Free Statistics

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Author's title

Author*The author of this computation has been verified*
R Software Module--
Title produced by softwareMultiple Regression
Date of computationTue, 22 Nov 2011 01:58:35 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/22/t13219451239joxez0lqp1aj71.htm/, Retrieved Thu, 28 Mar 2024 15:59:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146062, Retrieved Thu, 28 Mar 2024 15:59:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact119
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [] [2011-11-21 21:57:45] [77e355412ccdb651b3c7eae41c3da865]
-    D    [Multiple Regression] [] [2011-11-22 06:38:07] [aefb5c2d4042694c5b6b82f93ac1885a]
-           [Multiple Regression] [] [2011-11-22 06:41:14] [aefb5c2d4042694c5b6b82f93ac1885a]
-  M            [Multiple Regression] [] [2011-11-22 06:58:35] [5f7ae77ad4c15dc18491c39fdf8bddde] [Current]
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Dataseries X:
9.5	5.569	1.933	0.226
9.6	5.634	1.947	0.231
9.4	5.433	1.936	0.225
9.4	5.425	1.956	0.229
9.5	5.412	1.965	0.236
9.4	5.247	1.973	0.234
9.7	5.31	1.988	0.253
9.5	5.168	1.985	0.251
9.5	4.927	1.986	0.243
9.3	4.929	1.993	0.239
9.4	4.902	2.003	0.237
9.3	4.82	2	0.23
9.1	4.588	2.015	0.221
8.8	4.312	2.001	0.203
8.8	4.269	2.025	0.195
8.6	4.137	2.035	0.182
8.7	4.099	2.049	0.183
8.5	4.016	2.04	0.175
8.7	4.121	2.079	0.181
8.6	3.97	2.064	0.176
8.5	3.89	2.083	0.172
8.6	3.889	2.091	0.176
8.6	3.788	2.108	0.172
8.7	3.75	2.113	0.174
8.7	3.651	2.115	0.172
8.7	3.559	2.117	0.174
8.8	3.525	2.125	0.18
8.7	3.32	2.142	0.205
8.6	3.218	2.16	0.207
8.5	3.138	2.158	0.207
8.5	3.061	2.143	0.208
8.8	3.099	2.146	0.22
8.8	2.997	2.131	0.227
8.8	2.963	2.117	0.234
8.8	2.883	2.087	0.24
8.6	2.804	2.057	0.24
8.6	2.724	2.024	0.242
8.8	2.678	2.027	0.252
8.7	2.576	1.996	0.25
8.5	2.478	1.96	0.253




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=146062&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=146062&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146062&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 time4 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
Rate[t] = + 3.10886257200391 + 0.370918572255257Heart_disease[t] + 1.28807495597693Cancer[t] + 7.87100858055455`Diabetes `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Rate[t] =  +  3.10886257200391 +  0.370918572255257Heart_disease[t] +  1.28807495597693Cancer[t] +  7.87100858055455`Diabetes
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146062&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Rate[t] =  +  3.10886257200391 +  0.370918572255257Heart_disease[t] +  1.28807495597693Cancer[t] +  7.87100858055455`Diabetes
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146062&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146062&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
Rate[t] = + 3.10886257200391 + 0.370918572255257Heart_disease[t] + 1.28807495597693Cancer[t] + 7.87100858055455`Diabetes `[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.108862572003910.9219713.3720.0017950.000898
Heart_disease0.3709185722552570.02240316.556900
Cancer1.288074955976930.3704023.47750.001340.00067
`Diabetes `7.871008580554550.66836211.776600

