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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 20 Nov 2011 05:15:29 -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/2011/Nov/20/t13217841709j9pc6ddttfogqv.htm/, Retrieved Thu, 28 Mar 2024 21:49:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145558, Retrieved Thu, 28 Mar 2024 21:49:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact164
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [test] [2010-11-22 21:34:59] [3635fb7041b1998c5a1332cf9de22bce]
-    D    [Multiple Regression] [Workshop 7, tutor...] [2010-11-22 21:53:32] [3635fb7041b1998c5a1332cf9de22bce]
- R PD        [Multiple Regression] [] [2011-11-20 10:15:29] [a1e1d0bae7c18896aaea36b6ddc51406] [Current]
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Dataseries X:
1	7	1
1	5	1
1	5	2
2	5	3
1	8	2
1	6	1
2	6	3
1	4	3
1	5	1
1	5	2
2	5	1
2	6	3
1	7	1
1	7	4
1	6	1
2	7	2
1	7	2
2	5	3
2	8	1
1	7	1
2	5	3
1	7	2
1	5	1
2	10	3
1	5	3
1	4	1
1	4	1
1	5	1
2	5	3
1	6	3
2	5	1
1	5	3
1	8	3
2	5	3
2	5	3
2	5	1
1	5	4
2	5	1
2	5	2
1	7	1
1	7	2
1	7	1
1	4	3
2	5	1
2	5	3
1	4	1
2	5	3
2	5	3
1	6	1
2	5	3
1	5	3
1	6	3
1	6	2
1	4	3
2	6	2
1	6	1
1	5	3
1	5	1
2	5	3
2	7	1
1	6	1
1	8	3
1	7	1
2	5	3
1	6	1
2	6	2
2	5	3
2	5	3
1	5	2
1	5	3
1	4	2
2	6	3
2	6	1
1	6	4
1	6	1
1	7	3
1	7	3
2	5	1
2	7	3
2	5	4
2	5	3
1	5	3
1	8	3
1	8	3
2	5	1
2	4	1
1	6	1
1	4	1
1	5	1
1	5	3
1	5	2
2	6	1
1	6	2
1	6	3
1	5	1
1	6	3
2	5	3
1	5	2
2	7	3
2	6	3
1	6	3
2	6	2
1	7	1
1	5	4
2	6	1
1	6	1
2	5	1
1	4	1
1	5	3
1	5	3
1	9	1
1	6	3
1	5	3
1	6	4
1	5	1
2	6	1
1	5	3
1	7	3
2	6	3
1	7	3
1	5	2
1	6	3
1	9	3
1	4	3
1	6	4
2	6	2
1	6	4
1	6	2
2	5	3
2	5	4
1	5	1
2	7	1
2	5	4
2	4	3
2	5	4
1	6	3
1	7	3
2	5	2
2	7	1
1	7	4
1	6	3
1	8	1
1	5	3
2	5	1
1	6	1
1	4	1
2	5	3
2	5	2
2	5	3
2	7	1
1	5	4
1	6	1
1	7	3
2	8	1
2	10	3
2	5	3
1	6	3
1	4	3
1	6	2
1	7	4
2	5	3
1	7	3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'AstonUniversity' @ aston.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 & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145558&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]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145558&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145558&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'AstonUniversity' @ aston.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Browser[t] = + 2.2508308386481 + 0.0570595976687348Geslacht[t] -0.0112130678120525Leeftijd[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Browser[t] =  +  2.2508308386481 +  0.0570595976687348Geslacht[t] -0.0112130678120525Leeftijd[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145558&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Browser[t] =  +  2.2508308386481 +  0.0570595976687348Geslacht[t] -0.0112130678120525Leeftijd[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145558&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145558&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Browser[t] = + 2.2508308386481 + 0.0570595976687348Geslacht[t] -0.0112130678120525Leeftijd[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.25083083864810.4842214.64847e-063e-06
Geslacht0.05705959766873480.1668710.34190.7328490.366424
Leeftijd-0.01121306781205250.069323-0.16180.8717070.435853

\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) & 2.2508308386481 & 0.484221 & 4.6484 & 7e-06 & 3e-06 \tabularnewline
Geslacht & 0.0570595976687348 & 0.166871 & 0.3419 & 0.732849 & 0.366424 \tabularnewline
Leeftijd & -0.0112130678120525 & 0.069323 & -0.1618 & 0.871707 & 0.435853 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145558&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]2.2508308386481[/C][C]0.484221[/C][C]4.6484[/C][C]7e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]Geslacht[/C][C]0.0570595976687348[/C][C]0.166871[/C][C]0.3419[/C][C]0.732849[/C][C]0.366424[/C][/ROW]
[ROW][C]Leeftijd[/C][C]-0.0112130678120525[/C][C]0.069323[/C][C]-0.1618[/C][C]0.871707[/C][C]0.435853[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145558&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145558&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.25083083864810.4842214.64847e-063e-06
Geslacht0.05705959766873480.1668710.34190.7328490.366424
Leeftijd-0.01121306781205250.069323-0.16180.8717070.435853







Multiple Linear Regression - Regression Statistics
Multiple R0.0309682849398595
R-squared0.000959034672116332
Adjusted R-squared-0.0116075183508759
F-TEST (value)0.076316446551544
F-TEST (DF numerator)2
F-TEST (DF denominator)159
p-value0.92655688279474
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.03225886671091
Sum Squared Residuals169.423780496623

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.0309682849398595 \tabularnewline
R-squared & 0.000959034672116332 \tabularnewline
Adjusted R-squared & -0.0116075183508759 \tabularnewline
F-TEST (value) & 0.076316446551544 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 159 \tabularnewline
p-value & 0.92655688279474 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.03225886671091 \tabularnewline
Sum Squared Residuals & 169.423780496623 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145558&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.0309682849398595[/C][/ROW]
[ROW][C]R-squared[/C][C]0.000959034672116332[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0116075183508759[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.076316446551544[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]159[/C][/ROW]
[ROW][C]p-value[/C][C]0.92655688279474[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.03225886671091[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]169.423780496623[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145558&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145558&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.0309682849398595
R-squared0.000959034672116332
Adjusted R-squared-0.0116075183508759
F-TEST (value)0.076316446551544
F-TEST (DF numerator)2
F-TEST (DF denominator)159
p-value0.92655688279474
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.03225886671091
Sum Squared Residuals169.423780496623







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.22939896163247-1.22939896163247
212.25182509725657-1.25182509725657
322.25182509725657-0.251825097256569
432.30888469492530.691115305074696
522.21818589382041-0.218185893820412
612.24061202944452-1.24061202944452
732.297671627113250.702328372886748
832.263038165068620.736961834931378
912.25182509725657-1.25182509725657
1022.25182509725657-0.251825097256569
1112.3088846949253-1.3088846949253
1232.297671627113250.702328372886748
1312.22939896163246-1.22939896163246
1442.229398961632461.77060103836754
1512.24061202944452-1.24061202944452
1622.2864585593012-0.286458559301199
1722.22939896163246-0.229398961632464
1832.30888469492530.691115305074696
1912.27524549148915-1.27524549148915
2012.22939896163246-1.22939896163246
2132.30888469492530.691115305074696
2222.22939896163246-0.229398961632464
2312.25182509725657-1.25182509725657
2432.252819355865040.747180644134958
2532.251825097256570.748174902743431
2612.26303816506862-1.26303816506862
2712.26303816506862-1.26303816506862
2812.25182509725657-1.25182509725657
2932.30888469492530.691115305074696
3032.240612029444520.759387970555483
3112.3088846949253-1.3088846949253
3232.251825097256570.748174902743431
3332.218185893820410.781814106179588
3432.30888469492530.691115305074696
3532.30888469492530.691115305074696
3612.3088846949253-1.3088846949253
3742.251825097256571.74817490274343
3812.3088846949253-1.3088846949253
3922.3088846949253-0.308884694925304
4012.22939896163246-1.22939896163246
4122.22939896163246-0.229398961632464
4212.22939896163246-1.22939896163246
4332.263038165068620.736961834931378
4412.3088846949253-1.3088846949253
4532.30888469492530.691115305074696
4612.26303816506862-1.26303816506862
4732.30888469492530.691115305074696
4832.30888469492530.691115305074696
4912.24061202944452-1.24061202944452
5032.30888469492530.691115305074696
5132.251825097256570.748174902743431
5232.240612029444520.759387970555483
5322.24061202944452-0.240612029444517
5432.263038165068620.736961834931378
5522.29767162711325-0.297671627113252
5612.24061202944452-1.24061202944452
5732.251825097256570.748174902743431
5812.25182509725657-1.25182509725657
5932.30888469492530.691115305074696
6012.2864585593012-1.2864585593012
6112.24061202944452-1.24061202944452
6232.218185893820410.781814106179588
6312.22939896163246-1.22939896163246
6432.30888469492530.691115305074696
6512.24061202944452-1.24061202944452
6622.29767162711325-0.297671627113252
6732.30888469492530.691115305074696
6832.30888469492530.691115305074696
6922.25182509725657-0.251825097256569
7032.251825097256570.748174902743431
7122.26303816506862-0.263038165068622
7232.297671627113250.702328372886748
7312.29767162711325-1.29767162711325
7442.240612029444521.75938797055548
7512.24061202944452-1.24061202944452
7632.229398961632460.770601038367536
7732.229398961632460.770601038367536
7812.3088846949253-1.3088846949253
7932.28645855930120.7135414406988
8042.30888469492531.6911153050747
8132.30888469492530.691115305074696
8232.251825097256570.748174902743431
8332.218185893820410.781814106179588
8432.218185893820410.781814106179588
8512.3088846949253-1.3088846949253
8612.32009776273736-1.32009776273736
8712.24061202944452-1.24061202944452
8812.26303816506862-1.26303816506862
8912.25182509725657-1.25182509725657
9032.251825097256570.748174902743431
9122.25182509725657-0.251825097256569
9212.29767162711325-1.29767162711325
9322.24061202944452-0.240612029444517
9432.240612029444520.759387970555483
9512.25182509725657-1.25182509725657
9632.240612029444520.759387970555483
9732.30888469492530.691115305074696
9822.25182509725657-0.251825097256569
9932.28645855930120.7135414406988
10032.297671627113250.702328372886748
10132.240612029444520.759387970555483
10222.29767162711325-0.297671627113252
10312.22939896163246-1.22939896163246
10442.251825097256571.74817490274343
10512.29767162711325-1.29767162711325
10612.24061202944452-1.24061202944452
10712.3088846949253-1.3088846949253
10812.26303816506862-1.26303816506862
10932.251825097256570.748174902743431
11032.251825097256570.748174902743431
11112.20697282600836-1.20697282600836
11232.240612029444520.759387970555483
11332.251825097256570.748174902743431
11442.240612029444521.75938797055548
11512.25182509725657-1.25182509725657
11612.29767162711325-1.29767162711325
11732.251825097256570.748174902743431
11832.229398961632460.770601038367536
11932.297671627113250.702328372886748
12032.229398961632460.770601038367536
