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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 computationThu, 18 Mar 2010 13:12:28 -0600
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/Mar/18/t1268940308w34o755nwfys7gv.htm/, Retrieved Tue, 18 Jan 2022 13:59:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=74552, Retrieved Tue, 18 Jan 2022 13:59:17 +0000
QR Codes:

Original text written by user:T. Allison, D. V. Cicchetti (1976), Sleep in Mammals: Ecological and Constitutional Correlates, Science, vol. 194
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact291
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Sleep in Mammals ...] [2010-03-18 19:12:28] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
-    D    [Multiple Regression] [] [2010-03-26 10:01:56] [b98453cac15ba1066b407e146608df68]
- RMP     [Recursive Partitioning (Regression Trees)] [Review of Sleep A...] [2010-05-01 12:04:20] [b98453cac15ba1066b407e146608df68]
- RMP       [Recursive Partitioning (Regression Trees)] [] [2010-12-17 13:55:13] [94f495cfd7e7946e5228cbd267a6841d]
- RMPD        [Recursive Partitioning (Regression Trees)] [bonustaak, regres...] [2010-12-20 12:33:09] [94f495cfd7e7946e5228cbd267a6841d]
-    D          [Recursive Partitioning (Regression Trees)] [bonustaak, regres...] [2010-12-20 12:40:26] [94f495cfd7e7946e5228cbd267a6841d]
- RMPD        [Recursive Partitioning (Regression Trees)] [bonustaak, regres...] [2010-12-20 13:03:05] [698bec0d9310438da89fe441e967c51c]
- RMPD        [Recursive Partitioning (Regression Trees)] [bonustaak, regres...] [2010-12-20 13:10:46] [94f495cfd7e7946e5228cbd267a6841d]
-   PD    [Multiple Regression] [Review of Sleep A...] [2010-05-01 12:15:06] [b98453cac15ba1066b407e146608df68]
- RMPD    [Recursive Partitioning (Regression Trees)] [Review of Sleep A...] [2010-05-01 12:21:49] [b98453cac15ba1066b407e146608df68]
- RMP       [Recursive Partitioning (Regression Trees)] [] [2010-12-17 13:58:41] [94f495cfd7e7946e5228cbd267a6841d]
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Dataseries X:
6.3	0	3
2.1	3.40602894496362	4
9.1	1.02325245963371	4
15.8	-1.69897000433602	1
5.2	2.20411998265592	4
10.9	0.51851393987789	1
8.3	1.71733758272386	1
11	-0.36653154442041	4
3.2	2.66745295288995	5
6.3	-1.09691001300806	1
6.6	-0.10237290870956	2
9.5	-0.69897000433602	2
3.3	1.44185217577329	5
11	-0.92081875395238	2
4.7	1.92941892571429	1
10.4	-1	3
7.4	0.01703333929878	4
2.1	2.71683772329952	5
17.9	-2	1
6.1	1.79239168949825	1
11.9	-1.69897000433602	3
13.8	0.23044892137827	1
14.3	0.54406804435028	1
15.2	-0.31875876262441	2
10	1	4
11.9	0.20951501454263	2
6.5	2.28330122870355	4
7.5	0.39794000867204	5
10.6	-0.55284196865778	3
7.4	0.62736585659273	1
8.4	0.83250891270624	2
5.7	-0.1249387366083	2
4.9	0.55630250076729	3
3.2	1.74429298312268	5
11	-0.045757490560675	2
4.9	0.30102999566398	3
13.2	-1	2
9.7	0.6222140229663	4
12.8	0.54406804435028	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=74552&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=74552&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=74552&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 time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
SWS[t] = + 11.6923070380679 -1.81283463836901logWb[t] -0.805866957739349D[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
