Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 11:14:50 -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/t1258741016sykxmfxq11fztdq.htm/, Retrieved Fri, 29 Mar 2024 02:24:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58387, Retrieved Fri, 29 Mar 2024 02:24:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact147
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-   PD      [Multiple Regression] [workshop 7 data 2] [2009-11-20 18:14:50] [d41d8cd98f00b204e9800998ecf8427e] [Current]
-   P         [Multiple Regression] [] [2009-11-27 13:17:40] [09f192433169b2c787c4a71fde86e883]
Feedback Forum

Post a new message
Dataseries X:
543	0
594	0
611	0
613	0
611	0
594	0
595	0
591	0
589	0
584	0
573	0
567	0
569	0
621	0
629	0
628	0
612	0
595	0
597	0
593	0
590	0
580	0
574	0
573	0
573	0
620	0
626	0
620	0
588	0
566	0
557	0
561	0
549	0
532	0
526	0
511	0
499	0
555	0
565	0
542	0
527	0
510	0
514	0
517	0
508	0
493	0
490	0
469	1
478	1
528	1
534	1
518	1
506	1
502	1
516	1
528	1
533	1
536	1
537	1
524	1
536	1




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=58387&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=58387&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58387&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
Yt[t] = + 616.551900582166 + 7.38444103238997X[t] -1.95296039659737t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Yt[t] =  +  616.551900582166 +  7.38444103238997X[t] -1.95296039659737t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58387&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Yt[t] =  +  616.551900582166 +  7.38444103238997X[t] -1.95296039659737t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58387&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58387&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
Yt[t] = + 616.551900582166 + 7.38444103238997X[t] -1.95296039659737t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)616.5519005821668.11310475.994600
X7.3844410323899712.2828560.60120.550050.275025
t-1.952960396597370.29336-6.657200

\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) & 616.551900582166 & 8.113104 & 75.9946 & 0 & 0 \tabularnewline
X & 7.38444103238997 & 12.282856 & 0.6012 & 0.55005 & 0.275025 \tabularnewline
t & -1.95296039659737 & 0.29336 & -6.6572 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58387&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]616.551900582166[/C][C]8.113104[/C][C]75.9946[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]7.38444103238997[/C][C]12.282856[/C][C]0.6012[/C][C]0.55005[/C][C]0.275025[/C][/ROW]
[ROW][C]t[/C][C]-1.95296039659737[/C][C]0.29336[/C][C]-6.6572[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58387&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58387&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)616.5519005821668.11310475.994600
X7.3844410323899712.2828560.60120.550050.275025
t-1.952960396597370.29336-6.657200







Multiple Linear Regression - Regression Statistics
Multiple R0.766800045961603
R-squared0.587982310486717
Adjusted R-squared0.573774803951776
F-TEST (value)41.3853274704243
F-TEST (DF numerator)2
F-TEST (DF denominator)58
p-value6.80078215964386e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation27.6374538885241
Sum Squared Residuals44302.0737315372

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.766800045961603 \tabularnewline
R-squared & 0.587982310486717 \tabularnewline
Adjusted R-squared & 0.573774803951776 \tabularnewline
F-TEST (value) & 41.3853274704243 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 6.80078215964386e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 27.6374538885241 \tabularnewline
Sum Squared Residuals & 44302.0737315372 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58387&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.766800045961603[/C][/ROW]
[ROW][C]R-squared[/C][C]0.587982310486717[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.573774803951776[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]41.3853274704243[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]6.80078215964386e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]27.6374538885241[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]44302.0737315372[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58387&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58387&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 R0.766800045961603
