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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 13 Dec 2010 19:23:19 +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/2010/Dec/13/t1292268066ht8vq6y9p96hf4t.htm/, Retrieved Mon, 06 May 2024 21:32:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109096, Retrieved Mon, 06 May 2024 21:32:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact113

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [] [2010-12-13 19:23:19] [f9aa24c2294a5d3925c7278aa2e9a372] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
15	15	11	12	13	6	1
9	12	12	7	11	4	0
12	15	12	13	14	6	0
15	12	11	11	12	5	0
17	14	11	16	12	5	0
14	8	10	10	6	4	0
9	11	11	15	10	5	1
12	15	9	5	11	3	1
11	4	10	4	10	2	0
13	13	12	7	12	5	0
16	19	12	15	15	6	1
16	10	12	5	13	6	1
10	6	9	15	11	6	0
16	7	12	13	12	3	1
12	14	12	13	13	6	0
15	16	12	15	14	6	0
13	16	12	15	16	7	1
18	14	13	10	16	8	1
13	15	11	17	16	6	0
17	14	12	14	15	7	1
14	12	12	9	13	4	1
13	9	15	6	8	4	0
13	12	11	11	14	2	1
15	14	12	13	15	6	1
13	12	10	12	13	6	1
15	14	11	10	16	6	1
13	10	13	4	13	6	1
14	14	6	13	12	6	1
13	16	12	15	15	7	1
14	8	10	10	14	3	1
15	11	12	7	13	6	1
9	8	11	9	12	4	0
16	13	9	14	14	6	0
16	11	10	5	13	3	1
13	16	12	16	14	6	0
17	16	11	14	15	6	1
15	13	12	16	16	6	1
14	14	11	15	15	8	1
10	5	14	4	5	2	0
13	14	10	12	15	6	0
16	14	11	15	16	6	0
16	14	11	15	16	6	0
15	11	10	12	14	5	1
15	15	12	13	13	6	1
12	16	11	14	14	6	1
15	11	12	15	12	6	0
17	10	11	13	15	6	1
10	8	7	4	13	6	1
11	9	11	8	10	4	0
15	12	8	13	13	5	1
15	14	11	15	14	6	0
7	12	12	15	13	6	1
14	14	14	17	18	6	0
12	16	12	14	16	8	1
14	13	13	11	15	6	1
11	11	8	10	14	5	1
16	15	12	14	16	4	1
16	6	12	6	11	2	1
11	12	11	16	13	4	0
15	13	13	15	14	6	0
14	8	12	8	14	5	0
15	9	11	9	12	4	1
17	10	12	8	16	4	1
19	16	12	14	14	6	1
16	14	11	14	12	6	0
14	12	8	15	13	7	1
15	12	12	12	13	4	1
17	8	13	7	10	3	0
12	16	12	12	15	8	1
13	12	12	10	13	4	1
14	12	10	14	14	5	1
14	8	7	9	15	4	1
12	13	12	14	14	6	0
13	12	13	14	12	5	1
17	12	12	15	13	6	1
16	12	12	6	14	5	1
15	4	8	6	4	4	1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109096&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109096&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109096&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
Liked[t] = + 3.98257961482792 + 0.125971845421722Perceived_happiness[t] + 0.303663490104812Popularity[t] + 0.0425109403840113Finding_friends[t] + 0.101947787907027Knowing_people[t] + 0.308821987184138Celebrity[t] + 0.817714644076724Gender[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Liked[t] =  +  3.98257961482792 +  0.125971845421722Perceived_happiness[t] +  0.303663490104812Popularity[t] +  0.0425109403840113Finding_friends[t] +  0.101947787907027Knowing_people[t] +  0.308821987184138Celebrity[t] +  0.817714644076724Gender[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109096&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Liked[t] =  +  3.98257961482792 +  0.125971845421722Perceived_happiness[t] +  0.303663490104812Popularity[t] +  0.0425109403840113Finding_friends[t] +  0.101947787907027Knowing_people[t] +  0.308821987184138Celebrity[t] +  0.817714644076724Gender[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109096&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109096&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
Liked[t] = + 3.98257961482792 + 0.125971845421722Perceived_happiness[t] + 0.303663490104812Popularity[t] + 0.0425109403840113Finding_friends[t] + 0.101947787907027Knowing_people[t] + 0.308821987184138Celebrity[t] + 0.817714644076724Gender[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.982579614827921.9358292.05730.0433850.021692
Perceived_happiness0.1259718454217220.0934911.34740.182190.091095
Popularity0.3036634901048120.0979953.09880.0027980.001399
Finding_friends0.04251094038401130.1351550.31450.7540510.377026
Knowing_people0.1019477879070270.0756871.3470.1823360.091168
Celebrity0.3088219871841380.1980981.55890.1235210.06176
Gender0.8177146440767240.4600741.77740.0798540.039927

\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) & 3.98257961482792 & 1.935829 & 2.0573 & 0.043385 & 0.021692 \tabularnewline
Perceived_happiness & 0.125971845421722 & 0.093491 & 1.3474 & 0.18219 & 0.091095 \tabularnewline
Popularity & 0.303663490104812 & 0.097995 & 3.0988 & 0.002798 & 0.001399 \tabularnewline
Finding_friends & 0.0425109403840113 & 0.135155 & 0.3145 & 0.754051 & 0.377026 \tabularnewline
Knowing_people & 0.101947787907027 & 0.075687 & 1.347 & 0.182336 & 0.091168 \tabularnewline
Celebrity & 0.308821987184138 & 0.198098 & 1.5589 & 0.123521 & 0.06176 \tabularnewline
Gender & 0.817714644076724 & 0.460074 & 1.7774 & 0.079854 & 0.039927 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109096&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]3.98257961482792[/C][C]1.935829[/C][C]2.0573[/C][C]0.043385[/C][C]0.021692[/C][/ROW]
[ROW][C]Perceived_happiness[/C][C]0.125971845421722[/C][C]0.093491[/C][C]1.3474[/C][C]0.18219[/C][C]0.091095[/C][/ROW]
[ROW][C]Popularity[/C][C]0.303663490104812[/C][C]0.097995[/C][C]3.0988[/C][C]0.002798[/C][C]0.001399[/C][/ROW]
[ROW][C]Finding_friends[/C][C]0.0425109403840113[/C][C]0.135155[/C][C]0.3145[/C][C]0.754051[/C][C]0.377026[/C][/ROW]
[ROW][C]Knowing_people[/C][C]0.101947787907027[/C][C]0.075687[/C][C]1.347[/C][C]0.182336[/C][C]0.091168[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.308821987184138[/C][C]0.198098[/C][C]1.5589[/C][C]0.123521[/C][C]0.06176[/C][/ROW]