12122.25182509725657-0.251825097256569
12232.240612029444520.759387970555483
12332.206972826008360.79302717399164
12432.263038165068620.736961834931378
12542.240612029444521.75938797055548
12622.29767162711325-0.297671627113252
12742.240612029444521.75938797055548
12822.24061202944452-0.240612029444517
12932.30888469492530.691115305074696
13042.30888469492531.6911153050747
13112.25182509725657-1.25182509725657
13212.2864585593012-1.2864585593012
13342.30888469492531.6911153050747
13432.320097762737360.679902237262643
13542.30888469492531.6911153050747
13632.240612029444520.759387970555483
13732.229398961632460.770601038367536
13822.3088846949253-0.308884694925304
13912.2864585593012-1.2864585593012
14042.229398961632461.77060103836754
14132.240612029444520.759387970555483
14212.21818589382041-1.21818589382041
14332.251825097256570.748174902743431
14412.3088846949253-1.3088846949253
14512.24061202944452-1.24061202944452
14612.26303816506862-1.26303816506862
14732.30888469492530.691115305074696
14822.3088846949253-0.308884694925304
14932.30888469492530.691115305074696
15012.2864585593012-1.2864585593012
15142.251825097256571.74817490274343
15212.24061202944452-1.24061202944452
15332.229398961632460.770601038367536
15412.27524549148915-1.27524549148915
15532.252819355865040.747180644134958
15632.30888469492530.691115305074696
15732.240612029444520.759387970555483
15832.263038165068620.736961834931378
15922.24061202944452-0.240612029444517
16042.229398961632461.77060103836754
16132.30888469492530.691115305074696
16232.229398961632460.770601038367536

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 2.22939896163247 & -1.22939896163247 \tabularnewline
2 & 1 & 2.25182509725657 & -1.25182509725657 \tabularnewline
3 & 2 & 2.25182509725657 & -0.251825097256569 \tabularnewline
4 & 3 & 2.3088846949253 & 0.691115305074696 \tabularnewline
5 & 2 & 2.21818589382041 & -0.218185893820412 \tabularnewline
6 & 1 & 2.24061202944452 & -1.24061202944452 \tabularnewline
7 & 3 & 2.29767162711325 & 0.702328372886748 \tabularnewline
8 & 3 & 2.26303816506862 & 0.736961834931378 \tabularnewline
9 & 1 & 2.25182509725657 & -1.25182509725657 \tabularnewline
10 & 2 & 2.25182509725657 & -0.251825097256569 \tabularnewline
11 & 1 & 2.3088846949253 & -1.3088846949253 \tabularnewline
12 & 3 & 2.29767162711325 & 0.702328372886748 \tabularnewline
13 & 1 & 2.22939896163246 & -1.22939896163246 \tabularnewline
14 & 4 & 2.22939896163246 & 1.77060103836754 \tabularnewline
15 & 1 & 2.24061202944452 & -1.24061202944452 \tabularnewline
16 & 2 & 2.2864585593012 & -0.286458559301199 \tabularnewline
17 & 2 & 2.22939896163246 & -0.229398961632464 \tabularnewline
18 & 3 & 2.3088846949253 & 0.691115305074696 \tabularnewline
19 & 1 & 2.27524549148915 & -1.27524549148915 \tabularnewline
20 & 1 & 2.22939896163246 & -1.22939896163246 \tabularnewline
21 & 3 & 2.3088846949253 & 0.691115305074696 \tabularnewline
22 & 2 & 2.22939896163246 & -0.229398961632464 \tabularnewline
23 & 1 & 2.25182509725657 & -1.25182509725657 \tabularnewline
24 & 3 & 2.25281935586504 & 0.747180644134958 \tabularnewline
25 & 3 & 2.25182509725657 & 0.748174902743431 \tabularnewline
26 & 1 & 2.26303816506862 & -1.26303816506862 \tabularnewline
27 & 1 & 2.26303816506862 & -1.26303816506862 \tabularnewline
28 & 1 & 2.25182509725657 & -1.25182509725657 \tabularnewline
29 & 3 & 2.3088846949253 & 0.691115305074696 \tabularnewline
30 & 3 & 2.24061202944452 & 0.759387970555483 \tabularnewline
31 & 1 & 2.3088846949253 & -1.3088846949253 \tabularnewline
32 & 3 & 2.25182509725657 & 0.748174902743431 \tabularnewline
33 & 3 & 2.21818589382041 & 0.781814106179588 \tabularnewline
34 & 3 & 2.3088846949253 & 0.691115305074696 \tabularnewline
35 & 3 & 2.3088846949253 & 0.691115305074696 \tabularnewline
36 & 1 & 2.3088846949253 & -1.3088846949253 \tabularnewline
37 & 4 & 2.25182509725657 & 1.74817490274343 \tabularnewline
38 & 1 & 2.3088846949253 & -1.3088846949253 \tabularnewline
39 & 2 & 2.3088846949253 & -0.308884694925304 \tabularnewline
40 & 1 & 2.22939896163246 & -1.22939896163246 \tabularnewline
41 & 2 & 2.22939896163246 & -0.229398961632464 \tabularnewline
42 & 1 & 2.22939896163246 & -1.22939896163246 \tabularnewline
43 & 3 & 2.26303816506862 & 0.736961834931378 \tabularnewline
44 & 1 & 2.3088846949253 & -1.3088846949253 \tabularnewline
45 & 3 & 2.3088846949253 & 0.691115305074696 \tabularnewline
46 & 1 & 2.26303816506862 & -1.26303816506862 \tabularnewline
47 & 3 & 2.3088846949253 & 0.691115305074696 \tabularnewline
48 & 3 & 2.3088846949253 & 0.691115305074696 \tabularnewline
49 & 1 & 2.24061202944452 & -1.24061202944452 \tabularnewline
50 & 3 & 2.3088846949253 & 0.691115305074696 \tabularnewline
51 & 3 & 2.25182509725657 & 0.748174902743431 \tabularnewline
52 & 3 & 2.24061202944452 & 0.759387970555483 \tabularnewline
53 & 2 & 2.24061202944452 & -0.240612029444517 \tabularnewline
54 & 3 & 2.26303816506862 & 0.736961834931378 \tabularnewline
55 & 2 & 2.29767162711325 & -0.297671627113252 \tabularnewline
56 & 1 & 2.24061202944452 & -1.24061202944452 \tabularnewline
57 & 3 & 2.25182509725657 & 0.748174902743431 \tabularnewline
58 & 1 & 2.25182509725657 & -1.25182509725657 \tabularnewline
59 & 3 & 2.3088846949253 & 0.691115305074696 \tabularnewline
60 & 1 & 2.2864585593012 & -1.2864585593012 \tabularnewline
61 & 1 & 2.24061202944452 & -1.24061202944452 \tabularnewline
62 & 3 & 2.21818589382041 & 0.781814106179588 \tabularnewline
63 & 1 & 2.22939896163246 & -1.22939896163246 \tabularnewline
64 & 3 & 2.3088846949253 & 0.691115305074696 \tabularnewline
65 & 1 & 2.24061202944452 & -1.24061202944452 \tabularnewline
66 & 2 & 2.29767162711325 & -0.297671627113252 \tabularnewline
67 & 3 & 2.3088846949253 & 0.691115305074696 \tabularnewline
68 & 3 & 2.3088846949253 & 0.691115305074696 \tabularnewline
69 & 2 & 2.25182509725657 & -0.251825097256569 \tabularnewline
70 & 3 & 2.25182509725657 & 0.748174902743431 \tabularnewline
71 & 2 & 2.26303816506862 & -0.263038165068622 \tabularnewline
72 & 3 & 2.29767162711325 & 0.702328372886748 \tabularnewline
73 & 1 & 2.29767162711325 & -1.29767162711325 \tabularnewline
74 & 4 & 2.24061202944452 & 1.75938797055548 \tabularnewline
75 & 1 & 2.24061202944452 & -1.24061202944452 \tabularnewline
76 & 3 & 2.22939896163246 & 0.770601038367536 \tabularnewline
77 & 3 & 2.22939896163246 & 0.770601038367536 \tabularnewline
78 & 1 & 2.3088846949253 & -1.3088846949253 \tabularnewline
79 & 3 & 2.2864585593012 & 0.7135414406988 \tabularnewline
80 & 4 & 2.3088846949253 & 1.6911153050747 \tabularnewline
81 & 3 & 2.3088846949253 & 0.691115305074696 \tabularnewline
82 & 3 & 2.25182509725657 & 0.748174902743431 \tabularnewline
83 & 3 & 2.21818589382041 & 0.781814106179588 \tabularnewline
84 & 3 & 2.21818589382041 & 0.781814106179588 \tabularnewline
85 & 1 & 2.3088846949253 & -1.3088846949253 \tabularnewline
86 & 1 & 2.32009776273736 & -1.32009776273736 \tabularnewline
87 & 1 & 2.24061202944452 & -1.24061202944452 \tabularnewline
88 & 1 & 2.26303816506862 & -1.26303816506862 \tabularnewline
89 & 1 & 2.25182509725657 & -1.25182509725657 \tabularnewline
90 & 3 & 2.25182509725657 & 0.748174902743431 \tabularnewline
91 & 2 & 2.25182509725657 & -0.251825097256569 \tabularnewline
92 & 1 & 2.29767162711325 & -1.29767162711325 \tabularnewline
93 & 2 & 2.24061202944452 & -0.240612029444517 \tabularnewline
94 & 3 & 2.24061202944452 & 0.759387970555483 \tabularnewline
95 & 1 & 2.25182509725657 & -1.25182509725657 \tabularnewline
96 & 3 & 2.24061202944452 & 0.759387970555483 \tabularnewline
97 & 3 & 2.3088846949253 & 0.691115305074696 \tabularnewline
98 & 2 & 2.25182509725657 & -0.251825097256569 \tabularnewline
99 & 3 & 2.2864585593012 & 0.7135414406988 \tabularnewline
100 & 3 & 2.29767162711325 & 0.702328372886748 \tabularnewline
101 & 3 & 2.24061202944452 & 0.759387970555483 \tabularnewline
102 & 2 & 2.29767162711325 & -0.297671627113252 \tabularnewline
103 & 1 & 2.22939896163246 & -1.22939896163246 \tabularnewline
104 & 4 & 2.25182509725657 & 1.74817490274343 \tabularnewline
105 & 1 & 2.29767162711325 & -1.29767162711325 \tabularnewline
106 & 1 & 2.24061202944452 & -1.24061202944452 \tabularnewline
107 & 1 & 2.3088846949253 & -1.3088846949253 \tabularnewline
108 & 1 & 2.26303816506862 & -1.26303816506862 \tabularnewline
109 & 3 & 2.25182509725657 & 0.748174902743431 \tabularnewline
110 & 3 & 2.25182509725657 & 0.748174902743431 \tabularnewline
111 & 1 & 2.20697282600836 & -1.20697282600836 \tabularnewline
112 & 3 & 2.24061202944452 & 0.759387970555483 \tabularnewline
113 & 3 & 2.25182509725657 & 0.748174902743431 \tabularnewline
114 & 4 & 2.24061202944452 & 1.75938797055548 \tabularnewline
115 & 1 & 2.25182509725657 & -1.25182509725657 \tabularnewline
116 & 1 & 2.29767162711325 & -1.29767162711325 \tabularnewline
117 & 3 & 2.25182509725657 & 0.748174902743431 \tabularnewline
118 & 3 & 2.22939896163246 & 0.770601038367536 \tabularnewline
119 & 3 & 2.29767162711325 & 0.702328372886748 \tabularnewline
120 & 3 & 2.22939896163246 & 0.770601038367536 \tabularnewline
121 & 2 & 2.25182509725657 & -0.251825097256569 \tabularnewline
122 & 3 & 2.24061202944452 & 0.759387970555483 \tabularnewline
123 & 3 & 2.20697282600836 & 0.79302717399164 \tabularnewline
124 & 3 & 2.26303816506862 & 0.736961834931378 \tabularnewline
125 & 4 & 2.24061202944452 & 1.75938797055548 \tabularnewline
126 & 2 & 2.29767162711325 & -0.297671627113252 \tabularnewline
127 & 4 & 2.24061202944452 & 1.75938797055548 \tabularnewline
128 & 2 & 2.24061202944452 & -0.240612029444517 \tabularnewline
129 & 3 & 2.3088846949253 & 0.691115305074696 \tabularnewline
130 & 4 & 2.3088846949253 & 1.6911153050747 \tabularnewline
131 & 1 & 2.25182509725657 & -1.25182509725657 \tabularnewline
132 & 1 & 2.2864585593012 & -1.2864585593012 \tabularnewline
133 & 4 & 2.3088846949253 & 1.6911153050747 \tabularnewline
134 & 3 & 2.32009776273736 & 0.679902237262643 \tabularnewline
135 & 4 & 2.3088846949253 & 1.6911153050747 \tabularnewline
136 & 3 & 2.24061202944452 & 0.759387970555483 \tabularnewline
137 & 3 & 2.22939896163246 & 0.770601038367536 \tabularnewline
138 & 2 & 2.3088846949253 & -0.308884694925304 \tabularnewline
139 & 1 & 2.2864585593012 & -1.2864585593012 \tabularnewline
140 & 4 & 2.22939896163246 & 1.77060103836754 \tabularnewline
141 & 3 & 2.24061202944452 & 0.759387970555483 \tabularnewline
142 & 1 & 2.21818589382041 & -1.21818589382041 \tabularnewline
143 & 3 & 2.25182509725657 & 0.748174902743431 \tabularnewline
144 & 1 & 2.3088846949253 & -1.3088846949253 \tabularnewline
145 & 1 & 2.24061202944452 & -1.24061202944452 \tabularnewline
