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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 08:45:41 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t12586456162o6izwfin868nx8.htm/, Retrieved Thu, 28 Mar 2024 09:14:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57779, Retrieved Thu, 28 Mar 2024 09:14:38 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact155
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] [d45d8d97b86162be82506c3c0ea6e4a6] [Current]
-    D        [Multiple Regression] [Multiple Regression] [2009-11-19 15:52:15] [976efdaed7598845c859b86bc2e467ce]
-    D        [Multiple Regression] [] [2009-11-26 18:21:27] [58e1a7a2c10f1de09acf218271f55dfd]
Feedback Forum
2009-11-26 18:28:20 [c299e0eb981e6cab9be2a8b66230858e] [reply
Model 4 en 5 zijn naar mijn mening niet betrouwbaar en verkeerd samengesteld. Het is de bedoeling om de endogene variabele te vergelijken met het verleden. In model 4 wordt ervoor gekozen om tot 4 perioden terug te gaan. Hierdoor kan men voor de berekening met de endogene variabele pas starten bij periode 5. Bekijk via de link goed en bestudeer hoe de variabelen Y1-Y4 worden gevormd. Y1 gaat 1 periode terug tegenover de bekeken Y-waarde, Y2 gaat 2 perioden terug tegenover de bekeken Y-waarde, enz. Ik raad de student aan het nieuwe model 4 te interpreteren om indien nodig een nieuw model 5 (en 6) samen te stellen.

http://www.freestatistics.org/blog/index.php?v=date/2009/Nov/26/t1259259850ry8qlxqc77speev.htm/

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




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57779&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57779&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57779&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
bbp[t] = -0.658401005013655 + 0.862606433366398dnst[t] -0.0400106821199718y1[t] -0.0938026651675497y2[t] + 0.0544948659153816y3[t] + 0.389840352423138y4[t] + 0.0762801278972633M1[t] + 0.250610875389412M2[t] + 0.735839254790127M3[t] + 0.576575956377957M4[t] + 0.842257409297771M5[t] + 0.58792273935397M6[t] + 0.501342706463074M7[t] + 0.605788032914381M8[t] + 0.565962578885114M9[t] + 0.657248181394328M10[t] + 0.424207069846181M11[t] -0.00773667959424864t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
bbp[t] =  -0.658401005013655 +  0.862606433366398dnst[t] -0.0400106821199718y1[t] -0.0938026651675497y2[t] +  0.0544948659153816y3[t] +  0.389840352423138y4[t] +  0.0762801278972633M1[t] +  0.250610875389412M2[t] +  0.735839254790127M3[t] +  0.576575956377957M4[t] +  0.842257409297771M5[t] +  0.58792273935397M6[t] +  0.501342706463074M7[t] +  0.605788032914381M8[t] +  0.565962578885114M9[t] +  0.657248181394328M10[t] +  0.424207069846181M11[t] -0.00773667959424864t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57779&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]bbp[t] =  -0.658401005013655 +  0.862606433366398dnst[t] -0.0400106821199718y1[t] -0.0938026651675497y2[t] +  0.0544948659153816y3[t] +  0.389840352423138y4[t] +  0.0762801278972633M1[t] +  0.250610875389412M2[t] +  0.735839254790127M3[t] +  0.576575956377957M4[t] +  0.842257409297771M5[t] +  0.58792273935397M6[t] +  0.501342706463074M7[t] +  0.605788032914381M8[t] +  0.565962578885114M9[t] +  0.657248181394328M10[t] +  0.424207069846181M11[t] -0.00773667959424864t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57779&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57779&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
bbp[t] = -0.658401005013655 + 0.862606433366398dnst[t] -0.0400106821199718y1[t] -0.0938026651675497y2[t] + 0.0544948659153816y3[t] + 0.389840352423138y4[t] + 0.0762801278972633M1[t] + 0.250610875389412M2[t] + 0.735839254790127M3[t] + 0.576575956377957M4[t] + 0.842257409297771M5[t] + 0.58792273935397M6[t] + 0.501342706463074M7[t] + 0.605788032914381M8[t] + 0.565962578885114M9[t] + 0.657248181394328M10[t] + 0.424207069846181M11[t] -0.00773667959424864t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.6584010050136550.469228-1.40320.1680980.084049