SWS[t] =  +  11.6923070380679 -1.81283463836901logWb[t] -0.805866957739349D[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=74552&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]SWS[t] =  +  11.6923070380679 -1.81283463836901logWb[t] -0.805866957739349D[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=74552&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=74552&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
SWS[t] = + 11.6923070380679 -1.81283463836901logWb[t] -0.805866957739349D[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)11.69230703806790.9392712.448300
logWb-1.812834638369010.370561-4.89212.1e-051e-05
D-0.8058669577393490.336075-2.39790.02180.0109

\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) & 11.6923070380679 & 0.93927 & 12.4483 & 0 & 0 \tabularnewline
logWb & -1.81283463836901 & 0.370561 & -4.8921 & 2.1e-05 & 1e-05 \tabularnewline
D & -0.805866957739349 & 0.336075 & -2.3979 & 0.0218 & 0.0109 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=74552&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]11.6923070380679[/C][C]0.93927[/C][C]12.4483[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]logWb[/C][C]-1.81283463836901[/C][C]0.370561[/C][C]-4.8921[/C][C]2.1e-05[/C][C]1e-05[/C][/ROW]
[ROW][C]D[/C][C]-0.805866957739349[/C][C]0.336075[/C][C]-2.3979[/C][C]0.0218[/C][C]0.0109[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=74552&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=74552&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)11.69230703806790.9392712.448300
logWb-1.812834638369010.370561-4.89212.1e-051e-05
D-0.8058669577393490.336075-2.39790.02180.0109







Multiple Linear Regression - Regression Statistics
Multiple R0.758888821534529
R-squared0.575912243450066
Adjusted R-squared0.552351812530625
F-TEST (value)24.4440454174739
F-TEST (DF numerator)2
F-TEST (DF denominator)36
p-value1.96880734715243e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.6550561611856
Sum Squared Residuals253.775635885786

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.758888821534529 \tabularnewline
R-squared & 0.575912243450066 \tabularnewline
Adjusted R-squared & 0.552351812530625 \tabularnewline
F-TEST (value) & 24.4440454174739 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 36 \tabularnewline
p-value & 1.96880734715243e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.6550561611856 \tabularnewline
Sum Squared Residuals & 253.775635885786 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=74552&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.758888821534529[/C][/ROW]
[ROW][C]R-squared[/C][C]0.575912243450066[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.552351812530625[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]24.4440454174739[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]36[/C][/ROW]
[ROW][C]p-value[/C][C]1.96880734715243e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.6550561611856[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]253.775635885786[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=74552&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=74552&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 R0.758888821534529
R-squared0.575912243450066
Adjusted R-squared0.552351812530625
F-TEST (value)24.4440454174739
F-TEST (DF numerator)2
F-TEST (DF denominator)36
p-value1.96880734715243e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.6550561611856
Sum Squared Residuals253.775635885786







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16.39.27470616484984-2.97470616484984