146 & 1 & 2.26303816506862 & -1.26303816506862 \tabularnewline
147 & 3 & 2.3088846949253 & 0.691115305074696 \tabularnewline
148 & 2 & 2.3088846949253 & -0.308884694925304 \tabularnewline
149 & 3 & 2.3088846949253 & 0.691115305074696 \tabularnewline
150 & 1 & 2.2864585593012 & -1.2864585593012 \tabularnewline
151 & 4 & 2.25182509725657 & 1.74817490274343 \tabularnewline
152 & 1 & 2.24061202944452 & -1.24061202944452 \tabularnewline
153 & 3 & 2.22939896163246 & 0.770601038367536 \tabularnewline
154 & 1 & 2.27524549148915 & -1.27524549148915 \tabularnewline
155 & 3 & 2.25281935586504 & 0.747180644134958 \tabularnewline
156 & 3 & 2.3088846949253 & 0.691115305074696 \tabularnewline
157 & 3 & 2.24061202944452 & 0.759387970555483 \tabularnewline
158 & 3 & 2.26303816506862 & 0.736961834931378 \tabularnewline
159 & 2 & 2.24061202944452 & -0.240612029444517 \tabularnewline
160 & 4 & 2.22939896163246 & 1.77060103836754 \tabularnewline
161 & 3 & 2.3088846949253 & 0.691115305074696 \tabularnewline
162 & 3 & 2.22939896163246 & 0.770601038367536 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145558&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]1[/C][C]2.22939896163247[/C][C]-1.22939896163247[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]2.25182509725657[/C][C]-1.25182509725657[/C][/ROW]
[ROW][C]3[/C][C]2[/C][C]2.25182509725657[/C][C]-0.251825097256569[/C][/ROW]
[ROW][C]4[/C][C]3[/C][C]2.3088846949253[/C][C]0.691115305074696[/C][/ROW]
[ROW][C]5[/C][C]2[/C][C]2.21818589382041[/C][C]-0.218185893820412[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]2.24061202944452[/C][C]-1.24061202944452[/C][/ROW]
[ROW][C]7[/C][C]3[/C][C]2.29767162711325[/C][C]0.702328372886748[/C][/ROW]
[ROW][C]8[/C][C]3[/C][C]2.26303816506862[/C][C]0.736961834931378[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]2.25182509725657[/C][C]-1.25182509725657[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]2.25182509725657[/C][C]-0.251825097256569[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]2.3088846949253[/C][C]-1.3088846949253[/C][/ROW]
[ROW][C]12[/C][C]3[/C][C]2.29767162711325[/C][C]0.702328372886748[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]2.22939896163246[/C][C]-1.22939896163246[/C][/ROW]
[ROW][C]14[/C][C]4[/C][C]2.22939896163246[/C][C]1.77060103836754[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]2.24061202944452[/C][C]-1.24061202944452[/C][/ROW]
[ROW][C]16[/C][C]2[/C][C]2.2864585593012[/C][C]-0.286458559301199[/C][/ROW]
[ROW][C]17[/C][C]2[/C][C]2.22939896163246[/C][C]-0.229398961632464[/C][/ROW]
[ROW][C]18[/C][C]3[/C][C]2.3088846949253[/C][C]0.691115305074696[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]2.27524549148915[/C][C]-1.27524549148915[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]2.22939896163246[/C][C]-1.22939896163246[/C][/ROW]
[ROW][C]21[/C][C]3[/C][C]2.3088846949253[/C][C]0.691115305074696[/C][/ROW]
[ROW][C]22[/C][C]2[/C][C]2.22939896163246[/C][C]-0.229398961632464[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]2.25182509725657[/C][C]-1.25182509725657[/C][/ROW]
[ROW][C]24[/C][C]3[/C][C]2.25281935586504[/C][C]0.747180644134958[/C][/ROW]
[ROW][C]25[/C][C]3[/C][C]2.25182509725657[/C][C]0.748174902743431[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]2.26303816506862[/C][C]-1.26303816506862[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]2.26303816506862[/C][C]-1.26303816506862[/C][/ROW]
[ROW][C]28[/C][C]1[/C][C]2.25182509725657[/C][C]-1.25182509725657[/C][/ROW]
[ROW][C]29[/C][C]3[/C][C]2.3088846949253[/C][C]0.691115305074696[/C][/ROW]
[ROW][C]30[/C][C]3[/C][C]2.24061202944452[/C][C]0.759387970555483[/C][/ROW]
[ROW][C]31[/C][C]1[/C][C]2.3088846949253[/C][C]-1.3088846949253[/C][/ROW]
[ROW][C]32[/C][C]3[/C][C]2.25182509725657[/C][C]0.748174902743431[/C][/ROW]
[ROW][C]33[/C][C]3[/C][C]2.21818589382041[/C][C]0.781814106179588[/C][/ROW]
[ROW][C]34[/C][C]3[/C][C]2.3088846949253[/C][C]0.691115305074696[/C][/ROW]
[ROW][C]35[/C][C]3[/C][C]2.3088846949253[/C][C]0.691115305074696[/C][/ROW]
[ROW][C]36[/C][C]1[/C][C]2.3088846949253[/C][C]-1.3088846949253[/C][/ROW]
[ROW][C]37[/C][C]4[/C][C]2.25182509725657[/C][C]1.74817490274343[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]2.3088846949253[/C][C]-1.3088846949253[/C][/ROW]
[ROW][C]39[/C][C]2[/C][C]2.3088846949253[/C][C]-0.308884694925304[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]2.22939896163246[/C][C]-1.22939896163246[/C][/ROW]
[ROW][C]41[/C][C]2[/C][C]2.22939896163246[/C][C]-0.229398961632464[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]2.22939896163246[/C][C]-1.22939896163246[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]2.26303816506862[/C][C]0.736961834931378[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]2.3088846949253[/C][C]-1.3088846949253[/C][/ROW]
[ROW][C]45[/C][C]3[/C][C]2.3088846949253[/C][C]0.691115305074696[/C][/ROW]
[ROW][C]46[/C][C]1[/C][C]2.26303816506862[/C][C]-1.26303816506862[/C][/ROW]
[ROW][C]47[/C][C]3[/C][C]2.3088846949253[/C][C]0.691115305074696[/C][/ROW]
[ROW][C]48[/C][C]3[/C][C]2.3088846949253[/C][C]0.691115305074696[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]2.24061202944452[/C][C]-1.24061202944452[/C][/ROW]
[ROW][C]50[/C][C]3[/C][C]2.3088846949253[/C][C]0.691115305074696[/C][/ROW]
[ROW][C]51[/C][C]3[/C][C]2.25182509725657[/C][C]0.748174902743431[/C][/ROW]
[ROW][C]52[/C][C]3[/C][C]2.24061202944452[/C][C]0.759387970555483[/C][/ROW]
[ROW][C]53[/C][C]2[/C][C]2.24061202944452[/C][C]-0.240612029444517[/C][/ROW]
[ROW][C]54[/C][C]3[/C][C]2.26303816506862[/C][C]0.736961834931378[/C][/ROW]
[ROW][C]55[/C][C]2[/C][C]2.29767162711325[/C][C]-0.297671627113252[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]2.24061202944452[/C][C]-1.24061202944452[/C][/ROW]
[ROW][C]57[/C][C]3[/C][C]2.25182509725657[/C][C]0.748174902743431[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]2.25182509725657[/C][C]-1.25182509725657[/C][/ROW]
[ROW][C]59[/C][C]3[/C][C]2.3088846949253[/C][C]0.691115305074696[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]2.2864585593012[/C][C]-1.2864585593012[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]2.24061202944452[/C][C]-1.24061202944452[/C][/ROW]
[ROW][C]62[/C][C]3[/C][C]2.21818589382041[/C][C]0.781814106179588[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]2.22939896163246[/C][C]-1.22939896163246[/C][/ROW]
[ROW][C]64[/C][C]3[/C][C]2.3088846949253[/C][C]0.691115305074696[/C][/ROW]
[ROW][C]65[/C][C]1[/C][C]2.24061202944452[/C][C]-1.24061202944452[/C][/ROW]
[ROW][C]66[/C][C]2[/C][C]2.29767162711325[/C][C]-0.297671627113252[/C][/ROW]
[ROW][C]67[/C][C]3[/C][C]2.3088846949253[/C][C]0.691115305074696[/C][/ROW]
[ROW][C]68[/C][C]3[/C][C]2.3088846949253[/C][C]0.691115305074696[/C][/ROW]
[ROW][C]69[/C][C]2[/C][C]2.25182509725657[/C][C]-0.251825097256569[/C][/ROW]
[ROW][C]70[/C][C]3[/C][C]2.25182509725657[/C][C]0.748174902743431[/C][/ROW]
[ROW][C]71[/C][C]2[/C][C]2.26303816506862[/C][C]-0.263038165068622[/C][/ROW]
[ROW][C]72[/C][C]3[/C][C]2.29767162711325[/C][C]0.702328372886748[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]2.29767162711325[/C][C]-1.29767162711325[/C][/ROW]
[ROW][C]74[/C][C]4[/C][C]2.24061202944452[/C][C]1.75938797055548[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]2.24061202944452[/C][C]-1.24061202944452[/C][/ROW]
[ROW][C]76[/C][C]3[/C][C]2.22939896163246[/C][C]0.770601038367536[/C][/ROW]
[ROW][C]77[/C][C]3[/C][C]2.22939896163246[/C][C]0.770601038367536[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]2.3088846949253[/C][C]-1.3088846949253[/C][/ROW]
[ROW][C]79[/C][C]3[/C][C]2.2864585593012[/C][C]0.7135414406988[/C][/ROW]
[ROW][C]80[/C][C]4[/C][C]2.3088846949253[/C][C]1.6911153050747[/C][/ROW]
[ROW][C]81[/C][C]3[/C][C]2.3088846949253[/C][C]0.691115305074696[/C][/ROW]
[ROW][C]82[/C][C]3[/C][C]2.25182509725657[/C][C]0.748174902743431[/C][/ROW]
[ROW][C]83[/C][C]3[/C][C]2.21818589382041[/C][C]0.781814106179588[/C][/ROW]
[ROW][C]84[/C][C]3[/C][C]2.21818589382041[/C][C]0.781814106179588[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]2.3088846949253[/C][C]-1.3088846949253[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]2.32009776273736[/C][C]-1.32009776273736[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]2.24061202944452[/C][C]-1.24061202944452[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]2.26303816506862[/C][C]-1.26303816506862[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]2.25182509725657[/C][C]-1.25182509725657[/C][/ROW]
[ROW][C]90[/C][C]3[/C][C]2.25182509725657[/C][C]0.748174902743431[/C][/ROW]
[ROW][C]91[/C][C]2[/C][C]2.25182509725657[/C][C]-0.251825097256569[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]2.29767162711325[/C][C]-1.29767162711325[/C][/ROW]
[ROW][C]93[/C][C]2[/C][C]2.24061202944452[/C][C]-0.240612029444517[/C][/ROW]
[ROW][C]94[/C][C]3[/C][C]2.24061202944452[/C][C]0.759387970555483[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]2.25182509725657[/C][C]-1.25182509725657[/C][/ROW]
[ROW][C]96[/C][C]3[/C][C]2.24061202944452[/C][C]0.759387970555483[/C][/ROW]
[ROW][C]97[/C][C]3[/C][C]2.3088846949253[/C][C]0.691115305074696[/C][/ROW]
[ROW][C]98[/C][C]2[/C][C]2.25182509725657[/C][C]-0.251825097256569[/C][/ROW]
[ROW][C]99[/C][C]3[/C][C]2.2864585593012[/C][C]0.7135414406988[/C][/ROW]
[ROW][C]100[/C][C]3[/C][C]2.29767162711325[/C][C]0.702328372886748[/C][/ROW]
[ROW][C]101[/C][C]3[/C][C]2.24061202944452[/C][C]0.759387970555483[/C][/ROW]
[ROW][C]102[/C][C]2[/C][C]2.29767162711325[/C][C]-0.297671627113252[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]2.22939896163246[/C][C]-1.22939896163246[/C][/ROW]
[ROW][C]104[/C][C]4[/C][C]2.25182509725657[/C][C]1.74817490274343[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]2.29767162711325[/C][C]-1.29767162711325[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]2.24061202944452[/C][C]-1.24061202944452[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]2.3088846949253[/C][C]-1.3088846949253[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]2.26303816506862[/C][C]-1.26303816506862[/C][/ROW]
[ROW][C]109[/C][C]3[/C][C]2.25182509725657[/C][C]0.748174902743431[/C][/ROW]
[ROW][C]110[/C][C]3[/C][C]2.25182509725657[/C][C]0.748174902743431[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]2.20697282600836[/C][C]-1.20697282600836[/C][/ROW]
[ROW][C]112[/C][C]3[/C][C]2.24061202944452[/C][C]0.759387970555483[/C][/ROW]
[ROW][C]113[/C][C]3[/C][C]2.25182509725657[/C][C]0.748174902743431[/C][/ROW]
[ROW][C]114[/C][C]4[/C][C]2.24061202944452[/C][C]1.75938797055548[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]2.25182509725657[/C][C]-1.25182509725657[/C][/ROW]
