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of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 08:12:04 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t12587299518qafloih5gemur8.htm/, Retrieved Fri, 29 Mar 2024 07:43:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58255, Retrieved Fri, 29 Mar 2024 07:43:39 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact183
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2009-11-20 15:12:04] [d39d4e1021a28f94dc953cf77db656ab] [Current]
-    D    [Multiple Regression] [Model 3] [2009-12-19 14:46:02] [a542c511726eba04a1fc2f4bd37a90f8]
-    D      [Multiple Regression] [Model 3] [2009-12-20 01:11:18] [a542c511726eba04a1fc2f4bd37a90f8]
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Post a new message
Dataseries X:
4143	0
4429	0
5219	0
4929	0
5761	0
5592	0
4163	0
4962	0
5208	0
4755	0
4491	0
5732	0
5731	0
5040	0
6102	0
4904	0
5369	0
5578	0
4619	0
4731	0
5011	0
5299	0
4146	0
4625	0
4736	0
4219	0
5116	0
4205	1
4121	1
5103	1
4300	1
4578	1
3809	1
5526	1
4248	1
3830	1
4428	1
4834	1
4406	1
4565	1
4104	1
4798	1
3935	1
3792	1
4387	1
4006	1
4078	1
4724	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \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=58255&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/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=58255&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58255&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
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
y[t] = + 5134.77222222222 -467.044444444444`x `[t] -148.627777777778M1[t] -271.844444444445M2[t] + 314.188888888888M3[t] -123.266666666667M4[t] + 70.5166666666663M5[t] + 505.3M6[t] -502.416666666667M7[t] -235.133333333334M8[t] -141.350000000001M9[t] + 157.183333333333M10[t] -492.783333333334M11[t] -5.78333333333334t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
y[t] =  +  5134.77222222222 -467.044444444444`x
`[t] -148.627777777778M1[t] -271.844444444445M2[t] +  314.188888888888M3[t] -123.266666666667M4[t] +  70.5166666666663M5[t] +  505.3M6[t] -502.416666666667M7[t] -235.133333333334M8[t] -141.350000000001M9[t] +  157.183333333333M10[t] -492.783333333334M11[t] -5.78333333333334t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58255&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]y[t] =  +  5134.77222222222 -467.044444444444`x
`[t] -148.627777777778M1[t] -271.844444444445M2[t] +  314.188888888888M3[t] -123.266666666667M4[t] +  70.5166666666663M5[t] +  505.3M6[t] -502.416666666667M7[t] -235.133333333334M8[t] -141.350000000001M9[t] +  157.183333333333M10[t] -492.783333333334M11[t] -5.78333333333334t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58255&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58255&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
y[t] = + 5134.77222222222 -467.044444444444`x `[t] -148.627777777778M1[t] -271.844444444445M2[t] + 314.188888888888M3[t] -123.266666666667M4[t] + 70.5166666666663M5[t] + 505.3M6[t] -502.416666666667M7[t] -235.133333333334M8[t] -141.350000000001M9[t] + 157.183333333333M10[t] -492.783333333334M11[t] -5.78333333333334t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5134.77222222222307.39299516.704300
`x `-467.044444444444282.083158-1.65570.1069860.053493
M1-148.627777777778340.191246-0.43690.664950.332475
M2-271.844444444445338.817963-0.80230.4279340.213967
M3314.188888888888337.7459940.93030.3587990.179399
M4-123.266666666667344.278228-0.3580.7225240.361262
M570.5166666666663342.0137120.20620.8378790.41894
M6505.3340.0389331.4860.1464920.073246