22.12.29427195639296-0.194271956392962
39.16.61385170449022.48614829550980
415.813.96639175373881.83360824626119
55.24.473134155430520.726865844569485
610.99.94646004964070.953539950359305
78.37.77319102459380.526808975406202
8119.133300286890691.86669971310931
93.22.827321140152520.372678859847483
106.312.8749565470833-6.57495654708334
116.610.2661582775285-3.66615827752845
129.511.3476901576304-1.84769015763045
133.35.04913268172158-1.74913268172158
141111.7498652554138-0.749865255413841
154.77.38872261986893-2.68872261986893
1610.411.0875408032188-0.687540803218838
177.48.43796057962255-1.03796057962255
182.12.73779471774615-0.637794717746149
1917.914.51210935706653.38789064293345
206.17.63713034008134-1.53713034008134
2111.912.3546578382601-0.454657838260114
2213.810.46867429327923.33132570672078
2314.39.900134683900654.39986531609935
2415.210.65843004875834.54156995124165
25106.656004568741463.34399543125854
2611.99.70075704696792.19924295303209
276.54.329591649886152.17040835011385
287.56.941572817657590.558427182342413
2910.610.27691723517680.323082764823235
307.49.74912952456717-2.34912952456717
318.48.57137212888438-0.171372128884376
325.710.3070663919868-4.60706639198676
334.98.26622172204758-3.36622172204758
343.24.50085751010232-1.30085751010232
351110.16352388644240.836476113557592
364.98.72898856152209-3.82898856152209
3713.211.89340776095821.30659223904181
389.77.340868073798232.35913192620177
3912.89.900134683900652.89986531609935

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6.3 & 9.27470616484984 & -2.97470616484984 \tabularnewline
2 & 2.1 & 2.29427195639296 & -0.194271956392962 \tabularnewline
3 & 9.1 & 6.6138517044902 & 2.48614829550980 \tabularnewline
4 & 15.8 & 13.9663917537388 & 1.83360824626119 \tabularnewline
5 & 5.2 & 4.47313415543052 & 0.726865844569485 \tabularnewline
6 & 10.9 & 9.9464600496407 & 0.953539950359305 \tabularnewline
7 & 8.3 & 7.7731910245938 & 0.526808975406202 \tabularnewline
8 & 11 & 9.13330028689069 & 1.86669971310931 \tabularnewline
9 & 3.2 & 2.82732114015252 & 0.372678859847483 \tabularnewline
10 & 6.3 & 12.8749565470833 & -6.57495654708334 \tabularnewline
11 & 6.6 & 10.2661582775285 & -3.66615827752845 \tabularnewline
12 & 9.5 & 11.3476901576304 & -1.84769015763045 \tabularnewline
13 & 3.3 & 5.04913268172158 & -1.74913268172158 \tabularnewline
14 & 11 & 11.7498652554138 & -0.749865255413841 \tabularnewline
15 & 4.7 & 7.38872261986893 & -2.68872261986893 \tabularnewline
16 & 10.4 & 11.0875408032188 & -0.687540803218838 \tabularnewline
17 & 7.4 & 8.43796057962255 & -1.03796057962255 \tabularnewline
18 & 2.1 & 2.73779471774615 & -0.637794717746149 \tabularnewline
19 & 17.9 & 14.5121093570665 & 3.38789064293345 \tabularnewline
20 & 6.1 & 7.63713034008134 & -1.53713034008134 \tabularnewline
21 & 11.9 & 12.3546578382601 & -0.454657838260114 \tabularnewline
22 & 13.8 & 10.4686742932792 & 3.33132570672078 \tabularnewline
23 & 14.3 & 9.90013468390065 & 4.39986531609935 \tabularnewline
24 & 15.2 & 10.6584300487583 & 4.54156995124165 \tabularnewline
25 & 10 & 6.65600456874146 & 3.34399543125854 \tabularnewline
26 & 11.9 & 9.7007570469679 & 2.19924295303209 \tabularnewline
27 & 6.5 & 4.32959164988615 & 2.17040835011385 \tabularnewline
28 & 7.5 & 6.94157281765759 & 0.558427182342413 \tabularnewline
29 & 10.6 & 10.2769172351768 & 0.323082764823235 \tabularnewline
30 & 7.4 & 9.74912952456717 & -2.34912952456717 \tabularnewline
