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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 09:30:09 -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/t12587347755p4r74pktfedjit.htm/, Retrieved Thu, 28 Mar 2024 09:05:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58309, Retrieved Thu, 28 Mar 2024 09:05:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact133
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 14:03:14] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [WS7] [2009-11-20 16:30:09] [b8ce264f75295a954feffaf60221d1b0] [Current]
-    D        [Multiple Regression] [Multiple Regressi...] [2009-12-18 17:09:32] [4d62210f0915d3a20cbf115865da7cd4]
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Dataseries X:
363	14,3
364	14,2
363	15,9
358	15,3
357	15,5
357	15,1
380	15
378	12,1
376	15,8
380	16,9
379	15,1
384	13,7
392	14,8
394	14,7
392	16
396	15,4
392	15
396	15,5
419	15,1
421	11,7
420	16,3
418	16,7
410	15
418	14,9
426	14,6
428	15,3
430	17,9
424	16,4
423	15,4
427	17,9
441	15,9
449	13,9
452	17,8
462	17,9
455	17,4
461	16,7
461	16
463	16,6
462	19,1
456	17,8
455	17,2
456	18,6
472	16,3
472	15,1
471	19,2
465	17,7
459	19,1
465	18
468	17,5
467	17,8
463	21,1
460	17,2
462	19,4
461	19,8
476	17,6
476	16,2
471	19,5
453	19,9
443	20
442	17,3




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=58309&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=58309&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58309&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
WK>25j[t] = + 109.356227758007 + 20.1391918264263ExpBE[t] + 1.69465044197025M1[t] -2.74432326942943M2[t] -49.8616806336816M3[t] -21.2417575479279M4[t] -23.852892894042M5[t] -39.9753817012972M6[t] + 6.41948685569967M7[t] + 51.9229250373091M8[t] -28.2227069222822M9[t] -32.6366261049248M10[t] -28.9670301917116M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
WK>25j[t] =  +  109.356227758007 +  20.1391918264263ExpBE[t] +  1.69465044197025M1[t] -2.74432326942943M2[t] -49.8616806336816M3[t] -21.2417575479279M4[t] -23.852892894042M5[t] -39.9753817012972M6[t] +  6.41948685569967M7[t] +  51.9229250373091M8[t] -28.2227069222822M9[t] -32.6366261049248M10[t] -28.9670301917116M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58309&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]WK>25j[t] =  +  109.356227758007 +  20.1391918264263ExpBE[t] +  1.69465044197025M1[t] -2.74432326942943M2[t] -49.8616806336816M3[t] -21.2417575479279M4[t] -23.852892894042M5[t] -39.9753817012972M6[t] +  6.41948685569967M7[t] +  51.9229250373091M8[t] -28.2227069222822M9[t] -32.6366261049248M10[t] -28.9670301917116M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58309&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58309&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
WK>25j[t] = + 109.356227758007 + 20.1391918264263ExpBE[t] + 1.69465044197025M1[t] -2.74432326942943M2[t] -49.8616806336816M3[t] -21.2417575479279M4[t] -23.852892894042M5[t] -39.9753817012972M6[t] + 6.41948685569967M7[t] + 51.9229250373091M8[t] -28.2227069222822M9[t] -32.6366261049248M10[t] -28.9670301917116M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)109.35622775800732.2102343.39510.0014030.000702
ExpBE20.13919182642631.89889810.605700
M11.6946504419702514.2370240.1190.9057580.452879
M2-2.7443232694294314.198678-0.19330.8475720.423786
M3-49.861680633681614.620873-3.41030.0013420.000671
M4-21.241757547927914.189787-1.4970.1410860.070543
M5-23.85289289404214.196697-1.68020.099560.04978
M6-39.975381701297214.378808-2.78020.007790.003895
M76.4194868556996714.1808390.45270.6528570.326428
M851.922925037309114.8470023.49720.0010380.000519
M9-28.222706922282214.500221-1.94640.0576030.028801
M10-32.636626104924814.541194-2.24440.0295520.014776
M11-28.967030191711614.360289-2.01720.049410.024705

\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) & 109.356227758007 & 32.210234 & 3.3951 & 0.001403 & 0.000702 \tabularnewline
ExpBE & 20.1391918264263 & 1.898898 & 10.6057 & 0 & 0 \tabularnewline
