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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 13 Dec 2010 19:32:39 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/13/t1292268684hjx7nola9s2bk94.htm/, Retrieved Thu, 31 Oct 2024 22:51:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109104, Retrieved Thu, 31 Oct 2024 22:51:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact178
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [Pearsons Corr Mam...] [2010-12-09 13:00:40] [6ca0fc48dd5333d51a15728999009c83]
- RMPD  [Multiple Regression] [Bonus: MR SWS] [2010-12-11 15:01:53] [6ca0fc48dd5333d51a15728999009c83]
-    D    [Multiple Regression] [Bonus: MR SWS 2 var] [2010-12-11 15:29:26] [6ca0fc48dd5333d51a15728999009c83]
-    D      [Multiple Regression] [Bonus: MR SWS cor...] [2010-12-13 19:20:15] [6ca0fc48dd5333d51a15728999009c83]
-    D          [Multiple Regression] [Bonus: MR PS correct] [2010-12-13 19:32:39] [b4ba846736d082ffaee409a197f454c7] [Current]
-    D            [Multiple Regression] [Bonus: MR totaalm...] [2010-12-13 19:52:07] [6ca0fc48dd5333d51a15728999009c83]
-    D              [Multiple Regression] [Bonus: MR SWS tot...] [2010-12-13 19:58:24] [6ca0fc48dd5333d51a15728999009c83]
-    D            [Multiple Regression] [Bonus: MR PS tot...] [2010-12-13 20:09:08] [6ca0fc48dd5333d51a15728999009c83]
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Dataseries X:
0,30103000	3,00000000	1,62324929
0,25527251	4,00000000	2,79518459
-0,15490196	4,00000000	2,25527251
0,59106461	1,00000000	1,54406804
0,00000000	4,00000000	2,59328607
0,55630250	1,00000000	1,79934055
0,14612804	1,00000000	2,36172784
0,17609126	4,00000000	2,04921802
-0,15490196	5,00000000	2,44870632
0,32221929	1,00000000	1,62324929
0,61278386	2,00000000	1,62324929
0,07918125	2,00000000	2,07918125
-0,30103000	5,00000000	2,17026172
0,53147892	2,00000000	1,20411998
0,17609126	1,00000000	2,49136169
0,53147892	3,00000000	1,44715803
-0,09691001	4,00000000	1,83250891
-0,09691001	5,00000000	2,52633928
0,30103000	1,00000000	1,69897000
0,27875360	1,00000000	2,42651126
0,11394335	3,00000000	1,27875360
0,74818803	1,00000000	1,07918125
0,49136169	1,00000000	2,07918125
0,25527251	2,00000000	2,14612804
-0,04575749	4,00000000	2,23044892
0,25527251	2,00000000	1,23044892
0,27875360	4,00000000	2,06069784
-0,04575749	5,00000000	1,49136169
0,41497335	3,00000000	1,32221929
0,38021124	1,00000000	1,71600334
0,07918125	2,00000000	2,21484385
-0,04575749	2,00000000	2,35218252
-0,30103000	3,00000000	2,35218252
-0,22184875	5,00000000	2,17897695
0,36172784	2,00000000	1,77815125
-0,30103000	3,00000000	2,30103000
0,41497335	2,00000000	1,66275783
-0,22184875	4,00000000	2,32221929
0,81954394	1,00000000	1,14612804




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109104&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109104&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109104&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 time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







Multiple Linear Regression - Estimated Regression Equation
PS[t] = + 1.07450734352761 -0.110510500050775D[t] -0.303538869156499Tg[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PS[t] =  +  1.07450734352761 -0.110510500050775D[t] -0.303538869156499Tg[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109104&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PS[t] =  +  1.07450734352761 -0.110510500050775D[t] -0.303538869156499Tg[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109104&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109104&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