\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) & 3.10886257200391 & 0.921971 & 3.372 & 0.001795 & 0.000898 \tabularnewline
Heart_disease & 0.370918572255257 & 0.022403 & 16.5569 & 0 & 0 \tabularnewline
Cancer & 1.28807495597693 & 0.370402 & 3.4775 & 0.00134 & 0.00067 \tabularnewline
`Diabetes
` & 7.87100858055455 & 0.668362 & 11.7766 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146062&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]3.10886257200391[/C][C]0.921971[/C][C]3.372[/C][C]0.001795[/C][C]0.000898[/C][/ROW]
[ROW][C]Heart_disease[/C][C]0.370918572255257[/C][C]0.022403[/C][C]16.5569[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Cancer[/C][C]1.28807495597693[/C][C]0.370402[/C][C]3.4775[/C][C]0.00134[/C][C]0.00067[/C][/ROW]
[ROW][C]`Diabetes
`[/C][C]7.87100858055455[/C][C]0.668362[/C][C]11.7766[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146062&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146062&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.108862572003910.9219713.3720.0017950.000898
Heart_disease0.3709185722552570.02240316.556900
Cancer1.288074955976930.3704023.47750.001340.00067
`Diabetes `7.871008580554550.66836211.776600







Multiple Linear Regression - Regression Statistics
Multiple R0.973057227616687
R-squared0.946840368217073
Adjusted R-squared0.94241039890183
F-TEST (value)213.735197884762
F-TEST (DF numerator)3
F-TEST (DF denominator)36
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0918323740289117
Sum Squared Residuals0.303594657112294

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.973057227616687 \tabularnewline
R-squared & 0.946840368217073 \tabularnewline
Adjusted R-squared & 0.94241039890183 \tabularnewline
F-TEST (value) & 213.735197884762 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 36 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0918323740289117 \tabularnewline
Sum Squared Residuals & 0.303594657112294 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146062&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.973057227616687[/C][/ROW]
[ROW][C]R-squared[/C][C]0.946840368217073[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.94241039890183[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]213.735197884762[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]36[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0918323740289117[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.303594657112294[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146062&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146062&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 R0.973057227616687
R-squared0.946840368217073
Adjusted R-squared0.94241039890183
F-TEST (value)213.735197884762
F-TEST (DF numerator)3
F-TEST (DF denominator)36
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0918323740289117
Sum Squared Residuals0.303594657112294







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
19.59.443204930002170.0567950699978308
29.69.524702729485210.0752972705147905
39.49.388753220462830.0112467795371716
49.49.44303140532654-0.0430314053265431
59.59.5048991985549-0.00489919855489929
69.49.43826021661949-0.0382602166194881
79.79.630498374041760.0695016259582397
89.59.55822169475247-0.0582216947524736
99.59.40715032515050.092849674849503
109.39.38542465266463-0.0854246526646274
119.49.37254858361240.0274514163876041
129.39.283171975755650.016828024244348
139.19.1456009141071-0.0456009141070965
148.88.88351618433099-0.0835161843309856
158.88.83551241602302-0.0355124160230194
168.68.69710880249789-0.0971088024978867
178.78.70891795471642-0.0089179547164187
188.58.603570969971-0.103570969971003
198.78.73997839482423-0.0399783948242335
208.68.62529352317126-0.0252935231712625
218.58.58860942723219-0.0886094272321851
228.68.63002714262996-0.0300271426299637
238.68.582977606761570.0170223932384278
248.78.591065092956870.108934907043133
258.78.541178287054440.158821712945559
268.78.525371945480020.17462805451998
278.88.570291365154480.229708634845517
288.78.71292554660763-0.0129255466076278
298.68.71401921860629-0.114019218606285
308.58.68176958291391-0.18176958291391
318.58.64175873709116-0.141758737091156
328.88.754169970671440.0458300293285592
338.88.752112212025630.0478877879743677
348.88.776564991249160.0234350087508412
358.88.755475308272760.0445246917272422
368.68.68753049238529-0.0875304923852854
378.68.63109255021874-0.0310925502187356
388.88.696604606568470.103395393431531
398.78.603098571402040.0969014285979596
408.58.54399087864752-0.0439908786475188

