FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 15 Dec 2017 15:12:12 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/15/t1513347753aic6u7c53tm86g3.htm/, Retrieved Wed, 15 May 2024 03:02:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309782, Retrieved Wed, 15 May 2024 03:02:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact108

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2017-12-15 14:12:12] [ca643b0c409f93e6a7ce1fd0961340ec] [Current]
- RMPD    [Skewness-Kurtosis Plot] [] [2017-12-17 14:09:08] [c468faea9a2856906f4b319427cb0317]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
3,95508	78,70	56,90
4,15732	88,60	69,60
4,25277	104,20	82,60
4,16356	88,20	71,20
4,34640	94,70	74,10
4,27528	112,00	67,60
3,83514	78,90	56,30
4,11904	111,40	54,40
4,29456	132,50	65,00
4,31749	121,60	68,60
4,30946	116,10	80,30
4,31348	123,30	72,50
4,27249	107,90	88,30
4,19870	107,00	89,80
4,31882	115,80	103,50
4,21213	91,80	78,80
4,31214	93,50	85,70
4,33598	107,10	96,90
3,98713	80,50	66,90
4,24992	100,50	71,50
4,33205	100,20	89,50
4,37450	100,30	86,60
4,31482	96,60	91,20
4,17899	86,00	75,60
4,15104	76,90	75,60
4,16511	79,70	80,40
4,25277	93,10	91,60
4,31080	79,50	90,10
4,23989	80,30	90,80
4,31080	88,80	94,60
3,96651	72,40	62,60
4,11904	75,50	65,00
4,30271	92,90	87,60
4,37450	101,50	99,90
4,24563	94,70	85,10
4,34899	93,00	71,00
4,23989	79,80	73,00
4,31749	82,20	83,50
4,33598	87,60	86,50
4,32942	83,20	80,90
4,25277	81,60	80,80
4,49424	85,90	81,20
4,13517	71,90	61,90
4,07754	71,80	49,40
4,49424	98,30	79,20
4,42485	93,60	76,80
4,33073	86,10	81,20
4,45202	96,20	79,40
4,20320	78,60	74,00
4,32281	82,10	78,20
4,43793	94,40	98,90
4,40428	86,40	88,30
4,31749	82,20	79,30
4,52829	96,70	104,00
4,19570	84,20	60,50
4,32678	73,60	75,30
4,51415	94,90	106,20
4,48413	96,90	106,70
4,45202	90,20	95,40
4,46245	104,20	90,50
4,26268	78,40	113,80
4,42125	81,50	94,10
4,44265	96,70	109,90
4,37324	87,50	104,30
4,35028	86,20	80,70
4,56954	105,10	121,10
4,03424	72,90	68,80
4,32015	76,40	73,70
4,45783	100,50	104,20
4,44030	92,40	87,20
4,51743	96,30	94,50
4,70682	103,60	120,90
4,39445	75,10	88,50
4,40060	78,80	102,50
4,51086	93,70	118,60
4,39815	82,50	86,00
4,53796	88,30	110,60
4,61215	95,70	114,00
4,22683	73,30	72,60
4,35157	72,40	76,00
4,63181	94,00	114,60
4,72827	96,90	113,50
4,59006	92,40	115,20
4,68398	90,90	102,00
4,49536	93,50	101,50
4,53582	92,00	99,60
4,65014	115,90	113,80
4,54648	97,80	94,80
4,61215	97,70	102,00
4,71671	116,90	119,50
4,33205	96,70	88,00
4,62595	97,70	82,80
5,00529	103,90	112,10
5,14924	124,10	131,50
4,83310	117,30	110,00
4,88432	113,80	96,50
4,66814	100,00	101,90
4,75875	114,20	103,10
4,70773	116,30	103,50
4,80320	111,40	111,80
4,76388	103,40	100,30
4,81947	125,30	111,00
4,58497	92,50	84,60
4,53796	92,00	73,30
4,91486	121,60	112,00
4,87520	113,30	111,20
4,72916	92,50	82,40
4,61512	100,30	75,60
4,48526	83,20	64,20
4,57368	81,20	72,20
4,66155	94,50	80,80
4,55598	87,70	71,10
4,47734	82,30	153,20
4,67935	99,00	89,80
4,26409	72,40	57,30
4,28082	80,80	83,60
4,62006	105,50	88,40
4,63667	98,40	84,10
4,63473	94,50	95,50
4,48074	109,20	74,60
4,35671	84,10	79,80
4,51961	88,40	85,40
4,71402	111,30	106,40
4,60717	93,20	94,60
4,54648	86,30	94,60
4,77238	111,40	113,70
4,38826	85,40	66,70
4,52829	89,70	78,90
4,72827	110,90	126,30
4,71671	119,40	118,10
4,62203	109,30	117,30
4,66814	110,70	118,10
4,48751	101,30	108,60
4,61710	99,00	118,10
4,77912	117,90	141,00
4,65014	89,30	112,70
4,78916	105,40	131,90
4,72384	99,90	123,50
4,47847	79,50	81,30
4,59714	88,30	85,40
4,84024	116,20	138,50
4,72916	110,60	124,60
4,73795	99,30	125,80
4,85281	105,40	125,30
4,69318	89,90	111,00
4,67283	100,70	120,40
4,95794	122,50	141,40
4,66344	97,40	113,10
4,74667	97,90	114,00
4,86522	124,30	131,30
4,50424	94,70	77,80
4,57985	85,20	105,10
4,77322	101,90	125,40
4,79744	110,90	123,60
4,76644	102,00	107,90
4,65871	95,80	86,10
4,57780	86,90	97,80
4,58497	90,30	98,40
4,74319	97,90	118,00
4,69866	91,90	115,60
4,80320	90,40	114,50
4,81218	98,90	124,00
4,64535	81,30	101,80
4,60417	79,80	80,60
4,85593	93,70	129,70
4,84968	101,50	137,00
4,75961	88,60	127,30
4,71939	94,60	110,30
4,63279	84,20	134,90
4,70773	86,50	126,20
4,76899	92,60	130,50
4,80729	84,20	127,60
4,79082	85,90	134,80
4,78080	90,00	128,90
4,61809	79,10	101,10
4,61710	75,60	86,00
4,91339	97,00	139,20
4,89485	96,40	126,80
4,69684	85,20	117,10
4,75186	100,30	103,00
4,64727	76,70	108,70
4,74493	79,00	115,00
4,82511	94,40	133,20
4,81300	82,80	131,30
4,78749	74,60	119,60
4,88280	92,80	146,70
4,67470	69,70	101,00
4,61512	68,90	88,70
5,03109	97,50	143,70
4,97328	92,90	138,10
4,83469	93,40	139,80
4,83151	92,10	121,60
4,71582	80,60	112,60
4,77407	86,00	136,70
4,90971	93,60	147,40
4,87290	90,30	128,10
4,85593	81,30	117,50
4,92071	98,40	148,20
4,52287	73,30	101,60
4,64150	77,10	90,40
4,93447	91,40	148,60
4,82831	89,00	133,80
4,86907	94,10	130,30
4,75703	94,70	113,60
4,66721	80,70	105,80
4,79744	85,20	136,10
4,99451	107,90	160,30
4,75359	81,60	127,70
4,92362	83,80	141,80
4,91559	98,80	149,30
4,56226	75,60	94,50
4,84419	80,70	95,20

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time10 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309782&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]10 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309782&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309782&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R ServerBig Analytics Cloud Computing Center






Multiple Linear Regression - Estimated Regression Equation
(1-Bs)(1-B)a[t] = + 0.00101946 + 0.00328547`(1-Bs)(1-B)b`[t] + 0.000975155`(1-Bs)(1-B)c`[t] -0.344321`(1-Bs)(1-B)a(t-1s)`[t] -0.315358`(1-Bs)(1-B)a(t-2s)`[t] -0.229567`(1-Bs)(1-B)a(t-3s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
(1-Bs)(1-B)a[t] =  +  0.00101946 +  0.00328547`(1-Bs)(1-B)b`[t] +  0.000975155`(1-Bs)(1-B)c`[t] -0.344321`(1-Bs)(1-B)a(t-1s)`[t] -0.315358`(1-Bs)(1-B)a(t-2s)`[t] -0.229567`(1-Bs)(1-B)a(t-3s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309782&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C](1-Bs)(1-B)a[t] =  +  0.00101946 +  0.00328547`(1-Bs)(1-B)b`[t] +  0.000975155`(1-Bs)(1-B)c`[t] -0.344321`(1-Bs)(1-B)a(t-1s)`[t] -0.315358`(1-Bs)(1-B)a(t-2s)`[t] -0.229567`(1-Bs)(1-B)a(t-3s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309782&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309782&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
(1-Bs)(1-B)a[t] = + 0.00101946 + 0.00328547`(1-Bs)(1-B)b`[t] + 0.000975155`(1-Bs)(1-B)c`[t] -0.344321`(1-Bs)(1-B)a(t-1s)`[t] -0.315358`(1-Bs)(1-B)a(t-2s)`[t] -0.229567`(1-Bs)(1-B)a(t-3s)`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+0.00102 0.007092+1.4380e-01 0.8859 0.4429
`(1-Bs)(1-B)b`+0.003285 0.0007446+4.4120e+00 1.892e-05 9.461e-06
`(1-Bs)(1-B)c`+0.0009752 0.0003518+2.7720e+00 0.006247 0.003124
`(1-Bs)(1-B)a(t-1s)`-0.3443 0.0696-4.9470e+00 1.923e-06 9.615e-07
`(1-Bs)(1-B)a(t-2s)`-0.3154 0.06929-4.5510e+00 1.064e-05 5.318e-06
`(1-Bs)(1-B)a(t-3s)`-0.2296 0.06636-3.4600e+00 0.0006966 0.0003483

\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) & +0.00102 &  0.007092 & +1.4380e-01 &  0.8859 &  0.4429 \tabularnewline
`(1-Bs)(1-B)b` & +0.003285 &  0.0007446 & +4.4120e+00 &  1.892e-05 &  9.461e-06 \tabularnewline
`(1-Bs)(1-B)c` & +0.0009752 &  0.0003518 & +2.7720e+00 &  0.006247 &  0.003124 \tabularnewline
`(1-Bs)(1-B)a(t-1s)` & -0.3443 &  0.0696 & -4.9470e+00 &  1.923e-06 &  9.615e-07 \tabularnewline
`(1-Bs)(1-B)a(t-2s)` & -0.3154 &  0.06929 & -4.5510e+00 &  1.064e-05 &  5.318e-06 \tabularnewline
`(1-Bs)(1-B)a(t-3s)` & -0.2296 &  0.06636 & -3.4600e+00 &  0.0006966 &  0.0003483 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309782&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]+0.00102[/C][C] 0.007092[/C][C]+1.4380e-01[/C][C] 0.8859[/C][C] 0.4429[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)b`[/C][C]+0.003285[/C][C] 0.0007446[/C][C]+4.4120e+00[/C][C] 1.892e-05[/C][C] 9.461e-06[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)c`[/C][C]+0.0009752[/C][C] 0.0003518[/C][C]+2.7720e+00[/C][C] 0.006247[/C][C] 0.003124[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)a(t-1s)`[/C][C]-0.3443[/C][C] 0.0696[/C][C]-4.9470e+00[/C][C] 1.923e-06[/C][C] 9.615e-07[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)a(t-2s)`[/C][C]-0.3154[/C][C] 0.06929[/C][C]-4.5510e+00[/C][C] 1.064e-05[/C][C] 5.318e-06[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)a(t-3s)`[/C][C]-0.2296[/C][C] 0.06636[/C][C]-3.4600e+00[/C][C] 0.0006966[/C][C] 0.0003483[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309782&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309782&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+0.00102 0.007092+1.4380e-01 0.8859 0.4429
`(1-Bs)(1-B)b`+0.003285 0.0007446+4.4120e+00 1.892e-05 9.461e-06
`(1-Bs)(1-B)c`+0.0009752 0.0003518+2.7720e+00 0.006247 0.003124
`(1-Bs)(1-B)a(t-1s)`-0.3443 0.0696-4.9470e+00 1.923e-06 9.615e-07
`(1-Bs)(1-B)a(t-2s)`-0.3154 0.06929-4.5510e+00 1.064e-05 5.318e-06
`(1-Bs)(1-B)a(t-3s)`-0.2296 0.06636-3.4600e+00 0.0006966 0.0003483






Multiple Linear Regression - Regression Statistics
Multiple R 0.6061
R-squared 0.3674
Adjusted R-squared 0.3472
F-TEST (value) 18.23
F-TEST (DF numerator)5
F-TEST (DF denominator)157
p-value 3.031e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 0.09053
Sum Squared Residuals 1.287

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6061 \tabularnewline
R-squared &  0.3674 \tabularnewline
Adjusted R-squared &  0.3472 \tabularnewline
F-TEST (value) &  18.23 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value &  3.031e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.09053 \tabularnewline
Sum Squared Residuals &  1.287 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309782&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6061[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3674[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3472[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 18.23[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]157[/C][/ROW]
[ROW][C]p-value[/C][C] 3.031e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.09053[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1.287[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309782&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309782&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R 0.6061
R-squared 0.3674
Adjusted R-squared 0.3472
F-TEST (value) 18.23
F-TEST (DF numerator)5
F-TEST (DF denominator)157
p-value 3.031e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 0.09053
Sum Squared Residuals 1.287






