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Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 11:40:21 -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/t12587425281sev99q6hy0vo6x.htm/, Retrieved Thu, 28 Mar 2024 19:31:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58410, Retrieved Thu, 28 Mar 2024 19:31:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact123
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]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [Workshop 7 data 3] [2009-11-20 18:40:21] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
613	0	611	594	543
611	0	613	611	594
594	0	611	613	611
595	0	594	611	613
591	0	595	594	611
589	0	591	595	594
584	0	589	591	595
573	0	584	589	591
567	0	573	584	589
569	0	567	573	584
621	0	569	567	573
629	0	621	569	567
628	0	629	621	569
612	0	628	629	621
595	0	612	628	629
597	0	595	612	628
593	0	597	595	612
590	0	593	597	595
580	0	590	593	597
574	0	580	590	593
573	0	574	580	590
573	0	573	574	580
620	0	573	573	574
626	0	620	573	573
620	0	626	620	573
588	0	620	626	620
566	0	588	620	626
557	0	566	588	620
561	0	557	566	588
549	0	561	557	566
532	0	549	561	557
526	0	532	549	561
511	0	526	532	549
499	0	511	526	532
555	0	499	511	526
565	0	555	499	511
542	0	565	555	499
527	0	542	565	555
510	0	527	542	565
514	0	510	527	542
517	0	514	510	527
508	0	517	514	510
493	0	508	517	514
490	0	493	508	517
469	1	490	493	508
478	1	469	490	493
528	1	478	469	490
534	1	528	478	469
518	1	534	528	478
506	1	518	534	528
502	1	506	518	534
516	1	502	506	518
528	1	516	502	506
533	1	528	516	502
536	1	533	528	516
537	1	536	533	528
524	1	537	536	533
536	1	524	537	536




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=58410&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=58410&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58410&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] = + 27.2884150780906 + 7.47895824192816X[t] + 1.07957881958415Y1[t] + 0.0219700856362730Y2[t] -0.140078156472438Y3[t] -18.6096792509571M1[t] -17.3675468737958M2[t] -14.4556056693470M3[t] + 3.85191565250290M4[t] + 1.81841815303683M5[t] -6.79023110683807M6[t] -10.5917907190378M7[t] -5.5146948443727M8[t] -13.0312279400098M9[t] + 0.313573448969398M10[t] + 49.1524062059444M11[t] -0.176355958412839t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  27.2884150780906 +  7.47895824192816X[t] +  1.07957881958415Y1[t] +  0.0219700856362730Y2[t] -0.140078156472438Y3[t] -18.6096792509571M1[t] -17.3675468737958M2[t] -14.4556056693470M3[t] +  3.85191565250290M4[t] +  1.81841815303683M5[t] -6.79023110683807M6[t] -10.5917907190378M7[t] -5.5146948443727M8[t] -13.0312279400098M9[t] +  0.313573448969398M10[t] +  49.1524062059444M11[t] -0.176355958412839t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58410&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  27.2884150780906 +  7.47895824192816X[t] +  1.07957881958415Y1[t] +  0.0219700856362730Y2[t] -0.140078156472438Y3[t] -18.6096792509571M1[t] -17.3675468737958M2[t] -14.4556056693470M3[t] +  3.85191565250290M4[t] +  1.81841815303683M5[t] -6.79023110683807M6[t] -10.5917907190378M7[t] -5.5146948443727M8[t] -13.0312279400098M9[t] +  0.313573448969398M10[t] +  49.1524062059444M11[t] -0.176355958412839t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58410&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58410&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] = + 27.2884150780906 + 7.47895824192816X[t] + 1.07957881958415Y1[t] + 0.0219700856362730Y2[t] -0.140078156472438Y3[t] -18.6096792509571M1[t] -17.3675468737958M2[t] -14.4556056693470M3[t] + 3.85191565250290M4[t] + 1.81841815303683M5[t] -6.79023110683807M6[t] -10.5917907190378M7[t] -5.5146948443727M8[t] -13.0312279400098M9[t] + 0.313573448969398M10[t] + 49.1524062059444M11[t] -0.176355958412839t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)27.288415078090632.4546720.84080.4053280.202664
