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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 12 Dec 2010 18:08:28 +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/12/t1292177258etbndsi33otw3uh.htm/, Retrieved Thu, 31 Oct 2024 23:25:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108594, Retrieved Thu, 31 Oct 2024 23:25:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact199
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
-   PD  [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2010-12-11 15:32:45] [2960375a246cc0628590c95c4038a43c]
-    D    [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2010-12-12 10:40:43] [2960375a246cc0628590c95c4038a43c]
-   PD      [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2010-12-12 16:48:09] [2960375a246cc0628590c95c4038a43c]
-   P         [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2010-12-12 16:54:09] [2960375a246cc0628590c95c4038a43c]
- RMPD            [Multiple Regression] [Meervoudige Regre...] [2010-12-12 18:08:28] [4b5105369ca2b03f8f7589f5d63124c0] [Current]
-                   [Multiple Regression] [Meervoudig regres...] [2010-12-14 09:37:23] [62f7c80c4d96454bbd2b2b026ea9aad9]
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Dataseries X:
2.0	3	42.0
1.8	4	624.0
0.7	4	180.0
3.9	1	35.0
1.0	4	392.0
3.6	1	63.0
1.4	1	230.0
1.5	4	112.0
0.7	5	281.0
2.1	1	42.0
0.0	2	28.0
4.1	2	42.0
1.2	2	120.0
0.5	5	148.0
3.4	2	16.0
1.5	1	310.0
3.4	3	28.0
0.8	4	68.0
0.8	5	336.0
1.4	4	21.5
2.0	1	50.0
1.9	1	267.0
1.3	3	19.0
2.0	3	30.0
5.6	1	12.0
3.1	1	120.0
1.8	2	140.0
0.9	4	170.0
1.8	2	17.0
1.9	4	115.0
0.9	5	31.0
2.6	3	21.0
2.4	1	52.0
1.2	2	164.0
0.9	2	225.0
0.5	3	225.0
0.6	5	151.0
2.3	2	60.0
0.5	3	200.0
2.6	2	46.0
0.6	4	210.0
6.6	1	14.0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108594&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108594&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108594&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
PS[t] = + 3.59129827359983 -0.493271805642968D[t] -0.00280253109547180tg[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PS[t] =  +  3.59129827359983 -0.493271805642968D[t] -0.00280253109547180tg[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108594&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PS[t] =  +  3.59129827359983 -0.493271805642968D[t] -0.00280253109547180tg[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108594&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108594&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
PS[t] = + 3.59129827359983 -0.493271805642968D[t] -0.00280253109547180tg[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.591298273599830.3865169.291500
D-0.4932718056429680.131738-3.74430.0005830.000292
tg-0.002802531095471800.00143-1.95950.0572280.028614

\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) & 3.59129827359983 & 0.386516 & 9.2915 & 0 & 0 \tabularnewline
D & -0.493271805642968 & 0.131738 & -3.7443 & 0.000583 & 0.000292 \tabularnewline
tg & -0.00280253109547180 & 0.00143 & -1.9595 & 0.057228 & 0.028614 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108594&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]3.59129827359983[/C][C]0.386516[/C][C]9.2915[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]D[/C][C]-0.493271805642968[/C][C]0.131738[/C][C]-3.7443[/C][C]0.000583[/C][C]0.000292[/C][/ROW]
[ROW][C]tg[/C][C]-0.00280253109547180[/C][C]0.00143[/C][C]-1.9595[/C][C]0.057228[/C][C]0.028614[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108594&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108594&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.591298273599830.3865169.291500
