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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 computationSat, 22 Aug 2015 21:44:28 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Aug/22/t1440276354a8v26ncdk0qr9lq.htm/, Retrieved Thu, 16 May 2024 10:25:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=280301, Retrieved Thu, 16 May 2024 10:25:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact166

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2013-11-04 07:18:26] [0307e7a6407eb638caabc417e3a6b260]
- RMPD    [Multiple Regression] [] [2015-08-22 20:44:28] [3e99441ea7f7f69c8fa4628f6be951c3] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
22	20	20	24	24	24	11.3
22	18	16	21	19	21	9.6
21	16	20	25	20	23	16.1
20	18	13	20	21	20	13.4
20	19	17	19	24	21	12.7
14	9	7	25	18	22	12.3
23	20	18	28	17	24	7.9
16	22	9	20	21	22	12.3
18	22	16	25	26	24	11.6
20	16	14	21	21	21	6.7
23	24	20	28	20	25	12.1
13	20	8	24	24	27	5.7
20	14	11	24	21	18	8
19	19	10	23	20	20	13.3
20	14	10	23	17	22	9.1
16	14	7	24	18	22	12.2
20	20	16	21	20	19	8.8
23	21	22	23	17	16	14.6
17	13	8	19	17	23	12.6
13	13	8	25	21	22	9.9
20	15	14	23	18	25	10.5
22	18	15	25	22	26	13.4
19	21	9	26	20	26	10.9
21	17	21	26	21	24	4.3
15	18	7	16	21	22	10.3
21	20	17	23	20	21	11.8
24	18	18	26	18	22	11.2
22	25	16	25	25	28	11.4
20	20	16	23	23	22	8.6
21	19	14	26	21	26	13.2
19	18	15	22	20	20	12.6
14	12	8	20	21	24	5.6
25	22	22	27	20	21	9.9
11	16	5	20	22	23	8.8
17	18	13	22	15	23	7.7
22	23	22	24	24	23	9
20	20	18	21	22	22	7.3
22	20	15	24	21	23	11.4
15	16	11	26	17	21	13.6
23	22	19	24	23	27	7.9
20	19	19	24	22	23	10.7
22	23	21	27	23	26	10.3
16	6	4	25	16	27	8.3
25	19	17	27	18	27	9.6
18	24	10	19	25	23	14.2
19	19	13	22	18	23	8.5
25	15	15	22	14	23	13.5
21	18	11	25	20	28	4.9
22	18	20	23	19	24	6.4
21	22	13	24	18	20	9.6
22	23	18	24	22	23	11.6
23	18	20	23	21	22	11.1
24	16	12	26	11	20	16.6
22	16	17	18	20	18	12.6
26	25	21	28	22	21	18.9
11	12	10	14	19	23	11.6
24	20	22	27	8	27	14.6
28	19	19	24	15	20	13.85
23	22	19	22	14	21	14.85
19	12	9	21	21	24	11.75
18	17	11	24	18	21	18.45
23	18	17	26	18	25	15.9
17	24	10	17	17	14	19.9
15	18	17	23	20	23	10.95
21	18	13	21	24	28	18.45
20	23	11	21	22	24	15.1
26	21	19	24	15	22	15
19	21	21	22	22	24	11.35
28	28	24	24	26	25	15.95
21	17	13	24	17	21	18.1
19	21	16	24	23	22	14.6
20	18	15	24	23	27	17.6
17	17	13	23	16	24	15.35
20	18	12	24	13	21	13.4
17	14	8	21	18	19	13.9
21	20	17	23	21	28	15.25
12	14	9	20	23	19	12.9
23	17	18	23	16	23	16.1
22	21	17	23	17	25	17.35
22	23	17	23	20	26	13.15
21	24	18	23	18	25	12.15
20	21	12	27	20	25	12.6
18	14	14	19	19	24	10.35
21	24	22	25	26	24	15.4
24	16	19	25	9	24	9.6
22	21	21	21	23	22	18.2
20	8	10	25	9	21	13.6
17	17	16	17	13	17	14.85
16	17	15	23	22	17	14.1
19	16	12	27	12	25	14.9
23	22	21	27	18	19	16.25
22	21	20	19	17	14	13.6
15	20	9	23	22	25	15.65
21	8	14	25	19	25	14.6
18	11	9	16	17	15	12.65
23	15	18	23	18	25	11.9
20	13	12	25	24	24	19.2
21	18	11	26	20	28	16.6
21	19	14	24	18	24	11.2
22	22	11	27	25	26	13.2
15	11	11	19	16	25	15.85
19	14	13	20	23	26	11.15
18	21	12	21	24	20	15.65
20	21	23	26	26	26	7.65
18	18	11	21	21	21	15.2
22	21	19	25	23	23	15.6
25	23	19	27	28	25	13.1
23	20	13	26	24	23	11.85
21	21	23	25	22	22	12.4
19	18	13	28	28	28	11.4
21	19	17	22	18	24	14.9
16	18	13	21	23	14	19.9
21	18	8	24	15	20	11.2
22	19	16	26	24	28	14.6
18	18	14	23	18	26	14.75
4	11	7	15	20	25	15.15
22	20	17	22	20	24	16.85
17	20	19	25	25	24	7.85
20	21	12	24	25	23	12.6
18	12	12	21	14	20	7.85
19	15	18	17	16	16	10.95
20	18	16	26	24	24	12.35
15	14	15	20	13	20	9.95
24	18	20	22	19	23	14.9
21	16	16	24	18	23	16.65
19	19	12	23	16	18	13.4
19	7	10	22	8	21	13.95
27	21	28	28	27	25	15.7
23	24	19	21	23	23	16.85
23	21	18	24	20	26	10.95
20	20	19	28	20	26	15.35
17	22	8	25	26	24	12.2
21	17	17	24	23	23	15.1
23	19	16	24	24	21	17.75
22	20	18	21	21	23	15.2
20	20	17	26	22	23	16.65
16	16	13	16	25	24	8.1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 6 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280301&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]6 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]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280301&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280301&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 time6 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
TOT [t] = + 12.9226 + 0.186348I1[t] + 0.111836I2[t] -0.0780346I3[t] -0.0230923E1[t] -0.0223026E2[t] -0.167181E3[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT
[t] =  +  12.9226 +  0.186348I1[t] +  0.111836I2[t] -0.0780346I3[t] -0.0230923E1[t] -0.0223026E2[t] -0.167181E3[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280301&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT
[t] =  +  12.9226 +  0.186348I1[t] +  0.111836I2[t] -0.0780346I3[t] -0.0230923E1[t] -0.0223026E2[t] -0.167181E3[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280301&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280301&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] = + 12.9226 + 0.186348I1[t] + 0.111836I2[t] -0.0780346I3[t] -0.0230923E1[t] -0.0223026E2[t] -0.167181E3[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)12.92262.876454.4931.53748e-057.6874e-06
I10.1863480.1271841.4650.1452840.0726422
I20.1118360.0981391.140.2565620.128281
I3-0.07803460.0906918-0.86040.3911320.195566
E1-0.02309230.122281-0.18880.8505080.425254
E2-0.02230260.0856463-0.26040.7949650.397482
E3-0.1671810.107226-1.5590.1213920.0606962

\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) & 12.9226 & 2.87645 & 4.493 & 1.53748e-05 & 7.6874e-06 \tabularnewline
I1 & 0.186348 & 0.127184 & 1.465 & 0.145284 & 0.0726422 \tabularnewline
I2 & 0.111836 & 0.098139 & 1.14 & 0.256562 & 0.128281 \tabularnewline
I3 & -0.0780346 & 0.0906918 & -0.8604 & 0.391132 & 0.195566 \tabularnewline
E1 & -0.0230923 & 0.122281 & -0.1888 & 0.850508 & 0.425254 \tabularnewline
E2 & -0.0223026 & 0.0856463 & -0.2604 & 0.794965 & 0.397482 \tabularnewline
E3 & -0.167181 & 0.107226 & -1.559 & 0.121392 & 0.0606962 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280301&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]12.9226[/C][C]2.87645[/C][C]4.493[/C][C]1.53748e-05[/C][C]7.6874e-06[/C][/ROW]
[ROW][C]I1[/C][C]0.186348[/C][C]0.127184[/C][C]1.465[/C][C]0.145284[/C][C]0.0726422[/C][/ROW]
[ROW][C]I2[/C][C]0.111836[/C][C]0.098139[/C][C]1.14[/C][C]0.256562[/C][C]0.128281[/C][/ROW]
[ROW][C]I3[/C][C]-0.0780346[/C][C]0.0906918[/C][C]-0.8604[/C][C]0.391132[/C][C]0.195566[/C][/ROW]
