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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 02:24:05 -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/t1383549948ntljyx1l896lg4v.htm/, Retrieved Sun, 28 Apr 2024 18:33:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=222038, Retrieved Sun, 28 Apr 2024 18:33:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact365

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 07:24:05] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
- R       [Multiple Regression] [Workshop 7 - Seat...] [2013-11-20 15:24:23] [74be16979710d4c4e7c6647856088456]
- RM      [Multiple Regression] [] [2014-11-12 13:43:13] [394a9522c47495260fca596e959e6202]
- RM      [Multiple Regression] [Gordelwet - multi...] [2014-11-12 14:03:55] [81f624c2f0b20a2549c93e7c3dccf981]
- RM      [Multiple Regression] [ws7 v6] [2014-11-12 14:13:51] [e3727f74ca0896859afbe865e40a3465]
- RM      [Multiple Regression] [] [2014-11-12 14:25:15] [6795cd14e59cd8fafcdf800c40b889d9]
- RM      [Multiple Regression] [ws7 v8] [2014-11-12 14:28:07] [e3727f74ca0896859afbe865e40a3465]
- RM      [Multiple Regression] [] [2014-11-12 14:50:44] [fa1b8827d7de91b8b87087311d3d9fa1]
-    D      [Multiple Regression] [] [2014-12-16 12:37:15] [fa1b8827d7de91b8b87087311d3d9fa1]
- R  D      [Multiple Regression] [] [2014-12-16 13:43:10] [fa1b8827d7de91b8b87087311d3d9fa1]
-    D      [Multiple Regression] [] [2014-12-16 13:44:37] [fa1b8827d7de91b8b87087311d3d9fa1]
- R  D      [Multiple Regression] [] [2014-12-16 13:46:23] [fa1b8827d7de91b8b87087311d3d9fa1]
- RMP     [Multiple Regression] [Task 2.1 WS7] [2014-11-12 15:28:50] [805021881bfa5340347077d26b077617]
- RMP     [Multiple Regression] [] [2014-11-12 16:19:34] [c2c160edf30e228bd3a949bf24376c2c]
- RMP     [Multiple Regression] [] [2014-11-12 16:21:18] [c2c160edf30e228bd3a949bf24376c2c]
- RM      [Multiple Regression] [ws7 7] [2014-11-12 16:27:52] [15866c21ed6246d5efde5ff3ba421193]
- RM      [Multiple Regression] [] [2014-11-12 16:49:36] [6656361aa4da5489a6a45e803df0211c]
- RM      [Multiple Regression] [WS7 task 2] [2014-11-12 16:51:49] [67894a4ff6098ffac356bc81e6028257]
- RM      [Multiple Regression] [WS7 SHW] [2014-11-12 17:08:30] [cac6c5fb035463be46c296b46e439cb5]
- RM      [Multiple Regression] [] [2014-11-12 18:30:43] [69bf0eb8b9b38defaaf4848d8c317571]
-  M D    [Multiple Regression] [WS7] [2014-11-12 19:35:33] [074b0dbc8d3b700fa15927150828e345]
- RMP     [Multiple Regression] [WS7] [2014-11-12 19:41:31] [074b0dbc8d3b700fa15927150828e345]
-   P       [Multiple Regression] [WS7] [2014-11-12 20:02:42] [074b0dbc8d3b700fa15927150828e345]
-   P       [Multiple Regression] [WS7] [2014-11-12 20:03:11] [074b0dbc8d3b700fa15927150828e345]
- RM      [Multiple Regression] [WS7 - 7] [2014-11-12 20:20:56] [4d39cf209776852399955073f9d0ee7a]
-           [Multiple Regression] [WSH 7, 11] [2014-11-13 19:39:58] [e7da31d1eb6eab8d5ed70d87d07c747b]
- RM      [Multiple Regression] [] [2014-11-13 07:22:28] [1a6d42b46b3d01bc960fcfb45e99fecd]
- RM      [Multiple Regression] [] [2014-11-13 11:44:45] [ce58fb8a0a6d5fe2eedf5e527a9cf2f2]
- RM      [Multiple Regression] [seatbelt] [2014-11-13 13:22:28] [adaed729de74b66aca8005b12851d24b]
- RM      [Multiple Regression] [] [2014-11-13 14:51:17] [dd7a37d66cc3f8699a204e53c0324369]
- RM      [Multiple Regression] [] [2014-11-13 16:50:12] [d69b52d23ca73e15a0c741afa583703c]
- RM      [Multiple Regression] [w7 gordel] [2014-11-13 19:02:32] [673773038936aef3a5778d7e6bda5c1e]
- RM      [Multiple Regression] [ws7] [2014-11-13 19:07:37] [8523551e1e4e3cbe97fa25692e177b2e]
- RM      [Multiple Regression] [q] [2014-11-13 19:26:19] [1651e47f7f65f3a10bbbb444d4b26be7]
- RM      [Multiple Regression] [] [2014-11-13 21:11:54] [a86b94943a9e798c2d09bb837c6a8141]
- RM      [Multiple Regression] [ws7] [2014-11-13 21:25:43] [006b54b8ce76f482b86cd20c6480b526]
- RM D    [Multiple Regression] [] [2014-12-09 11:47:48] [c2c160edf30e228bd3a949bf24376c2c]
- RM D    [Multiple Regression] [] [2014-12-09 14:50:50] [c2c160edf30e228bd3a949bf24376c2c]
-  M      [Multiple Regression] [oef WS7 5] [2015-01-20 19:57:41] [bb1b6762b7e5624d262776d3f7139d34]
- RMPD      [Classical Decomposition] [oef WS8 1] [2015-01-20 20:13:51] [bb1b6762b7e5624d262776d3f7139d34]
- RMPD      [Spectral Analysis] [oef WS8 2] [2015-01-20 20:34:28] [bb1b6762b7e5624d262776d3f7139d34]
- RMPD      [Decomposition by Loess] [oef WS8 Loess] [2015-01-20 20:36:39] [bb1b6762b7e5624d262776d3f7139d34]
- RMPD      [Structural Time Series Models] [oef WS8 3] [2015-01-20 20:41:52] [bb1b6762b7e5624d262776d3f7139d34]
- RMPD      [Exponential Smoothing] [oef WS8 5] [2015-01-20 20:44:57] [bb1b6762b7e5624d262776d3f7139d34]
-   PD      [Multiple Regression] [oef ws8 6] [2015-01-20 20:49:30] [bb1b6762b7e5624d262776d3f7139d34]
- RMPD      [Variance Reduction Matrix] [oef WS9 1] [2015-01-20 21:06:54] [bb1b6762b7e5624d262776d3f7139d34]
- RMPD      [Spectral Analysis] [oef WS9 2] [2015-01-20 21:09:32] [bb1b6762b7e5624d262776d3f7139d34]
- RMPD      [Spectral Analysis] [oef WS9 2] [2015-01-20 21:09:32] [bb1b6762b7e5624d262776d3f7139d34]
- R           [Spectral Analysis] [oef WS9 3] [2015-01-20 21:12:20] [bb1b6762b7e5624d262776d3f7139d34]

[Truncated]
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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
1653 0
2152 0
2148 0
1752 0
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1575 0
1520 0
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1800 0
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
1689 0
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
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2118 0
2150 0
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1548 0
1382 0
1731 0
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1905 0
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2000 0
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1956 0
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2016 0
2207 0
1665 0
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1850 0
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 time8 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 & 8 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=222038&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]8 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=222038&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=222038&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 time8 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Accidents[t] = + 1717.75 -396.056Belt[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Accidents[t] =  +  1717.75 -396.056Belt[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=222038&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Accidents[t] =  +  1717.75 -396.056Belt[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=222038&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=222038&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] = + 1717.75 -396.056Belt[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1717.7520.000385.895.68074e-1542.84037e-154
Belt-396.05657.7862-6.8549.76295e-114.88148e-11

\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) & 1717.75 & 20.0003 & 85.89 & 5.68074e-154 & 2.84037e-154 \tabularnewline
Belt & -396.056 & 57.7862 & -6.854 & 9.76295e-11 & 4.88148e-11 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=222038&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]1717.75[/C][C]20.0003[/C][C]85.89[/C][C]5.68074e-154[/C][C]2.84037e-154[/C][/ROW]
[ROW][C]Belt[/C][C]-396.056[/C][C]57.7862[/C][C]-6.854[/C][C]9.76295e-11[/C][C]4.88148e-11[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=222038&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=222038&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)1717.7520.000385.895.68074e-1542.84037e-154
Belt-396.05657.7862-6.8549.76295e-114.88148e-11






Multiple Linear Regression - Regression Statistics
Multiple R0.445227
R-squared0.198227
Adjusted R-squared0.194007
F-TEST (value)46.9748
F-TEST (DF numerator)1
F-TEST (DF denominator)190
p-value9.76296e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation260.004
Sum Squared Residuals12844400

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.445227 \tabularnewline
R-squared & 0.198227 \tabularnewline
Adjusted R-squared & 0.194007 \tabularnewline
F-TEST (value) & 46.9748 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 190 \tabularnewline
p-value & 9.76296e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 260.004 \tabularnewline
Sum Squared Residuals & 12844400 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=222038&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.445227[/C][/ROW]
[ROW][C]R-squared[/C][C]0.198227[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.194007[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]46.9748[/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]9.76296e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]260.004[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]12844400[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=222038&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=222038&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 R0.445227
