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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 11:19:23 -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/t1258654819tpa9b6af0y2m642.htm/, Retrieved Thu, 28 Mar 2024 19:43:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57871, Retrieved Thu, 28 Mar 2024 19:43:34 +0000
QR Codes:

Original text written by user: Multiple lineair regression software (4)
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact69
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]
-    D      [Multiple Regression] [shw7: Multiple li...] [2009-11-19 18:19:23] [7a39e26d7a09dd77604df90cb29f8d39] [Current]
-   P         [Multiple Regression] [Paper: Y(t-4)] [2009-12-13 12:17:00] [3c8b83428ce260cd44df892bb7619588]
-               [Multiple Regression] [Y(t-4)] [2009-12-17 17:12:35] [1433a524809eda02c3198b3ae6eebb69]
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Dataseries X:
0.7905	0.313	0.7744	0.779	0.7775	0.7461
0.7719	0.364	0.7905	0.7744	0.779	0.7775
0.7811	0.363	0.7719	0.7905	0.7744	0.779
0.7557	-0.155	0.7811	0.7719	0.7905	0.7744
0.7637	0.052	0.7557	0.7811	0.7719	0.7905
0.7595	0.568	0.7637	0.7557	0.7811	0.7719
0.7471	0.668	0.7595	0.7637	0.7557	0.7811
0.7615	1.378	0.7471	0.7595	0.7637	0.7557
0.7487	0.252	0.7615	0.7471	0.7595	0.7637
0.7389	-0.402	0.7487	0.7615	0.7471	0.7595
0.7337	-0.05	0.7389	0.7487	0.7615	0.7471
0.751	0.555	0.7337	0.7389	0.7487	0.7615
0.7382	0.05	0.751	0.7337	0.7389	0.7487
0.7159	0.15	0.7382	0.751	0.7337	0.7389
0.7542	0.45	0.7159	0.7382	0.751	0.7337
0.7636	0.299	0.7542	0.7159	0.7382	0.751
0.7433	0.199	0.7636	0.7542	0.7159	0.7382
0.7658	0.496	0.7433	0.7636	0.7542	0.7159
0.7627	0.444	0.7658	0.7433	0.7636	0.7542
0.748	-0.393	0.7627	0.7658	0.7433	0.7636
0.7692	-0.444	0.748	0.7627	0.7658	0.7433
0.785	0.198	0.7692	0.748	0.7627	0.7658
0.7913	0.494	0.785	0.7692	0.748	0.7627
0.772	0.133	0.7913	0.785	0.7692	0.748
0.788	0.388	0.772	0.7913	0.785	0.7692
0.807	0.484	0.788	0.772	0.7913	0.785
0.8268	0.278	0.807	0.788	0.772	0.7913
0.8244	0.369	0.8268	0.807	0.788	0.772
0.8487	0.165	0.8244	0.8268	0.807	0.788
0.8572	0.155	0.8487	0.8244	0.8268	0.807
0.8214	0.087	0.8572	0.8487	0.8244	0.8268
0.8827	0.414	0.8214	0.8572	0.8487	0.8244
0.9216	0.36	0.8827	0.8214	0.8572	0.8487
0.8865	0.975	0.9216	0.8827	0.8214	0.8572
0.8816	0.27	0.8865	0.9216	0.8827	0.8214
0.8884	0.359	0.8816	0.8865	0.9216	0.8827
0.9466	0.169	0.8884	0.8816	0.8865	0.9216
0.918	0.381	0.9466	0.8884	0.8816	0.8865
0.9337	0.154	0.918	0.9466	0.8884	0.8816
0.9559	0.486	0.9337	0.918	0.9466	0.8884
0.9626	0.925	0.9559	0.9337	0.918	0.9466
0.9434	0.728	0.9626	0.9559	0.9337	0.918
0.8639	-0.014	0.9434	0.9626	0.9559	0.9337
0.7996	0.046	0.8639	0.9434	0.9626	0.9559
0.668	-0.819	0.7996	0.8639	0.9434	0.9626
0.6572	-1.674	0.668	0.7996	0.8639	0.9434
0.6928	-0.788	0.6572	0.668	0.7996	0.8639
0.6438	0.279	0.6928	0.6572	0.668	0.7996
0.6454	0.396	0.6438	0.6928	0.6572	0.668
0.6873	-0.141	0.6454	0.6438	0.6928	0.6572
0.7265	-0.019	0.6873	0.6454	0.6438	0.6928
0.7912	0.099	0.7265	0.6873	0.6454	0.6438
0.8114	0.742	0.7912	0.7265	0.6873	0.6454
0.8281	0.005	0.8114	0.7912	0.7265	0.6873
0.8393	0.448	0.8281	0.8114	0.7912	0.7265




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=57871&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=57871&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57871&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
USDOLLAR[t] = + 0.105597037179138 + 0.00941197428820186Amerikaanse_inflatie[t] + 1.20596116767540`Y[t-1]`[t] -0.527201466526525`Y[t-2]`[t] + 0.547813323376132`Y[t-3]`[t] -0.388942635999796`Y[t-4]`[t] + 0.0293145636322456M1[t] -0.00111634037128101M2[t] + 0.0413735451661571M3[t] + 0.0121472277427398M4[t] + 0.0212072164688286M5[t] + 0.0097722538432314M6[t] -0.0136301901705398M7[t] + 0.0219708478339727M8[t] -0.00996368572382476M9[t] + 0.0239631461363912M10[t] + 0.0182904283986546M11[t] + 0.000324299601614479t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