31 & 8.4 & 8.57137212888438 & -0.171372128884376 \tabularnewline
32 & 5.7 & 10.3070663919868 & -4.60706639198676 \tabularnewline
33 & 4.9 & 8.26622172204758 & -3.36622172204758 \tabularnewline
34 & 3.2 & 4.50085751010232 & -1.30085751010232 \tabularnewline
35 & 11 & 10.1635238864424 & 0.836476113557592 \tabularnewline
36 & 4.9 & 8.72898856152209 & -3.82898856152209 \tabularnewline
37 & 13.2 & 11.8934077609582 & 1.30659223904181 \tabularnewline
38 & 9.7 & 7.34086807379823 & 2.35913192620177 \tabularnewline
39 & 12.8 & 9.90013468390065 & 2.89986531609935 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=74552&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]9.27470616484984[/C][C]-2.97470616484984[/C][/ROW]
[ROW][C]2[/C][C]2.1[/C][C]2.29427195639296[/C][C]-0.194271956392962[/C][/ROW]
[ROW][C]3[/C][C]9.1[/C][C]6.6138517044902[/C][C]2.48614829550980[/C][/ROW]
[ROW][C]4[/C][C]15.8[/C][C]13.9663917537388[/C][C]1.83360824626119[/C][/ROW]
[ROW][C]5[/C][C]5.2[/C][C]4.47313415543052[/C][C]0.726865844569485[/C][/ROW]
[ROW][C]6[/C][C]10.9[/C][C]9.9464600496407[/C][C]0.953539950359305[/C][/ROW]
[ROW][C]7[/C][C]8.3[/C][C]7.7731910245938[/C][C]0.526808975406202[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]9.13330028689069[/C][C]1.86669971310931[/C][/ROW]
[ROW][C]9[/C][C]3.2[/C][C]2.82732114015252[/C][C]0.372678859847483[/C][/ROW]
[ROW][C]10[/C][C]6.3[/C][C]12.8749565470833[/C][C]-6.57495654708334[/C][/ROW]
[ROW][C]11[/C][C]6.6[/C][C]10.2661582775285[/C][C]-3.66615827752845[/C][/ROW]
[ROW][C]12[/C][C]9.5[/C][C]11.3476901576304[/C][C]-1.84769015763045[/C][/ROW]
[ROW][C]13[/C][C]3.3[/C][C]5.04913268172158[/C][C]-1.74913268172158[/C][/ROW]
[ROW][C]14[/C][C]11[/C][C]11.7498652554138[/C][C]-0.749865255413841[/C][/ROW]
[ROW][C]15[/C][C]4.7[/C][C]7.38872261986893[/C][C]-2.68872261986893[/C][/ROW]
[ROW][C]16[/C][C]10.4[/C][C]11.0875408032188[/C][C]-0.687540803218838[/C][/ROW]
[ROW][C]17[/C][C]7.4[/C][C]8.43796057962255[/C][C]-1.03796057962255[/C][/ROW]
[ROW][C]18[/C][C]2.1[/C][C]2.73779471774615[/C][C]-0.637794717746149[/C][/ROW]
[ROW][C]19[/C][C]17.9[/C][C]14.5121093570665[/C][C]3.38789064293345[/C][/ROW]
[ROW][C]20[/C][C]6.1[/C][C]7.63713034008134[/C][C]-1.53713034008134[/C][/ROW]
[ROW][C]21[/C][C]11.9[/C][C]12.3546578382601[/C][C]-0.454657838260114[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]10.4686742932792[/C][C]3.33132570672078[/C][/ROW]
[ROW][C]23[/C][C]14.3[/C][C]9.90013468390065[/C][C]4.39986531609935[/C][/ROW]
[ROW][C]24[/C][C]15.2[/C][C]10.6584300487583[/C][C]4.54156995124165[/C][/ROW]
[ROW][C]25[/C][C]10[/C][C]6.65600456874146[/C][C]3.34399543125854[/C][/ROW]
[ROW][C]26[/C][C]11.9[/C][C]9.7007570469679[/C][C]2.19924295303209[/C][/ROW]
[ROW][C]27[/C][C]6.5[/C][C]4.32959164988615[/C][C]2.17040835011385[/C][/ROW]
[ROW][C]28[/C][C]7.5[/C][C]6.94157281765759[/C][C]0.558427182342413[/C][/ROW]
[ROW][C]29[/C][C]10.6[/C][C]10.2769172351768[/C][C]0.323082764823235[/C][/ROW]
[ROW][C]30[/C][C]7.4[/C][C]9.74912952456717[/C][C]-2.34912952456717[/C][/ROW]
[ROW][C]31[/C][C]8.4[/C][C]8.57137212888438[/C][C]-0.171372128884376[/C][/ROW]
[ROW][C]32[/C][C]5.7[/C][C]10.3070663919868[/C][C]-4.60706639198676[/C][/ROW]
[ROW][C]33[/C][C]4.9[/C][C]8.26622172204758[/C][C]-3.36622172204758[/C][/ROW]
[ROW][C]34[/C][C]3.2[/C][C]4.50085751010232[/C][C]-1.30085751010232[/C][/ROW]
[ROW][C]35[/C][C]11[/C][C]10.1635238864424[/C][C]0.836476113557592[/C][/ROW]
[ROW][C]36[/C][C]4.9[/C][C]8.72898856152209[/C][C]-3.82898856152209[/C][/ROW]
