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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 04 Nov 2013 02:28:31 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Nov/04/t1383550159ii67vv6nubn1d7d.htm/, Retrieved Sun, 28 Apr 2024 04:23:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=222039, Retrieved Sun, 28 Apr 2024 04:23:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact288

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2013-11-04 07:28:31] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
- R       [Multiple Regression] [Workshop 7 - Seat...] [2013-11-20 15:49:10] [74be16979710d4c4e7c6647856088456]
- RM      [Multiple Regression] [] [2014-11-12 13:53:02] [394a9522c47495260fca596e959e6202]
- RM      [Multiple Regression] [Gordelwet - maand...] [2014-11-12 14:13:38] [81f624c2f0b20a2549c93e7c3dccf981]
- RM      [Multiple Regression] [ws7 v7] [2014-11-12 14:20:29] [e3727f74ca0896859afbe865e40a3465]
- RM      [Multiple Regression] [] [2014-11-12 14:32:28] [6795cd14e59cd8fafcdf800c40b889d9]
- RMP     [Multiple Regression] [Task 2.2 WS7] [2014-11-12 15:36:29] [805021881bfa5340347077d26b077617]
- RM      [Multiple Regression] [] [2014-11-12 15:42:39] [fa1b8827d7de91b8b87087311d3d9fa1]
- RMP     [Multiple Regression] [] [2014-11-12 16:58:06] [c2c160edf30e228bd3a949bf24376c2c]
- RM      [Multiple Regression] [WS7 task 2] [2014-11-12 17:01:49] [67894a4ff6098ffac356bc81e6028257]
- RM      [Multiple Regression] [] [2014-11-12 17:03:19] [6656361aa4da5489a6a45e803df0211c]
- RMP     [Multiple Regression] [WS7 SHW] [2014-11-12 17:17:20] [cac6c5fb035463be46c296b46e439cb5]
- RM      [Multiple Regression] [] [2014-11-12 18:38:05] [69bf0eb8b9b38defaaf4848d8c317571]
- RMPD    [Central Tendency] [] [2014-11-12 18:50:22] [69bf0eb8b9b38defaaf4848d8c317571]
- RMPD    [Skewness and Kurtosis Test] [] [2014-11-12 18:51:37] [69bf0eb8b9b38defaaf4848d8c317571]
- RM      [Multiple Regression] [WS7 - 8] [2014-11-12 20:42:59] [4d39cf209776852399955073f9d0ee7a]
-           [Multiple Regression] [WSH 7, 12b] [2014-11-13 19:49:05] [e7da31d1eb6eab8d5ed70d87d07c747b]
- RM      [Multiple Regression] [] [2014-11-13 07:24:13] [1a6d42b46b3d01bc960fcfb45e99fecd]
- RM      [Multiple Regression] [] [2014-11-13 11:57:35] [ce58fb8a0a6d5fe2eedf5e527a9cf2f2]
- RMPD    [Standard Deviation Plot] [] [2014-11-13 12:06:20] [ce58fb8a0a6d5fe2eedf5e527a9cf2f2]
- RMPD    [Skewness and Kurtosis Test] [] [2014-11-13 12:25:29] [ce58fb8a0a6d5fe2eedf5e527a9cf2f2]
- RM      [Multiple Regression] [] [2014-11-13 14:53:49] [dd7a37d66cc3f8699a204e53c0324369]
- RM      [Multiple Regression] [] [2014-11-13 17:16:10] [d69b52d23ca73e15a0c741afa583703c]
-  MPD      [Multiple Regression] [huwelijk vs conju...] [2014-12-13 10:47:33] [189b7d469e4e3b4e868a6af83e3b3816]
-  MPD      [Multiple Regression] [huwelijken vs con...] [2014-12-13 10:56:41] [189b7d469e4e3b4e868a6af83e3b3816]
-   P         [Multiple Regression] [aantal huwelijken...] [2014-12-13 11:05:02] [189b7d469e4e3b4e868a6af83e3b3816]
-    D          [Multiple Regression] [scheidingen vs co...] [2014-12-14 15:52:33] [189b7d469e4e3b4e868a6af83e3b3816]
-  M            [Multiple Regression] [] [2014-12-15 13:47:17] [78252ca1523d3477f114bddbfa59edb4]
-  MP           [Multiple Regression] [] [2014-12-15 14:06:54] [78252ca1523d3477f114bddbfa59edb4]
-  M D            [Multiple Regression] [] [2014-12-15 14:24:30] [78252ca1523d3477f114bddbfa59edb4]
-    D              [Multiple Regression] [] [2014-12-15 14:48:59] [78252ca1523d3477f114bddbfa59edb4]
- RMPD        [Exponential Smoothing] [] [2014-12-13 13:28:12] [189b7d469e4e3b4e868a6af83e3b3816]
- RMPD        [Exponential Smoothing] [] [2014-12-13 13:30:12] [189b7d469e4e3b4e868a6af83e3b3816]
- RMPD        [Exponential Smoothing] [] [2014-12-13 13:31:04] [189b7d469e4e3b4e868a6af83e3b3816]
- RMPD        [Exponential Smoothing] [] [2014-12-13 13:31:50] [189b7d469e4e3b4e868a6af83e3b3816]
- RMPD        [Exponential Smoothing] [] [2014-12-13 13:32:46] [189b7d469e4e3b4e868a6af83e3b3816]
- RMPD        [Exponential Smoothing] [] [2014-12-13 13:33:24] [189b7d469e4e3b4e868a6af83e3b3816]
- RM      [Multiple Regression] [w7-seat] [2014-11-13 19:07:02] [673773038936aef3a5778d7e6bda5c1e]
- RM      [Multiple Regression] [ws7] [2014-11-13 19:20:30] [8523551e1e4e3cbe97fa25692e177b2e]
- RM      [Multiple Regression] [q] [2014-11-13 19:27:43] [1651e47f7f65f3a10bbbb444d4b26be7]
- RM      [Multiple Regression] [q] [2014-11-13 19:27:43] [1651e47f7f65f3a10bbbb444d4b26be7]
- RM      [Multiple Regression] [ws7] [2014-11-13 21:33:17] [006b54b8ce76f482b86cd20c6480b526]
- RMPD    [Multiple Regression] [ex WS7seatbelt se...] [2015-01-18 12:53:49] [bb1b6762b7e5624d262776d3f7139d34]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1687 0
1508 0
1507 0
1385 0
1632 0
1511 0
1559 0
1630 0
1579 0
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1719 0
2008 0
2242 0
2478 0
2030 0
1655 0
1693 0
1623 0
1805 0
1746 0
1795 0
1926 0
1619 0
1992 0
2233 0
2192 0
2080 0
1768 0
1835 0
1569 0
1976 0
1853 0
1965 0
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1778 0
1976 0
2397 0
2654 0
2097 0
1963 0
1677 0
1941 0
2003 0
1813 0
2012 0
1912 0
2084 0
2080 0
2118 0
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1731 0
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1887 0
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1421 0
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1656 0
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1905 0
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2016 0
2207 0
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1938 0
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1998 0
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1057 1
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1168 1
1236 1
1076 1
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1139 1
1427 1
1487 1
1483 1
1513 1
1357 1
1165 1
1282 1
1110 1
1297 1
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 time9 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=222039&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=222039&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=222039&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 time9 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Accidents[t] = + 2165.23 -395.811Belt[t] -442.551M1[t] -617.812M2[t] -567.25M3[t] -680.438M4[t] -543.125M5[t] -598.875M6[t] -523.25M7[t] -508.375M8[t] -455.562M9[t] -316.188M10[t] -116.625M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Accidents[t] =  +  2165.23 -395.811Belt[t] -442.551M1[t] -617.812M2[t] -567.25M3[t] -680.438M4[t] -543.125M5[t] -598.875M6[t] -523.25M7[t] -508.375M8[t] -455.562M9[t] -316.188M10[t] -116.625M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=222039&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Accidents[t] =  +  2165.23 -395.811Belt[t] -442.551M1[t] -617.812M2[t] -567.25M3[t] -680.438M4[t] -543.125M5[t] -598.875M6[t] -523.25M7[t] -508.375M8[t] -455.562M9[t] -316.188M10[t] -116.625M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=222039&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=222039&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
Accidents[t] = + 2165.23 -395.811Belt[t] -442.551M1[t] -617.812M2[t] -567.25M3[t] -680.438M4[t] -543.125M5[t] -598.875M6[t] -523.25M7[t] -508.375M8[t] -455.562M9[t] -316.188M10[t] -116.625M11[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2165.2343.631949.621.45481e-1067.27407e-107
Belt-395.81138.6056-10.251.06923e-195.34613e-20
M1-442.55161.3737-7.2111.50917e-117.54584e-12
M2-617.81261.3262-10.073.41462e-191.70731e-19
M3-567.2561.3262-9.256.76088e-173.38044e-17
M4-680.43861.3262-11.14.19121e-222.09561e-22
M5-543.12561.3262-8.8568.00478e-164.00239e-16
M6-598.87561.3262-9.7652.51305e-181.25653e-18
M7-523.2561.3262-8.5325.95196e-152.97598e-15
M8-508.37561.3262-8.292.62012e-141.31006e-14
M9-455.56261.3262-7.4294.33013e-122.16506e-12
M10-316.18861.3262-5.1566.64186e-073.32093e-07
M11-116.62561.3262-1.9020.05881470.0294073

\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) & 2165.23 & 43.6319 & 49.62 & 1.45481e-106 & 7.27407e-107 \tabularnewline
Belt & -395.811 & 38.6056 & -10.25 & 1.06923e-19 & 5.34613e-20 \tabularnewline
M1 & -442.551 & 61.3737 & -7.211 & 1.50917e-11 & 7.54584e-12 \tabularnewline
M2 & -617.812 & 61.3262 & -10.07 & 3.41462e-19 & 1.70731e-19 \tabularnewline
M3 & -567.25 & 61.3262 & -9.25 & 6.76088e-17 & 3.38044e-17 \tabularnewline
M4 & -680.438 & 61.3262 & -11.1 & 4.19121e-22 & 2.09561e-22 \tabularnewline
M5 & -543.125 & 61.3262 & -8.856 & 8.00478e-16 & 4.00239e-16 \tabularnewline
M6 & -598.875 & 61.3262 & -9.765 & 2.51305e-18 & 1.25653e-18 \tabularnewline
M7 & -523.25 & 61.3262 & -8.532 & 5.95196e-15 & 2.97598e-15 \tabularnewline
M8 & -508.375 & 61.3262 & -8.29 & 2.62012e-14 & 1.31006e-14 \tabularnewline
M9 & -455.562 & 61.3262 & -7.429 & 4.33013e-12 & 2.16506e-12 \tabularnewline
M10 & -316.188 & 61.3262 & -5.156 & 6.64186e-07 & 3.32093e-07 \tabularnewline
M11 & -116.625 & 61.3262 & -1.902 & 0.0588147 & 0.0294073 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=222039&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]2165.23[/C][C]43.6319[/C][C]49.62[/C][C]1.45481e-106[/C][C]7.27407e-107[/C][/ROW]