R-squared0.198227
Adjusted R-squared0.194007
F-TEST (value)46.9748
F-TEST (DF numerator)1
F-TEST (DF denominator)190
p-value9.76296e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation260.004
Sum Squared Residuals12844400






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
116871717.75-30.7515
215081717.75-209.751
315071717.75-210.751
413851717.75-332.751
516321717.75-85.7515
615111717.75-206.751
715591717.75-158.751
816301717.75-87.7515
915791717.75-138.751
1016531717.75-64.7515
1121521717.75434.249
1221481717.75430.249
1317521717.7534.2485
1417651717.7547.2485
1517171717.75-0.751479
1615581717.75-159.751
1715751717.75-142.751
1815201717.75-197.751
1918051717.7587.2485
2018001717.7582.2485
2117191717.751.24852
2220081717.75290.249
2322421717.75524.249
2424781717.75760.249
2520301717.75312.249
2616551717.75-62.7515
2716931717.75-24.7515
2816231717.75-94.7515
2918051717.7587.2485
3017461717.7528.2485
3117951717.7577.2485
3219261717.75208.249
3316191717.75-98.7515
3419921717.75274.249
3522331717.75515.249
3621921717.75474.249
3720801717.75362.249
3817681717.7550.2485
3918351717.75117.249
4015691717.75-148.751
4119761717.75258.249
4218531717.75135.249
4319651717.75247.249
4416891717.75-28.7515
4517781717.7560.2485
4619761717.75258.249
4723971717.75679.249
4826541717.75936.249
4920971717.75379.249
5019631717.75245.249
5116771717.75-40.7515
5219411717.75223.249
5320031717.75285.249
5418131717.7595.2485
5520121717.75294.249
5619121717.75194.249
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5820801717.75362.249
5921181717.75400.249
6021501717.75432.249
6116081717.75-109.751
6215031717.75-214.751
6315481717.75-169.751
6413821717.75-335.751
6517311717.7513.2485
6617981717.7580.2485
6717791717.7561.2485
6818871717.75169.249
6920041717.75286.249
7020771717.75359.249
7120921717.75374.249
7220511717.75333.249
7315771717.75-140.751
7413561717.75-361.751
7516521717.75-65.7515
7613821717.75-335.751
7715191717.75-198.751
7814211717.75-296.751
7914421717.75-275.751
8015431717.75-174.751
8116561717.75-61.7515
8215611717.75-156.751
8319051717.75187.249
8421991717.75481.249
8514731717.75-244.751
8616551717.75-62.7515
8714071717.75-310.751
8813951717.75-322.751
8915301717.75-187.751
9013091717.75-408.751
9115261717.75-191.751
9213271717.75-390.751
9316271717.75-90.7515
9417481717.7530.2485
9519581717.75240.249
9622741717.75556.249
9716481717.75-69.7515
9814011717.75-316.751
9914111717.75-306.751
10014031717.75-314.751
10113941717.75-323.751
10215201717.75-197.751
10315281717.75-189.751
10416431717.75-74.7515
10515151717.75-202.751
10616851717.75-32.7515
10720001717.75282.249
10822151717.75497.249
10919561717.75238.249
11014621717.75-255.751
11115631717.75-154.751
11214591717.75-258.751
11314461717.75-271.751
11416221717.75-95.7515
11516571717.75-60.7515
11616381717.75-79.7515
11716431717.75-74.7515
11816831717.75-34.7515
11920501717.75332.249
12022621717.75544.249
12118131717.7595.2485
12214451717.75-272.751
12317621717.7544.2485
12414611717.75-256.751
12515561717.75-161.751
12614311717.75-286.751
12714271717.75-290.751
12815541717.75-163.751
12916451717.75-72.7515
13016531717.75-64.7515
13120161717.75298.249
13222071717.75489.249
13316651717.75-52.7515
13413611717.75-356.751
13515061717.75-211.751
13613601717.75-357.751
13714531717.75-264.751
13815221717.75-195.751
13914601717.75-257.751
14015521717.75-165.751
14115481717.75-169.751
14218271717.75109.249
14317371717.7519.2485
14419411717.75223.249
14514741717.75-243.751
14614581717.75-259.751
14715421717.75-175.751
14814041717.75-313.751
14915221717.75-195.751
15013851717.75-332.751
15116411717.75-76.7515
15215101717.75-207.751
15316811717.75-36.7515
15419381717.75220.249
15518681717.75150.249
15617261717.758.24852
15714561717.75-261.751
15814451717.75-272.751
15914561717.75-261.751
16013651717.75-352.751
16114871717.75-230.751
16215581717.75-159.751
16314881717.75-229.751
16416841717.75-33.7515
16515941717.75-123.751
16618501717.75132.249
16719981717.75280.249
16820791717.75361.249
16914941717.75-223.751
17010571321.7-264.696
17112181321.7-103.696
17211681321.7-153.696
17312361321.7-85.6957
17410761321.7-245.696
17511741321.7-147.696
17611391321.7-182.696
17714271321.7105.304
17814871321.7165.304
17914831321.7161.304
18015131321.7191.304
18113571321.735.3043
18211651321.7-156.696
18312821321.7-39.6957
18411101321.7-211.696
18512971321.7-24.6957
18611851321.7-136.696
18712221321.7-99.6957
18812841321.7-37.6957
18914441321.7122.304
19015751321.7253.304
19117371321.7415.304
19217631321.7441.304

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1687 & 1717.75 & -30.7515 \tabularnewline
2 & 1508 & 1717.75 & -209.751 \tabularnewline
3 & 1507 & 1717.75 & -210.751 \tabularnewline
4 & 1385 & 1717.75 & -332.751 \tabularnewline
5 & 1632 & 1717.75 & -85.7515 \tabularnewline
6 & 1511 & 1717.75 & -206.751 \tabularnewline
7 & 1559 & 1717.75 & -158.751 \tabularnewline
8 & 1630 & 1717.75 & -87.7515 \tabularnewline
9 & 1579 & 1717.75 & -138.751 \tabularnewline
10 & 1653 & 1717.75 & -64.7515 \tabularnewline
11 & 2152 & 1717.75 & 434.249 \tabularnewline
12 & 2148 & 1717.75 & 430.249 \tabularnewline
13 & 1752 & 1717.75 & 34.2485 \tabularnewline
14 & 1765 & 1717.75 & 47.2485 \tabularnewline
15 & 1717 & 1717.75 & -0.751479 \tabularnewline
16 & 1558 & 1717.75 & -159.751 \tabularnewline
17 & 1575 & 1717.75 & -142.751 \tabularnewline
18 & 1520 & 1717.75 & -197.751 \tabularnewline
19 & 1805 & 1717.75 & 87.2485 \tabularnewline
20 & 1800 & 1717.75 & 82.2485 \tabularnewline
21 & 1719 & 1717.75 & 1.24852 \tabularnewline
22 & 2008 & 1717.75 & 290.249 \tabularnewline
23 & 2242 & 1717.75 & 524.249 \tabularnewline
24 & 2478 & 1717.75 & 760.249 \tabularnewline
25 & 2030 & 1717.75 & 312.249 \tabularnewline
26 & 1655 & 1717.75 & -62.7515 \tabularnewline
27 & 1693 & 1717.75 & -24.7515 \tabularnewline
28 & 1623 & 1717.75 & -94.7515 \tabularnewline
29 & 1805 & 1717.75 & 87.2485 \tabularnewline
30 & 1746 & 1717.75 & 28.2485 \tabularnewline
31 & 1795 & 1717.75 & 77.2485 \tabularnewline
32 & 1926 & 1717.75 & 208.249 \tabularnewline
33 & 1619 & 1717.75 & -98.7515 \tabularnewline
34 & 1992 & 1717.75 & 274.249 \tabularnewline
35 & 2233 & 1717.75 & 515.249 \tabularnewline
36 & 2192 & 1717.75 & 474.249 \tabularnewline
37 & 2080 & 1717.75 & 362.249 \tabularnewline
38 & 1768 & 1717.75 & 50.2485 \tabularnewline
39 & 1835 & 1717.75 & 117.249 \tabularnewline
40 & 1569 & 1717.75 & -148.751 \tabularnewline
41 & 1976 & 1717.75 & 258.249 \tabularnewline
42 & 1853 & 1717.75 & 135.249 \tabularnewline
43 & 1965 & 1717.75 & 247.249 \tabularnewline
44 & 1689 & 1717.75 & -28.7515 \tabularnewline
45 & 1778 & 1717.75 & 60.2485 \tabularnewline
46 & 1976 & 1717.75 & 258.249 \tabularnewline
47 & 2397 & 1717.75 & 679.249 \tabularnewline
48 & 2654 & 1717.75 & 936.249 \tabularnewline
49 & 2097 & 1717.75 & 379.249 \tabularnewline
50 & 1963 & 1717.75 & 245.249 \tabularnewline
51 & 1677 & 1717.75 & -40.7515 \tabularnewline
52 & 1941 & 1717.75 & 223.249 \tabularnewline
53 & 2003 & 1717.75 & 285.249 \tabularnewline
54 & 1813 & 1717.75 & 95.2485 \tabularnewline
55 & 2012 & 1717.75 & 294.249 \tabularnewline
56 & 1912 & 1717.75 & 194.249 \tabularnewline
57 & 2084 & 1717.75 & 366.249 \tabularnewline
58 & 2080 & 1717.75 & 362.249 \tabularnewline
59 & 2118 & 1717.75 & 400.249 \tabularnewline
60 & 2150 & 1717.75 & 432.249 \tabularnewline
61 & 1608 & 1717.75 & -109.751 \tabularnewline
62 & 1503 & 1717.75 & -214.751 \tabularnewline
63 & 1548 & 1717.75 & -169.751 \tabularnewline
64 & 1382 & 1717.75 & -335.751 \tabularnewline
65 & 1731 & 1717.75 & 13.2485 \tabularnewline
66 & 1798 & 1717.75 & 80.2485 \tabularnewline
67 & 1779 & 1717.75 & 61.2485 \tabularnewline
68 & 1887 & 1717.75 & 169.249 \tabularnewline
69 & 2004 & 1717.75 & 286.249 \tabularnewline
70 & 2077 & 1717.75 & 359.249 \tabularnewline
71 & 2092 & 1717.75 & 374.249 \tabularnewline
72 & 2051 & 1717.75 & 333.249 \tabularnewline
73 & 1577 & 1717.75 & -140.751 \tabularnewline
74 & 1356 & 1717.75 & -361.751 \tabularnewline
75 & 1652 & 1717.75 & -65.7515 \tabularnewline
76 & 1382 & 1717.75 & -335.751 \tabularnewline
77 & 1519 & 1717.75 & -198.751 \tabularnewline
78 & 1421 & 1717.75 & -296.751 \tabularnewline
79 & 1442 & 1717.75 & -275.751 \tabularnewline
80 & 1543 & 1717.75 & -174.751 \tabularnewline
81 & 1656 & 1717.75 & -61.7515 \tabularnewline
82 & 1561 & 1717.75 & -156.751 \tabularnewline
83 & 1905 & 1717.75 & 187.249 \tabularnewline
84 & 2199 & 1717.75 & 481.249 \tabularnewline
85 & 1473 & 1717.75 & -244.751 \tabularnewline
86 & 1655 & 1717.75 & -62.7515 \tabularnewline
87 & 1407 & 1717.75 & -310.751 \tabularnewline
88 & 1395 & 1717.75 & -322.751 \tabularnewline
89 & 1530 & 1717.75 & -187.751 \tabularnewline
90 & 1309 & 1717.75 & -408.751 \tabularnewline
91 & 1526 & 1717.75 & -191.751 \tabularnewline
92 & 1327 & 1717.75 & -390.751 \tabularnewline
93 & 1627 & 1717.75 & -90.7515 \tabularnewline
94 & 1748 & 1717.75 & 30.2485 \tabularnewline
95 & 1958 & 1717.75 & 240.249 \tabularnewline
