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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 computationTue, 14 Dec 2010 09:50:13 +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/14/t129232024680b1pjqfhaxggsd.htm/, Retrieved Fri, 03 May 2024 03:35:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109331, Retrieved Fri, 03 May 2024 03:35:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact102

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Central Tendency] [] [2010-10-20 19:08:13] [b98453cac15ba1066b407e146608df68]
- RMPD  [Kendall tau Correlation Matrix] [] [2010-12-08 16:19:32] [3074aa973ede76ac75d398946b01602f]
-   PD    [Kendall tau Correlation Matrix] [] [2010-12-08 16:43:18] [13c73ac943380855a1c72833078e44d2]
- RMPD      [Multiple Regression] [Multiple regressi...] [2010-12-14 09:24:11] [3074aa973ede76ac75d398946b01602f]
-               [Multiple Regression] [Multiple Regressi...] [2010-12-14 09:50:13] [8e16b01a5be2b3f7f3ad6418d9d6fd5b] [Current]
-                 [Multiple Regression] [Multiple regression] [2010-12-14 15:56:53] [13c73ac943380855a1c72833078e44d2]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
6.3	2	4.5	1	6.6	42	3	1	3
2.1	1.8	69	2547	4603	624	3	5	4
9.1	0.7	27	10.55	179.5	180	4	4	4
15.8	3.9	19	0.023	0.3	35	1	1	1
5.2	1	30.4	160	169	392	4	5	4
10.9	3.6	28	3.3	25.6	63	1	2	1
8.3	1.4	50	52.16	440	230	1	1	1
11	1.5	7	0.42	6.4	112	5	4	4
3.2	0.7	30	465	423	281	5	5	5
6.3	2.1	3.5	0.075	1.2	42	1	1	1
6.6	4.1	6	0.785	3.5	42	2	2	2
9.5	1.2	10.4	0.2	5	120	2	2	2
3.3	0.5	20	27.66	115	148	5	5	5
11	3.4	3.9	0.12	1	16	3	1	2
4.7	1.5	41	85	325	310	1	3	1
10.4	3.4	9	0.101	4	28	5	1	3
7.4	0.8	7.6	1.04	5.5	68	5	3	4
2.1	0.8	46	521	655	336	5	5	5
17.9	2	24	0.1	0.25	50	1	1	1
6.1	1.9	100	62	1320	267	1	1	1
11.9	1.3	3.2	0.023	0.4	19	4	1	3
13.8	5.6	5	1.7	6.3	12	2	1	1
14.3	14.3	6.5	3.5	10.8	120	2	1	1
15.2	1.8	12	0.48	15.5	140	2	2	2
10	0.9	20.2	10	115	170	4	4	4
11.9	1.8	13	1.62	11.4	17	2	1	2
6.5	1.9	27	192	180	115	4	4	4
7.5	0.9	18	2.5	12.1	31	5	5	5
10.6	2.6	4.7	0.28	1.9	21	3	1	3
7.4	2.4	9.8	4.235	50.4	52	1	1	1
8.4	1.2	29	6.8	179	164	2	3	2
5.7	0.9	7	0.75	12.3	225	2	2	2
4.9	0.5	6	3.6	21	225	3	2	3
3.2	0.6	20	55.5	175	151	5	5	5
11	2.3	4.5	0.9	2.6	60	2	1	2
4.9	0.5	7.5	2	12.3	200	3	1	3
13.2	2.6	2.3	0.104	2.5	46	3	2	2
9.7	0.6	24	4.19	58	210	4	3	4
12.8	6.6	3	3.5	3.9	14	2	1	1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'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 & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109331&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109331&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109331&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
PS[t] = + 3.1953114478424 + 0.090823714491886SWS[t] -0.00941744275009553L[t] + 0.00306860442319590BW[t] -0.000795427276824133BRW[t] -0.000926904983833428Tg[t] + 1.80501265303085P[t] + 0.374434799750786S[t] -2.89072374549107D[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PS[t] =  +  3.1953114478424 +  0.090823714491886SWS[t] -0.00941744275009553L[t] +  0.00306860442319590BW[t] -0.000795427276824133BRW[t] -0.000926904983833428Tg[t] +  1.80501265303085P[t] +  0.374434799750786S[t] -2.89072374549107D[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109331&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PS[t] =  +  3.1953114478424 +  0.090823714491886SWS[t] -0.00941744275009553L[t] +  0.00306860442319590BW[t] -0.000795427276824133BRW[t] -0.000926904983833428Tg[t] +  1.80501265303085P[t] +  0.374434799750786S[t] -2.89072374549107D[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109331&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109331&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
PS[t] = + 3.1953114478424 + 0.090823714491886SWS[t] -0.00941744275009553L[t] + 0.00306860442319590BW[t] -0.000795427276824133BRW[t] -0.000926904983833428Tg[t] + 1.80501265303085P[t] + 0.374434799750786S[t] -2.89072374549107D[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.19531144784241.846951.730.0938990.046949
SWS0.0908237144918860.1218990.74510.4620260.231013
L-0.009417442750095530.036499-0.2580.7981530.399077
BW0.003068604423195900.0043310.70860.4840650.242032
BRW-0.0007954272768241330.002586-0.30750.7605560.380278
Tg-0.0009269049838334280.005299-0.17490.8623130.431157
P1.805012653030850.7523052.39930.0228420.011421
S0.3744347997507860.465880.80370.4278840.213942
D-2.890723745491070.953311-3.03230.0049690.002484

