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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 22 Nov 2010 10:55:52 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/22/t1290423305qig4gczb8mce8s4.htm/, Retrieved Sat, 27 Apr 2024 23:28:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98445, Retrieved Sat, 27 Apr 2024 23:28:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact282

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [WS7 Tutorial Popu...] [2010-11-22 10:55:52] [aa6b599ccd367bc74fed0d8f67004a46] [Current]
-           [Multiple Regression] [] [2010-11-23 10:29:39] [3fb95cad3bbcce10c72dbbcc5bec5662]
-   PD      [Multiple Regression] [WS7 - Multiple re...] [2010-11-23 20:19:24] [1f5baf2b24e732d76900bb8178fc04e7]
-    D        [Multiple Regression] [WS7 - Multiple re...] [2010-11-23 20:47:17] [1f5baf2b24e732d76900bb8178fc04e7]
- RM            [Multiple Regression] [] [2011-11-23 00:30:44] [19d77e37efa419fdc040c74a96874aff]
-   PD      [Multiple Regression] [verbetering ] [2010-11-28 15:38:22] [8214fe6d084e5ad7598b249a26cc9f06]
-   PD      [Multiple Regression] [] [2010-12-02 19:12:54] [94f4aa1c01e87d8321fffb341ed4df07]
- R           [Multiple Regression] [] [2011-11-25 00:51:26] [74be16979710d4c4e7c6647856088456]
- R P           [Multiple Regression] [] [2011-11-27 17:12:25] [3931071255a6f7f4a767409781cc5f7d]
-   PD      [Multiple Regression] [minitutorial ws 4] [2010-12-03 15:39:39] [e4afca2801c0b93eac84a600ed82fb9c]
-             [Multiple Regression] [minitutorial ws 4] [2010-12-03 15:46:44] [e4afca2801c0b93eac84a600ed82fb9c]
-    D      [Multiple Regression] [paper] [2010-12-29 12:34:21] [52986265a8945c3b72cdef4e8a412754]
-             [Multiple Regression] [] [2010-12-29 14:00:34] [20c5a34fea7ed3b9b27ff444f2eb4dfe]
- R         [Multiple Regression] [WS 7 -3 ] [2011-11-22 21:48:55] [74be16979710d4c4e7c6647856088456]
- R         [Multiple Regression] [] [2012-11-19 09:25:27] [d1865ed705b6ad9ba3d459a02c528b22]
- R P         [Multiple Regression] [] [2012-11-19 09:32:19] [d1865ed705b6ad9ba3d459a02c528b22]
- R  D      [Multiple Regression] [] [2012-11-19 19:26:45] [74be16979710d4c4e7c6647856088456]
- R         [Multiple Regression] [] [2012-11-20 03:56:45] [74be16979710d4c4e7c6647856088456]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9	13	13	14	13	3	1	1	0
9	12	12	8	13	5	1	0	0
9	15	10	12	16	6	0	0	0
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9	10	10	10	11	5	0	1	2
9	12	12	7	12	3	0	0	1
9	15	13	16	18	8	1	1	1
9	9	12	11	11	4	1	0	0
9	12	12	14	14	4	4	0	0
9	11	6	6	9	4	0	0	0
9	11	5	16	14	6	0	2	1
9	11	12	11	12	6	2	0	0
9	15	11	16	11	5	0	2	2
9	7	14	12	12	4	1	1	1
9	11	14	7	13	6	0	1	0
9	11	12	13	11	4	0	0	1
9	10	12	11	12	6	1	1	0
9	14	11	15	16	6	2	0	1
9	10	11	7	9	4	1	0	0
9	6	7	9	11	4	1	0	0
9	11	9	7	13	2	0	1	1
9	15	11	14	15	7	1	2	0
9	11	11	15	10	5	1	2	1
9	12	12	7	11	4	2	0	0
9	14	12	15	13	6	1	0	0
9	15	11	17	16	6	1	1	0
9	9	11	15	15	7	1	1	0
9	13	8	14	14	5	2	2	0
9	13	9	14	14	6	0	0	2
9	16	12	8	14	4	1	1	1
9	13	10	8	8	4	0	1	2
9	12	10	14	13	7	1	1	1
9	14	12	14	15	7	1	2	1
9	11	8	8	13	4	0	2	0
9	9	12	11	11	4	1	1	0
9	16	11	16	15	6	2	2	0
9	12	12	10	15	6	1	1	1
9	10	7	8	9	5	1	1	2
9	13	11	14	13	6	1	0	1
9	16	11	16	16	7	1	3	1
9	14	12	13	13	6	0	1	2
9	15	9	5	11	3	1	0	0
9	5	15	8	12	3	1	0	0
9	8	11	10	12	4	1	0	0
9	11	11	8	12	6	0	1	1
9	16	11	13	14	7	2	0	1
9	17	11	15	14	5	1	4	4
9	9	15	6	8	4	0	0	0
9	9	11	12	13	5	0	0	0
9	13	12	16	16	6	1	0	1
9	10	12	5	13	6	1	1	0
9	6	9	15	11	6	0	2	1
9	12	12	12	14	5	0	1	0
9	8	12	8	13	4	0	1	1
9	14	13	13	13	5	0	0	0
9	12	11	14	13	5	1	2	2
10	11	9	12	12	4	0	0	2
10	16	9	16	16	6	0	3	1
10	8	11	10	15	2	1	2	0
10	15	11	15	15	8	0	0	0
10	7	12	8	12	3	0	0	0
10	16	12	16	14	6	2	2	0
10	14	9	19	12	6	0	1	0
10	16	11	14	15	6	0	0	1
10	9	9	6	12	5	1	2	1
10	14	12	13	13	5	2	0	0
10	11	12	15	12	6	3	1	0
10	13	12	7	12	5	1	0	0
10	15	12	13	13	6	1	2	1
10	5	14	4	5	2	2	0	0
10	15	11	14	13	5	1	2	2
10	13	12	13	13	5	1	3	0
10	11	11	11	14	5	2	0	2
10	11	6	14	17	6	1	2	1
10	12	10	12	13	6	0	3	1
10	12	12	15	13	6	1	1	1
10	12	13	14	12	5	1	0	2
10	12	8	13	13	5	0	1	2
10	14	12	8	14	4	2	0	0
10	6	12	6	11	2	1	0	0
10	7	12	7	12	4	0	1	0
10	14	6	13	12	6	3	1	1
10	14	11	13	16	6	1	2	1
10	10	10	11	12	5	1	1	0
10	13	12	5	12	3	3	0	0
10	12	13	12	12	6	2	0	0
10	9	11	8	10	4	1	1	0
10	12	7	11	15	5	0	0	2
10	16	11	14	15	8	1	0	1
10	10	11	9	12	4	2	0	1
10	14	11	10	16	6	1	1	0
10	10	11	13	15	6	1	1	1
10	16	12	16	16	7	0	3	1
10	15	10	16	13	6	2	1	0
10	12	11	11	12	5	1	1	1
10	10	12	8	11	4	0	0	0
10	8	7	4	13	6	0	0	1
10	8	13	7	10	3	1	1	0
10	11	8	14	15	5	1	1	0
10	13	12	11	13	6	1	0	2
10	16	11	17	16	7	1	1	2
10	16	12	15	15	7	1	1	2
10	14	14	17	18	6	0	0	1
10	11	10	5	13	3	0	1	1
10	4	10	4	10	2	1	0	1
10	14	13	10	16	8	2	1	0
10	9	10	11	13	3	1	1	1
10	14	11	15	15	8	1	1	1
10	8	10	10	14	3	0	1	0
10	8	7	9	15	4	0	1	0
10	11	10	12	14	5	1	0	0
10	12	8	15	13	7	1	0	0
10	11	12	7	13	6	0	0	0
10	14	12	13	15	6	0	1	0
10	15	12	12	16	7	2	1	0
10	16	11	14	14	6	2	1	0
10	16	12	14	14	6	0	0	0
10	11	12	8	16	6	1	1	0
10	14	12	15	14	6	0	4	1
10	14	11	12	12	4	2	0	0
10	12	12	12	13	4	1	1	1
10	14	11	16	12	5	0	0	3
10	8	11	9	12	4	1	2	2
10	13	13	15	14	6	1	1	2
10	16	12	15	14	6	2	0	2
10	12	12	6	14	5	0	0	0
10	16	12	14	16	8	2	0	1
10	12	12	15	13	6	0	0	0
10	11	8	10	14	5	1	1	0
10	4	8	6	4	4	0	0	0
10	16	12	14	16	8	3	2	1
10	15	11	12	13	6	1	0	2
10	10	12	8	16	4	0	1	0
10	13	13	11	15	6	0	2	4
10	15	12	13	14	6	0	2	0
10	12	12	9	13	4	0	1	0
10	14	11	15	14	6	0	3	0
10	7	12	13	12	3	1	0	0
10	19	12	15	15	6	1	1	0
10	12	10	14	14	5	2	1	1
10	12	11	16	13	4	1	0	0
10	13	12	14	14	6	0	1	1
10	15	12	14	16	4	0	0	0
10	8	10	10	6	4	2	1	2
10	12	12	10	13	4	1	0	1
10	10	13	4	13	6	0	1	0
10	8	12	8	14	5	1	0	0
10	10	15	15	15	6	2	2	0
10	15	11	16	14	6	2	0	1
10	16	12	12	15	8	0	0	0
10	13	11	12	13	7	1	1	1
10	16	12	15	16	7	2	1	0
10	9	11	9	12	4	0	0	0
10	14	10	12	15	6	1	0	1
10	14	11	14	12	6	2	1	2
10	12	11	11	14	2	1	1	0

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98445&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98445&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98445&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = + 0.291123164546368 -0.0511359771771675month[t] + 0.100278815914936FindingFriends[t] + 0.211899695274710KnowingPeople[t] + 0.384406986575042Liked[t] + 0.591650456104061Celebrity[t] + 0.312334530791981bestfriend[t] -0.0295870251376916secondbestfriend[t] + 0.409233788979355thirdbestfriend[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Popularity[t] =  +  0.291123164546368 -0.0511359771771675month[t] +  0.100278815914936FindingFriends[t] +  0.211899695274710KnowingPeople[t] +  0.384406986575042Liked[t] +  0.591650456104061Celebrity[t] +  0.312334530791981bestfriend[t] -0.0295870251376916secondbestfriend[t] +  0.409233788979355thirdbestfriend[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98445&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Popularity[t] =  +  0.291123164546368 -0.0511359771771675month[t] +  0.100278815914936FindingFriends[t] +  0.211899695274710KnowingPeople[t] +  0.384406986575042Liked[t] +  0.591650456104061Celebrity[t] +  0.312334530791981bestfriend[t] -0.0295870251376916secondbestfriend[t] +  0.409233788979355thirdbestfriend[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98445&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98445&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
Popularity[t] = + 0.291123164546368 -0.0511359771771675month[t] + 0.100278815914936FindingFriends[t] + 0.211899695274710KnowingPeople[t] + 0.384406986575042Liked[t] + 0.591650456104061Celebrity[t] + 0.312334530791981bestfriend[t] -0.0295870251376916secondbestfriend[t] + 0.409233788979355thirdbestfriend[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.2911231645463683.5635910.08170.9350010.467501
month-0.05113597717716750.35484-0.14410.8856110.442805
FindingFriends0.1002788159149360.0970161.03360.3030070.151504
KnowingPeople0.2118996952747100.0638393.31930.0011380.000569
Liked0.3844069865750420.0986793.89550.0001487.4e-05
Celebrity0.5916504561040610.1561153.78980.0002190.00011
bestfriend0.3123345307919810.2105761.48320.1401520.070076
secondbestfriend-0.02958702513769160.201438-0.14690.8834290.441714
thirdbestfriend0.4092337889793550.2137521.91450.0574960.028748

\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) & 0.291123164546368 & 3.563591 & 0.0817 & 0.935001 & 0.467501 \tabularnewline
month & -0.0511359771771675 & 0.35484 & -0.1441 & 0.885611 & 0.442805 \tabularnewline
FindingFriends & 0.100278815914936 & 0.097016 & 1.0336 & 0.303007 & 0.151504 \tabularnewline
KnowingPeople & 0.211899695274710 & 0.063839 & 3.3193 & 0.001138 & 0.000569 \tabularnewline
Liked & 0.384406986575042 & 0.098679 & 3.8955 & 0.000148 & 7.4e-05 \tabularnewline
Celebrity & 0.591650456104061 & 0.156115 & 3.7898 & 0.000219 & 0.00011 \tabularnewline
bestfriend & 0.312334530791981 & 0.210576 & 1.4832 & 0.140152 & 0.070076 \tabularnewline
secondbestfriend & -0.0295870251376916 & 0.201438 & -0.1469 & 0.883429 & 0.441714 \tabularnewline
thirdbestfriend & 0.409233788979355 & 0.213752 & 1.9145 & 0.057496 & 0.028748 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98445&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]0.291123164546368[/C][C]3.563591[/C][C]0.0817[/C][C]0.935001[/C][C]0.467501[/C][/ROW]
[ROW][C]month[/C][C]-0.0511359771771675[/C][C]0.35484[/C][C]-0.1441[/C][C]0.885611[/C][C]0.442805[/C][/ROW]
[ROW][C]FindingFriends[/C][C]0.100278815914936[/C][C]0.097016[/C][C]1.0336[/C][C]0.303007[/C][C]0.151504[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.211899695274710[/C][C]0.063839[/C][C]3.3193[/C][C]0.001138[/C][C]0.000569[/C][/ROW]
[ROW][C]Liked[/C][C]0.384406986575042[/C][C]0.098679[/C][C]3.8955[/C][C]0.000148[/C][C]7.4e-05[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.591650456104061[/C][C]0.156115[/C][C]3.7898[/C][C]0.000219[/C][C]0.00011[/C][/ROW]
[ROW][C]bestfriend[/C][C]0.312334530791981[/C][C]0.210576[/C][C]1.4832[/C][C]0.140152[/C][C]0.070076[/C][/ROW]
[ROW][C]secondbestfriend[/C][C]-0.0295870251376916[/C][C]0.201438[/C][C]-0.1469[/C][C]0.883429[/C][C]0.441714[/C][/ROW]
[ROW][C]thirdbestfriend[/C][C]0.409233788979355[/C][C]0.213752[/C][C]1.9145[/C][C]0.057496[/C][C]0.028748[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98445&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98445&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)0.2911231645463683.5635910.08170.9350010.467501
month-0.05113597717716750.35484-0.14410.8856110.442805
FindingFriends0.1002788159149360.0970161.03360.3030070.151504
KnowingPeople0.2118996952747100.0638393.31930.0011380.000569
Liked0.3844069865750420.0986793.89550.0001487.4e-05
Celebrity0.5916504561040610.1561153.78980.0002190.00011
bestfriend0.3123345307919810.2105761.48320.1401520.070076
secondbestfriend-0.02958702513769160.201438-0.14690.8834290.441714
thirdbestfriend0.4092337889793550.2137521.91450.0574960.028748






Multiple Linear Regression - Regression Statistics
Multiple R0.718941993490984
R-squared0.51687759000479
Adjusted R-squared0.490585213950629
F-TEST (value)19.6588390847615
