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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 computationThu, 19 Nov 2009 11:55:57 -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/19/t1258657074i9urw99ge7hmcvd.htm/, Retrieved Thu, 28 Mar 2024 14:46:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57895, Retrieved Thu, 28 Mar 2024 14:46:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact133
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 14:03:14] [b98453cac15ba1066b407e146608df68]
- R  D      [Multiple Regression] [] [2009-11-19 18:55:57] [6974478841a4d28b8cb590971bfdefb0] [Current]
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Dataseries X:
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	0
478	0
528	0
534	0
518	1
506	1
502	1
516	1
528	1
533	1
536	1
537	1
524	1
536	1
587	1
597	1
581	1




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=57895&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=57895&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57895&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] = + 588.264 -26.1600000000000X[t] -14.2319999999999M1[t] -29.632M2[t] -27.232M3[t] -25.0320000000000M4[t] -29.232M5[t] -38.032M6[t] -43.032M7[t] -54.232M8[t] -52.032M9[t] -0.831999999999988M10[t] + 7.16800000000001M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  588.264 -26.1600000000000X[t] -14.2319999999999M1[t] -29.632M2[t] -27.232M3[t] -25.0320000000000M4[t] -29.232M5[t] -38.032M6[t] -43.032M7[t] -54.232M8[t] -52.032M9[t] -0.831999999999988M10[t] +  7.16800000000001M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57895&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  588.264 -26.1600000000000X[t] -14.2319999999999M1[t] -29.632M2[t] -27.232M3[t] -25.0320000000000M4[t] -29.232M5[t] -38.032M6[t] -43.032M7[t] -54.232M8[t] -52.032M9[t] -0.831999999999988M10[t] +  7.16800000000001M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57895&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57895&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] = + 588.264 -26.1600000000000X[t] -14.2319999999999M1[t] -29.632M2[t] -27.232M3[t] -25.0320000000000M4[t] -29.232M5[t] -38.032M6[t] -43.032M7[t] -54.232M8[t] -52.032M9[t] -0.831999999999988M10[t] + 7.16800000000001M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)588.26418.5625331.690900
X-26.160000000000012.630202-2.07120.0438530.021926
M1-14.231999999999925.386392-0.56060.5777230.288861
M2-29.63225.386392-1.16720.2490020.124501
M3-27.23225.386392-1.07270.2888830.144441
M4-25.032000000000025.386392-0.9860.3291630.164581
M5-29.23225.386392-1.15150.2553580.127679
M6-38.03225.386392-1.49810.1407870.070394
M7-43.03225.386392-1.69510.0966760.048338
M8-54.23225.386392-2.13630.0378960.018948
M9-52.03225.386392-2.04960.0460070.023003
M10-0.83199999999998825.386392-0.03280.9739940.486997
M117.1680000000000125.3863920.28240.778910.389455

\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) & 588.264 & 18.56253 & 31.6909 & 0 & 0 \tabularnewline
X & -26.1600000000000 & 12.630202 & -2.0712 & 0.043853 & 0.021926 \tabularnewline
M1 & -14.2319999999999 & 25.386392 & -0.5606 & 0.577723 & 0.288861 \tabularnewline
M2 & -29.632 & 25.386392 & -1.1672 & 0.249002 & 0.124501 \tabularnewline
M3 & -27.232 & 25.386392 & -1.0727 & 0.288883 & 0.144441 \tabularnewline
M4 & -25.0320000000000 & 25.386392 & -0.986 & 0.329163 & 0.164581 \tabularnewline