USDOLLAR[t] =  +  0.105597037179138 +  0.00941197428820186Amerikaanse_inflatie[t] +  1.20596116767540`Y[t-1]`[t] -0.527201466526525`Y[t-2]`[t] +  0.547813323376132`Y[t-3]`[t] -0.388942635999796`Y[t-4]`[t] +  0.0293145636322456M1[t] -0.00111634037128101M2[t] +  0.0413735451661571M3[t] +  0.0121472277427398M4[t] +  0.0212072164688286M5[t] +  0.0097722538432314M6[t] -0.0136301901705398M7[t] +  0.0219708478339727M8[t] -0.00996368572382476M9[t] +  0.0239631461363912M10[t] +  0.0182904283986546M11[t] +  0.000324299601614479t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57871&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]USDOLLAR[t] =  +  0.105597037179138 +  0.00941197428820186Amerikaanse_inflatie[t] +  1.20596116767540`Y[t-1]`[t] -0.527201466526525`Y[t-2]`[t] +  0.547813323376132`Y[t-3]`[t] -0.388942635999796`Y[t-4]`[t] +  0.0293145636322456M1[t] -0.00111634037128101M2[t] +  0.0413735451661571M3[t] +  0.0121472277427398M4[t] +  0.0212072164688286M5[t] +  0.0097722538432314M6[t] -0.0136301901705398M7[t] +  0.0219708478339727M8[t] -0.00996368572382476M9[t] +  0.0239631461363912M10[t] +  0.0182904283986546M11[t] +  0.000324299601614479t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57871&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57871&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
USDOLLAR[t] = + 0.105597037179138 + 0.00941197428820186Amerikaanse_inflatie[t] + 1.20596116767540`Y[t-1]`[t] -0.527201466526525`Y[t-2]`[t] + 0.547813323376132`Y[t-3]`[t] -0.388942635999796`Y[t-4]`[t] + 0.0293145636322456M1[t] -0.00111634037128101M2[t] + 0.0413735451661571M3[t] + 0.0121472277427398M4[t] + 0.0212072164688286M5[t] + 0.0097722538432314M6[t] -0.0136301901705398M7[t] + 0.0219708478339727M8[t] -0.00996368572382476M9[t] + 0.0239631461363912M10[t] + 0.0182904283986546M11[t] + 0.000324299601614479t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.1055970371791380.0496542.12670.0401810.020091
Amerikaanse_inflatie0.009411974288201860.013810.68150.4997820.249891
`Y[t-1]`1.205961167675400.1833096.578800
`Y[t-2]`-0.5272014665265250.247678-2.12860.0400120.020006
`Y[t-3]`0.5478133233761320.2389982.29210.0276850.013843
`Y[t-4]`-0.3889426359997960.151709-2.56370.0145520.007276
M10.02931456363224560.0210121.39520.1712860.085643
M2-0.001116340371281010.021164-0.05270.9582170.479109
M30.04137354516615710.0210361.96680.0567450.028372
M40.01214722774273980.0218180.55680.5810420.290521
M50.02120721646882860.021161.00220.3227390.16137
M60.00977225384323140.0215680.45310.6531290.326565
M7-0.01363019017053980.021231-0.6420.5248320.262416
M80.02197084783397270.0228590.96110.3427260.171363
M9-0.009963685723824760.023219-0.42910.6703270.335163
M100.02396314613639120.0229111.04590.3023910.151196
M110.01829042839865460.0222310.82270.4159240.207962
t0.0003242996016144790.0002821.15150.2569160.128458

\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) & 0.105597037179138 & 0.049654 & 2.1267 & 0.040181 & 0.020091 \tabularnewline
Amerikaanse_inflatie & 0.00941197428820186 & 0.01381 & 0.6815 & 0.499782 & 0.249891 \tabularnewline
`Y[t-1]` & 1.20596116767540 & 0.183309 & 6.5788 & 0 & 0 \tabularnewline
`Y[t-2]` & -0.527201466526525 & 0.247678 & -2.1286 & 0.040012 & 0.020006 \tabularnewline
`Y[t-3]` & 0.547813323376132 & 0.238998 & 2.2921 & 0.027685 & 0.013843 \tabularnewline
`Y[t-4]` & -0.388942635999796 & 0.151709 & -2.5637 & 0.014552 & 0.007276 \tabularnewline
M1 & 0.0293145636322456 & 0.021012 & 1.3952 & 0.171286 & 0.085643 \tabularnewline
M2 & -0.00111634037128101 & 0.021164 & -0.0527 & 0.958217 & 0.479109 \tabularnewline
M3 & 0.0413735451661571 & 0.021036 & 1.9668 & 0.056745 & 0.028372 \tabularnewline
M4 & 0.0121472277427398 & 0.021818 & 0.5568 & 0.581042 & 0.290521 \tabularnewline
M5 & 0.0212072164688286 & 0.02116 & 1.0022 & 0.322739 & 0.16137 \tabularnewline
M6 & 0.0097722538432314 & 0.021568 & 0.4531 & 0.653129 & 0.326565 \tabularnewline
M7 & -0.0136301901705398 & 0.021231 & -0.642 & 0.524832 & 0.262416 \tabularnewline
M8 & 0.0219708478339727 & 0.022859 & 0.9611 & 0.342726 & 0.171363 \tabularnewline
M9 & -0.00996368572382476 & 0.023219 & -0.4291 & 0.670327 & 0.335163 \tabularnewline
