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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 computationSun, 27 Dec 2009 04:45:56 -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/Dec/27/t1261914499yfttb38cqyxkm7s.htm/, Retrieved Sun, 28 Apr 2024 20:19:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=70851, Retrieved Sun, 28 Apr 2024 20:19:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact148

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]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [JJ Workshop 7, Mu...] [2009-11-20 19:02:44] [96e597a9107bfe8c07649cce3d4f6fec]
-           [Multiple Regression] [Paper, Multiple R...] [2009-12-25 13:56:37] [96e597a9107bfe8c07649cce3d4f6fec]
-   PD        [Multiple Regression] [Paper, Multiple R...] [2009-12-26 14:04:00] [96e597a9107bfe8c07649cce3d4f6fec]
-    D            [Multiple Regression] [Paper, Multiple R...] [2009-12-27 11:45:56] [e31f2fa83f4a5291b9a51009566cf69b] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
95,1	93,8	96,9	98,6	111,7
97	93,8	95,1	96,9	98,6
112,7	107,6	97	95,1	96,9
102,9	101	112,7	97	95,1
97,4	95,4	102,9	112,7	97
111,4	96,5	97,4	102,9	112,7
87,4	89,2	111,4	97,4	102,9
96,8	87,1	87,4	111,4	97,4
114,1	110,5	96,8	87,4	111,4
110,3	110,8	114,1	96,8	87,4
103,9	104,2	110,3	114,1	96,8
101,6	88,9	103,9	110,3	114,1
94,6	89,8	101,6	103,9	110,3
95,9	90	94,6	101,6	103,9
104,7	93,9	95,9	94,6	101,6
102,8	91,3	104,7	95,9	94,6
98,1	87,8	102,8	104,7	95,9
113,9	99,7	98,1	102,8	104,7
80,9	73,5	113,9	98,1	102,8
95,7	79,2	80,9	113,9	98,1
113,2	96,9	95,7	80,9	113,9
105,9	95,2	113,2	95,7	80,9
108,8	95,6	105,9	113,2	95,7
102,3	89,7	108,8	105,9	113,2
99	92,8	102,3	108,8	105,9
100,7	88	99	102,3	108,8
115,5	101,1	100,7	99	102,3
100,7	92,7	115,5	100,7	99
109,9	95,8	100,7	115,5	100,7
114,6	103,8	109,9	100,7	115,5
85,4	81,8	114,6	109,9	100,7
100,5	87,1	85,4	114,6	109,9
114,8	105,9	100,5	85,4	114,6
116,5	108,1	114,8	100,5	85,4
112,9	102,6	116,5	114,8	100,5
102	93,7	112,9	116,5	114,8
106	103,5	102	112,9	116,5
105,3	100,6	106	102	112,9
118,8	113,3	105,3	106	102
106,1	102,4	118,8	105,3	106
109,3	102,1	106,1	118,8	105,3
117,2	106,9	109,3	106,1	118,8
92,5	87,3	117,2	109,3	106,1
104,2	93,1	92,5	117,2	109,3
112,5	109,1	104,2	92,5	117,2
122,4	120,3	112,5	104,2	92,5
113,3	104,9	122,4	112,5	104,2
100	92,6	113,3	122,4	112,5
110,7	109,8	100	113,3	122,4
112,8	111,4	110,7	100	113,3
109,8	117,9	112,8	110,7	100
117,3	121,6	109,8	112,8	110,7
109,1	117,8	117,3	109,8	112,8
115,9	124,2	109,1	117,3	109,8
96	106,8	115,9	109,1	117,3
99,8	102,7	96	115,9	109,1
116,8	116,8	99,8	96	115,9
115,7	113,6	116,8	99,8	96
99,4	96,1	115,7	116,8	99,8
94,3	85	99,4	115,7	116,8

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=70851&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=70851&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70851&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
Y(t)[t] = + 38.1816714317873 + 0.311752284546608`X(t)`[t] -0.126216251824039`Y(t-1)`[t] + 0.0510552001571137`Y(t-2)`[t] + 0.363728019129759`Y(t-3)`[t] -1.66302616229589M1[t] + 2.51624326560834M2[t] + 12.0154766461194M3[t] + 8.22541372349253M4[t] + 5.88813520404393M5[t] + 10.2699498878386M6[t] -6.51336304862936M7[t] + 0.417995413824675M8[t] + 8.82630473985045M9[t] + 18.9912785329026M10[t] + 10.5108024928395M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y(t)[t] =  +  38.1816714317873 +  0.311752284546608`X(t)`[t] -0.126216251824039`Y(t-1)`[t] +  0.0510552001571137`Y(t-2)`[t] +  0.363728019129759`Y(t-3)`[t] -1.66302616229589M1[t] +  2.51624326560834M2[t] +  12.0154766461194M3[t] +  8.22541372349253M4[t] +  5.88813520404393M5[t] +  10.2699498878386M6[t] -6.51336304862936M7[t] +  0.417995413824675M8[t] +  8.82630473985045M9[t] +  18.9912785329026M10[t] +  10.5108024928395M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70851&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y(t)[t] =  +  38.1816714317873 +  0.311752284546608`X(t)`[t] -0.126216251824039`Y(t-1)`[t] +  0.0510552001571137`Y(t-2)`[t] +  0.363728019129759`Y(t-3)`[t] -1.66302616229589M1[t] +  2.51624326560834M2[t] +  12.0154766461194M3[t] +  8.22541372349253M4[t] +  5.88813520404393M5[t] +  10.2699498878386M6[t] -6.51336304862936M7[t] +  0.417995413824675M8[t] +  8.82630473985045M9[t] +  18.9912785329026M10[t] +  10.5108024928395M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70851&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70851&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