M5 & -29.232 & 25.386392 & -1.1515 & 0.255358 & 0.127679 \tabularnewline
M6 & -38.032 & 25.386392 & -1.4981 & 0.140787 & 0.070394 \tabularnewline
M7 & -43.032 & 25.386392 & -1.6951 & 0.096676 & 0.048338 \tabularnewline
M8 & -54.232 & 25.386392 & -2.1363 & 0.037896 & 0.018948 \tabularnewline
M9 & -52.032 & 25.386392 & -2.0496 & 0.046007 & 0.023003 \tabularnewline
M10 & -0.831999999999988 & 25.386392 & -0.0328 & 0.973994 & 0.486997 \tabularnewline
M11 & 7.16800000000001 & 25.386392 & 0.2824 & 0.77891 & 0.389455 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57895&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]588.264[/C][C]18.56253[/C][C]31.6909[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-26.1600000000000[/C][C]12.630202[/C][C]-2.0712[/C][C]0.043853[/C][C]0.021926[/C][/ROW]
[ROW][C]M1[/C][C]-14.2319999999999[/C][C]25.386392[/C][C]-0.5606[/C][C]0.577723[/C][C]0.288861[/C][/ROW]
[ROW][C]M2[/C][C]-29.632[/C][C]25.386392[/C][C]-1.1672[/C][C]0.249002[/C][C]0.124501[/C][/ROW]
[ROW][C]M3[/C][C]-27.232[/C][C]25.386392[/C][C]-1.0727[/C][C]0.288883[/C][C]0.144441[/C][/ROW]
[ROW][C]M4[/C][C]-25.0320000000000[/C][C]25.386392[/C][C]-0.986[/C][C]0.329163[/C][C]0.164581[/C][/ROW]
[ROW][C]M5[/C][C]-29.232[/C][C]25.386392[/C][C]-1.1515[/C][C]0.255358[/C][C]0.127679[/C][/ROW]
[ROW][C]M6[/C][C]-38.032[/C][C]25.386392[/C][C]-1.4981[/C][C]0.140787[/C][C]0.070394[/C][/ROW]
[ROW][C]M7[/C][C]-43.032[/C][C]25.386392[/C][C]-1.6951[/C][C]0.096676[/C][C]0.048338[/C][/ROW]
[ROW][C]M8[/C][C]-54.232[/C][C]25.386392[/C][C]-2.1363[/C][C]0.037896[/C][C]0.018948[/C][/ROW]
[ROW][C]M9[/C][C]-52.032[/C][C]25.386392[/C][C]-2.0496[/C][C]0.046007[/C][C]0.023003[/C][/ROW]
[ROW][C]M10[/C][C]-0.831999999999988[/C][C]25.386392[/C][C]-0.0328[/C][C]0.973994[/C][C]0.486997[/C][/ROW]
[ROW][C]M11[/C][C]7.16800000000001[/C][C]25.386392[/C][C]0.2824[/C][C]0.77891[/C][C]0.389455[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57895&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57895&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)588.26418.5625331.690900
X-26.160000000000012.630202-2.07120.0438530.021926
M1-14.231999999999925.386392-0.56060.5777230.288861
M2-29.63225.386392-1.16720.2490020.124501
M3-27.23225.386392-1.07270.2888830.144441
M4-25.032000000000025.386392-0.9860.3291630.164581
M5-29.23225.386392-1.15150.2553580.127679
M6-38.03225.386392-1.49810.1407870.070394
M7-43.03225.386392-1.69510.0966760.048338
M8-54.23225.386392-2.13630.0378960.018948
M9-52.03225.386392-2.04960.0460070.023003
M10-0.83199999999998825.386392-0.03280.9739940.486997
M117.1680000000000125.3863920.28240.778910.389455







Multiple Linear Regression - Regression Statistics
Multiple R0.521709767358670
R-squared0.272181081357437
Adjusted R-squared0.0863549744699744
F-TEST (value)1.46470851656098
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value0.171785357661580
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation39.9402063728567
Sum Squared Residuals74975.344

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.521709767358670 \tabularnewline
R-squared & 0.272181081357437 \tabularnewline
Adjusted R-squared & 0.0863549744699744 \tabularnewline
F-TEST (value) & 1.46470851656098 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 0.171785357661580 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 39.9402063728567 \tabularnewline