[ROW][C]Gender[/C][C]0.817714644076724[/C][C]0.460074[/C][C]1.7774[/C][C]0.079854[/C][C]0.039927[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109096&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109096&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)3.982579614827921.9358292.05730.0433850.021692
Perceived_happiness0.1259718454217220.0934911.34740.182190.091095
Popularity0.3036634901048120.0979953.09880.0027980.001399
Finding_friends0.04251094038401130.1351550.31450.7540510.377026
Knowing_people0.1019477879070270.0756871.3470.1823360.091168
Celebrity0.3088219871841380.1980981.55890.1235210.06176
Gender0.8177146440767240.4600741.77740.0798540.039927






Multiple Linear Regression - Regression Statistics
Multiple R0.703702909052231
R-squared0.495197784208573
Adjusted R-squared0.451929022855022
F-TEST (value)11.4446951730901
F-TEST (DF numerator)6
F-TEST (DF denominator)70
p-value7.02814140218777e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.80833332620379
Sum Squared Residuals228.904859306149

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.703702909052231 \tabularnewline
R-squared & 0.495197784208573 \tabularnewline
Adjusted R-squared & 0.451929022855022 \tabularnewline
F-TEST (value) & 11.4446951730901 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 70 \tabularnewline
p-value & 7.02814140218777e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.80833332620379 \tabularnewline
Sum Squared Residuals & 228.904859306149 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109096&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.703702909052231[/C][/ROW]
[ROW][C]R-squared[/C][C]0.495197784208573[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.451929022855022[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]11.4446951730901[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]70[/C][/ROW]
[ROW][C]p-value[/C][C]7.02814140218777e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.80833332620379[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]228.904859306149[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109096&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109096&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 R0.703702909052231
R-squared0.495197784208573
Adjusted R-squared0.451929022855022
F-TEST (value)11.4446951730901
F-TEST (DF numerator)6
F-TEST (DF denominator)70
p-value7.02814140218777e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.80833332620379
Sum Squared Residuals228.904859306149






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11314.7887500140159-1.78875001401595
21111.2193418535750-0.219341853575041
31413.73757856196510.262421438034914
41212.6492751245336-0.649275124533604
51214.0182847351218-2.01828473512181
6610.8553686032175-4.85536860321745
71012.8152863576033-2.81528635760329
81112.6857121200811-1.68571212008114
9108.03346840472261.96653159527740
101212.3357147125509-0.335714712550877
111516.477730123962-1.47773012396199
121312.72528083394840.274719166051585
131110.82902621484030.170973785159657
141211.70340670533780.296593294662223
151313.4339150718603-0.433915071860267
161414.6230531641491-0.62305316414911
171615.49764610456650.502353895433471
181615.36177233949850.638227660501471
191614.22883061863091.77116938136910
201515.2922587181368-0.292258718136765
211312.87081130057440.129188699425574
22810.8378237981925-2.8378237981925
231412.28858011621451.71141988378553
241514.62954525220220.370454747797843
251313.5833049124740-0.583304912474038
261614.28119094809711.71880905190294
271312.28792845016020.712071549839767
281214.2485077644764-2.24850776447637
291515.4976461045665-0.497646104566529
301411.36426126011002.63573873988996
311313.1068680544456-0.106868054445558
321210.16607252858581.83392747141417
331413.60855393019730.391446069802666
341312.01745648173280.98254351826721
351414.4730572612127-0.473057261212694
361515.5482527707782-0.548252770778241
371614.63172512581841.36827487418158
381515.2826020165788-0.282602016578752
3958.38120381094174-3.38120381094174
401513.37291724860691.62708275139306
411614.09918708897721.90081291102280
421614.09918708897721.90081291102280
431413.22276312602850.777236873971468
441314.9332087423070-1.93320874230697
451414.9183935436696-0.91839354366963
461213.1047357136250-1.10473571362505
471513.62432404224231.37567595775766
481311.04762029138141.95237970861863
491010.6197319216271-0.619731921627061
501313.5433525232723-0.543352523272348
511413.97321524355550.0267847564445257
521313.2183390844328-0.21833908443281
531814.17867179509983.82132820490016
541615.57854845842190.421451541578082
551514.03852528124560.96147471875442
561412.42995828775961.57004171224043
571614.54348440126741.45651559873256
581110.37728671269960.62271328730036
591312.34630469519770.653695304802288
601413.75457363421870.245426365781316
611411.04531689535562.95468310464444
621212.0432817352977-0.0432817352976996
631612.53945206872293.46054793127706
641415.8427074020057-1.84270740200570
651213.9972393010702-1.99723930107017
661314.2389202280330-1.23892022803296
671313.3026265097172-0.302626509717229
681010.7461516097294-0.746151609729437
691515.3746528826079-0.374652882607865
701312.84678724305970.153212756940269