X7.478958241928163.9323941.90190.0642280.032114
Y11.079578819584150.1593326.775700
Y20.02197008563627300.2353540.09330.9260810.46304
Y3-0.1400781564724380.16043-0.87310.387670.193835
M1-18.609679250957112.046999-1.54480.130090.065045
M2-17.367546873795810.959982-1.58460.1207340.060367
M3-14.455605669347011.530624-1.25370.2170630.108532
M43.8519156525029011.5025590.33490.7394270.369713
M51.818418153036839.0830380.20020.8423140.421157
M6-6.790231106838079.113171-0.74510.4604610.23023
M7-10.59179071903789.953327-1.06410.293490.146745
M8-5.514694844372710.450702-0.52770.6005610.300281
M9-13.031227940009810.161493-1.28240.2069010.103451
M100.31357344896939811.0103390.02850.9774180.488709
M1149.15240620594449.5932025.12378e-064e-06
t-0.1763559584128390.13019-1.35460.1829620.091481

\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) & 27.2884150780906 & 32.454672 & 0.8408 & 0.405328 & 0.202664 \tabularnewline
X & 7.47895824192816 & 3.932394 & 1.9019 & 0.064228 & 0.032114 \tabularnewline
Y1 & 1.07957881958415 & 0.159332 & 6.7757 & 0 & 0 \tabularnewline
Y2 & 0.0219700856362730 & 0.235354 & 0.0933 & 0.926081 & 0.46304 \tabularnewline
Y3 & -0.140078156472438 & 0.16043 & -0.8731 & 0.38767 & 0.193835 \tabularnewline
M1 & -18.6096792509571 & 12.046999 & -1.5448 & 0.13009 & 0.065045 \tabularnewline
M2 & -17.3675468737958 & 10.959982 & -1.5846 & 0.120734 & 0.060367 \tabularnewline
M3 & -14.4556056693470 & 11.530624 & -1.2537 & 0.217063 & 0.108532 \tabularnewline
M4 & 3.85191565250290 & 11.502559 & 0.3349 & 0.739427 & 0.369713 \tabularnewline
M5 & 1.81841815303683 & 9.083038 & 0.2002 & 0.842314 & 0.421157 \tabularnewline
M6 & -6.79023110683807 & 9.113171 & -0.7451 & 0.460461 & 0.23023 \tabularnewline
M7 & -10.5917907190378 & 9.953327 & -1.0641 & 0.29349 & 0.146745 \tabularnewline
M8 & -5.5146948443727 & 10.450702 & -0.5277 & 0.600561 & 0.300281 \tabularnewline
M9 & -13.0312279400098 & 10.161493 & -1.2824 & 0.206901 & 0.103451 \tabularnewline
M10 & 0.313573448969398 & 11.010339 & 0.0285 & 0.977418 & 0.488709 \tabularnewline
M11 & 49.1524062059444 & 9.593202 & 5.1237 & 8e-06 & 4e-06 \tabularnewline
t & -0.176355958412839 & 0.13019 & -1.3546 & 0.182962 & 0.091481 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58410&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]27.2884150780906[/C][C]32.454672[/C][C]0.8408[/C][C]0.405328[/C][C]0.202664[/C][/ROW]
[ROW][C]X[/C][C]7.47895824192816[/C][C]3.932394[/C][C]1.9019[/C][C]0.064228[/C][C]0.032114[/C][/ROW]
[ROW][C]Y1[/C][C]1.07957881958415[/C][C]0.159332[/C][C]6.7757[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y2[/C][C]0.0219700856362730[/C][C]0.235354[/C][C]0.0933[/C][C]0.926081[/C][C]0.46304[/C][/ROW]
[ROW][C]Y3[/C][C]-0.140078156472438[/C][C]0.16043[/C][C]-0.8731[/C][C]0.38767[/C][C]0.193835[/C][/ROW]
[ROW][C]M1[/C][C]-18.6096792509571[/C][C]12.046999[/C][C]-1.5448[/C][C]0.13009[/C][C]0.065045[/C][/ROW]
[ROW][C]M2[/C][C]-17.3675468737958[/C][C]10.959982[/C][C]-1.5846[/C][C]0.120734[/C][C]0.060367[/C][/ROW]
[ROW][C]M3[/C][C]-14.4556056693470[/C][C]11.530624[/C][C]-1.2537[/C][C]0.217063[/C][C]0.108532[/C][/ROW]
[ROW][C]M4[/C][C]3.85191565250290[/C][C]11.502559[/C][C]0.3349[/C][C]0.739427[/C][C]0.369713[/C][/ROW]
[ROW][C]M5[/C][C]1.81841815303683[/C][C]9.083038[/C][C]0.2002[/C][C]0.842314[/C][C]0.421157[/C][/ROW]
[ROW][C]M6[/C][C]-6.79023110683807[/C][C]9.113171[/C][C]-0.7451[/C][C]0.460461[/C][C]0.23023[/C][/ROW]
[ROW][C]M7[/C][C]-10.5917907190378[/C][C]9.953327[/C][C]-1.0641[/C][C]0.29349[/C][C]0.146745[/C][/ROW]
[ROW][C]M8[/C][C]-5.5146948443727[/C][C]10.450702[/C][C]-0.5277[/C][C]0.600561[/C][C]0.300281[/C][/ROW]
