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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 11:17:33 -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/t1258741728jv37hpc280dbg3y.htm/, Retrieved Fri, 29 Mar 2024 14:37:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58395, Retrieved Fri, 29 Mar 2024 14:37:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact134
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] [] [2009-11-20 18:17:33] [aa8eb70c35ea8a87edcd21d6427e653e] [Current]
-    D        [Multiple Regression] [] [2009-12-15 18:47:48] [73863f7f907331e734eff34b7de6fc83]
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Dataseries X:
594	139
595	135
591	130
589	127
584	122
573	117
567	112
569	113
621	149
629	157
628	157
612	147
595	137
597	132
593	125
590	123
580	117
574	114
573	111
573	112
620	144
626	150
620	149
588	134
566	123
557	116
561	117
549	111
532	105
526	102
511	95
499	93
555	124
565	130
542	124
527	115
510	106
514	105
517	105
508	101
493	95
490	93
469	84
478	87
528	116
534	120
518	117
506	109
502	105
516	107
528	109
533	109
536	108
537	107
524	99
536	103
587	131
597	137
581	135
564	124




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=58395&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=58395&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58395&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 176.905248685416 + 3.04049881808095X[t] + 5.55389550870783M1[t] + 17.0753919629505M2[t] + 24.7482898354962M3[t] + 29.669786289739M4[t] + 35.4641806165276M5[t] + 38.9775773071543M6[t] + 47.2367697428724M7[t] + 45.180071397559M8[t] + 1.51650827343334M9[t] -8.72648463505236M10[t] -13.8292874716581M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  176.905248685416 +  3.04049881808095X[t] +  5.55389550870783M1[t] +  17.0753919629505M2[t] +  24.7482898354962M3[t] +  29.669786289739M4[t] +  35.4641806165276M5[t] +  38.9775773071543M6[t] +  47.2367697428724M7[t] +  45.180071397559M8[t] +  1.51650827343334M9[t] -8.72648463505236M10[t] -13.8292874716581M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58395&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  176.905248685416 +  3.04049881808095X[t] +  5.55389550870783M1[t] +  17.0753919629505M2[t] +  24.7482898354962M3[t] +  29.669786289739M4[t] +  35.4641806165276M5[t] +  38.9775773071543M6[t] +  47.2367697428724M7[t] +  45.180071397559M8[t] +  1.51650827343334M9[t] -8.72648463505236M10[t] -13.8292874716581M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58395&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58395&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] = + 176.905248685416 + 3.04049881808095X[t] + 5.55389550870783M1[t] + 17.0753919629505M2[t] + 24.7482898354962M3[t] + 29.669786289739M4[t] + 35.4641806165276M5[t] + 38.9775773071543M6[t] + 47.2367697428724M7[t] + 45.180071397559M8[t] + 1.51650827343334M9[t] -8.72648463505236M10[t] -13.8292874716581M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)176.90524868541611.53630415.334700
X3.040498818080950.08724634.849600
M15.553895508707835.0354621.1030.2756620.137831
M217.07539196295055.059443.3750.0014880.000744
M324.74828983549625.080254.87151.3e-056e-06
M429.6697862897395.1254485.78871e-060
M535.46418061652765.2242956.788300
M638.97757730715435.2964157.359200
M747.23676974287245.4985918.590700
M845.1800713975595.4501198.289700
M91.516508273433345.0615160.29960.7657920.382896
M10-8.726484635052365.150959-1.69410.0968550.048427
M11-13.82928747165815.108937-2.70690.0094380.004719

\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) & 176.905248685416 & 11.536304 & 15.3347 & 0 & 0 \tabularnewline
X & 3.04049881808095 & 0.087246 & 34.8496 & 0 & 0 \tabularnewline
M1 & 5.55389550870783 & 5.035462 & 1.103 & 0.275662 & 0.137831 \tabularnewline
M2 & 17.0753919629505 & 5.05944 & 3.375 & 0.001488 & 0.000744 \tabularnewline
M3 & 24.7482898354962 & 5.08025 & 4.8715 & 1.3e-05 & 6e-06 \tabularnewline