Y(t)[t] = + 38.1816714317873 + 0.311752284546608`X(t)`[t] -0.126216251824039`Y(t-1)`[t] + 0.0510552001571137`Y(t-2)`[t] + 0.363728019129759`Y(t-3)`[t] -1.66302616229589M1[t] + 2.51624326560834M2[t] + 12.0154766461194M3[t] + 8.22541372349253M4[t] + 5.88813520404393M5[t] + 10.2699498878386M6[t] -6.51336304862936M7[t] + 0.417995413824675M8[t] + 8.82630473985045M9[t] + 18.9912785329026M10[t] + 10.5108024928395M11[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)38.181671431787316.4833842.31640.0252570.012629
`X(t)`0.3117522845466080.0870363.58190.0008480.000424
`Y(t-1)`-0.1262162518240390.116877-1.07990.2860660.143033
`Y(t-2)`0.05105520015711370.117730.43370.6666520.333326
`Y(t-3)`0.3637280191297590.1257812.89180.0059330.002966
M1-1.663026162295892.766841-0.60110.5508870.275443
M22.516243265608343.2501370.77420.4429550.221478
M312.01547664611944.1792882.8750.0062030.003102
M48.225413723492533.7488932.19410.0335540.016777
M55.888135204043933.0714971.9170.0617430.030871
M610.26994988783863.1534113.25680.0021730.001087
M7-6.513363048629362.852894-2.28310.0273130.013656
M80.4179954138246753.2322660.12930.8976940.448847
M98.826304739850454.7485721.85870.0697620.034881
M1018.99127853290265.5095253.4470.0012590.00063
M1110.51080249283953.4114713.0810.0035510.001776

\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) & 38.1816714317873 & 16.483384 & 2.3164 & 0.025257 & 0.012629 \tabularnewline
`X(t)` & 0.311752284546608 & 0.087036 & 3.5819 & 0.000848 & 0.000424 \tabularnewline
`Y(t-1)` & -0.126216251824039 & 0.116877 & -1.0799 & 0.286066 & 0.143033 \tabularnewline
`Y(t-2)` & 0.0510552001571137 & 0.11773 & 0.4337 & 0.666652 & 0.333326 \tabularnewline
`Y(t-3)` & 0.363728019129759 & 0.125781 & 2.8918 & 0.005933 & 0.002966 \tabularnewline
M1 & -1.66302616229589 & 2.766841 & -0.6011 & 0.550887 & 0.275443 \tabularnewline
M2 & 2.51624326560834 & 3.250137 & 0.7742 & 0.442955 & 0.221478 \tabularnewline
M3 & 12.0154766461194 & 4.179288 & 2.875 & 0.006203 & 0.003102 \tabularnewline
M4 & 8.22541372349253 & 3.748893 & 2.1941 & 0.033554 & 0.016777 \tabularnewline
M5 & 5.88813520404393 & 3.071497 & 1.917 & 0.061743 & 0.030871 \tabularnewline
M6 & 10.2699498878386 & 3.153411 & 3.2568 & 0.002173 & 0.001087 \tabularnewline
M7 & -6.51336304862936 & 2.852894 & -2.2831 & 0.027313 & 0.013656 \tabularnewline
M8 & 0.417995413824675 & 3.232266 & 0.1293 & 0.897694 & 0.448847 \tabularnewline
M9 & 8.82630473985045 & 4.748572 & 1.8587 & 0.069762 & 0.034881 \tabularnewline
M10 & 18.9912785329026 & 5.509525 & 3.447 & 0.001259 & 0.00063 \tabularnewline
M11 & 10.5108024928395 & 3.411471 & 3.081 & 0.003551 & 0.001776 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70851&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]38.1816714317873[/C][C]16.483384[/C][C]2.3164[/C][C]0.025257[/C][C]0.012629[/C][/ROW]
[ROW][C]`X(t)`[/C][C]0.311752284546608[/C][C]0.087036[/C][C]3.5819[/C][C]0.000848[/C][C]0.000424[/C][/ROW]
[ROW][C]`Y(t-1)`[/C][C]-0.126216251824039[/C][C]0.116877[/C][C]-1.0799[/C][C]0.286066[/C][C]0.143033[/C][/ROW]
[ROW][C]`Y(t-2)`[/C][C]0.0510552001571137[/C][C]0.11773[/C][C]0.4337[/C][C]0.666652[/C][C]0.333326[/C][/ROW]
[ROW][C]`Y(t-3)`[/C][C]0.363728019129759[/C][C]0.125781[/C][C]2.8918[/C][C]0.005933[/C][C]0.002966[/C][/ROW]
[ROW][C]M1[/C][C]-1.66302616229589[/C][C]2.766841[/C][C]-0.6011[/C][C]0.550887[/C][C]0.275443[/C][/ROW]
[ROW][C]M2[/C][C]2.51624326560834[/C][C]3.250137[/C][C]0.7742[/C][C]0.442955[/C][C]0.221478[/C][/ROW]
[ROW][C]M3[/C][C]12.0154766461194[/C][C]4.179288[/C][C]2.875[/C][C]0.006203[/C][C]0.003102[/C][/ROW]
[ROW][C]M4[/C][C]8.22541372349253[/C][C]3.748893[/C][C]2.1941[/C][C]0.033554[/C][C]0.016777[/C][/ROW]
[ROW][C]M5[/C][C]5.88813520404393[/C][C]3.071497[/C][C]1.917[/C][C]0.061743[/C][C]0.030871[/C][/ROW]