[ROW][C]E1[/C][C]-0.0230923[/C][C]0.122281[/C][C]-0.1888[/C][C]0.850508[/C][C]0.425254[/C][/ROW]
[ROW][C]E2[/C][C]-0.0223026[/C][C]0.0856463[/C][C]-0.2604[/C][C]0.794965[/C][C]0.397482[/C][/ROW]
[ROW][C]E3[/C][C]-0.167181[/C][C]0.107226[/C][C]-1.559[/C][C]0.121392[/C][C]0.0606962[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280301&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280301&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)12.92262.876454.4931.53748e-057.6874e-06
I10.1863480.1271841.4650.1452840.0726422
I20.1118360.0981391.140.2565620.128281
I3-0.07803460.0906918-0.86040.3911320.195566
E1-0.02309230.122281-0.18880.8505080.425254
E2-0.02230260.0856463-0.26040.7949650.397482
E3-0.1671810.107226-1.5590.1213920.0606962






Multiple Linear Regression - Regression Statistics
Multiple R0.24968
R-squared0.0623401
Adjusted R-squared0.0190635
F-TEST (value)1.4405
F-TEST (DF numerator)6
F-TEST (DF denominator)130
p-value0.204018
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.25029
Sum Squared Residuals1373.37

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.24968 \tabularnewline
R-squared & 0.0623401 \tabularnewline
Adjusted R-squared & 0.0190635 \tabularnewline
F-TEST (value) & 1.4405 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 130 \tabularnewline
p-value & 0.204018 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.25029 \tabularnewline
Sum Squared Residuals & 1373.37 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280301&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.24968[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0623401[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0190635[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.4405[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]130[/C][/ROW]
[ROW][C]p-value[/C][C]0.204018[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.25029[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1373.37[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280301&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280301&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 R0.24968
R-squared0.0623401
Adjusted R-squared0.0190635
F-TEST (value)1.4405
F-TEST (DF numerator)6
F-TEST (DF denominator)130
p-value0.204018
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.25029
Sum Squared Residuals1373.37






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
111.312.5965-1.29645
29.613.3673-3.76725
316.112.19613.90394
413.413.37430.0256704
512.712.963-0.263031
612.311.3350.964993
77.913.0026-5.10262
812.313.0541-0.754058
911.612.3192-0.719175
106.712.8823-6.18235
1112.113.0598-0.959807
125.711.3542-5.65419
13813.325-5.32505
1413.313.4869-0.186947
159.112.8467-3.74666
1612.212.29-0.0899766
178.813.5303-4.73029
1814.614.25520.344772
1912.612.2570.342963
209.911.4511-1.55106
2110.512.1225-1.62251
2213.412.45010.949893
2310.912.7163-1.81629
244.312.0173-7.71729
2510.312.6688-2.3688
2611.813.2581-1.45806
2711.213.3235-2.12354
2811.412.7537-1.35365
298.612.9157-4.31565
3013.212.45280.747161
3112.613.008-0.40803
325.611.3067-5.70667
339.913.7446-3.84458
348.811.574-2.77395
357.712.4014-4.70137
36912.9431-3.94307
377.312.8281-5.52807
3811.413.2207-1.82072
3913.612.15851.44154
407.912.6053-4.70527
4110.712.4017-1.70174
4210.312.4726-2.17259
438.310.815-2.515
449.612.8408-3.24076
4514.213.33910.860907
468.512.819-4.319
4713.513.42290.0771163
484.912.2861-7.38614
496.412.5074-6.10739
509.613.9826-4.38256
5111.613.2998-1.69982
5211.112.9835-1.88349
5316.614.05862.54144
5412.613.6141-1.01406
5518.914.27684.62323
5611.610.94190.658101
5714.612.59912.0009
5813.8514.5502-0.70019
5914.8513.85530.994737
6011.7512.1373-0.387287
6118.4512.85325.59678
6215.912.71373.18632
6319.914.8825.01802
6410.9511.5819-0.631933
6518.4512.13326.31677
6615.113.37551.72454
671514.06680.933196
6811.3512.162-0.812001
6915.9514.08531.86469
7018.113.27854.8215
7114.612.8181.78195
7217.611.9115.68898
7315.3512.0773.27304
7413.413.37120.0287654
7513.912.96910.93089
7615.2512.06553.18451
7712.911.87091.02909
7816.112.97213.12794
7917.3512.95444.39558
8013.1512.9440.205992
8112.1513.0032-0.853247
8212.612.8126-0.212624
8310.3511.8752-1.52523
8415.412.63372.76632
859.612.9113-3.31129
8618.213.05625.1438
8713.612.47511.12494
8814.8513.21861.63141
8914.112.7711.32901
9014.912.24552.65448
9116.2513.82892.42112
9213.614.6517-1.05168
9315.6512.05093.59909
9414.611.45753.14248
9512.6513.5484-0.898404
9611.912.3694-0.469419
9719.212.04217.15791
9816.612.2634.33695
9911.212.9003-1.70029
10013.213.09650.103503
10115.8511.11454.73549
10211.1511.6929-0.542946
10315.6513.32522.32482
1047.6511.6763-4.02634
10515.212.96742.23257
10615.612.95272.64728
10713.113.2434-0.143374
10811.8513.45-1.60004
10912.412.6437-0.243714
11011.411.5097-0.109677
11114.912.71242.18763
11219.913.56436.33567
11311.213.9923-2.7923
11414.612.08182.51815
11514.7511.91812.83186
11615.159.379975.77003
11716.8512.9663.88405
1187.8511.6974-3.84735
11912.613.1047-0.50475
1207.8512.5417-4.69168
12110.9513.3118-2.36181
12212.3512.2660.0839599
1239.9512.0176-2.0676
12414.913.07041.82964
12516.6512.57594.0741
12613.413.7544-0.35445
12713.9512.26851.68154
12815.712.68933.0107
12916.8513.56693.28306
13010.9512.8056-1.85556
13115.3511.96433.38573
13212.212.7571-0.557104
13315.112.49822.60181
13417.7513.48464.26535
13515.213.05592.14411
13616.6512.62354.02654
1378.111.7397-3.6397

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 11.3 & 12.5965 & -1.29645 \tabularnewline
2 & 9.6 & 13.3673 & -3.76725 \tabularnewline
3 & 16.1 & 12.1961 & 3.90394 \tabularnewline
4 & 13.4 & 13.3743 & 0.0256704 \tabularnewline
5 & 12.7 & 12.963 & -0.263031 \tabularnewline
6 & 12.3 & 11.335 & 0.964993 \tabularnewline
7 & 7.9 & 13.0026 & -5.10262 \tabularnewline
8 & 12.3 & 13.0541 & -0.754058 \tabularnewline
9 & 11.6 & 12.3192 & -0.719175 \tabularnewline
10 & 6.7 & 12.8823 & -6.18235 \tabularnewline
11 & 12.1 & 13.0598 & -0.959807 \tabularnewline
12 & 5.7 & 11.3542 & -5.65419 \tabularnewline
13 & 8 & 13.325 & -5.32505 \tabularnewline
14 & 13.3 & 13.4869 & -0.186947 \tabularnewline
15 & 9.1 & 12.8467 & -3.74666 \tabularnewline
16 & 12.2 & 12.29 & -0.0899766 \tabularnewline
17 & 8.8 & 13.5303 & -4.73029 \tabularnewline
18 & 14.6 & 14.2552 & 0.344772 \tabularnewline
19 & 12.6 & 12.257 & 0.342963 \tabularnewline
20 & 9.9 & 11.4511 & -1.55106 \tabularnewline
21 & 10.5 & 12.1225 & -1.62251 \tabularnewline
