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Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 14 Dec 2012 03:29:44 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/14/t1355473829214iy40fmnd1bcp.htm/, Retrieved Fri, 29 Mar 2024 13:02:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=199456, Retrieved Fri, 29 Mar 2024 13:02:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact183
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [] [2012-10-21 14:51:03] [235928acca9c96310100390b3cde8f3b]
-    D  [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [] [2012-12-09 16:20:41] [235928acca9c96310100390b3cde8f3b]
- RMPD    [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2012-12-12 12:23:23] [235928acca9c96310100390b3cde8f3b]
- RMPD      [Multiple Regression] [] [2012-12-12 13:15:40] [235928acca9c96310100390b3cde8f3b]
- R  D        [Multiple Regression] [Paper- anova(test...] [2012-12-13 18:15:58] [b43eb6e2e60f3928e6b8367ff6c5b484]
-   P             [Multiple Regression] [Paper- Multiple r...] [2012-12-14 08:29:44] [d41d8cd98f00b204e9800998ecf8427e] [Current]
-   P               [Multiple Regression] [Multiple regressi...] [2012-12-14 08:35:54] [74be16979710d4c4e7c6647856088456]
-                     [Multiple Regression] [Paper deel 5 Mult...] [2012-12-20 16:36:40] [8c30f4dd45e15fd207e4faf2fdf6253e]
-  MP                   [Multiple Regression] [PAPER Multiple Re...] [2012-12-20 17:37:39] [4beecb4e29f2a257543dd9eec92fc58e]
-                   [Multiple Regression] [Paper deel 5 Mult...] [2012-12-20 16:10:52] [8c30f4dd45e15fd207e4faf2fdf6253e]
-                   [Multiple Regression] [Paper deel 5 Mult...] [2012-12-20 16:10:52] [8c30f4dd45e15fd207e4faf2fdf6253e]
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Dataseries X:
4	1	1	0	0	0	0	1
4	0	2	0	0	0	0	0
4	0	2	0	0	0	0	0
4	0	2	0	0	0	0	0
4	0	2	0	0	0	0	0
4	1	2	0	0	0	1	1
4	0	2	0	0	0	0	0
4	0	1	0	0	0	0	0
4	0	2	0	0	0	0	1
4	1	2	0	0	0	0	0
4	1	1	0	0	0	0	0
4	0	2	0	0	0	0	0
4	0	2	0	1	0	1	0
4	1	1	0	0	0	0	0
4	0	2	0	1	0	1	1
4	0	1	0	1	0	1	1
4	1	1	0	1	1	1	0
4	1	1	0	0	0	0	0
4	0	2	0	0	0	0	1
4	0	1	0	1	1	1	1
4	1	2	0	0	0	1	0
4	1	2	0	1	0	1	1
4	0	2	0	0	0	1	1
4	1	2	0	0	0	1	1
4	0	1	0	1	0	0	1
4	0	2	0	1	0	1	0
4	1	2	0	0	0	0	1
4	0	2	0	1	0	0	0
4	0	2	0	0	0	0	1
4	0	2	0	0	0	1	0
4	0	2	0	0	0	0	0
4	1	2	0	0	0	0	0
4	1	2	0	0	0	1	0
4	0	1	0	0	0	0	1
4	0	2	0	0	0	0	0
4	0	2	0	0	0	0	0
4	1	1	0	1	0	1	0
4	0	2	0	1	0	0	1
4	0	2	0	0	0	1	1
4	0	1	0	0	0	1	0
4	0	2	0	1	1	1	1
4	0	2	0	1	0	0	1
4	1	2	0	0	0	1	1
4	1	1	0	0	0	0	0
4	0	2	0	0	0	1	0
4	0	2	0	0	0	1	1
4	0	2	0	0	0	0	0
4	0	2	0	0	0	0	1
4	0	2	0	0	0	1	1
4	0	2	0	0	0	0	0
4	0	1	0	1	0	0	0
4	1	1	0	1	1	1	0
4	0	2	0	0	0	0	1
4	0	2	0	1	1	0	0
4	0	2	0	0	0	0	0
4	0	1	0	1	0	0	1
4	0	2	0	1	0	1	1
4	0	2	0	0	0	0	1
4	0	2	0	0	0	0	1
4	1	1	0	1	1	1	1
4	1	1	0	0	0	0	1
4	0	2	0	1	0	1	0
4	0	2	0	0	0	0	0
4	1	1	0	0	0	0	1
4	0	2	0	0	0	0	0
4	0	2	0	0	0	0	0
4	0	1	0	1	1	1	0
4	1	2	0	0	0	0	0
4	0	2	0	0	0	0	1
4	0	2	0	1	0	0	0
4	0	2	0	0	0	0	0
4	0	2	0	0	0	0	1
4	0	2	0	1	0	0	1
4	1	2	0	1	0	0	0
4	0	2	0	0	0	0	1
4	0	1	0	0	0	1	1
4	0	2	0	0	0	0	1
4	0	2	0	1	0	1	1
4	0	1	0	1	1	0	1
4	0	1	0	0	0	1	0
4	0	2	0	0	0	0	0
4	1	2	0	1	0	0	1
4	0	2	0	0	0	0	0
4	0	2	0	1	1	0	0
4	0	2	0	0	0	1	1
4	1	2	0	0	0	0	0
2	1	0	2	0	0	0	1
2	1	0	1	1	0	0	1
2	0	0	2	0	0	0	0
2	0	0	2	0	0	0	1
2	0	0	2	0	0	1	0
2	1	0	1	0	0	0	0
2	1	0	2	0	0	1	0
2	0	0	2	0	0	0	0
2	0	0	1	0	0	0	0
2	0	0	2	0	0	0	1
2	1	0	1	0	0	0	0
2	0	0	2	0	0	0	0
2	1	0	2	0	0	0	0
2	0	0	2	0	0	0	1
2	1	0	2	0	0	0	1
2	0	0	2	0	0	0	0
2	0	0	2	0	0	0	0
2	0	0	2	0	0	0	0
2	0	0	1	1	0	0	0
2	0	0	2	0	0	0	0
2	0	0	2	0	0	0	0
2	1	0	1	1	0	0	0
2	0	0	2	0	0	0	0
2	1	0	2	0	0	0	0
2	1	0	1	1	0	1	0
2	0	0	1	0	0	0	0
2	0	0	2	1	0	0	0
2	1	0	1	1	0	0	0
2	1	0	2	0	0	0	0
2	0	0	2	0	0	0	0
2	1	0	2	0	0	0	1
2	1	0	2	0	0	0	0
2	0	0	2	0	0	0	0
2	0	0	2	0	0	0	1
2	1	0	2	0	0	0	0
2	0	0	2	0	0	0	0
2	1	0	1	1	0	0	0
2	0	0	2	1	0	1	1
2	0	0	2	0	0	0	1
2	0	0	1	0	0	0	0
2	0	0	2	0	0	1	0
2	0	0	2	0	0	0	1
2	0	0	2	0	0	0	0
2	0	0	2	0	0	0	1
2	1	0	2	0	0	0	0
2	1	0	2	0	0	0	1
2	1	0	2	1	0	0	0
2	0	0	2	0	0	0	0
2	0	0	2	0	0	0	0
2	0	0	2	0	0	0	0
2	1	0	2	1	0	1	1
2	1	0	1	1	0	1	1
2	0	0	1	0	0	0	0
2	0	0	2	0	0	0	0
2	0	0	2	1	1	0	1
2	0	0	1	1	0	0	1
2	1	0	2	0	0	0	0
2	0	0	2	0	0	1	1
2	0	0	2	0	0	1	0
2	0	0	1	0	0	0	1
2	0	0	1	1	0	0	0
2	0	0	1	0	0	0	0
2	1	0	2	0	0	0	0
2	0	0	2	0	0	1	1
2	0	0	2	0	0	0	1
2	1	0	2	1	1	0	0
2	1	0	2	1	1	1	0
2	1	0	2	1	0	0	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199456&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199456&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199456&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Weeks[t] = + 3.12215877450008 + 0.0240600096945622UseLimit[t] + 0.457803059764332T40[t] -0.615927691063681T20[t] -0.130947731103206Used[t] + 0.292427873146047CorrectAnalysis[t] + 0.0635211726862047Useful[t] + 0.0466628346837525Outcome[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Weeks[t] =  +  3.12215877450008 +  0.0240600096945622UseLimit[t] +  0.457803059764332T40[t] -0.615927691063681T20[t] -0.130947731103206Used[t] +  0.292427873146047CorrectAnalysis[t] +  0.0635211726862047Useful[t] +  0.0466628346837525Outcome[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199456&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Weeks[t] =  +  3.12215877450008 +  0.0240600096945622UseLimit[t] +  0.457803059764332T40[t] -0.615927691063681T20[t] -0.130947731103206Used[t] +  0.292427873146047CorrectAnalysis[t] +  0.0635211726862047Useful[t] +  0.0466628346837525Outcome[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199456&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199456&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Weeks[t] = + 3.12215877450008 + 0.0240600096945622UseLimit[t] + 0.457803059764332T40[t] -0.615927691063681T20[t] -0.130947731103206Used[t] + 0.292427873146047CorrectAnalysis[t] + 0.0635211726862047Useful[t] + 0.0466628346837525Outcome[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.122158774500080.08873935.183500
UseLimit0.02406000969456220.0414840.580.5628190.281409
T400.4578030597643320.0464439.857300
T20-0.6159276910636810.046632-13.208400
Used-0.1309477311032060.048688-2.68950.0079890.003995
CorrectAnalysis0.2924278731460470.0792043.69210.0003140.000157
Useful0.06352117268620470.0454131.39870.1640140.082007
Outcome0.04666283468375250.0394881.18170.2392530.119626

\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) & 3.12215877450008 & 0.088739 & 35.1835 & 0 & 0 \tabularnewline
UseLimit & 0.0240600096945622 & 0.041484 & 0.58 & 0.562819 & 0.281409 \tabularnewline
T40 & 0.457803059764332 & 0.046443 & 9.8573 & 0 & 0 \tabularnewline
T20 & -0.615927691063681 & 0.046632 & -13.2084 & 0 & 0 \tabularnewline
Used & -0.130947731103206 & 0.048688 & -2.6895 & 0.007989 & 0.003995 \tabularnewline
CorrectAnalysis & 0.292427873146047 & 0.079204 & 3.6921 & 0.000314 & 0.000157 \tabularnewline
Useful & 0.0635211726862047 & 0.045413 & 1.3987 & 0.164014 & 0.082007 \tabularnewline
Outcome & 0.0466628346837525 & 0.039488 & 1.1817 & 0.239253 & 0.119626 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199456&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]3.12215877450008[/C][C]0.088739[/C][C]35.1835[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]UseLimit[/C][C]0.0240600096945622[/C][C]0.041484[/C][C]0.58[/C][C]0.562819[/C][C]0.281409[/C][/ROW]
[ROW][C]T40[/C][C]0.457803059764332[/C][C]0.046443[/C][C]9.8573[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]T20[/C][C]-0.615927691063681[/C][C]0.046632[/C][C]-13.2084[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Used[/C][C]-0.130947731103206[/C][C]0.048688[/C][C]-2.6895[/C][C]0.007989[/C][C]0.003995[/C][/ROW]
[ROW][C]CorrectAnalysis[/C][C]0.292427873146047[/C][C]0.079204[/C][C]3.6921[/C][C]0.000314[/C][C]0.000157[/C][/ROW]
