/C][C]2.8[/C][C]2.12161449237473[/C][C]0.678385507625273[/C][/ROW]
[ROW][C]42[/C][C]3.9[/C][C]2.65061104546766[/C][C]1.24938895453234[/C][/ROW]
[ROW][C]43[/C][C]3.5[/C][C]3.03964324495767[/C][C]0.460356755042335[/C][/ROW]
[ROW][C]44[/C][C]2[/C][C]2.11323229707945[/C][C]-0.113232297079453[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]1.60905379082675[/C][C]0.390946209173255[/C][/ROW]
[ROW][C]46[/C][C]1.5[/C][C]1.87512367080771[/C][C]-0.375123670807705[/C][/ROW]
[ROW][C]47[/C][C]2.5[/C][C]2.05055441680450[/C][C]0.449445583195496[/C][/ROW]
[ROW][C]48[/C][C]3.1[/C][C]2.40478530434258[/C][C]0.695214695657419[/C][/ROW]
[ROW][C]49[/C][C]2.7[/C][C]2.91868854047944[/C][C]-0.218688540479442[/C][/ROW]
[ROW][C]50[/C][C]2.8[/C][C]2.09469447500358[/C][C]0.705305524996419[/C][/ROW]
[ROW][C]51[/C][C]2.5[/C][C]2.17582345366542[/C][C]0.324176546334578[/C][/ROW]
[ROW][C]52[/C][C]3[/C][C]2.48392860187825[/C][C]0.516071398121749[/C][/ROW]
[ROW][C]53[/C][C]3.2[/C][C]2.78053373026944[/C][C]0.419466269730559[/C][/ROW]
[ROW][C]54[/C][C]2.8[/C][C]3.04716036773560[/C][C]-0.247160367735605[/C][/ROW]
[ROW][C]55[/C][C]2.4[/C][C]2.35664859010549[/C][C]0.043351409894506[/C][/ROW]
[ROW][C]56[/C][C]2[/C][C]1.61158812156267[/C][C]0.38841187843733[/C][/ROW]
[ROW][C]57[/C][C]1.8[/C][C]1.69401019498560[/C][C]0.105989805014398[/C][/ROW]
[ROW][C]58[/C][C]1.1[/C][C]1.00818977906058[/C][C]0.0918102209394215[/C][/ROW]
[ROW][C]59[/C][C]-1.5[/C][C]-0.385097943018238[/C][C]-1.11490205698176[/C][/ROW]
[ROW][C]60[/C][C]-3.7[/C][C]-3.04092405706406[/C][C]-0.659075942935939[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60229&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60229&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
1-0.7-1.207065030977280.507065030977279
2-0.7-0.346661174496943-0.353338825503057
31.51.484918930767720.0150810692322801
433.29666648210517-0.29666648210517
53.23.75729963051641-0.557299630516412
63.13.46163862838131-0.36163862838131
73.93.530896363823070.369103636176928
811.97515499251186-0.975154992511862
91.30.7998710222206370.500128977779363
100.8-0.07601182384173130.876011823841731
111.20.06779003402093211.13220996597907
122.91.810344822467061.08965517753294
133.93.431399290515560.468600709484437
144.54.162249563508730.337750436491273
154.54.461944339851430.0380556601485679
163.33.30734341960794-0.00734341960794171
1722.21169066861462-0.211690668614617
181.51.492915044240390.00708495575961385
1911.24434434742973-0.244344347429733
202.12.16930712237691-0.0693071223769058
2133.53691916042892-0.536919160428917
2244.59995729493411-0.599957294934111
235.15.13499798691171-0.0349979869117128
244.54.6059692233846-0.105969223384603
254.23.949069054493670.250930945506331
263.33.244948584859520.0550514151404846
272.73.03776895087547-0.337768950875472
281.82.59066527304983-0.790665273049833
291.41.72886147822480-0.328861478224804
300.51.14767491417504-0.647674914175043
31-0.40.228467453684035-0.628467453684035
320.80.03071746646911110.769282533530889
330.71.16014583153810-0.460145831538099
341.91.892741079039340.00725892096066377
3522.43175550528109-0.431755505281089
361.12.11982470686981-1.01982470686981
370.91.90790814548861-1.00790814548861
380.41.14476855112512-0.744768551125121
390.70.739544324839955-0.0395443248399549
402.11.521396223358800.578603776641196
412.82.121614492374730.678385507625273
