FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 18 Dec 2014 15:00:58 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/18/t14189148691ti4awahd59138z.htm/, Retrieved Sun, 19 May 2024 19:50:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271031, Retrieved Sun, 19 May 2024 19:50:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact72

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2014-12-18 14:22:41] [8e3afc5508de37bed770d90d46857754]
-    D    [Multiple Regression] [] [2014-12-18 15:00:58] [ce2f801bda31f4b58163e4bbe4fada83] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12.9 8 12 18 68 1.8
12.2 18 8 31 39 2.1
12.8 12 11 39 32 2.2
7.4 24 13 46 62 2.3
6.7 16 11 31 33 2.1
12.6 19 10 67 52 2.7
14.8 16 7 35 62 2.1
13.3 15 10 52 77 2.4
11.1 28 15 77 76 2.9
8.2 21 12 37 41 2.2
11.4 18 12 32 48 2.1
6.4 22 10 36 63 2.2
10.6 19 10 38 30 2.2
12 22 14 69 78 2.7
6.3 25 6 21 19 1.9
11.9 16 14 54 66 2.5
9.3 19 11 36 35 2.2
10 26 12 23 45 1.9
6.4 24 15 34 21 2.1
13.8 20 13 112 25 3.5
10.8 19 11 35 44 2.1
13.8 19 12 47 69 2.3
11.7 23 7 47 54 2.3
10.9 18 11 37 74 2.2
9.9 21 12 20 61 1.9
11.5 20 13 22 41 1.9
8.3 15 9 23 46 1.9
11.7 19 11 32 39 2.1
9 19 12 30 34 2
9.7 7 15 92 51 3.2
10.8 20 12 43 42 2.3
10.3 20 6 55 31 2.5
10.4 19 5 16 39 1.8
9.3 20 11 71 49 2.8
11.8 18 6 43 53 2.3
5.9 14 12 29 31 2
11.4 17 10 56 39 2.5
13 17 6 46 54 2.3
10.8 8 12 19 49 1.8
11.3 22 6 59 46 2.6
11.8 20 12 30 55 2
12.7 22 8 7 50 1.6
10.9 14 12 19 30 1.8
13.3 21 14 48 45 2.4
10.1 20 12 23 35 1.9
14.3 18 14 33 41 2.1
9.3 24 11 34 73 2.1
12.5 19 10 48 17 2.4
7.6 16 7 18 40 1.8
15.9 16 12 43 64 2.3
9.2 16 7 33 37 2.1
11.1 22 12 71 65 2.8
13 21 10 26 100 2
14.5 15 10 67 28 2.7
12.3 15 12 80 56 2.9
11.4 14 12 29 29 2
12.6 14 5 32 50 2.1
NA 19 10 47 3 2.3
13 16 10 43 59 2.3
13.2 26 11 29 61 2
7.7 18 12 32 51 2.1
4.35 17 9 23 12 1
12.7 6 11 16 45 1
18.1 22 12 33 37 4
17.85 20 12 32 37 4
17.1 17 12 52 68 4
19.1 20 12 75 72 4
16.1 23 10 72 143 4
13.35 18 15 15 9 2
18.4 13 10 29 55 4
14.7 22 15 13 17 1
10.6 20 10 40 37 3
12.6 20 15 19 27 3
16.2 13 9 24 37 4
13.6 16 15 121 58 3
14.1 16 13 36 21 3
14.5 15 12 23 19 3
16.15 19 12 85 78 4
14.75 19 8 41 35 3
14.8 24 9 46 48 3
12.45 9 15 18 27 2
12.65 22 12 35 43 2
17.35 15 12 17 30 3
8.6 22 15 4 25 1
18.4 22 11 28 69 4
16.1 24 12 44 72 3
17.75 21 14 38 13 4
15.25 25 12 57 61 4
17.65 26 12 23 43 4
16.35 21 12 36 51 4
17.65 14 11 22 67 4
13.6 28 12 40 36 3
14.35 21 12 31 44 3
14.75 16 12 11 45 4
18.25 16 12 38 34 4
9.9 25 8 24 36 4
16 21 8 37 72 3
18.25 22 12 37 39 4
16.85 9 12 22 43 4
18.95 24 11 43 80 4
15.6 22 12 31 40 3
17.1 10 10 31 61 4
16.1 22 11 -4 23 1
15.4 21 11 21 29 4
15.4 20 11 21 29 4
13.35 17 13 32 54 3
19.1 7 7 26 43 4
7.6 14 8 32 20 1
19.1 23 11 33 61 4
14.75 18 8 30 57 4
19.25 17 14 67 54 4
13.6 20 9 22 36 4
12.75 19 13 33 16 4
9.85 19 13 24 40 1
15.25 23 11 28 27 4
11.9 20 9 41 61 3
16.35 19 12 31 69 4
12.4 16 12 33 34 3
18.15 21 13 21 34 4
17.75 20 11 52 34 4
12.35 20 11 29 13 3
15.6 19 9 11 12 4
19.3 19 12 26 51 4
17.1 20 15 7 19 4
18.4 22 14 13 81 3
19.05 19 12 20 42 4
18.55 23 9 52 22 4
19.1 16 9 28 85 4
12.85 18 13 39 25 4
9.5 23 15 9 22 2
4.5 20 11 19 19 1
13.6 23 10 60 45 4
11.7 13 11 19 45 2
13.35 26 14 14 51 3
17.6 13 12 -2 73 4
14.05 10 13 51 24 3
16.1 21 11 2 61 4
13.35 24 11 24 23 4
11.85 21 13 40 14 4
11.95 23 12 20 54 2
13.2 16 9 20 36 4
7.7 26 13 25 26 2
14.6 16 12 38 30 3

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 5 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271031&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271031&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271031&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 5.25895 -0.090384AMS.I2[t] + 0.129582CONFSOFTTOT[t] -0.0298986PRH[t] + 0.0339948CH[t] + 2.72119PR[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  5.25895 -0.090384AMS.I2[t] +  0.129582CONFSOFTTOT[t] -0.0298986PRH[t] +  0.0339948CH[t] +  2.72119PR[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271031&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  5.25895 -0.090384AMS.I2[t] +  0.129582CONFSOFTTOT[t] -0.0298986PRH[t] +  0.0339948CH[t] +  2.72119PR[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271031&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271031&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 5.25895 -0.090384AMS.I2[t] + 0.129582CONFSOFTTOT[t] -0.0298986PRH[t] + 0.0339948CH[t] + 2.72119PR[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.258951.376633.820.0002020310.000101015
AMS.I2-0.0903840.0444309-2.0340.04386950.0219347
CONFSOFTTOT0.1295820.08539011.5180.1314540.0657268
PRH-0.02989860.0101591-2.9430.003822570.00191128
CH0.03399480.01006813.3760.0009575220.000478761
PR2.721190.21020912.951.76718e-258.83592e-26

\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) & 5.25895 & 1.37663 & 3.82 & 0.000202031 & 0.000101015 \tabularnewline
AMS.I2 & -0.090384 & 0.0444309 & -2.034 & 0.0438695 & 0.0219347 \tabularnewline
CONFSOFTTOT & 0.129582 & 0.0853901 & 1.518 & 0.131454 & 0.0657268 \tabularnewline
PRH & -0.0298986 & 0.0101591 & -2.943 & 0.00382257 & 0.00191128 \tabularnewline
CH & 0.0339948 & 0.0100681 & 3.376 & 0.000957522 & 0.000478761 \tabularnewline
PR & 2.72119 & 0.210209 & 12.95 & 1.76718e-25 & 8.83592e-26 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271031&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]5.25895[/C][C]1.37663[/C][C]3.82[/C][C]0.000202031[/C][C]0.000101015[/C][/ROW]
[ROW][C]AMS.I2[/C][C]-0.090384[/C][C]0.0444309[/C][C]-2.034[/C][C]0.0438695[/C][C]0.0219347[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.129582[/C][C]0.0853901[/C][C]1.518[/C][C]0.131454[/C][C]0.0657268[/C][/ROW]
[ROW][C]PRH[/C][C]-0.0298986[/C][C]0.0101591[/C][C]-2.943[/C][C]0.00382257[/C][C]0.00191128[/C][/ROW]
[ROW][C]CH[/C][C]0.0339948[/C][C]0.0100681[/C][C]3.376[/C][C]0.000957522[/C][C]0.000478761[/C][/ROW]
[ROW][C]PR[/C][C]2.72119[/C][C]0.210209[/C][C]12.95[/C][C]1.76718e-25[/C][C]8.83592e-26[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271031&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271031&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.258951.376633.820.0002020310.000101015
AMS.I2-0.0903840.0444309-2.0340.04386950.0219347
CONFSOFTTOT0.1295820.08539011.5180.1314540.0657268
PRH-0.02989860.0101591-2.9430.003822570.00191128
CH0.03399480.01006813.3760.0009575220.000478761
PR2.721190.21020912.951.76718e-258.83592e-26






Multiple Linear Regression - Regression Statistics
Multiple R0.765888
R-squared0.586585
Adjusted R-squared0.571386
F-TEST (value)38.5935
F-TEST (DF numerator)5
F-TEST (DF denominator)136