R-squared0.587982310486717
Adjusted R-squared0.573774803951776
F-TEST (value)41.3853274704243
F-TEST (DF numerator)2
F-TEST (DF denominator)58
p-value6.80078215964386e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation27.6374538885241
Sum Squared Residuals44302.0737315372







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1543614.598940185571-71.5989401855713
2594612.645979788972-18.6459797889719
3611610.6930193923750.306980607625428
4613608.7400589957774.25994100422288
5611606.787098599184.21290140082024
6594604.834138202582-10.8341382025824
7595602.881177805985-7.88117780598502
8591600.928217409388-9.92821740938765
9589598.97525701279-9.97525701279028
10584597.022296616193-13.0222966161929
11573595.069336219596-22.0693362195955
12567593.116375822998-26.1163758229982
13569591.1634154264-22.1634154264008
14621589.21045502980331.7895449701966
15629587.25749463320641.7425053667939
16628585.30453423660942.6954657633913
17612583.35157384001128.6484261599887
18595581.39861344341413.6013865565860
19597579.44565304681717.5543469531834
20593577.49269265021915.5073073497808
21590575.53973225362214.4602677463782
22580573.5867718570246.41322814297552
23574571.6338114604272.36618853957289
24573569.680851063833.31914893617026
25573567.7278906672325.27210933276763
26620565.77493027063554.225069729365
27626563.82196987403862.1780301259624
28620561.8690094774458.1309905225597
29588559.91604908084328.0839509191571
30566557.9630886842468.03691131575447
31557556.0101282876480.989871712351839
32561554.0571678910516.94283210894921
33549552.104207494453-3.10420749445342
34532550.151247097856-18.1512470978561
35526548.198286701259-22.1982867012587
36511546.245326304661-35.2453263046613
37499544.292365908064-45.2923659080639
38555542.33940551146712.6605944885334
39565540.38644511486924.6135548851308
40542538.4334847182723.56651528172816
41527536.480524321674-9.48052432167447
42510534.527563925077-24.5275639250771
43514532.57460352848-18.5746035284797
44517530.621643131882-13.6216431318824
45508528.668682735285-20.668682735285
46493526.715722338688-33.7157223386876
47490524.76276194209-34.7627619420903
48469530.194242577883-61.1942425778829
49478528.241282181286-50.2412821812855
50528526.2883217846881.71167821531184
51534524.3353613880919.6646386119092
52518522.382400991493-4.38240099149343
53506520.429440594896-14.4294405948961
54502518.476480198299-16.4764801982987
55516516.523519801701-0.523519801701317
56528514.57055940510413.4294405948960
57533512.61759900850720.3824009914934
58536510.66463861190925.3353613880908
59537508.71167821531228.2883217846882
60524506.75871781871417.2412821812855
61536504.80575742211731.1942425778829

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 543 & 614.598940185571 & -71.5989401855713 \tabularnewline
2 & 594 & 612.645979788972 & -18.6459797889719 \tabularnewline
3 & 611 & 610.693019392375 & 0.306980607625428 \tabularnewline
4 & 613 & 608.740058995777 & 4.25994100422288 \tabularnewline
5 & 611 & 606.78709859918 & 4.21290140082024 \tabularnewline
6 & 594 & 604.834138202582 & -10.8341382025824 \tabularnewline
7 & 595 & 602.881177805985 & -7.88117780598502 \tabularnewline
8 & 591 & 600.928217409388 & -9.92821740938765 \tabularnewline
9 & 589 & 598.97525701279 & -9.97525701279028 \tabularnewline
10 & 584 & 597.022296616193 & -13.0222966161929 \tabularnewline
11 & 573 & 595.069336219596 & -22.0693362195955 \tabularnewline
12 & 567 & 593.116375822998 & -26.1163758229982 \tabularnewline
13 & 569 & 591.1634154264 & -22.1634154264008 \tabularnewline
14 & 621 & 589.210455029803 & 31.7895449701966 \tabularnewline
15 & 629 & 587.257494633206 & 41.7425053667939 \tabularnewline
16 & 628 & 585.304534236609 & 42.6954657633913 \tabularnewline
17 & 612 & 583.351573840011 & 28.6484261599887 \tabularnewline
18 & 595 & 581.398613443414 & 13.6013865565860 \tabularnewline
19 & 597 & 579.445653046817 & 17.5543469531834 \tabularnewline
20 & 593 & 577.492692650219 & 15.5073073497808 \tabularnewline