22 & 13.4 & 12.4501 & 0.949893 \tabularnewline
23 & 10.9 & 12.7163 & -1.81629 \tabularnewline
24 & 4.3 & 12.0173 & -7.71729 \tabularnewline
25 & 10.3 & 12.6688 & -2.3688 \tabularnewline
26 & 11.8 & 13.2581 & -1.45806 \tabularnewline
27 & 11.2 & 13.3235 & -2.12354 \tabularnewline
28 & 11.4 & 12.7537 & -1.35365 \tabularnewline
29 & 8.6 & 12.9157 & -4.31565 \tabularnewline
30 & 13.2 & 12.4528 & 0.747161 \tabularnewline
31 & 12.6 & 13.008 & -0.40803 \tabularnewline
32 & 5.6 & 11.3067 & -5.70667 \tabularnewline
33 & 9.9 & 13.7446 & -3.84458 \tabularnewline
34 & 8.8 & 11.574 & -2.77395 \tabularnewline
35 & 7.7 & 12.4014 & -4.70137 \tabularnewline
36 & 9 & 12.9431 & -3.94307 \tabularnewline
37 & 7.3 & 12.8281 & -5.52807 \tabularnewline
38 & 11.4 & 13.2207 & -1.82072 \tabularnewline
39 & 13.6 & 12.1585 & 1.44154 \tabularnewline
40 & 7.9 & 12.6053 & -4.70527 \tabularnewline
41 & 10.7 & 12.4017 & -1.70174 \tabularnewline
42 & 10.3 & 12.4726 & -2.17259 \tabularnewline
43 & 8.3 & 10.815 & -2.515 \tabularnewline
44 & 9.6 & 12.8408 & -3.24076 \tabularnewline
45 & 14.2 & 13.3391 & 0.860907 \tabularnewline
46 & 8.5 & 12.819 & -4.319 \tabularnewline
47 & 13.5 & 13.4229 & 0.0771163 \tabularnewline
48 & 4.9 & 12.2861 & -7.38614 \tabularnewline
49 & 6.4 & 12.5074 & -6.10739 \tabularnewline
50 & 9.6 & 13.9826 & -4.38256 \tabularnewline
51 & 11.6 & 13.2998 & -1.69982 \tabularnewline
52 & 11.1 & 12.9835 & -1.88349 \tabularnewline
53 & 16.6 & 14.0586 & 2.54144 \tabularnewline
54 & 12.6 & 13.6141 & -1.01406 \tabularnewline
55 & 18.9 & 14.2768 & 4.62323 \tabularnewline
56 & 11.6 & 10.9419 & 0.658101 \tabularnewline
57 & 14.6 & 12.5991 & 2.0009 \tabularnewline
58 & 13.85 & 14.5502 & -0.70019 \tabularnewline
59 & 14.85 & 13.8553 & 0.994737 \tabularnewline
60 & 11.75 & 12.1373 & -0.387287 \tabularnewline
61 & 18.45 & 12.8532 & 5.59678 \tabularnewline
62 & 15.9 & 12.7137 & 3.18632 \tabularnewline
63 & 19.9 & 14.882 & 5.01802 \tabularnewline
64 & 10.95 & 11.5819 & -0.631933 \tabularnewline
65 & 18.45 & 12.1332 & 6.31677 \tabularnewline
66 & 15.1 & 13.3755 & 1.72454 \tabularnewline
67 & 15 & 14.0668 & 0.933196 \tabularnewline
68 & 11.35 & 12.162 & -0.812001 \tabularnewline
69 & 15.95 & 14.0853 & 1.86469 \tabularnewline
70 & 18.1 & 13.2785 & 4.8215 \tabularnewline
71 & 14.6 & 12.818 & 1.78195 \tabularnewline
72 & 17.6 & 11.911 & 5.68898 \tabularnewline
73 & 15.35 & 12.077 & 3.27304 \tabularnewline
74 & 13.4 & 13.3712 & 0.0287654 \tabularnewline
75 & 13.9 & 12.9691 & 0.93089 \tabularnewline
76 & 15.25 & 12.0655 & 3.18451 \tabularnewline
77 & 12.9 & 11.8709 & 1.02909 \tabularnewline
78 & 16.1 & 12.9721 & 3.12794 \tabularnewline
79 & 17.35 & 12.9544 & 4.39558 \tabularnewline
80 & 13.15 & 12.944 & 0.205992 \tabularnewline
81 & 12.15 & 13.0032 & -0.853247 \tabularnewline
82 & 12.6 & 12.8126 & -0.212624 \tabularnewline
83 & 10.35 & 11.8752 & -1.52523 \tabularnewline
84 & 15.4 & 12.6337 & 2.76632 \tabularnewline
85 & 9.6 & 12.9113 & -3.31129 \tabularnewline
86 & 18.2 & 13.0562 & 5.1438 \tabularnewline
87 & 13.6 & 12.4751 & 1.12494 \tabularnewline
88 & 14.85 & 13.2186 & 1.63141 \tabularnewline
89 & 14.1 & 12.771 & 1.32901 \tabularnewline
90 & 14.9 & 12.2455 & 2.65448 \tabularnewline
91 & 16.25 & 13.8289 & 2.42112 \tabularnewline
92 & 13.6 & 14.6517 & -1.05168 \tabularnewline
93 & 15.65 & 12.0509 & 3.59909 \tabularnewline
94 & 14.6 & 11.4575 & 3.14248 \tabularnewline
95 & 12.65 & 13.5484 & -0.898404 \tabularnewline
96 & 11.9 & 12.3694 & -0.469419 \tabularnewline
97 & 19.2 & 12.0421 & 7.15791 \tabularnewline
98 & 16.6 & 12.263 & 4.33695 \tabularnewline
99 & 11.2 & 12.9003 & -1.70029 \tabularnewline
100 & 13.2 & 13.0965 & 0.103503 \tabularnewline
101 & 15.85 & 11.1145 & 4.73549 \tabularnewline
102 & 11.15 & 11.6929 & -0.542946 \tabularnewline
103 & 15.65 & 13.3252 & 2.32482 \tabularnewline
104 & 7.65 & 11.6763 & -4.02634 \tabularnewline
105 & 15.2 & 12.9674 & 2.23257 \tabularnewline
106 & 15.6 & 12.9527 & 2.64728 \tabularnewline
107 & 13.1 & 13.2434 & -0.143374 \tabularnewline
108 & 11.85 & 13.45 & -1.60004 \tabularnewline
109 & 12.4 & 12.6437 & -0.243714 \tabularnewline
110 & 11.4 & 11.5097 & -0.109677 \tabularnewline
111 & 14.9 & 12.7124 & 2.18763 \tabularnewline
112 & 19.9 & 13.5643 & 6.33567 \tabularnewline
113 & 11.2 & 13.9923 & -2.7923 \tabularnewline
114 & 14.6 & 12.0818 & 2.51815 \tabularnewline
115 & 14.75 & 11.9181 & 2.83186 \tabularnewline
116 & 15.15 & 9.37997 & 5.77003 \tabularnewline
117 & 16.85 & 12.966 & 3.88405 \tabularnewline
118 & 7.85 & 11.6974 & -3.84735 \tabularnewline
119 & 12.6 & 13.1047 & -0.50475 \tabularnewline
120 & 7.85 & 12.5417 & -4.69168 \tabularnewline
121 & 10.95 & 13.3118 & -2.36181 \tabularnewline
122 & 12.35 & 12.266 & 0.0839599 \tabularnewline
123 & 9.95 & 12.0176 & -2.0676 \tabularnewline
124 & 14.9 & 13.0704 & 1.82964 \tabularnewline
125 & 16.65 & 12.5759 & 4.0741 \tabularnewline
126 & 13.4 & 13.7544 & -0.35445 \tabularnewline
127 & 13.95 & 12.2685 & 1.68154 \tabularnewline
128 & 15.7 & 12.6893 & 3.0107 \tabularnewline
129 & 16.85 & 13.5669 & 3.28306 \tabularnewline
130 & 10.95 & 12.8056 & -1.85556 \tabularnewline
131 & 15.35 & 11.9643 & 3.38573 \tabularnewline
132 & 12.2 & 12.7571 & -0.557104 \tabularnewline
133 & 15.1 & 12.4982 & 2.60181 \tabularnewline
134 & 17.75 & 13.4846 & 4.26535 \tabularnewline
135 & 15.2 & 13.0559 & 2.14411 \tabularnewline
136 & 16.65 & 12.6235 & 4.02654 \tabularnewline
137 & 8.1 & 11.7397 & -3.6397 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280301&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]11.3[/C][C]12.5965[/C][C]-1.29645[/C][/ROW]
[ROW][C]2[/C][C]9.6[/C][C]13.3673[/C][C]-3.76725[/C][/ROW]
[ROW][C]3[/C][C]16.1[/C][C]12.1961[/C][C]3.90394[/C][/ROW]
[ROW][C]4[/C][C]13.4[/C][C]13.3743[/C][C]0.0256704[/C][/ROW]
[ROW][C]5[/C][C]12.7[/C][C]12.963[/C][C]-0.263031[/C][/ROW]
[ROW][C]6[/C][C]12.3[/C][C]11.335[/C][C]0.964993[/C][/ROW]
[ROW][C]7[/C][C]7.9[/C][C]13.0026[/C][C]-5.10262[/C][/ROW]
[ROW][C]8[/C][C]12.3[/C][C]13.0541[/C][C]-0.754058[/C][/ROW]
[ROW][C]9[/C][C]11.6[/C][C]12.3192[/C][C]-0.719175[/C][/ROW]
[ROW][C]10[/C][C]6.7[/C][C]12.8823[/C][C]-6.18235[/C][/ROW]
[ROW][C]11[/C][C]12.1[/C][C]13.0598[/C][C]-0.959807[/C][/ROW]
[ROW][C]12[/C][C]5.7[/C][C]11.3542[/C][C]-5.65419[/C][/ROW]
[ROW][C]13[/C][C]8[/C][C]13.325[/C][C]-5.32505[/C][/ROW]
[ROW][C]14[/C][C]13.3[/C][C]13.4869[/C][C]-0.186947[/C][/ROW]
[ROW][C]15[/C][C]9.1[/C][C]12.8467[/C][C]-3.74666[/C][/ROW]