M4 & 29.669786289739 & 5.125448 & 5.7887 & 1e-06 & 0 \tabularnewline
M5 & 35.4641806165276 & 5.224295 & 6.7883 & 0 & 0 \tabularnewline
M6 & 38.9775773071543 & 5.296415 & 7.3592 & 0 & 0 \tabularnewline
M7 & 47.2367697428724 & 5.498591 & 8.5907 & 0 & 0 \tabularnewline
M8 & 45.180071397559 & 5.450119 & 8.2897 & 0 & 0 \tabularnewline
M9 & 1.51650827343334 & 5.061516 & 0.2996 & 0.765792 & 0.382896 \tabularnewline
M10 & -8.72648463505236 & 5.150959 & -1.6941 & 0.096855 & 0.048427 \tabularnewline
M11 & -13.8292874716581 & 5.108937 & -2.7069 & 0.009438 & 0.004719 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58395&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]176.905248685416[/C][C]11.536304[/C][C]15.3347[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]3.04049881808095[/C][C]0.087246[/C][C]34.8496[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]5.55389550870783[/C][C]5.035462[/C][C]1.103[/C][C]0.275662[/C][C]0.137831[/C][/ROW]
[ROW][C]M2[/C][C]17.0753919629505[/C][C]5.05944[/C][C]3.375[/C][C]0.001488[/C][C]0.000744[/C][/ROW]
[ROW][C]M3[/C][C]24.7482898354962[/C][C]5.08025[/C][C]4.8715[/C][C]1.3e-05[/C][C]6e-06[/C][/ROW]
[ROW][C]M4[/C][C]29.669786289739[/C][C]5.125448[/C][C]5.7887[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]M5[/C][C]35.4641806165276[/C][C]5.224295[/C][C]6.7883[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M6[/C][C]38.9775773071543[/C][C]5.296415[/C][C]7.3592[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M7[/C][C]47.2367697428724[/C][C]5.498591[/C][C]8.5907[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M8[/C][C]45.180071397559[/C][C]5.450119[/C][C]8.2897[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]1.51650827343334[/C][C]5.061516[/C][C]0.2996[/C][C]0.765792[/C][C]0.382896[/C][/ROW]
[ROW][C]M10[/C][C]-8.72648463505236[/C][C]5.150959[/C][C]-1.6941[/C][C]0.096855[/C][C]0.048427[/C][/ROW]
[ROW][C]M11[/C][C]-13.8292874716581[/C][C]5.108937[/C][C]-2.7069[/C][C]0.009438[/C][C]0.004719[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58395&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58395&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)176.90524868541611.53630415.334700
X3.040498818080950.08724634.849600
M15.553895508707835.0354621.1030.2756620.137831
M217.07539196295055.059443.3750.0014880.000744
M324.74828983549625.080254.87151.3e-056e-06
M429.6697862897395.1254485.78871e-060
M535.46418061652765.2242956.788300
M638.97757730715435.2964157.359200
M747.23676974287245.4985918.590700
M845.1800713975595.4501198.289700
M91.516508273433345.0615160.29960.7657920.382896
M10-8.726484635052365.150959-1.69410.0968550.048427
M11-13.82928747165815.108937-2.70690.0094380.004719







Multiple Linear Regression - Regression Statistics
Multiple R0.985073814323171
R-squared0.970370419665202
Adjusted R-squared0.962805420430785
F-TEST (value)128.271053254117
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation7.94448891931475
Sum Squared Residuals2966.40049688840

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.985073814323171 \tabularnewline
R-squared & 0.970370419665202 \tabularnewline
Adjusted R-squared & 0.962805420430785 \tabularnewline
F-TEST (value) & 128.271053254117 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 7.94448891931475 \tabularnewline
Sum Squared Residuals & 2966.40049688840 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58395&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.985073814323171[/C][/ROW]
[ROW][C]R-squared[/C][C]0.970370419665202[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.962805420430785[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]128.271053254117[/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]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]7.94448891931475[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2966.40049688840[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58395&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58395&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 R0.985073814323171
