Free Statistics

of Irreproducible Research!

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 13:36:59 -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/t1268941220dz7izehsdveugfr.htm/, Retrieved Tue, 18 Jan 2022 13:27:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=74553, Retrieved Tue, 18 Jan 2022 13:27:27 +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 Impact190
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 19:36:59] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
- RMP     [Recursive Partitioning (Regression Trees)] [Review of Sleep A...] [2010-05-01 12:50:19] [b98453cac15ba1066b407e146608df68]
- RMP       [Recursive Partitioning (Regression Trees)] [] [2010-12-17 14:00:39] [94f495cfd7e7946e5228cbd267a6841d]
-   PD    [Multiple Regression] [Review of Sleep A...] [2010-05-01 12:55:00] [b98453cac15ba1066b407e146608df68]
- RMPD    [Recursive Partitioning (Regression Trees)] [Review of Sleep A...] [2010-05-01 12:59:14] [b98453cac15ba1066b407e146608df68]
- RMP       [Recursive Partitioning (Regression Trees)] [] [2010-12-17 14:02:31] [94f495cfd7e7946e5228cbd267a6841d]
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Dataseries X:
0.30102999566398	3	1.6232492903979
0.25527250510331	4	2.79518458968242
-0.15490195998574	4	2.25527250510331
0.5910646070265	1	1.54406804435028
0	4	2.59328606702046
0.55630250076729	1	1.79934054945358
0.14612803567824	1	2.36172783601759
0.17609125905568	4	2.04921802267018
-0.15490195998574	5	2.44870631990508
0.32221929473392	1	1.6232492903979
0.61278385671974	2	1.6232492903979
0.079181246047625	2	2.07918124604762
-0.30102999566398	5	2.17026171539496
0.53147891704226	2	1.20411998265592
0.17609125905568	1	2.49136169383427
0.53147891704226	3	1.44715803134222
-0.096910013008056	4	1.83250891270624
-0.096910013008056	5	2.52633927738984
0.30102999566398	1	1.69897000433602
0.27875360095283	1	2.42651126136458
0.11394335230684	3	1.27875360095283
0.7481880270062	1	1.07918124604762
0.49136169383427	1	2.07918124604762
0.25527250510331	2	2.14612803567824
-0.045757490560675	4	2.23044892137827
0.25527250510331	2	1.23044892137827
0.27875360095283	4	2.06069784035361
-0.045757490560675	5	1.49136169383427
0.41497334797082	3	1.32221929473392
0.38021124171161	1	1.7160033436348
0.079181246047625	2	2.2148438480477
-0.045757490560675	2	2.35218251811136
-0.30102999566398	3	2.35218251811136
-0.22184874961636	5	2.17897694729317
0.36172783601759	2	1.77815125038364
-0.30102999566398	3	2.30102999566398
0.41497334797082	2	1.66275783168157
-0.22184874961636	4	2.32221929473392
0.81954393554187	1	1.14612803567824




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

\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
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=74553&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]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=74553&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=74553&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







Multiple Linear Regression - Estimated Regression Equation
logPS[t] = + 1.07450734042495 -0.110510499814237D[t] -0.303538868483004logtg[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
logPS[t] =  +  1.07450734042495 -0.110510499814237D[t] -0.303538868483004logtg[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=74553&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]logPS[t] =  +  1.07450734042495 -0.110510499814237D[t] -0.303538868483004logtg[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=74553&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=74553&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
logPS[t] = + 1.07450734042495 -0.110510499814237D[t] -0.303538868483004logtg[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.074507340424950.1287518.345600
D-0.1105104998142370.022191-4.981.6e-058e-06
logtg-0.3035388684830040.068904-4.40539.1e-054.5e-05

\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) & 1.07450734042495 & 0.128751 & 8.3456 & 0 & 0 \tabularnewline
D & -0.110510499814237 & 0.022191 & -4.98 & 1.6e-05 & 8e-06 \tabularnewline
logtg & -0.303538868483004 & 0.068904 & -4.4053 & 9.1e-05 & 4.5e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=74553&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]1.07450734042495[/C][C]0.128751[/C][C]8.3456[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]D[/C][C]-0.110510499814237[/C][C]0.022191[/C][C]-4.98[/C][C]1.6e-05[/C][C]8e-06[/C][/ROW]
[ROW][C]logtg[/C][C]-0.303538868483004[/C][C]0.068904[/C][C]-4.4053[/C][C]9.1e-05[/C][C]4.5e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=74553&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=74553&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)1.074507340424950.1287518.345600
D-0.1105104998142370.022191-4.981.6e-058e-06
logtg-0.3035388684830040.068904-4.40539.1e-054.5e-05