[ROW][C]M9[/C][C]-13.0312279400098[/C][C]10.161493[/C][C]-1.2824[/C][C]0.206901[/C][C]0.103451[/C][/ROW]
[ROW][C]M10[/C][C]0.313573448969398[/C][C]11.010339[/C][C]0.0285[/C][C]0.977418[/C][C]0.488709[/C][/ROW]
[ROW][C]M11[/C][C]49.1524062059444[/C][C]9.593202[/C][C]5.1237[/C][C]8e-06[/C][C]4e-06[/C][/ROW]
[ROW][C]t[/C][C]-0.176355958412839[/C][C]0.13019[/C][C]-1.3546[/C][C]0.182962[/C][C]0.091481[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58410&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58410&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)27.288415078090632.4546720.84080.4053280.202664
X7.478958241928163.9323941.90190.0642280.032114
Y11.079578819584150.1593326.775700
Y20.02197008563627300.2353540.09330.9260810.46304
Y3-0.1400781564724380.16043-0.87310.387670.193835
M1-18.609679250957112.046999-1.54480.130090.065045
M2-17.367546873795810.959982-1.58460.1207340.060367
M3-14.455605669347011.530624-1.25370.2170630.108532
M43.8519156525029011.5025590.33490.7394270.369713
M51.818418153036839.0830380.20020.8423140.421157
M6-6.790231106838079.113171-0.74510.4604610.23023
M7-10.59179071903789.953327-1.06410.293490.146745
M8-5.514694844372710.450702-0.52770.6005610.300281
M9-13.031227940009810.161493-1.28240.2069010.103451
M100.31357344896939811.0103390.02850.9774180.488709
M1149.15240620594449.5932025.12378e-064e-06
t-0.1763559584128390.13019-1.35460.1829620.091481







Multiple Linear Regression - Regression Statistics
Multiple R0.989562151332385
R-squared0.979233251349577
Adjusted R-squared0.971129154315266
F-TEST (value)120.831876420492
F-TEST (DF numerator)16
F-TEST (DF denominator)41
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation7.2247844893341
Sum Squared Residuals2140.09794761022

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.989562151332385 \tabularnewline
R-squared & 0.979233251349577 \tabularnewline
Adjusted R-squared & 0.971129154315266 \tabularnewline
F-TEST (value) & 120.831876420492 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 41 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 7.2247844893341 \tabularnewline
Sum Squared Residuals & 2140.09794761022 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58410&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.989562151332385[/C][/ROW]
[ROW][C]R-squared[/C][C]0.979233251349577[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.971129154315266[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]120.831876420492[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]41[/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]7.2247844893341[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2140.09794761022[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58410&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58410&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 R0.989562151332385
R-squared0.979233251349577
Adjusted R-squared0.971129154315266
F-TEST (value)120.831876420492
F-TEST (DF numerator)16
F-TEST (DF denominator)41
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation7.2247844893341
Sum Squared Residuals2140.09794761022







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1613605.1128305380477.88716946195325
2611601.5672700716859.43272992831453
3594599.806309189794-5.80630918979425
4595599.260538136083-4.26053813608336
5591598.036928354917-7.03692835491685
6589587.336906603961.66309339603974
7584580.9718748951623.02812510483808
8573580.99109316811-7.99109316811058
9567561.5931429833995.40685701660145
10569568.7428353368230.257164663176796
11621620.9735089819330.0264910180672184
12629628.6672545460580.332745453941555
13628619.3801380335038.61986196649697
14612612.258032181191-0.258032181190727