R-squared0.970370419665202
Adjusted R-squared0.962805420430785
F-TEST (value)128.271053254117
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation7.94448891931475
Sum Squared Residuals2966.40049688840







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1594605.088479907375-11.0884799073753
2595604.447981089295-9.4479810892953
3591596.918384871436-5.91838487143616
4589592.718384871436-3.71838487143619
5584583.310285107820.689714892180035
6573571.6211877080421.37881229195811
7567564.6778860533552.32211394664476
8569565.6616865261233.33831347387716
9621631.456080852911-10.4560808529114
10629645.537078489073-16.5370784890733
11628640.434275652468-12.4342756524676
12612623.858574943316-11.8585749433162
13595599.007482271214-4.00748227121449
14597595.3264846350521.67351536494766
15593581.71589078103111.2841092189686
16590580.5563895991129.44361040088764
17580568.10779101741511.8922089825848
18574562.49969125379911.5003087462010
19573561.63738723527411.3626127647257
20573562.62118770804210.3788122919581
21620616.2535867625073.74641323749335
22626624.2535867625071.74641323749335
23620616.110285107823.88971489218002
24588584.3320903082643.6679096917362
25566556.4404988180819.55950118191883
26557546.67850354575710.3214964542429
27561557.3919002363843.60809976361619
28549544.0704037821414.92959621785904
29532531.6218052004440.378194799556179
30526526.013705436828-0.0137054368276273
31511512.989406145979-1.98940614597905
32499504.851710164504-5.85171016450381
33555555.443610400888-0.443610400887622
34565563.4436104008881.55638959911237
35542540.0978146557961.90218534420379
36527526.5626127647260.43738723527428
37510504.7520189107055.247981089295
38514513.2330165468670.766983453133338
39517520.905914419412-3.9059144194124
40508513.665415601331-5.66541560133144
41493501.216817019634-8.2168170196343
42490498.649216074099-8.64921607409907
43469479.543919147089-10.5439191470886
44478486.608717256018-8.6087172560181
45528531.11961985624-3.11961985624001
46534533.0386222200780.961377779921877
47518518.81432292923-0.814322929229542
48506508.31961985624-2.31961985624002
49502501.7115200926240.288479907375944
50516519.314014183029-3.31401418302857
51528533.067909691736-5.0679096917362
52533537.989406145979-4.98940614597904
53536540.743301654687-4.74330165468667
54537541.216199527232-4.21619952723238
55524525.151401418303-1.15140141830285
56536535.2566983453130.743301654686659
57587576.72710212745410.2728978725457
58597584.72710212745412.2728978725457
59581573.5433016546877.45669834531333
60564553.92710212745410.0728978725457

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 594 & 605.088479907375 & -11.0884799073753 \tabularnewline
2 & 595 & 604.447981089295 & -9.4479810892953 \tabularnewline
3 & 591 & 596.918384871436 & -5.91838487143616 \tabularnewline
4 & 589 & 592.718384871436 & -3.71838487143619 \tabularnewline
5 & 584 & 583.31028510782 & 0.689714892180035 \tabularnewline
6 & 573 & 571.621187708042 & 1.37881229195811 \tabularnewline
7 & 567 & 564.677886053355 & 2.32211394664476 \tabularnewline
8 & 569 & 565.661686526123 & 3.33831347387716 \tabularnewline
9 & 621 & 631.456080852911 & -10.4560808529114 \tabularnewline
10 & 629 & 645.537078489073 & -16.5370784890733 \tabularnewline
11 & 628 & 640.434275652468 & -12.4342756524676 \tabularnewline
12 & 612 & 623.858574943316 & -11.8585749433162 \tabularnewline
13 & 595 & 599.007482271214 & -4.00748227121449 \tabularnewline
14 & 597 & 595.326484635052 & 1.67351536494766 \tabularnewline
15 & 593 & 581.715890781031 & 11.2841092189686 \tabularnewline
16 & 590 & 580.556389599112 & 9.44361040088764 \tabularnewline
17 & 580 & 568.107791017415 & 11.8922089825848 \tabularnewline
18 & 574 & 562.499691253799 & 11.5003087462010 \tabularnewline
19 & 573 & 561.637387235274 & 11.3626127647257 \tabularnewline