21 & 590 & 575.539732253622 & 14.4602677463782 \tabularnewline
22 & 580 & 573.586771857024 & 6.41322814297552 \tabularnewline
23 & 574 & 571.633811460427 & 2.36618853957289 \tabularnewline
24 & 573 & 569.68085106383 & 3.31914893617026 \tabularnewline
25 & 573 & 567.727890667232 & 5.27210933276763 \tabularnewline
26 & 620 & 565.774930270635 & 54.225069729365 \tabularnewline
27 & 626 & 563.821969874038 & 62.1780301259624 \tabularnewline
28 & 620 & 561.86900947744 & 58.1309905225597 \tabularnewline
29 & 588 & 559.916049080843 & 28.0839509191571 \tabularnewline
30 & 566 & 557.963088684246 & 8.03691131575447 \tabularnewline
31 & 557 & 556.010128287648 & 0.989871712351839 \tabularnewline
32 & 561 & 554.057167891051 & 6.94283210894921 \tabularnewline
33 & 549 & 552.104207494453 & -3.10420749445342 \tabularnewline
34 & 532 & 550.151247097856 & -18.1512470978561 \tabularnewline
35 & 526 & 548.198286701259 & -22.1982867012587 \tabularnewline
36 & 511 & 546.245326304661 & -35.2453263046613 \tabularnewline
37 & 499 & 544.292365908064 & -45.2923659080639 \tabularnewline
38 & 555 & 542.339405511467 & 12.6605944885334 \tabularnewline
39 & 565 & 540.386445114869 & 24.6135548851308 \tabularnewline
40 & 542 & 538.433484718272 & 3.56651528172816 \tabularnewline
41 & 527 & 536.480524321674 & -9.48052432167447 \tabularnewline
42 & 510 & 534.527563925077 & -24.5275639250771 \tabularnewline
43 & 514 & 532.57460352848 & -18.5746035284797 \tabularnewline
44 & 517 & 530.621643131882 & -13.6216431318824 \tabularnewline
45 & 508 & 528.668682735285 & -20.668682735285 \tabularnewline
46 & 493 & 526.715722338688 & -33.7157223386876 \tabularnewline
47 & 490 & 524.76276194209 & -34.7627619420903 \tabularnewline
48 & 469 & 530.194242577883 & -61.1942425778829 \tabularnewline
49 & 478 & 528.241282181286 & -50.2412821812855 \tabularnewline
50 & 528 & 526.288321784688 & 1.71167821531184 \tabularnewline
51 & 534 & 524.335361388091 & 9.6646386119092 \tabularnewline
52 & 518 & 522.382400991493 & -4.38240099149343 \tabularnewline
53 & 506 & 520.429440594896 & -14.4294405948961 \tabularnewline
54 & 502 & 518.476480198299 & -16.4764801982987 \tabularnewline
55 & 516 & 516.523519801701 & -0.523519801701317 \tabularnewline
56 & 528 & 514.570559405104 & 13.4294405948960 \tabularnewline
57 & 533 & 512.617599008507 & 20.3824009914934 \tabularnewline
58 & 536 & 510.664638611909 & 25.3353613880908 \tabularnewline
59 & 537 & 508.711678215312 & 28.2883217846882 \tabularnewline
60 & 524 & 506.758717818714 & 17.2412821812855 \tabularnewline
61 & 536 & 504.805757422117 & 31.1942425778829 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58387&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]543[/C][C]614.598940185571[/C][C]-71.5989401855713[/C][/ROW]
[ROW][C]2[/C][C]594[/C][C]612.645979788972[/C][C]-18.6459797889719[/C][/ROW]
[ROW][C]3[/C][C]611[/C][C]610.693019392375[/C][C]0.306980607625428[/C][/ROW]
[ROW][C]4[/C][C]613[/C][C]608.740058995777[/C][C]4.25994100422288[/C][/ROW]
[ROW][C]5[/C][C]611[/C][C]606.78709859918[/C][C]4.21290140082024[/C][/ROW]
[ROW][C]6[/C][C]594[/C][C]604.834138202582[/C][C]-10.8341382025824[/C][/ROW]
[ROW][C]7[/C][C]595[/C][C]602.881177805985[/C][C]-7.88117780598502[/C][/ROW]
[ROW][C]8[/C][C]591[/C][C]600.928217409388[/C][C]-9.92821740938765[/C][/ROW]
[ROW][C]9[/C][C]589[/C][C]598.97525701279[/C][C]-9.97525701279028[/C][/ROW]
[ROW][C]10[/C][C]584[/C][C]597.022296616193[/C][C]-13.0222966161929[/C][/ROW]
[ROW][C]11[/C][C]573[/C][C]595.069336219596[/C][C]-22.0693362195955[/C][/ROW]
[ROW][C]12[/C][C]567[/C][C]593.116375822998[/C][C]-26.1163758229982[/C][/ROW]
[ROW][C]13[/C][C]569[/C][C]591.1634154264[/C][C]-22.1634154264008[/C][/ROW]
[ROW][C]14[/C][C]621[/C][C]589.210455029803[/C][C]31.7895449701966[/C][/ROW]
[ROW][C]15[/C][C]629[/C][C]587.257494633206[/C][C]41.7425053667939[/C][/ROW]
[ROW][C]16[/C][C]628[/C][C]585.304534236609[/C][C]42.6954657633913[/C][/ROW]
[ROW][C]17[/C][C]612[/C][C]583.351573840011[/C][C]28.6484261599887[/C][/ROW]
[ROW][C]18[/C][C]595[/C][C]581.398613443414[/C][C]13.6013865565860[/C][/ROW]
[ROW][C]19[/C][C]597[/C][C]579.445653046817[/C][C]17.5543469531834[/C][/ROW]
[ROW][C]20[/C][C]593[/C][C]577.492692650219[/C][C]15.5073073497808[/C][/ROW]