15595596.577760976465-1.57776097646458
16597596.1446431932630.85535680673684
17593597.961706422295-4.96170642229492
18590587.2836547569752.71634524302540
19580579.698966072120.301033927880349
20574574.298320161511-0.298320161511326
21573560.32849180301112.6715081969889
22573573.6863194649-0.686319464900039
23620623.167295116661-3.16729511666052
24626624.7188156292311.28118437076930
25620613.442847362276.55715263772948
26588601.579298023127-13.5792980231271
27566568.796071589818-2.7960715898181
28557563.313929120878-6.31392912087777
29561555.3870254098625.61297459013844
30549553.804324141578-4.80432414157761
31532538.220046486752-6.22004648675236
32526523.9439928165492.05600718345097
33511511.081077266847-0.0810772668468236
34499510.305348549865-11.3053485498648
35555546.5237971677088.47620283229231
36565559.4889802195145.51101978048596
37542554.409995879286-12.4099958792862
38527523.0207835415053.97921645849463
39510507.656592959422.3434070405795
40514510.3271647042493.67283529575101
41517514.1633074159772.83669258402338
42508511.086247659018-3.08624765901787
43493496.897720343167-3.89772034316707
44490484.9868127255135.01318727448669
45469482.465297578347-13.4652975783470
46478474.9978498878243.00215011217601
47528533.335398733699-5.33539873369901
48534541.124949605197-7.12494960519685
49518528.654188186894-10.6541881868935
50506505.5746161824910.425383817508676
51502494.1632652845037.83673471549744
52516509.9537248455276.04627515447328
53528524.451032396953.54896760304995
54533529.488866838473.51113316153034
55536529.2113922027996.788607797201
56537535.7797811283161.22021887168424
57524528.531990368397-4.53199036839658
58536527.2676467605888.73235323941201

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 613 & 605.112830538047 & 7.88716946195325 \tabularnewline
2 & 611 & 601.567270071685 & 9.43272992831453 \tabularnewline
3 & 594 & 599.806309189794 & -5.80630918979425 \tabularnewline
4 & 595 & 599.260538136083 & -4.26053813608336 \tabularnewline
5 & 591 & 598.036928354917 & -7.03692835491685 \tabularnewline
6 & 589 & 587.33690660396 & 1.66309339603974 \tabularnewline
7 & 584 & 580.971874895162 & 3.02812510483808 \tabularnewline
8 & 573 & 580.99109316811 & -7.99109316811058 \tabularnewline
9 & 567 & 561.593142983399 & 5.40685701660145 \tabularnewline
10 & 569 & 568.742835336823 & 0.257164663176796 \tabularnewline
11 & 621 & 620.973508981933 & 0.0264910180672184 \tabularnewline
12 & 629 & 628.667254546058 & 0.332745453941555 \tabularnewline
13 & 628 & 619.380138033503 & 8.61986196649697 \tabularnewline
14 & 612 & 612.258032181191 & -0.258032181190727 \tabularnewline
15 & 595 & 596.577760976465 & -1.57776097646458 \tabularnewline
16 & 597 & 596.144643193263 & 0.85535680673684 \tabularnewline
17 & 593 & 597.961706422295 & -4.96170642229492 \tabularnewline
18 & 590 & 587.283654756975 & 2.71634524302540 \tabularnewline
19 & 580 & 579.69896607212 & 0.301033927880349 \tabularnewline
20 & 574 & 574.298320161511 & -0.298320161511326 \tabularnewline
21 & 573 & 560.328491803011 & 12.6715081969889 \tabularnewline
22 & 573 & 573.6863194649 & -0.686319464900039 \tabularnewline
23 & 620 & 623.167295116661 & -3.16729511666052 \tabularnewline
24 & 626 & 624.718815629231 & 1.28118437076930 \tabularnewline
25 & 620 & 613.44284736227 & 6.55715263772948 \tabularnewline
26 & 588 & 601.579298023127 & -13.5792980231271 \tabularnewline
27 & 566 & 568.796071589818 & -2.7960715898181 \tabularnewline
28 & 557 & 563.313929120878 & -6.31392912087777 \tabularnewline
29 & 561 & 555.387025409862 & 5.61297459013844 \tabularnewline
30 & 549 & 553.804324141578 & -4.80432414157761 \tabularnewline
31 & 532 & 538.220046486752 & -6.22004648675236 \tabularnewline
32 & 526 & 523.943992816549 & 2.05600718345097 \tabularnewline