[ROW][C]Useful[/C][C]0.0635211726862047[/C][C]0.045413[/C][C]1.3987[/C][C]0.164014[/C][C]0.082007[/C][/ROW]
[ROW][C]Outcome[/C][C]0.0466628346837525[/C][C]0.039488[/C][C]1.1817[/C][C]0.239253[/C][C]0.119626[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199456&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199456&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.122158774500080.08873935.183500
UseLimit0.02406000969456220.0414840.580.5628190.281409
T400.4578030597643320.0464439.857300
T20-0.6159276910636810.046632-13.208400
Used-0.1309477311032060.048688-2.68950.0079890.003995
CorrectAnalysis0.2924278731460470.0792043.69210.0003140.000157
Useful0.06352117268620470.0454131.39870.1640140.082007
Outcome0.04666283468375250.0394881.18170.2392530.119626







Multiple Linear Regression - Regression Statistics
Multiple R0.973534664318095
R-squared0.947769742628946
Adjusted R-squared0.945265552207046
F-TEST (value)378.473511575009
F-TEST (DF numerator)7
F-TEST (DF denominator)146
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.233108426182364
Sum Squared Residuals7.93357260015392

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.973534664318095 \tabularnewline
R-squared & 0.947769742628946 \tabularnewline
Adjusted R-squared & 0.945265552207046 \tabularnewline
F-TEST (value) & 378.473511575009 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 146 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.233108426182364 \tabularnewline
Sum Squared Residuals & 7.93357260015392 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199456&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.973534664318095[/C][/ROW]
[ROW][C]R-squared[/C][C]0.947769742628946[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.945265552207046[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]378.473511575009[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]146[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.233108426182364[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]7.93357260015392[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199456&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199456&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.973534664318095
R-squared0.947769742628946
Adjusted R-squared0.945265552207046
F-TEST (value)378.473511575009
F-TEST (DF numerator)7
F-TEST (DF denominator)146
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.233108426182364
Sum Squared Residuals7.93357260015392







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
143.650684678642730.349315321357272
244.03776489402875-0.037764894028747
344.03776489402875-0.0377648940287478
444.03776489402875-0.0377648940287474
544.03776489402875-0.0377648940287473
644.17200891109327-0.172008911093267
744.03776489402875-0.0377648940287474
843.579961834264420.420038165735584
944.0844277287125-0.0844277287124999
1044.06182490372331-0.0618249037233096
1143.604021843958980.395978156041022
1244.03776489402875-0.0377648940287474
1343.970338335611750.0296616643882534
1443.604021843958980.395978156041022
1544.0170011702955-0.0170011702954991
1643.559198110531170.440801889468833
1743.829023158688020.170976841311976
1843.604021843958980.395978156041022
1944.0844277287125-0.0844277287124999
2043.851625983677210.148374016322786
2144.12534607640951-0.125346076409514
2244.04106117999006-0.0410611799900612
2344.1479489013987-0.147948901398705
2444.17200891109327-0.172008911093267
2543.495676937844960.504323062155037
2643.970338335611750.0296616643882534
2744.10848773840706-0.108487738407062
2843.906817162925540.0931828370744582
2944.0844277287125-0.0844277287124999
3044.10128606671495-0.101286066714952
3144.03776489402875-0.0377648940287474
3244.06182490372331-0.0618249037233096
3344.12534607640951-0.125346076409514
3443.626624668948170.373375331051832
3544.03776489402875-0.0377648940287474
3644.03776489402875-0.0377648940287474
3743.536595285541980.463404714458023
3843.953479997609290.0465200023907057
3944.1479489013987-0.147948901398705
4043.643483006950620.35651699304938
4144.30942904344155-0.309429043441546
4243.953479997609290.0465200023907057
4344.17200891109327-0.172008911093267
4443.604021843958980.395978156041022
4544.10128606671495-0.101286066714952
4644.1479489013987-0.147948901398705
4744.03776489402875-0.0377648940287474
4844.0844277287125-0.0844277287124999
4944.1479489013987-0.147948901398705
5044.03776489402875-0.0377648940287474
5143.449014103161210.55098589683879
5243.829023158688020.170976841311976
5344.0844277287125-0.0844277287124999
5444.19924503607159-0.199245036071588
5544.03776489402875-0.0377648940287474
5643.495676937844960.504323062155037
5744.0170011702955-0.0170011702954991
5844.0844277287125-0.0844277287124999
5944.0844277287125-0.0844277287124999
6043.875685993371780.124314006628224
6143.650684678642730.34931532135727
6243.970338335611750.0296616643882534
6344.03776489402875-0.0377648940287474
6443.650684678642730.34931532135727
6544.03776489402875-0.0377648940287474
6644.03776489402875-0.0377648940287474
6743.804963148993460.195036851006538
6844.06182490372331-0.0618249037233096
6944.0844277287125-0.0844277287124999
7043.906817162925540.0931828370744582
7144.03776489402875-0.0377648940287474
7244.0844277287125-0.0844277287124999
7343.953479997609290.0465200023907057
7443.93087717262010.069122827379896
7544.0844277287125-0.0844277287124999
7643.690145841634370.309854158365627
7744.0844277287125-0.0844277287124999
7844.0170011702955-0.0170011702954991
7943.788104810991010.211895189008991
8043.643483006950620.35651699304938
8144.03776489402875-0.0377648940287474
8243.977540007303860.0224599926961435
8344.03776489402875-0.0377648940287474
8444.19924503607159-0.199245036071588
8544.1479489013987-0.147948901398705
8644.06182490372331-0.0618249037233096
8721.961026236751040.0389737632489639
8822.44600619671151-0.446006196711512
8921.890303392372720.109696607627279
9021.936966227056470.0630337729435261
9121.953824565058930.0461754349410738
9222.53029109313097-0.530291093130965
9321.977884574753490.0221154252465116
9421.890303392372720.109696607627279
9522.5062310834364-0.506231083436403
9621.936966227056470.0630337729435261
9722.53029109313097-0.530291093130965
9821.890303392372720.109696607627279
9921.914363402067280.0856365979327164
10021.936966227056470.0630337729435261
10121.961026236751040.0389737632489639
10221.890303392372720.109696607627279
10321.890303392372720.109696607627279
10421.890303392372720.109696607627279
10522.3752833523332-0.375283352333197
10621.890303392372720.109696607627279
10721.890303392372720.109696607627279
10822.39934336202776-0.399343362027759
10921.890303392372720.109696607627279
11021.914363402067280.0856365979327164
11122.46286453471396-0.462864534713964
11222.5062310834364-0.506231083436403
11321.759355661269520.240644338730484
11422.39934336202776-0.399343362027759
11521.914363402067280.0856365979327164
11621.890303392372720.109696607627279
11721.961026236751040.0389737632489639
11821.914363402067280.0856365979327164
11921.890303392372720.109696607627279
12021.936966227056470.0630337729435261
12121.914363402067280.0856365979327164
12221.890303392372720.109696607627279
12322.39934336202776-0.399343362027759
12421.869539668639470.130460331360527
12521.936966227056470.0630337729435261
12622.5062310834364-0.506231083436403
12721.953824565058930.0461754349410738
12821.936966227056470.0630337729435261
12921.890303392372720.109696607627279
13021.936966227056470.0630337729435261
13121.914363402067280.0856365979327164
13221.961026236751040.0389737632489639
13321.783415670964080.216584329035922
13421.890303392372720.109696607627279