20 & 573 & 562.621187708042 & 10.3788122919581 \tabularnewline
21 & 620 & 616.253586762507 & 3.74641323749335 \tabularnewline
22 & 626 & 624.253586762507 & 1.74641323749335 \tabularnewline
23 & 620 & 616.11028510782 & 3.88971489218002 \tabularnewline
24 & 588 & 584.332090308264 & 3.6679096917362 \tabularnewline
25 & 566 & 556.440498818081 & 9.55950118191883 \tabularnewline
26 & 557 & 546.678503545757 & 10.3214964542429 \tabularnewline
27 & 561 & 557.391900236384 & 3.60809976361619 \tabularnewline
28 & 549 & 544.070403782141 & 4.92959621785904 \tabularnewline
29 & 532 & 531.621805200444 & 0.378194799556179 \tabularnewline
30 & 526 & 526.013705436828 & -0.0137054368276273 \tabularnewline
31 & 511 & 512.989406145979 & -1.98940614597905 \tabularnewline
32 & 499 & 504.851710164504 & -5.85171016450381 \tabularnewline
33 & 555 & 555.443610400888 & -0.443610400887622 \tabularnewline
34 & 565 & 563.443610400888 & 1.55638959911237 \tabularnewline
35 & 542 & 540.097814655796 & 1.90218534420379 \tabularnewline
36 & 527 & 526.562612764726 & 0.43738723527428 \tabularnewline
37 & 510 & 504.752018910705 & 5.247981089295 \tabularnewline
38 & 514 & 513.233016546867 & 0.766983453133338 \tabularnewline
39 & 517 & 520.905914419412 & -3.9059144194124 \tabularnewline
40 & 508 & 513.665415601331 & -5.66541560133144 \tabularnewline
41 & 493 & 501.216817019634 & -8.2168170196343 \tabularnewline
42 & 490 & 498.649216074099 & -8.64921607409907 \tabularnewline
43 & 469 & 479.543919147089 & -10.5439191470886 \tabularnewline
44 & 478 & 486.608717256018 & -8.6087172560181 \tabularnewline
45 & 528 & 531.11961985624 & -3.11961985624001 \tabularnewline
46 & 534 & 533.038622220078 & 0.961377779921877 \tabularnewline
47 & 518 & 518.81432292923 & -0.814322929229542 \tabularnewline
48 & 506 & 508.31961985624 & -2.31961985624002 \tabularnewline
49 & 502 & 501.711520092624 & 0.288479907375944 \tabularnewline
50 & 516 & 519.314014183029 & -3.31401418302857 \tabularnewline
51 & 528 & 533.067909691736 & -5.0679096917362 \tabularnewline
52 & 533 & 537.989406145979 & -4.98940614597904 \tabularnewline
53 & 536 & 540.743301654687 & -4.74330165468667 \tabularnewline
54 & 537 & 541.216199527232 & -4.21619952723238 \tabularnewline
55 & 524 & 525.151401418303 & -1.15140141830285 \tabularnewline
56 & 536 & 535.256698345313 & 0.743301654686659 \tabularnewline
57 & 587 & 576.727102127454 & 10.2728978725457 \tabularnewline
58 & 597 & 584.727102127454 & 12.2728978725457 \tabularnewline
59 & 581 & 573.543301654687 & 7.45669834531333 \tabularnewline
60 & 564 & 553.927102127454 & 10.0728978725457 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58395&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]594[/C][C]605.088479907375[/C][C]-11.0884799073753[/C][/ROW]
[ROW][C]2[/C][C]595[/C][C]604.447981089295[/C][C]-9.4479810892953[/C][/ROW]
[ROW][C]3[/C][C]591[/C][C]596.918384871436[/C][C]-5.91838487143616[/C][/ROW]
[ROW][C]4[/C][C]589[/C][C]592.718384871436[/C][C]-3.71838487143619[/C][/ROW]
[ROW][C]5[/C][C]584[/C][C]583.31028510782[/C][C]0.689714892180035[/C][/ROW]
[ROW][C]6[/C][C]573[/C][C]571.621187708042[/C][C]1.37881229195811[/C][/ROW]
[ROW][C]7[/C][C]567[/C][C]564.677886053355[/C][C]2.32211394664476[/C][/ROW]
[ROW][C]8[/C][C]569[/C][C]565.661686526123[/C][C]3.33831347387716[/C][/ROW]
[ROW][C]9[/C][C]621[/C][C]631.456080852911[/C][C]-10.4560808529114[/C][/ROW]
[ROW][C]10[/C][C]629[/C][C]645.537078489073[/C][C]-16.5370784890733[/C][/ROW]
[ROW][C]11[/C][C]628[/C][C]640.434275652468[/C][C]-12.4342756524676[/C][/ROW]
[ROW][C]12[/C][C]612[/C][C]623.858574943316[/C][C]-11.8585749433162[/C][/ROW]
[ROW][C]13[/C][C]595[/C][C]599.007482271214[/C][C]-4.00748227121449[/C][/ROW]
[ROW][C]14[/C][C]597[/C][C]595.326484635052[/C][C]1.67351536494766[/C][/ROW]
[ROW][C]15[/C][C]593[/C][C]581.715890781031[/C][C]11.2841092189686[/C][/ROW]
[ROW][C]16[/C][C]590[/C][C]580.556389599112[/C][C]9.44361040088764[/C][/ROW]
