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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 computationThu, 12 Nov 2009 14:54:52 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/12/t12580343339tx3tb331y2nqii.htm/, Retrieved Thu, 25 Apr 2024 21:07:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=55987, Retrieved Thu, 25 Apr 2024 21:07:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact914

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD    [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
-    D      [Multiple Regression] [Regressiemodel zo...] [2009-11-14 11:09:10] [e2a6b1b31bd881219e1879835b4c60d0]
-    D      [Multiple Regression] [WS 7 1] [2009-11-14 13:01:08] [6e4e01d7eb22a9f33d58ebb35753a195]
-    D      [Multiple Regression] [] [2009-11-14 17:08:20] [ca7a691f2b8ebdc7b81799394c1aa70d]
-    D        [Multiple Regression] [] [2009-11-20 13:28:10] [74be16979710d4c4e7c6647856088456]
-    D      [Multiple Regression] [] [2009-11-17 09:44:54] [639dd97b6eeebe46a3c92d62cb04fb95]
-   PD      [Multiple Regression] [] [2009-11-17 09:50:15] [11ac052cc87d77b9933b02bea117068e]
-    D      [Multiple Regression] [model 1] [2009-11-17 14:29:51] [ed603017d2bee8fbd82b6d5ec04e12c3]
-    D      [Multiple Regression] [ws3] [2009-11-17 14:22:36] [ca30429b07824e7c5d48293114d35d71]
-    D      [Multiple Regression] [model 1] [2009-11-17 14:36:29] [ed603017d2bee8fbd82b6d5ec04e12c3]
-    D        [Multiple Regression] [miltiple regression] [2009-11-19 18:03:37] [ed603017d2bee8fbd82b6d5ec04e12c3]
-    D        [Multiple Regression] [multiple regression] [2009-11-19 21:38:11] [ed603017d2bee8fbd82b6d5ec04e12c3]
-   P           [Multiple Regression] [monthly dummies] [2009-11-19 22:00:07] [ed603017d2bee8fbd82b6d5ec04e12c3]
-   P             [Multiple Regression] [model3] [2009-11-20 08:47:44] [ed603017d2bee8fbd82b6d5ec04e12c3]
-    D              [Multiple Regression] [model 4] [2009-11-20 08:59:37] [ed603017d2bee8fbd82b6d5ec04e12c3]
-    D                [Multiple Regression] [W7: Model 4] [2009-11-22 13:34:45] [03d5b865e91ca35b5a5d21b8d6da5aba]
-    D                  [Multiple Regression] [review 7] [2009-11-24 21:51:11] [309ee52d0058ff0a6f7eec15e07b2d9f]
-    D                [Multiple Regression] [] [2009-11-22 15:02:10] [9f35ad889e41dd0c9322ca60d75b9f47]
-    D                [Multiple Regression] [Beste model] [2009-12-05 15:17:52] [34b80aeb109c116fd63bf2eb7493a276]
-    D              [Multiple Regression] [Workshop7] [2009-11-20 13:14:04] [34b80aeb109c116fd63bf2eb7493a276]
-    D                [Multiple Regression] [workshop7] [2009-11-20 13:34:45] [34b80aeb109c116fd63bf2eb7493a276]
-   P                   [Multiple Regression] [Workshop 7: verbe...] [2009-11-27 14:49:24] [7c2a5b25a196bd646844b8f5223c9b3e]
-   PD                [Multiple Regression] [Workshop 7] [2009-11-20 16:57:31] [78762f311bef5a0e45c439762ada383c]
-   P                   [Multiple Regression] [verb ws 7] [2009-11-21 09:45:52] [134dc66689e3d457a82860db6471d419]
-    D                [Multiple Regression] [model 3] [2009-12-05 14:58:14] [34b80aeb109c116fd63bf2eb7493a276]
-    D              [Multiple Regression] [W7: Linear Trend] [2009-11-21 14:22:35] [03d5b865e91ca35b5a5d21b8d6da5aba]
-    D              [Multiple Regression] [] [2009-11-22 14:13:11] [9f35ad889e41dd0c9322ca60d75b9f47]
-    D            [Multiple Regression] [W7: Monthly Dummies] [2009-11-21 14:07:55] [03d5b865e91ca35b5a5d21b8d6da5aba]
-    D            [Multiple Regression] [WS7] [2009-11-21 15:04:45] [9f35ad889e41dd0c9322ca60d75b9f47]
-    D          [Multiple Regression] [WS7] [2009-11-20 22:38:21] [9f35ad889e41dd0c9322ca60d75b9f47]
-    D          [Multiple Regression] [W7: Multiple regr...] [2009-11-21 13:40:01] [03d5b865e91ca35b5a5d21b8d6da5aba]
-    D          [Multiple Regression] [model 1 multiple ...] [2009-12-06 11:43:28] [ed603017d2bee8fbd82b6d5ec04e12c3]
-   PD          [Multiple Regression] [multiple regressi...] [2009-12-06 12:47:31] [ed603017d2bee8fbd82b6d5ec04e12c3]
-   PD            [Multiple Regression] [multiple regressi...] [2009-12-08 20:02:18] [ed603017d2bee8fbd82b6d5ec04e12c3]
-   PD          [Multiple Regression] [multiple regressi...] [2009-12-06 13:06:41] [ed603017d2bee8fbd82b6d5ec04e12c3]
-   PD      [Multiple Regression] [ws3] [2009-11-17 15:02:21] [ca30429b07824e7c5d48293114d35d71]
-   PD      [Multiple Regression] [ws3] [2009-11-17 15:40:23] [ca30429b07824e7c5d48293114d35d71]
-   PD      [Multiple Regression] [ws3] [2009-11-17 15:57:36] [ca30429b07824e7c5d48293114d35d71]
-    D      [Multiple Regression] [] [2009-11-17 16:40:29] [1eab65e90adf64584b8e6f0da23ff414]
-   PD      [Multiple Regression] [] [2009-11-17 16:45:02] [1eab65e90adf64584b8e6f0da23ff414]
-   PD      [Multiple Regression] [] [2009-11-17 16:47:07] [1eab65e90adf64584b8e6f0da23ff414]
-    D      [Multiple Regression] [] [2009-11-17 18:05:50] [96d96f181930b548ce74f8c3116c4873]
-    D      [Multiple Regression] [workshop 7 bereke...] [2009-11-17 18:48:27] [eaf42bcf5162b5692bb3c7f9d4636222]
-   PD      [Multiple Regression] [Multuple regression] [2009-11-17 19:25:44] [214e6e00abbde49700521a7ef1d30da2]
-   PD      [Multiple Regression] [Multiple regressi...] [2009-11-17 19:32:14] [214e6e00abbde49700521a7ef1d30da2]
-   PD      [Multiple Regression] [Multiple regression] [2009-11-17 19:34:37] [214e6e00abbde49700521a7ef1d30da2]
-   PD      [Multiple Regression] [] [2009-11-17 19:35:42] [5edbdb7a459c4059b6c3b063ba86821c]
-   PD      [Multiple Regression] [] [2009-11-17 19:35:42] [5edbdb7a459c4059b6c3b063ba86821c]
-   PD      [Multiple Regression] [] [2009-11-17 19:35:42] [5edbdb7a459c4059b6c3b063ba86821c]

[Truncated]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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1385	0
1632	0
1511	0
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2008	0
2242	0
2478	0
2030	0
1655	0
1693	0
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1992	0
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1569	0
1976	0
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1998	0
2079	0
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1057	1
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1168	1
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1185	1
1222	1
1284	1
1444	1
1575	1
1737	1
1763	1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 7 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55987&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55987&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55987&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 time7 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 1717.75147928994 -396.055827116028X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  1717.75147928994 -396.055827116028X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55987&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  1717.75147928994 -396.055827116028X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55987&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55987&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
Y[t] = + 1717.75147928994 -396.055827116028X[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1717.7514792899420.00033485.886100
X-396.05582711602857.786173-6.853800

\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) & 1717.75147928994 & 20.000334 & 85.8861 & 0 & 0 \tabularnewline
X & -396.055827116028 & 57.786173 & -6.8538 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55987&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]1717.75147928994[/C][C]20.000334[/C][C]85.8861[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-396.055827116028[/C][C]57.786173[/C][C]-6.8538[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55987&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55987&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)1717.7514792899420.00033485.886100
X-396.05582711602857.786173-6.853800






Multiple Linear Regression - Regression Statistics
Multiple R0.445226892939612
R-squared0.198226986196661
Adjusted R-squared0.194007128229275
F-TEST (value)46.9748005095662
F-TEST (DF numerator)1
F-TEST (DF denominator)190
p-value9.762957109416e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation260.004336317031
Sum Squared Residuals12844428.4316954

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.445226892939612 \tabularnewline
R-squared & 0.198226986196661 \tabularnewline
Adjusted R-squared & 0.194007128229275 \tabularnewline
F-TEST (value) & 46.9748005095662 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 190 \tabularnewline
p-value & 9.762957109416e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 260.004336317031 \tabularnewline
Sum Squared Residuals & 12844428.4316954 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55987&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.445226892939612[/C][/ROW]
[ROW][C]R-squared[/C][C]0.198226986196661[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.194007128229275[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]46.9748005095662[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]190[/C][/ROW]
[ROW][C]p-value[/C][C]9.762957109416e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]260.004336317031[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]12844428.4316954[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55987&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55987&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.445226892939612
R-squared0.198226986196661
Adjusted R-squared0.194007128229275
F-TEST (value)46.9748005095662
F-TEST (DF numerator)1
F-TEST (DF denominator)190
p-value9.762957109416e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation260.004336317031
Sum Squared Residuals12844428.4316954






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
116871717.75147928994-30.7514792899354
215081717.75147928994-209.751479289941
315071717.75147928994-210.751479289941
413851717.75147928994-332.751479289941
516321717.75147928994-85.7514792899409
615111717.75147928994-206.751479289941
715591717.75147928994-158.751479289941
816301717.75147928994-87.7514792899409
915791717.75147928994-138.751479289941
1016531717.75147928994-64.7514792899408
1121521717.75147928994434.248520710059
1221481717.75147928994430.248520710059
1317521717.7514792899434.2485207100591
1417651717.7514792899447.2485207100591
1517171717.75147928994-0.751479289940856
1615581717.75147928994-159.751479289941
1715751717.75147928994-142.751479289941
1815201717.75147928994-197.751479289941
1918051717.7514792899487.2485207100591
2018001717.7514792899482.2485207100591
2117191717.751479289941.24852071005914
2220081717.75147928994290.248520710059
2322421717.75147928994524.248520710059
2424781717.75147928994760.248520710059
2520301717.75147928994312.248520710059
2616551717.75147928994-62.7514792899409
2716931717.75147928994-24.7514792899409
2816231717.75147928994-94.751479289941
2918051717.7514792899487.2485207100591
3017461717.7514792899428.2485207100591
3117951717.7514792899477.2485207100591
3219261717.75147928994208.248520710059
3316191717.75147928994-98.7514792899409
3419921717.75147928994274.248520710059
3522331717.75147928994515.248520710059
3621921717.75147928994474.248520710059
3720801717.75147928994362.248520710059
3817681717.7514792899450.2485207100591
3918351717.75147928994117.248520710059
4015691717.75147928994-148.751479289941
4119761717.75147928994258.248520710059
4218531717.75147928994135.248520710059
4319651717.75147928994247.248520710059
4416891717.75147928994-28.7514792899409
4517781717.7514792899460.2485207100591
4619761717.75147928994258.248520710059
4723971717.75147928994679.248520710059
4826541717.75147928994936.24852071006
4920971717.75147928994379.248520710059
5019631717.75147928994245.248520710059
5116771717.75147928994-40.7514792899409
5219411717.75147928994223.248520710059
5320031717.75147928994285.248520710059
5418131717.7514792899495.248520710059
5520121717.75147928994294.248520710059
5619121717.75147928994194.248520710059
5720841717.75147928994366.248520710059
5820801717.75147928994362.248520710059
5921181717.75147928994400.248520710059
6021501717.75147928994432.248520710059
6116081717.75147928994-109.751479289941
6215031717.75147928994-214.751479289941
6315481717.75147928994-169.751479289941
6413821717.75147928994-335.751479289941
6517311717.7514792899413.2485207100591
6617981717.7514792899480.2485207100591
6717791717.7514792899461.2485207100591
6818871717.75147928994169.248520710059
6920041717.75147928994286.248520710059
7020771717.75147928994359.248520710059
7120921717.75147928994374.248520710059
7220511717.75147928994333.248520710059
7315771717.75147928994-140.751479289941
7413561717.75147928994-361.751479289941
7516521717.75147928994-65.7514792899408
7613821717.75147928994-335.751479289941
7715191717.75147928994-198.751479289941
7814211717.75147928994-296.751479289941
7914421717.75147928994-275.751479289941
8015431717.75147928994-174.751479289941
8116561717.75147928994-61.7514792899409
8215611717.75147928994-156.751479289941
8319051717.75147928994187.248520710059
8421991717.75147928994481.248520710059
8514731717.75147928994-244.751479289941
8616551717.75147928994-62.7514792899409
8714071717.75147928994-310.751479289941
8813951717.75147928994-322.751479289941
8915301717.75147928994-187.751479289941
9013091717.75147928994-408.751479289941
9115261717.75147928994-191.751479289941
9213271717.75147928994-390.751479289941
9316271717.75147928994-90.7514792899409
9417481717.7514792899430.2485207100591
9519581717.75147928994240.248520710059
9622741717.75147928994556.248520710059
9716481717.75147928994-69.7514792899408
9814011717.75147928994-316.751479289941
9914111717.75147928994-306.751479289941
10014031717.75147928994-314.751479289941
10113941717.75147928994-323.751479289941
10215201717.75147928994-197.751479289941
10315281717.75147928994-189.751479289941
10416431717.75147928994-74.7514792899409
10515151717.75147928994-202.751479289941
10616851717.75147928994-32.7514792899409
10720001717.75147928994282.248520710059
10822151717.75147928994497.248520710059
10919561717.75147928994238.248520710059
11014621717.75147928994-255.751479289941
11115631717.75147928994-154.751479289941
11214591717.75147928994-258.751479289941
11314461717.75147928994-271.751479289941
11416221717.75147928994-95.7514792899409
11516571717.75147928994-60.7514792899409
11616381717.75147928994-79.7514792899409
11716431717.75147928994-74.7514792899409
11816831717.75147928994-34.7514792899409
11920501717.75147928994332.248520710059
12022621717.75147928994544.248520710059
12118131717.7514792899495.248520710059
12214451717.75147928994-272.751479289941
12317621717.7514792899444.2485207100591
12414611717.75147928994-256.751479289941
12515561717.75147928994-161.751479289941
12614311717.75147928994-286.751479289941
12714271717.75147928994-290.751479289941
12815541717.75147928994-163.751479289941
12916451717.75147928994-72.7514792899409
13016531717.75147928994-64.7514792899408
13120161717.75147928994298.248520710059
13222071717.75147928994489.248520710059
13316651717.75147928994-52.7514792899409
13413611717.75147928994-356.751479289941
13515061717.75147928994-211.751479289941
13613601717.75147928994-357.751479289941
13714531717.75147928994-264.751479289941
13815221717.75147928994-195.751479289941
13914601717.75147928994-257.751479289941
14015521717.75147928994-165.751479289941
14115481717.75147928994-169.751479289941
14218271717.75147928994109.248520710059
14317371717.7514792899419.2485207100591
14419411717.75147928994223.248520710059
14514741717.75147928994-243.751479289941
14614581717.75147928994-259.751479289941
14715421717.75147928994-175.751479289941
14814041717.75147928994-313.751479289941
14915221717.75147928994-195.751479289941
15013851717.75147928994-332.751479289941
15116411717.75147928994-76.7514792899409
15215101717.75147928994-207.751479289941
15316811717.75147928994-36.7514792899409
15419381717.75147928994220.248520710059
15518681717.75147928994150.248520710059
15617261717.751479289948.24852071005914
15714561717.75147928994-261.751479289941
15814451717.75147928994-272.751479289941
15914561717.75147928994-261.751479289941
16013651717.75147928994-352.751479289941
16114871717.75147928994-230.751479289941
16215581717.75147928994-159.751479289941
16314881717.75147928994-229.751479289941
16416841717.75147928994-33.7514792899409
16515941717.75147928994-123.751479289941
16618501717.75147928994132.248520710059
16719981717.75147928994280.248520710059
16820791717.75147928994361.248520710059
16914941717.75147928994-223.751479289941
17010571321.69565217391-264.695652173913
17112181321.69565217391-103.695652173913
17211681321.69565217391-153.695652173913
17312361321.69565217391-85.695652173913
17410761321.69565217391-245.695652173913
17511741321.69565217391-147.695652173913
17611391321.69565217391-182.695652173913
17714271321.69565217391105.304347826087
17814871321.69565217391165.304347826087
17914831321.69565217391161.304347826087
18015131321.69565217391191.304347826087
18113571321.6956521739135.3043478260869
18211651321.69565217391-156.695652173913
18312821321.69565217391-39.6956521739131
18411101321.69565217391-211.695652173913
18512971321.69565217391-24.6956521739131
18611851321.69565217391-136.695652173913
18712221321.69565217391-99.695652173913
18812841321.69565217391-37.6956521739131
18914441321.69565217391122.304347826087
19015751321.69565217391253.304347826087
19117371321.69565217391415.304347826087
19217631321.69565217391441.304347826087