96 & 2274 & 1717.75 & 556.249 \tabularnewline
97 & 1648 & 1717.75 & -69.7515 \tabularnewline
98 & 1401 & 1717.75 & -316.751 \tabularnewline
99 & 1411 & 1717.75 & -306.751 \tabularnewline
100 & 1403 & 1717.75 & -314.751 \tabularnewline
101 & 1394 & 1717.75 & -323.751 \tabularnewline
102 & 1520 & 1717.75 & -197.751 \tabularnewline
103 & 1528 & 1717.75 & -189.751 \tabularnewline
104 & 1643 & 1717.75 & -74.7515 \tabularnewline
105 & 1515 & 1717.75 & -202.751 \tabularnewline
106 & 1685 & 1717.75 & -32.7515 \tabularnewline
107 & 2000 & 1717.75 & 282.249 \tabularnewline
108 & 2215 & 1717.75 & 497.249 \tabularnewline
109 & 1956 & 1717.75 & 238.249 \tabularnewline
110 & 1462 & 1717.75 & -255.751 \tabularnewline
111 & 1563 & 1717.75 & -154.751 \tabularnewline
112 & 1459 & 1717.75 & -258.751 \tabularnewline
113 & 1446 & 1717.75 & -271.751 \tabularnewline
114 & 1622 & 1717.75 & -95.7515 \tabularnewline
115 & 1657 & 1717.75 & -60.7515 \tabularnewline
116 & 1638 & 1717.75 & -79.7515 \tabularnewline
117 & 1643 & 1717.75 & -74.7515 \tabularnewline
118 & 1683 & 1717.75 & -34.7515 \tabularnewline
119 & 2050 & 1717.75 & 332.249 \tabularnewline
120 & 2262 & 1717.75 & 544.249 \tabularnewline
121 & 1813 & 1717.75 & 95.2485 \tabularnewline
122 & 1445 & 1717.75 & -272.751 \tabularnewline
123 & 1762 & 1717.75 & 44.2485 \tabularnewline
124 & 1461 & 1717.75 & -256.751 \tabularnewline
125 & 1556 & 1717.75 & -161.751 \tabularnewline
126 & 1431 & 1717.75 & -286.751 \tabularnewline
127 & 1427 & 1717.75 & -290.751 \tabularnewline
128 & 1554 & 1717.75 & -163.751 \tabularnewline
129 & 1645 & 1717.75 & -72.7515 \tabularnewline
130 & 1653 & 1717.75 & -64.7515 \tabularnewline
131 & 2016 & 1717.75 & 298.249 \tabularnewline
132 & 2207 & 1717.75 & 489.249 \tabularnewline
133 & 1665 & 1717.75 & -52.7515 \tabularnewline
134 & 1361 & 1717.75 & -356.751 \tabularnewline
135 & 1506 & 1717.75 & -211.751 \tabularnewline
136 & 1360 & 1717.75 & -357.751 \tabularnewline
137 & 1453 & 1717.75 & -264.751 \tabularnewline
138 & 1522 & 1717.75 & -195.751 \tabularnewline
139 & 1460 & 1717.75 & -257.751 \tabularnewline
140 & 1552 & 1717.75 & -165.751 \tabularnewline
141 & 1548 & 1717.75 & -169.751 \tabularnewline
142 & 1827 & 1717.75 & 109.249 \tabularnewline
143 & 1737 & 1717.75 & 19.2485 \tabularnewline
144 & 1941 & 1717.75 & 223.249 \tabularnewline
145 & 1474 & 1717.75 & -243.751 \tabularnewline
146 & 1458 & 1717.75 & -259.751 \tabularnewline
147 & 1542 & 1717.75 & -175.751 \tabularnewline
148 & 1404 & 1717.75 & -313.751 \tabularnewline
149 & 1522 & 1717.75 & -195.751 \tabularnewline
150 & 1385 & 1717.75 & -332.751 \tabularnewline
151 & 1641 & 1717.75 & -76.7515 \tabularnewline
152 & 1510 & 1717.75 & -207.751 \tabularnewline
153 & 1681 & 1717.75 & -36.7515 \tabularnewline
154 & 1938 & 1717.75 & 220.249 \tabularnewline
155 & 1868 & 1717.75 & 150.249 \tabularnewline
156 & 1726 & 1717.75 & 8.24852 \tabularnewline
157 & 1456 & 1717.75 & -261.751 \tabularnewline
158 & 1445 & 1717.75 & -272.751 \tabularnewline
159 & 1456 & 1717.75 & -261.751 \tabularnewline
160 & 1365 & 1717.75 & -352.751 \tabularnewline
161 & 1487 & 1717.75 & -230.751 \tabularnewline
162 & 1558 & 1717.75 & -159.751 \tabularnewline
163 & 1488 & 1717.75 & -229.751 \tabularnewline
164 & 1684 & 1717.75 & -33.7515 \tabularnewline
165 & 1594 & 1717.75 & -123.751 \tabularnewline
166 & 1850 & 1717.75 & 132.249 \tabularnewline
167 & 1998 & 1717.75 & 280.249 \tabularnewline
168 & 2079 & 1717.75 & 361.249 \tabularnewline
169 & 1494 & 1717.75 & -223.751 \tabularnewline
170 & 1057 & 1321.7 & -264.696 \tabularnewline
171 & 1218 & 1321.7 & -103.696 \tabularnewline
172 & 1168 & 1321.7 & -153.696 \tabularnewline
173 & 1236 & 1321.7 & -85.6957 \tabularnewline
174 & 1076 & 1321.7 & -245.696 \tabularnewline
175 & 1174 & 1321.7 & -147.696 \tabularnewline
176 & 1139 & 1321.7 & -182.696 \tabularnewline
177 & 1427 & 1321.7 & 105.304 \tabularnewline
178 & 1487 & 1321.7 & 165.304 \tabularnewline
179 & 1483 & 1321.7 & 161.304 \tabularnewline
180 & 1513 & 1321.7 & 191.304 \tabularnewline
181 & 1357 & 1321.7 & 35.3043 \tabularnewline
182 & 1165 & 1321.7 & -156.696 \tabularnewline
183 & 1282 & 1321.7 & -39.6957 \tabularnewline
184 & 1110 & 1321.7 & -211.696 \tabularnewline
185 & 1297 & 1321.7 & -24.6957 \tabularnewline
186 & 1185 & 1321.7 & -136.696 \tabularnewline
187 & 1222 & 1321.7 & -99.6957 \tabularnewline
188 & 1284 & 1321.7 & -37.6957 \tabularnewline
189 & 1444 & 1321.7 & 122.304 \tabularnewline
190 & 1575 & 1321.7 & 253.304 \tabularnewline
191 & 1737 & 1321.7 & 415.304 \tabularnewline
192 & 1763 & 1321.7 & 441.304 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=222038&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]1717.75[/C][C]-30.7515[/C][/ROW]
[ROW][C]2[/C][C]1508[/C][C]1717.75[/C][C]-209.751[/C][/ROW]
[ROW][C]3[/C][C]1507[/C][C]1717.75[/C][C]-210.751[/C][/ROW]
[ROW][C]4[/C][C]1385[/C][C]1717.75[/C][C]-332.751[/C][/ROW]
[ROW][C]5[/C][C]1632[/C][C]1717.75[/C][C]-85.7515[/C][/ROW]
[ROW][C]6[/C][C]1511[/C][C]1717.75[/C][C]-206.751[/C][/ROW]
[ROW][C]7[/C][C]1559[/C][C]1717.75[/C][C]-158.751[/C][/ROW]
[ROW][C]8[/C][C]1630[/C][C]1717.75[/C][C]-87.7515[/C][/ROW]
[ROW][C]9[/C][C]1579[/C][C]1717.75[/C][C]-138.751[/C][/ROW]
[ROW][C]10[/C][C]1653[/C][C]1717.75[/C][C]-64.7515[/C][/ROW]
[ROW][C]11[/C][C]2152[/C][C]1717.75[/C][C]434.249[/C][/ROW]
[ROW][C]12[/C][C]2148[/C][C]1717.75[/C][C]430.249[/C][/ROW]
[ROW][C]13[/C][C]1752[/C][C]1717.75[/C][C]34.2485[/C][/ROW]
[ROW][C]14[/C][C]1765[/C][C]1717.75[/C][C]47.2485[/C][/ROW]
[ROW][C]15[/C][C]1717[/C][C]1717.75[/C][C]-0.751479[/C][/ROW]
[ROW][C]16[/C][C]1558[/C][C]1717.75[/C][C]-159.751[/C][/ROW]
[ROW][C]17[/C][C]1575[/C][C]1717.75[/C][C]-142.751[/C][/ROW]
[ROW][C]18[/C][C]1520[/C][C]1717.75[/C][C]-197.751[/C][/ROW]
[ROW][C]19[/C][C]1805[/C][C]1717.75[/C][C]87.2485[/C][/ROW]
[ROW][C]20[/C][C]1800[/C][C]1717.75[/C][C]82.2485[/C][/ROW]
[ROW][C]21[/C][C]1719[/C][C]1717.75[/C][C]1.24852[/C][/ROW]
[ROW][C]22[/C][C]2008[/C][C]1717.75[/C][C]290.249[/C][/ROW]
[ROW][C]23[/C][C]2242[/C][C]1717.75[/C][C]524.249[/C][/ROW]
[ROW][C]24[/C][C]2478[/C][C]1717.75[/C][C]760.249[/C][/ROW]
[ROW][C]25[/C][C]2030[/C][C]1717.75[/C][C]312.249[/C][/ROW]
[ROW][C]26[/C][C]1655[/C][C]1717.75[/C][C]-62.7515[/C][/ROW]
[ROW][C]27[/C][C]1693[/C][C]1717.75[/C][C]-24.7515[/C][/ROW]
[ROW][C]28[/C][C]1623[/C][C]1717.75[/C][C]-94.7515[/C][/ROW]
[ROW][C]29[/C][C]1805[/C][C]1717.75[/C][C]87.2485[/C][/ROW]
[ROW][C]30[/C][C]1746[/C][C]1717.75[/C][C]28.2485[/C][/ROW]
[ROW][C]31[/C][C]1795[/C][C]1717.75[/C][C]77.2485[/C][/ROW]
[ROW][C]32[/C][C]1926[/C][C]1717.75[/C][C]208.249[/C][/ROW]
[ROW][C]33[/C][C]1619[/C][C]1717.75[/C][C]-98.7515[/C][/ROW]
[ROW][C]34[/C][C]1992[/C][C]1717.75[/C][C]274.249[/C][/ROW]
[ROW][C]35[/C][C]2233[/C][C]1717.75[/C][C]515.249[/C][/ROW]
[ROW][C]36[/C][C]2192[/C][C]1717.75[/C][C]474.249[/C][/ROW]
[ROW][C]37[/C][C]2080[/C][C]1717.75[/C][C]362.249[/C][/ROW]
[ROW][C]38[/C][C]1768[/C][C]1717.75[/C][C]50.2485[/C][/ROW]
[ROW][C]39[/C][C]1835[/C][C]1717.75[/C][C]117.249[/C][/ROW]
[ROW][C]40[/C][C]1569[/C][C]1717.75[/C][C]-148.751[/C][/ROW]
[ROW][C]41[/C][C]1976[/C][C]1717.75[/C][C]258.249[/C][/ROW]
[ROW][C]42[/C][C]1853[/C][C]1717.75[/C][C]135.249[/C][/ROW]
[ROW][C]43[/C][C]1965[/C][C]1717.75[/C][C]247.249[/C][/ROW]
[ROW][C]44[/C][C]1689[/C][C]1717.75[/C][C]-28.7515[/C][/ROW]
[ROW][C]45[/C][C]1778[/C][C]1717.75[/C][C]60.2485[/C][/ROW]
[ROW][C]46[/C][C]1976[/C][C]1717.75[/C][C]258.249[/C][/ROW]
[ROW][C]47[/C][C]2397[/C][C]1717.75[/C][C]679.249[/C][/ROW]
[ROW][C]48[/C][C]2654[/C][C]1717.75[/C][C]936.249[/C][/ROW]
[ROW][C]49[/C][C]2097[/C][C]1717.75[/C][C]379.249[/C][/ROW]
[ROW][C]50[/C][C]1963[/C][C]1717.75[/C][C]245.249[/C][/ROW]
[ROW][C]51[/C][C]1677[/C][C]1717.75[/C][C]-40.7515[/C][/ROW]
[ROW][C]52[/C][C]1941[/C][C]1717.75[/C][C]223.249[/C][/ROW]
[ROW][C]53[/C][C]2003[/C][C]1717.75[/C][C]285.249[/C][/ROW]
[ROW][C]54[/C][C]1813[/C][C]1717.75[/C][C]95.2485[/C][/ROW]
[ROW][C]55[/C][C]2012[/C][C]1717.75[/C][C]294.249[/C][/ROW]
[ROW][C]56[/C][C]1912[/C][C]1717.75[/C][C]194.249[/C][/ROW]
[ROW][C]57[/C][C]2084[/C][C]1717.75[/C][C]366.249[/C][/ROW]
[ROW][C]58[/C][C]2080[/C][C]1717.75[/C][C]362.249[/C][/ROW]
[ROW][C]59[/C][C]2118[/C][C]1717.75[/C][C]400.249[/C][/ROW]
[ROW][C]60[/C][C]2150[/C][C]1717.75[/C][C]432.249[/C][/ROW]
[ROW][C]61[/C][C]1608[/C][C]1717.75[/C][C]-109.751[/C][/ROW]
[ROW][C]62[/C][C]1503[/C][C]1717.75[/C][C]-214.751[/C][/ROW]
[ROW][C]63[/C][C]1548[/C][C]1717.75[/C][C]-169.751[/C][/ROW]
[ROW][C]64[/C][C]1382[/C][C]1717.75[/C][C]-335.751[/C][/ROW]
[ROW][C]65[/C][C]1731[/C][C]1717.75[/C][C]13.2485[/C][/ROW]
[ROW][C]66[/C][C]1798[/C][C]1717.75[/C][C]80.2485[/C][/ROW]
[ROW][C]67[/C][C]1779[/C][C]1717.75[/C][C]61.2485[/C][/ROW]
[ROW][C]68[/C][C]1887[/C][C]1717.75[/C][C]169.249[/C][/ROW]
[ROW][C]69[/C][C]2004[/C][C]1717.75[/C][C]286.249[/C][/ROW]
[ROW][C]70[/C][C]2077[/C][C]1717.75[/C][C]359.249[/C][/ROW]
[ROW][C]71[/C][C]2092[/C][C]1717.75[/C][C]374.249[/C][/ROW]
[ROW][C]72[/C][C]2051[/C][C]1717.75[/C][C]333.249[/C][/ROW]
[ROW][C]73[/C][C]1577[/C][C]1717.75[/C][C]-140.751[/C][/ROW]
[ROW][C]74[/C][C]1356[/C][C]1717.75[/C][C]-361.751[/C][/ROW]