[ROW][C]Belt[/C][C]-395.811[/C][C]38.6056[/C][C]-10.25[/C][C]1.06923e-19[/C][C]5.34613e-20[/C][/ROW]
[ROW][C]M1[/C][C]-442.551[/C][C]61.3737[/C][C]-7.211[/C][C]1.50917e-11[/C][C]7.54584e-12[/C][/ROW]
[ROW][C]M2[/C][C]-617.812[/C][C]61.3262[/C][C]-10.07[/C][C]3.41462e-19[/C][C]1.70731e-19[/C][/ROW]
[ROW][C]M3[/C][C]-567.25[/C][C]61.3262[/C][C]-9.25[/C][C]6.76088e-17[/C][C]3.38044e-17[/C][/ROW]
[ROW][C]M4[/C][C]-680.438[/C][C]61.3262[/C][C]-11.1[/C][C]4.19121e-22[/C][C]2.09561e-22[/C][/ROW]
[ROW][C]M5[/C][C]-543.125[/C][C]61.3262[/C][C]-8.856[/C][C]8.00478e-16[/C][C]4.00239e-16[/C][/ROW]
[ROW][C]M6[/C][C]-598.875[/C][C]61.3262[/C][C]-9.765[/C][C]2.51305e-18[/C][C]1.25653e-18[/C][/ROW]
[ROW][C]M7[/C][C]-523.25[/C][C]61.3262[/C][C]-8.532[/C][C]5.95196e-15[/C][C]2.97598e-15[/C][/ROW]
[ROW][C]M8[/C][C]-508.375[/C][C]61.3262[/C][C]-8.29[/C][C]2.62012e-14[/C][C]1.31006e-14[/C][/ROW]
[ROW][C]M9[/C][C]-455.562[/C][C]61.3262[/C][C]-7.429[/C][C]4.33013e-12[/C][C]2.16506e-12[/C][/ROW]
[ROW][C]M10[/C][C]-316.188[/C][C]61.3262[/C][C]-5.156[/C][C]6.64186e-07[/C][C]3.32093e-07[/C][/ROW]
[ROW][C]M11[/C][C]-116.625[/C][C]61.3262[/C][C]-1.902[/C][C]0.0588147[/C][C]0.0294073[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=222039&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=222039&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)2165.2343.631949.621.45481e-1067.27407e-107
Belt-395.81138.6056-10.251.06923e-195.34613e-20
M1-442.55161.3737-7.2111.50917e-117.54584e-12
M2-617.81261.3262-10.073.41462e-191.70731e-19
M3-567.2561.3262-9.256.76088e-173.38044e-17
M4-680.43861.3262-11.14.19121e-222.09561e-22
M5-543.12561.3262-8.8568.00478e-164.00239e-16
M6-598.87561.3262-9.7652.51305e-181.25653e-18
M7-523.2561.3262-8.5325.95196e-152.97598e-15
M8-508.37561.3262-8.292.62012e-141.31006e-14
M9-455.56261.3262-7.4294.33013e-122.16506e-12
M10-316.18861.3262-5.1566.64186e-073.32093e-07
M11-116.62561.3262-1.9020.05881470.0294073






Multiple Linear Regression - Regression Statistics
Multiple R0.814751
R-squared0.66382
Adjusted R-squared0.641282
F-TEST (value)29.4544
F-TEST (DF numerator)12
F-TEST (DF denominator)179
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation173.457
Sum Squared Residuals5385620

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.814751 \tabularnewline
R-squared & 0.66382 \tabularnewline
Adjusted R-squared & 0.641282 \tabularnewline
F-TEST (value) & 29.4544 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 179 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 173.457 \tabularnewline
Sum Squared Residuals & 5385620 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=222039&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.814751[/C][/ROW]
[ROW][C]R-squared[/C][C]0.66382[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.641282[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]29.4544[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]179[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]173.457[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5385620[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=222039&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=222039&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 R0.814751
R-squared0.66382
Adjusted R-squared0.641282
F-TEST (value)29.4544
F-TEST (DF numerator)12
F-TEST (DF denominator)179
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation173.457
Sum Squared Residuals5385620






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
116871722.68-35.6757
215081547.41-39.4139
315071597.98-90.9764
413851484.79-99.7889
516321622.19.89861
615111566.35-55.3514
715591641.98-82.9764
816301656.85-26.8514
915791709.66-130.664
1016531849.04-196.039
1121522048.6103.399
1221482165.23-17.2264
1317521722.6829.3243
1417651547.41217.586
1517171597.98119.024
1615581484.7973.2111
1715751622.1-47.1014
1815201566.35-46.3514
1918051641.98163.024
2018001656.85143.149
2117191709.669.33611
2220081849.04158.961
2322422048.6193.399
2424782165.23312.774
2520301722.68307.324
2616551547.41107.586
2716931597.9895.0236
2816231484.79138.211
2918051622.1182.899
3017461566.35179.649
3117951641.98153.024
3219261656.85269.149
3316191709.66-90.6639
3419921849.04142.961
3522332048.6184.399
3621922165.2326.7736
3720801722.68357.324
3817681547.41220.586
3918351597.98237.024
4015691484.7984.2111
4119761622.1353.899
4218531566.35286.649
4319651641.98323.024
4416891656.8532.1486
4517781709.6668.3361
4619761849.04126.961
4723972048.6348.399
4826542165.23488.774
4920971722.68374.324
5019631547.41415.586
5116771597.9879.0236
5219411484.79456.211
5320031622.1380.899
5418131566.35246.649
5520121641.98370.024
5619121656.85255.149
5720841709.66374.336
5820801849.04230.961
5921182048.669.3986
6021502165.23-15.2264
6116081722.68-114.676
6215031547.41-44.4139
6315481597.98-49.9764
6413821484.79-102.789
6517311622.1108.899
6617981566.35231.649
6717791641.98137.024
6818871656.85230.149
6920041709.66294.336
7020771849.04227.961
7120922048.643.3986
7220512165.23-114.226
7315771722.68-145.676
7413561547.41-191.414
7516521597.9854.0236
7613821484.79-102.789
7715191622.1-103.101
7814211566.35-145.351
7914421641.98-199.976
8015431656.85-113.851
8116561709.66-53.6639
8215611849.04-288.039
8319052048.6-143.601
8421992165.2333.7736
8514731722.68-249.676
8616551547.41107.586
8714071597.98-190.976
8813951484.79-89.7889
8915301622.1-92.1014
9013091566.35-257.351
9115261641.98-115.976
9213271656.85-329.851
9316271709.66-82.6639
9417481849.04-101.039
9519582048.6-90.6014
9622742165.23108.774
9716481722.68-74.6757
9814011547.41-146.414
9914111597.98-186.976
10014031484.79-81.7889
10113941622.1-228.101
10215201566.35-46.3514
10315281641.98-113.976
10416431656.85-13.8514
10515151709.66-194.664
10616851849.04-164.039
10720002048.6-48.6014
10822152165.2349.7736
10919561722.68233.324
11014621547.41-85.4139
11115631597.98-34.9764
11214591484.79-25.7889
11314461622.1-176.101
11416221566.3555.6486
11516571641.9815.0236
11616381656.85-18.8514
11716431709.66-66.6639
11816831849.04-166.039
11920502048.61.39861
12022622165.2396.7736
12118131722.6890.3243
12214451547.41-102.414
12317621597.98164.024
12414611484.79-23.7889
12515561622.1-66.1014
12614311566.35-135.351
12714271641.98-214.976
12815541656.85-102.851
12916451709.66-64.6639
13016531849.04-196.039
13120162048.6-32.6014
13222072165.2341.7736
13316651722.68-57.6757
13413611547.41-186.414
13515061597.98-91.9764
13613601484.79-124.789
13714531622.1-169.101
13815221566.35-44.3514
13914601641.98-181.976
14015521656.85-104.851
14115481709.66-161.664
14218271849.04-22.0389
14317372048.6-311.601
14419412165.23-224.226
14514741722.68-248.676
14614581547.41-89.4139
14715421597.98-55.9764
14814041484.79-80.7889
14915221622.1-100.101
15013851566.35-181.351
15116411641.98-0.976393
15215101656.85-146.851
15316811709.66-28.6639
15419381849.0488.9611
15518682048.6-180.601
15617262165.23-439.226
15714561722.68-266.676
15814451547.41-102.414
15914561597.98-141.976
16013651484.79-119.789
16114871622.1-135.101
16215581566.35-8.35139
16314881641.98-153.976
16416841656.8527.1486
16515941709.66-115.664
16618501849.040.961107
16719982048.6-50.6014
16820792165.23-86.2264
16914941722.68-228.676
17010571151.6-94.6027
17112181202.1715.8348
17211681088.9879.0223
17312361226.299.70975
17410761170.54-94.5402
17511741246.17-72.1652
17611391261.04-122.04
17714271313.85113.147
17814871453.2333.7723
17914831652.79-169.79
18015131769.42-256.415
18113571326.8630.1354
18211651151.613.3973
18312821202.1779.8348
18411101088.9821.0223
18512971226.2970.7098
18611851170.5414.4598
18712221246.17-24.1652
18812841261.0422.9598
18914441313.85130.147
19015751453.23121.772
19117371652.7984.2098
19217631769.42-6.41525

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1687 & 1722.68 & -35.6757 \tabularnewline
2 & 1508 & 1547.41 & -39.4139 \tabularnewline
3 & 1507 & 1597.98 & -90.9764 \tabularnewline
4 & 1385 & 1484.79 & -99.7889 \tabularnewline
5 & 1632 & 1622.1 & 9.89861 \tabularnewline
6 & 1511 & 1566.35 & -55.3514 \tabularnewline
7 & 1559 & 1641.98 & -82.9764 \tabularnewline
8 & 1630 & 1656.85 & -26.8514 \tabularnewline
9 & 1579 & 1709.66 & -130.664 \tabularnewline
10 & 1653 & 1849.04 & -196.039 \tabularnewline
11 & 2152 & 2048.6 & 103.399 \tabularnewline
12 & 2148 & 2165.23 & -17.2264 \tabularnewline
13 & 1752 & 1722.68 & 29.3243 \tabularnewline
14 & 1765 & 1547.41 & 217.586 \tabularnewline
15 & 1717 & 1597.98 & 119.024 \tabularnewline
16 & 1558 & 1484.79 & 73.2111 \tabularnewline
17 & 1575 & 1622.1 & -47.1014 \tabularnewline
18 & 1520 & 1566.35 & -46.3514 \tabularnewline
19 & 1805 & 1641.98 & 163.024 \tabularnewline
20 & 1800 & 1656.85 & 143.149 \tabularnewline
21 & 1719 & 1709.66 & 9.33611 \tabularnewline
22 & 2008 & 1849.04 & 158.961 \tabularnewline
23 & 2242 & 2048.6 & 193.399 \tabularnewline
24 & 2478 & 2165.23 & 312.774 \tabularnewline
25 & 2030 & 1722.68 & 307.324 \tabularnewline
26 & 1655 & 1547.41 & 107.586 \tabularnewline
27 & 1693 & 1597.98 & 95.0236 \tabularnewline
28 & 1623 & 1484.79 & 138.211 \tabularnewline
29 & 1805 & 1622.1 & 182.899 \tabularnewline