dnst0.8626064333663980.1303786.616200
y1-0.04001068211997180.112815-0.35470.7246650.362332
y2-0.09380266516754970.145014-0.64690.5213330.260667
y30.05449486591538160.1467180.37140.7122320.356116
y40.3898403524231380.1332752.92510.0055890.002795
M10.07628012789726330.4507180.16920.8664390.43322
M20.2506108753894120.4452990.56280.5766410.288321
M30.7358392547901270.4512971.63050.1106560.055328
M40.5765759563779570.4580381.25880.2152250.107613
M50.8422574092977710.4459361.88870.0660150.033008
M60.587922739353970.4570281.28640.2055190.102759
M70.5013427064630740.4551311.10150.2770880.138544
M80.6057880329143810.4566441.32660.191980.09599
M90.5659625788851140.4594731.23180.2250550.112528
M100.6572481813943280.4504721.4590.1521790.076089
M110.4242070698461810.4450810.95310.3461250.173062
t-0.007736679594248640.005566-1.39010.1720040.086002

\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) & -0.658401005013655 & 0.469228 & -1.4032 & 0.168098 & 0.084049 \tabularnewline
dnst & 0.862606433366398 & 0.130378 & 6.6162 & 0 & 0 \tabularnewline
y1 & -0.0400106821199718 & 0.112815 & -0.3547 & 0.724665 & 0.362332 \tabularnewline
y2 & -0.0938026651675497 & 0.145014 & -0.6469 & 0.521333 & 0.260667 \tabularnewline
y3 & 0.0544948659153816 & 0.146718 & 0.3714 & 0.712232 & 0.356116 \tabularnewline
y4 & 0.389840352423138 & 0.133275 & 2.9251 & 0.005589 & 0.002795 \tabularnewline
M1 & 0.0762801278972633 & 0.450718 & 0.1692 & 0.866439 & 0.43322 \tabularnewline
M2 & 0.250610875389412 & 0.445299 & 0.5628 & 0.576641 & 0.288321 \tabularnewline
M3 & 0.735839254790127 & 0.451297 & 1.6305 & 0.110656 & 0.055328 \tabularnewline
M4 & 0.576575956377957 & 0.458038 & 1.2588 & 0.215225 & 0.107613 \tabularnewline
M5 & 0.842257409297771 & 0.445936 & 1.8887 & 0.066015 & 0.033008 \tabularnewline
M6 & 0.58792273935397 & 0.457028 & 1.2864 & 0.205519 & 0.102759 \tabularnewline
M7 & 0.501342706463074 & 0.455131 & 1.1015 & 0.277088 & 0.138544 \tabularnewline
M8 & 0.605788032914381 & 0.456644 & 1.3266 & 0.19198 & 0.09599 \tabularnewline
M9 & 0.565962578885114 & 0.459473 & 1.2318 & 0.225055 & 0.112528 \tabularnewline
M10 & 0.657248181394328 & 0.450472 & 1.459 & 0.152179 & 0.076089 \tabularnewline
M11 & 0.424207069846181 & 0.445081 & 0.9531 & 0.346125 & 0.173062 \tabularnewline
t & -0.00773667959424864 & 0.005566 & -1.3901 & 0.172004 & 0.086002 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57779&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]-0.658401005013655[/C][C]0.469228[/C][C]-1.4032[/C][C]0.168098[/C][C]0.084049[/C][/ROW]
[ROW][C]dnst[/C][C]0.862606433366398[/C][C]0.130378[/C][C]6.6162[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]y1[/C][C]-0.0400106821199718[/C][C]0.112815[/C][C]-0.3547[/C][C]0.724665[/C][C]0.362332[/C][/ROW]
[ROW][C]y2[/C][C]-0.0938026651675497[/C][C]0.145014[/C][C]-0.6469[/C][C]0.521333[/C][C]0.260667[/C][/ROW]
[ROW][C]y3[/C][C]0.0544948659153816[/C][C]0.146718[/C][C]0.3714[/C][C]0.712232[/C][C]0.356116[/C][/ROW]
[ROW][C]y4[/C][C]0.389840352423138[/C][C]0.133275[/C][C]2.9251[/C][C]0.005589[/C][C]0.002795[/C][/ROW]
[ROW][C]M1[/C][C]0.0762801278972633[/C][C]0.450718[/C][C]0.1692[/C][C]0.866439[/C][C]0.43322[/C][/ROW]
[ROW][C]M2[/C][C]0.250610875389412[/C][C]0.445299[/C][C]0.5628[/C][C]0.576641[/C][C]0.288321[/C][/ROW]
[ROW][C]M3[/C][C]0.735839254790127[/C][C]0.451297[/C][C]1.6305[/C][C]0.110656[/C][C]0.055328[/C][/ROW]
[ROW][C]M4[/C][C]0.576575956377957[/C][C]0.458038[/C][C]1.2588[/C][C]0.215225[/C][C]0.107613[/C][/ROW]
[ROW][C]M5[/C][C]0.842257409297771[/C][C]0.445936[/C][C]1.8887[/C][C]0.066015[/C][C]0.033008[/C][/ROW]