33 & 511 & 511.081077266847 & -0.0810772668468236 \tabularnewline
34 & 499 & 510.305348549865 & -11.3053485498648 \tabularnewline
35 & 555 & 546.523797167708 & 8.47620283229231 \tabularnewline
36 & 565 & 559.488980219514 & 5.51101978048596 \tabularnewline
37 & 542 & 554.409995879286 & -12.4099958792862 \tabularnewline
38 & 527 & 523.020783541505 & 3.97921645849463 \tabularnewline
39 & 510 & 507.65659295942 & 2.3434070405795 \tabularnewline
40 & 514 & 510.327164704249 & 3.67283529575101 \tabularnewline
41 & 517 & 514.163307415977 & 2.83669258402338 \tabularnewline
42 & 508 & 511.086247659018 & -3.08624765901787 \tabularnewline
43 & 493 & 496.897720343167 & -3.89772034316707 \tabularnewline
44 & 490 & 484.986812725513 & 5.01318727448669 \tabularnewline
45 & 469 & 482.465297578347 & -13.4652975783470 \tabularnewline
46 & 478 & 474.997849887824 & 3.00215011217601 \tabularnewline
47 & 528 & 533.335398733699 & -5.33539873369901 \tabularnewline
48 & 534 & 541.124949605197 & -7.12494960519685 \tabularnewline
49 & 518 & 528.654188186894 & -10.6541881868935 \tabularnewline
50 & 506 & 505.574616182491 & 0.425383817508676 \tabularnewline
51 & 502 & 494.163265284503 & 7.83673471549744 \tabularnewline
52 & 516 & 509.953724845527 & 6.04627515447328 \tabularnewline
53 & 528 & 524.45103239695 & 3.54896760304995 \tabularnewline
54 & 533 & 529.48886683847 & 3.51113316153034 \tabularnewline
55 & 536 & 529.211392202799 & 6.788607797201 \tabularnewline
56 & 537 & 535.779781128316 & 1.22021887168424 \tabularnewline
57 & 524 & 528.531990368397 & -4.53199036839658 \tabularnewline
58 & 536 & 527.267646760588 & 8.73235323941201 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58410&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]613[/C][C]605.112830538047[/C][C]7.88716946195325[/C][/ROW]
[ROW][C]2[/C][C]611[/C][C]601.567270071685[/C][C]9.43272992831453[/C][/ROW]
[ROW][C]3[/C][C]594[/C][C]599.806309189794[/C][C]-5.80630918979425[/C][/ROW]
[ROW][C]4[/C][C]595[/C][C]599.260538136083[/C][C]-4.26053813608336[/C][/ROW]
[ROW][C]5[/C][C]591[/C][C]598.036928354917[/C][C]-7.03692835491685[/C][/ROW]
[ROW][C]6[/C][C]589[/C][C]587.33690660396[/C][C]1.66309339603974[/C][/ROW]
[ROW][C]7[/C][C]584[/C][C]580.971874895162[/C][C]3.02812510483808[/C][/ROW]
[ROW][C]8[/C][C]573[/C][C]580.99109316811[/C][C]-7.99109316811058[/C][/ROW]
[ROW][C]9[/C][C]567[/C][C]561.593142983399[/C][C]5.40685701660145[/C][/ROW]
[ROW][C]10[/C][C]569[/C][C]568.742835336823[/C][C]0.257164663176796[/C][/ROW]
[ROW][C]11[/C][C]621[/C][C]620.973508981933[/C][C]0.0264910180672184[/C][/ROW]
[ROW][C]12[/C][C]629[/C][C]628.667254546058[/C][C]0.332745453941555[/C][/ROW]
[ROW][C]13[/C][C]628[/C][C]619.380138033503[/C][C]8.61986196649697[/C][/ROW]
[ROW][C]14[/C][C]612[/C][C]612.258032181191[/C][C]-0.258032181190727[/C][/ROW]
[ROW][C]15[/C][C]595[/C][C]596.577760976465[/C][C]-1.57776097646458[/C][/ROW]
[ROW][C]16[/C][C]597[/C][C]596.144643193263[/C][C]0.85535680673684[/C][/ROW]
[ROW][C]17[/C][C]593[/C][C]597.961706422295[/C][C]-4.96170642229492[/C][/ROW]
[ROW][C]18[/C][C]590[/C][C]587.283654756975[/C][C]2.71634524302540[/C][/ROW]
[ROW][C]19[/C][C]580[/C][C]579.69896607212[/C][C]0.301033927880349[/C][/ROW]
[ROW][C]20[/C][C]574[/C][C]574.298320161511[/C][C]-0.298320161511326[/C][/ROW]
[ROW][C]21[/C][C]573[/C][C]560.328491803011[/C][C]12.6715081969889[/C][/ROW]
[ROW][C]22[/C][C]573[/C][C]573.6863194649[/C][C]-0.686319464900039[/C][/ROW]
[ROW][C]23[/C][C]620[/C][C]623.167295116661[/C][C]-3.16729511666052[/C][/ROW]
[ROW][C]24[/C][C]626[/C][C]624.718815629231[/C][C]1.28118437076930[/C][/ROW]
[ROW][C]25[/C][C]620[/C][C]613.44284736227[/C][C]6.55715263772948[/C][/ROW]
[ROW][C]26[/C][C]588[/C][C]601.579298023127[/C][C]-13.5792980231271[/C][/ROW]