[ROW][C]37[/C][C]13.2[/C][C]11.8934077609582[/C][C]1.30659223904181[/C][/ROW]
[ROW][C]38[/C][C]9.7[/C][C]7.34086807379823[/C][C]2.35913192620177[/C][/ROW]
[ROW][C]39[/C][C]12.8[/C][C]9.90013468390065[/C][C]2.89986531609935[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=74552&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=74552&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
16.39.27470616484984-2.97470616484984
22.12.29427195639296-0.194271956392962
39.16.61385170449022.48614829550980
415.813.96639175373881.83360824626119
55.24.473134155430520.726865844569485
610.99.94646004964070.953539950359305
78.37.77319102459380.526808975406202
8119.133300286890691.86669971310931
93.22.827321140152520.372678859847483
106.312.8749565470833-6.57495654708334
116.610.2661582775285-3.66615827752845
129.511.3476901576304-1.84769015763045
133.35.04913268172158-1.74913268172158
141111.7498652554138-0.749865255413841
154.77.38872261986893-2.68872261986893
1610.411.0875408032188-0.687540803218838
177.48.43796057962255-1.03796057962255
182.12.73779471774615-0.637794717746149
1917.914.51210935706653.38789064293345
206.17.63713034008134-1.53713034008134
2111.912.3546578382601-0.454657838260114
2213.810.46867429327923.33132570672078
2314.39.900134683900654.39986531609935
2415.210.65843004875834.54156995124165
25106.656004568741463.34399543125854
2611.99.70075704696792.19924295303209
276.54.329591649886152.17040835011385
287.56.941572817657590.558427182342413
2910.610.27691723517680.323082764823235
307.49.74912952456717-2.34912952456717
318.48.57137212888438-0.171372128884376
325.710.3070663919868-4.60706639198676
334.98.26622172204758-3.36622172204758
343.24.50085751010232-1.30085751010232
351110.16352388644240.836476113557592
364.98.72898856152209-3.82898856152209
3713.211.89340776095821.30659223904181
389.77.340868073798232.35913192620177
3912.89.900134683900652.89986531609935







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.4832784011554960.9665568023109930.516721598844504
70.3103762783513990.6207525567027980.689623721648601
80.2098384641653670.4196769283307350.790161535834633
90.1172386693498230.2344773386996450.882761330650177
100.6755192147846560.6489615704306880.324480785215344
110.7046081564191590.5907836871616830.295391843580841
120.628985505986740.7420289880265210.371014494013260
130.5737237288999770.8525525422000460.426276271100023
140.4808186729277080.9616373458554160.519181327072292
150.4532449017384680.9064898034769370.546755098261531
160.3602716002334840.7205432004669680.639728399766516
170.2800542372458380.5601084744916750.719945762754162
180.2069738372658310.4139476745316620.793026162734169
190.2986206528884920.5972413057769840.701379347111508
200.2558315540439630.5116631080879270.744168445956037
210.1820563680115160.3641127360230310.817943631988484
220.2221257117314060.4442514234628130.777874288268594
230.3347293812575990.6694587625151970.665270618742401
240.4997517294002780.9995034588005570.500248270599722
250.5363812346596830.9272375306806340.463618765340317
260.5103887735166050.979222452966790.489611226483395
270.4884302773486480.9768605546972950.511569722651352
280.3886925670338670.7773851340677340.611307432966133
290.2870767655951620.5741535311903240.712923234404838
300.2457997268217000.4915994536433990.7542002731783
310.1542562163897450.3085124327794890.845743783610255
320.2924046944430350.584809388886070.707595305556965
330.3323444524255080.6646889048510160.667655547574492

