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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 18 Dec 2014 15:15:39 +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/t1418915764n0k5s0xx0oq6erx.htm/, Retrieved Sun, 19 May 2024 18:06:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271060, Retrieved Sun, 19 May 2024 18:06:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact80

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 15:15:39] [ce2f801bda31f4b58163e4bbe4fada83] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12.9 8 12 12 18 68 1.8
12.2 18 20 8 31 39 2.1
12.8 12 14 11 39 32 2.2
7.4 24 25 13 46 62 2.3
6.7 16 15 11 31 33 2.1
12.6 19 20 10 67 52 2.7
14.8 16 21 7 35 62 2.1
13.3 15 15 10 52 77 2.4
11.1 28 28 15 77 76 2.9
8.2 21 11 12 37 41 2.2
11.4 18 22 12 32 48 2.1
6.4 22 22 10 36 63 2.2
10.6 19 27 10 38 30 2.2
12 22 24 14 69 78 2.7
6.3 25 23 6 21 19 1.9
11.9 16 21 14 54 66 2.5
9.3 19 20 11 36 35 2.2
10 26 25 12 23 45 1.9
6.4 24 16 15 34 21 2.1
13.8 20 24 13 112 25 3.5
10.8 19 21 11 35 44 2.1
13.8 19 22 12 47 69 2.3
11.7 23 25 7 47 54 2.3
10.9 18 23 11 37 74 2.2
9.9 21 22 12 20 61 1.9
11.5 20 25 13 22 41 1.9
8.3 15 23 9 23 46 1.9
11.7 19 19 11 32 39 2.1
9 19 21 12 30 34 2
9.7 7 19 15 92 51 3.2
10.8 20 25 12 43 42 2.3
10.3 20 16 6 55 31 2.5
10.4 19 24 5 16 39 1.8
9.3 20 18 11 71 49 2.8
11.8 18 28 6 43 53 2.3
5.9 14 15 12 29 31 2
11.4 17 17 10 56 39 2.5
13 17 18 6 46 54 2.3
10.8 8 26 12 19 49 1.8
11.3 22 22 6 59 46 2.6
11.8 20 19 12 30 55 2
12.7 22 26 8 7 50 1.6
10.9 14 12 12 19 30 1.8
13.3 21 20 14 48 45 2.4
10.1 20 24 12 23 35 1.9
14.3 18 22 14 33 41 2.1
9.3 24 23 11 34 73 2.1
12.5 19 19 10 48 17 2.4
7.6 16 24 7 18 40 1.8
15.9 16 21 12 43 64 2.3
9.2 16 16 7 33 37 2.1
11.1 22 23 12 71 65 2.8
13 21 20 10 26 100 2
14.5 15 19 10 67 28 2.7
12.3 15 18 12 80 56 2.9
11.4 14 21 12 29 29 2
13 16 17 10 43 59 2.3
13.2 26 24 11 29 61 2
7.7 18 22 12 32 51 2.1
4.35 17 14 9 23 12 1
12.7 6 5 11 16 45 1
18.1 22 25 12 33 37 4
17.85 20 21 12 32 37 4
17.1 17 9 12 52 68 4
19.1 20 15 12 75 72 4
16.1 23 23 10 72 143 4
13.35 18 21 15 15 9 2
18.4 13 9 10 29 55 4
14.7 22 24 15 13 17 1
10.6 20 16 10 40 37 3
12.6 20 20 15 19 27 3
13.6 16 18 15 121 58 3
14.1 16 21 13 36 21 3
14.5 15 21 12 23 19 3
16.15 19 21 12 85 78 4
14.75 19 20 8 41 35 3
14.8 24 24 9 46 48 3
12.45 9 15 15 18 27 2
12.65 22 24 12 35 43 2
17.35 15 18 12 17 30 3
8.6 22 24 15 4 25 1
18.4 22 24 11 28 69 4
16.1 24 15 12 44 72 3
17.75 21 20 14 38 13 4
15.25 25 26 12 57 61 4
17.65 26 26 12 23 43 4
16.35 21 23 12 36 51 4
17.65 14 13 11 22 67 4
13.6 28 16 12 40 36 3
14.35 21 22 12 31 44 3
14.75 16 21 12 11 45 4
18.25 16 11 12 38 34 4
9.9 25 23 8 24 36 4
16 21 18 8 37 72 3
18.25 22 19 12 37 39 4
16.85 9 15 12 22 43 4
18.95 24 21 11 43 80 4
15.6 22 25 12 31 40 3
17.1 10 12 10 31 61 4
15.4 21 19 11 21 29 4
15.4 20 21 11 21 29 4
13.35 17 19 13 32 54 3
19.1 7 18 7 26 43 4
7.6 14 23 8 32 20 1
19.1 23 23 11 33 61 4
14.75 18 27 8 30 57 4
19.25 17 6 14 67 54 4
13.6 20 22 9 22 36 4
12.75 19 23 13 33 16 4
9.85 19 20 13 24 40 1
15.25 23 23 11 28 27 4
11.9 20 27 9 41 61 3
16.35 19 24 12 31 69 4
12.4 16 12 12 33 34 3
18.15 21 24 13 21 34 4
17.75 20 24 11 52 34 4
12.35 20 19 11 29 13 3
15.6 19 28 9 11 12 4
19.3 19 23 12 26 51 4
17.1 20 19 15 7 19 4
18.4 22 23 14 13 81 3
19.05 19 20 12 20 42 4
18.55 23 18 9 52 22 4
19.1 16 20 9 28 85 4
12.85 18 21 13 39 25 4
9.5 23 25 15 9 22 2
4.5 20 18 11 19 19 1
13.6 23 28 10 60 45 4
11.7 13 9 11 19 45 2
13.35 26 26 14 14 51 3
17.6 13 12 12 -2 73 4
14.05 10 12 13 51 24 3
16.1 21 20 11 2 61 4
13.35 24 25 11 24 23 4
11.85 21 24 13 40 14 4
11.95 23 23 12 20 54 2
13.2 16 22 9 20 36 4
7.7 26 28 13 25 26 2
14.6 16 15 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 time7 seconds
R Server'George Udny Yule' @ yule.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 & 7 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.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=271060&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.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=271060&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271060&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'George Udny Yule' @ yule.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] = + 5.24205 -0.0743944AMS.I2[t] -0.0447161AMS.E2[t] + 0.134435CONFSOFTTOT[t] -0.0259279PRH[t] + 0.0349741CH[t] + 2.83228PR[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  5.24205 -0.0743944AMS.I2[t] -0.0447161AMS.E2[t] +  0.134435CONFSOFTTOT[t] -0.0259279PRH[t] +  0.0349741CH[t] +  2.83228PR[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271060&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  5.24205 -0.0743944AMS.I2[t] -0.0447161AMS.E2[t] +  0.134435CONFSOFTTOT[t] -0.0259279PRH[t] +  0.0349741CH[t] +  2.83228PR[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271060&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271060&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] = + 5.24205 -0.0743944AMS.I2[t] -0.0447161AMS.E2[t] + 0.134435CONFSOFTTOT[t] -0.0259279PRH[t] + 0.0349741CH[t] + 2.83228PR[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.242051.478613.5450.0005432920.000271646
AMS.I2-0.07439440.0523434-1.4210.1575940.0787972
AMS.E2-0.04471610.0478453-0.93460.3517020.175851
CONFSOFTTOT0.1344350.08487241.5840.1155960.0577978
PRH-0.02592790.00987989-2.6240.009706010.004853
CH0.03497410.009704163.6040.0004427530.000221376
PR2.832280.2065713.713.73918e-271.86959e-27

\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) & 5.24205 & 1.47861 & 3.545 & 0.000543292 & 0.000271646 \tabularnewline
AMS.I2 & -0.0743944 & 0.0523434 & -1.421 & 0.157594 & 0.0787972 \tabularnewline
AMS.E2 & -0.0447161 & 0.0478453 & -0.9346 & 0.351702 & 0.175851 \tabularnewline
CONFSOFTTOT & 0.134435 & 0.0848724 & 1.584 & 0.115596 & 0.0577978 \tabularnewline
PRH & -0.0259279 & 0.00987989 & -2.624 & 0.00970601 & 0.004853 \tabularnewline
CH & 0.0349741 & 0.00970416 & 3.604 & 0.000442753 & 0.000221376 \tabularnewline
PR & 2.83228 & 0.20657 & 13.71 & 3.73918e-27 & 1.86959e-27 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271060&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]5.24205[/C][C]1.47861[/C][C]3.545[/C][C]0.000543292[/C][C]0.000271646[/C][/ROW]
[ROW][C]AMS.I2[/C][C]-0.0743944[/C][C]0.0523434[/C][C]-1.421[/C][C]0.157594[/C][C]0.0787972[/C][/ROW]
[ROW][C]AMS.E2[/C][C]-0.0447161[/C][C]0.0478453[/C][C]-0.9346[/C][C]0.351702[/C][C]0.175851[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.134435[/C][C]0.0848724[/C][C]1.584[/C][C]0.115596[/C][C]0.0577978[/C][/ROW]