Menu of Residual Diagnostics
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309782&T=4

[TABLE]
[ROW][C]Menu of Residual Diagnostics[/C][/ROW]
[ROW][C]Description[/C][C]Link[/C][/ROW]
[ROW][C]Histogram[/C][C]Compute[/C][/ROW]
[ROW][C]Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]QQ Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Kernel Density Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness/Kurtosis Test[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness-Kurtosis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Harrell-Davis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]Blocked Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C](Partial) Autocorrelation Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Spectral Analysis[/C][C]Compute[/C][/ROW]
[ROW][C]Tukey lambda PPCC Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Box-Cox Normality Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Summary Statistics[/C][C]Compute[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309782&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309782&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Menu of Residual Diagnostics
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 0.04201 0.01228 0.02973
2 0.09663 0.06934 0.02729
3-0.02709-0.04138 0.01429
4-0.01014 0.05869-0.06883
5-0.03067-0.03714 0.006473
6 0.02648-0.03496 0.06144
7 0.1887 0.1051 0.08359
8-0.2293-0.1058-0.1235
9 0.03937 0.06074-0.02137
10 0.06201 0.01005 0.05196
11-0.1109-0.03869-0.07217
12 0.04905 0.07277-0.02372
13 0.03896-0.07827 0.1172
14-0.09372 0.001762-0.09548
15-0.03576-0.006165-0.0296
16 0.06383 0.04085 0.02298
17 0.00846-0.02325 0.03171
18-0.2027-0.07779-0.1249
19 0.1548 0.0643 0.09053
20-0.04969-0.008006-0.04168
21 0.01249-0.025 0.03749
22 0.1092 0.03756 0.07168
23 0.179-0.01286 0.1918
24-0.1126-0.01637-0.09623
25-0.1524-0.005394-0.147
26 0.08886 0.018 0.07086
27-0.0433 0.003803-0.0471
28 0.1628 0.05389 0.1089
29-0.1451-0.1052-0.03983
30 0.15 0.1087 0.04129
31-0.1612-0.07948-0.08169
32 0.1426 0.03664 0.1059
33 0.114 0.06836 0.04563
34-0.2153-0.09719-0.1182
35-0.09547-0.09728 0.001814
36 0.1237 0.1897-0.06593
37 0.03431-0.001019 0.03533
38 0.00406 0.005512-0.001452
39 0.00905 0.02402-0.01497
40-0.07414-0.1092 0.03504
41 0.03037 0.1079-0.07749
42 0.00066 0.02411-0.02345
43 0.1692-0.03778 0.2069
44 0.0991-0.03942 0.1385
45 0.04749 0.02562 0.02187
46-0.1779-0.0037-0.1742
47-0.0427-0.003958-0.03874
48-0.02756-0.06547 0.03791
49 0.05015 0.08293-0.03278
50-0.1653-0.09197-0.07337
51 0.1991 0.08976 0.1094
52-0.105-0.08363-0.02136
53-0.04897 0.03661-0.08558
54 0.1502-0.03639 0.1866
55-0.3409-0.05282-0.2881
56-0.00244 0.01939-0.02183
57-0.1836-0.1675-0.01613
58 0.1701 0.052 0.1181
59-0.1653 0.04841-0.2137
60 0.08632-0.02989 0.1162
61-0.00219-0.03867 0.03648
62 0.1389 0.08106 0.05783
63-0.201-0.08425-0.1168
64-0.03932 0.123-0.1623
65 0.1464-0.04774 0.1942
66-0.1808-0.0709-0.1099
67 0.06374 0.168-0.1042
68-0.03766-0.1113 0.07362
69 0.05627 0.02363 0.03265
70 0.1441 0.1427 0.001377
71-0.03995 0.1022-0.1422
72 0.00583-0.05852 0.06435
73 0.07448-0.00356 0.07804
74 0.1065 0.04804 0.0585
75-0.00128-0.03381 0.03253
76 0.01795-0.0203 0.03825
77 0.02389 0.06712-0.04323
78 0.03114 0.003585 0.02756
79 0.1233 0.02053 0.1028
80-0.1393 0.02205-0.1613
81-0.02817 0.0761-0.1043
82-0.09274-0.09366 0.0009195
83 0.2001 0.05416 0.1459
84-0.0566 0.01536-0.07196
85-0.03331-0.05333 0.02002
86-0.03239-0.0528 0.02041
87-0.02213-0.03144 0.009311
88 0.1997 0.1256 0.07408
89-0.2912-0.1695-0.1217
90 0.1388 0.03591 0.1028
91-0.02136 0.02361-0.04497
92 0.04312 0.08898-0.04586
93-0.09952-0.01676-0.08276
94 0.1035-0.05353 0.157
95 0.06875-0.003168 0.07192
96 0.021-0.02587 0.04687
97-0.1499 0.03245-0.1824
98 0.1231-0.04564 0.1687
99-0.1655 0.06669-0.2322
100-0.05579-0.1335 0.07769
101 0.1839 0.19-0.006143
102-0.1156-0.05632-0.05929
103-0.04306-0.08264 0.03958
104-0.04973-0.03005-0.01968
105 0.1353 0.09102 0.04428
106-0.03979-0.04704 0.007247
107-0.2226-0.1378-0.08482
108 0.07872 0.05734 0.02138
109 0.02752 0.01316 0.01436
110-0.1269-0.1036-0.02326
111 0.25 0.1533 0.09668
112 0.02131-0.05539 0.0767
113-0.1096-0.04235-0.06722
114 0.1941 0.05987 0.1343
115-0.1168-0.02673-0.09006
116 0.05839 0.0554 0.002991
117-0.03047-0.002784-0.02769
118-0.05907-0.003911-0.05516
119 0.06751 0.05481 0.0127
120-0.00569-0.01206 0.006373
121 0.06777 0.03379 0.03398
122-0.09696-0.006519-0.09044
123 0.08283-0.03614 0.119
124-0.121-0.01596-0.105
125-0.019 0.01814-0.03714
126 0.00412-0.04467 0.04879
127 0.04019 0.05909-0.0189
128 0.04453 0.01534 0.02919
129-0.01229-0.05512 0.04283
130-0.1079 0.01574-0.1237
131 0.09524 0.06491 0.03033
132-0.01799-0.08847 0.07048
133 0.02272 0.01805 0.004665
134 0.01892 0.09027-0.07135
135-0.05041-0.07787 0.02746
136-0.00904-0.002184-0.006856
137 0.1053 0.07841 0.02692
138-0.04539-0.09262 0.04723
139-0.05859 0.0455-0.1041
140 0.1197 0.0041 0.1156
141-0.03927-0.02271-0.01656
142 0.05942 0.1155-0.05608
143-0.0582-0.05984 0.001644
144-0.0111 0.01636-0.02746
145-0.03941-0.00695-0.03246
146 0.05546 0.02127 0.03419
147-0.0247-0.05483 0.03013
148 0.00854 0.03585-0.02731
149-0.03053-0.004206-0.02632
150-0.1897-0.03667-0.1531
151 0.1782 0.05152 0.1267
152-0.123-0.1115-0.0115
153-0.04835 0.02367-0.07202
154 0.1794 0.0382 0.1411
155-0.1089-0.01677-0.09209
156 0.02587 0.004777 0.02109
157 0.07198-0.005044 0.07702
158 0.06143 0.06099 0.0004392
159-0.2041-0.08213-0.122
160 0.187 0.08959 0.09741
161-0.07281-0.04685-0.02596
162 0.04451 0.07797-0.03346
163 0.1633-0.03522 0.1985