PS[t] = + 1.07450734352761 -0.110510500050775D[t] -0.303538869156499Tg[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.074507343527610.1287518.345600
D-0.1105105000507750.022191-4.981.6e-058e-06
Tg-0.3035388691564990.068904-4.40539.1e-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) & 1.07450734352761 & 0.128751 & 8.3456 & 0 & 0 \tabularnewline
D & -0.110510500050775 & 0.022191 & -4.98 & 1.6e-05 & 8e-06 \tabularnewline
Tg & -0.303538869156499 & 0.068904 & -4.4053 & 9.1e-05 & 4.5e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109104&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]1.07450734352761[/C][C]0.128751[/C][C]8.3456[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]D[/C][C]-0.110510500050775[/C][C]0.022191[/C][C]-4.98[/C][C]1.6e-05[/C][C]8e-06[/C][/ROW]
[ROW][C]Tg[/C][C]-0.303538869156499[/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=109104&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109104&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)1.074507343527610.1287518.345600
D-0.1105105000507750.022191-4.981.6e-058e-06
Tg-0.3035388691564990.068904-4.40539.1e-054.5e-05







Multiple Linear Regression - Regression Statistics
Multiple R0.809091682302199
R-squared0.654629350370602
Adjusted R-squared0.635442092057858
F-TEST (value)34.1179203250624
F-TEST (DF numerator)2
F-TEST (DF denominator)36
p-value4.88807316845197e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.181764011717535
Sum Squared Residuals1.18937361440348

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.809091682302199 \tabularnewline
R-squared & 0.654629350370602 \tabularnewline
Adjusted R-squared & 0.635442092057858 \tabularnewline
F-TEST (value) & 34.1179203250624 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 36 \tabularnewline
p-value & 4.88807316845197e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.181764011717535 \tabularnewline
Sum Squared Residuals & 1.18937361440348 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109104&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.809091682302199[/C][/ROW]
[ROW][C]R-squared[/C][C]0.654629350370602[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.635442092057858[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]34.1179203250624[/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]4.88807316845197e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.181764011717535[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1.18937361440348[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109104&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109104&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 R0.809091682302199
R-squared0.654629350370602
Adjusted R-squared0.635442092057858
F-TEST (value)34.1179203250624
F-TEST (DF numerator)2
F-TEST (DF denominator)36
p-value4.88807316845197e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.181764011717535
Sum Squared Residuals1.18937361440348







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
10.301030.2502565895295930.0507734104704075
20.25527251-0.2159818262077660.471254336207766
3-0.15490196-0.0520975240006324-0.102804435999368
40.591064610.495312176714540.09575243328546
50-0.1546977777625950.154697777762595
60.55630250.4178270477023990.138475452297601
70.146128040.247120645667811-0.100992605667811
80.176091260.01044802287858660.165643237121413
9-0.15490196-0.2213227039954410.0664207439954406
100.322219290.471277589631142-0.149058299631142
110.612783860.3607670895803670.252016770419633
120.079181250.222374018029661-0.143192768029661