[ROW][C]PRH[/C][C]-0.0259279[/C][C]0.00987989[/C][C]-2.624[/C][C]0.00970601[/C][C]0.004853[/C][/ROW]
[ROW][C]CH[/C][C]0.0349741[/C][C]0.00970416[/C][C]3.604[/C][C]0.000442753[/C][C]0.000221376[/C][/ROW]
[ROW][C]PR[/C][C]2.83228[/C][C]0.20657[/C][C]13.71[/C][C]3.73918e-27[/C][C]1.86959e-27[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271060&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271060&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)5.242051.478613.5450.0005432920.000271646
AMS.I2-0.07439440.0523434-1.4210.1575940.0787972
AMS.E2-0.04471610.0478453-0.93460.3517020.175851
CONFSOFTTOT0.1344350.08487241.5840.1155960.0577978
PRH-0.02592790.00987989-2.6240.009706010.004853
CH0.03497410.009704163.6040.0004427530.000221376
PR2.832280.2065713.713.73918e-271.86959e-27






Multiple Linear Regression - Regression Statistics
Multiple R0.790246
R-squared0.624489
Adjusted R-squared0.607421
F-TEST (value)36.5869
F-TEST (DF numerator)6
F-TEST (DF denominator)132
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.19543
Sum Squared Residuals636.227

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.790246 \tabularnewline
R-squared & 0.624489 \tabularnewline
Adjusted R-squared & 0.607421 \tabularnewline
F-TEST (value) & 36.5869 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 132 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.19543 \tabularnewline
Sum Squared Residuals & 636.227 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271060&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.790246[/C][/ROW]
[ROW][C]R-squared[/C][C]0.624489[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.607421[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]36.5869[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]132[/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]2.19543[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]636.227[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271060&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271060&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 R0.790246
R-squared0.624489
Adjusted R-squared0.607421
F-TEST (value)36.5869
F-TEST (DF numerator)6
F-TEST (DF denominator)132
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.19543
Sum Squared Residuals636.227






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.73320.166835
212.210.59211.60787
312.811.54111.25892
47.411.5763-4.1763
56.711.158-4.45796
612.612.00720.592767
714.811.26253.53754
813.312.9420.358024
911.112.7987-1.69868
108.211.5067-3.30674
1111.411.32930.0707269
126.411.467-5.06695
1310.610.26060.339443
141213.0004-1.00039
156.38.66169-2.36169
1611.912.9837-1.08368
179.310.9347-1.63473
181010.1619-0.161941
196.410.5584-4.15835
2013.812.3121.48795
2110.810.9475-0.14748
2213.812.16691.63313
2311.710.53841.16164
2410.912.213-1.31304
259.911.3054-1.40543
2611.510.62880.871226
278.310.7014-2.40138
2811.710.93980.760174
29910.5786-1.57859
309.714.3498-4.64983
3110.811.1177-0.317742
3210.310.5842-0.284187
3310.49.47480.925201
349.312.2313-2.9313
3511.810.71051.08951
365.911.1399-5.23986
3711.411.5543-0.154257
381311.18921.81076
3910.811.4167-0.616704
4011.310.87120.428771
4111.811.32810.471921
4212.79.61713.0829
4310.910.9319-0.0318561
4413.311.79431.50569
4510.110.3033-0.203283
4614.311.32742.9726
479.311.5263-2.22625
4812.510.47082.0292
497.69.94997-2.34997
5015.912.36363.53639
519.210.6635-1.46354
5211.112.5529-1.45295
531312.61760.382357
5414.511.51012.98985
5512.313.0324-0.732402
5611.410.80160.598385
571312.09870.901262
5813.210.75952.44053
597.711.4342-3.7342
604.357.21687-2.86687
6112.710.04222.65784
6218.115.86822.23176
6317.8516.22181.62818
6417.117.5472-0.447239
6519.116.59932.50069
6616.118.3105-2.21048
6713.3510.57082.77915
6818.417.71760.682375
6914.77.638497.06151
7010.613.1368-2.53683
7112.613.8249-1.22488
7213.612.65140.948555
7314.113.15830.941744
7414.513.36531.13467
7516.1516.356-0.205978
7614.7512.66762.08239
7714.812.57622.22376
7812.4512.06040.389554
7912.6510.40642.24362
8017.3514.03983.31024
818.68.151630.448368
8218.417.02731.37267
8316.114.27321.82678
8417.7515.46612.28393
8515.2515.8175-0.567452
8617.6515.99511.65493
8716.3516.4439-0.0939223
8817.6518.2-0.549985
8913.612.77560.824431
9014.3513.54120.808824
9114.7517.3437-2.59368
9218.2516.70611.54393
939.915.3951-5.49513
941614.0061.99399
9518.2516.10282.14722
9616.8517.7776-0.927582
9718.9517.00851.94151
9815.613.19272.40726
9917.117.9646-0.864649
10015.416.1078-0.707841
10115.416.0928-0.692803
10213.3514.4311-1.08115
10319.117.01632.08366
1047.66.949610.650389
10519.116.58822.51178
10614.7516.3159-1.56591
10719.2517.07172.1783
10813.615.9981-2.39811
10912.7515.5808-2.83084
1109.858.290861.55914
11115.2515.5287-0.278744
11211.913.324-1.42397
11316.3517.3072-0.957168
11412.413.9587-1.55871
11518.1516.3281.822
11617.7515.32982.42024
11712.3512.5829-0.232944
11815.615.250.349965
11919.316.8522.44801
12017.116.73320.366777
12118.415.45172.94832
12219.0516.82692.22306
12318.5514.68633.86368
12419.117.94331.15672
12512.8515.9039-3.05386
1269.510.6302-1.13024
1274.57.43222-2.93222
12813.614.9706-1.37057
12911.712.097-0.397032
13013.3513.9448-0.594801
13117.619.2856-1.68564
13214.0513.72310.326928
13316.117.6749-1.57492
13413.3515.3287-1.97873
13511.8515.1359-3.28589
13611.9511.15030.799665
13713.216.3475-3.14754
1387.79.72909-2.02909
13914.613.5551.04497

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 12.7332 & 0.166835 \tabularnewline
2 & 12.2 & 10.5921 & 1.60787 \tabularnewline
3 & 12.8 & 11.5411 & 1.25892 \tabularnewline
4 & 7.4 & 11.5763 & -4.1763 \tabularnewline
5 & 6.7 & 11.158 & -4.45796 \tabularnewline
6 & 12.6 & 12.0072 & 0.592767 \tabularnewline
7 & 14.8 & 11.2625 & 3.53754 \tabularnewline
8 & 13.3 & 12.942 & 0.358024 \tabularnewline
9 & 11.1 & 12.7987 & -1.69868 \tabularnewline
10 & 8.2 & 11.5067 & -3.30674 \tabularnewline
11 & 11.4 & 11.3293 & 0.0707269 \tabularnewline
12 & 6.4 & 11.467 & -5.06695 \tabularnewline
13 & 10.6 & 10.2606 & 0.339443 \tabularnewline
14 & 12 & 13.0004 & -1.00039 \tabularnewline
15 & 6.3 & 8.66169 & -2.36169 \tabularnewline
16 & 11.9 & 12.9837 & -1.08368 \tabularnewline
17 & 9.3 & 10.9347 & -1.63473 \tabularnewline
18 & 10 & 10.1619 & -0.161941 \tabularnewline
19 & 6.4 & 10.5584 & -4.15835 \tabularnewline
20 & 13.8 & 12.312 & 1.48795 \tabularnewline
21 & 10.8 & 10.9475 & -0.14748 \tabularnewline
22 & 13.8 & 12.1669 & 1.63313 \tabularnewline
23 & 11.7 & 10.5384 & 1.16164 \tabularnewline
24 & 10.9 & 12.213 & -1.31304 \tabularnewline