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  0.04201 &  0.01228 &  0.02973 \tabularnewline
2 &  0.09663 &  0.06934 &  0.02729 \tabularnewline
3 & -0.02709 & -0.04138 &  0.01429 \tabularnewline
4 & -0.01014 &  0.05869 & -0.06883 \tabularnewline
5 & -0.03067 & -0.03714 &  0.006473 \tabularnewline
6 &  0.02648 & -0.03496 &  0.06144 \tabularnewline
7 &  0.1887 &  0.1051 &  0.08359 \tabularnewline
8 & -0.2293 & -0.1058 & -0.1235 \tabularnewline
9 &  0.03937 &  0.06074 & -0.02137 \tabularnewline
10 &  0.06201 &  0.01005 &  0.05196 \tabularnewline
11 & -0.1109 & -0.03869 & -0.07217 \tabularnewline
12 &  0.04905 &  0.07277 & -0.02372 \tabularnewline
13 &  0.03896 & -0.07827 &  0.1172 \tabularnewline
14 & -0.09372 &  0.001762 & -0.09548 \tabularnewline
15 & -0.03576 & -0.006165 & -0.0296 \tabularnewline
16 &  0.06383 &  0.04085 &  0.02298 \tabularnewline
17 &  0.00846 & -0.02325 &  0.03171 \tabularnewline
18 & -0.2027 & -0.07779 & -0.1249 \tabularnewline
19 &  0.1548 &  0.0643 &  0.09053 \tabularnewline
20 & -0.04969 & -0.008006 & -0.04168 \tabularnewline
21 &  0.01249 & -0.025 &  0.03749 \tabularnewline
22 &  0.1092 &  0.03756 &  0.07168 \tabularnewline
23 &  0.179 & -0.01286 &  0.1918 \tabularnewline
24 & -0.1126 & -0.01637 & -0.09623 \tabularnewline
25 & -0.1524 & -0.005394 & -0.147 \tabularnewline
26 &  0.08886 &  0.018 &  0.07086 \tabularnewline
27 & -0.0433 &  0.003803 & -0.0471 \tabularnewline
28 &  0.1628 &  0.05389 &  0.1089 \tabularnewline
29 & -0.1451 & -0.1052 & -0.03983 \tabularnewline
30 &  0.15 &  0.1087 &  0.04129 \tabularnewline
31 & -0.1612 & -0.07948 & -0.08169 \tabularnewline
32 &  0.1426 &  0.03664 &  0.1059 \tabularnewline
33 &  0.114 &  0.06836 &  0.04563 \tabularnewline
34 & -0.2153 & -0.09719 & -0.1182 \tabularnewline
35 & -0.09547 & -0.09728 &  0.001814 \tabularnewline
36 &  0.1237 &  0.1897 & -0.06593 \tabularnewline
37 &  0.03431 & -0.001019 &  0.03533 \tabularnewline
38 &  0.00406 &  0.005512 & -0.001452 \tabularnewline
39 &  0.00905 &  0.02402 & -0.01497 \tabularnewline
40 & -0.07414 & -0.1092 &  0.03504 \tabularnewline
41 &  0.03037 &  0.1079 & -0.07749 \tabularnewline
42 &  0.00066 &  0.02411 & -0.02345 \tabularnewline
43 &  0.1692 & -0.03778 &  0.2069 \tabularnewline
44 &  0.0991 & -0.03942 &  0.1385 \tabularnewline
45 &  0.04749 &  0.02562 &  0.02187 \tabularnewline
46 & -0.1779 & -0.0037 & -0.1742 \tabularnewline
47 & -0.0427 & -0.003958 & -0.03874 \tabularnewline
48 & -0.02756 & -0.06547 &  0.03791 \tabularnewline
49 &  0.05015 &  0.08293 & -0.03278 \tabularnewline
50 & -0.1653 & -0.09197 & -0.07337 \tabularnewline
51 &  0.1991 &  0.08976 &  0.1094 \tabularnewline
52 & -0.105 & -0.08363 & -0.02136 \tabularnewline
53 & -0.04897 &  0.03661 & -0.08558 \tabularnewline
54 &  0.1502 & -0.03639 &  0.1866 \tabularnewline
55 & -0.3409 & -0.05282 & -0.2881 \tabularnewline
56 & -0.00244 &  0.01939 & -0.02183 \tabularnewline
57 & -0.1836 & -0.1675 & -0.01613 \tabularnewline
58 &  0.1701 &  0.052 &  0.1181 \tabularnewline
59 & -0.1653 &  0.04841 & -0.2137 \tabularnewline
60 &  0.08632 & -0.02989 &  0.1162 \tabularnewline
61 & -0.00219 & -0.03867 &  0.03648 \tabularnewline
62 &  0.1389 &  0.08106 &  0.05783 \tabularnewline
63 & -0.201 & -0.08425 & -0.1168 \tabularnewline
64 & -0.03932 &  0.123 & -0.1623 \tabularnewline
65 &  0.1464 & -0.04774 &  0.1942 \tabularnewline
66 & -0.1808 & -0.0709 & -0.1099 \tabularnewline
67 &  0.06374 &  0.168 & -0.1042 \tabularnewline
68 & -0.03766 & -0.1113 &  0.07362 \tabularnewline
69 &  0.05627 &  0.02363 &  0.03265 \tabularnewline
70 &  0.1441 &  0.1427 &  0.001377 \tabularnewline
71 & -0.03995 &  0.1022 & -0.1422 \tabularnewline
72 &  0.00583 & -0.05852 &  0.06435 \tabularnewline
73 &  0.07448 & -0.00356 &  0.07804 \tabularnewline
74 &  0.1065 &  0.04804 &  0.0585 \tabularnewline
75 & -0.00128 & -0.03381 &  0.03253 \tabularnewline
76 &  0.01795 & -0.0203 &  0.03825 \tabularnewline
77 &  0.02389 &  0.06712 & -0.04323 \tabularnewline
78 &  0.03114 &  0.003585 &  0.02756 \tabularnewline
79 &  0.1233 &  0.02053 &  0.1028 \tabularnewline
80 & -0.1393 &  0.02205 & -0.1613 \tabularnewline
81 & -0.02817 &  0.0761 & -0.1043 \tabularnewline
82 & -0.09274 & -0.09366 &  0.0009195 \tabularnewline
83 &  0.2001 &  0.05416 &  0.1459 \tabularnewline
84 & -0.0566 &  0.01536 & -0.07196 \tabularnewline
85 & -0.03331 & -0.05333 &  0.02002 \tabularnewline
86 & -0.03239 & -0.0528 &  0.02041 \tabularnewline
87 & -0.02213 & -0.03144 &  0.009311 \tabularnewline
88 &  0.1997 &  0.1256 &  0.07408 \tabularnewline
89 & -0.2912 & -0.1695 & -0.1217 \tabularnewline
90 &  0.1388 &  0.03591 &  0.1028 \tabularnewline
91 & -0.02136 &  0.02361 & -0.04497 \tabularnewline
92 &  0.04312 &  0.08898 & -0.04586 \tabularnewline
93 & -0.09952 & -0.01676 & -0.08276 \tabularnewline
94 &  0.1035 & -0.05353 &  0.157 \tabularnewline
95 &  0.06875 & -0.003168 &  0.07192 \tabularnewline
96 &  0.021 & -0.02587 &  0.04687 \tabularnewline
97 & -0.1499 &  0.03245 & -0.1824 \tabularnewline
98 &  0.1231 & -0.04564 &  0.1687 \tabularnewline
99 & -0.1655 &  0.06669 & -0.2322 \tabularnewline
100 & -0.05579 & -0.1335 &  0.07769 \tabularnewline
101 &  0.1839 &  0.19 & -0.006143 \tabularnewline
102 & -0.1156 & -0.05632 & -0.05929 \tabularnewline
103 & -0.04306 & -0.08264 &  0.03958 \tabularnewline
104 & -0.04973 & -0.03005 & -0.01968 \tabularnewline
105 &  0.1353 &  0.09102 &  0.04428 \tabularnewline
106 & -0.03979 & -0.04704 &  0.007247 \tabularnewline
107 & -0.2226 & -0.1378 & -0.08482 \tabularnewline
108 &  0.07872 &  0.05734 &  0.02138 \tabularnewline
109 &  0.02752 &  0.01316 &  0.01436 \tabularnewline
110 & -0.1269 & -0.1036 & -0.02326 \tabularnewline
111 &  0.25 &  0.1533 &  0.09668 \tabularnewline
112 &  0.02131 & -0.05539 &  0.0767 \tabularnewline
113 & -0.1096 & -0.04235 & -0.06722 \tabularnewline
114 &  0.1941 &  0.05987 &  0.1343 \tabularnewline
115 & -0.1168 & -0.02673 & -0.09006 \tabularnewline
116 &  0.05839 &  0.0554 &  0.002991 \tabularnewline
117 & -0.03047 & -0.002784 & -0.02769 \tabularnewline
118 & -0.05907 & -0.003911 & -0.05516 \tabularnewline
119 &  0.06751 &  0.05481 &  0.0127 \tabularnewline
120 & -0.00569 & -0.01206 &  0.006373 \tabularnewline
121 &  0.06777 &  0.03379 &  0.03398 \tabularnewline
122 & -0.09696 & -0.006519 & -0.09044 \tabularnewline
123 &  0.08283 & -0.03614 &  0.119 \tabularnewline
124 & -0.121 & -0.01596 & -0.105 \tabularnewline
125 & -0.019 &  0.01814 & -0.03714 \tabularnewline
126 &  0.00412 & -0.04467 &  0.04879 \tabularnewline
127 &  0.04019 &  0.05909 & -0.0189 \tabularnewline
128 &  0.04453 &  0.01534 &  0.02919 \tabularnewline
129 & -0.01229 & -0.05512 &  0.04283 \tabularnewline
130 & -0.1079 &  0.01574 & -0.1237 \tabularnewline
131 &  0.09524 &  0.06491 &  0.03033 \tabularnewline
132 & -0.01799 & -0.08847 &  0.07048 \tabularnewline
133 &  0.02272 &  0.01805 &  0.004665 \tabularnewline
134 &  0.01892 &  0.09027 & -0.07135 \tabularnewline
135 & -0.05041 & -0.07787 &  0.02746 \tabularnewline
136 & -0.00904 & -0.002184 & -0.006856 \tabularnewline
137 &  0.1053 &  0.07841 &  0.02692 \tabularnewline
138 & -0.04539 & -0.09262 &  0.04723 \tabularnewline
139 & -0.05859 &  0.0455 & -0.1041 \tabularnewline
140 &  0.1197 &  0.0041 &  0.1156 \tabularnewline
141 & -0.03927 & -0.02271 & -0.01656 \tabularnewline
142 &  0.05942 &  0.1155 & -0.05608 \tabularnewline
143 & -0.0582 & -0.05984 &  0.001644 \tabularnewline
144 & -0.0111 &  0.01636 & -0.02746 \tabularnewline
145 & -0.03941 & -0.00695 & -0.03246 \tabularnewline
146 &  0.05546 &  0.02127 &  0.03419 \tabularnewline
147 & -0.0247 & -0.05483 &  0.03013 \tabularnewline
148 &  0.00854 &  0.03585 & -0.02731 \tabularnewline
149 & -0.03053 & -0.004206 & -0.02632 \tabularnewline
150 & -0.1897 & -0.03667 & -0.1531 \tabularnewline
151 &  0.1782 &  0.05152 &  0.1267 \tabularnewline
152 & -0.123 & -0.1115 & -0.0115 \tabularnewline
153 & -0.04835 &  0.02367 & -0.07202 \tabularnewline
154 &  0.1794 &  0.0382 &  0.1411 \tabularnewline
155 & -0.1089 & -0.01677 & -0.09209 \tabularnewline
156 &  0.02587 &  0.004777 &  0.02109 \tabularnewline
157 &  0.07198 & -0.005044 &  0.07702 \tabularnewline
158 &  0.06143 &  0.06099 &  0.0004392 \tabularnewline
159 & -0.2041 & -0.08213 & -0.122 \tabularnewline
160 &  0.187 &  0.08959 &  0.09741 \tabularnewline
161 & -0.07281 & -0.04685 & -0.02596 \tabularnewline
162 &  0.04451 &  0.07797 & -0.03346 \tabularnewline
163 &  0.1633 & -0.03522 &  0.1985 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309782&T=5