\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) & 3.1953114478424 & 1.84695 & 1.73 & 0.093899 & 0.046949 \tabularnewline
SWS & 0.090823714491886 & 0.121899 & 0.7451 & 0.462026 & 0.231013 \tabularnewline
L & -0.00941744275009553 & 0.036499 & -0.258 & 0.798153 & 0.399077 \tabularnewline
BW & 0.00306860442319590 & 0.004331 & 0.7086 & 0.484065 & 0.242032 \tabularnewline
BRW & -0.000795427276824133 & 0.002586 & -0.3075 & 0.760556 & 0.380278 \tabularnewline
Tg & -0.000926904983833428 & 0.005299 & -0.1749 & 0.862313 & 0.431157 \tabularnewline
P & 1.80501265303085 & 0.752305 & 2.3993 & 0.022842 & 0.011421 \tabularnewline
S & 0.374434799750786 & 0.46588 & 0.8037 & 0.427884 & 0.213942 \tabularnewline
D & -2.89072374549107 & 0.953311 & -3.0323 & 0.004969 & 0.002484 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109331&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]3.1953114478424[/C][C]1.84695[/C][C]1.73[/C][C]0.093899[/C][C]0.046949[/C][/ROW]
[ROW][C]SWS[/C][C]0.090823714491886[/C][C]0.121899[/C][C]0.7451[/C][C]0.462026[/C][C]0.231013[/C][/ROW]
[ROW][C]L[/C][C]-0.00941744275009553[/C][C]0.036499[/C][C]-0.258[/C][C]0.798153[/C][C]0.399077[/C][/ROW]
[ROW][C]BW[/C][C]0.00306860442319590[/C][C]0.004331[/C][C]0.7086[/C][C]0.484065[/C][C]0.242032[/C][/ROW]
[ROW][C]BRW[/C][C]-0.000795427276824133[/C][C]0.002586[/C][C]-0.3075[/C][C]0.760556[/C][C]0.380278[/C][/ROW]
[ROW][C]Tg[/C][C]-0.000926904983833428[/C][C]0.005299[/C][C]-0.1749[/C][C]0.862313[/C][C]0.431157[/C][/ROW]
[ROW][C]P[/C][C]1.80501265303085[/C][C]0.752305[/C][C]2.3993[/C][C]0.022842[/C][C]0.011421[/C][/ROW]
[ROW][C]S[/C][C]0.374434799750786[/C][C]0.46588[/C][C]0.8037[/C][C]0.427884[/C][C]0.213942[/C][/ROW]
[ROW][C]D[/C][C]-2.89072374549107[/C][C]0.953311[/C][C]-3.0323[/C][C]0.004969[/C][C]0.002484[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109331&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109331&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)3.19531144784241.846951.730.0938990.046949
SWS0.0908237144918860.1218990.74510.4620260.231013
L-0.009417442750095530.036499-0.2580.7981530.399077
BW0.003068604423195900.0043310.70860.4840650.242032
BRW-0.0007954272768241330.002586-0.30750.7605560.380278
Tg-0.0009269049838334280.005299-0.17490.8623130.431157
P1.805012653030850.7523052.39930.0228420.011421
S0.3744347997507860.465880.80370.4278840.213942
D-2.890723745491070.953311-3.03230.0049690.002484