D-0.4932718056429680.131738-3.74430.0005830.000292
tg-0.002802531095471800.00143-1.95950.0572280.028614







Multiple Linear Regression - Regression Statistics
Multiple R0.622442364712445
R-squared0.38743449738882
Adjusted R-squared0.356020881870298
F-TEST (value)12.3333303408002
F-TEST (DF numerator)2
F-TEST (DF denominator)39
p-value7.07089582455689e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.11449287014425
Sum Squared Residuals48.4416799464921

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.622442364712445 \tabularnewline
R-squared & 0.38743449738882 \tabularnewline
Adjusted R-squared & 0.356020881870298 \tabularnewline
F-TEST (value) & 12.3333303408002 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 39 \tabularnewline
p-value & 7.07089582455689e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.11449287014425 \tabularnewline
Sum Squared Residuals & 48.4416799464921 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108594&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.622442364712445[/C][/ROW]
[ROW][C]R-squared[/C][C]0.38743449738882[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.356020881870298[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]12.3333303408002[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]39[/C][/ROW]
[ROW][C]p-value[/C][C]7.07089582455689e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.11449287014425[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]48.4416799464921[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108594&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108594&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.622442364712445
R-squared0.38743449738882
Adjusted R-squared0.356020881870298
F-TEST (value)12.3333303408002
F-TEST (DF numerator)2
F-TEST (DF denominator)39
p-value7.07089582455689e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.11449287014425
Sum Squared Residuals48.4416799464921







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
121.993776550661110.0062234493388883
21.8-0.1305683525464541.93056835254645
30.71.11375545384303-0.413755453843029
43.92.999937879615350.900062120384652
510.5196188616030070.480381138396993
63.62.921467008942140.678532991057863
71.42.45344431599835-1.05344431599835
81.51.304327568335110.195672431664888
90.70.3374280075574090.362571992442591
102.12.98032016194704-0.880320161947045
1102.52628379164068-2.52628379164068
124.12.487048356304081.61295164369592
131.22.26845093085728-1.06845093085728
140.50.710164643255159-0.210164643255159
153.42.559914164786340.840085835213657
161.52.22924182836060-0.729241828360602
173.42.033011985997711.36698801400229
180.81.42763893653587-0.627638936535872
190.80.1832887973064600.61671120269354
201.41.55795663247531-0.157956632475311
2122.95789991318327-0.95789991318327
221.92.34975066546589-0.449750665465889
231.32.05823476585696-0.758234765856959
2422.02740692380677-0.027406923806769
255.63.06439609481122.5356039051888
263.12.761722736500240.338277263499756
271.82.21240030894784-0.412400308947839
280.91.14178076479775-0.241780764797748
291.82.55711163369087-0.757111633690871
301.91.295919975048700.604080024951303
310.91.03806078142536-0.138060781425360
322.62.052629703666020.547370296333985
332.42.95229485099233-0.552294850992327
341.22.14513956265652-0.945139562656516
350.91.97418516583274-1.07418516583274
360.51.48091336018977-0.980913360189767