M1 & 1.69465044197025 & 14.237024 & 0.119 & 0.905758 & 0.452879 \tabularnewline
M2 & -2.74432326942943 & 14.198678 & -0.1933 & 0.847572 & 0.423786 \tabularnewline
M3 & -49.8616806336816 & 14.620873 & -3.4103 & 0.001342 & 0.000671 \tabularnewline
M4 & -21.2417575479279 & 14.189787 & -1.497 & 0.141086 & 0.070543 \tabularnewline
M5 & -23.852892894042 & 14.196697 & -1.6802 & 0.09956 & 0.04978 \tabularnewline
M6 & -39.9753817012972 & 14.378808 & -2.7802 & 0.00779 & 0.003895 \tabularnewline
M7 & 6.41948685569967 & 14.180839 & 0.4527 & 0.652857 & 0.326428 \tabularnewline
M8 & 51.9229250373091 & 14.847002 & 3.4972 & 0.001038 & 0.000519 \tabularnewline
M9 & -28.2227069222822 & 14.500221 & -1.9464 & 0.057603 & 0.028801 \tabularnewline
M10 & -32.6366261049248 & 14.541194 & -2.2444 & 0.029552 & 0.014776 \tabularnewline
M11 & -28.9670301917116 & 14.360289 & -2.0172 & 0.04941 & 0.024705 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58309&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]109.356227758007[/C][C]32.210234[/C][C]3.3951[/C][C]0.001403[/C][C]0.000702[/C][/ROW]
[ROW][C]ExpBE[/C][C]20.1391918264263[/C][C]1.898898[/C][C]10.6057[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]1.69465044197025[/C][C]14.237024[/C][C]0.119[/C][C]0.905758[/C][C]0.452879[/C][/ROW]
[ROW][C]M2[/C][C]-2.74432326942943[/C][C]14.198678[/C][C]-0.1933[/C][C]0.847572[/C][C]0.423786[/C][/ROW]
[ROW][C]M3[/C][C]-49.8616806336816[/C][C]14.620873[/C][C]-3.4103[/C][C]0.001342[/C][C]0.000671[/C][/ROW]
[ROW][C]M4[/C][C]-21.2417575479279[/C][C]14.189787[/C][C]-1.497[/C][C]0.141086[/C][C]0.070543[/C][/ROW]
[ROW][C]M5[/C][C]-23.852892894042[/C][C]14.196697[/C][C]-1.6802[/C][C]0.09956[/C][C]0.04978[/C][/ROW]
[ROW][C]M6[/C][C]-39.9753817012972[/C][C]14.378808[/C][C]-2.7802[/C][C]0.00779[/C][C]0.003895[/C][/ROW]
[ROW][C]M7[/C][C]6.41948685569967[/C][C]14.180839[/C][C]0.4527[/C][C]0.652857[/C][C]0.326428[/C][/ROW]
[ROW][C]M8[/C][C]51.9229250373091[/C][C]14.847002[/C][C]3.4972[/C][C]0.001038[/C][C]0.000519[/C][/ROW]
[ROW][C]M9[/C][C]-28.2227069222822[/C][C]14.500221[/C][C]-1.9464[/C][C]0.057603[/C][C]0.028801[/C][/ROW]
[ROW][C]M10[/C][C]-32.6366261049248[/C][C]14.541194[/C][C]-2.2444[/C][C]0.029552[/C][C]0.014776[/C][/ROW]
[ROW][C]M11[/C][C]-28.9670301917116[/C][C]14.360289[/C][C]-2.0172[/C][C]0.04941[/C][C]0.024705[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58309&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58309&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)109.35622775800732.2102343.39510.0014030.000702
ExpBE20.13919182642631.89889810.605700
M11.6946504419702514.2370240.1190.9057580.452879
M2-2.7443232694294314.198678-0.19330.8475720.423786
M3-49.861680633681614.620873-3.41030.0013420.000671
M4-21.241757547927914.189787-1.4970.1410860.070543
M5-23.85289289404214.196697-1.68020.099560.04978
M6-39.975381701297214.378808-2.78020.007790.003895
M76.4194868556996714.1808390.45270.6528570.326428
M851.922925037309114.8470023.49720.0010380.000519
M9-28.222706922282214.500221-1.94640.0576030.028801
M10-32.636626104924814.541194-2.24440.0295520.014776
M11-28.967030191711614.360289-2.01720.049410.024705







Multiple Linear Regression - Regression Statistics
Multiple R0.847826266166205
R-squared0.718809377601329
Adjusted R-squared0.647016027201668
F-TEST (value)10.0121999265927
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value2.5348314558471e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation22.4179352962809
Sum Squared Residuals23620.4996785672

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.847826266166205 \tabularnewline
R-squared & 0.718809377601329 \tabularnewline
Adjusted R-squared & 0.647016027201668 \tabularnewline
F-TEST (value) & 10.0121999265927 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 2.5348314558471e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 22.4179352962809 \tabularnewline
Sum Squared Residuals & 23620.4996785672 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58309&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.847826266166205[/C][/ROW]