Multiple Linear Regression - Regression Statistics
Multiple R0.680451393267708
R-squared0.463014098599965
Adjusted R-squared0.319817858226622
F-TEST (value)3.23342356889252
F-TEST (DF numerator)8
F-TEST (DF denominator)30
p-value0.0090051591885767
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.99725666621076
Sum Squared Residuals119.6710257217

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.680451393267708 \tabularnewline
R-squared & 0.463014098599965 \tabularnewline
Adjusted R-squared & 0.319817858226622 \tabularnewline
F-TEST (value) & 3.23342356889252 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 30 \tabularnewline
p-value & 0.0090051591885767 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.99725666621076 \tabularnewline
Sum Squared Residuals & 119.6710257217 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109331&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.680451393267708[/C][/ROW]
[ROW][C]R-squared[/C][C]0.463014098599965[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.319817858226622[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.23342356889252[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]30[/C][/ROW]
[ROW][C]p-value[/C][C]0.0090051591885767[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.99725666621076[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]119.6710257217[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109331&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109331&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 R0.680451393267708
R-squared0.463014098599965
Adjusted R-squared0.319817858226622
F-TEST (value)3.23342356889252
F-TEST (DF numerator)8
F-TEST (DF denominator)30
p-value0.0090051591885767
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.99725666621076
Sum Squared Residuals119.6710257217






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
120.8013126542111551.19868734578884
21.82.0365496751474-0.236549675147398
30.70.645182808013020.0548171919869793
43.93.707508707137470.192491292862533
510.9038368767757190.0961631232242814
63.63.516128488170980.0838715118290233
71.42.36388210654043-0.963882106540434
81.52.98074241129233-1.48074241129233
90.70.4770176063986940.222982393601306
102.12.98360913008506-0.883609130085062
114.12.276385571251731.82361442874827
121.22.42305073193597-1.22305073193597
130.5-0.3934790889799770.893479088979977
143.44.1504118739016-0.750411873901599
151.52.98863802600793-1.48863802600793
163.44.7536228026311-1.3536228026311
170.82.31709401230088-1.51709401230088
180.80.1627553818210270.637244618178973
1923.83752377296687-1.83752377296687
201.90.9891213766706870.910878623329313
211.33.15043322119469-1.85043322119469
225.65.484410430270810.115589569729190
2314.35.417534450353648.88246554964636
241.82.89964711929473-1.09964711929473
250.90.84984913851710.0501508614829003
261.82.33684539285956-0.536845392859563
271.91.025690533233770.874309466766228
280.90.119906659992240.78009334000776
292.61.210965255653101.38903474434690
302.42.98854664924295-0.588546649242949
311.22.36347963433092-1.16347963433092
320.92.00849601222656-1.10849601222656
330.50.861368696220661-0.361368696220661
340.6-0.3676378648483390.967637864848339
352.32.30008576373919-8.5763739192668e-05
360.50.4979908071718230.00200919282817660
372.64.71067736583368-2.11067736583368
380.60.4028155056952540.197184494304746
396.65.418000304737581.18199969526242