711413.60435034652570.395649653474324
721511.44360263823513.55639736176488
731413.23219936966250.76780063033752
741213.605911322256-1.60591132225599
751314.4780575386500-1.47805753865003
761413.12573361488090.874266385119072
77410.0915880999005-6.09158809990052

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 14.7887500140159 & -1.78875001401595 \tabularnewline
2 & 11 & 11.2193418535750 & -0.219341853575041 \tabularnewline
3 & 14 & 13.7375785619651 & 0.262421438034914 \tabularnewline
4 & 12 & 12.6492751245336 & -0.649275124533604 \tabularnewline
5 & 12 & 14.0182847351218 & -2.01828473512181 \tabularnewline
6 & 6 & 10.8553686032175 & -4.85536860321745 \tabularnewline
7 & 10 & 12.8152863576033 & -2.81528635760329 \tabularnewline
8 & 11 & 12.6857121200811 & -1.68571212008114 \tabularnewline
9 & 10 & 8.0334684047226 & 1.96653159527740 \tabularnewline
10 & 12 & 12.3357147125509 & -0.335714712550877 \tabularnewline
11 & 15 & 16.477730123962 & -1.47773012396199 \tabularnewline
12 & 13 & 12.7252808339484 & 0.274719166051585 \tabularnewline
13 & 11 & 10.8290262148403 & 0.170973785159657 \tabularnewline
14 & 12 & 11.7034067053378 & 0.296593294662223 \tabularnewline
15 & 13 & 13.4339150718603 & -0.433915071860267 \tabularnewline
16 & 14 & 14.6230531641491 & -0.62305316414911 \tabularnewline
17 & 16 & 15.4976461045665 & 0.502353895433471 \tabularnewline
18 & 16 & 15.3617723394985 & 0.638227660501471 \tabularnewline
19 & 16 & 14.2288306186309 & 1.77116938136910 \tabularnewline
20 & 15 & 15.2922587181368 & -0.292258718136765 \tabularnewline
21 & 13 & 12.8708113005744 & 0.129188699425574 \tabularnewline
22 & 8 & 10.8378237981925 & -2.8378237981925 \tabularnewline
23 & 14 & 12.2885801162145 & 1.71141988378553 \tabularnewline
24 & 15 & 14.6295452522022 & 0.370454747797843 \tabularnewline
25 & 13 & 13.5833049124740 & -0.583304912474038 \tabularnewline
26 & 16 & 14.2811909480971 & 1.71880905190294 \tabularnewline
27 & 13 & 12.2879284501602 & 0.712071549839767 \tabularnewline
28 & 12 & 14.2485077644764 & -2.24850776447637 \tabularnewline
29 & 15 & 15.4976461045665 & -0.497646104566529 \tabularnewline
30 & 14 & 11.3642612601100 & 2.63573873988996 \tabularnewline
31 & 13 & 13.1068680544456 & -0.106868054445558 \tabularnewline
32 & 12 & 10.1660725285858 & 1.83392747141417 \tabularnewline
33 & 14 & 13.6085539301973 & 0.391446069802666 \tabularnewline
34 & 13 & 12.0174564817328 & 0.98254351826721 \tabularnewline
35 & 14 & 14.4730572612127 & -0.473057261212694 \tabularnewline
36 & 15 & 15.5482527707782 & -0.548252770778241 \tabularnewline
37 & 16 & 14.6317251258184 & 1.36827487418158 \tabularnewline
38 & 15 & 15.2826020165788 & -0.282602016578752 \tabularnewline
39 & 5 & 8.38120381094174 & -3.38120381094174 \tabularnewline
40 & 15 & 13.3729172486069 & 1.62708275139306 \tabularnewline
41 & 16 & 14.0991870889772 & 1.90081291102280 \tabularnewline
42 & 16 & 14.0991870889772 & 1.90081291102280 \tabularnewline
43 & 14 & 13.2227631260285 & 0.777236873971468 \tabularnewline
44 & 13 & 14.9332087423070 & -1.93320874230697 \tabularnewline
45 & 14 & 14.9183935436696 & -0.91839354366963 \tabularnewline
46 & 12 & 13.1047357136250 & -1.10473571362505 \tabularnewline
47 & 15 & 13.6243240422423 & 1.37567595775766 \tabularnewline
48 & 13 & 11.0476202913814 & 1.95237970861863 \tabularnewline
49 & 10 & 10.6197319216271 & -0.619731921627061 \tabularnewline
50 & 13 & 13.5433525232723 & -0.543352523272348 \tabularnewline
51 & 14 & 13.9732152435555 & 0.0267847564445257 \tabularnewline
52 & 13 & 13.2183390844328 & -0.21833908443281 \tabularnewline
53 & 18 & 14.1786717950998 & 3.82132820490016 \tabularnewline
54 & 16 & 15.5785484584219 & 0.421451541578082 \tabularnewline
55 & 15 & 14.0385252812456 & 0.96147471875442 \tabularnewline
56 & 14 & 12.4299582877596 & 1.57004171224043 \tabularnewline
57 & 16 & 14.5434844012674 & 1.45651559873256 \tabularnewline
58 & 11 & 10.3772867126996 & 0.62271328730036 \tabularnewline
59 & 13 & 12.3463046951977 & 0.653695304802288 \tabularnewline
60 & 14 & 13.7545736342187 & 0.245426365781316 \tabularnewline
61 & 14 & 11.0453168953556 & 2.95468310464444 \tabularnewline
62 & 12 & 12.0432817352977 & -0.0432817352976996 \tabularnewline
63 & 16 & 12.5394520687229 & 3.46054793127706 \tabularnewline
64 & 14 & 15.8427074020057 & -1.84270740200570 \tabularnewline
65 & 12 & 13.9972393010702 & -1.99723930107017 \tabularnewline
66 & 13 & 14.2389202280330 & -1.23892022803296 \tabularnewline
67 & 13 & 13.3026265097172 & -0.302626509717229 \tabularnewline
68 & 10 & 10.7461516097294 & -0.746151609729437 \tabularnewline
69 & 15 & 15.3746528826079 & -0.374652882607865 \tabularnewline
70 & 13 & 12.8467872430597 & 0.153212756940269 \tabularnewline
71 & 14 & 13.6043503465257 & 0.395649653474324 \tabularnewline
72 & 15 & 11.4436026382351 & 3.55639736176488 \tabularnewline
73 & 14 & 13.2321993696625 & 0.76780063033752 \tabularnewline
74 & 12 & 13.605911322256 & -1.60591132225599 \tabularnewline
75 & 13 & 14.4780575386500 & -1.47805753865003 \tabularnewline
76 & 14 & 13.1257336148809 & 0.874266385119072 \tabularnewline
77 & 4 & 10.0915880999005 & -6.09158809990052 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109096&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]13[/C][C]14.7887500140159[/C][C]-1.78875001401595[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]11.2193418535750[/C][C]-0.219341853575041[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]13.7375785619651[/C][C]0.262421438034914[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]12.6492751245336[/C][C]-0.649275124533604[/C][/ROW]