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.28141
Sum Squared Residuals707.857

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.765888 \tabularnewline
R-squared & 0.586585 \tabularnewline
Adjusted R-squared & 0.571386 \tabularnewline
F-TEST (value) & 38.5935 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 136 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.28141 \tabularnewline
Sum Squared Residuals & 707.857 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271031&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.765888[/C][/ROW]
[ROW][C]R-squared[/C][C]0.586585[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.571386[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]38.5935[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]136[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.28141[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]707.857[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271031&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271031&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.765888
R-squared0.586585
Adjusted R-squared0.571386
F-TEST (value)38.5935
F-TEST (DF numerator)5
F-TEST (DF denominator)136
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.28141
Sum Squared Residuals707.857






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.76250.137524
212.210.78211.41787
312.811.50811.29185
47.411.7654-4.36538
56.711.1477-4.44768
612.611.94920.650792
714.811.49563.3044
813.312.79270.507263
911.112.8448-1.74479
108.211.19-2.99003
1111.411.5765-0.176516
126.411.6183-5.21826
1310.610.7078-0.107789
141213.0205-1.02045
156.38.96513-2.66513
1611.913.0591-1.15906
179.311.0671-1.76714
181010.4763-0.476309
196.410.4453-4.0453
2013.812.16121.63877
2110.811.1309-0.330875
2213.812.29581.50422
2311.710.77640.923588
2410.912.4534-1.55342
259.911.5618-1.66184
2611.511.04210.457885
278.311.1158-2.81578
2811.711.05060.649403
29910.7979-1.79788
309.714.2609-4.56086
3110.811.4071-0.607132
3210.310.4412-0.141151
3310.49.935120.464875
349.312.0389-2.73895
3511.811.18430.61565
365.911.1777-5.27772
3711.411.4727-0.0726912
381311.2191.78097
3910.812.0867-1.28668
4011.310.92280.377171
4111.811.42140.37861
4212.710.15152.54849
4310.910.89850.00152899
4413.311.80051.49948
4510.110.6787-0.578665
4614.311.56782.73218
479.311.6947-2.3947
4812.510.51111.98889
497.610.4396-2.83964
5015.912.51663.38345
519.210.7055-1.50553
5211.112.5317-1.43168
531312.72120.278799
5414.511.49493.00513
5512.312.8614-0.561444
5611.411.10970.290272
5712.611.0991.50103
58NANA0.912585
591310.78342.21663
6013.217.1785-3.9785
617.710.6801-2.98012
624.351.564632.78537
6312.710.58142.1186
6418.116.44211.65793
6517.8517.66910.180916
6617.114.09623.00376
6719.121.0693-1.96925
6816.113.62562.47438
6913.3512.21721.13281
7018.411.82476.57535
7114.717.0725-2.37252
7210.611.9084-1.30836
7312.613.0752-0.475197
7416.214.87411.32593
7513.612.79850.801517
7614.113.180.920022
7714.514.44160.058391
7816.1514.10592.04415
7914.7512.6262.12404
8014.814.56130.23871
8112.4510.48321.96681
8212.659.433313.21669
8317.3517.4157-0.065699
848.67.289141.31086
8518.416.24042.15963
8616.113.71562.38442
8717.7518.3086-0.558555
8815.2513.72281.52718
8917.6517.758-0.108015
9016.3516.6236-0.273618
9117.6516.52461.12538
9213.612.89840.701646
9314.3517.0534-2.70343
9414.7512.77221.97777
9518.2523.777-5.52701
969.97.802492.09751
971613.67982.32021
9818.2519.0892-0.839246
9916.8514.73382.11616
10018.9516.7722.17801
10115.616.1825-0.582516
10217.19.318577.78143
10316.116.729-0.629027
10415.416.1194-0.719411
10515.416.4995-1.09952
10613.3511.35251.99749
10719.118.97460.12544
1087.65.077312.52269
10919.120.9442-1.8442
11014.7511.75382.99616
11119.2521.7183-2.46831
11213.616.5182-2.91824
11312.7511.48961.26036
1149.8510.171-0.320979
11515.2516.9789-1.72892
11611.912.9502-1.05018
11716.3517.6505-1.30053
11812.410.70821.69183
11918.1515.76252.38747
12017.7518.0151-0.265115
12112.3512.4217-0.0717055
12215.613.23782.36223
12319.318.91640.383627
12417.114.31312.78688
12518.416.16122.23879
12619.0514.92434.12573
12718.5517.36621.1838
12819.122.1352-3.03519
12912.8514.395-1.54503
1309.512.6757-3.17569
1314.55.99655-1.49655
13213.613.8134-0.213433
13311.712.5518-0.851839
13413.3514.8151-1.46512
13517.617.04430.555708
13614.0515.6349-1.58493
13716.118.2142-2.11421
13813.3516.7102-3.3602
13911.8511.31520.534771
14011.9515.2396-3.28964
14113.215.6723-2.47231
1427.76.515061.18494
14314.6NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 12.7625 & 0.137524 \tabularnewline
2 & 12.2 & 10.7821 & 1.41787 \tabularnewline
3 & 12.8 & 11.5081 & 1.29185 \tabularnewline
4 & 7.4 & 11.7654 & -4.36538 \tabularnewline
5 & 6.7 & 11.1477 & -4.44768 \tabularnewline
6 & 12.6 & 11.9492 & 0.650792 \tabularnewline
7 & 14.8 & 11.4956 & 3.3044 \tabularnewline
8 & 13.3 & 12.7927 & 0.507263 \tabularnewline
9 & 11.1 & 12.8448 & -1.74479 \tabularnewline
10 & 8.2 & 11.19 & -2.99003 \tabularnewline
11 & 11.4 & 11.5765 & -0.176516 \tabularnewline
12 & 6.4 & 11.6183 & -5.21826 \tabularnewline
13 & 10.6 & 10.7078 & -0.107789 \tabularnewline
14 & 12 & 13.0205 & -1.02045 \tabularnewline
15 & 6.3 & 8.96513 & -2.66513 \tabularnewline
16 & 11.9 & 13.0591 & -1.15906 \tabularnewline
17 & 9.3 & 11.0671 & -1.76714 \tabularnewline
18 & 10 & 10.4763 & -0.476309 \tabularnewline
19 & 6.4 & 10.4453 & -4.0453 \tabularnewline
20 & 13.8 & 12.1612 & 1.63877 \tabularnewline
21 & 10.8 & 11.1309 & -0.330875 \tabularnewline
22 & 13.8 & 12.2958 & 1.50422 \tabularnewline
23 & 11.7 & 10.7764 & 0.923588 \tabularnewline
24 & 10.9 & 12.4534 & -1.55342 \tabularnewline
25 & 9.9 & 11.5618 & -1.66184 \tabularnewline
26 & 11.5 & 11.0421 & 0.457885 \tabularnewline
27 & 8.3 & 11.1158 & -2.81578 \tabularnewline
28 & 11.7 & 11.0506 & 0.649403 \tabularnewline
29 & 9 & 10.7979 & -1.79788 \tabularnewline
30 & 9.7 & 14.2609 & -4.56086 \tabularnewline
31 & 10.8 & 11.4071 & -0.607132 \tabularnewline
32 & 10.3 & 10.4412 & -0.141151 \tabularnewline
33 & 10.4 & 9.93512 & 0.464875 \tabularnewline
34 & 9.3 & 12.0389 & -2.73895 \tabularnewline
35 & 11.8 & 11.1843 & 0.61565 \tabularnewline
36 & 5.9 & 11.1777 & -5.27772 \tabularnewline
37 & 11.4 & 11.4727 & -0.0726912 \tabularnewline
38 & 13 & 11.219 & 1.78097 \tabularnewline
39 & 10.8 & 12.0867 & -1.28668 \tabularnewline
40 & 11.3 & 10.9228 & 0.377171 \tabularnewline
41 & 11.8 & 11.4214 & 0.37861 \tabularnewline
42 & 12.7 & 10.1515 & 2.54849 \tabularnewline
43 & 10.9 & 10.8985 & 0.00152899 \tabularnewline
44 & 13.3 & 11.8005 & 1.49948 \tabularnewline
45 & 10.1 & 10.6787 & -0.578665 \tabularnewline
46 & 14.3 & 11.5678 & 2.73218 \tabularnewline