[ROW][C]M6[/C][C]0.58792273935397[/C][C]0.457028[/C][C]1.2864[/C][C]0.205519[/C][C]0.102759[/C][/ROW]
[ROW][C]M7[/C][C]0.501342706463074[/C][C]0.455131[/C][C]1.1015[/C][C]0.277088[/C][C]0.138544[/C][/ROW]
[ROW][C]M8[/C][C]0.605788032914381[/C][C]0.456644[/C][C]1.3266[/C][C]0.19198[/C][C]0.09599[/C][/ROW]
[ROW][C]M9[/C][C]0.565962578885114[/C][C]0.459473[/C][C]1.2318[/C][C]0.225055[/C][C]0.112528[/C][/ROW]
[ROW][C]M10[/C][C]0.657248181394328[/C][C]0.450472[/C][C]1.459[/C][C]0.152179[/C][C]0.076089[/C][/ROW]
[ROW][C]M11[/C][C]0.424207069846181[/C][C]0.445081[/C][C]0.9531[/C][C]0.346125[/C][C]0.173062[/C][/ROW]
[ROW][C]t[/C][C]-0.00773667959424864[/C][C]0.005566[/C][C]-1.3901[/C][C]0.172004[/C][C]0.086002[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57779&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57779&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.6584010050136550.469228-1.40320.1680980.084049
dnst0.8626064333663980.1303786.616200
y1-0.04001068211997180.112815-0.35470.7246650.362332
y2-0.09380266516754970.145014-0.64690.5213330.260667
y30.05449486591538160.1467180.37140.7122320.356116
y40.3898403524231380.1332752.92510.0055890.002795
M10.07628012789726330.4507180.16920.8664390.43322
M20.2506108753894120.4452990.56280.5766410.288321
M30.7358392547901270.4512971.63050.1106560.055328
M40.5765759563779570.4580381.25880.2152250.107613
M50.8422574092977710.4459361.88870.0660150.033008
M60.587922739353970.4570281.28640.2055190.102759
M70.5013427064630740.4551311.10150.2770880.138544
M80.6057880329143810.4566441.32660.191980.09599
M90.5659625788851140.4594731.23180.2250550.112528
M100.6572481813943280.4504721.4590.1521790.076089
M110.4242070698461810.4450810.95310.3461250.173062
t-0.007736679594248640.005566-1.39010.1720040.086002







Multiple Linear Regression - Regression Statistics
Multiple R0.934025997985242
R-squared0.872404564912327
Adjusted R-squared0.819499140607682
F-TEST (value)16.4898888229072
F-TEST (DF numerator)17
F-TEST (DF denominator)41
p-value3.46611628287974e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.652448276746705
Sum Squared Residuals17.4532389070196

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.934025997985242 \tabularnewline
R-squared & 0.872404564912327 \tabularnewline
Adjusted R-squared & 0.819499140607682 \tabularnewline
F-TEST (value) & 16.4898888229072 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 41 \tabularnewline
p-value & 3.46611628287974e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.652448276746705 \tabularnewline
Sum Squared Residuals & 17.4532389070196 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57779&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.934025997985242[/C][/ROW]
[ROW][C]R-squared[/C][C]0.872404564912327[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.819499140607682[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]16.4898888229072[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]41[/C][/ROW]
[ROW][C]p-value[/C][C]3.46611628287974e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.652448276746705[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]17.4532389070196[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57779&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57779&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.934025997985242
R-squared0.872404564912327
Adjusted R-squared0.819499140607682
F-TEST (value)16.4898888229072
F-TEST (DF numerator)17
F-TEST (DF denominator)41
p-value3.46611628287974e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.652448276746705
Sum Squared Residuals17.4532389070196







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.41.69537433284622-0.295374332846218
210.5806687545816490.419331245418351
3-0.8-1.108809374736810.308809374736813
4-2.9-1.80593082733721-1.09406917266279