[ROW][C]116[/C][C]1[/C][C]2.29767162711325[/C][C]-1.29767162711325[/C][/ROW]
[ROW][C]117[/C][C]3[/C][C]2.25182509725657[/C][C]0.748174902743431[/C][/ROW]
[ROW][C]118[/C][C]3[/C][C]2.22939896163246[/C][C]0.770601038367536[/C][/ROW]
[ROW][C]119[/C][C]3[/C][C]2.29767162711325[/C][C]0.702328372886748[/C][/ROW]
[ROW][C]120[/C][C]3[/C][C]2.22939896163246[/C][C]0.770601038367536[/C][/ROW]
[ROW][C]121[/C][C]2[/C][C]2.25182509725657[/C][C]-0.251825097256569[/C][/ROW]
[ROW][C]122[/C][C]3[/C][C]2.24061202944452[/C][C]0.759387970555483[/C][/ROW]
[ROW][C]123[/C][C]3[/C][C]2.20697282600836[/C][C]0.79302717399164[/C][/ROW]
[ROW][C]124[/C][C]3[/C][C]2.26303816506862[/C][C]0.736961834931378[/C][/ROW]
[ROW][C]125[/C][C]4[/C][C]2.24061202944452[/C][C]1.75938797055548[/C][/ROW]
[ROW][C]126[/C][C]2[/C][C]2.29767162711325[/C][C]-0.297671627113252[/C][/ROW]
[ROW][C]127[/C][C]4[/C][C]2.24061202944452[/C][C]1.75938797055548[/C][/ROW]
[ROW][C]128[/C][C]2[/C][C]2.24061202944452[/C][C]-0.240612029444517[/C][/ROW]
[ROW][C]129[/C][C]3[/C][C]2.3088846949253[/C][C]0.691115305074696[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]2.3088846949253[/C][C]1.6911153050747[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]2.25182509725657[/C][C]-1.25182509725657[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]2.2864585593012[/C][C]-1.2864585593012[/C][/ROW]
[ROW][C]133[/C][C]4[/C][C]2.3088846949253[/C][C]1.6911153050747[/C][/ROW]
[ROW][C]134[/C][C]3[/C][C]2.32009776273736[/C][C]0.679902237262643[/C][/ROW]
[ROW][C]135[/C][C]4[/C][C]2.3088846949253[/C][C]1.6911153050747[/C][/ROW]
[ROW][C]136[/C][C]3[/C][C]2.24061202944452[/C][C]0.759387970555483[/C][/ROW]
[ROW][C]137[/C][C]3[/C][C]2.22939896163246[/C][C]0.770601038367536[/C][/ROW]
[ROW][C]138[/C][C]2[/C][C]2.3088846949253[/C][C]-0.308884694925304[/C][/ROW]
[ROW][C]139[/C][C]1[/C][C]2.2864585593012[/C][C]-1.2864585593012[/C][/ROW]
[ROW][C]140[/C][C]4[/C][C]2.22939896163246[/C][C]1.77060103836754[/C][/ROW]
[ROW][C]141[/C][C]3[/C][C]2.24061202944452[/C][C]0.759387970555483[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]2.21818589382041[/C][C]-1.21818589382041[/C][/ROW]
[ROW][C]143[/C][C]3[/C][C]2.25182509725657[/C][C]0.748174902743431[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]2.3088846949253[/C][C]-1.3088846949253[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]2.24061202944452[/C][C]-1.24061202944452[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]2.26303816506862[/C][C]-1.26303816506862[/C][/ROW]
[ROW][C]147[/C][C]3[/C][C]2.3088846949253[/C][C]0.691115305074696[/C][/ROW]
[ROW][C]148[/C][C]2[/C][C]2.3088846949253[/C][C]-0.308884694925304[/C][/ROW]
[ROW][C]149[/C][C]3[/C][C]2.3088846949253[/C][C]0.691115305074696[/C][/ROW]
[ROW][C]150[/C][C]1[/C][C]2.2864585593012[/C][C]-1.2864585593012[/C][/ROW]
[ROW][C]151[/C][C]4[/C][C]2.25182509725657[/C][C]1.74817490274343[/C][/ROW]
[ROW][C]152[/C][C]1[/C][C]2.24061202944452[/C][C]-1.24061202944452[/C][/ROW]
[ROW][C]153[/C][C]3[/C][C]2.22939896163246[/C][C]0.770601038367536[/C][/ROW]
[ROW][C]154[/C][C]1[/C][C]2.27524549148915[/C][C]-1.27524549148915[/C][/ROW]
[ROW][C]155[/C][C]3[/C][C]2.25281935586504[/C][C]0.747180644134958[/C][/ROW]
[ROW][C]156[/C][C]3[/C][C]2.3088846949253[/C][C]0.691115305074696[/C][/ROW]
[ROW][C]157[/C][C]3[/C][C]2.24061202944452[/C][C]0.759387970555483[/C][/ROW]
[ROW][C]158[/C][C]3[/C][C]2.26303816506862[/C][C]0.736961834931378[/C][/ROW]
[ROW][C]159[/C][C]2[/C][C]2.24061202944452[/C][C]-0.240612029444517[/C][/ROW]
[ROW][C]160[/C][C]4[/C][C]2.22939896163246[/C][C]1.77060103836754[/C][/ROW]
[ROW][C]161[/C][C]3[/C][C]2.3088846949253[/C][C]0.691115305074696[/C][/ROW]
[ROW][C]162[/C][C]3[/C][C]2.22939896163246[/C][C]0.770601038367536[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145558&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145558&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.22939896163247-1.22939896163247
212.25182509725657-1.25182509725657
322.25182509725657-0.251825097256569
432.30888469492530.691115305074696
522.21818589382041-0.218185893820412
612.24061202944452-1.24061202944452
732.297671627113250.702328372886748
832.263038165068620.736961834931378
912.25182509725657-1.25182509725657
1022.25182509725657-0.251825097256569
1112.3088846949253-1.3088846949253
1232.297671627113250.702328372886748
1312.22939896163246-1.22939896163246
1442.229398961632461.77060103836754
1512.24061202944452-1.24061202944452
1622.2864585593012-0.286458559301199
1722.22939896163246-0.229398961632464
1832.30888469492530.691115305074696
1912.27524549148915-1.27524549148915
2012.22939896163246-1.22939896163246
2132.30888469492530.691115305074696
2222.22939896163246-0.229398961632464
2312.25182509725657-1.25182509725657
2432.252819355865040.747180644134958
2532.251825097256570.748174902743431
2612.26303816506862-1.26303816506862
2712.26303816506862-1.26303816506862
2812.25182509725657-1.25182509725657
2932.30888469492530.691115305074696
3032.240612029444520.759387970555483
3112.3088846949253-1.3088846949253
3232.251825097256570.748174902743431
3332.218185893820410.781814106179588
3432.30888469492530.691115305074696
3532.30888469492530.691115305074696
3612.3088846949253-1.3088846949253
3742.251825097256571.74817490274343
3812.3088846949253-1.3088846949253
3922.3088846949253-0.308884694925304
4012.22939896163246-1.22939896163246
4122.22939896163246-0.229398961632464
4212.22939896163246-1.22939896163246
4332.263038165068620.736961834931378
4412.3088846949253-1.3088846949253
4532.30888469492530.691115305074696
4612.26303816506862-1.26303816506862
4732.30888469492530.691115305074696
4832.30888469492530.691115305074696
4912.24061202944452-1.24061202944452
5032.30888469492530.691115305074696
5132.251825097256570.748174902743431
5232.240612029444520.759387970555483
5322.24061202944452-0.240612029444517
5432.263038165068620.736961834931378
5522.29767162711325-0.297671627113252
5612.24061202944452-1.24061202944452
5732.251825097256570.748174902743431
5812.25182509725657-1.25182509725657
5932.30888469492530.691115305074696
6012.2864585593012-1.2864585593012
6112.24061202944452-1.24061202944452
6232.218185893820410.781814106179588
6312.22939896163246-1.22939896163246
6432.30888469492530.691115305074696
6512.24061202944452-1.24061202944452
6622.29767162711325-0.297671627113252
6732.30888469492530.691115305074696
6832.30888469492530.691115305074696
6922.25182509725657-0.251825097256569
7032.251825097256570.748174902743431
7122.26303816506862-0.263038165068622
7232.297671627113250.702328372886748
7312.29767162711325-1.29767162711325
7442.240612029444521.75938797055548
7512.24061202944452-1.24061202944452
7632.229398961632460.770601038367536
7732.229398961632460.770601038367536
7812.3088846949253-1.3088846949253
7932.28645855930120.7135414406988
8042.30888469492531.6911153050747
8132.30888469492530.691115305074696
8232.251825097256570.748174902743431
8332.218185893820410.781814106179588
8432.218185893820410.781814106179588
8512.3088846949253-1.3088846949253
8612.32009776273736-1.32009776273736
8712.24061202944452-1.24061202944452
8812.26303816506862-1.26303816506862
8912.25182509725657-1.25182509725657
9032.251825097256570.748174902743431
9122.25182509725657-0.251825097256569
9212.29767162711325-1.29767162711325
9322.24061202944452-0.240612029444517
9432.240612029444520.759387970555483
9512.25182509725657-1.25182509725657
9632.240612029444520.759387970555483
9732.30888469492530.691115305074696
9822.25182509725657-0.251825097256569
9932.28645855930120.7135414406988
10032.297671627113250.702328372886748
10132.240612029444520.759387970555483
10222.29767162711325-0.297671627113252
10312.22939896163246-1.22939896163246
10442.251825097256571.74817490274343
10512.29767162711325-1.29767162711325
10612.24061202944452-1.24061202944452
10712.3088846949253-1.3088846949253
10812.26303816506862-1.26303816506862
10932.251825097256570.748174902743431
11032.251825097256570.748174902743431
11112.20697282600836-1.20697282600836
11232.240612029444520.759387970555483
11332.251825097256570.748174902743431
11442.240612029444521.75938797055548
11512.25182509725657-1.25182509725657
11612.29767162711325-1.29767162711325
11732.251825097256570.748174902743431
11832.229398961632460.770601038367536
11932.297671627113250.702328372886748
12032.229398961632460.770601038367536
12122.25182509725657-0.251825097256569
12232.240612029444520.759387970555483
12332.206972826008360.79302717399164
12432.263038165068620.736961834931378
12542.240612029444521.75938797055548
12622.29767162711325-0.297671627113252
12742.240612029444521.75938797055548
12822.24061202944452-0.240612029444517
12932.30888469492530.691115305074696
13042.30888469492531.6911153050747
13112.25182509725657-1.25182509725657
13212.2864585593012-1.2864585593012
13342.30888469492531.6911153050747
13432.320097762737360.679902237262643
13542.30888469492531.6911153050747
13632.240612029444520.759387970555483
13732.229398961632460.770601038367536
13822.3088846949253-0.308884694925304
13912.2864585593012-1.2864585593012
14042.229398961632461.77060103836754
14132.240612029444520.759387970555483
14212.21818589382041-1.21818589382041
14332.251825097256570.748174902743431
14412.3088846949253-1.3088846949253
14512.24061202944452-1.24061202944452
14612.26303816506862-1.26303816506862
14732.30888469492530.691115305074696
14822.3088846949253-0.308884694925304
14932.30888469492530.691115305074696
15012.2864585593012-1.2864585593012
15142.251825097256571.74817490274343
15212.24061202944452-1.24061202944452
15332.229398961632460.770601038367536
15412.27524549148915-1.27524549148915
15532.252819355865040.747180644134958
15632.30888469492530.691115305074696
15732.240612029444520.759387970555483
15832.263038165068620.736961834931378
15922.24061202944452-0.240612029444517
16042.229398961632461.77060103836754
16132.30888469492530.691115305074696
16232.229398961632460.770601038367536







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.215625991613670.431251983227340.78437400838633
70.1014667834227830.2029335668455650.898533216577217
80.2456419502791540.4912839005583080.754358049720846
90.2157331294413610.4314662588827220.784266870558639
100.140271277580330.280542555160660.85972872241967
110.3428736000562940.6857472001125870.657126399943706
120.2881585130384050.5763170260768090.711841486961595
130.2287679470245490.4575358940490980.77123205297545
140.6119953612238940.7760092775522130.388004638776107
150.5776989993821390.8446020012357220.422301000617861
160.5101636364273460.9796727271453080.489836363572654