47 & 9.3 & 11.6947 & -2.3947 \tabularnewline
48 & 12.5 & 10.5111 & 1.98889 \tabularnewline
49 & 7.6 & 10.4396 & -2.83964 \tabularnewline
50 & 15.9 & 12.5166 & 3.38345 \tabularnewline
51 & 9.2 & 10.7055 & -1.50553 \tabularnewline
52 & 11.1 & 12.5317 & -1.43168 \tabularnewline
53 & 13 & 12.7212 & 0.278799 \tabularnewline
54 & 14.5 & 11.4949 & 3.00513 \tabularnewline
55 & 12.3 & 12.8614 & -0.561444 \tabularnewline
56 & 11.4 & 11.1097 & 0.290272 \tabularnewline
57 & 12.6 & 11.099 & 1.50103 \tabularnewline
58 & NA & NA & 0.912585 \tabularnewline
59 & 13 & 10.7834 & 2.21663 \tabularnewline
60 & 13.2 & 17.1785 & -3.9785 \tabularnewline
61 & 7.7 & 10.6801 & -2.98012 \tabularnewline
62 & 4.35 & 1.56463 & 2.78537 \tabularnewline
63 & 12.7 & 10.5814 & 2.1186 \tabularnewline
64 & 18.1 & 16.4421 & 1.65793 \tabularnewline
65 & 17.85 & 17.6691 & 0.180916 \tabularnewline
66 & 17.1 & 14.0962 & 3.00376 \tabularnewline
67 & 19.1 & 21.0693 & -1.96925 \tabularnewline
68 & 16.1 & 13.6256 & 2.47438 \tabularnewline
69 & 13.35 & 12.2172 & 1.13281 \tabularnewline
70 & 18.4 & 11.8247 & 6.57535 \tabularnewline
71 & 14.7 & 17.0725 & -2.37252 \tabularnewline
72 & 10.6 & 11.9084 & -1.30836 \tabularnewline
73 & 12.6 & 13.0752 & -0.475197 \tabularnewline
74 & 16.2 & 14.8741 & 1.32593 \tabularnewline
75 & 13.6 & 12.7985 & 0.801517 \tabularnewline
76 & 14.1 & 13.18 & 0.920022 \tabularnewline
77 & 14.5 & 14.4416 & 0.058391 \tabularnewline
78 & 16.15 & 14.1059 & 2.04415 \tabularnewline
79 & 14.75 & 12.626 & 2.12404 \tabularnewline
80 & 14.8 & 14.5613 & 0.23871 \tabularnewline
81 & 12.45 & 10.4832 & 1.96681 \tabularnewline
82 & 12.65 & 9.43331 & 3.21669 \tabularnewline
83 & 17.35 & 17.4157 & -0.065699 \tabularnewline
84 & 8.6 & 7.28914 & 1.31086 \tabularnewline
85 & 18.4 & 16.2404 & 2.15963 \tabularnewline
86 & 16.1 & 13.7156 & 2.38442 \tabularnewline
87 & 17.75 & 18.3086 & -0.558555 \tabularnewline
88 & 15.25 & 13.7228 & 1.52718 \tabularnewline
89 & 17.65 & 17.758 & -0.108015 \tabularnewline
90 & 16.35 & 16.6236 & -0.273618 \tabularnewline
91 & 17.65 & 16.5246 & 1.12538 \tabularnewline
92 & 13.6 & 12.8984 & 0.701646 \tabularnewline
93 & 14.35 & 17.0534 & -2.70343 \tabularnewline
94 & 14.75 & 12.7722 & 1.97777 \tabularnewline
95 & 18.25 & 23.777 & -5.52701 \tabularnewline
96 & 9.9 & 7.80249 & 2.09751 \tabularnewline
97 & 16 & 13.6798 & 2.32021 \tabularnewline
98 & 18.25 & 19.0892 & -0.839246 \tabularnewline
99 & 16.85 & 14.7338 & 2.11616 \tabularnewline
100 & 18.95 & 16.772 & 2.17801 \tabularnewline
101 & 15.6 & 16.1825 & -0.582516 \tabularnewline
102 & 17.1 & 9.31857 & 7.78143 \tabularnewline
103 & 16.1 & 16.729 & -0.629027 \tabularnewline
104 & 15.4 & 16.1194 & -0.719411 \tabularnewline
105 & 15.4 & 16.4995 & -1.09952 \tabularnewline
106 & 13.35 & 11.3525 & 1.99749 \tabularnewline
107 & 19.1 & 18.9746 & 0.12544 \tabularnewline
108 & 7.6 & 5.07731 & 2.52269 \tabularnewline
109 & 19.1 & 20.9442 & -1.8442 \tabularnewline
110 & 14.75 & 11.7538 & 2.99616 \tabularnewline
111 & 19.25 & 21.7183 & -2.46831 \tabularnewline
112 & 13.6 & 16.5182 & -2.91824 \tabularnewline
113 & 12.75 & 11.4896 & 1.26036 \tabularnewline
114 & 9.85 & 10.171 & -0.320979 \tabularnewline
115 & 15.25 & 16.9789 & -1.72892 \tabularnewline
116 & 11.9 & 12.9502 & -1.05018 \tabularnewline
117 & 16.35 & 17.6505 & -1.30053 \tabularnewline
118 & 12.4 & 10.7082 & 1.69183 \tabularnewline
119 & 18.15 & 15.7625 & 2.38747 \tabularnewline
120 & 17.75 & 18.0151 & -0.265115 \tabularnewline
121 & 12.35 & 12.4217 & -0.0717055 \tabularnewline
122 & 15.6 & 13.2378 & 2.36223 \tabularnewline
123 & 19.3 & 18.9164 & 0.383627 \tabularnewline
124 & 17.1 & 14.3131 & 2.78688 \tabularnewline
125 & 18.4 & 16.1612 & 2.23879 \tabularnewline
126 & 19.05 & 14.9243 & 4.12573 \tabularnewline
127 & 18.55 & 17.3662 & 1.1838 \tabularnewline
128 & 19.1 & 22.1352 & -3.03519 \tabularnewline
129 & 12.85 & 14.395 & -1.54503 \tabularnewline
130 & 9.5 & 12.6757 & -3.17569 \tabularnewline
131 & 4.5 & 5.99655 & -1.49655 \tabularnewline
132 & 13.6 & 13.8134 & -0.213433 \tabularnewline
133 & 11.7 & 12.5518 & -0.851839 \tabularnewline
134 & 13.35 & 14.8151 & -1.46512 \tabularnewline
135 & 17.6 & 17.0443 & 0.555708 \tabularnewline
136 & 14.05 & 15.6349 & -1.58493 \tabularnewline
137 & 16.1 & 18.2142 & -2.11421 \tabularnewline
138 & 13.35 & 16.7102 & -3.3602 \tabularnewline
139 & 11.85 & 11.3152 & 0.534771 \tabularnewline
140 & 11.95 & 15.2396 & -3.28964 \tabularnewline
141 & 13.2 & 15.6723 & -2.47231 \tabularnewline
142 & 7.7 & 6.51506 & 1.18494 \tabularnewline
143 & 14.6 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271031&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]12.9[/C][C]12.7625[/C][C]0.137524[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]10.7821[/C][C]1.41787[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]11.5081[/C][C]1.29185[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]11.7654[/C][C]-4.36538[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]11.1477[/C][C]-4.44768[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.9492[/C][C]0.650792[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]11.4956[/C][C]3.3044[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]12.7927[/C][C]0.507263[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]12.8448[/C][C]-1.74479[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]11.19[/C][C]-2.99003[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]11.5765[/C][C]-0.176516[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]11.6183[/C][C]-5.21826[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]10.7078[/C][C]-0.107789[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]13.0205[/C][C]-1.02045[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]8.96513[/C][C]-2.66513[/C][/ROW]
[ROW][C]16[/C][C]11.9[/C][C]13.0591[/C][C]-1.15906[/C][/ROW]
[ROW][C]17[/C][C]9.3[/C][C]11.0671[/C][C]-1.76714[/C][/ROW]
[ROW][C]18[/C][C]10[/C][C]10.4763[/C][C]-0.476309[/C][/ROW]
[ROW][C]19[/C][C]6.4[/C][C]10.4453[/C][C]-4.0453[/C][/ROW]
[ROW][C]20[/C][C]13.8[/C][C]12.1612[/C][C]1.63877[/C][/ROW]
[ROW][C]21[/C][C]10.8[/C][C]11.1309[/C][C]-0.330875[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]12.2958[/C][C]1.50422[/C][/ROW]
[ROW][C]23[/C][C]11.7[/C][C]10.7764[/C][C]0.923588[/C][/ROW]
[ROW][C]24[/C][C]10.9[/C][C]12.4534[/C][C]-1.55342[/C][/ROW]
[ROW][C]25[/C][C]9.9[/C][C]11.5618[/C][C]-1.66184[/C][/ROW]
[ROW][C]26[/C][C]11.5[/C][C]11.0421[/C][C]0.457885[/C][/ROW]
[ROW][C]27[/C][C]8.3[/C][C]11.1158[/C][C]-2.81578[/C][/ROW]
[ROW][C]28[/C][C]11.7[/C][C]11.0506[/C][C]0.649403[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]10.7979[/C][C]-1.79788[/C][/ROW]