Sum Squared Residuals & 74975.344 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57895&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.521709767358670[/C][/ROW]
[ROW][C]R-squared[/C][C]0.272181081357437[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0863549744699744[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.46470851656098[/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]0.171785357661580[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]39.9402063728567[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]74975.344[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57895&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57895&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 R0.521709767358670
R-squared0.272181081357437
Adjusted R-squared0.0863549744699744
F-TEST (value)1.46470851656098
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value0.171785357661580
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation39.9402063728567
Sum Squared Residuals74975.344







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1611574.03236.9680000000002
2594558.63235.368
3595561.03233.968
4591563.23227.768
5589559.03229.9680000000000
6584550.23233.768
7573545.23227.768
8567534.03232.9680000000001
9569536.23232.768
10621587.43233.568
11629595.43233.568
12628588.26439.736
13612574.03237.9680000000000
14595558.63236.3680
15597561.03235.968
16593563.23229.768
17590559.03230.968
18580550.23229.768
19574545.23228.768
20573534.03238.968
21573536.23236.768
22620587.43232.568
23626595.43230.568
24620588.26431.736
25588574.03213.9679999999999
26566558.6327.36799999999997
27557561.032-4.03199999999999
28561563.232-2.23200000000002
29549559.032-10.032
30532550.232-18.232
31526545.232-19.232
32511534.032-23.0320000000000
33499536.232-37.232
34555587.432-32.432
35565595.432-30.432
36542588.264-46.264
37527574.032-47.0320000000001
38510558.632-48.6320000000000
39514561.032-47.032
40517563.232-46.232
41508559.032-51.032
42493550.232-57.232
43490545.232-55.232
44469534.032-65.032
45478536.232-58.232
46528587.432-59.432
47534595.432-61.432
48518562.104-44.104
49506547.872-41.8720000000001
50502532.472-30.472
51516534.872-18.8720000000000
52528537.072-9.0720
53533532.8720.128000000000013
54536524.07211.928
55537519.07217.9280000000000
56524507.87216.128
57536510.07225.928
58587561.27225.728
59597569.27227.728
60581562.10418.8960000000000

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 611 & 574.032 & 36.9680000000002 \tabularnewline
2 & 594 & 558.632 & 35.368 \tabularnewline
3 & 595 & 561.032 & 33.968 \tabularnewline
4 & 591 & 563.232 & 27.768 \tabularnewline
5 & 589 & 559.032 & 29.9680000000000 \tabularnewline
6 & 584 & 550.232 & 33.768 \tabularnewline
7 & 573 & 545.232 & 27.768 \tabularnewline
8 & 567 & 534.032 & 32.9680000000001 \tabularnewline
9 & 569 & 536.232 & 32.768 \tabularnewline
10 & 621 & 587.432 & 33.568 \tabularnewline
11 & 629 & 595.432 & 33.568 \tabularnewline
12 & 628 & 588.264 & 39.736 \tabularnewline
13 & 612 & 574.032 & 37.9680000000000 \tabularnewline
14 & 595 & 558.632 & 36.3680 \tabularnewline
15 & 597 & 561.032 & 35.968 \tabularnewline
16 & 593 & 563.232 & 29.768 \tabularnewline
17 & 590 & 559.032 & 30.968 \tabularnewline
18 & 580 & 550.232 & 29.768 \tabularnewline
19 & 574 & 545.232 & 28.768 \tabularnewline
20 & 573 & 534.032 & 38.968 \tabularnewline