25 & 9.9 & 11.3054 & -1.40543 \tabularnewline
26 & 11.5 & 10.6288 & 0.871226 \tabularnewline
27 & 8.3 & 10.7014 & -2.40138 \tabularnewline
28 & 11.7 & 10.9398 & 0.760174 \tabularnewline
29 & 9 & 10.5786 & -1.57859 \tabularnewline
30 & 9.7 & 14.3498 & -4.64983 \tabularnewline
31 & 10.8 & 11.1177 & -0.317742 \tabularnewline
32 & 10.3 & 10.5842 & -0.284187 \tabularnewline
33 & 10.4 & 9.4748 & 0.925201 \tabularnewline
34 & 9.3 & 12.2313 & -2.9313 \tabularnewline
35 & 11.8 & 10.7105 & 1.08951 \tabularnewline
36 & 5.9 & 11.1399 & -5.23986 \tabularnewline
37 & 11.4 & 11.5543 & -0.154257 \tabularnewline
38 & 13 & 11.1892 & 1.81076 \tabularnewline
39 & 10.8 & 11.4167 & -0.616704 \tabularnewline
40 & 11.3 & 10.8712 & 0.428771 \tabularnewline
41 & 11.8 & 11.3281 & 0.471921 \tabularnewline
42 & 12.7 & 9.6171 & 3.0829 \tabularnewline
43 & 10.9 & 10.9319 & -0.0318561 \tabularnewline
44 & 13.3 & 11.7943 & 1.50569 \tabularnewline
45 & 10.1 & 10.3033 & -0.203283 \tabularnewline
46 & 14.3 & 11.3274 & 2.9726 \tabularnewline
47 & 9.3 & 11.5263 & -2.22625 \tabularnewline
48 & 12.5 & 10.4708 & 2.0292 \tabularnewline
49 & 7.6 & 9.94997 & -2.34997 \tabularnewline
50 & 15.9 & 12.3636 & 3.53639 \tabularnewline
51 & 9.2 & 10.6635 & -1.46354 \tabularnewline
52 & 11.1 & 12.5529 & -1.45295 \tabularnewline
53 & 13 & 12.6176 & 0.382357 \tabularnewline
54 & 14.5 & 11.5101 & 2.98985 \tabularnewline
55 & 12.3 & 13.0324 & -0.732402 \tabularnewline
56 & 11.4 & 10.8016 & 0.598385 \tabularnewline
57 & 13 & 12.0987 & 0.901262 \tabularnewline
58 & 13.2 & 10.7595 & 2.44053 \tabularnewline
59 & 7.7 & 11.4342 & -3.7342 \tabularnewline
60 & 4.35 & 7.21687 & -2.86687 \tabularnewline
61 & 12.7 & 10.0422 & 2.65784 \tabularnewline
62 & 18.1 & 15.8682 & 2.23176 \tabularnewline
63 & 17.85 & 16.2218 & 1.62818 \tabularnewline
64 & 17.1 & 17.5472 & -0.447239 \tabularnewline
65 & 19.1 & 16.5993 & 2.50069 \tabularnewline
66 & 16.1 & 18.3105 & -2.21048 \tabularnewline
67 & 13.35 & 10.5708 & 2.77915 \tabularnewline
68 & 18.4 & 17.7176 & 0.682375 \tabularnewline
69 & 14.7 & 7.63849 & 7.06151 \tabularnewline
70 & 10.6 & 13.1368 & -2.53683 \tabularnewline
71 & 12.6 & 13.8249 & -1.22488 \tabularnewline
72 & 13.6 & 12.6514 & 0.948555 \tabularnewline
73 & 14.1 & 13.1583 & 0.941744 \tabularnewline
74 & 14.5 & 13.3653 & 1.13467 \tabularnewline
75 & 16.15 & 16.356 & -0.205978 \tabularnewline
76 & 14.75 & 12.6676 & 2.08239 \tabularnewline
77 & 14.8 & 12.5762 & 2.22376 \tabularnewline
78 & 12.45 & 12.0604 & 0.389554 \tabularnewline
79 & 12.65 & 10.4064 & 2.24362 \tabularnewline
80 & 17.35 & 14.0398 & 3.31024 \tabularnewline
81 & 8.6 & 8.15163 & 0.448368 \tabularnewline
82 & 18.4 & 17.0273 & 1.37267 \tabularnewline
83 & 16.1 & 14.2732 & 1.82678 \tabularnewline
84 & 17.75 & 15.4661 & 2.28393 \tabularnewline
85 & 15.25 & 15.8175 & -0.567452 \tabularnewline
86 & 17.65 & 15.9951 & 1.65493 \tabularnewline
87 & 16.35 & 16.4439 & -0.0939223 \tabularnewline
88 & 17.65 & 18.2 & -0.549985 \tabularnewline
89 & 13.6 & 12.7756 & 0.824431 \tabularnewline
90 & 14.35 & 13.5412 & 0.808824 \tabularnewline
91 & 14.75 & 17.3437 & -2.59368 \tabularnewline
92 & 18.25 & 16.7061 & 1.54393 \tabularnewline
93 & 9.9 & 15.3951 & -5.49513 \tabularnewline
94 & 16 & 14.006 & 1.99399 \tabularnewline
95 & 18.25 & 16.1028 & 2.14722 \tabularnewline
96 & 16.85 & 17.7776 & -0.927582 \tabularnewline
97 & 18.95 & 17.0085 & 1.94151 \tabularnewline
98 & 15.6 & 13.1927 & 2.40726 \tabularnewline
99 & 17.1 & 17.9646 & -0.864649 \tabularnewline
100 & 15.4 & 16.1078 & -0.707841 \tabularnewline
101 & 15.4 & 16.0928 & -0.692803 \tabularnewline
102 & 13.35 & 14.4311 & -1.08115 \tabularnewline
103 & 19.1 & 17.0163 & 2.08366 \tabularnewline
104 & 7.6 & 6.94961 & 0.650389 \tabularnewline
105 & 19.1 & 16.5882 & 2.51178 \tabularnewline
106 & 14.75 & 16.3159 & -1.56591 \tabularnewline
107 & 19.25 & 17.0717 & 2.1783 \tabularnewline
108 & 13.6 & 15.9981 & -2.39811 \tabularnewline
109 & 12.75 & 15.5808 & -2.83084 \tabularnewline
110 & 9.85 & 8.29086 & 1.55914 \tabularnewline
111 & 15.25 & 15.5287 & -0.278744 \tabularnewline
112 & 11.9 & 13.324 & -1.42397 \tabularnewline
113 & 16.35 & 17.3072 & -0.957168 \tabularnewline
114 & 12.4 & 13.9587 & -1.55871 \tabularnewline
115 & 18.15 & 16.328 & 1.822 \tabularnewline
116 & 17.75 & 15.3298 & 2.42024 \tabularnewline
117 & 12.35 & 12.5829 & -0.232944 \tabularnewline
118 & 15.6 & 15.25 & 0.349965 \tabularnewline
119 & 19.3 & 16.852 & 2.44801 \tabularnewline
120 & 17.1 & 16.7332 & 0.366777 \tabularnewline
121 & 18.4 & 15.4517 & 2.94832 \tabularnewline
122 & 19.05 & 16.8269 & 2.22306 \tabularnewline
123 & 18.55 & 14.6863 & 3.86368 \tabularnewline
124 & 19.1 & 17.9433 & 1.15672 \tabularnewline
125 & 12.85 & 15.9039 & -3.05386 \tabularnewline
126 & 9.5 & 10.6302 & -1.13024 \tabularnewline
127 & 4.5 & 7.43222 & -2.93222 \tabularnewline
128 & 13.6 & 14.9706 & -1.37057 \tabularnewline
129 & 11.7 & 12.097 & -0.397032 \tabularnewline
130 & 13.35 & 13.9448 & -0.594801 \tabularnewline
131 & 17.6 & 19.2856 & -1.68564 \tabularnewline
132 & 14.05 & 13.7231 & 0.326928 \tabularnewline
133 & 16.1 & 17.6749 & -1.57492 \tabularnewline
134 & 13.35 & 15.3287 & -1.97873 \tabularnewline
135 & 11.85 & 15.1359 & -3.28589 \tabularnewline
136 & 11.95 & 11.1503 & 0.799665 \tabularnewline
137 & 13.2 & 16.3475 & -3.14754 \tabularnewline
138 & 7.7 & 9.72909 & -2.02909 \tabularnewline
139 & 14.6 & 13.555 & 1.04497 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271060&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]12.7332[/C][C]0.166835[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]10.5921[/C][C]1.60787[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]11.5411[/C][C]1.25892[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]11.5763[/C][C]-4.1763[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]11.158[/C][C]-4.45796[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]12.0072[/C][C]0.592767[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]11.2625[/C][C]3.53754[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]12.942[/C][C]0.358024[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]12.7987[/C][C]-1.69868[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]11.5067[/C][C]-3.30674[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]11.3293[/C][C]0.0707269[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]11.467[/C][C]-5.06695[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]10.2606[/C][C]0.339443[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]13.0004[/C][C]-1.00039[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]8.66169[/C][C]-2.36169[/C][/ROW]