M7-502.416666666667338.358964-1.48490.1467930.073397
M8-235.133333333334336.978214-0.69780.4900660.245033
M9-141.350000000001335.900374-0.42080.6765420.338271
M10157.183333333333335.1283650.4690.6420460.321023
M11-492.783333333334334.664305-1.47250.1500910.075046
t-5.7833333333333410.178799-0.56820.573650.286825

\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) & 5134.77222222222 & 307.392995 & 16.7043 & 0 & 0 \tabularnewline
`x
` & -467.044444444444 & 282.083158 & -1.6557 & 0.106986 & 0.053493 \tabularnewline
M1 & -148.627777777778 & 340.191246 & -0.4369 & 0.66495 & 0.332475 \tabularnewline
M2 & -271.844444444445 & 338.817963 & -0.8023 & 0.427934 & 0.213967 \tabularnewline
M3 & 314.188888888888 & 337.745994 & 0.9303 & 0.358799 & 0.179399 \tabularnewline
M4 & -123.266666666667 & 344.278228 & -0.358 & 0.722524 & 0.361262 \tabularnewline
M5 & 70.5166666666663 & 342.013712 & 0.2062 & 0.837879 & 0.41894 \tabularnewline
M6 & 505.3 & 340.038933 & 1.486 & 0.146492 & 0.073246 \tabularnewline
M7 & -502.416666666667 & 338.358964 & -1.4849 & 0.146793 & 0.073397 \tabularnewline
M8 & -235.133333333334 & 336.978214 & -0.6978 & 0.490066 & 0.245033 \tabularnewline
M9 & -141.350000000001 & 335.900374 & -0.4208 & 0.676542 & 0.338271 \tabularnewline
M10 & 157.183333333333 & 335.128365 & 0.469 & 0.642046 & 0.321023 \tabularnewline
M11 & -492.783333333334 & 334.664305 & -1.4725 & 0.150091 & 0.075046 \tabularnewline
t & -5.78333333333334 & 10.178799 & -0.5682 & 0.57365 & 0.286825 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58255&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]5134.77222222222[/C][C]307.392995[/C][C]16.7043[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`x
`[/C][C]-467.044444444444[/C][C]282.083158[/C][C]-1.6557[/C][C]0.106986[/C][C]0.053493[/C][/ROW]
[ROW][C]M1[/C][C]-148.627777777778[/C][C]340.191246[/C][C]-0.4369[/C][C]0.66495[/C][C]0.332475[/C][/ROW]
[ROW][C]M2[/C][C]-271.844444444445[/C][C]338.817963[/C][C]-0.8023[/C][C]0.427934[/C][C]0.213967[/C][/ROW]
[ROW][C]M3[/C][C]314.188888888888[/C][C]337.745994[/C][C]0.9303[/C][C]0.358799[/C][C]0.179399[/C][/ROW]
[ROW][C]M4[/C][C]-123.266666666667[/C][C]344.278228[/C][C]-0.358[/C][C]0.722524[/C][C]0.361262[/C][/ROW]
[ROW][C]M5[/C][C]70.5166666666663[/C][C]342.013712[/C][C]0.2062[/C][C]0.837879[/C][C]0.41894[/C][/ROW]
[ROW][C]M6[/C][C]505.3[/C][C]340.038933[/C][C]1.486[/C][C]0.146492[/C][C]0.073246[/C][/ROW]
[ROW][C]M7[/C][C]-502.416666666667[/C][C]338.358964[/C][C]-1.4849[/C][C]0.146793[/C][C]0.073397[/C][/ROW]
[ROW][C]M8[/C][C]-235.133333333334[/C][C]336.978214[/C][C]-0.6978[/C][C]0.490066[/C][C]0.245033[/C][/ROW]
[ROW][C]M9[/C][C]-141.350000000001[/C][C]335.900374[/C][C]-0.4208[/C][C]0.676542[/C][C]0.338271[/C][/ROW]
[ROW][C]M10[/C][C]157.183333333333[/C][C]335.128365[/C][C]0.469[/C][C]0.642046[/C][C]0.321023[/C][/ROW]
[ROW][C]M11[/C][C]-492.783333333334[/C][C]334.664305[/C][C]-1.4725[/C][C]0.150091[/C][C]0.075046[/C][/ROW]
[ROW][C]t[/C][C]-5.78333333333334[/C][C]10.178799[/C][C]-0.5682[/C][C]0.57365[/C][C]0.286825[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58255&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58255&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)5134.77222222222307.39299516.704300
`x `-467.044444444444282.083158-1.65570.1069860.053493
M1-148.627777777778340.191246-0.43690.664950.332475
M2-271.844444444445338.817963-0.80230.4279340.213967
M3314.188888888888337.7459940.93030.3587990.179399
M4-123.266666666667344.278228-0.3580.7225240.361262
M570.5166666666663342.0137120.20620.8378790.41894
M6505.3340.0389331.4860.1464920.073246