F-TEST (DF numerator)8
F-TEST (DF denominator)147
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.09596780395353
Sum Squared Residuals645.782912175841

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.718941993490984 \tabularnewline
R-squared & 0.51687759000479 \tabularnewline
Adjusted R-squared & 0.490585213950629 \tabularnewline
F-TEST (value) & 19.6588390847615 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 147 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.09596780395353 \tabularnewline
Sum Squared Residuals & 645.782912175841 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98445&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.718941993490984[/C][/ROW]
[ROW][C]R-squared[/C][C]0.51687759000479[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.490585213950629[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]19.6588390847615[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]147[/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]2.09596780395353[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]645.782912175841[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98445&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98445&T=3

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

Multiple Linear Regression - Regression Statistics
Multiple R0.718941993490984
R-squared0.51687759000479
Adjusted R-squared0.490585213950629
F-TEST (value)19.6588390847615
F-TEST (DF numerator)8
F-TEST (DF denominator)147
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.09596780395353
Sum Squared Residuals645.782912175841






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11311.15610941013391.84389058986607
21210.99732035991661.00267964008338
31513.07689839422281.92310160577722
41211.41339600619740.586603993802554
51010.9282941675151-0.928294167515113
6129.314612024046112.68538797595389
71516.8694298030583-1.86942980305829
8910.2725550164866-1.27255501648660
91212.9984786544118-0.998478654411798
10117.530235140681363.46976485931864
111113.0043488613008-2.00434886130083
121112.1525974460617-1.15259744606174
131512.27038412994062.72961587005938
14711.4490660940079-4.44906609400788
151111.2357071966462-0.235707196646161
161110.79325366522340.206746334776610
171011.8106758901321-1.81067589013207
181414.8467791465252-0.84677914652517
19108.555863446322731.44413655367727
2069.34736154636249-3.34736154636249
21118.776945081634592.22305491836541
221514.06138055073280.93861944926724
231111.5771781899035-0.577178189903492
24129.737290766179742.26270923382026
251413.07226868294360.927731317056356
261514.51942319216560.480576807834437
27914.3028672711452-5.30286727114516
281312.50517073499680.494829265003233
291313.4500725736659-0.450072573665895
301611.16972365422934.83027634577075
31138.75962336113654.2403766388635
321213.6311085757848-1.63110857578479
331414.5708931556271-0.570893155627051
34119.633046059085441.36695394091456
35910.2429679913489-1.24296799134890
361614.20586401597011.7941359840299
371213.1612309435618-1.16123094356184
38109.747178886862780.252821113137221
391313.1693239607334-0.169323960733352
401615.24923369169890.750766308301114
411413.12501531442330.87498468557674
42158.108669940989466.89133005901054
4359.73044890887825-4.73044890887825
44810.3447834918720-2.34478349187199
451111.1715972465804-0.171597246580375
461614.24581623892971.75418376107027
471714.28333345286632.71666654713366
4898.048337497340740.951662502659255
49911.4323057943085-2.43230579430853
501314.8466231269228-1.84662312692284
511010.9236847050589-0.92368470505885
52612.0403434699607-6.04034346996074
531211.88740457166080.112595428339179
54810.4729821368622-2.47298213686223
551411.84476312141312.15523687858688
561212.9277332433333-0.927733243333263
571111.0230223205811-0.0230223205810992
581614.09355509579581.90644490420420
59810.2043935119364-2.20439351193644
601514.56063424441780.439365755582235
6179.0661419531643-2.06614195316430
621613.87059986813282.12940013186717
631412.84156649661581.15843350338421
641613.57466742591432.42533257408571
65910.1872012965741-1.18720129657414
661412.31801738990501.68198261009502
671113.2318077556377-2.23180775563770
681310.34987770088972.65012229911031
691512.94739305392102.05260694607897
7055.76127050334993-0.761270503349934
711512.87659726615612.12340273384390
721311.91692178369991.08307821630008
731112.9968137479744-1.99681374797437
741114.0952478000063-3.09524780000629
751212.1930141708868-0.193014170886771
761213.4007794696081-1.40077946960814
771212.7519219616863-0.751921961686308
781212.0811136174823-0.0811136174822852
791411.05127544400242.94872455599760
8067.97861965072775-1.97861965072775
8179.41630568885595-2.41630568885595
821412.61556925857801.38443074142198
831414.0003351977312-0.00033519773121668
841010.9673318250210-0.967331825020967
85139.367446459716113.63255354028389
861212.4136399800742-0.41363998007422
8799.07144712585763-0.0714471258576263
881212.3554364093057-0.355436409305704
891615.07030286891440.929697131085609
901010.8033161391914-0.803316139191449
911412.98498934806541.01501065193458
921013.6455152362939-3.64551523629387
931614.98604199964471.01395800035533
941513.31522227486561.68477772513440
951211.47684442991530.523155570084742
96109.273385422693310.726614577306685
97810.2857412363574-2.28574123635735
9888.46845460630873-0.468454606308727
991112.5556942387404-1.55569423874035
1001312.99200150262630.0079984973736558
1011615.87840524905120.121594750948834
1021615.17047768784160.82952231215836
1031415.6644239192084-1.66442391920835
104118.9939389859272.00606101407300
10547.37908943075277-3.37908943075277
1061414.6811824228954-0.681182422895399
107910.5776716883672-1.57767168836724
1081415.2526155390514-1.25261553905141
109810.0286106598962-2.02861065989624
110810.4919319595558-2.49193195955582
1111111.9776325185835-0.977632518583453
1121213.2116678982108-1.21166789821079
1131111.0136006127768-0.0136006127768117
1141413.02422573243750.975774267562534
1151514.41305254142580.586947458574178
1161613.37610868680622.62389131319384
1171612.88130546627483.11869453372517
1181112.6614687734309-1.66146877343094
1191413.38409084997810.615909150021876
1201411.02978143603622.97021856396378
1211211.58177947157590.418220528424115
1221413.07206297859320.92793702140677
123810.8410413471034-2.84104134710344
1241314.2946990610775-1.29469906107747
1251614.53634180109221.46365819890779
1261210.59445744797311.40554255202692
1271615.86732320219640.132676797803649
1281212.7087981749745-0.708798174974494
1291111.3236884710665-0.323688471066468
13045.75762186245885-1.75762186245885
1311616.1204836827130-0.120483682712950
1321513.10362238198611.89637761801388
1331011.1658333304308-1.16583333043083
1341314.3080532885827-1.30805328858271
1351512.61023172072472.38976827927527
1361210.22451206598041.77548793401958
1371412.90416527022151.09583472977847
138710.4379749603298-3.43797496032983
1391913.76035965377895.23964034622113
1401213.0934132037665-1.09341320376652
1411211.94945267291810.0505473270818735
1421313.2609522301165-0.260952230116489
1431512.46681852721682.53318147278321
14488.98814186294263-0.988141862942634
1451211.18756710616420.812432893835844
1461010.4485933177299-0.448593317729926
147811.3305913693145-3.33059136931448
1481014.3439436071780-4.34394360717797
1491514.23872889147260.761271108527372
1501614.02521397450861.97478602549143
1511313.2564520239731-0.256452023973133
1521615.04875162725000.951248372750046
15399.76941328862813-0.769413288628131
1541413.36292375024190.637076249758089
1551413.42576229161480.574237708385213
1561210.06147324577381.93852675422620

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 11.1561094101339 & 1.84389058986607 \tabularnewline
2 & 12 & 10.9973203599166 & 1.00267964008338 \tabularnewline
3 & 15 & 13.0768983942228 & 1.92310160577722 \tabularnewline
4 & 12 & 11.4133960061974 & 0.586603993802554 \tabularnewline
5 & 10 & 10.9282941675151 & -0.928294167515113 \tabularnewline
6 & 12 & 9.31461202404611 & 2.68538797595389 \tabularnewline
7 & 15 & 16.8694298030583 & -1.86942980305829 \tabularnewline
8 & 9 & 10.2725550164866 & -1.27255501648660 \tabularnewline
9 & 12 & 12.9984786544118 & -0.998478654411798 \tabularnewline
10 & 11 & 7.53023514068136 & 3.46976485931864 \tabularnewline
11 & 11 & 13.0043488613008 & -2.00434886130083 \tabularnewline
12 & 11 & 12.1525974460617 & -1.15259744606174 \tabularnewline
13 & 15 & 12.2703841299406 & 2.72961587005938 \tabularnewline
14 & 7 & 11.4490660940079 & -4.44906609400788 \tabularnewline
15 & 11 & 11.2357071966462 & -0.235707196646161 \tabularnewline
16 & 11 & 10.7932536652234 & 0.206746334776610 \tabularnewline
17 & 10 & 11.8106758901321 & -1.81067589013207 \tabularnewline
18 & 14 & 14.8467791465252 & -0.84677914652517 \tabularnewline
19 & 10 & 8.55586344632273 & 1.44413655367727 \tabularnewline
20 & 6 & 9.34736154636249 & -3.34736154636249 \tabularnewline
21 & 11 & 8.77694508163459 & 2.22305491836541 \tabularnewline
22 & 15 & 14.0613805507328 & 0.93861944926724 \tabularnewline
23 & 11 & 11.5771781899035 & -0.577178189903492 \tabularnewline
24 & 12 & 9.73729076617974 & 2.26270923382026 \tabularnewline
25 & 14 & 13.0722686829436 & 0.927731317056356 \tabularnewline
26 & 15 & 14.5194231921656 & 0.480576807834437 \tabularnewline
27 & 9 & 14.3028672711452 & -5.30286727114516 \tabularnewline
28 & 13 & 12.5051707349968 & 0.494829265003233 \tabularnewline
29 & 13 & 13.4500725736659 & -0.450072573665895 \tabularnewline
30 & 16 & 11.1697236542293 & 4.83027634577075 \tabularnewline
31 & 13 & 8.7596233611365 & 4.2403766388635 \tabularnewline
32 & 12 & 13.6311085757848 & -1.63110857578479 \tabularnewline
33 & 14 & 14.5708931556271 & -0.570893155627051 \tabularnewline
34 & 11 & 9.63304605908544 & 1.36695394091456 \tabularnewline
35 & 9 & 10.2429679913489 & -1.24296799134890 \tabularnewline
36 & 16 & 14.2058640159701 & 1.7941359840299 \tabularnewline
37 & 12 & 13.1612309435618 & -1.16123094356184 \tabularnewline
38 & 10 & 9.74717888686278 & 0.252821113137221 \tabularnewline
39 & 13 & 13.1693239607334 & -0.169323960733352 \tabularnewline
40 & 16 & 15.2492336916989 & 0.750766308301114 \tabularnewline
41 & 14 & 13.1250153144233 & 0.87498468557674 \tabularnewline
42 & 15 & 8.10866994098946 & 6.89133005901054 \tabularnewline
43 & 5 & 9.73044890887825 & -4.73044890887825 \tabularnewline
44 & 8 & 10.3447834918720 & -2.34478349187199 \tabularnewline
45 & 11 & 11.1715972465804 & -0.171597246580375 \tabularnewline
46 & 16 & 14.2458162389297 & 1.75418376107027 \tabularnewline
47 & 17 & 14.2833334528663 & 2.71666654713366 \tabularnewline
48 & 9 & 8.04833749734074 & 0.951662502659255 \tabularnewline
49 & 9 & 11.4323057943085 & -2.43230579430853 \tabularnewline
50 & 13 & 14.8466231269228 & -1.84662312692284 \tabularnewline
51 & 10 & 10.9236847050589 & -0.92368470505885 \tabularnewline
52 & 6 & 12.0403434699607 & -6.04034346996074 \tabularnewline
53 & 12 & 11.8874045716608 & 0.112595428339179 \tabularnewline