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 0.801312654211155 & 1.19868734578884 \tabularnewline
2 & 1.8 & 2.0365496751474 & -0.236549675147398 \tabularnewline
3 & 0.7 & 0.64518280801302 & 0.0548171919869793 \tabularnewline
4 & 3.9 & 3.70750870713747 & 0.192491292862533 \tabularnewline
5 & 1 & 0.903836876775719 & 0.0961631232242814 \tabularnewline
6 & 3.6 & 3.51612848817098 & 0.0838715118290233 \tabularnewline
7 & 1.4 & 2.36388210654043 & -0.963882106540434 \tabularnewline
8 & 1.5 & 2.98074241129233 & -1.48074241129233 \tabularnewline
9 & 0.7 & 0.477017606398694 & 0.222982393601306 \tabularnewline
10 & 2.1 & 2.98360913008506 & -0.883609130085062 \tabularnewline
11 & 4.1 & 2.27638557125173 & 1.82361442874827 \tabularnewline
12 & 1.2 & 2.42305073193597 & -1.22305073193597 \tabularnewline
13 & 0.5 & -0.393479088979977 & 0.893479088979977 \tabularnewline
14 & 3.4 & 4.1504118739016 & -0.750411873901599 \tabularnewline
15 & 1.5 & 2.98863802600793 & -1.48863802600793 \tabularnewline
16 & 3.4 & 4.7536228026311 & -1.3536228026311 \tabularnewline
17 & 0.8 & 2.31709401230088 & -1.51709401230088 \tabularnewline
18 & 0.8 & 0.162755381821027 & 0.637244618178973 \tabularnewline
19 & 2 & 3.83752377296687 & -1.83752377296687 \tabularnewline
20 & 1.9 & 0.989121376670687 & 0.910878623329313 \tabularnewline
21 & 1.3 & 3.15043322119469 & -1.85043322119469 \tabularnewline
22 & 5.6 & 5.48441043027081 & 0.115589569729190 \tabularnewline
23 & 14.3 & 5.41753445035364 & 8.88246554964636 \tabularnewline
24 & 1.8 & 2.89964711929473 & -1.09964711929473 \tabularnewline
25 & 0.9 & 0.8498491385171 & 0.0501508614829003 \tabularnewline
26 & 1.8 & 2.33684539285956 & -0.536845392859563 \tabularnewline
27 & 1.9 & 1.02569053323377 & 0.874309466766228 \tabularnewline
28 & 0.9 & 0.11990665999224 & 0.78009334000776 \tabularnewline
29 & 2.6 & 1.21096525565310 & 1.38903474434690 \tabularnewline
30 & 2.4 & 2.98854664924295 & -0.588546649242949 \tabularnewline
31 & 1.2 & 2.36347963433092 & -1.16347963433092 \tabularnewline
32 & 0.9 & 2.00849601222656 & -1.10849601222656 \tabularnewline
33 & 0.5 & 0.861368696220661 & -0.361368696220661 \tabularnewline
34 & 0.6 & -0.367637864848339 & 0.967637864848339 \tabularnewline
35 & 2.3 & 2.30008576373919 & -8.5763739192668e-05 \tabularnewline
36 & 0.5 & 0.497990807171823 & 0.00200919282817660 \tabularnewline
37 & 2.6 & 4.71067736583368 & -2.11067736583368 \tabularnewline
38 & 0.6 & 0.402815505695254 & 0.197184494304746 \tabularnewline
39 & 6.6 & 5.41800030473758 & 1.18199969526242 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109331&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]0.801312654211155[/C][C]1.19868734578884[/C][/ROW]
[ROW][C]2[/C][C]1.8[/C][C]2.0365496751474[/C][C]-0.236549675147398[/C][/ROW]
[ROW][C]3[/C][C]0.7[/C][C]0.64518280801302[/C][C]0.0548171919869793[/C][/ROW]
[ROW][C]4[/C][C]3.9[/C][C]3.70750870713747[/C][C]0.192491292862533[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]0.903836876775719[/C][C]0.0961631232242814[/C][/ROW]
[ROW][C]6[/C][C]3.6[/C][C]3.51612848817098[/C][C]0.0838715118290233[/C][/ROW]
[ROW][C]7[/C][C]1.4[/C][C]2.36388210654043[/C][C]-0.963882106540434[/C][/ROW]
[ROW][C]8[/C][C]1.5[/C][C]2.98074241129233[/C][C]-1.48074241129233[/C][/ROW]
[ROW][C]9[/C][C]0.7[/C][C]0.477017606398694[/C][C]0.222982393601306[/C][/ROW]
[ROW][C]10[/C][C]2.1[/C][C]2.98360913008506[/C][C]-0.883609130085062[/C][/ROW]
[ROW][C]11[/C][C]4.1[/C][C]2.27638557125173[/C][C]1.82361442874827[/C][/ROW]
[ROW][C]12[/C][C]1.2[/C][C]2.42305073193597[/C][C]-1.22305073193597[/C][/ROW]
[ROW][C]13[/C][C]0.5[/C][C]-0.393479088979977[/C][C]0.893479088979977[/C][/ROW]
[ROW][C]14[/C][C]3.4[/C][C]4.1504118739016[/C][C]-0.750411873901599[/C][/ROW]