13-0.30103-0.136803944988707-0.164226055011293
140.531478920.4879891263681110.0434897936318892
150.176091260.207771733434407-0.0316804734344075
160.531478920.3037071314583350.227771788541665
17-0.096910010.076227661063898-0.173137671063898
18-0.09691001-0.2448873248831120.147977314883112
190.301030.448293410946015-0.147263410946015
200.27875360.227456359620920.0512972403790797
210.113943350.35482442170148-0.24088107170148
220.748188030.6364233872369350.111764642763065
230.491361690.3328845180804360.158477171919564
240.255272510.2020530650994030.0532194449005968
25-0.04575749-0.0445625995636279-0.00119489043637205
260.255272510.479997269694421-0.224724759694421
270.27875360.006963451297666510.271790148702333
28-0.045757490.0692686023878065-0.115026092387806
290.414973350.3416308953117730.0733424546882272
300.380211240.443123130184457-0.0629118901844566
310.079181250.18119514583883-0.10201389583883
32-0.045757490.139507521255573-0.185265011255573
33-0.301030.0289970212047976-0.330027021204798
34-0.22184875-0.139449356047346-0.0823993939526542
350.361727840.3137483238118420.0479795161881583
36-0.301030.0445237992801028-0.345553799280103
370.414973350.3487747120267430.0661986379732574
38-0.22184875-0.0724184738955014-0.149430276104499
390.819543940.6161024343066770.203441505693323

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0.30103 & 0.250256589529593 & 0.0507734104704075 \tabularnewline
2 & 0.25527251 & -0.215981826207766 & 0.471254336207766 \tabularnewline
3 & -0.15490196 & -0.0520975240006324 & -0.102804435999368 \tabularnewline
4 & 0.59106461 & 0.49531217671454 & 0.09575243328546 \tabularnewline
5 & 0 & -0.154697777762595 & 0.154697777762595 \tabularnewline
6 & 0.5563025 & 0.417827047702399 & 0.138475452297601 \tabularnewline
7 & 0.14612804 & 0.247120645667811 & -0.100992605667811 \tabularnewline
8 & 0.17609126 & 0.0104480228785866 & 0.165643237121413 \tabularnewline
9 & -0.15490196 & -0.221322703995441 & 0.0664207439954406 \tabularnewline
10 & 0.32221929 & 0.471277589631142 & -0.149058299631142 \tabularnewline
11 & 0.61278386 & 0.360767089580367 & 0.252016770419633 \tabularnewline
12 & 0.07918125 & 0.222374018029661 & -0.143192768029661 \tabularnewline
13 & -0.30103 & -0.136803944988707 & -0.164226055011293 \tabularnewline
14 & 0.53147892 & 0.487989126368111 & 0.0434897936318892 \tabularnewline
15 & 0.17609126 & 0.207771733434407 & -0.0316804734344075 \tabularnewline
16 & 0.53147892 & 0.303707131458335 & 0.227771788541665 \tabularnewline
17 & -0.09691001 & 0.076227661063898 & -0.173137671063898 \tabularnewline
18 & -0.09691001 & -0.244887324883112 & 0.147977314883112 \tabularnewline
19 & 0.30103 & 0.448293410946015 & -0.147263410946015 \tabularnewline
20 & 0.2787536 & 0.22745635962092 & 0.0512972403790797 \tabularnewline
21 & 0.11394335 & 0.35482442170148 & -0.24088107170148 \tabularnewline
22 & 0.74818803 & 0.636423387236935 & 0.111764642763065 \tabularnewline
23 & 0.49136169 & 0.332884518080436 & 0.158477171919564 \tabularnewline
24 & 0.25527251 & 0.202053065099403 & 0.0532194449005968 \tabularnewline
25 & -0.04575749 & -0.0445625995636279 & -0.00119489043637205 \tabularnewline
26 & 0.25527251 & 0.479997269694421 & -0.224724759694421 \tabularnewline
27 & 0.2787536 & 0.00696345129766651 & 0.271790148702333 \tabularnewline
28 & -0.04575749 & 0.0692686023878065 & -0.115026092387806 \tabularnewline
29 & 0.41497335 & 0.341630895311773 & 0.0733424546882272 \tabularnewline
30 & 0.38021124 & 0.443123130184457 & -0.0629118901844566 \tabularnewline
31 & 0.07918125 & 0.18119514583883 & -0.10201389583883 \tabularnewline
32 & -0.04575749 & 0.139507521255573 & -0.185265011255573 \tabularnewline