[ROW][C]5[/C][C]12[/C][C]14.0182847351218[/C][C]-2.01828473512181[/C][/ROW]
[ROW][C]6[/C][C]6[/C][C]10.8553686032175[/C][C]-4.85536860321745[/C][/ROW]
[ROW][C]7[/C][C]10[/C][C]12.8152863576033[/C][C]-2.81528635760329[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]12.6857121200811[/C][C]-1.68571212008114[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]8.0334684047226[/C][C]1.96653159527740[/C][/ROW]
[ROW][C]10[/C][C]12[/C][C]12.3357147125509[/C][C]-0.335714712550877[/C][/ROW]
[ROW][C]11[/C][C]15[/C][C]16.477730123962[/C][C]-1.47773012396199[/C][/ROW]
[ROW][C]12[/C][C]13[/C][C]12.7252808339484[/C][C]0.274719166051585[/C][/ROW]
[ROW][C]13[/C][C]11[/C][C]10.8290262148403[/C][C]0.170973785159657[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]11.7034067053378[/C][C]0.296593294662223[/C][/ROW]
[ROW][C]15[/C][C]13[/C][C]13.4339150718603[/C][C]-0.433915071860267[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]14.6230531641491[/C][C]-0.62305316414911[/C][/ROW]
[ROW][C]17[/C][C]16[/C][C]15.4976461045665[/C][C]0.502353895433471[/C][/ROW]
[ROW][C]18[/C][C]16[/C][C]15.3617723394985[/C][C]0.638227660501471[/C][/ROW]
[ROW][C]19[/C][C]16[/C][C]14.2288306186309[/C][C]1.77116938136910[/C][/ROW]
[ROW][C]20[/C][C]15[/C][C]15.2922587181368[/C][C]-0.292258718136765[/C][/ROW]
[ROW][C]21[/C][C]13[/C][C]12.8708113005744[/C][C]0.129188699425574[/C][/ROW]
[ROW][C]22[/C][C]8[/C][C]10.8378237981925[/C][C]-2.8378237981925[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]12.2885801162145[/C][C]1.71141988378553[/C][/ROW]
[ROW][C]24[/C][C]15[/C][C]14.6295452522022[/C][C]0.370454747797843[/C][/ROW]
[ROW][C]25[/C][C]13[/C][C]13.5833049124740[/C][C]-0.583304912474038[/C][/ROW]
[ROW][C]26[/C][C]16[/C][C]14.2811909480971[/C][C]1.71880905190294[/C][/ROW]
[ROW][C]27[/C][C]13[/C][C]12.2879284501602[/C][C]0.712071549839767[/C][/ROW]
[ROW][C]28[/C][C]12[/C][C]14.2485077644764[/C][C]-2.24850776447637[/C][/ROW]
[ROW][C]29[/C][C]15[/C][C]15.4976461045665[/C][C]-0.497646104566529[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]11.3642612601100[/C][C]2.63573873988996[/C][/ROW]
[ROW][C]31[/C][C]13[/C][C]13.1068680544456[/C][C]-0.106868054445558[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]10.1660725285858[/C][C]1.83392747141417[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]13.6085539301973[/C][C]0.391446069802666[/C][/ROW]
[ROW][C]34[/C][C]13[/C][C]12.0174564817328[/C][C]0.98254351826721[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]14.4730572612127[/C][C]-0.473057261212694[/C][/ROW]
[ROW][C]36[/C][C]15[/C][C]15.5482527707782[/C][C]-0.548252770778241[/C][/ROW]
[ROW][C]37[/C][C]16[/C][C]14.6317251258184[/C][C]1.36827487418158[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]15.2826020165788[/C][C]-0.282602016578752[/C][/ROW]
[ROW][C]39[/C][C]5[/C][C]8.38120381094174[/C][C]-3.38120381094174[/C][/ROW]
[ROW][C]40[/C][C]15[/C][C]13.3729172486069[/C][C]1.62708275139306[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]14.0991870889772[/C][C]1.90081291102280[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]14.0991870889772[/C][C]1.90081291102280[/C][/ROW]
[ROW][C]43[/C][C]14[/C][C]13.2227631260285[/C][C]0.777236873971468[/C][/ROW]
[ROW][C]44[/C][C]13[/C][C]14.9332087423070[/C][C]-1.93320874230697[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.9183935436696[/C][C]-0.91839354366963[/C][/ROW]
[ROW][C]46[/C][C]12[/C][C]13.1047357136250[/C][C]-1.10473571362505[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]13.6243240422423[/C][C]1.37567595775766[/C][/ROW]
[ROW][C]48[/C][C]13[/C][C]11.0476202913814[/C][C]1.95237970861863[/C][/ROW]
[ROW][C]49[/C][C]10[/C][C]10.6197319216271[/C][C]-0.619731921627061[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]13.5433525232723[/C][C]-0.543352523272348[/C][/ROW]
[ROW][C]51[/C][C]14[/C][C]13.9732152435555[/C][C]0.0267847564445257[/C][/ROW]
[ROW][C]52[/C][C]13[/C][C]13.2183390844328[/C][C]-0.21833908443281[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]14.1786717950998[/C][C]3.82132820490016[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.5785484584219[/C][C]0.421451541578082[/C][/ROW]
[ROW][C]55[/C][C]15[/C][C]14.0385252812456[/C][C]0.96147471875442[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.4299582877596[/C][C]1.57004171224043[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]14.5434844012674[/C][C]1.45651559873256[/C][/ROW]
[ROW][C]58[/C][C]11[/C][C]10.3772867126996[/C][C]0.62271328730036[/C][/ROW]
[ROW][C]59[/C][C]13[/C][C]12.3463046951977[/C][C]0.653695304802288[/C][/ROW]
[ROW][C]60[/C][C]14[/C][C]13.7545736342187[/C][C]0.245426365781316[/C][/ROW]
[ROW][C]61[/C][C]14[/C][C]11.0453168953556[/C][C]2.95468310464444[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]12.0432817352977[/C][C]-0.0432817352976996[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]12.5394520687229[/C][C]3.46054793127706[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]15.8427074020057[/C][C]-1.84270740200570[/C][/ROW]
[ROW][C]65[/C][C]12[/C][C]13.9972393010702[/C][C]-1.99723930107017[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]14.2389202280330[/C][C]-1.23892022803296[/C][/ROW]