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1687 & 1717.75147928994 & -30.7514792899354 \tabularnewline
2 & 1508 & 1717.75147928994 & -209.751479289941 \tabularnewline
3 & 1507 & 1717.75147928994 & -210.751479289941 \tabularnewline
4 & 1385 & 1717.75147928994 & -332.751479289941 \tabularnewline
5 & 1632 & 1717.75147928994 & -85.7514792899409 \tabularnewline
6 & 1511 & 1717.75147928994 & -206.751479289941 \tabularnewline
7 & 1559 & 1717.75147928994 & -158.751479289941 \tabularnewline
8 & 1630 & 1717.75147928994 & -87.7514792899409 \tabularnewline
9 & 1579 & 1717.75147928994 & -138.751479289941 \tabularnewline
10 & 1653 & 1717.75147928994 & -64.7514792899408 \tabularnewline
11 & 2152 & 1717.75147928994 & 434.248520710059 \tabularnewline
12 & 2148 & 1717.75147928994 & 430.248520710059 \tabularnewline
13 & 1752 & 1717.75147928994 & 34.2485207100591 \tabularnewline
14 & 1765 & 1717.75147928994 & 47.2485207100591 \tabularnewline
15 & 1717 & 1717.75147928994 & -0.751479289940856 \tabularnewline
16 & 1558 & 1717.75147928994 & -159.751479289941 \tabularnewline
17 & 1575 & 1717.75147928994 & -142.751479289941 \tabularnewline
18 & 1520 & 1717.75147928994 & -197.751479289941 \tabularnewline
19 & 1805 & 1717.75147928994 & 87.2485207100591 \tabularnewline
20 & 1800 & 1717.75147928994 & 82.2485207100591 \tabularnewline
21 & 1719 & 1717.75147928994 & 1.24852071005914 \tabularnewline
22 & 2008 & 1717.75147928994 & 290.248520710059 \tabularnewline
23 & 2242 & 1717.75147928994 & 524.248520710059 \tabularnewline
24 & 2478 & 1717.75147928994 & 760.248520710059 \tabularnewline
25 & 2030 & 1717.75147928994 & 312.248520710059 \tabularnewline
26 & 1655 & 1717.75147928994 & -62.7514792899409 \tabularnewline
27 & 1693 & 1717.75147928994 & -24.7514792899409 \tabularnewline
28 & 1623 & 1717.75147928994 & -94.751479289941 \tabularnewline
29 & 1805 & 1717.75147928994 & 87.2485207100591 \tabularnewline
30 & 1746 & 1717.75147928994 & 28.2485207100591 \tabularnewline
31 & 1795 & 1717.75147928994 & 77.2485207100591 \tabularnewline
32 & 1926 & 1717.75147928994 & 208.248520710059 \tabularnewline
33 & 1619 & 1717.75147928994 & -98.7514792899409 \tabularnewline
34 & 1992 & 1717.75147928994 & 274.248520710059 \tabularnewline
35 & 2233 & 1717.75147928994 & 515.248520710059 \tabularnewline
36 & 2192 & 1717.75147928994 & 474.248520710059 \tabularnewline
37 & 2080 & 1717.75147928994 & 362.248520710059 \tabularnewline
38 & 1768 & 1717.75147928994 & 50.2485207100591 \tabularnewline
39 & 1835 & 1717.75147928994 & 117.248520710059 \tabularnewline
40 & 1569 & 1717.75147928994 & -148.751479289941 \tabularnewline
41 & 1976 & 1717.75147928994 & 258.248520710059 \tabularnewline
42 & 1853 & 1717.75147928994 & 135.248520710059 \tabularnewline
43 & 1965 & 1717.75147928994 & 247.248520710059 \tabularnewline
44 & 1689 & 1717.75147928994 & -28.7514792899409 \tabularnewline
45 & 1778 & 1717.75147928994 & 60.2485207100591 \tabularnewline
46 & 1976 & 1717.75147928994 & 258.248520710059 \tabularnewline
47 & 2397 & 1717.75147928994 & 679.248520710059 \tabularnewline
48 & 2654 & 1717.75147928994 & 936.24852071006 \tabularnewline
49 & 2097 & 1717.75147928994 & 379.248520710059 \tabularnewline
50 & 1963 & 1717.75147928994 & 245.248520710059 \tabularnewline
51 & 1677 & 1717.75147928994 & -40.7514792899409 \tabularnewline
52 & 1941 & 1717.75147928994 & 223.248520710059 \tabularnewline
53 & 2003 & 1717.75147928994 & 285.248520710059 \tabularnewline
54 & 1813 & 1717.75147928994 & 95.248520710059 \tabularnewline
55 & 2012 & 1717.75147928994 & 294.248520710059 \tabularnewline
56 & 1912 & 1717.75147928994 & 194.248520710059 \tabularnewline
57 & 2084 & 1717.75147928994 & 366.248520710059 \tabularnewline
58 & 2080 & 1717.75147928994 & 362.248520710059 \tabularnewline
59 & 2118 & 1717.75147928994 & 400.248520710059 \tabularnewline
60 & 2150 & 1717.75147928994 & 432.248520710059 \tabularnewline
61 & 1608 & 1717.75147928994 & -109.751479289941 \tabularnewline
62 & 1503 & 1717.75147928994 & -214.751479289941 \tabularnewline
63 & 1548 & 1717.75147928994 & -169.751479289941 \tabularnewline
64 & 1382 & 1717.75147928994 & -335.751479289941 \tabularnewline
65 & 1731 & 1717.75147928994 & 13.2485207100591 \tabularnewline
66 & 1798 & 1717.75147928994 & 80.2485207100591 \tabularnewline
67 & 1779 & 1717.75147928994 & 61.2485207100591 \tabularnewline
68 & 1887 & 1717.75147928994 & 169.248520710059 \tabularnewline
69 & 2004 & 1717.75147928994 & 286.248520710059 \tabularnewline
70 & 2077 & 1717.75147928994 & 359.248520710059 \tabularnewline
71 & 2092 & 1717.75147928994 & 374.248520710059 \tabularnewline
72 & 2051 & 1717.75147928994 & 333.248520710059 \tabularnewline
73 & 1577 & 1717.75147928994 & -140.751479289941 \tabularnewline
74 & 1356 & 1717.75147928994 & -361.751479289941 \tabularnewline
75 & 1652 & 1717.75147928994 & -65.7514792899408 \tabularnewline
76 & 1382 & 1717.75147928994 & -335.751479289941 \tabularnewline
77 & 1519 & 1717.75147928994 & -198.751479289941 \tabularnewline
78 & 1421 & 1717.75147928994 & -296.751479289941 \tabularnewline
79 & 1442 & 1717.75147928994 & -275.751479289941 \tabularnewline
80 & 1543 & 1717.75147928994 & -174.751479289941 \tabularnewline
81 & 1656 & 1717.75147928994 & -61.7514792899409 \tabularnewline
82 & 1561 & 1717.75147928994 & -156.751479289941 \tabularnewline
83 & 1905 & 1717.75147928994 & 187.248520710059 \tabularnewline
84 & 2199 & 1717.75147928994 & 481.248520710059 \tabularnewline
85 & 1473 & 1717.75147928994 & -244.751479289941 \tabularnewline
86 & 1655 & 1717.75147928994 & -62.7514792899409 \tabularnewline
87 & 1407 & 1717.75147928994 & -310.751479289941 \tabularnewline
88 & 1395 & 1717.75147928994 & -322.751479289941 \tabularnewline
89 & 1530 & 1717.75147928994 & -187.751479289941 \tabularnewline
90 & 1309 & 1717.75147928994 & -408.751479289941 \tabularnewline
91 & 1526 & 1717.75147928994 & -191.751479289941 \tabularnewline
92 & 1327 & 1717.75147928994 & -390.751479289941 \tabularnewline
93 & 1627 & 1717.75147928994 & -90.7514792899409 \tabularnewline
94 & 1748 & 1717.75147928994 & 30.2485207100591 \tabularnewline
95 & 1958 & 1717.75147928994 & 240.248520710059 \tabularnewline
96 & 2274 & 1717.75147928994 & 556.248520710059 \tabularnewline
97 & 1648 & 1717.75147928994 & -69.7514792899408 \tabularnewline
98 & 1401 & 1717.75147928994 & -316.751479289941 \tabularnewline
99 & 1411 & 1717.75147928994 & -306.751479289941 \tabularnewline
100 & 1403 & 1717.75147928994 & -314.751479289941 \tabularnewline
101 & 1394 & 1717.75147928994 & -323.751479289941 \tabularnewline
102 & 1520 & 1717.75147928994 & -197.751479289941 \tabularnewline
103 & 1528 & 1717.75147928994 & -189.751479289941 \tabularnewline
104 & 1643 & 1717.75147928994 & -74.7514792899409 \tabularnewline
105 & 1515 & 1717.75147928994 & -202.751479289941 \tabularnewline
106 & 1685 & 1717.75147928994 & -32.7514792899409 \tabularnewline
107 & 2000 & 1717.75147928994 & 282.248520710059 \tabularnewline
108 & 2215 & 1717.75147928994 & 497.248520710059 \tabularnewline
109 & 1956 & 1717.75147928994 & 238.248520710059 \tabularnewline
110 & 1462 & 1717.75147928994 & -255.751479289941 \tabularnewline
111 & 1563 & 1717.75147928994 & -154.751479289941 \tabularnewline
112 & 1459 & 1717.75147928994 & -258.751479289941 \tabularnewline
113 & 1446 & 1717.75147928994 & -271.751479289941 \tabularnewline
114 & 1622 & 1717.75147928994 & -95.7514792899409 \tabularnewline
115 & 1657 & 1717.75147928994 & -60.7514792899409 \tabularnewline
116 & 1638 & 1717.75147928994 & -79.7514792899409 \tabularnewline
117 & 1643 & 1717.75147928994 & -74.7514792899409 \tabularnewline
118 & 1683 & 1717.75147928994 & -34.7514792899409 \tabularnewline
119 & 2050 & 1717.75147928994 & 332.248520710059 \tabularnewline
120 & 2262 & 1717.75147928994 & 544.248520710059 \tabularnewline
121 & 1813 & 1717.75147928994 & 95.248520710059 \tabularnewline
122 & 1445 & 1717.75147928994 & -272.751479289941 \tabularnewline
123 & 1762 & 1717.75147928994 & 44.2485207100591 \tabularnewline
124 & 1461 & 1717.75147928994 & -256.751479289941 \tabularnewline
125 & 1556 & 1717.75147928994 & -161.751479289941 \tabularnewline
126 & 1431 & 1717.75147928994 & -286.751479289941 \tabularnewline
127 & 1427 & 1717.75147928994 & -290.751479289941 \tabularnewline
128 & 1554 & 1717.75147928994 & -163.751479289941 \tabularnewline
129 & 1645 & 1717.75147928994 & -72.7514792899409 \tabularnewline
130 & 1653 & 1717.75147928994 & -64.7514792899408 \tabularnewline
131 & 2016 & 1717.75147928994 & 298.248520710059 \tabularnewline
132 & 2207 & 1717.75147928994 & 489.248520710059 \tabularnewline
133 & 1665 & 1717.75147928994 & -52.7514792899409 \tabularnewline
134 & 1361 & 1717.75147928994 & -356.751479289941 \tabularnewline
135 & 1506 & 1717.75147928994 & -211.751479289941 \tabularnewline
136 & 1360 & 1717.75147928994 & -357.751479289941 \tabularnewline
137 & 1453 & 1717.75147928994 & -264.751479289941 \tabularnewline
138 & 1522 & 1717.75147928994 & -195.751479289941 \tabularnewline
139 & 1460 & 1717.75147928994 & -257.751479289941 \tabularnewline
140 & 1552 & 1717.75147928994 & -165.751479289941 \tabularnewline
141 & 1548 & 1717.75147928994 & -169.751479289941 \tabularnewline
142 & 1827 & 1717.75147928994 & 109.248520710059 \tabularnewline
143 & 1737 & 1717.75147928994 & 19.2485207100591 \tabularnewline
144 & 1941 & 1717.75147928994 & 223.248520710059 \tabularnewline
145 & 1474 & 1717.75147928994 & -243.751479289941 \tabularnewline
146 & 1458 & 1717.75147928994 & -259.751479289941 \tabularnewline
147 & 1542 & 1717.75147928994 & -175.751479289941 \tabularnewline
148 & 1404 & 1717.75147928994 & -313.751479289941 \tabularnewline
149 & 1522 & 1717.75147928994 & -195.751479289941 \tabularnewline
150 & 1385 & 1717.75147928994 & -332.751479289941 \tabularnewline
151 & 1641 & 1717.75147928994 & -76.7514792899409 \tabularnewline
152 & 1510 & 1717.75147928994 & -207.751479289941 \tabularnewline
153 & 1681 & 1717.75147928994 & -36.7514792899409 \tabularnewline
154 & 1938 & 1717.75147928994 & 220.248520710059 \tabularnewline
155 & 1868 & 1717.75147928994 & 150.248520710059 \tabularnewline
156 & 1726 & 1717.75147928994 & 8.24852071005914 \tabularnewline
157 & 1456 & 1717.75147928994 & -261.751479289941 \tabularnewline
158 & 1445 & 1717.75147928994 & -272.751479289941 \tabularnewline
159 & 1456 & 1717.75147928994 & -261.751479289941 \tabularnewline
160 & 1365 & 1717.75147928994 & -352.751479289941 \tabularnewline
161 & 1487 & 1717.75147928994 & -230.751479289941 \tabularnewline
162 & 1558 & 1717.75147928994 & -159.751479289941 \tabularnewline
163 & 1488 & 1717.75147928994 & -229.751479289941 \tabularnewline
164 & 1684 & 1717.75147928994 & -33.7514792899409 \tabularnewline
165 & 1594 & 1717.75147928994 & -123.751479289941 \tabularnewline
166 & 1850 & 1717.75147928994 & 132.248520710059 \tabularnewline
167 & 1998 & 1717.75147928994 & 280.248520710059 \tabularnewline
168 & 2079 & 1717.75147928994 & 361.248520710059 \tabularnewline
169 & 1494 & 1717.75147928994 & -223.751479289941 \tabularnewline
170 & 1057 & 1321.69565217391 & -264.695652173913 \tabularnewline
171 & 1218 & 1321.69565217391 & -103.695652173913 \tabularnewline
172 & 1168 & 1321.69565217391 & -153.695652173913 \tabularnewline
173 & 1236 & 1321.69565217391 & -85.695652173913 \tabularnewline
174 & 1076 & 1321.69565217391 & -245.695652173913 \tabularnewline
175 & 1174 & 1321.69565217391 & -147.695652173913 \tabularnewline
176 & 1139 & 1321.69565217391 & -182.695652173913 \tabularnewline
177 & 1427 & 1321.69565217391 & 105.304347826087 \tabularnewline
178 & 1487 & 1321.69565217391 & 165.304347826087 \tabularnewline
179 & 1483 & 1321.69565217391 & 161.304347826087 \tabularnewline
180 & 1513 & 1321.69565217391 & 191.304347826087 \tabularnewline
181 & 1357 & 1321.69565217391 & 35.3043478260869 \tabularnewline
182 & 1165 & 1321.69565217391 & -156.695652173913 \tabularnewline