[ROW][C]27[/C][C]566[/C][C]568.796071589818[/C][C]-2.7960715898181[/C][/ROW]
[ROW][C]28[/C][C]557[/C][C]563.313929120878[/C][C]-6.31392912087777[/C][/ROW]
[ROW][C]29[/C][C]561[/C][C]555.387025409862[/C][C]5.61297459013844[/C][/ROW]
[ROW][C]30[/C][C]549[/C][C]553.804324141578[/C][C]-4.80432414157761[/C][/ROW]
[ROW][C]31[/C][C]532[/C][C]538.220046486752[/C][C]-6.22004648675236[/C][/ROW]
[ROW][C]32[/C][C]526[/C][C]523.943992816549[/C][C]2.05600718345097[/C][/ROW]
[ROW][C]33[/C][C]511[/C][C]511.081077266847[/C][C]-0.0810772668468236[/C][/ROW]
[ROW][C]34[/C][C]499[/C][C]510.305348549865[/C][C]-11.3053485498648[/C][/ROW]
[ROW][C]35[/C][C]555[/C][C]546.523797167708[/C][C]8.47620283229231[/C][/ROW]
[ROW][C]36[/C][C]565[/C][C]559.488980219514[/C][C]5.51101978048596[/C][/ROW]
[ROW][C]37[/C][C]542[/C][C]554.409995879286[/C][C]-12.4099958792862[/C][/ROW]
[ROW][C]38[/C][C]527[/C][C]523.020783541505[/C][C]3.97921645849463[/C][/ROW]
[ROW][C]39[/C][C]510[/C][C]507.65659295942[/C][C]2.3434070405795[/C][/ROW]
[ROW][C]40[/C][C]514[/C][C]510.327164704249[/C][C]3.67283529575101[/C][/ROW]
[ROW][C]41[/C][C]517[/C][C]514.163307415977[/C][C]2.83669258402338[/C][/ROW]
[ROW][C]42[/C][C]508[/C][C]511.086247659018[/C][C]-3.08624765901787[/C][/ROW]
[ROW][C]43[/C][C]493[/C][C]496.897720343167[/C][C]-3.89772034316707[/C][/ROW]
[ROW][C]44[/C][C]490[/C][C]484.986812725513[/C][C]5.01318727448669[/C][/ROW]
[ROW][C]45[/C][C]469[/C][C]482.465297578347[/C][C]-13.4652975783470[/C][/ROW]
[ROW][C]46[/C][C]478[/C][C]474.997849887824[/C][C]3.00215011217601[/C][/ROW]
[ROW][C]47[/C][C]528[/C][C]533.335398733699[/C][C]-5.33539873369901[/C][/ROW]
[ROW][C]48[/C][C]534[/C][C]541.124949605197[/C][C]-7.12494960519685[/C][/ROW]
[ROW][C]49[/C][C]518[/C][C]528.654188186894[/C][C]-10.6541881868935[/C][/ROW]
[ROW][C]50[/C][C]506[/C][C]505.574616182491[/C][C]0.425383817508676[/C][/ROW]
[ROW][C]51[/C][C]502[/C][C]494.163265284503[/C][C]7.83673471549744[/C][/ROW]
[ROW][C]52[/C][C]516[/C][C]509.953724845527[/C][C]6.04627515447328[/C][/ROW]
[ROW][C]53[/C][C]528[/C][C]524.45103239695[/C][C]3.54896760304995[/C][/ROW]
[ROW][C]54[/C][C]533[/C][C]529.48886683847[/C][C]3.51113316153034[/C][/ROW]
[ROW][C]55[/C][C]536[/C][C]529.211392202799[/C][C]6.788607797201[/C][/ROW]
[ROW][C]56[/C][C]537[/C][C]535.779781128316[/C][C]1.22021887168424[/C][/ROW]
[ROW][C]57[/C][C]524[/C][C]528.531990368397[/C][C]-4.53199036839658[/C][/ROW]
[ROW][C]58[/C][C]536[/C][C]527.267646760588[/C][C]8.73235323941201[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58410&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58410&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
1613605.1128305380477.88716946195325
2611601.5672700716859.43272992831453
3594599.806309189794-5.80630918979425
4595599.260538136083-4.26053813608336
5591598.036928354917-7.03692835491685
6589587.336906603961.66309339603974
7584580.9718748951623.02812510483808
8573580.99109316811-7.99109316811058
9567561.5931429833995.40685701660145
10569568.7428353368230.257164663176796
11621620.9735089819330.0264910180672184
12629628.6672545460580.332745453941555
13628619.3801380335038.61986196649697
14612612.258032181191-0.258032181190727
15595596.577760976465-1.57776097646458
16597596.1446431932630.85535680673684
17593597.961706422295-4.96170642229492
18590587.2836547569752.71634524302540
19580579.698966072120.301033927880349
20574574.298320161511-0.298320161511326
21573560.32849180301112.6715081969889
22573573.6863194649-0.686319464900039
23620623.167295116661-3.16729511666052
24626624.7188156292311.28118437076930
25620613.442847362276.55715263772948
26588601.579298023127-13.5792980231271
27566568.796071589818-2.7960715898181
28557563.313929120878-6.31392912087777
29561555.3870254098625.61297459013844
30549553.804324141578-4.80432414157761