[ROW][C]16[/C][C]12.2[/C][C]12.29[/C][C]-0.0899766[/C][/ROW]
[ROW][C]17[/C][C]8.8[/C][C]13.5303[/C][C]-4.73029[/C][/ROW]
[ROW][C]18[/C][C]14.6[/C][C]14.2552[/C][C]0.344772[/C][/ROW]
[ROW][C]19[/C][C]12.6[/C][C]12.257[/C][C]0.342963[/C][/ROW]
[ROW][C]20[/C][C]9.9[/C][C]11.4511[/C][C]-1.55106[/C][/ROW]
[ROW][C]21[/C][C]10.5[/C][C]12.1225[/C][C]-1.62251[/C][/ROW]
[ROW][C]22[/C][C]13.4[/C][C]12.4501[/C][C]0.949893[/C][/ROW]
[ROW][C]23[/C][C]10.9[/C][C]12.7163[/C][C]-1.81629[/C][/ROW]
[ROW][C]24[/C][C]4.3[/C][C]12.0173[/C][C]-7.71729[/C][/ROW]
[ROW][C]25[/C][C]10.3[/C][C]12.6688[/C][C]-2.3688[/C][/ROW]
[ROW][C]26[/C][C]11.8[/C][C]13.2581[/C][C]-1.45806[/C][/ROW]
[ROW][C]27[/C][C]11.2[/C][C]13.3235[/C][C]-2.12354[/C][/ROW]
[ROW][C]28[/C][C]11.4[/C][C]12.7537[/C][C]-1.35365[/C][/ROW]
[ROW][C]29[/C][C]8.6[/C][C]12.9157[/C][C]-4.31565[/C][/ROW]
[ROW][C]30[/C][C]13.2[/C][C]12.4528[/C][C]0.747161[/C][/ROW]
[ROW][C]31[/C][C]12.6[/C][C]13.008[/C][C]-0.40803[/C][/ROW]
[ROW][C]32[/C][C]5.6[/C][C]11.3067[/C][C]-5.70667[/C][/ROW]
[ROW][C]33[/C][C]9.9[/C][C]13.7446[/C][C]-3.84458[/C][/ROW]
[ROW][C]34[/C][C]8.8[/C][C]11.574[/C][C]-2.77395[/C][/ROW]
[ROW][C]35[/C][C]7.7[/C][C]12.4014[/C][C]-4.70137[/C][/ROW]
[ROW][C]36[/C][C]9[/C][C]12.9431[/C][C]-3.94307[/C][/ROW]
[ROW][C]37[/C][C]7.3[/C][C]12.8281[/C][C]-5.52807[/C][/ROW]
[ROW][C]38[/C][C]11.4[/C][C]13.2207[/C][C]-1.82072[/C][/ROW]
[ROW][C]39[/C][C]13.6[/C][C]12.1585[/C][C]1.44154[/C][/ROW]
[ROW][C]40[/C][C]7.9[/C][C]12.6053[/C][C]-4.70527[/C][/ROW]
[ROW][C]41[/C][C]10.7[/C][C]12.4017[/C][C]-1.70174[/C][/ROW]
[ROW][C]42[/C][C]10.3[/C][C]12.4726[/C][C]-2.17259[/C][/ROW]
[ROW][C]43[/C][C]8.3[/C][C]10.815[/C][C]-2.515[/C][/ROW]
[ROW][C]44[/C][C]9.6[/C][C]12.8408[/C][C]-3.24076[/C][/ROW]
[ROW][C]45[/C][C]14.2[/C][C]13.3391[/C][C]0.860907[/C][/ROW]
[ROW][C]46[/C][C]8.5[/C][C]12.819[/C][C]-4.319[/C][/ROW]
[ROW][C]47[/C][C]13.5[/C][C]13.4229[/C][C]0.0771163[/C][/ROW]
[ROW][C]48[/C][C]4.9[/C][C]12.2861[/C][C]-7.38614[/C][/ROW]
[ROW][C]49[/C][C]6.4[/C][C]12.5074[/C][C]-6.10739[/C][/ROW]
[ROW][C]50[/C][C]9.6[/C][C]13.9826[/C][C]-4.38256[/C][/ROW]
[ROW][C]51[/C][C]11.6[/C][C]13.2998[/C][C]-1.69982[/C][/ROW]
[ROW][C]52[/C][C]11.1[/C][C]12.9835[/C][C]-1.88349[/C][/ROW]
[ROW][C]53[/C][C]16.6[/C][C]14.0586[/C][C]2.54144[/C][/ROW]
[ROW][C]54[/C][C]12.6[/C][C]13.6141[/C][C]-1.01406[/C][/ROW]
[ROW][C]55[/C][C]18.9[/C][C]14.2768[/C][C]4.62323[/C][/ROW]
[ROW][C]56[/C][C]11.6[/C][C]10.9419[/C][C]0.658101[/C][/ROW]
[ROW][C]57[/C][C]14.6[/C][C]12.5991[/C][C]2.0009[/C][/ROW]
[ROW][C]58[/C][C]13.85[/C][C]14.5502[/C][C]-0.70019[/C][/ROW]
[ROW][C]59[/C][C]14.85[/C][C]13.8553[/C][C]0.994737[/C][/ROW]
[ROW][C]60[/C][C]11.75[/C][C]12.1373[/C][C]-0.387287[/C][/ROW]
[ROW][C]61[/C][C]18.45[/C][C]12.8532[/C][C]5.59678[/C][/ROW]
[ROW][C]62[/C][C]15.9[/C][C]12.7137[/C][C]3.18632[/C][/ROW]
[ROW][C]63[/C][C]19.9[/C][C]14.882[/C][C]5.01802[/C][/ROW]
[ROW][C]64[/C][C]10.95[/C][C]11.5819[/C][C]-0.631933[/C][/ROW]
[ROW][C]65[/C][C]18.45[/C][C]12.1332[/C][C]6.31677[/C][/ROW]
[ROW][C]66[/C][C]15.1[/C][C]13.3755[/C][C]1.72454[/C][/ROW]
[ROW][C]67[/C][C]15[/C][C]14.0668[/C][C]0.933196[/C][/ROW]
[ROW][C]68[/C][C]11.35[/C][C]12.162[/C][C]-0.812001[/C][/ROW]
[ROW][C]69[/C][C]15.95[/C][C]14.0853[/C][C]1.86469[/C][/ROW]
[ROW][C]70[/C][C]18.1[/C][C]13.2785[/C][C]4.8215[/C][/ROW]
[ROW][C]71[/C][C]14.6[/C][C]12.818[/C][C]1.78195[/C][/ROW]
[ROW][C]72[/C][C]17.6[/C][C]11.911[/C][C]5.68898[/C][/ROW]
[ROW][C]73[/C][C]15.35[/C][C]12.077[/C][C]3.27304[/C][/ROW]
[ROW][C]74[/C][C]13.4[/C][C]13.3712[/C][C]0.0287654[/C][/ROW]
[ROW][C]75[/C][C]13.9[/C][C]12.9691[/C][C]0.93089[/C][/ROW]
[ROW][C]76[/C][C]15.25[/C][C]12.0655[/C][C]3.18451[/C][/ROW]
[ROW][C]77[/C][C]12.9[/C][C]11.8709[/C][C]1.02909[/C][/ROW]
[ROW][C]78[/C][C]16.1[/C][C]12.9721[/C][C]3.12794[/C][/ROW]
[ROW][C]79[/C][C]17.35[/C][C]12.9544[/C][C]4.39558[/C][/ROW]
[ROW][C]80[/C][C]13.15[/C][C]12.944[/C][C]0.205992[/C][/ROW]
[ROW][C]81[/C][C]12.15[/C][C]13.0032[/C][C]-0.853247[/C][/ROW]
[ROW][C]82[/C][C]12.6[/C][C]12.8126[/C][C]-0.212624[/C][/ROW]
[ROW][C]83[/C][C]10.35[/C][C]11.8752[/C][C]-1.52523[/C][/ROW]
[ROW][C]84[/C][C]15.4[/C][C]12.6337[/C][C]2.76632[/C][/ROW]
[ROW][C]85[/C][C]9.6[/C][C]12.9113[/C][C]-3.31129[/C][/ROW]
[ROW][C]86[/C][C]18.2[/C][C]13.0562[/C][C]5.1438[/C][/ROW]
[ROW][C]87[/C][C]13.6[/C][C]12.4751[/C][C]1.12494[/C][/ROW]
[ROW][C]88[/C][C]14.85[/C][C]13.2186[/C][C]1.63141[/C][/ROW]
[ROW][C]89[/C][C]14.1[/C][C]12.771[/C][C]1.32901[/C][/ROW]
[ROW][C]90[/C][C]14.9[/C][C]12.2455[/C][C]2.65448[/C][/ROW]
[ROW][C]91[/C][C]16.25[/C][C]13.8289[/C][C]2.42112[/C][/ROW]
[ROW][C]92[/C][C]13.6[/C][C]14.6517[/C][C]-1.05168[/C][/ROW]
[ROW][C]93[/C][C]15.65[/C][C]12.0509[/C][C]3.59909[/C][/ROW]
[ROW][C]94[/C][C]14.6[/C][C]11.4575[/C][C]3.14248[/C][/ROW]
[ROW][C]95[/C][C]12.65[/C][C]13.5484[/C][C]-0.898404[/C][/ROW]
[ROW][C]96[/C][C]11.9[/C][C]12.3694[/C][C]-0.469419[/C][/ROW]
[ROW][C]97[/C][C]19.2[/C][C]12.0421[/C][C]7.15791[/C][/ROW]
[ROW][C]98[/C][C]16.6[/C][C]12.263[/C][C]4.33695[/C][/ROW]
[ROW][C]99[/C][C]11.2[/C][C]12.9003[/C][C]-1.70029[/C][/ROW]
[ROW][C]100[/C][C]13.2[/C][C]13.0965[/C][C]0.103503[/C][/ROW]
[ROW][C]101[/C][C]15.85[/C][C]11.1145[/C][C]4.73549[/C][/ROW]
[ROW][C]102[/C][C]11.15[/C][C]11.6929[/C][C]-0.542946[/C][/ROW]
[ROW][C]103[/C][C]15.65[/C][C]13.3252[/C][C]2.32482[/C][/ROW]
[ROW][C]104[/C][C]7.65[/C][C]11.6763[/C][C]-4.02634[/C][/ROW]
[ROW][C]105[/C][C]15.2[/C][C]12.9674[/C][C]2.23257[/C][/ROW]
[ROW][C]106[/C][C]15.6[/C][C]12.9527[/C][C]2.64728[/C][/ROW]
[ROW][C]107[/C][C]13.1[/C][C]13.2434[/C][C]-0.143374[/C][/ROW]
[ROW][C]108[/C][C]11.85[/C][C]13.45[/C][C]-1.60004[/C][/ROW]
[ROW][C]109[/C][C]12.4[/C][C]12.6437[/C][C]-0.243714[/C][/ROW]
[ROW][C]110[/C][C]11.4[/C][C]11.5097[/C][C]-0.109677[/C][/ROW]
[ROW][C]111[/C][C]14.9[/C][C]12.7124[/C][C]2.18763[/C][/ROW]
[ROW][C]112[/C][C]19.9[/C][C]13.5643[/C][C]6.33567[/C][/ROW]
[ROW][C]113[/C][C]11.2[/C][C]13.9923[/C][C]-2.7923[/C][/ROW]
[ROW][C]114[/C][C]14.6[/C][C]12.0818[/C][C]2.51815[/C][/ROW]
[ROW][C]115[/C][C]14.75[/C][C]11.9181[/C][C]2.83186[/C][/ROW]
[ROW][C]116[/C][C]15.15[/C][C]9.37997[/C][C]5.77003[/C][/ROW]
[ROW][C]117[/C][C]16.85[/C][C]12.966[/C][C]3.88405[/C][/ROW]
[ROW][C]118[/C][C]7.85[/C][C]11.6974[/C][C]-3.84735[/C][/ROW]
[ROW][C]119[/C][C]12.6[/C][C]13.1047[/C][C]-0.50475[/C][/ROW]
[ROW][C]120[/C][C]7.85[/C][C]12.5417[/C][C]-4.69168[/C][/ROW]
[ROW][C]121[/C][C]10.95[/C][C]13.3118[/C][C]-2.36181[/C][/ROW]