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9.5 & 9.44320493000217 & 0.0567950699978308 \tabularnewline
2 & 9.6 & 9.52470272948521 & 0.0752972705147905 \tabularnewline
3 & 9.4 & 9.38875322046283 & 0.0112467795371716 \tabularnewline
4 & 9.4 & 9.44303140532654 & -0.0430314053265431 \tabularnewline
5 & 9.5 & 9.5048991985549 & -0.00489919855489929 \tabularnewline
6 & 9.4 & 9.43826021661949 & -0.0382602166194881 \tabularnewline
7 & 9.7 & 9.63049837404176 & 0.0695016259582397 \tabularnewline
8 & 9.5 & 9.55822169475247 & -0.0582216947524736 \tabularnewline
9 & 9.5 & 9.4071503251505 & 0.092849674849503 \tabularnewline
10 & 9.3 & 9.38542465266463 & -0.0854246526646274 \tabularnewline
11 & 9.4 & 9.3725485836124 & 0.0274514163876041 \tabularnewline
12 & 9.3 & 9.28317197575565 & 0.016828024244348 \tabularnewline
13 & 9.1 & 9.1456009141071 & -0.0456009141070965 \tabularnewline
14 & 8.8 & 8.88351618433099 & -0.0835161843309856 \tabularnewline
15 & 8.8 & 8.83551241602302 & -0.0355124160230194 \tabularnewline
16 & 8.6 & 8.69710880249789 & -0.0971088024978867 \tabularnewline
17 & 8.7 & 8.70891795471642 & -0.0089179547164187 \tabularnewline
18 & 8.5 & 8.603570969971 & -0.103570969971003 \tabularnewline
19 & 8.7 & 8.73997839482423 & -0.0399783948242335 \tabularnewline
20 & 8.6 & 8.62529352317126 & -0.0252935231712625 \tabularnewline
21 & 8.5 & 8.58860942723219 & -0.0886094272321851 \tabularnewline
22 & 8.6 & 8.63002714262996 & -0.0300271426299637 \tabularnewline
23 & 8.6 & 8.58297760676157 & 0.0170223932384278 \tabularnewline
24 & 8.7 & 8.59106509295687 & 0.108934907043133 \tabularnewline
25 & 8.7 & 8.54117828705444 & 0.158821712945559 \tabularnewline
26 & 8.7 & 8.52537194548002 & 0.17462805451998 \tabularnewline
27 & 8.8 & 8.57029136515448 & 0.229708634845517 \tabularnewline
28 & 8.7 & 8.71292554660763 & -0.0129255466076278 \tabularnewline
29 & 8.6 & 8.71401921860629 & -0.114019218606285 \tabularnewline
30 & 8.5 & 8.68176958291391 & -0.18176958291391 \tabularnewline
31 & 8.5 & 8.64175873709116 & -0.141758737091156 \tabularnewline
32 & 8.8 & 8.75416997067144 & 0.0458300293285592 \tabularnewline
33 & 8.8 & 8.75211221202563 & 0.0478877879743677 \tabularnewline
34 & 8.8 & 8.77656499124916 & 0.0234350087508412 \tabularnewline
35 & 8.8 & 8.75547530827276 & 0.0445246917272422 \tabularnewline
36 & 8.6 & 8.68753049238529 & -0.0875304923852854 \tabularnewline
37 & 8.6 & 8.63109255021874 & -0.0310925502187356 \tabularnewline
38 & 8.8 & 8.69660460656847 & 0.103395393431531 \tabularnewline
39 & 8.7 & 8.60309857140204 & 0.0969014285979596 \tabularnewline
40 & 8.5 & 8.54399087864752 & -0.0439908786475188 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146062&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]9.5[/C][C]9.44320493000217[/C][C]0.0567950699978308[/C][/ROW]
[ROW][C]2[/C][C]9.6[/C][C]9.52470272948521[/C][C]0.0752972705147905[/C][/ROW]
[ROW][C]3[/C][C]9.4[/C][C]9.38875322046283[/C][C]0.0112467795371716[/C][/ROW]
[ROW][C]4[/C][C]9.4[/C][C]9.44303140532654[/C][C]-0.0430314053265431[/C][/ROW]
[ROW][C]5[/C][C]9.5[/C][C]9.5048991985549[/C][C]-0.00489919855489929[/C][/ROW]
[ROW][C]6[/C][C]9.4[/C][C]9.43826021661949[/C][C]-0.0382602166194881[/C][/ROW]
[ROW][C]7[/C][C]9.7[/C][C]9.63049837404176[/C][C]0.0695016259582397[/C][/ROW]
[ROW][C]8[/C][C]9.5[/C][C]9.55822169475247[/C][C]-0.0582216947524736[/C][/ROW]
[ROW][C]9[/C][C]9.5[/C][C]9.4071503251505[/C][C]0.092849674849503[/C][/ROW]
[ROW][C]10[/C][C]9.3[/C][C]9.38542465266463[/C][C]-0.0854246526646274[/C][/ROW]
[ROW][C]11[/C][C]9.4[/C][C]9.3725485836124[/C][C]0.0274514163876041[/C][/ROW]
[ROW][C]12[/C][C]9.3[/C][C]9.28317197575565[/C][C]0.016828024244348[/C][/ROW]
[ROW][C]13[/C][C]9.1[/C][C]9.1456009141071[/C][C]-0.0456009141070965[/C][/ROW]
[ROW][C]14[/C][C]8.8[/C][C]8.88351618433099[/C][C]-0.0835161843309856[/C][/ROW]