21 & 573 & 536.232 & 36.768 \tabularnewline
22 & 620 & 587.432 & 32.568 \tabularnewline
23 & 626 & 595.432 & 30.568 \tabularnewline
24 & 620 & 588.264 & 31.736 \tabularnewline
25 & 588 & 574.032 & 13.9679999999999 \tabularnewline
26 & 566 & 558.632 & 7.36799999999997 \tabularnewline
27 & 557 & 561.032 & -4.03199999999999 \tabularnewline
28 & 561 & 563.232 & -2.23200000000002 \tabularnewline
29 & 549 & 559.032 & -10.032 \tabularnewline
30 & 532 & 550.232 & -18.232 \tabularnewline
31 & 526 & 545.232 & -19.232 \tabularnewline
32 & 511 & 534.032 & -23.0320000000000 \tabularnewline
33 & 499 & 536.232 & -37.232 \tabularnewline
34 & 555 & 587.432 & -32.432 \tabularnewline
35 & 565 & 595.432 & -30.432 \tabularnewline
36 & 542 & 588.264 & -46.264 \tabularnewline
37 & 527 & 574.032 & -47.0320000000001 \tabularnewline
38 & 510 & 558.632 & -48.6320000000000 \tabularnewline
39 & 514 & 561.032 & -47.032 \tabularnewline
40 & 517 & 563.232 & -46.232 \tabularnewline
41 & 508 & 559.032 & -51.032 \tabularnewline
42 & 493 & 550.232 & -57.232 \tabularnewline
43 & 490 & 545.232 & -55.232 \tabularnewline
44 & 469 & 534.032 & -65.032 \tabularnewline
45 & 478 & 536.232 & -58.232 \tabularnewline
46 & 528 & 587.432 & -59.432 \tabularnewline
47 & 534 & 595.432 & -61.432 \tabularnewline
48 & 518 & 562.104 & -44.104 \tabularnewline
49 & 506 & 547.872 & -41.8720000000001 \tabularnewline
50 & 502 & 532.472 & -30.472 \tabularnewline
51 & 516 & 534.872 & -18.8720000000000 \tabularnewline
52 & 528 & 537.072 & -9.0720 \tabularnewline
53 & 533 & 532.872 & 0.128000000000013 \tabularnewline
54 & 536 & 524.072 & 11.928 \tabularnewline
55 & 537 & 519.072 & 17.9280000000000 \tabularnewline
56 & 524 & 507.872 & 16.128 \tabularnewline
57 & 536 & 510.072 & 25.928 \tabularnewline
58 & 587 & 561.272 & 25.728 \tabularnewline
59 & 597 & 569.272 & 27.728 \tabularnewline
60 & 581 & 562.104 & 18.8960000000000 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57895&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]611[/C][C]574.032[/C][C]36.9680000000002[/C][/ROW]
[ROW][C]2[/C][C]594[/C][C]558.632[/C][C]35.368[/C][/ROW]
[ROW][C]3[/C][C]595[/C][C]561.032[/C][C]33.968[/C][/ROW]
[ROW][C]4[/C][C]591[/C][C]563.232[/C][C]27.768[/C][/ROW]
[ROW][C]5[/C][C]589[/C][C]559.032[/C][C]29.9680000000000[/C][/ROW]
[ROW][C]6[/C][C]584[/C][C]550.232[/C][C]33.768[/C][/ROW]
[ROW][C]7[/C][C]573[/C][C]545.232[/C][C]27.768[/C][/ROW]
[ROW][C]8[/C][C]567[/C][C]534.032[/C][C]32.9680000000001[/C][/ROW]
[ROW][C]9[/C][C]569[/C][C]536.232[/C][C]32.768[/C][/ROW]
[ROW][C]10[/C][C]621[/C][C]587.432[/C][C]33.568[/C][/ROW]
[ROW][C]11[/C][C]629[/C][C]595.432[/C][C]33.568[/C][/ROW]
[ROW][C]12[/C][C]628[/C][C]588.264[/C][C]39.736[/C][/ROW]
[ROW][C]13[/C][C]612[/C][C]574.032[/C][C]37.9680000000000[/C][/ROW]
[ROW][C]14[/C][C]595[/C][C]558.632[/C][C]36.3680[/C][/ROW]
[ROW][C]15[/C][C]597[/C][C]561.032[/C][C]35.968[/C][/ROW]
[ROW][C]16[/C][C]593[/C][C]563.232[/C][C]29.768[/C][/ROW]
[ROW][C]17[/C][C]590[/C][C]559.032[/C][C]30.968[/C][/ROW]
[ROW][C]18[/C][C]580[/C][C]550.232[/C][C]29.768[/C][/ROW]
[ROW][C]19[/C][C]574[/C][C]545.232[/C][C]28.768[/C][/ROW]
[ROW][C]20[/C][C]573[/C][C]534.032[/C][C]38.968[/C][/ROW]
[ROW][C]21[/C][C]573[/C][C]536.232[/C][C]36.768[/C][/ROW]
[ROW][C]22[/C][C]620[/C][C]587.432[/C][C]32.568[/C][/ROW]
[ROW][C]23[/C][C]626[/C][C]595.432[/C][C]30.568[/C][/ROW]