Multiple Linear Regression - Regression Statistics
Multiple R0.809091683132234
R-squared0.654629351713752
Adjusted R-squared0.635442093475627
F-TEST (value)34.1179205277495
F-TEST (DF numerator)2
F-TEST (DF denominator)36
p-value4.88807283538506e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.181764010644749
Sum Squared Residuals1.18937360036392

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.809091683132234 \tabularnewline
R-squared & 0.654629351713752 \tabularnewline
Adjusted R-squared & 0.635442093475627 \tabularnewline
F-TEST (value) & 34.1179205277495 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 36 \tabularnewline
p-value & 4.88807283538506e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.181764010644749 \tabularnewline
Sum Squared Residuals & 1.18937360036392 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=74553&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.809091683132234[/C][/ROW]
[ROW][C]R-squared[/C][C]0.654629351713752[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.635442093475627[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]34.1179205277495[/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]4.88807283538506e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.181764010644749[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1.18937360036392[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=74553&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=74553&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 R0.809091683132234
R-squared0.654629351713752
Adjusted R-squared0.635442093475627
F-TEST (value)34.1179205277495
F-TEST (DF numerator)2
F-TEST (DF denominator)36
p-value4.88807283538506e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.181764010644749
Sum Squared Residuals1.18937360036392







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
10.301029995663980.2502565881090250.0507734075549551
20.25527250510331-0.2159818263853270.471254331488637
3-0.15490195998574-0.0520975231518843-0.102804436833856
40.59106460702650.4953121735678660.0957524334586344
50-0.1546977772681260.154697777268126
60.556302500767290.4178270462139880.138475454553302
70.146128035678240.247120645601122-0.100992609922882
80.176091259055680.01044802129171850.165643237763961
9-0.15490195998574-0.2213227042374020.0664207442516616
100.322219294733920.471277587737497-0.149058293003577
110.612783856719740.360767087923260.252016768796480
120.0791812460476250.222374018000101-0.143192771952476
13-0.30102999566398-0.136803944049202-0.164226051614778
140.531478917042260.4879891237433250.0434897932989351
150.176091259055680.207771731082360-0.0316804720266803
160.531478917042260.3037071296325310.227771787409729
17-0.0969100130080560.0762276593201318-0.173137672328188
18-0.096910013008056-0.2448873243093150.147977311301259
190.301029995663980.448293407907994-0.147263412244015
200.278753600952830.2274563579748430.0512972429779865
210.113943352306840.354824419880452-0.240881067573612
220.74818802700620.6364233862973410.111764640708859
230.491361693834270.3328845178143380.158477176019932
240.255272505103310.2020530652270530.0532194398762572
25-0.045757490560675-0.0445626006362929-0.00119488992438214
260.255272505103310.479997267475184-0.224724762371874
270.278753600952830.00696345042169910.271790150531131
28-0.0457574905606750.0692686003084161-0.115026090869091
290.414973347970820.3416308923723110.0733424555985092
300.380211241711610.443123127370756-0.0629118856591458
310.0791812460476250.181195145293536-0.102013899245911
32-0.0457574905606750.139507520783453-0.185265011344128
33-0.301029995663980.0289970209692157-0.330027016633196
34-0.22184874961636-0.139449355678152-0.082399393938208
350.361727836017590.3137483222633890.0479795137542013
36-0.301029995663980.044523799752945-0.345553795416925
370.414973347970820.3487747100066000.0661986379642195
38-0.22184874961636-0.0724184759249296-0.149430273691430
390.819543935541870.6161024335242930.203441502017577