370.60.701757049968743-0.101757049968743
382.32.43660279658558-0.136602796585584
390.51.55097663757656-1.05097663757656
402.62.475838231922190.124161768077811
410.61.02967952097888-0.429679520978876
426.63.058791032620263.54120896737974

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 1.99377655066111 & 0.0062234493388883 \tabularnewline
2 & 1.8 & -0.130568352546454 & 1.93056835254645 \tabularnewline
3 & 0.7 & 1.11375545384303 & -0.413755453843029 \tabularnewline
4 & 3.9 & 2.99993787961535 & 0.900062120384652 \tabularnewline
5 & 1 & 0.519618861603007 & 0.480381138396993 \tabularnewline
6 & 3.6 & 2.92146700894214 & 0.678532991057863 \tabularnewline
7 & 1.4 & 2.45344431599835 & -1.05344431599835 \tabularnewline
8 & 1.5 & 1.30432756833511 & 0.195672431664888 \tabularnewline
9 & 0.7 & 0.337428007557409 & 0.362571992442591 \tabularnewline
10 & 2.1 & 2.98032016194704 & -0.880320161947045 \tabularnewline
11 & 0 & 2.52628379164068 & -2.52628379164068 \tabularnewline
12 & 4.1 & 2.48704835630408 & 1.61295164369592 \tabularnewline
13 & 1.2 & 2.26845093085728 & -1.06845093085728 \tabularnewline
14 & 0.5 & 0.710164643255159 & -0.210164643255159 \tabularnewline
15 & 3.4 & 2.55991416478634 & 0.840085835213657 \tabularnewline
16 & 1.5 & 2.22924182836060 & -0.729241828360602 \tabularnewline
17 & 3.4 & 2.03301198599771 & 1.36698801400229 \tabularnewline
18 & 0.8 & 1.42763893653587 & -0.627638936535872 \tabularnewline
19 & 0.8 & 0.183288797306460 & 0.61671120269354 \tabularnewline
20 & 1.4 & 1.55795663247531 & -0.157956632475311 \tabularnewline
21 & 2 & 2.95789991318327 & -0.95789991318327 \tabularnewline
22 & 1.9 & 2.34975066546589 & -0.449750665465889 \tabularnewline
23 & 1.3 & 2.05823476585696 & -0.758234765856959 \tabularnewline
24 & 2 & 2.02740692380677 & -0.027406923806769 \tabularnewline
25 & 5.6 & 3.0643960948112 & 2.5356039051888 \tabularnewline
26 & 3.1 & 2.76172273650024 & 0.338277263499756 \tabularnewline
27 & 1.8 & 2.21240030894784 & -0.412400308947839 \tabularnewline
28 & 0.9 & 1.14178076479775 & -0.241780764797748 \tabularnewline
29 & 1.8 & 2.55711163369087 & -0.757111633690871 \tabularnewline
30 & 1.9 & 1.29591997504870 & 0.604080024951303 \tabularnewline
31 & 0.9 & 1.03806078142536 & -0.138060781425360 \tabularnewline
32 & 2.6 & 2.05262970366602 & 0.547370296333985 \tabularnewline
33 & 2.4 & 2.95229485099233 & -0.552294850992327 \tabularnewline
34 & 1.2 & 2.14513956265652 & -0.945139562656516 \tabularnewline
35 & 0.9 & 1.97418516583274 & -1.07418516583274 \tabularnewline
36 & 0.5 & 1.48091336018977 & -0.980913360189767 \tabularnewline
37 & 0.6 & 0.701757049968743 & -0.101757049968743 \tabularnewline
38 & 2.3 & 2.43660279658558 & -0.136602796585584 \tabularnewline
39 & 0.5 & 1.55097663757656 & -1.05097663757656 \tabularnewline
40 & 2.6 & 2.47583823192219 & 0.124161768077811 \tabularnewline
41 & 0.6 & 1.02967952097888 & -0.429679520978876 \tabularnewline
42 & 6.6 & 3.05879103262026 & 3.54120896737974 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108594&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]2[/C][C]1.99377655066111[/C][C]0.0062234493388883[/C][/ROW]
[ROW][C]2[/C][C]1.8[/C][C]-0.130568352546454[/C][C]1.93056835254645[/C][/ROW]
[ROW][C]3[/C][C]0.7[/C][C]1.11375545384303[/C][C]-0.413755453843029[/C][/ROW]
[ROW][C]4[/C][C]3.9[/C][C]2.99993787961535[/C][C]0.900062120384652[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]0.519618861603007[/C][C]0.480381138396993[/C][/ROW]
[ROW][C]6[/C][C]3.6[/C][C]2.92146700894214[/C][C]0.678532991057863[/C][/ROW]
