FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationWed, 22 Dec 2010 20:47:35 +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/22/t12930507624f1irq4vm2jf3bq.htm/, Retrieved Mon, 06 May 2024 08:29:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114583, Retrieved Mon, 06 May 2024 08:29:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact121

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]
- R PD    [Multiple Regression] [2] [2010-12-22 20:47:35] [062de5fc17e30860c0960288bdb996a8] [Current]
-           [Multiple Regression] [3] [2010-12-22 20:49:49] [a7c91bc614e4e21e8b9c8593f39a36f1]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
6.3	2.0	4.5	1.000	6.600	42	3	1	3
2.1	1.8	69.0	2547.000	4603.000	624	3	5	4
9.1	0.7	27.0	10.550	179.500	180	4	4	4
15.8	3.9	19.0	0.023	0.300	35	1	1	1
5.2	1.0	30.4	160.000	169.000	392	4	5	4
10.9	3.6	28.0	3.300	25.600	63	1	2	1
8.3	1.4	50.0	52.160	440.000	230	1	1	1
11.0	1.5	7.0	0.425	6400.000	112	5	4	4
3.2	0.7	30.0	46.500	423.000	281	5	5	5
6.3	2.1	3.5	0.075	1.200	42	1	1	1
6.6	4.1	6.0	0.785	3.500	42	2	2	2
9.5	1.2	10.4	0.200	5.000	120	2	2	2
3.3	0.5	20.0	27.660	115.000	148	5	5	5
11.0	3.4	3.9	0.120	1.000	16	3	1	2
4.7	1.5	41.0	85.000	325.000	310	1	3	1
10.4	3.4	9.0	0.101	4.000	28	5	1	3
7.4	0.8	7.6	1.040	5.500	68	5	3	4
2.1	0.8	46.0	521.000	655.000	336	5	5	5
17.9	2.0	24.0	0.010	0.250	50	1	1	1
6.1	1.9	100.0	62.000	1320.000	267	1	1	1
11.9	1.3	3.2	0.023	0.400	19	4	1	3
13.8	5.6	5.0	1.700	6.300	12	2	1	1
14.3	3.1	6.5	3.500	10.800	120	2	1	1
15.2	1.8	12.0	0.480	15.500	140	2	2	2
10.0	0.9	20.2	10.000	115.000	170	4	4	4
11.9	1.8	13.0	1.620	11.400	17	2	1	2
6.5	1.9	27.0	192.000	180.000	115	4	4	4
7.5	0.9	18.0	2.500	12.100	31	5	5	5
10.6	2.6	4.7	0.280	1.900	21	3	1	3
7.4	2.4	9.8	4.235	50.400	52	1	1	1
8.4	1.2	29.0	6.800	179.000	164	2	3	2
5.7	0.9	7.0	0.750	12.300	225	2	2	2
4.9	0.5	6.0	3.600	21.000	225	3	2	3
3.2	0.6	20.0	5.550	175.000	151	5	5	5
11.0	2.3	4.5	0.900	2.600	60	2	1	2
4.9	0.5	7.5	2.000	12.300	200	3	1	3
13.2	2.6	2.3	0.104	2.500	46	3	2	2
9.7	0.6	24.0	4.190	58.000	210	4	3	4
12.8	6.6	3.0	3.500	3.900	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 time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 5 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114583&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114583&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114583&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 time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Multiple Linear Regression - Estimated Regression Equation
SWS[t] = + 13.6544237163211 -0.0585807794285882PS[t] -0.00582010443309297L[t] + 0.00110522505298075Wb[t] + 0.000312379340050593Wbr[t] -0.0164356921292093tg[t] + 1.2609612380151P[t] + 0.241262992690819S[t] -2.58964116718938D[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
SWS[t] =  +  13.6544237163211 -0.0585807794285882PS[t] -0.00582010443309297L[t] +  0.00110522505298075Wb[t] +  0.000312379340050593Wbr[t] -0.0164356921292093tg[t] +  1.2609612380151P[t] +  0.241262992690819S[t] -2.58964116718938D[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114583&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]SWS[t] =  +  13.6544237163211 -0.0585807794285882PS[t] -0.00582010443309297L[t] +  0.00110522505298075Wb[t] +  0.000312379340050593Wbr[t] -0.0164356921292093tg[t] +  1.2609612380151P[t] +  0.241262992690819S[t] -2.58964116718938D[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114583&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114583&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