5-0.7-0.8004576931433680.100457693143368
6-0.7-0.0516367899623158-0.648363210037684
71.51.89350865691398-0.393508656913976
833.13885245371768-0.138852453717678
93.23.52844798860734-0.328447988607336
103.13.58517733714265-0.485177337142647
113.92.951193190176670.94880680982333
1211.03698395111164-0.0369839511116440
131.30.3016267399812370.998373260018763
140.8-0.4361385735746681.23613857357467
151.20.7022949921659850.497705007834015
162.92.386015398062130.513984601937865
173.93.818787257828360.0812127421716392
184.54.237095378326130.262904621673867
194.53.843554877560270.656445122439733
203.32.460246138858320.839753861141675
2121.706281277126710.293718722873288
221.51.357900008892980.142099991107018
2311.62969885591364-0.629698855913642
242.12.73157877387005-0.631578773870052
2533.74949494752284-0.749494947522844
2644.89393075701504-0.893930757015044
275.15.31585923685243-0.215859236852432
284.54.212546553437330.287453446562665
294.23.756061544848430.443938455151569
303.33.112049997834370.187950002165628
312.72.85177167769766-0.151771677697659
321.82.52112645435853-0.721126454358529
331.41.53379727755809-0.133797277558085
340.50.775687310343635-0.275687310343635
35-0.40.53566161575785-0.93566161575785
360.80.5017995909113430.298200409088657
370.71.30854677046173-0.60854677046173
381.92.02140922839143-0.121409228391434
3922.17204376578178-0.172043765781784
401.11.47062995646826-0.370629956468263
410.91.47693244797496-0.576932447974963
420.40.953389827149578-0.553389827149578
430.71.01077478201472-0.310774782014717
442.12.14167762067835-0.0416776206783466
452.82.81436721277874-0.0143672127787354
463.92.939024966921280.960975033078722
473.52.551680514291180.948319485708824
4821.629637684106960.370362315893035
4921.344957209187970.655042790812029
501.52.14012983358654-0.64012983358654
512.52.91861137993661-0.418611379936613
523.12.436738919369480.663261080630523
532.72.74867644249161-0.048676442491612
542.82.049101586652230.750898413347767
552.52.300390005813380.199609994186619
5632.938097332387120.0619026676128784
573.23.017106243929130.182893756070868
582.83.14221037669946-0.342210376699459
592.42.73176582386066-0.331765823860662

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.4 & 1.69537433284622 & -0.295374332846218 \tabularnewline
2 & 1 & 0.580668754581649 & 0.419331245418351 \tabularnewline
3 & -0.8 & -1.10880937473681 & 0.308809374736813 \tabularnewline
4 & -2.9 & -1.80593082733721 & -1.09406917266279 \tabularnewline
5 & -0.7 & -0.800457693143368 & 0.100457693143368 \tabularnewline
6 & -0.7 & -0.0516367899623158 & -0.648363210037684 \tabularnewline
7 & 1.5 & 1.89350865691398 & -0.393508656913976 \tabularnewline
8 & 3 & 3.13885245371768 & -0.138852453717678 \tabularnewline
9 & 3.2 & 3.52844798860734 & -0.328447988607336 \tabularnewline
10 & 3.1 & 3.58517733714265 & -0.485177337142647 \tabularnewline
11 & 3.9 & 2.95119319017667 & 0.94880680982333 \tabularnewline
12 & 1 & 1.03698395111164 & -0.0369839511116440 \tabularnewline
13 & 1.3 & 0.301626739981237 & 0.998373260018763 \tabularnewline
14 & 0.8 & -0.436138573574668 & 1.23613857357467 \tabularnewline
15 & 1.2 & 0.702294992165985 & 0.497705007834015 \tabularnewline
16 & 2.9 & 2.38601539806213 & 0.513984601937865 \tabularnewline
17 & 3.9 & 3.81878725782836 & 0.0812127421716392 \tabularnewline
18 & 4.5 & 4.23709537832613 & 0.262904621673867 \tabularnewline
19 & 4.5 & 3.84355487756027 & 0.656445122439733 \tabularnewline
20 & 3.3 & 2.46024613885832 & 0.839753861141675 \tabularnewline
21 & 2 & 1.70628127712671 & 0.293718722873288 \tabularnewline
22 & 1.5 & 1.35790000889298 & 0.142099991107018 \tabularnewline
23 & 1 & 1.62969885591364 & -0.629698855913642 \tabularnewline
24 & 2.1 & 2.73157877387005 & -0.631578773870052 \tabularnewline
25 & 3 & 3.74949494752284 & -0.749494947522844 \tabularnewline