[ROW][C]75[/C][C]1652[/C][C]1717.75[/C][C]-65.7515[/C][/ROW]
[ROW][C]76[/C][C]1382[/C][C]1717.75[/C][C]-335.751[/C][/ROW]
[ROW][C]77[/C][C]1519[/C][C]1717.75[/C][C]-198.751[/C][/ROW]
[ROW][C]78[/C][C]1421[/C][C]1717.75[/C][C]-296.751[/C][/ROW]
[ROW][C]79[/C][C]1442[/C][C]1717.75[/C][C]-275.751[/C][/ROW]
[ROW][C]80[/C][C]1543[/C][C]1717.75[/C][C]-174.751[/C][/ROW]
[ROW][C]81[/C][C]1656[/C][C]1717.75[/C][C]-61.7515[/C][/ROW]
[ROW][C]82[/C][C]1561[/C][C]1717.75[/C][C]-156.751[/C][/ROW]
[ROW][C]83[/C][C]1905[/C][C]1717.75[/C][C]187.249[/C][/ROW]
[ROW][C]84[/C][C]2199[/C][C]1717.75[/C][C]481.249[/C][/ROW]
[ROW][C]85[/C][C]1473[/C][C]1717.75[/C][C]-244.751[/C][/ROW]
[ROW][C]86[/C][C]1655[/C][C]1717.75[/C][C]-62.7515[/C][/ROW]
[ROW][C]87[/C][C]1407[/C][C]1717.75[/C][C]-310.751[/C][/ROW]
[ROW][C]88[/C][C]1395[/C][C]1717.75[/C][C]-322.751[/C][/ROW]
[ROW][C]89[/C][C]1530[/C][C]1717.75[/C][C]-187.751[/C][/ROW]
[ROW][C]90[/C][C]1309[/C][C]1717.75[/C][C]-408.751[/C][/ROW]
[ROW][C]91[/C][C]1526[/C][C]1717.75[/C][C]-191.751[/C][/ROW]
[ROW][C]92[/C][C]1327[/C][C]1717.75[/C][C]-390.751[/C][/ROW]
[ROW][C]93[/C][C]1627[/C][C]1717.75[/C][C]-90.7515[/C][/ROW]
[ROW][C]94[/C][C]1748[/C][C]1717.75[/C][C]30.2485[/C][/ROW]
[ROW][C]95[/C][C]1958[/C][C]1717.75[/C][C]240.249[/C][/ROW]
[ROW][C]96[/C][C]2274[/C][C]1717.75[/C][C]556.249[/C][/ROW]
[ROW][C]97[/C][C]1648[/C][C]1717.75[/C][C]-69.7515[/C][/ROW]
[ROW][C]98[/C][C]1401[/C][C]1717.75[/C][C]-316.751[/C][/ROW]
[ROW][C]99[/C][C]1411[/C][C]1717.75[/C][C]-306.751[/C][/ROW]
[ROW][C]100[/C][C]1403[/C][C]1717.75[/C][C]-314.751[/C][/ROW]
[ROW][C]101[/C][C]1394[/C][C]1717.75[/C][C]-323.751[/C][/ROW]
[ROW][C]102[/C][C]1520[/C][C]1717.75[/C][C]-197.751[/C][/ROW]
[ROW][C]103[/C][C]1528[/C][C]1717.75[/C][C]-189.751[/C][/ROW]
[ROW][C]104[/C][C]1643[/C][C]1717.75[/C][C]-74.7515[/C][/ROW]
[ROW][C]105[/C][C]1515[/C][C]1717.75[/C][C]-202.751[/C][/ROW]
[ROW][C]106[/C][C]1685[/C][C]1717.75[/C][C]-32.7515[/C][/ROW]
[ROW][C]107[/C][C]2000[/C][C]1717.75[/C][C]282.249[/C][/ROW]
[ROW][C]108[/C][C]2215[/C][C]1717.75[/C][C]497.249[/C][/ROW]
[ROW][C]109[/C][C]1956[/C][C]1717.75[/C][C]238.249[/C][/ROW]
[ROW][C]110[/C][C]1462[/C][C]1717.75[/C][C]-255.751[/C][/ROW]
[ROW][C]111[/C][C]1563[/C][C]1717.75[/C][C]-154.751[/C][/ROW]
[ROW][C]112[/C][C]1459[/C][C]1717.75[/C][C]-258.751[/C][/ROW]
[ROW][C]113[/C][C]1446[/C][C]1717.75[/C][C]-271.751[/C][/ROW]
[ROW][C]114[/C][C]1622[/C][C]1717.75[/C][C]-95.7515[/C][/ROW]
[ROW][C]115[/C][C]1657[/C][C]1717.75[/C][C]-60.7515[/C][/ROW]
[ROW][C]116[/C][C]1638[/C][C]1717.75[/C][C]-79.7515[/C][/ROW]
[ROW][C]117[/C][C]1643[/C][C]1717.75[/C][C]-74.7515[/C][/ROW]
[ROW][C]118[/C][C]1683[/C][C]1717.75[/C][C]-34.7515[/C][/ROW]
[ROW][C]119[/C][C]2050[/C][C]1717.75[/C][C]332.249[/C][/ROW]
[ROW][C]120[/C][C]2262[/C][C]1717.75[/C][C]544.249[/C][/ROW]
[ROW][C]121[/C][C]1813[/C][C]1717.75[/C][C]95.2485[/C][/ROW]
[ROW][C]122[/C][C]1445[/C][C]1717.75[/C][C]-272.751[/C][/ROW]
[ROW][C]123[/C][C]1762[/C][C]1717.75[/C][C]44.2485[/C][/ROW]
[ROW][C]124[/C][C]1461[/C][C]1717.75[/C][C]-256.751[/C][/ROW]
[ROW][C]125[/C][C]1556[/C][C]1717.75[/C][C]-161.751[/C][/ROW]
[ROW][C]126[/C][C]1431[/C][C]1717.75[/C][C]-286.751[/C][/ROW]
[ROW][C]127[/C][C]1427[/C][C]1717.75[/C][C]-290.751[/C][/ROW]
[ROW][C]128[/C][C]1554[/C][C]1717.75[/C][C]-163.751[/C][/ROW]
[ROW][C]129[/C][C]1645[/C][C]1717.75[/C][C]-72.7515[/C][/ROW]
[ROW][C]130[/C][C]1653[/C][C]1717.75[/C][C]-64.7515[/C][/ROW]
[ROW][C]131[/C][C]2016[/C][C]1717.75[/C][C]298.249[/C][/ROW]
[ROW][C]132[/C][C]2207[/C][C]1717.75[/C][C]489.249[/C][/ROW]
[ROW][C]133[/C][C]1665[/C][C]1717.75[/C][C]-52.7515[/C][/ROW]
[ROW][C]134[/C][C]1361[/C][C]1717.75[/C][C]-356.751[/C][/ROW]
[ROW][C]135[/C][C]1506[/C][C]1717.75[/C][C]-211.751[/C][/ROW]
[ROW][C]136[/C][C]1360[/C][C]1717.75[/C][C]-357.751[/C][/ROW]
[ROW][C]137[/C][C]1453[/C][C]1717.75[/C][C]-264.751[/C][/ROW]
[ROW][C]138[/C][C]1522[/C][C]1717.75[/C][C]-195.751[/C][/ROW]
[ROW][C]139[/C][C]1460[/C][C]1717.75[/C][C]-257.751[/C][/ROW]
[ROW][C]140[/C][C]1552[/C][C]1717.75[/C][C]-165.751[/C][/ROW]
[ROW][C]141[/C][C]1548[/C][C]1717.75[/C][C]-169.751[/C][/ROW]
[ROW][C]142[/C][C]1827[/C][C]1717.75[/C][C]109.249[/C][/ROW]
[ROW][C]143[/C][C]1737[/C][C]1717.75[/C][C]19.2485[/C][/ROW]
[ROW][C]144[/C][C]1941[/C][C]1717.75[/C][C]223.249[/C][/ROW]
[ROW][C]145[/C][C]1474[/C][C]1717.75[/C][C]-243.751[/C][/ROW]
[ROW][C]146[/C][C]1458[/C][C]1717.75[/C][C]-259.751[/C][/ROW]
[ROW][C]147[/C][C]1542[/C][C]1717.75[/C][C]-175.751[/C][/ROW]
[ROW][C]148[/C][C]1404[/C][C]1717.75[/C][C]-313.751[/C][/ROW]
[ROW][C]149[/C][C]1522[/C][C]1717.75[/C][C]-195.751[/C][/ROW]
[ROW][C]150[/C][C]1385[/C][C]1717.75[/C][C]-332.751[/C][/ROW]
[ROW][C]151[/C][C]1641[/C][C]1717.75[/C][C]-76.7515[/C][/ROW]
[ROW][C]152[/C][C]1510[/C][C]1717.75[/C][C]-207.751[/C][/ROW]
[ROW][C]153[/C][C]1681[/C][C]1717.75[/C][C]-36.7515[/C][/ROW]
[ROW][C]154[/C][C]1938[/C][C]1717.75[/C][C]220.249[/C][/ROW]
[ROW][C]155[/C][C]1868[/C][C]1717.75[/C][C]150.249[/C][/ROW]
[ROW][C]156[/C][C]1726[/C][C]1717.75[/C][C]8.24852[/C][/ROW]
[ROW][C]157[/C][C]1456[/C][C]1717.75[/C][C]-261.751[/C][/ROW]
[ROW][C]158[/C][C]1445[/C][C]1717.75[/C][C]-272.751[/C][/ROW]
[ROW][C]159[/C][C]1456[/C][C]1717.75[/C][C]-261.751[/C][/ROW]
[ROW][C]160[/C][C]1365[/C][C]1717.75[/C][C]-352.751[/C][/ROW]
[ROW][C]161[/C][C]1487[/C][C]1717.75[/C][C]-230.751[/C][/ROW]
[ROW][C]162[/C][C]1558[/C][C]1717.75[/C][C]-159.751[/C][/ROW]
[ROW][C]163[/C][C]1488[/C][C]1717.75[/C][C]-229.751[/C][/ROW]
[ROW][C]164[/C][C]1684[/C][C]1717.75[/C][C]-33.7515[/C][/ROW]
[ROW][C]165[/C][C]1594[/C][C]1717.75[/C][C]-123.751[/C][/ROW]
[ROW][C]166[/C][C]1850[/C][C]1717.75[/C][C]132.249[/C][/ROW]
[ROW][C]167[/C][C]1998[/C][C]1717.75[/C][C]280.249[/C][/ROW]
[ROW][C]168[/C][C]2079[/C][C]1717.75[/C][C]361.249[/C][/ROW]
[ROW][C]169[/C][C]1494[/C][C]1717.75[/C][C]-223.751[/C][/ROW]
[ROW][C]170[/C][C]1057[/C][C]1321.7[/C][C]-264.696[/C][/ROW]
[ROW][C]171[/C][C]1218[/C][C]1321.7[/C][C]-103.696[/C][/ROW]
[ROW][C]172[/C][C]1168[/C][C]1321.7[/C][C]-153.696[/C][/ROW]
[ROW][C]173[/C][C]1236[/C][C]1321.7[/C][C]-85.6957[/C][/ROW]
[ROW][C]174[/C][C]1076[/C][C]1321.7[/C][C]-245.696[/C][/ROW]
[ROW][C]175[/C][C]1174[/C][C]1321.7[/C][C]-147.696[/C][/ROW]
[ROW][C]176[/C][C]1139[/C][C]1321.7[/C][C]-182.696[/C][/ROW]
[ROW][C]177[/C][C]1427[/C][C]1321.7[/C][C]105.304[/C][/ROW]
[ROW][C]178[/C][C]1487[/C][C]1321.7[/C][C]165.304[/C][/ROW]
[ROW][C]179[/C][C]1483[/C][C]1321.7[/C][C]161.304[/C][/ROW]
[ROW][C]180[/C][C]1513[/C][C]1321.7[/C][C]191.304[/C][/ROW]
[ROW][C]181[/C][C]1357[/C][C]1321.7[/C][C]35.3043[/C][/ROW]
[ROW][C]182[/C][C]1165[/C][C]1321.7[/C][C]-156.696[/C][/ROW]
[ROW][C]183[/C][C]1282[/C][C]1321.7[/C][C]-39.6957[/C][/ROW]
[ROW][C]184[/C][C]1110[/C][C]1321.7[/C][C]-211.696[/C][/ROW]
[ROW][C]185[/C][C]1297[/C][C]1321.7[/C][C]-24.6957[/C][/ROW]
[ROW][C]186[/C][C]1185[/C][C]1321.7[/C][C]-136.696[/C][/ROW]
[ROW][C]187[/C][C]1222[/C][C]1321.7[/C][C]-99.6957[/C][/ROW]
[ROW][C]188[/C][C]1284[/C][C]1321.7[/C][C]-37.6957[/C][/ROW]
[ROW][C]189[/C][C]1444[/C][C]1321.7[/C][C]122.304[/C][/ROW]
[ROW][C]190[/C][C]1575[/C][C]1321.7[/C][C]253.304[/C][/ROW]
[ROW][C]191[/C][C]1737[/C][C]1321.7[/C][C]415.304[/C][/ROW]
[ROW][C]192[/C][C]1763[/C][C]1321.7[/C][C]441.304[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=222038&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=222038&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
116871717.75-30.7515
215081717.75-209.751
315071717.75-210.751
413851717.75-332.751
516321717.75-85.7515
615111717.75-206.751
715591717.75-158.751
816301717.75-87.7515
915791717.75-138.751
1016531717.75-64.7515
1121521717.75434.249
1221481717.75430.249
1317521717.7534.2485
1417651717.7547.2485
1517171717.75-0.751479
1615581717.75-159.751
1715751717.75-142.751
1815201717.75-197.751
1918051717.7587.2485
2018001717.7582.2485
2117191717.751.24852
2220081717.75290.249
2322421717.75524.249
2424781717.75760.249
2520301717.75312.249
2616551717.75-62.7515
2716931717.75-24.7515
2816231717.75-94.7515
2918051717.7587.2485
3017461717.7528.2485
3117951717.7577.2485
3219261717.75208.249
3316191717.75-98.7515
3419921717.75274.249
3522331717.75515.249
3621921717.75474.249
3720801717.75362.249
3817681717.7550.2485
3918351717.75117.249
4015691717.75-148.751
4119761717.75258.249
4218531717.75135.249
4319651717.75247.249
4416891717.75-28.7515
4517781717.7560.2485
4619761717.75258.249
4723971717.75679.249
4826541717.75936.249
4920971717.75379.249
5019631717.75245.249
5116771717.75-40.7515
5219411717.75223.249
5320031717.75285.249
5418131717.7595.2485
5520121717.75294.249
5619121717.75194.249
5720841717.75366.249
5820801717.75362.249
5921181717.75400.249
6021501717.75432.249
6116081717.75-109.751
6215031717.75-214.751
6315481717.75-169.751
6413821717.75-335.751
6517311717.7513.2485
6617981717.7580.2485
6717791717.7561.2485
6818871717.75169.249
6920041717.75286.249
7020771717.75359.249
7120921717.75374.249
7220511717.75333.249
7315771717.75-140.751
7413561717.75-361.751
7516521717.75-65.7515
7613821717.75-335.751
7715191717.75-198.751
7814211717.75-296.751
7914421717.75-275.751