30 & 1746 & 1566.35 & 179.649 \tabularnewline
31 & 1795 & 1641.98 & 153.024 \tabularnewline
32 & 1926 & 1656.85 & 269.149 \tabularnewline
33 & 1619 & 1709.66 & -90.6639 \tabularnewline
34 & 1992 & 1849.04 & 142.961 \tabularnewline
35 & 2233 & 2048.6 & 184.399 \tabularnewline
36 & 2192 & 2165.23 & 26.7736 \tabularnewline
37 & 2080 & 1722.68 & 357.324 \tabularnewline
38 & 1768 & 1547.41 & 220.586 \tabularnewline
39 & 1835 & 1597.98 & 237.024 \tabularnewline
40 & 1569 & 1484.79 & 84.2111 \tabularnewline
41 & 1976 & 1622.1 & 353.899 \tabularnewline
42 & 1853 & 1566.35 & 286.649 \tabularnewline
43 & 1965 & 1641.98 & 323.024 \tabularnewline
44 & 1689 & 1656.85 & 32.1486 \tabularnewline
45 & 1778 & 1709.66 & 68.3361 \tabularnewline
46 & 1976 & 1849.04 & 126.961 \tabularnewline
47 & 2397 & 2048.6 & 348.399 \tabularnewline
48 & 2654 & 2165.23 & 488.774 \tabularnewline
49 & 2097 & 1722.68 & 374.324 \tabularnewline
50 & 1963 & 1547.41 & 415.586 \tabularnewline
51 & 1677 & 1597.98 & 79.0236 \tabularnewline
52 & 1941 & 1484.79 & 456.211 \tabularnewline
53 & 2003 & 1622.1 & 380.899 \tabularnewline
54 & 1813 & 1566.35 & 246.649 \tabularnewline
55 & 2012 & 1641.98 & 370.024 \tabularnewline
56 & 1912 & 1656.85 & 255.149 \tabularnewline
57 & 2084 & 1709.66 & 374.336 \tabularnewline
58 & 2080 & 1849.04 & 230.961 \tabularnewline
59 & 2118 & 2048.6 & 69.3986 \tabularnewline
60 & 2150 & 2165.23 & -15.2264 \tabularnewline
61 & 1608 & 1722.68 & -114.676 \tabularnewline
62 & 1503 & 1547.41 & -44.4139 \tabularnewline
63 & 1548 & 1597.98 & -49.9764 \tabularnewline
64 & 1382 & 1484.79 & -102.789 \tabularnewline
65 & 1731 & 1622.1 & 108.899 \tabularnewline
66 & 1798 & 1566.35 & 231.649 \tabularnewline
67 & 1779 & 1641.98 & 137.024 \tabularnewline
68 & 1887 & 1656.85 & 230.149 \tabularnewline
69 & 2004 & 1709.66 & 294.336 \tabularnewline
70 & 2077 & 1849.04 & 227.961 \tabularnewline
71 & 2092 & 2048.6 & 43.3986 \tabularnewline
72 & 2051 & 2165.23 & -114.226 \tabularnewline
73 & 1577 & 1722.68 & -145.676 \tabularnewline
74 & 1356 & 1547.41 & -191.414 \tabularnewline
75 & 1652 & 1597.98 & 54.0236 \tabularnewline
76 & 1382 & 1484.79 & -102.789 \tabularnewline
77 & 1519 & 1622.1 & -103.101 \tabularnewline
78 & 1421 & 1566.35 & -145.351 \tabularnewline
79 & 1442 & 1641.98 & -199.976 \tabularnewline
80 & 1543 & 1656.85 & -113.851 \tabularnewline
81 & 1656 & 1709.66 & -53.6639 \tabularnewline
82 & 1561 & 1849.04 & -288.039 \tabularnewline
83 & 1905 & 2048.6 & -143.601 \tabularnewline
84 & 2199 & 2165.23 & 33.7736 \tabularnewline
85 & 1473 & 1722.68 & -249.676 \tabularnewline
86 & 1655 & 1547.41 & 107.586 \tabularnewline
87 & 1407 & 1597.98 & -190.976 \tabularnewline
88 & 1395 & 1484.79 & -89.7889 \tabularnewline
89 & 1530 & 1622.1 & -92.1014 \tabularnewline
90 & 1309 & 1566.35 & -257.351 \tabularnewline
91 & 1526 & 1641.98 & -115.976 \tabularnewline
92 & 1327 & 1656.85 & -329.851 \tabularnewline
93 & 1627 & 1709.66 & -82.6639 \tabularnewline
94 & 1748 & 1849.04 & -101.039 \tabularnewline
95 & 1958 & 2048.6 & -90.6014 \tabularnewline
96 & 2274 & 2165.23 & 108.774 \tabularnewline
97 & 1648 & 1722.68 & -74.6757 \tabularnewline
98 & 1401 & 1547.41 & -146.414 \tabularnewline
99 & 1411 & 1597.98 & -186.976 \tabularnewline
100 & 1403 & 1484.79 & -81.7889 \tabularnewline
101 & 1394 & 1622.1 & -228.101 \tabularnewline
102 & 1520 & 1566.35 & -46.3514 \tabularnewline
103 & 1528 & 1641.98 & -113.976 \tabularnewline
104 & 1643 & 1656.85 & -13.8514 \tabularnewline
105 & 1515 & 1709.66 & -194.664 \tabularnewline
106 & 1685 & 1849.04 & -164.039 \tabularnewline
107 & 2000 & 2048.6 & -48.6014 \tabularnewline
108 & 2215 & 2165.23 & 49.7736 \tabularnewline
109 & 1956 & 1722.68 & 233.324 \tabularnewline
110 & 1462 & 1547.41 & -85.4139 \tabularnewline
111 & 1563 & 1597.98 & -34.9764 \tabularnewline
112 & 1459 & 1484.79 & -25.7889 \tabularnewline
113 & 1446 & 1622.1 & -176.101 \tabularnewline
114 & 1622 & 1566.35 & 55.6486 \tabularnewline
115 & 1657 & 1641.98 & 15.0236 \tabularnewline
116 & 1638 & 1656.85 & -18.8514 \tabularnewline
117 & 1643 & 1709.66 & -66.6639 \tabularnewline
118 & 1683 & 1849.04 & -166.039 \tabularnewline
119 & 2050 & 2048.6 & 1.39861 \tabularnewline
120 & 2262 & 2165.23 & 96.7736 \tabularnewline
121 & 1813 & 1722.68 & 90.3243 \tabularnewline
122 & 1445 & 1547.41 & -102.414 \tabularnewline
123 & 1762 & 1597.98 & 164.024 \tabularnewline
124 & 1461 & 1484.79 & -23.7889 \tabularnewline
125 & 1556 & 1622.1 & -66.1014 \tabularnewline
126 & 1431 & 1566.35 & -135.351 \tabularnewline
127 & 1427 & 1641.98 & -214.976 \tabularnewline
128 & 1554 & 1656.85 & -102.851 \tabularnewline
129 & 1645 & 1709.66 & -64.6639 \tabularnewline
130 & 1653 & 1849.04 & -196.039 \tabularnewline
131 & 2016 & 2048.6 & -32.6014 \tabularnewline
132 & 2207 & 2165.23 & 41.7736 \tabularnewline
133 & 1665 & 1722.68 & -57.6757 \tabularnewline
134 & 1361 & 1547.41 & -186.414 \tabularnewline
135 & 1506 & 1597.98 & -91.9764 \tabularnewline
136 & 1360 & 1484.79 & -124.789 \tabularnewline
137 & 1453 & 1622.1 & -169.101 \tabularnewline
138 & 1522 & 1566.35 & -44.3514 \tabularnewline
139 & 1460 & 1641.98 & -181.976 \tabularnewline
140 & 1552 & 1656.85 & -104.851 \tabularnewline
141 & 1548 & 1709.66 & -161.664 \tabularnewline
142 & 1827 & 1849.04 & -22.0389 \tabularnewline
143 & 1737 & 2048.6 & -311.601 \tabularnewline
144 & 1941 & 2165.23 & -224.226 \tabularnewline
145 & 1474 & 1722.68 & -248.676 \tabularnewline
146 & 1458 & 1547.41 & -89.4139 \tabularnewline
147 & 1542 & 1597.98 & -55.9764 \tabularnewline
148 & 1404 & 1484.79 & -80.7889 \tabularnewline
149 & 1522 & 1622.1 & -100.101 \tabularnewline
150 & 1385 & 1566.35 & -181.351 \tabularnewline
151 & 1641 & 1641.98 & -0.976393 \tabularnewline
152 & 1510 & 1656.85 & -146.851 \tabularnewline
153 & 1681 & 1709.66 & -28.6639 \tabularnewline
154 & 1938 & 1849.04 & 88.9611 \tabularnewline
155 & 1868 & 2048.6 & -180.601 \tabularnewline
156 & 1726 & 2165.23 & -439.226 \tabularnewline
157 & 1456 & 1722.68 & -266.676 \tabularnewline
158 & 1445 & 1547.41 & -102.414 \tabularnewline
159 & 1456 & 1597.98 & -141.976 \tabularnewline
160 & 1365 & 1484.79 & -119.789 \tabularnewline
161 & 1487 & 1622.1 & -135.101 \tabularnewline
162 & 1558 & 1566.35 & -8.35139 \tabularnewline
163 & 1488 & 1641.98 & -153.976 \tabularnewline
164 & 1684 & 1656.85 & 27.1486 \tabularnewline
165 & 1594 & 1709.66 & -115.664 \tabularnewline
166 & 1850 & 1849.04 & 0.961107 \tabularnewline
167 & 1998 & 2048.6 & -50.6014 \tabularnewline
168 & 2079 & 2165.23 & -86.2264 \tabularnewline
169 & 1494 & 1722.68 & -228.676 \tabularnewline
170 & 1057 & 1151.6 & -94.6027 \tabularnewline
171 & 1218 & 1202.17 & 15.8348 \tabularnewline
172 & 1168 & 1088.98 & 79.0223 \tabularnewline
173 & 1236 & 1226.29 & 9.70975 \tabularnewline
174 & 1076 & 1170.54 & -94.5402 \tabularnewline
175 & 1174 & 1246.17 & -72.1652 \tabularnewline
176 & 1139 & 1261.04 & -122.04 \tabularnewline
177 & 1427 & 1313.85 & 113.147 \tabularnewline
178 & 1487 & 1453.23 & 33.7723 \tabularnewline
179 & 1483 & 1652.79 & -169.79 \tabularnewline
180 & 1513 & 1769.42 & -256.415 \tabularnewline
181 & 1357 & 1326.86 & 30.1354 \tabularnewline
182 & 1165 & 1151.6 & 13.3973 \tabularnewline
183 & 1282 & 1202.17 & 79.8348 \tabularnewline
184 & 1110 & 1088.98 & 21.0223 \tabularnewline
185 & 1297 & 1226.29 & 70.7098 \tabularnewline
186 & 1185 & 1170.54 & 14.4598 \tabularnewline
187 & 1222 & 1246.17 & -24.1652 \tabularnewline
188 & 1284 & 1261.04 & 22.9598 \tabularnewline
189 & 1444 & 1313.85 & 130.147 \tabularnewline
190 & 1575 & 1453.23 & 121.772 \tabularnewline
191 & 1737 & 1652.79 & 84.2098 \tabularnewline
192 & 1763 & 1769.42 & -6.41525 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=222039&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]1722.68[/C][C]-35.6757[/C][/ROW]
[ROW][C]2[/C][C]1508[/C][C]1547.41[/C][C]-39.4139[/C][/ROW]
[ROW][C]3[/C][C]1507[/C][C]1597.98[/C][C]-90.9764[/C][/ROW]
[ROW][C]4[/C][C]1385[/C][C]1484.79[/C][C]-99.7889[/C][/ROW]
[ROW][C]5[/C][C]1632[/C][C]1622.1[/C][C]9.89861[/C][/ROW]
[ROW][C]6[/C][C]1511[/C][C]1566.35[/C][C]-55.3514[/C][/ROW]
[ROW][C]7[/C][C]1559[/C][C]1641.98[/C][C]-82.9764[/C][/ROW]
[ROW][C]8[/C][C]1630[/C][C]1656.85[/C][C]-26.8514[/C][/ROW]
[ROW][C]9[/C][C]1579[/C][C]1709.66[/C][C]-130.664[/C][/ROW]
[ROW][C]10[/C][C]1653[/C][C]1849.04[/C][C]-196.039[/C][/ROW]
[ROW][C]11[/C][C]2152[/C][C]2048.6[/C][C]103.399[/C][/ROW]
[ROW][C]12[/C][C]2148[/C][C]2165.23[/C][C]-17.2264[/C][/ROW]
[ROW][C]13[/C][C]1752[/C][C]1722.68[/C][C]29.3243[/C][/ROW]
[ROW][C]14[/C][C]1765[/C][C]1547.41[/C][C]217.586[/C][/ROW]
[ROW][C]15[/C][C]1717[/C][C]1597.98[/C][C]119.024[/C][/ROW]
[ROW][C]16[/C][C]1558[/C][C]1484.79[/C][C]73.2111[/C][/ROW]
[ROW][C]17[/C][C]1575[/C][C]1622.1[/C][C]-47.1014[/C][/ROW]
[ROW][C]18[/C][C]1520[/C][C]1566.35[/C][C]-46.3514[/C][/ROW]
[ROW][C]19[/C][C]1805[/C][C]1641.98[/C][C]163.024[/C][/ROW]
[ROW][C]20[/C][C]1800[/C][C]1656.85[/C][C]143.149[/C][/ROW]
[ROW][C]21[/C][C]1719[/C][C]1709.66[/C][C]9.33611[/C][/ROW]
[ROW][C]22[/C][C]2008[/C][C]1849.04[/C][C]158.961[/C][/ROW]
[ROW][C]23[/C][C]2242[/C][C]2048.6[/C][C]193.399[/C][/ROW]
[ROW][C]24[/C][C]2478[/C][C]2165.23[/C][C]312.774[/C][/ROW]