54 & 8 & 10.4729821368622 & -2.47298213686223 \tabularnewline
55 & 14 & 11.8447631214131 & 2.15523687858688 \tabularnewline
56 & 12 & 12.9277332433333 & -0.927733243333263 \tabularnewline
57 & 11 & 11.0230223205811 & -0.0230223205810992 \tabularnewline
58 & 16 & 14.0935550957958 & 1.90644490420420 \tabularnewline
59 & 8 & 10.2043935119364 & -2.20439351193644 \tabularnewline
60 & 15 & 14.5606342444178 & 0.439365755582235 \tabularnewline
61 & 7 & 9.0661419531643 & -2.06614195316430 \tabularnewline
62 & 16 & 13.8705998681328 & 2.12940013186717 \tabularnewline
63 & 14 & 12.8415664966158 & 1.15843350338421 \tabularnewline
64 & 16 & 13.5746674259143 & 2.42533257408571 \tabularnewline
65 & 9 & 10.1872012965741 & -1.18720129657414 \tabularnewline
66 & 14 & 12.3180173899050 & 1.68198261009502 \tabularnewline
67 & 11 & 13.2318077556377 & -2.23180775563770 \tabularnewline
68 & 13 & 10.3498777008897 & 2.65012229911031 \tabularnewline
69 & 15 & 12.9473930539210 & 2.05260694607897 \tabularnewline
70 & 5 & 5.76127050334993 & -0.761270503349934 \tabularnewline
71 & 15 & 12.8765972661561 & 2.12340273384390 \tabularnewline
72 & 13 & 11.9169217836999 & 1.08307821630008 \tabularnewline
73 & 11 & 12.9968137479744 & -1.99681374797437 \tabularnewline
74 & 11 & 14.0952478000063 & -3.09524780000629 \tabularnewline
75 & 12 & 12.1930141708868 & -0.193014170886771 \tabularnewline
76 & 12 & 13.4007794696081 & -1.40077946960814 \tabularnewline
77 & 12 & 12.7519219616863 & -0.751921961686308 \tabularnewline
78 & 12 & 12.0811136174823 & -0.0811136174822852 \tabularnewline
79 & 14 & 11.0512754440024 & 2.94872455599760 \tabularnewline
80 & 6 & 7.97861965072775 & -1.97861965072775 \tabularnewline
81 & 7 & 9.41630568885595 & -2.41630568885595 \tabularnewline
82 & 14 & 12.6155692585780 & 1.38443074142198 \tabularnewline
83 & 14 & 14.0003351977312 & -0.00033519773121668 \tabularnewline
84 & 10 & 10.9673318250210 & -0.967331825020967 \tabularnewline
85 & 13 & 9.36744645971611 & 3.63255354028389 \tabularnewline
86 & 12 & 12.4136399800742 & -0.41363998007422 \tabularnewline
87 & 9 & 9.07144712585763 & -0.0714471258576263 \tabularnewline
88 & 12 & 12.3554364093057 & -0.355436409305704 \tabularnewline
89 & 16 & 15.0703028689144 & 0.929697131085609 \tabularnewline
90 & 10 & 10.8033161391914 & -0.803316139191449 \tabularnewline
91 & 14 & 12.9849893480654 & 1.01501065193458 \tabularnewline
92 & 10 & 13.6455152362939 & -3.64551523629387 \tabularnewline
93 & 16 & 14.9860419996447 & 1.01395800035533 \tabularnewline
94 & 15 & 13.3152222748656 & 1.68477772513440 \tabularnewline
95 & 12 & 11.4768444299153 & 0.523155570084742 \tabularnewline
96 & 10 & 9.27338542269331 & 0.726614577306685 \tabularnewline
97 & 8 & 10.2857412363574 & -2.28574123635735 \tabularnewline
98 & 8 & 8.46845460630873 & -0.468454606308727 \tabularnewline
99 & 11 & 12.5556942387404 & -1.55569423874035 \tabularnewline
100 & 13 & 12.9920015026263 & 0.0079984973736558 \tabularnewline
101 & 16 & 15.8784052490512 & 0.121594750948834 \tabularnewline
102 & 16 & 15.1704776878416 & 0.82952231215836 \tabularnewline
103 & 14 & 15.6644239192084 & -1.66442391920835 \tabularnewline
104 & 11 & 8.993938985927 & 2.00606101407300 \tabularnewline
105 & 4 & 7.37908943075277 & -3.37908943075277 \tabularnewline
106 & 14 & 14.6811824228954 & -0.681182422895399 \tabularnewline
107 & 9 & 10.5776716883672 & -1.57767168836724 \tabularnewline
108 & 14 & 15.2526155390514 & -1.25261553905141 \tabularnewline
109 & 8 & 10.0286106598962 & -2.02861065989624 \tabularnewline
110 & 8 & 10.4919319595558 & -2.49193195955582 \tabularnewline
111 & 11 & 11.9776325185835 & -0.977632518583453 \tabularnewline
112 & 12 & 13.2116678982108 & -1.21166789821079 \tabularnewline
113 & 11 & 11.0136006127768 & -0.0136006127768117 \tabularnewline
114 & 14 & 13.0242257324375 & 0.975774267562534 \tabularnewline
115 & 15 & 14.4130525414258 & 0.586947458574178 \tabularnewline
116 & 16 & 13.3761086868062 & 2.62389131319384 \tabularnewline
117 & 16 & 12.8813054662748 & 3.11869453372517 \tabularnewline
118 & 11 & 12.6614687734309 & -1.66146877343094 \tabularnewline
119 & 14 & 13.3840908499781 & 0.615909150021876 \tabularnewline
120 & 14 & 11.0297814360362 & 2.97021856396378 \tabularnewline
121 & 12 & 11.5817794715759 & 0.418220528424115 \tabularnewline
122 & 14 & 13.0720629785932 & 0.92793702140677 \tabularnewline
123 & 8 & 10.8410413471034 & -2.84104134710344 \tabularnewline
124 & 13 & 14.2946990610775 & -1.29469906107747 \tabularnewline
125 & 16 & 14.5363418010922 & 1.46365819890779 \tabularnewline
126 & 12 & 10.5944574479731 & 1.40554255202692 \tabularnewline
127 & 16 & 15.8673232021964 & 0.132676797803649 \tabularnewline
128 & 12 & 12.7087981749745 & -0.708798174974494 \tabularnewline
129 & 11 & 11.3236884710665 & -0.323688471066468 \tabularnewline
130 & 4 & 5.75762186245885 & -1.75762186245885 \tabularnewline
131 & 16 & 16.1204836827130 & -0.120483682712950 \tabularnewline
132 & 15 & 13.1036223819861 & 1.89637761801388 \tabularnewline
133 & 10 & 11.1658333304308 & -1.16583333043083 \tabularnewline
134 & 13 & 14.3080532885827 & -1.30805328858271 \tabularnewline
135 & 15 & 12.6102317207247 & 2.38976827927527 \tabularnewline
136 & 12 & 10.2245120659804 & 1.77548793401958 \tabularnewline
137 & 14 & 12.9041652702215 & 1.09583472977847 \tabularnewline
138 & 7 & 10.4379749603298 & -3.43797496032983 \tabularnewline
139 & 19 & 13.7603596537789 & 5.23964034622113 \tabularnewline
140 & 12 & 13.0934132037665 & -1.09341320376652 \tabularnewline
141 & 12 & 11.9494526729181 & 0.0505473270818735 \tabularnewline
142 & 13 & 13.2609522301165 & -0.260952230116489 \tabularnewline
143 & 15 & 12.4668185272168 & 2.53318147278321 \tabularnewline
144 & 8 & 8.98814186294263 & -0.988141862942634 \tabularnewline
145 & 12 & 11.1875671061642 & 0.812432893835844 \tabularnewline
146 & 10 & 10.4485933177299 & -0.448593317729926 \tabularnewline
147 & 8 & 11.3305913693145 & -3.33059136931448 \tabularnewline
148 & 10 & 14.3439436071780 & -4.34394360717797 \tabularnewline
149 & 15 & 14.2387288914726 & 0.761271108527372 \tabularnewline
150 & 16 & 14.0252139745086 & 1.97478602549143 \tabularnewline
151 & 13 & 13.2564520239731 & -0.256452023973133 \tabularnewline
152 & 16 & 15.0487516272500 & 0.951248372750046 \tabularnewline
153 & 9 & 9.76941328862813 & -0.769413288628131 \tabularnewline
154 & 14 & 13.3629237502419 & 0.637076249758089 \tabularnewline
155 & 14 & 13.4257622916148 & 0.574237708385213 \tabularnewline
156 & 12 & 10.0614732457738 & 1.93852675422620 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98445&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]13[/C][C]11.1561094101339[/C][C]1.84389058986607[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]10.9973203599166[/C][C]1.00267964008338[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.0768983942228[/C][C]1.92310160577722[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]11.4133960061974[/C][C]0.586603993802554[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]10.9282941675151[/C][C]-0.928294167515113[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]9.31461202404611[/C][C]2.68538797595389[/C][/ROW]
[ROW][C]7[/C][C]15[/C][C]16.8694298030583[/C][C]-1.86942980305829[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]10.2725550164866[/C][C]-1.27255501648660[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]12.9984786544118[/C][C]-0.998478654411798[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]7.53023514068136[/C][C]3.46976485931864[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]13.0043488613008[/C][C]-2.00434886130083[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]12.1525974460617[/C][C]-1.15259744606174[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]12.2703841299406[/C][C]2.72961587005938[/C][/ROW]
[ROW][C]14[/C][C]7[/C][C]11.4490660940079[/C][C]-4.44906609400788[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]11.2357071966462[/C][C]-0.235707196646161[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]10.7932536652234[/C][C]0.206746334776610[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]11.8106758901321[/C][C]-1.81067589013207[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]14.8467791465252[/C][C]-0.84677914652517[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]8.55586344632273[/C][C]1.44413655367727[/C][/ROW]
[ROW][C]20[/C][C]6[/C][C]9.34736154636249[/C][C]-3.34736154636249[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]8.77694508163459[/C][C]2.22305491836541[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]14.0613805507328[/C][C]0.93861944926724[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]11.5771781899035[/C][C]-0.577178189903492[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]9.73729076617974[/C][C]2.26270923382026[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]13.0722686829436[/C][C]0.927731317056356[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]14.5194231921656[/C][C]0.480576807834437[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]14.3028672711452[/C][C]-5.30286727114516[/C][/ROW]
[ROW][C]28[/C][C]13[/C][C]12.5051707349968[/C][C]0.494829265003233[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]13.4500725736659[/C][C]-0.450072573665895[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]11.1697236542293[/C][C]4.83027634577075[/C][/ROW]
[ROW][C]31[/C][C]13[/C][C]8.7596233611365[/C][C]4.2403766388635[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.6311085757848[/C][C]-1.63110857578479[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.5708931556271[/C][C]-0.570893155627051[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]9.63304605908544[/C][C]1.36695394091456[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]10.2429679913489[/C][C]-1.24296799134890[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]14.2058640159701[/C][C]1.7941359840299[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]13.1612309435618[/C][C]-1.16123094356184[/C][/ROW]
[ROW][C]38[/C][C]10[/C][C]9.74717888686278[/C][C]0.252821113137221[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]13.1693239607334[/C][C]-0.169323960733352[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.2492336916989[/C][C]0.750766308301114[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]13.1250153144233[/C][C]0.87498468557674[/C][/ROW]
[ROW][C]42[/C][C]15[/C][C]8.10866994098946[/C][C]6.89133005901054[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]9.73044890887825[/C][C]-4.73044890887825[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]10.3447834918720[/C][C]-2.34478349187199[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]11.1715972465804[/C][C]-0.171597246580375[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]14.2458162389297[/C][C]1.75418376107027[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]14.2833334528663[/C][C]2.71666654713366[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]8.04833749734074[/C][C]0.951662502659255[/C][/ROW]