33 & -0.30103 & 0.0289970212047976 & -0.330027021204798 \tabularnewline
34 & -0.22184875 & -0.139449356047346 & -0.0823993939526542 \tabularnewline
35 & 0.36172784 & 0.313748323811842 & 0.0479795161881583 \tabularnewline
36 & -0.30103 & 0.0445237992801028 & -0.345553799280103 \tabularnewline
37 & 0.41497335 & 0.348774712026743 & 0.0661986379732574 \tabularnewline
38 & -0.22184875 & -0.0724184738955014 & -0.149430276104499 \tabularnewline
39 & 0.81954394 & 0.616102434306677 & 0.203441505693323 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109104&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]0.30103[/C][C]0.250256589529593[/C][C]0.0507734104704075[/C][/ROW]
[ROW][C]2[/C][C]0.25527251[/C][C]-0.215981826207766[/C][C]0.471254336207766[/C][/ROW]
[ROW][C]3[/C][C]-0.15490196[/C][C]-0.0520975240006324[/C][C]-0.102804435999368[/C][/ROW]
[ROW][C]4[/C][C]0.59106461[/C][C]0.49531217671454[/C][C]0.09575243328546[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]-0.154697777762595[/C][C]0.154697777762595[/C][/ROW]
[ROW][C]6[/C][C]0.5563025[/C][C]0.417827047702399[/C][C]0.138475452297601[/C][/ROW]
[ROW][C]7[/C][C]0.14612804[/C][C]0.247120645667811[/C][C]-0.100992605667811[/C][/ROW]
[ROW][C]8[/C][C]0.17609126[/C][C]0.0104480228785866[/C][C]0.165643237121413[/C][/ROW]
[ROW][C]9[/C][C]-0.15490196[/C][C]-0.221322703995441[/C][C]0.0664207439954406[/C][/ROW]
[ROW][C]10[/C][C]0.32221929[/C][C]0.471277589631142[/C][C]-0.149058299631142[/C][/ROW]
[ROW][C]11[/C][C]0.61278386[/C][C]0.360767089580367[/C][C]0.252016770419633[/C][/ROW]
[ROW][C]12[/C][C]0.07918125[/C][C]0.222374018029661[/C][C]-0.143192768029661[/C][/ROW]
[ROW][C]13[/C][C]-0.30103[/C][C]-0.136803944988707[/C][C]-0.164226055011293[/C][/ROW]
[ROW][C]14[/C][C]0.53147892[/C][C]0.487989126368111[/C][C]0.0434897936318892[/C][/ROW]
[ROW][C]15[/C][C]0.17609126[/C][C]0.207771733434407[/C][C]-0.0316804734344075[/C][/ROW]
[ROW][C]16[/C][C]0.53147892[/C][C]0.303707131458335[/C][C]0.227771788541665[/C][/ROW]
[ROW][C]17[/C][C]-0.09691001[/C][C]0.076227661063898[/C][C]-0.173137671063898[/C][/ROW]
[ROW][C]18[/C][C]-0.09691001[/C][C]-0.244887324883112[/C][C]0.147977314883112[/C][/ROW]
[ROW][C]19[/C][C]0.30103[/C][C]0.448293410946015[/C][C]-0.147263410946015[/C][/ROW]
[ROW][C]20[/C][C]0.2787536[/C][C]0.22745635962092[/C][C]0.0512972403790797[/C][/ROW]
[ROW][C]21[/C][C]0.11394335[/C][C]0.35482442170148[/C][C]-0.24088107170148[/C][/ROW]
[ROW][C]22[/C][C]0.74818803[/C][C]0.636423387236935[/C][C]0.111764642763065[/C][/ROW]
[ROW][C]23[/C][C]0.49136169[/C][C]0.332884518080436[/C][C]0.158477171919564[/C][/ROW]
[ROW][C]24[/C][C]0.25527251[/C][C]0.202053065099403[/C][C]0.0532194449005968[/C][/ROW]
[ROW][C]25[/C][C]-0.04575749[/C][C]-0.0445625995636279[/C][C]-0.00119489043637205[/C][/ROW]
[ROW][C]26[/C][C]0.25527251[/C][C]0.479997269694421[/C][C]-0.224724759694421[/C][/ROW]
[ROW][C]27[/C][C]0.2787536[/C][C]0.00696345129766651[/C][C]0.271790148702333[/C][/ROW]
[ROW][C]28[/C][C]-0.04575749[/C][C]0.0692686023878065[/C][C]-0.115026092387806[/C][/ROW]
[ROW][C]29[/C][C]0.41497335[/C][C]0.341630895311773[/C][C]0.0733424546882272[/C][/ROW]
[ROW][C]30[/C][C]0.38021124[/C][C]0.443123130184457[/C][C]-0.0629118901844566[/C][/ROW]
[ROW][C]31[/C][C]0.07918125[/C][C]0.18119514583883[/C][C]-0.10201389583883[/C][/ROW]
[ROW][C]32[/C][C]-0.04575749[/C][C]0.139507521255573[/C][C]-0.185265011255573[/C][/ROW]
[ROW][C]33[/C][C]-0.30103[/C][C]0.0289970212047976[/C][C]-0.330027021204798[/C][/ROW]
[ROW][C]34[/C][C]-0.22184875[/C][C]-0.139449356047346[/C][C]-0.0823993939526542[/C][/ROW]