[ROW][C]16[/C][C]11.9[/C][C]12.9837[/C][C]-1.08368[/C][/ROW]
[ROW][C]17[/C][C]9.3[/C][C]10.9347[/C][C]-1.63473[/C][/ROW]
[ROW][C]18[/C][C]10[/C][C]10.1619[/C][C]-0.161941[/C][/ROW]
[ROW][C]19[/C][C]6.4[/C][C]10.5584[/C][C]-4.15835[/C][/ROW]
[ROW][C]20[/C][C]13.8[/C][C]12.312[/C][C]1.48795[/C][/ROW]
[ROW][C]21[/C][C]10.8[/C][C]10.9475[/C][C]-0.14748[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]12.1669[/C][C]1.63313[/C][/ROW]
[ROW][C]23[/C][C]11.7[/C][C]10.5384[/C][C]1.16164[/C][/ROW]
[ROW][C]24[/C][C]10.9[/C][C]12.213[/C][C]-1.31304[/C][/ROW]
[ROW][C]25[/C][C]9.9[/C][C]11.3054[/C][C]-1.40543[/C][/ROW]
[ROW][C]26[/C][C]11.5[/C][C]10.6288[/C][C]0.871226[/C][/ROW]
[ROW][C]27[/C][C]8.3[/C][C]10.7014[/C][C]-2.40138[/C][/ROW]
[ROW][C]28[/C][C]11.7[/C][C]10.9398[/C][C]0.760174[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]10.5786[/C][C]-1.57859[/C][/ROW]
[ROW][C]30[/C][C]9.7[/C][C]14.3498[/C][C]-4.64983[/C][/ROW]
[ROW][C]31[/C][C]10.8[/C][C]11.1177[/C][C]-0.317742[/C][/ROW]
[ROW][C]32[/C][C]10.3[/C][C]10.5842[/C][C]-0.284187[/C][/ROW]
[ROW][C]33[/C][C]10.4[/C][C]9.4748[/C][C]0.925201[/C][/ROW]
[ROW][C]34[/C][C]9.3[/C][C]12.2313[/C][C]-2.9313[/C][/ROW]
[ROW][C]35[/C][C]11.8[/C][C]10.7105[/C][C]1.08951[/C][/ROW]
[ROW][C]36[/C][C]5.9[/C][C]11.1399[/C][C]-5.23986[/C][/ROW]
[ROW][C]37[/C][C]11.4[/C][C]11.5543[/C][C]-0.154257[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]11.1892[/C][C]1.81076[/C][/ROW]
[ROW][C]39[/C][C]10.8[/C][C]11.4167[/C][C]-0.616704[/C][/ROW]
[ROW][C]40[/C][C]11.3[/C][C]10.8712[/C][C]0.428771[/C][/ROW]
[ROW][C]41[/C][C]11.8[/C][C]11.3281[/C][C]0.471921[/C][/ROW]
[ROW][C]42[/C][C]12.7[/C][C]9.6171[/C][C]3.0829[/C][/ROW]
[ROW][C]43[/C][C]10.9[/C][C]10.9319[/C][C]-0.0318561[/C][/ROW]
[ROW][C]44[/C][C]13.3[/C][C]11.7943[/C][C]1.50569[/C][/ROW]
[ROW][C]45[/C][C]10.1[/C][C]10.3033[/C][C]-0.203283[/C][/ROW]
[ROW][C]46[/C][C]14.3[/C][C]11.3274[/C][C]2.9726[/C][/ROW]
[ROW][C]47[/C][C]9.3[/C][C]11.5263[/C][C]-2.22625[/C][/ROW]
[ROW][C]48[/C][C]12.5[/C][C]10.4708[/C][C]2.0292[/C][/ROW]
[ROW][C]49[/C][C]7.6[/C][C]9.94997[/C][C]-2.34997[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]12.3636[/C][C]3.53639[/C][/ROW]
[ROW][C]51[/C][C]9.2[/C][C]10.6635[/C][C]-1.46354[/C][/ROW]
[ROW][C]52[/C][C]11.1[/C][C]12.5529[/C][C]-1.45295[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]12.6176[/C][C]0.382357[/C][/ROW]
[ROW][C]54[/C][C]14.5[/C][C]11.5101[/C][C]2.98985[/C][/ROW]
[ROW][C]55[/C][C]12.3[/C][C]13.0324[/C][C]-0.732402[/C][/ROW]
[ROW][C]56[/C][C]11.4[/C][C]10.8016[/C][C]0.598385[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]12.0987[/C][C]0.901262[/C][/ROW]
[ROW][C]58[/C][C]13.2[/C][C]10.7595[/C][C]2.44053[/C][/ROW]
[ROW][C]59[/C][C]7.7[/C][C]11.4342[/C][C]-3.7342[/C][/ROW]
[ROW][C]60[/C][C]4.35[/C][C]7.21687[/C][C]-2.86687[/C][/ROW]
[ROW][C]61[/C][C]12.7[/C][C]10.0422[/C][C]2.65784[/C][/ROW]
[ROW][C]62[/C][C]18.1[/C][C]15.8682[/C][C]2.23176[/C][/ROW]
[ROW][C]63[/C][C]17.85[/C][C]16.2218[/C][C]1.62818[/C][/ROW]
[ROW][C]64[/C][C]17.1[/C][C]17.5472[/C][C]-0.447239[/C][/ROW]
[ROW][C]65[/C][C]19.1[/C][C]16.5993[/C][C]2.50069[/C][/ROW]
[ROW][C]66[/C][C]16.1[/C][C]18.3105[/C][C]-2.21048[/C][/ROW]
[ROW][C]67[/C][C]13.35[/C][C]10.5708[/C][C]2.77915[/C][/ROW]
[ROW][C]68[/C][C]18.4[/C][C]17.7176[/C][C]0.682375[/C][/ROW]
[ROW][C]69[/C][C]14.7[/C][C]7.63849[/C][C]7.06151[/C][/ROW]
[ROW][C]70[/C][C]10.6[/C][C]13.1368[/C][C]-2.53683[/C][/ROW]
[ROW][C]71[/C][C]12.6[/C][C]13.8249[/C][C]-1.22488[/C][/ROW]
[ROW][C]72[/C][C]13.6[/C][C]12.6514[/C][C]0.948555[/C][/ROW]
[ROW][C]73[/C][C]14.1[/C][C]13.1583[/C][C]0.941744[/C][/ROW]
[ROW][C]74[/C][C]14.5[/C][C]13.3653[/C][C]1.13467[/C][/ROW]
[ROW][C]75[/C][C]16.15[/C][C]16.356[/C][C]-0.205978[/C][/ROW]
[ROW][C]76[/C][C]14.75[/C][C]12.6676[/C][C]2.08239[/C][/ROW]
[ROW][C]77[/C][C]14.8[/C][C]12.5762[/C][C]2.22376[/C][/ROW]
[ROW][C]78[/C][C]12.45[/C][C]12.0604[/C][C]0.389554[/C][/ROW]
[ROW][C]79[/C][C]12.65[/C][C]10.4064[/C][C]2.24362[/C][/ROW]
[ROW][C]80[/C][C]17.35[/C][C]14.0398[/C][C]3.31024[/C][/ROW]
[ROW][C]81[/C][C]8.6[/C][C]8.15163[/C][C]0.448368[/C][/ROW]
[ROW][C]82[/C][C]18.4[/C][C]17.0273[/C][C]1.37267[/C][/ROW]
[ROW][C]83[/C][C]16.1[/C][C]14.2732[/C][C]1.82678[/C][/ROW]
[ROW][C]84[/C][C]17.75[/C][C]15.4661[/C][C]2.28393[/C][/ROW]
[ROW][C]85[/C][C]15.25[/C][C]15.8175[/C][C]-0.567452[/C][/ROW]
[ROW][C]86[/C][C]17.65[/C][C]15.9951[/C][C]1.65493[/C][/ROW]
[ROW][C]87[/C][C]16.35[/C][C]16.4439[/C][C]-0.0939223[/C][/ROW]
[ROW][C]88[/C][C]17.65[/C][C]18.2[/C][C]-0.549985[/C][/ROW]
[ROW][C]89[/C][C]13.6[/C][C]12.7756[/C][C]0.824431[/C][/ROW]
[ROW][C]90[/C][C]14.35[/C][C]13.5412[/C][C]0.808824[/C][/ROW]
[ROW][C]91[/C][C]14.75[/C][C]17.3437[/C][C]-2.59368[/C][/ROW]
[ROW][C]92[/C][C]18.25[/C][C]16.7061[/C][C]1.54393[/C][/ROW]
[ROW][C]93[/C][C]9.9[/C][C]15.3951[/C][C]-5.49513[/C][/ROW]
[ROW][C]94[/C][C]16[/C][C]14.006[/C][C]1.99399[/C][/ROW]
[ROW][C]95[/C][C]18.25[/C][C]16.1028[/C][C]2.14722[/C][/ROW]
[ROW][C]96[/C][C]16.85[/C][C]17.7776[/C][C]-0.927582[/C][/ROW]
[ROW][C]97[/C][C]18.95[/C][C]17.0085[/C][C]1.94151[/C][/ROW]
[ROW][C]98[/C][C]15.6[/C][C]13.1927[/C][C]2.40726[/C][/ROW]
[ROW][C]99[/C][C]17.1[/C][C]17.9646[/C][C]-0.864649[/C][/ROW]
[ROW][C]100[/C][C]15.4[/C][C]16.1078[/C][C]-0.707841[/C][/ROW]
[ROW][C]101[/C][C]15.4[/C][C]16.0928[/C][C]-0.692803[/C][/ROW]
[ROW][C]102[/C][C]13.35[/C][C]14.4311[/C][C]-1.08115[/C][/ROW]
[ROW][C]103[/C][C]19.1[/C][C]17.0163[/C][C]2.08366[/C][/ROW]
[ROW][C]104[/C][C]7.6[/C][C]6.94961[/C][C]0.650389[/C][/ROW]
[ROW][C]105[/C][C]19.1[/C][C]16.5882[/C][C]2.51178[/C][/ROW]
[ROW][C]106[/C][C]14.75[/C][C]16.3159[/C][C]-1.56591[/C][/ROW]
[ROW][C]107[/C][C]19.25[/C][C]17.0717[/C][C]2.1783[/C][/ROW]
[ROW][C]108[/C][C]13.6[/C][C]15.9981[/C][C]-2.39811[/C][/ROW]
[ROW][C]109[/C][C]12.75[/C][C]15.5808[/C][C]-2.83084[/C][/ROW]
[ROW][C]110[/C][C]9.85[/C][C]8.29086[/C][C]1.55914[/C][/ROW]
[ROW][C]111[/C][C]15.25[/C][C]15.5287[/C][C]-0.278744[/C][/ROW]
[ROW][C]112[/C][C]11.9[/C][C]13.324[/C][C]-1.42397[/C][/ROW]
[ROW][C]113[/C][C]16.35[/C][C]17.3072[/C][C]-0.957168[/C][/ROW]
[ROW][C]114[/C][C]12.4[/C][C]13.9587[/C][C]-1.55871[/C][/ROW]
[ROW][C]115[/C][C]18.15[/C][C]16.328[/C][C]1.822[/C][/ROW]
[ROW][C]116[/C][C]17.75[/C][C]15.3298[/C][C]2.42024[/C][/ROW]
[ROW][C]117[/C][C]12.35[/C][C]12.5829[/C][C]-0.232944[/C][/ROW]
[ROW][C]118[/C][C]15.6[/C][C]15.25[/C][C]0.349965[/C][/ROW]
[ROW][C]119[/C][C]19.3[/C][C]16.852[/C][C]2.44801[/C][/ROW]
[ROW][C]120[/C][C]17.1[/C][C]16.7332[/C][C]0.366777[/C][/ROW]
