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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 09:03:46 -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/t12587330883yxo4o9ljdybd34.htm/, Retrieved Thu, 28 Mar 2024 13:09:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58294, Retrieved Thu, 28 Mar 2024 13:09:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact103
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] [Model 4] [2009-11-18 19:08:24] [9c2d53170eb755e9ae5fcf19d2174a32]
-   PD        [Multiple Regression] [Model_2] [2009-11-20 16:03:46] [82f29a5d509ab8039aab37a0145f886d] [Current]
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Dataseries X:
562	573
561	572
555	566
544	555
537	548
543	554
594	605
611	622
613	624
611	622
594	605
595	606
591	602
589	600
584	595
573	584
567	578
569	580
621	632
629	640
628	639
612	623
595	606
597	608
593	604
590	601
580	591
574	585
573	584
573	584
620	631
626	637
620	631
588	599
566	577
557	568
561	572
549	560
532	543
526	537
511	522
499	510
555	566
565	576
542	553
527	538
510	521
514	525
517	528
508	519
493	504
490	501
469	480
478	489
528	539
534	545
518	529
506	517
502	513
516	527
528	539




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58294&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58294&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58294&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = -10.9999999999998 + 1X[t] + 1.49768332069831e-13M1[t] + 3.15212593919212e-15M2[t] + 7.17426655440003e-17M3[t] + 1.26161441554288e-15M4[t] + 2.12539972606287e-15M5[t] + 2.66074608880097e-15M6[t] + 2.71458453422716e-15M7[t] -2.73366500849629e-15M8[t] + 4.2792421394502e-15M9[t] + 4.75800251784279e-16M10[t] + 1.29088614655903e-15M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  -10.9999999999998 +  1X[t] +  1.49768332069831e-13M1[t] +  3.15212593919212e-15M2[t] +  7.17426655440003e-17M3[t] +  1.26161441554288e-15M4[t] +  2.12539972606287e-15M5[t] +  2.66074608880097e-15M6[t] +  2.71458453422716e-15M7[t] -2.73366500849629e-15M8[t] +  4.2792421394502e-15M9[t] +  4.75800251784279e-16M10[t] +  1.29088614655903e-15M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58294&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  -10.9999999999998 +  1X[t] +  1.49768332069831e-13M1[t] +  3.15212593919212e-15M2[t] +  7.17426655440003e-17M3[t] +  1.26161441554288e-15M4[t] +  2.12539972606287e-15M5[t] +  2.66074608880097e-15M6[t] +  2.71458453422716e-15M7[t] -2.73366500849629e-15M8[t] +  4.2792421394502e-15M9[t] +  4.75800251784279e-16M10[t] +  1.29088614655903e-15M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58294&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58294&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] = -10.9999999999998 + 1X[t] + 1.49768332069831e-13M1[t] + 3.15212593919212e-15M2[t] + 7.17426655440003e-17M3[t] + 1.26161441554288e-15M4[t] + 2.12539972606287e-15M5[t] + 2.66074608880097e-15M6[t] + 2.71458453422716e-15M7[t] -2.73366500849629e-15M8[t] + 4.2792421394502e-15M9[t] + 4.75800251784279e-16M10[t] + 1.29088614655903e-15M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-10.99999999999980-46461366442033.200
X10245498026668437500
M11.49768332069831e-1302.10940.0401490.020074
M23.15212593919212e-1500.04250.9662730.483137
M37.17426655440003e-1700.0010.9992330.499616
M41.26161441554288e-1500.0170.9865370.493269
M52.12539972606287e-1500.02840.9774520.488726
M62.66074608880097e-1500.03560.9717550.485878
M72.71458453422716e-1500.03620.971280.48564
M8-2.73366500849629e-150-0.03610.9713350.485668
M94.2792421394502e-1500.0570.9547630.477382
M104.75800251784279e-1600.00640.994920.49746