[ROW][C]7[/C][C]1.4[/C][C]2.45344431599835[/C][C]-1.05344431599835[/C][/ROW]
[ROW][C]8[/C][C]1.5[/C][C]1.30432756833511[/C][C]0.195672431664888[/C][/ROW]
[ROW][C]9[/C][C]0.7[/C][C]0.337428007557409[/C][C]0.362571992442591[/C][/ROW]
[ROW][C]10[/C][C]2.1[/C][C]2.98032016194704[/C][C]-0.880320161947045[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]2.52628379164068[/C][C]-2.52628379164068[/C][/ROW]
[ROW][C]12[/C][C]4.1[/C][C]2.48704835630408[/C][C]1.61295164369592[/C][/ROW]
[ROW][C]13[/C][C]1.2[/C][C]2.26845093085728[/C][C]-1.06845093085728[/C][/ROW]
[ROW][C]14[/C][C]0.5[/C][C]0.710164643255159[/C][C]-0.210164643255159[/C][/ROW]
[ROW][C]15[/C][C]3.4[/C][C]2.55991416478634[/C][C]0.840085835213657[/C][/ROW]
[ROW][C]16[/C][C]1.5[/C][C]2.22924182836060[/C][C]-0.729241828360602[/C][/ROW]
[ROW][C]17[/C][C]3.4[/C][C]2.03301198599771[/C][C]1.36698801400229[/C][/ROW]
[ROW][C]18[/C][C]0.8[/C][C]1.42763893653587[/C][C]-0.627638936535872[/C][/ROW]
[ROW][C]19[/C][C]0.8[/C][C]0.183288797306460[/C][C]0.61671120269354[/C][/ROW]
[ROW][C]20[/C][C]1.4[/C][C]1.55795663247531[/C][C]-0.157956632475311[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]2.95789991318327[/C][C]-0.95789991318327[/C][/ROW]
[ROW][C]22[/C][C]1.9[/C][C]2.34975066546589[/C][C]-0.449750665465889[/C][/ROW]
[ROW][C]23[/C][C]1.3[/C][C]2.05823476585696[/C][C]-0.758234765856959[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]2.02740692380677[/C][C]-0.027406923806769[/C][/ROW]
[ROW][C]25[/C][C]5.6[/C][C]3.0643960948112[/C][C]2.5356039051888[/C][/ROW]
[ROW][C]26[/C][C]3.1[/C][C]2.76172273650024[/C][C]0.338277263499756[/C][/ROW]
[ROW][C]27[/C][C]1.8[/C][C]2.21240030894784[/C][C]-0.412400308947839[/C][/ROW]
[ROW][C]28[/C][C]0.9[/C][C]1.14178076479775[/C][C]-0.241780764797748[/C][/ROW]
[ROW][C]29[/C][C]1.8[/C][C]2.55711163369087[/C][C]-0.757111633690871[/C][/ROW]
[ROW][C]30[/C][C]1.9[/C][C]1.29591997504870[/C][C]0.604080024951303[/C][/ROW]
[ROW][C]31[/C][C]0.9[/C][C]1.03806078142536[/C][C]-0.138060781425360[/C][/ROW]
[ROW][C]32[/C][C]2.6[/C][C]2.05262970366602[/C][C]0.547370296333985[/C][/ROW]
[ROW][C]33[/C][C]2.4[/C][C]2.95229485099233[/C][C]-0.552294850992327[/C][/ROW]
[ROW][C]34[/C][C]1.2[/C][C]2.14513956265652[/C][C]-0.945139562656516[/C][/ROW]
[ROW][C]35[/C][C]0.9[/C][C]1.97418516583274[/C][C]-1.07418516583274[/C][/ROW]
[ROW][C]36[/C][C]0.5[/C][C]1.48091336018977[/C][C]-0.980913360189767[/C][/ROW]
[ROW][C]37[/C][C]0.6[/C][C]0.701757049968743[/C][C]-0.101757049968743[/C][/ROW]
[ROW][C]38[/C][C]2.3[/C][C]2.43660279658558[/C][C]-0.136602796585584[/C][/ROW]
[ROW][C]39[/C][C]0.5[/C][C]1.55097663757656[/C][C]-1.05097663757656[/C][/ROW]
[ROW][C]40[/C][C]2.6[/C][C]2.47583823192219[/C][C]0.124161768077811[/C][/ROW]
[ROW][C]41[/C][C]0.6[/C][C]1.02967952097888[/C][C]-0.429679520978876[/C][/ROW]
[ROW][C]42[/C][C]6.6[/C][C]3.05879103262026[/C][C]3.54120896737974[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108594&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108594&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
121.993776550661110.0062234493388883
21.8-0.1305683525464541.93056835254645
30.71.11375545384303-0.413755453843029
43.92.999937879615350.900062120384652
510.5196188616030070.480381138396993
63.62.921467008942140.678532991057863
71.42.45344431599835-1.05344431599835
81.51.304327568335110.195672431664888
90.70.3374280075574090.362571992442591
102.12.98032016194704-0.880320161947045
1102.52628379164068-2.52628379164068
124.12.487048356304081.61295164369592
131.22.26845093085728-1.06845093085728
140.50.710164643255159-0.210164643255159