[ROW][C]21[/C][C]590[/C][C]575.539732253622[/C][C]14.4602677463782[/C][/ROW]
[ROW][C]22[/C][C]580[/C][C]573.586771857024[/C][C]6.41322814297552[/C][/ROW]
[ROW][C]23[/C][C]574[/C][C]571.633811460427[/C][C]2.36618853957289[/C][/ROW]
[ROW][C]24[/C][C]573[/C][C]569.68085106383[/C][C]3.31914893617026[/C][/ROW]
[ROW][C]25[/C][C]573[/C][C]567.727890667232[/C][C]5.27210933276763[/C][/ROW]
[ROW][C]26[/C][C]620[/C][C]565.774930270635[/C][C]54.225069729365[/C][/ROW]
[ROW][C]27[/C][C]626[/C][C]563.821969874038[/C][C]62.1780301259624[/C][/ROW]
[ROW][C]28[/C][C]620[/C][C]561.86900947744[/C][C]58.1309905225597[/C][/ROW]
[ROW][C]29[/C][C]588[/C][C]559.916049080843[/C][C]28.0839509191571[/C][/ROW]
[ROW][C]30[/C][C]566[/C][C]557.963088684246[/C][C]8.03691131575447[/C][/ROW]
[ROW][C]31[/C][C]557[/C][C]556.010128287648[/C][C]0.989871712351839[/C][/ROW]
[ROW][C]32[/C][C]561[/C][C]554.057167891051[/C][C]6.94283210894921[/C][/ROW]
[ROW][C]33[/C][C]549[/C][C]552.104207494453[/C][C]-3.10420749445342[/C][/ROW]
[ROW][C]34[/C][C]532[/C][C]550.151247097856[/C][C]-18.1512470978561[/C][/ROW]
[ROW][C]35[/C][C]526[/C][C]548.198286701259[/C][C]-22.1982867012587[/C][/ROW]
[ROW][C]36[/C][C]511[/C][C]546.245326304661[/C][C]-35.2453263046613[/C][/ROW]
[ROW][C]37[/C][C]499[/C][C]544.292365908064[/C][C]-45.2923659080639[/C][/ROW]
[ROW][C]38[/C][C]555[/C][C]542.339405511467[/C][C]12.6605944885334[/C][/ROW]
[ROW][C]39[/C][C]565[/C][C]540.386445114869[/C][C]24.6135548851308[/C][/ROW]
[ROW][C]40[/C][C]542[/C][C]538.433484718272[/C][C]3.56651528172816[/C][/ROW]
[ROW][C]41[/C][C]527[/C][C]536.480524321674[/C][C]-9.48052432167447[/C][/ROW]
[ROW][C]42[/C][C]510[/C][C]534.527563925077[/C][C]-24.5275639250771[/C][/ROW]
[ROW][C]43[/C][C]514[/C][C]532.57460352848[/C][C]-18.5746035284797[/C][/ROW]
[ROW][C]44[/C][C]517[/C][C]530.621643131882[/C][C]-13.6216431318824[/C][/ROW]
[ROW][C]45[/C][C]508[/C][C]528.668682735285[/C][C]-20.668682735285[/C][/ROW]
[ROW][C]46[/C][C]493[/C][C]526.715722338688[/C][C]-33.7157223386876[/C][/ROW]
[ROW][C]47[/C][C]490[/C][C]524.76276194209[/C][C]-34.7627619420903[/C][/ROW]
[ROW][C]48[/C][C]469[/C][C]530.194242577883[/C][C]-61.1942425778829[/C][/ROW]
[ROW][C]49[/C][C]478[/C][C]528.241282181286[/C][C]-50.2412821812855[/C][/ROW]
[ROW][C]50[/C][C]528[/C][C]526.288321784688[/C][C]1.71167821531184[/C][/ROW]
[ROW][C]51[/C][C]534[/C][C]524.335361388091[/C][C]9.6646386119092[/C][/ROW]
[ROW][C]52[/C][C]518[/C][C]522.382400991493[/C][C]-4.38240099149343[/C][/ROW]
[ROW][C]53[/C][C]506[/C][C]520.429440594896[/C][C]-14.4294405948961[/C][/ROW]
[ROW][C]54[/C][C]502[/C][C]518.476480198299[/C][C]-16.4764801982987[/C][/ROW]
[ROW][C]55[/C][C]516[/C][C]516.523519801701[/C][C]-0.523519801701317[/C][/ROW]
[ROW][C]56[/C][C]528[/C][C]514.570559405104[/C][C]13.4294405948960[/C][/ROW]
[ROW][C]57[/C][C]533[/C][C]512.617599008507[/C][C]20.3824009914934[/C][/ROW]
[ROW][C]58[/C][C]536[/C][C]510.664638611909[/C][C]25.3353613880908[/C][/ROW]
[ROW][C]59[/C][C]537[/C][C]508.711678215312[/C][C]28.2883217846882[/C][/ROW]
[ROW][C]60[/C][C]524[/C][C]506.758717818714[/C][C]17.2412821812855[/C][/ROW]
[ROW][C]61[/C][C]536[/C][C]504.805757422117[/C][C]31.1942425778829[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58387&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58387&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
1543614.598940185571-71.5989401855713
2594612.645979788972-18.6459797889719
3611610.6930193923750.306980607625428
4613608.7400589957774.25994100422288
5611606.787098599184.21290140082024
6594604.834138202582-10.8341382025824
7595602.881177805985-7.88117780598502
8591600.928217409388-9.92821740938765
9589598.97525701279-9.97525701279028
10584597.022296616193-13.0222966161929
11573595.069336219596-22.0693362195955
12567593.116375822998-26.1163758229982
13569591.1634154264-22.1634154264008
14621589.21045502980331.7895449701966
15629587.25749463320641.7425053667939
16628585.30453423660942.6954657633913
17612583.35157384001128.6484261599887
18595581.39861344341413.6013865565860
19597579.44565304681717.5543469531834
20593577.49269265021915.5073073497808
21590575.53973225362214.4602677463782
22580573.5867718570246.41322814297552
23574571.6338114604272.36618853957289