26 & 4 & 4.89393075701504 & -0.893930757015044 \tabularnewline
27 & 5.1 & 5.31585923685243 & -0.215859236852432 \tabularnewline
28 & 4.5 & 4.21254655343733 & 0.287453446562665 \tabularnewline
29 & 4.2 & 3.75606154484843 & 0.443938455151569 \tabularnewline
30 & 3.3 & 3.11204999783437 & 0.187950002165628 \tabularnewline
31 & 2.7 & 2.85177167769766 & -0.151771677697659 \tabularnewline
32 & 1.8 & 2.52112645435853 & -0.721126454358529 \tabularnewline
33 & 1.4 & 1.53379727755809 & -0.133797277558085 \tabularnewline
34 & 0.5 & 0.775687310343635 & -0.275687310343635 \tabularnewline
35 & -0.4 & 0.53566161575785 & -0.93566161575785 \tabularnewline
36 & 0.8 & 0.501799590911343 & 0.298200409088657 \tabularnewline
37 & 0.7 & 1.30854677046173 & -0.60854677046173 \tabularnewline
38 & 1.9 & 2.02140922839143 & -0.121409228391434 \tabularnewline
39 & 2 & 2.17204376578178 & -0.172043765781784 \tabularnewline
40 & 1.1 & 1.47062995646826 & -0.370629956468263 \tabularnewline
41 & 0.9 & 1.47693244797496 & -0.576932447974963 \tabularnewline
42 & 0.4 & 0.953389827149578 & -0.553389827149578 \tabularnewline
43 & 0.7 & 1.01077478201472 & -0.310774782014717 \tabularnewline
44 & 2.1 & 2.14167762067835 & -0.0416776206783466 \tabularnewline
45 & 2.8 & 2.81436721277874 & -0.0143672127787354 \tabularnewline
46 & 3.9 & 2.93902496692128 & 0.960975033078722 \tabularnewline
47 & 3.5 & 2.55168051429118 & 0.948319485708824 \tabularnewline
48 & 2 & 1.62963768410696 & 0.370362315893035 \tabularnewline
49 & 2 & 1.34495720918797 & 0.655042790812029 \tabularnewline
50 & 1.5 & 2.14012983358654 & -0.64012983358654 \tabularnewline
51 & 2.5 & 2.91861137993661 & -0.418611379936613 \tabularnewline
52 & 3.1 & 2.43673891936948 & 0.663261080630523 \tabularnewline
53 & 2.7 & 2.74867644249161 & -0.048676442491612 \tabularnewline
54 & 2.8 & 2.04910158665223 & 0.750898413347767 \tabularnewline
55 & 2.5 & 2.30039000581338 & 0.199609994186619 \tabularnewline
56 & 3 & 2.93809733238712 & 0.0619026676128784 \tabularnewline
57 & 3.2 & 3.01710624392913 & 0.182893756070868 \tabularnewline
58 & 2.8 & 3.14221037669946 & -0.342210376699459 \tabularnewline
59 & 2.4 & 2.73176582386066 & -0.331765823860662 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57779&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]1.4[/C][C]1.69537433284622[/C][C]-0.295374332846218[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]0.580668754581649[/C][C]0.419331245418351[/C][/ROW]
[ROW][C]3[/C][C]-0.8[/C][C]-1.10880937473681[/C][C]0.308809374736813[/C][/ROW]
[ROW][C]4[/C][C]-2.9[/C][C]-1.80593082733721[/C][C]-1.09406917266279[/C][/ROW]
[ROW][C]5[/C][C]-0.7[/C][C]-0.800457693143368[/C][C]0.100457693143368[/C][/ROW]
[ROW][C]6[/C][C]-0.7[/C][C]-0.0516367899623158[/C][C]-0.648363210037684[/C][/ROW]
[ROW][C]7[/C][C]1.5[/C][C]1.89350865691398[/C][C]-0.393508656913976[/C][/ROW]
[ROW][C]8[/C][C]3[/C][C]3.13885245371768[/C][C]-0.138852453717678[/C][/ROW]
[ROW][C]9[/C][C]3.2[/C][C]3.52844798860734[/C][C]-0.328447988607336[/C][/ROW]
[ROW][C]10[/C][C]3.1[/C][C]3.58517733714265[/C][C]-0.485177337142647[/C][/ROW]
[ROW][C]11[/C][C]3.9[/C][C]2.95119319017667[/C][C]0.94880680982333[/C][/ROW]
[ROW][C]12[/C][C]1[/C][C]1.03698395111164[/C][C]-0.0369839511116440[/C][/ROW]
[ROW][C]13[/C][C]1.3[/C][C]0.301626739981237[/C][C]0.998373260018763[/C][/ROW]
[ROW][C]14[/C][C]0.8[/C][C]-0.436138573574668[/C][C]1.23613857357467[/C][/ROW]
[ROW][C]15[/C][C]1.2[/C][C]0.702294992165985[/C][C]0.497705007834015[/C][/ROW]
[ROW][C]16[/C][C]2.9[/C][C]2.38601539806213[/C][C]0.513984601937865[/C][/ROW]
[ROW][C]17[/C][C]3.9[/C][C]3.81878725782836[/C][C]0.0812127421716392[/C][/ROW]
[ROW][C]18[/C][C]4.5[/C][C]4.23709537832613[/C][C]0.262904621673867[/C][/ROW]
[ROW][C]19[/C][C]4.5[/C][C]3.84355487756027[/C][C]0.656445122439733[/C][/ROW]