M10 & 0.0239631461363912 & 0.022911 & 1.0459 & 0.302391 & 0.151196 \tabularnewline
M11 & 0.0182904283986546 & 0.022231 & 0.8227 & 0.415924 & 0.207962 \tabularnewline
t & 0.000324299601614479 & 0.000282 & 1.1515 & 0.256916 & 0.128458 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57871&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]0.105597037179138[/C][C]0.049654[/C][C]2.1267[/C][C]0.040181[/C][C]0.020091[/C][/ROW]
[ROW][C]Amerikaanse_inflatie[/C][C]0.00941197428820186[/C][C]0.01381[/C][C]0.6815[/C][C]0.499782[/C][C]0.249891[/C][/ROW]
[ROW][C]`Y[t-1]`[/C][C]1.20596116767540[/C][C]0.183309[/C][C]6.5788[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Y[t-2]`[/C][C]-0.527201466526525[/C][C]0.247678[/C][C]-2.1286[/C][C]0.040012[/C][C]0.020006[/C][/ROW]
[ROW][C]`Y[t-3]`[/C][C]0.547813323376132[/C][C]0.238998[/C][C]2.2921[/C][C]0.027685[/C][C]0.013843[/C][/ROW]
[ROW][C]`Y[t-4]`[/C][C]-0.388942635999796[/C][C]0.151709[/C][C]-2.5637[/C][C]0.014552[/C][C]0.007276[/C][/ROW]
[ROW][C]M1[/C][C]0.0293145636322456[/C][C]0.021012[/C][C]1.3952[/C][C]0.171286[/C][C]0.085643[/C][/ROW]
[ROW][C]M2[/C][C]-0.00111634037128101[/C][C]0.021164[/C][C]-0.0527[/C][C]0.958217[/C][C]0.479109[/C][/ROW]
[ROW][C]M3[/C][C]0.0413735451661571[/C][C]0.021036[/C][C]1.9668[/C][C]0.056745[/C][C]0.028372[/C][/ROW]
[ROW][C]M4[/C][C]0.0121472277427398[/C][C]0.021818[/C][C]0.5568[/C][C]0.581042[/C][C]0.290521[/C][/ROW]
[ROW][C]M5[/C][C]0.0212072164688286[/C][C]0.02116[/C][C]1.0022[/C][C]0.322739[/C][C]0.16137[/C][/ROW]
[ROW][C]M6[/C][C]0.0097722538432314[/C][C]0.021568[/C][C]0.4531[/C][C]0.653129[/C][C]0.326565[/C][/ROW]
[ROW][C]M7[/C][C]-0.0136301901705398[/C][C]0.021231[/C][C]-0.642[/C][C]0.524832[/C][C]0.262416[/C][/ROW]
[ROW][C]M8[/C][C]0.0219708478339727[/C][C]0.022859[/C][C]0.9611[/C][C]0.342726[/C][C]0.171363[/C][/ROW]
[ROW][C]M9[/C][C]-0.00996368572382476[/C][C]0.023219[/C][C]-0.4291[/C][C]0.670327[/C][C]0.335163[/C][/ROW]
[ROW][C]M10[/C][C]0.0239631461363912[/C][C]0.022911[/C][C]1.0459[/C][C]0.302391[/C][C]0.151196[/C][/ROW]
[ROW][C]M11[/C][C]0.0182904283986546[/C][C]0.022231[/C][C]0.8227[/C][C]0.415924[/C][C]0.207962[/C][/ROW]
[ROW][C]t[/C][C]0.000324299601614479[/C][C]0.000282[/C][C]1.1515[/C][C]0.256916[/C][C]0.128458[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57871&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57871&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)0.1055970371791380.0496542.12670.0401810.020091
Amerikaanse_inflatie0.009411974288201860.013810.68150.4997820.249891
`Y[t-1]`1.205961167675400.1833096.578800
`Y[t-2]`-0.5272014665265250.247678-2.12860.0400120.020006
`Y[t-3]`0.5478133233761320.2389982.29210.0276850.013843
`Y[t-4]`-0.3889426359997960.151709-2.56370.0145520.007276
M10.02931456363224560.0210121.39520.1712860.085643
M2-0.001116340371281010.021164-0.05270.9582170.479109
M30.04137354516615710.0210361.96680.0567450.028372
M40.01214722774273980.0218180.55680.5810420.290521
M50.02120721646882860.021161.00220.3227390.16137
M60.00977225384323140.0215680.45310.6531290.326565
M7-0.01363019017053980.021231-0.6420.5248320.262416
M80.02197084783397270.0228590.96110.3427260.171363
M9-0.009963685723824760.023219-0.42910.6703270.335163
M100.02396314613639120.0229111.04590.3023910.151196
M110.01829042839865460.0222310.82270.4159240.207962
t0.0003242996016144790.0002821.15150.2569160.128458







Multiple Linear Regression - Regression Statistics
Multiple R0.94880027255588
R-squared0.900221957202111
Adjusted R-squared0.85437799159227
F-TEST (value)19.6366510886849
F-TEST (DF numerator)17
F-TEST (DF denominator)37
p-value1.49324996812084e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0305246824388129
Sum Squared Residuals0.0344749808056438

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.94880027255588 \tabularnewline
R-squared & 0.900221957202111 \tabularnewline
Adjusted R-squared & 0.85437799159227 \tabularnewline
F-TEST (value) & 19.6366510886849 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 37 \tabularnewline