SWS[t] = + 13.6544237163211 -0.0585807794285882PS[t] -0.00582010443309297L[t] + 0.00110522505298075Wb[t] + 0.000312379340050593Wbr[t] -0.0164356921292093tg[t] + 1.2609612380151P[t] + 0.241262992690819S[t] -2.58964116718938D[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)13.65442371632112.5940615.26371.1e-056e-06
PS-0.05858077942858820.605897-0.09670.923620.46181
L-0.005820104433092970.035279-0.1650.8700730.435036
Wb0.001105225052980750.0022070.50090.6201170.310058
Wbr0.0003123793400505930.0004990.62560.5363370.268168
tg-0.01643569212920930.008345-1.96950.0581880.029094
P1.26096123801511.2751440.98890.3306320.165316
S0.2412629926908190.6963650.34650.7314150.365707
D-2.589641167189381.730261-1.49670.1449250.072463

\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) & 13.6544237163211 & 2.594061 & 5.2637 & 1.1e-05 & 6e-06 \tabularnewline
PS & -0.0585807794285882 & 0.605897 & -0.0967 & 0.92362 & 0.46181 \tabularnewline
L & -0.00582010443309297 & 0.035279 & -0.165 & 0.870073 & 0.435036 \tabularnewline
Wb & 0.00110522505298075 & 0.002207 & 0.5009 & 0.620117 & 0.310058 \tabularnewline
Wbr & 0.000312379340050593 & 0.000499 & 0.6256 & 0.536337 & 0.268168 \tabularnewline
tg & -0.0164356921292093 & 0.008345 & -1.9695 & 0.058188 & 0.029094 \tabularnewline
P & 1.2609612380151 & 1.275144 & 0.9889 & 0.330632 & 0.165316 \tabularnewline
S & 0.241262992690819 & 0.696365 & 0.3465 & 0.731415 & 0.365707 \tabularnewline
D & -2.58964116718938 & 1.730261 & -1.4967 & 0.144925 & 0.072463 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114583&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]13.6544237163211[/C][C]2.594061[/C][C]5.2637[/C][C]1.1e-05[/C][C]6e-06[/C][/ROW]
[ROW][C]PS[/C][C]-0.0585807794285882[/C][C]0.605897[/C][C]-0.0967[/C][C]0.92362[/C][C]0.46181[/C][/ROW]
[ROW][C]L[/C][C]-0.00582010443309297[/C][C]0.035279[/C][C]-0.165[/C][C]0.870073[/C][C]0.435036[/C][/ROW]
[ROW][C]Wb[/C][C]0.00110522505298075[/C][C]0.002207[/C][C]0.5009[/C][C]0.620117[/C][C]0.310058[/C][/ROW]
[ROW][C]Wbr[/C][C]0.000312379340050593[/C][C]0.000499[/C][C]0.6256[/C][C]0.536337[/C][C]0.268168[/C][/ROW]
[ROW][C]tg[/C][C]-0.0164356921292093[/C][C]0.008345[/C][C]-1.9695[/C][C]0.058188[/C][C]0.029094[/C][/ROW]
[ROW][C]P[/C][C]1.2609612380151[/C][C]1.275144[/C][C]0.9889[/C][C]0.330632[/C][C]0.165316[/C][/ROW]
[ROW][C]S[/C][C]0.241262992690819[/C][C]0.696365[/C][C]0.3465[/C][C]0.731415[/C][C]0.365707[/C][/ROW]
[ROW][C]D[/C][C]-2.58964116718938[/C][C]1.730261[/C][C]-1.4967[/C][C]0.144925[/C][C]0.072463[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114583&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114583&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)13.65442371632112.5940615.26371.1e-056e-06
PS-0.05858077942858820.605897-0.09670.923620.46181
L-0.005820104433092970.035279-0.1650.8700730.435036
Wb0.001105225052980750.0022070.50090.6201170.310058
Wbr0.0003123793400505930.0004990.62560.5363370.268168