[ROW][C]24[/C][C]620[/C][C]588.264[/C][C]31.736[/C][/ROW]
[ROW][C]25[/C][C]588[/C][C]574.032[/C][C]13.9679999999999[/C][/ROW]
[ROW][C]26[/C][C]566[/C][C]558.632[/C][C]7.36799999999997[/C][/ROW]
[ROW][C]27[/C][C]557[/C][C]561.032[/C][C]-4.03199999999999[/C][/ROW]
[ROW][C]28[/C][C]561[/C][C]563.232[/C][C]-2.23200000000002[/C][/ROW]
[ROW][C]29[/C][C]549[/C][C]559.032[/C][C]-10.032[/C][/ROW]
[ROW][C]30[/C][C]532[/C][C]550.232[/C][C]-18.232[/C][/ROW]
[ROW][C]31[/C][C]526[/C][C]545.232[/C][C]-19.232[/C][/ROW]
[ROW][C]32[/C][C]511[/C][C]534.032[/C][C]-23.0320000000000[/C][/ROW]
[ROW][C]33[/C][C]499[/C][C]536.232[/C][C]-37.232[/C][/ROW]
[ROW][C]34[/C][C]555[/C][C]587.432[/C][C]-32.432[/C][/ROW]
[ROW][C]35[/C][C]565[/C][C]595.432[/C][C]-30.432[/C][/ROW]
[ROW][C]36[/C][C]542[/C][C]588.264[/C][C]-46.264[/C][/ROW]
[ROW][C]37[/C][C]527[/C][C]574.032[/C][C]-47.0320000000001[/C][/ROW]
[ROW][C]38[/C][C]510[/C][C]558.632[/C][C]-48.6320000000000[/C][/ROW]
[ROW][C]39[/C][C]514[/C][C]561.032[/C][C]-47.032[/C][/ROW]
[ROW][C]40[/C][C]517[/C][C]563.232[/C][C]-46.232[/C][/ROW]
[ROW][C]41[/C][C]508[/C][C]559.032[/C][C]-51.032[/C][/ROW]
[ROW][C]42[/C][C]493[/C][C]550.232[/C][C]-57.232[/C][/ROW]
[ROW][C]43[/C][C]490[/C][C]545.232[/C][C]-55.232[/C][/ROW]
[ROW][C]44[/C][C]469[/C][C]534.032[/C][C]-65.032[/C][/ROW]
[ROW][C]45[/C][C]478[/C][C]536.232[/C][C]-58.232[/C][/ROW]
[ROW][C]46[/C][C]528[/C][C]587.432[/C][C]-59.432[/C][/ROW]
[ROW][C]47[/C][C]534[/C][C]595.432[/C][C]-61.432[/C][/ROW]
[ROW][C]48[/C][C]518[/C][C]562.104[/C][C]-44.104[/C][/ROW]
[ROW][C]49[/C][C]506[/C][C]547.872[/C][C]-41.8720000000001[/C][/ROW]
[ROW][C]50[/C][C]502[/C][C]532.472[/C][C]-30.472[/C][/ROW]
[ROW][C]51[/C][C]516[/C][C]534.872[/C][C]-18.8720000000000[/C][/ROW]
[ROW][C]52[/C][C]528[/C][C]537.072[/C][C]-9.0720[/C][/ROW]
[ROW][C]53[/C][C]533[/C][C]532.872[/C][C]0.128000000000013[/C][/ROW]
[ROW][C]54[/C][C]536[/C][C]524.072[/C][C]11.928[/C][/ROW]
[ROW][C]55[/C][C]537[/C][C]519.072[/C][C]17.9280000000000[/C][/ROW]
[ROW][C]56[/C][C]524[/C][C]507.872[/C][C]16.128[/C][/ROW]
[ROW][C]57[/C][C]536[/C][C]510.072[/C][C]25.928[/C][/ROW]
[ROW][C]58[/C][C]587[/C][C]561.272[/C][C]25.728[/C][/ROW]
[ROW][C]59[/C][C]597[/C][C]569.272[/C][C]27.728[/C][/ROW]
[ROW][C]60[/C][C]581[/C][C]562.104[/C][C]18.8960000000000[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57895&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57895&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
1611574.03236.9680000000002
2594558.63235.368
3595561.03233.968
4591563.23227.768
5589559.03229.9680000000000
6584550.23233.768
7573545.23227.768
8567534.03232.9680000000001
9569536.23232.768
10621587.43233.568
11629595.43233.568
12628588.26439.736
13612574.03237.9680000000000
14595558.63236.3680
15597561.03235.968
16593563.23229.768
17590559.03230.968
18580550.23229.768
19574545.23228.768
20573534.03238.968
21573536.23236.768
22620587.43232.568
23626595.43230.568
24620588.26431.736
25588574.03213.9679999999999
26566558.6327.36799999999997
27557561.032-4.03199999999999
28561563.232-2.23200000000002
29549559.032-10.032
30532550.232-18.232
31526545.232-19.232
32511534.032-23.0320000000000
33499536.232-37.232
34555587.432-32.432
35565595.432-30.432
36542588.264-46.264
37527574.032-47.0320000000001
38510558.632-48.6320000000000
39514561.032-47.032
40517563.232-46.232
41508559.032-51.032
42493550.232-57.232
43490545.232-55.232