[ROW][C]122[/C][C]12.35[/C][C]12.266[/C][C]0.0839599[/C][/ROW]
[ROW][C]123[/C][C]9.95[/C][C]12.0176[/C][C]-2.0676[/C][/ROW]
[ROW][C]124[/C][C]14.9[/C][C]13.0704[/C][C]1.82964[/C][/ROW]
[ROW][C]125[/C][C]16.65[/C][C]12.5759[/C][C]4.0741[/C][/ROW]
[ROW][C]126[/C][C]13.4[/C][C]13.7544[/C][C]-0.35445[/C][/ROW]
[ROW][C]127[/C][C]13.95[/C][C]12.2685[/C][C]1.68154[/C][/ROW]
[ROW][C]128[/C][C]15.7[/C][C]12.6893[/C][C]3.0107[/C][/ROW]
[ROW][C]129[/C][C]16.85[/C][C]13.5669[/C][C]3.28306[/C][/ROW]
[ROW][C]130[/C][C]10.95[/C][C]12.8056[/C][C]-1.85556[/C][/ROW]
[ROW][C]131[/C][C]15.35[/C][C]11.9643[/C][C]3.38573[/C][/ROW]
[ROW][C]132[/C][C]12.2[/C][C]12.7571[/C][C]-0.557104[/C][/ROW]
[ROW][C]133[/C][C]15.1[/C][C]12.4982[/C][C]2.60181[/C][/ROW]
[ROW][C]134[/C][C]17.75[/C][C]13.4846[/C][C]4.26535[/C][/ROW]
[ROW][C]135[/C][C]15.2[/C][C]13.0559[/C][C]2.14411[/C][/ROW]
[ROW][C]136[/C][C]16.65[/C][C]12.6235[/C][C]4.02654[/C][/ROW]
[ROW][C]137[/C][C]8.1[/C][C]11.7397[/C][C]-3.6397[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280301&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280301&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
111.312.5965-1.29645
29.613.3673-3.76725
316.112.19613.90394
413.413.37430.0256704
512.712.963-0.263031
612.311.3350.964993
77.913.0026-5.10262
812.313.0541-0.754058
911.612.3192-0.719175
106.712.8823-6.18235
1112.113.0598-0.959807
125.711.3542-5.65419
13813.325-5.32505
1413.313.4869-0.186947
159.112.8467-3.74666
1612.212.29-0.0899766
178.813.5303-4.73029
1814.614.25520.344772
1912.612.2570.342963
209.911.4511-1.55106
2110.512.1225-1.62251
2213.412.45010.949893
2310.912.7163-1.81629
244.312.0173-7.71729
2510.312.6688-2.3688
2611.813.2581-1.45806
2711.213.3235-2.12354
2811.412.7537-1.35365
298.612.9157-4.31565
3013.212.45280.747161
3112.613.008-0.40803
325.611.3067-5.70667
339.913.7446-3.84458
348.811.574-2.77395
357.712.4014-4.70137
36912.9431-3.94307
377.312.8281-5.52807
3811.413.2207-1.82072
3913.612.15851.44154
407.912.6053-4.70527
4110.712.4017-1.70174
4210.312.4726-2.17259
438.310.815-2.515
449.612.8408-3.24076
4514.213.33910.860907
468.512.819-4.319
4713.513.42290.0771163
484.912.2861-7.38614
496.412.5074-6.10739
509.613.9826-4.38256
5111.613.2998-1.69982
5211.112.9835-1.88349
5316.614.05862.54144
5412.613.6141-1.01406
5518.914.27684.62323
5611.610.94190.658101
5714.612.59912.0009
5813.8514.5502-0.70019
5914.8513.85530.994737
6011.7512.1373-0.387287
6118.4512.85325.59678
6215.912.71373.18632
6319.914.8825.01802
6410.9511.5819-0.631933
6518.4512.13326.31677
6615.113.37551.72454
671514.06680.933196
6811.3512.162-0.812001
6915.9514.08531.86469
7018.113.27854.8215
7114.612.8181.78195
7217.611.9115.68898
7315.3512.0773.27304
7413.413.37120.0287654
7513.912.96910.93089
7615.2512.06553.18451
7712.911.87091.02909
7816.112.97213.12794
7917.3512.95444.39558
8013.1512.9440.205992
8112.1513.0032-0.853247
8212.612.8126-0.212624
8310.3511.8752-1.52523
8415.412.63372.76632
859.612.9113-3.31129
8618.213.05625.1438
8713.612.47511.12494
8814.8513.21861.63141
8914.112.7711.32901
9014.912.24552.65448
9116.2513.82892.42112
9213.614.6517-1.05168
9315.6512.05093.59909
9414.611.45753.14248
9512.6513.5484-0.898404
9611.912.3694-0.469419
9719.212.04217.15791
9816.612.2634.33695
9911.212.9003-1.70029
10013.213.09650.103503
10115.8511.11454.73549
10211.1511.6929-0.542946
10315.6513.32522.32482
1047.6511.6763-4.02634
10515.212.96742.23257
10615.612.95272.64728
10713.113.2434-0.143374
10811.8513.45-1.60004
10912.412.6437-0.243714
11011.411.5097-0.109677
11114.912.71242.18763
11219.913.56436.33567
11311.213.9923-2.7923
11414.612.08182.51815
11514.7511.91812.83186
11615.159.379975.77003
11716.8512.9663.88405
1187.8511.6974-3.84735
11912.613.1047-0.50475
1207.8512.5417-4.69168
12110.9513.3118-2.36181
12212.3512.2660.0839599
1239.9512.0176-2.0676
12414.913.07041.82964
12516.6512.57594.0741
12613.413.7544-0.35445
12713.9512.26851.68154
12815.712.68933.0107
12916.8513.56693.28306
13010.9512.8056-1.85556
13115.3511.96433.38573
13212.212.7571-0.557104
13315.112.49822.60181
13417.7513.48464.26535
13515.213.05592.14411
13616.6512.62354.02654
1378.111.7397-3.6397






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.509660.9806810.49034
110.386490.772980.61351
120.2608730.5217460.739127
130.1852220.3704430.814778
140.2321550.4643110.767845
150.3224710.6449430.677529
160.2790920.5581840.720908
170.5573610.8852780.442639
180.4704630.9409260.529537
190.4088180.8176350.591182
200.3466810.6933620.653319
210.2750390.5500770.724961
220.3116620.6233230.688338
230.2648310.5296620.735169
240.5480660.9038670.451934
250.4899110.9798220.510089
260.4224280.8448570.577572
270.3602680.7205360.639732
280.3064210.6128430.693579
290.2943270.5886530.705673
300.2741960.5483930.725804
310.2321140.4642290.767886
320.2902570.5805130.709743
330.2754330.5508660.724567
340.2424760.4849510.757524
350.2561580.5123150.743842
360.239780.4795590.76022
370.273860.547720.72614
380.2338150.4676290.766185
390.2346040.4692090.765396
400.2375450.4750890.762455
410.2068120.4136250.793188
420.1799450.359890.820055
430.1600840.3201670.839916
440.1457840.2915670.854216
450.1409190.2818390.859081
460.1515260.3030520.848474
470.1380580.2761160.861942
480.3086770.6173540.691323
490.3987180.7974350.601282
500.4709080.9418160.529092
510.4454320.8908640.554568
520.4163820.8327650.583618
530.4251130.8502250.574887
540.3863810.7727620.613619
550.4883650.9767290.511635
560.5422540.9154920.457746
570.5445680.9108630.455432
580.4980790.9961580.501921
590.4544130.9088260.545587
600.4443710.8887430.555629
610.6007460.7985090.399254
620.6429860.7140280.357014
630.6905730.6188550.309427
640.657790.6844190.34221
650.867050.26590.13295
660.8483250.3033510.151675
670.8186320.3627370.181368
680.7983960.4032070.201604
690.7854360.4291280.214564
700.835860.3282790.16414
710.8215180.3569640.178482
720.8996230.2007540.100377
730.9000890.1998210.0999107
740.8759010.2481980.124099
750.8504920.2990160.149508
760.8516740.2966530.148326
770.8325810.3348380.167419
780.8311680.3376640.168832
790.8586110.2827790.141389
800.8271070.3457850.172893
810.7951920.4096170.204808
820.7610.4779990.239
830.7404710.5190590.259529
840.7345210.5309580.265479
850.7488610.5022780.251139
860.8139530.3720940.186047
870.782380.4352390.21762
880.7495640.5008730.250436
890.7126220.5747570.287378
900.6813630.6372730.318637
910.6565270.6869470.343473
920.608820.7823610.39118