[ROW][C]20[/C][C]3.3[/C][C]2.46024613885832[/C][C]0.839753861141675[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]1.70628127712671[/C][C]0.293718722873288[/C][/ROW]
[ROW][C]22[/C][C]1.5[/C][C]1.35790000889298[/C][C]0.142099991107018[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]1.62969885591364[/C][C]-0.629698855913642[/C][/ROW]
[ROW][C]24[/C][C]2.1[/C][C]2.73157877387005[/C][C]-0.631578773870052[/C][/ROW]
[ROW][C]25[/C][C]3[/C][C]3.74949494752284[/C][C]-0.749494947522844[/C][/ROW]
[ROW][C]26[/C][C]4[/C][C]4.89393075701504[/C][C]-0.893930757015044[/C][/ROW]
[ROW][C]27[/C][C]5.1[/C][C]5.31585923685243[/C][C]-0.215859236852432[/C][/ROW]
[ROW][C]28[/C][C]4.5[/C][C]4.21254655343733[/C][C]0.287453446562665[/C][/ROW]
[ROW][C]29[/C][C]4.2[/C][C]3.75606154484843[/C][C]0.443938455151569[/C][/ROW]
[ROW][C]30[/C][C]3.3[/C][C]3.11204999783437[/C][C]0.187950002165628[/C][/ROW]
[ROW][C]31[/C][C]2.7[/C][C]2.85177167769766[/C][C]-0.151771677697659[/C][/ROW]
[ROW][C]32[/C][C]1.8[/C][C]2.52112645435853[/C][C]-0.721126454358529[/C][/ROW]
[ROW][C]33[/C][C]1.4[/C][C]1.53379727755809[/C][C]-0.133797277558085[/C][/ROW]
[ROW][C]34[/C][C]0.5[/C][C]0.775687310343635[/C][C]-0.275687310343635[/C][/ROW]
[ROW][C]35[/C][C]-0.4[/C][C]0.53566161575785[/C][C]-0.93566161575785[/C][/ROW]
[ROW][C]36[/C][C]0.8[/C][C]0.501799590911343[/C][C]0.298200409088657[/C][/ROW]
[ROW][C]37[/C][C]0.7[/C][C]1.30854677046173[/C][C]-0.60854677046173[/C][/ROW]
[ROW][C]38[/C][C]1.9[/C][C]2.02140922839143[/C][C]-0.121409228391434[/C][/ROW]
[ROW][C]39[/C][C]2[/C][C]2.17204376578178[/C][C]-0.172043765781784[/C][/ROW]
[ROW][C]40[/C][C]1.1[/C][C]1.47062995646826[/C][C]-0.370629956468263[/C][/ROW]
[ROW][C]41[/C][C]0.9[/C][C]1.47693244797496[/C][C]-0.576932447974963[/C][/ROW]
[ROW][C]42[/C][C]0.4[/C][C]0.953389827149578[/C][C]-0.553389827149578[/C][/ROW]
[ROW][C]43[/C][C]0.7[/C][C]1.01077478201472[/C][C]-0.310774782014717[/C][/ROW]
[ROW][C]44[/C][C]2.1[/C][C]2.14167762067835[/C][C]-0.0416776206783466[/C][/ROW]
[ROW][C]45[/C][C]2.8[/C][C]2.81436721277874[/C][C]-0.0143672127787354[/C][/ROW]
[ROW][C]46[/C][C]3.9[/C][C]2.93902496692128[/C][C]0.960975033078722[/C][/ROW]
[ROW][C]47[/C][C]3.5[/C][C]2.55168051429118[/C][C]0.948319485708824[/C][/ROW]
[ROW][C]48[/C][C]2[/C][C]1.62963768410696[/C][C]0.370362315893035[/C][/ROW]
[ROW][C]49[/C][C]2[/C][C]1.34495720918797[/C][C]0.655042790812029[/C][/ROW]
[ROW][C]50[/C][C]1.5[/C][C]2.14012983358654[/C][C]-0.64012983358654[/C][/ROW]
[ROW][C]51[/C][C]2.5[/C][C]2.91861137993661[/C][C]-0.418611379936613[/C][/ROW]
[ROW][C]52[/C][C]3.1[/C][C]2.43673891936948[/C][C]0.663261080630523[/C][/ROW]
[ROW][C]53[/C][C]2.7[/C][C]2.74867644249161[/C][C]-0.048676442491612[/C][/ROW]
[ROW][C]54[/C][C]2.8[/C][C]2.04910158665223[/C][C]0.750898413347767[/C][/ROW]
[ROW][C]55[/C][C]2.5[/C][C]2.30039000581338[/C][C]0.199609994186619[/C][/ROW]
[ROW][C]56[/C][C]3[/C][C]2.93809733238712[/C][C]0.0619026676128784[/C][/ROW]
[ROW][C]57[/C][C]3.2[/C][C]3.01710624392913[/C][C]0.182893756070868[/C][/ROW]
[ROW][C]58[/C][C]2.8[/C][C]3.14221037669946[/C][C]-0.342210376699459[/C][/ROW]
[ROW][C]59[/C][C]2.4[/C][C]2.73176582386066[/C][C]-0.331765823860662[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57779&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57779&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.41.69537433284622-0.295374332846218
210.5806687545816490.419331245418351
3-0.8-1.108809374736810.308809374736813
4-2.9-1.80593082733721-1.09406917266279
5-0.7-0.8004576931433680.100457693143368
6-0.7-0.0516367899623158-0.648363210037684
71.51.89350865691398-0.393508656913976
833.13885245371768-0.138852453717678
93.23.52844798860734-0.328447988607336
103.13.58517733714265-0.485177337142647
113.92.951193190176670.94880680982333
1211.03698395111164-0.0369839511116440
131.30.3016267399812370.998373260018763
140.8-0.4361385735746681.23613857357467
151.20.7022949921659850.497705007834015