[ROW][C]121[/C][C]18.4[/C][C]15.4517[/C][C]2.94832[/C][/ROW]
[ROW][C]122[/C][C]19.05[/C][C]16.8269[/C][C]2.22306[/C][/ROW]
[ROW][C]123[/C][C]18.55[/C][C]14.6863[/C][C]3.86368[/C][/ROW]
[ROW][C]124[/C][C]19.1[/C][C]17.9433[/C][C]1.15672[/C][/ROW]
[ROW][C]125[/C][C]12.85[/C][C]15.9039[/C][C]-3.05386[/C][/ROW]
[ROW][C]126[/C][C]9.5[/C][C]10.6302[/C][C]-1.13024[/C][/ROW]
[ROW][C]127[/C][C]4.5[/C][C]7.43222[/C][C]-2.93222[/C][/ROW]
[ROW][C]128[/C][C]13.6[/C][C]14.9706[/C][C]-1.37057[/C][/ROW]
[ROW][C]129[/C][C]11.7[/C][C]12.097[/C][C]-0.397032[/C][/ROW]
[ROW][C]130[/C][C]13.35[/C][C]13.9448[/C][C]-0.594801[/C][/ROW]
[ROW][C]131[/C][C]17.6[/C][C]19.2856[/C][C]-1.68564[/C][/ROW]
[ROW][C]132[/C][C]14.05[/C][C]13.7231[/C][C]0.326928[/C][/ROW]
[ROW][C]133[/C][C]16.1[/C][C]17.6749[/C][C]-1.57492[/C][/ROW]
[ROW][C]134[/C][C]13.35[/C][C]15.3287[/C][C]-1.97873[/C][/ROW]
[ROW][C]135[/C][C]11.85[/C][C]15.1359[/C][C]-3.28589[/C][/ROW]
[ROW][C]136[/C][C]11.95[/C][C]11.1503[/C][C]0.799665[/C][/ROW]
[ROW][C]137[/C][C]13.2[/C][C]16.3475[/C][C]-3.14754[/C][/ROW]
[ROW][C]138[/C][C]7.7[/C][C]9.72909[/C][C]-2.02909[/C][/ROW]
[ROW][C]139[/C][C]14.6[/C][C]13.555[/C][C]1.04497[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271060&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271060&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
112.912.73320.166835
212.210.59211.60787
312.811.54111.25892
47.411.5763-4.1763
56.711.158-4.45796
612.612.00720.592767
714.811.26253.53754
813.312.9420.358024
911.112.7987-1.69868
108.211.5067-3.30674
1111.411.32930.0707269
126.411.467-5.06695
1310.610.26060.339443
141213.0004-1.00039
156.38.66169-2.36169
1611.912.9837-1.08368
179.310.9347-1.63473
181010.1619-0.161941
196.410.5584-4.15835
2013.812.3121.48795
2110.810.9475-0.14748
2213.812.16691.63313
2311.710.53841.16164
2410.912.213-1.31304
259.911.3054-1.40543
2611.510.62880.871226
278.310.7014-2.40138
2811.710.93980.760174
29910.5786-1.57859
309.714.3498-4.64983
3110.811.1177-0.317742
3210.310.5842-0.284187
3310.49.47480.925201
349.312.2313-2.9313
3511.810.71051.08951
365.911.1399-5.23986
3711.411.5543-0.154257
381311.18921.81076
3910.811.4167-0.616704
4011.310.87120.428771
4111.811.32810.471921
4212.79.61713.0829
4310.910.9319-0.0318561
4413.311.79431.50569
4510.110.3033-0.203283
4614.311.32742.9726
479.311.5263-2.22625
4812.510.47082.0292
497.69.94997-2.34997
5015.912.36363.53639
519.210.6635-1.46354
5211.112.5529-1.45295
531312.61760.382357
5414.511.51012.98985
5512.313.0324-0.732402
5611.410.80160.598385
571312.09870.901262
5813.210.75952.44053
597.711.4342-3.7342
604.357.21687-2.86687
6112.710.04222.65784
6218.115.86822.23176
6317.8516.22181.62818
6417.117.5472-0.447239
6519.116.59932.50069
6616.118.3105-2.21048
6713.3510.57082.77915
6818.417.71760.682375
6914.77.638497.06151
7010.613.1368-2.53683
7112.613.8249-1.22488
7213.612.65140.948555
7314.113.15830.941744
7414.513.36531.13467
7516.1516.356-0.205978
7614.7512.66762.08239
7714.812.57622.22376
7812.4512.06040.389554
7912.6510.40642.24362
8017.3514.03983.31024
818.68.151630.448368
8218.417.02731.37267
8316.114.27321.82678
8417.7515.46612.28393
8515.2515.8175-0.567452
8617.6515.99511.65493
8716.3516.4439-0.0939223
8817.6518.2-0.549985
8913.612.77560.824431
9014.3513.54120.808824
9114.7517.3437-2.59368
9218.2516.70611.54393
939.915.3951-5.49513
941614.0061.99399
9518.2516.10282.14722
9616.8517.7776-0.927582
9718.9517.00851.94151
9815.613.19272.40726
9917.117.9646-0.864649
10015.416.1078-0.707841
10115.416.0928-0.692803
10213.3514.4311-1.08115
10319.117.01632.08366
1047.66.949610.650389
10519.116.58822.51178
10614.7516.3159-1.56591
10719.2517.07172.1783
10813.615.9981-2.39811
10912.7515.5808-2.83084
1109.858.290861.55914
11115.2515.5287-0.278744
11211.913.324-1.42397
11316.3517.3072-0.957168
11412.413.9587-1.55871
11518.1516.3281.822
11617.7515.32982.42024
11712.3512.5829-0.232944
11815.615.250.349965
11919.316.8522.44801
12017.116.73320.366777
12118.415.45172.94832
12219.0516.82692.22306
12318.5514.68633.86368
12419.117.94331.15672
12512.8515.9039-3.05386
1269.510.6302-1.13024
1274.57.43222-2.93222
12813.614.9706-1.37057
12911.712.097-0.397032
13013.3513.9448-0.594801
13117.619.2856-1.68564
13214.0513.72310.326928
13316.117.6749-1.57492
13413.3515.3287-1.97873
13511.8515.1359-3.28589
13611.9511.15030.799665
13713.216.3475-3.14754
1387.79.72909-2.02909
13914.613.5551.04497






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.6836380.6327250.316362
110.5714430.8571150.428557
120.7293020.5413970.270698
130.6258630.7482740.374137
140.513270.9734590.48673
150.4144330.8288650.585567
160.318920.6378390.68108
170.2388820.4777640.761118
180.3528710.7057430.647129
190.3101880.6203750.689812
200.2415990.4831970.758401
210.1802990.3605990.819701
220.1475350.2950690.852465
230.108120.216240.89188
240.07648190.1529640.923518
250.06857870.1371570.931421
260.05903720.1180740.940963
270.1758770.3517540.824123
280.1850740.3701490.814926
290.1676030.3352060.832397
300.3811430.7622860.618857
310.3337470.6674950.666253
320.2773810.5547620.722619
330.2294210.4588430.770579
340.2231410.4462830.776859
350.182280.364560.81772
360.4088930.8177870.591107
370.3562070.7124140.643793
380.3144680.6289370.685532
390.2924840.5849680.707516
400.2450290.4900590.754971
410.2143460.4286920.785654
420.2394820.4789640.760518
430.2102060.4204110.789794
440.3238930.6477850.676107
450.277910.5558210.72209
460.4157320.8314640.584268
470.4198950.839790.580105
480.458980.917960.54102
490.526960.9460790.47304
500.6607930.6784150.339207
510.6302170.7395670.369783
520.6046340.7907310.395366
530.5634960.8730080.436504
540.6067020.7865960.393298
550.5710760.8578480.428924
560.5234780.9530450.476522
570.4904850.980970.509515
580.506360.9872810.49364
590.598240.8035190.40176
600.6858540.6282930.314146
610.719690.560620.28031
620.7298220.5403560.270178
630.6995970.6008060.300403
640.6645890.6708230.335411
650.656680.686640.34332
660.7143830.5712340.285617
670.7434510.5130980.256549
680.7045280.5909440.295472
690.9610150.07796970.0389849
700.9683270.06334530.0316727
710.9626540.0746930.0373465
720.9599550.08009040.0400452
730.9492210.1015570.0507786
740.9401760.1196470.0598236
750.9389960.1220080.0610038
760.9356080.1287840.0643918
770.9312220.1375560.0687778
780.9124620.1750760.0875382
790.9084450.183110.0915548
800.9398130.1203750.0601873
810.9259930.1480150.0740075
820.9095540.1808910.0904456
830.8985440.2029130.101456
840.8994590.2010810.100541
850.8928040.2143910.107196
860.8835520.2328950.116448
870.8585710.2828590.141429
880.8336270.3327460.166373