[ROW][C]M6[/C][C]10.2699498878386[/C][C]3.153411[/C][C]3.2568[/C][C]0.002173[/C][C]0.001087[/C][/ROW]
[ROW][C]M7[/C][C]-6.51336304862936[/C][C]2.852894[/C][C]-2.2831[/C][C]0.027313[/C][C]0.013656[/C][/ROW]
[ROW][C]M8[/C][C]0.417995413824675[/C][C]3.232266[/C][C]0.1293[/C][C]0.897694[/C][C]0.448847[/C][/ROW]
[ROW][C]M9[/C][C]8.82630473985045[/C][C]4.748572[/C][C]1.8587[/C][C]0.069762[/C][C]0.034881[/C][/ROW]
[ROW][C]M10[/C][C]18.9912785329026[/C][C]5.509525[/C][C]3.447[/C][C]0.001259[/C][C]0.00063[/C][/ROW]
[ROW][C]M11[/C][C]10.5108024928395[/C][C]3.411471[/C][C]3.081[/C][C]0.003551[/C][C]0.001776[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70851&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70851&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)38.181671431787316.4833842.31640.0252570.012629
`X(t)`0.3117522845466080.0870363.58190.0008480.000424
`Y(t-1)`-0.1262162518240390.116877-1.07990.2860660.143033
`Y(t-2)`0.05105520015711370.117730.43370.6666520.333326
`Y(t-3)`0.3637280191297590.1257812.89180.0059330.002966
M1-1.663026162295892.766841-0.60110.5508870.275443
M22.516243265608343.2501370.77420.4429550.221478
M312.01547664611944.1792882.8750.0062030.003102
M48.225413723492533.7488932.19410.0335540.016777
M55.888135204043933.0714971.9170.0617430.030871
M610.26994988783863.1534113.25680.0021730.001087
M7-6.513363048629362.852894-2.28310.0273130.013656
M80.4179954138246753.2322660.12930.8976940.448847
M98.826304739850454.7485721.85870.0697620.034881
M1018.99127853290265.5095253.4470.0012590.00063
M1110.51080249283953.4114713.0810.0035510.001776






Multiple Linear Regression - Regression Statistics
Multiple R0.940717446934413
R-squared0.8849493149668
Adjusted R-squared0.845727490523663
F-TEST (value)22.5626759471577
F-TEST (DF numerator)15
F-TEST (DF denominator)44
p-value7.7715611723761e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.53485642588882
Sum Squared Residuals549.78923787249

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.940717446934413 \tabularnewline
R-squared & 0.8849493149668 \tabularnewline
Adjusted R-squared & 0.845727490523663 \tabularnewline
F-TEST (value) & 22.5626759471577 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 44 \tabularnewline
p-value & 7.7715611723761e-16 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.53485642588882 \tabularnewline
Sum Squared Residuals & 549.78923787249 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70851&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.940717446934413[/C][/ROW]
[ROW][C]R-squared[/C][C]0.8849493149668[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.845727490523663[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]22.5626759471577[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]44[/C][/ROW]
[ROW][C]p-value[/C][C]7.7715611723761e-16[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.53485642588882[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]549.78923787249[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70851&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70851&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 R0.940717446934413
R-squared0.8849493149668
Adjusted R-squared0.845727490523663
F-TEST (value)22.5626759471577
F-TEST (DF numerator)15
F-TEST (DF denominator)44
p-value7.7715611723761e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.53485642588882
Sum Squared Residuals549.78923787249






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
195.199.1931172304992-4.09311723049916
29798.7479450208199-1.74794502081988
3112.7111.5993120568051.10068794319494
4102.9103.212383348398-0.312383348398138
597.4101.858861182177-4.45886118217734
6111.4112.487981702803-1.08798170280302
787.487.8165113752725-0.416511375272481
896.895.83664878094150.963351219058486
9114.1114.220396262258-0.120396262257859
10110.3114.045801006481-3.74580100648082
11103.9108.289679987879-4.38967998787919
12101.699.91533652349821.68466347650176
1394.697.114265042791-2.51426504279097
1495.999.794112407581-3.89411240758097
15104.7109.151137725354-4.45113772535427
16102.8100.9600914731511.83990852684935
1798.198.693623022506-0.593623022505887