[ROW][C]17[/C][C]580[/C][C]568.107791017415[/C][C]11.8922089825848[/C][/ROW]
[ROW][C]18[/C][C]574[/C][C]562.499691253799[/C][C]11.5003087462010[/C][/ROW]
[ROW][C]19[/C][C]573[/C][C]561.637387235274[/C][C]11.3626127647257[/C][/ROW]
[ROW][C]20[/C][C]573[/C][C]562.621187708042[/C][C]10.3788122919581[/C][/ROW]
[ROW][C]21[/C][C]620[/C][C]616.253586762507[/C][C]3.74641323749335[/C][/ROW]
[ROW][C]22[/C][C]626[/C][C]624.253586762507[/C][C]1.74641323749335[/C][/ROW]
[ROW][C]23[/C][C]620[/C][C]616.11028510782[/C][C]3.88971489218002[/C][/ROW]
[ROW][C]24[/C][C]588[/C][C]584.332090308264[/C][C]3.6679096917362[/C][/ROW]
[ROW][C]25[/C][C]566[/C][C]556.440498818081[/C][C]9.55950118191883[/C][/ROW]
[ROW][C]26[/C][C]557[/C][C]546.678503545757[/C][C]10.3214964542429[/C][/ROW]
[ROW][C]27[/C][C]561[/C][C]557.391900236384[/C][C]3.60809976361619[/C][/ROW]
[ROW][C]28[/C][C]549[/C][C]544.070403782141[/C][C]4.92959621785904[/C][/ROW]
[ROW][C]29[/C][C]532[/C][C]531.621805200444[/C][C]0.378194799556179[/C][/ROW]
[ROW][C]30[/C][C]526[/C][C]526.013705436828[/C][C]-0.0137054368276273[/C][/ROW]
[ROW][C]31[/C][C]511[/C][C]512.989406145979[/C][C]-1.98940614597905[/C][/ROW]
[ROW][C]32[/C][C]499[/C][C]504.851710164504[/C][C]-5.85171016450381[/C][/ROW]
[ROW][C]33[/C][C]555[/C][C]555.443610400888[/C][C]-0.443610400887622[/C][/ROW]
[ROW][C]34[/C][C]565[/C][C]563.443610400888[/C][C]1.55638959911237[/C][/ROW]
[ROW][C]35[/C][C]542[/C][C]540.097814655796[/C][C]1.90218534420379[/C][/ROW]
[ROW][C]36[/C][C]527[/C][C]526.562612764726[/C][C]0.43738723527428[/C][/ROW]
[ROW][C]37[/C][C]510[/C][C]504.752018910705[/C][C]5.247981089295[/C][/ROW]
[ROW][C]38[/C][C]514[/C][C]513.233016546867[/C][C]0.766983453133338[/C][/ROW]
[ROW][C]39[/C][C]517[/C][C]520.905914419412[/C][C]-3.9059144194124[/C][/ROW]
[ROW][C]40[/C][C]508[/C][C]513.665415601331[/C][C]-5.66541560133144[/C][/ROW]
[ROW][C]41[/C][C]493[/C][C]501.216817019634[/C][C]-8.2168170196343[/C][/ROW]
[ROW][C]42[/C][C]490[/C][C]498.649216074099[/C][C]-8.64921607409907[/C][/ROW]
[ROW][C]43[/C][C]469[/C][C]479.543919147089[/C][C]-10.5439191470886[/C][/ROW]
[ROW][C]44[/C][C]478[/C][C]486.608717256018[/C][C]-8.6087172560181[/C][/ROW]
[ROW][C]45[/C][C]528[/C][C]531.11961985624[/C][C]-3.11961985624001[/C][/ROW]
[ROW][C]46[/C][C]534[/C][C]533.038622220078[/C][C]0.961377779921877[/C][/ROW]
[ROW][C]47[/C][C]518[/C][C]518.81432292923[/C][C]-0.814322929229542[/C][/ROW]
[ROW][C]48[/C][C]506[/C][C]508.31961985624[/C][C]-2.31961985624002[/C][/ROW]
[ROW][C]49[/C][C]502[/C][C]501.711520092624[/C][C]0.288479907375944[/C][/ROW]
[ROW][C]50[/C][C]516[/C][C]519.314014183029[/C][C]-3.31401418302857[/C][/ROW]
[ROW][C]51[/C][C]528[/C][C]533.067909691736[/C][C]-5.0679096917362[/C][/ROW]
[ROW][C]52[/C][C]533[/C][C]537.989406145979[/C][C]-4.98940614597904[/C][/ROW]
[ROW][C]53[/C][C]536[/C][C]540.743301654687[/C][C]-4.74330165468667[/C][/ROW]
[ROW][C]54[/C][C]537[/C][C]541.216199527232[/C][C]-4.21619952723238[/C][/ROW]
[ROW][C]55[/C][C]524[/C][C]525.151401418303[/C][C]-1.15140141830285[/C][/ROW]
[ROW][C]56[/C][C]536[/C][C]535.256698345313[/C][C]0.743301654686659[/C][/ROW]
[ROW][C]57[/C][C]587[/C][C]576.727102127454[/C][C]10.2728978725457[/C][/ROW]
[ROW][C]58[/C][C]597[/C][C]584.727102127454[/C][C]12.2728978725457[/C][/ROW]
[ROW][C]59[/C][C]581[/C][C]573.543301654687[/C][C]7.45669834531333[/C][/ROW]
[ROW][C]60[/C][C]564[/C][C]553.927102127454[/C][C]10.0728978725457[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58395&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58395&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
1594605.088479907375-11.0884799073753
2595604.447981089295-9.4479810892953
3591596.918384871436-5.91838487143616
4589592.718384871436-3.71838487143619
5584583.310285107820.689714892180035
6573571.6211877080421.37881229195811
7567564.6778860533552.32211394664476
8569565.6616865261233.33831347387716
9621631.456080852911-10.4560808529114
10629645.537078489073-16.5370784890733