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0.30102999566398 & 0.250256588109025 & 0.0507734075549551 \tabularnewline
2 & 0.25527250510331 & -0.215981826385327 & 0.471254331488637 \tabularnewline
3 & -0.15490195998574 & -0.0520975231518843 & -0.102804436833856 \tabularnewline
4 & 0.5910646070265 & 0.495312173567866 & 0.0957524334586344 \tabularnewline
5 & 0 & -0.154697777268126 & 0.154697777268126 \tabularnewline
6 & 0.55630250076729 & 0.417827046213988 & 0.138475454553302 \tabularnewline
7 & 0.14612803567824 & 0.247120645601122 & -0.100992609922882 \tabularnewline
8 & 0.17609125905568 & 0.0104480212917185 & 0.165643237763961 \tabularnewline
9 & -0.15490195998574 & -0.221322704237402 & 0.0664207442516616 \tabularnewline
10 & 0.32221929473392 & 0.471277587737497 & -0.149058293003577 \tabularnewline
11 & 0.61278385671974 & 0.36076708792326 & 0.252016768796480 \tabularnewline
12 & 0.079181246047625 & 0.222374018000101 & -0.143192771952476 \tabularnewline
13 & -0.30102999566398 & -0.136803944049202 & -0.164226051614778 \tabularnewline
14 & 0.53147891704226 & 0.487989123743325 & 0.0434897932989351 \tabularnewline
15 & 0.17609125905568 & 0.207771731082360 & -0.0316804720266803 \tabularnewline
16 & 0.53147891704226 & 0.303707129632531 & 0.227771787409729 \tabularnewline
17 & -0.096910013008056 & 0.0762276593201318 & -0.173137672328188 \tabularnewline
18 & -0.096910013008056 & -0.244887324309315 & 0.147977311301259 \tabularnewline
19 & 0.30102999566398 & 0.448293407907994 & -0.147263412244015 \tabularnewline
20 & 0.27875360095283 & 0.227456357974843 & 0.0512972429779865 \tabularnewline
21 & 0.11394335230684 & 0.354824419880452 & -0.240881067573612 \tabularnewline
22 & 0.7481880270062 & 0.636423386297341 & 0.111764640708859 \tabularnewline
23 & 0.49136169383427 & 0.332884517814338 & 0.158477176019932 \tabularnewline
24 & 0.25527250510331 & 0.202053065227053 & 0.0532194398762572 \tabularnewline
25 & -0.045757490560675 & -0.0445626006362929 & -0.00119488992438214 \tabularnewline
26 & 0.25527250510331 & 0.479997267475184 & -0.224724762371874 \tabularnewline
27 & 0.27875360095283 & 0.0069634504216991 & 0.271790150531131 \tabularnewline
28 & -0.045757490560675 & 0.0692686003084161 & -0.115026090869091 \tabularnewline
29 & 0.41497334797082 & 0.341630892372311 & 0.0733424555985092 \tabularnewline
30 & 0.38021124171161 & 0.443123127370756 & -0.0629118856591458 \tabularnewline
31 & 0.079181246047625 & 0.181195145293536 & -0.102013899245911 \tabularnewline
32 & -0.045757490560675 & 0.139507520783453 & -0.185265011344128 \tabularnewline
33 & -0.30102999566398 & 0.0289970209692157 & -0.330027016633196 \tabularnewline
34 & -0.22184874961636 & -0.139449355678152 & -0.082399393938208 \tabularnewline
35 & 0.36172783601759 & 0.313748322263389 & 0.0479795137542013 \tabularnewline
36 & -0.30102999566398 & 0.044523799752945 & -0.345553795416925 \tabularnewline
37 & 0.41497334797082 & 0.348774710006600 & 0.0661986379642195 \tabularnewline
38 & -0.22184874961636 & -0.0724184759249296 & -0.149430273691430 \tabularnewline
39 & 0.81954393554187 & 0.616102433524293 & 0.203441502017577 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=74553&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]0.30102999566398[/C][C]0.250256588109025[/C][C]0.0507734075549551[/C][/ROW]
[ROW][C]2[/C][C]0.25527250510331[/C][C]-0.215981826385327[/C][C]0.471254331488637[/C][/ROW]
[ROW][C]3[/C][C]-0.15490195998574[/C][C]-0.0520975231518843[/C][C]-0.102804436833856[/C][/ROW]
[ROW][C]4[/C][C]0.5910646070265[/C][C]0.495312173567866[/C][C]0.0957524334586344[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]-0.154697777268126[/C][C]0.154697777268126[/C][/ROW]
[ROW][C]6[/C][C]0.55630250076729[/C][C]0.417827046213988[/C][C]0.138475454553302[/C][/ROW]
[ROW][C]7[/C][C]0.14612803567824[/C][C]0.247120645601122[/C][C]-0.100992609922882[/C][/ROW]
[ROW][C]8[/C][C]0.17609125905568[/C][C]0.0104480212917185[/C][C]0.165643237763961[/C][/ROW]
[ROW][C]9[/C][C]-0.15490195998574[/C][C]-0.221322704237402[/C][C]0.0664207442516616[/C][/ROW]
[ROW][C]10[/C][C]0.32221929473392[/C][C]0.471277587737497[/C][C]-0.149058293003577[/C][/ROW]
[ROW][C]11[/C][C]0.61278385671974[/C][C]0.36076708792326[/C][C]0.252016768796480[/C][/ROW]
[ROW][C]12[/C][C]0.079181246047625[/C][C]0.222374018000101[/C][C]-0.143192771952476[/C][/ROW]
[ROW][C]13[/C][C]-0.30102999566398[/C][C]-0.136803944049202[/C][C]-0.164226051614778[/C][/ROW]
[ROW][C]14[/C][C]0.53147891704226[/C][C]0.487989123743325[/C][C]0.0434897932989351[/C][/ROW]
[ROW][C]15[/C][C]0.17609125905568[/C][C]0.207771731082360[/C][C]-0.0316804720266803[/C][/ROW]