8015431717.75-174.751
8116561717.75-61.7515
8215611717.75-156.751
8319051717.75187.249
8421991717.75481.249
8514731717.75-244.751
8616551717.75-62.7515
8714071717.75-310.751
8813951717.75-322.751
8915301717.75-187.751
9013091717.75-408.751
9115261717.75-191.751
9213271717.75-390.751
9316271717.75-90.7515
9417481717.7530.2485
9519581717.75240.249
9622741717.75556.249
9716481717.75-69.7515
9814011717.75-316.751
9914111717.75-306.751
10014031717.75-314.751
10113941717.75-323.751
10215201717.75-197.751
10315281717.75-189.751
10416431717.75-74.7515
10515151717.75-202.751
10616851717.75-32.7515
10720001717.75282.249
10822151717.75497.249
10919561717.75238.249
11014621717.75-255.751
11115631717.75-154.751
11214591717.75-258.751
11314461717.75-271.751
11416221717.75-95.7515
11516571717.75-60.7515
11616381717.75-79.7515
11716431717.75-74.7515
11816831717.75-34.7515
11920501717.75332.249
12022621717.75544.249
12118131717.7595.2485
12214451717.75-272.751
12317621717.7544.2485
12414611717.75-256.751
12515561717.75-161.751
12614311717.75-286.751
12714271717.75-290.751
12815541717.75-163.751
12916451717.75-72.7515
13016531717.75-64.7515
13120161717.75298.249
13222071717.75489.249
13316651717.75-52.7515
13413611717.75-356.751
13515061717.75-211.751
13613601717.75-357.751
13714531717.75-264.751
13815221717.75-195.751
13914601717.75-257.751
14015521717.75-165.751
14115481717.75-169.751
14218271717.75109.249
14317371717.7519.2485
14419411717.75223.249
14514741717.75-243.751
14614581717.75-259.751
14715421717.75-175.751
14814041717.75-313.751
14915221717.75-195.751
15013851717.75-332.751
15116411717.75-76.7515
15215101717.75-207.751
15316811717.75-36.7515
15419381717.75220.249
15518681717.75150.249
15617261717.758.24852
15714561717.75-261.751
15814451717.75-272.751
15914561717.75-261.751
16013651717.75-352.751
16114871717.75-230.751
16215581717.75-159.751
16314881717.75-229.751
16416841717.75-33.7515
16515941717.75-123.751
16618501717.75132.249
16719981717.75280.249
16820791717.75361.249
16914941717.75-223.751
17010571321.7-264.696
17112181321.7-103.696
17211681321.7-153.696
17312361321.7-85.6957
17410761321.7-245.696
17511741321.7-147.696
17611391321.7-182.696
17714271321.7105.304
17814871321.7165.304
17914831321.7161.304
18015131321.7191.304
18113571321.735.3043
18211651321.7-156.696
18312821321.7-39.6957
18411101321.7-211.696
18512971321.7-24.6957
18611851321.7-136.696
18712221321.7-99.6957
18812841321.7-37.6957
18914441321.7122.304
19015751321.7253.304
19117371321.7415.304
19217631321.7441.304






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.1559310.3118620.844069
60.06649350.1329870.933506
70.02567380.05134770.974326
80.01221740.02443480.987783
90.004379980.008759960.99562
100.002240160.004480320.99776
110.223580.447160.77642
120.5039810.9920370.496019
130.4208410.8416820.579159
140.3453660.6907310.654634
150.2700180.5400360.729982
160.2190640.4381290.780936
170.1710750.342150.828925
180.1407120.2814230.859288
190.1140920.2281840.885908
200.09007930.1801590.909921
210.06388480.127770.936115
220.0882860.1765720.911714
230.2540370.5080730.745963
240.7041350.5917310.295865
250.7099490.5801010.290051
260.6617330.6765340.338267
270.6065220.7869550.393478
280.5597190.8805620.440281
290.5037380.9925250.496262
300.4449610.8899220.555039
310.3901830.7803660.609817
320.3621280.7242560.637872
330.3227250.645450.677275
340.318470.636940.68153
350.4475030.8950060.552497
360.5401910.9196190.459809
370.5622950.8754110.437705
380.5106860.9786280.489314
390.4622780.9245560.537722
400.4432410.8864830.556759
410.4257390.8514770.574261
420.3820740.7641480.617926
430.3620120.7240230.637988
440.3219930.6439850.678007
450.2794280.5588570.720572
460.2656490.5312970.734351
470.4937820.9875640.506218
480.8899380.2201250.110062
490.9011140.1977730.0988863
500.8919310.2161370.108069
510.8749590.2500820.125041
520.8619240.2761510.138076
530.8577420.2845160.142258
540.8337870.3324260.166213
550.8316840.3366330.168316
560.8134520.3730960.186548
570.829990.340020.17001
580.8452940.3094120.154706
590.8698870.2602270.130113
600.8999730.2000540.100027
610.8929120.2141770.107088
620.8998140.2003730.100186
630.8987190.2025620.101281
640.9242220.1515570.0757784
650.9102740.1794520.0897258
660.8947930.2104150.105207
670.877170.245660.12283
680.8640970.2718050.135903
690.8678740.2642530.132126
700.8871760.2256470.112824
710.908450.1831010.0915504
720.920870.1582610.0791305
730.916010.1679790.0839897
740.9404030.1191950.0595975
750.9312920.1374160.0687078
760.9469560.1060880.0530439
770.9458820.1082370.0541184
780.9534360.09312740.0465637
790.9577670.08446620.0422331
800.9544120.09117650.0455882
810.9457910.1084180.0542088
820.9401750.119650.059825
830.9361310.1277380.0638689
840.9663390.06732290.0336614
850.9669160.06616790.0330839
860.9601640.07967180.0398359
870.9652770.06944590.0347229
880.9703790.05924110.0296205
890.9674640.06507190.0325359
900.9777370.04452540.0222627
910.9753230.04935470.0246774
920.9821280.03574480.0178724
930.9779040.04419210.0220961
940.972580.05483990.02742
950.9736520.05269560.0263478
960.9921480.01570430.00785213
970.9899440.02011160.0100558
980.991080.01783910.00891953
990.9918520.01629590.00814795
1000.9926890.01462170.00731084
1010.9935880.01282410.00641203
1020.9925870.01482510.00741253
1030.9913340.01733140.00866569
1040.9887870.02242680.0112134
1050.9871970.02560510.0128026
1060.9834890.03302250.0165113
1070.986210.02757990.0137899
1080.9954810.009037550.00451877
1090.9960740.007851320.00392566
1100.9958080.008383570.00419179
1110.9947130.01057490.00528744
1120.9943840.01123260.00561628
1130.9942030.01159450.00579723
1140.9923720.01525620.00762811
1150.9899370.02012560.0100628
1160.986890.02622010.0131101
1170.9830390.03392250.0169612
1180.9782010.04359790.021799
1190.9854990.02900190.014501
1200.9972740.005452520.00272626
1210.9968410.0063190.0031595
1220.9966040.006791340.00339567
1230.9957320.008536190.0042681
1240.9952320.009536130.00476806
1250.9938360.01232860.00616428
1260.9935790.01284180.00642089
1270.9933940.0132130.0066065
1280.9915250.01695060.00847531
1290.9886550.0226890.0113445
1300.9849590.03008130.0150406
1310.9902220.01955670.00977837
1320.998280.003440020.00172001
1330.9975920.004816750.00240838
1340.9978930.004214370.00210719
1350.9972940.00541220.0027061
1360.9976780.004644620.00232231
1370.9973550.005290820.00264541
1380.9965470.006906890.00345345
1390.9960450.007909420.00395471
1400.9946870.01062610.00531303
1410.9929570.01408510.00704257
1420.9920330.0159340.00796699
1430.9895840.02083250.0104162
1440.9920820.01583670.00791834
1450.9904490.0191010.00955052
1460.988950.02210060.0110503
1470.9854560.02908790.0145439
1480.9855710.02885770.0144289
1490.981780.03643970.0182198
1500.9835230.03295460.0164773
1510.9772490.04550220.0227511
1520.972640.05471980.0273599
1530.9629260.07414890.0370745
1540.9673030.0653950.0326975
1550.9666860.06662890.0333145
1560.9571920.08561660.0428083
1570.9511040.09779230.0488962
1580.9465730.1068530.0534267
1590.941990.116020.0580102
1600.9539460.09210720.0460536
1610.9517820.0964360.048218
1620.9437820.1124370.0562183
1630.9488530.1022940.0511472
1640.933870.1322610.0661304
1650.9320180.1359640.067982
1660.9084080.1831830.0915916
1670.8966960.2066090.103304
1680.9463250.107350.0536751
1690.9257620.1484760.074238
1700.9320740.1358520.0679259
1710.9121990.1756030.0878014
1720.897140.205720.10286
1730.8679720.2640550.132028
1740.8840550.2318890.115945
1750.8717410.2565180.128259
1760.875980.2480390.12402
1770.8294720.3410550.170528
1780.7812490.4375020.218751
1790.7230850.5538290.276915
1800.6680980.6638040.331902
1810.5736340.8527310.426366
1820.5400640.9198720.459936
1830.4498560.8997120.550144
1840.4910290.9820590.508971
1850.4034630.8069250.596537
1860.4288960.8577910.571104
1870.4881410.9762820.511859

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.155931 & 0.311862 & 0.844069 \tabularnewline
6 & 0.0664935 & 0.132987 & 0.933506 \tabularnewline
7 & 0.0256738 & 0.0513477 & 0.974326 \tabularnewline
8 & 0.0122174 & 0.0244348 & 0.987783 \tabularnewline
9 & 0.00437998 & 0.00875996 & 0.99562 \tabularnewline
10 & 0.00224016 & 0.00448032 & 0.99776 \tabularnewline
11 & 0.22358 & 0.44716 & 0.77642 \tabularnewline
12 & 0.503981 & 0.992037 & 0.496019 \tabularnewline
13 & 0.420841 & 0.841682 & 0.579159 \tabularnewline
14 & 0.345366 & 0.690731 & 0.654634 \tabularnewline
15 & 0.270018 & 0.540036 & 0.729982 \tabularnewline
16 & 0.219064 & 0.438129 & 0.780936 \tabularnewline
17 & 0.171075 & 0.34215 & 0.828925 \tabularnewline
18 & 0.140712 & 0.281423 & 0.859288 \tabularnewline
19 & 0.114092 & 0.228184 & 0.885908 \tabularnewline
20 & 0.0900793 & 0.180159 & 0.909921 \tabularnewline
21 & 0.0638848 & 0.12777 & 0.936115 \tabularnewline
22 & 0.088286 & 0.176572 & 0.911714 \tabularnewline
23 & 0.254037 & 0.508073 & 0.745963 \tabularnewline
24 & 0.704135 & 0.591731 & 0.295865 \tabularnewline
25 & 0.709949 & 0.580101 & 0.290051 \tabularnewline
26 & 0.661733 & 0.676534 & 0.338267 \tabularnewline
27 & 0.606522 & 0.786955 & 0.393478 \tabularnewline
28 & 0.559719 & 0.880562 & 0.440281 \tabularnewline
29 & 0.503738 & 0.992525 & 0.496262 \tabularnewline