31532538.220046486752-6.22004648675236
32526523.9439928165492.05600718345097
33511511.081077266847-0.0810772668468236
34499510.305348549865-11.3053485498648
35555546.5237971677088.47620283229231
36565559.4889802195145.51101978048596
37542554.409995879286-12.4099958792862
38527523.0207835415053.97921645849463
39510507.656592959422.3434070405795
40514510.3271647042493.67283529575101
41517514.1633074159772.83669258402338
42508511.086247659018-3.08624765901787
43493496.897720343167-3.89772034316707
44490484.9868127255135.01318727448669
45469482.465297578347-13.4652975783470
46478474.9978498878243.00215011217601
47528533.335398733699-5.33539873369901
48534541.124949605197-7.12494960519685
49518528.654188186894-10.6541881868935
50506505.5746161824910.425383817508676
51502494.1632652845037.83673471549744
52516509.9537248455276.04627515447328
53528524.451032396953.54896760304995
54533529.488866838473.51113316153034
55536529.2113922027996.788607797201
56537535.7797811283161.22021887168424
57524528.531990368397-4.53199036839658
58536527.2676467605888.73235323941201







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
200.1297580417837720.2595160835675440.870241958216228
210.1451759213855000.2903518427710010.8548240786145
220.07380359005255880.1476071801051180.926196409947441
230.04199307147557450.0839861429511490.958006928524425
240.02096008062155350.04192016124310690.979039919378446
250.04525410400561220.09050820801122450.954745895994388
260.5117502309331910.9764995381336190.488249769066809
270.404776716698050.80955343339610.59522328330195
280.4371751264041280.8743502528082550.562824873595872
290.3703624686090780.7407249372181560.629637531390922
300.3706123933170220.7412247866340450.629387606682978
310.4578108567983400.9156217135966790.54218914320166
320.4730010175665280.9460020351330550.526998982433472
330.4882656846867650.976531369373530.511734315313235
340.6257605189487310.7484789621025370.374239481051269
350.6991427129983170.6017145740033670.300857287001683
360.8366550490405670.3266899019188670.163344950959433
370.7841910662763610.4316178674472780.215808933723639
380.7580153622512130.4839692754975730.241984637748787

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
20 & 0.129758041783772 & 0.259516083567544 & 0.870241958216228 \tabularnewline
21 & 0.145175921385500 & 0.290351842771001 & 0.8548240786145 \tabularnewline
22 & 0.0738035900525588 & 0.147607180105118 & 0.926196409947441 \tabularnewline
23 & 0.0419930714755745 & 0.083986142951149 & 0.958006928524425 \tabularnewline
24 & 0.0209600806215535 & 0.0419201612431069 & 0.979039919378446 \tabularnewline
25 & 0.0452541040056122 & 0.0905082080112245 & 0.954745895994388 \tabularnewline
26 & 0.511750230933191 & 0.976499538133619 & 0.488249769066809 \tabularnewline
27 & 0.40477671669805 & 0.8095534333961 & 0.59522328330195 \tabularnewline
28 & 0.437175126404128 & 0.874350252808255 & 0.562824873595872 \tabularnewline
29 & 0.370362468609078 & 0.740724937218156 & 0.629637531390922 \tabularnewline
30 & 0.370612393317022 & 0.741224786634045 & 0.629387606682978 \tabularnewline
31 & 0.457810856798340 & 0.915621713596679 & 0.54218914320166 \tabularnewline
32 & 0.473001017566528 & 0.946002035133055 & 0.526998982433472 \tabularnewline
33 & 0.488265684686765 & 0.97653136937353 & 0.511734315313235 \tabularnewline
34 & 0.625760518948731 & 0.748478962102537 & 0.374239481051269 \tabularnewline
35 & 0.699142712998317 & 0.601714574003367 & 0.300857287001683 \tabularnewline
36 & 0.836655049040567 & 0.326689901918867 & 0.163344950959433 \tabularnewline
37 & 0.784191066276361 & 0.431617867447278 & 0.215808933723639 \tabularnewline