[ROW][C]35[/C][C]0.36172784[/C][C]0.313748323811842[/C][C]0.0479795161881583[/C][/ROW]
[ROW][C]36[/C][C]-0.30103[/C][C]0.0445237992801028[/C][C]-0.345553799280103[/C][/ROW]
[ROW][C]37[/C][C]0.41497335[/C][C]0.348774712026743[/C][C]0.0661986379732574[/C][/ROW]
[ROW][C]38[/C][C]-0.22184875[/C][C]-0.0724184738955014[/C][C]-0.149430276104499[/C][/ROW]
[ROW][C]39[/C][C]0.81954394[/C][C]0.616102434306677[/C][C]0.203441505693323[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109104&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109104&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
10.301030.2502565895295930.0507734104704075
20.25527251-0.2159818262077660.471254336207766
3-0.15490196-0.0520975240006324-0.102804435999368
40.591064610.495312176714540.09575243328546
50-0.1546977777625950.154697777762595
60.55630250.4178270477023990.138475452297601
70.146128040.247120645667811-0.100992605667811
80.176091260.01044802287858660.165643237121413
9-0.15490196-0.2213227039954410.0664207439954406
100.322219290.471277589631142-0.149058299631142
110.612783860.3607670895803670.252016770419633
120.079181250.222374018029661-0.143192768029661
13-0.30103-0.136803944988707-0.164226055011293
140.531478920.4879891263681110.0434897936318892
150.176091260.207771733434407-0.0316804734344075
160.531478920.3037071314583350.227771788541665
17-0.096910010.076227661063898-0.173137671063898
18-0.09691001-0.2448873248831120.147977314883112
190.301030.448293410946015-0.147263410946015
200.27875360.227456359620920.0512972403790797
210.113943350.35482442170148-0.24088107170148
220.748188030.6364233872369350.111764642763065
230.491361690.3328845180804360.158477171919564
240.255272510.2020530650994030.0532194449005968
25-0.04575749-0.0445625995636279-0.00119489043637205
260.255272510.479997269694421-0.224724759694421
270.27875360.006963451297666510.271790148702333
28-0.045757490.0692686023878065-0.115026092387806
290.414973350.3416308953117730.0733424546882272
300.380211240.443123130184457-0.0629118901844566
310.079181250.18119514583883-0.10201389583883
32-0.045757490.139507521255573-0.185265011255573
33-0.301030.0289970212047976-0.330027021204798
34-0.22184875-0.139449356047346-0.0823993939526542
350.361727840.3137483238118420.0479795161881583
36-0.301030.0445237992801028-0.345553799280103
370.414973350.3487747120267430.0661986379732574
38-0.22184875-0.0724184738955014-0.149430276104499
390.819543940.6161024343066770.203441505693323







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.5979289810166790.8041420379666420.402071018983321
70.8058149738732330.3883700522535340.194185026126767
80.7209818132054270.5580363735891450.279018186794573
90.6497647874229490.7004704251541020.350235212577051
100.6130048061514350.7739903876971310.386995193848565
110.6901071899554090.6197856200891820.309892810044591
120.6911996529982730.6176006940034540.308800347001727
130.7378984241983130.5242031516033740.262101575801687
140.6517730967498560.6964538065002870.348226903250144
150.5666429756465680.8667140487068650.433357024353432
160.5946890732242670.8106218535514660.405310926775733
170.6108801446816290.7782397106367430.389119855318371
180.6134410857790130.7731178284419750.386558914220987
190.5892053648542630.8215892702914730.410794635145737
200.5034278226626260.9931443546747480.496572177337374
210.5914000344703680.8171999310592630.408599965529631
220.5262808924015390.9474382151969210.473719107598461
230.534351613299260.9312967734014810.46564838670074
240.482913741963180.9658274839263610.51708625803682
250.4143011308671430.8286022617342860.585698869132857
260.602854834681830.7942903306363410.397145165318171