M111.29088614655903e-1500.01740.9861830.493091

\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) & -10.9999999999998 & 0 & -46461366442033.2 & 0 & 0 \tabularnewline
X & 1 & 0 & 2454980266684375 & 0 & 0 \tabularnewline
M1 & 1.49768332069831e-13 & 0 & 2.1094 & 0.040149 & 0.020074 \tabularnewline
M2 & 3.15212593919212e-15 & 0 & 0.0425 & 0.966273 & 0.483137 \tabularnewline
M3 & 7.17426655440003e-17 & 0 & 0.001 & 0.999233 & 0.499616 \tabularnewline
M4 & 1.26161441554288e-15 & 0 & 0.017 & 0.986537 & 0.493269 \tabularnewline
M5 & 2.12539972606287e-15 & 0 & 0.0284 & 0.977452 & 0.488726 \tabularnewline
M6 & 2.66074608880097e-15 & 0 & 0.0356 & 0.971755 & 0.485878 \tabularnewline
M7 & 2.71458453422716e-15 & 0 & 0.0362 & 0.97128 & 0.48564 \tabularnewline
M8 & -2.73366500849629e-15 & 0 & -0.0361 & 0.971335 & 0.485668 \tabularnewline
M9 & 4.2792421394502e-15 & 0 & 0.057 & 0.954763 & 0.477382 \tabularnewline
M10 & 4.75800251784279e-16 & 0 & 0.0064 & 0.99492 & 0.49746 \tabularnewline
M11 & 1.29088614655903e-15 & 0 & 0.0174 & 0.986183 & 0.493091 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58294&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]-10.9999999999998[/C][C]0[/C][C]-46461366442033.2[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]1[/C][C]0[/C][C]2454980266684375[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]1.49768332069831e-13[/C][C]0[/C][C]2.1094[/C][C]0.040149[/C][C]0.020074[/C][/ROW]
[ROW][C]M2[/C][C]3.15212593919212e-15[/C][C]0[/C][C]0.0425[/C][C]0.966273[/C][C]0.483137[/C][/ROW]
[ROW][C]M3[/C][C]7.17426655440003e-17[/C][C]0[/C][C]0.001[/C][C]0.999233[/C][C]0.499616[/C][/ROW]
[ROW][C]M4[/C][C]1.26161441554288e-15[/C][C]0[/C][C]0.017[/C][C]0.986537[/C][C]0.493269[/C][/ROW]
[ROW][C]M5[/C][C]2.12539972606287e-15[/C][C]0[/C][C]0.0284[/C][C]0.977452[/C][C]0.488726[/C][/ROW]
[ROW][C]M6[/C][C]2.66074608880097e-15[/C][C]0[/C][C]0.0356[/C][C]0.971755[/C][C]0.485878[/C][/ROW]
[ROW][C]M7[/C][C]2.71458453422716e-15[/C][C]0[/C][C]0.0362[/C][C]0.97128[/C][C]0.48564[/C][/ROW]
[ROW][C]M8[/C][C]-2.73366500849629e-15[/C][C]0[/C][C]-0.0361[/C][C]0.971335[/C][C]0.485668[/C][/ROW]
[ROW][C]M9[/C][C]4.2792421394502e-15[/C][C]0[/C][C]0.057[/C][C]0.954763[/C][C]0.477382[/C][/ROW]
[ROW][C]M10[/C][C]4.75800251784279e-16[/C][C]0[/C][C]0.0064[/C][C]0.99492[/C][C]0.49746[/C][/ROW]
[ROW][C]M11[/C][C]1.29088614655903e-15[/C][C]0[/C][C]0.0174[/C][C]0.986183[/C][C]0.493091[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58294&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58294&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)-10.99999999999980-46461366442033.200
X10245498026668437500
M11.49768332069831e-1302.10940.0401490.020074
M23.15212593919212e-1500.04250.9662730.483137
M37.17426655440003e-1700.0010.9992330.499616
M41.26161441554288e-1500.0170.9865370.493269
M52.12539972606287e-1500.02840.9774520.488726
M62.66074608880097e-1500.03560.9717550.485878
M72.71458453422716e-1500.03620.971280.48564
M8-2.73366500849629e-150-0.03610.9713350.485668
M94.2792421394502e-1500.0570.9547630.477382
M104.75800251784279e-1600.00640.994920.49746
M111.29088614655903e-1500.01740.9861830.493091







Multiple Linear Regression - Regression Statistics
Multiple R1
R-squared1
Adjusted R-squared1
F-TEST (value)6.36136484099676e+29
F-TEST (DF numerator)12
F-TEST (DF denominator)48
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.17235962979347e-13
Sum Squared Residuals6.5972500875335e-25

\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) & 6.36136484099676e+29 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 48 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.17235962979347e-13 \tabularnewline