30 & 0.444961 & 0.889922 & 0.555039 \tabularnewline
31 & 0.390183 & 0.780366 & 0.609817 \tabularnewline
32 & 0.362128 & 0.724256 & 0.637872 \tabularnewline
33 & 0.322725 & 0.64545 & 0.677275 \tabularnewline
34 & 0.31847 & 0.63694 & 0.68153 \tabularnewline
35 & 0.447503 & 0.895006 & 0.552497 \tabularnewline
36 & 0.540191 & 0.919619 & 0.459809 \tabularnewline
37 & 0.562295 & 0.875411 & 0.437705 \tabularnewline
38 & 0.510686 & 0.978628 & 0.489314 \tabularnewline
39 & 0.462278 & 0.924556 & 0.537722 \tabularnewline
40 & 0.443241 & 0.886483 & 0.556759 \tabularnewline
41 & 0.425739 & 0.851477 & 0.574261 \tabularnewline
42 & 0.382074 & 0.764148 & 0.617926 \tabularnewline
43 & 0.362012 & 0.724023 & 0.637988 \tabularnewline
44 & 0.321993 & 0.643985 & 0.678007 \tabularnewline
45 & 0.279428 & 0.558857 & 0.720572 \tabularnewline
46 & 0.265649 & 0.531297 & 0.734351 \tabularnewline
47 & 0.493782 & 0.987564 & 0.506218 \tabularnewline
48 & 0.889938 & 0.220125 & 0.110062 \tabularnewline
49 & 0.901114 & 0.197773 & 0.0988863 \tabularnewline
50 & 0.891931 & 0.216137 & 0.108069 \tabularnewline
51 & 0.874959 & 0.250082 & 0.125041 \tabularnewline
52 & 0.861924 & 0.276151 & 0.138076 \tabularnewline
53 & 0.857742 & 0.284516 & 0.142258 \tabularnewline
54 & 0.833787 & 0.332426 & 0.166213 \tabularnewline
55 & 0.831684 & 0.336633 & 0.168316 \tabularnewline
56 & 0.813452 & 0.373096 & 0.186548 \tabularnewline
57 & 0.82999 & 0.34002 & 0.17001 \tabularnewline
58 & 0.845294 & 0.309412 & 0.154706 \tabularnewline
59 & 0.869887 & 0.260227 & 0.130113 \tabularnewline
60 & 0.899973 & 0.200054 & 0.100027 \tabularnewline
61 & 0.892912 & 0.214177 & 0.107088 \tabularnewline
62 & 0.899814 & 0.200373 & 0.100186 \tabularnewline
63 & 0.898719 & 0.202562 & 0.101281 \tabularnewline
64 & 0.924222 & 0.151557 & 0.0757784 \tabularnewline
65 & 0.910274 & 0.179452 & 0.0897258 \tabularnewline
66 & 0.894793 & 0.210415 & 0.105207 \tabularnewline
67 & 0.87717 & 0.24566 & 0.12283 \tabularnewline
68 & 0.864097 & 0.271805 & 0.135903 \tabularnewline
69 & 0.867874 & 0.264253 & 0.132126 \tabularnewline
70 & 0.887176 & 0.225647 & 0.112824 \tabularnewline
71 & 0.90845 & 0.183101 & 0.0915504 \tabularnewline
72 & 0.92087 & 0.158261 & 0.0791305 \tabularnewline
73 & 0.91601 & 0.167979 & 0.0839897 \tabularnewline
74 & 0.940403 & 0.119195 & 0.0595975 \tabularnewline
75 & 0.931292 & 0.137416 & 0.0687078 \tabularnewline
76 & 0.946956 & 0.106088 & 0.0530439 \tabularnewline
77 & 0.945882 & 0.108237 & 0.0541184 \tabularnewline
78 & 0.953436 & 0.0931274 & 0.0465637 \tabularnewline
79 & 0.957767 & 0.0844662 & 0.0422331 \tabularnewline
80 & 0.954412 & 0.0911765 & 0.0455882 \tabularnewline
81 & 0.945791 & 0.108418 & 0.0542088 \tabularnewline
82 & 0.940175 & 0.11965 & 0.059825 \tabularnewline
83 & 0.936131 & 0.127738 & 0.0638689 \tabularnewline
84 & 0.966339 & 0.0673229 & 0.0336614 \tabularnewline
85 & 0.966916 & 0.0661679 & 0.0330839 \tabularnewline
86 & 0.960164 & 0.0796718 & 0.0398359 \tabularnewline
87 & 0.965277 & 0.0694459 & 0.0347229 \tabularnewline
88 & 0.970379 & 0.0592411 & 0.0296205 \tabularnewline
89 & 0.967464 & 0.0650719 & 0.0325359 \tabularnewline
90 & 0.977737 & 0.0445254 & 0.0222627 \tabularnewline
91 & 0.975323 & 0.0493547 & 0.0246774 \tabularnewline
92 & 0.982128 & 0.0357448 & 0.0178724 \tabularnewline
93 & 0.977904 & 0.0441921 & 0.0220961 \tabularnewline
94 & 0.97258 & 0.0548399 & 0.02742 \tabularnewline
95 & 0.973652 & 0.0526956 & 0.0263478 \tabularnewline
96 & 0.992148 & 0.0157043 & 0.00785213 \tabularnewline
97 & 0.989944 & 0.0201116 & 0.0100558 \tabularnewline
98 & 0.99108 & 0.0178391 & 0.00891953 \tabularnewline
99 & 0.991852 & 0.0162959 & 0.00814795 \tabularnewline
100 & 0.992689 & 0.0146217 & 0.00731084 \tabularnewline
101 & 0.993588 & 0.0128241 & 0.00641203 \tabularnewline
102 & 0.992587 & 0.0148251 & 0.00741253 \tabularnewline
103 & 0.991334 & 0.0173314 & 0.00866569 \tabularnewline
104 & 0.988787 & 0.0224268 & 0.0112134 \tabularnewline
105 & 0.987197 & 0.0256051 & 0.0128026 \tabularnewline
106 & 0.983489 & 0.0330225 & 0.0165113 \tabularnewline
107 & 0.98621 & 0.0275799 & 0.0137899 \tabularnewline
108 & 0.995481 & 0.00903755 & 0.00451877 \tabularnewline
109 & 0.996074 & 0.00785132 & 0.00392566 \tabularnewline
110 & 0.995808 & 0.00838357 & 0.00419179 \tabularnewline
111 & 0.994713 & 0.0105749 & 0.00528744 \tabularnewline
112 & 0.994384 & 0.0112326 & 0.00561628 \tabularnewline
113 & 0.994203 & 0.0115945 & 0.00579723 \tabularnewline
114 & 0.992372 & 0.0152562 & 0.00762811 \tabularnewline
115 & 0.989937 & 0.0201256 & 0.0100628 \tabularnewline
116 & 0.98689 & 0.0262201 & 0.0131101 \tabularnewline
117 & 0.983039 & 0.0339225 & 0.0169612 \tabularnewline
118 & 0.978201 & 0.0435979 & 0.021799 \tabularnewline
119 & 0.985499 & 0.0290019 & 0.014501 \tabularnewline
120 & 0.997274 & 0.00545252 & 0.00272626 \tabularnewline
121 & 0.996841 & 0.006319 & 0.0031595 \tabularnewline
122 & 0.996604 & 0.00679134 & 0.00339567 \tabularnewline
123 & 0.995732 & 0.00853619 & 0.0042681 \tabularnewline
124 & 0.995232 & 0.00953613 & 0.00476806 \tabularnewline
125 & 0.993836 & 0.0123286 & 0.00616428 \tabularnewline
126 & 0.993579 & 0.0128418 & 0.00642089 \tabularnewline
127 & 0.993394 & 0.013213 & 0.0066065 \tabularnewline
128 & 0.991525 & 0.0169506 & 0.00847531 \tabularnewline
129 & 0.988655 & 0.022689 & 0.0113445 \tabularnewline
130 & 0.984959 & 0.0300813 & 0.0150406 \tabularnewline
131 & 0.990222 & 0.0195567 & 0.00977837 \tabularnewline
132 & 0.99828 & 0.00344002 & 0.00172001 \tabularnewline
133 & 0.997592 & 0.00481675 & 0.00240838 \tabularnewline
134 & 0.997893 & 0.00421437 & 0.00210719 \tabularnewline
135 & 0.997294 & 0.0054122 & 0.0027061 \tabularnewline
136 & 0.997678 & 0.00464462 & 0.00232231 \tabularnewline
137 & 0.997355 & 0.00529082 & 0.00264541 \tabularnewline
138 & 0.996547 & 0.00690689 & 0.00345345 \tabularnewline
139 & 0.996045 & 0.00790942 & 0.00395471 \tabularnewline
140 & 0.994687 & 0.0106261 & 0.00531303 \tabularnewline
141 & 0.992957 & 0.0140851 & 0.00704257 \tabularnewline
142 & 0.992033 & 0.015934 & 0.00796699 \tabularnewline
143 & 0.989584 & 0.0208325 & 0.0104162 \tabularnewline
144 & 0.992082 & 0.0158367 & 0.00791834 \tabularnewline
145 & 0.990449 & 0.019101 & 0.00955052 \tabularnewline
146 & 0.98895 & 0.0221006 & 0.0110503 \tabularnewline
147 & 0.985456 & 0.0290879 & 0.0145439 \tabularnewline
148 & 0.985571 & 0.0288577 & 0.0144289 \tabularnewline
149 & 0.98178 & 0.0364397 & 0.0182198 \tabularnewline
150 & 0.983523 & 0.0329546 & 0.0164773 \tabularnewline
151 & 0.977249 & 0.0455022 & 0.0227511 \tabularnewline
152 & 0.97264 & 0.0547198 & 0.0273599 \tabularnewline
153 & 0.962926 & 0.0741489 & 0.0370745 \tabularnewline
154 & 0.967303 & 0.065395 & 0.0326975 \tabularnewline
155 & 0.966686 & 0.0666289 & 0.0333145 \tabularnewline
156 & 0.957192 & 0.0856166 & 0.0428083 \tabularnewline
157 & 0.951104 & 0.0977923 & 0.0488962 \tabularnewline
158 & 0.946573 & 0.106853 & 0.0534267 \tabularnewline
159 & 0.94199 & 0.11602 & 0.0580102 \tabularnewline
160 & 0.953946 & 0.0921072 & 0.0460536 \tabularnewline
161 & 0.951782 & 0.096436 & 0.048218 \tabularnewline
162 & 0.943782 & 0.112437 & 0.0562183 \tabularnewline
163 & 0.948853 & 0.102294 & 0.0511472 \tabularnewline
164 & 0.93387 & 0.132261 & 0.0661304 \tabularnewline
165 & 0.932018 & 0.135964 & 0.067982 \tabularnewline
166 & 0.908408 & 0.183183 & 0.0915916 \tabularnewline
167 & 0.896696 & 0.206609 & 0.103304 \tabularnewline
168 & 0.946325 & 0.10735 & 0.0536751 \tabularnewline
169 & 0.925762 & 0.148476 & 0.074238 \tabularnewline
170 & 0.932074 & 0.135852 & 0.0679259 \tabularnewline
171 & 0.912199 & 0.175603 & 0.0878014 \tabularnewline
172 & 0.89714 & 0.20572 & 0.10286 \tabularnewline
173 & 0.867972 & 0.264055 & 0.132028 \tabularnewline
174 & 0.884055 & 0.231889 & 0.115945 \tabularnewline
175 & 0.871741 & 0.256518 & 0.128259 \tabularnewline
176 & 0.87598 & 0.248039 & 0.12402 \tabularnewline
177 & 0.829472 & 0.341055 & 0.170528 \tabularnewline
178 & 0.781249 & 0.437502 & 0.218751 \tabularnewline
179 & 0.723085 & 0.553829 & 0.276915 \tabularnewline
180 & 0.668098 & 0.663804 & 0.331902 \tabularnewline
181 & 0.573634 & 0.852731 & 0.426366 \tabularnewline
182 & 0.540064 & 0.919872 & 0.459936 \tabularnewline
183 & 0.449856 & 0.899712 & 0.550144 \tabularnewline
184 & 0.491029 & 0.982059 & 0.508971 \tabularnewline
185 & 0.403463 & 0.806925 & 0.596537 \tabularnewline
186 & 0.428896 & 0.857791 & 0.571104 \tabularnewline
187 & 0.488141 & 0.976282 & 0.511859 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=222038&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]0.155931[/C][C]0.311862[/C][C]0.844069[/C][/ROW]
[ROW][C]6[/C][C]0.0664935[/C][C]0.132987[/C][C]0.933506[/C][/ROW]
[ROW][C]7[/C][C]0.0256738[/C][C]0.0513477[/C][C]0.974326[/C][/ROW]
[ROW][C]8[/C][C]0.0122174[/C][C]0.0244348[/C][C]0.987783[/C][/ROW]
[ROW][C]9[/C][C]0.00437998[/C][C]0.00875996[/C][C]0.99562[/C][/ROW]
[ROW][C]10[/C][C]0.00224016[/C][C]0.00448032[/C][C]0.99776[/C][/ROW]
[ROW][C]11[/C][C]0.22358[/C][C]0.44716[/C][C]0.77642[/C][/ROW]
[ROW][C]12[/C][C]0.503981[/C][C]0.992037[/C][C]0.496019[/C][/ROW]