[ROW][C]16[/C][C]0.53147891704226[/C][C]0.303707129632531[/C][C]0.227771787409729[/C][/ROW]
[ROW][C]17[/C][C]-0.096910013008056[/C][C]0.0762276593201318[/C][C]-0.173137672328188[/C][/ROW]
[ROW][C]18[/C][C]-0.096910013008056[/C][C]-0.244887324309315[/C][C]0.147977311301259[/C][/ROW]
[ROW][C]19[/C][C]0.30102999566398[/C][C]0.448293407907994[/C][C]-0.147263412244015[/C][/ROW]
[ROW][C]20[/C][C]0.27875360095283[/C][C]0.227456357974843[/C][C]0.0512972429779865[/C][/ROW]
[ROW][C]21[/C][C]0.11394335230684[/C][C]0.354824419880452[/C][C]-0.240881067573612[/C][/ROW]
[ROW][C]22[/C][C]0.7481880270062[/C][C]0.636423386297341[/C][C]0.111764640708859[/C][/ROW]
[ROW][C]23[/C][C]0.49136169383427[/C][C]0.332884517814338[/C][C]0.158477176019932[/C][/ROW]
[ROW][C]24[/C][C]0.25527250510331[/C][C]0.202053065227053[/C][C]0.0532194398762572[/C][/ROW]
[ROW][C]25[/C][C]-0.045757490560675[/C][C]-0.0445626006362929[/C][C]-0.00119488992438214[/C][/ROW]
[ROW][C]26[/C][C]0.25527250510331[/C][C]0.479997267475184[/C][C]-0.224724762371874[/C][/ROW]
[ROW][C]27[/C][C]0.27875360095283[/C][C]0.0069634504216991[/C][C]0.271790150531131[/C][/ROW]
[ROW][C]28[/C][C]-0.045757490560675[/C][C]0.0692686003084161[/C][C]-0.115026090869091[/C][/ROW]
[ROW][C]29[/C][C]0.41497334797082[/C][C]0.341630892372311[/C][C]0.0733424555985092[/C][/ROW]
[ROW][C]30[/C][C]0.38021124171161[/C][C]0.443123127370756[/C][C]-0.0629118856591458[/C][/ROW]
[ROW][C]31[/C][C]0.079181246047625[/C][C]0.181195145293536[/C][C]-0.102013899245911[/C][/ROW]
[ROW][C]32[/C][C]-0.045757490560675[/C][C]0.139507520783453[/C][C]-0.185265011344128[/C][/ROW]
[ROW][C]33[/C][C]-0.30102999566398[/C][C]0.0289970209692157[/C][C]-0.330027016633196[/C][/ROW]
[ROW][C]34[/C][C]-0.22184874961636[/C][C]-0.139449355678152[/C][C]-0.082399393938208[/C][/ROW]
[ROW][C]35[/C][C]0.36172783601759[/C][C]0.313748322263389[/C][C]0.0479795137542013[/C][/ROW]
[ROW][C]36[/C][C]-0.30102999566398[/C][C]0.044523799752945[/C][C]-0.345553795416925[/C][/ROW]
[ROW][C]37[/C][C]0.41497334797082[/C][C]0.348774710006600[/C][C]0.0661986379642195[/C][/ROW]
[ROW][C]38[/C][C]-0.22184874961636[/C][C]-0.0724184759249296[/C][C]-0.149430273691430[/C][/ROW]
[ROW][C]39[/C][C]0.81954393554187[/C][C]0.616102433524293[/C][C]0.203441502017577[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=74553&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=74553&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
10.301029995663980.2502565881090250.0507734075549551
20.25527250510331-0.2159818263853270.471254331488637
3-0.15490195998574-0.0520975231518843-0.102804436833856
40.59106460702650.4953121735678660.0957524334586344
50-0.1546977772681260.154697777268126
60.556302500767290.4178270462139880.138475454553302
70.146128035678240.247120645601122-0.100992609922882
80.176091259055680.01044802129171850.165643237763961
9-0.15490195998574-0.2213227042374020.0664207442516616
100.322219294733920.471277587737497-0.149058293003577
110.612783856719740.360767087923260.252016768796480
120.0791812460476250.222374018000101-0.143192771952476
13-0.30102999566398-0.136803944049202-0.164226051614778
140.531478917042260.4879891237433250.0434897932989351
150.176091259055680.207771731082360-0.0316804720266803
160.531478917042260.3037071296325310.227771787409729
17-0.0969100130080560.0762276593201318-0.173137672328188
18-0.096910013008056-0.2448873243093150.147977311301259
190.301029995663980.448293407907994-0.147263412244015
200.278753600952830.2274563579748430.0512972429779865
210.113943352306840.354824419880452-0.240881067573612
220.74818802700620.6364233862973410.111764640708859
230.491361693834270.3328845178143380.158477176019932
240.255272505103310.2020530652270530.0532194398762572
25-0.045757490560675-0.0445626006362929-0.00119488992438214
260.255272505103310.479997267475184-0.224724762371874
270.278753600952830.00696345042169910.271790150531131
28-0.0457574905606750.0692686003084161-0.115026090869091
290.414973347970820.3416308923723110.0733424555985092
300.380211241711610.443123127370756-0.0629118856591458
310.0791812460476250.181195145293536-0.102013899245911
32-0.0457574905606750.139507520783453-0.185265011344128
33-0.301029995663980.0289970209692157-0.330027016633196
34-0.22184874961636-0.139449355678152-0.082399393938208
350.361727836017590.3137483222633890.0479795137542013
36-0.301029995663980.044523799752945-0.345553795416925
370.414973347970820.3487747100066000.0661986379642195
38-0.22184874961636-0.0724184759249296-0.149430273691430
390.819543935541870.6161024335242930.203441502017577