18113.9110.4823079640223.41769203597844
1980.982.6058257165276-1.70582571652763
2095.794.57645898366321.12354101633684
21113.2110.6968643162342.50313568376643
22105.9106.875667149679-0.97566714967909
23108.8105.717911347623.08208865238
24102.398.99428061928973.30571938071035
259996.6109377167532.38906228324711
26100.7100.4332622643080.266737735692224
27115.5111.2691686594174.2308313405834
28100.7101.878877396741-1.17887739674137
29109.9103.7499860812296.1500139187711
30114.6114.0921872454100.507812754589537
3185.484.94364082366910.45635917633089
32100.5100.799058164214-0.299058164214328
33114.8113.3811548824961.41884511750449
34116.5112.577166664253.92283333575005
35112.9108.3898678821864.51013211781433
36102100.8469530772711.15304692272946
37106104.0493953603681.95060463963158
38105.3104.9537956052120.346204394788341
39118.8114.7402197678564.05978023214448
40106.1107.267310980455-1.16731098045519
41109.3106.8740887625382.42591123746191
42117.2116.6103496225760.589650377424014
4392.588.26361431713944.23638568286061
44104.2101.6879431924742.51205680752606
45112.5115.219946832149-2.71994683214859
46122.4119.4422150913162.95778490868354
47113.3109.5905889613003.7094110387002
4810099.91819030046820.0818096995318184
49110.7108.4322846495892.26771535041144
50112.8107.7708847020805.02911529792028
51109.8114.740161790569-4.94016179056855
52117.3116.4813368012550.818663198745358
53109.1112.623440951550-3.52344095154978
54115.9119.327173465189-3.42717346518897
559698.5704077673914-2.57040776739138
5699.8104.099890878707-4.29989087870706
57116.8117.881637706864-1.08163770686447
58115.7117.859150088274-2.15915008827368
5999.4106.311951821015-6.91195182101534
6094.3100.525239479473-6.2252394794734

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 95.1 & 99.1931172304992 & -4.09311723049916 \tabularnewline
2 & 97 & 98.7479450208199 & -1.74794502081988 \tabularnewline
3 & 112.7 & 111.599312056805 & 1.10068794319494 \tabularnewline
4 & 102.9 & 103.212383348398 & -0.312383348398138 \tabularnewline
5 & 97.4 & 101.858861182177 & -4.45886118217734 \tabularnewline
6 & 111.4 & 112.487981702803 & -1.08798170280302 \tabularnewline
7 & 87.4 & 87.8165113752725 & -0.416511375272481 \tabularnewline
8 & 96.8 & 95.8366487809415 & 0.963351219058486 \tabularnewline
9 & 114.1 & 114.220396262258 & -0.120396262257859 \tabularnewline
10 & 110.3 & 114.045801006481 & -3.74580100648082 \tabularnewline
11 & 103.9 & 108.289679987879 & -4.38967998787919 \tabularnewline
12 & 101.6 & 99.9153365234982 & 1.68466347650176 \tabularnewline
13 & 94.6 & 97.114265042791 & -2.51426504279097 \tabularnewline
14 & 95.9 & 99.794112407581 & -3.89411240758097 \tabularnewline
15 & 104.7 & 109.151137725354 & -4.45113772535427 \tabularnewline
16 & 102.8 & 100.960091473151 & 1.83990852684935 \tabularnewline
17 & 98.1 & 98.693623022506 & -0.593623022505887 \tabularnewline
18 & 113.9 & 110.482307964022 & 3.41769203597844 \tabularnewline
19 & 80.9 & 82.6058257165276 & -1.70582571652763 \tabularnewline
20 & 95.7 & 94.5764589836632 & 1.12354101633684 \tabularnewline
21 & 113.2 & 110.696864316234 & 2.50313568376643 \tabularnewline
22 & 105.9 & 106.875667149679 & -0.97566714967909 \tabularnewline
23 & 108.8 & 105.71791134762 & 3.08208865238 \tabularnewline
24 & 102.3 & 98.9942806192897 & 3.30571938071035 \tabularnewline
25 & 99 & 96.610937716753 & 2.38906228324711 \tabularnewline
26 & 100.7 & 100.433262264308 & 0.266737735692224 \tabularnewline
27 & 115.5 & 111.269168659417 & 4.2308313405834 \tabularnewline
28 & 100.7 & 101.878877396741 & -1.17887739674137 \tabularnewline
29 & 109.9 & 103.749986081229 & 6.1500139187711 \tabularnewline
30 & 114.6 & 114.092187245410 & 0.507812754589537 \tabularnewline
31 & 85.4 & 84.9436408236691 & 0.45635917633089 \tabularnewline
32 & 100.5 & 100.799058164214 & -0.299058164214328 \tabularnewline
33 & 114.8 & 113.381154882496 & 1.41884511750449 \tabularnewline
34 & 116.5 & 112.57716666425 & 3.92283333575005 \tabularnewline
35 & 112.9 & 108.389867882186 & 4.51013211781433 \tabularnewline
36 & 102 & 100.846953077271 & 1.15304692272946 \tabularnewline
37 & 106 & 104.049395360368 & 1.95060463963158 \tabularnewline
