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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 computationFri, 20 Nov 2009 10:35:09 -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/t12587387043cpt9t1n3cq810k.htm/, Retrieved Thu, 28 Mar 2024 12:18:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58361, Retrieved Thu, 28 Mar 2024 12:18:50 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact118
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-   PD      [Multiple Regression] [WS 7: model met s...] [2009-11-20 17:35:09] [17b3de9cda9f51722106e41c76160a49] [Current]
-             [Multiple Regression] [WS 7: Model 4: Ve...] [2009-11-20 17:55:48] [8cf9233b7464ea02e32be3b30fdac052]
-             [Multiple Regression] [Paper: Multiple L...] [2009-12-16 15:05:39] [b97b96148b0223bc16666763988dc147]
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Dataseries X:
423	114	449	441	427	423
427	116	452	449	441	427
441	153	462	452	449	441
449	162	455	462	452	449
452	161	461	455	462	452
462	149	461	461	455	462
455	139	463	461	461	455
461	135	462	463	461	461
461	130	456	462	463	461
463	127	455	456	462	463
462	122	456	455	456	462
456	117	472	456	455	456
455	112	472	472	456	455
456	113	471	472	472	456
472	149	465	471	472	472
472	157	459	465	471	472
471	157	465	459	465	471
465	147	468	465	459	465
459	137	467	468	465	459
465	132	463	467	468	465
468	125	460	463	467	468
467	123	462	460	463	467
463	117	461	462	460	463
460	114	476	461	462	460
462	111	476	476	461	462
461	112	471	476	476	461
476	144	453	471	476	476
476	150	443	453	471	476
471	149	442	443	453	471
453	134	444	442	443	453
443	123	438	444	442	443
442	116	427	438	444	442
444	117	424	427	438	444
438	111	416	424	427	438
427	105	406	416	424	427
424	102	431	406	416	424
416	95	434	431	406	416
406	93	418	434	431	406
431	124	412	418	434	431
434	130	404	412	418	434
418	124	409	404	412	418
412	115	412	409	404	412
404	106	406	412	409	404
409	105	398	406	412	409
412	105	397	398	406	412
406	101	385	397	398	406
398	95	390	385	397	398
397	93	413	390	385	397
385	84	413	413	390	385
390	87	401	413	413	390
413	116	397	401	413	413
413	120	397	397	401	413
401	117	409	397	397	401
397	109	419	409	397	397
397	105	424	419	409	397
409	107	428	424	419	409
419	109	430	428	424	419




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=58361&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=58361&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58361&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
Y[t] = -1.26945508398474e-14 -2.22449408643527e-17X[t] -5.7380076558157e-17Y1[t] + 1.99131271067708e-16Y2[t] -5.19124269428038e-16Y3[t] + 1Y4[t] -1.36208603520825e-15M1[t] + 7.14770130673045e-16M2[t] -2.34701997419123e-15M3[t] -2.93422111390953e-15M4[t] -2.67617627599303e-15M5[t] + 1.68530062700541e-15M6[t] -7.7618351318877e-16M7[t] -8.7509539066569e-16M8[t] -1.06494618745309e-15M9[t] -1.50208739479063e-15M10[t] -4.61356709651356e-16M11[t] -1.42183864708931e-17t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  -1.26945508398474e-14 -2.22449408643527e-17X[t] -5.7380076558157e-17Y1[t] +  1.99131271067708e-16Y2[t] -5.19124269428038e-16Y3[t] +  1Y4[t] -1.36208603520825e-15M1[t] +  7.14770130673045e-16M2[t] -2.34701997419123e-15M3[t] -2.93422111390953e-15M4[t] -2.67617627599303e-15M5[t] +  1.68530062700541e-15M6[t] -7.7618351318877e-16M7[t] -8.7509539066569e-16M8[t] -1.06494618745309e-15M9[t] -1.50208739479063e-15M10[t] -4.61356709651356e-16M11[t] -1.42183864708931e-17t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58361&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  -1.26945508398474e-14 -2.22449408643527e-17X[t] -5.7380076558157e-17Y1[t] +  1.99131271067708e-16Y2[t] -5.19124269428038e-16Y3[t] +  1Y4[t] -1.36208603520825e-15M1[t] +  7.14770130673045e-16M2[t] -2.34701997419123e-15M3[t] -2.93422111390953e-15M4[t] -2.67617627599303e-15M5[t] +  1.68530062700541e-15M6[t] -7.7618351318877e-16M7[t] -8.7509539066569e-16M8[t] -1.06494618745309e-15M9[t] -1.50208739479063e-15M10[t] -4.61356709651356e-16M11[t] -1.42183864708931e-17t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58361&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58361&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] = -1.26945508398474e-14 -2.22449408643527e-17X[t] -5.7380076558157e-17Y1[t] + 1.99131271067708e-16Y2[t] -5.19124269428038e-16Y3[t] + 1Y4[t] -1.36208603520825e-15M1[t] + 7.14770130673045e-16M2[t] -2.34701997419123e-15M3[t] -2.93422111390953e-15M4[t] -2.67617627599303e-15M5[t] + 1.68530062700541e-15M6[t] -7.7618351318877e-16M7[t] -8.7509539066569e-16M8[t] -1.06494618745309e-15M9[t] -1.50208739479063e-15M10[t] -4.61356709651356e-16M11[t] -1.42183864708931e-17t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1.26945508398474e-140-0.91920.3636360.181818
X-2.22449408643527e-170-0.15480.8778050.438903
Y1-5.7380076558157e-170-0.68170.4994440.249722
Y21.99131271067708e-1601.55470.1280930.064046
Y3-5.19124269428038e-160-4.13270.0001849.2e-05
Y4101232649587414302800
M1-1.36208603520825e-150-0.41740.6786650.339332
M27.14770130673045e-1600.19570.8458730.422937
M3-2.34701997419123e-150-0.48130.6329750.316488
M4-2.93422111390953e-150-0.50080.6193180.309659
M5-2.67617627599303e-150-0.49120.6260070.313004
M61.68530062700541e-1500.39770.6930120.346506
M7-7.7618351318877e-160-0.21730.8291390.41457
M8-8.7509539066569e-160-0.25810.7976780.398839
M9-1.06494618745309e-150-0.3390.7364290.368214
M10-1.50208739479063e-150-0.48620.6295760.314788
M11-4.61356709651356e-160-0.16850.8670370.433519
t-1.42183864708931e-170-0.18090.8574110.428705

\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) & -1.26945508398474e-14 & 0 & -0.9192 & 0.363636 & 0.181818 \tabularnewline
X & -2.22449408643527e-17 & 0 & -0.1548 & 0.877805 & 0.438903 \tabularnewline
Y1 & -5.7380076558157e-17 & 0 & -0.6817 & 0.499444 & 0.249722 \tabularnewline
Y2 & 1.99131271067708e-16 & 0 & 1.5547 & 0.128093 & 0.064046 \tabularnewline
Y3 & -5.19124269428038e-16 & 0 & -4.1327 & 0.000184 & 9.2e-05 \tabularnewline
Y4 & 1 & 0 & 12326495874143028 & 0 & 0 \tabularnewline
M1 & -1.36208603520825e-15 & 0 & -0.4174 & 0.678665 & 0.339332 \tabularnewline
M2 & 7.14770130673045e-16 & 0 & 0.1957 & 0.845873 & 0.422937 \tabularnewline
M3 & -2.34701997419123e-15 & 0 & -0.4813 & 0.632975 & 0.316488 \tabularnewline
M4 & -2.93422111390953e-15 & 0 & -0.5008 & 0.619318 & 0.309659 \tabularnewline
M5 & -2.67617627599303e-15 & 0 & -0.4912 & 0.626007 & 0.313004 \tabularnewline
M6 & 1.68530062700541e-15 & 0 & 0.3977 & 0.693012 & 0.346506 \tabularnewline
M7 & -7.7618351318877e-16 & 0 & -0.2173 & 0.829139 & 0.41457 \tabularnewline
M8 & -8.7509539066569e-16 & 0 & -0.2581 & 0.797678 & 0.398839 \tabularnewline
M9 & -1.06494618745309e-15 & 0 & -0.339 & 0.736429 & 0.368214 \tabularnewline
M10 & -1.50208739479063e-15 & 0 & -0.4862 & 0.629576 & 0.314788 \tabularnewline
M11 & -4.61356709651356e-16 & 0 & -0.1685 & 0.867037 & 0.433519 \tabularnewline