153.42.559914164786340.840085835213657
161.52.22924182836060-0.729241828360602
173.42.033011985997711.36698801400229
180.81.42763893653587-0.627638936535872
190.80.1832887973064600.61671120269354
201.41.55795663247531-0.157956632475311
2122.95789991318327-0.95789991318327
221.92.34975066546589-0.449750665465889
231.32.05823476585696-0.758234765856959
2422.02740692380677-0.027406923806769
255.63.06439609481122.5356039051888
263.12.761722736500240.338277263499756
271.82.21240030894784-0.412400308947839
280.91.14178076479775-0.241780764797748
291.82.55711163369087-0.757111633690871
301.91.295919975048700.604080024951303
310.91.03806078142536-0.138060781425360
322.62.052629703666020.547370296333985
332.42.95229485099233-0.552294850992327
341.22.14513956265652-0.945139562656516
350.91.97418516583274-1.07418516583274
360.51.48091336018977-0.980913360189767
370.60.701757049968743-0.101757049968743
382.32.43660279658558-0.136602796585584
390.51.55097663757656-1.05097663757656
402.62.475838231922190.124161768077811
410.61.02967952097888-0.429679520978876
426.63.058791032620263.54120896737974







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.02789614214253830.05579228428507660.972103857857462
70.518835514445620.962328971108760.48116448555438
80.3677655753812350.735531150762470.632234424618765
90.2517790250004960.5035580500009920.748220974999504
100.2189215245726590.4378430491453180.781078475427341
110.6411768742812830.7176462514374340.358823125718717
120.7975320548268750.404935890346250.202467945173125
130.7817452566119330.4365094867761350.218254743388067
140.6996418569134130.6007162861731740.300358143086587
150.6798684842593690.6402630314812630.320131515740631
160.6292970080064910.7414059839870170.370702991993509
170.6656988870968210.6686022258063570.334301112903179
180.6087458354879560.7825083290240880.391254164512044
190.6487256398113980.7025487203772050.351274360188602
200.561974131925740.876051736148520.43802586807426
210.584694778496470.830610443007060.41530522150353
220.4994184434198560.9988368868397120.500581556580144
230.5053293698633470.9893412602733060.494670630136653
240.4321257775401770.8642515550803530.567874222459823
250.731801288123120.5363974237537610.268198711876880
260.6555821795503170.6888356408993670.344417820449683
270.5637159685547680.8725680628904630.436284031445232
280.4707319649315660.9414639298631320.529268035068434
290.5256475699539820.9487048600920350.474352430046018
300.4659571603641230.9319143207282460.534042839635877
310.3953800179810430.7907600359620850.604619982018958
320.3158353206317770.6316706412635540.684164679368223
330.3686711656354230.7373423312708460.631328834364577
340.2997360906335990.5994721812671990.7002639093664
350.2040156102005640.4080312204011270.795984389799436
360.1190118200245090.2380236400490180.880988179975491

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.0278961421425383 & 0.0557922842850766 & 0.972103857857462 \tabularnewline
7 & 0.51883551444562 & 0.96232897110876 & 0.48116448555438 \tabularnewline
8 & 0.367765575381235 & 0.73553115076247 & 0.632234424618765 \tabularnewline
9 & 0.251779025000496 & 0.503558050000992 & 0.748220974999504 \tabularnewline
10 & 0.218921524572659 & 0.437843049145318 & 0.781078475427341 \tabularnewline
11 & 0.641176874281283 & 0.717646251437434 & 0.358823125718717 \tabularnewline
12 & 0.797532054826875 & 0.40493589034625 & 0.202467945173125 \tabularnewline
13 & 0.781745256611933 & 0.436509486776135 & 0.218254743388067 \tabularnewline
14 & 0.699641856913413 & 0.600716286173174 & 0.300358143086587 \tabularnewline