[ROW][C]R-squared[/C][C]0.718809377601329[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.647016027201668[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.0121999265927[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]2.5348314558471e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]22.4179352962809[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]23620.4996785672[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58309&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58309&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 R0.847826266166205
R-squared0.718809377601329
Adjusted R-squared0.647016027201668
F-TEST (value)10.0121999265927
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value2.5348314558471e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation22.4179352962809
Sum Squared Residuals23620.4996785672







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1363399.041321317873-36.0413213178727
2364392.588428423832-28.5884284238318
3363379.707697164505-16.7076971645047
4358396.244105154403-38.2441051544025
5357397.660808173574-40.6608081735736
6357373.482642635748-16.4826426357479
7380417.863592010102-37.8635920101022
8378404.963373895075-26.9633738950752
9376399.332751693261-23.3327516932614
10380417.071943519688-37.0719435196877
11379384.490994145334-5.49099414533349
12384385.263155780048-1.26315578004820
13392409.110917231087-17.1109172310875
14394402.658024337045-8.65802433704512
15392381.72161634714710.2783836528527
16396398.258024337045-2.25802433704512
17392387.5912122603604.40878773963953
18396381.53831936631814.4616806336815
19419419.877511192745-0.877511192744778
20421396.90769716450524.0923028354953
21420409.40234760647510.5976523935254
22418413.0441051544024.95589484559751
23410382.47707496269127.5229250373092
24418409.430185971768.56981402824016
25426405.08307886580220.9169211341978
26428414.74153943290113.2584605670990
27430419.98608081735710.0139191826427
28424418.3972161634715.60278383652856
29423395.64688899093127.353111009069
30427429.872379749742-2.87237974974167
31441435.9888646538865.01113534611412
32449441.2139191826437.78608081735736
33452439.61113534611412.3888646538859
34462437.21113534611424.7888646538859
35455430.81113534611424.1888646538859
36461445.68073125932715.3192687406727
37461433.27794742279927.7220525772009
38463440.92248880725522.0775111927448
39462444.15311100906917.8468889909310
40456446.5920847204689.40791527953162
41455431.89743427849823.1025657215015
42456443.9698140282412.0301859717598
43472444.04454138445627.9554586155436
44472465.3809493743546.61905062564574
45471467.8060039031113.19399609688900
46465433.18329698082931.8167030191712
47459465.047761451039-6.04776145103894
48465471.861680633682-6.86168063368154
49468463.4867351624394.51326483756139
50467465.0895189989671.91048100103314
51463484.431494661922-21.4314946619217
52460434.50856962461325.4914303753875
53462476.203656296636-14.2036562966364
54461468.136844219952-7.13684421995179
55476470.2254907588115.7745092411893
56476487.534060383423-11.5340603834232
57471473.847761451039-2.84776145103891
58453477.489518998967-24.4895189989668
59443483.173034094823-40.1730340948226
60442457.764246355183-15.7642463551831

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 363 & 399.041321317873 & -36.0413213178727 \tabularnewline
2 & 364 & 392.588428423832 & -28.5884284238318 \tabularnewline
3 & 363 & 379.707697164505 & -16.7076971645047 \tabularnewline
4 & 358 & 396.244105154403 & -38.2441051544025 \tabularnewline
5 & 357 & 397.660808173574 & -40.6608081735736 \tabularnewline
6 & 357 & 373.482642635748 & -16.4826426357479 \tabularnewline
7 & 380 & 417.863592010102 & -37.8635920101022 \tabularnewline
8 & 378 & 404.963373895075 & -26.9633738950752 \tabularnewline
9 & 376 & 399.332751693261 & -23.3327516932614 \tabularnewline
10 & 380 & 417.071943519688 & -37.0719435196877 \tabularnewline
11 & 379 & 384.490994145334 & -5.49099414533349 \tabularnewline
12 & 384 & 385.263155780048 & -1.26315578004820 \tabularnewline