11628640.434275652468-12.4342756524676
12612623.858574943316-11.8585749433162
13595599.007482271214-4.00748227121449
14597595.3264846350521.67351536494766
15593581.71589078103111.2841092189686
16590580.5563895991129.44361040088764
17580568.10779101741511.8922089825848
18574562.49969125379911.5003087462010
19573561.63738723527411.3626127647257
20573562.62118770804210.3788122919581
21620616.2535867625073.74641323749335
22626624.2535867625071.74641323749335
23620616.110285107823.88971489218002
24588584.3320903082643.6679096917362
25566556.4404988180819.55950118191883
26557546.67850354575710.3214964542429
27561557.3919002363843.60809976361619
28549544.0704037821414.92959621785904
29532531.6218052004440.378194799556179
30526526.013705436828-0.0137054368276273
31511512.989406145979-1.98940614597905
32499504.851710164504-5.85171016450381
33555555.443610400888-0.443610400887622
34565563.4436104008881.55638959911237
35542540.0978146557961.90218534420379
36527526.5626127647260.43738723527428
37510504.7520189107055.247981089295
38514513.2330165468670.766983453133338
39517520.905914419412-3.9059144194124
40508513.665415601331-5.66541560133144
41493501.216817019634-8.2168170196343
42490498.649216074099-8.64921607409907
43469479.543919147089-10.5439191470886
44478486.608717256018-8.6087172560181
45528531.11961985624-3.11961985624001
46534533.0386222200780.961377779921877
47518518.81432292923-0.814322929229542
48506508.31961985624-2.31961985624002
49502501.7115200926240.288479907375944
50516519.314014183029-3.31401418302857
51528533.067909691736-5.0679096917362
52533537.989406145979-4.98940614597904
53536540.743301654687-4.74330165468667
54537541.216199527232-4.21619952723238
55524525.151401418303-1.15140141830285
56536535.2566983453130.743301654686659
57587576.72710212745410.2728978725457
58597584.72710212745412.2728978725457
59581573.5433016546877.45669834531333
60564553.92710212745410.0728978725457







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.0008606974095109950.001721394819021990.99913930259049
170.03605285310486040.07210570620972090.96394714689514
180.01584058631071760.03168117262143530.984159413689282
190.05348527825213010.1069705565042600.94651472174787
200.05774616666128510.1154923333225700.942253833338715
210.03472210193322660.06944420386645320.965277898066773
220.04923817269622480.09847634539244970.950761827303775
230.1657108133033240.3314216266066480.834289186696676
240.8694870356717140.2610259286565720.130512964328286
250.9538015748006540.09239685039869230.0461984251993462
260.98836105555230.02327788889539790.0116389444476989
270.9948722686282260.01025546274354840.00512773137177421
280.9990834475552120.001833104889576130.000916552444788067
290.99988979388360.0002204122328014280.000110206116400714
300.999969358225586.12835488412234e-053.06417744206117e-05
310.9999778400339394.43199321225238e-052.21599660612619e-05
320.9999802759446663.94481106685859e-051.97240553342929e-05
330.9999678898469786.42203060430797e-053.21101530215398e-05
340.9999762341628664.75316742689485e-052.37658371344742e-05
350.9999162321557370.0001675356885254628.37678442627312e-05
360.999790541901540.0004189161969196730.000209458098459837
370.9996341981418870.0007316037162269910.000365801858113495
380.99956838731760.0008632253647994760.000431612682399738
390.9992304126673150.001539174665370520.00076958733268526
400.998853110598750.002293778802500470.00114688940125023
410.9986205517147810.002758896570438080.00137944828521904
420.99850152289220.002996954215597410.00149847710779870
430.9938473177494440.01230536450111170.00615268225055586
440.975878684702410.04824263059518160.0241213152975908

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.000860697409510995 & 0.00172139481902199 & 0.99913930259049 \tabularnewline
17 & 0.0360528531048604 & 0.0721057062097209 & 0.96394714689514 \tabularnewline
18 & 0.0158405863107176 & 0.0316811726214353 & 0.984159413689282 \tabularnewline