Sum Squared Residuals & 6.5972500875335e-25 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58294&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]6.36136484099676e+29[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]48[/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]1.17235962979347e-13[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]6.5972500875335e-25[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58294&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58294&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)6.36136484099676e+29
F-TEST (DF numerator)12
F-TEST (DF denominator)48
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.17235962979347e-13
Sum Squared Residuals6.5972500875335e-25







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1562561.9999999999997.41176493519634e-13
25615617.1728427640018e-17
35555551.36515492563589e-15
45445445.03722123418926e-16
5537537-3.21930930656747e-15
6543543-2.31893433227744e-15
75945949.49074390035476e-16
86116113.99178964404305e-16
9613613-2.1396493986529e-16
10611611-2.81028629587628e-15
115945942.37277277770357e-15
12595595-3.79460049030042e-15
13591591-1.48598890653482e-13
14589589-1.27702040891977e-15
155845841.48094707379684e-17
16573573-8.4662333147891e-16
17567567-4.81594719926861e-16
18569569-8.3442677688888e-16
19621621-1.47196379033856e-15
20629629-5.95179806091241e-15
216286281.59898156330514e-15
226126123.94230900476268e-15
23595595-5.08548663685942e-15
24597597-4.50026460422447e-15
25593593-1.52857268446207e-13
26590590-1.62985246588190e-15
27580580-2.12657598021436e-15
28574574-1.19945538844097e-15
29573573-1.71040864199892e-15
30573573-2.24575500473702e-15
31620620-1.11913173337654e-15
32626626-4.8933018900263e-15
33620620-2.68378933859956e-15
345885881.75213733545007e-15
35566566-1.82427503161073e-16
365575572.06350110680601e-15
37561561-1.48227980771173e-13
385495494.01763993760036e-16
395325324.15122171756194e-15
405265265.0783423093354e-15
415115112.40161049564492e-15
424994996.10024881645128e-15
43555555-1.27768694304731e-15
445655654.1949557088563e-15
45542542-3.58459832596457e-15
46527527-1.59410294146905e-15
475105103.58895613211083e-15
485145143.46851405082166e-15
49517517-1.47358314189895e-13
505085082.43338045340181e-15
51493493-3.40461013372152e-15
52490490-3.53598571283429e-15
534694693.00970217284851e-15
54478478-7.0113270254792e-16
555285282.91970807672699e-15
565345346.25096527767821e-15
575185184.88337104112435e-15
58506506-1.29005710286727e-15
59502502-6.938147697939e-16
605165162.76284993689764e-15
61528528-1.44134039458876e-13

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 562 & 561.999999999999 & 7.41176493519634e-13 \tabularnewline
2 & 561 & 561 & 7.1728427640018e-17 \tabularnewline
3 & 555 & 555 & 1.36515492563589e-15 \tabularnewline
4 & 544 & 544 & 5.03722123418926e-16 \tabularnewline
5 & 537 & 537 & -3.21930930656747e-15 \tabularnewline
6 & 543 & 543 & -2.31893433227744e-15 \tabularnewline
7 & 594 & 594 & 9.49074390035476e-16 \tabularnewline
8 & 611 & 611 & 3.99178964404305e-16 \tabularnewline
9 & 613 & 613 & -2.1396493986529e-16 \tabularnewline
10 & 611 & 611 & -2.81028629587628e-15 \tabularnewline
11 & 594 & 594 & 2.37277277770357e-15 \tabularnewline
12 & 595 & 595 & -3.79460049030042e-15 \tabularnewline
13 & 591 & 591 & -1.48598890653482e-13 \tabularnewline
14 & 589 & 589 & -1.27702040891977e-15 \tabularnewline
15 & 584 & 584 & 1.48094707379684e-17 \tabularnewline
16 & 573 & 573 & -8.4662333147891e-16 \tabularnewline
17 & 567 & 567 & -4.81594719926861e-16 \tabularnewline
18 & 569 & 569 & -8.3442677688888e-16 \tabularnewline
19 & 621 & 621 & -1.47196379033856e-15 \tabularnewline