13 & 392 & 409.110917231087 & -17.1109172310875 \tabularnewline
14 & 394 & 402.658024337045 & -8.65802433704512 \tabularnewline
15 & 392 & 381.721616347147 & 10.2783836528527 \tabularnewline
16 & 396 & 398.258024337045 & -2.25802433704512 \tabularnewline
17 & 392 & 387.591212260360 & 4.40878773963953 \tabularnewline
18 & 396 & 381.538319366318 & 14.4616806336815 \tabularnewline
19 & 419 & 419.877511192745 & -0.877511192744778 \tabularnewline
20 & 421 & 396.907697164505 & 24.0923028354953 \tabularnewline
21 & 420 & 409.402347606475 & 10.5976523935254 \tabularnewline
22 & 418 & 413.044105154402 & 4.95589484559751 \tabularnewline
23 & 410 & 382.477074962691 & 27.5229250373092 \tabularnewline
24 & 418 & 409.43018597176 & 8.56981402824016 \tabularnewline
25 & 426 & 405.083078865802 & 20.9169211341978 \tabularnewline
26 & 428 & 414.741539432901 & 13.2584605670990 \tabularnewline
27 & 430 & 419.986080817357 & 10.0139191826427 \tabularnewline
28 & 424 & 418.397216163471 & 5.60278383652856 \tabularnewline
29 & 423 & 395.646888990931 & 27.353111009069 \tabularnewline
30 & 427 & 429.872379749742 & -2.87237974974167 \tabularnewline
31 & 441 & 435.988864653886 & 5.01113534611412 \tabularnewline
32 & 449 & 441.213919182643 & 7.78608081735736 \tabularnewline
33 & 452 & 439.611135346114 & 12.3888646538859 \tabularnewline
34 & 462 & 437.211135346114 & 24.7888646538859 \tabularnewline
35 & 455 & 430.811135346114 & 24.1888646538859 \tabularnewline
36 & 461 & 445.680731259327 & 15.3192687406727 \tabularnewline
37 & 461 & 433.277947422799 & 27.7220525772009 \tabularnewline
38 & 463 & 440.922488807255 & 22.0775111927448 \tabularnewline
39 & 462 & 444.153111009069 & 17.8468889909310 \tabularnewline
40 & 456 & 446.592084720468 & 9.40791527953162 \tabularnewline
41 & 455 & 431.897434278498 & 23.1025657215015 \tabularnewline
42 & 456 & 443.96981402824 & 12.0301859717598 \tabularnewline
43 & 472 & 444.044541384456 & 27.9554586155436 \tabularnewline
44 & 472 & 465.380949374354 & 6.61905062564574 \tabularnewline
45 & 471 & 467.806003903111 & 3.19399609688900 \tabularnewline
46 & 465 & 433.183296980829 & 31.8167030191712 \tabularnewline
47 & 459 & 465.047761451039 & -6.04776145103894 \tabularnewline
48 & 465 & 471.861680633682 & -6.86168063368154 \tabularnewline
49 & 468 & 463.486735162439 & 4.51326483756139 \tabularnewline
50 & 467 & 465.089518998967 & 1.91048100103314 \tabularnewline
51 & 463 & 484.431494661922 & -21.4314946619217 \tabularnewline
52 & 460 & 434.508569624613 & 25.4914303753875 \tabularnewline
53 & 462 & 476.203656296636 & -14.2036562966364 \tabularnewline
54 & 461 & 468.136844219952 & -7.13684421995179 \tabularnewline
55 & 476 & 470.225490758811 & 5.7745092411893 \tabularnewline
56 & 476 & 487.534060383423 & -11.5340603834232 \tabularnewline
57 & 471 & 473.847761451039 & -2.84776145103891 \tabularnewline
58 & 453 & 477.489518998967 & -24.4895189989668 \tabularnewline
59 & 443 & 483.173034094823 & -40.1730340948226 \tabularnewline
60 & 442 & 457.764246355183 & -15.7642463551831 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58309&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]363[/C][C]399.041321317873[/C][C]-36.0413213178727[/C][/ROW]
[ROW][C]2[/C][C]364[/C][C]392.588428423832[/C][C]-28.5884284238318[/C][/ROW]
[ROW][C]3[/C][C]363[/C][C]379.707697164505[/C][C]-16.7076971645047[/C][/ROW]
[ROW][C]4[/C][C]358[/C][C]396.244105154403[/C][C]-38.2441051544025[/C][/ROW]
[ROW][C]5[/C][C]357[/C][C]397.660808173574[/C][C]-40.6608081735736[/C][/ROW]
[ROW][C]6[/C][C]357[/C][C]373.482642635748[/C][C]-16.4826426357479[/C][/ROW]
[ROW][C]7[/C][C]380[/C][C]417.863592010102[/C][C]-37.8635920101022[/C][/ROW]
[ROW][C]8[/C][C]378[/C][C]404.963373895075[/C][C]-26.9633738950752[/C][/ROW]
[ROW][C]9[/C][C]376[/C][C]399.332751693261[/C][C]-23.3327516932614[/C][/ROW]
[ROW][C]10[/C][C]380[/C][C]417.071943519688[/C][C]-37.0719435196877[/C][/ROW]
[ROW][C]11[/C][C]379[/C][C]384.490994145334[/C][C]-5.49099414533349[/C][/ROW]