[ROW][C]49[/C][C]9[/C][C]11.4323057943085[/C][C]-2.43230579430853[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.8466231269228[/C][C]-1.84662312692284[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]10.9236847050589[/C][C]-0.92368470505885[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]12.0403434699607[/C][C]-6.04034346996074[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]11.8874045716608[/C][C]0.112595428339179[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]10.4729821368622[/C][C]-2.47298213686223[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]11.8447631214131[/C][C]2.15523687858688[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.9277332433333[/C][C]-0.927733243333263[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]11.0230223205811[/C][C]-0.0230223205810992[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.0935550957958[/C][C]1.90644490420420[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]10.2043935119364[/C][C]-2.20439351193644[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]14.5606342444178[/C][C]0.439365755582235[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]9.0661419531643[/C][C]-2.06614195316430[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]13.8705998681328[/C][C]2.12940013186717[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]12.8415664966158[/C][C]1.15843350338421[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]13.5746674259143[/C][C]2.42533257408571[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]10.1872012965741[/C][C]-1.18720129657414[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]12.3180173899050[/C][C]1.68198261009502[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]13.2318077556377[/C][C]-2.23180775563770[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]10.3498777008897[/C][C]2.65012229911031[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]12.9473930539210[/C][C]2.05260694607897[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]5.76127050334993[/C][C]-0.761270503349934[/C][/ROW]
[ROW][C]71[/C][C]15[/C][C]12.8765972661561[/C][C]2.12340273384390[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]11.9169217836999[/C][C]1.08307821630008[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]12.9968137479744[/C][C]-1.99681374797437[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]14.0952478000063[/C][C]-3.09524780000629[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]12.1930141708868[/C][C]-0.193014170886771[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]13.4007794696081[/C][C]-1.40077946960814[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]12.7519219616863[/C][C]-0.751921961686308[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]12.0811136174823[/C][C]-0.0811136174822852[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]11.0512754440024[/C][C]2.94872455599760[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]7.97861965072775[/C][C]-1.97861965072775[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]9.41630568885595[/C][C]-2.41630568885595[/C][/ROW]
[ROW][C]82[/C][C]14[/C][C]12.6155692585780[/C][C]1.38443074142198[/C][/ROW]
[ROW][C]83[/C][C]14[/C][C]14.0003351977312[/C][C]-0.00033519773121668[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]10.9673318250210[/C][C]-0.967331825020967[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]9.36744645971611[/C][C]3.63255354028389[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]12.4136399800742[/C][C]-0.41363998007422[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]9.07144712585763[/C][C]-0.0714471258576263[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.3554364093057[/C][C]-0.355436409305704[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]15.0703028689144[/C][C]0.929697131085609[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]10.8033161391914[/C][C]-0.803316139191449[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]12.9849893480654[/C][C]1.01501065193458[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]13.6455152362939[/C][C]-3.64551523629387[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]14.9860419996447[/C][C]1.01395800035533[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]13.3152222748656[/C][C]1.68477772513440[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]11.4768444299153[/C][C]0.523155570084742[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]9.27338542269331[/C][C]0.726614577306685[/C][/ROW]
[ROW][C]97[/C][C]8[/C][C]10.2857412363574[/C][C]-2.28574123635735[/C][/ROW]
[ROW][C]98[/C][C]8[/C][C]8.46845460630873[/C][C]-0.468454606308727[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]12.5556942387404[/C][C]-1.55569423874035[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]12.9920015026263[/C][C]0.0079984973736558[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]15.8784052490512[/C][C]0.121594750948834[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]15.1704776878416[/C][C]0.82952231215836[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]15.6644239192084[/C][C]-1.66442391920835[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]8.993938985927[/C][C]2.00606101407300[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]7.37908943075277[/C][C]-3.37908943075277[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]14.6811824228954[/C][C]-0.681182422895399[/C][/ROW]
[ROW][C]107[/C][C]9[/C][C]10.5776716883672[/C][C]-1.57767168836724[/C][/ROW]
[ROW][C]108[/C][C]14[/C][C]15.2526155390514[/C][C]-1.25261553905141[/C][/ROW]
[ROW][C]109[/C][C]8[/C][C]10.0286106598962[/C][C]-2.02861065989624[/C][/ROW]
[ROW][C]110[/C][C]8[/C][C]10.4919319595558[/C][C]-2.49193195955582[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]11.9776325185835[/C][C]-0.977632518583453[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]13.2116678982108[/C][C]-1.21166789821079[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]11.0136006127768[/C][C]-0.0136006127768117[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]13.0242257324375[/C][C]0.975774267562534[/C][/ROW]
[ROW][C]115[/C][C]15[/C][C]14.4130525414258[/C][C]0.586947458574178[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]13.3761086868062[/C][C]2.62389131319384[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]12.8813054662748[/C][C]3.11869453372517[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]12.6614687734309[/C][C]-1.66146877343094[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]13.3840908499781[/C][C]0.615909150021876[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]11.0297814360362[/C][C]2.97021856396378[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]11.5817794715759[/C][C]0.418220528424115[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]13.0720629785932[/C][C]0.92793702140677[/C][/ROW]
[ROW][C]123[/C][C]8[/C][C]10.8410413471034[/C][C]-2.84104134710344[/C][/ROW]
[ROW][C]124[/C][C]13[/C][C]14.2946990610775[/C][C]-1.29469906107747[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]14.5363418010922[/C][C]1.46365819890779[/C][/ROW]
[ROW][C]126[/C][C]12[/C][C]10.5944574479731[/C][C]1.40554255202692[/C][/ROW]
[ROW][C]127[/C][C]16[/C][C]15.8673232021964[/C][C]0.132676797803649[/C][/ROW]
[ROW][C]128[/C][C]12[/C][C]12.7087981749745[/C][C]-0.708798174974494[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]11.3236884710665[/C][C]-0.323688471066468[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]5.75762186245885[/C][C]-1.75762186245885[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]16.1204836827130[/C][C]-0.120483682712950[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]13.1036223819861[/C][C]1.89637761801388[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]11.1658333304308[/C][C]-1.16583333043083[/C][/ROW]
[ROW][C]134[/C][C]13[/C][C]14.3080532885827[/C][C]-1.30805328858271[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]12.6102317207247[/C][C]2.38976827927527[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]10.2245120659804[/C][C]1.77548793401958[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]12.9041652702215[/C][C]1.09583472977847[/C][/ROW]
[ROW][C]138[/C][C]7[/C][C]10.4379749603298[/C][C]-3.43797496032983[/C][/ROW]
[ROW][C]139[/C][C]19[/C][C]13.7603596537789[/C][C]5.23964034622113[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.0934132037665[/C][C]-1.09341320376652[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]11.9494526729181[/C][C]0.0505473270818735[/C][/ROW]
[ROW][C]142[/C][C]13[/C][C]13.2609522301165[/C][C]-0.260952230116489[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]12.4668185272168[/C][C]2.53318147278321[/C][/ROW]
[ROW][C]144[/C][C]8[/C][C]8.98814186294263[/C][C]-0.988141862942634[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]11.1875671061642[/C][C]0.812432893835844[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]10.4485933177299[/C][C]-0.448593317729926[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]11.3305913693145[/C][C]-3.33059136931448[/C][/ROW]
[ROW][C]148[/C][C]10[/C][C]14.3439436071780[/C][C]-4.34394360717797[/C][/ROW]
[ROW][C]149[/C][C]15[/C][C]14.2387288914726[/C][C]0.761271108527372[/C][/ROW]
[ROW][C]150[/C][C]16[/C][C]14.0252139745086[/C][C]1.97478602549143[/C][/ROW]
[ROW][C]151[/C][C]13[/C][C]13.2564520239731[/C][C]-0.256452023973133[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]15.0487516272500[/C][C]0.951248372750046[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]9.76941328862813[/C][C]-0.769413288628131[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]13.3629237502419[/C][C]0.637076249758089[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]13.4257622916148[/C][C]0.574237708385213[/C][/ROW]
[ROW][C]156[/C][C]12[/C][C]10.0614732457738[/C][C]1.93852675422620[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98445&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98445&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11311.15610941013391.84389058986607
21210.99732035991661.00267964008338
31513.07689839422281.92310160577722
41211.41339600619740.586603993802554
51010.9282941675151-0.928294167515113
6129.314612024046112.68538797595389
71516.8694298030583-1.86942980305829
8910.2725550164866-1.27255501648660
91212.9984786544118-0.998478654411798
10117.530235140681363.46976485931864
111113.0043488613008-2.00434886130083
121112.1525974460617-1.15259744606174
131512.27038412994062.72961587005938
14711.4490660940079-4.44906609400788
151111.2357071966462-0.235707196646161
161110.79325366522340.206746334776610
171011.8106758901321-1.81067589013207
181414.8467791465252-0.84677914652517
19108.555863446322731.44413655367727
2069.34736154636249-3.34736154636249
21118.776945081634592.22305491836541
221514.06138055073280.93861944926724
231111.5771781899035-0.577178189903492
24129.737290766179742.26270923382026
251413.07226868294360.927731317056356
261514.51942319216560.480576807834437