tg-0.01643569212920930.008345-1.96950.0581880.029094
P1.26096123801511.2751440.98890.3306320.165316
S0.2412629926908190.6963650.34650.7314150.365707
D-2.589641167189381.730261-1.49670.1449250.072463






Multiple Linear Regression - Regression Statistics
Multiple R0.74423501919545
R-squared0.553885763796852
Adjusted R-squared0.434921967476012
F-TEST (value)4.65591869902209
F-TEST (DF numerator)8
F-TEST (DF denominator)30
p-value0.000891925292758833
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.98304296750641
Sum Squared Residuals266.956360379684

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.74423501919545 \tabularnewline
R-squared & 0.553885763796852 \tabularnewline
Adjusted R-squared & 0.434921967476012 \tabularnewline
F-TEST (value) & 4.65591869902209 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 30 \tabularnewline
p-value & 0.000891925292758833 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.98304296750641 \tabularnewline
Sum Squared Residuals & 266.956360379684 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114583&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.74423501919545[/C][/ROW]
[ROW][C]R-squared[/C][C]0.553885763796852[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.434921967476012[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.65591869902209[/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]0.000891925292758833[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.98304296750641[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]266.956360379684[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114583&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114583&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 R0.74423501919545
R-squared0.553885763796852
Adjusted R-squared0.434921967476012
F-TEST (value)4.65591869902209
F-TEST (DF numerator)8
F-TEST (DF denominator)30
p-value0.000891925292758833
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.98304296750641
Sum Squared Residuals266.956360379684






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16.39.07916275195347-2.77916275195347
22.11.775043539776280.324956460223719
39.16.215914237684042.88408576231596
415.811.65282966529324.14717033470676
55.23.097343811178832.10265618882117
610.911.4106115920988-0.510611592098838
78.38.60887472565052-0.308874725650516
81110.59600528672760.403994713272354
93.23.56682929280814-0.366829292808135
106.311.733775455182-5.43377545518198
116.610.5161498810283-3.91614988102834
129.59.37826370814140.121736291858607
133.35.70565826947588-2.40565826947588
141112.0148889636112-1.01488896361121
154.77.82344016928446-3.12344016928446
1610.411.7211755730369-1.32117557303688
177.49.11859725411626-1.71859725411626
182.13.16078777136038-1.06078777136038
1917.911.48846725521136.41153274478873
206.17.96622773926668-1.86622773926668
2111.910.76370103351831.13629896648167
2213.813.2774336977690.522566302230992
2314.311.64349585186182.65650414813817
2415.29.008678676892476.19132132310753
25106.387375372023043.61262462797696
2611.910.78316491292751.11683508707251
276.57.41463656630172-0.91463656630172
287.57.55689084926392-0.0568908492639201
2910.69.38573585318671.2142641468133
307.411.5351444418837-4.13514444188371
318.48.84975079520995-0.449750795209949