183 & 1282 & 1321.69565217391 & -39.6956521739131 \tabularnewline
184 & 1110 & 1321.69565217391 & -211.695652173913 \tabularnewline
185 & 1297 & 1321.69565217391 & -24.6956521739131 \tabularnewline
186 & 1185 & 1321.69565217391 & -136.695652173913 \tabularnewline
187 & 1222 & 1321.69565217391 & -99.695652173913 \tabularnewline
188 & 1284 & 1321.69565217391 & -37.6956521739131 \tabularnewline
189 & 1444 & 1321.69565217391 & 122.304347826087 \tabularnewline
190 & 1575 & 1321.69565217391 & 253.304347826087 \tabularnewline
191 & 1737 & 1321.69565217391 & 415.304347826087 \tabularnewline
192 & 1763 & 1321.69565217391 & 441.304347826087 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55987&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]1687[/C][C]1717.75147928994[/C][C]-30.7514792899354[/C][/ROW]
[ROW][C]2[/C][C]1508[/C][C]1717.75147928994[/C][C]-209.751479289941[/C][/ROW]
[ROW][C]3[/C][C]1507[/C][C]1717.75147928994[/C][C]-210.751479289941[/C][/ROW]
[ROW][C]4[/C][C]1385[/C][C]1717.75147928994[/C][C]-332.751479289941[/C][/ROW]
[ROW][C]5[/C][C]1632[/C][C]1717.75147928994[/C][C]-85.7514792899409[/C][/ROW]
[ROW][C]6[/C][C]1511[/C][C]1717.75147928994[/C][C]-206.751479289941[/C][/ROW]
[ROW][C]7[/C][C]1559[/C][C]1717.75147928994[/C][C]-158.751479289941[/C][/ROW]
[ROW][C]8[/C][C]1630[/C][C]1717.75147928994[/C][C]-87.7514792899409[/C][/ROW]
[ROW][C]9[/C][C]1579[/C][C]1717.75147928994[/C][C]-138.751479289941[/C][/ROW]
[ROW][C]10[/C][C]1653[/C][C]1717.75147928994[/C][C]-64.7514792899408[/C][/ROW]
[ROW][C]11[/C][C]2152[/C][C]1717.75147928994[/C][C]434.248520710059[/C][/ROW]
[ROW][C]12[/C][C]2148[/C][C]1717.75147928994[/C][C]430.248520710059[/C][/ROW]
[ROW][C]13[/C][C]1752[/C][C]1717.75147928994[/C][C]34.2485207100591[/C][/ROW]
[ROW][C]14[/C][C]1765[/C][C]1717.75147928994[/C][C]47.2485207100591[/C][/ROW]
[ROW][C]15[/C][C]1717[/C][C]1717.75147928994[/C][C]-0.751479289940856[/C][/ROW]
[ROW][C]16[/C][C]1558[/C][C]1717.75147928994[/C][C]-159.751479289941[/C][/ROW]
[ROW][C]17[/C][C]1575[/C][C]1717.75147928994[/C][C]-142.751479289941[/C][/ROW]
[ROW][C]18[/C][C]1520[/C][C]1717.75147928994[/C][C]-197.751479289941[/C][/ROW]
[ROW][C]19[/C][C]1805[/C][C]1717.75147928994[/C][C]87.2485207100591[/C][/ROW]
[ROW][C]20[/C][C]1800[/C][C]1717.75147928994[/C][C]82.2485207100591[/C][/ROW]
[ROW][C]21[/C][C]1719[/C][C]1717.75147928994[/C][C]1.24852071005914[/C][/ROW]
[ROW][C]22[/C][C]2008[/C][C]1717.75147928994[/C][C]290.248520710059[/C][/ROW]
[ROW][C]23[/C][C]2242[/C][C]1717.75147928994[/C][C]524.248520710059[/C][/ROW]
[ROW][C]24[/C][C]2478[/C][C]1717.75147928994[/C][C]760.248520710059[/C][/ROW]
[ROW][C]25[/C][C]2030[/C][C]1717.75147928994[/C][C]312.248520710059[/C][/ROW]
[ROW][C]26[/C][C]1655[/C][C]1717.75147928994[/C][C]-62.7514792899409[/C][/ROW]
[ROW][C]27[/C][C]1693[/C][C]1717.75147928994[/C][C]-24.7514792899409[/C][/ROW]
[ROW][C]28[/C][C]1623[/C][C]1717.75147928994[/C][C]-94.751479289941[/C][/ROW]
[ROW][C]29[/C][C]1805[/C][C]1717.75147928994[/C][C]87.2485207100591[/C][/ROW]
[ROW][C]30[/C][C]1746[/C][C]1717.75147928994[/C][C]28.2485207100591[/C][/ROW]
[ROW][C]31[/C][C]1795[/C][C]1717.75147928994[/C][C]77.2485207100591[/C][/ROW]
[ROW][C]32[/C][C]1926[/C][C]1717.75147928994[/C][C]208.248520710059[/C][/ROW]
[ROW][C]33[/C][C]1619[/C][C]1717.75147928994[/C][C]-98.7514792899409[/C][/ROW]
[ROW][C]34[/C][C]1992[/C][C]1717.75147928994[/C][C]274.248520710059[/C][/ROW]
[ROW][C]35[/C][C]2233[/C][C]1717.75147928994[/C][C]515.248520710059[/C][/ROW]
[ROW][C]36[/C][C]2192[/C][C]1717.75147928994[/C][C]474.248520710059[/C][/ROW]
[ROW][C]37[/C][C]2080[/C][C]1717.75147928994[/C][C]362.248520710059[/C][/ROW]
[ROW][C]38[/C][C]1768[/C][C]1717.75147928994[/C][C]50.2485207100591[/C][/ROW]
[ROW][C]39[/C][C]1835[/C][C]1717.75147928994[/C][C]117.248520710059[/C][/ROW]
[ROW][C]40[/C][C]1569[/C][C]1717.75147928994[/C][C]-148.751479289941[/C][/ROW]
[ROW][C]41[/C][C]1976[/C][C]1717.75147928994[/C][C]258.248520710059[/C][/ROW]
[ROW][C]42[/C][C]1853[/C][C]1717.75147928994[/C][C]135.248520710059[/C][/ROW]
[ROW][C]43[/C][C]1965[/C][C]1717.75147928994[/C][C]247.248520710059[/C][/ROW]
[ROW][C]44[/C][C]1689[/C][C]1717.75147928994[/C][C]-28.7514792899409[/C][/ROW]
[ROW][C]45[/C][C]1778[/C][C]1717.75147928994[/C][C]60.2485207100591[/C][/ROW]
[ROW][C]46[/C][C]1976[/C][C]1717.75147928994[/C][C]258.248520710059[/C][/ROW]
[ROW][C]47[/C][C]2397[/C][C]1717.75147928994[/C][C]679.248520710059[/C][/ROW]
[ROW][C]48[/C][C]2654[/C][C]1717.75147928994[/C][C]936.24852071006[/C][/ROW]
[ROW][C]49[/C][C]2097[/C][C]1717.75147928994[/C][C]379.248520710059[/C][/ROW]
[ROW][C]50[/C][C]1963[/C][C]1717.75147928994[/C][C]245.248520710059[/C][/ROW]
[ROW][C]51[/C][C]1677[/C][C]1717.75147928994[/C][C]-40.7514792899409[/C][/ROW]
[ROW][C]52[/C][C]1941[/C][C]1717.75147928994[/C][C]223.248520710059[/C][/ROW]
[ROW][C]53[/C][C]2003[/C][C]1717.75147928994[/C][C]285.248520710059[/C][/ROW]
[ROW][C]54[/C][C]1813[/C][C]1717.75147928994[/C][C]95.248520710059[/C][/ROW]
[ROW][C]55[/C][C]2012[/C][C]1717.75147928994[/C][C]294.248520710059[/C][/ROW]
[ROW][C]56[/C][C]1912[/C][C]1717.75147928994[/C][C]194.248520710059[/C][/ROW]
[ROW][C]57[/C][C]2084[/C][C]1717.75147928994[/C][C]366.248520710059[/C][/ROW]
[ROW][C]58[/C][C]2080[/C][C]1717.75147928994[/C][C]362.248520710059[/C][/ROW]
[ROW][C]59[/C][C]2118[/C][C]1717.75147928994[/C][C]400.248520710059[/C][/ROW]
[ROW][C]60[/C][C]2150[/C][C]1717.75147928994[/C][C]432.248520710059[/C][/ROW]
[ROW][C]61[/C][C]1608[/C][C]1717.75147928994[/C][C]-109.751479289941[/C][/ROW]
[ROW][C]62[/C][C]1503[/C][C]1717.75147928994[/C][C]-214.751479289941[/C][/ROW]
[ROW][C]63[/C][C]1548[/C][C]1717.75147928994[/C][C]-169.751479289941[/C][/ROW]
[ROW][C]64[/C][C]1382[/C][C]1717.75147928994[/C][C]-335.751479289941[/C][/ROW]
[ROW][C]65[/C][C]1731[/C][C]1717.75147928994[/C][C]13.2485207100591[/C][/ROW]
[ROW][C]66[/C][C]1798[/C][C]1717.75147928994[/C][C]80.2485207100591[/C][/ROW]
[ROW][C]67[/C][C]1779[/C][C]1717.75147928994[/C][C]61.2485207100591[/C][/ROW]
[ROW][C]68[/C][C]1887[/C][C]1717.75147928994[/C][C]169.248520710059[/C][/ROW]
[ROW][C]69[/C][C]2004[/C][C]1717.75147928994[/C][C]286.248520710059[/C][/ROW]
[ROW][C]70[/C][C]2077[/C][C]1717.75147928994[/C][C]359.248520710059[/C][/ROW]
[ROW][C]71[/C][C]2092[/C][C]1717.75147928994[/C][C]374.248520710059[/C][/ROW]
[ROW][C]72[/C][C]2051[/C][C]1717.75147928994[/C][C]333.248520710059[/C][/ROW]
[ROW][C]73[/C][C]1577[/C][C]1717.75147928994[/C][C]-140.751479289941[/C][/ROW]
[ROW][C]74[/C][C]1356[/C][C]1717.75147928994[/C][C]-361.751479289941[/C][/ROW]
[ROW][C]75[/C][C]1652[/C][C]1717.75147928994[/C][C]-65.7514792899408[/C][/ROW]
[ROW][C]76[/C][C]1382[/C][C]1717.75147928994[/C][C]-335.751479289941[/C][/ROW]
[ROW][C]77[/C][C]1519[/C][C]1717.75147928994[/C][C]-198.751479289941[/C][/ROW]
[ROW][C]78[/C][C]1421[/C][C]1717.75147928994[/C][C]-296.751479289941[/C][/ROW]
[ROW][C]79[/C][C]1442[/C][C]1717.75147928994[/C][C]-275.751479289941[/C][/ROW]
[ROW][C]80[/C][C]1543[/C][C]1717.75147928994[/C][C]-174.751479289941[/C][/ROW]
[ROW][C]81[/C][C]1656[/C][C]1717.75147928994[/C][C]-61.7514792899409[/C][/ROW]
[ROW][C]82[/C][C]1561[/C][C]1717.75147928994[/C][C]-156.751479289941[/C][/ROW]
[ROW][C]83[/C][C]1905[/C][C]1717.75147928994[/C][C]187.248520710059[/C][/ROW]
[ROW][C]84[/C][C]2199[/C][C]1717.75147928994[/C][C]481.248520710059[/C][/ROW]
[ROW][C]85[/C][C]1473[/C][C]1717.75147928994[/C][C]-244.751479289941[/C][/ROW]
[ROW][C]86[/C][C]1655[/C][C]1717.75147928994[/C][C]-62.7514792899409[/C][/ROW]
[ROW][C]87[/C][C]1407[/C][C]1717.75147928994[/C][C]-310.751479289941[/C][/ROW]
[ROW][C]88[/C][C]1395[/C][C]1717.75147928994[/C][C]-322.751479289941[/C][/ROW]
[ROW][C]89[/C][C]1530[/C][C]1717.75147928994[/C][C]-187.751479289941[/C][/ROW]
[ROW][C]90[/C][C]1309[/C][C]1717.75147928994[/C][C]-408.751479289941[/C][/ROW]
[ROW][C]91[/C][C]1526[/C][C]1717.75147928994[/C][C]-191.751479289941[/C][/ROW]
[ROW][C]92[/C][C]1327[/C][C]1717.75147928994[/C][C]-390.751479289941[/C][/ROW]
[ROW][C]93[/C][C]1627[/C][C]1717.75147928994[/C][C]-90.7514792899409[/C][/ROW]
[ROW][C]94[/C][C]1748[/C][C]1717.75147928994[/C][C]30.2485207100591[/C][/ROW]
[ROW][C]95[/C][C]1958[/C][C]1717.75147928994[/C][C]240.248520710059[/C][/ROW]
[ROW][C]96[/C][C]2274[/C][C]1717.75147928994[/C][C]556.248520710059[/C][/ROW]
[ROW][C]97[/C][C]1648[/C][C]1717.75147928994[/C][C]-69.7514792899408[/C][/ROW]
[ROW][C]98[/C][C]1401[/C][C]1717.75147928994[/C][C]-316.751479289941[/C][/ROW]
[ROW][C]99[/C][C]1411[/C][C]1717.75147928994[/C][C]-306.751479289941[/C][/ROW]
[ROW][C]100[/C][C]1403[/C][C]1717.75147928994[/C][C]-314.751479289941[/C][/ROW]
[ROW][C]101[/C][C]1394[/C][C]1717.75147928994[/C][C]-323.751479289941[/C][/ROW]
[ROW][C]102[/C][C]1520[/C][C]1717.75147928994[/C][C]-197.751479289941[/C][/ROW]
[ROW][C]103[/C][C]1528[/C][C]1717.75147928994[/C][C]-189.751479289941[/C][/ROW]
[ROW][C]104[/C][C]1643[/C][C]1717.75147928994[/C][C]-74.7514792899409[/C][/ROW]
[ROW][C]105[/C][C]1515[/C][C]1717.75147928994[/C][C]-202.751479289941[/C][/ROW]
[ROW][C]106[/C][C]1685[/C][C]1717.75147928994[/C][C]-32.7514792899409[/C][/ROW]
[ROW][C]107[/C][C]2000[/C][C]1717.75147928994[/C][C]282.248520710059[/C][/ROW]
[ROW][C]108[/C][C]2215[/C][C]1717.75147928994[/C][C]497.248520710059[/C][/ROW]
[ROW][C]109[/C][C]1956[/C][C]1717.75147928994[/C][C]238.248520710059[/C][/ROW]
[ROW][C]110[/C][C]1462[/C][C]1717.75147928994[/C][C]-255.751479289941[/C][/ROW]
[ROW][C]111[/C][C]1563[/C][C]1717.75147928994[/C][C]-154.751479289941[/C][/ROW]
[ROW][C]112[/C][C]1459[/C][C]1717.75147928994[/C][C]-258.751479289941[/C][/ROW]
[ROW][C]113[/C][C]1446[/C][C]1717.75147928994[/C][C]-271.751479289941[/C][/ROW]
[ROW][C]114[/C][C]1622[/C][C]1717.75147928994[/C][C]-95.7514792899409[/C][/ROW]
[ROW][C]115[/C][C]1657[/C][C]1717.75147928994[/C][C]-60.7514792899409[/C][/ROW]
[ROW][C]116[/C][C]1638[/C][C]1717.75147928994[/C][C]-79.7514792899409[/C][/ROW]
[ROW][C]117[/C][C]1643[/C][C]1717.75147928994[/C][C]-74.7514792899409[/C][/ROW]
[ROW][C]118[/C][C]1683[/C][C]1717.75147928994[/C][C]-34.7514792899409[/C][/ROW]
[ROW][C]119[/C][C]2050[/C][C]1717.75147928994[/C][C]332.248520710059[/C][/ROW]
[ROW][C]120[/C][C]2262[/C][C]1717.75147928994[/C][C]544.248520710059[/C][/ROW]
[ROW][C]121[/C][C]1813[/C][C]1717.75147928994[/C][C]95.248520710059[/C][/ROW]
[ROW][C]122[/C][C]1445[/C][C]1717.75147928994[/C][C]-272.751479289941[/C][/ROW]
[ROW][C]123[/C][C]1762[/C][C]1717.75147928994[/C][C]44.2485207100591[/C][/ROW]
[ROW][C]124[/C][C]1461[/C][C]1717.75147928994[/C][C]-256.751479289941[/C][/ROW]
[ROW][C]125[/C][C]1556[/C][C]1717.75147928994[/C][C]-161.751479289941[/C][/ROW]
[ROW][C]126[/C][C]1431[/C][C]1717.75147928994[/C][C]-286.751479289941[/C][/ROW]
[ROW][C]127[/C][C]1427[/C][C]1717.75147928994[/C][C]-290.751479289941[/C][/ROW]
[ROW][C]128[/C][C]1554[/C][C]1717.75147928994[/C][C]-163.751479289941[/C][/ROW]
[ROW][C]129[/C][C]1645[/C][C]1717.75147928994[/C][C]-72.7514792899409[/C][/ROW]
[ROW][C]130[/C][C]1653[/C][C]1717.75147928994[/C][C]-64.7514792899408[/C][/ROW]
[ROW][C]131[/C][C]2016[/C][C]1717.75147928994[/C][C]298.248520710059[/C][/ROW]
[ROW][C]132[/C][C]2207[/C][C]1717.75147928994[/C][C]489.248520710059[/C][/ROW]
[ROW][C]133[/C][C]1665[/C][C]1717.75147928994[/C][C]-52.7514792899409[/C][/ROW]
[ROW][C]134[/C][C]1361[/C][C]1717.75147928994[/C][C]-356.751479289941[/C][/ROW]
[ROW][C]135[/C][C]1506[/C][C]1717.75147928994[/C][C]-211.751479289941[/C][/ROW]
[ROW][C]136[/C][C]1360[/C][C]1717.75147928994[/C][C]-357.751479289941[/C][/ROW]
[ROW][C]137[/C][C]1453[/C][C]1717.75147928994[/C][C]-264.751479289941[/C][/ROW]
[ROW][C]138[/C][C]1522[/C][C]1717.75147928994[/C][C]-195.751479289941[/C][/ROW]
[ROW][C]139[/C][C]1460[/C][C]1717.75147928994[/C][C]-257.751479289941[/C][/ROW]
[ROW][C]140[/C][C]1552[/C][C]1717.75147928994[/C][C]-165.751479289941[/C][/ROW]
[ROW][C]141[/C][C]1548[/C][C]1717.75147928994[/C][C]-169.751479289941[/C][/ROW]