[ROW][C]30[/C][C]9.7[/C][C]14.2609[/C][C]-4.56086[/C][/ROW]
[ROW][C]31[/C][C]10.8[/C][C]11.4071[/C][C]-0.607132[/C][/ROW]
[ROW][C]32[/C][C]10.3[/C][C]10.4412[/C][C]-0.141151[/C][/ROW]
[ROW][C]33[/C][C]10.4[/C][C]9.93512[/C][C]0.464875[/C][/ROW]
[ROW][C]34[/C][C]9.3[/C][C]12.0389[/C][C]-2.73895[/C][/ROW]
[ROW][C]35[/C][C]11.8[/C][C]11.1843[/C][C]0.61565[/C][/ROW]
[ROW][C]36[/C][C]5.9[/C][C]11.1777[/C][C]-5.27772[/C][/ROW]
[ROW][C]37[/C][C]11.4[/C][C]11.4727[/C][C]-0.0726912[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]11.219[/C][C]1.78097[/C][/ROW]
[ROW][C]39[/C][C]10.8[/C][C]12.0867[/C][C]-1.28668[/C][/ROW]
[ROW][C]40[/C][C]11.3[/C][C]10.9228[/C][C]0.377171[/C][/ROW]
[ROW][C]41[/C][C]11.8[/C][C]11.4214[/C][C]0.37861[/C][/ROW]
[ROW][C]42[/C][C]12.7[/C][C]10.1515[/C][C]2.54849[/C][/ROW]
[ROW][C]43[/C][C]10.9[/C][C]10.8985[/C][C]0.00152899[/C][/ROW]
[ROW][C]44[/C][C]13.3[/C][C]11.8005[/C][C]1.49948[/C][/ROW]
[ROW][C]45[/C][C]10.1[/C][C]10.6787[/C][C]-0.578665[/C][/ROW]
[ROW][C]46[/C][C]14.3[/C][C]11.5678[/C][C]2.73218[/C][/ROW]
[ROW][C]47[/C][C]9.3[/C][C]11.6947[/C][C]-2.3947[/C][/ROW]
[ROW][C]48[/C][C]12.5[/C][C]10.5111[/C][C]1.98889[/C][/ROW]
[ROW][C]49[/C][C]7.6[/C][C]10.4396[/C][C]-2.83964[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]12.5166[/C][C]3.38345[/C][/ROW]
[ROW][C]51[/C][C]9.2[/C][C]10.7055[/C][C]-1.50553[/C][/ROW]
[ROW][C]52[/C][C]11.1[/C][C]12.5317[/C][C]-1.43168[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]12.7212[/C][C]0.278799[/C][/ROW]
[ROW][C]54[/C][C]14.5[/C][C]11.4949[/C][C]3.00513[/C][/ROW]
[ROW][C]55[/C][C]12.3[/C][C]12.8614[/C][C]-0.561444[/C][/ROW]
[ROW][C]56[/C][C]11.4[/C][C]11.1097[/C][C]0.290272[/C][/ROW]
[ROW][C]57[/C][C]12.6[/C][C]11.099[/C][C]1.50103[/C][/ROW]
[ROW][C]58[/C][C]NA[/C][C]NA[/C][C]0.912585[/C][/ROW]
[ROW][C]59[/C][C]13[/C][C]10.7834[/C][C]2.21663[/C][/ROW]
[ROW][C]60[/C][C]13.2[/C][C]17.1785[/C][C]-3.9785[/C][/ROW]
[ROW][C]61[/C][C]7.7[/C][C]10.6801[/C][C]-2.98012[/C][/ROW]
[ROW][C]62[/C][C]4.35[/C][C]1.56463[/C][C]2.78537[/C][/ROW]
[ROW][C]63[/C][C]12.7[/C][C]10.5814[/C][C]2.1186[/C][/ROW]
[ROW][C]64[/C][C]18.1[/C][C]16.4421[/C][C]1.65793[/C][/ROW]
[ROW][C]65[/C][C]17.85[/C][C]17.6691[/C][C]0.180916[/C][/ROW]
[ROW][C]66[/C][C]17.1[/C][C]14.0962[/C][C]3.00376[/C][/ROW]
[ROW][C]67[/C][C]19.1[/C][C]21.0693[/C][C]-1.96925[/C][/ROW]
[ROW][C]68[/C][C]16.1[/C][C]13.6256[/C][C]2.47438[/C][/ROW]
[ROW][C]69[/C][C]13.35[/C][C]12.2172[/C][C]1.13281[/C][/ROW]
[ROW][C]70[/C][C]18.4[/C][C]11.8247[/C][C]6.57535[/C][/ROW]
[ROW][C]71[/C][C]14.7[/C][C]17.0725[/C][C]-2.37252[/C][/ROW]
[ROW][C]72[/C][C]10.6[/C][C]11.9084[/C][C]-1.30836[/C][/ROW]
[ROW][C]73[/C][C]12.6[/C][C]13.0752[/C][C]-0.475197[/C][/ROW]
[ROW][C]74[/C][C]16.2[/C][C]14.8741[/C][C]1.32593[/C][/ROW]
[ROW][C]75[/C][C]13.6[/C][C]12.7985[/C][C]0.801517[/C][/ROW]
[ROW][C]76[/C][C]14.1[/C][C]13.18[/C][C]0.920022[/C][/ROW]
[ROW][C]77[/C][C]14.5[/C][C]14.4416[/C][C]0.058391[/C][/ROW]
[ROW][C]78[/C][C]16.15[/C][C]14.1059[/C][C]2.04415[/C][/ROW]
[ROW][C]79[/C][C]14.75[/C][C]12.626[/C][C]2.12404[/C][/ROW]
[ROW][C]80[/C][C]14.8[/C][C]14.5613[/C][C]0.23871[/C][/ROW]
[ROW][C]81[/C][C]12.45[/C][C]10.4832[/C][C]1.96681[/C][/ROW]
[ROW][C]82[/C][C]12.65[/C][C]9.43331[/C][C]3.21669[/C][/ROW]
[ROW][C]83[/C][C]17.35[/C][C]17.4157[/C][C]-0.065699[/C][/ROW]
[ROW][C]84[/C][C]8.6[/C][C]7.28914[/C][C]1.31086[/C][/ROW]
[ROW][C]85[/C][C]18.4[/C][C]16.2404[/C][C]2.15963[/C][/ROW]
[ROW][C]86[/C][C]16.1[/C][C]13.7156[/C][C]2.38442[/C][/ROW]
[ROW][C]87[/C][C]17.75[/C][C]18.3086[/C][C]-0.558555[/C][/ROW]
[ROW][C]88[/C][C]15.25[/C][C]13.7228[/C][C]1.52718[/C][/ROW]
[ROW][C]89[/C][C]17.65[/C][C]17.758[/C][C]-0.108015[/C][/ROW]
[ROW][C]90[/C][C]16.35[/C][C]16.6236[/C][C]-0.273618[/C][/ROW]
[ROW][C]91[/C][C]17.65[/C][C]16.5246[/C][C]1.12538[/C][/ROW]
[ROW][C]92[/C][C]13.6[/C][C]12.8984[/C][C]0.701646[/C][/ROW]
[ROW][C]93[/C][C]14.35[/C][C]17.0534[/C][C]-2.70343[/C][/ROW]
[ROW][C]94[/C][C]14.75[/C][C]12.7722[/C][C]1.97777[/C][/ROW]
[ROW][C]95[/C][C]18.25[/C][C]23.777[/C][C]-5.52701[/C][/ROW]
[ROW][C]96[/C][C]9.9[/C][C]7.80249[/C][C]2.09751[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]13.6798[/C][C]2.32021[/C][/ROW]
[ROW][C]98[/C][C]18.25[/C][C]19.0892[/C][C]-0.839246[/C][/ROW]
[ROW][C]99[/C][C]16.85[/C][C]14.7338[/C][C]2.11616[/C][/ROW]
[ROW][C]100[/C][C]18.95[/C][C]16.772[/C][C]2.17801[/C][/ROW]
[ROW][C]101[/C][C]15.6[/C][C]16.1825[/C][C]-0.582516[/C][/ROW]
[ROW][C]102[/C][C]17.1[/C][C]9.31857[/C][C]7.78143[/C][/ROW]
[ROW][C]103[/C][C]16.1[/C][C]16.729[/C][C]-0.629027[/C][/ROW]
[ROW][C]104[/C][C]15.4[/C][C]16.1194[/C][C]-0.719411[/C][/ROW]
[ROW][C]105[/C][C]15.4[/C][C]16.4995[/C][C]-1.09952[/C][/ROW]
[ROW][C]106[/C][C]13.35[/C][C]11.3525[/C][C]1.99749[/C][/ROW]
[ROW][C]107[/C][C]19.1[/C][C]18.9746[/C][C]0.12544[/C][/ROW]
[ROW][C]108[/C][C]7.6[/C][C]5.07731[/C][C]2.52269[/C][/ROW]
[ROW][C]109[/C][C]19.1[/C][C]20.9442[/C][C]-1.8442[/C][/ROW]
[ROW][C]110[/C][C]14.75[/C][C]11.7538[/C][C]2.99616[/C][/ROW]
[ROW][C]111[/C][C]19.25[/C][C]21.7183[/C][C]-2.46831[/C][/ROW]
[ROW][C]112[/C][C]13.6[/C][C]16.5182[/C][C]-2.91824[/C][/ROW]
[ROW][C]113[/C][C]12.75[/C][C]11.4896[/C][C]1.26036[/C][/ROW]
[ROW][C]114[/C][C]9.85[/C][C]10.171[/C][C]-0.320979[/C][/ROW]
[ROW][C]115[/C][C]15.25[/C][C]16.9789[/C][C]-1.72892[/C][/ROW]
[ROW][C]116[/C][C]11.9[/C][C]12.9502[/C][C]-1.05018[/C][/ROW]
[ROW][C]117[/C][C]16.35[/C][C]17.6505[/C][C]-1.30053[/C][/ROW]
[ROW][C]118[/C][C]12.4[/C][C]10.7082[/C][C]1.69183[/C][/ROW]
[ROW][C]119[/C][C]18.15[/C][C]15.7625[/C][C]2.38747[/C][/ROW]
[ROW][C]120[/C][C]17.75[/C][C]18.0151[/C][C]-0.265115[/C][/ROW]
[ROW][C]121[/C][C]12.35[/C][C]12.4217[/C][C]-0.0717055[/C][/ROW]
[ROW][C]122[/C][C]15.6[/C][C]13.2378[/C][C]2.36223[/C][/ROW]
[ROW][C]123[/C][C]19.3[/C][C]18.9164[/C][C]0.383627[/C][/ROW]
[ROW][C]124[/C][C]17.1[/C][C]14.3131[/C][C]2.78688[/C][/ROW]
[ROW][C]125[/C][C]18.4[/C][C]16.1612[/C][C]2.23879[/C][/ROW]
[ROW][C]126[/C][C]19.05[/C][C]14.9243[/C][C]4.12573[/C][/ROW]
[ROW][C]127[/C][C]18.55[/C][C]17.3662[/C][C]1.1838[/C][/ROW]
[ROW][C]128[/C][C]19.1[/C][C]22.1352[/C][C]-3.03519[/C][/ROW]
[ROW][C]129[/C][C]12.85[/C][C]14.395[/C][C]-1.54503[/C][/ROW]
[ROW][C]130[/C][C]9.5[/C][C]12.6757[/C][C]-3.17569[/C][/ROW]
[ROW][C]131[/C][C]4.5[/C][C]5.99655[/C][C]-1.49655[/C][/ROW]
[ROW][C]132[/C][C]13.6[/C][C]13.8134[/C][C]-0.213433[/C][/ROW]
[ROW][C]133[/C][C]11.7[/C][C]12.5518[/C][C]-0.851839[/C][/ROW]
[ROW][C]134[/C][C]13.35[/C][C]14.8151[/C][C]-1.46512[/C][/ROW]
[ROW][C]135[/C][C]17.6[/C][C]17.0443[/C][C]0.555708[/C][/ROW]
[ROW][C]136[/C][C]14.05[/C][C]15.6349[/C][C]-1.58493[/C][/ROW]