325.77.69276686643702-1.99276686643702
334.96.3992069451267-1.4992069451267
343.25.64479934962703-2.44479934962703
351110.09306594910790.906934050892101
364.96.55562003867326-1.65562003867326
3713.211.81970886847081.3802911315292
389.75.459915551205974.24008444879403
3912.813.1988614376274-0.398861437627432

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6.3 & 9.07916275195347 & -2.77916275195347 \tabularnewline
2 & 2.1 & 1.77504353977628 & 0.324956460223719 \tabularnewline
3 & 9.1 & 6.21591423768404 & 2.88408576231596 \tabularnewline
4 & 15.8 & 11.6528296652932 & 4.14717033470676 \tabularnewline
5 & 5.2 & 3.09734381117883 & 2.10265618882117 \tabularnewline
6 & 10.9 & 11.4106115920988 & -0.510611592098838 \tabularnewline
7 & 8.3 & 8.60887472565052 & -0.308874725650516 \tabularnewline
8 & 11 & 10.5960052867276 & 0.403994713272354 \tabularnewline
9 & 3.2 & 3.56682929280814 & -0.366829292808135 \tabularnewline
10 & 6.3 & 11.733775455182 & -5.43377545518198 \tabularnewline
11 & 6.6 & 10.5161498810283 & -3.91614988102834 \tabularnewline
12 & 9.5 & 9.3782637081414 & 0.121736291858607 \tabularnewline
13 & 3.3 & 5.70565826947588 & -2.40565826947588 \tabularnewline
14 & 11 & 12.0148889636112 & -1.01488896361121 \tabularnewline
15 & 4.7 & 7.82344016928446 & -3.12344016928446 \tabularnewline
16 & 10.4 & 11.7211755730369 & -1.32117557303688 \tabularnewline
17 & 7.4 & 9.11859725411626 & -1.71859725411626 \tabularnewline
18 & 2.1 & 3.16078777136038 & -1.06078777136038 \tabularnewline
19 & 17.9 & 11.4884672552113 & 6.41153274478873 \tabularnewline
20 & 6.1 & 7.96622773926668 & -1.86622773926668 \tabularnewline
21 & 11.9 & 10.7637010335183 & 1.13629896648167 \tabularnewline
22 & 13.8 & 13.277433697769 & 0.522566302230992 \tabularnewline
23 & 14.3 & 11.6434958518618 & 2.65650414813817 \tabularnewline
24 & 15.2 & 9.00867867689247 & 6.19132132310753 \tabularnewline
25 & 10 & 6.38737537202304 & 3.61262462797696 \tabularnewline
26 & 11.9 & 10.7831649129275 & 1.11683508707251 \tabularnewline
27 & 6.5 & 7.41463656630172 & -0.91463656630172 \tabularnewline
28 & 7.5 & 7.55689084926392 & -0.0568908492639201 \tabularnewline
29 & 10.6 & 9.3857358531867 & 1.2142641468133 \tabularnewline
30 & 7.4 & 11.5351444418837 & -4.13514444188371 \tabularnewline
31 & 8.4 & 8.84975079520995 & -0.449750795209949 \tabularnewline
32 & 5.7 & 7.69276686643702 & -1.99276686643702 \tabularnewline
33 & 4.9 & 6.3992069451267 & -1.4992069451267 \tabularnewline
34 & 3.2 & 5.64479934962703 & -2.44479934962703 \tabularnewline
35 & 11 & 10.0930659491079 & 0.906934050892101 \tabularnewline
36 & 4.9 & 6.55562003867326 & -1.65562003867326 \tabularnewline
37 & 13.2 & 11.8197088684708 & 1.3802911315292 \tabularnewline
38 & 9.7 & 5.45991555120597 & 4.24008444879403 \tabularnewline
39 & 12.8 & 13.1988614376274 & -0.398861437627432 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114583&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]6.3[/C][C]9.07916275195347[/C][C]-2.77916275195347[/C][/ROW]
[ROW][C]2[/C][C]2.1[/C][C]1.77504353977628[/C][C]0.324956460223719[/C][/ROW]
[ROW][C]3[/C][C]9.1[/C][C]6.21591423768404[/C][C]2.88408576231596[/C][/ROW]
[ROW][C]4[/C][C]15.8[/C][C]11.6528296652932[/C][C]4.14717033470676[/C][/ROW]