423.92.650611045467661.24938895453234
433.53.039643244957670.460356755042335
4422.11323229707945-0.113232297079453
4521.609053790826750.390946209173255
461.51.87512367080771-0.375123670807705
472.52.050554416804500.449445583195496
483.12.404785304342580.695214695657419
492.72.91868854047944-0.218688540479442
502.82.094694475003580.705305524996419
512.52.175823453665420.324176546334578
5232.483928601878250.516071398121749
533.22.780533730269440.419466269730559
542.83.04716036773560-0.247160367735605
552.42.356648590105490.043351409894506
5621.611588121562670.38841187843733
571.81.694010194985600.105989805014398
581.11.008189779060580.0918102209394215
59-1.5-0.385097943018238-1.11490205698176
60-3.7-3.04092405706406-0.659075942935939






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.1491022045248860.2982044090497720.850897795475114
220.5973933040706470.8052133918587060.402606695929353
230.5532571412174270.8934857175651460.446742858782573
240.5206836620614860.9586326758770280.479316337938514
250.4825622229898070.9651244459796130.517437777010193
260.4031278496509430.8062556993018850.596872150349057
270.3177736868592190.6355473737184370.682226313140782
280.2568224598068020.5136449196136040.743177540193198
290.1831262784779730.3662525569559450.816873721522027
300.1141525537599930.2283051075199860.885847446240007
310.06913930712619260.1382786142523850.930860692873807
320.1881258521224900.3762517042449800.81187414787751
330.1248874529889450.2497749059778890.875112547011055
340.07377017435186720.1475403487037340.926229825648133
350.07368597357715690.1473719471543140.926314026422843
360.5510925991441770.8978148017116470.448907400855823
370.7993032167278250.401393566544350.200696783272175
380.6726229096460280.6547541807079430.327377090353972
390.5452837470824120.9094325058351750.454716252917588

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.149102204524886 & 0.298204409049772 & 0.850897795475114 \tabularnewline
22 & 0.597393304070647 & 0.805213391858706 & 0.402606695929353 \tabularnewline
23 & 0.553257141217427 & 0.893485717565146 & 0.446742858782573 \tabularnewline
24 & 0.520683662061486 & 0.958632675877028 & 0.479316337938514 \tabularnewline
25 & 0.482562222989807 & 0.965124445979613 & 0.517437777010193 \tabularnewline
26 & 0.403127849650943 & 0.806255699301885 & 0.596872150349057 \tabularnewline
27 & 0.317773686859219 & 0.635547373718437 & 0.682226313140782 \tabularnewline
28 & 0.256822459806802 & 0.513644919613604 & 0.743177540193198 \tabularnewline
29 & 0.183126278477973 & 0.366252556955945 & 0.816873721522027 \tabularnewline
30 & 0.114152553759993 & 0.228305107519986 & 0.885847446240007 \tabularnewline
31 & 0.0691393071261926 & 0.138278614252385 & 0.930860692873807 \tabularnewline
32 & 0.188125852122490 & 0.376251704244980 & 0.81187414787751 \tabularnewline
33 & 0.124887452988945 & 0.249774905977889 & 0.875112547011055 \tabularnewline
34 & 0.0737701743518672 & 0.147540348703734 & 0.926229825648133 \tabularnewline
35 & 0.0736859735771569 & 0.147371947154314 & 0.926314026422843 \tabularnewline
36 & 0.551092599144177 & 0.897814801711647 & 0.448907400855823 \tabularnewline
37 & 0.799303216727825 & 0.40139356654435 & 0.200696783272175 \tabularnewline
38 & 0.672622909646028 & 0.654754180707943 & 0.327377090353972 \tabularnewline