38 & 105.3 & 104.953795605212 & 0.346204394788341 \tabularnewline
39 & 118.8 & 114.740219767856 & 4.05978023214448 \tabularnewline
40 & 106.1 & 107.267310980455 & -1.16731098045519 \tabularnewline
41 & 109.3 & 106.874088762538 & 2.42591123746191 \tabularnewline
42 & 117.2 & 116.610349622576 & 0.589650377424014 \tabularnewline
43 & 92.5 & 88.2636143171394 & 4.23638568286061 \tabularnewline
44 & 104.2 & 101.687943192474 & 2.51205680752606 \tabularnewline
45 & 112.5 & 115.219946832149 & -2.71994683214859 \tabularnewline
46 & 122.4 & 119.442215091316 & 2.95778490868354 \tabularnewline
47 & 113.3 & 109.590588961300 & 3.7094110387002 \tabularnewline
48 & 100 & 99.9181903004682 & 0.0818096995318184 \tabularnewline
49 & 110.7 & 108.432284649589 & 2.26771535041144 \tabularnewline
50 & 112.8 & 107.770884702080 & 5.02911529792028 \tabularnewline
51 & 109.8 & 114.740161790569 & -4.94016179056855 \tabularnewline
52 & 117.3 & 116.481336801255 & 0.818663198745358 \tabularnewline
53 & 109.1 & 112.623440951550 & -3.52344095154978 \tabularnewline
54 & 115.9 & 119.327173465189 & -3.42717346518897 \tabularnewline
55 & 96 & 98.5704077673914 & -2.57040776739138 \tabularnewline
56 & 99.8 & 104.099890878707 & -4.29989087870706 \tabularnewline
57 & 116.8 & 117.881637706864 & -1.08163770686447 \tabularnewline
58 & 115.7 & 117.859150088274 & -2.15915008827368 \tabularnewline
59 & 99.4 & 106.311951821015 & -6.91195182101534 \tabularnewline
60 & 94.3 & 100.525239479473 & -6.2252394794734 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70851&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]95.1[/C][C]99.1931172304992[/C][C]-4.09311723049916[/C][/ROW]
[ROW][C]2[/C][C]97[/C][C]98.7479450208199[/C][C]-1.74794502081988[/C][/ROW]
[ROW][C]3[/C][C]112.7[/C][C]111.599312056805[/C][C]1.10068794319494[/C][/ROW]
[ROW][C]4[/C][C]102.9[/C][C]103.212383348398[/C][C]-0.312383348398138[/C][/ROW]
[ROW][C]5[/C][C]97.4[/C][C]101.858861182177[/C][C]-4.45886118217734[/C][/ROW]
[ROW][C]6[/C][C]111.4[/C][C]112.487981702803[/C][C]-1.08798170280302[/C][/ROW]
[ROW][C]7[/C][C]87.4[/C][C]87.8165113752725[/C][C]-0.416511375272481[/C][/ROW]
[ROW][C]8[/C][C]96.8[/C][C]95.8366487809415[/C][C]0.963351219058486[/C][/ROW]
[ROW][C]9[/C][C]114.1[/C][C]114.220396262258[/C][C]-0.120396262257859[/C][/ROW]
[ROW][C]10[/C][C]110.3[/C][C]114.045801006481[/C][C]-3.74580100648082[/C][/ROW]
[ROW][C]11[/C][C]103.9[/C][C]108.289679987879[/C][C]-4.38967998787919[/C][/ROW]
[ROW][C]12[/C][C]101.6[/C][C]99.9153365234982[/C][C]1.68466347650176[/C][/ROW]
[ROW][C]13[/C][C]94.6[/C][C]97.114265042791[/C][C]-2.51426504279097[/C][/ROW]
[ROW][C]14[/C][C]95.9[/C][C]99.794112407581[/C][C]-3.89411240758097[/C][/ROW]
[ROW][C]15[/C][C]104.7[/C][C]109.151137725354[/C][C]-4.45113772535427[/C][/ROW]
[ROW][C]16[/C][C]102.8[/C][C]100.960091473151[/C][C]1.83990852684935[/C][/ROW]
[ROW][C]17[/C][C]98.1[/C][C]98.693623022506[/C][C]-0.593623022505887[/C][/ROW]
[ROW][C]18[/C][C]113.9[/C][C]110.482307964022[/C][C]3.41769203597844[/C][/ROW]
[ROW][C]19[/C][C]80.9[/C][C]82.6058257165276[/C][C]-1.70582571652763[/C][/ROW]
[ROW][C]20[/C][C]95.7[/C][C]94.5764589836632[/C][C]1.12354101633684[/C][/ROW]
[ROW][C]21[/C][C]113.2[/C][C]110.696864316234[/C][C]2.50313568376643[/C][/ROW]
[ROW][C]22[/C][C]105.9[/C][C]106.875667149679[/C][C]-0.97566714967909[/C][/ROW]
[ROW][C]23[/C][C]108.8[/C][C]105.71791134762[/C][C]3.08208865238[/C][/ROW]
[ROW][C]24[/C][C]102.3[/C][C]98.9942806192897[/C][C]3.30571938071035[/C][/ROW]
[ROW][C]25[/C][C]99[/C][C]96.610937716753[/C][C]2.38906228324711[/C][/ROW]
[ROW][C]26[/C][C]100.7[/C][C]100.433262264308[/C][C]0.266737735692224[/C][/ROW]
[ROW][C]27[/C][C]115.5[/C][C]111.269168659417[/C][C]4.2308313405834[/C][/ROW]
[ROW][C]28[/C][C]100.7[/C][C]101.878877396741[/C][C]-1.17887739674137[/C][/ROW]
[ROW][C]29[/C][C]109.9[/C][C]103.749986081229[/C][C]6.1500139187711[/C][/ROW]
[ROW][C]30[/C][C]114.6[/C][C]114.092187245410[/C][C]0.507812754589537[/C][/ROW]
[ROW][C]31[/C][C]85.4[/C][C]84.9436408236691[/C][C]0.45635917633089[/C][/ROW]
[ROW][C]32[/C][C]100.5[/C][C]100.799058164214[/C][C]-0.299058164214328[/C][/ROW]
[ROW][C]33[/C][C]114.8[/C][C]113.381154882496[/C][C]1.41884511750449[/C][/ROW]