[ROW][C]12[/C][C]384[/C][C]385.263155780048[/C][C]-1.26315578004820[/C][/ROW]
[ROW][C]13[/C][C]392[/C][C]409.110917231087[/C][C]-17.1109172310875[/C][/ROW]
[ROW][C]14[/C][C]394[/C][C]402.658024337045[/C][C]-8.65802433704512[/C][/ROW]
[ROW][C]15[/C][C]392[/C][C]381.721616347147[/C][C]10.2783836528527[/C][/ROW]
[ROW][C]16[/C][C]396[/C][C]398.258024337045[/C][C]-2.25802433704512[/C][/ROW]
[ROW][C]17[/C][C]392[/C][C]387.591212260360[/C][C]4.40878773963953[/C][/ROW]
[ROW][C]18[/C][C]396[/C][C]381.538319366318[/C][C]14.4616806336815[/C][/ROW]
[ROW][C]19[/C][C]419[/C][C]419.877511192745[/C][C]-0.877511192744778[/C][/ROW]
[ROW][C]20[/C][C]421[/C][C]396.907697164505[/C][C]24.0923028354953[/C][/ROW]
[ROW][C]21[/C][C]420[/C][C]409.402347606475[/C][C]10.5976523935254[/C][/ROW]
[ROW][C]22[/C][C]418[/C][C]413.044105154402[/C][C]4.95589484559751[/C][/ROW]
[ROW][C]23[/C][C]410[/C][C]382.477074962691[/C][C]27.5229250373092[/C][/ROW]
[ROW][C]24[/C][C]418[/C][C]409.43018597176[/C][C]8.56981402824016[/C][/ROW]
[ROW][C]25[/C][C]426[/C][C]405.083078865802[/C][C]20.9169211341978[/C][/ROW]
[ROW][C]26[/C][C]428[/C][C]414.741539432901[/C][C]13.2584605670990[/C][/ROW]
[ROW][C]27[/C][C]430[/C][C]419.986080817357[/C][C]10.0139191826427[/C][/ROW]
[ROW][C]28[/C][C]424[/C][C]418.397216163471[/C][C]5.60278383652856[/C][/ROW]
[ROW][C]29[/C][C]423[/C][C]395.646888990931[/C][C]27.353111009069[/C][/ROW]
[ROW][C]30[/C][C]427[/C][C]429.872379749742[/C][C]-2.87237974974167[/C][/ROW]
[ROW][C]31[/C][C]441[/C][C]435.988864653886[/C][C]5.01113534611412[/C][/ROW]
[ROW][C]32[/C][C]449[/C][C]441.213919182643[/C][C]7.78608081735736[/C][/ROW]
[ROW][C]33[/C][C]452[/C][C]439.611135346114[/C][C]12.3888646538859[/C][/ROW]
[ROW][C]34[/C][C]462[/C][C]437.211135346114[/C][C]24.7888646538859[/C][/ROW]
[ROW][C]35[/C][C]455[/C][C]430.811135346114[/C][C]24.1888646538859[/C][/ROW]
[ROW][C]36[/C][C]461[/C][C]445.680731259327[/C][C]15.3192687406727[/C][/ROW]
[ROW][C]37[/C][C]461[/C][C]433.277947422799[/C][C]27.7220525772009[/C][/ROW]
[ROW][C]38[/C][C]463[/C][C]440.922488807255[/C][C]22.0775111927448[/C][/ROW]
[ROW][C]39[/C][C]462[/C][C]444.153111009069[/C][C]17.8468889909310[/C][/ROW]
[ROW][C]40[/C][C]456[/C][C]446.592084720468[/C][C]9.40791527953162[/C][/ROW]
[ROW][C]41[/C][C]455[/C][C]431.897434278498[/C][C]23.1025657215015[/C][/ROW]
[ROW][C]42[/C][C]456[/C][C]443.96981402824[/C][C]12.0301859717598[/C][/ROW]
[ROW][C]43[/C][C]472[/C][C]444.044541384456[/C][C]27.9554586155436[/C][/ROW]
[ROW][C]44[/C][C]472[/C][C]465.380949374354[/C][C]6.61905062564574[/C][/ROW]
[ROW][C]45[/C][C]471[/C][C]467.806003903111[/C][C]3.19399609688900[/C][/ROW]
[ROW][C]46[/C][C]465[/C][C]433.183296980829[/C][C]31.8167030191712[/C][/ROW]
[ROW][C]47[/C][C]459[/C][C]465.047761451039[/C][C]-6.04776145103894[/C][/ROW]
[ROW][C]48[/C][C]465[/C][C]471.861680633682[/C][C]-6.86168063368154[/C][/ROW]
[ROW][C]49[/C][C]468[/C][C]463.486735162439[/C][C]4.51326483756139[/C][/ROW]
[ROW][C]50[/C][C]467[/C][C]465.089518998967[/C][C]1.91048100103314[/C][/ROW]
[ROW][C]51[/C][C]463[/C][C]484.431494661922[/C][C]-21.4314946619217[/C][/ROW]
[ROW][C]52[/C][C]460[/C][C]434.508569624613[/C][C]25.4914303753875[/C][/ROW]
[ROW][C]53[/C][C]462[/C][C]476.203656296636[/C][C]-14.2036562966364[/C][/ROW]
[ROW][C]54[/C][C]461[/C][C]468.136844219952[/C][C]-7.13684421995179[/C][/ROW]
[ROW][C]55[/C][C]476[/C][C]470.225490758811[/C][C]5.7745092411893[/C][/ROW]
[ROW][C]56[/C][C]476[/C][C]487.534060383423[/C][C]-11.5340603834232[/C][/ROW]
[ROW][C]57[/C][C]471[/C][C]473.847761451039[/C][C]-2.84776145103891[/C][/ROW]
[ROW][C]58[/C][C]453[/C][C]477.489518998967[/C][C]-24.4895189989668[/C][/ROW]
[ROW][C]59[/C][C]443[/C][C]483.173034094823[/C][C]-40.1730340948226[/C][/ROW]
[ROW][C]60[/C][C]442[/C][C]457.764246355183[/C][C]-15.7642463551831[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58309&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58309&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
1363399.041321317873-36.0413213178727
2364392.588428423832-28.5884284238318
3363379.707697164505-16.7076971645047