[ROW][C]67[/C][C]13[/C][C]13.3026265097172[/C][C]-0.302626509717229[/C][/ROW]
[ROW][C]68[/C][C]10[/C][C]10.7461516097294[/C][C]-0.746151609729437[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]15.3746528826079[/C][C]-0.374652882607865[/C][/ROW]
[ROW][C]70[/C][C]13[/C][C]12.8467872430597[/C][C]0.153212756940269[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]13.6043503465257[/C][C]0.395649653474324[/C][/ROW]
[ROW][C]72[/C][C]15[/C][C]11.4436026382351[/C][C]3.55639736176488[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]13.2321993696625[/C][C]0.76780063033752[/C][/ROW]
[ROW][C]74[/C][C]12[/C][C]13.605911322256[/C][C]-1.60591132225599[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]14.4780575386500[/C][C]-1.47805753865003[/C][/ROW]
[ROW][C]76[/C][C]14[/C][C]13.1257336148809[/C][C]0.874266385119072[/C][/ROW]
[ROW][C]77[/C][C]4[/C][C]10.0915880999005[/C][C]-6.09158809990052[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109096&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109096&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
11314.7887500140159-1.78875001401595
21111.2193418535750-0.219341853575041
31413.73757856196510.262421438034914
41212.6492751245336-0.649275124533604
51214.0182847351218-2.01828473512181
6610.8553686032175-4.85536860321745
71012.8152863576033-2.81528635760329
81112.6857121200811-1.68571212008114
9108.03346840472261.96653159527740
101212.3357147125509-0.335714712550877
111516.477730123962-1.47773012396199
121312.72528083394840.274719166051585
131110.82902621484030.170973785159657
141211.70340670533780.296593294662223
151313.4339150718603-0.433915071860267
161414.6230531641491-0.62305316414911
171615.49764610456650.502353895433471
181615.36177233949850.638227660501471
191614.22883061863091.77116938136910
201515.2922587181368-0.292258718136765
211312.87081130057440.129188699425574
22810.8378237981925-2.8378237981925
231412.28858011621451.71141988378553
241514.62954525220220.370454747797843
251313.5833049124740-0.583304912474038
261614.28119094809711.71880905190294
271312.28792845016020.712071549839767
281214.2485077644764-2.24850776447637
291515.4976461045665-0.497646104566529
301411.36426126011002.63573873988996
311313.1068680544456-0.106868054445558
321210.16607252858581.83392747141417
331413.60855393019730.391446069802666
341312.01745648173280.98254351826721
351414.4730572612127-0.473057261212694
361515.5482527707782-0.548252770778241
371614.63172512581841.36827487418158
381515.2826020165788-0.282602016578752
3958.38120381094174-3.38120381094174
401513.37291724860691.62708275139306
411614.09918708897721.90081291102280
421614.09918708897721.90081291102280
431413.22276312602850.777236873971468
441314.9332087423070-1.93320874230697
451414.9183935436696-0.91839354366963
461213.1047357136250-1.10473571362505
471513.62432404224231.37567595775766
481311.04762029138141.95237970861863
491010.6197319216271-0.619731921627061
501313.5433525232723-0.543352523272348
511413.97321524355550.0267847564445257
521313.2183390844328-0.21833908443281
531814.17867179509983.82132820490016
541615.57854845842190.421451541578082
551514.03852528124560.96147471875442
561412.42995828775961.57004171224043
571614.54348440126741.45651559873256
581110.37728671269960.62271328730036
591312.34630469519770.653695304802288
601413.75457363421870.245426365781316
611411.04531689535562.95468310464444
621212.0432817352977-0.0432817352976996
631612.53945206872293.46054793127706
641415.8427074020057-1.84270740200570
651213.9972393010702-1.99723930107017
661314.2389202280330-1.23892022803296
671313.3026265097172-0.302626509717229
681010.7461516097294-0.746151609729437
691515.3746528826079-0.374652882607865
701312.84678724305970.153212756940269
711413.60435034652570.395649653474324
721511.44360263823513.55639736176488
731413.23219936966250.76780063033752
741213.605911322256-1.60591132225599
751314.4780575386500-1.47805753865003
761413.12573361488090.874266385119072
77410.0915880999005-6.09158809990052






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.841923999533150.31615200093370.15807600046685
110.7518206868415490.4963586263169030.248179313158451
120.6272001689647750.745599662070450.372799831035225
130.7092980629479690.5814038741040620.290701937052031
140.6747462953496240.6505074093007520.325253704650376
150.5721894336074940.8556211327850110.427810566392506
160.4841924162504550.968384832500910.515807583749545
170.4447268955569170.8894537911138330.555273104443083
180.3529656392493350.705931278498670.647034360750665
190.4933826490222170.9867652980444350.506617350977783
200.403802552100180.807605104200360.59619744789982
210.3268643928990420.6537287857980840.673135607100958
220.5126927184025680.9746145631948640.487307281597432
230.572028200062460.855943599875080.42797179993754
240.4997105175991510.9994210351983020.500289482400849
250.4233372387938720.8466744775877450.576662761206128
260.4232959147884760.8465918295769510.576704085211524
270.3551954500824640.7103909001649280.644804549917536
280.3669061463115260.7338122926230520.633093853688474
290.3016599478774750.6033198957549490.698340052122525
300.3863479341919750.7726958683839490.613652065808025
310.3171360425034500.6342720850069010.68286395749655
320.3185721833069840.6371443666139690.681427816693016