[ROW][C]15[/C][C]8.8[/C][C]8.83551241602302[/C][C]-0.0355124160230194[/C][/ROW]
[ROW][C]16[/C][C]8.6[/C][C]8.69710880249789[/C][C]-0.0971088024978867[/C][/ROW]
[ROW][C]17[/C][C]8.7[/C][C]8.70891795471642[/C][C]-0.0089179547164187[/C][/ROW]
[ROW][C]18[/C][C]8.5[/C][C]8.603570969971[/C][C]-0.103570969971003[/C][/ROW]
[ROW][C]19[/C][C]8.7[/C][C]8.73997839482423[/C][C]-0.0399783948242335[/C][/ROW]
[ROW][C]20[/C][C]8.6[/C][C]8.62529352317126[/C][C]-0.0252935231712625[/C][/ROW]
[ROW][C]21[/C][C]8.5[/C][C]8.58860942723219[/C][C]-0.0886094272321851[/C][/ROW]
[ROW][C]22[/C][C]8.6[/C][C]8.63002714262996[/C][C]-0.0300271426299637[/C][/ROW]
[ROW][C]23[/C][C]8.6[/C][C]8.58297760676157[/C][C]0.0170223932384278[/C][/ROW]
[ROW][C]24[/C][C]8.7[/C][C]8.59106509295687[/C][C]0.108934907043133[/C][/ROW]
[ROW][C]25[/C][C]8.7[/C][C]8.54117828705444[/C][C]0.158821712945559[/C][/ROW]
[ROW][C]26[/C][C]8.7[/C][C]8.52537194548002[/C][C]0.17462805451998[/C][/ROW]
[ROW][C]27[/C][C]8.8[/C][C]8.57029136515448[/C][C]0.229708634845517[/C][/ROW]
[ROW][C]28[/C][C]8.7[/C][C]8.71292554660763[/C][C]-0.0129255466076278[/C][/ROW]
[ROW][C]29[/C][C]8.6[/C][C]8.71401921860629[/C][C]-0.114019218606285[/C][/ROW]
[ROW][C]30[/C][C]8.5[/C][C]8.68176958291391[/C][C]-0.18176958291391[/C][/ROW]
[ROW][C]31[/C][C]8.5[/C][C]8.64175873709116[/C][C]-0.141758737091156[/C][/ROW]
[ROW][C]32[/C][C]8.8[/C][C]8.75416997067144[/C][C]0.0458300293285592[/C][/ROW]
[ROW][C]33[/C][C]8.8[/C][C]8.75211221202563[/C][C]0.0478877879743677[/C][/ROW]
[ROW][C]34[/C][C]8.8[/C][C]8.77656499124916[/C][C]0.0234350087508412[/C][/ROW]
[ROW][C]35[/C][C]8.8[/C][C]8.75547530827276[/C][C]0.0445246917272422[/C][/ROW]
[ROW][C]36[/C][C]8.6[/C][C]8.68753049238529[/C][C]-0.0875304923852854[/C][/ROW]
[ROW][C]37[/C][C]8.6[/C][C]8.63109255021874[/C][C]-0.0310925502187356[/C][/ROW]
[ROW][C]38[/C][C]8.8[/C][C]8.69660460656847[/C][C]0.103395393431531[/C][/ROW]
[ROW][C]39[/C][C]8.7[/C][C]8.60309857140204[/C][C]0.0969014285979596[/C][/ROW]
[ROW][C]40[/C][C]8.5[/C][C]8.54399087864752[/C][C]-0.0439908786475188[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146062&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146062&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
19.59.443204930002170.0567950699978308
29.69.524702729485210.0752972705147905
39.49.388753220462830.0112467795371716
49.49.44303140532654-0.0430314053265431
59.59.5048991985549-0.00489919855489929
69.49.43826021661949-0.0382602166194881
79.79.630498374041760.0695016259582397
89.59.55822169475247-0.0582216947524736
99.59.40715032515050.092849674849503
109.39.38542465266463-0.0854246526646274
119.49.37254858361240.0274514163876041
129.39.283171975755650.016828024244348
139.19.1456009141071-0.0456009141070965
148.88.88351618433099-0.0835161843309856
158.88.83551241602302-0.0355124160230194
168.68.69710880249789-0.0971088024978867
178.78.70891795471642-0.0089179547164187
188.58.603570969971-0.103570969971003
198.78.73997839482423-0.0399783948242335
208.68.62529352317126-0.0252935231712625
218.58.58860942723219-0.0886094272321851
228.68.63002714262996-0.0300271426299637
238.68.582977606761570.0170223932384278
248.78.591065092956870.108934907043133
258.78.541178287054440.158821712945559
268.78.525371945480020.17462805451998
278.88.570291365154480.229708634845517
288.78.71292554660763-0.0129255466076278
298.68.71401921860629-0.114019218606285
308.58.68176958291391-0.18176958291391
318.58.64175873709116-0.141758737091156
328.88.754169970671440.0458300293285592
338.88.752112212025630.0478877879743677
348.88.776564991249160.0234350087508412
358.88.755475308272760.0445246917272422
368.68.68753049238529-0.0875304923852854
378.68.63109255021874-0.0310925502187356
388.88.696604606568470.103395393431531
398.78.603098571402040.0969014285979596
408.58.54399087864752-0.0439908786475188