[ROW][C]25[/C][C]2030[/C][C]1722.68[/C][C]307.324[/C][/ROW]
[ROW][C]26[/C][C]1655[/C][C]1547.41[/C][C]107.586[/C][/ROW]
[ROW][C]27[/C][C]1693[/C][C]1597.98[/C][C]95.0236[/C][/ROW]
[ROW][C]28[/C][C]1623[/C][C]1484.79[/C][C]138.211[/C][/ROW]
[ROW][C]29[/C][C]1805[/C][C]1622.1[/C][C]182.899[/C][/ROW]
[ROW][C]30[/C][C]1746[/C][C]1566.35[/C][C]179.649[/C][/ROW]
[ROW][C]31[/C][C]1795[/C][C]1641.98[/C][C]153.024[/C][/ROW]
[ROW][C]32[/C][C]1926[/C][C]1656.85[/C][C]269.149[/C][/ROW]
[ROW][C]33[/C][C]1619[/C][C]1709.66[/C][C]-90.6639[/C][/ROW]
[ROW][C]34[/C][C]1992[/C][C]1849.04[/C][C]142.961[/C][/ROW]
[ROW][C]35[/C][C]2233[/C][C]2048.6[/C][C]184.399[/C][/ROW]
[ROW][C]36[/C][C]2192[/C][C]2165.23[/C][C]26.7736[/C][/ROW]
[ROW][C]37[/C][C]2080[/C][C]1722.68[/C][C]357.324[/C][/ROW]
[ROW][C]38[/C][C]1768[/C][C]1547.41[/C][C]220.586[/C][/ROW]
[ROW][C]39[/C][C]1835[/C][C]1597.98[/C][C]237.024[/C][/ROW]
[ROW][C]40[/C][C]1569[/C][C]1484.79[/C][C]84.2111[/C][/ROW]
[ROW][C]41[/C][C]1976[/C][C]1622.1[/C][C]353.899[/C][/ROW]
[ROW][C]42[/C][C]1853[/C][C]1566.35[/C][C]286.649[/C][/ROW]
[ROW][C]43[/C][C]1965[/C][C]1641.98[/C][C]323.024[/C][/ROW]
[ROW][C]44[/C][C]1689[/C][C]1656.85[/C][C]32.1486[/C][/ROW]
[ROW][C]45[/C][C]1778[/C][C]1709.66[/C][C]68.3361[/C][/ROW]
[ROW][C]46[/C][C]1976[/C][C]1849.04[/C][C]126.961[/C][/ROW]
[ROW][C]47[/C][C]2397[/C][C]2048.6[/C][C]348.399[/C][/ROW]
[ROW][C]48[/C][C]2654[/C][C]2165.23[/C][C]488.774[/C][/ROW]
[ROW][C]49[/C][C]2097[/C][C]1722.68[/C][C]374.324[/C][/ROW]
[ROW][C]50[/C][C]1963[/C][C]1547.41[/C][C]415.586[/C][/ROW]
[ROW][C]51[/C][C]1677[/C][C]1597.98[/C][C]79.0236[/C][/ROW]
[ROW][C]52[/C][C]1941[/C][C]1484.79[/C][C]456.211[/C][/ROW]
[ROW][C]53[/C][C]2003[/C][C]1622.1[/C][C]380.899[/C][/ROW]
[ROW][C]54[/C][C]1813[/C][C]1566.35[/C][C]246.649[/C][/ROW]
[ROW][C]55[/C][C]2012[/C][C]1641.98[/C][C]370.024[/C][/ROW]
[ROW][C]56[/C][C]1912[/C][C]1656.85[/C][C]255.149[/C][/ROW]
[ROW][C]57[/C][C]2084[/C][C]1709.66[/C][C]374.336[/C][/ROW]
[ROW][C]58[/C][C]2080[/C][C]1849.04[/C][C]230.961[/C][/ROW]
[ROW][C]59[/C][C]2118[/C][C]2048.6[/C][C]69.3986[/C][/ROW]
[ROW][C]60[/C][C]2150[/C][C]2165.23[/C][C]-15.2264[/C][/ROW]
[ROW][C]61[/C][C]1608[/C][C]1722.68[/C][C]-114.676[/C][/ROW]
[ROW][C]62[/C][C]1503[/C][C]1547.41[/C][C]-44.4139[/C][/ROW]
[ROW][C]63[/C][C]1548[/C][C]1597.98[/C][C]-49.9764[/C][/ROW]
[ROW][C]64[/C][C]1382[/C][C]1484.79[/C][C]-102.789[/C][/ROW]
[ROW][C]65[/C][C]1731[/C][C]1622.1[/C][C]108.899[/C][/ROW]
[ROW][C]66[/C][C]1798[/C][C]1566.35[/C][C]231.649[/C][/ROW]
[ROW][C]67[/C][C]1779[/C][C]1641.98[/C][C]137.024[/C][/ROW]
[ROW][C]68[/C][C]1887[/C][C]1656.85[/C][C]230.149[/C][/ROW]
[ROW][C]69[/C][C]2004[/C][C]1709.66[/C][C]294.336[/C][/ROW]
[ROW][C]70[/C][C]2077[/C][C]1849.04[/C][C]227.961[/C][/ROW]
[ROW][C]71[/C][C]2092[/C][C]2048.6[/C][C]43.3986[/C][/ROW]
[ROW][C]72[/C][C]2051[/C][C]2165.23[/C][C]-114.226[/C][/ROW]
[ROW][C]73[/C][C]1577[/C][C]1722.68[/C][C]-145.676[/C][/ROW]
[ROW][C]74[/C][C]1356[/C][C]1547.41[/C][C]-191.414[/C][/ROW]
[ROW][C]75[/C][C]1652[/C][C]1597.98[/C][C]54.0236[/C][/ROW]
[ROW][C]76[/C][C]1382[/C][C]1484.79[/C][C]-102.789[/C][/ROW]
[ROW][C]77[/C][C]1519[/C][C]1622.1[/C][C]-103.101[/C][/ROW]
[ROW][C]78[/C][C]1421[/C][C]1566.35[/C][C]-145.351[/C][/ROW]
[ROW][C]79[/C][C]1442[/C][C]1641.98[/C][C]-199.976[/C][/ROW]
[ROW][C]80[/C][C]1543[/C][C]1656.85[/C][C]-113.851[/C][/ROW]
[ROW][C]81[/C][C]1656[/C][C]1709.66[/C][C]-53.6639[/C][/ROW]
[ROW][C]82[/C][C]1561[/C][C]1849.04[/C][C]-288.039[/C][/ROW]
[ROW][C]83[/C][C]1905[/C][C]2048.6[/C][C]-143.601[/C][/ROW]
[ROW][C]84[/C][C]2199[/C][C]2165.23[/C][C]33.7736[/C][/ROW]
[ROW][C]85[/C][C]1473[/C][C]1722.68[/C][C]-249.676[/C][/ROW]
[ROW][C]86[/C][C]1655[/C][C]1547.41[/C][C]107.586[/C][/ROW]
[ROW][C]87[/C][C]1407[/C][C]1597.98[/C][C]-190.976[/C][/ROW]
[ROW][C]88[/C][C]1395[/C][C]1484.79[/C][C]-89.7889[/C][/ROW]
[ROW][C]89[/C][C]1530[/C][C]1622.1[/C][C]-92.1014[/C][/ROW]
[ROW][C]90[/C][C]1309[/C][C]1566.35[/C][C]-257.351[/C][/ROW]
[ROW][C]91[/C][C]1526[/C][C]1641.98[/C][C]-115.976[/C][/ROW]
[ROW][C]92[/C][C]1327[/C][C]1656.85[/C][C]-329.851[/C][/ROW]
[ROW][C]93[/C][C]1627[/C][C]1709.66[/C][C]-82.6639[/C][/ROW]
[ROW][C]94[/C][C]1748[/C][C]1849.04[/C][C]-101.039[/C][/ROW]
[ROW][C]95[/C][C]1958[/C][C]2048.6[/C][C]-90.6014[/C][/ROW]
[ROW][C]96[/C][C]2274[/C][C]2165.23[/C][C]108.774[/C][/ROW]
[ROW][C]97[/C][C]1648[/C][C]1722.68[/C][C]-74.6757[/C][/ROW]
[ROW][C]98[/C][C]1401[/C][C]1547.41[/C][C]-146.414[/C][/ROW]
[ROW][C]99[/C][C]1411[/C][C]1597.98[/C][C]-186.976[/C][/ROW]
[ROW][C]100[/C][C]1403[/C][C]1484.79[/C][C]-81.7889[/C][/ROW]
[ROW][C]101[/C][C]1394[/C][C]1622.1[/C][C]-228.101[/C][/ROW]
[ROW][C]102[/C][C]1520[/C][C]1566.35[/C][C]-46.3514[/C][/ROW]
[ROW][C]103[/C][C]1528[/C][C]1641.98[/C][C]-113.976[/C][/ROW]
[ROW][C]104[/C][C]1643[/C][C]1656.85[/C][C]-13.8514[/C][/ROW]
[ROW][C]105[/C][C]1515[/C][C]1709.66[/C][C]-194.664[/C][/ROW]
[ROW][C]106[/C][C]1685[/C][C]1849.04[/C][C]-164.039[/C][/ROW]
[ROW][C]107[/C][C]2000[/C][C]2048.6[/C][C]-48.6014[/C][/ROW]
[ROW][C]108[/C][C]2215[/C][C]2165.23[/C][C]49.7736[/C][/ROW]
[ROW][C]109[/C][C]1956[/C][C]1722.68[/C][C]233.324[/C][/ROW]
[ROW][C]110[/C][C]1462[/C][C]1547.41[/C][C]-85.4139[/C][/ROW]
[ROW][C]111[/C][C]1563[/C][C]1597.98[/C][C]-34.9764[/C][/ROW]
[ROW][C]112[/C][C]1459[/C][C]1484.79[/C][C]-25.7889[/C][/ROW]
[ROW][C]113[/C][C]1446[/C][C]1622.1[/C][C]-176.101[/C][/ROW]
[ROW][C]114[/C][C]1622[/C][C]1566.35[/C][C]55.6486[/C][/ROW]
[ROW][C]115[/C][C]1657[/C][C]1641.98[/C][C]15.0236[/C][/ROW]
[ROW][C]116[/C][C]1638[/C][C]1656.85[/C][C]-18.8514[/C][/ROW]
[ROW][C]117[/C][C]1643[/C][C]1709.66[/C][C]-66.6639[/C][/ROW]
[ROW][C]118[/C][C]1683[/C][C]1849.04[/C][C]-166.039[/C][/ROW]
[ROW][C]119[/C][C]2050[/C][C]2048.6[/C][C]1.39861[/C][/ROW]
[ROW][C]120[/C][C]2262[/C][C]2165.23[/C][C]96.7736[/C][/ROW]
[ROW][C]121[/C][C]1813[/C][C]1722.68[/C][C]90.3243[/C][/ROW]
[ROW][C]122[/C][C]1445[/C][C]1547.41[/C][C]-102.414[/C][/ROW]
[ROW][C]123[/C][C]1762[/C][C]1597.98[/C][C]164.024[/C][/ROW]
[ROW][C]124[/C][C]1461[/C][C]1484.79[/C][C]-23.7889[/C][/ROW]
[ROW][C]125[/C][C]1556[/C][C]1622.1[/C][C]-66.1014[/C][/ROW]
[ROW][C]126[/C][C]1431[/C][C]1566.35[/C][C]-135.351[/C][/ROW]
[ROW][C]127[/C][C]1427[/C][C]1641.98[/C][C]-214.976[/C][/ROW]
[ROW][C]128[/C][C]1554[/C][C]1656.85[/C][C]-102.851[/C][/ROW]
[ROW][C]129[/C][C]1645[/C][C]1709.66[/C][C]-64.6639[/C][/ROW]
[ROW][C]130[/C][C]1653[/C][C]1849.04[/C][C]-196.039[/C][/ROW]
[ROW][C]131[/C][C]2016[/C][C]2048.6[/C][C]-32.6014[/C][/ROW]
[ROW][C]132[/C][C]2207[/C][C]2165.23[/C][C]41.7736[/C][/ROW]
[ROW][C]133[/C][C]1665[/C][C]1722.68[/C][C]-57.6757[/C][/ROW]
[ROW][C]134[/C][C]1361[/C][C]1547.41[/C][C]-186.414[/C][/ROW]
[ROW][C]135[/C][C]1506[/C][C]1597.98[/C][C]-91.9764[/C][/ROW]
[ROW][C]136[/C][C]1360[/C][C]1484.79[/C][C]-124.789[/C][/ROW]
[ROW][C]137[/C][C]1453[/C][C]1622.1[/C][C]-169.101[/C][/ROW]
[ROW][C]138[/C][C]1522[/C][C]1566.35[/C][C]-44.3514[/C][/ROW]
[ROW][C]139[/C][C]1460[/C][C]1641.98[/C][C]-181.976[/C][/ROW]
[ROW][C]140[/C][C]1552[/C][C]1656.85[/C][C]-104.851[/C][/ROW]
[ROW][C]141[/C][C]1548[/C][C]1709.66[/C][C]-161.664[/C][/ROW]
[ROW][C]142[/C][C]1827[/C][C]1849.04[/C][C]-22.0389[/C][/ROW]
[ROW][C]143[/C][C]1737[/C][C]2048.6[/C][C]-311.601[/C][/ROW]
[ROW][C]144[/C][C]1941[/C][C]2165.23[/C][C]-224.226[/C][/ROW]
[ROW][C]145[/C][C]1474[/C][C]1722.68[/C][C]-248.676[/C][/ROW]
[ROW][C]146[/C][C]1458[/C][C]1547.41[/C][C]-89.4139[/C][/ROW]
[ROW][C]147[/C][C]1542[/C][C]1597.98[/C][C]-55.9764[/C][/ROW]
[ROW][C]148[/C][C]1404[/C][C]1484.79[/C][C]-80.7889[/C][/ROW]
[ROW][C]149[/C][C]1522[/C][C]1622.1[/C][C]-100.101[/C][/ROW]
[ROW][C]150[/C][C]1385[/C][C]1566.35[/C][C]-181.351[/C][/ROW]
[ROW][C]151[/C][C]1641[/C][C]1641.98[/C][C]-0.976393[/C][/ROW]
[ROW][C]152[/C][C]1510[/C][C]1656.85[/C][C]-146.851[/C][/ROW]
[ROW][C]153[/C][C]1681[/C][C]1709.66[/C][C]-28.6639[/C][/ROW]
[ROW][C]154[/C][C]1938[/C][C]1849.04[/C][C]88.9611[/C][/ROW]
[ROW][C]155[/C][C]1868[/C][C]2048.6[/C][C]-180.601[/C][/ROW]
[ROW][C]156[/C][C]1726[/C][C]2165.23[/C][C]-439.226[/C][/ROW]
[ROW][C]157[/C][C]1456[/C][C]1722.68[/C][C]-266.676[/C][/ROW]
[ROW][C]158[/C][C]1445[/C][C]1547.41[/C][C]-102.414[/C][/ROW]
[ROW][C]159[/C][C]1456[/C][C]1597.98[/C][C]-141.976[/C][/ROW]
[ROW][C]160[/C][C]1365[/C][C]1484.79[/C][C]-119.789[/C][/ROW]
[ROW][C]161[/C][C]1487[/C][C]1622.1[/C][C]-135.101[/C][/ROW]
[ROW][C]162[/C][C]1558[/C][C]1566.35[/C][C]-8.35139[/C][/ROW]
[ROW][C]163[/C][C]1488[/C][C]1641.98[/C][C]-153.976[/C][/ROW]
[ROW][C]164[/C][C]1684[/C][C]1656.85[/C][C]27.1486[/C][/ROW]
[ROW][C]165[/C][C]1594[/C][C]1709.66[/C][C]-115.664[/C][/ROW]
[ROW][C]166[/C][C]1850[/C][C]1849.04[/C][C]0.961107[/C][/ROW]
[ROW][C]167[/C][C]1998[/C][C]2048.6[/C][C]-50.6014[/C][/ROW]
[ROW][C]168[/C][C]2079[/C][C]2165.23[/C][C]-86.2264[/C][/ROW]
[ROW][C]169[/C][C]1494[/C][C]1722.68[/C][C]-228.676[/C][/ROW]
[ROW][C]170[/C][C]1057[/C][C]1151.6[/C][C]-94.6027[/C][/ROW]
[ROW][C]171[/C][C]1218[/C][C]1202.17[/C][C]15.8348[/C][/ROW]
[ROW][C]172[/C][C]1168[/C][C]1088.98[/C][C]79.0223[/C][/ROW]
[ROW][C]173[/C][C]1236[/C][C]1226.29[/C][C]9.70975[/C][/ROW]
[ROW][C]174[/C][C]1076[/C][C]1170.54[/C][C]-94.5402[/C][/ROW]
[ROW][C]175[/C][C]1174[/C][C]1246.17[/C][C]-72.1652[/C][/ROW]