13521.890303392372720.109696607627279
13621.890303392372720.109696607627279
13721.893599678334040.106400321665965
13822.50952736939772-0.509527369397717
13922.5062310834364-0.506231083436403
14021.890303392372720.109696607627279
14122.09844636909932-0.098446369099315
14222.42194618701695-0.42194618701695
14321.914363402067280.0856365979327164
14422.00048739974268-0.0004873997426787
14521.953824565058930.0461754349410738
14622.55289391812016-0.552893918120155
14722.3752833523332-0.375283352333197
14822.5062310834364-0.506231083436403
14921.914363402067280.0856365979327164
15022.00048739974268-0.0004873997426787
15121.936966227056470.0630337729435261
15222.07584354411012-0.0758435441101246
15322.13936471679633-0.139364716796329
15421.783415670964080.216584329035922

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4 & 3.65068467864273 & 0.349315321357272 \tabularnewline
2 & 4 & 4.03776489402875 & -0.037764894028747 \tabularnewline
3 & 4 & 4.03776489402875 & -0.0377648940287478 \tabularnewline
4 & 4 & 4.03776489402875 & -0.0377648940287474 \tabularnewline
5 & 4 & 4.03776489402875 & -0.0377648940287473 \tabularnewline
6 & 4 & 4.17200891109327 & -0.172008911093267 \tabularnewline
7 & 4 & 4.03776489402875 & -0.0377648940287474 \tabularnewline
8 & 4 & 3.57996183426442 & 0.420038165735584 \tabularnewline
9 & 4 & 4.0844277287125 & -0.0844277287124999 \tabularnewline
10 & 4 & 4.06182490372331 & -0.0618249037233096 \tabularnewline
11 & 4 & 3.60402184395898 & 0.395978156041022 \tabularnewline
12 & 4 & 4.03776489402875 & -0.0377648940287474 \tabularnewline
13 & 4 & 3.97033833561175 & 0.0296616643882534 \tabularnewline
14 & 4 & 3.60402184395898 & 0.395978156041022 \tabularnewline
15 & 4 & 4.0170011702955 & -0.0170011702954991 \tabularnewline
16 & 4 & 3.55919811053117 & 0.440801889468833 \tabularnewline
17 & 4 & 3.82902315868802 & 0.170976841311976 \tabularnewline
18 & 4 & 3.60402184395898 & 0.395978156041022 \tabularnewline
19 & 4 & 4.0844277287125 & -0.0844277287124999 \tabularnewline
20 & 4 & 3.85162598367721 & 0.148374016322786 \tabularnewline
21 & 4 & 4.12534607640951 & -0.125346076409514 \tabularnewline
22 & 4 & 4.04106117999006 & -0.0410611799900612 \tabularnewline
23 & 4 & 4.1479489013987 & -0.147948901398705 \tabularnewline
24 & 4 & 4.17200891109327 & -0.172008911093267 \tabularnewline
25 & 4 & 3.49567693784496 & 0.504323062155037 \tabularnewline
26 & 4 & 3.97033833561175 & 0.0296616643882534 \tabularnewline
27 & 4 & 4.10848773840706 & -0.108487738407062 \tabularnewline
28 & 4 & 3.90681716292554 & 0.0931828370744582 \tabularnewline
29 & 4 & 4.0844277287125 & -0.0844277287124999 \tabularnewline
30 & 4 & 4.10128606671495 & -0.101286066714952 \tabularnewline
31 & 4 & 4.03776489402875 & -0.0377648940287474 \tabularnewline
32 & 4 & 4.06182490372331 & -0.0618249037233096 \tabularnewline
33 & 4 & 4.12534607640951 & -0.125346076409514 \tabularnewline
34 & 4 & 3.62662466894817 & 0.373375331051832 \tabularnewline
35 & 4 & 4.03776489402875 & -0.0377648940287474 \tabularnewline
36 & 4 & 4.03776489402875 & -0.0377648940287474 \tabularnewline
37 & 4 & 3.53659528554198 & 0.463404714458023 \tabularnewline
38 & 4 & 3.95347999760929 & 0.0465200023907057 \tabularnewline
39 & 4 & 4.1479489013987 & -0.147948901398705 \tabularnewline
40 & 4 & 3.64348300695062 & 0.35651699304938 \tabularnewline
41 & 4 & 4.30942904344155 & -0.309429043441546 \tabularnewline
42 & 4 & 3.95347999760929 & 0.0465200023907057 \tabularnewline
43 & 4 & 4.17200891109327 & -0.172008911093267 \tabularnewline
44 & 4 & 3.60402184395898 & 0.395978156041022 \tabularnewline
45 & 4 & 4.10128606671495 & -0.101286066714952 \tabularnewline
46 & 4 & 4.1479489013987 & -0.147948901398705 \tabularnewline
47 & 4 & 4.03776489402875 & -0.0377648940287474 \tabularnewline
48 & 4 & 4.0844277287125 & -0.0844277287124999 \tabularnewline
49 & 4 & 4.1479489013987 & -0.147948901398705 \tabularnewline
50 & 4 & 4.03776489402875 & -0.0377648940287474 \tabularnewline
51 & 4 & 3.44901410316121 & 0.55098589683879 \tabularnewline
52 & 4 & 3.82902315868802 & 0.170976841311976 \tabularnewline
53 & 4 & 4.0844277287125 & -0.0844277287124999 \tabularnewline
54 & 4 & 4.19924503607159 & -0.199245036071588 \tabularnewline
55 & 4 & 4.03776489402875 & -0.0377648940287474 \tabularnewline
56 & 4 & 3.49567693784496 & 0.504323062155037 \tabularnewline
57 & 4 & 4.0170011702955 & -0.0170011702954991 \tabularnewline
58 & 4 & 4.0844277287125 & -0.0844277287124999 \tabularnewline
59 & 4 & 4.0844277287125 & -0.0844277287124999 \tabularnewline
60 & 4 & 3.87568599337178 & 0.124314006628224 \tabularnewline
61 & 4 & 3.65068467864273 & 0.34931532135727 \tabularnewline
62 & 4 & 3.97033833561175 & 0.0296616643882534 \tabularnewline
63 & 4 & 4.03776489402875 & -0.0377648940287474 \tabularnewline
64 & 4 & 3.65068467864273 & 0.34931532135727 \tabularnewline
65 & 4 & 4.03776489402875 & -0.0377648940287474 \tabularnewline
66 & 4 & 4.03776489402875 & -0.0377648940287474 \tabularnewline
67 & 4 & 3.80496314899346 & 0.195036851006538 \tabularnewline
68 & 4 & 4.06182490372331 & -0.0618249037233096 \tabularnewline
69 & 4 & 4.0844277287125 & -0.0844277287124999 \tabularnewline
70 & 4 & 3.90681716292554 & 0.0931828370744582 \tabularnewline
71 & 4 & 4.03776489402875 & -0.0377648940287474 \tabularnewline
72 & 4 & 4.0844277287125 & -0.0844277287124999 \tabularnewline
73 & 4 & 3.95347999760929 & 0.0465200023907057 \tabularnewline
74 & 4 & 3.9308771726201 & 0.069122827379896 \tabularnewline
75 & 4 & 4.0844277287125 & -0.0844277287124999 \tabularnewline
76 & 4 & 3.69014584163437 & 0.309854158365627 \tabularnewline
77 & 4 & 4.0844277287125 & -0.0844277287124999 \tabularnewline
78 & 4 & 4.0170011702955 & -0.0170011702954991 \tabularnewline
79 & 4 & 3.78810481099101 & 0.211895189008991 \tabularnewline
80 & 4 & 3.64348300695062 & 0.35651699304938 \tabularnewline
81 & 4 & 4.03776489402875 & -0.0377648940287474 \tabularnewline
82 & 4 & 3.97754000730386 & 0.0224599926961435 \tabularnewline
83 & 4 & 4.03776489402875 & -0.0377648940287474 \tabularnewline
84 & 4 & 4.19924503607159 & -0.199245036071588 \tabularnewline
85 & 4 & 4.1479489013987 & -0.147948901398705 \tabularnewline
86 & 4 & 4.06182490372331 & -0.0618249037233096 \tabularnewline
87 & 2 & 1.96102623675104 & 0.0389737632489639 \tabularnewline
88 & 2 & 2.44600619671151 & -0.446006196711512 \tabularnewline
89 & 2 & 1.89030339237272 & 0.109696607627279 \tabularnewline
90 & 2 & 1.93696622705647 & 0.0630337729435261 \tabularnewline
91 & 2 & 1.95382456505893 & 0.0461754349410738 \tabularnewline
92 & 2 & 2.53029109313097 & -0.530291093130965 \tabularnewline
93 & 2 & 1.97788457475349 & 0.0221154252465116 \tabularnewline
94 & 2 & 1.89030339237272 & 0.109696607627279 \tabularnewline
95 & 2 & 2.5062310834364 & -0.506231083436403 \tabularnewline
96 & 2 & 1.93696622705647 & 0.0630337729435261 \tabularnewline
97 & 2 & 2.53029109313097 & -0.530291093130965 \tabularnewline
98 & 2 & 1.89030339237272 & 0.109696607627279 \tabularnewline
99 & 2 & 1.91436340206728 & 0.0856365979327164 \tabularnewline
100 & 2 & 1.93696622705647 & 0.0630337729435261 \tabularnewline
101 & 2 & 1.96102623675104 & 0.0389737632489639 \tabularnewline
102 & 2 & 1.89030339237272 & 0.109696607627279 \tabularnewline
103 & 2 & 1.89030339237272 & 0.109696607627279 \tabularnewline
104 & 2 & 1.89030339237272 & 0.109696607627279 \tabularnewline
105 & 2 & 2.3752833523332 & -0.375283352333197 \tabularnewline
106 & 2 & 1.89030339237272 & 0.109696607627279 \tabularnewline
107 & 2 & 1.89030339237272 & 0.109696607627279 \tabularnewline
108 & 2 & 2.39934336202776 & -0.399343362027759 \tabularnewline
109 & 2 & 1.89030339237272 & 0.109696607627279 \tabularnewline