t & -1.42183864708931e-17 & 0 & -0.1809 & 0.857411 & 0.428705 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58361&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]-1.26945508398474e-14[/C][C]0[/C][C]-0.9192[/C][C]0.363636[/C][C]0.181818[/C][/ROW]
[ROW][C]X[/C][C]-2.22449408643527e-17[/C][C]0[/C][C]-0.1548[/C][C]0.877805[/C][C]0.438903[/C][/ROW]
[ROW][C]Y1[/C][C]-5.7380076558157e-17[/C][C]0[/C][C]-0.6817[/C][C]0.499444[/C][C]0.249722[/C][/ROW]
[ROW][C]Y2[/C][C]1.99131271067708e-16[/C][C]0[/C][C]1.5547[/C][C]0.128093[/C][C]0.064046[/C][/ROW]
[ROW][C]Y3[/C][C]-5.19124269428038e-16[/C][C]0[/C][C]-4.1327[/C][C]0.000184[/C][C]9.2e-05[/C][/ROW]
[ROW][C]Y4[/C][C]1[/C][C]0[/C][C]12326495874143028[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-1.36208603520825e-15[/C][C]0[/C][C]-0.4174[/C][C]0.678665[/C][C]0.339332[/C][/ROW]
[ROW][C]M2[/C][C]7.14770130673045e-16[/C][C]0[/C][C]0.1957[/C][C]0.845873[/C][C]0.422937[/C][/ROW]
[ROW][C]M3[/C][C]-2.34701997419123e-15[/C][C]0[/C][C]-0.4813[/C][C]0.632975[/C][C]0.316488[/C][/ROW]
[ROW][C]M4[/C][C]-2.93422111390953e-15[/C][C]0[/C][C]-0.5008[/C][C]0.619318[/C][C]0.309659[/C][/ROW]
[ROW][C]M5[/C][C]-2.67617627599303e-15[/C][C]0[/C][C]-0.4912[/C][C]0.626007[/C][C]0.313004[/C][/ROW]
[ROW][C]M6[/C][C]1.68530062700541e-15[/C][C]0[/C][C]0.3977[/C][C]0.693012[/C][C]0.346506[/C][/ROW]
[ROW][C]M7[/C][C]-7.7618351318877e-16[/C][C]0[/C][C]-0.2173[/C][C]0.829139[/C][C]0.41457[/C][/ROW]
[ROW][C]M8[/C][C]-8.7509539066569e-16[/C][C]0[/C][C]-0.2581[/C][C]0.797678[/C][C]0.398839[/C][/ROW]
[ROW][C]M9[/C][C]-1.06494618745309e-15[/C][C]0[/C][C]-0.339[/C][C]0.736429[/C][C]0.368214[/C][/ROW]
[ROW][C]M10[/C][C]-1.50208739479063e-15[/C][C]0[/C][C]-0.4862[/C][C]0.629576[/C][C]0.314788[/C][/ROW]
[ROW][C]M11[/C][C]-4.61356709651356e-16[/C][C]0[/C][C]-0.1685[/C][C]0.867037[/C][C]0.433519[/C][/ROW]
[ROW][C]t[/C][C]-1.42183864708931e-17[/C][C]0[/C][C]-0.1809[/C][C]0.857411[/C][C]0.428705[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58361&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58361&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)-1.26945508398474e-140-0.91920.3636360.181818
X-2.22449408643527e-170-0.15480.8778050.438903
Y1-5.7380076558157e-170-0.68170.4994440.249722
Y21.99131271067708e-1601.55470.1280930.064046
Y3-5.19124269428038e-160-4.13270.0001849.2e-05
Y4101232649587414302800
M1-1.36208603520825e-150-0.41740.6786650.339332
M27.14770130673045e-1600.19570.8458730.422937
M3-2.34701997419123e-150-0.48130.6329750.316488
M4-2.93422111390953e-150-0.50080.6193180.309659
M5-2.67617627599303e-150-0.49120.6260070.313004
M61.68530062700541e-1500.39770.6930120.346506
M7-7.7618351318877e-160-0.21730.8291390.41457
M8-8.7509539066569e-160-0.25810.7976780.398839
M9-1.06494618745309e-150-0.3390.7364290.368214
M10-1.50208739479063e-150-0.48620.6295760.314788
M11-4.61356709651356e-160-0.16850.8670370.433519
t-1.42183864708931e-170-0.18090.8574110.428705







Multiple Linear Regression - Regression Statistics
Multiple R1
R-squared1
Adjusted R-squared1
F-TEST (value)2.80584054414827e+32
F-TEST (DF numerator)17
F-TEST (DF denominator)39
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.91412542240633e-15
Sum Squared Residuals3.3119295212308e-28

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 1 \tabularnewline
R-squared & 1 \tabularnewline
Adjusted R-squared & 1 \tabularnewline