4358396.244105154403-38.2441051544025
5357397.660808173574-40.6608081735736
6357373.482642635748-16.4826426357479
7380417.863592010102-37.8635920101022
8378404.963373895075-26.9633738950752
9376399.332751693261-23.3327516932614
10380417.071943519688-37.0719435196877
11379384.490994145334-5.49099414533349
12384385.263155780048-1.26315578004820
13392409.110917231087-17.1109172310875
14394402.658024337045-8.65802433704512
15392381.72161634714710.2783836528527
16396398.258024337045-2.25802433704512
17392387.5912122603604.40878773963953
18396381.53831936631814.4616806336815
19419419.877511192745-0.877511192744778
20421396.90769716450524.0923028354953
21420409.40234760647510.5976523935254
22418413.0441051544024.95589484559751
23410382.47707496269127.5229250373092
24418409.430185971768.56981402824016
25426405.08307886580220.9169211341978
26428414.74153943290113.2584605670990
27430419.98608081735710.0139191826427
28424418.3972161634715.60278383652856
29423395.64688899093127.353111009069
30427429.872379749742-2.87237974974167
31441435.9888646538865.01113534611412
32449441.2139191826437.78608081735736
33452439.61113534611412.3888646538859
34462437.21113534611424.7888646538859
35455430.81113534611424.1888646538859
36461445.68073125932715.3192687406727
37461433.27794742279927.7220525772009
38463440.92248880725522.0775111927448
39462444.15311100906917.8468889909310
40456446.5920847204689.40791527953162
41455431.89743427849823.1025657215015
42456443.9698140282412.0301859717598
43472444.04454138445627.9554586155436
44472465.3809493743546.61905062564574
45471467.8060039031113.19399609688900
46465433.18329698082931.8167030191712
47459465.047761451039-6.04776145103894
48465471.861680633682-6.86168063368154
49468463.4867351624394.51326483756139
50467465.0895189989671.91048100103314
51463484.431494661922-21.4314946619217
52460434.50856962461325.4914303753875
53462476.203656296636-14.2036562966364
54461468.136844219952-7.13684421995179
55476470.2254907588115.7745092411893
56476487.534060383423-11.5340603834232
57471473.847761451039-2.84776145103891
58453477.489518998967-24.4895189989668
59443483.173034094823-40.1730340948226
60442457.764246355183-15.7642463551831







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.7359729288184820.5280541423630350.264027071181518
170.9817543654115450.03649126917690990.0182456345884550
180.979352320533740.04129535893251890.0206476794662594
190.989544241773010.02091151645397710.0104557582269886
200.9969971576780440.006005684643911950.00300284232195598
210.9970596865985470.00588062680290560.0029403134014528
220.9987066190771930.002586761845614590.00129338092280729
230.998292170128790.003415659742420240.00170782987121012
240.9970866296435250.005826740712950210.00291337035647511
250.9987745492199540.002450901560091490.00122545078004574
260.9988148715657430.002370256868513460.00118512843425673
270.998228967292880.003542065414240460.00177103270712023
280.9987712556651960.002457488669607610.00122874433480381
290.9993687742141360.001262451571727470.000631225785863735
300.9997219771600760.0005560456798483040.000278022839924152
310.999953079314289.38413714405145e-054.69206857202573e-05
320.9999818620532273.62758935469469e-051.81379467734735e-05
330.9999909478659851.81042680293711e-059.05213401468556e-06
340.9999777095254034.45809491942825e-052.22904745971412e-05
350.9999303675668210.0001392648663570636.96324331785316e-05
360.9998397421774510.0003205156450978440.000160257822548922
370.9996189586664540.0007620826670915950.000381041333545797
380.9988665125072340.002266974985531680.00113348749276584
390.9965751135796440.006849772840711610.00342488642035580
400.990735481133130.01852903773374050.00926451886687027
410.9787314807123360.04253703857532850.0212685192876642
420.9517641201384740.0964717597230520.048235879861526
430.9046000935615220.1907998128769550.0953999064384775
440.809934923856130.3801301522877390.190065076143869

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.735972928818482 & 0.528054142363035 & 0.264027071181518 \tabularnewline