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.5979289695738070.8041420608523850.402071030426193
70.8058149775079140.3883700449841720.194185022492086
80.7209818186943910.5580363626112180.279018181305609
90.6497647928589610.7004704142820790.350235207141039
100.6130048052770260.7739903894459490.386995194722974
110.6901071880975610.6197856238048780.309892811902439
120.6911996559582270.6176006880835460.308800344041773
130.7378984236749420.5242031526501170.262101576325058
140.651773096047830.696453807904340.34822690395217
150.5666429745195110.8667140509609780.433357025480489
160.5946890723195130.8106218553609740.405310927680487
170.6108801461677360.7782397076645280.389119853832264
180.6134410839960510.7731178320078980.386558916003949
190.5892053647130370.8215892705739260.410794635286963
200.5034278235504710.9931443528990580.496572176449529
210.591400030643520.817199938712960.40859996935648
220.526280887806520.947438224386960.47371911219348
230.5343516146572730.9312967706854530.465648385342727
240.4829137399356250.965827479871250.517086260064375
250.4143011284503860.8286022569007730.585698871549614
260.6028548390685170.7942903218629660.397145160931483
270.9605582441800020.07888351163999550.0394417558199978
280.9705526834379710.05889463312405720.0294473165620286
290.9617218150631290.0765563698737420.038278184936871
300.9327454850026110.1345090299947780.0672545149973888
310.913605273138470.172789453723060.0863947268615299
320.9363536407600480.1272927184799030.0636463592399516
330.8803569925688070.2392860148623870.119643007431193