F-TEST (value) & 2.80584054414827e+32 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 39 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.91412542240633e-15 \tabularnewline
Sum Squared Residuals & 3.3119295212308e-28 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58361&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]1[/C][/ROW]
[ROW][C]R-squared[/C][C]1[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.80584054414827e+32[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]39[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.91412542240633e-15[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3.3119295212308e-28[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58361&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58361&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 R1
R-squared1
Adjusted R-squared1
F-TEST (value)2.80584054414827e+32
F-TEST (DF numerator)17
F-TEST (DF denominator)39
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.91412542240633e-15
Sum Squared Residuals3.3119295212308e-28







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1423423-1.5756660649759e-15
24274272.05826417672047e-15
3441441-1.37862820027747e-15
4449449-1.21580093961209e-15
5452452-2.02414321847352e-15
64624621.47573744853726e-14
7455455-6.54641376215501e-16
8461461-1.1016798355215e-15
9461461-7.06789786257863e-16
104634631.20517493284047e-16
11462462-1.82892527268994e-15
12456456-3.60995230708738e-16
13455455-1.84022052772984e-16
14456456-4.79024480723306e-16
15472472-2.41970383018809e-16
164724722.50641779754823e-16
17471471-2.64946915677257e-16
18465465-4.05587282464333e-15
19459459-5.83982853098776e-16
20465465-5.97858284924235e-16
21468468-3.17149285580468e-16
224674675.58347472896395e-16
234634638.4947621776597e-16
244604602.63288391538352e-16
254624622.68497714247626e-15
26461461-3.30552625562589e-16
27476476-4.04822303853878e-16
284764764.59532949691034e-16
29471471-4.75808655795954e-16
30453453-4.31759982777839e-15
31443443-4.62580974135499e-16
32442442-2.14530336827656e-16
33444444-1.13820253539286e-16
344384381.96873641622148e-16
35427427-3.3488550983607e-16
364244243.11964772464467e-16
37416416-1.90394293208803e-15
384064065.09484047473316e-16
394314314.111946569591e-16
40434434-6.64743970712905e-16
414184181.06155131207386e-15
42412412-4.05772806784273e-15
434044041.41899780458316e-16
444094095.25470132693299e-17
454124122.81578831769808e-16
46406406-8.75738607802591e-16
473983981.31433456476004e-15
48397397-2.14257933294079e-16
493853859.7865390736065e-16
50390390-1.75817111790789e-15
514134131.61422623019105e-15
524134131.17037018087915e-15
534014011.70334747787287e-15
54397397-2.32617376510813e-15
553973971.55930542299146e-15
564094091.86152144400406e-15
574194198.5618049360781e-16

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 423 & 423 & -1.5756660649759e-15 \tabularnewline
2 & 427 & 427 & 2.05826417672047e-15 \tabularnewline
3 & 441 & 441 & -1.37862820027747e-15 \tabularnewline
4 & 449 & 449 & -1.21580093961209e-15 \tabularnewline
5 & 452 & 452 & -2.02414321847352e-15 \tabularnewline
6 & 462 & 462 & 1.47573744853726e-14 \tabularnewline
7 & 455 & 455 & -6.54641376215501e-16 \tabularnewline
8 & 461 & 461 & -1.1016798355215e-15 \tabularnewline
9 & 461 & 461 & -7.06789786257863e-16 \tabularnewline
10 & 463 & 463 & 1.20517493284047e-16 \tabularnewline
11 & 462 & 462 & -1.82892527268994e-15 \tabularnewline
12 & 456 & 456 & -3.60995230708738e-16 \tabularnewline
13 & 455 & 455 & -1.84022052772984e-16 \tabularnewline