[ROW][C]137[/C][C]16.1[/C][C]18.2142[/C][C]-2.11421[/C][/ROW]
[ROW][C]138[/C][C]13.35[/C][C]16.7102[/C][C]-3.3602[/C][/ROW]
[ROW][C]139[/C][C]11.85[/C][C]11.3152[/C][C]0.534771[/C][/ROW]
[ROW][C]140[/C][C]11.95[/C][C]15.2396[/C][C]-3.28964[/C][/ROW]
[ROW][C]141[/C][C]13.2[/C][C]15.6723[/C][C]-2.47231[/C][/ROW]
[ROW][C]142[/C][C]7.7[/C][C]6.51506[/C][C]1.18494[/C][/ROW]
[ROW][C]143[/C][C]14.6[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271031&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271031&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.76250.137524
212.210.78211.41787
312.811.50811.29185
47.411.7654-4.36538
56.711.1477-4.44768
612.611.94920.650792
714.811.49563.3044
813.312.79270.507263
911.112.8448-1.74479
108.211.19-2.99003
1111.411.5765-0.176516
126.411.6183-5.21826
1310.610.7078-0.107789
141213.0205-1.02045
156.38.96513-2.66513
1611.913.0591-1.15906
179.311.0671-1.76714
181010.4763-0.476309
196.410.4453-4.0453
2013.812.16121.63877
2110.811.1309-0.330875
2213.812.29581.50422
2311.710.77640.923588
2410.912.4534-1.55342
259.911.5618-1.66184
2611.511.04210.457885
278.311.1158-2.81578
2811.711.05060.649403
29910.7979-1.79788
309.714.2609-4.56086
3110.811.4071-0.607132
3210.310.4412-0.141151
3310.49.935120.464875
349.312.0389-2.73895
3511.811.18430.61565
365.911.1777-5.27772
3711.411.4727-0.0726912
381311.2191.78097
3910.812.0867-1.28668
4011.310.92280.377171
4111.811.42140.37861
4212.710.15152.54849
4310.910.89850.00152899
4413.311.80051.49948
4510.110.6787-0.578665
4614.311.56782.73218
479.311.6947-2.3947
4812.510.51111.98889
497.610.4396-2.83964
5015.912.51663.38345
519.210.7055-1.50553
5211.112.5317-1.43168
531312.72120.278799
5414.511.49493.00513
5512.312.8614-0.561444
5611.411.10970.290272
5712.611.0991.50103
58NANA0.912585
591310.78342.21663
6013.217.1785-3.9785
617.710.6801-2.98012
624.351.564632.78537
6312.710.58142.1186
6418.116.44211.65793
6517.8517.66910.180916
6617.114.09623.00376
6719.121.0693-1.96925
6816.113.62562.47438
6913.3512.21721.13281
7018.411.82476.57535
7114.717.0725-2.37252
7210.611.9084-1.30836
7312.613.0752-0.475197
7416.214.87411.32593
7513.612.79850.801517
7614.113.180.920022
7714.514.44160.058391
7816.1514.10592.04415
7914.7512.6262.12404
8014.814.56130.23871
8112.4510.48321.96681
8212.659.433313.21669
8317.3517.4157-0.065699
848.67.289141.31086
8518.416.24042.15963
8616.113.71562.38442
8717.7518.3086-0.558555
8815.2513.72281.52718
8917.6517.758-0.108015
9016.3516.6236-0.273618
9117.6516.52461.12538
9213.612.89840.701646
9314.3517.0534-2.70343
9414.7512.77221.97777
9518.2523.777-5.52701
969.97.802492.09751
971613.67982.32021
9818.2519.0892-0.839246
9916.8514.73382.11616
10018.9516.7722.17801
10115.616.1825-0.582516
10217.19.318577.78143
10316.116.729-0.629027
10415.416.1194-0.719411
10515.416.4995-1.09952
10613.3511.35251.99749
10719.118.97460.12544
1087.65.077312.52269
10919.120.9442-1.8442
11014.7511.75382.99616
11119.2521.7183-2.46831
11213.616.5182-2.91824
11312.7511.48961.26036
1149.8510.171-0.320979
11515.2516.9789-1.72892
11611.912.9502-1.05018
11716.3517.6505-1.30053
11812.410.70821.69183
11918.1515.76252.38747
12017.7518.0151-0.265115
12112.3512.4217-0.0717055
12215.613.23782.36223
12319.318.91640.383627
12417.114.31312.78688
12518.416.16122.23879
12619.0514.92434.12573
12718.5517.36621.1838
12819.122.1352-3.03519
12912.8514.395-1.54503
1309.512.6757-3.17569
1314.55.99655-1.49655
13213.613.8134-0.213433
13311.712.5518-0.851839
13413.3514.8151-1.46512
13517.617.04430.555708
13614.0515.6349-1.58493
13716.118.2142-2.11421
13813.3516.7102-3.3602
13911.8511.31520.534771
14011.9515.2396-3.28964
14113.215.6723-2.47231
1427.76.515061.18494
14314.6NANA






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.6802540.6394920.319746
100.5302450.9395110.469755
110.5018370.9963270.498163
120.6462620.7074760.353738
130.5385760.9228490.461424
140.4299470.8598940.570053
150.356950.7138990.64305
160.269460.538920.73054
170.2015010.4030010.798499
180.3002290.6004580.699771
190.2733270.5466540.726673
200.2082030.4164060.791797
210.153120.3062410.84688
220.1269860.2539710.873014
230.09247480.184950.907525
240.06595040.1319010.93405
250.0628980.1257960.937102
260.08189460.1637890.918105
270.1340560.2681120.865944
280.1347490.2694990.865251
290.1171230.2342450.882877
300.2467470.4934950.753253
310.2162020.4324030.783798
320.1766010.3532030.823399
330.1385180.2770370.861482
340.1365780.2731570.863422
350.1069590.2139180.893041
360.2799880.5599760.720012
370.2331690.4663380.766831
380.1956850.391370.804315
390.168170.336340.83183
400.1341350.2682690.865865
410.1113550.2227110.888645
420.1217710.2435410.878229
430.09899730.1979950.901003
440.1678960.3357920.832104
450.1401550.2803090.859845
460.242970.485940.75703
470.2514840.5029680.748516
480.2788550.557710.721145
490.3367140.6734280.663286
500.4577030.9154060.542297
510.4332170.8664340.566783
520.4065720.8131430.593428
530.3641250.7282510.635875
540.3985890.7971770.601411
550.3637390.7274780.636261
560.3227790.6455590.677221
570.2910750.582150.708925
580.2592050.5184110.740795
590.265540.5310810.73446
600.3514420.7028840.648558
610.4467060.8934130.553294
620.4721280.9442550.527872
630.4720310.9440620.527969
640.4357220.8714450.564278
650.395170.790340.60483
660.3950310.7900630.604969
670.4532550.906510.546745
680.479290.958580.52071
690.4413080.8826150.558692
700.7993770.4012450.200623
710.8141140.3717720.185886
720.793760.412480.20624
730.7618480.4763030.238152
740.7408380.5183240.259162
750.7019520.5960950.298048
760.6645040.6709910.335496
770.6369110.7261780.363089
780.6186230.7627540.381377
790.5992480.8015050.400752
800.5511860.8976290.448814
810.5275450.9449090.472455
820.5742020.8515950.425798
830.5274720.9450560.472528
840.4846520.9693030.515348
850.4585240.9170480.541476
860.4598310.9196620.540169
870.4279030.8558050.572097
880.3976620.7953230.602338
890.3520880.7041760.647912
900.3095720.6191430.690428
910.2695810.5391610.730419
920.2290380.4580750.770962
930.2460950.4921910.753905
940.2326880.4653770.767312
950.4522670.9045330.547733
960.4199980.8399960.580002
970.4168230.8336460.583177
980.3699680.7399360.630032
990.3406930.6813850.659307
1000.3274830.6549670.672517
1010.2839740.5679480.716026
1020.8914510.2170990.108549
1030.8638650.2722710.136135
1040.8311820.3376370.168818
1050.8124640.3750720.187536
1060.8185450.362910.181455
1070.799580.400840.20042
1080.7947540.4104920.205246
1090.771660.456680.22834
1100.7470260.5059480.252974
1110.7320730.5358550.267927
1120.7503460.4993080.249654
1130.7418760.5162480.258124
1140.6847690.6304620.315231
1150.6690640.6618730.330936