[ROW][C]15[/C][C]1.5[/C][C]2.98863802600793[/C][C]-1.48863802600793[/C][/ROW]
[ROW][C]16[/C][C]3.4[/C][C]4.7536228026311[/C][C]-1.3536228026311[/C][/ROW]
[ROW][C]17[/C][C]0.8[/C][C]2.31709401230088[/C][C]-1.51709401230088[/C][/ROW]
[ROW][C]18[/C][C]0.8[/C][C]0.162755381821027[/C][C]0.637244618178973[/C][/ROW]
[ROW][C]19[/C][C]2[/C][C]3.83752377296687[/C][C]-1.83752377296687[/C][/ROW]
[ROW][C]20[/C][C]1.9[/C][C]0.989121376670687[/C][C]0.910878623329313[/C][/ROW]
[ROW][C]21[/C][C]1.3[/C][C]3.15043322119469[/C][C]-1.85043322119469[/C][/ROW]
[ROW][C]22[/C][C]5.6[/C][C]5.48441043027081[/C][C]0.115589569729190[/C][/ROW]
[ROW][C]23[/C][C]14.3[/C][C]5.41753445035364[/C][C]8.88246554964636[/C][/ROW]
[ROW][C]24[/C][C]1.8[/C][C]2.89964711929473[/C][C]-1.09964711929473[/C][/ROW]
[ROW][C]25[/C][C]0.9[/C][C]0.8498491385171[/C][C]0.0501508614829003[/C][/ROW]
[ROW][C]26[/C][C]1.8[/C][C]2.33684539285956[/C][C]-0.536845392859563[/C][/ROW]
[ROW][C]27[/C][C]1.9[/C][C]1.02569053323377[/C][C]0.874309466766228[/C][/ROW]
[ROW][C]28[/C][C]0.9[/C][C]0.11990665999224[/C][C]0.78009334000776[/C][/ROW]
[ROW][C]29[/C][C]2.6[/C][C]1.21096525565310[/C][C]1.38903474434690[/C][/ROW]
[ROW][C]30[/C][C]2.4[/C][C]2.98854664924295[/C][C]-0.588546649242949[/C][/ROW]
[ROW][C]31[/C][C]1.2[/C][C]2.36347963433092[/C][C]-1.16347963433092[/C][/ROW]
[ROW][C]32[/C][C]0.9[/C][C]2.00849601222656[/C][C]-1.10849601222656[/C][/ROW]
[ROW][C]33[/C][C]0.5[/C][C]0.861368696220661[/C][C]-0.361368696220661[/C][/ROW]
[ROW][C]34[/C][C]0.6[/C][C]-0.367637864848339[/C][C]0.967637864848339[/C][/ROW]
[ROW][C]35[/C][C]2.3[/C][C]2.30008576373919[/C][C]-8.5763739192668e-05[/C][/ROW]
[ROW][C]36[/C][C]0.5[/C][C]0.497990807171823[/C][C]0.00200919282817660[/C][/ROW]
[ROW][C]37[/C][C]2.6[/C][C]4.71067736583368[/C][C]-2.11067736583368[/C][/ROW]
[ROW][C]38[/C][C]0.6[/C][C]0.402815505695254[/C][C]0.197184494304746[/C][/ROW]
[ROW][C]39[/C][C]6.6[/C][C]5.41800030473758[/C][C]1.18199969526242[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109331&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109331&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
120.8013126542111551.19868734578884
21.82.0365496751474-0.236549675147398
30.70.645182808013020.0548171919869793
43.93.707508707137470.192491292862533
510.9038368767757190.0961631232242814
63.63.516128488170980.0838715118290233
71.42.36388210654043-0.963882106540434
81.52.98074241129233-1.48074241129233
90.70.4770176063986940.222982393601306
102.12.98360913008506-0.883609130085062
114.12.276385571251731.82361442874827
121.22.42305073193597-1.22305073193597
130.5-0.3934790889799770.893479088979977
143.44.1504118739016-0.750411873901599
151.52.98863802600793-1.48863802600793
163.44.7536228026311-1.3536228026311
170.82.31709401230088-1.51709401230088
180.80.1627553818210270.637244618178973
1923.83752377296687-1.83752377296687
201.90.9891213766706870.910878623329313
211.33.15043322119469-1.85043322119469
225.65.484410430270810.115589569729190
2314.35.417534450353648.88246554964636
241.82.89964711929473-1.09964711929473
250.90.84984913851710.0501508614829003
261.82.33684539285956-0.536845392859563
271.91.025690533233770.874309466766228
280.90.119906659992240.78009334000776
292.61.210965255653101.38903474434690
302.42.98854664924295-0.588546649242949
311.22.36347963433092-1.16347963433092
320.92.00849601222656-1.10849601222656
330.50.861368696220661-0.361368696220661
340.6-0.3676378648483390.967637864848339
352.32.30008576373919-8.5763739192668e-05
360.50.4979908071718230.00200919282817660
372.64.71067736583368-2.11067736583368
380.60.4028155056952540.197184494304746
396.65.418000304737581.18199969526242