17 & 0.981754365411545 & 0.0364912691769099 & 0.0182456345884550 \tabularnewline
18 & 0.97935232053374 & 0.0412953589325189 & 0.0206476794662594 \tabularnewline
19 & 0.98954424177301 & 0.0209115164539771 & 0.0104557582269886 \tabularnewline
20 & 0.996997157678044 & 0.00600568464391195 & 0.00300284232195598 \tabularnewline
21 & 0.997059686598547 & 0.0058806268029056 & 0.0029403134014528 \tabularnewline
22 & 0.998706619077193 & 0.00258676184561459 & 0.00129338092280729 \tabularnewline
23 & 0.99829217012879 & 0.00341565974242024 & 0.00170782987121012 \tabularnewline
24 & 0.997086629643525 & 0.00582674071295021 & 0.00291337035647511 \tabularnewline
25 & 0.998774549219954 & 0.00245090156009149 & 0.00122545078004574 \tabularnewline
26 & 0.998814871565743 & 0.00237025686851346 & 0.00118512843425673 \tabularnewline
27 & 0.99822896729288 & 0.00354206541424046 & 0.00177103270712023 \tabularnewline
28 & 0.998771255665196 & 0.00245748866960761 & 0.00122874433480381 \tabularnewline
29 & 0.999368774214136 & 0.00126245157172747 & 0.000631225785863735 \tabularnewline
30 & 0.999721977160076 & 0.000556045679848304 & 0.000278022839924152 \tabularnewline
31 & 0.99995307931428 & 9.38413714405145e-05 & 4.69206857202573e-05 \tabularnewline
32 & 0.999981862053227 & 3.62758935469469e-05 & 1.81379467734735e-05 \tabularnewline
33 & 0.999990947865985 & 1.81042680293711e-05 & 9.05213401468556e-06 \tabularnewline
34 & 0.999977709525403 & 4.45809491942825e-05 & 2.22904745971412e-05 \tabularnewline
35 & 0.999930367566821 & 0.000139264866357063 & 6.96324331785316e-05 \tabularnewline
36 & 0.999839742177451 & 0.000320515645097844 & 0.000160257822548922 \tabularnewline
37 & 0.999618958666454 & 0.000762082667091595 & 0.000381041333545797 \tabularnewline
38 & 0.998866512507234 & 0.00226697498553168 & 0.00113348749276584 \tabularnewline
39 & 0.996575113579644 & 0.00684977284071161 & 0.00342488642035580 \tabularnewline
40 & 0.99073548113313 & 0.0185290377337405 & 0.00926451886687027 \tabularnewline
41 & 0.978731480712336 & 0.0425370385753285 & 0.0212685192876642 \tabularnewline
42 & 0.951764120138474 & 0.096471759723052 & 0.048235879861526 \tabularnewline
43 & 0.904600093561522 & 0.190799812876955 & 0.0953999064384775 \tabularnewline
44 & 0.80993492385613 & 0.380130152287739 & 0.190065076143869 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58309&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]0.735972928818482[/C][C]0.528054142363035[/C][C]0.264027071181518[/C][/ROW]
[ROW][C]17[/C][C]0.981754365411545[/C][C]0.0364912691769099[/C][C]0.0182456345884550[/C][/ROW]
[ROW][C]18[/C][C]0.97935232053374[/C][C]0.0412953589325189[/C][C]0.0206476794662594[/C][/ROW]
[ROW][C]19[/C][C]0.98954424177301[/C][C]0.0209115164539771[/C][C]0.0104557582269886[/C][/ROW]
[ROW][C]20[/C][C]0.996997157678044[/C][C]0.00600568464391195[/C][C]0.00300284232195598[/C][/ROW]
[ROW][C]21[/C][C]0.997059686598547[/C][C]0.0058806268029056[/C][C]0.0029403134014528[/C][/ROW]
[ROW][C]22[/C][C]0.998706619077193[/C][C]0.00258676184561459[/C][C]0.00129338092280729[/C][/ROW]
[ROW][C]23[/C][C]0.99829217012879[/C][C]0.00341565974242024[/C][C]0.00170782987121012[/C][/ROW]
[ROW][C]24[/C][C]0.997086629643525[/C][C]0.00582674071295021[/C][C]0.00291337035647511[/C][/ROW]
[ROW][C]25[/C][C]0.998774549219954[/C][C]0.00245090156009149[/C][C]0.00122545078004574[/C][/ROW]
[ROW][C]26[/C][C]0.998814871565743[/C][C]0.00237025686851346[/C][C]0.00118512843425673[/C][/ROW]
[ROW][C]27[/C][C]0.99822896729288[/C][C]0.00354206541424046[/C][C]0.00177103270712023[/C][/ROW]
[ROW][C]28[/C][C]0.998771255665196[/C][C]0.00245748866960761[/C][C]0.00122874433480381[/C][/ROW]
[ROW][C]29[/C][C]0.999368774214136[/C][C]0.00126245157172747[/C][C]0.000631225785863735[/C][/ROW]
[ROW][C]30[/C][C]0.999721977160076[/C][C]0.000556045679848304[/C][C]0.000278022839924152[/C][/ROW]
[ROW][C]31[/C][C]0.99995307931428[/C][C]9.38413714405145e-05[/C][C]4.69206857202573e-05[/C][/ROW]
[ROW][C]32[/C][C]0.999981862053227[/C][C]3.62758935469469e-05[/C][C]1.81379467734735e-05[/C][/ROW]