44469534.032-65.032
45478536.232-58.232
46528587.432-59.432
47534595.432-61.432
48518562.104-44.104
49506547.872-41.8720000000001
50502532.472-30.472
51516534.872-18.8720000000000
52528537.072-9.0720
53533532.8720.128000000000013
54536524.07211.928
55537519.07217.9280000000000
56524507.87216.128
57536510.07225.928
58587561.27225.728
59597569.27227.728
60581562.10418.8960000000000







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
165.49470362031783e-050.0001098940724063570.999945052963797
172.00506099788873e-064.01012199577746e-060.999997994939002
185.68416998379384e-071.13683399675877e-060.999999431583002
192.85295091729473e-085.70590183458946e-080.99999997147049
202.68181243090685e-085.3636248618137e-080.999999973181876
215.39219011652879e-091.07843802330576e-080.99999999460781
225.36949944686265e-101.07389988937253e-090.99999999946305
239.13524024705453e-111.82704804941091e-100.999999999908648
243.20922885527082e-106.41845771054165e-100.999999999679077
252.670198908114e-065.340397816228e-060.999997329801092
260.000195528400186030.000391056800372060.999804471599814
270.006275775103616030.01255155020723210.993724224896384
280.02244809956059310.04489619912118630.977551900439407
290.08194732632743140.1638946526548630.918052673672569
300.2190422943132720.4380845886265430.780957705686728
310.3371617023660730.6743234047321460.662838297633927
320.5224010872109450.9551978255781110.477598912789055
330.6709843794544510.6580312410910980.329015620545549
340.7264355859142620.5471288281714760.273564414085738
350.7464334720546580.5071330558906830.253566527945342
360.8098948341071540.3802103317856930.190105165892846
370.907201870937650.1855962581247000.0927981290623502
380.9460981637173860.1078036725652290.0539018362826144
390.96126379975540.0774724004891990.0387362002445995
400.966524484099560.06695103180088160.0334755159004408
410.9593650602456570.0812698795086860.040634939754343
420.9296280704345170.1407438591309670.0703719295654834
430.8706093394846660.2587813210306690.129390660515334
440.7660565574564330.4678868850871330.233943442543567

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 5.49470362031783e-05 & 0.000109894072406357 & 0.999945052963797 \tabularnewline
17 & 2.00506099788873e-06 & 4.01012199577746e-06 & 0.999997994939002 \tabularnewline
18 & 5.68416998379384e-07 & 1.13683399675877e-06 & 0.999999431583002 \tabularnewline
19 & 2.85295091729473e-08 & 5.70590183458946e-08 & 0.99999997147049 \tabularnewline
20 & 2.68181243090685e-08 & 5.3636248618137e-08 & 0.999999973181876 \tabularnewline
21 & 5.39219011652879e-09 & 1.07843802330576e-08 & 0.99999999460781 \tabularnewline
22 & 5.36949944686265e-10 & 1.07389988937253e-09 & 0.99999999946305 \tabularnewline
23 & 9.13524024705453e-11 & 1.82704804941091e-10 & 0.999999999908648 \tabularnewline
24 & 3.20922885527082e-10 & 6.41845771054165e-10 & 0.999999999679077 \tabularnewline
25 & 2.670198908114e-06 & 5.340397816228e-06 & 0.999997329801092 \tabularnewline
26 & 0.00019552840018603 & 0.00039105680037206 & 0.999804471599814 \tabularnewline
27 & 0.00627577510361603 & 0.0125515502072321 & 0.993724224896384 \tabularnewline
28 & 0.0224480995605931 & 0.0448961991211863 & 0.977551900439407 \tabularnewline
29 & 0.0819473263274314 & 0.163894652654863 & 0.918052673672569 \tabularnewline
30 & 0.219042294313272 & 0.438084588626543 & 0.780957705686728 \tabularnewline