M7-502.416666666667338.358964-1.48490.1467930.073397
M8-235.133333333334336.978214-0.69780.4900660.245033
M9-141.350000000001335.900374-0.42080.6765420.338271
M10157.183333333333335.1283650.4690.6420460.321023
M11-492.783333333334334.664305-1.47250.1500910.075046
t-5.7833333333333410.178799-0.56820.573650.286825







Multiple Linear Regression - Regression Statistics
Multiple R0.728609903988344
R-squared0.530872392189904
Adjusted R-squared0.351500071556632
F-TEST (value)2.95961155163553
F-TEST (DF numerator)13
F-TEST (DF denominator)34
p-value0.00554959611353678
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation473.067837374483
Sum Squared Residuals7608968.07777778

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.728609903988344 \tabularnewline
R-squared & 0.530872392189904 \tabularnewline
Adjusted R-squared & 0.351500071556632 \tabularnewline
F-TEST (value) & 2.95961155163553 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 34 \tabularnewline
p-value & 0.00554959611353678 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 473.067837374483 \tabularnewline
Sum Squared Residuals & 7608968.07777778 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58255&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.728609903988344[/C][/ROW]
[ROW][C]R-squared[/C][C]0.530872392189904[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.351500071556632[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.95961155163553[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]34[/C][/ROW]
[ROW][C]p-value[/C][C]0.00554959611353678[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]473.067837374483[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]7608968.07777778[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58255&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58255&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 R0.728609903988344
R-squared0.530872392189904
Adjusted R-squared0.351500071556632
F-TEST (value)2.95961155163553
F-TEST (DF numerator)13
F-TEST (DF denominator)34
p-value0.00554959611353678
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation473.067837374483
Sum Squared Residuals7608968.07777778







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
141434980.36111111111-837.36111111111
244294851.36111111111-422.361111111112
352195431.61111111111-212.611111111111
449294988.37222222222-59.3722222222223
557615176.37222222222584.627777777778
655925605.37222222222-13.3722222222220
741634591.87222222222-428.872222222223
849624853.37222222222108.627777777778
952084941.37222222222266.627777777778
1047555234.12222222222-479.122222222223
1144914578.37222222222-87.3722222222222
1257325065.37222222222666.627777777778
1357314910.96111111111820.038888888889
1450404781.96111111111258.038888888889
1561025362.21111111111739.788888888889
1649044918.97222222222-14.9722222222223
1753695106.97222222222262.027777777778
1855785535.9722222222242.0277777777776
1946194522.4722222222296.5277777777778
2047314783.97222222222-52.9722222222222
2150114871.97222222222139.027777777778
2252995164.72222222222134.277777777777
2341464508.97222222222-362.972222222222
2446254995.97222222222-370.972222222223
2547364841.56111111111-105.561111111112
2642194712.56111111111-493.561111111111
2751165292.81111111111-176.811111111111
2842054382.52777777778-177.527777777778
2941214570.52777777778-449.527777777777
3051034999.52777777778103.472222222222
3143003986.02777777778313.972222222223
3245784247.52777777778330.472222222222
3338094335.52777777778-526.527777777778
3455264628.27777777778897.722222222222
3542483972.52777777778275.472222222223