930.6011710.7976590.398829
940.5869310.8261370.413069
950.5492230.9015540.450777
960.5044160.9911680.495584
970.654430.6911390.34557
980.6763790.6472410.323621
990.6471570.7056870.352843
1000.5911070.8177870.408893
1010.6298780.7402430.370122
1020.5771390.8457230.422861
1030.5320130.9359740.467987
1040.6373990.7252020.362601
1050.5972440.8055130.402756
1060.5544480.8911030.445552
1070.498270.9965390.50173
1080.459230.9184610.54077
1090.4223660.8447310.577634
1100.3790450.758090.620955
1110.3304460.6608910.669554
1120.5664640.8670710.433536
1130.5328290.9343430.467171
1140.4687090.9374180.531291
1150.4015160.8030310.598484
1160.8926930.2146140.107307
1170.8824070.2351860.117593
1180.8848170.2303660.115183
1190.8475230.3049550.152477
1200.9221780.1556450.0778224
1210.901870.196260.0981299
1220.8737850.252430.126215
1230.8263580.3472840.173642
1240.7339030.5321930.266097
1250.6806260.6387470.319374
1260.9939460.01210720.00605362
1270.975040.04992030.0249601

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.50966 & 0.980681 & 0.49034 \tabularnewline
11 & 0.38649 & 0.77298 & 0.61351 \tabularnewline
12 & 0.260873 & 0.521746 & 0.739127 \tabularnewline
13 & 0.185222 & 0.370443 & 0.814778 \tabularnewline
14 & 0.232155 & 0.464311 & 0.767845 \tabularnewline
15 & 0.322471 & 0.644943 & 0.677529 \tabularnewline
16 & 0.279092 & 0.558184 & 0.720908 \tabularnewline
17 & 0.557361 & 0.885278 & 0.442639 \tabularnewline
18 & 0.470463 & 0.940926 & 0.529537 \tabularnewline
19 & 0.408818 & 0.817635 & 0.591182 \tabularnewline
20 & 0.346681 & 0.693362 & 0.653319 \tabularnewline
21 & 0.275039 & 0.550077 & 0.724961 \tabularnewline
22 & 0.311662 & 0.623323 & 0.688338 \tabularnewline
23 & 0.264831 & 0.529662 & 0.735169 \tabularnewline
24 & 0.548066 & 0.903867 & 0.451934 \tabularnewline
25 & 0.489911 & 0.979822 & 0.510089 \tabularnewline
26 & 0.422428 & 0.844857 & 0.577572 \tabularnewline
27 & 0.360268 & 0.720536 & 0.639732 \tabularnewline
28 & 0.306421 & 0.612843 & 0.693579 \tabularnewline
29 & 0.294327 & 0.588653 & 0.705673 \tabularnewline
30 & 0.274196 & 0.548393 & 0.725804 \tabularnewline
31 & 0.232114 & 0.464229 & 0.767886 \tabularnewline
32 & 0.290257 & 0.580513 & 0.709743 \tabularnewline
33 & 0.275433 & 0.550866 & 0.724567 \tabularnewline
34 & 0.242476 & 0.484951 & 0.757524 \tabularnewline
35 & 0.256158 & 0.512315 & 0.743842 \tabularnewline
36 & 0.23978 & 0.479559 & 0.76022 \tabularnewline
37 & 0.27386 & 0.54772 & 0.72614 \tabularnewline
38 & 0.233815 & 0.467629 & 0.766185 \tabularnewline
39 & 0.234604 & 0.469209 & 0.765396 \tabularnewline
40 & 0.237545 & 0.475089 & 0.762455 \tabularnewline
41 & 0.206812 & 0.413625 & 0.793188 \tabularnewline
42 & 0.179945 & 0.35989 & 0.820055 \tabularnewline
43 & 0.160084 & 0.320167 & 0.839916 \tabularnewline
44 & 0.145784 & 0.291567 & 0.854216 \tabularnewline
45 & 0.140919 & 0.281839 & 0.859081 \tabularnewline
46 & 0.151526 & 0.303052 & 0.848474 \tabularnewline
47 & 0.138058 & 0.276116 & 0.861942 \tabularnewline
48 & 0.308677 & 0.617354 & 0.691323 \tabularnewline
49 & 0.398718 & 0.797435 & 0.601282 \tabularnewline
50 & 0.470908 & 0.941816 & 0.529092 \tabularnewline
51 & 0.445432 & 0.890864 & 0.554568 \tabularnewline
52 & 0.416382 & 0.832765 & 0.583618 \tabularnewline
53 & 0.425113 & 0.850225 & 0.574887 \tabularnewline
54 & 0.386381 & 0.772762 & 0.613619 \tabularnewline
55 & 0.488365 & 0.976729 & 0.511635 \tabularnewline
56 & 0.542254 & 0.915492 & 0.457746 \tabularnewline
57 & 0.544568 & 0.910863 & 0.455432 \tabularnewline
58 & 0.498079 & 0.996158 & 0.501921 \tabularnewline
59 & 0.454413 & 0.908826 & 0.545587 \tabularnewline
60 & 0.444371 & 0.888743 & 0.555629 \tabularnewline
61 & 0.600746 & 0.798509 & 0.399254 \tabularnewline
62 & 0.642986 & 0.714028 & 0.357014 \tabularnewline
63 & 0.690573 & 0.618855 & 0.309427 \tabularnewline
64 & 0.65779 & 0.684419 & 0.34221 \tabularnewline
65 & 0.86705 & 0.2659 & 0.13295 \tabularnewline
66 & 0.848325 & 0.303351 & 0.151675 \tabularnewline
67 & 0.818632 & 0.362737 & 0.181368 \tabularnewline
68 & 0.798396 & 0.403207 & 0.201604 \tabularnewline
69 & 0.785436 & 0.429128 & 0.214564 \tabularnewline
70 & 0.83586 & 0.328279 & 0.16414 \tabularnewline
71 & 0.821518 & 0.356964 & 0.178482 \tabularnewline
72 & 0.899623 & 0.200754 & 0.100377 \tabularnewline
73 & 0.900089 & 0.199821 & 0.0999107 \tabularnewline
74 & 0.875901 & 0.248198 & 0.124099 \tabularnewline
75 & 0.850492 & 0.299016 & 0.149508 \tabularnewline
76 & 0.851674 & 0.296653 & 0.148326 \tabularnewline
77 & 0.832581 & 0.334838 & 0.167419 \tabularnewline
78 & 0.831168 & 0.337664 & 0.168832 \tabularnewline
79 & 0.858611 & 0.282779 & 0.141389 \tabularnewline
80 & 0.827107 & 0.345785 & 0.172893 \tabularnewline
81 & 0.795192 & 0.409617 & 0.204808 \tabularnewline
82 & 0.761 & 0.477999 & 0.239 \tabularnewline
83 & 0.740471 & 0.519059 & 0.259529 \tabularnewline
84 & 0.734521 & 0.530958 & 0.265479 \tabularnewline
85 & 0.748861 & 0.502278 & 0.251139 \tabularnewline
86 & 0.813953 & 0.372094 & 0.186047 \tabularnewline
87 & 0.78238 & 0.435239 & 0.21762 \tabularnewline
88 & 0.749564 & 0.500873 & 0.250436 \tabularnewline
89 & 0.712622 & 0.574757 & 0.287378 \tabularnewline
90 & 0.681363 & 0.637273 & 0.318637 \tabularnewline
91 & 0.656527 & 0.686947 & 0.343473 \tabularnewline
92 & 0.60882 & 0.782361 & 0.39118 \tabularnewline
93 & 0.601171 & 0.797659 & 0.398829 \tabularnewline
94 & 0.586931 & 0.826137 & 0.413069 \tabularnewline
95 & 0.549223 & 0.901554 & 0.450777 \tabularnewline
96 & 0.504416 & 0.991168 & 0.495584 \tabularnewline
97 & 0.65443 & 0.691139 & 0.34557 \tabularnewline
98 & 0.676379 & 0.647241 & 0.323621 \tabularnewline
99 & 0.647157 & 0.705687 & 0.352843 \tabularnewline
100 & 0.591107 & 0.817787 & 0.408893 \tabularnewline
101 & 0.629878 & 0.740243 & 0.370122 \tabularnewline
102 & 0.577139 & 0.845723 & 0.422861 \tabularnewline
103 & 0.532013 & 0.935974 & 0.467987 \tabularnewline
104 & 0.637399 & 0.725202 & 0.362601 \tabularnewline
105 & 0.597244 & 0.805513 & 0.402756 \tabularnewline
106 & 0.554448 & 0.891103 & 0.445552 \tabularnewline
107 & 0.49827 & 0.996539 & 0.50173 \tabularnewline
108 & 0.45923 & 0.918461 & 0.54077 \tabularnewline
109 & 0.422366 & 0.844731 & 0.577634 \tabularnewline
110 & 0.379045 & 0.75809 & 0.620955 \tabularnewline
111 & 0.330446 & 0.660891 & 0.669554 \tabularnewline
112 & 0.566464 & 0.867071 & 0.433536 \tabularnewline
113 & 0.532829 & 0.934343 & 0.467171 \tabularnewline
114 & 0.468709 & 0.937418 & 0.531291 \tabularnewline