1160.656740.6865210.34326
1170.6059630.7880740.394037
1180.5853250.8293490.414675
1190.5628980.8742050.437102
1200.5018240.9963520.498176
1210.4969950.9939890.503005
1220.4902940.9805880.509706
1230.5186710.9626570.481329
1240.5032770.9934460.496723
1250.643260.713480.35674
1260.9862310.02753820.0137691
1270.9738820.0522360.026118
1280.9719210.05615870.0280794
1290.9445940.1108110.0554056
1300.9375570.1248870.0624433
1310.951030.097940.04897
1320.9003190.1993620.0996808
1330.7858630.4282730.214137
1340.5881750.8236490.411825

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.680254 & 0.639492 & 0.319746 \tabularnewline
10 & 0.530245 & 0.939511 & 0.469755 \tabularnewline
11 & 0.501837 & 0.996327 & 0.498163 \tabularnewline
12 & 0.646262 & 0.707476 & 0.353738 \tabularnewline
13 & 0.538576 & 0.922849 & 0.461424 \tabularnewline
14 & 0.429947 & 0.859894 & 0.570053 \tabularnewline
15 & 0.35695 & 0.713899 & 0.64305 \tabularnewline
16 & 0.26946 & 0.53892 & 0.73054 \tabularnewline
17 & 0.201501 & 0.403001 & 0.798499 \tabularnewline
18 & 0.300229 & 0.600458 & 0.699771 \tabularnewline
19 & 0.273327 & 0.546654 & 0.726673 \tabularnewline
20 & 0.208203 & 0.416406 & 0.791797 \tabularnewline
21 & 0.15312 & 0.306241 & 0.84688 \tabularnewline
22 & 0.126986 & 0.253971 & 0.873014 \tabularnewline
23 & 0.0924748 & 0.18495 & 0.907525 \tabularnewline
24 & 0.0659504 & 0.131901 & 0.93405 \tabularnewline
25 & 0.062898 & 0.125796 & 0.937102 \tabularnewline
26 & 0.0818946 & 0.163789 & 0.918105 \tabularnewline
27 & 0.134056 & 0.268112 & 0.865944 \tabularnewline
28 & 0.134749 & 0.269499 & 0.865251 \tabularnewline
29 & 0.117123 & 0.234245 & 0.882877 \tabularnewline
30 & 0.246747 & 0.493495 & 0.753253 \tabularnewline
31 & 0.216202 & 0.432403 & 0.783798 \tabularnewline
32 & 0.176601 & 0.353203 & 0.823399 \tabularnewline
33 & 0.138518 & 0.277037 & 0.861482 \tabularnewline
34 & 0.136578 & 0.273157 & 0.863422 \tabularnewline
35 & 0.106959 & 0.213918 & 0.893041 \tabularnewline
36 & 0.279988 & 0.559976 & 0.720012 \tabularnewline
37 & 0.233169 & 0.466338 & 0.766831 \tabularnewline
38 & 0.195685 & 0.39137 & 0.804315 \tabularnewline
39 & 0.16817 & 0.33634 & 0.83183 \tabularnewline
40 & 0.134135 & 0.268269 & 0.865865 \tabularnewline
41 & 0.111355 & 0.222711 & 0.888645 \tabularnewline
42 & 0.121771 & 0.243541 & 0.878229 \tabularnewline
43 & 0.0989973 & 0.197995 & 0.901003 \tabularnewline
44 & 0.167896 & 0.335792 & 0.832104 \tabularnewline
45 & 0.140155 & 0.280309 & 0.859845 \tabularnewline
46 & 0.24297 & 0.48594 & 0.75703 \tabularnewline
47 & 0.251484 & 0.502968 & 0.748516 \tabularnewline
48 & 0.278855 & 0.55771 & 0.721145 \tabularnewline
49 & 0.336714 & 0.673428 & 0.663286 \tabularnewline
50 & 0.457703 & 0.915406 & 0.542297 \tabularnewline
51 & 0.433217 & 0.866434 & 0.566783 \tabularnewline
52 & 0.406572 & 0.813143 & 0.593428 \tabularnewline
53 & 0.364125 & 0.728251 & 0.635875 \tabularnewline
54 & 0.398589 & 0.797177 & 0.601411 \tabularnewline
55 & 0.363739 & 0.727478 & 0.636261 \tabularnewline
56 & 0.322779 & 0.645559 & 0.677221 \tabularnewline
57 & 0.291075 & 0.58215 & 0.708925 \tabularnewline
58 & 0.259205 & 0.518411 & 0.740795 \tabularnewline
59 & 0.26554 & 0.531081 & 0.73446 \tabularnewline
60 & 0.351442 & 0.702884 & 0.648558 \tabularnewline
61 & 0.446706 & 0.893413 & 0.553294 \tabularnewline
62 & 0.472128 & 0.944255 & 0.527872 \tabularnewline
63 & 0.472031 & 0.944062 & 0.527969 \tabularnewline
64 & 0.435722 & 0.871445 & 0.564278 \tabularnewline
65 & 0.39517 & 0.79034 & 0.60483 \tabularnewline
66 & 0.395031 & 0.790063 & 0.604969 \tabularnewline
67 & 0.453255 & 0.90651 & 0.546745 \tabularnewline
68 & 0.47929 & 0.95858 & 0.52071 \tabularnewline
69 & 0.441308 & 0.882615 & 0.558692 \tabularnewline
70 & 0.799377 & 0.401245 & 0.200623 \tabularnewline
71 & 0.814114 & 0.371772 & 0.185886 \tabularnewline
72 & 0.79376 & 0.41248 & 0.20624 \tabularnewline
73 & 0.761848 & 0.476303 & 0.238152 \tabularnewline
74 & 0.740838 & 0.518324 & 0.259162 \tabularnewline
75 & 0.701952 & 0.596095 & 0.298048 \tabularnewline
76 & 0.664504 & 0.670991 & 0.335496 \tabularnewline
77 & 0.636911 & 0.726178 & 0.363089 \tabularnewline
78 & 0.618623 & 0.762754 & 0.381377 \tabularnewline
79 & 0.599248 & 0.801505 & 0.400752 \tabularnewline
80 & 0.551186 & 0.897629 & 0.448814 \tabularnewline
81 & 0.527545 & 0.944909 & 0.472455 \tabularnewline
82 & 0.574202 & 0.851595 & 0.425798 \tabularnewline
83 & 0.527472 & 0.945056 & 0.472528 \tabularnewline
84 & 0.484652 & 0.969303 & 0.515348 \tabularnewline
85 & 0.458524 & 0.917048 & 0.541476 \tabularnewline
86 & 0.459831 & 0.919662 & 0.540169 \tabularnewline
87 & 0.427903 & 0.855805 & 0.572097 \tabularnewline
88 & 0.397662 & 0.795323 & 0.602338 \tabularnewline
89 & 0.352088 & 0.704176 & 0.647912 \tabularnewline
90 & 0.309572 & 0.619143 & 0.690428 \tabularnewline
91 & 0.269581 & 0.539161 & 0.730419 \tabularnewline
92 & 0.229038 & 0.458075 & 0.770962 \tabularnewline
93 & 0.246095 & 0.492191 & 0.753905 \tabularnewline
94 & 0.232688 & 0.465377 & 0.767312 \tabularnewline
95 & 0.452267 & 0.904533 & 0.547733 \tabularnewline
96 & 0.419998 & 0.839996 & 0.580002 \tabularnewline
97 & 0.416823 & 0.833646 & 0.583177 \tabularnewline
98 & 0.369968 & 0.739936 & 0.630032 \tabularnewline
99 & 0.340693 & 0.681385 & 0.659307 \tabularnewline
100 & 0.327483 & 0.654967 & 0.672517 \tabularnewline
101 & 0.283974 & 0.567948 & 0.716026 \tabularnewline
102 & 0.891451 & 0.217099 & 0.108549 \tabularnewline
103 & 0.863865 & 0.272271 & 0.136135 \tabularnewline
104 & 0.831182 & 0.337637 & 0.168818 \tabularnewline
105 & 0.812464 & 0.375072 & 0.187536 \tabularnewline
106 & 0.818545 & 0.36291 & 0.181455 \tabularnewline
107 & 0.79958 & 0.40084 & 0.20042 \tabularnewline
108 & 0.794754 & 0.410492 & 0.205246 \tabularnewline
109 & 0.77166 & 0.45668 & 0.22834 \tabularnewline
110 & 0.747026 & 0.505948 & 0.252974 \tabularnewline
111 & 0.732073 & 0.535855 & 0.267927 \tabularnewline
112 & 0.750346 & 0.499308 & 0.249654 \tabularnewline
113 & 0.741876 & 0.516248 & 0.258124 \tabularnewline
114 & 0.684769 & 0.630462 & 0.315231 \tabularnewline
115 & 0.669064 & 0.661873 & 0.330936 \tabularnewline
116 & 0.65674 & 0.686521 & 0.34326 \tabularnewline
117 & 0.605963 & 0.788074 & 0.394037 \tabularnewline
118 & 0.585325 & 0.829349 & 0.414675 \tabularnewline
119 & 0.562898 & 0.874205 & 0.437102 \tabularnewline
120 & 0.501824 & 0.996352 & 0.498176 \tabularnewline
121 & 0.496995 & 0.993989 & 0.503005 \tabularnewline
122 & 0.490294 & 0.980588 & 0.509706 \tabularnewline
123 & 0.518671 & 0.962657 & 0.481329 \tabularnewline
124 & 0.503277 & 0.993446 & 0.496723 \tabularnewline