3638304459.52777777778-629.527777777778
3744284305.11666666667122.883333333333
3848344176.11666666667657.883333333334
3944064756.36666666667-350.366666666667
4045654313.12777777778251.872222222222
4141044501.12777777778-397.127777777778
4247984930.12777777778-132.127777777778
4339353916.6277777777818.3722222222223
4437924178.12777777778-386.127777777778
4543874266.12777777778120.872222222222
4640064558.87777777778-552.877777777778
4740783903.12777777778174.872222222222
4847244390.12777777778333.872222222222

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4143 & 4980.36111111111 & -837.36111111111 \tabularnewline
2 & 4429 & 4851.36111111111 & -422.361111111112 \tabularnewline
3 & 5219 & 5431.61111111111 & -212.611111111111 \tabularnewline
4 & 4929 & 4988.37222222222 & -59.3722222222223 \tabularnewline
5 & 5761 & 5176.37222222222 & 584.627777777778 \tabularnewline
6 & 5592 & 5605.37222222222 & -13.3722222222220 \tabularnewline
7 & 4163 & 4591.87222222222 & -428.872222222223 \tabularnewline
8 & 4962 & 4853.37222222222 & 108.627777777778 \tabularnewline
9 & 5208 & 4941.37222222222 & 266.627777777778 \tabularnewline
10 & 4755 & 5234.12222222222 & -479.122222222223 \tabularnewline
11 & 4491 & 4578.37222222222 & -87.3722222222222 \tabularnewline
12 & 5732 & 5065.37222222222 & 666.627777777778 \tabularnewline
13 & 5731 & 4910.96111111111 & 820.038888888889 \tabularnewline
14 & 5040 & 4781.96111111111 & 258.038888888889 \tabularnewline
15 & 6102 & 5362.21111111111 & 739.788888888889 \tabularnewline
16 & 4904 & 4918.97222222222 & -14.9722222222223 \tabularnewline
17 & 5369 & 5106.97222222222 & 262.027777777778 \tabularnewline
18 & 5578 & 5535.97222222222 & 42.0277777777776 \tabularnewline
19 & 4619 & 4522.47222222222 & 96.5277777777778 \tabularnewline
20 & 4731 & 4783.97222222222 & -52.9722222222222 \tabularnewline
21 & 5011 & 4871.97222222222 & 139.027777777778 \tabularnewline
22 & 5299 & 5164.72222222222 & 134.277777777777 \tabularnewline
23 & 4146 & 4508.97222222222 & -362.972222222222 \tabularnewline
24 & 4625 & 4995.97222222222 & -370.972222222223 \tabularnewline
25 & 4736 & 4841.56111111111 & -105.561111111112 \tabularnewline
26 & 4219 & 4712.56111111111 & -493.561111111111 \tabularnewline
27 & 5116 & 5292.81111111111 & -176.811111111111 \tabularnewline
28 & 4205 & 4382.52777777778 & -177.527777777778 \tabularnewline
29 & 4121 & 4570.52777777778 & -449.527777777777 \tabularnewline
30 & 5103 & 4999.52777777778 & 103.472222222222 \tabularnewline
31 & 4300 & 3986.02777777778 & 313.972222222223 \tabularnewline
32 & 4578 & 4247.52777777778 & 330.472222222222 \tabularnewline
33 & 3809 & 4335.52777777778 & -526.527777777778 \tabularnewline
34 & 5526 & 4628.27777777778 & 897.722222222222 \tabularnewline
35 & 4248 & 3972.52777777778 & 275.472222222223 \tabularnewline
36 & 3830 & 4459.52777777778 & -629.527777777778 \tabularnewline
37 & 4428 & 4305.11666666667 & 122.883333333333 \tabularnewline
38 & 4834 & 4176.11666666667 & 657.883333333334 \tabularnewline
39 & 4406 & 4756.36666666667 & -350.366666666667 \tabularnewline
40 & 4565 & 4313.12777777778 & 251.872222222222 \tabularnewline
41 & 4104 & 4501.12777777778 & -397.127777777778 \tabularnewline
42 & 4798 & 4930.12777777778 & -132.127777777778 \tabularnewline
43 & 3935 & 3916.62777777778 & 18.3722222222223 \tabularnewline
44 & 3792 & 4178.12777777778 & -386.127777777778 \tabularnewline
45 & 4387 & 4266.12777777778 & 120.872222222222 \tabularnewline
46 & 4006 & 4558.87777777778 & -552.877777777778 \tabularnewline
47 & 4078 & 3903.12777777778 & 174.872222222222 \tabularnewline