24573569.680851063833.31914893617026
25573567.7278906672325.27210933276763
26620565.77493027063554.225069729365
27626563.82196987403862.1780301259624
28620561.8690094774458.1309905225597
29588559.91604908084328.0839509191571
30566557.9630886842468.03691131575447
31557556.0101282876480.989871712351839
32561554.0571678910516.94283210894921
33549552.104207494453-3.10420749445342
34532550.151247097856-18.1512470978561
35526548.198286701259-22.1982867012587
36511546.245326304661-35.2453263046613
37499544.292365908064-45.2923659080639
38555542.33940551146712.6605944885334
39565540.38644511486924.6135548851308
40542538.4334847182723.56651528172816
41527536.480524321674-9.48052432167447
42510534.527563925077-24.5275639250771
43514532.57460352848-18.5746035284797
44517530.621643131882-13.6216431318824
45508528.668682735285-20.668682735285
46493526.715722338688-33.7157223386876
47490524.76276194209-34.7627619420903
48469530.194242577883-61.1942425778829
49478528.241282181286-50.2412821812855
50528526.2883217846881.71167821531184
51534524.3353613880919.6646386119092
52518522.382400991493-4.38240099149343
53506520.429440594896-14.4294405948961
54502518.476480198299-16.4764801982987
55516516.523519801701-0.523519801701317
56528514.57055940510413.4294405948960
57533512.61759900850720.3824009914934
58536510.66463861190925.3353613880908
59537508.71167821531228.2883217846882
60524506.75871781871417.2412821812855
61536504.80575742211731.1942425778829







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.6035617709783990.7928764580432020.396438229021601
70.5380781115901270.9238437768197460.461921888409873
80.4677993727459830.9355987454919650.532200627254017
90.3880824495150540.7761648990301080.611917550484946
100.3246655999146990.6493311998293990.6753344000853
110.3110134584195980.6220269168391950.688986541580402
120.3105101718131400.6210203436262810.68948982818686
130.2866245176613770.5732490353227530.713375482338623
140.3877568371339480.7755136742678960.612243162866052
150.4542024991140230.9084049982280470.545797500885977
160.4466267705869960.8932535411739920.553373229413004
170.3622712343638140.7245424687276280.637728765636186
180.2985735062415710.5971470124831430.701426493758429
190.2347333471706280.4694666943412550.765266652829372
200.1846498424914730.3692996849829450.815350157508527
210.1445057397259260.2890114794518510.855494260274074
220.1258939867500050.2517879735000110.874106013249994
230.1154390462648390.2308780925296790.884560953735161
240.09978365458784860.1995673091756970.900216345412151
250.08057012812332010.1611402562466400.91942987187668
260.1043062720875600.2086125441751210.89569372791244
270.1830484833267680.3660969666535360.816951516673232
280.3158553832360490.6317107664720970.684144616763951
290.3632375279035430.7264750558070850.636762472096457
300.4214246211928120.8428492423856240.578575378807188
310.4875334819401950.975066963880390.512466518059805
320.558242535112510.883514929774980.44175746488749
330.628954527745440.7420909445091210.371045472254561
340.6931750445704130.6136499108591740.306824955429587
350.730130660730950.5397386785381010.269869339269050
360.7734512418170270.4530975163659460.226548758182973
370.8292382374116740.3415235251766510.170761762588325
380.8590213928466210.2819572143067580.140978607153379
390.9580182865657890.08396342686842220.0419817134342111
400.9811893633111850.03762127337763060.0188106366888153
410.9874072711587730.02518545768245370.0125927288412268
420.984983106912230.03003378617553800.0150168930877690
430.9831429984189130.03371400316217440.0168570015810872
440.9848420096768130.03031598064637320.0151579903231866
450.982819391877370.03436121624525770.0171806081226288
460.9734163331287930.05316733374241490.0265836668712074
470.9574155859121930.08516882817561370.0425844140878068
480.9738671111754510.05226577764909780.0261328888245489
490.9934012098039740.01319758039205180.00659879019602592
500.991646213848790.01670757230242080.00835378615121039
510.997296524249070.005406951501857960.00270347575092898
520.9953270724892650.009345855021470.004672927510735
530.985498006193530.02900398761294070.0145019938064704
540.9867908487246580.02641830255068300.0132091512753415