270.9605582396754680.07888352064906470.0394417603245324
280.9705526793881930.05889464122361360.0294473206118068
290.9617218108826470.07655637823470650.0382781891173532
300.9327454812471530.1345090375056930.0672545187528466
310.9136052715760.1727894568480010.0863947284240004
320.9363536448533140.1272927102933720.0636463551466858
330.8803569984999060.2392860030001870.119643001500094

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.597928981016679 & 0.804142037966642 & 0.402071018983321 \tabularnewline
7 & 0.805814973873233 & 0.388370052253534 & 0.194185026126767 \tabularnewline
8 & 0.720981813205427 & 0.558036373589145 & 0.279018186794573 \tabularnewline
9 & 0.649764787422949 & 0.700470425154102 & 0.350235212577051 \tabularnewline
10 & 0.613004806151435 & 0.773990387697131 & 0.386995193848565 \tabularnewline
11 & 0.690107189955409 & 0.619785620089182 & 0.309892810044591 \tabularnewline
12 & 0.691199652998273 & 0.617600694003454 & 0.308800347001727 \tabularnewline
13 & 0.737898424198313 & 0.524203151603374 & 0.262101575801687 \tabularnewline
14 & 0.651773096749856 & 0.696453806500287 & 0.348226903250144 \tabularnewline
15 & 0.566642975646568 & 0.866714048706865 & 0.433357024353432 \tabularnewline
16 & 0.594689073224267 & 0.810621853551466 & 0.405310926775733 \tabularnewline
17 & 0.610880144681629 & 0.778239710636743 & 0.389119855318371 \tabularnewline
18 & 0.613441085779013 & 0.773117828441975 & 0.386558914220987 \tabularnewline
19 & 0.589205364854263 & 0.821589270291473 & 0.410794635145737 \tabularnewline
20 & 0.503427822662626 & 0.993144354674748 & 0.496572177337374 \tabularnewline
21 & 0.591400034470368 & 0.817199931059263 & 0.408599965529631 \tabularnewline
22 & 0.526280892401539 & 0.947438215196921 & 0.473719107598461 \tabularnewline
23 & 0.53435161329926 & 0.931296773401481 & 0.46564838670074 \tabularnewline
24 & 0.48291374196318 & 0.965827483926361 & 0.51708625803682 \tabularnewline
25 & 0.414301130867143 & 0.828602261734286 & 0.585698869132857 \tabularnewline
26 & 0.60285483468183 & 0.794290330636341 & 0.397145165318171 \tabularnewline
27 & 0.960558239675468 & 0.0788835206490647 & 0.0394417603245324 \tabularnewline
28 & 0.970552679388193 & 0.0588946412236136 & 0.0294473206118068 \tabularnewline
29 & 0.961721810882647 & 0.0765563782347065 & 0.0382781891173532 \tabularnewline
30 & 0.932745481247153 & 0.134509037505693 & 0.0672545187528466 \tabularnewline
31 & 0.913605271576 & 0.172789456848001 & 0.0863947284240004 \tabularnewline
32 & 0.936353644853314 & 0.127292710293372 & 0.0636463551466858 \tabularnewline
33 & 0.880356998499906 & 0.239286003000187 & 0.119643001500094 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109104&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]0.597928981016679[/C][C]0.804142037966642[/C][C]0.402071018983321[/C][/ROW]
[ROW][C]7[/C][C]0.805814973873233[/C][C]0.388370052253534[/C][C]0.194185026126767[/C][/ROW]
[ROW][C]8[/C][C]0.720981813205427[/C][C]0.558036373589145[/C][C]0.279018186794573[/C][/ROW]
[ROW][C]9[/C][C]0.649764787422949[/C][C]0.700470425154102[/C][C]0.350235212577051[/C][/ROW]
[ROW][C]10[/C][C]0.613004806151435[/C][C]0.773990387697131[/C][C]0.386995193848565[/C][/ROW]
[ROW][C]11[/C][C]0.690107189955409[/C][C]0.619785620089182[/C][C]0.309892810044591[/C][/ROW]
[ROW][C]12[/C][C]0.691199652998273[/C][C]0.617600694003454[/C][C]0.308800347001727[/C][/ROW]
[ROW][C]13[/C][C]0.737898424198313[/C][C]0.524203151603374[/C][C]0.262101575801687[/C][/ROW]
[ROW][C]14[/C][C]0.651773096749856[/C][C]0.696453806500287[/C][C]0.348226903250144[/C][/ROW]
[ROW][C]15[/C][C]0.566642975646568[/C][C]0.866714048706865[/C][C]0.433357024353432[/C][/ROW]