[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] 0.04201[/C][C] 0.01228[/C][C] 0.02973[/C][/ROW]
[ROW][C]2[/C][C] 0.09663[/C][C] 0.06934[/C][C] 0.02729[/C][/ROW]
[ROW][C]3[/C][C]-0.02709[/C][C]-0.04138[/C][C] 0.01429[/C][/ROW]
[ROW][C]4[/C][C]-0.01014[/C][C] 0.05869[/C][C]-0.06883[/C][/ROW]
[ROW][C]5[/C][C]-0.03067[/C][C]-0.03714[/C][C] 0.006473[/C][/ROW]
[ROW][C]6[/C][C] 0.02648[/C][C]-0.03496[/C][C] 0.06144[/C][/ROW]
[ROW][C]7[/C][C] 0.1887[/C][C] 0.1051[/C][C] 0.08359[/C][/ROW]
[ROW][C]8[/C][C]-0.2293[/C][C]-0.1058[/C][C]-0.1235[/C][/ROW]
[ROW][C]9[/C][C] 0.03937[/C][C] 0.06074[/C][C]-0.02137[/C][/ROW]
[ROW][C]10[/C][C] 0.06201[/C][C] 0.01005[/C][C] 0.05196[/C][/ROW]
[ROW][C]11[/C][C]-0.1109[/C][C]-0.03869[/C][C]-0.07217[/C][/ROW]
[ROW][C]12[/C][C] 0.04905[/C][C] 0.07277[/C][C]-0.02372[/C][/ROW]
[ROW][C]13[/C][C] 0.03896[/C][C]-0.07827[/C][C] 0.1172[/C][/ROW]
[ROW][C]14[/C][C]-0.09372[/C][C] 0.001762[/C][C]-0.09548[/C][/ROW]
[ROW][C]15[/C][C]-0.03576[/C][C]-0.006165[/C][C]-0.0296[/C][/ROW]
[ROW][C]16[/C][C] 0.06383[/C][C] 0.04085[/C][C] 0.02298[/C][/ROW]
[ROW][C]17[/C][C] 0.00846[/C][C]-0.02325[/C][C] 0.03171[/C][/ROW]
[ROW][C]18[/C][C]-0.2027[/C][C]-0.07779[/C][C]-0.1249[/C][/ROW]
[ROW][C]19[/C][C] 0.1548[/C][C] 0.0643[/C][C] 0.09053[/C][/ROW]
[ROW][C]20[/C][C]-0.04969[/C][C]-0.008006[/C][C]-0.04168[/C][/ROW]
[ROW][C]21[/C][C] 0.01249[/C][C]-0.025[/C][C] 0.03749[/C][/ROW]
[ROW][C]22[/C][C] 0.1092[/C][C] 0.03756[/C][C] 0.07168[/C][/ROW]
[ROW][C]23[/C][C] 0.179[/C][C]-0.01286[/C][C] 0.1918[/C][/ROW]
[ROW][C]24[/C][C]-0.1126[/C][C]-0.01637[/C][C]-0.09623[/C][/ROW]
[ROW][C]25[/C][C]-0.1524[/C][C]-0.005394[/C][C]-0.147[/C][/ROW]
[ROW][C]26[/C][C] 0.08886[/C][C] 0.018[/C][C] 0.07086[/C][/ROW]
[ROW][C]27[/C][C]-0.0433[/C][C] 0.003803[/C][C]-0.0471[/C][/ROW]
[ROW][C]28[/C][C] 0.1628[/C][C] 0.05389[/C][C] 0.1089[/C][/ROW]
[ROW][C]29[/C][C]-0.1451[/C][C]-0.1052[/C][C]-0.03983[/C][/ROW]
[ROW][C]30[/C][C] 0.15[/C][C] 0.1087[/C][C] 0.04129[/C][/ROW]
[ROW][C]31[/C][C]-0.1612[/C][C]-0.07948[/C][C]-0.08169[/C][/ROW]
[ROW][C]32[/C][C] 0.1426[/C][C] 0.03664[/C][C] 0.1059[/C][/ROW]
[ROW][C]33[/C][C] 0.114[/C][C] 0.06836[/C][C] 0.04563[/C][/ROW]
[ROW][C]34[/C][C]-0.2153[/C][C]-0.09719[/C][C]-0.1182[/C][/ROW]
[ROW][C]35[/C][C]-0.09547[/C][C]-0.09728[/C][C] 0.001814[/C][/ROW]
[ROW][C]36[/C][C] 0.1237[/C][C] 0.1897[/C][C]-0.06593[/C][/ROW]
[ROW][C]37[/C][C] 0.03431[/C][C]-0.001019[/C][C] 0.03533[/C][/ROW]
[ROW][C]38[/C][C] 0.00406[/C][C] 0.005512[/C][C]-0.001452[/C][/ROW]
[ROW][C]39[/C][C] 0.00905[/C][C] 0.02402[/C][C]-0.01497[/C][/ROW]
[ROW][C]40[/C][C]-0.07414[/C][C]-0.1092[/C][C] 0.03504[/C][/ROW]
[ROW][C]41[/C][C] 0.03037[/C][C] 0.1079[/C][C]-0.07749[/C][/ROW]
[ROW][C]42[/C][C] 0.00066[/C][C] 0.02411[/C][C]-0.02345[/C][/ROW]
[ROW][C]43[/C][C] 0.1692[/C][C]-0.03778[/C][C] 0.2069[/C][/ROW]
[ROW][C]44[/C][C] 0.0991[/C][C]-0.03942[/C][C] 0.1385[/C][/ROW]
[ROW][C]45[/C][C] 0.04749[/C][C] 0.02562[/C][C] 0.02187[/C][/ROW]
[ROW][C]46[/C][C]-0.1779[/C][C]-0.0037[/C][C]-0.1742[/C][/ROW]
[ROW][C]47[/C][C]-0.0427[/C][C]-0.003958[/C][C]-0.03874[/C][/ROW]
[ROW][C]48[/C][C]-0.02756[/C][C]-0.06547[/C][C] 0.03791[/C][/ROW]
[ROW][C]49[/C][C] 0.05015[/C][C] 0.08293[/C][C]-0.03278[/C][/ROW]
[ROW][C]50[/C][C]-0.1653[/C][C]-0.09197[/C][C]-0.07337[/C][/ROW]
[ROW][C]51[/C][C] 0.1991[/C][C] 0.08976[/C][C] 0.1094[/C][/ROW]
[ROW][C]52[/C][C]-0.105[/C][C]-0.08363[/C][C]-0.02136[/C][/ROW]
[ROW][C]53[/C][C]-0.04897[/C][C] 0.03661[/C][C]-0.08558[/C][/ROW]
[ROW][C]54[/C][C] 0.1502[/C][C]-0.03639[/C][C] 0.1866[/C][/ROW]
[ROW][C]55[/C][C]-0.3409[/C][C]-0.05282[/C][C]-0.2881[/C][/ROW]
[ROW][C]56[/C][C]-0.00244[/C][C] 0.01939[/C][C]-0.02183[/C][/ROW]
[ROW][C]57[/C][C]-0.1836[/C][C]-0.1675[/C][C]-0.01613[/C][/ROW]
[ROW][C]58[/C][C] 0.1701[/C][C] 0.052[/C][C] 0.1181[/C][/ROW]
[ROW][C]59[/C][C]-0.1653[/C][C] 0.04841[/C][C]-0.2137[/C][/ROW]
[ROW][C]60[/C][C] 0.08632[/C][C]-0.02989[/C][C] 0.1162[/C][/ROW]
[ROW][C]61[/C][C]-0.00219[/C][C]-0.03867[/C][C] 0.03648[/C][/ROW]
[ROW][C]62[/C][C] 0.1389[/C][C] 0.08106[/C][C] 0.05783[/C][/ROW]
[ROW][C]63[/C][C]-0.201[/C][C]-0.08425[/C][C]-0.1168[/C][/ROW]
[ROW][C]64[/C][C]-0.03932[/C][C] 0.123[/C][C]-0.1623[/C][/ROW]
[ROW][C]65[/C][C] 0.1464[/C][C]-0.04774[/C][C] 0.1942[/C][/ROW]
[ROW][C]66[/C][C]-0.1808[/C][C]-0.0709[/C][C]-0.1099[/C][/ROW]
[ROW][C]67[/C][C] 0.06374[/C][C] 0.168[/C][C]-0.1042[/C][/ROW]
[ROW][C]68[/C][C]-0.03766[/C][C]-0.1113[/C][C] 0.07362[/C][/ROW]
[ROW][C]69[/C][C] 0.05627[/C][C] 0.02363[/C][C] 0.03265[/C][/ROW]
[ROW][C]70[/C][C] 0.1441[/C][C] 0.1427[/C][C] 0.001377[/C][/ROW]
[ROW][C]71[/C][C]-0.03995[/C][C] 0.1022[/C][C]-0.1422[/C][/ROW]
[ROW][C]72[/C][C] 0.00583[/C][C]-0.05852[/C][C] 0.06435[/C][/ROW]
[ROW][C]73[/C][C] 0.07448[/C][C]-0.00356[/C][C] 0.07804[/C][/ROW]
[ROW][C]74[/C][C] 0.1065[/C][C] 0.04804[/C][C] 0.0585[/C][/ROW]
[ROW][C]75[/C][C]-0.00128[/C][C]-0.03381[/C][C] 0.03253[/C][/ROW]
[ROW][C]76[/C][C] 0.01795[/C][C]-0.0203[/C][C] 0.03825[/C][/ROW]
[ROW][C]77[/C][C] 0.02389[/C][C] 0.06712[/C][C]-0.04323[/C][/ROW]
[ROW][C]78[/C][C] 0.03114[/C][C] 0.003585[/C][C] 0.02756[/C][/ROW]
[ROW][C]79[/C][C] 0.1233[/C][C] 0.02053[/C][C] 0.1028[/C][/ROW]
[ROW][C]80[/C][C]-0.1393[/C][C] 0.02205[/C][C]-0.1613[/C][/ROW]
[ROW][C]81[/C][C]-0.02817[/C][C] 0.0761[/C][C]-0.1043[/C][/ROW]
[ROW][C]82[/C][C]-0.09274[/C][C]-0.09366[/C][C] 0.0009195[/C][/ROW]
[ROW][C]83[/C][C] 0.2001[/C][C] 0.05416[/C][C] 0.1459[/C][/ROW]
[ROW][C]84[/C][C]-0.0566[/C][C] 0.01536[/C][C]-0.07196[/C][/ROW]
[ROW][C]85[/C][C]-0.03331[/C][C]-0.05333[/C][C] 0.02002[/C][/ROW]
[ROW][C]86[/C][C]-0.03239[/C][C]-0.0528[/C][C] 0.02041[/C][/ROW]
[ROW][C]87[/C][C]-0.02213[/C][C]-0.03144[/C][C] 0.009311[/C][/ROW]
[ROW][C]88[/C][C] 0.1997[/C][C] 0.1256[/C][C] 0.07408[/C][/ROW]
[ROW][C]89[/C][C]-0.2912[/C][C]-0.1695[/C][C]-0.1217[/C][/ROW]
[ROW][C]90[/C][C] 0.1388[/C][C] 0.03591[/C][C] 0.1028[/C][/ROW]
[ROW][C]91[/C][C]-0.02136[/C][C] 0.02361[/C][C]-0.04497[/C][/ROW]
[ROW][C]92[/C][C] 0.04312[/C][C] 0.08898[/C][C]-0.04586[/C][/ROW]
[ROW][C]93[/C][C]-0.09952[/C][C]-0.01676[/C][C]-0.08276[/C][/ROW]
[ROW][C]94[/C][C] 0.1035[/C][C]-0.05353[/C][C] 0.157[/C][/ROW]
[ROW][C]95[/C][C] 0.06875[/C][C]-0.003168[/C][C] 0.07192[/C][/ROW]
[ROW][C]96[/C][C] 0.021[/C][C]-0.02587[/C][C] 0.04687[/C][/ROW]
[ROW][C]97[/C][C]-0.1499[/C][C] 0.03245[/C][C]-0.1824[/C][/ROW]
[ROW][C]98[/C][C] 0.1231[/C][C]-0.04564[/C][C] 0.1687[/C][/ROW]
[ROW][C]99[/C][C]-0.1655[/C][C] 0.06669[/C][C]-0.2322[/C][/ROW]
[ROW][C]100[/C][C]-0.05579[/C][C]-0.1335[/C][C] 0.07769[/C][/ROW]
[ROW][C]101[/C][C] 0.1839[/C][C] 0.19[/C][C]-0.006143[/C][/ROW]
[ROW][C]102[/C][C]-0.1156[/C][C]-0.05632[/C][C]-0.05929[/C][/ROW]
[ROW][C]103[/C][C]-0.04306[/C][C]-0.08264[/C][C] 0.03958[/C][/ROW]
[ROW][C]104[/C][C]-0.04973[/C][C]-0.03005[/C][C]-0.01968[/C][/ROW]
[ROW][C]105[/C][C] 0.1353[/C][C] 0.09102[/C][C] 0.04428[/C][/ROW]
[ROW][C]106[/C][C]-0.03979[/C][C]-0.04704[/C][C] 0.007247[/C][/ROW]
[ROW][C]107[/C][C]-0.2226[/C][C]-0.1378[/C][C]-0.08482[/C][/ROW]
[ROW][C]108[/C][C] 0.07872[/C][C] 0.05734[/C][C] 0.02138[/C][/ROW]
[ROW][C]109[/C][C] 0.02752[/C][C] 0.01316[/C][C] 0.01436[/C][/ROW]
[ROW][C]110[/C][C]-0.1269[/C][C]-0.1036[/C][C]-0.02326[/C][/ROW]
[ROW][C]111[/C][C] 0.25[/C][C] 0.1533[/C][C] 0.09668[/C][/ROW]
[ROW][C]112[/C][C] 0.02131[/C][C]-0.05539[/C][C] 0.0767[/C][/ROW]
[ROW][C]113[/C][C]-0.1096[/C][C]-0.04235[/C][C]-0.06722[/C][/ROW]
[ROW][C]114[/C][C] 0.1941[/C][C] 0.05987[/C][C] 0.1343[/C][/ROW]
[ROW][C]115[/C][C]-0.1168[/C][C]-0.02673[/C][C]-0.09006[/C][/ROW]
[ROW][C]116[/C][C] 0.05839[/C][C] 0.0554[/C][C] 0.002991[/C][/ROW]
[ROW][C]117[/C][C]-0.03047[/C][C]-0.002784[/C][C]-0.02769[/C][/ROW]
[ROW][C]118[/C][C]-0.05907[/C][C]-0.003911[/C][C]-0.05516[/C][/ROW]
[ROW][C]119[/C][C] 0.06751[/C][C] 0.05481[/C][C] 0.0127[/C][/ROW]
[ROW][C]120[/C][C]-0.00569[/C][C]-0.01206[/C][C] 0.006373[/C][/ROW]
[ROW][C]121[/C][C] 0.06777[/C][C] 0.03379[/C][C] 0.03398[/C][/ROW]
[ROW][C]122[/C][C]-0.09696[/C][C]-0.006519[/C][C]-0.09044[/C][/ROW]
[ROW][C]123[/C][C] 0.08283[/C][C]-0.03614[/C][C] 0.119[/C][/ROW]
[ROW][C]124[/C][C]-0.121[/C][C]-0.01596[/C][C]-0.105[/C][/ROW]
[ROW][C]125[/C][C]-0.019[/C][C] 0.01814[/C][C]-0.03714[/C][/ROW]
[ROW][C]126[/C][C] 0.00412[/C][C]-0.04467[/C][C] 0.04879[/C][/ROW]
[ROW][C]127[/C][C] 0.04019[/C][C] 0.05909[/C][C]-0.0189[/C][/ROW]
[ROW][C]128[/C][C] 0.04453[/C][C] 0.01534[/C][C] 0.02919[/C][/ROW]
[ROW][C]129[/C][C]-0.01229[/C][C]-0.05512[/C][C] 0.04283[/C][/ROW]
[ROW][C]130[/C][C]-0.1079[/C][C] 0.01574[/C][C]-0.1237[/C][/ROW]
[ROW][C]131[/C][C] 0.09524[/C][C] 0.06491[/C][C] 0.03033[/C][/ROW]
[ROW][C]132[/C][C]-0.01799[/C][C]-0.08847[/C][C] 0.07048[/C][/ROW]
[ROW][C]133[/C][C] 0.02272[/C][C] 0.01805[/C][C] 0.004665[/C][/ROW]
[ROW][C]134[/C][C] 0.01892[/C][C] 0.09027[/C][C]-0.07135[/C][/ROW]
[ROW][C]135[/C][C]-0.05041[/C][C]-0.07787[/C][C] 0.02746[/C][/ROW]
[ROW][C]136[/C][C]-0.00904[/C][C]-0.002184[/C][C]-0.006856[/C][/ROW]
[ROW][C]137[/C][C] 0.1053[/C][C] 0.07841[/C][C] 0.02692[/C][/ROW]
[ROW][C]138[/C][C]-0.04539[/C][C]-0.09262[/C][C] 0.04723[/C][/ROW]
[ROW][C]139[/C][C]-0.05859[/C][C] 0.0455[/C][C]-0.1041[/C][/ROW]
[ROW][C]140[/C][C] 0.1197[/C][C] 0.0041[/C][C] 0.1156[/C][/ROW]
[ROW][C]141[/C][C]-0.03927[/C][C]-0.02271[/C][C]-0.01656[/C][/ROW]
[ROW][C]142[/C][C] 0.05942[/C][C] 0.1155[/C][C]-0.05608[/C][/ROW]
[ROW][C]143[/C][C]-0.0582[/C][C]-0.05984[/C][C] 0.001644[/C][/ROW]
[ROW][C]144[/C][C]-0.0111[/C][C] 0.01636[/C][C]-0.02746[/C][/ROW]
[ROW][C]145[/C][C]-0.03941[/C][C]-0.00695[/C][C]-0.03246[/C][/ROW]
[ROW][C]146[/C][C] 0.05546[/C][C] 0.02127[/C][C] 0.03419[/C][/ROW]
[ROW][C]147[/C][C]-0.0247[/C][C]-0.05483[/C][C] 0.03013[/C][/ROW]
[ROW][C]148[/C][C] 0.00854[/C][C] 0.03585[/C][C]-0.02731[/C][/ROW]
[ROW][C]149[/C][C]-0.03053[/C][C]-0.004206[/C][C]-0.02632[/C][/ROW]
[ROW][C]150[/C][C]-0.1897[/C][C]-0.03667[/C][C]-0.1531[/C][/ROW]
[ROW][C]151[/C][C] 0.1782[/C][C] 0.05152[/C][C] 0.1267[/C][/ROW]
[ROW][C]152[/C][C]-0.123[/C][C]-0.1115[/C][C]-0.0115[/C][/ROW]
[ROW][C]153[/C][C]-0.04835[/C][C] 0.02367[/C][C]-0.07202[/C][/ROW]
[ROW][C]154[/C][C] 0.1794[/C][C] 0.0382[/C][C] 0.1411[/C][/ROW]
[ROW][C]155[/C][C]-0.1089[/C][C]-0.01677[/C][C]-0.09209[/C][/ROW]
[ROW][C]156[/C][C] 0.02587[/C][C] 0.004777[/C][C] 0.02109[/C][/ROW]
[ROW][C]157[/C][C] 0.07198[/C][C]-0.005044[/C][C] 0.07702[/C][/ROW]
[ROW][C]158[/C][C] 0.06143[/C][C] 0.06099[/C][C] 0.0004392[/C][/ROW]
[ROW][C]159[/C][C]-0.2041[/C][C]-0.08213[/C][C]-0.122[/C][/ROW]
[ROW][C]160[/C][C] 0.187[/C][C] 0.08959[/C][C] 0.09741[/C][/ROW]
[ROW][C]161[/C][C]-0.07281[/C][C]-0.04685[/C][C]-0.02596[/C][/ROW]
[ROW][C]162[/C][C] 0.04451[/C][C] 0.07797[/C][C]-0.03346[/C][/ROW]
[ROW][C]163[/C][C] 0.1633[/C][C]-0.03522[/C][C] 0.1985[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309782&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309782&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 0.04201 0.01228 0.02973
2 0.09663 0.06934 0.02729
3-0.02709-0.04138 0.01429
4-0.01014 0.05869-0.06883
5-0.03067-0.03714 0.006473
6 0.02648-0.03496 0.06144
7 0.1887 0.1051 0.08359
8-0.2293-0.1058-0.1235
9 0.03937 0.06074-0.02137
10 0.06201 0.01005 0.05196
11-0.1109-0.03869-0.07217
12 0.04905 0.07277-0.02372
13 0.03896-0.07827 0.1172
14-0.09372 0.001762-0.09548
15-0.03576-0.006165-0.0296
16 0.06383 0.04085 0.02298
17 0.00846-0.02325 0.03171
18-0.2027-0.07779-0.1249
19 0.1548 0.0643 0.09053
20-0.04969-0.008006-0.04168
21 0.01249-0.025 0.03749
22 0.1092 0.03756 0.07168
23 0.179-0.01286 0.1918
24-0.1126-0.01637-0.09623
25-0.1524-0.005394-0.147
26 0.08886 0.018 0.07086
27-0.0433 0.003803-0.0471
28 0.1628 0.05389 0.1089
29-0.1451-0.1052-0.03983
30 0.15 0.1087 0.04129
31-0.1612-0.07948-0.08169
32 0.1426 0.03664 0.1059
33 0.114 0.06836 0.04563
34-0.2153-0.09719-0.1182
35-0.09547-0.09728 0.001814
36 0.1237 0.1897-0.06593
37 0.03431-0.001019 0.03533
38 0.00406 0.005512-0.001452
39 0.00905 0.02402-0.01497
40-0.07414-0.1092 0.03504
41 0.03037 0.1079-0.07749
42 0.00066 0.02411-0.02345
43 0.1692-0.03778 0.2069
44 0.0991-0.03942 0.1385
45 0.04749 0.02562 0.02187
46-0.1779-0.0037-0.1742
47-0.0427-0.003958-0.03874
48-0.02756-0.06547 0.03791
49 0.05015 0.08293-0.03278
50-0.1653-0.09197-0.07337
51 0.1991 0.08976 0.1094
52-0.105-0.08363-0.02136
53-0.04897 0.03661-0.08558
54 0.1502-0.03639 0.1866
55-0.3409-0.05282-0.2881
56-0.00244 0.01939-0.02183
57-0.1836-0.1675-0.01613
58 0.1701 0.052 0.1181
59-0.1653 0.04841-0.2137
60 0.08632-0.02989 0.1162
61-0.00219-0.03867 0.03648
62 0.1389 0.08106 0.05783
63-0.201-0.08425-0.1168
64-0.03932 0.123-0.1623
65 0.1464-0.04774 0.1942
66-0.1808-0.0709-0.1099
67 0.06374 0.168-0.1042
68-0.03766-0.1113 0.07362
69 0.05627 0.02363 0.03265
70 0.1441 0.1427 0.001377
71-0.03995 0.1022-0.1422
72 0.00583-0.05852 0.06435
73 0.07448-0.00356 0.07804
74 0.1065 0.04804 0.0585
75-0.00128-0.03381 0.03253
76 0.01795-0.0203 0.03825
77 0.02389 0.06712-0.04323
78 0.03114 0.003585 0.02756
79 0.1233 0.02053 0.1028
80-0.1393 0.02205-0.1613
81-0.02817 0.0761-0.1043
82-0.09274-0.09366 0.0009195
83 0.2001 0.05416 0.1459
84-0.0566 0.01536-0.07196
85-0.03331-0.05333 0.02002
86-0.03239-0.0528 0.02041
87-0.02213-0.03144 0.009311
88 0.1997 0.1256 0.07408
89-0.2912-0.1695-0.1217
90 0.1388 0.03591 0.1028
91-0.02136 0.02361-0.04497
92 0.04312 0.08898-0.04586
93-0.09952-0.01676-0.08276
94 0.1035-0.05353 0.157
95 0.06875-0.003168 0.07192
96 0.021-0.02587 0.04687
97-0.1499 0.03245-0.1824
98 0.1231-0.04564 0.1687
99-0.1655 0.06669-0.2322
100-0.05579-0.1335 0.07769
101 0.1839 0.19-0.006143
102-0.1156-0.05632-0.05929
103-0.04306-0.08264 0.03958
104-0.04973-0.03005-0.01968
105 0.1353 0.09102 0.04428
106-0.03979-0.04704 0.007247
107-0.2226-0.1378-0.08482
108 0.07872 0.05734 0.02138
109 0.02752 0.01316 0.01436
110-0.1269-0.1036-0.02326
111 0.25 0.1533 0.09668
112 0.02131-0.05539 0.0767
113-0.1096-0.04235-0.06722
114 0.1941 0.05987 0.1343
115-0.1168-0.02673-0.09006
116 0.05839 0.0554 0.002991
117-0.03047-0.002784-0.02769
118-0.05907-0.003911-0.05516
119 0.06751 0.05481 0.0127
120-0.00569-0.01206 0.006373
121 0.06777 0.03379 0.03398
122-0.09696-0.006519-0.09044
123 0.08283-0.03614 0.119
124-0.121-0.01596-0.105
125-0.019 0.01814-0.03714
126 0.00412-0.04467 0.04879
127 0.04019 0.05909-0.0189
128 0.04453 0.01534 0.02919
129-0.01229-0.05512 0.04283
130-0.1079 0.01574-0.1237
131 0.09524 0.06491 0.03033
132-0.01799-0.08847 0.07048
133 0.02272 0.01805 0.004665
134 0.01892 0.09027-0.07135
135-0.05041-0.07787 0.02746
136-0.00904-0.002184-0.006856
137 0.1053 0.07841 0.02692
138-0.04539-0.09262 0.04723
139-0.05859 0.0455-0.1041
140 0.1197 0.0041 0.1156
141-0.03927-0.02271-0.01656
142 0.05942 0.1155-0.05608
143-0.0582-0.05984 0.001644
144-0.0111 0.01636-0.02746
145-0.03941-0.00695-0.03246
146 0.05546 0.02127 0.03419
147-0.0247-0.05483 0.03013
148 0.00854 0.03585-0.02731
149-0.03053-0.004206-0.02632
150-0.1897-0.03667-0.1531
151 0.1782 0.05152 0.1267
152-0.123-0.1115-0.0115
153-0.04835 0.02367-0.07202
154 0.1794 0.0382 0.1411
155-0.1089-0.01677-0.09209
156 0.02587 0.004777 0.02109
157 0.07198-0.005044 0.07702
158 0.06143 0.06099 0.0004392
159-0.2041-0.08213-0.122
160 0.187 0.08959 0.09741
161-0.07281-0.04685-0.02596
162 0.04451 0.07797-0.03346
163 0.1633-0.03522 0.1985