330.2804589069788850.560917813957770.719541093021115
340.2337383415663510.4674766831327020.766261658433649
350.1920609355982430.3841218711964850.807939064401757
360.1534105617490950.306821123498190.846589438250905
370.1392519473727730.2785038947455450.860748052627228
380.1029934343554860.2059868687109720.897006565644514
390.2230191216549590.4460382433099170.776980878345041
400.2113444358735830.4226888717471660.788655564126417
410.2125517145716390.4251034291432780.78744828542836
420.2102019443748440.4204038887496870.789798055625156
430.1709353497218990.3418706994437980.8290646502781
440.1757950748983410.3515901497966820.82420492510166
450.1522019759699070.3044039519398140.847798024030093
460.1220192468603010.2440384937206010.8779807531397
470.1342086501481320.2684173002962640.865791349851868
480.1217163440002530.2434326880005070.878283655999747
490.1158472078355650.2316944156711300.884152792164435
500.08712044639070790.1742408927814160.912879553609292
510.06207936761147440.1241587352229490.937920632388526
520.04533839581505190.09067679163010390.954661604184948
530.1508878737681720.3017757475363450.849112126231828
540.1105479902633570.2210959805267140.889452009736643
550.08550593706235980.1710118741247200.91449406293764
560.06287380871931250.1257476174386250.937126191280688
570.0461962387736810.0923924775473620.953803761226319
580.02972720794888360.05945441589776720.970272792051116
590.01922729672908600.03845459345817210.980772703270914
600.01186949834984780.02373899669969570.988130501650152
610.04803481765308050.0960696353061610.95196518234692
620.02869159837372310.05738319674744620.971308401626277
630.1577113055246310.3154226110492620.84228869447537
640.1491969110921760.2983938221843520.850803088907824
650.5004182339617910.9991635320764170.499581766038209
660.5093896339886930.9812207320226140.490610366011307
670.4560994114922220.9121988229844450.543900588507778

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.84192399953315 & 0.3161520009337 & 0.15807600046685 \tabularnewline
11 & 0.751820686841549 & 0.496358626316903 & 0.248179313158451 \tabularnewline
12 & 0.627200168964775 & 0.74559966207045 & 0.372799831035225 \tabularnewline
13 & 0.709298062947969 & 0.581403874104062 & 0.290701937052031 \tabularnewline
14 & 0.674746295349624 & 0.650507409300752 & 0.325253704650376 \tabularnewline
15 & 0.572189433607494 & 0.855621132785011 & 0.427810566392506 \tabularnewline
16 & 0.484192416250455 & 0.96838483250091 & 0.515807583749545 \tabularnewline
17 & 0.444726895556917 & 0.889453791113833 & 0.555273104443083 \tabularnewline
18 & 0.352965639249335 & 0.70593127849867 & 0.647034360750665 \tabularnewline
19 & 0.493382649022217 & 0.986765298044435 & 0.506617350977783 \tabularnewline
20 & 0.40380255210018 & 0.80760510420036 & 0.59619744789982 \tabularnewline
21 & 0.326864392899042 & 0.653728785798084 & 0.673135607100958 \tabularnewline
22 & 0.512692718402568 & 0.974614563194864 & 0.487307281597432 \tabularnewline
23 & 0.57202820006246 & 0.85594359987508 & 0.42797179993754 \tabularnewline
24 & 0.499710517599151 & 0.999421035198302 & 0.500289482400849 \tabularnewline
25 & 0.423337238793872 & 0.846674477587745 & 0.576662761206128 \tabularnewline
26 & 0.423295914788476 & 0.846591829576951 & 0.576704085211524 \tabularnewline
27 & 0.355195450082464 & 0.710390900164928 & 0.644804549917536 \tabularnewline
28 & 0.366906146311526 & 0.733812292623052 & 0.633093853688474 \tabularnewline
29 & 0.301659947877475 & 0.603319895754949 & 0.698340052122525 \tabularnewline
30 & 0.386347934191975 & 0.772695868383949 & 0.613652065808025 \tabularnewline
31 & 0.317136042503450 & 0.634272085006901 & 0.68286395749655 \tabularnewline
32 & 0.318572183306984 & 0.637144366613969 & 0.681427816693016 \tabularnewline
33 & 0.280458906978885 & 0.56091781395777 & 0.719541093021115 \tabularnewline
34 & 0.233738341566351 & 0.467476683132702 & 0.766261658433649 \tabularnewline
35 & 0.192060935598243 & 0.384121871196485 & 0.807939064401757 \tabularnewline
36 & 0.153410561749095 & 0.30682112349819 & 0.846589438250905 \tabularnewline
37 & 0.139251947372773 & 0.278503894745545 & 0.860748052627228 \tabularnewline
38 & 0.102993434355486 & 0.205986868710972 & 0.897006565644514 \tabularnewline
39 & 0.223019121654959 & 0.446038243309917 & 0.776980878345041 \tabularnewline
40 & 0.211344435873583 & 0.422688871747166 & 0.788655564126417 \tabularnewline
41 & 0.212551714571639 & 0.425103429143278 & 0.78744828542836 \tabularnewline
42 & 0.210201944374844 & 0.420403888749687 & 0.789798055625156 \tabularnewline
43 & 0.170935349721899 & 0.341870699443798 & 0.8290646502781 \tabularnewline
44 & 0.175795074898341 & 0.351590149796682 & 0.82420492510166 \tabularnewline
45 & 0.152201975969907 & 0.304403951939814 & 0.847798024030093 \tabularnewline
46 & 0.122019246860301 & 0.244038493720601 & 0.8779807531397 \tabularnewline
47 & 0.134208650148132 & 0.268417300296264 & 0.865791349851868 \tabularnewline
48 & 0.121716344000253 & 0.243432688000507 & 0.878283655999747 \tabularnewline
49 & 0.115847207835565 & 0.231694415671130 & 0.884152792164435 \tabularnewline
50 & 0.0871204463907079 & 0.174240892781416 & 0.912879553609292 \tabularnewline
51 & 0.0620793676114744 & 0.124158735222949 & 0.937920632388526 \tabularnewline
52 & 0.0453383958150519 & 0.0906767916301039 & 0.954661604184948 \tabularnewline
53 & 0.150887873768172 & 0.301775747536345 & 0.849112126231828 \tabularnewline