[ROW][C]5[/C][C]5.2[/C][C]3.09734381117883[/C][C]2.10265618882117[/C][/ROW]
[ROW][C]6[/C][C]10.9[/C][C]11.4106115920988[/C][C]-0.510611592098838[/C][/ROW]
[ROW][C]7[/C][C]8.3[/C][C]8.60887472565052[/C][C]-0.308874725650516[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]10.5960052867276[/C][C]0.403994713272354[/C][/ROW]
[ROW][C]9[/C][C]3.2[/C][C]3.56682929280814[/C][C]-0.366829292808135[/C][/ROW]
[ROW][C]10[/C][C]6.3[/C][C]11.733775455182[/C][C]-5.43377545518198[/C][/ROW]
[ROW][C]11[/C][C]6.6[/C][C]10.5161498810283[/C][C]-3.91614988102834[/C][/ROW]
[ROW][C]12[/C][C]9.5[/C][C]9.3782637081414[/C][C]0.121736291858607[/C][/ROW]
[ROW][C]13[/C][C]3.3[/C][C]5.70565826947588[/C][C]-2.40565826947588[/C][/ROW]
[ROW][C]14[/C][C]11[/C][C]12.0148889636112[/C][C]-1.01488896361121[/C][/ROW]
[ROW][C]15[/C][C]4.7[/C][C]7.82344016928446[/C][C]-3.12344016928446[/C][/ROW]
[ROW][C]16[/C][C]10.4[/C][C]11.7211755730369[/C][C]-1.32117557303688[/C][/ROW]
[ROW][C]17[/C][C]7.4[/C][C]9.11859725411626[/C][C]-1.71859725411626[/C][/ROW]
[ROW][C]18[/C][C]2.1[/C][C]3.16078777136038[/C][C]-1.06078777136038[/C][/ROW]
[ROW][C]19[/C][C]17.9[/C][C]11.4884672552113[/C][C]6.41153274478873[/C][/ROW]
[ROW][C]20[/C][C]6.1[/C][C]7.96622773926668[/C][C]-1.86622773926668[/C][/ROW]
[ROW][C]21[/C][C]11.9[/C][C]10.7637010335183[/C][C]1.13629896648167[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]13.277433697769[/C][C]0.522566302230992[/C][/ROW]
[ROW][C]23[/C][C]14.3[/C][C]11.6434958518618[/C][C]2.65650414813817[/C][/ROW]
[ROW][C]24[/C][C]15.2[/C][C]9.00867867689247[/C][C]6.19132132310753[/C][/ROW]
[ROW][C]25[/C][C]10[/C][C]6.38737537202304[/C][C]3.61262462797696[/C][/ROW]
[ROW][C]26[/C][C]11.9[/C][C]10.7831649129275[/C][C]1.11683508707251[/C][/ROW]
[ROW][C]27[/C][C]6.5[/C][C]7.41463656630172[/C][C]-0.91463656630172[/C][/ROW]
[ROW][C]28[/C][C]7.5[/C][C]7.55689084926392[/C][C]-0.0568908492639201[/C][/ROW]
[ROW][C]29[/C][C]10.6[/C][C]9.3857358531867[/C][C]1.2142641468133[/C][/ROW]
[ROW][C]30[/C][C]7.4[/C][C]11.5351444418837[/C][C]-4.13514444188371[/C][/ROW]
[ROW][C]31[/C][C]8.4[/C][C]8.84975079520995[/C][C]-0.449750795209949[/C][/ROW]
[ROW][C]32[/C][C]5.7[/C][C]7.69276686643702[/C][C]-1.99276686643702[/C][/ROW]
[ROW][C]33[/C][C]4.9[/C][C]6.3992069451267[/C][C]-1.4992069451267[/C][/ROW]
[ROW][C]34[/C][C]3.2[/C][C]5.64479934962703[/C][C]-2.44479934962703[/C][/ROW]
[ROW][C]35[/C][C]11[/C][C]10.0930659491079[/C][C]0.906934050892101[/C][/ROW]
[ROW][C]36[/C][C]4.9[/C][C]6.55562003867326[/C][C]-1.65562003867326[/C][/ROW]
[ROW][C]37[/C][C]13.2[/C][C]11.8197088684708[/C][C]1.3802911315292[/C][/ROW]
[ROW][C]38[/C][C]9.7[/C][C]5.45991555120597[/C][C]4.24008444879403[/C][/ROW]
[ROW][C]39[/C][C]12.8[/C][C]13.1988614376274[/C][C]-0.398861437627432[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114583&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114583&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
16.39.07916275195347-2.77916275195347
22.11.775043539776280.324956460223719
39.16.215914237684042.88408576231596
415.811.65282966529324.14717033470676
55.23.097343811178832.10265618882117
610.911.4106115920988-0.510611592098838
78.38.60887472565052-0.308874725650516
81110.59600528672760.403994713272354
93.23.56682929280814-0.366829292808135