[ROW][C]33[/C][C]0.999990947865985[/C][C]1.81042680293711e-05[/C][C]9.05213401468556e-06[/C][/ROW]
[ROW][C]34[/C][C]0.999977709525403[/C][C]4.45809491942825e-05[/C][C]2.22904745971412e-05[/C][/ROW]
[ROW][C]35[/C][C]0.999930367566821[/C][C]0.000139264866357063[/C][C]6.96324331785316e-05[/C][/ROW]
[ROW][C]36[/C][C]0.999839742177451[/C][C]0.000320515645097844[/C][C]0.000160257822548922[/C][/ROW]
[ROW][C]37[/C][C]0.999618958666454[/C][C]0.000762082667091595[/C][C]0.000381041333545797[/C][/ROW]
[ROW][C]38[/C][C]0.998866512507234[/C][C]0.00226697498553168[/C][C]0.00113348749276584[/C][/ROW]
[ROW][C]39[/C][C]0.996575113579644[/C][C]0.00684977284071161[/C][C]0.00342488642035580[/C][/ROW]
[ROW][C]40[/C][C]0.99073548113313[/C][C]0.0185290377337405[/C][C]0.00926451886687027[/C][/ROW]
[ROW][C]41[/C][C]0.978731480712336[/C][C]0.0425370385753285[/C][C]0.0212685192876642[/C][/ROW]
[ROW][C]42[/C][C]0.951764120138474[/C][C]0.096471759723052[/C][C]0.048235879861526[/C][/ROW]
[ROW][C]43[/C][C]0.904600093561522[/C][C]0.190799812876955[/C][C]0.0953999064384775[/C][/ROW]
[ROW][C]44[/C][C]0.80993492385613[/C][C]0.380130152287739[/C][C]0.190065076143869[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58309&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58309&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
160.7359729288184820.5280541423630350.264027071181518
170.9817543654115450.03649126917690990.0182456345884550
180.979352320533740.04129535893251890.0206476794662594
190.989544241773010.02091151645397710.0104557582269886
200.9969971576780440.006005684643911950.00300284232195598
210.9970596865985470.00588062680290560.0029403134014528
220.9987066190771930.002586761845614590.00129338092280729
230.998292170128790.003415659742420240.00170782987121012
240.9970866296435250.005826740712950210.00291337035647511
250.9987745492199540.002450901560091490.00122545078004574
260.9988148715657430.002370256868513460.00118512843425673
270.998228967292880.003542065414240460.00177103270712023
280.9987712556651960.002457488669607610.00122874433480381
290.9993687742141360.001262451571727470.000631225785863735
300.9997219771600760.0005560456798483040.000278022839924152
310.999953079314289.38413714405145e-054.69206857202573e-05
320.9999818620532273.62758935469469e-051.81379467734735e-05
330.9999909478659851.81042680293711e-059.05213401468556e-06
340.9999777095254034.45809491942825e-052.22904745971412e-05
350.9999303675668210.0001392648663570636.96324331785316e-05
360.9998397421774510.0003205156450978440.000160257822548922
370.9996189586664540.0007620826670915950.000381041333545797
380.9988665125072340.002266974985531680.00113348749276584
390.9965751135796440.006849772840711610.00342488642035580
400.990735481133130.01852903773374050.00926451886687027
410.9787314807123360.04253703857532850.0212685192876642
420.9517641201384740.0964717597230520.048235879861526
430.9046000935615220.1907998128769550.0953999064384775
440.809934923856130.3801301522877390.190065076143869







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level200.689655172413793NOK
5% type I error level250.862068965517241NOK
10% type I error level260.896551724137931NOK

\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 & 20 & 0.689655172413793 & NOK \tabularnewline
5% type I error level & 25 & 0.862068965517241 & NOK \tabularnewline
10% type I error level & 26 & 0.896551724137931 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58309&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]20[/C][C]0.689655172413793[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]25[/C][C]0.862068965517241[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]26[/C][C]0.896551724137931[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58309&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58309&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 level200.689655172413793NOK
5% type I error level250.862068965517241NOK
10% type I error level260.896551724137931NOK



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')
}