27914.3028672711452-5.30286727114516
281312.50517073499680.494829265003233
291313.4500725736659-0.450072573665895
301611.16972365422934.83027634577075
31138.75962336113654.2403766388635
321213.6311085757848-1.63110857578479
331414.5708931556271-0.570893155627051
34119.633046059085441.36695394091456
35910.2429679913489-1.24296799134890
361614.20586401597011.7941359840299
371213.1612309435618-1.16123094356184
38109.747178886862780.252821113137221
391313.1693239607334-0.169323960733352
401615.24923369169890.750766308301114
411413.12501531442330.87498468557674
42158.108669940989466.89133005901054
4359.73044890887825-4.73044890887825
44810.3447834918720-2.34478349187199
451111.1715972465804-0.171597246580375
461614.24581623892971.75418376107027
471714.28333345286632.71666654713366
4898.048337497340740.951662502659255
49911.4323057943085-2.43230579430853
501314.8466231269228-1.84662312692284
511010.9236847050589-0.92368470505885
52612.0403434699607-6.04034346996074
531211.88740457166080.112595428339179
54810.4729821368622-2.47298213686223
551411.84476312141312.15523687858688
561212.9277332433333-0.927733243333263
571111.0230223205811-0.0230223205810992
581614.09355509579581.90644490420420
59810.2043935119364-2.20439351193644
601514.56063424441780.439365755582235
6179.0661419531643-2.06614195316430
621613.87059986813282.12940013186717
631412.84156649661581.15843350338421
641613.57466742591432.42533257408571
65910.1872012965741-1.18720129657414
661412.31801738990501.68198261009502
671113.2318077556377-2.23180775563770
681310.34987770088972.65012229911031
691512.94739305392102.05260694607897
7055.76127050334993-0.761270503349934
711512.87659726615612.12340273384390
721311.91692178369991.08307821630008
731112.9968137479744-1.99681374797437
741114.0952478000063-3.09524780000629
751212.1930141708868-0.193014170886771
761213.4007794696081-1.40077946960814
771212.7519219616863-0.751921961686308
781212.0811136174823-0.0811136174822852
791411.05127544400242.94872455599760
8067.97861965072775-1.97861965072775
8179.41630568885595-2.41630568885595
821412.61556925857801.38443074142198
831414.0003351977312-0.00033519773121668
841010.9673318250210-0.967331825020967
85139.367446459716113.63255354028389
861212.4136399800742-0.41363998007422
8799.07144712585763-0.0714471258576263
881212.3554364093057-0.355436409305704
891615.07030286891440.929697131085609
901010.8033161391914-0.803316139191449
911412.98498934806541.01501065193458
921013.6455152362939-3.64551523629387
931614.98604199964471.01395800035533
941513.31522227486561.68477772513440
951211.47684442991530.523155570084742
96109.273385422693310.726614577306685
97810.2857412363574-2.28574123635735
9888.46845460630873-0.468454606308727
991112.5556942387404-1.55569423874035
1001312.99200150262630.0079984973736558
1011615.87840524905120.121594750948834
1021615.17047768784160.82952231215836
1031415.6644239192084-1.66442391920835
104118.9939389859272.00606101407300
10547.37908943075277-3.37908943075277
1061414.6811824228954-0.681182422895399
107910.5776716883672-1.57767168836724
1081415.2526155390514-1.25261553905141
109810.0286106598962-2.02861065989624
110810.4919319595558-2.49193195955582
1111111.9776325185835-0.977632518583453
1121213.2116678982108-1.21166789821079
1131111.0136006127768-0.0136006127768117
1141413.02422573243750.975774267562534
1151514.41305254142580.586947458574178
1161613.37610868680622.62389131319384
1171612.88130546627483.11869453372517
1181112.6614687734309-1.66146877343094
1191413.38409084997810.615909150021876
1201411.02978143603622.97021856396378
1211211.58177947157590.418220528424115
1221413.07206297859320.92793702140677
123810.8410413471034-2.84104134710344
1241314.2946990610775-1.29469906107747
1251614.53634180109221.46365819890779
1261210.59445744797311.40554255202692
1271615.86732320219640.132676797803649
1281212.7087981749745-0.708798174974494
1291111.3236884710665-0.323688471066468
13045.75762186245885-1.75762186245885
1311616.1204836827130-0.120483682712950
1321513.10362238198611.89637761801388
1331011.1658333304308-1.16583333043083
1341314.3080532885827-1.30805328858271
1351512.61023172072472.38976827927527
1361210.22451206598041.77548793401958
1371412.90416527022151.09583472977847
138710.4379749603298-3.43797496032983
1391913.76035965377895.23964034622113
1401213.0934132037665-1.09341320376652
1411211.94945267291810.0505473270818735
1421313.2609522301165-0.260952230116489
1431512.46681852721682.53318147278321
14488.98814186294263-0.988141862942634
1451211.18756710616420.812432893835844
1461010.4485933177299-0.448593317729926
147811.3305913693145-3.33059136931448
1481014.3439436071780-4.34394360717797
1491514.23872889147260.761271108527372
1501614.02521397450861.97478602549143
1511313.2564520239731-0.256452023973133
1521615.04875162725000.951248372750046
15399.76941328862813-0.769413288628131
1541413.36292375024190.637076249758089
1551413.42576229161480.574237708385213
1561210.06147324577381.93852675422620






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.2678513345002960.5357026690005920.732148665499704
130.7315296066593030.5369407866813930.268470393340697
140.9142176081100610.1715647837798780.085782391889939
150.866476907794970.2670461844100600.133523092205030
160.809325610501750.3813487789965000.190674389498250
170.7396762307510420.5206475384979170.260323769248958
180.653738513018350.6925229739633010.346261486981651
190.5735676834704780.8528646330590440.426432316529522
200.7980922531473420.4038154937053160.201907746852658
210.7415994501696420.5168010996607170.258400549830358
220.7398475105646740.5203049788706520.260152489435326
230.6738257356869080.6523485286261850.326174264313092
240.6681044889121870.6637910221756260.331895511087813
250.6320021330011760.7359957339976490.367997866998824
260.5711288379068880.8577423241862240.428871162093112
270.782043545414310.4359129091713810.217956454585690
280.741339637825130.5173207243497410.258660362174870
290.6832311636062760.6335376727874480.316768836393724
300.7894184284235510.4211631431528970.210581571576449
310.8471088776428070.3057822447143850.152891122357193
320.8128608530515020.3742782938969970.187139146948498
330.7686127680653280.4627744638693440.231387231934672
340.7318356037058850.536328792588230.268164396294115
350.706367759331850.5872644813362990.293632240668150
360.7354872440465790.5290255119068420.264512755953421
370.7124346299322570.5751307401354870.287565370067743
380.6666289978551480.6667420042897050.333371002144852
390.6176513638903460.7646972722193070.382348636109653
400.5752149597017590.8495700805964830.424785040298241
410.5279938500366200.944012299926760.47200614996338
420.8612217805111670.2775564389776660.138778219488833
430.9665833104382970.06683337912340580.0334166895617029
440.968195765983340.06360846803331810.0318042340166591
450.959256047933050.08148790413390190.0407439520669509
460.9647205182928310.07055896341433750.0352794817071687
470.9717868031831920.05642639363361630.0282131968168081
480.968360992512940.06327801497412170.0316390074870608
490.9646124249083240.0707751501833520.035387575091676
500.9555286995619040.08894260087619240.0444713004380962
510.9493328443042320.1013343113915360.0506671556957681
520.9900909833618450.01981803327631050.00990901663815524
530.9864360035328250.02712799293434910.0135639964671746
540.9910223060725720.01795538785485600.00897769392742801
550.9930041335620150.01399173287596970.00699586643798484
560.9908837337966130.01823253240677310.00911626620338653
570.9874740424208530.02505191515829300.0125259575791465
580.9871492056746050.02570158865079020.0128507943253951
590.990200165216620.01959966956675940.0097998347833797
600.9890189551237470.02196208975250500.0109810448762525
610.988863729907920.02227254018415810.0111362700920791
620.990426082140180.01914783571963880.00957391785981939
630.9888803808176050.02223923836478990.0111196191823949
640.9892955367363910.02140892652721720.0107044632636086
650.9887657389000370.02246852219992600.0112342610999630
660.986713682394570.02657263521086170.0132863176054308
670.9884284032135680.02314319357286320.0115715967864316
680.9898264183192030.02034716336159370.0101735816807968
690.9892063818901250.02158723621975060.0107936181098753
700.9863757381272550.02724852374549050.0136242618727453
710.9864624439497350.02707511210052910.0135375560502646
720.9829212683772070.03415746324558660.0170787316227933
730.9841504936372280.03169901272554330.0158495063627716
740.9894996134871930.02100077302561450.0105003865128073
750.9859931529239940.02801369415201260.0140068470760063
760.9837312215888440.03253755682231220.0162687784111561
770.9790275260196430.04194494796071370.0209724739803568
780.9727438300408540.0545123399182930.0272561699591465
790.9789046939270370.04219061214592530.0210953060729627
800.9786668339147240.04266633217055130.0213331660852757
810.9802987951330930.03940240973381480.0197012048669074
820.9780726521365580.04385469572688340.0219273478634417
830.9708758887860570.05824822242788530.0291241112139427
840.9636129145806250.07277417083875080.0363870854193754
850.9844686979080290.03106260418394280.0155313020919714
860.9795301760248670.04093964795026590.0204698239751329
870.972941769530300.05411646093939820.0270582304696991
880.9653188183054410.06936236338911840.0346811816945592
890.9565035496042840.08699290079143180.0434964503957159
900.9456790051839530.1086419896320940.0543209948160468
910.9360495390388550.1279009219222900.0639504609611449
920.9631666497959860.07366670040802800.0368333502040140
930.9539761038777330.09204779224453340.0460238961222667
940.948960354309090.1020792913818190.0510396456909093
950.9363909376317440.1272181247365130.0636090623682564
960.9214250050037870.1571499899924260.0785749949962131
970.917424809309990.1651503813800180.0825751906900092
980.897594806931740.2048103861365220.102405193068261
990.888133783090760.2237324338184810.111866216909240
1000.861792857181190.2764142856376210.138207142818810
1010.832625671896550.3347486562068990.167374328103450
1020.8018491369719110.3963017260561770.198150863028089
1030.830250346198640.3394993076027190.169749653801360
1040.8725282875519130.2549434248961740.127471712448087
1050.8794083399137970.2411833201724050.120591660086203
1060.8524915602195250.2950168795609490.147508439780475
1070.8286865565190890.3426268869618230.171313443480911
1080.8225860546060120.3548278907879760.177413945393988
1090.8125769717782440.3748460564435120.187423028221756
1100.8347472123309760.3305055753380480.165252787669024
1110.8205409284868470.3589181430263060.179459071513153
1120.8675319755252580.2649360489494830.132468024474742
1130.8344548966231930.3310902067536140.165545103376807
1140.8010761977835750.3978476044328490.198923802216425
1150.7600172246084920.4799655507830150.239982775391508
1160.7723196136389310.4553607727221380.227680386361069
1170.782083959981740.4358320800365180.217916040018259
1180.7622000186412860.4755999627174280.237799981358714
1190.7165573747786850.5668852504426290.283442625221315