162.92.386015398062130.513984601937865
173.93.818787257828360.0812127421716392
184.54.237095378326130.262904621673867
194.53.843554877560270.656445122439733
203.32.460246138858320.839753861141675
2121.706281277126710.293718722873288
221.51.357900008892980.142099991107018
2311.62969885591364-0.629698855913642
242.12.73157877387005-0.631578773870052
2533.74949494752284-0.749494947522844
2644.89393075701504-0.893930757015044
275.15.31585923685243-0.215859236852432
284.54.212546553437330.287453446562665
294.23.756061544848430.443938455151569
303.33.112049997834370.187950002165628
312.72.85177167769766-0.151771677697659
321.82.52112645435853-0.721126454358529
331.41.53379727755809-0.133797277558085
340.50.775687310343635-0.275687310343635
35-0.40.53566161575785-0.93566161575785
360.80.5017995909113430.298200409088657
370.71.30854677046173-0.60854677046173
381.92.02140922839143-0.121409228391434
3922.17204376578178-0.172043765781784
401.11.47062995646826-0.370629956468263
410.91.47693244797496-0.576932447974963
420.40.953389827149578-0.553389827149578
430.71.01077478201472-0.310774782014717
442.12.14167762067835-0.0416776206783466
452.82.81436721277874-0.0143672127787354
463.92.939024966921280.960975033078722
473.52.551680514291180.948319485708824
4821.629637684106960.370362315893035
4921.344957209187970.655042790812029
501.52.14012983358654-0.64012983358654
512.52.91861137993661-0.418611379936613
523.12.436738919369480.663261080630523
532.72.74867644249161-0.048676442491612
542.82.049101586652230.750898413347767
552.52.300390005813380.199609994186619
5632.938097332387120.0619026676128784
573.23.017106243929130.182893756070868
582.83.14221037669946-0.342210376699459
592.42.73176582386066-0.331765823860662







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.1770943134276060.3541886268552120.822905686572394
220.3086499536549480.6172999073098960.691350046345052
230.7472366835451280.5055266329097430.252763316454872
240.8625326605579250.274934678884150.137467339442075
250.9419412688018920.1161174623962160.0580587311981079
260.9594015761403020.08119684771939670.0405984238596984
270.9367461655620140.1265076688759720.0632538344379858
280.9172282395720670.1655435208558660.0827717604279328
290.8701415746128760.2597168507742480.129858425387124
300.8321170823413070.3357658353173870.167882917658693
310.846892362062110.3062152758757780.153107637937889
320.8805939321386110.2388121357227770.119406067861389
330.806042112181090.3879157756378190.193957887818910
340.7900847244050890.4198305511898220.209915275594911
350.7149953652886110.5700092694227780.285004634711389
360.7371556111237880.5256887777524230.262844388876212
370.7594150471544900.4811699056910190.240584952845510
380.8115731966938060.3768536066123890.188426803306194

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.177094313427606 & 0.354188626855212 & 0.822905686572394 \tabularnewline
22 & 0.308649953654948 & 0.617299907309896 & 0.691350046345052 \tabularnewline
23 & 0.747236683545128 & 0.505526632909743 & 0.252763316454872 \tabularnewline
24 & 0.862532660557925 & 0.27493467888415 & 0.137467339442075 \tabularnewline
25 & 0.941941268801892 & 0.116117462396216 & 0.0580587311981079 \tabularnewline
26 & 0.959401576140302 & 0.0811968477193967 & 0.0405984238596984 \tabularnewline
27 & 0.936746165562014 & 0.126507668875972 & 0.0632538344379858 \tabularnewline
28 & 0.917228239572067 & 0.165543520855866 & 0.0827717604279328 \tabularnewline
29 & 0.870141574612876 & 0.259716850774248 & 0.129858425387124 \tabularnewline
30 & 0.832117082341307 & 0.335765835317387 & 0.167882917658693 \tabularnewline
31 & 0.84689236206211 & 0.306215275875778 & 0.153107637937889 \tabularnewline
32 & 0.880593932138611 & 0.238812135722777 & 0.119406067861389 \tabularnewline
33 & 0.80604211218109 & 0.387915775637819 & 0.193957887818910 \tabularnewline