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.483278401155496 & 0.966556802310993 & 0.516721598844504 \tabularnewline
7 & 0.310376278351399 & 0.620752556702798 & 0.689623721648601 \tabularnewline
8 & 0.209838464165367 & 0.419676928330735 & 0.790161535834633 \tabularnewline
9 & 0.117238669349823 & 0.234477338699645 & 0.882761330650177 \tabularnewline
10 & 0.675519214784656 & 0.648961570430688 & 0.324480785215344 \tabularnewline
11 & 0.704608156419159 & 0.590783687161683 & 0.295391843580841 \tabularnewline
12 & 0.62898550598674 & 0.742028988026521 & 0.371014494013260 \tabularnewline
13 & 0.573723728899977 & 0.852552542200046 & 0.426276271100023 \tabularnewline
14 & 0.480818672927708 & 0.961637345855416 & 0.519181327072292 \tabularnewline
15 & 0.453244901738468 & 0.906489803476937 & 0.546755098261531 \tabularnewline
16 & 0.360271600233484 & 0.720543200466968 & 0.639728399766516 \tabularnewline
17 & 0.280054237245838 & 0.560108474491675 & 0.719945762754162 \tabularnewline
18 & 0.206973837265831 & 0.413947674531662 & 0.793026162734169 \tabularnewline
19 & 0.298620652888492 & 0.597241305776984 & 0.701379347111508 \tabularnewline
20 & 0.255831554043963 & 0.511663108087927 & 0.744168445956037 \tabularnewline
21 & 0.182056368011516 & 0.364112736023031 & 0.817943631988484 \tabularnewline
22 & 0.222125711731406 & 0.444251423462813 & 0.777874288268594 \tabularnewline
23 & 0.334729381257599 & 0.669458762515197 & 0.665270618742401 \tabularnewline
24 & 0.499751729400278 & 0.999503458800557 & 0.500248270599722 \tabularnewline
25 & 0.536381234659683 & 0.927237530680634 & 0.463618765340317 \tabularnewline
26 & 0.510388773516605 & 0.97922245296679 & 0.489611226483395 \tabularnewline
27 & 0.488430277348648 & 0.976860554697295 & 0.511569722651352 \tabularnewline
28 & 0.388692567033867 & 0.777385134067734 & 0.611307432966133 \tabularnewline
29 & 0.287076765595162 & 0.574153531190324 & 0.712923234404838 \tabularnewline
30 & 0.245799726821700 & 0.491599453643399 & 0.7542002731783 \tabularnewline
31 & 0.154256216389745 & 0.308512432779489 & 0.845743783610255 \tabularnewline
32 & 0.292404694443035 & 0.58480938888607 & 0.707595305556965 \tabularnewline
33 & 0.332344452425508 & 0.664688904851016 & 0.667655547574492 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=74552&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]0.483278401155496[/C][C]0.966556802310993[/C][C]0.516721598844504[/C][/ROW]
[ROW][C]7[/C][C]0.310376278351399[/C][C]0.620752556702798[/C][C]0.689623721648601[/C][/ROW]
[ROW][C]8[/C][C]0.209838464165367[/C][C]0.419676928330735[/C][C]0.790161535834633[/C][/ROW]
[ROW][C]9[/C][C]0.117238669349823[/C][C]0.234477338699645[/C][C]0.882761330650177[/C][/ROW]
[ROW][C]10[/C][C]0.675519214784656[/C][C]0.648961570430688[/C][C]0.324480785215344[/C][/ROW]
[ROW][C]11[/C][C]0.704608156419159[/C][C]0.590783687161683[/C][C]0.295391843580841[/C][/ROW]
[ROW][C]12[/C][C]0.62898550598674[/C][C]0.742028988026521[/C][C]0.371014494013260[/C][/ROW]
[ROW][C]13[/C][C]0.573723728899977[/C][C]0.852552542200046[/C][C]0.426276271100023[/C][/ROW]
[ROW][C]14[/C][C]0.480818672927708[/C][C]0.961637345855416[/C][C]0.519181327072292[/C][/ROW]
[ROW][C]15[/C][C]0.453244901738468[/C][C]0.906489803476937[/C][C]0.546755098261531[/C][/ROW]
[ROW][C]16[/C][C]0.360271600233484[/C][C]0.720543200466968[/C][C]0.639728399766516[/C][/ROW]
[ROW][C]17[/C][C]0.280054237245838[/C][C]0.560108474491675[/C][C]0.719945762754162[/C][/ROW]