115 & 0.401516 & 0.803031 & 0.598484 \tabularnewline
116 & 0.892693 & 0.214614 & 0.107307 \tabularnewline
117 & 0.882407 & 0.235186 & 0.117593 \tabularnewline
118 & 0.884817 & 0.230366 & 0.115183 \tabularnewline
119 & 0.847523 & 0.304955 & 0.152477 \tabularnewline
120 & 0.922178 & 0.155645 & 0.0778224 \tabularnewline
121 & 0.90187 & 0.19626 & 0.0981299 \tabularnewline
122 & 0.873785 & 0.25243 & 0.126215 \tabularnewline
123 & 0.826358 & 0.347284 & 0.173642 \tabularnewline
124 & 0.733903 & 0.532193 & 0.266097 \tabularnewline
125 & 0.680626 & 0.638747 & 0.319374 \tabularnewline
126 & 0.993946 & 0.0121072 & 0.00605362 \tabularnewline
127 & 0.97504 & 0.0499203 & 0.0249601 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280301&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]0.50966[/C][C]0.980681[/C][C]0.49034[/C][/ROW]
[ROW][C]11[/C][C]0.38649[/C][C]0.77298[/C][C]0.61351[/C][/ROW]
[ROW][C]12[/C][C]0.260873[/C][C]0.521746[/C][C]0.739127[/C][/ROW]
[ROW][C]13[/C][C]0.185222[/C][C]0.370443[/C][C]0.814778[/C][/ROW]
[ROW][C]14[/C][C]0.232155[/C][C]0.464311[/C][C]0.767845[/C][/ROW]
[ROW][C]15[/C][C]0.322471[/C][C]0.644943[/C][C]0.677529[/C][/ROW]
[ROW][C]16[/C][C]0.279092[/C][C]0.558184[/C][C]0.720908[/C][/ROW]
[ROW][C]17[/C][C]0.557361[/C][C]0.885278[/C][C]0.442639[/C][/ROW]
[ROW][C]18[/C][C]0.470463[/C][C]0.940926[/C][C]0.529537[/C][/ROW]
[ROW][C]19[/C][C]0.408818[/C][C]0.817635[/C][C]0.591182[/C][/ROW]
[ROW][C]20[/C][C]0.346681[/C][C]0.693362[/C][C]0.653319[/C][/ROW]
[ROW][C]21[/C][C]0.275039[/C][C]0.550077[/C][C]0.724961[/C][/ROW]
[ROW][C]22[/C][C]0.311662[/C][C]0.623323[/C][C]0.688338[/C][/ROW]
[ROW][C]23[/C][C]0.264831[/C][C]0.529662[/C][C]0.735169[/C][/ROW]
[ROW][C]24[/C][C]0.548066[/C][C]0.903867[/C][C]0.451934[/C][/ROW]
[ROW][C]25[/C][C]0.489911[/C][C]0.979822[/C][C]0.510089[/C][/ROW]
[ROW][C]26[/C][C]0.422428[/C][C]0.844857[/C][C]0.577572[/C][/ROW]
[ROW][C]27[/C][C]0.360268[/C][C]0.720536[/C][C]0.639732[/C][/ROW]
[ROW][C]28[/C][C]0.306421[/C][C]0.612843[/C][C]0.693579[/C][/ROW]
[ROW][C]29[/C][C]0.294327[/C][C]0.588653[/C][C]0.705673[/C][/ROW]
[ROW][C]30[/C][C]0.274196[/C][C]0.548393[/C][C]0.725804[/C][/ROW]
[ROW][C]31[/C][C]0.232114[/C][C]0.464229[/C][C]0.767886[/C][/ROW]
[ROW][C]32[/C][C]0.290257[/C][C]0.580513[/C][C]0.709743[/C][/ROW]
[ROW][C]33[/C][C]0.275433[/C][C]0.550866[/C][C]0.724567[/C][/ROW]
[ROW][C]34[/C][C]0.242476[/C][C]0.484951[/C][C]0.757524[/C][/ROW]
[ROW][C]35[/C][C]0.256158[/C][C]0.512315[/C][C]0.743842[/C][/ROW]
[ROW][C]36[/C][C]0.23978[/C][C]0.479559[/C][C]0.76022[/C][/ROW]
[ROW][C]37[/C][C]0.27386[/C][C]0.54772[/C][C]0.72614[/C][/ROW]
[ROW][C]38[/C][C]0.233815[/C][C]0.467629[/C][C]0.766185[/C][/ROW]
[ROW][C]39[/C][C]0.234604[/C][C]0.469209[/C][C]0.765396[/C][/ROW]
[ROW][C]40[/C][C]0.237545[/C][C]0.475089[/C][C]0.762455[/C][/ROW]
[ROW][C]41[/C][C]0.206812[/C][C]0.413625[/C][C]0.793188[/C][/ROW]
[ROW][C]42[/C][C]0.179945[/C][C]0.35989[/C][C]0.820055[/C][/ROW]
[ROW][C]43[/C][C]0.160084[/C][C]0.320167[/C][C]0.839916[/C][/ROW]
[ROW][C]44[/C][C]0.145784[/C][C]0.291567[/C][C]0.854216[/C][/ROW]
[ROW][C]45[/C][C]0.140919[/C][C]0.281839[/C][C]0.859081[/C][/ROW]
[ROW][C]46[/C][C]0.151526[/C][C]0.303052[/C][C]0.848474[/C][/ROW]
[ROW][C]47[/C][C]0.138058[/C][C]0.276116[/C][C]0.861942[/C][/ROW]
[ROW][C]48[/C][C]0.308677[/C][C]0.617354[/C][C]0.691323[/C][/ROW]
[ROW][C]49[/C][C]0.398718[/C][C]0.797435[/C][C]0.601282[/C][/ROW]
[ROW][C]50[/C][C]0.470908[/C][C]0.941816[/C][C]0.529092[/C][/ROW]
[ROW][C]51[/C][C]0.445432[/C][C]0.890864[/C][C]0.554568[/C][/ROW]
[ROW][C]52[/C][C]0.416382[/C][C]0.832765[/C][C]0.583618[/C][/ROW]
[ROW][C]53[/C][C]0.425113[/C][C]0.850225[/C][C]0.574887[/C][/ROW]
[ROW][C]54[/C][C]0.386381[/C][C]0.772762[/C][C]0.613619[/C][/ROW]
[ROW][C]55[/C][C]0.488365[/C][C]0.976729[/C][C]0.511635[/C][/ROW]
[ROW][C]56[/C][C]0.542254[/C][C]0.915492[/C][C]0.457746[/C][/ROW]
[ROW][C]57[/C][C]0.544568[/C][C]0.910863[/C][C]0.455432[/C][/ROW]
[ROW][C]58[/C][C]0.498079[/C][C]0.996158[/C][C]0.501921[/C][/ROW]
[ROW][C]59[/C][C]0.454413[/C][C]0.908826[/C][C]0.545587[/C][/ROW]
[ROW][C]60[/C][C]0.444371[/C][C]0.888743[/C][C]0.555629[/C][/ROW]
[ROW][C]61[/C][C]0.600746[/C][C]0.798509[/C][C]0.399254[/C][/ROW]
[ROW][C]62[/C][C]0.642986[/C][C]0.714028[/C][C]0.357014[/C][/ROW]
[ROW][C]63[/C][C]0.690573[/C][C]0.618855[/C][C]0.309427[/C][/ROW]
[ROW][C]64[/C][C]0.65779[/C][C]0.684419[/C][C]0.34221[/C][/ROW]
[ROW][C]65[/C][C]0.86705[/C][C]0.2659[/C][C]0.13295[/C][/ROW]
[ROW][C]66[/C][C]0.848325[/C][C]0.303351[/C][C]0.151675[/C][/ROW]
[ROW][C]67[/C][C]0.818632[/C][C]0.362737[/C][C]0.181368[/C][/ROW]
[ROW][C]68[/C][C]0.798396[/C][C]0.403207[/C][C]0.201604[/C][/ROW]
[ROW][C]69[/C][C]0.785436[/C][C]0.429128[/C][C]0.214564[/C][/ROW]
[ROW][C]70[/C][C]0.83586[/C][C]0.328279[/C][C]0.16414[/C][/ROW]
[ROW][C]71[/C][C]0.821518[/C][C]0.356964[/C][C]0.178482[/C][/ROW]
[ROW][C]72[/C][C]0.899623[/C][C]0.200754[/C][C]0.100377[/C][/ROW]
[ROW][C]73[/C][C]0.900089[/C][C]0.199821[/C][C]0.0999107[/C][/ROW]
[ROW][C]74[/C][C]0.875901[/C][C]0.248198[/C][C]0.124099[/C][/ROW]
[ROW][C]75[/C][C]0.850492[/C][C]0.299016[/C][C]0.149508[/C][/ROW]
[ROW][C]76[/C][C]0.851674[/C][C]0.296653[/C][C]0.148326[/C][/ROW]
[ROW][C]77[/C][C]0.832581[/C][C]0.334838[/C][C]0.167419[/C][/ROW]
[ROW][C]78[/C][C]0.831168[/C][C]0.337664[/C][C]0.168832[/C][/ROW]
[ROW][C]79[/C][C]0.858611[/C][C]0.282779[/C][C]0.141389[/C][/ROW]
[ROW][C]80[/C][C]0.827107[/C][C]0.345785[/C][C]0.172893[/C][/ROW]
[ROW][C]81[/C][C]0.795192[/C][C]0.409617[/C][C]0.204808[/C][/ROW]
[ROW][C]82[/C][C]0.761[/C][C]0.477999[/C][C]0.239[/C][/ROW]
[ROW][C]83[/C][C]0.740471[/C][C]0.519059[/C][C]0.259529[/C][/ROW]
[ROW][C]84[/C][C]0.734521[/C][C]0.530958[/C][C]0.265479[/C][/ROW]
[ROW][C]85[/C][C]0.748861[/C][C]0.502278[/C][C]0.251139[/C][/ROW]
[ROW][C]86[/C][C]0.813953[/C][C]0.372094[/C][C]0.186047[/C][/ROW]
[ROW][C]87[/C][C]0.78238[/C][C]0.435239[/C][C]0.21762[/C][/ROW]
[ROW][C]88[/C][C]0.749564[/C][C]0.500873[/C][C]0.250436[/C][/ROW]
[ROW][C]89[/C][C]0.712622[/C][C]0.574757[/C][C]0.287378[/C][/ROW]
[ROW][C]90[/C][C]0.681363[/C][C]0.637273[/C][C]0.318637[/C][/ROW]
[ROW][C]91[/C][C]0.656527[/C][C]0.686947[/C][C]0.343473[/C][/ROW]
[ROW][C]92[/C][C]0.60882[/C][C]0.782361[/C][C]0.39118[/C][/ROW]
[ROW][C]93[/C][C]0.601171[/C][C]0.797659[/C][C]0.398829[/C][/ROW]
[ROW][C]94[/C][C]0.586931[/C][C]0.826137[/C][C]0.413069[/C][/ROW]
[ROW][C]95[/C][C]0.549223[/C][C]0.901554[/C][C]0.450777[/C][/ROW]