1200.8021962436530250.3956075126939500.197803756346975
1210.7680665521219130.4638668957561740.231933447878087
1220.7181076494505730.5637847010988530.281892350549427
1230.7167784968328570.5664430063342870.283221503167144
1240.681936715058390.6361265698832210.318063284941610
1250.6551771566105570.6896456867788850.344822843389443
1260.6366249755225130.7267500489549740.363375024477487
1270.5703123004906650.859375399018670.429687699509335
1280.5529379248307320.8941241503385360.447062075169268
1290.5415609757494840.9168780485010310.458439024250516
1300.5273171683015330.9453656633969330.472682831698467
1310.4582107475922270.9164214951844530.541789252407773
1320.4275904170900050.855180834180010.572409582909995
1330.3913229988154910.7826459976309820.608677001184509
1340.3463124146818660.6926248293637330.653687585318134
1350.2987739248101240.5975478496202480.701226075189876
1360.2741180170187510.5482360340375020.725881982981249
1370.2413305637247180.4826611274494360.758669436275282
1380.2642179808010550.528435961602110.735782019198945
1390.6435463020351730.7129073959296530.356453697964826
1400.6332558716228390.7334882567543220.366744128377161
1410.5260238330694660.9479523338610670.473976166930534
1420.512125676302660.975748647394680.48787432369734
1430.3780826783545200.7561653567090390.62191732164548
1440.2578906186196720.5157812372393440.742109381380328

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.267851334500296 & 0.535702669000592 & 0.732148665499704 \tabularnewline
13 & 0.731529606659303 & 0.536940786681393 & 0.268470393340697 \tabularnewline
14 & 0.914217608110061 & 0.171564783779878 & 0.085782391889939 \tabularnewline
15 & 0.86647690779497 & 0.267046184410060 & 0.133523092205030 \tabularnewline
16 & 0.80932561050175 & 0.381348778996500 & 0.190674389498250 \tabularnewline
17 & 0.739676230751042 & 0.520647538497917 & 0.260323769248958 \tabularnewline
18 & 0.65373851301835 & 0.692522973963301 & 0.346261486981651 \tabularnewline
19 & 0.573567683470478 & 0.852864633059044 & 0.426432316529522 \tabularnewline
20 & 0.798092253147342 & 0.403815493705316 & 0.201907746852658 \tabularnewline
21 & 0.741599450169642 & 0.516801099660717 & 0.258400549830358 \tabularnewline
22 & 0.739847510564674 & 0.520304978870652 & 0.260152489435326 \tabularnewline
23 & 0.673825735686908 & 0.652348528626185 & 0.326174264313092 \tabularnewline
24 & 0.668104488912187 & 0.663791022175626 & 0.331895511087813 \tabularnewline
25 & 0.632002133001176 & 0.735995733997649 & 0.367997866998824 \tabularnewline
26 & 0.571128837906888 & 0.857742324186224 & 0.428871162093112 \tabularnewline
27 & 0.78204354541431 & 0.435912909171381 & 0.217956454585690 \tabularnewline
28 & 0.74133963782513 & 0.517320724349741 & 0.258660362174870 \tabularnewline
29 & 0.683231163606276 & 0.633537672787448 & 0.316768836393724 \tabularnewline
30 & 0.789418428423551 & 0.421163143152897 & 0.210581571576449 \tabularnewline
31 & 0.847108877642807 & 0.305782244714385 & 0.152891122357193 \tabularnewline
32 & 0.812860853051502 & 0.374278293896997 & 0.187139146948498 \tabularnewline
33 & 0.768612768065328 & 0.462774463869344 & 0.231387231934672 \tabularnewline
34 & 0.731835603705885 & 0.53632879258823 & 0.268164396294115 \tabularnewline
35 & 0.70636775933185 & 0.587264481336299 & 0.293632240668150 \tabularnewline
36 & 0.735487244046579 & 0.529025511906842 & 0.264512755953421 \tabularnewline
37 & 0.712434629932257 & 0.575130740135487 & 0.287565370067743 \tabularnewline
38 & 0.666628997855148 & 0.666742004289705 & 0.333371002144852 \tabularnewline
39 & 0.617651363890346 & 0.764697272219307 & 0.382348636109653 \tabularnewline
40 & 0.575214959701759 & 0.849570080596483 & 0.424785040298241 \tabularnewline
41 & 0.527993850036620 & 0.94401229992676 & 0.47200614996338 \tabularnewline
42 & 0.861221780511167 & 0.277556438977666 & 0.138778219488833 \tabularnewline
43 & 0.966583310438297 & 0.0668333791234058 & 0.0334166895617029 \tabularnewline
44 & 0.96819576598334 & 0.0636084680333181 & 0.0318042340166591 \tabularnewline
45 & 0.95925604793305 & 0.0814879041339019 & 0.0407439520669509 \tabularnewline
46 & 0.964720518292831 & 0.0705589634143375 & 0.0352794817071687 \tabularnewline
47 & 0.971786803183192 & 0.0564263936336163 & 0.0282131968168081 \tabularnewline
48 & 0.96836099251294 & 0.0632780149741217 & 0.0316390074870608 \tabularnewline
49 & 0.964612424908324 & 0.070775150183352 & 0.035387575091676 \tabularnewline
50 & 0.955528699561904 & 0.0889426008761924 & 0.0444713004380962 \tabularnewline
51 & 0.949332844304232 & 0.101334311391536 & 0.0506671556957681 \tabularnewline
52 & 0.990090983361845 & 0.0198180332763105 & 0.00990901663815524 \tabularnewline
53 & 0.986436003532825 & 0.0271279929343491 & 0.0135639964671746 \tabularnewline
54 & 0.991022306072572 & 0.0179553878548560 & 0.00897769392742801 \tabularnewline
55 & 0.993004133562015 & 0.0139917328759697 & 0.00699586643798484 \tabularnewline
56 & 0.990883733796613 & 0.0182325324067731 & 0.00911626620338653 \tabularnewline
57 & 0.987474042420853 & 0.0250519151582930 & 0.0125259575791465 \tabularnewline
58 & 0.987149205674605 & 0.0257015886507902 & 0.0128507943253951 \tabularnewline
59 & 0.99020016521662 & 0.0195996695667594 & 0.0097998347833797 \tabularnewline
60 & 0.989018955123747 & 0.0219620897525050 & 0.0109810448762525 \tabularnewline
61 & 0.98886372990792 & 0.0222725401841581 & 0.0111362700920791 \tabularnewline
62 & 0.99042608214018 & 0.0191478357196388 & 0.00957391785981939 \tabularnewline
63 & 0.988880380817605 & 0.0222392383647899 & 0.0111196191823949 \tabularnewline
64 & 0.989295536736391 & 0.0214089265272172 & 0.0107044632636086 \tabularnewline
65 & 0.988765738900037 & 0.0224685221999260 & 0.0112342610999630 \tabularnewline
66 & 0.98671368239457 & 0.0265726352108617 & 0.0132863176054308 \tabularnewline
67 & 0.988428403213568 & 0.0231431935728632 & 0.0115715967864316 \tabularnewline
68 & 0.989826418319203 & 0.0203471633615937 & 0.0101735816807968 \tabularnewline
69 & 0.989206381890125 & 0.0215872362197506 & 0.0107936181098753 \tabularnewline
70 & 0.986375738127255 & 0.0272485237454905 & 0.0136242618727453 \tabularnewline
71 & 0.986462443949735 & 0.0270751121005291 & 0.0135375560502646 \tabularnewline
72 & 0.982921268377207 & 0.0341574632455866 & 0.0170787316227933 \tabularnewline
73 & 0.984150493637228 & 0.0316990127255433 & 0.0158495063627716 \tabularnewline
74 & 0.989499613487193 & 0.0210007730256145 & 0.0105003865128073 \tabularnewline
75 & 0.985993152923994 & 0.0280136941520126 & 0.0140068470760063 \tabularnewline
76 & 0.983731221588844 & 0.0325375568223122 & 0.0162687784111561 \tabularnewline
77 & 0.979027526019643 & 0.0419449479607137 & 0.0209724739803568 \tabularnewline
78 & 0.972743830040854 & 0.054512339918293 & 0.0272561699591465 \tabularnewline
79 & 0.978904693927037 & 0.0421906121459253 & 0.0210953060729627 \tabularnewline
80 & 0.978666833914724 & 0.0426663321705513 & 0.0213331660852757 \tabularnewline
81 & 0.980298795133093 & 0.0394024097338148 & 0.0197012048669074 \tabularnewline
82 & 0.978072652136558 & 0.0438546957268834 & 0.0219273478634417 \tabularnewline
83 & 0.970875888786057 & 0.0582482224278853 & 0.0291241112139427 \tabularnewline
84 & 0.963612914580625 & 0.0727741708387508 & 0.0363870854193754 \tabularnewline
85 & 0.984468697908029 & 0.0310626041839428 & 0.0155313020919714 \tabularnewline
86 & 0.979530176024867 & 0.0409396479502659 & 0.0204698239751329 \tabularnewline
87 & 0.97294176953030 & 0.0541164609393982 & 0.0270582304696991 \tabularnewline
88 & 0.965318818305441 & 0.0693623633891184 & 0.0346811816945592 \tabularnewline
89 & 0.956503549604284 & 0.0869929007914318 & 0.0434964503957159 \tabularnewline
90 & 0.945679005183953 & 0.108641989632094 & 0.0543209948160468 \tabularnewline
91 & 0.936049539038855 & 0.127900921922290 & 0.0639504609611449 \tabularnewline
92 & 0.963166649795986 & 0.0736667004080280 & 0.0368333502040140 \tabularnewline
93 & 0.953976103877733 & 0.0920477922445334 & 0.0460238961222667 \tabularnewline
94 & 0.94896035430909 & 0.102079291381819 & 0.0510396456909093 \tabularnewline
95 & 0.936390937631744 & 0.127218124736513 & 0.0636090623682564 \tabularnewline
96 & 0.921425005003787 & 0.157149989992426 & 0.0785749949962131 \tabularnewline
97 & 0.91742480930999 & 0.165150381380018 & 0.0825751906900092 \tabularnewline
98 & 0.89759480693174 & 0.204810386136522 & 0.102405193068261 \tabularnewline
99 & 0.88813378309076 & 0.223732433818481 & 0.111866216909240 \tabularnewline
100 & 0.86179285718119 & 0.276414285637621 & 0.138207142818810 \tabularnewline
101 & 0.83262567189655 & 0.334748656206899 & 0.167374328103450 \tabularnewline
102 & 0.801849136971911 & 0.396301726056177 & 0.198150863028089 \tabularnewline
103 & 0.83025034619864 & 0.339499307602719 & 0.169749653801360 \tabularnewline
104 & 0.872528287551913 & 0.254943424896174 & 0.127471712448087 \tabularnewline
105 & 0.879408339913797 & 0.241183320172405 & 0.120591660086203 \tabularnewline
106 & 0.852491560219525 & 0.295016879560949 & 0.147508439780475 \tabularnewline
107 & 0.828686556519089 & 0.342626886961823 & 0.171313443480911 \tabularnewline
108 & 0.822586054606012 & 0.354827890787976 & 0.177413945393988 \tabularnewline
109 & 0.812576971778244 & 0.374846056443512 & 0.187423028221756 \tabularnewline
110 & 0.834747212330976 & 0.330505575338048 & 0.165252787669024 \tabularnewline
111 & 0.820540928486847 & 0.358918143026306 & 0.179459071513153 \tabularnewline
112 & 0.867531975525258 & 0.264936048949483 & 0.132468024474742 \tabularnewline
113 & 0.834454896623193 & 0.331090206753614 & 0.165545103376807 \tabularnewline
114 & 0.801076197783575 & 0.397847604432849 & 0.198923802216425 \tabularnewline
115 & 0.760017224608492 & 0.479965550783015 & 0.239982775391508 \tabularnewline
116 & 0.772319613638931 & 0.455360772722138 & 0.227680386361069 \tabularnewline
117 & 0.78208395998174 & 0.435832080036518 & 0.217916040018259 \tabularnewline
118 & 0.762200018641286 & 0.475599962717428 & 0.237799981358714 \tabularnewline
119 & 0.716557374778685 & 0.566885250442629 & 0.283442625221315 \tabularnewline
120 & 0.802196243653025 & 0.395607512693950 & 0.197803756346975 \tabularnewline
121 & 0.768066552121913 & 0.463866895756174 & 0.231933447878087 \tabularnewline
122 & 0.718107649450573 & 0.563784701098853 & 0.281892350549427 \tabularnewline
123 & 0.716778496832857 & 0.566443006334287 & 0.283221503167144 \tabularnewline
124 & 0.68193671505839 & 0.636126569883221 & 0.318063284941610 \tabularnewline
125 & 0.655177156610557 & 0.689645686778885 & 0.344822843389443 \tabularnewline
126 & 0.636624975522513 & 0.726750048954974 & 0.363375024477487 \tabularnewline
127 & 0.570312300490665 & 0.85937539901867 & 0.429687699509335 \tabularnewline
128 & 0.552937924830732 & 0.894124150338536 & 0.447062075169268 \tabularnewline
129 & 0.541560975749484 & 0.916878048501031 & 0.458439024250516 \tabularnewline
130 & 0.527317168301533 & 0.945365663396933 & 0.472682831698467 \tabularnewline