14 & 456 & 456 & -4.79024480723306e-16 \tabularnewline
15 & 472 & 472 & -2.41970383018809e-16 \tabularnewline
16 & 472 & 472 & 2.50641779754823e-16 \tabularnewline
17 & 471 & 471 & -2.64946915677257e-16 \tabularnewline
18 & 465 & 465 & -4.05587282464333e-15 \tabularnewline
19 & 459 & 459 & -5.83982853098776e-16 \tabularnewline
20 & 465 & 465 & -5.97858284924235e-16 \tabularnewline
21 & 468 & 468 & -3.17149285580468e-16 \tabularnewline
22 & 467 & 467 & 5.58347472896395e-16 \tabularnewline
23 & 463 & 463 & 8.4947621776597e-16 \tabularnewline
24 & 460 & 460 & 2.63288391538352e-16 \tabularnewline
25 & 462 & 462 & 2.68497714247626e-15 \tabularnewline
26 & 461 & 461 & -3.30552625562589e-16 \tabularnewline
27 & 476 & 476 & -4.04822303853878e-16 \tabularnewline
28 & 476 & 476 & 4.59532949691034e-16 \tabularnewline
29 & 471 & 471 & -4.75808655795954e-16 \tabularnewline
30 & 453 & 453 & -4.31759982777839e-15 \tabularnewline
31 & 443 & 443 & -4.62580974135499e-16 \tabularnewline
32 & 442 & 442 & -2.14530336827656e-16 \tabularnewline
33 & 444 & 444 & -1.13820253539286e-16 \tabularnewline
34 & 438 & 438 & 1.96873641622148e-16 \tabularnewline
35 & 427 & 427 & -3.3488550983607e-16 \tabularnewline
36 & 424 & 424 & 3.11964772464467e-16 \tabularnewline
37 & 416 & 416 & -1.90394293208803e-15 \tabularnewline
38 & 406 & 406 & 5.09484047473316e-16 \tabularnewline
39 & 431 & 431 & 4.111946569591e-16 \tabularnewline
40 & 434 & 434 & -6.64743970712905e-16 \tabularnewline
41 & 418 & 418 & 1.06155131207386e-15 \tabularnewline
42 & 412 & 412 & -4.05772806784273e-15 \tabularnewline
43 & 404 & 404 & 1.41899780458316e-16 \tabularnewline
44 & 409 & 409 & 5.25470132693299e-17 \tabularnewline
45 & 412 & 412 & 2.81578831769808e-16 \tabularnewline
46 & 406 & 406 & -8.75738607802591e-16 \tabularnewline
47 & 398 & 398 & 1.31433456476004e-15 \tabularnewline
48 & 397 & 397 & -2.14257933294079e-16 \tabularnewline
49 & 385 & 385 & 9.7865390736065e-16 \tabularnewline
50 & 390 & 390 & -1.75817111790789e-15 \tabularnewline
51 & 413 & 413 & 1.61422623019105e-15 \tabularnewline
52 & 413 & 413 & 1.17037018087915e-15 \tabularnewline
53 & 401 & 401 & 1.70334747787287e-15 \tabularnewline
54 & 397 & 397 & -2.32617376510813e-15 \tabularnewline
55 & 397 & 397 & 1.55930542299146e-15 \tabularnewline
56 & 409 & 409 & 1.86152144400406e-15 \tabularnewline
57 & 419 & 419 & 8.5618049360781e-16 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58361&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]423[/C][C]423[/C][C]-1.5756660649759e-15[/C][/ROW]
[ROW][C]2[/C][C]427[/C][C]427[/C][C]2.05826417672047e-15[/C][/ROW]
[ROW][C]3[/C][C]441[/C][C]441[/C][C]-1.37862820027747e-15[/C][/ROW]
[ROW][C]4[/C][C]449[/C][C]449[/C][C]-1.21580093961209e-15[/C][/ROW]
[ROW][C]5[/C][C]452[/C][C]452[/C][C]-2.02414321847352e-15[/C][/ROW]
[ROW][C]6[/C][C]462[/C][C]462[/C][C]1.47573744853726e-14[/C][/ROW]
[ROW][C]7[/C][C]455[/C][C]455[/C][C]-6.54641376215501e-16[/C][/ROW]
[ROW][C]8[/C][C]461[/C][C]461[/C][C]-1.1016798355215e-15[/C][/ROW]
[ROW][C]9[/C][C]461[/C][C]461[/C][C]-7.06789786257863e-16[/C][/ROW]
[ROW][C]10[/C][C]463[/C][C]463[/C][C]1.20517493284047e-16[/C][/ROW]
[ROW][C]11[/C][C]462[/C][C]462[/C][C]-1.82892527268994e-15[/C][/ROW]
[ROW][C]12[/C][C]456[/C][C]456[/C][C]-3.60995230708738e-16[/C][/ROW]
[ROW][C]13[/C][C]455[/C][C]455[/C][C]-1.84022052772984e-16[/C][/ROW]
[ROW][C]14[/C][C]456[/C][C]456[/C][C]-4.79024480723306e-16[/C][/ROW]