15 & 0.679868484259369 & 0.640263031481263 & 0.320131515740631 \tabularnewline
16 & 0.629297008006491 & 0.741405983987017 & 0.370702991993509 \tabularnewline
17 & 0.665698887096821 & 0.668602225806357 & 0.334301112903179 \tabularnewline
18 & 0.608745835487956 & 0.782508329024088 & 0.391254164512044 \tabularnewline
19 & 0.648725639811398 & 0.702548720377205 & 0.351274360188602 \tabularnewline
20 & 0.56197413192574 & 0.87605173614852 & 0.43802586807426 \tabularnewline
21 & 0.58469477849647 & 0.83061044300706 & 0.41530522150353 \tabularnewline
22 & 0.499418443419856 & 0.998836886839712 & 0.500581556580144 \tabularnewline
23 & 0.505329369863347 & 0.989341260273306 & 0.494670630136653 \tabularnewline
24 & 0.432125777540177 & 0.864251555080353 & 0.567874222459823 \tabularnewline
25 & 0.73180128812312 & 0.536397423753761 & 0.268198711876880 \tabularnewline
26 & 0.655582179550317 & 0.688835640899367 & 0.344417820449683 \tabularnewline
27 & 0.563715968554768 & 0.872568062890463 & 0.436284031445232 \tabularnewline
28 & 0.470731964931566 & 0.941463929863132 & 0.529268035068434 \tabularnewline
29 & 0.525647569953982 & 0.948704860092035 & 0.474352430046018 \tabularnewline
30 & 0.465957160364123 & 0.931914320728246 & 0.534042839635877 \tabularnewline
31 & 0.395380017981043 & 0.790760035962085 & 0.604619982018958 \tabularnewline
32 & 0.315835320631777 & 0.631670641263554 & 0.684164679368223 \tabularnewline
33 & 0.368671165635423 & 0.737342331270846 & 0.631328834364577 \tabularnewline
34 & 0.299736090633599 & 0.599472181267199 & 0.7002639093664 \tabularnewline
35 & 0.204015610200564 & 0.408031220401127 & 0.795984389799436 \tabularnewline
36 & 0.119011820024509 & 0.238023640049018 & 0.880988179975491 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108594&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]6[/C][C]0.0278961421425383[/C][C]0.0557922842850766[/C][C]0.972103857857462[/C][/ROW]
[ROW][C]7[/C][C]0.51883551444562[/C][C]0.96232897110876[/C][C]0.48116448555438[/C][/ROW]
[ROW][C]8[/C][C]0.367765575381235[/C][C]0.73553115076247[/C][C]0.632234424618765[/C][/ROW]
[ROW][C]9[/C][C]0.251779025000496[/C][C]0.503558050000992[/C][C]0.748220974999504[/C][/ROW]
[ROW][C]10[/C][C]0.218921524572659[/C][C]0.437843049145318[/C][C]0.781078475427341[/C][/ROW]
[ROW][C]11[/C][C]0.641176874281283[/C][C]0.717646251437434[/C][C]0.358823125718717[/C][/ROW]
[ROW][C]12[/C][C]0.797532054826875[/C][C]0.40493589034625[/C][C]0.202467945173125[/C][/ROW]
[ROW][C]13[/C][C]0.781745256611933[/C][C]0.436509486776135[/C][C]0.218254743388067[/C][/ROW]
[ROW][C]14[/C][C]0.699641856913413[/C][C]0.600716286173174[/C][C]0.300358143086587[/C][/ROW]
[ROW][C]15[/C][C]0.679868484259369[/C][C]0.640263031481263[/C][C]0.320131515740631[/C][/ROW]
[ROW][C]16[/C][C]0.629297008006491[/C][C]0.741405983987017[/C][C]0.370702991993509[/C][/ROW]
[ROW][C]17[/C][C]0.665698887096821[/C][C]0.668602225806357[/C][C]0.334301112903179[/C][/ROW]
[ROW][C]18[/C][C]0.608745835487956[/C][C]0.782508329024088[/C][C]0.391254164512044[/C][/ROW]
[ROW][C]19[/C][C]0.648725639811398[/C][C]0.702548720377205[/C][C]0.351274360188602[/C][/ROW]
[ROW][C]20[/C][C]0.56197413192574[/C][C]0.87605173614852[/C][C]0.43802586807426[/C][/ROW]
[ROW][C]21[/C][C]0.58469477849647[/C][C]0.83061044300706[/C][C]0.41530522150353[/C][/ROW]
[ROW][C]22[/C][C]0.499418443419856[/C][C]0.998836886839712[/C][C]0.500581556580144[/C][/ROW]
[ROW][C]23[/C][C]0.505329369863347[/C][C]0.989341260273306[/C][C]0.494670630136653[/C][/ROW]
[ROW][C]24[/C][C]0.432125777540177[/C][C]0.864251555080353[/C][C]0.567874222459823[/C][/ROW]