[ROW][C]96[/C][C]0.504416[/C][C]0.991168[/C][C]0.495584[/C][/ROW]
[ROW][C]97[/C][C]0.65443[/C][C]0.691139[/C][C]0.34557[/C][/ROW]
[ROW][C]98[/C][C]0.676379[/C][C]0.647241[/C][C]0.323621[/C][/ROW]
[ROW][C]99[/C][C]0.647157[/C][C]0.705687[/C][C]0.352843[/C][/ROW]
[ROW][C]100[/C][C]0.591107[/C][C]0.817787[/C][C]0.408893[/C][/ROW]
[ROW][C]101[/C][C]0.629878[/C][C]0.740243[/C][C]0.370122[/C][/ROW]
[ROW][C]102[/C][C]0.577139[/C][C]0.845723[/C][C]0.422861[/C][/ROW]
[ROW][C]103[/C][C]0.532013[/C][C]0.935974[/C][C]0.467987[/C][/ROW]
[ROW][C]104[/C][C]0.637399[/C][C]0.725202[/C][C]0.362601[/C][/ROW]
[ROW][C]105[/C][C]0.597244[/C][C]0.805513[/C][C]0.402756[/C][/ROW]
[ROW][C]106[/C][C]0.554448[/C][C]0.891103[/C][C]0.445552[/C][/ROW]
[ROW][C]107[/C][C]0.49827[/C][C]0.996539[/C][C]0.50173[/C][/ROW]
[ROW][C]108[/C][C]0.45923[/C][C]0.918461[/C][C]0.54077[/C][/ROW]
[ROW][C]109[/C][C]0.422366[/C][C]0.844731[/C][C]0.577634[/C][/ROW]
[ROW][C]110[/C][C]0.379045[/C][C]0.75809[/C][C]0.620955[/C][/ROW]
[ROW][C]111[/C][C]0.330446[/C][C]0.660891[/C][C]0.669554[/C][/ROW]
[ROW][C]112[/C][C]0.566464[/C][C]0.867071[/C][C]0.433536[/C][/ROW]
[ROW][C]113[/C][C]0.532829[/C][C]0.934343[/C][C]0.467171[/C][/ROW]
[ROW][C]114[/C][C]0.468709[/C][C]0.937418[/C][C]0.531291[/C][/ROW]
[ROW][C]115[/C][C]0.401516[/C][C]0.803031[/C][C]0.598484[/C][/ROW]
[ROW][C]116[/C][C]0.892693[/C][C]0.214614[/C][C]0.107307[/C][/ROW]
[ROW][C]117[/C][C]0.882407[/C][C]0.235186[/C][C]0.117593[/C][/ROW]
[ROW][C]118[/C][C]0.884817[/C][C]0.230366[/C][C]0.115183[/C][/ROW]
[ROW][C]119[/C][C]0.847523[/C][C]0.304955[/C][C]0.152477[/C][/ROW]
[ROW][C]120[/C][C]0.922178[/C][C]0.155645[/C][C]0.0778224[/C][/ROW]
[ROW][C]121[/C][C]0.90187[/C][C]0.19626[/C][C]0.0981299[/C][/ROW]
[ROW][C]122[/C][C]0.873785[/C][C]0.25243[/C][C]0.126215[/C][/ROW]
[ROW][C]123[/C][C]0.826358[/C][C]0.347284[/C][C]0.173642[/C][/ROW]
[ROW][C]124[/C][C]0.733903[/C][C]0.532193[/C][C]0.266097[/C][/ROW]
[ROW][C]125[/C][C]0.680626[/C][C]0.638747[/C][C]0.319374[/C][/ROW]
[ROW][C]126[/C][C]0.993946[/C][C]0.0121072[/C][C]0.00605362[/C][/ROW]
[ROW][C]127[/C][C]0.97504[/C][C]0.0499203[/C][C]0.0249601[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280301&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280301&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
100.509660.9806810.49034
110.386490.772980.61351
120.2608730.5217460.739127
130.1852220.3704430.814778
140.2321550.4643110.767845
150.3224710.6449430.677529
160.2790920.5581840.720908
170.5573610.8852780.442639
180.4704630.9409260.529537
190.4088180.8176350.591182
200.3466810.6933620.653319
210.2750390.5500770.724961
220.3116620.6233230.688338
230.2648310.5296620.735169
240.5480660.9038670.451934
250.4899110.9798220.510089
260.4224280.8448570.577572
270.3602680.7205360.639732
280.3064210.6128430.693579
290.2943270.5886530.705673
300.2741960.5483930.725804
310.2321140.4642290.767886
320.2902570.5805130.709743
330.2754330.5508660.724567
340.2424760.4849510.757524
350.2561580.5123150.743842
360.239780.4795590.76022
370.273860.547720.72614
380.2338150.4676290.766185
390.2346040.4692090.765396
400.2375450.4750890.762455
410.2068120.4136250.793188
420.1799450.359890.820055
430.1600840.3201670.839916
440.1457840.2915670.854216
450.1409190.2818390.859081
460.1515260.3030520.848474
470.1380580.2761160.861942
480.3086770.6173540.691323
490.3987180.7974350.601282
500.4709080.9418160.529092
510.4454320.8908640.554568
520.4163820.8327650.583618
530.4251130.8502250.574887
540.3863810.7727620.613619
550.4883650.9767290.511635
560.5422540.9154920.457746
570.5445680.9108630.455432
580.4980790.9961580.501921
590.4544130.9088260.545587
600.4443710.8887430.555629
610.6007460.7985090.399254
620.6429860.7140280.357014
630.6905730.6188550.309427
640.657790.6844190.34221
650.867050.26590.13295
660.8483250.3033510.151675
670.8186320.3627370.181368
680.7983960.4032070.201604
690.7854360.4291280.214564
700.835860.3282790.16414
710.8215180.3569640.178482
720.8996230.2007540.100377
730.9000890.1998210.0999107
740.8759010.2481980.124099
750.8504920.2990160.149508
760.8516740.2966530.148326
770.8325810.3348380.167419
780.8311680.3376640.168832
790.8586110.2827790.141389
800.8271070.3457850.172893
810.7951920.4096170.204808
820.7610.4779990.239
830.7404710.5190590.259529
840.7345210.5309580.265479
850.7488610.5022780.251139
860.8139530.3720940.186047
870.782380.4352390.21762
880.7495640.5008730.250436
890.7126220.5747570.287378
900.6813630.6372730.318637
910.6565270.6869470.343473
920.608820.7823610.39118
930.6011710.7976590.398829
940.5869310.8261370.413069
950.5492230.9015540.450777
960.5044160.9911680.495584
970.654430.6911390.34557
980.6763790.6472410.323621
990.6471570.7056870.352843
1000.5911070.8177870.408893
1010.6298780.7402430.370122
1020.5771390.8457230.422861
1030.5320130.9359740.467987
1040.6373990.7252020.362601
1050.5972440.8055130.402756
1060.5544480.8911030.445552
1070.498270.9965390.50173
1080.459230.9184610.54077
1090.4223660.8447310.577634
1100.3790450.758090.620955
1110.3304460.6608910.669554
1120.5664640.8670710.433536
1130.5328290.9343430.467171
1140.4687090.9374180.531291
1150.4015160.8030310.598484
1160.8926930.2146140.107307
1170.8824070.2351860.117593
1180.8848170.2303660.115183
1190.8475230.3049550.152477
1200.9221780.1556450.0778224
1210.901870.196260.0981299
1220.8737850.252430.126215
1230.8263580.3472840.173642
1240.7339030.5321930.266097
1250.6806260.6387470.319374
1260.9939460.01210720.00605362
1270.975040.04992030.0249601






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0169492OK
10% type I error level20.0169492OK

\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 & 2 & 0.0169492 & OK \tabularnewline
10% type I error level & 2 & 0.0169492 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280301&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]2[/C][C]0.0169492[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0169492[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280301&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280301&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 level20.0169492OK
10% type I error level20.0169492OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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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 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 7 ; 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')
}