20 & 629 & 629 & -5.95179806091241e-15 \tabularnewline
21 & 628 & 628 & 1.59898156330514e-15 \tabularnewline
22 & 612 & 612 & 3.94230900476268e-15 \tabularnewline
23 & 595 & 595 & -5.08548663685942e-15 \tabularnewline
24 & 597 & 597 & -4.50026460422447e-15 \tabularnewline
25 & 593 & 593 & -1.52857268446207e-13 \tabularnewline
26 & 590 & 590 & -1.62985246588190e-15 \tabularnewline
27 & 580 & 580 & -2.12657598021436e-15 \tabularnewline
28 & 574 & 574 & -1.19945538844097e-15 \tabularnewline
29 & 573 & 573 & -1.71040864199892e-15 \tabularnewline
30 & 573 & 573 & -2.24575500473702e-15 \tabularnewline
31 & 620 & 620 & -1.11913173337654e-15 \tabularnewline
32 & 626 & 626 & -4.8933018900263e-15 \tabularnewline
33 & 620 & 620 & -2.68378933859956e-15 \tabularnewline
34 & 588 & 588 & 1.75213733545007e-15 \tabularnewline
35 & 566 & 566 & -1.82427503161073e-16 \tabularnewline
36 & 557 & 557 & 2.06350110680601e-15 \tabularnewline
37 & 561 & 561 & -1.48227980771173e-13 \tabularnewline
38 & 549 & 549 & 4.01763993760036e-16 \tabularnewline
39 & 532 & 532 & 4.15122171756194e-15 \tabularnewline
40 & 526 & 526 & 5.0783423093354e-15 \tabularnewline
41 & 511 & 511 & 2.40161049564492e-15 \tabularnewline
42 & 499 & 499 & 6.10024881645128e-15 \tabularnewline
43 & 555 & 555 & -1.27768694304731e-15 \tabularnewline
44 & 565 & 565 & 4.1949557088563e-15 \tabularnewline
45 & 542 & 542 & -3.58459832596457e-15 \tabularnewline
46 & 527 & 527 & -1.59410294146905e-15 \tabularnewline
47 & 510 & 510 & 3.58895613211083e-15 \tabularnewline
48 & 514 & 514 & 3.46851405082166e-15 \tabularnewline
49 & 517 & 517 & -1.47358314189895e-13 \tabularnewline
50 & 508 & 508 & 2.43338045340181e-15 \tabularnewline
51 & 493 & 493 & -3.40461013372152e-15 \tabularnewline
52 & 490 & 490 & -3.53598571283429e-15 \tabularnewline
53 & 469 & 469 & 3.00970217284851e-15 \tabularnewline
54 & 478 & 478 & -7.0113270254792e-16 \tabularnewline
55 & 528 & 528 & 2.91970807672699e-15 \tabularnewline
56 & 534 & 534 & 6.25096527767821e-15 \tabularnewline
57 & 518 & 518 & 4.88337104112435e-15 \tabularnewline
58 & 506 & 506 & -1.29005710286727e-15 \tabularnewline
59 & 502 & 502 & -6.938147697939e-16 \tabularnewline
60 & 516 & 516 & 2.76284993689764e-15 \tabularnewline
61 & 528 & 528 & -1.44134039458876e-13 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58294&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]562[/C][C]561.999999999999[/C][C]7.41176493519634e-13[/C][/ROW]
[ROW][C]2[/C][C]561[/C][C]561[/C][C]7.1728427640018e-17[/C][/ROW]
[ROW][C]3[/C][C]555[/C][C]555[/C][C]1.36515492563589e-15[/C][/ROW]
[ROW][C]4[/C][C]544[/C][C]544[/C][C]5.03722123418926e-16[/C][/ROW]
[ROW][C]5[/C][C]537[/C][C]537[/C][C]-3.21930930656747e-15[/C][/ROW]
[ROW][C]6[/C][C]543[/C][C]543[/C][C]-2.31893433227744e-15[/C][/ROW]
[ROW][C]7[/C][C]594[/C][C]594[/C][C]9.49074390035476e-16[/C][/ROW]
[ROW][C]8[/C][C]611[/C][C]611[/C][C]3.99178964404305e-16[/C][/ROW]
[ROW][C]9[/C][C]613[/C][C]613[/C][C]-2.1396493986529e-16[/C][/ROW]
[ROW][C]10[/C][C]611[/C][C]611[/C][C]-2.81028629587628e-15[/C][/ROW]
[ROW][C]11[/C][C]594[/C][C]594[/C][C]2.37277277770357e-15[/C][/ROW]
[ROW][C]12[/C][C]595[/C][C]595[/C][C]-3.79460049030042e-15[/C][/ROW]
[ROW][C]13[/C][C]591[/C][C]591[/C][C]-1.48598890653482e-13[/C][/ROW]
[ROW][C]14[/C][C]589[/C][C]589[/C][C]-1.27702040891977e-15[/C][/ROW]
[ROW][C]15[/C][C]584[/C][C]584[/C][C]1.48094707379684e-17[/C][/ROW]
[ROW][C]16[/C][C]573[/C][C]573[/C][C]-8.4662333147891e-16[/C][/ROW]
[ROW][C]17[/C][C]567[/C][C]567[/C][C]-4.81594719926861e-16[/C][/ROW]
[ROW][C]18[/C][C]569[/C][C]569[/C][C]-8.3442677688888e-16[/C][/ROW]
[ROW][C]19[/C][C]621[/C][C]621[/C][C]-1.47196379033856e-15[/C][/ROW]