170.4325114045167320.8650228090334640.567488595483268
180.378741967254810.757483934509620.62125803274519
190.4182699255576880.8365398511153760.581730074442312
200.3792538975661410.7585077951322820.620746102433859
210.3302294968067870.6604589936135730.669770503193214
220.2783999458562170.5567998917124330.721600054143783
230.2685252835458980.5370505670917960.731474716454102
240.2521405885298040.5042811770596080.747859411470196
250.2788931104077970.5577862208155930.721106889592203
260.2673258254554670.5346516509109350.732674174544533
270.2506617449318930.5013234898637860.749338255068107
280.2322761886120050.4645523772240090.767723811387995
290.201229018916480.4024580378329610.79877098108352
300.231980403951290.4639608079025790.76801959604871
310.280276830007050.56055366001410.71972316999295
320.3047026785211680.6094053570423360.695297321478832
330.3104835763914830.6209671527829660.689516423608517
340.2827404619962280.5654809239924560.717259538003772
350.253692637798830.5073852755976610.74630736220117
360.2926289257025790.5852578514051570.707371074297421
370.4590824456598740.9181648913197490.540917554340126
380.4891794729486630.9783589458973260.510820527051337
390.4381023037037530.8762046074075070.561897696296247
400.4394391354544320.8788782709088650.560560864545568
410.3885715786163850.777143157232770.611428421383615
420.388434313453210.776868626906420.61156568654679
430.3908178013787010.7816356027574030.609182198621299
440.4152680165130170.8305360330260340.584731983486983
450.3931753973350220.7863507946700450.606824602664978
460.3944461520265570.7888923040531140.605553847973443
470.3713412654882880.7426825309765750.628658734511712
480.3469900767954540.6939801535909080.653009923204546
490.3478420449249180.6956840898498350.652157955075082
500.3230784863122850.6461569726245690.676921513687715
510.3236991298946870.6473982597893740.676300870105313
520.3220296775170860.6440593550341710.677970322482914
530.2812077511611850.562415502322370.718792248838815
540.2745367671307020.5490735342614050.725463232869298
550.23869774329230.47739548658460.7613022567077
560.2442052743869950.4884105487739890.755794725613005
570.2380553736362950.4761107472725890.761944626363705
580.2467732392581270.4935464785162530.753226760741873
590.2257379557094280.4514759114188560.774262044290572
600.2458309704405330.4916619408810650.754169029559467
610.2527016326878840.5054032653757670.747298367312117
620.2542508330135180.5085016660270360.745749166986482
630.2612312080842470.5224624161684930.738768791915753
640.2407104529776450.481420905955290.759289547022355
650.2499828621773290.4999657243546580.750017137822671
660.2172678303503190.4345356607006380.782732169649681
670.1988791265231780.3977582530463550.801120873476822
680.1812775777733510.3625551555467030.818722422226649
690.154691081051660.3093821621033210.84530891894834
700.149236844379290.298473688758580.85076315562071
710.1258623591728840.2517247183457690.874137640827116
720.1138754925065950.227750985013190.886124507493405
730.1296895281969540.2593790563939080.870310471803046
740.199013100231560.398026200463120.80098689976844
750.2111293985100480.4222587970200960.788870601489952
760.2052679505184620.4105359010369240.794732049481538
770.1977305770137110.3954611540274220.80226942298629
780.2193097787567350.4386195575134710.780690221243264
790.2029085800206370.4058171600412740.797091419979363
800.2623385880010340.5246771760020680.737661411998966
810.2425891894076620.4851783788153240.757410810592338
820.2290836767415740.4581673534831470.770916323258426
830.2186718734377330.4373437468754660.781328126562267
840.2066157812859940.4132315625719880.793384218714006
850.2266518020911150.453303604182230.773348197908885
860.2486416463679160.4972832927358310.751358353632084
870.2668203059382330.5336406118764650.733179694061767
880.291792189164830.5835843783296610.70820781083517
890.3189601137767160.6379202275534310.681039886223284
900.3014244280986780.6028488561973560.698575571901322
910.2699418509524830.5398837019049650.730058149047517
920.2939441178759080.5878882357518160.706055882124092
930.2613621668979620.5227243337959250.738637833102038
940.2440173241587320.4880346483174630.755982675841268
950.275334910506620.550669821013240.72466508949338
960.2565662538879190.5131325077758380.743433746112081
970.2351678100703110.4703356201406220.764832189929689
980.209049955750110.418099911500220.79095004424989
990.1937356525586570.3874713051173140.806264347441343
1000.1776043871951070.3552087743902140.822395612804893
1010.162013450349060.324026900698120.83798654965094
1020.1369668724256340.2739337448512690.863033127574366
1030.154866431727960.3097328634559210.84513356827204
1040.1998252970514150.399650594102830.800174702948585
1050.2189253265273510.4378506530547020.781074673472649
1060.2508679803828720.5017359607657440.749132019617128
1070.2811076854961450.562215370992290.718892314503855
1080.3436624066509140.6873248133018280.656337593349086
1090.3155480586227310.6310961172454630.684451941377269
1100.2876988690993640.5753977381987280.712301130900636
1110.3120661398363750.6241322796727490.687933860163625
1120.2842756295835280.5685512591670560.715724370416472
1130.2562131965496570.5124263930993130.743786803450343
1140.3080857837639220.6161715675278440.691914216236078
1150.3697003221442760.7394006442885510.630299677855724
1160.4074991692422790.8149983384845580.592500830757721
1170.3713045416353270.7426090832706530.628695458364673
1180.3392221949171420.6784443898342840.660777805082858
1190.3096609669761330.6193219339522670.690339033023867
1200.2793021991233720.5586043982467440.720697800876628
1210.2543058458389490.5086116916778990.74569415416105
1220.2240253327321710.4480506654643420.775974667267829
1230.2044714096616390.4089428193232780.795528590338361
1240.1756536441825560.3513072883651120.824346355817444
1250.2145664091693210.4291328183386420.785433590830679
1260.181713396533520.3634267930670390.81828660346648
1270.2257487995575580.4514975991151160.774251200442442
1280.193057056676220.386114113352440.80694294332378
1290.1653540593720620.3307081187441230.834645940627938
1300.207832703250360.415665406500720.79216729674964
1310.2641811328845780.5283622657691560.735818867115422
1320.2819704279693420.5639408559386840.718029572030658
1330.346891805519180.693783611038360.65310819448082
1340.3085218150680720.6170436301361440.691478184931928
1350.4104241465204680.8208482930409370.589575853479532
1360.362457254369750.7249145087394990.63754274563025
1370.3180197857528780.6360395715057560.681980214247122
1380.2638989980073560.5277979960147110.736101001992644
1390.2724296602378370.5448593204756740.727570339762163
1400.3402368612340690.6804737224681380.659763138765931
1410.2972160136723660.5944320273447310.702783986327634
1420.3402530672366290.6805061344732580.65974693276337
1430.2908187859621410.5816375719242810.70918121403786
1440.3195440166625340.6390880333250680.680455983337466
1450.3980008867339970.7960017734679940.601999113266003
1460.5697416194834440.8605167610331120.430258380516556
1470.5084627175537170.9830745648925660.491537282446283
1480.4368536178926710.8737072357853410.56314638210733
1490.3731232007493510.7462464014987030.626876799250649
1500.4279520295591710.8559040591183410.572047970440829
1510.4557432818145760.9114865636291520.544256718185424
1520.7074939790686710.5850120418626580.292506020931329
1530.5954640999329750.809071800134050.404535900067025
1540.8731323458331440.2537353083337120.126867654166856
1550.789389977849060.4212200443018790.21061002215094
1560.6352124982197350.729575003560530.364787501780265

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.21562599161367 & 0.43125198322734 & 0.78437400838633 \tabularnewline
7 & 0.101466783422783 & 0.202933566845565 & 0.898533216577217 \tabularnewline
8 & 0.245641950279154 & 0.491283900558308 & 0.754358049720846 \tabularnewline
9 & 0.215733129441361 & 0.431466258882722 & 0.784266870558639 \tabularnewline
10 & 0.14027127758033 & 0.28054255516066 & 0.85972872241967 \tabularnewline
11 & 0.342873600056294 & 0.685747200112587 & 0.657126399943706 \tabularnewline
12 & 0.288158513038405 & 0.576317026076809 & 0.711841486961595 \tabularnewline
13 & 0.228767947024549 & 0.457535894049098 & 0.77123205297545 \tabularnewline
14 & 0.611995361223894 & 0.776009277552213 & 0.388004638776107 \tabularnewline
15 & 0.577698999382139 & 0.844602001235722 & 0.422301000617861 \tabularnewline
16 & 0.510163636427346 & 0.979672727145308 & 0.489836363572654 \tabularnewline
17 & 0.432511404516732 & 0.865022809033464 & 0.567488595483268 \tabularnewline
18 & 0.37874196725481 & 0.75748393450962 & 0.62125803274519 \tabularnewline
19 & 0.418269925557688 & 0.836539851115376 & 0.581730074442312 \tabularnewline
20 & 0.379253897566141 & 0.758507795132282 & 0.620746102433859 \tabularnewline
21 & 0.330229496806787 & 0.660458993613573 & 0.669770503193214 \tabularnewline
22 & 0.278399945856217 & 0.556799891712433 & 0.721600054143783 \tabularnewline
23 & 0.268525283545898 & 0.537050567091796 & 0.731474716454102 \tabularnewline
24 & 0.252140588529804 & 0.504281177059608 & 0.747859411470196 \tabularnewline
25 & 0.278893110407797 & 0.557786220815593 & 0.721106889592203 \tabularnewline
26 & 0.267325825455467 & 0.534651650910935 & 0.732674174544533 \tabularnewline
27 & 0.250661744931893 & 0.501323489863786 & 0.749338255068107 \tabularnewline
28 & 0.232276188612005 & 0.464552377224009 & 0.767723811387995 \tabularnewline
29 & 0.20122901891648 & 0.402458037832961 & 0.79877098108352 \tabularnewline
30 & 0.23198040395129 & 0.463960807902579 & 0.76801959604871 \tabularnewline
31 & 0.28027683000705 & 0.5605536600141 & 0.71972316999295 \tabularnewline
32 & 0.304702678521168 & 0.609405357042336 & 0.695297321478832 \tabularnewline
33 & 0.310483576391483 & 0.620967152782966 & 0.689516423608517 \tabularnewline
34 & 0.282740461996228 & 0.565480923992456 & 0.717259538003772 \tabularnewline
35 & 0.25369263779883 & 0.507385275597661 & 0.74630736220117 \tabularnewline
36 & 0.292628925702579 & 0.585257851405157 & 0.707371074297421 \tabularnewline
37 & 0.459082445659874 & 0.918164891319749 & 0.540917554340126 \tabularnewline
38 & 0.489179472948663 & 0.978358945897326 & 0.510820527051337 \tabularnewline
39 & 0.438102303703753 & 0.876204607407507 & 0.561897696296247 \tabularnewline
40 & 0.439439135454432 & 0.878878270908865 & 0.560560864545568 \tabularnewline
41 & 0.388571578616385 & 0.77714315723277 & 0.611428421383615 \tabularnewline
42 & 0.38843431345321 & 0.77686862690642 & 0.61156568654679 \tabularnewline
43 & 0.390817801378701 & 0.781635602757403 & 0.609182198621299 \tabularnewline