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.597928969573807 & 0.804142060852385 & 0.402071030426193 \tabularnewline
7 & 0.805814977507914 & 0.388370044984172 & 0.194185022492086 \tabularnewline
8 & 0.720981818694391 & 0.558036362611218 & 0.279018181305609 \tabularnewline
9 & 0.649764792858961 & 0.700470414282079 & 0.350235207141039 \tabularnewline
10 & 0.613004805277026 & 0.773990389445949 & 0.386995194722974 \tabularnewline
11 & 0.690107188097561 & 0.619785623804878 & 0.309892811902439 \tabularnewline
12 & 0.691199655958227 & 0.617600688083546 & 0.308800344041773 \tabularnewline
13 & 0.737898423674942 & 0.524203152650117 & 0.262101576325058 \tabularnewline
14 & 0.65177309604783 & 0.69645380790434 & 0.34822690395217 \tabularnewline
15 & 0.566642974519511 & 0.866714050960978 & 0.433357025480489 \tabularnewline
16 & 0.594689072319513 & 0.810621855360974 & 0.405310927680487 \tabularnewline
17 & 0.610880146167736 & 0.778239707664528 & 0.389119853832264 \tabularnewline
18 & 0.613441083996051 & 0.773117832007898 & 0.386558916003949 \tabularnewline
19 & 0.589205364713037 & 0.821589270573926 & 0.410794635286963 \tabularnewline
20 & 0.503427823550471 & 0.993144352899058 & 0.496572176449529 \tabularnewline
21 & 0.59140003064352 & 0.81719993871296 & 0.40859996935648 \tabularnewline
22 & 0.52628088780652 & 0.94743822438696 & 0.47371911219348 \tabularnewline
23 & 0.534351614657273 & 0.931296770685453 & 0.465648385342727 \tabularnewline
24 & 0.482913739935625 & 0.96582747987125 & 0.517086260064375 \tabularnewline
25 & 0.414301128450386 & 0.828602256900773 & 0.585698871549614 \tabularnewline
26 & 0.602854839068517 & 0.794290321862966 & 0.397145160931483 \tabularnewline
27 & 0.960558244180002 & 0.0788835116399955 & 0.0394417558199978 \tabularnewline
28 & 0.970552683437971 & 0.0588946331240572 & 0.0294473165620286 \tabularnewline
29 & 0.961721815063129 & 0.076556369873742 & 0.038278184936871 \tabularnewline
30 & 0.932745485002611 & 0.134509029994778 & 0.0672545149973888 \tabularnewline
31 & 0.91360527313847 & 0.17278945372306 & 0.0863947268615299 \tabularnewline
32 & 0.936353640760048 & 0.127292718479903 & 0.0636463592399516 \tabularnewline
33 & 0.880356992568807 & 0.239286014862387 & 0.119643007431193 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=74553&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]0.597928969573807[/C][C]0.804142060852385[/C][C]0.402071030426193[/C][/ROW]
[ROW][C]7[/C][C]0.805814977507914[/C][C]0.388370044984172[/C][C]0.194185022492086[/C][/ROW]
[ROW][C]8[/C][C]0.720981818694391[/C][C]0.558036362611218[/C][C]0.279018181305609[/C][/ROW]
[ROW][C]9[/C][C]0.649764792858961[/C][C]0.700470414282079[/C][C]0.350235207141039[/C][/ROW]
[ROW][C]10[/C][C]0.613004805277026[/C][C]0.773990389445949[/C][C]0.386995194722974[/C][/ROW]
[ROW][C]11[/C][C]0.690107188097561[/C][C]0.619785623804878[/C][C]0.309892811902439[/C][/ROW]
[ROW][C]12[/C][C]0.691199655958227[/C][C]0.617600688083546[/C][C]0.308800344041773[/C][/ROW]
[ROW][C]13[/C][C]0.737898423674942[/C][C]0.524203152650117[/C][C]0.262101576325058[/C][/ROW]
[ROW][C]14[/C][C]0.65177309604783[/C][C]0.69645380790434[/C][C]0.34822690395217[/C][/ROW]
[ROW][C]15[/C][C]0.566642974519511[/C][C]0.866714050960978[/C][C]0.433357025480489[/C][/ROW]
[ROW][C]16[/C][C]0.594689072319513[/C][C]0.810621855360974[/C][C]0.405310927680487[/C][/ROW]
[ROW][C]17[/C][C]0.610880146167736[/C][C]0.778239707664528[/C][C]0.389119853832264[/C][/ROW]
[ROW][C]18[/C][C]0.613441083996051[/C][C]0.773117832007898[/C][C]0.386558916003949[/C][/ROW]
[ROW][C]19[/C][C]0.589205364713037[/C][C]0.821589270573926[/C][C]0.410794635286963[/C][/ROW]