54 & 0.110547990263357 & 0.221095980526714 & 0.889452009736643 \tabularnewline
55 & 0.0855059370623598 & 0.171011874124720 & 0.91449406293764 \tabularnewline
56 & 0.0628738087193125 & 0.125747617438625 & 0.937126191280688 \tabularnewline
57 & 0.046196238773681 & 0.092392477547362 & 0.953803761226319 \tabularnewline
58 & 0.0297272079488836 & 0.0594544158977672 & 0.970272792051116 \tabularnewline
59 & 0.0192272967290860 & 0.0384545934581721 & 0.980772703270914 \tabularnewline
60 & 0.0118694983498478 & 0.0237389966996957 & 0.988130501650152 \tabularnewline
61 & 0.0480348176530805 & 0.096069635306161 & 0.95196518234692 \tabularnewline
62 & 0.0286915983737231 & 0.0573831967474462 & 0.971308401626277 \tabularnewline
63 & 0.157711305524631 & 0.315422611049262 & 0.84228869447537 \tabularnewline
64 & 0.149196911092176 & 0.298393822184352 & 0.850803088907824 \tabularnewline
65 & 0.500418233961791 & 0.999163532076417 & 0.499581766038209 \tabularnewline
66 & 0.509389633988693 & 0.981220732022614 & 0.490610366011307 \tabularnewline
67 & 0.456099411492222 & 0.912198822984445 & 0.543900588507778 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109096&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]0.84192399953315[/C][C]0.3161520009337[/C][C]0.15807600046685[/C][/ROW]
[ROW][C]11[/C][C]0.751820686841549[/C][C]0.496358626316903[/C][C]0.248179313158451[/C][/ROW]
[ROW][C]12[/C][C]0.627200168964775[/C][C]0.74559966207045[/C][C]0.372799831035225[/C][/ROW]
[ROW][C]13[/C][C]0.709298062947969[/C][C]0.581403874104062[/C][C]0.290701937052031[/C][/ROW]
[ROW][C]14[/C][C]0.674746295349624[/C][C]0.650507409300752[/C][C]0.325253704650376[/C][/ROW]
[ROW][C]15[/C][C]0.572189433607494[/C][C]0.855621132785011[/C][C]0.427810566392506[/C][/ROW]
[ROW][C]16[/C][C]0.484192416250455[/C][C]0.96838483250091[/C][C]0.515807583749545[/C][/ROW]
[ROW][C]17[/C][C]0.444726895556917[/C][C]0.889453791113833[/C][C]0.555273104443083[/C][/ROW]
[ROW][C]18[/C][C]0.352965639249335[/C][C]0.70593127849867[/C][C]0.647034360750665[/C][/ROW]
[ROW][C]19[/C][C]0.493382649022217[/C][C]0.986765298044435[/C][C]0.506617350977783[/C][/ROW]
[ROW][C]20[/C][C]0.40380255210018[/C][C]0.80760510420036[/C][C]0.59619744789982[/C][/ROW]
[ROW][C]21[/C][C]0.326864392899042[/C][C]0.653728785798084[/C][C]0.673135607100958[/C][/ROW]
[ROW][C]22[/C][C]0.512692718402568[/C][C]0.974614563194864[/C][C]0.487307281597432[/C][/ROW]
[ROW][C]23[/C][C]0.57202820006246[/C][C]0.85594359987508[/C][C]0.42797179993754[/C][/ROW]
[ROW][C]24[/C][C]0.499710517599151[/C][C]0.999421035198302[/C][C]0.500289482400849[/C][/ROW]
[ROW][C]25[/C][C]0.423337238793872[/C][C]0.846674477587745[/C][C]0.576662761206128[/C][/ROW]
[ROW][C]26[/C][C]0.423295914788476[/C][C]0.846591829576951[/C][C]0.576704085211524[/C][/ROW]
[ROW][C]27[/C][C]0.355195450082464[/C][C]0.710390900164928[/C][C]0.644804549917536[/C][/ROW]
[ROW][C]28[/C][C]0.366906146311526[/C][C]0.733812292623052[/C][C]0.633093853688474[/C][/ROW]
[ROW][C]29[/C][C]0.301659947877475[/C][C]0.603319895754949[/C][C]0.698340052122525[/C][/ROW]
[ROW][C]30[/C][C]0.386347934191975[/C][C]0.772695868383949[/C][C]0.613652065808025[/C][/ROW]
[ROW][C]31[/C][C]0.317136042503450[/C][C]0.634272085006901[/C][C]0.68286395749655[/C][/ROW]
[ROW][C]32[/C][C]0.318572183306984[/C][C]0.637144366613969[/C][C]0.681427816693016[/C][/ROW]
[ROW][C]33[/C][C]0.280458906978885[/C][C]0.56091781395777[/C][C]0.719541093021115[/C][/ROW]
[ROW][C]34[/C][C]0.233738341566351[/C][C]0.467476683132702[/C][C]0.766261658433649[/C][/ROW]
[ROW][C]35[/C][C]0.192060935598243[/C][C]0.384121871196485[/C][C]0.807939064401757[/C][/ROW]
[ROW][C]36[/C][C]0.153410561749095[/C][C]0.30682112349819[/C][C]0.846589438250905[/C][/ROW]
[ROW][C]37[/C][C]0.139251947372773[/C][C]0.278503894745545[/C][C]0.860748052627228[/C][/ROW]
[ROW][C]38[/C][C]0.102993434355486[/C][C]0.205986868710972[/C][C]0.897006565644514[/C][/ROW]
[ROW][C]39[/C][C]0.223019121654959[/C][C]0.446038243309917[/C][C]0.776980878345041[/C][/ROW]
[ROW][C]40[/C][C]0.211344435873583[/C][C]0.422688871747166[/C][C]0.788655564126417[/C][/ROW]
[ROW][C]41[/C][C]0.212551714571639[/C][C]0.425103429143278[/C][C]0.78744828542836[/C][/ROW]
[ROW][C]42[/C][C]0.210201944374844[/C][C]0.420403888749687[/C][C]0.789798055625156[/C][/ROW]
[ROW][C]43[/C][C]0.170935349721899[/C][C]0.341870699443798[/C][C]0.8290646502781[/C][/ROW]
[ROW][C]44[/C][C]0.175795074898341[/C][C]0.351590149796682[/C][C]0.82420492510166[/C][/ROW]
[ROW][C]45[/C][C]0.152201975969907[/C][C]0.304403951939814[/C][C]0.847798024030093[/C][/ROW]
[ROW][C]46[/C][C]0.122019246860301[/C][C]0.244038493720601[/C][C]0.8779807531397[/C][/ROW]
[ROW][C]47[/C][C]0.134208650148132[/C][C]0.268417300296264[/C][C]0.865791349851868[/C][/ROW]
[ROW][C]48[/C][C]0.121716344000253[/C][C]0.243432688000507[/C][C]0.878283655999747[/C][/ROW]
[ROW][C]49[/C][C]0.115847207835565[/C][C]0.231694415671130[/C][C]0.884152792164435[/C][/ROW]
[ROW][C]50[/C][C]0.0871204463907079[/C][C]0.174240892781416[/C][C]0.912879553609292[/C][/ROW]
[ROW][C]51[/C][C]0.0620793676114744[/C][C]0.124158735222949[/C][C]0.937920632388526[/C][/ROW]
[ROW][C]52[/C][C]0.0453383958150519[/C][C]0.0906767916301039[/C][C]0.954661604184948[/C][/ROW]
[ROW][C]53[/C][C]0.150887873768172[/C][C]0.301775747536345[/C][C]0.849112126231828[/C][/ROW]