44 & 0.415268016513017 & 0.830536033026034 & 0.584731983486983 \tabularnewline
45 & 0.393175397335022 & 0.786350794670045 & 0.606824602664978 \tabularnewline
46 & 0.394446152026557 & 0.788892304053114 & 0.605553847973443 \tabularnewline
47 & 0.371341265488288 & 0.742682530976575 & 0.628658734511712 \tabularnewline
48 & 0.346990076795454 & 0.693980153590908 & 0.653009923204546 \tabularnewline
49 & 0.347842044924918 & 0.695684089849835 & 0.652157955075082 \tabularnewline
50 & 0.323078486312285 & 0.646156972624569 & 0.676921513687715 \tabularnewline
51 & 0.323699129894687 & 0.647398259789374 & 0.676300870105313 \tabularnewline
52 & 0.322029677517086 & 0.644059355034171 & 0.677970322482914 \tabularnewline
53 & 0.281207751161185 & 0.56241550232237 & 0.718792248838815 \tabularnewline
54 & 0.274536767130702 & 0.549073534261405 & 0.725463232869298 \tabularnewline
55 & 0.2386977432923 & 0.4773954865846 & 0.7613022567077 \tabularnewline
56 & 0.244205274386995 & 0.488410548773989 & 0.755794725613005 \tabularnewline
57 & 0.238055373636295 & 0.476110747272589 & 0.761944626363705 \tabularnewline
58 & 0.246773239258127 & 0.493546478516253 & 0.753226760741873 \tabularnewline
59 & 0.225737955709428 & 0.451475911418856 & 0.774262044290572 \tabularnewline
60 & 0.245830970440533 & 0.491661940881065 & 0.754169029559467 \tabularnewline
61 & 0.252701632687884 & 0.505403265375767 & 0.747298367312117 \tabularnewline
62 & 0.254250833013518 & 0.508501666027036 & 0.745749166986482 \tabularnewline
63 & 0.261231208084247 & 0.522462416168493 & 0.738768791915753 \tabularnewline
64 & 0.240710452977645 & 0.48142090595529 & 0.759289547022355 \tabularnewline
65 & 0.249982862177329 & 0.499965724354658 & 0.750017137822671 \tabularnewline
66 & 0.217267830350319 & 0.434535660700638 & 0.782732169649681 \tabularnewline
67 & 0.198879126523178 & 0.397758253046355 & 0.801120873476822 \tabularnewline
68 & 0.181277577773351 & 0.362555155546703 & 0.818722422226649 \tabularnewline
69 & 0.15469108105166 & 0.309382162103321 & 0.84530891894834 \tabularnewline
70 & 0.14923684437929 & 0.29847368875858 & 0.85076315562071 \tabularnewline
71 & 0.125862359172884 & 0.251724718345769 & 0.874137640827116 \tabularnewline
72 & 0.113875492506595 & 0.22775098501319 & 0.886124507493405 \tabularnewline
73 & 0.129689528196954 & 0.259379056393908 & 0.870310471803046 \tabularnewline
74 & 0.19901310023156 & 0.39802620046312 & 0.80098689976844 \tabularnewline
75 & 0.211129398510048 & 0.422258797020096 & 0.788870601489952 \tabularnewline
76 & 0.205267950518462 & 0.410535901036924 & 0.794732049481538 \tabularnewline
77 & 0.197730577013711 & 0.395461154027422 & 0.80226942298629 \tabularnewline
78 & 0.219309778756735 & 0.438619557513471 & 0.780690221243264 \tabularnewline
79 & 0.202908580020637 & 0.405817160041274 & 0.797091419979363 \tabularnewline
80 & 0.262338588001034 & 0.524677176002068 & 0.737661411998966 \tabularnewline
81 & 0.242589189407662 & 0.485178378815324 & 0.757410810592338 \tabularnewline
82 & 0.229083676741574 & 0.458167353483147 & 0.770916323258426 \tabularnewline
83 & 0.218671873437733 & 0.437343746875466 & 0.781328126562267 \tabularnewline
84 & 0.206615781285994 & 0.413231562571988 & 0.793384218714006 \tabularnewline
85 & 0.226651802091115 & 0.45330360418223 & 0.773348197908885 \tabularnewline
86 & 0.248641646367916 & 0.497283292735831 & 0.751358353632084 \tabularnewline
87 & 0.266820305938233 & 0.533640611876465 & 0.733179694061767 \tabularnewline
88 & 0.29179218916483 & 0.583584378329661 & 0.70820781083517 \tabularnewline
89 & 0.318960113776716 & 0.637920227553431 & 0.681039886223284 \tabularnewline
90 & 0.301424428098678 & 0.602848856197356 & 0.698575571901322 \tabularnewline
91 & 0.269941850952483 & 0.539883701904965 & 0.730058149047517 \tabularnewline
92 & 0.293944117875908 & 0.587888235751816 & 0.706055882124092 \tabularnewline
93 & 0.261362166897962 & 0.522724333795925 & 0.738637833102038 \tabularnewline
94 & 0.244017324158732 & 0.488034648317463 & 0.755982675841268 \tabularnewline
95 & 0.27533491050662 & 0.55066982101324 & 0.72466508949338 \tabularnewline
96 & 0.256566253887919 & 0.513132507775838 & 0.743433746112081 \tabularnewline
97 & 0.235167810070311 & 0.470335620140622 & 0.764832189929689 \tabularnewline
98 & 0.20904995575011 & 0.41809991150022 & 0.79095004424989 \tabularnewline
99 & 0.193735652558657 & 0.387471305117314 & 0.806264347441343 \tabularnewline
100 & 0.177604387195107 & 0.355208774390214 & 0.822395612804893 \tabularnewline
101 & 0.16201345034906 & 0.32402690069812 & 0.83798654965094 \tabularnewline
102 & 0.136966872425634 & 0.273933744851269 & 0.863033127574366 \tabularnewline
103 & 0.15486643172796 & 0.309732863455921 & 0.84513356827204 \tabularnewline
104 & 0.199825297051415 & 0.39965059410283 & 0.800174702948585 \tabularnewline
105 & 0.218925326527351 & 0.437850653054702 & 0.781074673472649 \tabularnewline
106 & 0.250867980382872 & 0.501735960765744 & 0.749132019617128 \tabularnewline
107 & 0.281107685496145 & 0.56221537099229 & 0.718892314503855 \tabularnewline
108 & 0.343662406650914 & 0.687324813301828 & 0.656337593349086 \tabularnewline
109 & 0.315548058622731 & 0.631096117245463 & 0.684451941377269 \tabularnewline
110 & 0.287698869099364 & 0.575397738198728 & 0.712301130900636 \tabularnewline
111 & 0.312066139836375 & 0.624132279672749 & 0.687933860163625 \tabularnewline
112 & 0.284275629583528 & 0.568551259167056 & 0.715724370416472 \tabularnewline
113 & 0.256213196549657 & 0.512426393099313 & 0.743786803450343 \tabularnewline
114 & 0.308085783763922 & 0.616171567527844 & 0.691914216236078 \tabularnewline
115 & 0.369700322144276 & 0.739400644288551 & 0.630299677855724 \tabularnewline
116 & 0.407499169242279 & 0.814998338484558 & 0.592500830757721 \tabularnewline
117 & 0.371304541635327 & 0.742609083270653 & 0.628695458364673 \tabularnewline
118 & 0.339222194917142 & 0.678444389834284 & 0.660777805082858 \tabularnewline
119 & 0.309660966976133 & 0.619321933952267 & 0.690339033023867 \tabularnewline
120 & 0.279302199123372 & 0.558604398246744 & 0.720697800876628 \tabularnewline
121 & 0.254305845838949 & 0.508611691677899 & 0.74569415416105 \tabularnewline
122 & 0.224025332732171 & 0.448050665464342 & 0.775974667267829 \tabularnewline
123 & 0.204471409661639 & 0.408942819323278 & 0.795528590338361 \tabularnewline
124 & 0.175653644182556 & 0.351307288365112 & 0.824346355817444 \tabularnewline
125 & 0.214566409169321 & 0.429132818338642 & 0.785433590830679 \tabularnewline
126 & 0.18171339653352 & 0.363426793067039 & 0.81828660346648 \tabularnewline
127 & 0.225748799557558 & 0.451497599115116 & 0.774251200442442 \tabularnewline
128 & 0.19305705667622 & 0.38611411335244 & 0.80694294332378 \tabularnewline
129 & 0.165354059372062 & 0.330708118744123 & 0.834645940627938 \tabularnewline
130 & 0.20783270325036 & 0.41566540650072 & 0.79216729674964 \tabularnewline
131 & 0.264181132884578 & 0.528362265769156 & 0.735818867115422 \tabularnewline
132 & 0.281970427969342 & 0.563940855938684 & 0.718029572030658 \tabularnewline
133 & 0.34689180551918 & 0.69378361103836 & 0.65310819448082 \tabularnewline
134 & 0.308521815068072 & 0.617043630136144 & 0.691478184931928 \tabularnewline
135 & 0.410424146520468 & 0.820848293040937 & 0.589575853479532 \tabularnewline
136 & 0.36245725436975 & 0.724914508739499 & 0.63754274563025 \tabularnewline
137 & 0.318019785752878 & 0.636039571505756 & 0.681980214247122 \tabularnewline
138 & 0.263898998007356 & 0.527797996014711 & 0.736101001992644 \tabularnewline
139 & 0.272429660237837 & 0.544859320475674 & 0.727570339762163 \tabularnewline
140 & 0.340236861234069 & 0.680473722468138 & 0.659763138765931 \tabularnewline
141 & 0.297216013672366 & 0.594432027344731 & 0.702783986327634 \tabularnewline
142 & 0.340253067236629 & 0.680506134473258 & 0.65974693276337 \tabularnewline
143 & 0.290818785962141 & 0.581637571924281 & 0.70918121403786 \tabularnewline
144 & 0.319544016662534 & 0.639088033325068 & 0.680455983337466 \tabularnewline
145 & 0.398000886733997 & 0.796001773467994 & 0.601999113266003 \tabularnewline
146 & 0.569741619483444 & 0.860516761033112 & 0.430258380516556 \tabularnewline
147 & 0.508462717553717 & 0.983074564892566 & 0.491537282446283 \tabularnewline
148 & 0.436853617892671 & 0.873707235785341 & 0.56314638210733 \tabularnewline
149 & 0.373123200749351 & 0.746246401498703 & 0.626876799250649 \tabularnewline
150 & 0.427952029559171 & 0.855904059118341 & 0.572047970440829 \tabularnewline
151 & 0.455743281814576 & 0.911486563629152 & 0.544256718185424 \tabularnewline
152 & 0.707493979068671 & 0.585012041862658 & 0.292506020931329 \tabularnewline
153 & 0.595464099932975 & 0.80907180013405 & 0.404535900067025 \tabularnewline
154 & 0.873132345833144 & 0.253735308333712 & 0.126867654166856 \tabularnewline
155 & 0.78938997784906 & 0.421220044301879 & 0.21061002215094 \tabularnewline
156 & 0.635212498219735 & 0.72957500356053 & 0.364787501780265 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145558&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]6[/C][C]0.21562599161367[/C][C]0.43125198322734[/C][C]0.78437400838633[/C][/ROW]
[ROW][C]7[/C][C]0.101466783422783[/C][C]0.202933566845565[/C][C]0.898533216577217[/C][/ROW]
[ROW][C]8[/C][C]0.245641950279154[/C][C]0.491283900558308[/C][C]0.754358049720846[/C][/ROW]
[ROW][C]9[/C][C]0.215733129441361[/C][C]0.431466258882722[/C][C]0.784266870558639[/C][/ROW]
[ROW][C]10[/C][C]0.14027127758033[/C][C]0.28054255516066[/C][C]0.85972872241967[/C][/ROW]
[ROW][C]11[/C][C]0.342873600056294[/C][C]0.685747200112587[/C][C]0.657126399943706[/C][/ROW]
[ROW][C]12[/C][C]0.288158513038405[/C][C]0.576317026076809[/C][C]0.711841486961595[/C][/ROW]
[ROW][C]13[/C][C]0.228767947024549[/C][C]0.457535894049098[/C][C]0.77123205297545[/C][/ROW]
[ROW][C]14[/C][C]0.611995361223894[/C][C]0.776009277552213[/C][C]0.388004638776107[/C][/ROW]
[ROW][C]15[/C][C]0.577698999382139[/C][C]0.844602001235722[/C][C]0.422301000617861[/C][/ROW]
[ROW][C]16[/C][C]0.510163636427346[/C][C]0.979672727145308[/C][C]0.489836363572654[/C][/ROW]
[ROW][C]17[/C][C]0.432511404516732[/C][C]0.865022809033464[/C][C]0.567488595483268[/C][/ROW]
[ROW][C]18[/C][C]0.37874196725481[/C][C]0.75748393450962[/C][C]0.62125803274519[/C][/ROW]
[ROW][C]19[/C][C]0.418269925557688[/C][C]0.836539851115376[/C][C]0.581730074442312[/C][/ROW]
[ROW][C]20[/C][C]0.379253897566141[/C][C]0.758507795132282[/C][C]0.620746102433859[/C][/ROW]
[ROW][C]21[/C][C]0.330229496806787[/C][C]0.660458993613573[/C][C]0.669770503193214[/C][/ROW]
[ROW][C]22[/C][C]0.278399945856217[/C][C]0.556799891712433[/C][C]0.721600054143783[/C][/ROW]