110 & 2 & 1.91436340206728 & 0.0856365979327164 \tabularnewline
111 & 2 & 2.46286453471396 & -0.462864534713964 \tabularnewline
112 & 2 & 2.5062310834364 & -0.506231083436403 \tabularnewline
113 & 2 & 1.75935566126952 & 0.240644338730484 \tabularnewline
114 & 2 & 2.39934336202776 & -0.399343362027759 \tabularnewline
115 & 2 & 1.91436340206728 & 0.0856365979327164 \tabularnewline
116 & 2 & 1.89030339237272 & 0.109696607627279 \tabularnewline
117 & 2 & 1.96102623675104 & 0.0389737632489639 \tabularnewline
118 & 2 & 1.91436340206728 & 0.0856365979327164 \tabularnewline
119 & 2 & 1.89030339237272 & 0.109696607627279 \tabularnewline
120 & 2 & 1.93696622705647 & 0.0630337729435261 \tabularnewline
121 & 2 & 1.91436340206728 & 0.0856365979327164 \tabularnewline
122 & 2 & 1.89030339237272 & 0.109696607627279 \tabularnewline
123 & 2 & 2.39934336202776 & -0.399343362027759 \tabularnewline
124 & 2 & 1.86953966863947 & 0.130460331360527 \tabularnewline
125 & 2 & 1.93696622705647 & 0.0630337729435261 \tabularnewline
126 & 2 & 2.5062310834364 & -0.506231083436403 \tabularnewline
127 & 2 & 1.95382456505893 & 0.0461754349410738 \tabularnewline
128 & 2 & 1.93696622705647 & 0.0630337729435261 \tabularnewline
129 & 2 & 1.89030339237272 & 0.109696607627279 \tabularnewline
130 & 2 & 1.93696622705647 & 0.0630337729435261 \tabularnewline
131 & 2 & 1.91436340206728 & 0.0856365979327164 \tabularnewline
132 & 2 & 1.96102623675104 & 0.0389737632489639 \tabularnewline
133 & 2 & 1.78341567096408 & 0.216584329035922 \tabularnewline
134 & 2 & 1.89030339237272 & 0.109696607627279 \tabularnewline
135 & 2 & 1.89030339237272 & 0.109696607627279 \tabularnewline
136 & 2 & 1.89030339237272 & 0.109696607627279 \tabularnewline
137 & 2 & 1.89359967833404 & 0.106400321665965 \tabularnewline
138 & 2 & 2.50952736939772 & -0.509527369397717 \tabularnewline
139 & 2 & 2.5062310834364 & -0.506231083436403 \tabularnewline
140 & 2 & 1.89030339237272 & 0.109696607627279 \tabularnewline
141 & 2 & 2.09844636909932 & -0.098446369099315 \tabularnewline
142 & 2 & 2.42194618701695 & -0.42194618701695 \tabularnewline
143 & 2 & 1.91436340206728 & 0.0856365979327164 \tabularnewline
144 & 2 & 2.00048739974268 & -0.0004873997426787 \tabularnewline
145 & 2 & 1.95382456505893 & 0.0461754349410738 \tabularnewline
146 & 2 & 2.55289391812016 & -0.552893918120155 \tabularnewline
147 & 2 & 2.3752833523332 & -0.375283352333197 \tabularnewline
148 & 2 & 2.5062310834364 & -0.506231083436403 \tabularnewline
149 & 2 & 1.91436340206728 & 0.0856365979327164 \tabularnewline
150 & 2 & 2.00048739974268 & -0.0004873997426787 \tabularnewline
151 & 2 & 1.93696622705647 & 0.0630337729435261 \tabularnewline
152 & 2 & 2.07584354411012 & -0.0758435441101246 \tabularnewline
153 & 2 & 2.13936471679633 & -0.139364716796329 \tabularnewline
154 & 2 & 1.78341567096408 & 0.216584329035922 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199456&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]4[/C][C]3.65068467864273[/C][C]0.349315321357272[/C][/ROW]
[ROW][C]2[/C][C]4[/C][C]4.03776489402875[/C][C]-0.037764894028747[/C][/ROW]
[ROW][C]3[/C][C]4[/C][C]4.03776489402875[/C][C]-0.0377648940287478[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]4.03776489402875[/C][C]-0.0377648940287474[/C][/ROW]
[ROW][C]5[/C][C]4[/C][C]4.03776489402875[/C][C]-0.0377648940287473[/C][/ROW]
[ROW][C]6[/C][C]4[/C][C]4.17200891109327[/C][C]-0.172008911093267[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]4.03776489402875[/C][C]-0.0377648940287474[/C][/ROW]
[ROW][C]8[/C][C]4[/C][C]3.57996183426442[/C][C]0.420038165735584[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]4.0844277287125[/C][C]-0.0844277287124999[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]4.06182490372331[/C][C]-0.0618249037233096[/C][/ROW]
[ROW][C]11[/C][C]4[/C][C]3.60402184395898[/C][C]0.395978156041022[/C][/ROW]
[ROW][C]12[/C][C]4[/C][C]4.03776489402875[/C][C]-0.0377648940287474[/C][/ROW]
[ROW][C]13[/C][C]4[/C][C]3.97033833561175[/C][C]0.0296616643882534[/C][/ROW]
[ROW][C]14[/C][C]4[/C][C]3.60402184395898[/C][C]0.395978156041022[/C][/ROW]
[ROW][C]15[/C][C]4[/C][C]4.0170011702955[/C][C]-0.0170011702954991[/C][/ROW]
[ROW][C]16[/C][C]4[/C][C]3.55919811053117[/C][C]0.440801889468833[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]3.82902315868802[/C][C]0.170976841311976[/C][/ROW]
[ROW][C]18[/C][C]4[/C][C]3.60402184395898[/C][C]0.395978156041022[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]4.0844277287125[/C][C]-0.0844277287124999[/C][/ROW]
[ROW][C]20[/C][C]4[/C][C]3.85162598367721[/C][C]0.148374016322786[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]4.12534607640951[/C][C]-0.125346076409514[/C][/ROW]
[ROW][C]22[/C][C]4[/C][C]4.04106117999006[/C][C]-0.0410611799900612[/C][/ROW]
[ROW][C]23[/C][C]4[/C][C]4.1479489013987[/C][C]-0.147948901398705[/C][/ROW]
[ROW][C]24[/C][C]4[/C][C]4.17200891109327[/C][C]-0.172008911093267[/C][/ROW]
[ROW][C]25[/C][C]4[/C][C]3.49567693784496[/C][C]0.504323062155037[/C][/ROW]
[ROW][C]26[/C][C]4[/C][C]3.97033833561175[/C][C]0.0296616643882534[/C][/ROW]
[ROW][C]27[/C][C]4[/C][C]4.10848773840706[/C][C]-0.108487738407062[/C][/ROW]
[ROW][C]28[/C][C]4[/C][C]3.90681716292554[/C][C]0.0931828370744582[/C][/ROW]
[ROW][C]29[/C][C]4[/C][C]4.0844277287125[/C][C]-0.0844277287124999[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]4.10128606671495[/C][C]-0.101286066714952[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]4.03776489402875[/C][C]-0.0377648940287474[/C][/ROW]
[ROW][C]32[/C][C]4[/C][C]4.06182490372331[/C][C]-0.0618249037233096[/C][/ROW]
[ROW][C]33[/C][C]4[/C][C]4.12534607640951[/C][C]-0.125346076409514[/C][/ROW]
[ROW][C]34[/C][C]4[/C][C]3.62662466894817[/C][C]0.373375331051832[/C][/ROW]
[ROW][C]35[/C][C]4[/C][C]4.03776489402875[/C][C]-0.0377648940287474[/C][/ROW]
[ROW][C]36[/C][C]4[/C][C]4.03776489402875[/C][C]-0.0377648940287474[/C][/ROW]
[ROW][C]37[/C][C]4[/C][C]3.53659528554198[/C][C]0.463404714458023[/C][/ROW]
[ROW][C]38[/C][C]4[/C][C]3.95347999760929[/C][C]0.0465200023907057[/C][/ROW]
[ROW][C]39[/C][C]4[/C][C]4.1479489013987[/C][C]-0.147948901398705[/C][/ROW]
[ROW][C]40[/C][C]4[/C][C]3.64348300695062[/C][C]0.35651699304938[/C][/ROW]
[ROW][C]41[/C][C]4[/C][C]4.30942904344155[/C][C]-0.309429043441546[/C][/ROW]
[ROW][C]42[/C][C]4[/C][C]3.95347999760929[/C][C]0.0465200023907057[/C][/ROW]
[ROW][C]43[/C][C]4[/C][C]4.17200891109327[/C][C]-0.172008911093267[/C][/ROW]
[ROW][C]44[/C][C]4[/C][C]3.60402184395898[/C][C]0.395978156041022[/C][/ROW]
[ROW][C]45[/C][C]4[/C][C]4.10128606671495[/C][C]-0.101286066714952[/C][/ROW]
[ROW][C]46[/C][C]4[/C][C]4.1479489013987[/C][C]-0.147948901398705[/C][/ROW]
[ROW][C]47[/C][C]4[/C][C]4.03776489402875[/C][C]-0.0377648940287474[/C][/ROW]
[ROW][C]48[/C][C]4[/C][C]4.0844277287125[/C][C]-0.0844277287124999[/C][/ROW]
[ROW][C]49[/C][C]4[/C][C]4.1479489013987[/C][C]-0.147948901398705[/C][/ROW]
[ROW][C]50[/C][C]4[/C][C]4.03776489402875[/C][C]-0.0377648940287474[/C][/ROW]
[ROW][C]51[/C][C]4[/C][C]3.44901410316121[/C][C]0.55098589683879[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]3.82902315868802[/C][C]0.170976841311976[/C][/ROW]
[ROW][C]53[/C][C]4[/C][C]4.0844277287125[/C][C]-0.0844277287124999[/C][/ROW]
[ROW][C]54[/C][C]4[/C][C]4.19924503607159[/C][C]-0.199245036071588[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]4.03776489402875[/C][C]-0.0377648940287474[/C][/ROW]
[ROW][C]56[/C][C]4[/C][C]3.49567693784496[/C][C]0.504323062155037[/C][/ROW]
[ROW][C]57[/C][C]4[/C][C]4.0170011702955[/C][C]-0.0170011702954991[/C][/ROW]
[ROW][C]58[/C][C]4[/C][C]4.0844277287125[/C][C]-0.0844277287124999[/C][/ROW]