131 & 0.458210747592227 & 0.916421495184453 & 0.541789252407773 \tabularnewline
132 & 0.427590417090005 & 0.85518083418001 & 0.572409582909995 \tabularnewline
133 & 0.391322998815491 & 0.782645997630982 & 0.608677001184509 \tabularnewline
134 & 0.346312414681866 & 0.692624829363733 & 0.653687585318134 \tabularnewline
135 & 0.298773924810124 & 0.597547849620248 & 0.701226075189876 \tabularnewline
136 & 0.274118017018751 & 0.548236034037502 & 0.725881982981249 \tabularnewline
137 & 0.241330563724718 & 0.482661127449436 & 0.758669436275282 \tabularnewline
138 & 0.264217980801055 & 0.52843596160211 & 0.735782019198945 \tabularnewline
139 & 0.643546302035173 & 0.712907395929653 & 0.356453697964826 \tabularnewline
140 & 0.633255871622839 & 0.733488256754322 & 0.366744128377161 \tabularnewline
141 & 0.526023833069466 & 0.947952333861067 & 0.473976166930534 \tabularnewline
142 & 0.51212567630266 & 0.97574864739468 & 0.48787432369734 \tabularnewline
143 & 0.378082678354520 & 0.756165356709039 & 0.62191732164548 \tabularnewline
144 & 0.257890618619672 & 0.515781237239344 & 0.742109381380328 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98445&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]12[/C][C]0.267851334500296[/C][C]0.535702669000592[/C][C]0.732148665499704[/C][/ROW]
[ROW][C]13[/C][C]0.731529606659303[/C][C]0.536940786681393[/C][C]0.268470393340697[/C][/ROW]
[ROW][C]14[/C][C]0.914217608110061[/C][C]0.171564783779878[/C][C]0.085782391889939[/C][/ROW]
[ROW][C]15[/C][C]0.86647690779497[/C][C]0.267046184410060[/C][C]0.133523092205030[/C][/ROW]
[ROW][C]16[/C][C]0.80932561050175[/C][C]0.381348778996500[/C][C]0.190674389498250[/C][/ROW]
[ROW][C]17[/C][C]0.739676230751042[/C][C]0.520647538497917[/C][C]0.260323769248958[/C][/ROW]
[ROW][C]18[/C][C]0.65373851301835[/C][C]0.692522973963301[/C][C]0.346261486981651[/C][/ROW]
[ROW][C]19[/C][C]0.573567683470478[/C][C]0.852864633059044[/C][C]0.426432316529522[/C][/ROW]
[ROW][C]20[/C][C]0.798092253147342[/C][C]0.403815493705316[/C][C]0.201907746852658[/C][/ROW]
[ROW][C]21[/C][C]0.741599450169642[/C][C]0.516801099660717[/C][C]0.258400549830358[/C][/ROW]
[ROW][C]22[/C][C]0.739847510564674[/C][C]0.520304978870652[/C][C]0.260152489435326[/C][/ROW]
[ROW][C]23[/C][C]0.673825735686908[/C][C]0.652348528626185[/C][C]0.326174264313092[/C][/ROW]
[ROW][C]24[/C][C]0.668104488912187[/C][C]0.663791022175626[/C][C]0.331895511087813[/C][/ROW]
[ROW][C]25[/C][C]0.632002133001176[/C][C]0.735995733997649[/C][C]0.367997866998824[/C][/ROW]
[ROW][C]26[/C][C]0.571128837906888[/C][C]0.857742324186224[/C][C]0.428871162093112[/C][/ROW]
[ROW][C]27[/C][C]0.78204354541431[/C][C]0.435912909171381[/C][C]0.217956454585690[/C][/ROW]
[ROW][C]28[/C][C]0.74133963782513[/C][C]0.517320724349741[/C][C]0.258660362174870[/C][/ROW]
[ROW][C]29[/C][C]0.683231163606276[/C][C]0.633537672787448[/C][C]0.316768836393724[/C][/ROW]
[ROW][C]30[/C][C]0.789418428423551[/C][C]0.421163143152897[/C][C]0.210581571576449[/C][/ROW]
[ROW][C]31[/C][C]0.847108877642807[/C][C]0.305782244714385[/C][C]0.152891122357193[/C][/ROW]
[ROW][C]32[/C][C]0.812860853051502[/C][C]0.374278293896997[/C][C]0.187139146948498[/C][/ROW]
[ROW][C]33[/C][C]0.768612768065328[/C][C]0.462774463869344[/C][C]0.231387231934672[/C][/ROW]
[ROW][C]34[/C][C]0.731835603705885[/C][C]0.53632879258823[/C][C]0.268164396294115[/C][/ROW]
[ROW][C]35[/C][C]0.70636775933185[/C][C]0.587264481336299[/C][C]0.293632240668150[/C][/ROW]
[ROW][C]36[/C][C]0.735487244046579[/C][C]0.529025511906842[/C][C]0.264512755953421[/C][/ROW]
[ROW][C]37[/C][C]0.712434629932257[/C][C]0.575130740135487[/C][C]0.287565370067743[/C][/ROW]
[ROW][C]38[/C][C]0.666628997855148[/C][C]0.666742004289705[/C][C]0.333371002144852[/C][/ROW]
[ROW][C]39[/C][C]0.617651363890346[/C][C]0.764697272219307[/C][C]0.382348636109653[/C][/ROW]
[ROW][C]40[/C][C]0.575214959701759[/C][C]0.849570080596483[/C][C]0.424785040298241[/C][/ROW]
[ROW][C]41[/C][C]0.527993850036620[/C][C]0.94401229992676[/C][C]0.47200614996338[/C][/ROW]
[ROW][C]42[/C][C]0.861221780511167[/C][C]0.277556438977666[/C][C]0.138778219488833[/C][/ROW]
[ROW][C]43[/C][C]0.966583310438297[/C][C]0.0668333791234058[/C][C]0.0334166895617029[/C][/ROW]
[ROW][C]44[/C][C]0.96819576598334[/C][C]0.0636084680333181[/C][C]0.0318042340166591[/C][/ROW]
[ROW][C]45[/C][C]0.95925604793305[/C][C]0.0814879041339019[/C][C]0.0407439520669509[/C][/ROW]
[ROW][C]46[/C][C]0.964720518292831[/C][C]0.0705589634143375[/C][C]0.0352794817071687[/C][/ROW]
[ROW][C]47[/C][C]0.971786803183192[/C][C]0.0564263936336163[/C][C]0.0282131968168081[/C][/ROW]
[ROW][C]48[/C][C]0.96836099251294[/C][C]0.0632780149741217[/C][C]0.0316390074870608[/C][/ROW]
[ROW][C]49[/C][C]0.964612424908324[/C][C]0.070775150183352[/C][C]0.035387575091676[/C][/ROW]
[ROW][C]50[/C][C]0.955528699561904[/C][C]0.0889426008761924[/C][C]0.0444713004380962[/C][/ROW]
[ROW][C]51[/C][C]0.949332844304232[/C][C]0.101334311391536[/C][C]0.0506671556957681[/C][/ROW]
[ROW][C]52[/C][C]0.990090983361845[/C][C]0.0198180332763105[/C][C]0.00990901663815524[/C][/ROW]
[ROW][C]53[/C][C]0.986436003532825[/C][C]0.0271279929343491[/C][C]0.0135639964671746[/C][/ROW]
[ROW][C]54[/C][C]0.991022306072572[/C][C]0.0179553878548560[/C][C]0.00897769392742801[/C][/ROW]
[ROW][C]55[/C][C]0.993004133562015[/C][C]0.0139917328759697[/C][C]0.00699586643798484[/C][/ROW]
[ROW][C]56[/C][C]0.990883733796613[/C][C]0.0182325324067731[/C][C]0.00911626620338653[/C][/ROW]
[ROW][C]57[/C][C]0.987474042420853[/C][C]0.0250519151582930[/C][C]0.0125259575791465[/C][/ROW]
[ROW][C]58[/C][C]0.987149205674605[/C][C]0.0257015886507902[/C][C]0.0128507943253951[/C][/ROW]
[ROW][C]59[/C][C]0.99020016521662[/C][C]0.0195996695667594[/C][C]0.0097998347833797[/C][/ROW]
[ROW][C]60[/C][C]0.989018955123747[/C][C]0.0219620897525050[/C][C]0.0109810448762525[/C][/ROW]
[ROW][C]61[/C][C]0.98886372990792[/C][C]0.0222725401841581[/C][C]0.0111362700920791[/C][/ROW]
[ROW][C]62[/C][C]0.99042608214018[/C][C]0.0191478357196388[/C][C]0.00957391785981939[/C][/ROW]
[ROW][C]63[/C][C]0.988880380817605[/C][C]0.0222392383647899[/C][C]0.0111196191823949[/C][/ROW]
[ROW][C]64[/C][C]0.989295536736391[/C][C]0.0214089265272172[/C][C]0.0107044632636086[/C][/ROW]
[ROW][C]65[/C][C]0.988765738900037[/C][C]0.0224685221999260[/C][C]0.0112342610999630[/C][/ROW]
[ROW][C]66[/C][C]0.98671368239457[/C][C]0.0265726352108617[/C][C]0.0132863176054308[/C][/ROW]
[ROW][C]67[/C][C]0.988428403213568[/C][C]0.0231431935728632[/C][C]0.0115715967864316[/C][/ROW]
[ROW][C]68[/C][C]0.989826418319203[/C][C]0.0203471633615937[/C][C]0.0101735816807968[/C][/ROW]
[ROW][C]69[/C][C]0.989206381890125[/C][C]0.0215872362197506[/C][C]0.0107936181098753[/C][/ROW]
[ROW][C]70[/C][C]0.986375738127255[/C][C]0.0272485237454905[/C][C]0.0136242618727453[/C][/ROW]
[ROW][C]71[/C][C]0.986462443949735[/C][C]0.0270751121005291[/C][C]0.0135375560502646[/C][/ROW]
[ROW][C]72[/C][C]0.982921268377207[/C][C]0.0341574632455866[/C][C]0.0170787316227933[/C][/ROW]
[ROW][C]73[/C][C]0.984150493637228[/C][C]0.0316990127255433[/C][C]0.0158495063627716[/C][/ROW]
[ROW][C]74[/C][C]0.989499613487193[/C][C]0.0210007730256145[/C][C]0.0105003865128073[/C][/ROW]
[ROW][C]75[/C][C]0.985993152923994[/C][C]0.0280136941520126[/C][C]0.0140068470760063[/C][/ROW]
[ROW][C]76[/C][C]0.983731221588844[/C][C]0.0325375568223122[/C][C]0.0162687784111561[/C][/ROW]
[ROW][C]77[/C][C]0.979027526019643[/C][C]0.0419449479607137[/C][C]0.0209724739803568[/C][/ROW]
[ROW][C]78[/C][C]0.972743830040854[/C][C]0.054512339918293[/C][C]0.0272561699591465[/C][/ROW]
[ROW][C]79[/C][C]0.978904693927037[/C][C]0.0421906121459253[/C][C]0.0210953060729627[/C][/ROW]
[ROW][C]80[/C][C]0.978666833914724[/C][C]0.0426663321705513[/C][C]0.0213331660852757[/C][/ROW]
[ROW][C]81[/C][C]0.980298795133093[/C][C]0.0394024097338148[/C][C]0.0197012048669074[/C][/ROW]
[ROW][C]82[/C][C]0.978072652136558[/C][C]0.0438546957268834[/C][C]0.0219273478634417[/C][/ROW]
[ROW][C]83[/C][C]0.970875888786057[/C][C]0.0582482224278853[/C][C]0.0291241112139427[/C][/ROW]
[ROW][C]84[/C][C]0.963612914580625[/C][C]0.0727741708387508[/C][C]0.0363870854193754[/C][/ROW]
[ROW][C]85[/C][C]0.984468697908029[/C][C]0.0310626041839428[/C][C]0.0155313020919714[/C][/ROW]
[ROW][C]86[/C][C]0.979530176024867[/C][C]0.0409396479502659[/C][C]0.0204698239751329[/C][/ROW]
[ROW][C]87[/C][C]0.97294176953030[/C][C]0.0541164609393982[/C][C]0.0270582304696991[/C][/ROW]
[ROW][C]88[/C][C]0.965318818305441[/C][C]0.0693623633891184[/C][C]0.0346811816945592[/C][/ROW]
[ROW][C]89[/C][C]0.956503549604284[/C][C]0.0869929007914318[/C][C]0.0434964503957159[/C][/ROW]
[ROW][C]90[/C][C]0.945679005183953[/C][C]0.108641989632094[/C][C]0.0543209948160468[/C][/ROW]
[ROW][C]91[/C][C]0.936049539038855[/C][C]0.127900921922290[/C][C]0.0639504609611449[/C][/ROW]
[ROW][C]92[/C][C]0.963166649795986[/C][C]0.0736667004080280[/C][C]0.0368333502040140[/C][/ROW]
[ROW][C]93[/C][C]0.953976103877733[/C][C]0.0920477922445334[/C][C]0.0460238961222667[/C][/ROW]
[ROW][C]94[/C][C]0.94896035430909[/C][C]0.102079291381819[/C][C]0.0510396456909093[/C][/ROW]
[ROW][C]95[/C][C]0.936390937631744[/C][C]0.127218124736513[/C][C]0.0636090623682564[/C][/ROW]
[ROW][C]96[/C][C]0.921425005003787[/C][C]0.157149989992426[/C][C]0.0785749949962131[/C][/ROW]
[ROW][C]97[/C][C]0.91742480930999[/C][C]0.165150381380018[/C][C]0.0825751906900092[/C][/ROW]
[ROW][C]98[/C][C]0.89759480693174[/C][C]0.204810386136522[/C][C]0.102405193068261[/C][/ROW]
[ROW][C]99[/C][C]0.88813378309076[/C][C]0.223732433818481[/C][C]0.111866216909240[/C][/ROW]
[ROW][C]100[/C][C]0.86179285718119[/C][C]0.276414285637621[/C][C]0.138207142818810[/C][/ROW]
[ROW][C]101[/C][C]0.83262567189655[/C][C]0.334748656206899[/C][C]0.167374328103450[/C][/ROW]
[ROW][C]102[/C][C]0.801849136971911[/C][C]0.396301726056177[/C][C]0.198150863028089[/C][/ROW]
[ROW][C]103[/C][C]0.83025034619864[/C][C]0.339499307602719[/C][C]0.169749653801360[/C][/ROW]
[ROW][C]104[/C][C]0.872528287551913[/C][C]0.254943424896174[/C][C]0.127471712448087[/C][/ROW]
[ROW][C]105[/C][C]0.879408339913797[/C][C]0.241183320172405[/C][C]0.120591660086203[/C][/ROW]
[ROW][C]106[/C][C]0.852491560219525[/C][C]0.295016879560949[/C][C]0.147508439780475[/C][/ROW]
[ROW][C]107[/C][C]0.828686556519089[/C][C]0.342626886961823[/C][C]0.171313443480911[/C][/ROW]
[ROW][C]108[/C][C]0.822586054606012[/C][C]0.354827890787976[/C][C]0.177413945393988[/C][/ROW]
[ROW][C]109[/C][C]0.812576971778244[/C][C]0.374846056443512[/C][C]0.187423028221756[/C][/ROW]
[ROW][C]110[/C][C]0.834747212330976[/C][C]0.330505575338048[/C][C]0.165252787669024[/C][/ROW]
[ROW][C]111[/C][C]0.820540928486847[/C][C]0.358918143026306[/C][C]0.179459071513153[/C][/ROW]
[ROW][C]112[/C][C]0.867531975525258[/C][C]0.264936048949483[/C][C]0.132468024474742[/C][/ROW]
[ROW][C]113[/C][C]0.834454896623193[/C][C]0.331090206753614[/C][C]0.165545103376807[/C][/ROW]
[ROW][C]114[/C][C]0.801076197783575[/C][C]0.397847604432849[/C][C]0.198923802216425[/C][/ROW]
[ROW][C]115[/C][C]0.760017224608492[/C][C]0.479965550783015[/C][C]0.239982775391508[/C][/ROW]