48 & 4724 & 4390.12777777778 & 333.872222222222 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58255&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]4143[/C][C]4980.36111111111[/C][C]-837.36111111111[/C][/ROW]
[ROW][C]2[/C][C]4429[/C][C]4851.36111111111[/C][C]-422.361111111112[/C][/ROW]
[ROW][C]3[/C][C]5219[/C][C]5431.61111111111[/C][C]-212.611111111111[/C][/ROW]
[ROW][C]4[/C][C]4929[/C][C]4988.37222222222[/C][C]-59.3722222222223[/C][/ROW]
[ROW][C]5[/C][C]5761[/C][C]5176.37222222222[/C][C]584.627777777778[/C][/ROW]
[ROW][C]6[/C][C]5592[/C][C]5605.37222222222[/C][C]-13.3722222222220[/C][/ROW]
[ROW][C]7[/C][C]4163[/C][C]4591.87222222222[/C][C]-428.872222222223[/C][/ROW]
[ROW][C]8[/C][C]4962[/C][C]4853.37222222222[/C][C]108.627777777778[/C][/ROW]
[ROW][C]9[/C][C]5208[/C][C]4941.37222222222[/C][C]266.627777777778[/C][/ROW]
[ROW][C]10[/C][C]4755[/C][C]5234.12222222222[/C][C]-479.122222222223[/C][/ROW]
[ROW][C]11[/C][C]4491[/C][C]4578.37222222222[/C][C]-87.3722222222222[/C][/ROW]
[ROW][C]12[/C][C]5732[/C][C]5065.37222222222[/C][C]666.627777777778[/C][/ROW]
[ROW][C]13[/C][C]5731[/C][C]4910.96111111111[/C][C]820.038888888889[/C][/ROW]
[ROW][C]14[/C][C]5040[/C][C]4781.96111111111[/C][C]258.038888888889[/C][/ROW]
[ROW][C]15[/C][C]6102[/C][C]5362.21111111111[/C][C]739.788888888889[/C][/ROW]
[ROW][C]16[/C][C]4904[/C][C]4918.97222222222[/C][C]-14.9722222222223[/C][/ROW]
[ROW][C]17[/C][C]5369[/C][C]5106.97222222222[/C][C]262.027777777778[/C][/ROW]
[ROW][C]18[/C][C]5578[/C][C]5535.97222222222[/C][C]42.0277777777776[/C][/ROW]
[ROW][C]19[/C][C]4619[/C][C]4522.47222222222[/C][C]96.5277777777778[/C][/ROW]
[ROW][C]20[/C][C]4731[/C][C]4783.97222222222[/C][C]-52.9722222222222[/C][/ROW]
[ROW][C]21[/C][C]5011[/C][C]4871.97222222222[/C][C]139.027777777778[/C][/ROW]
[ROW][C]22[/C][C]5299[/C][C]5164.72222222222[/C][C]134.277777777777[/C][/ROW]
[ROW][C]23[/C][C]4146[/C][C]4508.97222222222[/C][C]-362.972222222222[/C][/ROW]
[ROW][C]24[/C][C]4625[/C][C]4995.97222222222[/C][C]-370.972222222223[/C][/ROW]
[ROW][C]25[/C][C]4736[/C][C]4841.56111111111[/C][C]-105.561111111112[/C][/ROW]
[ROW][C]26[/C][C]4219[/C][C]4712.56111111111[/C][C]-493.561111111111[/C][/ROW]
[ROW][C]27[/C][C]5116[/C][C]5292.81111111111[/C][C]-176.811111111111[/C][/ROW]
[ROW][C]28[/C][C]4205[/C][C]4382.52777777778[/C][C]-177.527777777778[/C][/ROW]
[ROW][C]29[/C][C]4121[/C][C]4570.52777777778[/C][C]-449.527777777777[/C][/ROW]
[ROW][C]30[/C][C]5103[/C][C]4999.52777777778[/C][C]103.472222222222[/C][/ROW]
[ROW][C]31[/C][C]4300[/C][C]3986.02777777778[/C][C]313.972222222223[/C][/ROW]
[ROW][C]32[/C][C]4578[/C][C]4247.52777777778[/C][C]330.472222222222[/C][/ROW]
[ROW][C]33[/C][C]3809[/C][C]4335.52777777778[/C][C]-526.527777777778[/C][/ROW]
[ROW][C]34[/C][C]5526[/C][C]4628.27777777778[/C][C]897.722222222222[/C][/ROW]
[ROW][C]35[/C][C]4248[/C][C]3972.52777777778[/C][C]275.472222222223[/C][/ROW]
[ROW][C]36[/C][C]3830[/C][C]4459.52777777778[/C][C]-629.527777777778[/C][/ROW]
[ROW][C]37[/C][C]4428[/C][C]4305.11666666667[/C][C]122.883333333333[/C][/ROW]
[ROW][C]38[/C][C]4834[/C][C]4176.11666666667[/C][C]657.883333333334[/C][/ROW]
[ROW][C]39[/C][C]4406[/C][C]4756.36666666667[/C][C]-350.366666666667[/C][/ROW]
[ROW][C]40[/C][C]4565[/C][C]4313.12777777778[/C][C]251.872222222222[/C][/ROW]
[ROW][C]41[/C][C]4104[/C][C]4501.12777777778[/C][C]-397.127777777778[/C][/ROW]
[ROW][C]42[/C][C]4798[/C][C]4930.12777777778[/C][C]-132.127777777778[/C][/ROW]
[ROW][C]43[/C][C]3935[/C][C]3916.62777777778[/C][C]18.3722222222223[/C][/ROW]
[ROW][C]44[/C][C]3792[/C][C]4178.12777777778[/C][C]-386.127777777778[/C][/ROW]