39 & 0.545283747082412 & 0.909432505835175 & 0.454716252917588 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60229&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]21[/C][C]0.149102204524886[/C][C]0.298204409049772[/C][C]0.850897795475114[/C][/ROW]
[ROW][C]22[/C][C]0.597393304070647[/C][C]0.805213391858706[/C][C]0.402606695929353[/C][/ROW]
[ROW][C]23[/C][C]0.553257141217427[/C][C]0.893485717565146[/C][C]0.446742858782573[/C][/ROW]
[ROW][C]24[/C][C]0.520683662061486[/C][C]0.958632675877028[/C][C]0.479316337938514[/C][/ROW]
[ROW][C]25[/C][C]0.482562222989807[/C][C]0.965124445979613[/C][C]0.517437777010193[/C][/ROW]
[ROW][C]26[/C][C]0.403127849650943[/C][C]0.806255699301885[/C][C]0.596872150349057[/C][/ROW]
[ROW][C]27[/C][C]0.317773686859219[/C][C]0.635547373718437[/C][C]0.682226313140782[/C][/ROW]
[ROW][C]28[/C][C]0.256822459806802[/C][C]0.513644919613604[/C][C]0.743177540193198[/C][/ROW]
[ROW][C]29[/C][C]0.183126278477973[/C][C]0.366252556955945[/C][C]0.816873721522027[/C][/ROW]
[ROW][C]30[/C][C]0.114152553759993[/C][C]0.228305107519986[/C][C]0.885847446240007[/C][/ROW]
[ROW][C]31[/C][C]0.0691393071261926[/C][C]0.138278614252385[/C][C]0.930860692873807[/C][/ROW]
[ROW][C]32[/C][C]0.188125852122490[/C][C]0.376251704244980[/C][C]0.81187414787751[/C][/ROW]
[ROW][C]33[/C][C]0.124887452988945[/C][C]0.249774905977889[/C][C]0.875112547011055[/C][/ROW]
[ROW][C]34[/C][C]0.0737701743518672[/C][C]0.147540348703734[/C][C]0.926229825648133[/C][/ROW]
[ROW][C]35[/C][C]0.0736859735771569[/C][C]0.147371947154314[/C][C]0.926314026422843[/C][/ROW]
[ROW][C]36[/C][C]0.551092599144177[/C][C]0.897814801711647[/C][C]0.448907400855823[/C][/ROW]
[ROW][C]37[/C][C]0.799303216727825[/C][C]0.40139356654435[/C][C]0.200696783272175[/C][/ROW]
[ROW][C]38[/C][C]0.672622909646028[/C][C]0.654754180707943[/C][C]0.327377090353972[/C][/ROW]
[ROW][C]39[/C][C]0.545283747082412[/C][C]0.909432505835175[/C][C]0.454716252917588[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60229&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60229&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
210.1491022045248860.2982044090497720.850897795475114
220.5973933040706470.8052133918587060.402606695929353
230.5532571412174270.8934857175651460.446742858782573
240.5206836620614860.9586326758770280.479316337938514
250.4825622229898070.9651244459796130.517437777010193
260.4031278496509430.8062556993018850.596872150349057
270.3177736868592190.6355473737184370.682226313140782
280.2568224598068020.5136449196136040.743177540193198
290.1831262784779730.3662525569559450.816873721522027
300.1141525537599930.2283051075199860.885847446240007
310.06913930712619260.1382786142523850.930860692873807
320.1881258521224900.3762517042449800.81187414787751
330.1248874529889450.2497749059778890.875112547011055
340.07377017435186720.1475403487037340.926229825648133
350.07368597357715690.1473719471543140.926314026422843
360.5510925991441770.8978148017116470.448907400855823
370.7993032167278250.401393566544350.200696783272175
380.6726229096460280.6547541807079430.327377090353972
390.5452837470824120.9094325058351750.454716252917588






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=60229&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=60229&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60229&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 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}