[ROW][C]15[/C][C]472[/C][C]472[/C][C]-2.41970383018809e-16[/C][/ROW]
[ROW][C]16[/C][C]472[/C][C]472[/C][C]2.50641779754823e-16[/C][/ROW]
[ROW][C]17[/C][C]471[/C][C]471[/C][C]-2.64946915677257e-16[/C][/ROW]
[ROW][C]18[/C][C]465[/C][C]465[/C][C]-4.05587282464333e-15[/C][/ROW]
[ROW][C]19[/C][C]459[/C][C]459[/C][C]-5.83982853098776e-16[/C][/ROW]
[ROW][C]20[/C][C]465[/C][C]465[/C][C]-5.97858284924235e-16[/C][/ROW]
[ROW][C]21[/C][C]468[/C][C]468[/C][C]-3.17149285580468e-16[/C][/ROW]
[ROW][C]22[/C][C]467[/C][C]467[/C][C]5.58347472896395e-16[/C][/ROW]
[ROW][C]23[/C][C]463[/C][C]463[/C][C]8.4947621776597e-16[/C][/ROW]
[ROW][C]24[/C][C]460[/C][C]460[/C][C]2.63288391538352e-16[/C][/ROW]
[ROW][C]25[/C][C]462[/C][C]462[/C][C]2.68497714247626e-15[/C][/ROW]
[ROW][C]26[/C][C]461[/C][C]461[/C][C]-3.30552625562589e-16[/C][/ROW]
[ROW][C]27[/C][C]476[/C][C]476[/C][C]-4.04822303853878e-16[/C][/ROW]
[ROW][C]28[/C][C]476[/C][C]476[/C][C]4.59532949691034e-16[/C][/ROW]
[ROW][C]29[/C][C]471[/C][C]471[/C][C]-4.75808655795954e-16[/C][/ROW]
[ROW][C]30[/C][C]453[/C][C]453[/C][C]-4.31759982777839e-15[/C][/ROW]
[ROW][C]31[/C][C]443[/C][C]443[/C][C]-4.62580974135499e-16[/C][/ROW]
[ROW][C]32[/C][C]442[/C][C]442[/C][C]-2.14530336827656e-16[/C][/ROW]
[ROW][C]33[/C][C]444[/C][C]444[/C][C]-1.13820253539286e-16[/C][/ROW]
[ROW][C]34[/C][C]438[/C][C]438[/C][C]1.96873641622148e-16[/C][/ROW]
[ROW][C]35[/C][C]427[/C][C]427[/C][C]-3.3488550983607e-16[/C][/ROW]
[ROW][C]36[/C][C]424[/C][C]424[/C][C]3.11964772464467e-16[/C][/ROW]
[ROW][C]37[/C][C]416[/C][C]416[/C][C]-1.90394293208803e-15[/C][/ROW]
[ROW][C]38[/C][C]406[/C][C]406[/C][C]5.09484047473316e-16[/C][/ROW]
[ROW][C]39[/C][C]431[/C][C]431[/C][C]4.111946569591e-16[/C][/ROW]
[ROW][C]40[/C][C]434[/C][C]434[/C][C]-6.64743970712905e-16[/C][/ROW]
[ROW][C]41[/C][C]418[/C][C]418[/C][C]1.06155131207386e-15[/C][/ROW]
[ROW][C]42[/C][C]412[/C][C]412[/C][C]-4.05772806784273e-15[/C][/ROW]
[ROW][C]43[/C][C]404[/C][C]404[/C][C]1.41899780458316e-16[/C][/ROW]
[ROW][C]44[/C][C]409[/C][C]409[/C][C]5.25470132693299e-17[/C][/ROW]
[ROW][C]45[/C][C]412[/C][C]412[/C][C]2.81578831769808e-16[/C][/ROW]
[ROW][C]46[/C][C]406[/C][C]406[/C][C]-8.75738607802591e-16[/C][/ROW]
[ROW][C]47[/C][C]398[/C][C]398[/C][C]1.31433456476004e-15[/C][/ROW]
[ROW][C]48[/C][C]397[/C][C]397[/C][C]-2.14257933294079e-16[/C][/ROW]
[ROW][C]49[/C][C]385[/C][C]385[/C][C]9.7865390736065e-16[/C][/ROW]
[ROW][C]50[/C][C]390[/C][C]390[/C][C]-1.75817111790789e-15[/C][/ROW]
[ROW][C]51[/C][C]413[/C][C]413[/C][C]1.61422623019105e-15[/C][/ROW]
[ROW][C]52[/C][C]413[/C][C]413[/C][C]1.17037018087915e-15[/C][/ROW]
[ROW][C]53[/C][C]401[/C][C]401[/C][C]1.70334747787287e-15[/C][/ROW]
[ROW][C]54[/C][C]397[/C][C]397[/C][C]-2.32617376510813e-15[/C][/ROW]
[ROW][C]55[/C][C]397[/C][C]397[/C][C]1.55930542299146e-15[/C][/ROW]
[ROW][C]56[/C][C]409[/C][C]409[/C][C]1.86152144400406e-15[/C][/ROW]
[ROW][C]57[/C][C]419[/C][C]419[/C][C]8.5618049360781e-16[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58361&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58361&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
1423423-1.5756660649759e-15
24274272.05826417672047e-15
3441441-1.37862820027747e-15
4449449-1.21580093961209e-15
5452452-2.02414321847352e-15
64624621.47573744853726e-14
7455455-6.54641376215501e-16
8461461-1.1016798355215e-15
9461461-7.06789786257863e-16
104634631.20517493284047e-16
11462462-1.82892527268994e-15
12456456-3.60995230708738e-16
13455455-1.84022052772984e-16