[ROW][C]54[/C][C]0.110547990263357[/C][C]0.221095980526714[/C][C]0.889452009736643[/C][/ROW]
[ROW][C]55[/C][C]0.0855059370623598[/C][C]0.171011874124720[/C][C]0.91449406293764[/C][/ROW]
[ROW][C]56[/C][C]0.0628738087193125[/C][C]0.125747617438625[/C][C]0.937126191280688[/C][/ROW]
[ROW][C]57[/C][C]0.046196238773681[/C][C]0.092392477547362[/C][C]0.953803761226319[/C][/ROW]
[ROW][C]58[/C][C]0.0297272079488836[/C][C]0.0594544158977672[/C][C]0.970272792051116[/C][/ROW]
[ROW][C]59[/C][C]0.0192272967290860[/C][C]0.0384545934581721[/C][C]0.980772703270914[/C][/ROW]
[ROW][C]60[/C][C]0.0118694983498478[/C][C]0.0237389966996957[/C][C]0.988130501650152[/C][/ROW]
[ROW][C]61[/C][C]0.0480348176530805[/C][C]0.096069635306161[/C][C]0.95196518234692[/C][/ROW]
[ROW][C]62[/C][C]0.0286915983737231[/C][C]0.0573831967474462[/C][C]0.971308401626277[/C][/ROW]
[ROW][C]63[/C][C]0.157711305524631[/C][C]0.315422611049262[/C][C]0.84228869447537[/C][/ROW]
[ROW][C]64[/C][C]0.149196911092176[/C][C]0.298393822184352[/C][C]0.850803088907824[/C][/ROW]
[ROW][C]65[/C][C]0.500418233961791[/C][C]0.999163532076417[/C][C]0.499581766038209[/C][/ROW]
[ROW][C]66[/C][C]0.509389633988693[/C][C]0.981220732022614[/C][C]0.490610366011307[/C][/ROW]
[ROW][C]67[/C][C]0.456099411492222[/C][C]0.912198822984445[/C][C]0.543900588507778[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109096&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109096&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
100.841923999533150.31615200093370.15807600046685
110.7518206868415490.4963586263169030.248179313158451
120.6272001689647750.745599662070450.372799831035225
130.7092980629479690.5814038741040620.290701937052031
140.6747462953496240.6505074093007520.325253704650376
150.5721894336074940.8556211327850110.427810566392506
160.4841924162504550.968384832500910.515807583749545
170.4447268955569170.8894537911138330.555273104443083
180.3529656392493350.705931278498670.647034360750665
190.4933826490222170.9867652980444350.506617350977783
200.403802552100180.807605104200360.59619744789982
210.3268643928990420.6537287857980840.673135607100958
220.5126927184025680.9746145631948640.487307281597432
230.572028200062460.855943599875080.42797179993754
240.4997105175991510.9994210351983020.500289482400849
250.4233372387938720.8466744775877450.576662761206128
260.4232959147884760.8465918295769510.576704085211524
270.3551954500824640.7103909001649280.644804549917536
280.3669061463115260.7338122926230520.633093853688474
290.3016599478774750.6033198957549490.698340052122525
300.3863479341919750.7726958683839490.613652065808025
310.3171360425034500.6342720850069010.68286395749655
320.3185721833069840.6371443666139690.681427816693016
330.2804589069788850.560917813957770.719541093021115
340.2337383415663510.4674766831327020.766261658433649
350.1920609355982430.3841218711964850.807939064401757
360.1534105617490950.306821123498190.846589438250905
370.1392519473727730.2785038947455450.860748052627228
380.1029934343554860.2059868687109720.897006565644514
390.2230191216549590.4460382433099170.776980878345041
400.2113444358735830.4226888717471660.788655564126417
410.2125517145716390.4251034291432780.78744828542836
420.2102019443748440.4204038887496870.789798055625156
430.1709353497218990.3418706994437980.8290646502781
440.1757950748983410.3515901497966820.82420492510166
450.1522019759699070.3044039519398140.847798024030093
460.1220192468603010.2440384937206010.8779807531397
470.1342086501481320.2684173002962640.865791349851868
480.1217163440002530.2434326880005070.878283655999747
490.1158472078355650.2316944156711300.884152792164435
500.08712044639070790.1742408927814160.912879553609292
510.06207936761147440.1241587352229490.937920632388526
520.04533839581505190.09067679163010390.954661604184948
530.1508878737681720.3017757475363450.849112126231828
540.1105479902633570.2210959805267140.889452009736643
550.08550593706235980.1710118741247200.91449406293764
560.06287380871931250.1257476174386250.937126191280688
570.0461962387736810.0923924775473620.953803761226319
580.02972720794888360.05945441589776720.970272792051116
590.01922729672908600.03845459345817210.980772703270914
600.01186949834984780.02373899669969570.988130501650152
610.04803481765308050.0960696353061610.95196518234692
620.02869159837372310.05738319674744620.971308401626277
630.1577113055246310.3154226110492620.84228869447537
640.1491969110921760.2983938221843520.850803088907824
650.5004182339617910.9991635320764170.499581766038209
660.5093896339886930.9812207320226140.490610366011307
670.4560994114922220.9121988229844450.543900588507778






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0344827586206897OK
10% type I error level70.120689655172414NOK

\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 & 0.0344827586206897 & OK \tabularnewline
10% type I error level & 7 & 0.120689655172414 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109096&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]0.0344827586206897[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]7[/C][C]0.120689655172414[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109096&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109096&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 level20.0344827586206897OK
10% type I error level70.120689655172414NOK


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