[ROW][C]18[/C][C]0.206973837265831[/C][C]0.413947674531662[/C][C]0.793026162734169[/C][/ROW]
[ROW][C]19[/C][C]0.298620652888492[/C][C]0.597241305776984[/C][C]0.701379347111508[/C][/ROW]
[ROW][C]20[/C][C]0.255831554043963[/C][C]0.511663108087927[/C][C]0.744168445956037[/C][/ROW]
[ROW][C]21[/C][C]0.182056368011516[/C][C]0.364112736023031[/C][C]0.817943631988484[/C][/ROW]
[ROW][C]22[/C][C]0.222125711731406[/C][C]0.444251423462813[/C][C]0.777874288268594[/C][/ROW]
[ROW][C]23[/C][C]0.334729381257599[/C][C]0.669458762515197[/C][C]0.665270618742401[/C][/ROW]
[ROW][C]24[/C][C]0.499751729400278[/C][C]0.999503458800557[/C][C]0.500248270599722[/C][/ROW]
[ROW][C]25[/C][C]0.536381234659683[/C][C]0.927237530680634[/C][C]0.463618765340317[/C][/ROW]
[ROW][C]26[/C][C]0.510388773516605[/C][C]0.97922245296679[/C][C]0.489611226483395[/C][/ROW]
[ROW][C]27[/C][C]0.488430277348648[/C][C]0.976860554697295[/C][C]0.511569722651352[/C][/ROW]
[ROW][C]28[/C][C]0.388692567033867[/C][C]0.777385134067734[/C][C]0.611307432966133[/C][/ROW]
[ROW][C]29[/C][C]0.287076765595162[/C][C]0.574153531190324[/C][C]0.712923234404838[/C][/ROW]
[ROW][C]30[/C][C]0.245799726821700[/C][C]0.491599453643399[/C][C]0.7542002731783[/C][/ROW]
[ROW][C]31[/C][C]0.154256216389745[/C][C]0.308512432779489[/C][C]0.845743783610255[/C][/ROW]
[ROW][C]32[/C][C]0.292404694443035[/C][C]0.58480938888607[/C][C]0.707595305556965[/C][/ROW]
[ROW][C]33[/C][C]0.332344452425508[/C][C]0.664688904851016[/C][C]0.667655547574492[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=74552&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=74552&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
60.4832784011554960.9665568023109930.516721598844504
70.3103762783513990.6207525567027980.689623721648601
80.2098384641653670.4196769283307350.790161535834633
90.1172386693498230.2344773386996450.882761330650177
100.6755192147846560.6489615704306880.324480785215344
110.7046081564191590.5907836871616830.295391843580841
120.628985505986740.7420289880265210.371014494013260
130.5737237288999770.8525525422000460.426276271100023
140.4808186729277080.9616373458554160.519181327072292
150.4532449017384680.9064898034769370.546755098261531
160.3602716002334840.7205432004669680.639728399766516
170.2800542372458380.5601084744916750.719945762754162
180.2069738372658310.4139476745316620.793026162734169
190.2986206528884920.5972413057769840.701379347111508
200.2558315540439630.5116631080879270.744168445956037
210.1820563680115160.3641127360230310.817943631988484
220.2221257117314060.4442514234628130.777874288268594
230.3347293812575990.6694587625151970.665270618742401
240.4997517294002780.9995034588005570.500248270599722
250.5363812346596830.9272375306806340.463618765340317
260.5103887735166050.979222452966790.489611226483395
270.4884302773486480.9768605546972950.511569722651352
280.3886925670338670.7773851340677340.611307432966133
290.2870767655951620.5741535311903240.712923234404838
300.2457997268217000.4915994536433990.7542002731783
310.1542562163897450.3085124327794890.845743783610255
320.2924046944430350.584809388886070.707595305556965
330.3323444524255080.6646889048510160.667655547574492







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=74552&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=74552&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=74552&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 level00OK



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')
}