890.8003580.3992840.199642
900.7631610.4736770.236839
910.7793260.4413490.220674
920.751880.4962410.24812
930.926490.147020.07351
940.9121140.1757720.0878862
950.9055890.1888220.0944111
960.8820260.2359480.117974
970.8584310.2831380.141569
980.8693390.2613210.130661
990.8492860.3014270.150714
1000.8151230.3697540.184877
1010.7755620.4488750.224438
1020.7470780.5058440.252922
1030.7780720.4438550.221928
1040.7906530.4186950.209347
1050.776480.4470390.22352
1060.7396180.5207640.260382
1070.7033210.5933580.296679
1080.6933990.6132020.306601
1090.6981240.6037520.301876
1100.7149050.570190.285095
1110.6559090.6881820.344091
1120.6066980.7866050.393302
1130.5719480.8561040.428052
1140.5485980.9028040.451402
1150.5409130.9181730.459087
1160.5408210.9183580.459179
1170.4671120.9342240.532888
1180.7263670.5472660.273633
1190.822660.3546790.17734
1200.805420.389160.19458
1210.7921550.415690.207845
1220.9136970.1726060.0863031
1230.9737230.05255450.0262773
1240.9590690.08186120.0409306
1250.9571480.08570490.0428524
1260.9301310.1397380.069869
1270.9361810.1276380.0638191
1280.9824370.03512690.0175635
1290.9679960.06400810.0320041

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.683638 & 0.632725 & 0.316362 \tabularnewline
11 & 0.571443 & 0.857115 & 0.428557 \tabularnewline
12 & 0.729302 & 0.541397 & 0.270698 \tabularnewline
13 & 0.625863 & 0.748274 & 0.374137 \tabularnewline
14 & 0.51327 & 0.973459 & 0.48673 \tabularnewline
15 & 0.414433 & 0.828865 & 0.585567 \tabularnewline
16 & 0.31892 & 0.637839 & 0.68108 \tabularnewline
17 & 0.238882 & 0.477764 & 0.761118 \tabularnewline
18 & 0.352871 & 0.705743 & 0.647129 \tabularnewline
19 & 0.310188 & 0.620375 & 0.689812 \tabularnewline
20 & 0.241599 & 0.483197 & 0.758401 \tabularnewline
21 & 0.180299 & 0.360599 & 0.819701 \tabularnewline
22 & 0.147535 & 0.295069 & 0.852465 \tabularnewline
23 & 0.10812 & 0.21624 & 0.89188 \tabularnewline
24 & 0.0764819 & 0.152964 & 0.923518 \tabularnewline
25 & 0.0685787 & 0.137157 & 0.931421 \tabularnewline
26 & 0.0590372 & 0.118074 & 0.940963 \tabularnewline
27 & 0.175877 & 0.351754 & 0.824123 \tabularnewline
28 & 0.185074 & 0.370149 & 0.814926 \tabularnewline
29 & 0.167603 & 0.335206 & 0.832397 \tabularnewline
30 & 0.381143 & 0.762286 & 0.618857 \tabularnewline
31 & 0.333747 & 0.667495 & 0.666253 \tabularnewline
32 & 0.277381 & 0.554762 & 0.722619 \tabularnewline
33 & 0.229421 & 0.458843 & 0.770579 \tabularnewline
34 & 0.223141 & 0.446283 & 0.776859 \tabularnewline
35 & 0.18228 & 0.36456 & 0.81772 \tabularnewline
36 & 0.408893 & 0.817787 & 0.591107 \tabularnewline
37 & 0.356207 & 0.712414 & 0.643793 \tabularnewline
38 & 0.314468 & 0.628937 & 0.685532 \tabularnewline
39 & 0.292484 & 0.584968 & 0.707516 \tabularnewline
40 & 0.245029 & 0.490059 & 0.754971 \tabularnewline
41 & 0.214346 & 0.428692 & 0.785654 \tabularnewline
42 & 0.239482 & 0.478964 & 0.760518 \tabularnewline
43 & 0.210206 & 0.420411 & 0.789794 \tabularnewline
44 & 0.323893 & 0.647785 & 0.676107 \tabularnewline
45 & 0.27791 & 0.555821 & 0.72209 \tabularnewline
46 & 0.415732 & 0.831464 & 0.584268 \tabularnewline
47 & 0.419895 & 0.83979 & 0.580105 \tabularnewline
48 & 0.45898 & 0.91796 & 0.54102 \tabularnewline
49 & 0.52696 & 0.946079 & 0.47304 \tabularnewline
50 & 0.660793 & 0.678415 & 0.339207 \tabularnewline
51 & 0.630217 & 0.739567 & 0.369783 \tabularnewline
52 & 0.604634 & 0.790731 & 0.395366 \tabularnewline
53 & 0.563496 & 0.873008 & 0.436504 \tabularnewline
54 & 0.606702 & 0.786596 & 0.393298 \tabularnewline
55 & 0.571076 & 0.857848 & 0.428924 \tabularnewline
56 & 0.523478 & 0.953045 & 0.476522 \tabularnewline
57 & 0.490485 & 0.98097 & 0.509515 \tabularnewline
58 & 0.50636 & 0.987281 & 0.49364 \tabularnewline
59 & 0.59824 & 0.803519 & 0.40176 \tabularnewline
60 & 0.685854 & 0.628293 & 0.314146 \tabularnewline
61 & 0.71969 & 0.56062 & 0.28031 \tabularnewline
62 & 0.729822 & 0.540356 & 0.270178 \tabularnewline
63 & 0.699597 & 0.600806 & 0.300403 \tabularnewline
64 & 0.664589 & 0.670823 & 0.335411 \tabularnewline
65 & 0.65668 & 0.68664 & 0.34332 \tabularnewline
66 & 0.714383 & 0.571234 & 0.285617 \tabularnewline
67 & 0.743451 & 0.513098 & 0.256549 \tabularnewline
68 & 0.704528 & 0.590944 & 0.295472 \tabularnewline
69 & 0.961015 & 0.0779697 & 0.0389849 \tabularnewline
70 & 0.968327 & 0.0633453 & 0.0316727 \tabularnewline
71 & 0.962654 & 0.074693 & 0.0373465 \tabularnewline
72 & 0.959955 & 0.0800904 & 0.0400452 \tabularnewline
73 & 0.949221 & 0.101557 & 0.0507786 \tabularnewline
74 & 0.940176 & 0.119647 & 0.0598236 \tabularnewline
75 & 0.938996 & 0.122008 & 0.0610038 \tabularnewline
76 & 0.935608 & 0.128784 & 0.0643918 \tabularnewline
77 & 0.931222 & 0.137556 & 0.0687778 \tabularnewline
78 & 0.912462 & 0.175076 & 0.0875382 \tabularnewline
79 & 0.908445 & 0.18311 & 0.0915548 \tabularnewline
80 & 0.939813 & 0.120375 & 0.0601873 \tabularnewline
81 & 0.925993 & 0.148015 & 0.0740075 \tabularnewline
82 & 0.909554 & 0.180891 & 0.0904456 \tabularnewline
83 & 0.898544 & 0.202913 & 0.101456 \tabularnewline
84 & 0.899459 & 0.201081 & 0.100541 \tabularnewline
85 & 0.892804 & 0.214391 & 0.107196 \tabularnewline
86 & 0.883552 & 0.232895 & 0.116448 \tabularnewline
87 & 0.858571 & 0.282859 & 0.141429 \tabularnewline
88 & 0.833627 & 0.332746 & 0.166373 \tabularnewline
89 & 0.800358 & 0.399284 & 0.199642 \tabularnewline
90 & 0.763161 & 0.473677 & 0.236839 \tabularnewline
91 & 0.779326 & 0.441349 & 0.220674 \tabularnewline
92 & 0.75188 & 0.496241 & 0.24812 \tabularnewline
93 & 0.92649 & 0.14702 & 0.07351 \tabularnewline
94 & 0.912114 & 0.175772 & 0.0878862 \tabularnewline
95 & 0.905589 & 0.188822 & 0.0944111 \tabularnewline
96 & 0.882026 & 0.235948 & 0.117974 \tabularnewline
97 & 0.858431 & 0.283138 & 0.141569 \tabularnewline
98 & 0.869339 & 0.261321 & 0.130661 \tabularnewline
99 & 0.849286 & 0.301427 & 0.150714 \tabularnewline
100 & 0.815123 & 0.369754 & 0.184877 \tabularnewline
101 & 0.775562 & 0.448875 & 0.224438 \tabularnewline
102 & 0.747078 & 0.505844 & 0.252922 \tabularnewline
103 & 0.778072 & 0.443855 & 0.221928 \tabularnewline
104 & 0.790653 & 0.418695 & 0.209347 \tabularnewline
105 & 0.77648 & 0.447039 & 0.22352 \tabularnewline
106 & 0.739618 & 0.520764 & 0.260382 \tabularnewline
107 & 0.703321 & 0.593358 & 0.296679 \tabularnewline
108 & 0.693399 & 0.613202 & 0.306601 \tabularnewline
109 & 0.698124 & 0.603752 & 0.301876 \tabularnewline
110 & 0.714905 & 0.57019 & 0.285095 \tabularnewline
111 & 0.655909 & 0.688182 & 0.344091 \tabularnewline
112 & 0.606698 & 0.786605 & 0.393302 \tabularnewline