p-value & 1.49324996812084e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0305246824388129 \tabularnewline
Sum Squared Residuals & 0.0344749808056438 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57871&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.94880027255588[/C][/ROW]
[ROW][C]R-squared[/C][C]0.900221957202111[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.85437799159227[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]19.6366510886849[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]37[/C][/ROW]
[ROW][C]p-value[/C][C]1.49324996812084e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0305246824388129[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.0344749808056438[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57871&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57871&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 R0.94880027255588
R-squared0.900221957202111
Adjusted R-squared0.85437799159227
F-TEST (value)19.6366510886849
F-TEST (DF numerator)17
F-TEST (DF denominator)37
p-value1.49324996812084e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0305246824388129
Sum Squared Residuals0.0344749808056438







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
10.79050.79712299239437-0.00662299239437012
20.77190.777946421441421-0.00604642144142124
30.78110.786729018034817-0.00562901803481656
40.75570.784461318183687-0.0287613181836867
50.76370.7438609137836560.0198390862163442
60.75950.772919651688219-0.0134196516882189
70.74710.7240073254034810.0230926745965185
80.76150.768137141975872-0.00663714197587215
90.74870.744419806924450.00428019307554969
100.73890.744328176998905-0.00542817699890496
110.73370.749933933683564-0.0162339336835641
120.7510.7239448411333220.0270551588666775
130.73820.772045128350074-0.0338451283500743
140.71590.71928584161107-0.00338584161107040
150.75420.756278535970568-0.00207853597056763
160.76360.770159497314745-0.00655949731474485
170.74330.76250903565132-0.0192090356513203
180.76580.7544116945698750.0113883054301251
190.76270.758933385818860.00376661418113985
200.7480.756603717086188-0.00860371708618804
210.76920.7286415031094690.0405584968905306
220.7850.791801929764523-0.00680192976452251
230.79130.7902698376945870.00103016230541267
240.7720.785504857569513-0.0135048575695132
250.7880.79135722109776-0.00335722109775968
260.8070.7889304635027560.0180695364972439
270.82680.831260684911889-0.00446068491188936
280.82440.833347966055096-0.00894796605509596
290.84870.831664686756510.0170353132434903
300.85720.8544868376026720.00271316239732783
310.82140.8191925370586650.00220746294133468
320.88270.824786294073220.057913705926781
330.92160.8806721527795270.0409278472204730
340.88650.912388358570235-0.0258883585702349
350.88160.885072227619392-0.0034722276193915
360.88840.8780070809800170.0103929190199829
370.94660.8822833760343970.0643166239656029
380.9180.931741681406962-0.0137416814069616
390.93370.9128766831505890.0208233168494111
400.95590.9503489185633240.00555108143667627
410.96260.944056416037730.0185435839622705
420.94340.947192089912107-0.00379208991210688
430.86390.896498612726762-0.0325986127267623
440.79960.842272846864721-0.0426728468647208
450.6680.753766537186553-0.0857665371865532
460.65720.6190815346663380.0381184653336624
470.69280.6741240010024570.0186759989975429
480.64380.667743220317147-0.0239432203171471
490.64540.665891282123399-0.0204912821233989
500.68730.682195592037790.00510440796220938
510.72650.735155077932138-0.0086550779321376
520.79120.7524822998831490.0387177001168513
530.81140.847608947770785-0.0362089477707847
540.82810.8249897262271270.00311027377287283
550.83930.835768138992230.0035318610077692

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0.7905 & 0.79712299239437 & -0.00662299239437012 \tabularnewline
2 & 0.7719 & 0.777946421441421 & -0.00604642144142124 \tabularnewline
3 & 0.7811 & 0.786729018034817 & -0.00562901803481656 \tabularnewline
4 & 0.7557 & 0.784461318183687 & -0.0287613181836867 \tabularnewline
5 & 0.7637 & 0.743860913783656 & 0.0198390862163442 \tabularnewline