19 & 0.0534852782521301 & 0.106970556504260 & 0.94651472174787 \tabularnewline
20 & 0.0577461666612851 & 0.115492333322570 & 0.942253833338715 \tabularnewline
21 & 0.0347221019332266 & 0.0694442038664532 & 0.965277898066773 \tabularnewline
22 & 0.0492381726962248 & 0.0984763453924497 & 0.950761827303775 \tabularnewline
23 & 0.165710813303324 & 0.331421626606648 & 0.834289186696676 \tabularnewline
24 & 0.869487035671714 & 0.261025928656572 & 0.130512964328286 \tabularnewline
25 & 0.953801574800654 & 0.0923968503986923 & 0.0461984251993462 \tabularnewline
26 & 0.9883610555523 & 0.0232778888953979 & 0.0116389444476989 \tabularnewline
27 & 0.994872268628226 & 0.0102554627435484 & 0.00512773137177421 \tabularnewline
28 & 0.999083447555212 & 0.00183310488957613 & 0.000916552444788067 \tabularnewline
29 & 0.9998897938836 & 0.000220412232801428 & 0.000110206116400714 \tabularnewline
30 & 0.99996935822558 & 6.12835488412234e-05 & 3.06417744206117e-05 \tabularnewline
31 & 0.999977840033939 & 4.43199321225238e-05 & 2.21599660612619e-05 \tabularnewline
32 & 0.999980275944666 & 3.94481106685859e-05 & 1.97240553342929e-05 \tabularnewline
33 & 0.999967889846978 & 6.42203060430797e-05 & 3.21101530215398e-05 \tabularnewline
34 & 0.999976234162866 & 4.75316742689485e-05 & 2.37658371344742e-05 \tabularnewline
35 & 0.999916232155737 & 0.000167535688525462 & 8.37678442627312e-05 \tabularnewline
36 & 0.99979054190154 & 0.000418916196919673 & 0.000209458098459837 \tabularnewline
37 & 0.999634198141887 & 0.000731603716226991 & 0.000365801858113495 \tabularnewline
38 & 0.9995683873176 & 0.000863225364799476 & 0.000431612682399738 \tabularnewline
39 & 0.999230412667315 & 0.00153917466537052 & 0.00076958733268526 \tabularnewline
40 & 0.99885311059875 & 0.00229377880250047 & 0.00114688940125023 \tabularnewline
41 & 0.998620551714781 & 0.00275889657043808 & 0.00137944828521904 \tabularnewline
42 & 0.9985015228922 & 0.00299695421559741 & 0.00149847710779870 \tabularnewline
43 & 0.993847317749444 & 0.0123053645011117 & 0.00615268225055586 \tabularnewline
44 & 0.97587868470241 & 0.0482426305951816 & 0.0241213152975908 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58395&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]0.000860697409510995[/C][C]0.00172139481902199[/C][C]0.99913930259049[/C][/ROW]
[ROW][C]17[/C][C]0.0360528531048604[/C][C]0.0721057062097209[/C][C]0.96394714689514[/C][/ROW]
[ROW][C]18[/C][C]0.0158405863107176[/C][C]0.0316811726214353[/C][C]0.984159413689282[/C][/ROW]
[ROW][C]19[/C][C]0.0534852782521301[/C][C]0.106970556504260[/C][C]0.94651472174787[/C][/ROW]
[ROW][C]20[/C][C]0.0577461666612851[/C][C]0.115492333322570[/C][C]0.942253833338715[/C][/ROW]
[ROW][C]21[/C][C]0.0347221019332266[/C][C]0.0694442038664532[/C][C]0.965277898066773[/C][/ROW]
[ROW][C]22[/C][C]0.0492381726962248[/C][C]0.0984763453924497[/C][C]0.950761827303775[/C][/ROW]
[ROW][C]23[/C][C]0.165710813303324[/C][C]0.331421626606648[/C][C]0.834289186696676[/C][/ROW]
[ROW][C]24[/C][C]0.869487035671714[/C][C]0.261025928656572[/C][C]0.130512964328286[/C][/ROW]
[ROW][C]25[/C][C]0.953801574800654[/C][C]0.0923968503986923[/C][C]0.0461984251993462[/C][/ROW]
[ROW][C]26[/C][C]0.9883610555523[/C][C]0.0232778888953979[/C][C]0.0116389444476989[/C][/ROW]
[ROW][C]27[/C][C]0.994872268628226[/C][C]0.0102554627435484[/C][C]0.00512773137177421[/C][/ROW]
[ROW][C]28[/C][C]0.999083447555212[/C][C]0.00183310488957613[/C][C]0.000916552444788067[/C][/ROW]
[ROW][C]29[/C][C]0.9998897938836[/C][C]0.000220412232801428[/C][C]0.000110206116400714[/C][/ROW]
[ROW][C]30[/C][C]0.99996935822558[/C][C]6.12835488412234e-05[/C][C]3.06417744206117e-05[/C][/ROW]
[ROW][C]31[/C][C]0.999977840033939[/C][C]4.43199321225238e-05[/C][C]2.21599660612619e-05[/C][/ROW]
[ROW][C]32[/C][C]0.999980275944666[/C][C]3.94481106685859e-05[/C][C]1.97240553342929e-05[/C][/ROW]
[ROW][C]33[/C][C]0.999967889846978[/C][C]6.42203060430797e-05[/C][C]3.21101530215398e-05[/C][/ROW]