[ROW][C]20[/C][C]629[/C][C]629[/C][C]-5.95179806091241e-15[/C][/ROW]
[ROW][C]21[/C][C]628[/C][C]628[/C][C]1.59898156330514e-15[/C][/ROW]
[ROW][C]22[/C][C]612[/C][C]612[/C][C]3.94230900476268e-15[/C][/ROW]
[ROW][C]23[/C][C]595[/C][C]595[/C][C]-5.08548663685942e-15[/C][/ROW]
[ROW][C]24[/C][C]597[/C][C]597[/C][C]-4.50026460422447e-15[/C][/ROW]
[ROW][C]25[/C][C]593[/C][C]593[/C][C]-1.52857268446207e-13[/C][/ROW]
[ROW][C]26[/C][C]590[/C][C]590[/C][C]-1.62985246588190e-15[/C][/ROW]
[ROW][C]27[/C][C]580[/C][C]580[/C][C]-2.12657598021436e-15[/C][/ROW]
[ROW][C]28[/C][C]574[/C][C]574[/C][C]-1.19945538844097e-15[/C][/ROW]
[ROW][C]29[/C][C]573[/C][C]573[/C][C]-1.71040864199892e-15[/C][/ROW]
[ROW][C]30[/C][C]573[/C][C]573[/C][C]-2.24575500473702e-15[/C][/ROW]
[ROW][C]31[/C][C]620[/C][C]620[/C][C]-1.11913173337654e-15[/C][/ROW]
[ROW][C]32[/C][C]626[/C][C]626[/C][C]-4.8933018900263e-15[/C][/ROW]
[ROW][C]33[/C][C]620[/C][C]620[/C][C]-2.68378933859956e-15[/C][/ROW]
[ROW][C]34[/C][C]588[/C][C]588[/C][C]1.75213733545007e-15[/C][/ROW]
[ROW][C]35[/C][C]566[/C][C]566[/C][C]-1.82427503161073e-16[/C][/ROW]
[ROW][C]36[/C][C]557[/C][C]557[/C][C]2.06350110680601e-15[/C][/ROW]
[ROW][C]37[/C][C]561[/C][C]561[/C][C]-1.48227980771173e-13[/C][/ROW]
[ROW][C]38[/C][C]549[/C][C]549[/C][C]4.01763993760036e-16[/C][/ROW]
[ROW][C]39[/C][C]532[/C][C]532[/C][C]4.15122171756194e-15[/C][/ROW]
[ROW][C]40[/C][C]526[/C][C]526[/C][C]5.0783423093354e-15[/C][/ROW]
[ROW][C]41[/C][C]511[/C][C]511[/C][C]2.40161049564492e-15[/C][/ROW]
[ROW][C]42[/C][C]499[/C][C]499[/C][C]6.10024881645128e-15[/C][/ROW]
[ROW][C]43[/C][C]555[/C][C]555[/C][C]-1.27768694304731e-15[/C][/ROW]
[ROW][C]44[/C][C]565[/C][C]565[/C][C]4.1949557088563e-15[/C][/ROW]
[ROW][C]45[/C][C]542[/C][C]542[/C][C]-3.58459832596457e-15[/C][/ROW]
[ROW][C]46[/C][C]527[/C][C]527[/C][C]-1.59410294146905e-15[/C][/ROW]
[ROW][C]47[/C][C]510[/C][C]510[/C][C]3.58895613211083e-15[/C][/ROW]
[ROW][C]48[/C][C]514[/C][C]514[/C][C]3.46851405082166e-15[/C][/ROW]
[ROW][C]49[/C][C]517[/C][C]517[/C][C]-1.47358314189895e-13[/C][/ROW]
[ROW][C]50[/C][C]508[/C][C]508[/C][C]2.43338045340181e-15[/C][/ROW]
[ROW][C]51[/C][C]493[/C][C]493[/C][C]-3.40461013372152e-15[/C][/ROW]
[ROW][C]52[/C][C]490[/C][C]490[/C][C]-3.53598571283429e-15[/C][/ROW]
[ROW][C]53[/C][C]469[/C][C]469[/C][C]3.00970217284851e-15[/C][/ROW]
[ROW][C]54[/C][C]478[/C][C]478[/C][C]-7.0113270254792e-16[/C][/ROW]
[ROW][C]55[/C][C]528[/C][C]528[/C][C]2.91970807672699e-15[/C][/ROW]
[ROW][C]56[/C][C]534[/C][C]534[/C][C]6.25096527767821e-15[/C][/ROW]
[ROW][C]57[/C][C]518[/C][C]518[/C][C]4.88337104112435e-15[/C][/ROW]
[ROW][C]58[/C][C]506[/C][C]506[/C][C]-1.29005710286727e-15[/C][/ROW]
[ROW][C]59[/C][C]502[/C][C]502[/C][C]-6.938147697939e-16[/C][/ROW]
[ROW][C]60[/C][C]516[/C][C]516[/C][C]2.76284993689764e-15[/C][/ROW]
[ROW][C]61[/C][C]528[/C][C]528[/C][C]-1.44134039458876e-13[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58294&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58294&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
1562561.9999999999997.41176493519634e-13
25615617.1728427640018e-17
35555551.36515492563589e-15
45445445.03722123418926e-16
5537537-3.21930930656747e-15
6543543-2.31893433227744e-15
75945949.49074390035476e-16
86116113.99178964404305e-16
9613613-2.1396493986529e-16
10611611-2.81028629587628e-15
115945942.37277277770357e-15
12595595-3.79460049030042e-15
13591591-1.48598890653482e-13
14589589-1.27702040891977e-15
155845841.48094707379684e-17
16573573-8.4662333147891e-16
17567567-4.81594719926861e-16
18569569-8.3442677688888e-16
19621621-1.47196379033856e-15
20629629-5.95179806091241e-15
216286281.59898156330514e-15
226126123.94230900476268e-15