6 & 0.7595 & 0.772919651688219 & -0.0134196516882189 \tabularnewline
7 & 0.7471 & 0.724007325403481 & 0.0230926745965185 \tabularnewline
8 & 0.7615 & 0.768137141975872 & -0.00663714197587215 \tabularnewline
9 & 0.7487 & 0.74441980692445 & 0.00428019307554969 \tabularnewline
10 & 0.7389 & 0.744328176998905 & -0.00542817699890496 \tabularnewline
11 & 0.7337 & 0.749933933683564 & -0.0162339336835641 \tabularnewline
12 & 0.751 & 0.723944841133322 & 0.0270551588666775 \tabularnewline
13 & 0.7382 & 0.772045128350074 & -0.0338451283500743 \tabularnewline
14 & 0.7159 & 0.71928584161107 & -0.00338584161107040 \tabularnewline
15 & 0.7542 & 0.756278535970568 & -0.00207853597056763 \tabularnewline
16 & 0.7636 & 0.770159497314745 & -0.00655949731474485 \tabularnewline
17 & 0.7433 & 0.76250903565132 & -0.0192090356513203 \tabularnewline
18 & 0.7658 & 0.754411694569875 & 0.0113883054301251 \tabularnewline
19 & 0.7627 & 0.75893338581886 & 0.00376661418113985 \tabularnewline
20 & 0.748 & 0.756603717086188 & -0.00860371708618804 \tabularnewline
21 & 0.7692 & 0.728641503109469 & 0.0405584968905306 \tabularnewline
22 & 0.785 & 0.791801929764523 & -0.00680192976452251 \tabularnewline
23 & 0.7913 & 0.790269837694587 & 0.00103016230541267 \tabularnewline
24 & 0.772 & 0.785504857569513 & -0.0135048575695132 \tabularnewline
25 & 0.788 & 0.79135722109776 & -0.00335722109775968 \tabularnewline
26 & 0.807 & 0.788930463502756 & 0.0180695364972439 \tabularnewline
27 & 0.8268 & 0.831260684911889 & -0.00446068491188936 \tabularnewline
28 & 0.8244 & 0.833347966055096 & -0.00894796605509596 \tabularnewline
29 & 0.8487 & 0.83166468675651 & 0.0170353132434903 \tabularnewline
30 & 0.8572 & 0.854486837602672 & 0.00271316239732783 \tabularnewline
31 & 0.8214 & 0.819192537058665 & 0.00220746294133468 \tabularnewline
32 & 0.8827 & 0.82478629407322 & 0.057913705926781 \tabularnewline
33 & 0.9216 & 0.880672152779527 & 0.0409278472204730 \tabularnewline
34 & 0.8865 & 0.912388358570235 & -0.0258883585702349 \tabularnewline
35 & 0.8816 & 0.885072227619392 & -0.0034722276193915 \tabularnewline
36 & 0.8884 & 0.878007080980017 & 0.0103929190199829 \tabularnewline
37 & 0.9466 & 0.882283376034397 & 0.0643166239656029 \tabularnewline
38 & 0.918 & 0.931741681406962 & -0.0137416814069616 \tabularnewline
39 & 0.9337 & 0.912876683150589 & 0.0208233168494111 \tabularnewline
40 & 0.9559 & 0.950348918563324 & 0.00555108143667627 \tabularnewline
41 & 0.9626 & 0.94405641603773 & 0.0185435839622705 \tabularnewline
42 & 0.9434 & 0.947192089912107 & -0.00379208991210688 \tabularnewline
43 & 0.8639 & 0.896498612726762 & -0.0325986127267623 \tabularnewline
44 & 0.7996 & 0.842272846864721 & -0.0426728468647208 \tabularnewline
45 & 0.668 & 0.753766537186553 & -0.0857665371865532 \tabularnewline
46 & 0.6572 & 0.619081534666338 & 0.0381184653336624 \tabularnewline
47 & 0.6928 & 0.674124001002457 & 0.0186759989975429 \tabularnewline
48 & 0.6438 & 0.667743220317147 & -0.0239432203171471 \tabularnewline
49 & 0.6454 & 0.665891282123399 & -0.0204912821233989 \tabularnewline
50 & 0.6873 & 0.68219559203779 & 0.00510440796220938 \tabularnewline
51 & 0.7265 & 0.735155077932138 & -0.0086550779321376 \tabularnewline
52 & 0.7912 & 0.752482299883149 & 0.0387177001168513 \tabularnewline
53 & 0.8114 & 0.847608947770785 & -0.0362089477707847 \tabularnewline
54 & 0.8281 & 0.824989726227127 & 0.00311027377287283 \tabularnewline
55 & 0.8393 & 0.83576813899223 & 0.0035318610077692 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57871&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]0.7905[/C][C]0.79712299239437[/C][C]-0.00662299239437012[/C][/ROW]
[ROW][C]2[/C][C]0.7719[/C][C]0.777946421441421[/C][C]-0.00604642144142124[/C][/ROW]
[ROW][C]3[/C][C]0.7811[/C][C]0.786729018034817[/C][C]-0.00562901803481656[/C][/ROW]
[ROW][C]4[/C][C]0.7557[/C][C]0.784461318183687[/C][C]-0.0287613181836867[/C][/ROW]
[ROW][C]5[/C][C]0.7637[/C][C]0.743860913783656[/C][C]0.0198390862163442[/C][/ROW]
[ROW][C]6[/C][C]0.7595[/C][C]0.772919651688219[/C][C]-0.0134196516882189[/C][/ROW]