[ROW][C]176[/C][C]1139[/C][C]1261.04[/C][C]-122.04[/C][/ROW]
[ROW][C]177[/C][C]1427[/C][C]1313.85[/C][C]113.147[/C][/ROW]
[ROW][C]178[/C][C]1487[/C][C]1453.23[/C][C]33.7723[/C][/ROW]
[ROW][C]179[/C][C]1483[/C][C]1652.79[/C][C]-169.79[/C][/ROW]
[ROW][C]180[/C][C]1513[/C][C]1769.42[/C][C]-256.415[/C][/ROW]
[ROW][C]181[/C][C]1357[/C][C]1326.86[/C][C]30.1354[/C][/ROW]
[ROW][C]182[/C][C]1165[/C][C]1151.6[/C][C]13.3973[/C][/ROW]
[ROW][C]183[/C][C]1282[/C][C]1202.17[/C][C]79.8348[/C][/ROW]
[ROW][C]184[/C][C]1110[/C][C]1088.98[/C][C]21.0223[/C][/ROW]
[ROW][C]185[/C][C]1297[/C][C]1226.29[/C][C]70.7098[/C][/ROW]
[ROW][C]186[/C][C]1185[/C][C]1170.54[/C][C]14.4598[/C][/ROW]
[ROW][C]187[/C][C]1222[/C][C]1246.17[/C][C]-24.1652[/C][/ROW]
[ROW][C]188[/C][C]1284[/C][C]1261.04[/C][C]22.9598[/C][/ROW]
[ROW][C]189[/C][C]1444[/C][C]1313.85[/C][C]130.147[/C][/ROW]
[ROW][C]190[/C][C]1575[/C][C]1453.23[/C][C]121.772[/C][/ROW]
[ROW][C]191[/C][C]1737[/C][C]1652.79[/C][C]84.2098[/C][/ROW]
[ROW][C]192[/C][C]1763[/C][C]1769.42[/C][C]-6.41525[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=222039&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=222039&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
116871722.68-35.6757
215081547.41-39.4139
315071597.98-90.9764
413851484.79-99.7889
516321622.19.89861
615111566.35-55.3514
715591641.98-82.9764
816301656.85-26.8514
915791709.66-130.664
1016531849.04-196.039
1121522048.6103.399
1221482165.23-17.2264
1317521722.6829.3243
1417651547.41217.586
1517171597.98119.024
1615581484.7973.2111
1715751622.1-47.1014
1815201566.35-46.3514
1918051641.98163.024
2018001656.85143.149
2117191709.669.33611
2220081849.04158.961
2322422048.6193.399
2424782165.23312.774
2520301722.68307.324
2616551547.41107.586
2716931597.9895.0236
2816231484.79138.211
2918051622.1182.899
3017461566.35179.649
3117951641.98153.024
3219261656.85269.149
3316191709.66-90.6639
3419921849.04142.961
3522332048.6184.399
3621922165.2326.7736
3720801722.68357.324
3817681547.41220.586
3918351597.98237.024
4015691484.7984.2111
4119761622.1353.899
4218531566.35286.649
4319651641.98323.024
4416891656.8532.1486
4517781709.6668.3361
4619761849.04126.961
4723972048.6348.399
4826542165.23488.774
4920971722.68374.324
5019631547.41415.586
5116771597.9879.0236
5219411484.79456.211
5320031622.1380.899
5418131566.35246.649
5520121641.98370.024
5619121656.85255.149
5720841709.66374.336
5820801849.04230.961
5921182048.669.3986
6021502165.23-15.2264
6116081722.68-114.676
6215031547.41-44.4139
6315481597.98-49.9764
6413821484.79-102.789
6517311622.1108.899
6617981566.35231.649
6717791641.98137.024
6818871656.85230.149
6920041709.66294.336
7020771849.04227.961
7120922048.643.3986
7220512165.23-114.226
7315771722.68-145.676
7413561547.41-191.414
7516521597.9854.0236
7613821484.79-102.789
7715191622.1-103.101
7814211566.35-145.351
7914421641.98-199.976
8015431656.85-113.851
8116561709.66-53.6639
8215611849.04-288.039
8319052048.6-143.601
8421992165.2333.7736
8514731722.68-249.676
8616551547.41107.586
8714071597.98-190.976
8813951484.79-89.7889
8915301622.1-92.1014
9013091566.35-257.351
9115261641.98-115.976
9213271656.85-329.851
9316271709.66-82.6639
9417481849.04-101.039
9519582048.6-90.6014
9622742165.23108.774
9716481722.68-74.6757
9814011547.41-146.414
9914111597.98-186.976
10014031484.79-81.7889
10113941622.1-228.101
10215201566.35-46.3514
10315281641.98-113.976
10416431656.85-13.8514
10515151709.66-194.664
10616851849.04-164.039
10720002048.6-48.6014
10822152165.2349.7736
10919561722.68233.324
11014621547.41-85.4139
11115631597.98-34.9764
11214591484.79-25.7889
11314461622.1-176.101
11416221566.3555.6486
11516571641.9815.0236
11616381656.85-18.8514
11716431709.66-66.6639
11816831849.04-166.039
11920502048.61.39861
12022622165.2396.7736
12118131722.6890.3243
12214451547.41-102.414
12317621597.98164.024
12414611484.79-23.7889
12515561622.1-66.1014
12614311566.35-135.351
12714271641.98-214.976
12815541656.85-102.851
12916451709.66-64.6639
13016531849.04-196.039
13120162048.6-32.6014
13222072165.2341.7736
13316651722.68-57.6757
13413611547.41-186.414
13515061597.98-91.9764
13613601484.79-124.789
13714531622.1-169.101
13815221566.35-44.3514
13914601641.98-181.976
14015521656.85-104.851
14115481709.66-161.664
14218271849.04-22.0389
14317372048.6-311.601
14419412165.23-224.226
14514741722.68-248.676
14614581547.41-89.4139
14715421597.98-55.9764
14814041484.79-80.7889
14915221622.1-100.101
15013851566.35-181.351
15116411641.98-0.976393
15215101656.85-146.851
15316811709.66-28.6639
15419381849.0488.9611
15518682048.6-180.601
15617262165.23-439.226
15714561722.68-266.676
15814451547.41-102.414
15914561597.98-141.976
16013651484.79-119.789
16114871622.1-135.101
16215581566.35-8.35139
16314881641.98-153.976
16416841656.8527.1486
16515941709.66-115.664
16618501849.040.961107
16719982048.6-50.6014
16820792165.23-86.2264
16914941722.68-228.676
17010571151.6-94.6027
17112181202.1715.8348
17211681088.9879.0223
17312361226.299.70975
17410761170.54-94.5402
17511741246.17-72.1652
17611391261.04-122.04
17714271313.85113.147
17814871453.2333.7723
17914831652.79-169.79
18015131769.42-256.415
18113571326.8630.1354
18211651151.613.3973
18312821202.1779.8348
18411101088.9821.0223
18512971226.2970.7098
18611851170.5414.4598
18712221246.17-24.1652
18812841261.0422.9598
18914441313.85130.147
19015751453.23121.772
19117371652.7984.2098
19217631769.42-6.41525






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.4774120.9548250.522588
170.3177830.6355660.682217
180.1918850.383770.808115
190.2194960.4389930.780504
200.1828710.3657430.817129
210.1388510.2777010.861149
220.2470670.4941340.752933
230.1865730.3731460.813427
240.2681740.5363490.731826
250.3753170.7506350.624683
260.2984710.5969420.701529
270.2399250.4798510.760075
280.2130450.426090.786955
290.2137690.4275380.786231
300.2297750.459550.770225
310.1947720.3895430.805228
320.2077650.415530.792235
330.1621570.3243130.837843
340.1481160.2962330.851884
350.1178310.2356630.882169
360.09738210.1947640.902618
370.1414010.2828020.858599
380.1297820.2595650.870218
390.1397730.2795460.860227
400.1105850.2211710.889415
410.1841250.3682490.815875
420.2348990.4697990.765101
430.291650.58330.70835
440.2552540.5105090.744746
450.2319340.4638680.768066
460.203910.4078190.79609
470.2431480.4862950.756852
480.4738880.9477760.526112
490.5691990.8616030.430801
500.7264190.5471620.273581
510.6883970.6232060.311603
520.8882420.2235150.111758
530.9448770.1102450.0551225
540.9546550.09069090.0453454
550.9811150.03776960.0188848
560.9865140.02697210.013486
570.9978480.0043040.002152
580.9985630.002874210.00143711
590.99850.002999420.00149971
600.9985430.002913560.00145678
610.9990720.001856540.000928269
620.9991150.001769790.000884896
630.9988720.002256190.00112809
640.998910.002179310.00108965
650.9989310.002138750.00106938
660.9993980.001204990.000602494
670.9995050.0009904810.00049524
680.9997610.0004785730.000239286
690.9999480.0001030895.15444e-05
700.999984.00488e-052.00244e-05
710.999983.9388e-051.9694e-05
720.9999843.20918e-051.60459e-05
730.9999892.24415e-051.12208e-05
740.9999941.18084e-055.90421e-06
750.9999921.57216e-057.86078e-06
760.9999911.84852e-059.24259e-06
770.9999921.65881e-058.29404e-06
780.9999931.36074e-056.8037e-06
790.9999976.58202e-063.29101e-06
800.9999976.4345e-063.21725e-06
810.9999959.24198e-064.62099e-06
820.9999992.17048e-061.08524e-06
830.9999991.88926e-069.44632e-07
840.9999992.34076e-061.17038e-06
8519.72555e-074.86277e-07
8616.92191e-073.46095e-07
8715.55464e-072.77732e-07
8818.27693e-074.13846e-07
890.9999991.0317e-065.15849e-07
9014.34139e-072.17069e-07
9115.31761e-072.65881e-07
9218.13219e-084.06609e-08
9311.3024e-076.51202e-08
9411.97943e-079.89717e-08
9512.77558e-071.38779e-07
9611.7184e-078.592e-08
9712.79756e-071.39878e-07
9813.49958e-071.74979e-07
9913.06098e-071.53049e-07
10015.05691e-072.52845e-07
10113.74833e-071.87417e-07
10216.29696e-073.14848e-07
10319.02247e-074.51123e-07
1040.9999991.41555e-067.07774e-07
1050.9999991.27095e-066.35473e-07
1060.9999991.4055e-067.0275e-07
1070.9999992.08254e-061.04127e-06
1080.9999991.69571e-068.47854e-07
10911.60297e-078.01486e-08
11012.57826e-071.28913e-07
11114.78788e-072.39394e-07
11218.32068e-074.16034e-07
1130.9999991.01035e-065.05177e-07
1140.9999991.03946e-065.19732e-07
1150.9999991.0952e-065.47599e-07
1160.9999991.67425e-068.37126e-07
1170.9999992.93825e-061.46913e-06
1180.9999983.01449e-061.50725e-06
1190.9999983.35416e-061.67708e-06
12017.64266e-073.82133e-07
12112.13253e-071.06627e-07
12213.62777e-071.81389e-07
12311.01287e-075.06434e-08
12411.75868e-078.79338e-08
12513.15452e-071.57726e-07
12615.33745e-072.66872e-07
12716.21597e-073.10799e-07
1280.9999991.12245e-065.61224e-07
1290.9999992.12864e-061.06432e-06
1300.9999991.14455e-065.72274e-07
1310.9999991.21801e-066.09003e-07
13211.28511e-076.42555e-08
13311.0862e-075.43101e-08
13411.68881e-078.44404e-08
13513.45391e-071.72695e-07
13616.37774e-073.18887e-07
13719.67357e-074.83679e-07
1380.9999991.56366e-067.8183e-07
1390.9999992.42979e-061.2149e-06
1400.9999984.70826e-062.35413e-06
1410.9999984.97522e-062.48761e-06
1420.9999959.87784e-064.93892e-06
1430.9999984.52434e-062.26217e-06