23595595-5.08548663685942e-15
24597597-4.50026460422447e-15
25593593-1.52857268446207e-13
26590590-1.62985246588190e-15
27580580-2.12657598021436e-15
28574574-1.19945538844097e-15
29573573-1.71040864199892e-15
30573573-2.24575500473702e-15
31620620-1.11913173337654e-15
32626626-4.8933018900263e-15
33620620-2.68378933859956e-15
345885881.75213733545007e-15
35566566-1.82427503161073e-16
365575572.06350110680601e-15
37561561-1.48227980771173e-13
385495494.01763993760036e-16
395325324.15122171756194e-15
405265265.0783423093354e-15
415115112.40161049564492e-15
424994996.10024881645128e-15
43555555-1.27768694304731e-15
445655654.1949557088563e-15
45542542-3.58459832596457e-15
46527527-1.59410294146905e-15
475105103.58895613211083e-15
485145143.46851405082166e-15
49517517-1.47358314189895e-13
505085082.43338045340181e-15
51493493-3.40461013372152e-15
52490490-3.53598571283429e-15
534694693.00970217284851e-15
54478478-7.0113270254792e-16
555285282.91970807672699e-15
565345346.25096527767821e-15
575185184.88337104112435e-15
58506506-1.29005710286727e-15
59502502-6.938147697939e-16
605165162.76284993689764e-15
61528528-1.44134039458876e-13







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
168.226928087861e-061.6453856175722e-050.999991773071912
177.14836887896909e-071.42967377579382e-060.999999285163112
188.8158438321542e-141.76316876643084e-130.999999999999912
190.7029471347702090.5941057304595830.297052865229791
2012.08973960896685e-591.04486980448342e-59
210.9037655979342690.1924688041314620.0962344020657312
220.9978164264202120.004367147159576520.00218357357978826
230.9956650973838650.008669805232269450.00433490261613473
240.9941335063255570.01173298734888570.00586649367444284
256.22933043309322e-181.24586608661864e-171
264.40259020361679e-148.80518040723357e-140.999999999999956
2714.02516462900809e-232.01258231450405e-23
2812.84845798323531e-351.42422899161765e-35
290.9999999998944712.11057244765383e-101.05528622382691e-10
303.39909157459406e-056.79818314918813e-050.999966009084254
315.45329556195428e-291.09065911239086e-281
320.005614791207798140.01122958241559630.994385208792202
330.999999999838163.23682331136652e-101.61841165568326e-10
340.006690667622529760.01338133524505950.99330933237747
350.9866809515658440.02663809686831160.0133190484341558
366.66255810091468e-341.33251162018294e-331
370.008802335583069820.01760467116613960.99119766441693
380.598942842898860.802114314202280.40105715710114
390.999999999999983.97752381955655e-141.98876190977827e-14
405.58886542687237e-121.11777308537447e-110.999999999994411
410.9999999998317853.36430614514845e-101.68215307257423e-10
420.999999999999754.9801176358057e-132.49005881790285e-13
430.999999973292445.3415122004525e-082.67075610022625e-08
440.007050857024030190.01410171404806040.99294914297597
450.9999762761904244.74476191514882e-052.37238095757441e-05

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 8.226928087861e-06 & 1.6453856175722e-05 & 0.999991773071912 \tabularnewline
17 & 7.14836887896909e-07 & 1.42967377579382e-06 & 0.999999285163112 \tabularnewline
18 & 8.8158438321542e-14 & 1.76316876643084e-13 & 0.999999999999912 \tabularnewline
19 & 0.702947134770209 & 0.594105730459583 & 0.297052865229791 \tabularnewline
20 & 1 & 2.08973960896685e-59 & 1.04486980448342e-59 \tabularnewline
21 & 0.903765597934269 & 0.192468804131462 & 0.0962344020657312 \tabularnewline
22 & 0.997816426420212 & 0.00436714715957652 & 0.00218357357978826 \tabularnewline
23 & 0.995665097383865 & 0.00866980523226945 & 0.00433490261613473 \tabularnewline
24 & 0.994133506325557 & 0.0117329873488857 & 0.00586649367444284 \tabularnewline
25 & 6.22933043309322e-18 & 1.24586608661864e-17 & 1 \tabularnewline