106.311.733775455182-5.43377545518198
116.610.5161498810283-3.91614988102834
129.59.37826370814140.121736291858607
133.35.70565826947588-2.40565826947588
141112.0148889636112-1.01488896361121
154.77.82344016928446-3.12344016928446
1610.411.7211755730369-1.32117557303688
177.49.11859725411626-1.71859725411626
182.13.16078777136038-1.06078777136038
1917.911.48846725521136.41153274478873
206.17.96622773926668-1.86622773926668
2111.910.76370103351831.13629896648167
2213.813.2774336977690.522566302230992
2314.311.64349585186182.65650414813817
2415.29.008678676892476.19132132310753
25106.387375372023043.61262462797696
2611.910.78316491292751.11683508707251
276.57.41463656630172-0.91463656630172
287.57.55689084926392-0.0568908492639201
2910.69.38573585318671.2142641468133
307.411.5351444418837-4.13514444188371
318.48.84975079520995-0.449750795209949
325.77.69276686643702-1.99276686643702
334.96.3992069451267-1.4992069451267
343.25.64479934962703-2.44479934962703
351110.09306594910790.906934050892101
364.96.55562003867326-1.65562003867326
3713.211.81970886847081.3802911315292
389.75.459915551205974.24008444879403
3912.813.1988614376274-0.398861437627432






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.8208630694023620.3582738611952770.179136930597638
130.8318895463574140.3362209072851720.168110453642586
140.7492975299776730.5014049400446550.250702470022328
150.7943850228778530.4112299542442940.205614977122147
160.7639786372300430.4720427255399140.236021362769957
170.7008059564083940.5983880871832130.299194043591606
180.6781629414979590.6436741170040830.321837058502041
190.8181913406920610.3636173186158780.181808659307939
200.8085496004877980.3829007990244030.191450399512202
210.721210218100610.5575795637987790.27878978189939
220.6148690049454410.7702619901091180.385130995054559
230.5193389192981040.961322161403790.480661080701896
240.8221448746173830.3557102507652330.177855125382617
250.9017748915263380.1964502169473240.098225108473662
260.8022725188336910.3954549623326180.197727481166309
270.7761160351754170.4477679296491670.223883964824583

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.820863069402362 & 0.358273861195277 & 0.179136930597638 \tabularnewline
13 & 0.831889546357414 & 0.336220907285172 & 0.168110453642586 \tabularnewline
14 & 0.749297529977673 & 0.501404940044655 & 0.250702470022328 \tabularnewline
15 & 0.794385022877853 & 0.411229954244294 & 0.205614977122147 \tabularnewline
16 & 0.763978637230043 & 0.472042725539914 & 0.236021362769957 \tabularnewline
17 & 0.700805956408394 & 0.598388087183213 & 0.299194043591606 \tabularnewline
18 & 0.678162941497959 & 0.643674117004083 & 0.321837058502041 \tabularnewline
19 & 0.818191340692061 & 0.363617318615878 & 0.181808659307939 \tabularnewline
20 & 0.808549600487798 & 0.382900799024403 & 0.191450399512202 \tabularnewline
21 & 0.72121021810061 & 0.557579563798779 & 0.27878978189939 \tabularnewline
22 & 0.614869004945441 & 0.770261990109118 & 0.385130995054559 \tabularnewline
23 & 0.519338919298104 & 0.96132216140379 & 0.480661080701896 \tabularnewline
24 & 0.822144874617383 & 0.355710250765233 & 0.177855125382617 \tabularnewline
25 & 0.901774891526338 & 0.196450216947324 & 0.098225108473662 \tabularnewline