[ROW][C]59[/C][C]4[/C][C]4.0844277287125[/C][C]-0.0844277287124999[/C][/ROW]
[ROW][C]60[/C][C]4[/C][C]3.87568599337178[/C][C]0.124314006628224[/C][/ROW]
[ROW][C]61[/C][C]4[/C][C]3.65068467864273[/C][C]0.34931532135727[/C][/ROW]
[ROW][C]62[/C][C]4[/C][C]3.97033833561175[/C][C]0.0296616643882534[/C][/ROW]
[ROW][C]63[/C][C]4[/C][C]4.03776489402875[/C][C]-0.0377648940287474[/C][/ROW]
[ROW][C]64[/C][C]4[/C][C]3.65068467864273[/C][C]0.34931532135727[/C][/ROW]
[ROW][C]65[/C][C]4[/C][C]4.03776489402875[/C][C]-0.0377648940287474[/C][/ROW]
[ROW][C]66[/C][C]4[/C][C]4.03776489402875[/C][C]-0.0377648940287474[/C][/ROW]
[ROW][C]67[/C][C]4[/C][C]3.80496314899346[/C][C]0.195036851006538[/C][/ROW]
[ROW][C]68[/C][C]4[/C][C]4.06182490372331[/C][C]-0.0618249037233096[/C][/ROW]
[ROW][C]69[/C][C]4[/C][C]4.0844277287125[/C][C]-0.0844277287124999[/C][/ROW]
[ROW][C]70[/C][C]4[/C][C]3.90681716292554[/C][C]0.0931828370744582[/C][/ROW]
[ROW][C]71[/C][C]4[/C][C]4.03776489402875[/C][C]-0.0377648940287474[/C][/ROW]
[ROW][C]72[/C][C]4[/C][C]4.0844277287125[/C][C]-0.0844277287124999[/C][/ROW]
[ROW][C]73[/C][C]4[/C][C]3.95347999760929[/C][C]0.0465200023907057[/C][/ROW]
[ROW][C]74[/C][C]4[/C][C]3.9308771726201[/C][C]0.069122827379896[/C][/ROW]
[ROW][C]75[/C][C]4[/C][C]4.0844277287125[/C][C]-0.0844277287124999[/C][/ROW]
[ROW][C]76[/C][C]4[/C][C]3.69014584163437[/C][C]0.309854158365627[/C][/ROW]
[ROW][C]77[/C][C]4[/C][C]4.0844277287125[/C][C]-0.0844277287124999[/C][/ROW]
[ROW][C]78[/C][C]4[/C][C]4.0170011702955[/C][C]-0.0170011702954991[/C][/ROW]
[ROW][C]79[/C][C]4[/C][C]3.78810481099101[/C][C]0.211895189008991[/C][/ROW]
[ROW][C]80[/C][C]4[/C][C]3.64348300695062[/C][C]0.35651699304938[/C][/ROW]
[ROW][C]81[/C][C]4[/C][C]4.03776489402875[/C][C]-0.0377648940287474[/C][/ROW]
[ROW][C]82[/C][C]4[/C][C]3.97754000730386[/C][C]0.0224599926961435[/C][/ROW]
[ROW][C]83[/C][C]4[/C][C]4.03776489402875[/C][C]-0.0377648940287474[/C][/ROW]
[ROW][C]84[/C][C]4[/C][C]4.19924503607159[/C][C]-0.199245036071588[/C][/ROW]
[ROW][C]85[/C][C]4[/C][C]4.1479489013987[/C][C]-0.147948901398705[/C][/ROW]
[ROW][C]86[/C][C]4[/C][C]4.06182490372331[/C][C]-0.0618249037233096[/C][/ROW]
[ROW][C]87[/C][C]2[/C][C]1.96102623675104[/C][C]0.0389737632489639[/C][/ROW]
[ROW][C]88[/C][C]2[/C][C]2.44600619671151[/C][C]-0.446006196711512[/C][/ROW]
[ROW][C]89[/C][C]2[/C][C]1.89030339237272[/C][C]0.109696607627279[/C][/ROW]
[ROW][C]90[/C][C]2[/C][C]1.93696622705647[/C][C]0.0630337729435261[/C][/ROW]
[ROW][C]91[/C][C]2[/C][C]1.95382456505893[/C][C]0.0461754349410738[/C][/ROW]
[ROW][C]92[/C][C]2[/C][C]2.53029109313097[/C][C]-0.530291093130965[/C][/ROW]
[ROW][C]93[/C][C]2[/C][C]1.97788457475349[/C][C]0.0221154252465116[/C][/ROW]
[ROW][C]94[/C][C]2[/C][C]1.89030339237272[/C][C]0.109696607627279[/C][/ROW]
[ROW][C]95[/C][C]2[/C][C]2.5062310834364[/C][C]-0.506231083436403[/C][/ROW]
[ROW][C]96[/C][C]2[/C][C]1.93696622705647[/C][C]0.0630337729435261[/C][/ROW]
[ROW][C]97[/C][C]2[/C][C]2.53029109313097[/C][C]-0.530291093130965[/C][/ROW]
[ROW][C]98[/C][C]2[/C][C]1.89030339237272[/C][C]0.109696607627279[/C][/ROW]
[ROW][C]99[/C][C]2[/C][C]1.91436340206728[/C][C]0.0856365979327164[/C][/ROW]
[ROW][C]100[/C][C]2[/C][C]1.93696622705647[/C][C]0.0630337729435261[/C][/ROW]
[ROW][C]101[/C][C]2[/C][C]1.96102623675104[/C][C]0.0389737632489639[/C][/ROW]
[ROW][C]102[/C][C]2[/C][C]1.89030339237272[/C][C]0.109696607627279[/C][/ROW]
[ROW][C]103[/C][C]2[/C][C]1.89030339237272[/C][C]0.109696607627279[/C][/ROW]
[ROW][C]104[/C][C]2[/C][C]1.89030339237272[/C][C]0.109696607627279[/C][/ROW]
[ROW][C]105[/C][C]2[/C][C]2.3752833523332[/C][C]-0.375283352333197[/C][/ROW]
[ROW][C]106[/C][C]2[/C][C]1.89030339237272[/C][C]0.109696607627279[/C][/ROW]
[ROW][C]107[/C][C]2[/C][C]1.89030339237272[/C][C]0.109696607627279[/C][/ROW]
[ROW][C]108[/C][C]2[/C][C]2.39934336202776[/C][C]-0.399343362027759[/C][/ROW]
[ROW][C]109[/C][C]2[/C][C]1.89030339237272[/C][C]0.109696607627279[/C][/ROW]
[ROW][C]110[/C][C]2[/C][C]1.91436340206728[/C][C]0.0856365979327164[/C][/ROW]
[ROW][C]111[/C][C]2[/C][C]2.46286453471396[/C][C]-0.462864534713964[/C][/ROW]
[ROW][C]112[/C][C]2[/C][C]2.5062310834364[/C][C]-0.506231083436403[/C][/ROW]
[ROW][C]113[/C][C]2[/C][C]1.75935566126952[/C][C]0.240644338730484[/C][/ROW]
[ROW][C]114[/C][C]2[/C][C]2.39934336202776[/C][C]-0.399343362027759[/C][/ROW]
[ROW][C]115[/C][C]2[/C][C]1.91436340206728[/C][C]0.0856365979327164[/C][/ROW]
[ROW][C]116[/C][C]2[/C][C]1.89030339237272[/C][C]0.109696607627279[/C][/ROW]
[ROW][C]117[/C][C]2[/C][C]1.96102623675104[/C][C]0.0389737632489639[/C][/ROW]
[ROW][C]118[/C][C]2[/C][C]1.91436340206728[/C][C]0.0856365979327164[/C][/ROW]
[ROW][C]119[/C][C]2[/C][C]1.89030339237272[/C][C]0.109696607627279[/C][/ROW]
[ROW][C]120[/C][C]2[/C][C]1.93696622705647[/C][C]0.0630337729435261[/C][/ROW]
[ROW][C]121[/C][C]2[/C][C]1.91436340206728[/C][C]0.0856365979327164[/C][/ROW]
[ROW][C]122[/C][C]2[/C][C]1.89030339237272[/C][C]0.109696607627279[/C][/ROW]
[ROW][C]123[/C][C]2[/C][C]2.39934336202776[/C][C]-0.399343362027759[/C][/ROW]
[ROW][C]124[/C][C]2[/C][C]1.86953966863947[/C][C]0.130460331360527[/C][/ROW]
[ROW][C]125[/C][C]2[/C][C]1.93696622705647[/C][C]0.0630337729435261[/C][/ROW]
[ROW][C]126[/C][C]2[/C][C]2.5062310834364[/C][C]-0.506231083436403[/C][/ROW]
[ROW][C]127[/C][C]2[/C][C]1.95382456505893[/C][C]0.0461754349410738[/C][/ROW]
[ROW][C]128[/C][C]2[/C][C]1.93696622705647[/C][C]0.0630337729435261[/C][/ROW]
[ROW][C]129[/C][C]2[/C][C]1.89030339237272[/C][C]0.109696607627279[/C][/ROW]
[ROW][C]130[/C][C]2[/C][C]1.93696622705647[/C][C]0.0630337729435261[/C][/ROW]
[ROW][C]131[/C][C]2[/C][C]1.91436340206728[/C][C]0.0856365979327164[/C][/ROW]
[ROW][C]132[/C][C]2[/C][C]1.96102623675104[/C][C]0.0389737632489639[/C][/ROW]
[ROW][C]133[/C][C]2[/C][C]1.78341567096408[/C][C]0.216584329035922[/C][/ROW]
[ROW][C]134[/C][C]2[/C][C]1.89030339237272[/C][C]0.109696607627279[/C][/ROW]
[ROW][C]135[/C][C]2[/C][C]1.89030339237272[/C][C]0.109696607627279[/C][/ROW]
[ROW][C]136[/C][C]2[/C][C]1.89030339237272[/C][C]0.109696607627279[/C][/ROW]
[ROW][C]137[/C][C]2[/C][C]1.89359967833404[/C][C]0.106400321665965[/C][/ROW]
[ROW][C]138[/C][C]2[/C][C]2.50952736939772[/C][C]-0.509527369397717[/C][/ROW]
[ROW][C]139[/C][C]2[/C][C]2.5062310834364[/C][C]-0.506231083436403[/C][/ROW]
[ROW][C]140[/C][C]2[/C][C]1.89030339237272[/C][C]0.109696607627279[/C][/ROW]
[ROW][C]141[/C][C]2[/C][C]2.09844636909932[/C][C]-0.098446369099315[/C][/ROW]
[ROW][C]142[/C][C]2[/C][C]2.42194618701695[/C][C]-0.42194618701695[/C][/ROW]
[ROW][C]143[/C][C]2[/C][C]1.91436340206728[/C][C]0.0856365979327164[/C][/ROW]
[ROW][C]144[/C][C]2[/C][C]2.00048739974268[/C][C]-0.0004873997426787[/C][/ROW]
[ROW][C]145[/C][C]2[/C][C]1.95382456505893[/C][C]0.0461754349410738[/C][/ROW]
[ROW][C]146[/C][C]2[/C][C]2.55289391812016[/C][C]-0.552893918120155[/C][/ROW]
[ROW][C]147[/C][C]2[/C][C]2.3752833523332[/C][C]-0.375283352333197[/C][/ROW]
[ROW][C]148[/C][C]2[/C][C]2.5062310834364[/C][C]-0.506231083436403[/C][/ROW]
[ROW][C]149[/C][C]2[/C][C]1.91436340206728[/C][C]0.0856365979327164[/C][/ROW]
[ROW][C]150[/C][C]2[/C][C]2.00048739974268[/C][C]-0.0004873997426787[/C][/ROW]
[ROW][C]151[/C][C]2[/C][C]1.93696622705647[/C][C]0.0630337729435261[/C][/ROW]
[ROW][C]152[/C][C]2[/C][C]2.07584354411012[/C][C]-0.0758435441101246[/C][/ROW]
[ROW][C]153[/C][C]2[/C][C]2.13936471679633[/C][C]-0.139364716796329[/C][/ROW]
[ROW][C]154[/C][C]2[/C][C]1.78341567096408[/C][C]0.216584329035922[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199456&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199456&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
143.650684678642730.349315321357272
244.03776489402875-0.037764894028747