14456456-4.79024480723306e-16
15472472-2.41970383018809e-16
164724722.50641779754823e-16
17471471-2.64946915677257e-16
18465465-4.05587282464333e-15
19459459-5.83982853098776e-16
20465465-5.97858284924235e-16
21468468-3.17149285580468e-16
224674675.58347472896395e-16
234634638.4947621776597e-16
244604602.63288391538352e-16
254624622.68497714247626e-15
26461461-3.30552625562589e-16
27476476-4.04822303853878e-16
284764764.59532949691034e-16
29471471-4.75808655795954e-16
30453453-4.31759982777839e-15
31443443-4.62580974135499e-16
32442442-2.14530336827656e-16
33444444-1.13820253539286e-16
344384381.96873641622148e-16
35427427-3.3488550983607e-16
364244243.11964772464467e-16
37416416-1.90394293208803e-15
384064065.09484047473316e-16
394314314.111946569591e-16
40434434-6.64743970712905e-16
414184181.06155131207386e-15
42412412-4.05772806784273e-15
434044041.41899780458316e-16
444094095.25470132693299e-17
454124122.81578831769808e-16
46406406-8.75738607802591e-16
473983981.31433456476004e-15
48397397-2.14257933294079e-16
493853859.7865390736065e-16
50390390-1.75817111790789e-15
514134131.61422623019105e-15
524134131.17037018087915e-15
534014011.70334747787287e-15
54397397-2.32617376510813e-15
553973971.55930542299146e-15
564094091.86152144400406e-15
574194198.5618049360781e-16







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.02204133304504480.04408266609008960.977958666954955
220.05544755032004960.1108951006400990.94455244967995
230.6972602872193680.6054794255612640.302739712780632
240.4561400051135390.9122800102270780.543859994886461
250.0002108171261063380.0004216342522126770.999789182873894
260.09482377793388870.1896475558677770.905176222066111
271.51425479954453e-053.02850959908905e-050.999984857452004
280.9783625873401820.04327482531963570.0216374126598178
290.1580517246664370.3161034493328750.841948275333563
300.911058768033980.1778824639320390.0889412319660196
310.999789823366550.0004203532668987110.000210176633449356
322.12272602757383e-094.24545205514767e-090.999999997877274
330.9835637554335180.03287248913296450.0164362445664823
340.3319484877319290.6638969754638580.668051512268071
350.9373985625196420.1252028749607160.0626014374803581
36100

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.0220413330450448 & 0.0440826660900896 & 0.977958666954955 \tabularnewline
22 & 0.0554475503200496 & 0.110895100640099 & 0.94455244967995 \tabularnewline
23 & 0.697260287219368 & 0.605479425561264 & 0.302739712780632 \tabularnewline
24 & 0.456140005113539 & 0.912280010227078 & 0.543859994886461 \tabularnewline
25 & 0.000210817126106338 & 0.000421634252212677 & 0.999789182873894 \tabularnewline
26 & 0.0948237779338887 & 0.189647555867777 & 0.905176222066111 \tabularnewline
27 & 1.51425479954453e-05 & 3.02850959908905e-05 & 0.999984857452004 \tabularnewline
28 & 0.978362587340182 & 0.0432748253196357 & 0.0216374126598178 \tabularnewline
29 & 0.158051724666437 & 0.316103449332875 & 0.841948275333563 \tabularnewline
30 & 0.91105876803398 & 0.177882463932039 & 0.0889412319660196 \tabularnewline
31 & 0.99978982336655 & 0.000420353266898711 & 0.000210176633449356 \tabularnewline
32 & 2.12272602757383e-09 & 4.24545205514767e-09 & 0.999999997877274 \tabularnewline
33 & 0.983563755433518 & 0.0328724891329645 & 0.0164362445664823 \tabularnewline
34 & 0.331948487731929 & 0.663896975463858 & 0.668051512268071 \tabularnewline
35 & 0.937398562519642 & 0.125202874960716 & 0.0626014374803581 \tabularnewline