[ROW][C]7[/C][C]0.7471[/C][C]0.724007325403481[/C][C]0.0230926745965185[/C][/ROW]
[ROW][C]8[/C][C]0.7615[/C][C]0.768137141975872[/C][C]-0.00663714197587215[/C][/ROW]
[ROW][C]9[/C][C]0.7487[/C][C]0.74441980692445[/C][C]0.00428019307554969[/C][/ROW]
[ROW][C]10[/C][C]0.7389[/C][C]0.744328176998905[/C][C]-0.00542817699890496[/C][/ROW]
[ROW][C]11[/C][C]0.7337[/C][C]0.749933933683564[/C][C]-0.0162339336835641[/C][/ROW]
[ROW][C]12[/C][C]0.751[/C][C]0.723944841133322[/C][C]0.0270551588666775[/C][/ROW]
[ROW][C]13[/C][C]0.7382[/C][C]0.772045128350074[/C][C]-0.0338451283500743[/C][/ROW]
[ROW][C]14[/C][C]0.7159[/C][C]0.71928584161107[/C][C]-0.00338584161107040[/C][/ROW]
[ROW][C]15[/C][C]0.7542[/C][C]0.756278535970568[/C][C]-0.00207853597056763[/C][/ROW]
[ROW][C]16[/C][C]0.7636[/C][C]0.770159497314745[/C][C]-0.00655949731474485[/C][/ROW]
[ROW][C]17[/C][C]0.7433[/C][C]0.76250903565132[/C][C]-0.0192090356513203[/C][/ROW]
[ROW][C]18[/C][C]0.7658[/C][C]0.754411694569875[/C][C]0.0113883054301251[/C][/ROW]
[ROW][C]19[/C][C]0.7627[/C][C]0.75893338581886[/C][C]0.00376661418113985[/C][/ROW]
[ROW][C]20[/C][C]0.748[/C][C]0.756603717086188[/C][C]-0.00860371708618804[/C][/ROW]
[ROW][C]21[/C][C]0.7692[/C][C]0.728641503109469[/C][C]0.0405584968905306[/C][/ROW]
[ROW][C]22[/C][C]0.785[/C][C]0.791801929764523[/C][C]-0.00680192976452251[/C][/ROW]
[ROW][C]23[/C][C]0.7913[/C][C]0.790269837694587[/C][C]0.00103016230541267[/C][/ROW]
[ROW][C]24[/C][C]0.772[/C][C]0.785504857569513[/C][C]-0.0135048575695132[/C][/ROW]
[ROW][C]25[/C][C]0.788[/C][C]0.79135722109776[/C][C]-0.00335722109775968[/C][/ROW]
[ROW][C]26[/C][C]0.807[/C][C]0.788930463502756[/C][C]0.0180695364972439[/C][/ROW]
[ROW][C]27[/C][C]0.8268[/C][C]0.831260684911889[/C][C]-0.00446068491188936[/C][/ROW]
[ROW][C]28[/C][C]0.8244[/C][C]0.833347966055096[/C][C]-0.00894796605509596[/C][/ROW]
[ROW][C]29[/C][C]0.8487[/C][C]0.83166468675651[/C][C]0.0170353132434903[/C][/ROW]
[ROW][C]30[/C][C]0.8572[/C][C]0.854486837602672[/C][C]0.00271316239732783[/C][/ROW]
[ROW][C]31[/C][C]0.8214[/C][C]0.819192537058665[/C][C]0.00220746294133468[/C][/ROW]
[ROW][C]32[/C][C]0.8827[/C][C]0.82478629407322[/C][C]0.057913705926781[/C][/ROW]
[ROW][C]33[/C][C]0.9216[/C][C]0.880672152779527[/C][C]0.0409278472204730[/C][/ROW]
[ROW][C]34[/C][C]0.8865[/C][C]0.912388358570235[/C][C]-0.0258883585702349[/C][/ROW]
[ROW][C]35[/C][C]0.8816[/C][C]0.885072227619392[/C][C]-0.0034722276193915[/C][/ROW]
[ROW][C]36[/C][C]0.8884[/C][C]0.878007080980017[/C][C]0.0103929190199829[/C][/ROW]
[ROW][C]37[/C][C]0.9466[/C][C]0.882283376034397[/C][C]0.0643166239656029[/C][/ROW]
[ROW][C]38[/C][C]0.918[/C][C]0.931741681406962[/C][C]-0.0137416814069616[/C][/ROW]
[ROW][C]39[/C][C]0.9337[/C][C]0.912876683150589[/C][C]0.0208233168494111[/C][/ROW]
[ROW][C]40[/C][C]0.9559[/C][C]0.950348918563324[/C][C]0.00555108143667627[/C][/ROW]
[ROW][C]41[/C][C]0.9626[/C][C]0.94405641603773[/C][C]0.0185435839622705[/C][/ROW]
[ROW][C]42[/C][C]0.9434[/C][C]0.947192089912107[/C][C]-0.00379208991210688[/C][/ROW]
[ROW][C]43[/C][C]0.8639[/C][C]0.896498612726762[/C][C]-0.0325986127267623[/C][/ROW]
[ROW][C]44[/C][C]0.7996[/C][C]0.842272846864721[/C][C]-0.0426728468647208[/C][/ROW]
[ROW][C]45[/C][C]0.668[/C][C]0.753766537186553[/C][C]-0.0857665371865532[/C][/ROW]
[ROW][C]46[/C][C]0.6572[/C][C]0.619081534666338[/C][C]0.0381184653336624[/C][/ROW]
[ROW][C]47[/C][C]0.6928[/C][C]0.674124001002457[/C][C]0.0186759989975429[/C][/ROW]
[ROW][C]48[/C][C]0.6438[/C][C]0.667743220317147[/C][C]-0.0239432203171471[/C][/ROW]
[ROW][C]49[/C][C]0.6454[/C][C]0.665891282123399[/C][C]-0.0204912821233989[/C][/ROW]
[ROW][C]50[/C][C]0.6873[/C][C]0.68219559203779[/C][C]0.00510440796220938[/C][/ROW]
[ROW][C]51[/C][C]0.7265[/C][C]0.735155077932138[/C][C]-0.0086550779321376[/C][/ROW]
[ROW][C]52[/C][C]0.7912[/C][C]0.752482299883149[/C][C]0.0387177001168513[/C][/ROW]
[ROW][C]53[/C][C]0.8114[/C][C]0.847608947770785[/C][C]-0.0362089477707847[/C][/ROW]
[ROW][C]54[/C][C]0.8281[/C][C]0.824989726227127[/C][C]0.00311027377287283[/C][/ROW]