[ROW][C]34[/C][C]116.5[/C][C]112.57716666425[/C][C]3.92283333575005[/C][/ROW]
[ROW][C]35[/C][C]112.9[/C][C]108.389867882186[/C][C]4.51013211781433[/C][/ROW]
[ROW][C]36[/C][C]102[/C][C]100.846953077271[/C][C]1.15304692272946[/C][/ROW]
[ROW][C]37[/C][C]106[/C][C]104.049395360368[/C][C]1.95060463963158[/C][/ROW]
[ROW][C]38[/C][C]105.3[/C][C]104.953795605212[/C][C]0.346204394788341[/C][/ROW]
[ROW][C]39[/C][C]118.8[/C][C]114.740219767856[/C][C]4.05978023214448[/C][/ROW]
[ROW][C]40[/C][C]106.1[/C][C]107.267310980455[/C][C]-1.16731098045519[/C][/ROW]
[ROW][C]41[/C][C]109.3[/C][C]106.874088762538[/C][C]2.42591123746191[/C][/ROW]
[ROW][C]42[/C][C]117.2[/C][C]116.610349622576[/C][C]0.589650377424014[/C][/ROW]
[ROW][C]43[/C][C]92.5[/C][C]88.2636143171394[/C][C]4.23638568286061[/C][/ROW]
[ROW][C]44[/C][C]104.2[/C][C]101.687943192474[/C][C]2.51205680752606[/C][/ROW]
[ROW][C]45[/C][C]112.5[/C][C]115.219946832149[/C][C]-2.71994683214859[/C][/ROW]
[ROW][C]46[/C][C]122.4[/C][C]119.442215091316[/C][C]2.95778490868354[/C][/ROW]
[ROW][C]47[/C][C]113.3[/C][C]109.590588961300[/C][C]3.7094110387002[/C][/ROW]
[ROW][C]48[/C][C]100[/C][C]99.9181903004682[/C][C]0.0818096995318184[/C][/ROW]
[ROW][C]49[/C][C]110.7[/C][C]108.432284649589[/C][C]2.26771535041144[/C][/ROW]
[ROW][C]50[/C][C]112.8[/C][C]107.770884702080[/C][C]5.02911529792028[/C][/ROW]
[ROW][C]51[/C][C]109.8[/C][C]114.740161790569[/C][C]-4.94016179056855[/C][/ROW]
[ROW][C]52[/C][C]117.3[/C][C]116.481336801255[/C][C]0.818663198745358[/C][/ROW]
[ROW][C]53[/C][C]109.1[/C][C]112.623440951550[/C][C]-3.52344095154978[/C][/ROW]
[ROW][C]54[/C][C]115.9[/C][C]119.327173465189[/C][C]-3.42717346518897[/C][/ROW]
[ROW][C]55[/C][C]96[/C][C]98.5704077673914[/C][C]-2.57040776739138[/C][/ROW]
[ROW][C]56[/C][C]99.8[/C][C]104.099890878707[/C][C]-4.29989087870706[/C][/ROW]
[ROW][C]57[/C][C]116.8[/C][C]117.881637706864[/C][C]-1.08163770686447[/C][/ROW]
[ROW][C]58[/C][C]115.7[/C][C]117.859150088274[/C][C]-2.15915008827368[/C][/ROW]
[ROW][C]59[/C][C]99.4[/C][C]106.311951821015[/C][C]-6.91195182101534[/C][/ROW]
[ROW][C]60[/C][C]94.3[/C][C]100.525239479473[/C][C]-6.2252394794734[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70851&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70851&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
195.199.1931172304992-4.09311723049916
29798.7479450208199-1.74794502081988
3112.7111.5993120568051.10068794319494
4102.9103.212383348398-0.312383348398138
597.4101.858861182177-4.45886118217734
6111.4112.487981702803-1.08798170280302
787.487.8165113752725-0.416511375272481
896.895.83664878094150.963351219058486
9114.1114.220396262258-0.120396262257859
10110.3114.045801006481-3.74580100648082
11103.9108.289679987879-4.38967998787919
12101.699.91533652349821.68466347650176
1394.697.114265042791-2.51426504279097
1495.999.794112407581-3.89411240758097
15104.7109.151137725354-4.45113772535427
16102.8100.9600914731511.83990852684935
1798.198.693623022506-0.593623022505887
18113.9110.4823079640223.41769203597844
1980.982.6058257165276-1.70582571652763
2095.794.57645898366321.12354101633684
21113.2110.6968643162342.50313568376643
22105.9106.875667149679-0.97566714967909
23108.8105.717911347623.08208865238
24102.398.99428061928973.30571938071035
259996.6109377167532.38906228324711
26100.7100.4332622643080.266737735692224
27115.5111.2691686594174.2308313405834
28100.7101.878877396741-1.17887739674137
29109.9103.7499860812296.1500139187711
30114.6114.0921872454100.507812754589537
3185.484.94364082366910.45635917633089
32100.5100.799058164214-0.299058164214328
33114.8113.3811548824961.41884511750449
34116.5112.577166664253.92283333575005
35112.9108.3898678821864.51013211781433
36102100.8469530772711.15304692272946
37106104.0493953603681.95060463963158
38105.3104.9537956052120.346204394788341
39118.8114.7402197678564.05978023214448
40106.1107.267310980455-1.16731098045519
41109.3106.8740887625382.42591123746191
42117.2116.6103496225760.589650377424014
4392.588.26361431713944.23638568286061
44104.2101.6879431924742.51205680752606
45112.5115.219946832149-2.71994683214859
46122.4119.4422150913162.95778490868354