[ROW][C]142[/C][C]1827[/C][C]1717.75147928994[/C][C]109.248520710059[/C][/ROW]
[ROW][C]143[/C][C]1737[/C][C]1717.75147928994[/C][C]19.2485207100591[/C][/ROW]
[ROW][C]144[/C][C]1941[/C][C]1717.75147928994[/C][C]223.248520710059[/C][/ROW]
[ROW][C]145[/C][C]1474[/C][C]1717.75147928994[/C][C]-243.751479289941[/C][/ROW]
[ROW][C]146[/C][C]1458[/C][C]1717.75147928994[/C][C]-259.751479289941[/C][/ROW]
[ROW][C]147[/C][C]1542[/C][C]1717.75147928994[/C][C]-175.751479289941[/C][/ROW]
[ROW][C]148[/C][C]1404[/C][C]1717.75147928994[/C][C]-313.751479289941[/C][/ROW]
[ROW][C]149[/C][C]1522[/C][C]1717.75147928994[/C][C]-195.751479289941[/C][/ROW]
[ROW][C]150[/C][C]1385[/C][C]1717.75147928994[/C][C]-332.751479289941[/C][/ROW]
[ROW][C]151[/C][C]1641[/C][C]1717.75147928994[/C][C]-76.7514792899409[/C][/ROW]
[ROW][C]152[/C][C]1510[/C][C]1717.75147928994[/C][C]-207.751479289941[/C][/ROW]
[ROW][C]153[/C][C]1681[/C][C]1717.75147928994[/C][C]-36.7514792899409[/C][/ROW]
[ROW][C]154[/C][C]1938[/C][C]1717.75147928994[/C][C]220.248520710059[/C][/ROW]
[ROW][C]155[/C][C]1868[/C][C]1717.75147928994[/C][C]150.248520710059[/C][/ROW]
[ROW][C]156[/C][C]1726[/C][C]1717.75147928994[/C][C]8.24852071005914[/C][/ROW]
[ROW][C]157[/C][C]1456[/C][C]1717.75147928994[/C][C]-261.751479289941[/C][/ROW]
[ROW][C]158[/C][C]1445[/C][C]1717.75147928994[/C][C]-272.751479289941[/C][/ROW]
[ROW][C]159[/C][C]1456[/C][C]1717.75147928994[/C][C]-261.751479289941[/C][/ROW]
[ROW][C]160[/C][C]1365[/C][C]1717.75147928994[/C][C]-352.751479289941[/C][/ROW]
[ROW][C]161[/C][C]1487[/C][C]1717.75147928994[/C][C]-230.751479289941[/C][/ROW]
[ROW][C]162[/C][C]1558[/C][C]1717.75147928994[/C][C]-159.751479289941[/C][/ROW]
[ROW][C]163[/C][C]1488[/C][C]1717.75147928994[/C][C]-229.751479289941[/C][/ROW]
[ROW][C]164[/C][C]1684[/C][C]1717.75147928994[/C][C]-33.7514792899409[/C][/ROW]
[ROW][C]165[/C][C]1594[/C][C]1717.75147928994[/C][C]-123.751479289941[/C][/ROW]
[ROW][C]166[/C][C]1850[/C][C]1717.75147928994[/C][C]132.248520710059[/C][/ROW]
[ROW][C]167[/C][C]1998[/C][C]1717.75147928994[/C][C]280.248520710059[/C][/ROW]
[ROW][C]168[/C][C]2079[/C][C]1717.75147928994[/C][C]361.248520710059[/C][/ROW]
[ROW][C]169[/C][C]1494[/C][C]1717.75147928994[/C][C]-223.751479289941[/C][/ROW]
[ROW][C]170[/C][C]1057[/C][C]1321.69565217391[/C][C]-264.695652173913[/C][/ROW]
[ROW][C]171[/C][C]1218[/C][C]1321.69565217391[/C][C]-103.695652173913[/C][/ROW]
[ROW][C]172[/C][C]1168[/C][C]1321.69565217391[/C][C]-153.695652173913[/C][/ROW]
[ROW][C]173[/C][C]1236[/C][C]1321.69565217391[/C][C]-85.695652173913[/C][/ROW]
[ROW][C]174[/C][C]1076[/C][C]1321.69565217391[/C][C]-245.695652173913[/C][/ROW]
[ROW][C]175[/C][C]1174[/C][C]1321.69565217391[/C][C]-147.695652173913[/C][/ROW]
[ROW][C]176[/C][C]1139[/C][C]1321.69565217391[/C][C]-182.695652173913[/C][/ROW]
[ROW][C]177[/C][C]1427[/C][C]1321.69565217391[/C][C]105.304347826087[/C][/ROW]
[ROW][C]178[/C][C]1487[/C][C]1321.69565217391[/C][C]165.304347826087[/C][/ROW]
[ROW][C]179[/C][C]1483[/C][C]1321.69565217391[/C][C]161.304347826087[/C][/ROW]
[ROW][C]180[/C][C]1513[/C][C]1321.69565217391[/C][C]191.304347826087[/C][/ROW]
[ROW][C]181[/C][C]1357[/C][C]1321.69565217391[/C][C]35.3043478260869[/C][/ROW]
[ROW][C]182[/C][C]1165[/C][C]1321.69565217391[/C][C]-156.695652173913[/C][/ROW]
[ROW][C]183[/C][C]1282[/C][C]1321.69565217391[/C][C]-39.6956521739131[/C][/ROW]
[ROW][C]184[/C][C]1110[/C][C]1321.69565217391[/C][C]-211.695652173913[/C][/ROW]
[ROW][C]185[/C][C]1297[/C][C]1321.69565217391[/C][C]-24.6956521739131[/C][/ROW]
[ROW][C]186[/C][C]1185[/C][C]1321.69565217391[/C][C]-136.695652173913[/C][/ROW]
[ROW][C]187[/C][C]1222[/C][C]1321.69565217391[/C][C]-99.695652173913[/C][/ROW]
[ROW][C]188[/C][C]1284[/C][C]1321.69565217391[/C][C]-37.6956521739131[/C][/ROW]
[ROW][C]189[/C][C]1444[/C][C]1321.69565217391[/C][C]122.304347826087[/C][/ROW]
[ROW][C]190[/C][C]1575[/C][C]1321.69565217391[/C][C]253.304347826087[/C][/ROW]
[ROW][C]191[/C][C]1737[/C][C]1321.69565217391[/C][C]415.304347826087[/C][/ROW]
[ROW][C]192[/C][C]1763[/C][C]1321.69565217391[/C][C]441.304347826087[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55987&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55987&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
116871717.75147928994-30.7514792899354
215081717.75147928994-209.751479289941
315071717.75147928994-210.751479289941
413851717.75147928994-332.751479289941
516321717.75147928994-85.7514792899409
615111717.75147928994-206.751479289941
715591717.75147928994-158.751479289941
816301717.75147928994-87.7514792899409
915791717.75147928994-138.751479289941
1016531717.75147928994-64.7514792899408
1121521717.75147928994434.248520710059
1221481717.75147928994430.248520710059
1317521717.7514792899434.2485207100591
1417651717.7514792899447.2485207100591
1517171717.75147928994-0.751479289940856
1615581717.75147928994-159.751479289941
1715751717.75147928994-142.751479289941
1815201717.75147928994-197.751479289941
1918051717.7514792899487.2485207100591
2018001717.7514792899482.2485207100591
2117191717.751479289941.24852071005914
2220081717.75147928994290.248520710059
2322421717.75147928994524.248520710059
2424781717.75147928994760.248520710059
2520301717.75147928994312.248520710059
2616551717.75147928994-62.7514792899409
2716931717.75147928994-24.7514792899409
2816231717.75147928994-94.751479289941
2918051717.7514792899487.2485207100591
3017461717.7514792899428.2485207100591
3117951717.7514792899477.2485207100591
3219261717.75147928994208.248520710059
3316191717.75147928994-98.7514792899409
3419921717.75147928994274.248520710059
3522331717.75147928994515.248520710059
3621921717.75147928994474.248520710059
3720801717.75147928994362.248520710059
3817681717.7514792899450.2485207100591
3918351717.75147928994117.248520710059
4015691717.75147928994-148.751479289941
4119761717.75147928994258.248520710059
4218531717.75147928994135.248520710059
4319651717.75147928994247.248520710059
4416891717.75147928994-28.7514792899409
4517781717.7514792899460.2485207100591
4619761717.75147928994258.248520710059
4723971717.75147928994679.248520710059
4826541717.75147928994936.24852071006
4920971717.75147928994379.248520710059
5019631717.75147928994245.248520710059
5116771717.75147928994-40.7514792899409
5219411717.75147928994223.248520710059
5320031717.75147928994285.248520710059
5418131717.7514792899495.248520710059
5520121717.75147928994294.248520710059
5619121717.75147928994194.248520710059
5720841717.75147928994366.248520710059
5820801717.75147928994362.248520710059
5921181717.75147928994400.248520710059
6021501717.75147928994432.248520710059
6116081717.75147928994-109.751479289941
6215031717.75147928994-214.751479289941
6315481717.75147928994-169.751479289941
6413821717.75147928994-335.751479289941
6517311717.7514792899413.2485207100591
6617981717.7514792899480.2485207100591
6717791717.7514792899461.2485207100591
6818871717.75147928994169.248520710059
6920041717.75147928994286.248520710059
7020771717.75147928994359.248520710059
7120921717.75147928994374.248520710059
7220511717.75147928994333.248520710059
7315771717.75147928994-140.751479289941
7413561717.75147928994-361.751479289941
7516521717.75147928994-65.7514792899408
7613821717.75147928994-335.751479289941
7715191717.75147928994-198.751479289941
7814211717.75147928994-296.751479289941
7914421717.75147928994-275.751479289941
8015431717.75147928994-174.751479289941
8116561717.75147928994-61.7514792899409
8215611717.75147928994-156.751479289941
8319051717.75147928994187.248520710059
8421991717.75147928994481.248520710059
8514731717.75147928994-244.751479289941
8616551717.75147928994-62.7514792899409
8714071717.75147928994-310.751479289941
8813951717.75147928994-322.751479289941
8915301717.75147928994-187.751479289941
9013091717.75147928994-408.751479289941
9115261717.75147928994-191.751479289941
9213271717.75147928994-390.751479289941
9316271717.75147928994-90.7514792899409
9417481717.7514792899430.2485207100591
9519581717.75147928994240.248520710059
9622741717.75147928994556.248520710059
9716481717.75147928994-69.7514792899408
9814011717.75147928994-316.751479289941
9914111717.75147928994-306.751479289941
10014031717.75147928994-314.751479289941
10113941717.75147928994-323.751479289941
10215201717.75147928994-197.751479289941
10315281717.75147928994-189.751479289941
10416431717.75147928994-74.7514792899409
10515151717.75147928994-202.751479289941
10616851717.75147928994-32.7514792899409
10720001717.75147928994282.248520710059
10822151717.75147928994497.248520710059
10919561717.75147928994238.248520710059
11014621717.75147928994-255.751479289941
11115631717.75147928994-154.751479289941
11214591717.75147928994-258.751479289941
11314461717.75147928994-271.751479289941
11416221717.75147928994-95.7514792899409
11516571717.75147928994-60.7514792899409
11616381717.75147928994-79.7514792899409
11716431717.75147928994-74.7514792899409
11816831717.75147928994-34.7514792899409
11920501717.75147928994332.248520710059
12022621717.75147928994544.248520710059
12118131717.7514792899495.248520710059
12214451717.75147928994-272.751479289941
12317621717.7514792899444.2485207100591
12414611717.75147928994-256.751479289941
12515561717.75147928994-161.751479289941
12614311717.75147928994-286.751479289941
12714271717.75147928994-290.751479289941
12815541717.75147928994-163.751479289941
12916451717.75147928994-72.7514792899409
13016531717.75147928994-64.7514792899408
13120161717.75147928994298.248520710059
13222071717.75147928994489.248520710059
13316651717.75147928994-52.7514792899409
13413611717.75147928994-356.751479289941
13515061717.75147928994-211.751479289941
13613601717.75147928994-357.751479289941
13714531717.75147928994-264.751479289941
13815221717.75147928994-195.751479289941
13914601717.75147928994-257.751479289941
14015521717.75147928994-165.751479289941
14115481717.75147928994-169.751479289941
14218271717.75147928994109.248520710059
14317371717.7514792899419.2485207100591
14419411717.75147928994223.248520710059
14514741717.75147928994-243.751479289941
14614581717.75147928994-259.751479289941
14715421717.75147928994-175.751479289941
14814041717.75147928994-313.751479289941
14915221717.75147928994-195.751479289941
15013851717.75147928994-332.751479289941
15116411717.75147928994-76.7514792899409
15215101717.75147928994-207.751479289941
15316811717.75147928994-36.7514792899409
15419381717.75147928994220.248520710059
15518681717.75147928994150.248520710059
15617261717.751479289948.24852071005914
15714561717.75147928994-261.751479289941
15814451717.75147928994-272.751479289941
15914561717.75147928994-261.751479289941
16013651717.75147928994-352.751479289941
16114871717.75147928994-230.751479289941
16215581717.75147928994-159.751479289941
16314881717.75147928994-229.751479289941
16416841717.75147928994-33.7514792899409
16515941717.75147928994-123.751479289941
16618501717.75147928994132.248520710059
16719981717.75147928994280.248520710059
16820791717.75147928994361.248520710059
16914941717.75147928994-223.751479289941
17010571321.69565217391-264.695652173913
17112181321.69565217391-103.695652173913
17211681321.69565217391-153.695652173913
17312361321.69565217391-85.695652173913
17410761321.69565217391-245.695652173913
17511741321.69565217391-147.695652173913
17611391321.69565217391-182.695652173913
17714271321.69565217391105.304347826087
17814871321.69565217391165.304347826087
17914831321.69565217391161.304347826087
18015131321.69565217391191.304347826087
18113571321.6956521739135.3043478260869
18211651321.69565217391-156.695652173913
18312821321.69565217391-39.6956521739131
18411101321.69565217391-211.695652173913
18512971321.69565217391-24.6956521739131
18611851321.69565217391-136.695652173913
18712221321.69565217391-99.695652173913
18812841321.69565217391-37.6956521739131
18914441321.69565217391122.304347826087
19015751321.69565217391253.304347826087
19117371321.69565217391415.304347826087
19217631321.69565217391441.304347826087