344.03776489402875-0.0377648940287478
444.03776489402875-0.0377648940287474
544.03776489402875-0.0377648940287473
644.17200891109327-0.172008911093267
744.03776489402875-0.0377648940287474
843.579961834264420.420038165735584
944.0844277287125-0.0844277287124999
1044.06182490372331-0.0618249037233096
1143.604021843958980.395978156041022
1244.03776489402875-0.0377648940287474
1343.970338335611750.0296616643882534
1443.604021843958980.395978156041022
1544.0170011702955-0.0170011702954991
1643.559198110531170.440801889468833
1743.829023158688020.170976841311976
1843.604021843958980.395978156041022
1944.0844277287125-0.0844277287124999
2043.851625983677210.148374016322786
2144.12534607640951-0.125346076409514
2244.04106117999006-0.0410611799900612
2344.1479489013987-0.147948901398705
2444.17200891109327-0.172008911093267
2543.495676937844960.504323062155037
2643.970338335611750.0296616643882534
2744.10848773840706-0.108487738407062
2843.906817162925540.0931828370744582
2944.0844277287125-0.0844277287124999
3044.10128606671495-0.101286066714952
3144.03776489402875-0.0377648940287474
3244.06182490372331-0.0618249037233096
3344.12534607640951-0.125346076409514
3443.626624668948170.373375331051832
3544.03776489402875-0.0377648940287474
3644.03776489402875-0.0377648940287474
3743.536595285541980.463404714458023
3843.953479997609290.0465200023907057
3944.1479489013987-0.147948901398705
4043.643483006950620.35651699304938
4144.30942904344155-0.309429043441546
4243.953479997609290.0465200023907057
4344.17200891109327-0.172008911093267
4443.604021843958980.395978156041022
4544.10128606671495-0.101286066714952
4644.1479489013987-0.147948901398705
4744.03776489402875-0.0377648940287474
4844.0844277287125-0.0844277287124999
4944.1479489013987-0.147948901398705
5044.03776489402875-0.0377648940287474
5143.449014103161210.55098589683879
5243.829023158688020.170976841311976
5344.0844277287125-0.0844277287124999
5444.19924503607159-0.199245036071588
5544.03776489402875-0.0377648940287474
5643.495676937844960.504323062155037
5744.0170011702955-0.0170011702954991
5844.0844277287125-0.0844277287124999
5944.0844277287125-0.0844277287124999
6043.875685993371780.124314006628224
6143.650684678642730.34931532135727
6243.970338335611750.0296616643882534
6344.03776489402875-0.0377648940287474
6443.650684678642730.34931532135727
6544.03776489402875-0.0377648940287474
6644.03776489402875-0.0377648940287474
6743.804963148993460.195036851006538
6844.06182490372331-0.0618249037233096
6944.0844277287125-0.0844277287124999
7043.906817162925540.0931828370744582
7144.03776489402875-0.0377648940287474
7244.0844277287125-0.0844277287124999
7343.953479997609290.0465200023907057
7443.93087717262010.069122827379896
7544.0844277287125-0.0844277287124999
7643.690145841634370.309854158365627
7744.0844277287125-0.0844277287124999
7844.0170011702955-0.0170011702954991
7943.788104810991010.211895189008991
8043.643483006950620.35651699304938
8144.03776489402875-0.0377648940287474
8243.977540007303860.0224599926961435
8344.03776489402875-0.0377648940287474
8444.19924503607159-0.199245036071588
8544.1479489013987-0.147948901398705
8644.06182490372331-0.0618249037233096
8721.961026236751040.0389737632489639
8822.44600619671151-0.446006196711512
8921.890303392372720.109696607627279
9021.936966227056470.0630337729435261
9121.953824565058930.0461754349410738
9222.53029109313097-0.530291093130965
9321.977884574753490.0221154252465116
9421.890303392372720.109696607627279
9522.5062310834364-0.506231083436403
9621.936966227056470.0630337729435261
9722.53029109313097-0.530291093130965
9821.890303392372720.109696607627279
9921.914363402067280.0856365979327164
10021.936966227056470.0630337729435261
10121.961026236751040.0389737632489639
10221.890303392372720.109696607627279
10321.890303392372720.109696607627279
10421.890303392372720.109696607627279
10522.3752833523332-0.375283352333197
10621.890303392372720.109696607627279
10721.890303392372720.109696607627279
10822.39934336202776-0.399343362027759
10921.890303392372720.109696607627279
11021.914363402067280.0856365979327164
11122.46286453471396-0.462864534713964
11222.5062310834364-0.506231083436403
11321.759355661269520.240644338730484
11422.39934336202776-0.399343362027759
11521.914363402067280.0856365979327164
11621.890303392372720.109696607627279
11721.961026236751040.0389737632489639
11821.914363402067280.0856365979327164
11921.890303392372720.109696607627279
12021.936966227056470.0630337729435261
12121.914363402067280.0856365979327164
12221.890303392372720.109696607627279
12322.39934336202776-0.399343362027759
12421.869539668639470.130460331360527
12521.936966227056470.0630337729435261
12622.5062310834364-0.506231083436403
12721.953824565058930.0461754349410738
12821.936966227056470.0630337729435261
12921.890303392372720.109696607627279
13021.936966227056470.0630337729435261
13121.914363402067280.0856365979327164
13221.961026236751040.0389737632489639
13321.783415670964080.216584329035922
13421.890303392372720.109696607627279
13521.890303392372720.109696607627279
13621.890303392372720.109696607627279
13721.893599678334040.106400321665965
13822.50952736939772-0.509527369397717
13922.5062310834364-0.506231083436403
14021.890303392372720.109696607627279
14122.09844636909932-0.098446369099315
14222.42194618701695-0.42194618701695
14321.914363402067280.0856365979327164
14422.00048739974268-0.0004873997426787
14521.953824565058930.0461754349410738
14622.55289391812016-0.552893918120155
14722.3752833523332-0.375283352333197
14822.5062310834364-0.506231083436403
14921.914363402067280.0856365979327164
15022.00048739974268-0.0004873997426787
15121.936966227056470.0630337729435261
15222.07584354411012-0.0758435441101246
15322.13936471679633-0.139364716796329
15421.783415670964080.216584329035922







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
119.03506347918299e-481.8070126958366e-471
123.20626257281013e-596.41252514562025e-591
132.795071948978e-845.59014389795601e-841
148.9328405056152e-881.78656810112304e-871
153.47654186837015e-1026.9530837367403e-1021
16001
171.32177691364694e-1412.64355382729387e-1411
185.63295962419065e-1471.12659192483813e-1461
192.651778006317e-1605.30355601263401e-1601
201.05476923636003e-1832.10953847272006e-1831
211.71305456876909e-2153.42610913753818e-2151
229.8104908098296e-2071.96209816196592e-2061
231.00343225329699e-2172.00686450659398e-2171
243.13172667059181e-2356.26345334118363e-2351
259.33632547023392e-2531.86726509404678e-2521
265.3003114947839e-2931.06006229895678e-2921
279.03175813031148e-2811.8063516260623e-2801
281.367809490404e-2902.73561898080801e-2901
294.42612067857667e-3108.85224135715333e-3101
30001
31001
32001
33001
34001
35001
36001
37001
38001
39001
40001
41001
42001
43001
44001
45001
46001
47001
48001
49001
50001
51001
52001
53001
54001
55001
56001
57001
58001
59001
60001
61001
62001
63001
64001
65001
66001
67001
68001
69001
70001
71001
72001
73001
74001
75001
76001
77001
78001
79001
8011.50379188914343e-697.51895944571716e-70
810.9999867815454442.64369091115982e-051.32184545557991e-05
820.9768569299562180.04628614008756440.0231430700437822
8312.06851483078373e-621.03425741539187e-62
841.27346233440329e-612.54692466880657e-611
8514.83036160830791e-682.41518080415396e-68
8611.72556705773827e-188.62783528869134e-19
871.60308715223474e-233.20617430446947e-231
88100
89100
90100
91100
92100
93100
94100
95100
96100
97100
98100
99100
100100
101100
102100
103100
104100
105100
106100
107100
108100
109100
110100
111100
112100
113100
114100
115100
116100
117100
118100
119100
120100
121100
122100
123100
124100
12512.09288502019211e-3151.04644251009606e-315