1440.9999967.06157e-063.53079e-06
1450.9999951.02752e-055.1376e-06
1460.999991.94565e-059.72823e-06
1470.9999813.81091e-051.90546e-05
1480.9999637.49737e-053.74869e-05
1490.9999280.0001431347.15671e-05
1500.9998940.0002119950.000105997
1510.9998940.0002119580.000105979
1520.9998170.0003655170.000182758
1530.999650.0007000590.00035003
1540.9995310.0009376330.000468817
1550.9992520.00149580.000747898
1560.9998480.0003030270.000151513
1570.9998220.0003564520.000178226
1580.9996470.0007066720.000353336
1590.9994720.001055510.000527753
1600.9991620.001676120.000838058
1610.9987940.002411140.00120557
1620.9981320.003735160.00186758
1630.9965410.006917360.00345868
1640.9964310.007138720.00356936
1650.9963030.007394170.00369709
1660.9926250.01475040.00737518
1670.9869490.0261020.013051
1680.9909150.01817060.00908528
1690.9822710.03545790.017729
1700.9718770.05624570.0281228
1710.9497550.100490.0502448
1720.9122010.1755980.087799
1730.8524620.2950760.147538
1740.7823570.4352850.217643
1750.6569430.6861140.343057
1760.5500730.8998540.449927

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.477412 & 0.954825 & 0.522588 \tabularnewline
17 & 0.317783 & 0.635566 & 0.682217 \tabularnewline
18 & 0.191885 & 0.38377 & 0.808115 \tabularnewline
19 & 0.219496 & 0.438993 & 0.780504 \tabularnewline
20 & 0.182871 & 0.365743 & 0.817129 \tabularnewline
21 & 0.138851 & 0.277701 & 0.861149 \tabularnewline
22 & 0.247067 & 0.494134 & 0.752933 \tabularnewline
23 & 0.186573 & 0.373146 & 0.813427 \tabularnewline
24 & 0.268174 & 0.536349 & 0.731826 \tabularnewline
25 & 0.375317 & 0.750635 & 0.624683 \tabularnewline
26 & 0.298471 & 0.596942 & 0.701529 \tabularnewline
27 & 0.239925 & 0.479851 & 0.760075 \tabularnewline
28 & 0.213045 & 0.42609 & 0.786955 \tabularnewline
29 & 0.213769 & 0.427538 & 0.786231 \tabularnewline
30 & 0.229775 & 0.45955 & 0.770225 \tabularnewline
31 & 0.194772 & 0.389543 & 0.805228 \tabularnewline
32 & 0.207765 & 0.41553 & 0.792235 \tabularnewline
33 & 0.162157 & 0.324313 & 0.837843 \tabularnewline
34 & 0.148116 & 0.296233 & 0.851884 \tabularnewline
35 & 0.117831 & 0.235663 & 0.882169 \tabularnewline
36 & 0.0973821 & 0.194764 & 0.902618 \tabularnewline
37 & 0.141401 & 0.282802 & 0.858599 \tabularnewline
38 & 0.129782 & 0.259565 & 0.870218 \tabularnewline
39 & 0.139773 & 0.279546 & 0.860227 \tabularnewline
40 & 0.110585 & 0.221171 & 0.889415 \tabularnewline
41 & 0.184125 & 0.368249 & 0.815875 \tabularnewline
42 & 0.234899 & 0.469799 & 0.765101 \tabularnewline
43 & 0.29165 & 0.5833 & 0.70835 \tabularnewline
44 & 0.255254 & 0.510509 & 0.744746 \tabularnewline
45 & 0.231934 & 0.463868 & 0.768066 \tabularnewline
46 & 0.20391 & 0.407819 & 0.79609 \tabularnewline
47 & 0.243148 & 0.486295 & 0.756852 \tabularnewline
48 & 0.473888 & 0.947776 & 0.526112 \tabularnewline
49 & 0.569199 & 0.861603 & 0.430801 \tabularnewline
50 & 0.726419 & 0.547162 & 0.273581 \tabularnewline
51 & 0.688397 & 0.623206 & 0.311603 \tabularnewline
52 & 0.888242 & 0.223515 & 0.111758 \tabularnewline
53 & 0.944877 & 0.110245 & 0.0551225 \tabularnewline
54 & 0.954655 & 0.0906909 & 0.0453454 \tabularnewline
55 & 0.981115 & 0.0377696 & 0.0188848 \tabularnewline
56 & 0.986514 & 0.0269721 & 0.013486 \tabularnewline
57 & 0.997848 & 0.004304 & 0.002152 \tabularnewline
58 & 0.998563 & 0.00287421 & 0.00143711 \tabularnewline
59 & 0.9985 & 0.00299942 & 0.00149971 \tabularnewline
60 & 0.998543 & 0.00291356 & 0.00145678 \tabularnewline
61 & 0.999072 & 0.00185654 & 0.000928269 \tabularnewline
62 & 0.999115 & 0.00176979 & 0.000884896 \tabularnewline
63 & 0.998872 & 0.00225619 & 0.00112809 \tabularnewline
64 & 0.99891 & 0.00217931 & 0.00108965 \tabularnewline
65 & 0.998931 & 0.00213875 & 0.00106938 \tabularnewline
66 & 0.999398 & 0.00120499 & 0.000602494 \tabularnewline
67 & 0.999505 & 0.000990481 & 0.00049524 \tabularnewline
68 & 0.999761 & 0.000478573 & 0.000239286 \tabularnewline
69 & 0.999948 & 0.000103089 & 5.15444e-05 \tabularnewline
70 & 0.99998 & 4.00488e-05 & 2.00244e-05 \tabularnewline
71 & 0.99998 & 3.9388e-05 & 1.9694e-05 \tabularnewline
72 & 0.999984 & 3.20918e-05 & 1.60459e-05 \tabularnewline
73 & 0.999989 & 2.24415e-05 & 1.12208e-05 \tabularnewline
74 & 0.999994 & 1.18084e-05 & 5.90421e-06 \tabularnewline
75 & 0.999992 & 1.57216e-05 & 7.86078e-06 \tabularnewline
76 & 0.999991 & 1.84852e-05 & 9.24259e-06 \tabularnewline
77 & 0.999992 & 1.65881e-05 & 8.29404e-06 \tabularnewline
78 & 0.999993 & 1.36074e-05 & 6.8037e-06 \tabularnewline
79 & 0.999997 & 6.58202e-06 & 3.29101e-06 \tabularnewline
80 & 0.999997 & 6.4345e-06 & 3.21725e-06 \tabularnewline
81 & 0.999995 & 9.24198e-06 & 4.62099e-06 \tabularnewline
82 & 0.999999 & 2.17048e-06 & 1.08524e-06 \tabularnewline
83 & 0.999999 & 1.88926e-06 & 9.44632e-07 \tabularnewline
84 & 0.999999 & 2.34076e-06 & 1.17038e-06 \tabularnewline
85 & 1 & 9.72555e-07 & 4.86277e-07 \tabularnewline
86 & 1 & 6.92191e-07 & 3.46095e-07 \tabularnewline
87 & 1 & 5.55464e-07 & 2.77732e-07 \tabularnewline
88 & 1 & 8.27693e-07 & 4.13846e-07 \tabularnewline
89 & 0.999999 & 1.0317e-06 & 5.15849e-07 \tabularnewline
90 & 1 & 4.34139e-07 & 2.17069e-07 \tabularnewline
91 & 1 & 5.31761e-07 & 2.65881e-07 \tabularnewline
92 & 1 & 8.13219e-08 & 4.06609e-08 \tabularnewline
93 & 1 & 1.3024e-07 & 6.51202e-08 \tabularnewline
94 & 1 & 1.97943e-07 & 9.89717e-08 \tabularnewline
95 & 1 & 2.77558e-07 & 1.38779e-07 \tabularnewline
96 & 1 & 1.7184e-07 & 8.592e-08 \tabularnewline
97 & 1 & 2.79756e-07 & 1.39878e-07 \tabularnewline
98 & 1 & 3.49958e-07 & 1.74979e-07 \tabularnewline
99 & 1 & 3.06098e-07 & 1.53049e-07 \tabularnewline
100 & 1 & 5.05691e-07 & 2.52845e-07 \tabularnewline
101 & 1 & 3.74833e-07 & 1.87417e-07 \tabularnewline
102 & 1 & 6.29696e-07 & 3.14848e-07 \tabularnewline
103 & 1 & 9.02247e-07 & 4.51123e-07 \tabularnewline
104 & 0.999999 & 1.41555e-06 & 7.07774e-07 \tabularnewline
105 & 0.999999 & 1.27095e-06 & 6.35473e-07 \tabularnewline
106 & 0.999999 & 1.4055e-06 & 7.0275e-07 \tabularnewline
107 & 0.999999 & 2.08254e-06 & 1.04127e-06 \tabularnewline
108 & 0.999999 & 1.69571e-06 & 8.47854e-07 \tabularnewline
109 & 1 & 1.60297e-07 & 8.01486e-08 \tabularnewline
110 & 1 & 2.57826e-07 & 1.28913e-07 \tabularnewline
111 & 1 & 4.78788e-07 & 2.39394e-07 \tabularnewline
112 & 1 & 8.32068e-07 & 4.16034e-07 \tabularnewline
113 & 0.999999 & 1.01035e-06 & 5.05177e-07 \tabularnewline
114 & 0.999999 & 1.03946e-06 & 5.19732e-07 \tabularnewline
115 & 0.999999 & 1.0952e-06 & 5.47599e-07 \tabularnewline
116 & 0.999999 & 1.67425e-06 & 8.37126e-07 \tabularnewline
117 & 0.999999 & 2.93825e-06 & 1.46913e-06 \tabularnewline
118 & 0.999998 & 3.01449e-06 & 1.50725e-06 \tabularnewline
119 & 0.999998 & 3.35416e-06 & 1.67708e-06 \tabularnewline
120 & 1 & 7.64266e-07 & 3.82133e-07 \tabularnewline
121 & 1 & 2.13253e-07 & 1.06627e-07 \tabularnewline
122 & 1 & 3.62777e-07 & 1.81389e-07 \tabularnewline
123 & 1 & 1.01287e-07 & 5.06434e-08 \tabularnewline
124 & 1 & 1.75868e-07 & 8.79338e-08 \tabularnewline
125 & 1 & 3.15452e-07 & 1.57726e-07 \tabularnewline
126 & 1 & 5.33745e-07 & 2.66872e-07 \tabularnewline
127 & 1 & 6.21597e-07 & 3.10799e-07 \tabularnewline
128 & 0.999999 & 1.12245e-06 & 5.61224e-07 \tabularnewline
129 & 0.999999 & 2.12864e-06 & 1.06432e-06 \tabularnewline
130 & 0.999999 & 1.14455e-06 & 5.72274e-07 \tabularnewline
131 & 0.999999 & 1.21801e-06 & 6.09003e-07 \tabularnewline
132 & 1 & 1.28511e-07 & 6.42555e-08 \tabularnewline
133 & 1 & 1.0862e-07 & 5.43101e-08 \tabularnewline
134 & 1 & 1.68881e-07 & 8.44404e-08 \tabularnewline
135 & 1 & 3.45391e-07 & 1.72695e-07 \tabularnewline
136 & 1 & 6.37774e-07 & 3.18887e-07 \tabularnewline
137 & 1 & 9.67357e-07 & 4.83679e-07 \tabularnewline
138 & 0.999999 & 1.56366e-06 & 7.8183e-07 \tabularnewline
139 & 0.999999 & 2.42979e-06 & 1.2149e-06 \tabularnewline
140 & 0.999998 & 4.70826e-06 & 2.35413e-06 \tabularnewline
141 & 0.999998 & 4.97522e-06 & 2.48761e-06 \tabularnewline
142 & 0.999995 & 9.87784e-06 & 4.93892e-06 \tabularnewline
143 & 0.999998 & 4.52434e-06 & 2.26217e-06 \tabularnewline
144 & 0.999996 & 7.06157e-06 & 3.53079e-06 \tabularnewline
145 & 0.999995 & 1.02752e-05 & 5.1376e-06 \tabularnewline
146 & 0.99999 & 1.94565e-05 & 9.72823e-06 \tabularnewline
147 & 0.999981 & 3.81091e-05 & 1.90546e-05 \tabularnewline
148 & 0.999963 & 7.49737e-05 & 3.74869e-05 \tabularnewline
149 & 0.999928 & 0.000143134 & 7.15671e-05 \tabularnewline
150 & 0.999894 & 0.000211995 & 0.000105997 \tabularnewline
151 & 0.999894 & 0.000211958 & 0.000105979 \tabularnewline
152 & 0.999817 & 0.000365517 & 0.000182758 \tabularnewline
153 & 0.99965 & 0.000700059 & 0.00035003 \tabularnewline
154 & 0.999531 & 0.000937633 & 0.000468817 \tabularnewline
155 & 0.999252 & 0.0014958 & 0.000747898 \tabularnewline
156 & 0.999848 & 0.000303027 & 0.000151513 \tabularnewline
157 & 0.999822 & 0.000356452 & 0.000178226 \tabularnewline
158 & 0.999647 & 0.000706672 & 0.000353336 \tabularnewline
159 & 0.999472 & 0.00105551 & 0.000527753 \tabularnewline
160 & 0.999162 & 0.00167612 & 0.000838058 \tabularnewline
161 & 0.998794 & 0.00241114 & 0.00120557 \tabularnewline
162 & 0.998132 & 0.00373516 & 0.00186758 \tabularnewline
163 & 0.996541 & 0.00691736 & 0.00345868 \tabularnewline