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.01527714959560030.03055429919120050.9847228504044
80.02765785105420250.0553157021084050.972342148945798
90.213974608879050.42794921775810.78602539112095
100.1408946874502090.2817893749004190.859105312549791
110.1470559907973460.2941119815946910.852944009202654
120.1100482355472980.2200964710945960.889951764452702
130.07020751718554980.14041503437110.92979248281445
140.03907270199051260.07814540398102520.960927298009487
150.02498284666248690.04996569332497390.975017153337513
160.01316865921632960.02633731843265930.98683134078367
170.009358162348316020.0187163246966320.990641837651684
180.006372173599236740.01274434719847350.993627826400763
190.002859111389151450.005718222778302910.997140888610849
200.00157304130301240.00314608260602480.998426958696988
210.001856229483708380.003712458967416750.998143770516292
220.003253267813242560.006506535626485130.996746732186757
230.01048311628234580.02096623256469170.989516883717654
240.07123119707706160.1424623941541230.928768802922938
250.1659578155851370.3319156311702740.834042184414863
260.222859289925740.4457185798514790.77714071007426
270.7103128576775460.5793742846449090.289687142322454
280.756633919181690.4867321616366210.24336608081831
290.7763151115804780.4473697768390440.223684888419522
300.756527306511170.4869453869776610.24347269348883
310.6990060300568520.6019879398862960.300993969943148
320.7886626874836840.4226746250326310.211337312516316
330.7070689509398030.5858620981203940.292931049060197