113 & 0.571948 & 0.856104 & 0.428052 \tabularnewline
114 & 0.548598 & 0.902804 & 0.451402 \tabularnewline
115 & 0.540913 & 0.918173 & 0.459087 \tabularnewline
116 & 0.540821 & 0.918358 & 0.459179 \tabularnewline
117 & 0.467112 & 0.934224 & 0.532888 \tabularnewline
118 & 0.726367 & 0.547266 & 0.273633 \tabularnewline
119 & 0.82266 & 0.354679 & 0.17734 \tabularnewline
120 & 0.80542 & 0.38916 & 0.19458 \tabularnewline
121 & 0.792155 & 0.41569 & 0.207845 \tabularnewline
122 & 0.913697 & 0.172606 & 0.0863031 \tabularnewline
123 & 0.973723 & 0.0525545 & 0.0262773 \tabularnewline
124 & 0.959069 & 0.0818612 & 0.0409306 \tabularnewline
125 & 0.957148 & 0.0857049 & 0.0428524 \tabularnewline
126 & 0.930131 & 0.139738 & 0.069869 \tabularnewline
127 & 0.936181 & 0.127638 & 0.0638191 \tabularnewline
128 & 0.982437 & 0.0351269 & 0.0175635 \tabularnewline
129 & 0.967996 & 0.0640081 & 0.0320041 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271060&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]10[/C][C]0.683638[/C][C]0.632725[/C][C]0.316362[/C][/ROW]
[ROW][C]11[/C][C]0.571443[/C][C]0.857115[/C][C]0.428557[/C][/ROW]
[ROW][C]12[/C][C]0.729302[/C][C]0.541397[/C][C]0.270698[/C][/ROW]
[ROW][C]13[/C][C]0.625863[/C][C]0.748274[/C][C]0.374137[/C][/ROW]
[ROW][C]14[/C][C]0.51327[/C][C]0.973459[/C][C]0.48673[/C][/ROW]
[ROW][C]15[/C][C]0.414433[/C][C]0.828865[/C][C]0.585567[/C][/ROW]
[ROW][C]16[/C][C]0.31892[/C][C]0.637839[/C][C]0.68108[/C][/ROW]
[ROW][C]17[/C][C]0.238882[/C][C]0.477764[/C][C]0.761118[/C][/ROW]
[ROW][C]18[/C][C]0.352871[/C][C]0.705743[/C][C]0.647129[/C][/ROW]
[ROW][C]19[/C][C]0.310188[/C][C]0.620375[/C][C]0.689812[/C][/ROW]
[ROW][C]20[/C][C]0.241599[/C][C]0.483197[/C][C]0.758401[/C][/ROW]
[ROW][C]21[/C][C]0.180299[/C][C]0.360599[/C][C]0.819701[/C][/ROW]
[ROW][C]22[/C][C]0.147535[/C][C]0.295069[/C][C]0.852465[/C][/ROW]
[ROW][C]23[/C][C]0.10812[/C][C]0.21624[/C][C]0.89188[/C][/ROW]
[ROW][C]24[/C][C]0.0764819[/C][C]0.152964[/C][C]0.923518[/C][/ROW]
[ROW][C]25[/C][C]0.0685787[/C][C]0.137157[/C][C]0.931421[/C][/ROW]
[ROW][C]26[/C][C]0.0590372[/C][C]0.118074[/C][C]0.940963[/C][/ROW]
[ROW][C]27[/C][C]0.175877[/C][C]0.351754[/C][C]0.824123[/C][/ROW]
[ROW][C]28[/C][C]0.185074[/C][C]0.370149[/C][C]0.814926[/C][/ROW]
[ROW][C]29[/C][C]0.167603[/C][C]0.335206[/C][C]0.832397[/C][/ROW]
[ROW][C]30[/C][C]0.381143[/C][C]0.762286[/C][C]0.618857[/C][/ROW]
[ROW][C]31[/C][C]0.333747[/C][C]0.667495[/C][C]0.666253[/C][/ROW]
[ROW][C]32[/C][C]0.277381[/C][C]0.554762[/C][C]0.722619[/C][/ROW]
[ROW][C]33[/C][C]0.229421[/C][C]0.458843[/C][C]0.770579[/C][/ROW]
[ROW][C]34[/C][C]0.223141[/C][C]0.446283[/C][C]0.776859[/C][/ROW]
[ROW][C]35[/C][C]0.18228[/C][C]0.36456[/C][C]0.81772[/C][/ROW]
[ROW][C]36[/C][C]0.408893[/C][C]0.817787[/C][C]0.591107[/C][/ROW]
[ROW][C]37[/C][C]0.356207[/C][C]0.712414[/C][C]0.643793[/C][/ROW]
[ROW][C]38[/C][C]0.314468[/C][C]0.628937[/C][C]0.685532[/C][/ROW]
[ROW][C]39[/C][C]0.292484[/C][C]0.584968[/C][C]0.707516[/C][/ROW]
[ROW][C]40[/C][C]0.245029[/C][C]0.490059[/C][C]0.754971[/C][/ROW]
[ROW][C]41[/C][C]0.214346[/C][C]0.428692[/C][C]0.785654[/C][/ROW]
[ROW][C]42[/C][C]0.239482[/C][C]0.478964[/C][C]0.760518[/C][/ROW]
[ROW][C]43[/C][C]0.210206[/C][C]0.420411[/C][C]0.789794[/C][/ROW]
[ROW][C]44[/C][C]0.323893[/C][C]0.647785[/C][C]0.676107[/C][/ROW]
[ROW][C]45[/C][C]0.27791[/C][C]0.555821[/C][C]0.72209[/C][/ROW]
[ROW][C]46[/C][C]0.415732[/C][C]0.831464[/C][C]0.584268[/C][/ROW]
[ROW][C]47[/C][C]0.419895[/C][C]0.83979[/C][C]0.580105[/C][/ROW]
[ROW][C]48[/C][C]0.45898[/C][C]0.91796[/C][C]0.54102[/C][/ROW]
[ROW][C]49[/C][C]0.52696[/C][C]0.946079[/C][C]0.47304[/C][/ROW]
[ROW][C]50[/C][C]0.660793[/C][C]0.678415[/C][C]0.339207[/C][/ROW]
[ROW][C]51[/C][C]0.630217[/C][C]0.739567[/C][C]0.369783[/C][/ROW]
[ROW][C]52[/C][C]0.604634[/C][C]0.790731[/C][C]0.395366[/C][/ROW]
[ROW][C]53[/C][C]0.563496[/C][C]0.873008[/C][C]0.436504[/C][/ROW]
[ROW][C]54[/C][C]0.606702[/C][C]0.786596[/C][C]0.393298[/C][/ROW]
[ROW][C]55[/C][C]0.571076[/C][C]0.857848[/C][C]0.428924[/C][/ROW]
[ROW][C]56[/C][C]0.523478[/C][C]0.953045[/C][C]0.476522[/C][/ROW]
[ROW][C]57[/C][C]0.490485[/C][C]0.98097[/C][C]0.509515[/C][/ROW]
[ROW][C]58[/C][C]0.50636[/C][C]0.987281[/C][C]0.49364[/C][/ROW]
[ROW][C]59[/C][C]0.59824[/C][C]0.803519[/C][C]0.40176[/C][/ROW]
[ROW][C]60[/C][C]0.685854[/C][C]0.628293[/C][C]0.314146[/C][/ROW]
[ROW][C]61[/C][C]0.71969[/C][C]0.56062[/C][C]0.28031[/C][/ROW]
[ROW][C]62[/C][C]0.729822[/C][C]0.540356[/C][C]0.270178[/C][/ROW]
[ROW][C]63[/C][C]0.699597[/C][C]0.600806[/C][C]0.300403[/C][/ROW]
[ROW][C]64[/C][C]0.664589[/C][C]0.670823[/C][C]0.335411[/C][/ROW]
[ROW][C]65[/C][C]0.65668[/C][C]0.68664[/C][C]0.34332[/C][/ROW]
[ROW][C]66[/C][C]0.714383[/C][C]0.571234[/C][C]0.285617[/C][/ROW]
[ROW][C]67[/C][C]0.743451[/C][C]0.513098[/C][C]0.256549[/C][/ROW]
[ROW][C]68[/C][C]0.704528[/C][C]0.590944[/C][C]0.295472[/C][/ROW]
[ROW][C]69[/C][C]0.961015[/C][C]0.0779697[/C][C]0.0389849[/C][/ROW]
[ROW][C]70[/C][C]0.968327[/C][C]0.0633453[/C][C]0.0316727[/C][/ROW]
[ROW][C]71[/C][C]0.962654[/C][C]0.074693[/C][C]0.0373465[/C][/ROW]
[ROW][C]72[/C][C]0.959955[/C][C]0.0800904[/C][C]0.0400452[/C][/ROW]
[ROW][C]73[/C][C]0.949221[/C][C]0.101557[/C][C]0.0507786[/C][/ROW]
[ROW][C]74[/C][C]0.940176[/C][C]0.119647[/C][C]0.0598236[/C][/ROW]
[ROW][C]75[/C][C]0.938996[/C][C]0.122008[/C][C]0.0610038[/C][/ROW]
[ROW][C]76[/C][C]0.935608[/C][C]0.128784[/C][C]0.0643918[/C][/ROW]
[ROW][C]77[/C][C]0.931222[/C][C]0.137556[/C][C]0.0687778[/C][/ROW]
[ROW][C]78[/C][C]0.912462[/C][C]0.175076[/C][C]0.0875382[/C][/ROW]
[ROW][C]79[/C][C]0.908445[/C][C]0.18311[/C][C]0.0915548[/C][/ROW]
[ROW][C]80[/C][C]0.939813[/C][C]0.120375[/C][C]0.0601873[/C][/ROW]
[ROW][C]81[/C][C]0.925993[/C][C]0.148015[/C][C]0.0740075[/C][/ROW]
[ROW][C]82[/C][C]0.909554[/C][C]0.180891[/C][C]0.0904456[/C][/ROW]
[ROW][C]83[/C][C]0.898544[/C][C]0.202913[/C][C]0.101456[/C][/ROW]
[ROW][C]84[/C][C]0.899459[/C][C]0.201081[/C][C]0.100541[/C][/ROW]
[ROW][C]85[/C][C]0.892804[/C][C]0.214391[/C][C]0.107196[/C][/ROW]
[ROW][C]86[/C][C]0.883552[/C][C]0.232895[/C][C]0.116448[/C][/ROW]
[ROW][C]87[/C][C]0.858571[/C][C]0.282859[/C][C]0.141429[/C][/ROW]
[ROW][C]88[/C][C]0.833627[/C][C]0.332746[/C][C]0.166373[/C][/ROW]
[ROW][C]89[/C][C]0.800358[/C][C]0.399284[/C][C]0.199642[/C][/ROW]
[ROW][C]90[/C][C]0.763161[/C][C]0.473677[/C][C]0.236839[/C][/ROW]
[ROW][C]91[/C][C]0.779326[/C][C]0.441349[/C][C]0.220674[/C][/ROW]
[ROW][C]92[/C][C]0.75188[/C][C]0.496241[/C][C]0.24812[/C][/ROW]
[ROW][C]93[/C][C]0.92649[/C][C]0.14702[/C][C]0.07351[/C][/ROW]