[ROW][C]116[/C][C]0.772319613638931[/C][C]0.455360772722138[/C][C]0.227680386361069[/C][/ROW]
[ROW][C]117[/C][C]0.78208395998174[/C][C]0.435832080036518[/C][C]0.217916040018259[/C][/ROW]
[ROW][C]118[/C][C]0.762200018641286[/C][C]0.475599962717428[/C][C]0.237799981358714[/C][/ROW]
[ROW][C]119[/C][C]0.716557374778685[/C][C]0.566885250442629[/C][C]0.283442625221315[/C][/ROW]
[ROW][C]120[/C][C]0.802196243653025[/C][C]0.395607512693950[/C][C]0.197803756346975[/C][/ROW]
[ROW][C]121[/C][C]0.768066552121913[/C][C]0.463866895756174[/C][C]0.231933447878087[/C][/ROW]
[ROW][C]122[/C][C]0.718107649450573[/C][C]0.563784701098853[/C][C]0.281892350549427[/C][/ROW]
[ROW][C]123[/C][C]0.716778496832857[/C][C]0.566443006334287[/C][C]0.283221503167144[/C][/ROW]
[ROW][C]124[/C][C]0.68193671505839[/C][C]0.636126569883221[/C][C]0.318063284941610[/C][/ROW]
[ROW][C]125[/C][C]0.655177156610557[/C][C]0.689645686778885[/C][C]0.344822843389443[/C][/ROW]
[ROW][C]126[/C][C]0.636624975522513[/C][C]0.726750048954974[/C][C]0.363375024477487[/C][/ROW]
[ROW][C]127[/C][C]0.570312300490665[/C][C]0.85937539901867[/C][C]0.429687699509335[/C][/ROW]
[ROW][C]128[/C][C]0.552937924830732[/C][C]0.894124150338536[/C][C]0.447062075169268[/C][/ROW]
[ROW][C]129[/C][C]0.541560975749484[/C][C]0.916878048501031[/C][C]0.458439024250516[/C][/ROW]
[ROW][C]130[/C][C]0.527317168301533[/C][C]0.945365663396933[/C][C]0.472682831698467[/C][/ROW]
[ROW][C]131[/C][C]0.458210747592227[/C][C]0.916421495184453[/C][C]0.541789252407773[/C][/ROW]
[ROW][C]132[/C][C]0.427590417090005[/C][C]0.85518083418001[/C][C]0.572409582909995[/C][/ROW]
[ROW][C]133[/C][C]0.391322998815491[/C][C]0.782645997630982[/C][C]0.608677001184509[/C][/ROW]
[ROW][C]134[/C][C]0.346312414681866[/C][C]0.692624829363733[/C][C]0.653687585318134[/C][/ROW]
[ROW][C]135[/C][C]0.298773924810124[/C][C]0.597547849620248[/C][C]0.701226075189876[/C][/ROW]
[ROW][C]136[/C][C]0.274118017018751[/C][C]0.548236034037502[/C][C]0.725881982981249[/C][/ROW]
[ROW][C]137[/C][C]0.241330563724718[/C][C]0.482661127449436[/C][C]0.758669436275282[/C][/ROW]
[ROW][C]138[/C][C]0.264217980801055[/C][C]0.52843596160211[/C][C]0.735782019198945[/C][/ROW]
[ROW][C]139[/C][C]0.643546302035173[/C][C]0.712907395929653[/C][C]0.356453697964826[/C][/ROW]
[ROW][C]140[/C][C]0.633255871622839[/C][C]0.733488256754322[/C][C]0.366744128377161[/C][/ROW]
[ROW][C]141[/C][C]0.526023833069466[/C][C]0.947952333861067[/C][C]0.473976166930534[/C][/ROW]
[ROW][C]142[/C][C]0.51212567630266[/C][C]0.97574864739468[/C][C]0.48787432369734[/C][/ROW]
[ROW][C]143[/C][C]0.378082678354520[/C][C]0.756165356709039[/C][C]0.62191732164548[/C][/ROW]
[ROW][C]144[/C][C]0.257890618619672[/C][C]0.515781237239344[/C][C]0.742109381380328[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98445&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98445&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.2678513345002960.5357026690005920.732148665499704
130.7315296066593030.5369407866813930.268470393340697
140.9142176081100610.1715647837798780.085782391889939
150.866476907794970.2670461844100600.133523092205030
160.809325610501750.3813487789965000.190674389498250
170.7396762307510420.5206475384979170.260323769248958
180.653738513018350.6925229739633010.346261486981651
190.5735676834704780.8528646330590440.426432316529522
200.7980922531473420.4038154937053160.201907746852658
210.7415994501696420.5168010996607170.258400549830358
220.7398475105646740.5203049788706520.260152489435326
230.6738257356869080.6523485286261850.326174264313092
240.6681044889121870.6637910221756260.331895511087813
250.6320021330011760.7359957339976490.367997866998824
260.5711288379068880.8577423241862240.428871162093112
270.782043545414310.4359129091713810.217956454585690
280.741339637825130.5173207243497410.258660362174870
290.6832311636062760.6335376727874480.316768836393724
300.7894184284235510.4211631431528970.210581571576449
310.8471088776428070.3057822447143850.152891122357193
320.8128608530515020.3742782938969970.187139146948498
330.7686127680653280.4627744638693440.231387231934672
340.7318356037058850.536328792588230.268164396294115
350.706367759331850.5872644813362990.293632240668150
360.7354872440465790.5290255119068420.264512755953421
370.7124346299322570.5751307401354870.287565370067743
380.6666289978551480.6667420042897050.333371002144852
390.6176513638903460.7646972722193070.382348636109653
400.5752149597017590.8495700805964830.424785040298241
410.5279938500366200.944012299926760.47200614996338
420.8612217805111670.2775564389776660.138778219488833
430.9665833104382970.06683337912340580.0334166895617029
440.968195765983340.06360846803331810.0318042340166591
450.959256047933050.08148790413390190.0407439520669509
460.9647205182928310.07055896341433750.0352794817071687
470.9717868031831920.05642639363361630.0282131968168081
480.968360992512940.06327801497412170.0316390074870608
490.9646124249083240.0707751501833520.035387575091676
500.9555286995619040.08894260087619240.0444713004380962
510.9493328443042320.1013343113915360.0506671556957681
520.9900909833618450.01981803327631050.00990901663815524
530.9864360035328250.02712799293434910.0135639964671746
540.9910223060725720.01795538785485600.00897769392742801
550.9930041335620150.01399173287596970.00699586643798484
560.9908837337966130.01823253240677310.00911626620338653
570.9874740424208530.02505191515829300.0125259575791465
580.9871492056746050.02570158865079020.0128507943253951
590.990200165216620.01959966956675940.0097998347833797
600.9890189551237470.02196208975250500.0109810448762525
610.988863729907920.02227254018415810.0111362700920791
620.990426082140180.01914783571963880.00957391785981939
630.9888803808176050.02223923836478990.0111196191823949
640.9892955367363910.02140892652721720.0107044632636086
650.9887657389000370.02246852219992600.0112342610999630
660.986713682394570.02657263521086170.0132863176054308
670.9884284032135680.02314319357286320.0115715967864316
680.9898264183192030.02034716336159370.0101735816807968
690.9892063818901250.02158723621975060.0107936181098753
700.9863757381272550.02724852374549050.0136242618727453
710.9864624439497350.02707511210052910.0135375560502646
720.9829212683772070.03415746324558660.0170787316227933
730.9841504936372280.03169901272554330.0158495063627716
740.9894996134871930.02100077302561450.0105003865128073
750.9859931529239940.02801369415201260.0140068470760063
760.9837312215888440.03253755682231220.0162687784111561
770.9790275260196430.04194494796071370.0209724739803568
780.9727438300408540.0545123399182930.0272561699591465
790.9789046939270370.04219061214592530.0210953060729627
800.9786668339147240.04266633217055130.0213331660852757
810.9802987951330930.03940240973381480.0197012048669074
820.9780726521365580.04385469572688340.0219273478634417
830.9708758887860570.05824822242788530.0291241112139427
840.9636129145806250.07277417083875080.0363870854193754
850.9844686979080290.03106260418394280.0155313020919714
860.9795301760248670.04093964795026590.0204698239751329
870.972941769530300.05411646093939820.0270582304696991
880.9653188183054410.06936236338911840.0346811816945592
890.9565035496042840.08699290079143180.0434964503957159
900.9456790051839530.1086419896320940.0543209948160468
910.9360495390388550.1279009219222900.0639504609611449
920.9631666497959860.07366670040802800.0368333502040140
930.9539761038777330.09204779224453340.0460238961222667
940.948960354309090.1020792913818190.0510396456909093
950.9363909376317440.1272181247365130.0636090623682564
960.9214250050037870.1571499899924260.0785749949962131
970.917424809309990.1651503813800180.0825751906900092
980.897594806931740.2048103861365220.102405193068261
990.888133783090760.2237324338184810.111866216909240
1000.861792857181190.2764142856376210.138207142818810
1010.832625671896550.3347486562068990.167374328103450
1020.8018491369719110.3963017260561770.198150863028089
1030.830250346198640.3394993076027190.169749653801360
1040.8725282875519130.2549434248961740.127471712448087
1050.8794083399137970.2411833201724050.120591660086203
1060.8524915602195250.2950168795609490.147508439780475
1070.8286865565190890.3426268869618230.171313443480911
1080.8225860546060120.3548278907879760.177413945393988
1090.8125769717782440.3748460564435120.187423028221756
1100.8347472123309760.3305055753380480.165252787669024
1110.8205409284868470.3589181430263060.179459071513153
1120.8675319755252580.2649360489494830.132468024474742
1130.8344548966231930.3310902067536140.165545103376807
1140.8010761977835750.3978476044328490.198923802216425
1150.7600172246084920.4799655507830150.239982775391508
1160.7723196136389310.4553607727221380.227680386361069
1170.782083959981740.4358320800365180.217916040018259
1180.7622000186412860.4755999627174280.237799981358714
1190.7165573747786850.5668852504426290.283442625221315
1200.8021962436530250.3956075126939500.197803756346975
1210.7680665521219130.4638668957561740.231933447878087
1220.7181076494505730.5637847010988530.281892350549427
1230.7167784968328570.5664430063342870.283221503167144
1240.681936715058390.6361265698832210.318063284941610
1250.6551771566105570.6896456867788850.344822843389443
1260.6366249755225130.7267500489549740.363375024477487
1270.5703123004906650.859375399018670.429687699509335
1280.5529379248307320.8941241503385360.447062075169268
1290.5415609757494840.9168780485010310.458439024250516
1300.5273171683015330.9453656633969330.472682831698467
1310.4582107475922270.9164214951844530.541789252407773
1320.4275904170900050.855180834180010.572409582909995
1330.3913229988154910.7826459976309820.608677001184509
1340.3463124146818660.6926248293637330.653687585318134
1350.2987739248101240.5975478496202480.701226075189876
1360.2741180170187510.5482360340375020.725881982981249
1370.2413305637247180.4826611274494360.758669436275282
1380.2642179808010550.528435961602110.735782019198945
1390.6435463020351730.7129073959296530.356453697964826
1400.6332558716228390.7334882567543220.366744128377161
1410.5260238330694660.9479523338610670.473976166930534
1420.512125676302660.975748647394680.48787432369734
1430.3780826783545200.7561653567090390.62191732164548
1440.2578906186196720.5157812372393440.742109381380328






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level320.240601503759398NOK
10% type I error level480.360902255639098NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 32 & 0.240601503759398 & NOK \tabularnewline
10% type I error level & 48 & 0.360902255639098 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98445&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]32[/C][C]0.240601503759398[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]48[/C][C]0.360902255639098[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98445&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98445&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level320.240601503759398NOK
10% type I error level480.360902255639098NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}