[ROW][C]13[/C][C]0.420841[/C][C]0.841682[/C][C]0.579159[/C][/ROW]
[ROW][C]14[/C][C]0.345366[/C][C]0.690731[/C][C]0.654634[/C][/ROW]
[ROW][C]15[/C][C]0.270018[/C][C]0.540036[/C][C]0.729982[/C][/ROW]
[ROW][C]16[/C][C]0.219064[/C][C]0.438129[/C][C]0.780936[/C][/ROW]
[ROW][C]17[/C][C]0.171075[/C][C]0.34215[/C][C]0.828925[/C][/ROW]
[ROW][C]18[/C][C]0.140712[/C][C]0.281423[/C][C]0.859288[/C][/ROW]
[ROW][C]19[/C][C]0.114092[/C][C]0.228184[/C][C]0.885908[/C][/ROW]
[ROW][C]20[/C][C]0.0900793[/C][C]0.180159[/C][C]0.909921[/C][/ROW]
[ROW][C]21[/C][C]0.0638848[/C][C]0.12777[/C][C]0.936115[/C][/ROW]
[ROW][C]22[/C][C]0.088286[/C][C]0.176572[/C][C]0.911714[/C][/ROW]
[ROW][C]23[/C][C]0.254037[/C][C]0.508073[/C][C]0.745963[/C][/ROW]
[ROW][C]24[/C][C]0.704135[/C][C]0.591731[/C][C]0.295865[/C][/ROW]
[ROW][C]25[/C][C]0.709949[/C][C]0.580101[/C][C]0.290051[/C][/ROW]
[ROW][C]26[/C][C]0.661733[/C][C]0.676534[/C][C]0.338267[/C][/ROW]
[ROW][C]27[/C][C]0.606522[/C][C]0.786955[/C][C]0.393478[/C][/ROW]
[ROW][C]28[/C][C]0.559719[/C][C]0.880562[/C][C]0.440281[/C][/ROW]
[ROW][C]29[/C][C]0.503738[/C][C]0.992525[/C][C]0.496262[/C][/ROW]
[ROW][C]30[/C][C]0.444961[/C][C]0.889922[/C][C]0.555039[/C][/ROW]
[ROW][C]31[/C][C]0.390183[/C][C]0.780366[/C][C]0.609817[/C][/ROW]
[ROW][C]32[/C][C]0.362128[/C][C]0.724256[/C][C]0.637872[/C][/ROW]
[ROW][C]33[/C][C]0.322725[/C][C]0.64545[/C][C]0.677275[/C][/ROW]
[ROW][C]34[/C][C]0.31847[/C][C]0.63694[/C][C]0.68153[/C][/ROW]
[ROW][C]35[/C][C]0.447503[/C][C]0.895006[/C][C]0.552497[/C][/ROW]
[ROW][C]36[/C][C]0.540191[/C][C]0.919619[/C][C]0.459809[/C][/ROW]
[ROW][C]37[/C][C]0.562295[/C][C]0.875411[/C][C]0.437705[/C][/ROW]
[ROW][C]38[/C][C]0.510686[/C][C]0.978628[/C][C]0.489314[/C][/ROW]
[ROW][C]39[/C][C]0.462278[/C][C]0.924556[/C][C]0.537722[/C][/ROW]
[ROW][C]40[/C][C]0.443241[/C][C]0.886483[/C][C]0.556759[/C][/ROW]
[ROW][C]41[/C][C]0.425739[/C][C]0.851477[/C][C]0.574261[/C][/ROW]
[ROW][C]42[/C][C]0.382074[/C][C]0.764148[/C][C]0.617926[/C][/ROW]
[ROW][C]43[/C][C]0.362012[/C][C]0.724023[/C][C]0.637988[/C][/ROW]
[ROW][C]44[/C][C]0.321993[/C][C]0.643985[/C][C]0.678007[/C][/ROW]
[ROW][C]45[/C][C]0.279428[/C][C]0.558857[/C][C]0.720572[/C][/ROW]
[ROW][C]46[/C][C]0.265649[/C][C]0.531297[/C][C]0.734351[/C][/ROW]
[ROW][C]47[/C][C]0.493782[/C][C]0.987564[/C][C]0.506218[/C][/ROW]
[ROW][C]48[/C][C]0.889938[/C][C]0.220125[/C][C]0.110062[/C][/ROW]
[ROW][C]49[/C][C]0.901114[/C][C]0.197773[/C][C]0.0988863[/C][/ROW]
[ROW][C]50[/C][C]0.891931[/C][C]0.216137[/C][C]0.108069[/C][/ROW]
[ROW][C]51[/C][C]0.874959[/C][C]0.250082[/C][C]0.125041[/C][/ROW]
[ROW][C]52[/C][C]0.861924[/C][C]0.276151[/C][C]0.138076[/C][/ROW]
[ROW][C]53[/C][C]0.857742[/C][C]0.284516[/C][C]0.142258[/C][/ROW]
[ROW][C]54[/C][C]0.833787[/C][C]0.332426[/C][C]0.166213[/C][/ROW]
[ROW][C]55[/C][C]0.831684[/C][C]0.336633[/C][C]0.168316[/C][/ROW]
[ROW][C]56[/C][C]0.813452[/C][C]0.373096[/C][C]0.186548[/C][/ROW]
[ROW][C]57[/C][C]0.82999[/C][C]0.34002[/C][C]0.17001[/C][/ROW]
[ROW][C]58[/C][C]0.845294[/C][C]0.309412[/C][C]0.154706[/C][/ROW]
[ROW][C]59[/C][C]0.869887[/C][C]0.260227[/C][C]0.130113[/C][/ROW]
[ROW][C]60[/C][C]0.899973[/C][C]0.200054[/C][C]0.100027[/C][/ROW]
[ROW][C]61[/C][C]0.892912[/C][C]0.214177[/C][C]0.107088[/C][/ROW]
[ROW][C]62[/C][C]0.899814[/C][C]0.200373[/C][C]0.100186[/C][/ROW]
[ROW][C]63[/C][C]0.898719[/C][C]0.202562[/C][C]0.101281[/C][/ROW]
[ROW][C]64[/C][C]0.924222[/C][C]0.151557[/C][C]0.0757784[/C][/ROW]
[ROW][C]65[/C][C]0.910274[/C][C]0.179452[/C][C]0.0897258[/C][/ROW]
[ROW][C]66[/C][C]0.894793[/C][C]0.210415[/C][C]0.105207[/C][/ROW]
[ROW][C]67[/C][C]0.87717[/C][C]0.24566[/C][C]0.12283[/C][/ROW]
[ROW][C]68[/C][C]0.864097[/C][C]0.271805[/C][C]0.135903[/C][/ROW]
[ROW][C]69[/C][C]0.867874[/C][C]0.264253[/C][C]0.132126[/C][/ROW]
[ROW][C]70[/C][C]0.887176[/C][C]0.225647[/C][C]0.112824[/C][/ROW]
[ROW][C]71[/C][C]0.90845[/C][C]0.183101[/C][C]0.0915504[/C][/ROW]
[ROW][C]72[/C][C]0.92087[/C][C]0.158261[/C][C]0.0791305[/C][/ROW]
[ROW][C]73[/C][C]0.91601[/C][C]0.167979[/C][C]0.0839897[/C][/ROW]
[ROW][C]74[/C][C]0.940403[/C][C]0.119195[/C][C]0.0595975[/C][/ROW]
[ROW][C]75[/C][C]0.931292[/C][C]0.137416[/C][C]0.0687078[/C][/ROW]
[ROW][C]76[/C][C]0.946956[/C][C]0.106088[/C][C]0.0530439[/C][/ROW]
[ROW][C]77[/C][C]0.945882[/C][C]0.108237[/C][C]0.0541184[/C][/ROW]
[ROW][C]78[/C][C]0.953436[/C][C]0.0931274[/C][C]0.0465637[/C][/ROW]
[ROW][C]79[/C][C]0.957767[/C][C]0.0844662[/C][C]0.0422331[/C][/ROW]
[ROW][C]80[/C][C]0.954412[/C][C]0.0911765[/C][C]0.0455882[/C][/ROW]
[ROW][C]81[/C][C]0.945791[/C][C]0.108418[/C][C]0.0542088[/C][/ROW]
[ROW][C]82[/C][C]0.940175[/C][C]0.11965[/C][C]0.059825[/C][/ROW]
[ROW][C]83[/C][C]0.936131[/C][C]0.127738[/C][C]0.0638689[/C][/ROW]
[ROW][C]84[/C][C]0.966339[/C][C]0.0673229[/C][C]0.0336614[/C][/ROW]
[ROW][C]85[/C][C]0.966916[/C][C]0.0661679[/C][C]0.0330839[/C][/ROW]
[ROW][C]86[/C][C]0.960164[/C][C]0.0796718[/C][C]0.0398359[/C][/ROW]
[ROW][C]87[/C][C]0.965277[/C][C]0.0694459[/C][C]0.0347229[/C][/ROW]
[ROW][C]88[/C][C]0.970379[/C][C]0.0592411[/C][C]0.0296205[/C][/ROW]
[ROW][C]89[/C][C]0.967464[/C][C]0.0650719[/C][C]0.0325359[/C][/ROW]
[ROW][C]90[/C][C]0.977737[/C][C]0.0445254[/C][C]0.0222627[/C][/ROW]
[ROW][C]91[/C][C]0.975323[/C][C]0.0493547[/C][C]0.0246774[/C][/ROW]
[ROW][C]92[/C][C]0.982128[/C][C]0.0357448[/C][C]0.0178724[/C][/ROW]
[ROW][C]93[/C][C]0.977904[/C][C]0.0441921[/C][C]0.0220961[/C][/ROW]
[ROW][C]94[/C][C]0.97258[/C][C]0.0548399[/C][C]0.02742[/C][/ROW]
[ROW][C]95[/C][C]0.973652[/C][C]0.0526956[/C][C]0.0263478[/C][/ROW]
[ROW][C]96[/C][C]0.992148[/C][C]0.0157043[/C][C]0.00785213[/C][/ROW]
[ROW][C]97[/C][C]0.989944[/C][C]0.0201116[/C][C]0.0100558[/C][/ROW]
[ROW][C]98[/C][C]0.99108[/C][C]0.0178391[/C][C]0.00891953[/C][/ROW]
[ROW][C]99[/C][C]0.991852[/C][C]0.0162959[/C][C]0.00814795[/C][/ROW]
[ROW][C]100[/C][C]0.992689[/C][C]0.0146217[/C][C]0.00731084[/C][/ROW]
[ROW][C]101[/C][C]0.993588[/C][C]0.0128241[/C][C]0.00641203[/C][/ROW]
[ROW][C]102[/C][C]0.992587[/C][C]0.0148251[/C][C]0.00741253[/C][/ROW]
[ROW][C]103[/C][C]0.991334[/C][C]0.0173314[/C][C]0.00866569[/C][/ROW]
[ROW][C]104[/C][C]0.988787[/C][C]0.0224268[/C][C]0.0112134[/C][/ROW]
[ROW][C]105[/C][C]0.987197[/C][C]0.0256051[/C][C]0.0128026[/C][/ROW]
[ROW][C]106[/C][C]0.983489[/C][C]0.0330225[/C][C]0.0165113[/C][/ROW]
[ROW][C]107[/C][C]0.98621[/C][C]0.0275799[/C][C]0.0137899[/C][/ROW]
[ROW][C]108[/C][C]0.995481[/C][C]0.00903755[/C][C]0.00451877[/C][/ROW]
[ROW][C]109[/C][C]0.996074[/C][C]0.00785132[/C][C]0.00392566[/C][/ROW]
[ROW][C]110[/C][C]0.995808[/C][C]0.00838357[/C][C]0.00419179[/C][/ROW]
[ROW][C]111[/C][C]0.994713[/C][C]0.0105749[/C][C]0.00528744[/C][/ROW]
[ROW][C]112[/C][C]0.994384[/C][C]0.0112326[/C][C]0.00561628[/C][/ROW]
[ROW][C]113[/C][C]0.994203[/C][C]0.0115945[/C][C]0.00579723[/C][/ROW]
[ROW][C]114[/C][C]0.992372[/C][C]0.0152562[/C][C]0.00762811[/C][/ROW]
[ROW][C]115[/C][C]0.989937[/C][C]0.0201256[/C][C]0.0100628[/C][/ROW]
[ROW][C]116[/C][C]0.98689[/C][C]0.0262201[/C][C]0.0131101[/C][/ROW]
[ROW][C]117[/C][C]0.983039[/C][C]0.0339225[/C][C]0.0169612[/C][/ROW]
[ROW][C]118[/C][C]0.978201[/C][C]0.0435979[/C][C]0.021799[/C][/ROW]
[ROW][C]119[/C][C]0.985499[/C][C]0.0290019[/C][C]0.014501[/C][/ROW]
[ROW][C]120[/C][C]0.997274[/C][C]0.00545252[/C][C]0.00272626[/C][/ROW]
[ROW][C]121[/C][C]0.996841[/C][C]0.006319[/C][C]0.0031595[/C][/ROW]
[ROW][C]122[/C][C]0.996604[/C][C]0.00679134[/C][C]0.00339567[/C][/ROW]
[ROW][C]123[/C][C]0.995732[/C][C]0.00853619[/C][C]0.0042681[/C][/ROW]
[ROW][C]124[/C][C]0.995232[/C][C]0.00953613[/C][C]0.00476806[/C][/ROW]
[ROW][C]125[/C][C]0.993836[/C][C]0.0123286[/C][C]0.00616428[/C][/ROW]
[ROW][C]126[/C][C]0.993579[/C][C]0.0128418[/C][C]0.00642089[/C][/ROW]
[ROW][C]127[/C][C]0.993394[/C][C]0.013213[/C][C]0.0066065[/C][/ROW]
[ROW][C]128[/C][C]0.991525[/C][C]0.0169506[/C][C]0.00847531[/C][/ROW]
[ROW][C]129[/C][C]0.988655[/C][C]0.022689[/C][C]0.0113445[/C][/ROW]
[ROW][C]130[/C][C]0.984959[/C][C]0.0300813[/C][C]0.0150406[/C][/ROW]
[ROW][C]131[/C][C]0.990222[/C][C]0.0195567[/C][C]0.00977837[/C][/ROW]
[ROW][C]132[/C][C]0.99828[/C][C]0.00344002[/C][C]0.00172001[/C][/ROW]
[ROW][C]133[/C][C]0.997592[/C][C]0.00481675[/C][C]0.00240838[/C][/ROW]
[ROW][C]134[/C][C]0.997893[/C][C]0.00421437[/C][C]0.00210719[/C][/ROW]
[ROW][C]135[/C][C]0.997294[/C][C]0.0054122[/C][C]0.0027061[/C][/ROW]
[ROW][C]136[/C][C]0.997678[/C][C]0.00464462[/C][C]0.00232231[/C][/ROW]
[ROW][C]137[/C][C]0.997355[/C][C]0.00529082[/C][C]0.00264541[/C][/ROW]
[ROW][C]138[/C][C]0.996547[/C][C]0.00690689[/C][C]0.00345345[/C][/ROW]
[ROW][C]139[/C][C]0.996045[/C][C]0.00790942[/C][C]0.00395471[/C][/ROW]
[ROW][C]140[/C][C]0.994687[/C][C]0.0106261[/C][C]0.00531303[/C][/ROW]
[ROW][C]141[/C][C]0.992957[/C][C]0.0140851[/C][C]0.00704257[/C][/ROW]
[ROW][C]142[/C][C]0.992033[/C][C]0.015934[/C][C]0.00796699[/C][/ROW]
[ROW][C]143[/C][C]0.989584[/C][C]0.0208325[/C][C]0.0104162[/C][/ROW]
[ROW][C]144[/C][C]0.992082[/C][C]0.0158367[/C][C]0.00791834[/C][/ROW]
[ROW][C]145[/C][C]0.990449[/C][C]0.019101[/C][C]0.00955052[/C][/ROW]
[ROW][C]146[/C][C]0.98895[/C][C]0.0221006[/C][C]0.0110503[/C][/ROW]
[ROW][C]147[/C][C]0.985456[/C][C]0.0290879[/C][C]0.0145439[/C][/ROW]