[ROW][C]94[/C][C]0.912114[/C][C]0.175772[/C][C]0.0878862[/C][/ROW]
[ROW][C]95[/C][C]0.905589[/C][C]0.188822[/C][C]0.0944111[/C][/ROW]
[ROW][C]96[/C][C]0.882026[/C][C]0.235948[/C][C]0.117974[/C][/ROW]
[ROW][C]97[/C][C]0.858431[/C][C]0.283138[/C][C]0.141569[/C][/ROW]
[ROW][C]98[/C][C]0.869339[/C][C]0.261321[/C][C]0.130661[/C][/ROW]
[ROW][C]99[/C][C]0.849286[/C][C]0.301427[/C][C]0.150714[/C][/ROW]
[ROW][C]100[/C][C]0.815123[/C][C]0.369754[/C][C]0.184877[/C][/ROW]
[ROW][C]101[/C][C]0.775562[/C][C]0.448875[/C][C]0.224438[/C][/ROW]
[ROW][C]102[/C][C]0.747078[/C][C]0.505844[/C][C]0.252922[/C][/ROW]
[ROW][C]103[/C][C]0.778072[/C][C]0.443855[/C][C]0.221928[/C][/ROW]
[ROW][C]104[/C][C]0.790653[/C][C]0.418695[/C][C]0.209347[/C][/ROW]
[ROW][C]105[/C][C]0.77648[/C][C]0.447039[/C][C]0.22352[/C][/ROW]
[ROW][C]106[/C][C]0.739618[/C][C]0.520764[/C][C]0.260382[/C][/ROW]
[ROW][C]107[/C][C]0.703321[/C][C]0.593358[/C][C]0.296679[/C][/ROW]
[ROW][C]108[/C][C]0.693399[/C][C]0.613202[/C][C]0.306601[/C][/ROW]
[ROW][C]109[/C][C]0.698124[/C][C]0.603752[/C][C]0.301876[/C][/ROW]
[ROW][C]110[/C][C]0.714905[/C][C]0.57019[/C][C]0.285095[/C][/ROW]
[ROW][C]111[/C][C]0.655909[/C][C]0.688182[/C][C]0.344091[/C][/ROW]
[ROW][C]112[/C][C]0.606698[/C][C]0.786605[/C][C]0.393302[/C][/ROW]
[ROW][C]113[/C][C]0.571948[/C][C]0.856104[/C][C]0.428052[/C][/ROW]
[ROW][C]114[/C][C]0.548598[/C][C]0.902804[/C][C]0.451402[/C][/ROW]
[ROW][C]115[/C][C]0.540913[/C][C]0.918173[/C][C]0.459087[/C][/ROW]
[ROW][C]116[/C][C]0.540821[/C][C]0.918358[/C][C]0.459179[/C][/ROW]
[ROW][C]117[/C][C]0.467112[/C][C]0.934224[/C][C]0.532888[/C][/ROW]
[ROW][C]118[/C][C]0.726367[/C][C]0.547266[/C][C]0.273633[/C][/ROW]
[ROW][C]119[/C][C]0.82266[/C][C]0.354679[/C][C]0.17734[/C][/ROW]
[ROW][C]120[/C][C]0.80542[/C][C]0.38916[/C][C]0.19458[/C][/ROW]
[ROW][C]121[/C][C]0.792155[/C][C]0.41569[/C][C]0.207845[/C][/ROW]
[ROW][C]122[/C][C]0.913697[/C][C]0.172606[/C][C]0.0863031[/C][/ROW]
[ROW][C]123[/C][C]0.973723[/C][C]0.0525545[/C][C]0.0262773[/C][/ROW]
[ROW][C]124[/C][C]0.959069[/C][C]0.0818612[/C][C]0.0409306[/C][/ROW]
[ROW][C]125[/C][C]0.957148[/C][C]0.0857049[/C][C]0.0428524[/C][/ROW]
[ROW][C]126[/C][C]0.930131[/C][C]0.139738[/C][C]0.069869[/C][/ROW]
[ROW][C]127[/C][C]0.936181[/C][C]0.127638[/C][C]0.0638191[/C][/ROW]
[ROW][C]128[/C][C]0.982437[/C][C]0.0351269[/C][C]0.0175635[/C][/ROW]
[ROW][C]129[/C][C]0.967996[/C][C]0.0640081[/C][C]0.0320041[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271060&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271060&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
100.6836380.6327250.316362
110.5714430.8571150.428557
120.7293020.5413970.270698
130.6258630.7482740.374137
140.513270.9734590.48673
150.4144330.8288650.585567
160.318920.6378390.68108
170.2388820.4777640.761118
180.3528710.7057430.647129
190.3101880.6203750.689812
200.2415990.4831970.758401
210.1802990.3605990.819701
220.1475350.2950690.852465
230.108120.216240.89188
240.07648190.1529640.923518
250.06857870.1371570.931421
260.05903720.1180740.940963
270.1758770.3517540.824123
280.1850740.3701490.814926
290.1676030.3352060.832397
300.3811430.7622860.618857
310.3337470.6674950.666253
320.2773810.5547620.722619
330.2294210.4588430.770579
340.2231410.4462830.776859
350.182280.364560.81772
360.4088930.8177870.591107
370.3562070.7124140.643793
380.3144680.6289370.685532
390.2924840.5849680.707516
400.2450290.4900590.754971
410.2143460.4286920.785654
420.2394820.4789640.760518
430.2102060.4204110.789794
440.3238930.6477850.676107
450.277910.5558210.72209
460.4157320.8314640.584268
470.4198950.839790.580105
480.458980.917960.54102
490.526960.9460790.47304
500.6607930.6784150.339207
510.6302170.7395670.369783
520.6046340.7907310.395366
530.5634960.8730080.436504
540.6067020.7865960.393298
550.5710760.8578480.428924
560.5234780.9530450.476522
570.4904850.980970.509515
580.506360.9872810.49364
590.598240.8035190.40176
600.6858540.6282930.314146
610.719690.560620.28031
620.7298220.5403560.270178
630.6995970.6008060.300403
640.6645890.6708230.335411
650.656680.686640.34332
660.7143830.5712340.285617
670.7434510.5130980.256549
680.7045280.5909440.295472
690.9610150.07796970.0389849
700.9683270.06334530.0316727
710.9626540.0746930.0373465
720.9599550.08009040.0400452
730.9492210.1015570.0507786
740.9401760.1196470.0598236
750.9389960.1220080.0610038
760.9356080.1287840.0643918
770.9312220.1375560.0687778
780.9124620.1750760.0875382
790.9084450.183110.0915548
800.9398130.1203750.0601873
810.9259930.1480150.0740075
820.9095540.1808910.0904456
830.8985440.2029130.101456
840.8994590.2010810.100541
850.8928040.2143910.107196
860.8835520.2328950.116448
870.8585710.2828590.141429
880.8336270.3327460.166373
890.8003580.3992840.199642
900.7631610.4736770.236839
910.7793260.4413490.220674
920.751880.4962410.24812
930.926490.147020.07351
940.9121140.1757720.0878862
950.9055890.1888220.0944111
960.8820260.2359480.117974
970.8584310.2831380.141569
980.8693390.2613210.130661
990.8492860.3014270.150714
1000.8151230.3697540.184877
1010.7755620.4488750.224438
1020.7470780.5058440.252922
1030.7780720.4438550.221928
1040.7906530.4186950.209347
1050.776480.4470390.22352
1060.7396180.5207640.260382
1070.7033210.5933580.296679
1080.6933990.6132020.306601
1090.6981240.6037520.301876
1100.7149050.570190.285095
1110.6559090.6881820.344091
1120.6066980.7866050.393302
1130.5719480.8561040.428052
1140.5485980.9028040.451402
1150.5409130.9181730.459087
1160.5408210.9183580.459179
1170.4671120.9342240.532888
1180.7263670.5472660.273633
1190.822660.3546790.17734
1200.805420.389160.19458
1210.7921550.415690.207845
1220.9136970.1726060.0863031
1230.9737230.05255450.0262773
1240.9590690.08186120.0409306
1250.9571480.08570490.0428524
1260.9301310.1397380.069869
1270.9361810.1276380.0638191
1280.9824370.03512690.0175635
1290.9679960.06400810.0320041






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00833333OK
10% type I error level90.075OK

\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 & 0.00833333 & OK \tabularnewline
10% type I error level & 9 & 0.075 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271060&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]0.00833333[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]9[/C][C]0.075[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271060&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271060&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 level10.00833333OK
10% type I error level90.075OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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