[ROW][C]23[/C][C]0.268525283545898[/C][C]0.537050567091796[/C][C]0.731474716454102[/C][/ROW]
[ROW][C]24[/C][C]0.252140588529804[/C][C]0.504281177059608[/C][C]0.747859411470196[/C][/ROW]
[ROW][C]25[/C][C]0.278893110407797[/C][C]0.557786220815593[/C][C]0.721106889592203[/C][/ROW]
[ROW][C]26[/C][C]0.267325825455467[/C][C]0.534651650910935[/C][C]0.732674174544533[/C][/ROW]
[ROW][C]27[/C][C]0.250661744931893[/C][C]0.501323489863786[/C][C]0.749338255068107[/C][/ROW]
[ROW][C]28[/C][C]0.232276188612005[/C][C]0.464552377224009[/C][C]0.767723811387995[/C][/ROW]
[ROW][C]29[/C][C]0.20122901891648[/C][C]0.402458037832961[/C][C]0.79877098108352[/C][/ROW]
[ROW][C]30[/C][C]0.23198040395129[/C][C]0.463960807902579[/C][C]0.76801959604871[/C][/ROW]
[ROW][C]31[/C][C]0.28027683000705[/C][C]0.5605536600141[/C][C]0.71972316999295[/C][/ROW]
[ROW][C]32[/C][C]0.304702678521168[/C][C]0.609405357042336[/C][C]0.695297321478832[/C][/ROW]
[ROW][C]33[/C][C]0.310483576391483[/C][C]0.620967152782966[/C][C]0.689516423608517[/C][/ROW]
[ROW][C]34[/C][C]0.282740461996228[/C][C]0.565480923992456[/C][C]0.717259538003772[/C][/ROW]
[ROW][C]35[/C][C]0.25369263779883[/C][C]0.507385275597661[/C][C]0.74630736220117[/C][/ROW]
[ROW][C]36[/C][C]0.292628925702579[/C][C]0.585257851405157[/C][C]0.707371074297421[/C][/ROW]
[ROW][C]37[/C][C]0.459082445659874[/C][C]0.918164891319749[/C][C]0.540917554340126[/C][/ROW]
[ROW][C]38[/C][C]0.489179472948663[/C][C]0.978358945897326[/C][C]0.510820527051337[/C][/ROW]
[ROW][C]39[/C][C]0.438102303703753[/C][C]0.876204607407507[/C][C]0.561897696296247[/C][/ROW]
[ROW][C]40[/C][C]0.439439135454432[/C][C]0.878878270908865[/C][C]0.560560864545568[/C][/ROW]
[ROW][C]41[/C][C]0.388571578616385[/C][C]0.77714315723277[/C][C]0.611428421383615[/C][/ROW]
[ROW][C]42[/C][C]0.38843431345321[/C][C]0.77686862690642[/C][C]0.61156568654679[/C][/ROW]
[ROW][C]43[/C][C]0.390817801378701[/C][C]0.781635602757403[/C][C]0.609182198621299[/C][/ROW]
[ROW][C]44[/C][C]0.415268016513017[/C][C]0.830536033026034[/C][C]0.584731983486983[/C][/ROW]
[ROW][C]45[/C][C]0.393175397335022[/C][C]0.786350794670045[/C][C]0.606824602664978[/C][/ROW]
[ROW][C]46[/C][C]0.394446152026557[/C][C]0.788892304053114[/C][C]0.605553847973443[/C][/ROW]
[ROW][C]47[/C][C]0.371341265488288[/C][C]0.742682530976575[/C][C]0.628658734511712[/C][/ROW]
[ROW][C]48[/C][C]0.346990076795454[/C][C]0.693980153590908[/C][C]0.653009923204546[/C][/ROW]
[ROW][C]49[/C][C]0.347842044924918[/C][C]0.695684089849835[/C][C]0.652157955075082[/C][/ROW]
[ROW][C]50[/C][C]0.323078486312285[/C][C]0.646156972624569[/C][C]0.676921513687715[/C][/ROW]
[ROW][C]51[/C][C]0.323699129894687[/C][C]0.647398259789374[/C][C]0.676300870105313[/C][/ROW]
[ROW][C]52[/C][C]0.322029677517086[/C][C]0.644059355034171[/C][C]0.677970322482914[/C][/ROW]
[ROW][C]53[/C][C]0.281207751161185[/C][C]0.56241550232237[/C][C]0.718792248838815[/C][/ROW]
[ROW][C]54[/C][C]0.274536767130702[/C][C]0.549073534261405[/C][C]0.725463232869298[/C][/ROW]
[ROW][C]55[/C][C]0.2386977432923[/C][C]0.4773954865846[/C][C]0.7613022567077[/C][/ROW]
[ROW][C]56[/C][C]0.244205274386995[/C][C]0.488410548773989[/C][C]0.755794725613005[/C][/ROW]
[ROW][C]57[/C][C]0.238055373636295[/C][C]0.476110747272589[/C][C]0.761944626363705[/C][/ROW]
[ROW][C]58[/C][C]0.246773239258127[/C][C]0.493546478516253[/C][C]0.753226760741873[/C][/ROW]
[ROW][C]59[/C][C]0.225737955709428[/C][C]0.451475911418856[/C][C]0.774262044290572[/C][/ROW]
[ROW][C]60[/C][C]0.245830970440533[/C][C]0.491661940881065[/C][C]0.754169029559467[/C][/ROW]
[ROW][C]61[/C][C]0.252701632687884[/C][C]0.505403265375767[/C][C]0.747298367312117[/C][/ROW]
[ROW][C]62[/C][C]0.254250833013518[/C][C]0.508501666027036[/C][C]0.745749166986482[/C][/ROW]
[ROW][C]63[/C][C]0.261231208084247[/C][C]0.522462416168493[/C][C]0.738768791915753[/C][/ROW]
[ROW][C]64[/C][C]0.240710452977645[/C][C]0.48142090595529[/C][C]0.759289547022355[/C][/ROW]
[ROW][C]65[/C][C]0.249982862177329[/C][C]0.499965724354658[/C][C]0.750017137822671[/C][/ROW]
[ROW][C]66[/C][C]0.217267830350319[/C][C]0.434535660700638[/C][C]0.782732169649681[/C][/ROW]
[ROW][C]67[/C][C]0.198879126523178[/C][C]0.397758253046355[/C][C]0.801120873476822[/C][/ROW]
[ROW][C]68[/C][C]0.181277577773351[/C][C]0.362555155546703[/C][C]0.818722422226649[/C][/ROW]
[ROW][C]69[/C][C]0.15469108105166[/C][C]0.309382162103321[/C][C]0.84530891894834[/C][/ROW]
[ROW][C]70[/C][C]0.14923684437929[/C][C]0.29847368875858[/C][C]0.85076315562071[/C][/ROW]
[ROW][C]71[/C][C]0.125862359172884[/C][C]0.251724718345769[/C][C]0.874137640827116[/C][/ROW]
[ROW][C]72[/C][C]0.113875492506595[/C][C]0.22775098501319[/C][C]0.886124507493405[/C][/ROW]
[ROW][C]73[/C][C]0.129689528196954[/C][C]0.259379056393908[/C][C]0.870310471803046[/C][/ROW]
[ROW][C]74[/C][C]0.19901310023156[/C][C]0.39802620046312[/C][C]0.80098689976844[/C][/ROW]
[ROW][C]75[/C][C]0.211129398510048[/C][C]0.422258797020096[/C][C]0.788870601489952[/C][/ROW]
[ROW][C]76[/C][C]0.205267950518462[/C][C]0.410535901036924[/C][C]0.794732049481538[/C][/ROW]
[ROW][C]77[/C][C]0.197730577013711[/C][C]0.395461154027422[/C][C]0.80226942298629[/C][/ROW]
[ROW][C]78[/C][C]0.219309778756735[/C][C]0.438619557513471[/C][C]0.780690221243264[/C][/ROW]
[ROW][C]79[/C][C]0.202908580020637[/C][C]0.405817160041274[/C][C]0.797091419979363[/C][/ROW]
[ROW][C]80[/C][C]0.262338588001034[/C][C]0.524677176002068[/C][C]0.737661411998966[/C][/ROW]
[ROW][C]81[/C][C]0.242589189407662[/C][C]0.485178378815324[/C][C]0.757410810592338[/C][/ROW]
[ROW][C]82[/C][C]0.229083676741574[/C][C]0.458167353483147[/C][C]0.770916323258426[/C][/ROW]
[ROW][C]83[/C][C]0.218671873437733[/C][C]0.437343746875466[/C][C]0.781328126562267[/C][/ROW]
[ROW][C]84[/C][C]0.206615781285994[/C][C]0.413231562571988[/C][C]0.793384218714006[/C][/ROW]
[ROW][C]85[/C][C]0.226651802091115[/C][C]0.45330360418223[/C][C]0.773348197908885[/C][/ROW]
[ROW][C]86[/C][C]0.248641646367916[/C][C]0.497283292735831[/C][C]0.751358353632084[/C][/ROW]
[ROW][C]87[/C][C]0.266820305938233[/C][C]0.533640611876465[/C][C]0.733179694061767[/C][/ROW]
[ROW][C]88[/C][C]0.29179218916483[/C][C]0.583584378329661[/C][C]0.70820781083517[/C][/ROW]
[ROW][C]89[/C][C]0.318960113776716[/C][C]0.637920227553431[/C][C]0.681039886223284[/C][/ROW]
[ROW][C]90[/C][C]0.301424428098678[/C][C]0.602848856197356[/C][C]0.698575571901322[/C][/ROW]
[ROW][C]91[/C][C]0.269941850952483[/C][C]0.539883701904965[/C][C]0.730058149047517[/C][/ROW]
[ROW][C]92[/C][C]0.293944117875908[/C][C]0.587888235751816[/C][C]0.706055882124092[/C][/ROW]
[ROW][C]93[/C][C]0.261362166897962[/C][C]0.522724333795925[/C][C]0.738637833102038[/C][/ROW]
[ROW][C]94[/C][C]0.244017324158732[/C][C]0.488034648317463[/C][C]0.755982675841268[/C][/ROW]
[ROW][C]95[/C][C]0.27533491050662[/C][C]0.55066982101324[/C][C]0.72466508949338[/C][/ROW]
[ROW][C]96[/C][C]0.256566253887919[/C][C]0.513132507775838[/C][C]0.743433746112081[/C][/ROW]
[ROW][C]97[/C][C]0.235167810070311[/C][C]0.470335620140622[/C][C]0.764832189929689[/C][/ROW]
[ROW][C]98[/C][C]0.20904995575011[/C][C]0.41809991150022[/C][C]0.79095004424989[/C][/ROW]
[ROW][C]99[/C][C]0.193735652558657[/C][C]0.387471305117314[/C][C]0.806264347441343[/C][/ROW]
[ROW][C]100[/C][C]0.177604387195107[/C][C]0.355208774390214[/C][C]0.822395612804893[/C][/ROW]
[ROW][C]101[/C][C]0.16201345034906[/C][C]0.32402690069812[/C][C]0.83798654965094[/C][/ROW]
[ROW][C]102[/C][C]0.136966872425634[/C][C]0.273933744851269[/C][C]0.863033127574366[/C][/ROW]
[ROW][C]103[/C][C]0.15486643172796[/C][C]0.309732863455921[/C][C]0.84513356827204[/C][/ROW]
[ROW][C]104[/C][C]0.199825297051415[/C][C]0.39965059410283[/C][C]0.800174702948585[/C][/ROW]
[ROW][C]105[/C][C]0.218925326527351[/C][C]0.437850653054702[/C][C]0.781074673472649[/C][/ROW]
[ROW][C]106[/C][C]0.250867980382872[/C][C]0.501735960765744[/C][C]0.749132019617128[/C][/ROW]
[ROW][C]107[/C][C]0.281107685496145[/C][C]0.56221537099229[/C][C]0.718892314503855[/C][/ROW]
[ROW][C]108[/C][C]0.343662406650914[/C][C]0.687324813301828[/C][C]0.656337593349086[/C][/ROW]
[ROW][C]109[/C][C]0.315548058622731[/C][C]0.631096117245463[/C][C]0.684451941377269[/C][/ROW]
[ROW][C]110[/C][C]0.287698869099364[/C][C]0.575397738198728[/C][C]0.712301130900636[/C][/ROW]
[ROW][C]111[/C][C]0.312066139836375[/C][C]0.624132279672749[/C][C]0.687933860163625[/C][/ROW]
[ROW][C]112[/C][C]0.284275629583528[/C][C]0.568551259167056[/C][C]0.715724370416472[/C][/ROW]
[ROW][C]113[/C][C]0.256213196549657[/C][C]0.512426393099313[/C][C]0.743786803450343[/C][/ROW]
[ROW][C]114[/C][C]0.308085783763922[/C][C]0.616171567527844[/C][C]0.691914216236078[/C][/ROW]
[ROW][C]115[/C][C]0.369700322144276[/C][C]0.739400644288551[/C][C]0.630299677855724[/C][/ROW]
[ROW][C]116[/C][C]0.407499169242279[/C][C]0.814998338484558[/C][C]0.592500830757721[/C][/ROW]
[ROW][C]117[/C][C]0.371304541635327[/C][C]0.742609083270653[/C][C]0.628695458364673[/C][/ROW]
[ROW][C]118[/C][C]0.339222194917142[/C][C]0.678444389834284[/C][C]0.660777805082858[/C][/ROW]
[ROW][C]119[/C][C]0.309660966976133[/C][C]0.619321933952267[/C][C]0.690339033023867[/C][/ROW]
[ROW][C]120[/C][C]0.279302199123372[/C][C]0.558604398246744[/C][C]0.720697800876628[/C][/ROW]
[ROW][C]121[/C][C]0.254305845838949[/C][C]0.508611691677899[/C][C]0.74569415416105[/C][/ROW]
[ROW][C]122[/C][C]0.224025332732171[/C][C]0.448050665464342[/C][C]0.775974667267829[/C][/ROW]
[ROW][C]123[/C][C]0.204471409661639[/C][C]0.408942819323278[/C][C]0.795528590338361[/C][/ROW]
[ROW][C]124[/C][C]0.175653644182556[/C][C]0.351307288365112[/C][C]0.824346355817444[/C][/ROW]
[ROW][C]125[/C][C]0.214566409169321[/C][C]0.429132818338642[/C][C]0.785433590830679[/C][/ROW]
[ROW][C]126[/C][C]0.18171339653352[/C][C]0.363426793067039[/C][C]0.81828660346648[/C][/ROW]
[ROW][C]127[/C][C]0.225748799557558[/C][C]0.451497599115116[/C][C]0.774251200442442[/C][/ROW]
[ROW][C]128[/C][C]0.19305705667622[/C][C]0.38611411335244[/C][C]0.80694294332378[/C][/ROW]
[ROW][C]129[/C][C]0.165354059372062[/C][C]0.330708118744123[/C][C]0.834645940627938[/C][/ROW]
[ROW][C]130[/C][C]0.20783270325036[/C][C]0.41566540650072[/C][C]0.79216729674964[/C][/ROW]
[ROW][C]131[/C][C]0.264181132884578[/C][C]0.528362265769156[/C][C]0.735818867115422[/C][/ROW]
[ROW][C]132[/C][C]0.281970427969342[/C][C]0.563940855938684[/C][C]0.718029572030658[/C][/ROW]
[ROW][C]133[/C][C]0.34689180551918[/C][C]0.69378361103836[/C][C]0.65310819448082[/C][/ROW]
[ROW][C]134[/C][C]0.308521815068072[/C][C]0.617043630136144[/C][C]0.691478184931928[/C][/ROW]
[ROW][C]135[/C][C]0.410424146520468[/C][C]0.820848293040937[/C][C]0.589575853479532[/C][/ROW]
[ROW][C]136[/C][C]0.36245725436975[/C][C]0.724914508739499[/C][C]0.63754274563025[/C][/ROW]