[ROW][C]148[/C][C]0.985571[/C][C]0.0288577[/C][C]0.0144289[/C][/ROW]
[ROW][C]149[/C][C]0.98178[/C][C]0.0364397[/C][C]0.0182198[/C][/ROW]
[ROW][C]150[/C][C]0.983523[/C][C]0.0329546[/C][C]0.0164773[/C][/ROW]
[ROW][C]151[/C][C]0.977249[/C][C]0.0455022[/C][C]0.0227511[/C][/ROW]
[ROW][C]152[/C][C]0.97264[/C][C]0.0547198[/C][C]0.0273599[/C][/ROW]
[ROW][C]153[/C][C]0.962926[/C][C]0.0741489[/C][C]0.0370745[/C][/ROW]
[ROW][C]154[/C][C]0.967303[/C][C]0.065395[/C][C]0.0326975[/C][/ROW]
[ROW][C]155[/C][C]0.966686[/C][C]0.0666289[/C][C]0.0333145[/C][/ROW]
[ROW][C]156[/C][C]0.957192[/C][C]0.0856166[/C][C]0.0428083[/C][/ROW]
[ROW][C]157[/C][C]0.951104[/C][C]0.0977923[/C][C]0.0488962[/C][/ROW]
[ROW][C]158[/C][C]0.946573[/C][C]0.106853[/C][C]0.0534267[/C][/ROW]
[ROW][C]159[/C][C]0.94199[/C][C]0.11602[/C][C]0.0580102[/C][/ROW]
[ROW][C]160[/C][C]0.953946[/C][C]0.0921072[/C][C]0.0460536[/C][/ROW]
[ROW][C]161[/C][C]0.951782[/C][C]0.096436[/C][C]0.048218[/C][/ROW]
[ROW][C]162[/C][C]0.943782[/C][C]0.112437[/C][C]0.0562183[/C][/ROW]
[ROW][C]163[/C][C]0.948853[/C][C]0.102294[/C][C]0.0511472[/C][/ROW]
[ROW][C]164[/C][C]0.93387[/C][C]0.132261[/C][C]0.0661304[/C][/ROW]
[ROW][C]165[/C][C]0.932018[/C][C]0.135964[/C][C]0.067982[/C][/ROW]
[ROW][C]166[/C][C]0.908408[/C][C]0.183183[/C][C]0.0915916[/C][/ROW]
[ROW][C]167[/C][C]0.896696[/C][C]0.206609[/C][C]0.103304[/C][/ROW]
[ROW][C]168[/C][C]0.946325[/C][C]0.10735[/C][C]0.0536751[/C][/ROW]
[ROW][C]169[/C][C]0.925762[/C][C]0.148476[/C][C]0.074238[/C][/ROW]
[ROW][C]170[/C][C]0.932074[/C][C]0.135852[/C][C]0.0679259[/C][/ROW]
[ROW][C]171[/C][C]0.912199[/C][C]0.175603[/C][C]0.0878014[/C][/ROW]
[ROW][C]172[/C][C]0.89714[/C][C]0.20572[/C][C]0.10286[/C][/ROW]
[ROW][C]173[/C][C]0.867972[/C][C]0.264055[/C][C]0.132028[/C][/ROW]
[ROW][C]174[/C][C]0.884055[/C][C]0.231889[/C][C]0.115945[/C][/ROW]
[ROW][C]175[/C][C]0.871741[/C][C]0.256518[/C][C]0.128259[/C][/ROW]
[ROW][C]176[/C][C]0.87598[/C][C]0.248039[/C][C]0.12402[/C][/ROW]
[ROW][C]177[/C][C]0.829472[/C][C]0.341055[/C][C]0.170528[/C][/ROW]
[ROW][C]178[/C][C]0.781249[/C][C]0.437502[/C][C]0.218751[/C][/ROW]
[ROW][C]179[/C][C]0.723085[/C][C]0.553829[/C][C]0.276915[/C][/ROW]
[ROW][C]180[/C][C]0.668098[/C][C]0.663804[/C][C]0.331902[/C][/ROW]
[ROW][C]181[/C][C]0.573634[/C][C]0.852731[/C][C]0.426366[/C][/ROW]
[ROW][C]182[/C][C]0.540064[/C][C]0.919872[/C][C]0.459936[/C][/ROW]
[ROW][C]183[/C][C]0.449856[/C][C]0.899712[/C][C]0.550144[/C][/ROW]
[ROW][C]184[/C][C]0.491029[/C][C]0.982059[/C][C]0.508971[/C][/ROW]
[ROW][C]185[/C][C]0.403463[/C][C]0.806925[/C][C]0.596537[/C][/ROW]
[ROW][C]186[/C][C]0.428896[/C][C]0.857791[/C][C]0.571104[/C][/ROW]
[ROW][C]187[/C][C]0.488141[/C][C]0.976282[/C][C]0.511859[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=222038&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=222038&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
50.1559310.3118620.844069
60.06649350.1329870.933506
70.02567380.05134770.974326
80.01221740.02443480.987783
90.004379980.008759960.99562
100.002240160.004480320.99776
110.223580.447160.77642
120.5039810.9920370.496019
130.4208410.8416820.579159
140.3453660.6907310.654634
150.2700180.5400360.729982
160.2190640.4381290.780936
170.1710750.342150.828925
180.1407120.2814230.859288
190.1140920.2281840.885908
200.09007930.1801590.909921
210.06388480.127770.936115
220.0882860.1765720.911714
230.2540370.5080730.745963
240.7041350.5917310.295865
250.7099490.5801010.290051
260.6617330.6765340.338267
270.6065220.7869550.393478
280.5597190.8805620.440281
290.5037380.9925250.496262
300.4449610.8899220.555039
310.3901830.7803660.609817
320.3621280.7242560.637872
330.3227250.645450.677275
340.318470.636940.68153
350.4475030.8950060.552497
360.5401910.9196190.459809
370.5622950.8754110.437705
380.5106860.9786280.489314
390.4622780.9245560.537722
400.4432410.8864830.556759
410.4257390.8514770.574261
420.3820740.7641480.617926
430.3620120.7240230.637988
440.3219930.6439850.678007
450.2794280.5588570.720572
460.2656490.5312970.734351
470.4937820.9875640.506218
480.8899380.2201250.110062
490.9011140.1977730.0988863
500.8919310.2161370.108069
510.8749590.2500820.125041
520.8619240.2761510.138076
530.8577420.2845160.142258
540.8337870.3324260.166213
550.8316840.3366330.168316
560.8134520.3730960.186548
570.829990.340020.17001
580.8452940.3094120.154706
590.8698870.2602270.130113
600.8999730.2000540.100027
610.8929120.2141770.107088
620.8998140.2003730.100186
630.8987190.2025620.101281
640.9242220.1515570.0757784
650.9102740.1794520.0897258
660.8947930.2104150.105207
670.877170.245660.12283
680.8640970.2718050.135903
690.8678740.2642530.132126
700.8871760.2256470.112824
710.908450.1831010.0915504
720.920870.1582610.0791305
730.916010.1679790.0839897
740.9404030.1191950.0595975
750.9312920.1374160.0687078
760.9469560.1060880.0530439
770.9458820.1082370.0541184
780.9534360.09312740.0465637
790.9577670.08446620.0422331
800.9544120.09117650.0455882
810.9457910.1084180.0542088
820.9401750.119650.059825
830.9361310.1277380.0638689
840.9663390.06732290.0336614
850.9669160.06616790.0330839
860.9601640.07967180.0398359
870.9652770.06944590.0347229
880.9703790.05924110.0296205
890.9674640.06507190.0325359
900.9777370.04452540.0222627
910.9753230.04935470.0246774
920.9821280.03574480.0178724
930.9779040.04419210.0220961
940.972580.05483990.02742
950.9736520.05269560.0263478
960.9921480.01570430.00785213
970.9899440.02011160.0100558
980.991080.01783910.00891953
990.9918520.01629590.00814795
1000.9926890.01462170.00731084
1010.9935880.01282410.00641203
1020.9925870.01482510.00741253
1030.9913340.01733140.00866569
1040.9887870.02242680.0112134
1050.9871970.02560510.0128026
1060.9834890.03302250.0165113
1070.986210.02757990.0137899
1080.9954810.009037550.00451877
1090.9960740.007851320.00392566
1100.9958080.008383570.00419179
1110.9947130.01057490.00528744
1120.9943840.01123260.00561628
1130.9942030.01159450.00579723
1140.9923720.01525620.00762811
1150.9899370.02012560.0100628
1160.986890.02622010.0131101
1170.9830390.03392250.0169612
1180.9782010.04359790.021799
1190.9854990.02900190.014501
1200.9972740.005452520.00272626
1210.9968410.0063190.0031595
1220.9966040.006791340.00339567
1230.9957320.008536190.0042681
1240.9952320.009536130.00476806
1250.9938360.01232860.00616428
1260.9935790.01284180.00642089
1270.9933940.0132130.0066065
1280.9915250.01695060.00847531
1290.9886550.0226890.0113445
1300.9849590.03008130.0150406
1310.9902220.01955670.00977837
1320.998280.003440020.00172001
1330.9975920.004816750.00240838
1340.9978930.004214370.00210719
1350.9972940.00541220.0027061
1360.9976780.004644620.00232231
1370.9973550.005290820.00264541
1380.9965470.006906890.00345345
1390.9960450.007909420.00395471
1400.9946870.01062610.00531303
1410.9929570.01408510.00704257
1420.9920330.0159340.00796699
1430.9895840.02083250.0104162
1440.9920820.01583670.00791834
1450.9904490.0191010.00955052
1460.988950.02210060.0110503
1470.9854560.02908790.0145439
1480.9855710.02885770.0144289
1490.981780.03643970.0182198
1500.9835230.03295460.0164773
1510.9772490.04550220.0227511
1520.972640.05471980.0273599
1530.9629260.07414890.0370745
1540.9673030.0653950.0326975
1550.9666860.06662890.0333145
1560.9571920.08561660.0428083
1570.9511040.09779230.0488962
1580.9465730.1068530.0534267
1590.941990.116020.0580102
1600.9539460.09210720.0460536
1610.9517820.0964360.048218
1620.9437820.1124370.0562183
1630.9488530.1022940.0511472
1640.933870.1322610.0661304
1650.9320180.1359640.067982
1660.9084080.1831830.0915916
1670.8966960.2066090.103304
1680.9463250.107350.0536751
1690.9257620.1484760.074238
1700.9320740.1358520.0679259
1710.9121990.1756030.0878014
1720.897140.205720.10286
1730.8679720.2640550.132028
1740.8840550.2318890.115945
1750.8717410.2565180.128259
1760.875980.2480390.12402
1770.8294720.3410550.170528
1780.7812490.4375020.218751
1790.7230850.5538290.276915
1800.6680980.6638040.331902
1810.5736340.8527310.426366
1820.5400640.9198720.459936
1830.4498560.8997120.550144
1840.4910290.9820590.508971
1850.4034630.8069250.596537
1860.4288960.8577910.571104
1870.4881410.9762820.511859






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level180.0983607NOK
5% type I error level630.344262NOK
10% type I error level830.453552NOK

\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 & 0.0983607 & NOK \tabularnewline
5% type I error level & 63 & 0.344262 & NOK \tabularnewline
10% type I error level & 83 & 0.453552 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=222038&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]0.0983607[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]63[/C][C]0.344262[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]83[/C][C]0.453552[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=222038&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=222038&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 level180.0983607NOK
5% type I error level630.344262NOK
10% type I error level830.453552NOK


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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$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')
}