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.1559308029189440.3118616058378880.844069197081056
60.06649352737734470.1329870547546890.933506472622655
70.02567383314294040.05134766628588080.97432616685706
80.01221739680733030.02443479361466060.98778260319267
90.004379978104602920.008759956209205830.995620021895397
100.002240159817864130.004480319635728250.997759840182136
110.2235801583455510.4471603166911010.77641984165445
120.5039813540008250.992037291998350.496018645999175
130.4208412053123040.8416824106246070.579158794687696
140.3453656525640860.6907313051281720.654634347435914
150.2700182093786300.5400364187572610.72998179062137
160.2190644244158520.4381288488317040.780935575584148
170.1710750448748180.3421500897496370.828924955125182
180.1407115265465930.2814230530931850.859288473453407
190.1140922425588590.2281844851177170.885907757441141
200.09007928733986690.1801585746797340.909920712660133
210.0638847643680760.1277695287361520.936115235631924
220.08828604303362070.1765720860672410.91171395696638
230.2540366087170350.508073217434070.745963391282965
240.7041345159405080.5917309681189830.295865484059492
250.7099494063259890.5801011873480230.290050593674011
260.6617327503331520.6765344993336960.338267249666848
270.60652227047790.7869554590441980.393477729522099
280.5597189438136970.8805621123726060.440281056186303
290.5037375415697180.9925249168605640.496262458430282
300.4449608348989580.8899216697979160.555039165101042
310.3901828560403290.7803657120806570.609817143959671
320.3621281127465610.7242562254931220.637871887253439
330.3227248265256090.6454496530512180.677275173474391
340.3184699397357430.6369398794714860.681530060264257
350.4475027759995330.8950055519990660.552497224000467
360.5401905183494390.9196189633011220.459809481650561
370.5622945098448020.8754109803103960.437705490155198
380.5106860002932680.9786279994134630.489313999706732
390.4622777891552320.9245555783104650.537722210844768
400.4432414467682040.8864828935364090.556758553231796
410.4257386735252530.8514773470505050.574261326474747
420.3820739067215540.7641478134431070.617926093278446
430.3620115673263840.7240231346527670.637988432673616
440.3219927317477250.6439854634954510.678007268252275
450.2794282905145580.5588565810291160.720571709485442
460.2656486170042530.5312972340085060.734351382995747
470.4937822328476490.9875644656952970.506217767152351
480.8899375407916190.2201249184167620.110062459208381
490.9011136623684070.1977726752631870.0988863376315933
500.8919312802842850.2161374394314300.108068719715715
510.8749587704768280.2500824590463440.125041229523172
520.8619242688046230.2761514623907550.138075731195377
530.8577418676605710.2845162646788570.142258132339429
540.833787220623450.3324255587530990.166212779376550
550.8316835171299290.3366329657401420.168316482870071
560.8134521521322490.3730956957355030.186547847867751
570.8299900464796020.3400199070407960.170009953520398
580.8452939376681140.3094121246637720.154706062331886
590.869886556799950.2602268864001000.130113443200050
600.8999731806219020.2000536387561950.100026819378098
610.8929115381189770.2141769237620450.107088461881023
620.899813657648210.2003726847035800.100186342351790
630.8987191232890920.2025617534218150.101280876710908
640.9242215772354530.1515568455290940.0757784227645468
650.9102741546203670.1794516907592650.0897258453796325
660.8947925365983930.2104149268032140.105207463401607
670.8771697946178040.2456604107643920.122830205382196
680.8640972653708240.2718054692583510.135902734629176
690.8678736410287260.2642527179425490.132126358971274
700.8871764413456120.2256471173087750.112823558654388
710.9084496324115170.1831007351769660.0915503675884829
720.9208695043335420.1582609913329170.0791304956664583
730.9160102994141070.1679794011717860.0839897005858932
740.9404025394138450.1191949211723110.0595974605861555
750.9312922476935140.1374155046129730.0687077523064863
760.9469560558531370.1060878882937260.0530439441468632
770.9458816420170680.1082367159658630.0541183579829316
780.9534363149400040.09312737011999190.0465636850599959
790.9577669083245750.08446618335085020.0422330916754251
800.954411751576560.0911764968468810.0455882484234405
810.9457911981509690.1084176036980630.0542088018490313
820.9401750357771440.1196499284457110.0598249642228557
830.9361310619796970.1277378760406070.0638689380203034
840.9663385663714640.0673228672570730.0336614336285365
850.966916074785160.0661678504296810.0330839252148405
860.9601641193441550.07967176131169050.0398358806558452
870.9652770693352240.0694458613295530.0347229306647765
880.9703794550258670.05924108994826540.0296205449741327
890.9674640511590650.06507189768186920.0325359488409346
900.977737303238430.0445253935231380.022262696761569
910.97532264804520.04935470390960.0246773519548
920.9821276036826250.03574479263475090.0178723963173755
930.9779039316311570.04419213673768590.0220960683688429
940.9725800354559550.05483992908809090.0274199645440455
950.9736521992866830.05269560142663460.0263478007133173
960.9921478667486580.01570426650268470.00785213325134237
970.9899441772202630.02011164555947370.0100558227797368
980.991080474977210.01783905004557950.00891952502278976
990.991852053029110.01629589394178150.00814794697089075
1000.9926891558394360.01462168832112720.00731084416056362
1010.9935879656446240.01282406871075100.00641203435537552
1020.9925874706751830.01482505864963350.00741252932481676
1030.9913343111926220.01733137761475530.00866568880737765
1040.9887866234665020.02242675306699640.0112133765334982
1050.9871974269032140.02560514619357120.0128025730967856
1060.9834887348306130.03302253033877320.0165112651693866
1070.9862100740678750.02757985186424970.0137899259321249
1080.9954812256947070.009037548610585450.00451877430529273
1090.9960743394012850.007851321197429210.00392566059871461
1100.9958082135513870.008383572897225780.00419178644861289
1110.9947125552945240.01057488941095250.00528744470547626
1120.994383716676210.01123256664757900.00561628332378949
1130.9942027655540470.01159446889190660.0057972344459533
1140.9923718888698070.01525622226038540.0076281111301927
1150.989937178797550.02012564240489920.0100628212024496
1160.9868899382317050.02622012353658970.0131100617682949
1170.983038753707430.03392249258514230.0169612462925711
1180.978201029612120.04359794077576160.0217989703878808
1190.9854990285980250.02900194280394990.0145009714019749
1200.9972737379465190.005452524106962470.00272626205348123
1210.9968405001714030.006318999657194860.00315949982859743
1220.9966043287120030.006791342575994760.00339567128799738
1230.9957319034927090.008536193014582830.00426809650729142
1240.9952319354744660.009536129051068430.00476806452553421
1250.993835719564880.01232856087024070.00616428043512034
1260.9935791126770050.01284177464598930.00642088732299466
1270.9933935035118350.01321299297633020.00660649648816512
1280.9915246851074790.01695062978504220.00847531489252108
1290.9886554883036150.02268902339277020.0113445116963851
1300.984959359786210.03008128042757930.0150406402137896
1310.990221626959950.01955674608010090.00977837304005047
1320.9982799910709060.003440017858188500.00172000892909425
1330.9975916247830560.004816750433887580.00240837521694379
1340.997892812725710.004214374548578230.00210718727428912
1350.997293898802090.005412202395820770.00270610119791039
1360.9976776922153970.004644615569205940.00232230778460297
1370.997354591339330.005290817321341440.00264540866067072
1380.9965465538433560.006906892313288440.00345344615664422
1390.99604528987740.007909420245200820.00395471012260041
1400.9946869708300080.01062605833998440.00531302916999218
1410.9929574252000930.01408514959981320.00704257479990659
1420.9920330096054330.01593398078913350.00796699039456673
1430.9895837712358810.0208324575282370.0104162287641185
1440.9920816591491310.01583668170173770.00791834085086885
1450.9904494844043830.0191010311912350.0095505155956175
1460.9889497073390560.02210058532188870.0110502926609444
1470.9854560608942650.02908787821146920.0145439391057346
1480.9855711345140450.02885773097190940.0144288654859547
1490.9817801746925750.03643965061485060.0182198253074253
1500.9835227096634280.03295458067314440.0164772903365722
1510.9772488977752520.04550220444949580.0227511022247479
1520.9726400953125070.05471980937498520.0273599046874926
1530.9629255490837120.07414890183257640.0370744509162882
1540.9673025224909830.06539495501803460.0326974775090173
1550.9666855442480890.06662891150382250.0333144557519112
1560.9571917212437640.08561655751247290.0428082787562364
1570.9511038397950780.0977923204098450.0488961602049225
1580.9465733352094960.1068533295810070.0534266647905037
1590.941989759761460.1160204804770810.0580102402385407
1600.9539463886338830.09210722273223490.0460536113661174
1610.9517820151660470.09643596966790610.0482179848339531
1620.9437816756742470.1124366486515070.0562183243257533
1630.9488528298300860.1022943403398270.0511471701699137
1640.933869588053220.1322608238935590.0661304119467795
1650.932017991595130.135964016809740.06798200840487
1660.9084084086103110.1831831827793770.0915915913896886
1670.8966955716601020.2066088566797950.103304428339898
1680.9463249453451660.1073501093096680.0536750546548339
1690.925761978392230.1484760432155410.0742380216077706
1700.9320741472713060.1358517054573880.0679258527286941
1710.9121986283845660.1756027432308670.0878013716154336
1720.8971397501106840.2057204997786310.102860249889316
1730.8679723755156910.2640552489686180.132027624484309
1740.8840554441978720.2318891116042550.115944555802128
1750.8717410280954920.2565179438090150.128258971904507
1760.8759804634073520.2480390731852960.124019536592648
1770.8294723493283930.3410553013432150.170527650671607
1780.7812488548959410.4375022902081180.218751145104059
1790.7230854296552360.5538291406895280.276914570344764
1800.668097907094830.6638041858103410.331902092905171
1810.5736344001114190.8527311997771620.426365599888581
1820.5400640857924250.919871828415150.459935914207575
1830.4498561837194870.8997123674389750.550143816280512
1840.491029489233680.982058978467360.50897051076632
1850.4034626872200280.8069253744400560.596537312779972
1860.4288955432867320.8577910865734640.571104456713268
1870.4881409628525450.976281925705090.511859037147455