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
9 0.4803 0.9605 0.5197
10 0.4015 0.8031 0.5985
11 0.3232 0.6465 0.6768
12 0.2405 0.481 0.7595
13 0.2558 0.5115 0.7442
14 0.2531 0.5062 0.7469
15 0.2134 0.4269 0.7866
16 0.1501 0.3003 0.8499
17 0.1024 0.2049 0.8975
18 0.1019 0.2037 0.8981
19 0.09964 0.1993 0.9004
20 0.1002 0.2004 0.8998
21 0.07786 0.1557 0.9221
22 0.06629 0.1326 0.9337
23 0.2164 0.4328 0.7836
24 0.2052 0.4105 0.7948
25 0.3543 0.7087 0.6457
26 0.3321 0.6642 0.6679
27 0.286 0.5719 0.714
28 0.2768 0.5537 0.7232
29 0.2251 0.4501 0.7749
30 0.1858 0.3716 0.8142
31 0.1505 0.301 0.8495
32 0.1297 0.2594 0.8703
33 0.1011 0.2022 0.8989
34 0.0961 0.1922 0.9039
35 0.08207 0.1641 0.9179
36 0.1357 0.2714 0.8643
37 0.1096 0.2192 0.8904
38 0.08745 0.1749 0.9126
39 0.06696 0.1339 0.933
40 0.06188 0.1238 0.9381
41 0.07021 0.1404 0.9298
42 0.05659 0.1132 0.9434
43 0.1253 0.2506 0.8747
44 0.2555 0.5111 0.7445
45 0.2148 0.4296 0.7852
46 0.3492 0.6985 0.6508
47 0.3052 0.6104 0.6948
48 0.2651 0.5301 0.7349
49 0.2373 0.4745 0.7627
50 0.2139 0.4279 0.7861
51 0.2184 0.4368 0.7816
52 0.1833 0.3666 0.8167
53 0.1873 0.3746 0.8127
54 0.3445 0.689 0.6555
55 0.7528 0.4944 0.2472
56 0.7146 0.5708 0.2854
57 0.6726 0.6547 0.3274
58 0.6929 0.6141 0.3071
59 0.848 0.304 0.152
60 0.8654 0.2691 0.1346
61 0.842 0.3159 0.158
62 0.8234 0.3532 0.1766
63 0.8387 0.3227 0.1613
64 0.9041 0.1917 0.09587
65 0.9533 0.09341 0.04671
66 0.9596 0.08078 0.04039
67 0.967 0.06595 0.03298
68 0.9636 0.07271 0.03635
69 0.9551 0.0897 0.04485
70 0.9441 0.1118 0.0559
71 0.9607 0.07856 0.03928
72 0.956 0.0881 0.04405
73 0.9529 0.0942 0.0471
74 0.9463 0.1074 0.05368
75 0.9361 0.1277 0.06385
76 0.9271 0.1459 0.07294
77 0.9282 0.1435 0.07177
78 0.9166 0.1668 0.08341
79 0.9259 0.1482 0.07409
80 0.969 0.06206 0.03103
81 0.9713 0.05746 0.02873
82 0.9628 0.07443 0.03722
83 0.9762 0.04752 0.02376
84 0.9744 0.05128 0.02564
85 0.9669 0.06626 0.03313
86 0.9576 0.08483 0.04241
87 0.9485 0.103 0.05148
88 0.9422 0.1155 0.05775
89 0.9486 0.1029 0.05143
90 0.953 0.09398 0.04699
91 0.9451 0.1099 0.05494
92 0.9334 0.1332 0.06658
93 0.928 0.1441 0.07203
94 0.9554 0.08918 0.04459
95 0.9482 0.1035 0.05176
96 0.9415 0.117 0.05849
97 0.9805 0.03892 0.01946
98 0.9912 0.01752 0.008759
99 0.9995 0.001005 0.0005025
100 0.9995 0.001074 0.0005372
101 0.9992 0.001587 0.0007935
102 0.9992 0.001637 0.0008184
103 0.999 0.001973 0.0009867
104 0.9986 0.002856 0.001428
105 0.998 0.004031 0.002016
106 0.997 0.005931 0.002965
107 0.9972 0.005656 0.002828
108 0.9959 0.008209 0.004104
109 0.996 0.008094 0.004047
110 0.9941 0.01174 0.005868
111 0.9943 0.01131 0.005655
112 0.994 0.01209 0.006044
113 0.9921 0.01582 0.007911
114 0.9957 0.008509 0.004254
115 0.9961 0.007771 0.003886
116 0.9944 0.01115 0.005577
117 0.9935 0.01309 0.006545
118 0.9915 0.01703 0.008515
119 0.9928 0.01437 0.007186
120 0.9898 0.02049 0.01025
121 0.9855 0.02902 0.01451
122 0.983 0.03395 0.01697
123 0.9795 0.04102 0.02051
124 0.9805 0.03898 0.01949
125 0.9756 0.04874 0.02437
126 0.9667 0.06669 0.03335
127 0.9561 0.08787 0.04394
128 0.9401 0.1198 0.05992
129 0.9241 0.1518 0.07591
130 0.9309 0.1382 0.06911
131 0.9073 0.1855 0.09274
132 0.9139 0.1723 0.08613
133 0.8923 0.2154 0.1077
134 0.8668 0.2664 0.1332
135 0.8363 0.3274 0.1637
136 0.8112 0.3776 0.1888
137 0.7678 0.4645 0.2322
138 0.7285 0.543 0.2715
139 0.8281 0.3438 0.1719
140 0.8156 0.3687 0.1844
141 0.7617 0.4767 0.2383
142 0.7374 0.5252 0.2626
143 0.6661 0.6677 0.3339
144 0.6328 0.7343 0.3672
145 0.5645 0.871 0.4355
146 0.4792 0.9584 0.5208
147 0.462 0.9241 0.538
148 0.4049 0.8097 0.5951
149 0.5039 0.9921 0.4961
150 0.4794 0.9588 0.5206
151 0.6831 0.6338 0.3169
152 0.5547 0.8907 0.4453
153 0.5256 0.9489 0.4744
154 0.3701 0.7402 0.6299