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.1716267080986840.3432534161973670.828373291901316
130.07507122311224620.1501424462244920.924928776887754
140.03255249046751820.06510498093503630.967447509532482
150.01186440309977890.02372880619955780.988135596900221
160.004438588947922170.008877177895844340.995561411052078
170.002966654699164310.005933309398328630.997033345300836
180.0009843710148049330.001968742029609870.999015628985195
190.0007925811411899640.001585162282379930.99920741885881
200.0002831706526076210.0005663413052152420.999716829347392
210.0002855192781332020.0005710385562664050.999714480721867
220.0004878619806462090.0009757239612924170.999512138019354
230.9935129875425870.01297402491482520.00648701245741258
240.9827931069384530.03441378612309490.0172068930615475
250.9577196444808380.08456071103832470.0422803555191624
260.9168598157797860.1662803684404270.0831401842202136
270.868685913971970.2626281720560580.131314086028029

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.171626708098684 & 0.343253416197367 & 0.828373291901316 \tabularnewline
13 & 0.0750712231122462 & 0.150142446224492 & 0.924928776887754 \tabularnewline
14 & 0.0325524904675182 & 0.0651049809350363 & 0.967447509532482 \tabularnewline
15 & 0.0118644030997789 & 0.0237288061995578 & 0.988135596900221 \tabularnewline
16 & 0.00443858894792217 & 0.00887717789584434 & 0.995561411052078 \tabularnewline
17 & 0.00296665469916431 & 0.00593330939832863 & 0.997033345300836 \tabularnewline
18 & 0.000984371014804933 & 0.00196874202960987 & 0.999015628985195 \tabularnewline
19 & 0.000792581141189964 & 0.00158516228237993 & 0.99920741885881 \tabularnewline
20 & 0.000283170652607621 & 0.000566341305215242 & 0.999716829347392 \tabularnewline
21 & 0.000285519278133202 & 0.000571038556266405 & 0.999714480721867 \tabularnewline
22 & 0.000487861980646209 & 0.000975723961292417 & 0.999512138019354 \tabularnewline
23 & 0.993512987542587 & 0.0129740249148252 & 0.00648701245741258 \tabularnewline
24 & 0.982793106938453 & 0.0344137861230949 & 0.0172068930615475 \tabularnewline
25 & 0.957719644480838 & 0.0845607110383247 & 0.0422803555191624 \tabularnewline
26 & 0.916859815779786 & 0.166280368440427 & 0.0831401842202136 \tabularnewline
27 & 0.86868591397197 & 0.262628172056058 & 0.131314086028029 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109331&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]12[/C][C]0.171626708098684[/C][C]0.343253416197367[/C][C]0.828373291901316[/C][/ROW]
[ROW][C]13[/C][C]0.0750712231122462[/C][C]0.150142446224492[/C][C]0.924928776887754[/C][/ROW]
[ROW][C]14[/C][C]0.0325524904675182[/C][C]0.0651049809350363[/C][C]0.967447509532482[/C][/ROW]
[ROW][C]15[/C][C]0.0118644030997789[/C][C]0.0237288061995578[/C][C]0.988135596900221[/C][/ROW]
[ROW][C]16[/C][C]0.00443858894792217[/C][C]0.00887717789584434[/C][C]0.995561411052078[/C][/ROW]
[ROW][C]17[/C][C]0.00296665469916431[/C][C]0.00593330939832863[/C][C]0.997033345300836[/C][/ROW]