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.155930802918944 & 0.311861605837888 & 0.844069197081056 \tabularnewline
6 & 0.0664935273773447 & 0.132987054754689 & 0.933506472622655 \tabularnewline
7 & 0.0256738331429404 & 0.0513476662858808 & 0.97432616685706 \tabularnewline
8 & 0.0122173968073303 & 0.0244347936146606 & 0.98778260319267 \tabularnewline
9 & 0.00437997810460292 & 0.00875995620920583 & 0.995620021895397 \tabularnewline
10 & 0.00224015981786413 & 0.00448031963572825 & 0.997759840182136 \tabularnewline
11 & 0.223580158345551 & 0.447160316691101 & 0.77641984165445 \tabularnewline
12 & 0.503981354000825 & 0.99203729199835 & 0.496018645999175 \tabularnewline
13 & 0.420841205312304 & 0.841682410624607 & 0.579158794687696 \tabularnewline
14 & 0.345365652564086 & 0.690731305128172 & 0.654634347435914 \tabularnewline
15 & 0.270018209378630 & 0.540036418757261 & 0.72998179062137 \tabularnewline
16 & 0.219064424415852 & 0.438128848831704 & 0.780935575584148 \tabularnewline
17 & 0.171075044874818 & 0.342150089749637 & 0.828924955125182 \tabularnewline
18 & 0.140711526546593 & 0.281423053093185 & 0.859288473453407 \tabularnewline
19 & 0.114092242558859 & 0.228184485117717 & 0.885907757441141 \tabularnewline
20 & 0.0900792873398669 & 0.180158574679734 & 0.909920712660133 \tabularnewline
21 & 0.063884764368076 & 0.127769528736152 & 0.936115235631924 \tabularnewline
22 & 0.0882860430336207 & 0.176572086067241 & 0.91171395696638 \tabularnewline
23 & 0.254036608717035 & 0.50807321743407 & 0.745963391282965 \tabularnewline
24 & 0.704134515940508 & 0.591730968118983 & 0.295865484059492 \tabularnewline
25 & 0.709949406325989 & 0.580101187348023 & 0.290050593674011 \tabularnewline
26 & 0.661732750333152 & 0.676534499333696 & 0.338267249666848 \tabularnewline
27 & 0.6065222704779 & 0.786955459044198 & 0.393477729522099 \tabularnewline
28 & 0.559718943813697 & 0.880562112372606 & 0.440281056186303 \tabularnewline
29 & 0.503737541569718 & 0.992524916860564 & 0.496262458430282 \tabularnewline
30 & 0.444960834898958 & 0.889921669797916 & 0.555039165101042 \tabularnewline
31 & 0.390182856040329 & 0.780365712080657 & 0.609817143959671 \tabularnewline
32 & 0.362128112746561 & 0.724256225493122 & 0.637871887253439 \tabularnewline
33 & 0.322724826525609 & 0.645449653051218 & 0.677275173474391 \tabularnewline
34 & 0.318469939735743 & 0.636939879471486 & 0.681530060264257 \tabularnewline
35 & 0.447502775999533 & 0.895005551999066 & 0.552497224000467 \tabularnewline
36 & 0.540190518349439 & 0.919618963301122 & 0.459809481650561 \tabularnewline
37 & 0.562294509844802 & 0.875410980310396 & 0.437705490155198 \tabularnewline
38 & 0.510686000293268 & 0.978627999413463 & 0.489313999706732 \tabularnewline
39 & 0.462277789155232 & 0.924555578310465 & 0.537722210844768 \tabularnewline
40 & 0.443241446768204 & 0.886482893536409 & 0.556758553231796 \tabularnewline
41 & 0.425738673525253 & 0.851477347050505 & 0.574261326474747 \tabularnewline
42 & 0.382073906721554 & 0.764147813443107 & 0.617926093278446 \tabularnewline
43 & 0.362011567326384 & 0.724023134652767 & 0.637988432673616 \tabularnewline
44 & 0.321992731747725 & 0.643985463495451 & 0.678007268252275 \tabularnewline
45 & 0.279428290514558 & 0.558856581029116 & 0.720571709485442 \tabularnewline
46 & 0.265648617004253 & 0.531297234008506 & 0.734351382995747 \tabularnewline
47 & 0.493782232847649 & 0.987564465695297 & 0.506217767152351 \tabularnewline
48 & 0.889937540791619 & 0.220124918416762 & 0.110062459208381 \tabularnewline
49 & 0.901113662368407 & 0.197772675263187 & 0.0988863376315933 \tabularnewline
50 & 0.891931280284285 & 0.216137439431430 & 0.108068719715715 \tabularnewline
51 & 0.874958770476828 & 0.250082459046344 & 0.125041229523172 \tabularnewline
52 & 0.861924268804623 & 0.276151462390755 & 0.138075731195377 \tabularnewline
53 & 0.857741867660571 & 0.284516264678857 & 0.142258132339429 \tabularnewline
54 & 0.83378722062345 & 0.332425558753099 & 0.166212779376550 \tabularnewline
55 & 0.831683517129929 & 0.336632965740142 & 0.168316482870071 \tabularnewline
56 & 0.813452152132249 & 0.373095695735503 & 0.186547847867751 \tabularnewline
57 & 0.829990046479602 & 0.340019907040796 & 0.170009953520398 \tabularnewline
58 & 0.845293937668114 & 0.309412124663772 & 0.154706062331886 \tabularnewline
59 & 0.86988655679995 & 0.260226886400100 & 0.130113443200050 \tabularnewline
60 & 0.899973180621902 & 0.200053638756195 & 0.100026819378098 \tabularnewline
61 & 0.892911538118977 & 0.214176923762045 & 0.107088461881023 \tabularnewline
62 & 0.89981365764821 & 0.200372684703580 & 0.100186342351790 \tabularnewline
63 & 0.898719123289092 & 0.202561753421815 & 0.101280876710908 \tabularnewline
64 & 0.924221577235453 & 0.151556845529094 & 0.0757784227645468 \tabularnewline
65 & 0.910274154620367 & 0.179451690759265 & 0.0897258453796325 \tabularnewline
66 & 0.894792536598393 & 0.210414926803214 & 0.105207463401607 \tabularnewline
67 & 0.877169794617804 & 0.245660410764392 & 0.122830205382196 \tabularnewline
68 & 0.864097265370824 & 0.271805469258351 & 0.135902734629176 \tabularnewline
69 & 0.867873641028726 & 0.264252717942549 & 0.132126358971274 \tabularnewline
70 & 0.887176441345612 & 0.225647117308775 & 0.112823558654388 \tabularnewline
71 & 0.908449632411517 & 0.183100735176966 & 0.0915503675884829 \tabularnewline
72 & 0.920869504333542 & 0.158260991332917 & 0.0791304956664583 \tabularnewline
73 & 0.916010299414107 & 0.167979401171786 & 0.0839897005858932 \tabularnewline
74 & 0.940402539413845 & 0.119194921172311 & 0.0595974605861555 \tabularnewline
75 & 0.931292247693514 & 0.137415504612973 & 0.0687077523064863 \tabularnewline
76 & 0.946956055853137 & 0.106087888293726 & 0.0530439441468632 \tabularnewline
77 & 0.945881642017068 & 0.108236715965863 & 0.0541183579829316 \tabularnewline
78 & 0.953436314940004 & 0.0931273701199919 & 0.0465636850599959 \tabularnewline
79 & 0.957766908324575 & 0.0844661833508502 & 0.0422330916754251 \tabularnewline
80 & 0.95441175157656 & 0.091176496846881 & 0.0455882484234405 \tabularnewline
81 & 0.945791198150969 & 0.108417603698063 & 0.0542088018490313 \tabularnewline
82 & 0.940175035777144 & 0.119649928445711 & 0.0598249642228557 \tabularnewline
83 & 0.936131061979697 & 0.127737876040607 & 0.0638689380203034 \tabularnewline
84 & 0.966338566371464 & 0.067322867257073 & 0.0336614336285365 \tabularnewline
85 & 0.96691607478516 & 0.066167850429681 & 0.0330839252148405 \tabularnewline
86 & 0.960164119344155 & 0.0796717613116905 & 0.0398358806558452 \tabularnewline
87 & 0.965277069335224 & 0.069445861329553 & 0.0347229306647765 \tabularnewline
88 & 0.970379455025867 & 0.0592410899482654 & 0.0296205449741327 \tabularnewline
89 & 0.967464051159065 & 0.0650718976818692 & 0.0325359488409346 \tabularnewline
90 & 0.97773730323843 & 0.044525393523138 & 0.022262696761569 \tabularnewline
91 & 0.9753226480452 & 0.0493547039096 & 0.0246773519548 \tabularnewline
92 & 0.982127603682625 & 0.0357447926347509 & 0.0178723963173755 \tabularnewline
93 & 0.977903931631157 & 0.0441921367376859 & 0.0220960683688429 \tabularnewline
94 & 0.972580035455955 & 0.0548399290880909 & 0.0274199645440455 \tabularnewline
95 & 0.973652199286683 & 0.0526956014266346 & 0.0263478007133173 \tabularnewline
96 & 0.992147866748658 & 0.0157042665026847 & 0.00785213325134237 \tabularnewline
97 & 0.989944177220263 & 0.0201116455594737 & 0.0100558227797368 \tabularnewline
98 & 0.99108047497721 & 0.0178390500455795 & 0.00891952502278976 \tabularnewline
99 & 0.99185205302911 & 0.0162958939417815 & 0.00814794697089075 \tabularnewline
100 & 0.992689155839436 & 0.0146216883211272 & 0.00731084416056362 \tabularnewline
101 & 0.993587965644624 & 0.0128240687107510 & 0.00641203435537552 \tabularnewline
102 & 0.992587470675183 & 0.0148250586496335 & 0.00741252932481676 \tabularnewline
103 & 0.991334311192622 & 0.0173313776147553 & 0.00866568880737765 \tabularnewline
104 & 0.988786623466502 & 0.0224267530669964 & 0.0112133765334982 \tabularnewline
105 & 0.987197426903214 & 0.0256051461935712 & 0.0128025730967856 \tabularnewline
106 & 0.983488734830613 & 0.0330225303387732 & 0.0165112651693866 \tabularnewline
107 & 0.986210074067875 & 0.0275798518642497 & 0.0137899259321249 \tabularnewline
108 & 0.995481225694707 & 0.00903754861058545 & 0.00451877430529273 \tabularnewline
109 & 0.996074339401285 & 0.00785132119742921 & 0.00392566059871461 \tabularnewline
110 & 0.995808213551387 & 0.00838357289722578 & 0.00419178644861289 \tabularnewline
111 & 0.994712555294524 & 0.0105748894109525 & 0.00528744470547626 \tabularnewline
112 & 0.99438371667621 & 0.0112325666475790 & 0.00561628332378949 \tabularnewline
113 & 0.994202765554047 & 0.0115944688919066 & 0.0057972344459533 \tabularnewline
114 & 0.992371888869807 & 0.0152562222603854 & 0.0076281111301927 \tabularnewline
115 & 0.98993717879755 & 0.0201256424048992 & 0.0100628212024496 \tabularnewline
116 & 0.986889938231705 & 0.0262201235365897 & 0.0131100617682949 \tabularnewline
117 & 0.98303875370743 & 0.0339224925851423 & 0.0169612462925711 \tabularnewline
118 & 0.97820102961212 & 0.0435979407757616 & 0.0217989703878808 \tabularnewline
119 & 0.985499028598025 & 0.0290019428039499 & 0.0145009714019749 \tabularnewline
120 & 0.997273737946519 & 0.00545252410696247 & 0.00272626205348123 \tabularnewline
121 & 0.996840500171403 & 0.00631899965719486 & 0.00315949982859743 \tabularnewline
122 & 0.996604328712003 & 0.00679134257599476 & 0.00339567128799738 \tabularnewline
123 & 0.995731903492709 & 0.00853619301458283 & 0.00426809650729142 \tabularnewline
124 & 0.995231935474466 & 0.00953612905106843 & 0.00476806452553421 \tabularnewline
125 & 0.99383571956488 & 0.0123285608702407 & 0.00616428043512034 \tabularnewline
126 & 0.993579112677005 & 0.0128417746459893 & 0.00642088732299466 \tabularnewline
127 & 0.993393503511835 & 0.0132129929763302 & 0.00660649648816512 \tabularnewline
128 & 0.991524685107479 & 0.0169506297850422 & 0.00847531489252108 \tabularnewline
129 & 0.988655488303615 & 0.0226890233927702 & 0.0113445116963851 \tabularnewline
130 & 0.98495935978621 & 0.0300812804275793 & 0.0150406402137896 \tabularnewline
131 & 0.99022162695995 & 0.0195567460801009 & 0.00977837304005047 \tabularnewline
132 & 0.998279991070906 & 0.00344001785818850 & 0.00172000892909425 \tabularnewline
133 & 0.997591624783056 & 0.00481675043388758 & 0.00240837521694379 \tabularnewline
134 & 0.99789281272571 & 0.00421437454857823 & 0.00210718727428912 \tabularnewline
135 & 0.99729389880209 & 0.00541220239582077 & 0.00270610119791039 \tabularnewline
136 & 0.997677692215397 & 0.00464461556920594 & 0.00232230778460297 \tabularnewline
137 & 0.99735459133933 & 0.00529081732134144 & 0.00264540866067072 \tabularnewline
138 & 0.996546553843356 & 0.00690689231328844 & 0.00345344615664422 \tabularnewline
139 & 0.9960452898774 & 0.00790942024520082 & 0.00395471012260041 \tabularnewline
140 & 0.994686970830008 & 0.0106260583399844 & 0.00531302916999218 \tabularnewline
141 & 0.992957425200093 & 0.0140851495998132 & 0.00704257479990659 \tabularnewline
142 & 0.992033009605433 & 0.0159339807891335 & 0.00796699039456673 \tabularnewline
143 & 0.989583771235881 & 0.020832457528237 & 0.0104162287641185 \tabularnewline
144 & 0.992081659149131 & 0.0158366817017377 & 0.00791834085086885 \tabularnewline
145 & 0.990449484404383 & 0.019101031191235 & 0.0095505155956175 \tabularnewline
146 & 0.988949707339056 & 0.0221005853218887 & 0.0110502926609444 \tabularnewline
147 & 0.985456060894265 & 0.0290878782114692 & 0.0145439391057346 \tabularnewline
148 & 0.985571134514045 & 0.0288577309719094 & 0.0144288654859547 \tabularnewline
149 & 0.981780174692575 & 0.0364396506148506 & 0.0182198253074253 \tabularnewline
150 & 0.983522709663428 & 0.0329545806731444 & 0.0164772903365722 \tabularnewline
151 & 0.977248897775252 & 0.0455022044494958 & 0.0227511022247479 \tabularnewline
152 & 0.972640095312507 & 0.0547198093749852 & 0.0273599046874926 \tabularnewline
153 & 0.962925549083712 & 0.0741489018325764 & 0.0370744509162882 \tabularnewline
154 & 0.967302522490983 & 0.0653949550180346 & 0.0326974775090173 \tabularnewline
155 & 0.966685544248089 & 0.0666289115038225 & 0.0333144557519112 \tabularnewline
156 & 0.957191721243764 & 0.0856165575124729 & 0.0428082787562364 \tabularnewline
157 & 0.951103839795078 & 0.097792320409845 & 0.0488961602049225 \tabularnewline
158 & 0.946573335209496 & 0.106853329581007 & 0.0534266647905037 \tabularnewline
159 & 0.94198975976146 & 0.116020480477081 & 0.0580102402385407 \tabularnewline
160 & 0.953946388633883 & 0.0921072227322349 & 0.0460536113661174 \tabularnewline
161 & 0.951782015166047 & 0.0964359696679061 & 0.0482179848339531 \tabularnewline
162 & 0.943781675674247 & 0.112436648651507 & 0.0562183243257533 \tabularnewline
163 & 0.948852829830086 & 0.102294340339827 & 0.0511471701699137 \tabularnewline
164 & 0.93386958805322 & 0.132260823893559 & 0.0661304119467795 \tabularnewline
165 & 0.93201799159513 & 0.13596401680974 & 0.06798200840487 \tabularnewline
166 & 0.908408408610311 & 0.183183182779377 & 0.0915915913896886 \tabularnewline
167 & 0.896695571660102 & 0.206608856679795 & 0.103304428339898 \tabularnewline
168 & 0.946324945345166 & 0.107350109309668 & 0.0536750546548339 \tabularnewline
169 & 0.92576197839223 & 0.148476043215541 & 0.0742380216077706 \tabularnewline
170 & 0.932074147271306 & 0.135851705457388 & 0.0679258527286941 \tabularnewline
171 & 0.912198628384566 & 0.175602743230867 & 0.0878013716154336 \tabularnewline
172 & 0.897139750110684 & 0.205720499778631 & 0.102860249889316 \tabularnewline
173 & 0.867972375515691 & 0.264055248968618 & 0.132027624484309 \tabularnewline
174 & 0.884055444197872 & 0.231889111604255 & 0.115944555802128 \tabularnewline
175 & 0.871741028095492 & 0.256517943809015 & 0.128258971904507 \tabularnewline
176 & 0.875980463407352 & 0.248039073185296 & 0.124019536592648 \tabularnewline
177 & 0.829472349328393 & 0.341055301343215 & 0.170527650671607 \tabularnewline
178 & 0.781248854895941 & 0.437502290208118 & 0.218751145104059 \tabularnewline
179 & 0.723085429655236 & 0.553829140689528 & 0.276914570344764 \tabularnewline
180 & 0.66809790709483 & 0.663804185810341 & 0.331902092905171 \tabularnewline
181 & 0.573634400111419 & 0.852731199777162 & 0.426365599888581 \tabularnewline
182 & 0.540064085792425 & 0.91987182841515 & 0.459935914207575 \tabularnewline
183 & 0.449856183719487 & 0.899712367438975 & 0.550143816280512 \tabularnewline
184 & 0.49102948923368 & 0.98205897846736 & 0.50897051076632 \tabularnewline