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 &  0.4803 &  0.9605 &  0.5197 \tabularnewline
10 &  0.4015 &  0.8031 &  0.5985 \tabularnewline
11 &  0.3232 &  0.6465 &  0.6768 \tabularnewline
12 &  0.2405 &  0.481 &  0.7595 \tabularnewline
13 &  0.2558 &  0.5115 &  0.7442 \tabularnewline
14 &  0.2531 &  0.5062 &  0.7469 \tabularnewline
15 &  0.2134 &  0.4269 &  0.7866 \tabularnewline
16 &  0.1501 &  0.3003 &  0.8499 \tabularnewline
17 &  0.1024 &  0.2049 &  0.8975 \tabularnewline
18 &  0.1019 &  0.2037 &  0.8981 \tabularnewline
19 &  0.09964 &  0.1993 &  0.9004 \tabularnewline
20 &  0.1002 &  0.2004 &  0.8998 \tabularnewline
21 &  0.07786 &  0.1557 &  0.9221 \tabularnewline
22 &  0.06629 &  0.1326 &  0.9337 \tabularnewline
23 &  0.2164 &  0.4328 &  0.7836 \tabularnewline
24 &  0.2052 &  0.4105 &  0.7948 \tabularnewline
25 &  0.3543 &  0.7087 &  0.6457 \tabularnewline
26 &  0.3321 &  0.6642 &  0.6679 \tabularnewline
27 &  0.286 &  0.5719 &  0.714 \tabularnewline
28 &  0.2768 &  0.5537 &  0.7232 \tabularnewline
29 &  0.2251 &  0.4501 &  0.7749 \tabularnewline
30 &  0.1858 &  0.3716 &  0.8142 \tabularnewline
31 &  0.1505 &  0.301 &  0.8495 \tabularnewline
32 &  0.1297 &  0.2594 &  0.8703 \tabularnewline
33 &  0.1011 &  0.2022 &  0.8989 \tabularnewline
34 &  0.0961 &  0.1922 &  0.9039 \tabularnewline
35 &  0.08207 &  0.1641 &  0.9179 \tabularnewline
36 &  0.1357 &  0.2714 &  0.8643 \tabularnewline
37 &  0.1096 &  0.2192 &  0.8904 \tabularnewline
38 &  0.08745 &  0.1749 &  0.9126 \tabularnewline
39 &  0.06696 &  0.1339 &  0.933 \tabularnewline
40 &  0.06188 &  0.1238 &  0.9381 \tabularnewline
41 &  0.07021 &  0.1404 &  0.9298 \tabularnewline
42 &  0.05659 &  0.1132 &  0.9434 \tabularnewline
43 &  0.1253 &  0.2506 &  0.8747 \tabularnewline
44 &  0.2555 &  0.5111 &  0.7445 \tabularnewline
45 &  0.2148 &  0.4296 &  0.7852 \tabularnewline
46 &  0.3492 &  0.6985 &  0.6508 \tabularnewline
47 &  0.3052 &  0.6104 &  0.6948 \tabularnewline
48 &  0.2651 &  0.5301 &  0.7349 \tabularnewline
49 &  0.2373 &  0.4745 &  0.7627 \tabularnewline
50 &  0.2139 &  0.4279 &  0.7861 \tabularnewline
51 &  0.2184 &  0.4368 &  0.7816 \tabularnewline
52 &  0.1833 &  0.3666 &  0.8167 \tabularnewline
53 &  0.1873 &  0.3746 &  0.8127 \tabularnewline
54 &  0.3445 &  0.689 &  0.6555 \tabularnewline
55 &  0.7528 &  0.4944 &  0.2472 \tabularnewline
56 &  0.7146 &  0.5708 &  0.2854 \tabularnewline
57 &  0.6726 &  0.6547 &  0.3274 \tabularnewline
58 &  0.6929 &  0.6141 &  0.3071 \tabularnewline
59 &  0.848 &  0.304 &  0.152 \tabularnewline
60 &  0.8654 &  0.2691 &  0.1346 \tabularnewline
61 &  0.842 &  0.3159 &  0.158 \tabularnewline
62 &  0.8234 &  0.3532 &  0.1766 \tabularnewline
63 &  0.8387 &  0.3227 &  0.1613 \tabularnewline
64 &  0.9041 &  0.1917 &  0.09587 \tabularnewline
65 &  0.9533 &  0.09341 &  0.04671 \tabularnewline
66 &  0.9596 &  0.08078 &  0.04039 \tabularnewline
67 &  0.967 &  0.06595 &  0.03298 \tabularnewline
68 &  0.9636 &  0.07271 &  0.03635 \tabularnewline
69 &  0.9551 &  0.0897 &  0.04485 \tabularnewline
70 &  0.9441 &  0.1118 &  0.0559 \tabularnewline
71 &  0.9607 &  0.07856 &  0.03928 \tabularnewline
72 &  0.956 &  0.0881 &  0.04405 \tabularnewline
73 &  0.9529 &  0.0942 &  0.0471 \tabularnewline
74 &  0.9463 &  0.1074 &  0.05368 \tabularnewline
75 &  0.9361 &  0.1277 &  0.06385 \tabularnewline
76 &  0.9271 &  0.1459 &  0.07294 \tabularnewline
77 &  0.9282 &  0.1435 &  0.07177 \tabularnewline
78 &  0.9166 &  0.1668 &  0.08341 \tabularnewline
79 &  0.9259 &  0.1482 &  0.07409 \tabularnewline
80 &  0.969 &  0.06206 &  0.03103 \tabularnewline
81 &  0.9713 &  0.05746 &  0.02873 \tabularnewline
82 &  0.9628 &  0.07443 &  0.03722 \tabularnewline
83 &  0.9762 &  0.04752 &  0.02376 \tabularnewline
84 &  0.9744 &  0.05128 &  0.02564 \tabularnewline
85 &  0.9669 &  0.06626 &  0.03313 \tabularnewline
86 &  0.9576 &  0.08483 &  0.04241 \tabularnewline
87 &  0.9485 &  0.103 &  0.05148 \tabularnewline
88 &  0.9422 &  0.1155 &  0.05775 \tabularnewline
89 &  0.9486 &  0.1029 &  0.05143 \tabularnewline
90 &  0.953 &  0.09398 &  0.04699 \tabularnewline
91 &  0.9451 &  0.1099 &  0.05494 \tabularnewline
92 &  0.9334 &  0.1332 &  0.06658 \tabularnewline
93 &  0.928 &  0.1441 &  0.07203 \tabularnewline
94 &  0.9554 &  0.08918 &  0.04459 \tabularnewline
95 &  0.9482 &  0.1035 &  0.05176 \tabularnewline
96 &  0.9415 &  0.117 &  0.05849 \tabularnewline
97 &  0.9805 &  0.03892 &  0.01946 \tabularnewline
98 &  0.9912 &  0.01752 &  0.008759 \tabularnewline
99 &  0.9995 &  0.001005 &  0.0005025 \tabularnewline
100 &  0.9995 &  0.001074 &  0.0005372 \tabularnewline
101 &  0.9992 &  0.001587 &  0.0007935 \tabularnewline
102 &  0.9992 &  0.001637 &  0.0008184 \tabularnewline
103 &  0.999 &  0.001973 &  0.0009867 \tabularnewline
104 &  0.9986 &  0.002856 &  0.001428 \tabularnewline
105 &  0.998 &  0.004031 &  0.002016 \tabularnewline
106 &  0.997 &  0.005931 &  0.002965 \tabularnewline
107 &  0.9972 &  0.005656 &  0.002828 \tabularnewline
108 &  0.9959 &  0.008209 &  0.004104 \tabularnewline
109 &  0.996 &  0.008094 &  0.004047 \tabularnewline
110 &  0.9941 &  0.01174 &  0.005868 \tabularnewline
111 &  0.9943 &  0.01131 &  0.005655 \tabularnewline
112 &  0.994 &  0.01209 &  0.006044 \tabularnewline
113 &  0.9921 &  0.01582 &  0.007911 \tabularnewline
114 &  0.9957 &  0.008509 &  0.004254 \tabularnewline
115 &  0.9961 &  0.007771 &  0.003886 \tabularnewline
116 &  0.9944 &  0.01115 &  0.005577 \tabularnewline
117 &  0.9935 &  0.01309 &  0.006545 \tabularnewline
118 &  0.9915 &  0.01703 &  0.008515 \tabularnewline
119 &  0.9928 &  0.01437 &  0.007186 \tabularnewline
120 &  0.9898 &  0.02049 &  0.01025 \tabularnewline
121 &  0.9855 &  0.02902 &  0.01451 \tabularnewline
122 &  0.983 &  0.03395 &  0.01697 \tabularnewline
123 &  0.9795 &  0.04102 &  0.02051 \tabularnewline
124 &  0.9805 &  0.03898 &  0.01949 \tabularnewline
125 &  0.9756 &  0.04874 &  0.02437 \tabularnewline
126 &  0.9667 &  0.06669 &  0.03335 \tabularnewline
127 &  0.9561 &  0.08787 &  0.04394 \tabularnewline
128 &  0.9401 &  0.1198 &  0.05992 \tabularnewline
129 &  0.9241 &  0.1518 &  0.07591 \tabularnewline
130 &  0.9309 &  0.1382 &  0.06911 \tabularnewline
131 &  0.9073 &  0.1855 &  0.09274 \tabularnewline
132 &  0.9139 &  0.1723 &  0.08613 \tabularnewline
133 &  0.8923 &  0.2154 &  0.1077 \tabularnewline
134 &  0.8668 &  0.2664 &  0.1332 \tabularnewline
135 &  0.8363 &  0.3274 &  0.1637 \tabularnewline
136 &  0.8112 &  0.3776 &  0.1888 \tabularnewline
137 &  0.7678 &  0.4645 &  0.2322 \tabularnewline
138 &  0.7285 &  0.543 &  0.2715 \tabularnewline
139 &  0.8281 &  0.3438 &  0.1719 \tabularnewline
140 &  0.8156 &  0.3687 &  0.1844 \tabularnewline
141 &  0.7617 &  0.4767 &  0.2383 \tabularnewline
142 &  0.7374 &  0.5252 &  0.2626 \tabularnewline
143 &  0.6661 &  0.6677 &  0.3339 \tabularnewline
144 &  0.6328 &  0.7343 &  0.3672 \tabularnewline
145 &  0.5645 &  0.871 &  0.4355 \tabularnewline
146 &  0.4792 &  0.9584 &  0.5208 \tabularnewline
147 &  0.462 &  0.9241 &  0.538 \tabularnewline
148 &  0.4049 &  0.8097 &  0.5951 \tabularnewline
149 &  0.5039 &  0.9921 &  0.4961 \tabularnewline
150 &  0.4794 &  0.9588 &  0.5206 \tabularnewline
151 &  0.6831 &  0.6338 &  0.3169 \tabularnewline
152 &  0.5547 &  0.8907 &  0.4453 \tabularnewline
153 &  0.5256 &  0.9489 &  0.4744 \tabularnewline
154 &  0.3701 &  0.7402 &  0.6299 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309782&T=6