[ROW][C]18[/C][C]0.000984371014804933[/C][C]0.00196874202960987[/C][C]0.999015628985195[/C][/ROW]
[ROW][C]19[/C][C]0.000792581141189964[/C][C]0.00158516228237993[/C][C]0.99920741885881[/C][/ROW]
[ROW][C]20[/C][C]0.000283170652607621[/C][C]0.000566341305215242[/C][C]0.999716829347392[/C][/ROW]
[ROW][C]21[/C][C]0.000285519278133202[/C][C]0.000571038556266405[/C][C]0.999714480721867[/C][/ROW]
[ROW][C]22[/C][C]0.000487861980646209[/C][C]0.000975723961292417[/C][C]0.999512138019354[/C][/ROW]
[ROW][C]23[/C][C]0.993512987542587[/C][C]0.0129740249148252[/C][C]0.00648701245741258[/C][/ROW]
[ROW][C]24[/C][C]0.982793106938453[/C][C]0.0344137861230949[/C][C]0.0172068930615475[/C][/ROW]
[ROW][C]25[/C][C]0.957719644480838[/C][C]0.0845607110383247[/C][C]0.0422803555191624[/C][/ROW]
[ROW][C]26[/C][C]0.916859815779786[/C][C]0.166280368440427[/C][C]0.0831401842202136[/C][/ROW]
[ROW][C]27[/C][C]0.86868591397197[/C][C]0.262628172056058[/C][C]0.131314086028029[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109331&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109331&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
120.1716267080986840.3432534161973670.828373291901316
130.07507122311224620.1501424462244920.924928776887754
140.03255249046751820.06510498093503630.967447509532482
150.01186440309977890.02372880619955780.988135596900221
160.004438588947922170.008877177895844340.995561411052078
170.002966654699164310.005933309398328630.997033345300836
180.0009843710148049330.001968742029609870.999015628985195
190.0007925811411899640.001585162282379930.99920741885881
200.0002831706526076210.0005663413052152420.999716829347392
210.0002855192781332020.0005710385562664050.999714480721867
220.0004878619806462090.0009757239612924170.999512138019354
230.9935129875425870.01297402491482520.00648701245741258
240.9827931069384530.03441378612309490.0172068930615475
250.9577196444808380.08456071103832470.0422803555191624
260.9168598157797860.1662803684404270.0831401842202136
270.868685913971970.2626281720560580.131314086028029






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level70.4375NOK
5% type I error level100.625NOK
10% type I error level120.75NOK

\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 & 7 & 0.4375 & NOK \tabularnewline
5% type I error level & 10 & 0.625 & NOK \tabularnewline
10% type I error level & 12 & 0.75 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109331&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]7[/C][C]0.4375[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]10[/C][C]0.625[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]12[/C][C]0.75[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109331&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109331&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 level70.4375NOK
5% type I error level100.625NOK
10% type I error level120.75NOK


Figures (Output of Computation)

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

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