[ROW][C]20[/C][C]0.503427823550471[/C][C]0.993144352899058[/C][C]0.496572176449529[/C][/ROW]
[ROW][C]21[/C][C]0.59140003064352[/C][C]0.81719993871296[/C][C]0.40859996935648[/C][/ROW]
[ROW][C]22[/C][C]0.52628088780652[/C][C]0.94743822438696[/C][C]0.47371911219348[/C][/ROW]
[ROW][C]23[/C][C]0.534351614657273[/C][C]0.931296770685453[/C][C]0.465648385342727[/C][/ROW]
[ROW][C]24[/C][C]0.482913739935625[/C][C]0.96582747987125[/C][C]0.517086260064375[/C][/ROW]
[ROW][C]25[/C][C]0.414301128450386[/C][C]0.828602256900773[/C][C]0.585698871549614[/C][/ROW]
[ROW][C]26[/C][C]0.602854839068517[/C][C]0.794290321862966[/C][C]0.397145160931483[/C][/ROW]
[ROW][C]27[/C][C]0.960558244180002[/C][C]0.0788835116399955[/C][C]0.0394417558199978[/C][/ROW]
[ROW][C]28[/C][C]0.970552683437971[/C][C]0.0588946331240572[/C][C]0.0294473165620286[/C][/ROW]
[ROW][C]29[/C][C]0.961721815063129[/C][C]0.076556369873742[/C][C]0.038278184936871[/C][/ROW]
[ROW][C]30[/C][C]0.932745485002611[/C][C]0.134509029994778[/C][C]0.0672545149973888[/C][/ROW]
[ROW][C]31[/C][C]0.91360527313847[/C][C]0.17278945372306[/C][C]0.0863947268615299[/C][/ROW]
[ROW][C]32[/C][C]0.936353640760048[/C][C]0.127292718479903[/C][C]0.0636463592399516[/C][/ROW]
[ROW][C]33[/C][C]0.880356992568807[/C][C]0.239286014862387[/C][C]0.119643007431193[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=74553&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=74553&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
60.5979289695738070.8041420608523850.402071030426193
70.8058149775079140.3883700449841720.194185022492086
80.7209818186943910.5580363626112180.279018181305609
90.6497647928589610.7004704142820790.350235207141039
100.6130048052770260.7739903894459490.386995194722974
110.6901071880975610.6197856238048780.309892811902439
120.6911996559582270.6176006880835460.308800344041773
130.7378984236749420.5242031526501170.262101576325058
140.651773096047830.696453807904340.34822690395217
150.5666429745195110.8667140509609780.433357025480489
160.5946890723195130.8106218553609740.405310927680487
170.6108801461677360.7782397076645280.389119853832264
180.6134410839960510.7731178320078980.386558916003949
190.5892053647130370.8215892705739260.410794635286963
200.5034278235504710.9931443528990580.496572176449529
210.591400030643520.817199938712960.40859996935648
220.526280887806520.947438224386960.47371911219348
230.5343516146572730.9312967706854530.465648385342727
240.4829137399356250.965827479871250.517086260064375
250.4143011284503860.8286022569007730.585698871549614
260.6028548390685170.7942903218629660.397145160931483
270.9605582441800020.07888351163999550.0394417558199978
280.9705526834379710.05889463312405720.0294473165620286
290.9617218150631290.0765563698737420.038278184936871
300.9327454850026110.1345090299947780.0672545149973888
310.913605273138470.172789453723060.0863947268615299
320.9363536407600480.1272927184799030.0636463592399516
330.8803569925688070.2392860148623870.119643007431193







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 level30.107142857142857NOK

\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 & 3 & 0.107142857142857 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=74553&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]3[/C][C]0.107142857142857[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=74553&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=74553&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 level30.107142857142857NOK



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')
}