550.9803209167184750.03935816656305020.0196790832815251

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.603561770978399 & 0.792876458043202 & 0.396438229021601 \tabularnewline
7 & 0.538078111590127 & 0.923843776819746 & 0.461921888409873 \tabularnewline
8 & 0.467799372745983 & 0.935598745491965 & 0.532200627254017 \tabularnewline
9 & 0.388082449515054 & 0.776164899030108 & 0.611917550484946 \tabularnewline
10 & 0.324665599914699 & 0.649331199829399 & 0.6753344000853 \tabularnewline
11 & 0.311013458419598 & 0.622026916839195 & 0.688986541580402 \tabularnewline
12 & 0.310510171813140 & 0.621020343626281 & 0.68948982818686 \tabularnewline
13 & 0.286624517661377 & 0.573249035322753 & 0.713375482338623 \tabularnewline
14 & 0.387756837133948 & 0.775513674267896 & 0.612243162866052 \tabularnewline
15 & 0.454202499114023 & 0.908404998228047 & 0.545797500885977 \tabularnewline
16 & 0.446626770586996 & 0.893253541173992 & 0.553373229413004 \tabularnewline
17 & 0.362271234363814 & 0.724542468727628 & 0.637728765636186 \tabularnewline
18 & 0.298573506241571 & 0.597147012483143 & 0.701426493758429 \tabularnewline
19 & 0.234733347170628 & 0.469466694341255 & 0.765266652829372 \tabularnewline
20 & 0.184649842491473 & 0.369299684982945 & 0.815350157508527 \tabularnewline
21 & 0.144505739725926 & 0.289011479451851 & 0.855494260274074 \tabularnewline
22 & 0.125893986750005 & 0.251787973500011 & 0.874106013249994 \tabularnewline
23 & 0.115439046264839 & 0.230878092529679 & 0.884560953735161 \tabularnewline
24 & 0.0997836545878486 & 0.199567309175697 & 0.900216345412151 \tabularnewline
25 & 0.0805701281233201 & 0.161140256246640 & 0.91942987187668 \tabularnewline
26 & 0.104306272087560 & 0.208612544175121 & 0.89569372791244 \tabularnewline
27 & 0.183048483326768 & 0.366096966653536 & 0.816951516673232 \tabularnewline
28 & 0.315855383236049 & 0.631710766472097 & 0.684144616763951 \tabularnewline
29 & 0.363237527903543 & 0.726475055807085 & 0.636762472096457 \tabularnewline
30 & 0.421424621192812 & 0.842849242385624 & 0.578575378807188 \tabularnewline
31 & 0.487533481940195 & 0.97506696388039 & 0.512466518059805 \tabularnewline
32 & 0.55824253511251 & 0.88351492977498 & 0.44175746488749 \tabularnewline
33 & 0.62895452774544 & 0.742090944509121 & 0.371045472254561 \tabularnewline
34 & 0.693175044570413 & 0.613649910859174 & 0.306824955429587 \tabularnewline
35 & 0.73013066073095 & 0.539738678538101 & 0.269869339269050 \tabularnewline
36 & 0.773451241817027 & 0.453097516365946 & 0.226548758182973 \tabularnewline
37 & 0.829238237411674 & 0.341523525176651 & 0.170761762588325 \tabularnewline
38 & 0.859021392846621 & 0.281957214306758 & 0.140978607153379 \tabularnewline
39 & 0.958018286565789 & 0.0839634268684222 & 0.0419817134342111 \tabularnewline
40 & 0.981189363311185 & 0.0376212733776306 & 0.0188106366888153 \tabularnewline
41 & 0.987407271158773 & 0.0251854576824537 & 0.0125927288412268 \tabularnewline
42 & 0.98498310691223 & 0.0300337861755380 & 0.0150168930877690 \tabularnewline
43 & 0.983142998418913 & 0.0337140031621744 & 0.0168570015810872 \tabularnewline
44 & 0.984842009676813 & 0.0303159806463732 & 0.0151579903231866 \tabularnewline
45 & 0.98281939187737 & 0.0343612162452577 & 0.0171806081226288 \tabularnewline
46 & 0.973416333128793 & 0.0531673337424149 & 0.0265836668712074 \tabularnewline
47 & 0.957415585912193 & 0.0851688281756137 & 0.0425844140878068 \tabularnewline
48 & 0.973867111175451 & 0.0522657776490978 & 0.0261328888245489 \tabularnewline
49 & 0.993401209803974 & 0.0131975803920518 & 0.00659879019602592 \tabularnewline
50 & 0.99164621384879 & 0.0167075723024208 & 0.00835378615121039 \tabularnewline
51 & 0.99729652424907 & 0.00540695150185796 & 0.00270347575092898 \tabularnewline
52 & 0.995327072489265 & 0.00934585502147 & 0.004672927510735 \tabularnewline
53 & 0.98549800619353 & 0.0290039876129407 & 0.0145019938064704 \tabularnewline
54 & 0.986790848724658 & 0.0264183025506830 & 0.0132091512753415 \tabularnewline
55 & 0.980320916718475 & 0.0393581665630502 & 0.0196790832815251 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58387&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]6[/C][C]0.603561770978399[/C][C]0.792876458043202[/C][C]0.396438229021601[/C][/ROW]