[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]9[/C][C] 0.4803[/C][C] 0.9605[/C][C] 0.5197[/C][/ROW]
[ROW][C]10[/C][C] 0.4015[/C][C] 0.8031[/C][C] 0.5985[/C][/ROW]
[ROW][C]11[/C][C] 0.3232[/C][C] 0.6465[/C][C] 0.6768[/C][/ROW]
[ROW][C]12[/C][C] 0.2405[/C][C] 0.481[/C][C] 0.7595[/C][/ROW]
[ROW][C]13[/C][C] 0.2558[/C][C] 0.5115[/C][C] 0.7442[/C][/ROW]
[ROW][C]14[/C][C] 0.2531[/C][C] 0.5062[/C][C] 0.7469[/C][/ROW]
[ROW][C]15[/C][C] 0.2134[/C][C] 0.4269[/C][C] 0.7866[/C][/ROW]
[ROW][C]16[/C][C] 0.1501[/C][C] 0.3003[/C][C] 0.8499[/C][/ROW]
[ROW][C]17[/C][C] 0.1024[/C][C] 0.2049[/C][C] 0.8975[/C][/ROW]
[ROW][C]18[/C][C] 0.1019[/C][C] 0.2037[/C][C] 0.8981[/C][/ROW]
[ROW][C]19[/C][C] 0.09964[/C][C] 0.1993[/C][C] 0.9004[/C][/ROW]
[ROW][C]20[/C][C] 0.1002[/C][C] 0.2004[/C][C] 0.8998[/C][/ROW]
[ROW][C]21[/C][C] 0.07786[/C][C] 0.1557[/C][C] 0.9221[/C][/ROW]
[ROW][C]22[/C][C] 0.06629[/C][C] 0.1326[/C][C] 0.9337[/C][/ROW]
[ROW][C]23[/C][C] 0.2164[/C][C] 0.4328[/C][C] 0.7836[/C][/ROW]
[ROW][C]24[/C][C] 0.2052[/C][C] 0.4105[/C][C] 0.7948[/C][/ROW]
[ROW][C]25[/C][C] 0.3543[/C][C] 0.7087[/C][C] 0.6457[/C][/ROW]
[ROW][C]26[/C][C] 0.3321[/C][C] 0.6642[/C][C] 0.6679[/C][/ROW]
[ROW][C]27[/C][C] 0.286[/C][C] 0.5719[/C][C] 0.714[/C][/ROW]
[ROW][C]28[/C][C] 0.2768[/C][C] 0.5537[/C][C] 0.7232[/C][/ROW]
[ROW][C]29[/C][C] 0.2251[/C][C] 0.4501[/C][C] 0.7749[/C][/ROW]
[ROW][C]30[/C][C] 0.1858[/C][C] 0.3716[/C][C] 0.8142[/C][/ROW]
[ROW][C]31[/C][C] 0.1505[/C][C] 0.301[/C][C] 0.8495[/C][/ROW]
[ROW][C]32[/C][C] 0.1297[/C][C] 0.2594[/C][C] 0.8703[/C][/ROW]
[ROW][C]33[/C][C] 0.1011[/C][C] 0.2022[/C][C] 0.8989[/C][/ROW]
[ROW][C]34[/C][C] 0.0961[/C][C] 0.1922[/C][C] 0.9039[/C][/ROW]
[ROW][C]35[/C][C] 0.08207[/C][C] 0.1641[/C][C] 0.9179[/C][/ROW]
[ROW][C]36[/C][C] 0.1357[/C][C] 0.2714[/C][C] 0.8643[/C][/ROW]
[ROW][C]37[/C][C] 0.1096[/C][C] 0.2192[/C][C] 0.8904[/C][/ROW]
[ROW][C]38[/C][C] 0.08745[/C][C] 0.1749[/C][C] 0.9126[/C][/ROW]
[ROW][C]39[/C][C] 0.06696[/C][C] 0.1339[/C][C] 0.933[/C][/ROW]
[ROW][C]40[/C][C] 0.06188[/C][C] 0.1238[/C][C] 0.9381[/C][/ROW]
[ROW][C]41[/C][C] 0.07021[/C][C] 0.1404[/C][C] 0.9298[/C][/ROW]
[ROW][C]42[/C][C] 0.05659[/C][C] 0.1132[/C][C] 0.9434[/C][/ROW]
[ROW][C]43[/C][C] 0.1253[/C][C] 0.2506[/C][C] 0.8747[/C][/ROW]
[ROW][C]44[/C][C] 0.2555[/C][C] 0.5111[/C][C] 0.7445[/C][/ROW]
[ROW][C]45[/C][C] 0.2148[/C][C] 0.4296[/C][C] 0.7852[/C][/ROW]
[ROW][C]46[/C][C] 0.3492[/C][C] 0.6985[/C][C] 0.6508[/C][/ROW]
[ROW][C]47[/C][C] 0.3052[/C][C] 0.6104[/C][C] 0.6948[/C][/ROW]
[ROW][C]48[/C][C] 0.2651[/C][C] 0.5301[/C][C] 0.7349[/C][/ROW]
[ROW][C]49[/C][C] 0.2373[/C][C] 0.4745[/C][C] 0.7627[/C][/ROW]
[ROW][C]50[/C][C] 0.2139[/C][C] 0.4279[/C][C] 0.7861[/C][/ROW]
[ROW][C]51[/C][C] 0.2184[/C][C] 0.4368[/C][C] 0.7816[/C][/ROW]
[ROW][C]52[/C][C] 0.1833[/C][C] 0.3666[/C][C] 0.8167[/C][/ROW]
[ROW][C]53[/C][C] 0.1873[/C][C] 0.3746[/C][C] 0.8127[/C][/ROW]
[ROW][C]54[/C][C] 0.3445[/C][C] 0.689[/C][C] 0.6555[/C][/ROW]
[ROW][C]55[/C][C] 0.7528[/C][C] 0.4944[/C][C] 0.2472[/C][/ROW]
[ROW][C]56[/C][C] 0.7146[/C][C] 0.5708[/C][C] 0.2854[/C][/ROW]
[ROW][C]57[/C][C] 0.6726[/C][C] 0.6547[/C][C] 0.3274[/C][/ROW]
[ROW][C]58[/C][C] 0.6929[/C][C] 0.6141[/C][C] 0.3071[/C][/ROW]
[ROW][C]59[/C][C] 0.848[/C][C] 0.304[/C][C] 0.152[/C][/ROW]
[ROW][C]60[/C][C] 0.8654[/C][C] 0.2691[/C][C] 0.1346[/C][/ROW]
[ROW][C]61[/C][C] 0.842[/C][C] 0.3159[/C][C] 0.158[/C][/ROW]
[ROW][C]62[/C][C] 0.8234[/C][C] 0.3532[/C][C] 0.1766[/C][/ROW]
[ROW][C]63[/C][C] 0.8387[/C][C] 0.3227[/C][C] 0.1613[/C][/ROW]
[ROW][C]64[/C][C] 0.9041[/C][C] 0.1917[/C][C] 0.09587[/C][/ROW]
[ROW][C]65[/C][C] 0.9533[/C][C] 0.09341[/C][C] 0.04671[/C][/ROW]
[ROW][C]66[/C][C] 0.9596[/C][C] 0.08078[/C][C] 0.04039[/C][/ROW]
[ROW][C]67[/C][C] 0.967[/C][C] 0.06595[/C][C] 0.03298[/C][/ROW]
[ROW][C]68[/C][C] 0.9636[/C][C] 0.07271[/C][C] 0.03635[/C][/ROW]
[ROW][C]69[/C][C] 0.9551[/C][C] 0.0897[/C][C] 0.04485[/C][/ROW]
[ROW][C]70[/C][C] 0.9441[/C][C] 0.1118[/C][C] 0.0559[/C][/ROW]
[ROW][C]71[/C][C] 0.9607[/C][C] 0.07856[/C][C] 0.03928[/C][/ROW]
[ROW][C]72[/C][C] 0.956[/C][C] 0.0881[/C][C] 0.04405[/C][/ROW]
[ROW][C]73[/C][C] 0.9529[/C][C] 0.0942[/C][C] 0.0471[/C][/ROW]
[ROW][C]74[/C][C] 0.9463[/C][C] 0.1074[/C][C] 0.05368[/C][/ROW]
[ROW][C]75[/C][C] 0.9361[/C][C] 0.1277[/C][C] 0.06385[/C][/ROW]
[ROW][C]76[/C][C] 0.9271[/C][C] 0.1459[/C][C] 0.07294[/C][/ROW]
[ROW][C]77[/C][C] 0.9282[/C][C] 0.1435[/C][C] 0.07177[/C][/ROW]
[ROW][C]78[/C][C] 0.9166[/C][C] 0.1668[/C][C] 0.08341[/C][/ROW]
[ROW][C]79[/C][C] 0.9259[/C][C] 0.1482[/C][C] 0.07409[/C][/ROW]
[ROW][C]80[/C][C] 0.969[/C][C] 0.06206[/C][C] 0.03103[/C][/ROW]
[ROW][C]81[/C][C] 0.9713[/C][C] 0.05746[/C][C] 0.02873[/C][/ROW]
[ROW][C]82[/C][C] 0.9628[/C][C] 0.07443[/C][C] 0.03722[/C][/ROW]
[ROW][C]83[/C][C] 0.9762[/C][C] 0.04752[/C][C] 0.02376[/C][/ROW]
[ROW][C]84[/C][C] 0.9744[/C][C] 0.05128[/C][C] 0.02564[/C][/ROW]
[ROW][C]85[/C][C] 0.9669[/C][C] 0.06626[/C][C] 0.03313[/C][/ROW]
[ROW][C]86[/C][C] 0.9576[/C][C] 0.08483[/C][C] 0.04241[/C][/ROW]
[ROW][C]87[/C][C] 0.9485[/C][C] 0.103[/C][C] 0.05148[/C][/ROW]
[ROW][C]88[/C][C] 0.9422[/C][C] 0.1155[/C][C] 0.05775[/C][/ROW]
[ROW][C]89[/C][C] 0.9486[/C][C] 0.1029[/C][C] 0.05143[/C][/ROW]
[ROW][C]90[/C][C] 0.953[/C][C] 0.09398[/C][C] 0.04699[/C][/ROW]
[ROW][C]91[/C][C] 0.9451[/C][C] 0.1099[/C][C] 0.05494[/C][/ROW]
[ROW][C]92[/C][C] 0.9334[/C][C] 0.1332[/C][C] 0.06658[/C][/ROW]
[ROW][C]93[/C][C] 0.928[/C][C] 0.1441[/C][C] 0.07203[/C][/ROW]
[ROW][C]94[/C][C] 0.9554[/C][C] 0.08918[/C][C] 0.04459[/C][/ROW]
[ROW][C]95[/C][C] 0.9482[/C][C] 0.1035[/C][C] 0.05176[/C][/ROW]
[ROW][C]96[/C][C] 0.9415[/C][C] 0.117[/C][C] 0.05849[/C][/ROW]
[ROW][C]97[/C][C] 0.9805[/C][C] 0.03892[/C][C] 0.01946[/C][/ROW]
[ROW][C]98[/C][C] 0.9912[/C][C] 0.01752[/C][C] 0.008759[/C][/ROW]
[ROW][C]99[/C][C] 0.9995[/C][C] 0.001005[/C][C] 0.0005025[/C][/ROW]
[ROW][C]100[/C][C] 0.9995[/C][C] 0.001074[/C][C] 0.0005372[/C][/ROW]
[ROW][C]101[/C][C] 0.9992[/C][C] 0.001587[/C][C] 0.0007935[/C][/ROW]
[ROW][C]102[/C][C] 0.9992[/C][C] 0.001637[/C][C] 0.0008184[/C][/ROW]
[ROW][C]103[/C][C] 0.999[/C][C] 0.001973[/C][C] 0.0009867[/C][/ROW]
[ROW][C]104[/C][C] 0.9986[/C][C] 0.002856[/C][C] 0.001428[/C][/ROW]
[ROW][C]105[/C][C] 0.998[/C][C] 0.004031[/C][C] 0.002016[/C][/ROW]
[ROW][C]106[/C][C] 0.997[/C][C] 0.005931[/C][C] 0.002965[/C][/ROW]
[ROW][C]107[/C][C] 0.9972[/C][C] 0.005656[/C][C] 0.002828[/C][/ROW]
[ROW][C]108[/C][C] 0.9959[/C][C] 0.008209[/C][C] 0.004104[/C][/ROW]
[ROW][C]109[/C][C] 0.996[/C][C] 0.008094[/C][C] 0.004047[/C][/ROW]
[ROW][C]110[/C][C] 0.9941[/C][C] 0.01174[/C][C] 0.005868[/C][/ROW]
[ROW][C]111[/C][C] 0.9943[/C][C] 0.01131[/C][C] 0.005655[/C][/ROW]
[ROW][C]112[/C][C] 0.994[/C][C] 0.01209[/C][C] 0.006044[/C][/ROW]
[ROW][C]113[/C][C] 0.9921[/C][C] 0.01582[/C][C] 0.007911[/C][/ROW]
[ROW][C]114[/C][C] 0.9957[/C][C] 0.008509[/C][C] 0.004254[/C][/ROW]
[ROW][C]115[/C][C] 0.9961[/C][C] 0.007771[/C][C] 0.003886[/C][/ROW]
[ROW][C]116[/C][C] 0.9944[/C][C] 0.01115[/C][C] 0.005577[/C][/ROW]
[ROW][C]117[/C][C] 0.9935[/C][C] 0.01309[/C][C] 0.006545[/C][/ROW]
[ROW][C]118[/C][C] 0.9915[/C][C] 0.01703[/C][C] 0.008515[/C][/ROW]
[ROW][C]119[/C][C] 0.9928[/C][C] 0.01437[/C][C] 0.007186[/C][/ROW]
[ROW][C]120[/C][C] 0.9898[/C][C] 0.02049[/C][C] 0.01025[/C][/ROW]
[ROW][C]121[/C][C] 0.9855[/C][C] 0.02902[/C][C] 0.01451[/C][/ROW]
[ROW][C]122[/C][C] 0.983[/C][C] 0.03395[/C][C] 0.01697[/C][/ROW]
[ROW][C]123[/C][C] 0.9795[/C][C] 0.04102[/C][C] 0.02051[/C][/ROW]
[ROW][C]124[/C][C] 0.9805[/C][C] 0.03898[/C][C] 0.01949[/C][/ROW]
[ROW][C]125[/C][C] 0.9756[/C][C] 0.04874[/C][C] 0.02437[/C][/ROW]
[ROW][C]126[/C][C] 0.9667[/C][C] 0.06669[/C][C] 0.03335[/C][/ROW]
[ROW][C]127[/C][C] 0.9561[/C][C] 0.08787[/C][C] 0.04394[/C][/ROW]
[ROW][C]128[/C][C] 0.9401[/C][C] 0.1198[/C][C] 0.05992[/C][/ROW]
[ROW][C]129[/C][C] 0.9241[/C][C] 0.1518[/C][C] 0.07591[/C][/ROW]
[ROW][C]130[/C][C] 0.9309[/C][C] 0.1382[/C][C] 0.06911[/C][/ROW]
[ROW][C]131[/C][C] 0.9073[/C][C] 0.1855[/C][C] 0.09274[/C][/ROW]
[ROW][C]132[/C][C] 0.9139[/C][C] 0.1723[/C][C] 0.08613[/C][/ROW]
[ROW][C]133[/C][C] 0.8923[/C][C] 0.2154[/C][C] 0.1077[/C][/ROW]
[ROW][C]134[/C][C] 0.8668[/C][C] 0.2664[/C][C] 0.1332[/C][/ROW]
[ROW][C]135[/C][C] 0.8363[/C][C] 0.3274[/C][C] 0.1637[/C][/ROW]
[ROW][C]136[/C][C] 0.8112[/C][C] 0.3776[/C][C] 0.1888[/C][/ROW]
[ROW][C]137[/C][C] 0.7678[/C][C] 0.4645[/C][C] 0.2322[/C][/ROW]
[ROW][C]138[/C][C] 0.7285[/C][C] 0.543[/C][C] 0.2715[/C][/ROW]
[ROW][C]139[/C][C] 0.8281[/C][C] 0.3438[/C][C] 0.1719[/C][/ROW]
[ROW][C]140[/C][C] 0.8156[/C][C] 0.3687[/C][C] 0.1844[/C][/ROW]
[ROW][C]141[/C][C] 0.7617[/C][C] 0.4767[/C][C] 0.2383[/C][/ROW]
[ROW][C]142[/C][C] 0.7374[/C][C] 0.5252[/C][C] 0.2626[/C][/ROW]
[ROW][C]143[/C][C] 0.6661[/C][C] 0.6677[/C][C] 0.3339[/C][/ROW]
[ROW][C]144[/C][C] 0.6328[/C][C] 0.7343[/C][C] 0.3672[/C][/ROW]
[ROW][C]145[/C][C] 0.5645[/C][C] 0.871[/C][C] 0.4355[/C][/ROW]
[ROW][C]146[/C][C] 0.4792[/C][C] 0.9584[/C][C] 0.5208[/C][/ROW]
[ROW][C]147[/C][C] 0.462[/C][C] 0.9241[/C][C] 0.538[/C][/ROW]
[ROW][C]148[/C][C] 0.4049[/C][C] 0.8097[/C][C] 0.5951[/C][/ROW]
[ROW][C]149[/C][C] 0.5039[/C][C] 0.9921[/C][C] 0.4961[/C][/ROW]
[ROW][C]150[/C][C] 0.4794[/C][C] 0.9588[/C][C] 0.5206[/C][/ROW]
[ROW][C]151[/C][C] 0.6831[/C][C] 0.6338[/C][C] 0.3169[/C][/ROW]
[ROW][C]152[/C][C] 0.5547[/C][C] 0.8907[/C][C] 0.4453[/C][/ROW]
[ROW][C]153[/C][C] 0.5256[/C][C] 0.9489[/C][C] 0.4744[/C][/ROW]
[ROW][C]154[/C][C] 0.3701[/C][C] 0.7402[/C][C] 0.6299[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309782&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309782&T=6