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.0152771495956003 & 0.0305542991912005 & 0.9847228504044 \tabularnewline
8 & 0.0276578510542025 & 0.055315702108405 & 0.972342148945798 \tabularnewline
9 & 0.21397460887905 & 0.4279492177581 & 0.78602539112095 \tabularnewline
10 & 0.140894687450209 & 0.281789374900419 & 0.859105312549791 \tabularnewline
11 & 0.147055990797346 & 0.294111981594691 & 0.852944009202654 \tabularnewline
12 & 0.110048235547298 & 0.220096471094596 & 0.889951764452702 \tabularnewline
13 & 0.0702075171855498 & 0.1404150343711 & 0.92979248281445 \tabularnewline
14 & 0.0390727019905126 & 0.0781454039810252 & 0.960927298009487 \tabularnewline
15 & 0.0249828466624869 & 0.0499656933249739 & 0.975017153337513 \tabularnewline
16 & 0.0131686592163296 & 0.0263373184326593 & 0.98683134078367 \tabularnewline
17 & 0.00935816234831602 & 0.018716324696632 & 0.990641837651684 \tabularnewline
18 & 0.00637217359923674 & 0.0127443471984735 & 0.993627826400763 \tabularnewline
19 & 0.00285911138915145 & 0.00571822277830291 & 0.997140888610849 \tabularnewline
20 & 0.0015730413030124 & 0.0031460826060248 & 0.998426958696988 \tabularnewline
21 & 0.00185622948370838 & 0.00371245896741675 & 0.998143770516292 \tabularnewline
22 & 0.00325326781324256 & 0.00650653562648513 & 0.996746732186757 \tabularnewline
23 & 0.0104831162823458 & 0.0209662325646917 & 0.989516883717654 \tabularnewline
24 & 0.0712311970770616 & 0.142462394154123 & 0.928768802922938 \tabularnewline
25 & 0.165957815585137 & 0.331915631170274 & 0.834042184414863 \tabularnewline
26 & 0.22285928992574 & 0.445718579851479 & 0.77714071007426 \tabularnewline
27 & 0.710312857677546 & 0.579374284644909 & 0.289687142322454 \tabularnewline
28 & 0.75663391918169 & 0.486732161636621 & 0.24336608081831 \tabularnewline
29 & 0.776315111580478 & 0.447369776839044 & 0.223684888419522 \tabularnewline
30 & 0.75652730651117 & 0.486945386977661 & 0.24347269348883 \tabularnewline
31 & 0.699006030056852 & 0.601987939886296 & 0.300993969943148 \tabularnewline
32 & 0.788662687483684 & 0.422674625032631 & 0.211337312516316 \tabularnewline
33 & 0.707068950939803 & 0.585862098120394 & 0.292931049060197 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146062&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]7[/C][C]0.0152771495956003[/C][C]0.0305542991912005[/C][C]0.9847228504044[/C][/ROW]
[ROW][C]8[/C][C]0.0276578510542025[/C][C]0.055315702108405[/C][C]0.972342148945798[/C][/ROW]
[ROW][C]9[/C][C]0.21397460887905[/C][C]0.4279492177581[/C][C]0.78602539112095[/C][/ROW]
[ROW][C]10[/C][C]0.140894687450209[/C][C]0.281789374900419[/C][C]0.859105312549791[/C][/ROW]
[ROW][C]11[/C][C]0.147055990797346[/C][C]0.294111981594691[/C][C]0.852944009202654[/C][/ROW]
[ROW][C]12[/C][C]0.110048235547298[/C][C]0.220096471094596[/C][C]0.889951764452702[/C][/ROW]
[ROW][C]13[/C][C]0.0702075171855498[/C][C]0.1404150343711[/C][C]0.92979248281445[/C][/ROW]
[ROW][C]14[/C][C]0.0390727019905126[/C][C]0.0781454039810252[/C][C]0.960927298009487[/C][/ROW]
[ROW][C]15[/C][C]0.0249828466624869[/C][C]0.0499656933249739[/C][C]0.975017153337513[/C][/ROW]
[ROW][C]16[/C][C]0.0131686592163296[/C][C]0.0263373184326593[/C][C]0.98683134078367[/C][/ROW]
[ROW][C]17[/C][C]0.00935816234831602[/C][C]0.018716324696632[/C][C]0.990641837651684[/C][/ROW]
[ROW][C]18[/C][C]0.00637217359923674[/C][C]0.0127443471984735[/C][C]0.993627826400763[/C][/ROW]
[ROW][C]19[/C][C]0.00285911138915145[/C][C]0.00571822277830291[/C][C]0.997140888610849[/C][/ROW]
[ROW][C]20[/C][C]0.0015730413030124[/C][C]0.0031460826060248[/C][C]0.998426958696988[/C][/ROW]
[ROW][C]21[/C][C]0.00185622948370838[/C][C]0.00371245896741675[/C][C]0.998143770516292[/C][/ROW]
[ROW][C]22[/C][C]0.00325326781324256[/C][C]0.00650653562648513[/C][C]0.996746732186757[/C][/ROW]