[ROW][C]7[/C][C]0.538078111590127[/C][C]0.923843776819746[/C][C]0.461921888409873[/C][/ROW]
[ROW][C]8[/C][C]0.467799372745983[/C][C]0.935598745491965[/C][C]0.532200627254017[/C][/ROW]
[ROW][C]9[/C][C]0.388082449515054[/C][C]0.776164899030108[/C][C]0.611917550484946[/C][/ROW]
[ROW][C]10[/C][C]0.324665599914699[/C][C]0.649331199829399[/C][C]0.6753344000853[/C][/ROW]
[ROW][C]11[/C][C]0.311013458419598[/C][C]0.622026916839195[/C][C]0.688986541580402[/C][/ROW]
[ROW][C]12[/C][C]0.310510171813140[/C][C]0.621020343626281[/C][C]0.68948982818686[/C][/ROW]
[ROW][C]13[/C][C]0.286624517661377[/C][C]0.573249035322753[/C][C]0.713375482338623[/C][/ROW]
[ROW][C]14[/C][C]0.387756837133948[/C][C]0.775513674267896[/C][C]0.612243162866052[/C][/ROW]
[ROW][C]15[/C][C]0.454202499114023[/C][C]0.908404998228047[/C][C]0.545797500885977[/C][/ROW]
[ROW][C]16[/C][C]0.446626770586996[/C][C]0.893253541173992[/C][C]0.553373229413004[/C][/ROW]
[ROW][C]17[/C][C]0.362271234363814[/C][C]0.724542468727628[/C][C]0.637728765636186[/C][/ROW]
[ROW][C]18[/C][C]0.298573506241571[/C][C]0.597147012483143[/C][C]0.701426493758429[/C][/ROW]
[ROW][C]19[/C][C]0.234733347170628[/C][C]0.469466694341255[/C][C]0.765266652829372[/C][/ROW]
[ROW][C]20[/C][C]0.184649842491473[/C][C]0.369299684982945[/C][C]0.815350157508527[/C][/ROW]
[ROW][C]21[/C][C]0.144505739725926[/C][C]0.289011479451851[/C][C]0.855494260274074[/C][/ROW]
[ROW][C]22[/C][C]0.125893986750005[/C][C]0.251787973500011[/C][C]0.874106013249994[/C][/ROW]
[ROW][C]23[/C][C]0.115439046264839[/C][C]0.230878092529679[/C][C]0.884560953735161[/C][/ROW]
[ROW][C]24[/C][C]0.0997836545878486[/C][C]0.199567309175697[/C][C]0.900216345412151[/C][/ROW]
[ROW][C]25[/C][C]0.0805701281233201[/C][C]0.161140256246640[/C][C]0.91942987187668[/C][/ROW]
[ROW][C]26[/C][C]0.104306272087560[/C][C]0.208612544175121[/C][C]0.89569372791244[/C][/ROW]
[ROW][C]27[/C][C]0.183048483326768[/C][C]0.366096966653536[/C][C]0.816951516673232[/C][/ROW]
[ROW][C]28[/C][C]0.315855383236049[/C][C]0.631710766472097[/C][C]0.684144616763951[/C][/ROW]
[ROW][C]29[/C][C]0.363237527903543[/C][C]0.726475055807085[/C][C]0.636762472096457[/C][/ROW]
[ROW][C]30[/C][C]0.421424621192812[/C][C]0.842849242385624[/C][C]0.578575378807188[/C][/ROW]
[ROW][C]31[/C][C]0.487533481940195[/C][C]0.97506696388039[/C][C]0.512466518059805[/C][/ROW]
[ROW][C]32[/C][C]0.55824253511251[/C][C]0.88351492977498[/C][C]0.44175746488749[/C][/ROW]
[ROW][C]33[/C][C]0.62895452774544[/C][C]0.742090944509121[/C][C]0.371045472254561[/C][/ROW]
[ROW][C]34[/C][C]0.693175044570413[/C][C]0.613649910859174[/C][C]0.306824955429587[/C][/ROW]
[ROW][C]35[/C][C]0.73013066073095[/C][C]0.539738678538101[/C][C]0.269869339269050[/C][/ROW]
[ROW][C]36[/C][C]0.773451241817027[/C][C]0.453097516365946[/C][C]0.226548758182973[/C][/ROW]
[ROW][C]37[/C][C]0.829238237411674[/C][C]0.341523525176651[/C][C]0.170761762588325[/C][/ROW]
[ROW][C]38[/C][C]0.859021392846621[/C][C]0.281957214306758[/C][C]0.140978607153379[/C][/ROW]
[ROW][C]39[/C][C]0.958018286565789[/C][C]0.0839634268684222[/C][C]0.0419817134342111[/C][/ROW]
[ROW][C]40[/C][C]0.981189363311185[/C][C]0.0376212733776306[/C][C]0.0188106366888153[/C][/ROW]
[ROW][C]41[/C][C]0.987407271158773[/C][C]0.0251854576824537[/C][C]0.0125927288412268[/C][/ROW]
[ROW][C]42[/C][C]0.98498310691223[/C][C]0.0300337861755380[/C][C]0.0150168930877690[/C][/ROW]
[ROW][C]43[/C][C]0.983142998418913[/C][C]0.0337140031621744[/C][C]0.0168570015810872[/C][/ROW]
[ROW][C]44[/C][C]0.984842009676813[/C][C]0.0303159806463732[/C][C]0.0151579903231866[/C][/ROW]
[ROW][C]45[/C][C]0.98281939187737[/C][C]0.0343612162452577[/C][C]0.0171806081226288[/C][/ROW]
[ROW][C]46[/C][C]0.973416333128793[/C][C]0.0531673337424149[/C][C]0.0265836668712074[/C][/ROW]
[ROW][C]47[/C][C]0.957415585912193[/C][C]0.0851688281756137[/C][C]0.0425844140878068[/C][/ROW]
[ROW][C]48[/C][C]0.973867111175451[/C][C]0.0522657776490978[/C][C]0.0261328888245489[/C][/ROW]
[ROW][C]49[/C][C]0.993401209803974[/C][C]0.0131975803920518[/C][C]0.00659879019602592[/C][/ROW]
[ROW][C]50[/C][C]0.99164621384879[/C][C]0.0167075723024208[/C][C]0.00835378615121039[/C][/ROW]
[ROW][C]51[/C][C]0.99729652424907[/C][C]0.00540695150185796[/C][C]0.00270347575092898[/C][/ROW]