36 & 1 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58361&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]21[/C][C]0.0220413330450448[/C][C]0.0440826660900896[/C][C]0.977958666954955[/C][/ROW]
[ROW][C]22[/C][C]0.0554475503200496[/C][C]0.110895100640099[/C][C]0.94455244967995[/C][/ROW]
[ROW][C]23[/C][C]0.697260287219368[/C][C]0.605479425561264[/C][C]0.302739712780632[/C][/ROW]
[ROW][C]24[/C][C]0.456140005113539[/C][C]0.912280010227078[/C][C]0.543859994886461[/C][/ROW]
[ROW][C]25[/C][C]0.000210817126106338[/C][C]0.000421634252212677[/C][C]0.999789182873894[/C][/ROW]
[ROW][C]26[/C][C]0.0948237779338887[/C][C]0.189647555867777[/C][C]0.905176222066111[/C][/ROW]
[ROW][C]27[/C][C]1.51425479954453e-05[/C][C]3.02850959908905e-05[/C][C]0.999984857452004[/C][/ROW]
[ROW][C]28[/C][C]0.978362587340182[/C][C]0.0432748253196357[/C][C]0.0216374126598178[/C][/ROW]
[ROW][C]29[/C][C]0.158051724666437[/C][C]0.316103449332875[/C][C]0.841948275333563[/C][/ROW]
[ROW][C]30[/C][C]0.91105876803398[/C][C]0.177882463932039[/C][C]0.0889412319660196[/C][/ROW]
[ROW][C]31[/C][C]0.99978982336655[/C][C]0.000420353266898711[/C][C]0.000210176633449356[/C][/ROW]
[ROW][C]32[/C][C]2.12272602757383e-09[/C][C]4.24545205514767e-09[/C][C]0.999999997877274[/C][/ROW]
[ROW][C]33[/C][C]0.983563755433518[/C][C]0.0328724891329645[/C][C]0.0164362445664823[/C][/ROW]
[ROW][C]34[/C][C]0.331948487731929[/C][C]0.663896975463858[/C][C]0.668051512268071[/C][/ROW]
[ROW][C]35[/C][C]0.937398562519642[/C][C]0.125202874960716[/C][C]0.0626014374803581[/C][/ROW]
[ROW][C]36[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58361&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58361&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
210.02204133304504480.04408266609008960.977958666954955
220.05544755032004960.1108951006400990.94455244967995
230.6972602872193680.6054794255612640.302739712780632
240.4561400051135390.9122800102270780.543859994886461
250.0002108171261063380.0004216342522126770.999789182873894
260.09482377793388870.1896475558677770.905176222066111
271.51425479954453e-053.02850959908905e-050.999984857452004
280.9783625873401820.04327482531963570.0216374126598178
290.1580517246664370.3161034493328750.841948275333563
300.911058768033980.1778824639320390.0889412319660196
310.999789823366550.0004203532668987110.000210176633449356
322.12272602757383e-094.24545205514767e-090.999999997877274
330.9835637554335180.03287248913296450.0164362445664823
340.3319484877319290.6638969754638580.668051512268071
350.9373985625196420.1252028749607160.0626014374803581
36100







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level50.3125NOK
5% type I error level80.5NOK
10% type I error level80.5NOK

\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 & 5 & 0.3125 & NOK \tabularnewline
5% type I error level & 8 & 0.5 & NOK \tabularnewline
10% type I error level & 8 & 0.5 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58361&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]5[/C][C]0.3125[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]8[/C][C]0.5[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]8[/C][C]0.5[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58361&T=6

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Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level50.3125NOK
5% type I error level80.5NOK
10% type I error level80.5NOK



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')
}