185 & 0.403462687220028 & 0.806925374440056 & 0.596537312779972 \tabularnewline
186 & 0.428895543286732 & 0.857791086573464 & 0.571104456713268 \tabularnewline
187 & 0.488140962852545 & 0.97628192570509 & 0.511859037147455 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55987&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]5[/C][C]0.155930802918944[/C][C]0.311861605837888[/C][C]0.844069197081056[/C][/ROW]
[ROW][C]6[/C][C]0.0664935273773447[/C][C]0.132987054754689[/C][C]0.933506472622655[/C][/ROW]
[ROW][C]7[/C][C]0.0256738331429404[/C][C]0.0513476662858808[/C][C]0.97432616685706[/C][/ROW]
[ROW][C]8[/C][C]0.0122173968073303[/C][C]0.0244347936146606[/C][C]0.98778260319267[/C][/ROW]
[ROW][C]9[/C][C]0.00437997810460292[/C][C]0.00875995620920583[/C][C]0.995620021895397[/C][/ROW]
[ROW][C]10[/C][C]0.00224015981786413[/C][C]0.00448031963572825[/C][C]0.997759840182136[/C][/ROW]
[ROW][C]11[/C][C]0.223580158345551[/C][C]0.447160316691101[/C][C]0.77641984165445[/C][/ROW]
[ROW][C]12[/C][C]0.503981354000825[/C][C]0.99203729199835[/C][C]0.496018645999175[/C][/ROW]
[ROW][C]13[/C][C]0.420841205312304[/C][C]0.841682410624607[/C][C]0.579158794687696[/C][/ROW]
[ROW][C]14[/C][C]0.345365652564086[/C][C]0.690731305128172[/C][C]0.654634347435914[/C][/ROW]
[ROW][C]15[/C][C]0.270018209378630[/C][C]0.540036418757261[/C][C]0.72998179062137[/C][/ROW]
[ROW][C]16[/C][C]0.219064424415852[/C][C]0.438128848831704[/C][C]0.780935575584148[/C][/ROW]
[ROW][C]17[/C][C]0.171075044874818[/C][C]0.342150089749637[/C][C]0.828924955125182[/C][/ROW]
[ROW][C]18[/C][C]0.140711526546593[/C][C]0.281423053093185[/C][C]0.859288473453407[/C][/ROW]
[ROW][C]19[/C][C]0.114092242558859[/C][C]0.228184485117717[/C][C]0.885907757441141[/C][/ROW]
[ROW][C]20[/C][C]0.0900792873398669[/C][C]0.180158574679734[/C][C]0.909920712660133[/C][/ROW]
[ROW][C]21[/C][C]0.063884764368076[/C][C]0.127769528736152[/C][C]0.936115235631924[/C][/ROW]
[ROW][C]22[/C][C]0.0882860430336207[/C][C]0.176572086067241[/C][C]0.91171395696638[/C][/ROW]
[ROW][C]23[/C][C]0.254036608717035[/C][C]0.50807321743407[/C][C]0.745963391282965[/C][/ROW]
[ROW][C]24[/C][C]0.704134515940508[/C][C]0.591730968118983[/C][C]0.295865484059492[/C][/ROW]
[ROW][C]25[/C][C]0.709949406325989[/C][C]0.580101187348023[/C][C]0.290050593674011[/C][/ROW]
[ROW][C]26[/C][C]0.661732750333152[/C][C]0.676534499333696[/C][C]0.338267249666848[/C][/ROW]
[ROW][C]27[/C][C]0.6065222704779[/C][C]0.786955459044198[/C][C]0.393477729522099[/C][/ROW]
[ROW][C]28[/C][C]0.559718943813697[/C][C]0.880562112372606[/C][C]0.440281056186303[/C][/ROW]
[ROW][C]29[/C][C]0.503737541569718[/C][C]0.992524916860564[/C][C]0.496262458430282[/C][/ROW]
[ROW][C]30[/C][C]0.444960834898958[/C][C]0.889921669797916[/C][C]0.555039165101042[/C][/ROW]
[ROW][C]31[/C][C]0.390182856040329[/C][C]0.780365712080657[/C][C]0.609817143959671[/C][/ROW]
[ROW][C]32[/C][C]0.362128112746561[/C][C]0.724256225493122[/C][C]0.637871887253439[/C][/ROW]
[ROW][C]33[/C][C]0.322724826525609[/C][C]0.645449653051218[/C][C]0.677275173474391[/C][/ROW]
[ROW][C]34[/C][C]0.318469939735743[/C][C]0.636939879471486[/C][C]0.681530060264257[/C][/ROW]
[ROW][C]35[/C][C]0.447502775999533[/C][C]0.895005551999066[/C][C]0.552497224000467[/C][/ROW]
[ROW][C]36[/C][C]0.540190518349439[/C][C]0.919618963301122[/C][C]0.459809481650561[/C][/ROW]
[ROW][C]37[/C][C]0.562294509844802[/C][C]0.875410980310396[/C][C]0.437705490155198[/C][/ROW]
[ROW][C]38[/C][C]0.510686000293268[/C][C]0.978627999413463[/C][C]0.489313999706732[/C][/ROW]
[ROW][C]39[/C][C]0.462277789155232[/C][C]0.924555578310465[/C][C]0.537722210844768[/C][/ROW]
[ROW][C]40[/C][C]0.443241446768204[/C][C]0.886482893536409[/C][C]0.556758553231796[/C][/ROW]
[ROW][C]41[/C][C]0.425738673525253[/C][C]0.851477347050505[/C][C]0.574261326474747[/C][/ROW]
[ROW][C]42[/C][C]0.382073906721554[/C][C]0.764147813443107[/C][C]0.617926093278446[/C][/ROW]
[ROW][C]43[/C][C]0.362011567326384[/C][C]0.724023134652767[/C][C]0.637988432673616[/C][/ROW]
[ROW][C]44[/C][C]0.321992731747725[/C][C]0.643985463495451[/C][C]0.678007268252275[/C][/ROW]
[ROW][C]45[/C][C]0.279428290514558[/C][C]0.558856581029116[/C][C]0.720571709485442[/C][/ROW]
[ROW][C]46[/C][C]0.265648617004253[/C][C]0.531297234008506[/C][C]0.734351382995747[/C][/ROW]
[ROW][C]47[/C][C]0.493782232847649[/C][C]0.987564465695297[/C][C]0.506217767152351[/C][/ROW]
[ROW][C]48[/C][C]0.889937540791619[/C][C]0.220124918416762[/C][C]0.110062459208381[/C][/ROW]
[ROW][C]49[/C][C]0.901113662368407[/C][C]0.197772675263187[/C][C]0.0988863376315933[/C][/ROW]
[ROW][C]50[/C][C]0.891931280284285[/C][C]0.216137439431430[/C][C]0.108068719715715[/C][/ROW]
[ROW][C]51[/C][C]0.874958770476828[/C][C]0.250082459046344[/C][C]0.125041229523172[/C][/ROW]
[ROW][C]52[/C][C]0.861924268804623[/C][C]0.276151462390755[/C][C]0.138075731195377[/C][/ROW]
[ROW][C]53[/C][C]0.857741867660571[/C][C]0.284516264678857[/C][C]0.142258132339429[/C][/ROW]
[ROW][C]54[/C][C]0.83378722062345[/C][C]0.332425558753099[/C][C]0.166212779376550[/C][/ROW]
[ROW][C]55[/C][C]0.831683517129929[/C][C]0.336632965740142[/C][C]0.168316482870071[/C][/ROW]
[ROW][C]56[/C][C]0.813452152132249[/C][C]0.373095695735503[/C][C]0.186547847867751[/C][/ROW]
[ROW][C]57[/C][C]0.829990046479602[/C][C]0.340019907040796[/C][C]0.170009953520398[/C][/ROW]
[ROW][C]58[/C][C]0.845293937668114[/C][C]0.309412124663772[/C][C]0.154706062331886[/C][/ROW]
[ROW][C]59[/C][C]0.86988655679995[/C][C]0.260226886400100[/C][C]0.130113443200050[/C][/ROW]
[ROW][C]60[/C][C]0.899973180621902[/C][C]0.200053638756195[/C][C]0.100026819378098[/C][/ROW]
[ROW][C]61[/C][C]0.892911538118977[/C][C]0.214176923762045[/C][C]0.107088461881023[/C][/ROW]
[ROW][C]62[/C][C]0.89981365764821[/C][C]0.200372684703580[/C][C]0.100186342351790[/C][/ROW]
[ROW][C]63[/C][C]0.898719123289092[/C][C]0.202561753421815[/C][C]0.101280876710908[/C][/ROW]
[ROW][C]64[/C][C]0.924221577235453[/C][C]0.151556845529094[/C][C]0.0757784227645468[/C][/ROW]
[ROW][C]65[/C][C]0.910274154620367[/C][C]0.179451690759265[/C][C]0.0897258453796325[/C][/ROW]
[ROW][C]66[/C][C]0.894792536598393[/C][C]0.210414926803214[/C][C]0.105207463401607[/C][/ROW]
[ROW][C]67[/C][C]0.877169794617804[/C][C]0.245660410764392[/C][C]0.122830205382196[/C][/ROW]
[ROW][C]68[/C][C]0.864097265370824[/C][C]0.271805469258351[/C][C]0.135902734629176[/C][/ROW]
[ROW][C]69[/C][C]0.867873641028726[/C][C]0.264252717942549[/C][C]0.132126358971274[/C][/ROW]
[ROW][C]70[/C][C]0.887176441345612[/C][C]0.225647117308775[/C][C]0.112823558654388[/C][/ROW]
[ROW][C]71[/C][C]0.908449632411517[/C][C]0.183100735176966[/C][C]0.0915503675884829[/C][/ROW]
[ROW][C]72[/C][C]0.920869504333542[/C][C]0.158260991332917[/C][C]0.0791304956664583[/C][/ROW]
[ROW][C]73[/C][C]0.916010299414107[/C][C]0.167979401171786[/C][C]0.0839897005858932[/C][/ROW]
[ROW][C]74[/C][C]0.940402539413845[/C][C]0.119194921172311[/C][C]0.0595974605861555[/C][/ROW]
[ROW][C]75[/C][C]0.931292247693514[/C][C]0.137415504612973[/C][C]0.0687077523064863[/C][/ROW]
[ROW][C]76[/C][C]0.946956055853137[/C][C]0.106087888293726[/C][C]0.0530439441468632[/C][/ROW]
[ROW][C]77[/C][C]0.945881642017068[/C][C]0.108236715965863[/C][C]0.0541183579829316[/C][/ROW]
[ROW][C]78[/C][C]0.953436314940004[/C][C]0.0931273701199919[/C][C]0.0465636850599959[/C][/ROW]
[ROW][C]79[/C][C]0.957766908324575[/C][C]0.0844661833508502[/C][C]0.0422330916754251[/C][/ROW]
[ROW][C]80[/C][C]0.95441175157656[/C][C]0.091176496846881[/C][C]0.0455882484234405[/C][/ROW]
[ROW][C]81[/C][C]0.945791198150969[/C][C]0.108417603698063[/C][C]0.0542088018490313[/C][/ROW]
[ROW][C]82[/C][C]0.940175035777144[/C][C]0.119649928445711[/C][C]0.0598249642228557[/C][/ROW]
[ROW][C]83[/C][C]0.936131061979697[/C][C]0.127737876040607[/C][C]0.0638689380203034[/C][/ROW]
[ROW][C]84[/C][C]0.966338566371464[/C][C]0.067322867257073[/C][C]0.0336614336285365[/C][/ROW]
[ROW][C]85[/C][C]0.96691607478516[/C][C]0.066167850429681[/C][C]0.0330839252148405[/C][/ROW]
[ROW][C]86[/C][C]0.960164119344155[/C][C]0.0796717613116905[/C][C]0.0398358806558452[/C][/ROW]
[ROW][C]87[/C][C]0.965277069335224[/C][C]0.069445861329553[/C][C]0.0347229306647765[/C][/ROW]
[ROW][C]88[/C][C]0.970379455025867[/C][C]0.0592410899482654[/C][C]0.0296205449741327[/C][/ROW]
[ROW][C]89[/C][C]0.967464051159065[/C][C]0.0650718976818692[/C][C]0.0325359488409346[/C][/ROW]
[ROW][C]90[/C][C]0.97773730323843[/C][C]0.044525393523138[/C][C]0.022262696761569[/C][/ROW]
[ROW][C]91[/C][C]0.9753226480452[/C][C]0.0493547039096[/C][C]0.0246773519548[/C][/ROW]
[ROW][C]92[/C][C]0.982127603682625[/C][C]0.0357447926347509[/C][C]0.0178723963173755[/C][/ROW]
[ROW][C]93[/C][C]0.977903931631157[/C][C]0.0441921367376859[/C][C]0.0220960683688429[/C][/ROW]
[ROW][C]94[/C][C]0.972580035455955[/C][C]0.0548399290880909[/C][C]0.0274199645440455[/C][/ROW]
[ROW][C]95[/C][C]0.973652199286683[/C][C]0.0526956014266346[/C][C]0.0263478007133173[/C][/ROW]
[ROW][C]96[/C][C]0.992147866748658[/C][C]0.0157042665026847[/C][C]0.00785213325134237[/C][/ROW]
[ROW][C]97[/C][C]0.989944177220263[/C][C]0.0201116455594737[/C][C]0.0100558227797368[/C][/ROW]
[ROW][C]98[/C][C]0.99108047497721[/C][C]0.0178390500455795[/C][C]0.00891952502278976[/C][/ROW]
[ROW][C]99[/C][C]0.99185205302911[/C][C]0.0162958939417815[/C][C]0.00814794697089075[/C][/ROW]
[ROW][C]100[/C][C]0.992689155839436[/C][C]0.0146216883211272[/C][C]0.00731084416056362[/C][/ROW]
[ROW][C]101[/C][C]0.993587965644624[/C][C]0.0128240687107510[/C][C]0.00641203435537552[/C][/ROW]
[ROW][C]102[/C][C]0.992587470675183[/C][C]0.0148250586496335[/C][C]0.00741252932481676[/C][/ROW]
[ROW][C]103[/C][C]0.991334311192622[/C][C]0.0173313776147553[/C][C]0.00866568880737765[/C][/ROW]
[ROW][C]104[/C][C]0.988786623466502[/C][C]0.0224267530669964[/C][C]0.0112133765334982[/C][/ROW]
[ROW][C]105[/C][C]0.987197426903214[/C][C]0.0256051461935712[/C][C]0.0128025730967856[/C][/ROW]
[ROW][C]106[/C][C]0.983488734830613[/C][C]0.0330225303387732[/C][C]0.0165112651693866[/C][/ROW]
[ROW][C]107[/C][C]0.986210074067875[/C][C]0.0275798518642497[/C][C]0.0137899259321249[/C][/ROW]
[ROW][C]108[/C][C]0.995481225694707[/C][C]0.00903754861058545[/C][C]0.00451877430529273[/C][/ROW]
[ROW][C]109[/C][C]0.996074339401285[/C][C]0.00785132119742921[/C][C]0.00392566059871461[/C][/ROW]
[ROW][C]110[/C][C]0.995808213551387[/C][C]0.00838357289722578[/C][C]0.00419178644861289[/C][/ROW]
[ROW][C]111[/C][C]0.994712555294524[/C][C]0.0105748894109525[/C][C]0.00528744470547626[/C][/ROW]
[ROW][C]112[/C][C]0.99438371667621[/C][C]0.0112325666475790[/C][C]0.00561628332378949[/C][/ROW]
[ROW][C]113[/C][C]0.994202765554047[/C][C]0.0115944688919066[/C][C]0.0057972344459533[/C][/ROW]
[ROW][C]114[/C][C]0.992371888869807[/C][C]0.0152562222603854[/C][C]0.0076281111301927[/C][/ROW]
[ROW][C]115[/C][C]0.98993717879755[/C][C]0.0201256424048992[/C][C]0.0100628212024496[/C][/ROW]
[ROW][C]116[/C][C]0.986889938231705[/C][C]0.0262201235365897[/C][C]0.0131100617682949[/C][/ROW]
[ROW][C]117[/C][C]0.98303875370743[/C][C]0.0339224925851423[/C][C]0.0169612462925711[/C][/ROW]
[ROW][C]118[/C][C]0.97820102961212[/C][C]0.0435979407757616[/C][C]0.0217989703878808[/C][/ROW]
[ROW][C]119[/C][C]0.985499028598025[/C][C]0.0290019428039499[/C][C]0.0145009714019749[/C][/ROW]
[ROW][C]120[/C][C]0.997273737946519[/C][C]0.00545252410696247[/C][C]0.00272626205348123[/C][/ROW]
[ROW][C]121[/C][C]0.996840500171403[/C][C]0.00631899965719486[/C][C]0.00315949982859743[/C][/ROW]
[ROW][C]122[/C][C]0.996604328712003[/C][C]0.00679134257599476[/C][C]0.00339567128799738[/C][/ROW]
[ROW][C]123[/C][C]0.995731903492709[/C][C]0.00853619301458283[/C][C]0.00426809650729142[/C][/ROW]
[ROW][C]124[/C][C]0.995231935474466[/C][C]0.00953612905106843[/C][C]0.00476806452553421[/C][/ROW]
[ROW][C]125[/C][C]0.99383571956488[/C][C]0.0123285608702407[/C][C]0.00616428043512034[/C][/ROW]
[ROW][C]126[/C][C]0.993579112677005[/C][C]0.0128417746459893[/C][C]0.00642088732299466[/C][/ROW]
[ROW][C]127[/C][C]0.993393503511835[/C][C]0.0132129929763302[/C][C]0.00660649648816512[/C][/ROW]
[ROW][C]128[/C][C]0.991524685107479[/C][C]0.0169506297850422[/C][C]0.00847531489252108[/C][/ROW]
[ROW][C]129[/C][C]0.988655488303615[/C][C]0.0226890233927702[/C][C]0.0113445116963851[/C][/ROW]
[ROW][C]130[/C][C]0.98495935978621[/C][C]0.0300812804275793[/C][C]0.0150406402137896[/C][/ROW]
[ROW][C]131[/C][C]0.99022162695995[/C][C]0.0195567460801009[/C][C]0.00977837304005047[/C][/ROW]
[ROW][C]132[/C][C]0.998279991070906[/C][C]0.00344001785818850[/C][C]0.00172000892909425[/C][/ROW]
[ROW][C]133[/C][C]0.997591624783056[/C][C]0.00481675043388758[/C][C]0.00240837521694379[/C][/ROW]
[ROW][C]134[/C][C]0.99789281272571[/C][C]0.00421437454857823[/C][C]0.00210718727428912[/C][/ROW]
[ROW][C]135[/C][C]0.99729389880209[/C][C]0.00541220239582077[/C][C]0.00270610119791039[/C][/ROW]
[ROW][C]136[/C][C]0.997677692215397[/C][C]0.00464461556920594[/C][C]0.00232230778460297[/C][/ROW]
[ROW][C]137[/C][C]0.99735459133933[/C][C]0.00529081732134144[/C][C]0.00264540866067072[/C][/ROW]
[ROW][C]138[/C][C]0.996546553843356[/C][C]0.00690689231328844[/C][C]0.00345344615664422[/C][/ROW]
[ROW][C]139[/C][C]0.9960452898774[/C][C]0.00790942024520082[/C][C]0.00395471012260041[/C][/ROW]
[ROW][C]140[/C][C]0.994686970830008[/C][C]0.0106260583399844[/C][C]0.00531302916999218[/C][/ROW]
[ROW][C]141[/C][C]0.992957425200093[/C][C]0.0140851495998132[/C][C]0.00704257479990659[/C][/ROW]
[ROW][C]142[/C][C]0.992033009605433[/C][C]0.0159339807891335[/C][C]0.00796699039456673[/C][/ROW]
[ROW][C]143[/C][C]0.989583771235881[/C][C]0.020832457528237[/C][C]0.0104162287641185[/C][/ROW]
[ROW][C]144[/C][C]0.992081659149131[/C][C]0.0158366817017377[/C][C]0.00791834085086885[/C][/ROW]
[ROW][C]145[/C][C]0.990449484404383[/C][C]0.019101031191235[/C][C]0.0095505155956175[/C][/ROW]
[ROW][C]146[/C][C]0.988949707339056[/C][C]0.0221005853218887[/C][C]0.0110502926609444[/C][/ROW]
[ROW][C]147[/C][C]0.985456060894265[/C][C]0.0290878782114692[/C][C]0.0145439391057346[/C][/ROW]
[ROW][C]148[/C][C]0.985571134514045[/C][C]0.0288577309719094[/C][C]0.0144288654859547[/C][/ROW]
[ROW][C]149[/C][C]0.981780174692575[/C][C]0.0364396506148506[/C][C]0.0182198253074253[/C][/ROW]
[ROW][C]150[/C][C]0.983522709663428[/C][C]0.0329545806731444[/C][C]0.0164772903365722[/C][/ROW]
[ROW][C]151[/C][C]0.977248897775252[/C][C]0.0455022044494958[/C][C]0.0227511022247479[/C][/ROW]
[ROW][C]152[/C][C]0.972640095312507[/C][C]0.0547198093749852[/C][C]0.0273599046874926[/C][/ROW]
[ROW][C]153[/C][C]0.962925549083712[/C][C]0.0741489018325764[/C][C]0.0370744509162882[/C][/ROW]
[ROW][C]154[/C][C]0.967302522490983[/C][C]0.0653949550180346[/C][C]0.0326974775090173[/C][/ROW]
[ROW][C]155[/C][C]0.966685544248089[/C][C]0.0666289115038225[/C][C]0.0333144557519112[/C][/ROW]