47113.3109.5905889613003.7094110387002
4810099.91819030046820.0818096995318184
49110.7108.4322846495892.26771535041144
50112.8107.7708847020805.02911529792028
51109.8114.740161790569-4.94016179056855
52117.3116.4813368012550.818663198745358
53109.1112.623440951550-3.52344095154978
54115.9119.327173465189-3.42717346518897
559698.5704077673914-2.57040776739138
5699.8104.099890878707-4.29989087870706
57116.8117.881637706864-1.08163770686447
58115.7117.859150088274-2.15915008827368
5999.4106.311951821015-6.91195182101534
6094.3100.525239479473-6.2252394794734






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.1555010982380170.3110021964760350.844498901761983
200.06266270054969480.1253254010993900.937337299450305
210.04573558022430540.09147116044861080.954264419775695
220.02357337457698180.04714674915396370.976426625423018
230.06731274571442680.1346254914288540.932687254285573
240.03491223584342950.06982447168685890.96508776415657
250.04145418156795720.08290836313591440.958545818432043
260.1259232566277650.251846513255530.874076743372235
270.1690739311602050.338147862320410.830926068839795
280.1400381989548660.2800763979097320.859961801045134
290.2768153898915990.5536307797831990.7231846101084
300.2012278298133070.4024556596266150.798772170186693
310.1514359384164610.3028718768329230.848564061583539
320.09579524261434860.1915904852286970.904204757385651
330.0568514370553850.113702874110770.943148562944615
340.05025443222413670.1005088644482730.949745567775863
350.05531259849678390.1106251969935680.944687401503216
360.0441016481589150.088203296317830.955898351841085
370.02463121815593980.04926243631187960.97536878184406
380.01779727959476750.03559455918953490.982202720405233
390.03373145757350640.06746291514701270.966268542426494
400.02301897491137780.04603794982275560.976981025088622
410.01464492218433190.02928984436866380.985355077815668

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.155501098238017 & 0.311002196476035 & 0.844498901761983 \tabularnewline
20 & 0.0626627005496948 & 0.125325401099390 & 0.937337299450305 \tabularnewline
21 & 0.0457355802243054 & 0.0914711604486108 & 0.954264419775695 \tabularnewline
22 & 0.0235733745769818 & 0.0471467491539637 & 0.976426625423018 \tabularnewline
23 & 0.0673127457144268 & 0.134625491428854 & 0.932687254285573 \tabularnewline
24 & 0.0349122358434295 & 0.0698244716868589 & 0.96508776415657 \tabularnewline
25 & 0.0414541815679572 & 0.0829083631359144 & 0.958545818432043 \tabularnewline
26 & 0.125923256627765 & 0.25184651325553 & 0.874076743372235 \tabularnewline
27 & 0.169073931160205 & 0.33814786232041 & 0.830926068839795 \tabularnewline
28 & 0.140038198954866 & 0.280076397909732 & 0.859961801045134 \tabularnewline
29 & 0.276815389891599 & 0.553630779783199 & 0.7231846101084 \tabularnewline
30 & 0.201227829813307 & 0.402455659626615 & 0.798772170186693 \tabularnewline
31 & 0.151435938416461 & 0.302871876832923 & 0.848564061583539 \tabularnewline
32 & 0.0957952426143486 & 0.191590485228697 & 0.904204757385651 \tabularnewline
33 & 0.056851437055385 & 0.11370287411077 & 0.943148562944615 \tabularnewline
34 & 0.0502544322241367 & 0.100508864448273 & 0.949745567775863 \tabularnewline
35 & 0.0553125984967839 & 0.110625196993568 & 0.944687401503216 \tabularnewline
36 & 0.044101648158915 & 0.08820329631783 & 0.955898351841085 \tabularnewline
37 & 0.0246312181559398 & 0.0492624363118796 & 0.97536878184406 \tabularnewline
38 & 0.0177972795947675 & 0.0355945591895349 & 0.982202720405233 \tabularnewline
39 & 0.0337314575735064 & 0.0674629151470127 & 0.966268542426494 \tabularnewline
40 & 0.0230189749113778 & 0.0460379498227556 & 0.976981025088622 \tabularnewline
41 & 0.0146449221843319 & 0.0292898443686638 & 0.985355077815668 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70851&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]19[/C][C]0.155501098238017[/C][C]0.311002196476035[/C][C]0.844498901761983[/C][/ROW]
[ROW][C]20[/C][C]0.0626627005496948[/C][C]0.125325401099390[/C][C]0.937337299450305[/C][/ROW]
[ROW][C]21[/C][C]0.0457355802243054[/C][C]0.0914711604486108[/C][C]0.954264419775695[/C][/ROW]