[ROW][C]52[/C][C]0.995327072489265[/C][C]0.00934585502147[/C][C]0.004672927510735[/C][/ROW]
[ROW][C]53[/C][C]0.98549800619353[/C][C]0.0290039876129407[/C][C]0.0145019938064704[/C][/ROW]
[ROW][C]54[/C][C]0.986790848724658[/C][C]0.0264183025506830[/C][C]0.0132091512753415[/C][/ROW]
[ROW][C]55[/C][C]0.980320916718475[/C][C]0.0393581665630502[/C][C]0.0196790832815251[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58387&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58387&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
60.6035617709783990.7928764580432020.396438229021601
70.5380781115901270.9238437768197460.461921888409873
80.4677993727459830.9355987454919650.532200627254017
90.3880824495150540.7761648990301080.611917550484946
100.3246655999146990.6493311998293990.6753344000853
110.3110134584195980.6220269168391950.688986541580402
120.3105101718131400.6210203436262810.68948982818686
130.2866245176613770.5732490353227530.713375482338623
140.3877568371339480.7755136742678960.612243162866052
150.4542024991140230.9084049982280470.545797500885977
160.4466267705869960.8932535411739920.553373229413004
170.3622712343638140.7245424687276280.637728765636186
180.2985735062415710.5971470124831430.701426493758429
190.2347333471706280.4694666943412550.765266652829372
200.1846498424914730.3692996849829450.815350157508527
210.1445057397259260.2890114794518510.855494260274074
220.1258939867500050.2517879735000110.874106013249994
230.1154390462648390.2308780925296790.884560953735161
240.09978365458784860.1995673091756970.900216345412151
250.08057012812332010.1611402562466400.91942987187668
260.1043062720875600.2086125441751210.89569372791244
270.1830484833267680.3660969666535360.816951516673232
280.3158553832360490.6317107664720970.684144616763951
290.3632375279035430.7264750558070850.636762472096457
300.4214246211928120.8428492423856240.578575378807188
310.4875334819401950.975066963880390.512466518059805
320.558242535112510.883514929774980.44175746488749
330.628954527745440.7420909445091210.371045472254561
340.6931750445704130.6136499108591740.306824955429587
350.730130660730950.5397386785381010.269869339269050
360.7734512418170270.4530975163659460.226548758182973
370.8292382374116740.3415235251766510.170761762588325
380.8590213928466210.2819572143067580.140978607153379
390.9580182865657890.08396342686842220.0419817134342111
400.9811893633111850.03762127337763060.0188106366888153
410.9874072711587730.02518545768245370.0125927288412268
420.984983106912230.03003378617553800.0150168930877690
430.9831429984189130.03371400316217440.0168570015810872
440.9848420096768130.03031598064637320.0151579903231866
450.982819391877370.03436121624525770.0171806081226288
460.9734163331287930.05316733374241490.0265836668712074
470.9574155859121930.08516882817561370.0425844140878068
480.9738671111754510.05226577764909780.0261328888245489
490.9934012098039740.01319758039205180.00659879019602592
500.991646213848790.01670757230242080.00835378615121039
510.997296524249070.005406951501857960.00270347575092898
520.9953270724892650.009345855021470.004672927510735
530.985498006193530.02900398761294070.0145019938064704
540.9867908487246580.02641830255068300.0132091512753415
550.9803209167184750.03935816656305020.0196790832815251







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.04NOK
5% type I error level130.26NOK
10% type I error level170.34NOK

\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 & 2 & 0.04 & NOK \tabularnewline
5% type I error level & 13 & 0.26 & NOK \tabularnewline
10% type I error level & 17 & 0.34 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58387&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]2[/C][C]0.04[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]13[/C][C]0.26[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]17[/C][C]0.34[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58387&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58387&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 level20.04NOK
5% type I error level130.26NOK
10% type I error level170.34NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal 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')
}