[ROW][C]55[/C][C]0.8393[/C][C]0.83576813899223[/C][C]0.0035318610077692[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57871&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57871&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
10.79050.79712299239437-0.00662299239437012
20.77190.777946421441421-0.00604642144142124
30.78110.786729018034817-0.00562901803481656
40.75570.784461318183687-0.0287613181836867
50.76370.7438609137836560.0198390862163442
60.75950.772919651688219-0.0134196516882189
70.74710.7240073254034810.0230926745965185
80.76150.768137141975872-0.00663714197587215
90.74870.744419806924450.00428019307554969
100.73890.744328176998905-0.00542817699890496
110.73370.749933933683564-0.0162339336835641
120.7510.7239448411333220.0270551588666775
130.73820.772045128350074-0.0338451283500743
140.71590.71928584161107-0.00338584161107040
150.75420.756278535970568-0.00207853597056763
160.76360.770159497314745-0.00655949731474485
170.74330.76250903565132-0.0192090356513203
180.76580.7544116945698750.0113883054301251
190.76270.758933385818860.00376661418113985
200.7480.756603717086188-0.00860371708618804
210.76920.7286415031094690.0405584968905306
220.7850.791801929764523-0.00680192976452251
230.79130.7902698376945870.00103016230541267
240.7720.785504857569513-0.0135048575695132
250.7880.79135722109776-0.00335722109775968
260.8070.7889304635027560.0180695364972439
270.82680.831260684911889-0.00446068491188936
280.82440.833347966055096-0.00894796605509596
290.84870.831664686756510.0170353132434903
300.85720.8544868376026720.00271316239732783
310.82140.8191925370586650.00220746294133468
320.88270.824786294073220.057913705926781
330.92160.8806721527795270.0409278472204730
340.88650.912388358570235-0.0258883585702349
350.88160.885072227619392-0.0034722276193915
360.88840.8780070809800170.0103929190199829
370.94660.8822833760343970.0643166239656029
380.9180.931741681406962-0.0137416814069616
390.93370.9128766831505890.0208233168494111
400.95590.9503489185633240.00555108143667627
410.96260.944056416037730.0185435839622705
420.94340.947192089912107-0.00379208991210688
430.86390.896498612726762-0.0325986127267623
440.79960.842272846864721-0.0426728468647208
450.6680.753766537186553-0.0857665371865532
460.65720.6190815346663380.0381184653336624
470.69280.6741240010024570.0186759989975429
480.64380.667743220317147-0.0239432203171471
490.64540.665891282123399-0.0204912821233989
500.68730.682195592037790.00510440796220938
510.72650.735155077932138-0.0086550779321376
520.79120.7524822998831490.0387177001168513
530.81140.847608947770785-0.0362089477707847
540.82810.8249897262271270.00311027377287283
550.83930.835768138992230.0035318610077692







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.1457302762644270.2914605525288530.854269723735573
220.06665586414260120.1333117282852020.933344135857399
230.02367221452659020.04734442905318050.97632778547341
240.02323108261961320.04646216523922650.976768917380387
250.01583424232188330.03166848464376650.984165757678117
260.007442784635045820.01488556927009160.992557215364954
270.00282502292305590.00565004584611180.997174977076944
280.001222123525696610.002444247051393220.998777876474303
290.0004035254390582380.0008070508781164760.999596474560942
300.0002344835438393770.0004689670876787540.99976551645616
310.0006971880064060720.001394376012812140.999302811993594
320.0004908590582665380.0009817181165330760.999509140941733
330.00268973774368390.00537947548736780.997310262256316
340.001266992052512350.002533984105024710.998733007947488

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.145730276264427 & 0.291460552528853 & 0.854269723735573 \tabularnewline
22 & 0.0666558641426012 & 0.133311728285202 & 0.933344135857399 \tabularnewline
23 & 0.0236722145265902 & 0.0473444290531805 & 0.97632778547341 \tabularnewline
24 & 0.0232310826196132 & 0.0464621652392265 & 0.976768917380387 \tabularnewline
25 & 0.0158342423218833 & 0.0316684846437665 & 0.984165757678117 \tabularnewline
26 & 0.00744278463504582 & 0.0148855692700916 & 0.992557215364954 \tabularnewline