26 & 4.40259020361679e-14 & 8.80518040723357e-14 & 0.999999999999956 \tabularnewline
27 & 1 & 4.02516462900809e-23 & 2.01258231450405e-23 \tabularnewline
28 & 1 & 2.84845798323531e-35 & 1.42422899161765e-35 \tabularnewline
29 & 0.999999999894471 & 2.11057244765383e-10 & 1.05528622382691e-10 \tabularnewline
30 & 3.39909157459406e-05 & 6.79818314918813e-05 & 0.999966009084254 \tabularnewline
31 & 5.45329556195428e-29 & 1.09065911239086e-28 & 1 \tabularnewline
32 & 0.00561479120779814 & 0.0112295824155963 & 0.994385208792202 \tabularnewline
33 & 0.99999999983816 & 3.23682331136652e-10 & 1.61841165568326e-10 \tabularnewline
34 & 0.00669066762252976 & 0.0133813352450595 & 0.99330933237747 \tabularnewline
35 & 0.986680951565844 & 0.0266380968683116 & 0.0133190484341558 \tabularnewline
36 & 6.66255810091468e-34 & 1.33251162018294e-33 & 1 \tabularnewline
37 & 0.00880233558306982 & 0.0176046711661396 & 0.99119766441693 \tabularnewline
38 & 0.59894284289886 & 0.80211431420228 & 0.40105715710114 \tabularnewline
39 & 0.99999999999998 & 3.97752381955655e-14 & 1.98876190977827e-14 \tabularnewline
40 & 5.58886542687237e-12 & 1.11777308537447e-11 & 0.999999999994411 \tabularnewline
41 & 0.999999999831785 & 3.36430614514845e-10 & 1.68215307257423e-10 \tabularnewline
42 & 0.99999999999975 & 4.9801176358057e-13 & 2.49005881790285e-13 \tabularnewline
43 & 0.99999997329244 & 5.3415122004525e-08 & 2.67075610022625e-08 \tabularnewline
44 & 0.00705085702403019 & 0.0141017140480604 & 0.99294914297597 \tabularnewline
45 & 0.999976276190424 & 4.74476191514882e-05 & 2.37238095757441e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58294&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]16[/C][C]8.226928087861e-06[/C][C]1.6453856175722e-05[/C][C]0.999991773071912[/C][/ROW]
[ROW][C]17[/C][C]7.14836887896909e-07[/C][C]1.42967377579382e-06[/C][C]0.999999285163112[/C][/ROW]
[ROW][C]18[/C][C]8.8158438321542e-14[/C][C]1.76316876643084e-13[/C][C]0.999999999999912[/C][/ROW]
[ROW][C]19[/C][C]0.702947134770209[/C][C]0.594105730459583[/C][C]0.297052865229791[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]2.08973960896685e-59[/C][C]1.04486980448342e-59[/C][/ROW]
[ROW][C]21[/C][C]0.903765597934269[/C][C]0.192468804131462[/C][C]0.0962344020657312[/C][/ROW]
[ROW][C]22[/C][C]0.997816426420212[/C][C]0.00436714715957652[/C][C]0.00218357357978826[/C][/ROW]
[ROW][C]23[/C][C]0.995665097383865[/C][C]0.00866980523226945[/C][C]0.00433490261613473[/C][/ROW]
[ROW][C]24[/C][C]0.994133506325557[/C][C]0.0117329873488857[/C][C]0.00586649367444284[/C][/ROW]
[ROW][C]25[/C][C]6.22933043309322e-18[/C][C]1.24586608661864e-17[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]4.40259020361679e-14[/C][C]8.80518040723357e-14[/C][C]0.999999999999956[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]4.02516462900809e-23[/C][C]2.01258231450405e-23[/C][/ROW]
[ROW][C]28[/C][C]1[/C][C]2.84845798323531e-35[/C][C]1.42422899161765e-35[/C][/ROW]
[ROW][C]29[/C][C]0.999999999894471[/C][C]2.11057244765383e-10[/C][C]1.05528622382691e-10[/C][/ROW]
[ROW][C]30[/C][C]3.39909157459406e-05[/C][C]6.79818314918813e-05[/C][C]0.999966009084254[/C][/ROW]
[ROW][C]31[/C][C]5.45329556195428e-29[/C][C]1.09065911239086e-28[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]0.00561479120779814[/C][C]0.0112295824155963[/C][C]0.994385208792202[/C][/ROW]
[ROW][C]33[/C][C]0.99999999983816[/C][C]3.23682331136652e-10[/C][C]1.61841165568326e-10[/C][/ROW]
[ROW][C]34[/C][C]0.00669066762252976[/C][C]0.0133813352450595[/C][C]0.99330933237747[/C][/ROW]
[ROW][C]35[/C][C]0.986680951565844[/C][C]0.0266380968683116[/C][C]0.0133190484341558[/C][/ROW]