38 & 0.758015362251213 & 0.483969275497573 & 0.241984637748787 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58410&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]20[/C][C]0.129758041783772[/C][C]0.259516083567544[/C][C]0.870241958216228[/C][/ROW]
[ROW][C]21[/C][C]0.145175921385500[/C][C]0.290351842771001[/C][C]0.8548240786145[/C][/ROW]
[ROW][C]22[/C][C]0.0738035900525588[/C][C]0.147607180105118[/C][C]0.926196409947441[/C][/ROW]
[ROW][C]23[/C][C]0.0419930714755745[/C][C]0.083986142951149[/C][C]0.958006928524425[/C][/ROW]
[ROW][C]24[/C][C]0.0209600806215535[/C][C]0.0419201612431069[/C][C]0.979039919378446[/C][/ROW]
[ROW][C]25[/C][C]0.0452541040056122[/C][C]0.0905082080112245[/C][C]0.954745895994388[/C][/ROW]
[ROW][C]26[/C][C]0.511750230933191[/C][C]0.976499538133619[/C][C]0.488249769066809[/C][/ROW]
[ROW][C]27[/C][C]0.40477671669805[/C][C]0.8095534333961[/C][C]0.59522328330195[/C][/ROW]
[ROW][C]28[/C][C]0.437175126404128[/C][C]0.874350252808255[/C][C]0.562824873595872[/C][/ROW]
[ROW][C]29[/C][C]0.370362468609078[/C][C]0.740724937218156[/C][C]0.629637531390922[/C][/ROW]
[ROW][C]30[/C][C]0.370612393317022[/C][C]0.741224786634045[/C][C]0.629387606682978[/C][/ROW]
[ROW][C]31[/C][C]0.457810856798340[/C][C]0.915621713596679[/C][C]0.54218914320166[/C][/ROW]
[ROW][C]32[/C][C]0.473001017566528[/C][C]0.946002035133055[/C][C]0.526998982433472[/C][/ROW]
[ROW][C]33[/C][C]0.488265684686765[/C][C]0.97653136937353[/C][C]0.511734315313235[/C][/ROW]
[ROW][C]34[/C][C]0.625760518948731[/C][C]0.748478962102537[/C][C]0.374239481051269[/C][/ROW]
[ROW][C]35[/C][C]0.699142712998317[/C][C]0.601714574003367[/C][C]0.300857287001683[/C][/ROW]
[ROW][C]36[/C][C]0.836655049040567[/C][C]0.326689901918867[/C][C]0.163344950959433[/C][/ROW]
[ROW][C]37[/C][C]0.784191066276361[/C][C]0.431617867447278[/C][C]0.215808933723639[/C][/ROW]
[ROW][C]38[/C][C]0.758015362251213[/C][C]0.483969275497573[/C][C]0.241984637748787[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58410&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58410&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
200.1297580417837720.2595160835675440.870241958216228
210.1451759213855000.2903518427710010.8548240786145
220.07380359005255880.1476071801051180.926196409947441
230.04199307147557450.0839861429511490.958006928524425
240.02096008062155350.04192016124310690.979039919378446
250.04525410400561220.09050820801122450.954745895994388
260.5117502309331910.9764995381336190.488249769066809
270.404776716698050.80955343339610.59522328330195
280.4371751264041280.8743502528082550.562824873595872
290.3703624686090780.7407249372181560.629637531390922
300.3706123933170220.7412247866340450.629387606682978
310.4578108567983400.9156217135966790.54218914320166
320.4730010175665280.9460020351330550.526998982433472
330.4882656846867650.976531369373530.511734315313235
340.6257605189487310.7484789621025370.374239481051269
350.6991427129983170.6017145740033670.300857287001683
360.8366550490405670.3266899019188670.163344950959433
370.7841910662763610.4316178674472780.215808933723639
380.7580153622512130.4839692754975730.241984637748787







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0526315789473684NOK
10% type I error level30.157894736842105NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.0526315789473684 & NOK \tabularnewline
10% type I error level & 3 & 0.157894736842105 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58410&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.0526315789473684[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.157894736842105[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58410&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58410&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0526315789473684NOK
10% type I error level30.157894736842105NOK



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')
}