27 & 0.0028250229230559 & 0.0056500458461118 & 0.997174977076944 \tabularnewline
28 & 0.00122212352569661 & 0.00244424705139322 & 0.998777876474303 \tabularnewline
29 & 0.000403525439058238 & 0.000807050878116476 & 0.999596474560942 \tabularnewline
30 & 0.000234483543839377 & 0.000468967087678754 & 0.99976551645616 \tabularnewline
31 & 0.000697188006406072 & 0.00139437601281214 & 0.999302811993594 \tabularnewline
32 & 0.000490859058266538 & 0.000981718116533076 & 0.999509140941733 \tabularnewline
33 & 0.0026897377436839 & 0.0053794754873678 & 0.997310262256316 \tabularnewline
34 & 0.00126699205251235 & 0.00253398410502471 & 0.998733007947488 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57871&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]0.145730276264427[/C][C]0.291460552528853[/C][C]0.854269723735573[/C][/ROW]
[ROW][C]22[/C][C]0.0666558641426012[/C][C]0.133311728285202[/C][C]0.933344135857399[/C][/ROW]
[ROW][C]23[/C][C]0.0236722145265902[/C][C]0.0473444290531805[/C][C]0.97632778547341[/C][/ROW]
[ROW][C]24[/C][C]0.0232310826196132[/C][C]0.0464621652392265[/C][C]0.976768917380387[/C][/ROW]
[ROW][C]25[/C][C]0.0158342423218833[/C][C]0.0316684846437665[/C][C]0.984165757678117[/C][/ROW]
[ROW][C]26[/C][C]0.00744278463504582[/C][C]0.0148855692700916[/C][C]0.992557215364954[/C][/ROW]
[ROW][C]27[/C][C]0.0028250229230559[/C][C]0.0056500458461118[/C][C]0.997174977076944[/C][/ROW]
[ROW][C]28[/C][C]0.00122212352569661[/C][C]0.00244424705139322[/C][C]0.998777876474303[/C][/ROW]
[ROW][C]29[/C][C]0.000403525439058238[/C][C]0.000807050878116476[/C][C]0.999596474560942[/C][/ROW]
[ROW][C]30[/C][C]0.000234483543839377[/C][C]0.000468967087678754[/C][C]0.99976551645616[/C][/ROW]
[ROW][C]31[/C][C]0.000697188006406072[/C][C]0.00139437601281214[/C][C]0.999302811993594[/C][/ROW]
[ROW][C]32[/C][C]0.000490859058266538[/C][C]0.000981718116533076[/C][C]0.999509140941733[/C][/ROW]
[ROW][C]33[/C][C]0.0026897377436839[/C][C]0.0053794754873678[/C][C]0.997310262256316[/C][/ROW]
[ROW][C]34[/C][C]0.00126699205251235[/C][C]0.00253398410502471[/C][C]0.998733007947488[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57871&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57871&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
210.1457302762644270.2914605525288530.854269723735573
220.06665586414260120.1333117282852020.933344135857399
230.02367221452659020.04734442905318050.97632778547341
240.02323108261961320.04646216523922650.976768917380387
250.01583424232188330.03166848464376650.984165757678117
260.007442784635045820.01488556927009160.992557215364954
270.00282502292305590.00565004584611180.997174977076944
280.001222123525696610.002444247051393220.998777876474303
290.0004035254390582380.0008070508781164760.999596474560942
300.0002344835438393770.0004689670876787540.99976551645616
310.0006971880064060720.001394376012812140.999302811993594
320.0004908590582665380.0009817181165330760.999509140941733
330.00268973774368390.00537947548736780.997310262256316
340.001266992052512350.002533984105024710.998733007947488







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level80.571428571428571NOK
5% type I error level120.857142857142857NOK
10% type I error level120.857142857142857NOK

\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 & 8 & 0.571428571428571 & NOK \tabularnewline
5% type I error level & 12 & 0.857142857142857 & NOK \tabularnewline
10% type I error level & 12 & 0.857142857142857 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57871&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]8[/C][C]0.571428571428571[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]12[/C][C]0.857142857142857[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]12[/C][C]0.857142857142857[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57871&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57871&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 level80.571428571428571NOK
5% type I error level120.857142857142857NOK
10% type I error level120.857142857142857NOK



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')
}