164 & 0.996431 & 0.00713872 & 0.00356936 \tabularnewline
165 & 0.996303 & 0.00739417 & 0.00369709 \tabularnewline
166 & 0.992625 & 0.0147504 & 0.00737518 \tabularnewline
167 & 0.986949 & 0.026102 & 0.013051 \tabularnewline
168 & 0.990915 & 0.0181706 & 0.00908528 \tabularnewline
169 & 0.982271 & 0.0354579 & 0.017729 \tabularnewline
170 & 0.971877 & 0.0562457 & 0.0281228 \tabularnewline
171 & 0.949755 & 0.10049 & 0.0502448 \tabularnewline
172 & 0.912201 & 0.175598 & 0.087799 \tabularnewline
173 & 0.852462 & 0.295076 & 0.147538 \tabularnewline
174 & 0.782357 & 0.435285 & 0.217643 \tabularnewline
175 & 0.656943 & 0.686114 & 0.343057 \tabularnewline
176 & 0.550073 & 0.899854 & 0.449927 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=222039&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]16[/C][C]0.477412[/C][C]0.954825[/C][C]0.522588[/C][/ROW]
[ROW][C]17[/C][C]0.317783[/C][C]0.635566[/C][C]0.682217[/C][/ROW]
[ROW][C]18[/C][C]0.191885[/C][C]0.38377[/C][C]0.808115[/C][/ROW]
[ROW][C]19[/C][C]0.219496[/C][C]0.438993[/C][C]0.780504[/C][/ROW]
[ROW][C]20[/C][C]0.182871[/C][C]0.365743[/C][C]0.817129[/C][/ROW]
[ROW][C]21[/C][C]0.138851[/C][C]0.277701[/C][C]0.861149[/C][/ROW]
[ROW][C]22[/C][C]0.247067[/C][C]0.494134[/C][C]0.752933[/C][/ROW]
[ROW][C]23[/C][C]0.186573[/C][C]0.373146[/C][C]0.813427[/C][/ROW]
[ROW][C]24[/C][C]0.268174[/C][C]0.536349[/C][C]0.731826[/C][/ROW]
[ROW][C]25[/C][C]0.375317[/C][C]0.750635[/C][C]0.624683[/C][/ROW]
[ROW][C]26[/C][C]0.298471[/C][C]0.596942[/C][C]0.701529[/C][/ROW]
[ROW][C]27[/C][C]0.239925[/C][C]0.479851[/C][C]0.760075[/C][/ROW]
[ROW][C]28[/C][C]0.213045[/C][C]0.42609[/C][C]0.786955[/C][/ROW]
[ROW][C]29[/C][C]0.213769[/C][C]0.427538[/C][C]0.786231[/C][/ROW]
[ROW][C]30[/C][C]0.229775[/C][C]0.45955[/C][C]0.770225[/C][/ROW]
[ROW][C]31[/C][C]0.194772[/C][C]0.389543[/C][C]0.805228[/C][/ROW]
[ROW][C]32[/C][C]0.207765[/C][C]0.41553[/C][C]0.792235[/C][/ROW]
[ROW][C]33[/C][C]0.162157[/C][C]0.324313[/C][C]0.837843[/C][/ROW]
[ROW][C]34[/C][C]0.148116[/C][C]0.296233[/C][C]0.851884[/C][/ROW]
[ROW][C]35[/C][C]0.117831[/C][C]0.235663[/C][C]0.882169[/C][/ROW]
[ROW][C]36[/C][C]0.0973821[/C][C]0.194764[/C][C]0.902618[/C][/ROW]
[ROW][C]37[/C][C]0.141401[/C][C]0.282802[/C][C]0.858599[/C][/ROW]
[ROW][C]38[/C][C]0.129782[/C][C]0.259565[/C][C]0.870218[/C][/ROW]
[ROW][C]39[/C][C]0.139773[/C][C]0.279546[/C][C]0.860227[/C][/ROW]
[ROW][C]40[/C][C]0.110585[/C][C]0.221171[/C][C]0.889415[/C][/ROW]
[ROW][C]41[/C][C]0.184125[/C][C]0.368249[/C][C]0.815875[/C][/ROW]
[ROW][C]42[/C][C]0.234899[/C][C]0.469799[/C][C]0.765101[/C][/ROW]
[ROW][C]43[/C][C]0.29165[/C][C]0.5833[/C][C]0.70835[/C][/ROW]
[ROW][C]44[/C][C]0.255254[/C][C]0.510509[/C][C]0.744746[/C][/ROW]
[ROW][C]45[/C][C]0.231934[/C][C]0.463868[/C][C]0.768066[/C][/ROW]
[ROW][C]46[/C][C]0.20391[/C][C]0.407819[/C][C]0.79609[/C][/ROW]
[ROW][C]47[/C][C]0.243148[/C][C]0.486295[/C][C]0.756852[/C][/ROW]
[ROW][C]48[/C][C]0.473888[/C][C]0.947776[/C][C]0.526112[/C][/ROW]
[ROW][C]49[/C][C]0.569199[/C][C]0.861603[/C][C]0.430801[/C][/ROW]
[ROW][C]50[/C][C]0.726419[/C][C]0.547162[/C][C]0.273581[/C][/ROW]
[ROW][C]51[/C][C]0.688397[/C][C]0.623206[/C][C]0.311603[/C][/ROW]
[ROW][C]52[/C][C]0.888242[/C][C]0.223515[/C][C]0.111758[/C][/ROW]
[ROW][C]53[/C][C]0.944877[/C][C]0.110245[/C][C]0.0551225[/C][/ROW]
[ROW][C]54[/C][C]0.954655[/C][C]0.0906909[/C][C]0.0453454[/C][/ROW]
[ROW][C]55[/C][C]0.981115[/C][C]0.0377696[/C][C]0.0188848[/C][/ROW]
[ROW][C]56[/C][C]0.986514[/C][C]0.0269721[/C][C]0.013486[/C][/ROW]
[ROW][C]57[/C][C]0.997848[/C][C]0.004304[/C][C]0.002152[/C][/ROW]
[ROW][C]58[/C][C]0.998563[/C][C]0.00287421[/C][C]0.00143711[/C][/ROW]
[ROW][C]59[/C][C]0.9985[/C][C]0.00299942[/C][C]0.00149971[/C][/ROW]
[ROW][C]60[/C][C]0.998543[/C][C]0.00291356[/C][C]0.00145678[/C][/ROW]
[ROW][C]61[/C][C]0.999072[/C][C]0.00185654[/C][C]0.000928269[/C][/ROW]
[ROW][C]62[/C][C]0.999115[/C][C]0.00176979[/C][C]0.000884896[/C][/ROW]
[ROW][C]63[/C][C]0.998872[/C][C]0.00225619[/C][C]0.00112809[/C][/ROW]
[ROW][C]64[/C][C]0.99891[/C][C]0.00217931[/C][C]0.00108965[/C][/ROW]
[ROW][C]65[/C][C]0.998931[/C][C]0.00213875[/C][C]0.00106938[/C][/ROW]
[ROW][C]66[/C][C]0.999398[/C][C]0.00120499[/C][C]0.000602494[/C][/ROW]
[ROW][C]67[/C][C]0.999505[/C][C]0.000990481[/C][C]0.00049524[/C][/ROW]
[ROW][C]68[/C][C]0.999761[/C][C]0.000478573[/C][C]0.000239286[/C][/ROW]
[ROW][C]69[/C][C]0.999948[/C][C]0.000103089[/C][C]5.15444e-05[/C][/ROW]
[ROW][C]70[/C][C]0.99998[/C][C]4.00488e-05[/C][C]2.00244e-05[/C][/ROW]
[ROW][C]71[/C][C]0.99998[/C][C]3.9388e-05[/C][C]1.9694e-05[/C][/ROW]
[ROW][C]72[/C][C]0.999984[/C][C]3.20918e-05[/C][C]1.60459e-05[/C][/ROW]
[ROW][C]73[/C][C]0.999989[/C][C]2.24415e-05[/C][C]1.12208e-05[/C][/ROW]
[ROW][C]74[/C][C]0.999994[/C][C]1.18084e-05[/C][C]5.90421e-06[/C][/ROW]
[ROW][C]75[/C][C]0.999992[/C][C]1.57216e-05[/C][C]7.86078e-06[/C][/ROW]
[ROW][C]76[/C][C]0.999991[/C][C]1.84852e-05[/C][C]9.24259e-06[/C][/ROW]
[ROW][C]77[/C][C]0.999992[/C][C]1.65881e-05[/C][C]8.29404e-06[/C][/ROW]
[ROW][C]78[/C][C]0.999993[/C][C]1.36074e-05[/C][C]6.8037e-06[/C][/ROW]
[ROW][C]79[/C][C]0.999997[/C][C]6.58202e-06[/C][C]3.29101e-06[/C][/ROW]
[ROW][C]80[/C][C]0.999997[/C][C]6.4345e-06[/C][C]3.21725e-06[/C][/ROW]
[ROW][C]81[/C][C]0.999995[/C][C]9.24198e-06[/C][C]4.62099e-06[/C][/ROW]
[ROW][C]82[/C][C]0.999999[/C][C]2.17048e-06[/C][C]1.08524e-06[/C][/ROW]
[ROW][C]83[/C][C]0.999999[/C][C]1.88926e-06[/C][C]9.44632e-07[/C][/ROW]
[ROW][C]84[/C][C]0.999999[/C][C]2.34076e-06[/C][C]1.17038e-06[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]9.72555e-07[/C][C]4.86277e-07[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]6.92191e-07[/C][C]3.46095e-07[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]5.55464e-07[/C][C]2.77732e-07[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]8.27693e-07[/C][C]4.13846e-07[/C][/ROW]
[ROW][C]89[/C][C]0.999999[/C][C]1.0317e-06[/C][C]5.15849e-07[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]4.34139e-07[/C][C]2.17069e-07[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]5.31761e-07[/C][C]2.65881e-07[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]8.13219e-08[/C][C]4.06609e-08[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]1.3024e-07[/C][C]6.51202e-08[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]1.97943e-07[/C][C]9.89717e-08[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]2.77558e-07[/C][C]1.38779e-07[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]1.7184e-07[/C][C]8.592e-08[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]2.79756e-07[/C][C]1.39878e-07[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]3.49958e-07[/C][C]1.74979e-07[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]3.06098e-07[/C][C]1.53049e-07[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]5.05691e-07[/C][C]2.52845e-07[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]3.74833e-07[/C][C]1.87417e-07[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]6.29696e-07[/C][C]3.14848e-07[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]9.02247e-07[/C][C]4.51123e-07[/C][/ROW]
[ROW][C]104[/C][C]0.999999[/C][C]1.41555e-06[/C][C]7.07774e-07[/C][/ROW]
[ROW][C]105[/C][C]0.999999[/C][C]1.27095e-06[/C][C]6.35473e-07[/C][/ROW]
[ROW][C]106[/C][C]0.999999[/C][C]1.4055e-06[/C][C]7.0275e-07[/C][/ROW]
[ROW][C]107[/C][C]0.999999[/C][C]2.08254e-06[/C][C]1.04127e-06[/C][/ROW]
[ROW][C]108[/C][C]0.999999[/C][C]1.69571e-06[/C][C]8.47854e-07[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]1.60297e-07[/C][C]8.01486e-08[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]2.57826e-07[/C][C]1.28913e-07[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]4.78788e-07[/C][C]2.39394e-07[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]8.32068e-07[/C][C]4.16034e-07[/C][/ROW]
[ROW][C]113[/C][C]0.999999[/C][C]1.01035e-06[/C][C]5.05177e-07[/C][/ROW]
[ROW][C]114[/C][C]0.999999[/C][C]1.03946e-06[/C][C]5.19732e-07[/C][/ROW]
[ROW][C]115[/C][C]0.999999[/C][C]1.0952e-06[/C][C]5.47599e-07[/C][/ROW]
[ROW][C]116[/C][C]0.999999[/C][C]1.67425e-06[/C][C]8.37126e-07[/C][/ROW]
[ROW][C]117[/C][C]0.999999[/C][C]2.93825e-06[/C][C]1.46913e-06[/C][/ROW]
[ROW][C]118[/C][C]0.999998[/C][C]3.01449e-06[/C][C]1.50725e-06[/C][/ROW]
[ROW][C]119[/C][C]0.999998[/C][C]3.35416e-06[/C][C]1.67708e-06[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]7.64266e-07[/C][C]3.82133e-07[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]2.13253e-07[/C][C]1.06627e-07[/C][/ROW]
[ROW][C]122[/C][C]1[/C][C]3.62777e-07[/C][C]1.81389e-07[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]1.01287e-07[/C][C]5.06434e-08[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]1.75868e-07[/C][C]8.79338e-08[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]3.15452e-07[/C][C]1.57726e-07[/C][/ROW]
[ROW][C]126[/C][C]1[/C][C]5.33745e-07[/C][C]2.66872e-07[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]6.21597e-07[/C][C]3.10799e-07[/C][/ROW]
[ROW][C]128[/C][C]0.999999[/C][C]1.12245e-06[/C][C]5.61224e-07[/C][/ROW]
[ROW][C]129[/C][C]0.999999[/C][C]2.12864e-06[/C][C]1.06432e-06[/C][/ROW]
[ROW][C]130[/C][C]0.999999[/C][C]1.14455e-06[/C][C]5.72274e-07[/C][/ROW]