34 & 0.790084724405089 & 0.419830551189822 & 0.209915275594911 \tabularnewline
35 & 0.714995365288611 & 0.570009269422778 & 0.285004634711389 \tabularnewline
36 & 0.737155611123788 & 0.525688777752423 & 0.262844388876212 \tabularnewline
37 & 0.759415047154490 & 0.481169905691019 & 0.240584952845510 \tabularnewline
38 & 0.811573196693806 & 0.376853606612389 & 0.188426803306194 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57779&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]0.177094313427606[/C][C]0.354188626855212[/C][C]0.822905686572394[/C][/ROW]
[ROW][C]22[/C][C]0.308649953654948[/C][C]0.617299907309896[/C][C]0.691350046345052[/C][/ROW]
[ROW][C]23[/C][C]0.747236683545128[/C][C]0.505526632909743[/C][C]0.252763316454872[/C][/ROW]
[ROW][C]24[/C][C]0.862532660557925[/C][C]0.27493467888415[/C][C]0.137467339442075[/C][/ROW]
[ROW][C]25[/C][C]0.941941268801892[/C][C]0.116117462396216[/C][C]0.0580587311981079[/C][/ROW]
[ROW][C]26[/C][C]0.959401576140302[/C][C]0.0811968477193967[/C][C]0.0405984238596984[/C][/ROW]
[ROW][C]27[/C][C]0.936746165562014[/C][C]0.126507668875972[/C][C]0.0632538344379858[/C][/ROW]
[ROW][C]28[/C][C]0.917228239572067[/C][C]0.165543520855866[/C][C]0.0827717604279328[/C][/ROW]
[ROW][C]29[/C][C]0.870141574612876[/C][C]0.259716850774248[/C][C]0.129858425387124[/C][/ROW]
[ROW][C]30[/C][C]0.832117082341307[/C][C]0.335765835317387[/C][C]0.167882917658693[/C][/ROW]
[ROW][C]31[/C][C]0.84689236206211[/C][C]0.306215275875778[/C][C]0.153107637937889[/C][/ROW]
[ROW][C]32[/C][C]0.880593932138611[/C][C]0.238812135722777[/C][C]0.119406067861389[/C][/ROW]
[ROW][C]33[/C][C]0.80604211218109[/C][C]0.387915775637819[/C][C]0.193957887818910[/C][/ROW]
[ROW][C]34[/C][C]0.790084724405089[/C][C]0.419830551189822[/C][C]0.209915275594911[/C][/ROW]
[ROW][C]35[/C][C]0.714995365288611[/C][C]0.570009269422778[/C][C]0.285004634711389[/C][/ROW]
[ROW][C]36[/C][C]0.737155611123788[/C][C]0.525688777752423[/C][C]0.262844388876212[/C][/ROW]
[ROW][C]37[/C][C]0.759415047154490[/C][C]0.481169905691019[/C][C]0.240584952845510[/C][/ROW]
[ROW][C]38[/C][C]0.811573196693806[/C][C]0.376853606612389[/C][C]0.188426803306194[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57779&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57779&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.1770943134276060.3541886268552120.822905686572394
220.3086499536549480.6172999073098960.691350046345052
230.7472366835451280.5055266329097430.252763316454872
240.8625326605579250.274934678884150.137467339442075
250.9419412688018920.1161174623962160.0580587311981079
260.9594015761403020.08119684771939670.0405984238596984
270.9367461655620140.1265076688759720.0632538344379858
280.9172282395720670.1655435208558660.0827717604279328
290.8701415746128760.2597168507742480.129858425387124
300.8321170823413070.3357658353173870.167882917658693
310.846892362062110.3062152758757780.153107637937889
320.8805939321386110.2388121357227770.119406067861389
330.806042112181090.3879157756378190.193957887818910
340.7900847244050890.4198305511898220.209915275594911
350.7149953652886110.5700092694227780.285004634711389
360.7371556111237880.5256887777524230.262844388876212
370.7594150471544900.4811699056910190.240584952845510
380.8115731966938060.3768536066123890.188426803306194







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.0555555555555556OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 1 & 0.0555555555555556 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57779&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.0555555555555556[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57779&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57779&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.0555555555555556OK



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')
}