[ROW][C]156[/C][C]0.957191721243764[/C][C]0.0856165575124729[/C][C]0.0428082787562364[/C][/ROW]
[ROW][C]157[/C][C]0.951103839795078[/C][C]0.097792320409845[/C][C]0.0488961602049225[/C][/ROW]
[ROW][C]158[/C][C]0.946573335209496[/C][C]0.106853329581007[/C][C]0.0534266647905037[/C][/ROW]
[ROW][C]159[/C][C]0.94198975976146[/C][C]0.116020480477081[/C][C]0.0580102402385407[/C][/ROW]
[ROW][C]160[/C][C]0.953946388633883[/C][C]0.0921072227322349[/C][C]0.0460536113661174[/C][/ROW]
[ROW][C]161[/C][C]0.951782015166047[/C][C]0.0964359696679061[/C][C]0.0482179848339531[/C][/ROW]
[ROW][C]162[/C][C]0.943781675674247[/C][C]0.112436648651507[/C][C]0.0562183243257533[/C][/ROW]
[ROW][C]163[/C][C]0.948852829830086[/C][C]0.102294340339827[/C][C]0.0511471701699137[/C][/ROW]
[ROW][C]164[/C][C]0.93386958805322[/C][C]0.132260823893559[/C][C]0.0661304119467795[/C][/ROW]
[ROW][C]165[/C][C]0.93201799159513[/C][C]0.13596401680974[/C][C]0.06798200840487[/C][/ROW]
[ROW][C]166[/C][C]0.908408408610311[/C][C]0.183183182779377[/C][C]0.0915915913896886[/C][/ROW]
[ROW][C]167[/C][C]0.896695571660102[/C][C]0.206608856679795[/C][C]0.103304428339898[/C][/ROW]
[ROW][C]168[/C][C]0.946324945345166[/C][C]0.107350109309668[/C][C]0.0536750546548339[/C][/ROW]
[ROW][C]169[/C][C]0.92576197839223[/C][C]0.148476043215541[/C][C]0.0742380216077706[/C][/ROW]
[ROW][C]170[/C][C]0.932074147271306[/C][C]0.135851705457388[/C][C]0.0679258527286941[/C][/ROW]
[ROW][C]171[/C][C]0.912198628384566[/C][C]0.175602743230867[/C][C]0.0878013716154336[/C][/ROW]
[ROW][C]172[/C][C]0.897139750110684[/C][C]0.205720499778631[/C][C]0.102860249889316[/C][/ROW]
[ROW][C]173[/C][C]0.867972375515691[/C][C]0.264055248968618[/C][C]0.132027624484309[/C][/ROW]
[ROW][C]174[/C][C]0.884055444197872[/C][C]0.231889111604255[/C][C]0.115944555802128[/C][/ROW]
[ROW][C]175[/C][C]0.871741028095492[/C][C]0.256517943809015[/C][C]0.128258971904507[/C][/ROW]
[ROW][C]176[/C][C]0.875980463407352[/C][C]0.248039073185296[/C][C]0.124019536592648[/C][/ROW]
[ROW][C]177[/C][C]0.829472349328393[/C][C]0.341055301343215[/C][C]0.170527650671607[/C][/ROW]
[ROW][C]178[/C][C]0.781248854895941[/C][C]0.437502290208118[/C][C]0.218751145104059[/C][/ROW]
[ROW][C]179[/C][C]0.723085429655236[/C][C]0.553829140689528[/C][C]0.276914570344764[/C][/ROW]
[ROW][C]180[/C][C]0.66809790709483[/C][C]0.663804185810341[/C][C]0.331902092905171[/C][/ROW]
[ROW][C]181[/C][C]0.573634400111419[/C][C]0.852731199777162[/C][C]0.426365599888581[/C][/ROW]
[ROW][C]182[/C][C]0.540064085792425[/C][C]0.91987182841515[/C][C]0.459935914207575[/C][/ROW]
[ROW][C]183[/C][C]0.449856183719487[/C][C]0.899712367438975[/C][C]0.550143816280512[/C][/ROW]
[ROW][C]184[/C][C]0.49102948923368[/C][C]0.98205897846736[/C][C]0.50897051076632[/C][/ROW]
[ROW][C]185[/C][C]0.403462687220028[/C][C]0.806925374440056[/C][C]0.596537312779972[/C][/ROW]
[ROW][C]186[/C][C]0.428895543286732[/C][C]0.857791086573464[/C][C]0.571104456713268[/C][/ROW]
[ROW][C]187[/C][C]0.488140962852545[/C][C]0.97628192570509[/C][C]0.511859037147455[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55987&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55987&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
50.1559308029189440.3118616058378880.844069197081056
60.06649352737734470.1329870547546890.933506472622655
70.02567383314294040.05134766628588080.97432616685706
80.01221739680733030.02443479361466060.98778260319267
90.004379978104602920.008759956209205830.995620021895397
100.002240159817864130.004480319635728250.997759840182136
110.2235801583455510.4471603166911010.77641984165445
120.5039813540008250.992037291998350.496018645999175
130.4208412053123040.8416824106246070.579158794687696
140.3453656525640860.6907313051281720.654634347435914
150.2700182093786300.5400364187572610.72998179062137
160.2190644244158520.4381288488317040.780935575584148
170.1710750448748180.3421500897496370.828924955125182
180.1407115265465930.2814230530931850.859288473453407
190.1140922425588590.2281844851177170.885907757441141
200.09007928733986690.1801585746797340.909920712660133
210.0638847643680760.1277695287361520.936115235631924
220.08828604303362070.1765720860672410.91171395696638
230.2540366087170350.508073217434070.745963391282965
240.7041345159405080.5917309681189830.295865484059492
250.7099494063259890.5801011873480230.290050593674011
260.6617327503331520.6765344993336960.338267249666848
270.60652227047790.7869554590441980.393477729522099
280.5597189438136970.8805621123726060.440281056186303
290.5037375415697180.9925249168605640.496262458430282
300.4449608348989580.8899216697979160.555039165101042
310.3901828560403290.7803657120806570.609817143959671
320.3621281127465610.7242562254931220.637871887253439
330.3227248265256090.6454496530512180.677275173474391
340.3184699397357430.6369398794714860.681530060264257
350.4475027759995330.8950055519990660.552497224000467
360.5401905183494390.9196189633011220.459809481650561
370.5622945098448020.8754109803103960.437705490155198
380.5106860002932680.9786279994134630.489313999706732
390.4622777891552320.9245555783104650.537722210844768
400.4432414467682040.8864828935364090.556758553231796
410.4257386735252530.8514773470505050.574261326474747
420.3820739067215540.7641478134431070.617926093278446
430.3620115673263840.7240231346527670.637988432673616
440.3219927317477250.6439854634954510.678007268252275
450.2794282905145580.5588565810291160.720571709485442
460.2656486170042530.5312972340085060.734351382995747
470.4937822328476490.9875644656952970.506217767152351
480.8899375407916190.2201249184167620.110062459208381
490.9011136623684070.1977726752631870.0988863376315933
500.8919312802842850.2161374394314300.108068719715715
510.8749587704768280.2500824590463440.125041229523172
520.8619242688046230.2761514623907550.138075731195377
530.8577418676605710.2845162646788570.142258132339429
540.833787220623450.3324255587530990.166212779376550
550.8316835171299290.3366329657401420.168316482870071
560.8134521521322490.3730956957355030.186547847867751
570.8299900464796020.3400199070407960.170009953520398
580.8452939376681140.3094121246637720.154706062331886
590.869886556799950.2602268864001000.130113443200050
600.8999731806219020.2000536387561950.100026819378098
610.8929115381189770.2141769237620450.107088461881023
620.899813657648210.2003726847035800.100186342351790
630.8987191232890920.2025617534218150.101280876710908
640.9242215772354530.1515568455290940.0757784227645468
650.9102741546203670.1794516907592650.0897258453796325
660.8947925365983930.2104149268032140.105207463401607
670.8771697946178040.2456604107643920.122830205382196
680.8640972653708240.2718054692583510.135902734629176
690.8678736410287260.2642527179425490.132126358971274
700.8871764413456120.2256471173087750.112823558654388
710.9084496324115170.1831007351769660.0915503675884829
720.9208695043335420.1582609913329170.0791304956664583
730.9160102994141070.1679794011717860.0839897005858932
740.9404025394138450.1191949211723110.0595974605861555
750.9312922476935140.1374155046129730.0687077523064863
760.9469560558531370.1060878882937260.0530439441468632
770.9458816420170680.1082367159658630.0541183579829316
780.9534363149400040.09312737011999190.0465636850599959
790.9577669083245750.08446618335085020.0422330916754251
800.954411751576560.0911764968468810.0455882484234405
810.9457911981509690.1084176036980630.0542088018490313
820.9401750357771440.1196499284457110.0598249642228557
830.9361310619796970.1277378760406070.0638689380203034
840.9663385663714640.0673228672570730.0336614336285365
850.966916074785160.0661678504296810.0330839252148405
860.9601641193441550.07967176131169050.0398358806558452
870.9652770693352240.0694458613295530.0347229306647765
880.9703794550258670.05924108994826540.0296205449741327
890.9674640511590650.06507189768186920.0325359488409346
900.977737303238430.0445253935231380.022262696761569
910.97532264804520.04935470390960.0246773519548
920.9821276036826250.03574479263475090.0178723963173755
930.9779039316311570.04419213673768590.0220960683688429
940.9725800354559550.05483992908809090.0274199645440455
950.9736521992866830.05269560142663460.0263478007133173
960.9921478667486580.01570426650268470.00785213325134237
970.9899441772202630.02011164555947370.0100558227797368
980.991080474977210.01783905004557950.00891952502278976
990.991852053029110.01629589394178150.00814794697089075
1000.9926891558394360.01462168832112720.00731084416056362
1010.9935879656446240.01282406871075100.00641203435537552
1020.9925874706751830.01482505864963350.00741252932481676
1030.9913343111926220.01733137761475530.00866568880737765
1040.9887866234665020.02242675306699640.0112133765334982
1050.9871974269032140.02560514619357120.0128025730967856
1060.9834887348306130.03302253033877320.0165112651693866
1070.9862100740678750.02757985186424970.0137899259321249
1080.9954812256947070.009037548610585450.00451877430529273
1090.9960743394012850.007851321197429210.00392566059871461
1100.9958082135513870.008383572897225780.00419178644861289
1110.9947125552945240.01057488941095250.00528744470547626
1120.994383716676210.01123256664757900.00561628332378949
1130.9942027655540470.01159446889190660.0057972344459533
1140.9923718888698070.01525622226038540.0076281111301927
1150.989937178797550.02012564240489920.0100628212024496
1160.9868899382317050.02622012353658970.0131100617682949
1170.983038753707430.03392249258514230.0169612462925711
1180.978201029612120.04359794077576160.0217989703878808
1190.9854990285980250.02900194280394990.0145009714019749
1200.9972737379465190.005452524106962470.00272626205348123
1210.9968405001714030.006318999657194860.00315949982859743
1220.9966043287120030.006791342575994760.00339567128799738
1230.9957319034927090.008536193014582830.00426809650729142
1240.9952319354744660.009536129051068430.00476806452553421
1250.993835719564880.01232856087024070.00616428043512034
1260.9935791126770050.01284177464598930.00642088732299466
1270.9933935035118350.01321299297633020.00660649648816512
1280.9915246851074790.01695062978504220.00847531489252108
1290.9886554883036150.02268902339277020.0113445116963851
1300.984959359786210.03008128042757930.0150406402137896
1310.990221626959950.01955674608010090.00977837304005047
1320.9982799910709060.003440017858188500.00172000892909425
1330.9975916247830560.004816750433887580.00240837521694379
1340.997892812725710.004214374548578230.00210718727428912
1350.997293898802090.005412202395820770.00270610119791039
1360.9976776922153970.004644615569205940.00232230778460297
1370.997354591339330.005290817321341440.00264540866067072
1380.9965465538433560.006906892313288440.00345344615664422
1390.99604528987740.007909420245200820.00395471012260041
1400.9946869708300080.01062605833998440.00531302916999218
1410.9929574252000930.01408514959981320.00704257479990659
1420.9920330096054330.01593398078913350.00796699039456673
1430.9895837712358810.0208324575282370.0104162287641185
1440.9920816591491310.01583668170173770.00791834085086885
1450.9904494844043830.0191010311912350.0095505155956175
1460.9889497073390560.02210058532188870.0110502926609444
1470.9854560608942650.02908787821146920.0145439391057346
1480.9855711345140450.02885773097190940.0144288654859547
1490.9817801746925750.03643965061485060.0182198253074253
1500.9835227096634280.03295458067314440.0164772903365722
1510.9772488977752520.04550220444949580.0227511022247479
1520.9726400953125070.05471980937498520.0273599046874926
1530.9629255490837120.07414890183257640.0370744509162882
1540.9673025224909830.06539495501803460.0326974775090173
1550.9666855442480890.06662891150382250.0333144557519112
1560.9571917212437640.08561655751247290.0428082787562364
1570.9511038397950780.0977923204098450.0488961602049225
1580.9465733352094960.1068533295810070.0534266647905037
1590.941989759761460.1160204804770810.0580102402385407
1600.9539463886338830.09210722273223490.0460536113661174
1610.9517820151660470.09643596966790610.0482179848339531
1620.9437816756742470.1124366486515070.0562183243257533
1630.9488528298300860.1022943403398270.0511471701699137
1640.933869588053220.1322608238935590.0661304119467795
1650.932017991595130.135964016809740.06798200840487
1660.9084084086103110.1831831827793770.0915915913896886
1670.8966955716601020.2066088566797950.103304428339898
1680.9463249453451660.1073501093096680.0536750546548339
1690.925761978392230.1484760432155410.0742380216077706
1700.9320741472713060.1358517054573880.0679258527286941
1710.9121986283845660.1756027432308670.0878013716154336
1720.8971397501106840.2057204997786310.102860249889316
1730.8679723755156910.2640552489686180.132027624484309
1740.8840554441978720.2318891116042550.115944555802128
1750.8717410280954920.2565179438090150.128258971904507
1760.8759804634073520.2480390731852960.124019536592648
1770.8294723493283930.3410553013432150.170527650671607
1780.7812488548959410.4375022902081180.218751145104059
1790.7230854296552360.5538291406895280.276914570344764
1800.668097907094830.6638041858103410.331902092905171
1810.5736344001114190.8527311997771620.426365599888581
1820.5400640857924250.919871828415150.459935914207575
1830.4498561837194870.8997123674389750.550143816280512
1840.491029489233680.982058978467360.50897051076632
1850.4034626872200280.8069253744400560.596537312779972
1860.4288955432867320.8577910865734640.571104456713268
1870.4881409628525450.976281925705090.511859037147455






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level180.098360655737705NOK
5% type I error level630.344262295081967NOK
10% type I error level830.453551912568306NOK

\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 & 18 & 0.098360655737705 & NOK \tabularnewline
5% type I error level & 63 & 0.344262295081967 & NOK \tabularnewline
10% type I error level & 83 & 0.453551912568306 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55987&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]18[/C][C]0.098360655737705[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]63[/C][C]0.344262295081967[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]83[/C][C]0.453551912568306[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55987&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55987&T=6

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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 level180.098360655737705NOK
5% type I error level630.344262295081967NOK
10% type I error level830.453551912568306NOK


Figures (Output of Computation)

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Input Parameters & R Code

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