[ROW][C]45[/C][C]4387[/C][C]4266.12777777778[/C][C]120.872222222222[/C][/ROW]
[ROW][C]46[/C][C]4006[/C][C]4558.87777777778[/C][C]-552.877777777778[/C][/ROW]
[ROW][C]47[/C][C]4078[/C][C]3903.12777777778[/C][C]174.872222222222[/C][/ROW]
[ROW][C]48[/C][C]4724[/C][C]4390.12777777778[/C][C]333.872222222222[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58255&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58255&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
141434980.36111111111-837.36111111111
244294851.36111111111-422.361111111112
352195431.61111111111-212.611111111111
449294988.37222222222-59.3722222222223
557615176.37222222222584.627777777778
655925605.37222222222-13.3722222222220
741634591.87222222222-428.872222222223
849624853.37222222222108.627777777778
952084941.37222222222266.627777777778
1047555234.12222222222-479.122222222223
1144914578.37222222222-87.3722222222222
1257325065.37222222222666.627777777778
1357314910.96111111111820.038888888889
1450404781.96111111111258.038888888889
1561025362.21111111111739.788888888889
1649044918.97222222222-14.9722222222223
1753695106.97222222222262.027777777778
1855785535.9722222222242.0277777777776
1946194522.4722222222296.5277777777778
2047314783.97222222222-52.9722222222222
2150114871.97222222222139.027777777778
2252995164.72222222222134.277777777777
2341464508.97222222222-362.972222222222
2446254995.97222222222-370.972222222223
2547364841.56111111111-105.561111111112
2642194712.56111111111-493.561111111111
2751165292.81111111111-176.811111111111
2842054382.52777777778-177.527777777778
2941214570.52777777778-449.527777777777
3051034999.52777777778103.472222222222
3143003986.02777777778313.972222222223
3245784247.52777777778330.472222222222
3338094335.52777777778-526.527777777778
3455264628.27777777778897.722222222222
3542483972.52777777778275.472222222223
3638304459.52777777778-629.527777777778
3744284305.11666666667122.883333333333
3848344176.11666666667657.883333333334
3944064756.36666666667-350.366666666667
4045654313.12777777778251.872222222222
4141044501.12777777778-397.127777777778
4247984930.12777777778-132.127777777778
4339353916.6277777777818.3722222222223
4437924178.12777777778-386.127777777778
4543874266.12777777778120.872222222222
4640064558.87777777778-552.877777777778
4740783903.12777777778174.872222222222
4847244390.12777777778333.872222222222







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.8838750627563980.2322498744872040.116124937243602
180.8205684680837450.358863063832510.179431531916255
190.702403132085020.5951937358299590.297596867914979
200.6444928980632320.7110142038735360.355507101936768
210.606736008800830.786527982398340.39326399119917
220.495946793752680.991893587505360.50405320624732
230.4395246267534450.879049253506890.560475373246555
240.5386122682019380.9227754635961230.461387731798062
250.4334821066027790.8669642132055570.566517893397221
260.4631382695193850.926276539038770.536861730480615
270.3652843938926620.7305687877853230.634715606107338
280.2777597747593710.5555195495187410.72224022524063
290.1948924478615260.3897848957230530.805107552138474
300.1193764600402730.2387529200805450.880623539959727
310.07072582724104890.1414516544820980.929274172758951

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.883875062756398 & 0.232249874487204 & 0.116124937243602 \tabularnewline
18 & 0.820568468083745 & 0.35886306383251 & 0.179431531916255 \tabularnewline
19 & 0.70240313208502 & 0.595193735829959 & 0.297596867914979 \tabularnewline
20 & 0.644492898063232 & 0.711014203873536 & 0.355507101936768 \tabularnewline
21 & 0.60673600880083 & 0.78652798239834 & 0.39326399119917 \tabularnewline