[ROW][C]16[/C][C]0.594689073224267[/C][C]0.810621853551466[/C][C]0.405310926775733[/C][/ROW]
[ROW][C]17[/C][C]0.610880144681629[/C][C]0.778239710636743[/C][C]0.389119855318371[/C][/ROW]
[ROW][C]18[/C][C]0.613441085779013[/C][C]0.773117828441975[/C][C]0.386558914220987[/C][/ROW]
[ROW][C]19[/C][C]0.589205364854263[/C][C]0.821589270291473[/C][C]0.410794635145737[/C][/ROW]
[ROW][C]20[/C][C]0.503427822662626[/C][C]0.993144354674748[/C][C]0.496572177337374[/C][/ROW]
[ROW][C]21[/C][C]0.591400034470368[/C][C]0.817199931059263[/C][C]0.408599965529631[/C][/ROW]
[ROW][C]22[/C][C]0.526280892401539[/C][C]0.947438215196921[/C][C]0.473719107598461[/C][/ROW]
[ROW][C]23[/C][C]0.53435161329926[/C][C]0.931296773401481[/C][C]0.46564838670074[/C][/ROW]
[ROW][C]24[/C][C]0.48291374196318[/C][C]0.965827483926361[/C][C]0.51708625803682[/C][/ROW]
[ROW][C]25[/C][C]0.414301130867143[/C][C]0.828602261734286[/C][C]0.585698869132857[/C][/ROW]
[ROW][C]26[/C][C]0.60285483468183[/C][C]0.794290330636341[/C][C]0.397145165318171[/C][/ROW]
[ROW][C]27[/C][C]0.960558239675468[/C][C]0.0788835206490647[/C][C]0.0394417603245324[/C][/ROW]
[ROW][C]28[/C][C]0.970552679388193[/C][C]0.0588946412236136[/C][C]0.0294473206118068[/C][/ROW]
[ROW][C]29[/C][C]0.961721810882647[/C][C]0.0765563782347065[/C][C]0.0382781891173532[/C][/ROW]
[ROW][C]30[/C][C]0.932745481247153[/C][C]0.134509037505693[/C][C]0.0672545187528466[/C][/ROW]
[ROW][C]31[/C][C]0.913605271576[/C][C]0.172789456848001[/C][C]0.0863947284240004[/C][/ROW]
[ROW][C]32[/C][C]0.936353644853314[/C][C]0.127292710293372[/C][C]0.0636463551466858[/C][/ROW]
[ROW][C]33[/C][C]0.880356998499906[/C][C]0.239286003000187[/C][C]0.119643001500094[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109104&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109104&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
60.5979289810166790.8041420379666420.402071018983321
70.8058149738732330.3883700522535340.194185026126767
80.7209818132054270.5580363735891450.279018186794573
90.6497647874229490.7004704251541020.350235212577051
100.6130048061514350.7739903876971310.386995193848565
110.6901071899554090.6197856200891820.309892810044591
120.6911996529982730.6176006940034540.308800347001727
130.7378984241983130.5242031516033740.262101575801687
140.6517730967498560.6964538065002870.348226903250144
150.5666429756465680.8667140487068650.433357024353432
160.5946890732242670.8106218535514660.405310926775733
170.6108801446816290.7782397106367430.389119855318371
180.6134410857790130.7731178284419750.386558914220987
190.5892053648542630.8215892702914730.410794635145737
200.5034278226626260.9931443546747480.496572177337374
210.5914000344703680.8171999310592630.408599965529631
220.5262808924015390.9474382151969210.473719107598461
230.534351613299260.9312967734014810.46564838670074
240.482913741963180.9658274839263610.51708625803682
250.4143011308671430.8286022617342860.585698869132857
260.602854834681830.7942903306363410.397145165318171
270.9605582396754680.07888352064906470.0394417603245324
280.9705526793881930.05889464122361360.0294473206118068
290.9617218108826470.07655637823470650.0382781891173532
300.9327454812471530.1345090375056930.0672545187528466
310.9136052715760.1727894568480010.0863947284240004
320.9363536448533140.1272927102933720.0636463551466858
330.8803569984999060.2392860030001870.119643001500094







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=109104&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=109104&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109104&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')
}