[ROW][C]22[/C][C]0.0235733745769818[/C][C]0.0471467491539637[/C][C]0.976426625423018[/C][/ROW]
[ROW][C]23[/C][C]0.0673127457144268[/C][C]0.134625491428854[/C][C]0.932687254285573[/C][/ROW]
[ROW][C]24[/C][C]0.0349122358434295[/C][C]0.0698244716868589[/C][C]0.96508776415657[/C][/ROW]
[ROW][C]25[/C][C]0.0414541815679572[/C][C]0.0829083631359144[/C][C]0.958545818432043[/C][/ROW]
[ROW][C]26[/C][C]0.125923256627765[/C][C]0.25184651325553[/C][C]0.874076743372235[/C][/ROW]
[ROW][C]27[/C][C]0.169073931160205[/C][C]0.33814786232041[/C][C]0.830926068839795[/C][/ROW]
[ROW][C]28[/C][C]0.140038198954866[/C][C]0.280076397909732[/C][C]0.859961801045134[/C][/ROW]
[ROW][C]29[/C][C]0.276815389891599[/C][C]0.553630779783199[/C][C]0.7231846101084[/C][/ROW]
[ROW][C]30[/C][C]0.201227829813307[/C][C]0.402455659626615[/C][C]0.798772170186693[/C][/ROW]
[ROW][C]31[/C][C]0.151435938416461[/C][C]0.302871876832923[/C][C]0.848564061583539[/C][/ROW]
[ROW][C]32[/C][C]0.0957952426143486[/C][C]0.191590485228697[/C][C]0.904204757385651[/C][/ROW]
[ROW][C]33[/C][C]0.056851437055385[/C][C]0.11370287411077[/C][C]0.943148562944615[/C][/ROW]
[ROW][C]34[/C][C]0.0502544322241367[/C][C]0.100508864448273[/C][C]0.949745567775863[/C][/ROW]
[ROW][C]35[/C][C]0.0553125984967839[/C][C]0.110625196993568[/C][C]0.944687401503216[/C][/ROW]
[ROW][C]36[/C][C]0.044101648158915[/C][C]0.08820329631783[/C][C]0.955898351841085[/C][/ROW]
[ROW][C]37[/C][C]0.0246312181559398[/C][C]0.0492624363118796[/C][C]0.97536878184406[/C][/ROW]
[ROW][C]38[/C][C]0.0177972795947675[/C][C]0.0355945591895349[/C][C]0.982202720405233[/C][/ROW]
[ROW][C]39[/C][C]0.0337314575735064[/C][C]0.0674629151470127[/C][C]0.966268542426494[/C][/ROW]
[ROW][C]40[/C][C]0.0230189749113778[/C][C]0.0460379498227556[/C][C]0.976981025088622[/C][/ROW]
[ROW][C]41[/C][C]0.0146449221843319[/C][C]0.0292898443686638[/C][C]0.985355077815668[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70851&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70851&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
190.1555010982380170.3110021964760350.844498901761983
200.06266270054969480.1253254010993900.937337299450305
210.04573558022430540.09147116044861080.954264419775695
220.02357337457698180.04714674915396370.976426625423018
230.06731274571442680.1346254914288540.932687254285573
240.03491223584342950.06982447168685890.96508776415657
250.04145418156795720.08290836313591440.958545818432043
260.1259232566277650.251846513255530.874076743372235
270.1690739311602050.338147862320410.830926068839795
280.1400381989548660.2800763979097320.859961801045134
290.2768153898915990.5536307797831990.7231846101084
300.2012278298133070.4024556596266150.798772170186693
310.1514359384164610.3028718768329230.848564061583539
320.09579524261434860.1915904852286970.904204757385651
330.0568514370553850.113702874110770.943148562944615
340.05025443222413670.1005088644482730.949745567775863
350.05531259849678390.1106251969935680.944687401503216
360.0441016481589150.088203296317830.955898351841085
370.02463121815593980.04926243631187960.97536878184406
380.01779727959476750.03559455918953490.982202720405233
390.03373145757350640.06746291514701270.966268542426494
400.02301897491137780.04603794982275560.976981025088622
410.01464492218433190.02928984436866380.985355077815668






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level50.217391304347826NOK
10% type I error level100.434782608695652NOK

\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 & 5 & 0.217391304347826 & NOK \tabularnewline
10% type I error level & 10 & 0.434782608695652 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70851&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]5[/C][C]0.217391304347826[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]10[/C][C]0.434782608695652[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70851&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70851&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 level50.217391304347826NOK
10% type I error level100.434782608695652NOK


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 = 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')
}