[ROW][C]36[/C][C]6.66255810091468e-34[/C][C]1.33251162018294e-33[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]0.00880233558306982[/C][C]0.0176046711661396[/C][C]0.99119766441693[/C][/ROW]
[ROW][C]38[/C][C]0.59894284289886[/C][C]0.80211431420228[/C][C]0.40105715710114[/C][/ROW]
[ROW][C]39[/C][C]0.99999999999998[/C][C]3.97752381955655e-14[/C][C]1.98876190977827e-14[/C][/ROW]
[ROW][C]40[/C][C]5.58886542687237e-12[/C][C]1.11777308537447e-11[/C][C]0.999999999994411[/C][/ROW]
[ROW][C]41[/C][C]0.999999999831785[/C][C]3.36430614514845e-10[/C][C]1.68215307257423e-10[/C][/ROW]
[ROW][C]42[/C][C]0.99999999999975[/C][C]4.9801176358057e-13[/C][C]2.49005881790285e-13[/C][/ROW]
[ROW][C]43[/C][C]0.99999997329244[/C][C]5.3415122004525e-08[/C][C]2.67075610022625e-08[/C][/ROW]
[ROW][C]44[/C][C]0.00705085702403019[/C][C]0.0141017140480604[/C][C]0.99294914297597[/C][/ROW]
[ROW][C]45[/C][C]0.999976276190424[/C][C]4.74476191514882e-05[/C][C]2.37238095757441e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58294&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58294&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
168.226928087861e-061.6453856175722e-050.999991773071912
177.14836887896909e-071.42967377579382e-060.999999285163112
188.8158438321542e-141.76316876643084e-130.999999999999912
190.7029471347702090.5941057304595830.297052865229791
2012.08973960896685e-591.04486980448342e-59
210.9037655979342690.1924688041314620.0962344020657312
220.9978164264202120.004367147159576520.00218357357978826
230.9956650973838650.008669805232269450.00433490261613473
240.9941335063255570.01173298734888570.00586649367444284
256.22933043309322e-181.24586608661864e-171
264.40259020361679e-148.80518040723357e-140.999999999999956
2714.02516462900809e-232.01258231450405e-23
2812.84845798323531e-351.42422899161765e-35
290.9999999998944712.11057244765383e-101.05528622382691e-10
303.39909157459406e-056.79818314918813e-050.999966009084254
315.45329556195428e-291.09065911239086e-281
320.005614791207798140.01122958241559630.994385208792202
330.999999999838163.23682331136652e-101.61841165568326e-10
340.006690667622529760.01338133524505950.99330933237747
350.9866809515658440.02663809686831160.0133190484341558
366.66255810091468e-341.33251162018294e-331
370.008802335583069820.01760467116613960.99119766441693
380.598942842898860.802114314202280.40105715710114
390.999999999999983.97752381955655e-141.98876190977827e-14
405.58886542687237e-121.11777308537447e-110.999999999994411
410.9999999998317853.36430614514845e-101.68215307257423e-10
420.999999999999754.9801176358057e-132.49005881790285e-13
430.999999973292445.3415122004525e-082.67075610022625e-08
440.007050857024030190.01410171404806040.99294914297597
450.9999762761904244.74476191514882e-052.37238095757441e-05







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level210.7NOK
5% type I error level270.9NOK
10% type I error level270.9NOK

\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 & 21 & 0.7 & NOK \tabularnewline
5% type I error level & 27 & 0.9 & NOK \tabularnewline
10% type I error level & 27 & 0.9 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58294&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]21[/C][C]0.7[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]27[/C][C]0.9[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]27[/C][C]0.9[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58294&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58294&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 level210.7NOK
5% type I error level270.9NOK
10% type I error level270.9NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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')
}