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
9 0.4803 0.9605 0.5197
10 0.4015 0.8031 0.5985
11 0.3232 0.6465 0.6768
12 0.2405 0.481 0.7595
13 0.2558 0.5115 0.7442
14 0.2531 0.5062 0.7469
15 0.2134 0.4269 0.7866
16 0.1501 0.3003 0.8499
17 0.1024 0.2049 0.8975
18 0.1019 0.2037 0.8981
19 0.09964 0.1993 0.9004
20 0.1002 0.2004 0.8998
21 0.07786 0.1557 0.9221
22 0.06629 0.1326 0.9337
23 0.2164 0.4328 0.7836
24 0.2052 0.4105 0.7948
25 0.3543 0.7087 0.6457
26 0.3321 0.6642 0.6679
27 0.286 0.5719 0.714
28 0.2768 0.5537 0.7232
29 0.2251 0.4501 0.7749
30 0.1858 0.3716 0.8142
31 0.1505 0.301 0.8495
32 0.1297 0.2594 0.8703
33 0.1011 0.2022 0.8989
34 0.0961 0.1922 0.9039
35 0.08207 0.1641 0.9179
36 0.1357 0.2714 0.8643
37 0.1096 0.2192 0.8904
38 0.08745 0.1749 0.9126
39 0.06696 0.1339 0.933
40 0.06188 0.1238 0.9381
41 0.07021 0.1404 0.9298
42 0.05659 0.1132 0.9434
43 0.1253 0.2506 0.8747
44 0.2555 0.5111 0.7445
45 0.2148 0.4296 0.7852
46 0.3492 0.6985 0.6508
47 0.3052 0.6104 0.6948
48 0.2651 0.5301 0.7349
49 0.2373 0.4745 0.7627
50 0.2139 0.4279 0.7861
51 0.2184 0.4368 0.7816
52 0.1833 0.3666 0.8167
53 0.1873 0.3746 0.8127
54 0.3445 0.689 0.6555
55 0.7528 0.4944 0.2472
56 0.7146 0.5708 0.2854
57 0.6726 0.6547 0.3274
58 0.6929 0.6141 0.3071
59 0.848 0.304 0.152
60 0.8654 0.2691 0.1346
61 0.842 0.3159 0.158
62 0.8234 0.3532 0.1766
63 0.8387 0.3227 0.1613
64 0.9041 0.1917 0.09587
65 0.9533 0.09341 0.04671
66 0.9596 0.08078 0.04039
67 0.967 0.06595 0.03298
68 0.9636 0.07271 0.03635
69 0.9551 0.0897 0.04485
70 0.9441 0.1118 0.0559
71 0.9607 0.07856 0.03928
72 0.956 0.0881 0.04405
73 0.9529 0.0942 0.0471
74 0.9463 0.1074 0.05368
75 0.9361 0.1277 0.06385
76 0.9271 0.1459 0.07294
77 0.9282 0.1435 0.07177
78 0.9166 0.1668 0.08341
79 0.9259 0.1482 0.07409
80 0.969 0.06206 0.03103
81 0.9713 0.05746 0.02873
82 0.9628 0.07443 0.03722
83 0.9762 0.04752 0.02376
84 0.9744 0.05128 0.02564
85 0.9669 0.06626 0.03313
86 0.9576 0.08483 0.04241
87 0.9485 0.103 0.05148
88 0.9422 0.1155 0.05775
89 0.9486 0.1029 0.05143
90 0.953 0.09398 0.04699
91 0.9451 0.1099 0.05494
92 0.9334 0.1332 0.06658
93 0.928 0.1441 0.07203
94 0.9554 0.08918 0.04459
95 0.9482 0.1035 0.05176
96 0.9415 0.117 0.05849
97 0.9805 0.03892 0.01946
98 0.9912 0.01752 0.008759
99 0.9995 0.001005 0.0005025
100 0.9995 0.001074 0.0005372
101 0.9992 0.001587 0.0007935
102 0.9992 0.001637 0.0008184
103 0.999 0.001973 0.0009867
104 0.9986 0.002856 0.001428
105 0.998 0.004031 0.002016
106 0.997 0.005931 0.002965
107 0.9972 0.005656 0.002828
108 0.9959 0.008209 0.004104
109 0.996 0.008094 0.004047
110 0.9941 0.01174 0.005868
111 0.9943 0.01131 0.005655
112 0.994 0.01209 0.006044
113 0.9921 0.01582 0.007911
114 0.9957 0.008509 0.004254
115 0.9961 0.007771 0.003886
116 0.9944 0.01115 0.005577
117 0.9935 0.01309 0.006545
118 0.9915 0.01703 0.008515
119 0.9928 0.01437 0.007186
120 0.9898 0.02049 0.01025
121 0.9855 0.02902 0.01451
122 0.983 0.03395 0.01697
123 0.9795 0.04102 0.02051
124 0.9805 0.03898 0.01949
125 0.9756 0.04874 0.02437
126 0.9667 0.06669 0.03335
127 0.9561 0.08787 0.04394
128 0.9401 0.1198 0.05992
129 0.9241 0.1518 0.07591
130 0.9309 0.1382 0.06911
131 0.9073 0.1855 0.09274
132 0.9139 0.1723 0.08613
133 0.8923 0.2154 0.1077
134 0.8668 0.2664 0.1332
135 0.8363 0.3274 0.1637
136 0.8112 0.3776 0.1888
137 0.7678 0.4645 0.2322
138 0.7285 0.543 0.2715
139 0.8281 0.3438 0.1719
140 0.8156 0.3687 0.1844
141 0.7617 0.4767 0.2383
142 0.7374 0.5252 0.2626
143 0.6661 0.6677 0.3339
144 0.6328 0.7343 0.3672
145 0.5645 0.871 0.4355
146 0.4792 0.9584 0.5208
147 0.462 0.9241 0.538
148 0.4049 0.8097 0.5951
149 0.5039 0.9921 0.4961
150 0.4794 0.9588 0.5206
151 0.6831 0.6338 0.3169
152 0.5547 0.8907 0.4453
153 0.5256 0.9489 0.4744
154 0.3701 0.7402 0.6299






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level13 0.08904NOK
5% type I error level300.205479NOK
10% type I error level480.328767NOK

\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 & 13 &  0.08904 & NOK \tabularnewline
5% type I error level & 30 & 0.205479 & NOK \tabularnewline
10% type I error level & 48 & 0.328767 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309782&T=7

[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]13[/C][C] 0.08904[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]30[/C][C]0.205479[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]48[/C][C]0.328767[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309782&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309782&T=7

As an alternative you can also use a QR Code:  
QR Code


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level13 0.08904NOK
5% type I error level300.205479NOK
10% type I error level480.328767NOK






Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.2835, df1 = 2, df2 = 155, p-value = 0.28
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.7394, df1 = 10, df2 = 147, p-value = 0.07707
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 4.5697, df1 = 2, df2 = 155, p-value = 0.0118


\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline

> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.2835, df1 = 2, df2 = 155, p-value = 0.28

 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline

> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.7394, df1 = 10, df2 = 147, p-value = 0.07707

 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline

> reset_test_principal_components
	RESET test
data:  mylm
RESET = 4.5697, df1 = 2, df2 = 155, p-value = 0.0118

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309782&T=8

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.2835, df1 = 2, df2 = 155, p-value = 0.28

[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.7394, df1 = 10, df2 = 147, p-value = 0.07707

[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 4.5697, df1 = 2, df2 = 155, p-value = 0.0118

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309782&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309782&T=8

As an alternative you can also use a QR Code:  
QR Code


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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.2835, df1 = 2, df2 = 155, p-value = 0.28
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.7394, df1 = 10, df2 = 147, p-value = 0.07707
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 4.5697, df1 = 2, df2 = 155, p-value = 0.0118






Variance Inflation Factors (Multicollinearity)
> vif
      `(1-Bs)(1-B)b`       `(1-Bs)(1-B)c` `(1-Bs)(1-B)a(t-1s)` 
            1.188566             1.156145             1.202521 
`(1-Bs)(1-B)a(t-2s)` `(1-Bs)(1-B)a(t-3s)` 
            1.228352             1.174473 


\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline

> vif
      `(1-Bs)(1-B)b`       `(1-Bs)(1-B)c` `(1-Bs)(1-B)a(t-1s)` 
            1.188566             1.156145             1.202521 
`(1-Bs)(1-B)a(t-2s)` `(1-Bs)(1-B)a(t-3s)` 
            1.228352             1.174473 

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309782&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
      `(1-Bs)(1-B)b`       `(1-Bs)(1-B)c` `(1-Bs)(1-B)a(t-1s)` 
            1.188566             1.156145             1.202521 
`(1-Bs)(1-B)a(t-2s)` `(1-Bs)(1-B)a(t-3s)` 
            1.228352             1.174473 

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309782&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309782&T=9

As an alternative you can also use a QR Code:  
QR Code


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

Variance Inflation Factors (Multicollinearity)
> vif
      `(1-Bs)(1-B)b`       `(1-Bs)(1-B)c` `(1-Bs)(1-B)a(t-1s)` 
            1.188566             1.156145             1.202521 
`(1-Bs)(1-B)a(t-2s)` `(1-Bs)(1-B)a(t-3s)` 
            1.228352             1.174473


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s) ; par4 = 0 ; par5 = 3 ; par6 = 12 ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s) ; par4 = 0 ; par5 = 3 ; par6 = 12 ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$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, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$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.row.start(a)
a<-table.element(a, mywarning)
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,'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$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
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,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('
',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('
',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('
',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('
',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')