[ROW][C]34[/C][C]0.999976234162866[/C][C]4.75316742689485e-05[/C][C]2.37658371344742e-05[/C][/ROW]
[ROW][C]35[/C][C]0.999916232155737[/C][C]0.000167535688525462[/C][C]8.37678442627312e-05[/C][/ROW]
[ROW][C]36[/C][C]0.99979054190154[/C][C]0.000418916196919673[/C][C]0.000209458098459837[/C][/ROW]
[ROW][C]37[/C][C]0.999634198141887[/C][C]0.000731603716226991[/C][C]0.000365801858113495[/C][/ROW]
[ROW][C]38[/C][C]0.9995683873176[/C][C]0.000863225364799476[/C][C]0.000431612682399738[/C][/ROW]
[ROW][C]39[/C][C]0.999230412667315[/C][C]0.00153917466537052[/C][C]0.00076958733268526[/C][/ROW]
[ROW][C]40[/C][C]0.99885311059875[/C][C]0.00229377880250047[/C][C]0.00114688940125023[/C][/ROW]
[ROW][C]41[/C][C]0.998620551714781[/C][C]0.00275889657043808[/C][C]0.00137944828521904[/C][/ROW]
[ROW][C]42[/C][C]0.9985015228922[/C][C]0.00299695421559741[/C][C]0.00149847710779870[/C][/ROW]
[ROW][C]43[/C][C]0.993847317749444[/C][C]0.0123053645011117[/C][C]0.00615268225055586[/C][/ROW]
[ROW][C]44[/C][C]0.97587868470241[/C][C]0.0482426305951816[/C][C]0.0241213152975908[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58395&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58395&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
160.0008606974095109950.001721394819021990.99913930259049
170.03605285310486040.07210570620972090.96394714689514
180.01584058631071760.03168117262143530.984159413689282
190.05348527825213010.1069705565042600.94651472174787
200.05774616666128510.1154923333225700.942253833338715
210.03472210193322660.06944420386645320.965277898066773
220.04923817269622480.09847634539244970.950761827303775
230.1657108133033240.3314216266066480.834289186696676
240.8694870356717140.2610259286565720.130512964328286
250.9538015748006540.09239685039869230.0461984251993462
260.98836105555230.02327788889539790.0116389444476989
270.9948722686282260.01025546274354840.00512773137177421
280.9990834475552120.001833104889576130.000916552444788067
290.99988979388360.0002204122328014280.000110206116400714
300.999969358225586.12835488412234e-053.06417744206117e-05
310.9999778400339394.43199321225238e-052.21599660612619e-05
320.9999802759446663.94481106685859e-051.97240553342929e-05
330.9999678898469786.42203060430797e-053.21101530215398e-05
340.9999762341628664.75316742689485e-052.37658371344742e-05
350.9999162321557370.0001675356885254628.37678442627312e-05
360.999790541901540.0004189161969196730.000209458098459837
370.9996341981418870.0007316037162269910.000365801858113495
380.99956838731760.0008632253647994760.000431612682399738
390.9992304126673150.001539174665370520.00076958733268526
400.998853110598750.002293778802500470.00114688940125023
410.9986205517147810.002758896570438080.00137944828521904
420.99850152289220.002996954215597410.00149847710779870
430.9938473177494440.01230536450111170.00615268225055586
440.975878684702410.04824263059518160.0241213152975908







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level160.551724137931034NOK
5% type I error level210.724137931034483NOK
10% type I error level250.862068965517241NOK

\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 & 16 & 0.551724137931034 & NOK \tabularnewline
5% type I error level & 21 & 0.724137931034483 & NOK \tabularnewline
10% type I error level & 25 & 0.862068965517241 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58395&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]16[/C][C]0.551724137931034[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]21[/C][C]0.724137931034483[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]25[/C][C]0.862068965517241[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58395&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58395&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 level160.551724137931034NOK
5% type I error level210.724137931034483NOK
10% type I error level250.862068965517241NOK



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')
}