12613.68766736720773e-2961.84383368360387e-296
12711.50991039508717e-2857.54955197543587e-286
12813.48774596328724e-2971.74387298164362e-297
12917.56396178867496e-2573.78198089433748e-257
13011.19942788249676e-2385.99713941248381e-239
13117.71008253465043e-2223.85504126732521e-222
13214.78685891976034e-2102.39342945988017e-210
13316.3732325494721e-2183.18661627473605e-218
13417.00981797600438e-1863.50490898800219e-186
13512.37121508363949e-1621.18560754181975e-162
13613.60186676609673e-1491.80093338304836e-149
13711.27633580935966e-1446.38167904679829e-145
138100
13911.10039880719778e-1035.50199403598892e-104
14011.51451209276217e-897.57256046381086e-90
14114.40736631583747e-852.20368315791874e-85
14211.27357320360738e-596.36786601803688e-60
14316.25975718097643e-483.12987859048821e-48

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 9.03506347918299e-48 & 1.8070126958366e-47 & 1 \tabularnewline
12 & 3.20626257281013e-59 & 6.41252514562025e-59 & 1 \tabularnewline
13 & 2.795071948978e-84 & 5.59014389795601e-84 & 1 \tabularnewline
14 & 8.9328405056152e-88 & 1.78656810112304e-87 & 1 \tabularnewline
15 & 3.47654186837015e-102 & 6.9530837367403e-102 & 1 \tabularnewline
16 & 0 & 0 & 1 \tabularnewline
17 & 1.32177691364694e-141 & 2.64355382729387e-141 & 1 \tabularnewline
18 & 5.63295962419065e-147 & 1.12659192483813e-146 & 1 \tabularnewline
19 & 2.651778006317e-160 & 5.30355601263401e-160 & 1 \tabularnewline
20 & 1.05476923636003e-183 & 2.10953847272006e-183 & 1 \tabularnewline
21 & 1.71305456876909e-215 & 3.42610913753818e-215 & 1 \tabularnewline
22 & 9.8104908098296e-207 & 1.96209816196592e-206 & 1 \tabularnewline
23 & 1.00343225329699e-217 & 2.00686450659398e-217 & 1 \tabularnewline
24 & 3.13172667059181e-235 & 6.26345334118363e-235 & 1 \tabularnewline
25 & 9.33632547023392e-253 & 1.86726509404678e-252 & 1 \tabularnewline
26 & 5.3003114947839e-293 & 1.06006229895678e-292 & 1 \tabularnewline
27 & 9.03175813031148e-281 & 1.8063516260623e-280 & 1 \tabularnewline
28 & 1.367809490404e-290 & 2.73561898080801e-290 & 1 \tabularnewline
29 & 4.42612067857667e-310 & 8.85224135715333e-310 & 1 \tabularnewline
30 & 0 & 0 & 1 \tabularnewline
31 & 0 & 0 & 1 \tabularnewline
32 & 0 & 0 & 1 \tabularnewline
33 & 0 & 0 & 1 \tabularnewline
34 & 0 & 0 & 1 \tabularnewline
35 & 0 & 0 & 1 \tabularnewline
36 & 0 & 0 & 1 \tabularnewline
37 & 0 & 0 & 1 \tabularnewline
38 & 0 & 0 & 1 \tabularnewline
39 & 0 & 0 & 1 \tabularnewline
40 & 0 & 0 & 1 \tabularnewline
41 & 0 & 0 & 1 \tabularnewline
42 & 0 & 0 & 1 \tabularnewline
43 & 0 & 0 & 1 \tabularnewline
44 & 0 & 0 & 1 \tabularnewline
45 & 0 & 0 & 1 \tabularnewline
46 & 0 & 0 & 1 \tabularnewline
47 & 0 & 0 & 1 \tabularnewline
48 & 0 & 0 & 1 \tabularnewline
49 & 0 & 0 & 1 \tabularnewline
50 & 0 & 0 & 1 \tabularnewline
51 & 0 & 0 & 1 \tabularnewline
52 & 0 & 0 & 1 \tabularnewline
53 & 0 & 0 & 1 \tabularnewline
54 & 0 & 0 & 1 \tabularnewline
55 & 0 & 0 & 1 \tabularnewline
56 & 0 & 0 & 1 \tabularnewline
57 & 0 & 0 & 1 \tabularnewline
58 & 0 & 0 & 1 \tabularnewline
59 & 0 & 0 & 1 \tabularnewline
60 & 0 & 0 & 1 \tabularnewline
61 & 0 & 0 & 1 \tabularnewline
62 & 0 & 0 & 1 \tabularnewline
63 & 0 & 0 & 1 \tabularnewline
64 & 0 & 0 & 1 \tabularnewline
65 & 0 & 0 & 1 \tabularnewline
66 & 0 & 0 & 1 \tabularnewline
67 & 0 & 0 & 1 \tabularnewline
68 & 0 & 0 & 1 \tabularnewline
69 & 0 & 0 & 1 \tabularnewline
70 & 0 & 0 & 1 \tabularnewline
71 & 0 & 0 & 1 \tabularnewline
72 & 0 & 0 & 1 \tabularnewline
73 & 0 & 0 & 1 \tabularnewline
74 & 0 & 0 & 1 \tabularnewline
75 & 0 & 0 & 1 \tabularnewline
76 & 0 & 0 & 1 \tabularnewline
77 & 0 & 0 & 1 \tabularnewline
78 & 0 & 0 & 1 \tabularnewline
79 & 0 & 0 & 1 \tabularnewline
80 & 1 & 1.50379188914343e-69 & 7.51895944571716e-70 \tabularnewline
81 & 0.999986781545444 & 2.64369091115982e-05 & 1.32184545557991e-05 \tabularnewline
82 & 0.976856929956218 & 0.0462861400875644 & 0.0231430700437822 \tabularnewline
83 & 1 & 2.06851483078373e-62 & 1.03425741539187e-62 \tabularnewline
84 & 1.27346233440329e-61 & 2.54692466880657e-61 & 1 \tabularnewline
85 & 1 & 4.83036160830791e-68 & 2.41518080415396e-68 \tabularnewline
86 & 1 & 1.72556705773827e-18 & 8.62783528869134e-19 \tabularnewline
87 & 1.60308715223474e-23 & 3.20617430446947e-23 & 1 \tabularnewline
88 & 1 & 0 & 0 \tabularnewline
89 & 1 & 0 & 0 \tabularnewline
90 & 1 & 0 & 0 \tabularnewline
91 & 1 & 0 & 0 \tabularnewline
92 & 1 & 0 & 0 \tabularnewline
93 & 1 & 0 & 0 \tabularnewline
94 & 1 & 0 & 0 \tabularnewline
95 & 1 & 0 & 0 \tabularnewline
96 & 1 & 0 & 0 \tabularnewline
97 & 1 & 0 & 0 \tabularnewline
98 & 1 & 0 & 0 \tabularnewline
99 & 1 & 0 & 0 \tabularnewline
100 & 1 & 0 & 0 \tabularnewline
101 & 1 & 0 & 0 \tabularnewline
102 & 1 & 0 & 0 \tabularnewline
103 & 1 & 0 & 0 \tabularnewline
104 & 1 & 0 & 0 \tabularnewline
105 & 1 & 0 & 0 \tabularnewline
106 & 1 & 0 & 0 \tabularnewline
107 & 1 & 0 & 0 \tabularnewline
108 & 1 & 0 & 0 \tabularnewline
109 & 1 & 0 & 0 \tabularnewline
110 & 1 & 0 & 0 \tabularnewline
111 & 1 & 0 & 0 \tabularnewline
112 & 1 & 0 & 0 \tabularnewline
113 & 1 & 0 & 0 \tabularnewline
114 & 1 & 0 & 0 \tabularnewline
115 & 1 & 0 & 0 \tabularnewline
116 & 1 & 0 & 0 \tabularnewline
117 & 1 & 0 & 0 \tabularnewline
118 & 1 & 0 & 0 \tabularnewline
119 & 1 & 0 & 0 \tabularnewline
120 & 1 & 0 & 0 \tabularnewline
121 & 1 & 0 & 0 \tabularnewline
122 & 1 & 0 & 0 \tabularnewline
123 & 1 & 0 & 0 \tabularnewline
124 & 1 & 0 & 0 \tabularnewline
125 & 1 & 2.09288502019211e-315 & 1.04644251009606e-315 \tabularnewline
126 & 1 & 3.68766736720773e-296 & 1.84383368360387e-296 \tabularnewline
127 & 1 & 1.50991039508717e-285 & 7.54955197543587e-286 \tabularnewline
128 & 1 & 3.48774596328724e-297 & 1.74387298164362e-297 \tabularnewline
129 & 1 & 7.56396178867496e-257 & 3.78198089433748e-257 \tabularnewline
130 & 1 & 1.19942788249676e-238 & 5.99713941248381e-239 \tabularnewline
131 & 1 & 7.71008253465043e-222 & 3.85504126732521e-222 \tabularnewline
132 & 1 & 4.78685891976034e-210 & 2.39342945988017e-210 \tabularnewline
133 & 1 & 6.3732325494721e-218 & 3.18661627473605e-218 \tabularnewline
134 & 1 & 7.00981797600438e-186 & 3.50490898800219e-186 \tabularnewline
135 & 1 & 2.37121508363949e-162 & 1.18560754181975e-162 \tabularnewline
136 & 1 & 3.60186676609673e-149 & 1.80093338304836e-149 \tabularnewline
137 & 1 & 1.27633580935966e-144 & 6.38167904679829e-145 \tabularnewline
138 & 1 & 0 & 0 \tabularnewline
139 & 1 & 1.10039880719778e-103 & 5.50199403598892e-104 \tabularnewline
140 & 1 & 1.51451209276217e-89 & 7.57256046381086e-90 \tabularnewline
141 & 1 & 4.40736631583747e-85 & 2.20368315791874e-85 \tabularnewline
142 & 1 & 1.27357320360738e-59 & 6.36786601803688e-60 \tabularnewline
143 & 1 & 6.25975718097643e-48 & 3.12987859048821e-48 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199456&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]9.03506347918299e-48[/C][C]1.8070126958366e-47[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]3.20626257281013e-59[/C][C]6.41252514562025e-59[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]2.795071948978e-84[/C][C]5.59014389795601e-84[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]8.9328405056152e-88[/C][C]1.78656810112304e-87[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]3.47654186837015e-102[/C][C]6.9530837367403e-102[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]1.32177691364694e-141[/C][C]2.64355382729387e-141[/C][C]1[/C][/ROW]
[ROW][C]18[/C][C]5.63295962419065e-147[/C][C]1.12659192483813e-146[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]2.651778006317e-160[/C][C]5.30355601263401e-160[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]1.05476923636003e-183[/C][C]2.10953847272006e-183[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]1.71305456876909e-215[/C][C]3.42610913753818e-215[/C][C]1[/C][/ROW]