125 & 0.64326 & 0.71348 & 0.35674 \tabularnewline
126 & 0.986231 & 0.0275382 & 0.0137691 \tabularnewline
127 & 0.973882 & 0.052236 & 0.026118 \tabularnewline
128 & 0.971921 & 0.0561587 & 0.0280794 \tabularnewline
129 & 0.944594 & 0.110811 & 0.0554056 \tabularnewline
130 & 0.937557 & 0.124887 & 0.0624433 \tabularnewline
131 & 0.95103 & 0.09794 & 0.04897 \tabularnewline
132 & 0.900319 & 0.199362 & 0.0996808 \tabularnewline
133 & 0.785863 & 0.428273 & 0.214137 \tabularnewline
134 & 0.588175 & 0.823649 & 0.411825 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271031&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]9[/C][C]0.680254[/C][C]0.639492[/C][C]0.319746[/C][/ROW]
[ROW][C]10[/C][C]0.530245[/C][C]0.939511[/C][C]0.469755[/C][/ROW]
[ROW][C]11[/C][C]0.501837[/C][C]0.996327[/C][C]0.498163[/C][/ROW]
[ROW][C]12[/C][C]0.646262[/C][C]0.707476[/C][C]0.353738[/C][/ROW]
[ROW][C]13[/C][C]0.538576[/C][C]0.922849[/C][C]0.461424[/C][/ROW]
[ROW][C]14[/C][C]0.429947[/C][C]0.859894[/C][C]0.570053[/C][/ROW]
[ROW][C]15[/C][C]0.35695[/C][C]0.713899[/C][C]0.64305[/C][/ROW]
[ROW][C]16[/C][C]0.26946[/C][C]0.53892[/C][C]0.73054[/C][/ROW]
[ROW][C]17[/C][C]0.201501[/C][C]0.403001[/C][C]0.798499[/C][/ROW]
[ROW][C]18[/C][C]0.300229[/C][C]0.600458[/C][C]0.699771[/C][/ROW]
[ROW][C]19[/C][C]0.273327[/C][C]0.546654[/C][C]0.726673[/C][/ROW]
[ROW][C]20[/C][C]0.208203[/C][C]0.416406[/C][C]0.791797[/C][/ROW]
[ROW][C]21[/C][C]0.15312[/C][C]0.306241[/C][C]0.84688[/C][/ROW]
[ROW][C]22[/C][C]0.126986[/C][C]0.253971[/C][C]0.873014[/C][/ROW]
[ROW][C]23[/C][C]0.0924748[/C][C]0.18495[/C][C]0.907525[/C][/ROW]
[ROW][C]24[/C][C]0.0659504[/C][C]0.131901[/C][C]0.93405[/C][/ROW]
[ROW][C]25[/C][C]0.062898[/C][C]0.125796[/C][C]0.937102[/C][/ROW]
[ROW][C]26[/C][C]0.0818946[/C][C]0.163789[/C][C]0.918105[/C][/ROW]
[ROW][C]27[/C][C]0.134056[/C][C]0.268112[/C][C]0.865944[/C][/ROW]
[ROW][C]28[/C][C]0.134749[/C][C]0.269499[/C][C]0.865251[/C][/ROW]
[ROW][C]29[/C][C]0.117123[/C][C]0.234245[/C][C]0.882877[/C][/ROW]
[ROW][C]30[/C][C]0.246747[/C][C]0.493495[/C][C]0.753253[/C][/ROW]
[ROW][C]31[/C][C]0.216202[/C][C]0.432403[/C][C]0.783798[/C][/ROW]
[ROW][C]32[/C][C]0.176601[/C][C]0.353203[/C][C]0.823399[/C][/ROW]
[ROW][C]33[/C][C]0.138518[/C][C]0.277037[/C][C]0.861482[/C][/ROW]
[ROW][C]34[/C][C]0.136578[/C][C]0.273157[/C][C]0.863422[/C][/ROW]
[ROW][C]35[/C][C]0.106959[/C][C]0.213918[/C][C]0.893041[/C][/ROW]
[ROW][C]36[/C][C]0.279988[/C][C]0.559976[/C][C]0.720012[/C][/ROW]
[ROW][C]37[/C][C]0.233169[/C][C]0.466338[/C][C]0.766831[/C][/ROW]
[ROW][C]38[/C][C]0.195685[/C][C]0.39137[/C][C]0.804315[/C][/ROW]
[ROW][C]39[/C][C]0.16817[/C][C]0.33634[/C][C]0.83183[/C][/ROW]
[ROW][C]40[/C][C]0.134135[/C][C]0.268269[/C][C]0.865865[/C][/ROW]
[ROW][C]41[/C][C]0.111355[/C][C]0.222711[/C][C]0.888645[/C][/ROW]
[ROW][C]42[/C][C]0.121771[/C][C]0.243541[/C][C]0.878229[/C][/ROW]
[ROW][C]43[/C][C]0.0989973[/C][C]0.197995[/C][C]0.901003[/C][/ROW]
[ROW][C]44[/C][C]0.167896[/C][C]0.335792[/C][C]0.832104[/C][/ROW]
[ROW][C]45[/C][C]0.140155[/C][C]0.280309[/C][C]0.859845[/C][/ROW]
[ROW][C]46[/C][C]0.24297[/C][C]0.48594[/C][C]0.75703[/C][/ROW]
[ROW][C]47[/C][C]0.251484[/C][C]0.502968[/C][C]0.748516[/C][/ROW]
[ROW][C]48[/C][C]0.278855[/C][C]0.55771[/C][C]0.721145[/C][/ROW]
[ROW][C]49[/C][C]0.336714[/C][C]0.673428[/C][C]0.663286[/C][/ROW]
[ROW][C]50[/C][C]0.457703[/C][C]0.915406[/C][C]0.542297[/C][/ROW]
[ROW][C]51[/C][C]0.433217[/C][C]0.866434[/C][C]0.566783[/C][/ROW]
[ROW][C]52[/C][C]0.406572[/C][C]0.813143[/C][C]0.593428[/C][/ROW]
[ROW][C]53[/C][C]0.364125[/C][C]0.728251[/C][C]0.635875[/C][/ROW]
[ROW][C]54[/C][C]0.398589[/C][C]0.797177[/C][C]0.601411[/C][/ROW]
[ROW][C]55[/C][C]0.363739[/C][C]0.727478[/C][C]0.636261[/C][/ROW]
[ROW][C]56[/C][C]0.322779[/C][C]0.645559[/C][C]0.677221[/C][/ROW]
[ROW][C]57[/C][C]0.291075[/C][C]0.58215[/C][C]0.708925[/C][/ROW]
[ROW][C]58[/C][C]0.259205[/C][C]0.518411[/C][C]0.740795[/C][/ROW]
[ROW][C]59[/C][C]0.26554[/C][C]0.531081[/C][C]0.73446[/C][/ROW]
[ROW][C]60[/C][C]0.351442[/C][C]0.702884[/C][C]0.648558[/C][/ROW]
[ROW][C]61[/C][C]0.446706[/C][C]0.893413[/C][C]0.553294[/C][/ROW]
[ROW][C]62[/C][C]0.472128[/C][C]0.944255[/C][C]0.527872[/C][/ROW]
[ROW][C]63[/C][C]0.472031[/C][C]0.944062[/C][C]0.527969[/C][/ROW]
[ROW][C]64[/C][C]0.435722[/C][C]0.871445[/C][C]0.564278[/C][/ROW]
[ROW][C]65[/C][C]0.39517[/C][C]0.79034[/C][C]0.60483[/C][/ROW]
[ROW][C]66[/C][C]0.395031[/C][C]0.790063[/C][C]0.604969[/C][/ROW]
[ROW][C]67[/C][C]0.453255[/C][C]0.90651[/C][C]0.546745[/C][/ROW]
[ROW][C]68[/C][C]0.47929[/C][C]0.95858[/C][C]0.52071[/C][/ROW]
[ROW][C]69[/C][C]0.441308[/C][C]0.882615[/C][C]0.558692[/C][/ROW]
[ROW][C]70[/C][C]0.799377[/C][C]0.401245[/C][C]0.200623[/C][/ROW]
[ROW][C]71[/C][C]0.814114[/C][C]0.371772[/C][C]0.185886[/C][/ROW]
[ROW][C]72[/C][C]0.79376[/C][C]0.41248[/C][C]0.20624[/C][/ROW]
[ROW][C]73[/C][C]0.761848[/C][C]0.476303[/C][C]0.238152[/C][/ROW]
[ROW][C]74[/C][C]0.740838[/C][C]0.518324[/C][C]0.259162[/C][/ROW]
[ROW][C]75[/C][C]0.701952[/C][C]0.596095[/C][C]0.298048[/C][/ROW]
[ROW][C]76[/C][C]0.664504[/C][C]0.670991[/C][C]0.335496[/C][/ROW]
[ROW][C]77[/C][C]0.636911[/C][C]0.726178[/C][C]0.363089[/C][/ROW]
[ROW][C]78[/C][C]0.618623[/C][C]0.762754[/C][C]0.381377[/C][/ROW]
[ROW][C]79[/C][C]0.599248[/C][C]0.801505[/C][C]0.400752[/C][/ROW]
[ROW][C]80[/C][C]0.551186[/C][C]0.897629[/C][C]0.448814[/C][/ROW]
[ROW][C]81[/C][C]0.527545[/C][C]0.944909[/C][C]0.472455[/C][/ROW]
[ROW][C]82[/C][C]0.574202[/C][C]0.851595[/C][C]0.425798[/C][/ROW]
[ROW][C]83[/C][C]0.527472[/C][C]0.945056[/C][C]0.472528[/C][/ROW]
[ROW][C]84[/C][C]0.484652[/C][C]0.969303[/C][C]0.515348[/C][/ROW]
[ROW][C]85[/C][C]0.458524[/C][C]0.917048[/C][C]0.541476[/C][/ROW]
[ROW][C]86[/C][C]0.459831[/C][C]0.919662[/C][C]0.540169[/C][/ROW]
[ROW][C]87[/C][C]0.427903[/C][C]0.855805[/C][C]0.572097[/C][/ROW]
[ROW][C]88[/C][C]0.397662[/C][C]0.795323[/C][C]0.602338[/C][/ROW]
[ROW][C]89[/C][C]0.352088[/C][C]0.704176[/C][C]0.647912[/C][/ROW]
[ROW][C]90[/C][C]0.309572[/C][C]0.619143[/C][C]0.690428[/C][/ROW]
[ROW][C]91[/C][C]0.269581[/C][C]0.539161[/C][C]0.730419[/C][/ROW]
[ROW][C]92[/C][C]0.229038[/C][C]0.458075[/C][C]0.770962[/C][/ROW]
[ROW][C]93[/C][C]0.246095[/C][C]0.492191[/C][C]0.753905[/C][/ROW]
[ROW][C]94[/C][C]0.232688[/C][C]0.465377[/C][C]0.767312[/C][/ROW]
[ROW][C]95[/C][C]0.452267[/C][C]0.904533[/C][C]0.547733[/C][/ROW]
[ROW][C]96[/C][C]0.419998[/C][C]0.839996[/C][C]0.580002[/C][/ROW]
[ROW][C]97[/C][C]0.416823[/C][C]0.833646[/C][C]0.583177[/C][/ROW]
[ROW][C]98[/C][C]0.369968[/C][C]0.739936[/C][C]0.630032[/C][/ROW]
[ROW][C]99[/C][C]0.340693[/C][C]0.681385[/C][C]0.659307[/C][/ROW]
[ROW][C]100[/C][C]0.327483[/C][C]0.654967[/C][C]0.672517[/C][/ROW]