31 & 0.337161702366073 & 0.674323404732146 & 0.662838297633927 \tabularnewline
32 & 0.522401087210945 & 0.955197825578111 & 0.477598912789055 \tabularnewline
33 & 0.670984379454451 & 0.658031241091098 & 0.329015620545549 \tabularnewline
34 & 0.726435585914262 & 0.547128828171476 & 0.273564414085738 \tabularnewline
35 & 0.746433472054658 & 0.507133055890683 & 0.253566527945342 \tabularnewline
36 & 0.809894834107154 & 0.380210331785693 & 0.190105165892846 \tabularnewline
37 & 0.90720187093765 & 0.185596258124700 & 0.0927981290623502 \tabularnewline
38 & 0.946098163717386 & 0.107803672565229 & 0.0539018362826144 \tabularnewline
39 & 0.9612637997554 & 0.077472400489199 & 0.0387362002445995 \tabularnewline
40 & 0.96652448409956 & 0.0669510318008816 & 0.0334755159004408 \tabularnewline
41 & 0.959365060245657 & 0.081269879508686 & 0.040634939754343 \tabularnewline
42 & 0.929628070434517 & 0.140743859130967 & 0.0703719295654834 \tabularnewline
43 & 0.870609339484666 & 0.258781321030669 & 0.129390660515334 \tabularnewline
44 & 0.766056557456433 & 0.467886885087133 & 0.233943442543567 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57895&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]5.49470362031783e-05[/C][C]0.000109894072406357[/C][C]0.999945052963797[/C][/ROW]
[ROW][C]17[/C][C]2.00506099788873e-06[/C][C]4.01012199577746e-06[/C][C]0.999997994939002[/C][/ROW]
[ROW][C]18[/C][C]5.68416998379384e-07[/C][C]1.13683399675877e-06[/C][C]0.999999431583002[/C][/ROW]
[ROW][C]19[/C][C]2.85295091729473e-08[/C][C]5.70590183458946e-08[/C][C]0.99999997147049[/C][/ROW]
[ROW][C]20[/C][C]2.68181243090685e-08[/C][C]5.3636248618137e-08[/C][C]0.999999973181876[/C][/ROW]
[ROW][C]21[/C][C]5.39219011652879e-09[/C][C]1.07843802330576e-08[/C][C]0.99999999460781[/C][/ROW]
[ROW][C]22[/C][C]5.36949944686265e-10[/C][C]1.07389988937253e-09[/C][C]0.99999999946305[/C][/ROW]
[ROW][C]23[/C][C]9.13524024705453e-11[/C][C]1.82704804941091e-10[/C][C]0.999999999908648[/C][/ROW]
[ROW][C]24[/C][C]3.20922885527082e-10[/C][C]6.41845771054165e-10[/C][C]0.999999999679077[/C][/ROW]
[ROW][C]25[/C][C]2.670198908114e-06[/C][C]5.340397816228e-06[/C][C]0.999997329801092[/C][/ROW]
[ROW][C]26[/C][C]0.00019552840018603[/C][C]0.00039105680037206[/C][C]0.999804471599814[/C][/ROW]
[ROW][C]27[/C][C]0.00627577510361603[/C][C]0.0125515502072321[/C][C]0.993724224896384[/C][/ROW]
[ROW][C]28[/C][C]0.0224480995605931[/C][C]0.0448961991211863[/C][C]0.977551900439407[/C][/ROW]
[ROW][C]29[/C][C]0.0819473263274314[/C][C]0.163894652654863[/C][C]0.918052673672569[/C][/ROW]
[ROW][C]30[/C][C]0.219042294313272[/C][C]0.438084588626543[/C][C]0.780957705686728[/C][/ROW]
[ROW][C]31[/C][C]0.337161702366073[/C][C]0.674323404732146[/C][C]0.662838297633927[/C][/ROW]
[ROW][C]32[/C][C]0.522401087210945[/C][C]0.955197825578111[/C][C]0.477598912789055[/C][/ROW]
[ROW][C]33[/C][C]0.670984379454451[/C][C]0.658031241091098[/C][C]0.329015620545549[/C][/ROW]
[ROW][C]34[/C][C]0.726435585914262[/C][C]0.547128828171476[/C][C]0.273564414085738[/C][/ROW]
[ROW][C]35[/C][C]0.746433472054658[/C][C]0.507133055890683[/C][C]0.253566527945342[/C][/ROW]
[ROW][C]36[/C][C]0.809894834107154[/C][C]0.380210331785693[/C][C]0.190105165892846[/C][/ROW]
[ROW][C]37[/C][C]0.90720187093765[/C][C]0.185596258124700[/C][C]0.0927981290623502[/C][/ROW]
[ROW][C]38[/C][C]0.946098163717386[/C][C]0.107803672565229[/C][C]0.0539018362826144[/C][/ROW]