26 & 0.802272518833691 & 0.395454962332618 & 0.197727481166309 \tabularnewline
27 & 0.776116035175417 & 0.447767929649167 & 0.223883964824583 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114583&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]0.820863069402362[/C][C]0.358273861195277[/C][C]0.179136930597638[/C][/ROW]
[ROW][C]13[/C][C]0.831889546357414[/C][C]0.336220907285172[/C][C]0.168110453642586[/C][/ROW]
[ROW][C]14[/C][C]0.749297529977673[/C][C]0.501404940044655[/C][C]0.250702470022328[/C][/ROW]
[ROW][C]15[/C][C]0.794385022877853[/C][C]0.411229954244294[/C][C]0.205614977122147[/C][/ROW]
[ROW][C]16[/C][C]0.763978637230043[/C][C]0.472042725539914[/C][C]0.236021362769957[/C][/ROW]
[ROW][C]17[/C][C]0.700805956408394[/C][C]0.598388087183213[/C][C]0.299194043591606[/C][/ROW]
[ROW][C]18[/C][C]0.678162941497959[/C][C]0.643674117004083[/C][C]0.321837058502041[/C][/ROW]
[ROW][C]19[/C][C]0.818191340692061[/C][C]0.363617318615878[/C][C]0.181808659307939[/C][/ROW]
[ROW][C]20[/C][C]0.808549600487798[/C][C]0.382900799024403[/C][C]0.191450399512202[/C][/ROW]
[ROW][C]21[/C][C]0.72121021810061[/C][C]0.557579563798779[/C][C]0.27878978189939[/C][/ROW]
[ROW][C]22[/C][C]0.614869004945441[/C][C]0.770261990109118[/C][C]0.385130995054559[/C][/ROW]
[ROW][C]23[/C][C]0.519338919298104[/C][C]0.96132216140379[/C][C]0.480661080701896[/C][/ROW]
[ROW][C]24[/C][C]0.822144874617383[/C][C]0.355710250765233[/C][C]0.177855125382617[/C][/ROW]
[ROW][C]25[/C][C]0.901774891526338[/C][C]0.196450216947324[/C][C]0.098225108473662[/C][/ROW]
[ROW][C]26[/C][C]0.802272518833691[/C][C]0.395454962332618[/C][C]0.197727481166309[/C][/ROW]
[ROW][C]27[/C][C]0.776116035175417[/C][C]0.447767929649167[/C][C]0.223883964824583[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114583&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114583&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
120.8208630694023620.3582738611952770.179136930597638
130.8318895463574140.3362209072851720.168110453642586
140.7492975299776730.5014049400446550.250702470022328
150.7943850228778530.4112299542442940.205614977122147
160.7639786372300430.4720427255399140.236021362769957
170.7008059564083940.5983880871832130.299194043591606
180.6781629414979590.6436741170040830.321837058502041
190.8181913406920610.3636173186158780.181808659307939
200.8085496004877980.3829007990244030.191450399512202
210.721210218100610.5575795637987790.27878978189939
220.6148690049454410.7702619901091180.385130995054559
230.5193389192981040.961322161403790.480661080701896
240.8221448746173830.3557102507652330.177855125382617
250.9017748915263380.1964502169473240.098225108473662
260.8022725188336910.3954549623326180.197727481166309
270.7761160351754170.4477679296491670.223883964824583






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 level00OK

\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 & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114583&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114583&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114583&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 level00OK
10% type I error level00OK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

Parameters (Session):
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')
}