[ROW][C]22[/C][C]9.8104908098296e-207[/C][C]1.96209816196592e-206[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]1.00343225329699e-217[/C][C]2.00686450659398e-217[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]3.13172667059181e-235[/C][C]6.26345334118363e-235[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]9.33632547023392e-253[/C][C]1.86726509404678e-252[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]5.3003114947839e-293[/C][C]1.06006229895678e-292[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]9.03175813031148e-281[/C][C]1.8063516260623e-280[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]1.367809490404e-290[/C][C]2.73561898080801e-290[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]4.42612067857667e-310[/C][C]8.85224135715333e-310[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]60[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]67[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]79[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]1.50379188914343e-69[/C][C]7.51895944571716e-70[/C][/ROW]
[ROW][C]81[/C][C]0.999986781545444[/C][C]2.64369091115982e-05[/C][C]1.32184545557991e-05[/C][/ROW]
[ROW][C]82[/C][C]0.976856929956218[/C][C]0.0462861400875644[/C][C]0.0231430700437822[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]2.06851483078373e-62[/C][C]1.03425741539187e-62[/C][/ROW]
[ROW][C]84[/C][C]1.27346233440329e-61[/C][C]2.54692466880657e-61[/C][C]1[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]4.83036160830791e-68[/C][C]2.41518080415396e-68[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]1.72556705773827e-18[/C][C]8.62783528869134e-19[/C][/ROW]
[ROW][C]87[/C][C]1.60308715223474e-23[/C][C]3.20617430446947e-23[/C][C]1[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]104[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]116[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]119[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]122[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]2.09288502019211e-315[/C][C]1.04644251009606e-315[/C][/ROW]
[ROW][C]126[/C][C]1[/C][C]3.68766736720773e-296[/C][C]1.84383368360387e-296[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]1.50991039508717e-285[/C][C]7.54955197543587e-286[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]3.48774596328724e-297[/C][C]1.74387298164362e-297[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]7.56396178867496e-257[/C][C]3.78198089433748e-257[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]1.19942788249676e-238[/C][C]5.99713941248381e-239[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]7.71008253465043e-222[/C][C]3.85504126732521e-222[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]4.78685891976034e-210[/C][C]2.39342945988017e-210[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]6.3732325494721e-218[/C][C]3.18661627473605e-218[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]7.00981797600438e-186[/C][C]3.50490898800219e-186[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]2.37121508363949e-162[/C][C]1.18560754181975e-162[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]3.60186676609673e-149[/C][C]1.80093338304836e-149[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]1.27633580935966e-144[/C][C]6.38167904679829e-145[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]139[/C][C]1[/C][C]1.10039880719778e-103[/C][C]5.50199403598892e-104[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]1.51451209276217e-89[/C][C]7.57256046381086e-90[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]4.40736631583747e-85[/C][C]2.20368315791874e-85[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]1.27357320360738e-59[/C][C]6.36786601803688e-60[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]6.25975718097643e-48[/C][C]3.12987859048821e-48[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199456&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199456&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
119.03506347918299e-481.8070126958366e-471
123.20626257281013e-596.41252514562025e-591
132.795071948978e-845.59014389795601e-841
148.9328405056152e-881.78656810112304e-871
153.47654186837015e-1026.9530837367403e-1021
16001
171.32177691364694e-1412.64355382729387e-1411
185.63295962419065e-1471.12659192483813e-1461
192.651778006317e-1605.30355601263401e-1601
201.05476923636003e-1832.10953847272006e-1831
211.71305456876909e-2153.42610913753818e-2151
229.8104908098296e-2071.96209816196592e-2061
231.00343225329699e-2172.00686450659398e-2171
243.13172667059181e-2356.26345334118363e-2351
259.33632547023392e-2531.86726509404678e-2521
265.3003114947839e-2931.06006229895678e-2921
279.03175813031148e-2811.8063516260623e-2801
281.367809490404e-2902.73561898080801e-2901
294.42612067857667e-3108.85224135715333e-3101
30001
31001
32001
33001
34001
35001
36001
37001
38001
39001
40001
41001
42001
43001
44001
45001
46001
47001
48001
49001
50001
51001
52001
53001
54001
55001
56001
57001
58001
59001
60001
61001
62001
63001
64001
65001
66001
67001
68001
69001
70001
71001
72001
73001
74001
75001
76001
77001
78001
79001
8011.50379188914343e-697.51895944571716e-70
810.9999867815454442.64369091115982e-051.32184545557991e-05
820.9768569299562180.04628614008756440.0231430700437822
8312.06851483078373e-621.03425741539187e-62
841.27346233440329e-612.54692466880657e-611
8514.83036160830791e-682.41518080415396e-68
8611.72556705773827e-188.62783528869134e-19
871.60308715223474e-233.20617430446947e-231
88100
89100
90100
91100
92100
93100
94100
95100
96100
97100
98100
99100
100100
101100
102100
103100
104100
105100
106100
107100
108100
109100
110100
111100
112100
113100
114100
115100
116100
117100
118100
119100
120100
121100
122100
123100
124100
12512.09288502019211e-3151.04644251009606e-315
12613.68766736720773e-2961.84383368360387e-296
12711.50991039508717e-2857.54955197543587e-286
12813.48774596328724e-2971.74387298164362e-297
12917.56396178867496e-2573.78198089433748e-257
13011.19942788249676e-2385.99713941248381e-239
13117.71008253465043e-2223.85504126732521e-222
13214.78685891976034e-2102.39342945988017e-210
13316.3732325494721e-2183.18661627473605e-218
13417.00981797600438e-1863.50490898800219e-186
13512.37121508363949e-1621.18560754181975e-162
13613.60186676609673e-1491.80093338304836e-149
13711.27633580935966e-1446.38167904679829e-145
138100
13911.10039880719778e-1035.50199403598892e-104
14011.51451209276217e-897.57256046381086e-90
14114.40736631583747e-852.20368315791874e-85
14211.27357320360738e-596.36786601803688e-60
14316.25975718097643e-483.12987859048821e-48







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1320.992481203007519NOK
5% type I error level1331NOK
10% type I error level1331NOK

\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 & 132 & 0.992481203007519 & NOK \tabularnewline
5% type I error level & 133 & 1 & NOK \tabularnewline
10% type I error level & 133 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199456&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]132[/C][C]0.992481203007519[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]133[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]133[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199456&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199456&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1320.992481203007519NOK
5% type I error level1331NOK
10% type I error level1331NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}