[ROW][C]101[/C][C]0.283974[/C][C]0.567948[/C][C]0.716026[/C][/ROW]
[ROW][C]102[/C][C]0.891451[/C][C]0.217099[/C][C]0.108549[/C][/ROW]
[ROW][C]103[/C][C]0.863865[/C][C]0.272271[/C][C]0.136135[/C][/ROW]
[ROW][C]104[/C][C]0.831182[/C][C]0.337637[/C][C]0.168818[/C][/ROW]
[ROW][C]105[/C][C]0.812464[/C][C]0.375072[/C][C]0.187536[/C][/ROW]
[ROW][C]106[/C][C]0.818545[/C][C]0.36291[/C][C]0.181455[/C][/ROW]
[ROW][C]107[/C][C]0.79958[/C][C]0.40084[/C][C]0.20042[/C][/ROW]
[ROW][C]108[/C][C]0.794754[/C][C]0.410492[/C][C]0.205246[/C][/ROW]
[ROW][C]109[/C][C]0.77166[/C][C]0.45668[/C][C]0.22834[/C][/ROW]
[ROW][C]110[/C][C]0.747026[/C][C]0.505948[/C][C]0.252974[/C][/ROW]
[ROW][C]111[/C][C]0.732073[/C][C]0.535855[/C][C]0.267927[/C][/ROW]
[ROW][C]112[/C][C]0.750346[/C][C]0.499308[/C][C]0.249654[/C][/ROW]
[ROW][C]113[/C][C]0.741876[/C][C]0.516248[/C][C]0.258124[/C][/ROW]
[ROW][C]114[/C][C]0.684769[/C][C]0.630462[/C][C]0.315231[/C][/ROW]
[ROW][C]115[/C][C]0.669064[/C][C]0.661873[/C][C]0.330936[/C][/ROW]
[ROW][C]116[/C][C]0.65674[/C][C]0.686521[/C][C]0.34326[/C][/ROW]
[ROW][C]117[/C][C]0.605963[/C][C]0.788074[/C][C]0.394037[/C][/ROW]
[ROW][C]118[/C][C]0.585325[/C][C]0.829349[/C][C]0.414675[/C][/ROW]
[ROW][C]119[/C][C]0.562898[/C][C]0.874205[/C][C]0.437102[/C][/ROW]
[ROW][C]120[/C][C]0.501824[/C][C]0.996352[/C][C]0.498176[/C][/ROW]
[ROW][C]121[/C][C]0.496995[/C][C]0.993989[/C][C]0.503005[/C][/ROW]
[ROW][C]122[/C][C]0.490294[/C][C]0.980588[/C][C]0.509706[/C][/ROW]
[ROW][C]123[/C][C]0.518671[/C][C]0.962657[/C][C]0.481329[/C][/ROW]
[ROW][C]124[/C][C]0.503277[/C][C]0.993446[/C][C]0.496723[/C][/ROW]
[ROW][C]125[/C][C]0.64326[/C][C]0.71348[/C][C]0.35674[/C][/ROW]
[ROW][C]126[/C][C]0.986231[/C][C]0.0275382[/C][C]0.0137691[/C][/ROW]
[ROW][C]127[/C][C]0.973882[/C][C]0.052236[/C][C]0.026118[/C][/ROW]
[ROW][C]128[/C][C]0.971921[/C][C]0.0561587[/C][C]0.0280794[/C][/ROW]
[ROW][C]129[/C][C]0.944594[/C][C]0.110811[/C][C]0.0554056[/C][/ROW]
[ROW][C]130[/C][C]0.937557[/C][C]0.124887[/C][C]0.0624433[/C][/ROW]
[ROW][C]131[/C][C]0.95103[/C][C]0.09794[/C][C]0.04897[/C][/ROW]
[ROW][C]132[/C][C]0.900319[/C][C]0.199362[/C][C]0.0996808[/C][/ROW]
[ROW][C]133[/C][C]0.785863[/C][C]0.428273[/C][C]0.214137[/C][/ROW]
[ROW][C]134[/C][C]0.588175[/C][C]0.823649[/C][C]0.411825[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271031&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271031&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.6802540.6394920.319746
100.5302450.9395110.469755
110.5018370.9963270.498163
120.6462620.7074760.353738
130.5385760.9228490.461424
140.4299470.8598940.570053
150.356950.7138990.64305
160.269460.538920.73054
170.2015010.4030010.798499
180.3002290.6004580.699771
190.2733270.5466540.726673
200.2082030.4164060.791797
210.153120.3062410.84688
220.1269860.2539710.873014
230.09247480.184950.907525
240.06595040.1319010.93405
250.0628980.1257960.937102
260.08189460.1637890.918105
270.1340560.2681120.865944
280.1347490.2694990.865251
290.1171230.2342450.882877
300.2467470.4934950.753253
310.2162020.4324030.783798
320.1766010.3532030.823399
330.1385180.2770370.861482
340.1365780.2731570.863422
350.1069590.2139180.893041
360.2799880.5599760.720012
370.2331690.4663380.766831
380.1956850.391370.804315
390.168170.336340.83183
400.1341350.2682690.865865
410.1113550.2227110.888645
420.1217710.2435410.878229
430.09899730.1979950.901003
440.1678960.3357920.832104
450.1401550.2803090.859845
460.242970.485940.75703
470.2514840.5029680.748516
480.2788550.557710.721145
490.3367140.6734280.663286
500.4577030.9154060.542297
510.4332170.8664340.566783
520.4065720.8131430.593428
530.3641250.7282510.635875
540.3985890.7971770.601411
550.3637390.7274780.636261
560.3227790.6455590.677221
570.2910750.582150.708925
580.2592050.5184110.740795
590.265540.5310810.73446
600.3514420.7028840.648558
610.4467060.8934130.553294
620.4721280.9442550.527872
630.4720310.9440620.527969
640.4357220.8714450.564278
650.395170.790340.60483
660.3950310.7900630.604969
670.4532550.906510.546745
680.479290.958580.52071
690.4413080.8826150.558692
700.7993770.4012450.200623
710.8141140.3717720.185886
720.793760.412480.20624
730.7618480.4763030.238152
740.7408380.5183240.259162
750.7019520.5960950.298048
760.6645040.6709910.335496
770.6369110.7261780.363089
780.6186230.7627540.381377
790.5992480.8015050.400752
800.5511860.8976290.448814
810.5275450.9449090.472455
820.5742020.8515950.425798
830.5274720.9450560.472528
840.4846520.9693030.515348
850.4585240.9170480.541476
860.4598310.9196620.540169
870.4279030.8558050.572097
880.3976620.7953230.602338
890.3520880.7041760.647912
900.3095720.6191430.690428
910.2695810.5391610.730419
920.2290380.4580750.770962
930.2460950.4921910.753905
940.2326880.4653770.767312
950.4522670.9045330.547733
960.4199980.8399960.580002
970.4168230.8336460.583177
980.3699680.7399360.630032
990.3406930.6813850.659307
1000.3274830.6549670.672517
1010.2839740.5679480.716026
1020.8914510.2170990.108549
1030.8638650.2722710.136135
1040.8311820.3376370.168818
1050.8124640.3750720.187536
1060.8185450.362910.181455
1070.799580.400840.20042
1080.7947540.4104920.205246
1090.771660.456680.22834
1100.7470260.5059480.252974
1110.7320730.5358550.267927
1120.7503460.4993080.249654
1130.7418760.5162480.258124
1140.6847690.6304620.315231
1150.6690640.6618730.330936
1160.656740.6865210.34326
1170.6059630.7880740.394037
1180.5853250.8293490.414675
1190.5628980.8742050.437102
1200.5018240.9963520.498176
1210.4969950.9939890.503005
1220.4902940.9805880.509706
1230.5186710.9626570.481329
1240.5032770.9934460.496723
1250.643260.713480.35674
1260.9862310.02753820.0137691
1270.9738820.0522360.026118
1280.9719210.05615870.0280794
1290.9445940.1108110.0554056
1300.9375570.1248870.0624433
1310.951030.097940.04897
1320.9003190.1993620.0996808
1330.7858630.4282730.214137
1340.5881750.8236490.411825






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00793651OK
10% type I error level40.031746OK

\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 & 1 & 0.00793651 & OK \tabularnewline
10% type I error level & 4 & 0.031746 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271031&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]1[/C][C]0.00793651[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.031746[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271031&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271031&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00793651OK
10% type I error level40.031746OK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

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