[ROW][C]131[/C][C]0.999999[/C][C]1.21801e-06[/C][C]6.09003e-07[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]1.28511e-07[/C][C]6.42555e-08[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]1.0862e-07[/C][C]5.43101e-08[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]1.68881e-07[/C][C]8.44404e-08[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]3.45391e-07[/C][C]1.72695e-07[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]6.37774e-07[/C][C]3.18887e-07[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]9.67357e-07[/C][C]4.83679e-07[/C][/ROW]
[ROW][C]138[/C][C]0.999999[/C][C]1.56366e-06[/C][C]7.8183e-07[/C][/ROW]
[ROW][C]139[/C][C]0.999999[/C][C]2.42979e-06[/C][C]1.2149e-06[/C][/ROW]
[ROW][C]140[/C][C]0.999998[/C][C]4.70826e-06[/C][C]2.35413e-06[/C][/ROW]
[ROW][C]141[/C][C]0.999998[/C][C]4.97522e-06[/C][C]2.48761e-06[/C][/ROW]
[ROW][C]142[/C][C]0.999995[/C][C]9.87784e-06[/C][C]4.93892e-06[/C][/ROW]
[ROW][C]143[/C][C]0.999998[/C][C]4.52434e-06[/C][C]2.26217e-06[/C][/ROW]
[ROW][C]144[/C][C]0.999996[/C][C]7.06157e-06[/C][C]3.53079e-06[/C][/ROW]
[ROW][C]145[/C][C]0.999995[/C][C]1.02752e-05[/C][C]5.1376e-06[/C][/ROW]
[ROW][C]146[/C][C]0.99999[/C][C]1.94565e-05[/C][C]9.72823e-06[/C][/ROW]
[ROW][C]147[/C][C]0.999981[/C][C]3.81091e-05[/C][C]1.90546e-05[/C][/ROW]
[ROW][C]148[/C][C]0.999963[/C][C]7.49737e-05[/C][C]3.74869e-05[/C][/ROW]
[ROW][C]149[/C][C]0.999928[/C][C]0.000143134[/C][C]7.15671e-05[/C][/ROW]
[ROW][C]150[/C][C]0.999894[/C][C]0.000211995[/C][C]0.000105997[/C][/ROW]
[ROW][C]151[/C][C]0.999894[/C][C]0.000211958[/C][C]0.000105979[/C][/ROW]
[ROW][C]152[/C][C]0.999817[/C][C]0.000365517[/C][C]0.000182758[/C][/ROW]
[ROW][C]153[/C][C]0.99965[/C][C]0.000700059[/C][C]0.00035003[/C][/ROW]
[ROW][C]154[/C][C]0.999531[/C][C]0.000937633[/C][C]0.000468817[/C][/ROW]
[ROW][C]155[/C][C]0.999252[/C][C]0.0014958[/C][C]0.000747898[/C][/ROW]
[ROW][C]156[/C][C]0.999848[/C][C]0.000303027[/C][C]0.000151513[/C][/ROW]
[ROW][C]157[/C][C]0.999822[/C][C]0.000356452[/C][C]0.000178226[/C][/ROW]
[ROW][C]158[/C][C]0.999647[/C][C]0.000706672[/C][C]0.000353336[/C][/ROW]
[ROW][C]159[/C][C]0.999472[/C][C]0.00105551[/C][C]0.000527753[/C][/ROW]
[ROW][C]160[/C][C]0.999162[/C][C]0.00167612[/C][C]0.000838058[/C][/ROW]
[ROW][C]161[/C][C]0.998794[/C][C]0.00241114[/C][C]0.00120557[/C][/ROW]
[ROW][C]162[/C][C]0.998132[/C][C]0.00373516[/C][C]0.00186758[/C][/ROW]
[ROW][C]163[/C][C]0.996541[/C][C]0.00691736[/C][C]0.00345868[/C][/ROW]
[ROW][C]164[/C][C]0.996431[/C][C]0.00713872[/C][C]0.00356936[/C][/ROW]
[ROW][C]165[/C][C]0.996303[/C][C]0.00739417[/C][C]0.00369709[/C][/ROW]
[ROW][C]166[/C][C]0.992625[/C][C]0.0147504[/C][C]0.00737518[/C][/ROW]
[ROW][C]167[/C][C]0.986949[/C][C]0.026102[/C][C]0.013051[/C][/ROW]
[ROW][C]168[/C][C]0.990915[/C][C]0.0181706[/C][C]0.00908528[/C][/ROW]
[ROW][C]169[/C][C]0.982271[/C][C]0.0354579[/C][C]0.017729[/C][/ROW]
[ROW][C]170[/C][C]0.971877[/C][C]0.0562457[/C][C]0.0281228[/C][/ROW]
[ROW][C]171[/C][C]0.949755[/C][C]0.10049[/C][C]0.0502448[/C][/ROW]
[ROW][C]172[/C][C]0.912201[/C][C]0.175598[/C][C]0.087799[/C][/ROW]
[ROW][C]173[/C][C]0.852462[/C][C]0.295076[/C][C]0.147538[/C][/ROW]
[ROW][C]174[/C][C]0.782357[/C][C]0.435285[/C][C]0.217643[/C][/ROW]
[ROW][C]175[/C][C]0.656943[/C][C]0.686114[/C][C]0.343057[/C][/ROW]
[ROW][C]176[/C][C]0.550073[/C][C]0.899854[/C][C]0.449927[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=222039&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=222039&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
160.4774120.9548250.522588
170.3177830.6355660.682217
180.1918850.383770.808115
190.2194960.4389930.780504
200.1828710.3657430.817129
210.1388510.2777010.861149
220.2470670.4941340.752933
230.1865730.3731460.813427
240.2681740.5363490.731826
250.3753170.7506350.624683
260.2984710.5969420.701529
270.2399250.4798510.760075
280.2130450.426090.786955
290.2137690.4275380.786231
300.2297750.459550.770225
310.1947720.3895430.805228
320.2077650.415530.792235
330.1621570.3243130.837843
340.1481160.2962330.851884
350.1178310.2356630.882169
360.09738210.1947640.902618
370.1414010.2828020.858599
380.1297820.2595650.870218
390.1397730.2795460.860227
400.1105850.2211710.889415
410.1841250.3682490.815875
420.2348990.4697990.765101
430.291650.58330.70835
440.2552540.5105090.744746
450.2319340.4638680.768066
460.203910.4078190.79609
470.2431480.4862950.756852
480.4738880.9477760.526112
490.5691990.8616030.430801
500.7264190.5471620.273581
510.6883970.6232060.311603
520.8882420.2235150.111758
530.9448770.1102450.0551225
540.9546550.09069090.0453454
550.9811150.03776960.0188848
560.9865140.02697210.013486
570.9978480.0043040.002152
580.9985630.002874210.00143711
590.99850.002999420.00149971
600.9985430.002913560.00145678
610.9990720.001856540.000928269
620.9991150.001769790.000884896
630.9988720.002256190.00112809
640.998910.002179310.00108965
650.9989310.002138750.00106938
660.9993980.001204990.000602494
670.9995050.0009904810.00049524
680.9997610.0004785730.000239286
690.9999480.0001030895.15444e-05
700.999984.00488e-052.00244e-05
710.999983.9388e-051.9694e-05
720.9999843.20918e-051.60459e-05
730.9999892.24415e-051.12208e-05
740.9999941.18084e-055.90421e-06
750.9999921.57216e-057.86078e-06
760.9999911.84852e-059.24259e-06
770.9999921.65881e-058.29404e-06
780.9999931.36074e-056.8037e-06
790.9999976.58202e-063.29101e-06
800.9999976.4345e-063.21725e-06
810.9999959.24198e-064.62099e-06
820.9999992.17048e-061.08524e-06
830.9999991.88926e-069.44632e-07
840.9999992.34076e-061.17038e-06
8519.72555e-074.86277e-07
8616.92191e-073.46095e-07
8715.55464e-072.77732e-07
8818.27693e-074.13846e-07
890.9999991.0317e-065.15849e-07
9014.34139e-072.17069e-07
9115.31761e-072.65881e-07
9218.13219e-084.06609e-08
9311.3024e-076.51202e-08
9411.97943e-079.89717e-08
9512.77558e-071.38779e-07
9611.7184e-078.592e-08
9712.79756e-071.39878e-07
9813.49958e-071.74979e-07
9913.06098e-071.53049e-07
10015.05691e-072.52845e-07
10113.74833e-071.87417e-07
10216.29696e-073.14848e-07
10319.02247e-074.51123e-07
1040.9999991.41555e-067.07774e-07
1050.9999991.27095e-066.35473e-07
1060.9999991.4055e-067.0275e-07
1070.9999992.08254e-061.04127e-06
1080.9999991.69571e-068.47854e-07
10911.60297e-078.01486e-08
11012.57826e-071.28913e-07
11114.78788e-072.39394e-07
11218.32068e-074.16034e-07
1130.9999991.01035e-065.05177e-07
1140.9999991.03946e-065.19732e-07
1150.9999991.0952e-065.47599e-07
1160.9999991.67425e-068.37126e-07
1170.9999992.93825e-061.46913e-06
1180.9999983.01449e-061.50725e-06
1190.9999983.35416e-061.67708e-06
12017.64266e-073.82133e-07
12112.13253e-071.06627e-07
12213.62777e-071.81389e-07
12311.01287e-075.06434e-08
12411.75868e-078.79338e-08
12513.15452e-071.57726e-07
12615.33745e-072.66872e-07
12716.21597e-073.10799e-07
1280.9999991.12245e-065.61224e-07
1290.9999992.12864e-061.06432e-06
1300.9999991.14455e-065.72274e-07
1310.9999991.21801e-066.09003e-07
13211.28511e-076.42555e-08
13311.0862e-075.43101e-08
13411.68881e-078.44404e-08
13513.45391e-071.72695e-07
13616.37774e-073.18887e-07
13719.67357e-074.83679e-07
1380.9999991.56366e-067.8183e-07
1390.9999992.42979e-061.2149e-06
1400.9999984.70826e-062.35413e-06
1410.9999984.97522e-062.48761e-06
1420.9999959.87784e-064.93892e-06
1430.9999984.52434e-062.26217e-06
1440.9999967.06157e-063.53079e-06
1450.9999951.02752e-055.1376e-06
1460.999991.94565e-059.72823e-06
1470.9999813.81091e-051.90546e-05
1480.9999637.49737e-053.74869e-05
1490.9999280.0001431347.15671e-05
1500.9998940.0002119950.000105997
1510.9998940.0002119580.000105979
1520.9998170.0003655170.000182758
1530.999650.0007000590.00035003
1540.9995310.0009376330.000468817
1550.9992520.00149580.000747898
1560.9998480.0003030270.000151513
1570.9998220.0003564520.000178226
1580.9996470.0007066720.000353336
1590.9994720.001055510.000527753
1600.9991620.001676120.000838058
1610.9987940.002411140.00120557
1620.9981320.003735160.00186758
1630.9965410.006917360.00345868
1640.9964310.007138720.00356936
1650.9963030.007394170.00369709
1660.9926250.01475040.00737518
1670.9869490.0261020.013051
1680.9909150.01817060.00908528
1690.9822710.03545790.017729
1700.9718770.05624570.0281228
1710.9497550.100490.0502448
1720.9122010.1755980.087799
1730.8524620.2950760.147538
1740.7823570.4352850.217643
1750.6569430.6861140.343057
1760.5500730.8998540.449927






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1090.677019NOK
5% type I error level1150.714286NOK
10% type I error level1170.726708NOK

\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 & 109 & 0.677019 & NOK \tabularnewline
5% type I error level & 115 & 0.714286 & NOK \tabularnewline
10% type I error level & 117 & 0.726708 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=222039&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]109[/C][C]0.677019[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]115[/C][C]0.714286[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]117[/C][C]0.726708[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=222039&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=222039&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 level1090.677019NOK
5% type I error level1150.714286NOK
10% type I error level1170.726708NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}