[ROW][C]39[/C][C]0.9612637997554[/C][C]0.077472400489199[/C][C]0.0387362002445995[/C][/ROW]
[ROW][C]40[/C][C]0.96652448409956[/C][C]0.0669510318008816[/C][C]0.0334755159004408[/C][/ROW]
[ROW][C]41[/C][C]0.959365060245657[/C][C]0.081269879508686[/C][C]0.040634939754343[/C][/ROW]
[ROW][C]42[/C][C]0.929628070434517[/C][C]0.140743859130967[/C][C]0.0703719295654834[/C][/ROW]
[ROW][C]43[/C][C]0.870609339484666[/C][C]0.258781321030669[/C][C]0.129390660515334[/C][/ROW]
[ROW][C]44[/C][C]0.766056557456433[/C][C]0.467886885087133[/C][C]0.233943442543567[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57895&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57895&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
165.49470362031783e-050.0001098940724063570.999945052963797
172.00506099788873e-064.01012199577746e-060.999997994939002
185.68416998379384e-071.13683399675877e-060.999999431583002
192.85295091729473e-085.70590183458946e-080.99999997147049
202.68181243090685e-085.3636248618137e-080.999999973181876
215.39219011652879e-091.07843802330576e-080.99999999460781
225.36949944686265e-101.07389988937253e-090.99999999946305
239.13524024705453e-111.82704804941091e-100.999999999908648
243.20922885527082e-106.41845771054165e-100.999999999679077
252.670198908114e-065.340397816228e-060.999997329801092
260.000195528400186030.000391056800372060.999804471599814
270.006275775103616030.01255155020723210.993724224896384
280.02244809956059310.04489619912118630.977551900439407
290.08194732632743140.1638946526548630.918052673672569
300.2190422943132720.4380845886265430.780957705686728
310.3371617023660730.6743234047321460.662838297633927
320.5224010872109450.9551978255781110.477598912789055
330.6709843794544510.6580312410910980.329015620545549
340.7264355859142620.5471288281714760.273564414085738
350.7464334720546580.5071330558906830.253566527945342
360.8098948341071540.3802103317856930.190105165892846
370.907201870937650.1855962581247000.0927981290623502
380.9460981637173860.1078036725652290.0539018362826144
390.96126379975540.0774724004891990.0387362002445995
400.966524484099560.06695103180088160.0334755159004408
410.9593650602456570.0812698795086860.040634939754343
420.9296280704345170.1407438591309670.0703719295654834
430.8706093394846660.2587813210306690.129390660515334
440.7660565574564330.4678868850871330.233943442543567







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level110.379310344827586NOK
5% type I error level130.448275862068966NOK
10% type I error level160.551724137931034NOK

\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 & 11 & 0.379310344827586 & NOK \tabularnewline
5% type I error level & 13 & 0.448275862068966 & NOK \tabularnewline
10% type I error level & 16 & 0.551724137931034 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57895&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]11[/C][C]0.379310344827586[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]13[/C][C]0.448275862068966[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.551724137931034[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57895&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57895&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 level110.379310344827586NOK
5% type I error level130.448275862068966NOK
10% type I error level160.551724137931034NOK



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')
}