22 & 0.49594679375268 & 0.99189358750536 & 0.50405320624732 \tabularnewline
23 & 0.439524626753445 & 0.87904925350689 & 0.560475373246555 \tabularnewline
24 & 0.538612268201938 & 0.922775463596123 & 0.461387731798062 \tabularnewline
25 & 0.433482106602779 & 0.866964213205557 & 0.566517893397221 \tabularnewline
26 & 0.463138269519385 & 0.92627653903877 & 0.536861730480615 \tabularnewline
27 & 0.365284393892662 & 0.730568787785323 & 0.634715606107338 \tabularnewline
28 & 0.277759774759371 & 0.555519549518741 & 0.72224022524063 \tabularnewline
29 & 0.194892447861526 & 0.389784895723053 & 0.805107552138474 \tabularnewline
30 & 0.119376460040273 & 0.238752920080545 & 0.880623539959727 \tabularnewline
31 & 0.0707258272410489 & 0.141451654482098 & 0.929274172758951 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58255&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]17[/C][C]0.883875062756398[/C][C]0.232249874487204[/C][C]0.116124937243602[/C][/ROW]
[ROW][C]18[/C][C]0.820568468083745[/C][C]0.35886306383251[/C][C]0.179431531916255[/C][/ROW]
[ROW][C]19[/C][C]0.70240313208502[/C][C]0.595193735829959[/C][C]0.297596867914979[/C][/ROW]
[ROW][C]20[/C][C]0.644492898063232[/C][C]0.711014203873536[/C][C]0.355507101936768[/C][/ROW]
[ROW][C]21[/C][C]0.60673600880083[/C][C]0.78652798239834[/C][C]0.39326399119917[/C][/ROW]
[ROW][C]22[/C][C]0.49594679375268[/C][C]0.99189358750536[/C][C]0.50405320624732[/C][/ROW]
[ROW][C]23[/C][C]0.439524626753445[/C][C]0.87904925350689[/C][C]0.560475373246555[/C][/ROW]
[ROW][C]24[/C][C]0.538612268201938[/C][C]0.922775463596123[/C][C]0.461387731798062[/C][/ROW]
[ROW][C]25[/C][C]0.433482106602779[/C][C]0.866964213205557[/C][C]0.566517893397221[/C][/ROW]
[ROW][C]26[/C][C]0.463138269519385[/C][C]0.92627653903877[/C][C]0.536861730480615[/C][/ROW]
[ROW][C]27[/C][C]0.365284393892662[/C][C]0.730568787785323[/C][C]0.634715606107338[/C][/ROW]
[ROW][C]28[/C][C]0.277759774759371[/C][C]0.555519549518741[/C][C]0.72224022524063[/C][/ROW]
[ROW][C]29[/C][C]0.194892447861526[/C][C]0.389784895723053[/C][C]0.805107552138474[/C][/ROW]
[ROW][C]30[/C][C]0.119376460040273[/C][C]0.238752920080545[/C][C]0.880623539959727[/C][/ROW]
[ROW][C]31[/C][C]0.0707258272410489[/C][C]0.141451654482098[/C][C]0.929274172758951[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58255&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58255&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
170.8838750627563980.2322498744872040.116124937243602
180.8205684680837450.358863063832510.179431531916255
190.702403132085020.5951937358299590.297596867914979
200.6444928980632320.7110142038735360.355507101936768
210.606736008800830.786527982398340.39326399119917
220.495946793752680.991893587505360.50405320624732
230.4395246267534450.879049253506890.560475373246555
240.5386122682019380.9227754635961230.461387731798062
250.4334821066027790.8669642132055570.566517893397221
260.4631382695193850.926276539038770.536861730480615
270.3652843938926620.7305687877853230.634715606107338
280.2777597747593710.5555195495187410.72224022524063
290.1948924478615260.3897848957230530.805107552138474
300.1193764600402730.2387529200805450.880623539959727
310.07072582724104890.1414516544820980.929274172758951







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=58255&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=58255&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58255&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 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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')
}