[ROW][C]25[/C][C]0.73180128812312[/C][C]0.536397423753761[/C][C]0.268198711876880[/C][/ROW]
[ROW][C]26[/C][C]0.655582179550317[/C][C]0.688835640899367[/C][C]0.344417820449683[/C][/ROW]
[ROW][C]27[/C][C]0.563715968554768[/C][C]0.872568062890463[/C][C]0.436284031445232[/C][/ROW]
[ROW][C]28[/C][C]0.470731964931566[/C][C]0.941463929863132[/C][C]0.529268035068434[/C][/ROW]
[ROW][C]29[/C][C]0.525647569953982[/C][C]0.948704860092035[/C][C]0.474352430046018[/C][/ROW]
[ROW][C]30[/C][C]0.465957160364123[/C][C]0.931914320728246[/C][C]0.534042839635877[/C][/ROW]
[ROW][C]31[/C][C]0.395380017981043[/C][C]0.790760035962085[/C][C]0.604619982018958[/C][/ROW]
[ROW][C]32[/C][C]0.315835320631777[/C][C]0.631670641263554[/C][C]0.684164679368223[/C][/ROW]
[ROW][C]33[/C][C]0.368671165635423[/C][C]0.737342331270846[/C][C]0.631328834364577[/C][/ROW]
[ROW][C]34[/C][C]0.299736090633599[/C][C]0.599472181267199[/C][C]0.7002639093664[/C][/ROW]
[ROW][C]35[/C][C]0.204015610200564[/C][C]0.408031220401127[/C][C]0.795984389799436[/C][/ROW]
[ROW][C]36[/C][C]0.119011820024509[/C][C]0.238023640049018[/C][C]0.880988179975491[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108594&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108594&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.02789614214253830.05579228428507660.972103857857462
70.518835514445620.962328971108760.48116448555438
80.3677655753812350.735531150762470.632234424618765
90.2517790250004960.5035580500009920.748220974999504
100.2189215245726590.4378430491453180.781078475427341
110.6411768742812830.7176462514374340.358823125718717
120.7975320548268750.404935890346250.202467945173125
130.7817452566119330.4365094867761350.218254743388067
140.6996418569134130.6007162861731740.300358143086587
150.6798684842593690.6402630314812630.320131515740631
160.6292970080064910.7414059839870170.370702991993509
170.6656988870968210.6686022258063570.334301112903179
180.6087458354879560.7825083290240880.391254164512044
190.6487256398113980.7025487203772050.351274360188602
200.561974131925740.876051736148520.43802586807426
210.584694778496470.830610443007060.41530522150353
220.4994184434198560.9988368868397120.500581556580144
230.5053293698633470.9893412602733060.494670630136653
240.4321257775401770.8642515550803530.567874222459823
250.731801288123120.5363974237537610.268198711876880
260.6555821795503170.6888356408993670.344417820449683
270.5637159685547680.8725680628904630.436284031445232
280.4707319649315660.9414639298631320.529268035068434
290.5256475699539820.9487048600920350.474352430046018
300.4659571603641230.9319143207282460.534042839635877
310.3953800179810430.7907600359620850.604619982018958
320.3158353206317770.6316706412635540.684164679368223
330.3686711656354230.7373423312708460.631328834364577
340.2997360906335990.5994721812671990.7002639093664
350.2040156102005640.4080312204011270.795984389799436
360.1190118200245090.2380236400490180.880988179975491







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.032258064516129OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 1 & 0.032258064516129 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108594&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.032258064516129[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108594&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108594&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.032258064516129OK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$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')
}