[ROW][C]23[/C][C]0.0104831162823458[/C][C]0.0209662325646917[/C][C]0.989516883717654[/C][/ROW]
[ROW][C]24[/C][C]0.0712311970770616[/C][C]0.142462394154123[/C][C]0.928768802922938[/C][/ROW]
[ROW][C]25[/C][C]0.165957815585137[/C][C]0.331915631170274[/C][C]0.834042184414863[/C][/ROW]
[ROW][C]26[/C][C]0.22285928992574[/C][C]0.445718579851479[/C][C]0.77714071007426[/C][/ROW]
[ROW][C]27[/C][C]0.710312857677546[/C][C]0.579374284644909[/C][C]0.289687142322454[/C][/ROW]
[ROW][C]28[/C][C]0.75663391918169[/C][C]0.486732161636621[/C][C]0.24336608081831[/C][/ROW]
[ROW][C]29[/C][C]0.776315111580478[/C][C]0.447369776839044[/C][C]0.223684888419522[/C][/ROW]
[ROW][C]30[/C][C]0.75652730651117[/C][C]0.486945386977661[/C][C]0.24347269348883[/C][/ROW]
[ROW][C]31[/C][C]0.699006030056852[/C][C]0.601987939886296[/C][C]0.300993969943148[/C][/ROW]
[ROW][C]32[/C][C]0.788662687483684[/C][C]0.422674625032631[/C][C]0.211337312516316[/C][/ROW]
[ROW][C]33[/C][C]0.707068950939803[/C][C]0.585862098120394[/C][C]0.292931049060197[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146062&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146062&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
70.01527714959560030.03055429919120050.9847228504044
80.02765785105420250.0553157021084050.972342148945798
90.213974608879050.42794921775810.78602539112095
100.1408946874502090.2817893749004190.859105312549791
110.1470559907973460.2941119815946910.852944009202654
120.1100482355472980.2200964710945960.889951764452702
130.07020751718554980.14041503437110.92979248281445
140.03907270199051260.07814540398102520.960927298009487
150.02498284666248690.04996569332497390.975017153337513
160.01316865921632960.02633731843265930.98683134078367
170.009358162348316020.0187163246966320.990641837651684
180.006372173599236740.01274434719847350.993627826400763
190.002859111389151450.005718222778302910.997140888610849
200.00157304130301240.00314608260602480.998426958696988
210.001856229483708380.003712458967416750.998143770516292
220.003253267813242560.006506535626485130.996746732186757
230.01048311628234580.02096623256469170.989516883717654
240.07123119707706160.1424623941541230.928768802922938
250.1659578155851370.3319156311702740.834042184414863
260.222859289925740.4457185798514790.77714071007426
270.7103128576775460.5793742846449090.289687142322454
280.756633919181690.4867321616366210.24336608081831
290.7763151115804780.4473697768390440.223684888419522
300.756527306511170.4869453869776610.24347269348883
310.6990060300568520.6019879398862960.300993969943148
320.7886626874836840.4226746250326310.211337312516316
330.7070689509398030.5858620981203940.292931049060197







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level40.148148148148148NOK
5% type I error level100.37037037037037NOK
10% type I error level120.444444444444444NOK

\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 & 4 & 0.148148148148148 & NOK \tabularnewline
5% type I error level & 10 & 0.37037037037037 & NOK \tabularnewline
10% type I error level & 12 & 0.444444444444444 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146062&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]4[/C][C]0.148148148148148[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]10[/C][C]0.37037037037037[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]12[/C][C]0.444444444444444[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146062&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146062&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 level40.148148148148148NOK
5% type I error level100.37037037037037NOK
10% type I error level120.444444444444444NOK



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 ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')
}