[ROW][C]137[/C][C]0.318019785752878[/C][C]0.636039571505756[/C][C]0.681980214247122[/C][/ROW]
[ROW][C]138[/C][C]0.263898998007356[/C][C]0.527797996014711[/C][C]0.736101001992644[/C][/ROW]
[ROW][C]139[/C][C]0.272429660237837[/C][C]0.544859320475674[/C][C]0.727570339762163[/C][/ROW]
[ROW][C]140[/C][C]0.340236861234069[/C][C]0.680473722468138[/C][C]0.659763138765931[/C][/ROW]
[ROW][C]141[/C][C]0.297216013672366[/C][C]0.594432027344731[/C][C]0.702783986327634[/C][/ROW]
[ROW][C]142[/C][C]0.340253067236629[/C][C]0.680506134473258[/C][C]0.65974693276337[/C][/ROW]
[ROW][C]143[/C][C]0.290818785962141[/C][C]0.581637571924281[/C][C]0.70918121403786[/C][/ROW]
[ROW][C]144[/C][C]0.319544016662534[/C][C]0.639088033325068[/C][C]0.680455983337466[/C][/ROW]
[ROW][C]145[/C][C]0.398000886733997[/C][C]0.796001773467994[/C][C]0.601999113266003[/C][/ROW]
[ROW][C]146[/C][C]0.569741619483444[/C][C]0.860516761033112[/C][C]0.430258380516556[/C][/ROW]
[ROW][C]147[/C][C]0.508462717553717[/C][C]0.983074564892566[/C][C]0.491537282446283[/C][/ROW]
[ROW][C]148[/C][C]0.436853617892671[/C][C]0.873707235785341[/C][C]0.56314638210733[/C][/ROW]
[ROW][C]149[/C][C]0.373123200749351[/C][C]0.746246401498703[/C][C]0.626876799250649[/C][/ROW]
[ROW][C]150[/C][C]0.427952029559171[/C][C]0.855904059118341[/C][C]0.572047970440829[/C][/ROW]
[ROW][C]151[/C][C]0.455743281814576[/C][C]0.911486563629152[/C][C]0.544256718185424[/C][/ROW]
[ROW][C]152[/C][C]0.707493979068671[/C][C]0.585012041862658[/C][C]0.292506020931329[/C][/ROW]
[ROW][C]153[/C][C]0.595464099932975[/C][C]0.80907180013405[/C][C]0.404535900067025[/C][/ROW]
[ROW][C]154[/C][C]0.873132345833144[/C][C]0.253735308333712[/C][C]0.126867654166856[/C][/ROW]
[ROW][C]155[/C][C]0.78938997784906[/C][C]0.421220044301879[/C][C]0.21061002215094[/C][/ROW]
[ROW][C]156[/C][C]0.635212498219735[/C][C]0.72957500356053[/C][C]0.364787501780265[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145558&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145558&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.215625991613670.431251983227340.78437400838633
70.1014667834227830.2029335668455650.898533216577217
80.2456419502791540.4912839005583080.754358049720846
90.2157331294413610.4314662588827220.784266870558639
100.140271277580330.280542555160660.85972872241967
110.3428736000562940.6857472001125870.657126399943706
120.2881585130384050.5763170260768090.711841486961595
130.2287679470245490.4575358940490980.77123205297545
140.6119953612238940.7760092775522130.388004638776107
150.5776989993821390.8446020012357220.422301000617861
160.5101636364273460.9796727271453080.489836363572654
170.4325114045167320.8650228090334640.567488595483268
180.378741967254810.757483934509620.62125803274519
190.4182699255576880.8365398511153760.581730074442312
200.3792538975661410.7585077951322820.620746102433859
210.3302294968067870.6604589936135730.669770503193214
220.2783999458562170.5567998917124330.721600054143783
230.2685252835458980.5370505670917960.731474716454102
240.2521405885298040.5042811770596080.747859411470196
250.2788931104077970.5577862208155930.721106889592203
260.2673258254554670.5346516509109350.732674174544533
270.2506617449318930.5013234898637860.749338255068107
280.2322761886120050.4645523772240090.767723811387995
290.201229018916480.4024580378329610.79877098108352
300.231980403951290.4639608079025790.76801959604871
310.280276830007050.56055366001410.71972316999295
320.3047026785211680.6094053570423360.695297321478832
330.3104835763914830.6209671527829660.689516423608517
340.2827404619962280.5654809239924560.717259538003772
350.253692637798830.5073852755976610.74630736220117
360.2926289257025790.5852578514051570.707371074297421
370.4590824456598740.9181648913197490.540917554340126
380.4891794729486630.9783589458973260.510820527051337
390.4381023037037530.8762046074075070.561897696296247
400.4394391354544320.8788782709088650.560560864545568
410.3885715786163850.777143157232770.611428421383615
420.388434313453210.776868626906420.61156568654679
430.3908178013787010.7816356027574030.609182198621299
440.4152680165130170.8305360330260340.584731983486983
450.3931753973350220.7863507946700450.606824602664978
460.3944461520265570.7888923040531140.605553847973443
470.3713412654882880.7426825309765750.628658734511712
480.3469900767954540.6939801535909080.653009923204546
490.3478420449249180.6956840898498350.652157955075082
500.3230784863122850.6461569726245690.676921513687715
510.3236991298946870.6473982597893740.676300870105313
520.3220296775170860.6440593550341710.677970322482914
530.2812077511611850.562415502322370.718792248838815
540.2745367671307020.5490735342614050.725463232869298
550.23869774329230.47739548658460.7613022567077
560.2442052743869950.4884105487739890.755794725613005
570.2380553736362950.4761107472725890.761944626363705
580.2467732392581270.4935464785162530.753226760741873
590.2257379557094280.4514759114188560.774262044290572
600.2458309704405330.4916619408810650.754169029559467
610.2527016326878840.5054032653757670.747298367312117
620.2542508330135180.5085016660270360.745749166986482
630.2612312080842470.5224624161684930.738768791915753
640.2407104529776450.481420905955290.759289547022355
650.2499828621773290.4999657243546580.750017137822671
660.2172678303503190.4345356607006380.782732169649681
670.1988791265231780.3977582530463550.801120873476822
680.1812775777733510.3625551555467030.818722422226649
690.154691081051660.3093821621033210.84530891894834
700.149236844379290.298473688758580.85076315562071
710.1258623591728840.2517247183457690.874137640827116
720.1138754925065950.227750985013190.886124507493405
730.1296895281969540.2593790563939080.870310471803046
740.199013100231560.398026200463120.80098689976844
750.2111293985100480.4222587970200960.788870601489952
760.2052679505184620.4105359010369240.794732049481538
770.1977305770137110.3954611540274220.80226942298629
780.2193097787567350.4386195575134710.780690221243264
790.2029085800206370.4058171600412740.797091419979363
800.2623385880010340.5246771760020680.737661411998966
810.2425891894076620.4851783788153240.757410810592338
820.2290836767415740.4581673534831470.770916323258426
830.2186718734377330.4373437468754660.781328126562267
840.2066157812859940.4132315625719880.793384218714006
850.2266518020911150.453303604182230.773348197908885
860.2486416463679160.4972832927358310.751358353632084
870.2668203059382330.5336406118764650.733179694061767
880.291792189164830.5835843783296610.70820781083517
890.3189601137767160.6379202275534310.681039886223284
900.3014244280986780.6028488561973560.698575571901322
910.2699418509524830.5398837019049650.730058149047517
920.2939441178759080.5878882357518160.706055882124092
930.2613621668979620.5227243337959250.738637833102038
940.2440173241587320.4880346483174630.755982675841268
950.275334910506620.550669821013240.72466508949338
960.2565662538879190.5131325077758380.743433746112081
970.2351678100703110.4703356201406220.764832189929689
980.209049955750110.418099911500220.79095004424989
990.1937356525586570.3874713051173140.806264347441343
1000.1776043871951070.3552087743902140.822395612804893
1010.162013450349060.324026900698120.83798654965094
1020.1369668724256340.2739337448512690.863033127574366
1030.154866431727960.3097328634559210.84513356827204
1040.1998252970514150.399650594102830.800174702948585
1050.2189253265273510.4378506530547020.781074673472649
1060.2508679803828720.5017359607657440.749132019617128
1070.2811076854961450.562215370992290.718892314503855
1080.3436624066509140.6873248133018280.656337593349086
1090.3155480586227310.6310961172454630.684451941377269
1100.2876988690993640.5753977381987280.712301130900636
1110.3120661398363750.6241322796727490.687933860163625
1120.2842756295835280.5685512591670560.715724370416472
1130.2562131965496570.5124263930993130.743786803450343
1140.3080857837639220.6161715675278440.691914216236078
1150.3697003221442760.7394006442885510.630299677855724
1160.4074991692422790.8149983384845580.592500830757721
1170.3713045416353270.7426090832706530.628695458364673
1180.3392221949171420.6784443898342840.660777805082858
1190.3096609669761330.6193219339522670.690339033023867
1200.2793021991233720.5586043982467440.720697800876628
1210.2543058458389490.5086116916778990.74569415416105
1220.2240253327321710.4480506654643420.775974667267829
1230.2044714096616390.4089428193232780.795528590338361
1240.1756536441825560.3513072883651120.824346355817444
1250.2145664091693210.4291328183386420.785433590830679
1260.181713396533520.3634267930670390.81828660346648
1270.2257487995575580.4514975991151160.774251200442442
1280.193057056676220.386114113352440.80694294332378
1290.1653540593720620.3307081187441230.834645940627938
1300.207832703250360.415665406500720.79216729674964
1310.2641811328845780.5283622657691560.735818867115422
1320.2819704279693420.5639408559386840.718029572030658
1330.346891805519180.693783611038360.65310819448082
1340.3085218150680720.6170436301361440.691478184931928
1350.4104241465204680.8208482930409370.589575853479532
1360.362457254369750.7249145087394990.63754274563025
1370.3180197857528780.6360395715057560.681980214247122
1380.2638989980073560.5277979960147110.736101001992644
1390.2724296602378370.5448593204756740.727570339762163
1400.3402368612340690.6804737224681380.659763138765931
1410.2972160136723660.5944320273447310.702783986327634
1420.3402530672366290.6805061344732580.65974693276337
1430.2908187859621410.5816375719242810.70918121403786
1440.3195440166625340.6390880333250680.680455983337466
1450.3980008867339970.7960017734679940.601999113266003
1460.5697416194834440.8605167610331120.430258380516556
1470.5084627175537170.9830745648925660.491537282446283
1480.4368536178926710.8737072357853410.56314638210733
1490.3731232007493510.7462464014987030.626876799250649
1500.4279520295591710.8559040591183410.572047970440829
1510.4557432818145760.9114865636291520.544256718185424
1520.7074939790686710.5850120418626580.292506020931329
1530.5954640999329750.809071800134050.404535900067025
1540.8731323458331440.2537353083337120.126867654166856
1550.789389977849060.4212200443018790.21061002215094
1560.6352124982197350.729575003560530.364787501780265







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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145558&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145558&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145558&T=6

As an alternative you can also use a QR Code:  

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

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



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