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Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 10:42:34 -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/t1258739488n7gtf948m7i48t6.htm/, Retrieved Thu, 28 Mar 2024 19:44:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58370, Retrieved Thu, 28 Mar 2024 19:44:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact106
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]
-    D      [Multiple Regression] [Workshop 7 data 1] [2009-11-20 17:42:34] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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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 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=58370&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=58370&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58370&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
Yt[t] = + 569.68085106383 -52.1808510638297X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Yt[t] =  +  569.68085106383 -52.1808510638297X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58370&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Yt[t] =  +  569.68085106383 -52.1808510638297X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58370&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58370&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] = + 569.68085106383 -52.1808510638297X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)569.680851063835.308843107.307900
X-52.180851063829711.081557-4.70881.6e-058e-06

\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) & 569.68085106383 & 5.308843 & 107.3079 & 0 & 0 \tabularnewline
X & -52.1808510638297 & 11.081557 & -4.7088 & 1.6e-05 & 8e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58370&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]569.68085106383[/C][C]5.308843[/C][C]107.3079[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-52.1808510638297[/C][C]11.081557[/C][C]-4.7088[/C][C]1.6e-05[/C][C]8e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58370&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58370&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)569.680851063835.308843107.307900
X-52.180851063829711.081557-4.70881.6e-058e-06







Multiple Linear Regression - Regression Statistics
Multiple R0.522642943159927
R-squared0.273155646034871
Adjusted R-squared0.260836250204954
F-TEST (value)22.1728118656207
F-TEST (DF numerator)1
F-TEST (DF denominator)59
p-value1.55495900285851e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation36.3955931319028
Sum Squared Residuals78153.7127659575

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.522642943159927 \tabularnewline
R-squared & 0.273155646034871 \tabularnewline
Adjusted R-squared & 0.260836250204954 \tabularnewline
F-TEST (value) & 22.1728118656207 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 1.55495900285851e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 36.3955931319028 \tabularnewline
Sum Squared Residuals & 78153.7127659575 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58370&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.522642943159927[/C][/ROW]
[ROW][C]R-squared[/C][C]0.273155646034871[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.260836250204954[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]22.1728118656207[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]59[/C][/ROW]
[ROW][C]p-value[/C][C]1.55495900285851e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]36.3955931319028[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]78153.7127659575[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58370&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58370&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 R0.522642943159927
R-squared0.273155646034871
Adjusted R-squared0.260836250204954
F-TEST (value)22.1728118656207
F-TEST (DF numerator)1
F-TEST (DF denominator)59
p-value1.55495900285851e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation36.3955931319028
Sum Squared Residuals78153.7127659575







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1543569.680851063832-26.6808510638318
2594569.6808510638324.3191489361702
3611569.6808510638341.3191489361703
4613569.6808510638343.3191489361703
5611569.6808510638341.3191489361703
6594569.6808510638324.3191489361703
7595569.6808510638325.3191489361703
8591569.6808510638321.3191489361703
9589569.6808510638319.3191489361703
10584569.6808510638314.3191489361703
11573569.680851063833.31914893617026
12567569.68085106383-2.68085106382974
13569569.68085106383-0.680851063829743
14621569.6808510638351.3191489361703
15629569.6808510638359.3191489361703
16628569.6808510638358.3191489361703
17612569.6808510638342.3191489361703
18595569.6808510638325.3191489361703
19597569.6808510638327.3191489361703
20593569.6808510638323.3191489361703
21590569.6808510638320.3191489361703
22580569.6808510638310.3191489361703
23574569.680851063834.31914893617026
24573569.680851063833.31914893617026
25573569.680851063833.31914893617026
26620569.6808510638350.3191489361703
27626569.6808510638356.3191489361703
28620569.6808510638350.3191489361703
29588569.6808510638318.3191489361703
30566569.68085106383-3.68085106382974
31557569.68085106383-12.6808510638297
32561569.68085106383-8.68085106382974
33549569.68085106383-20.6808510638297
34532569.68085106383-37.6808510638297
35526569.68085106383-43.6808510638297
36511569.68085106383-58.6808510638297
37499569.68085106383-70.6808510638297
38555569.68085106383-14.6808510638297
39565569.68085106383-4.68085106382974
40542569.68085106383-27.6808510638297
41527569.68085106383-42.6808510638297
42510569.68085106383-59.6808510638297
43514569.68085106383-55.6808510638297
44517569.68085106383-52.6808510638297
45508569.68085106383-61.6808510638297
46493569.68085106383-76.6808510638297
47490569.68085106383-79.6808510638297
48469517.5-48.5
49478517.5-39.5
50528517.510.5
51534517.516.5
52518517.50.500000000000005
53506517.5-11.5
54502517.5-15.5
55516517.5-1.49999999999999
56528517.510.5
57533517.515.5
58536517.518.5
59537517.519.5
60524517.56.5
61536517.518.5

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 543 & 569.680851063832 & -26.6808510638318 \tabularnewline
2 & 594 & 569.68085106383 & 24.3191489361702 \tabularnewline
3 & 611 & 569.68085106383 & 41.3191489361703 \tabularnewline
4 & 613 & 569.68085106383 & 43.3191489361703 \tabularnewline
5 & 611 & 569.68085106383 & 41.3191489361703 \tabularnewline
6 & 594 & 569.68085106383 & 24.3191489361703 \tabularnewline
7 & 595 & 569.68085106383 & 25.3191489361703 \tabularnewline
8 & 591 & 569.68085106383 & 21.3191489361703 \tabularnewline
9 & 589 & 569.68085106383 & 19.3191489361703 \tabularnewline
10 & 584 & 569.68085106383 & 14.3191489361703 \tabularnewline
11 & 573 & 569.68085106383 & 3.31914893617026 \tabularnewline
12 & 567 & 569.68085106383 & -2.68085106382974 \tabularnewline
13 & 569 & 569.68085106383 & -0.680851063829743 \tabularnewline
14 & 621 & 569.68085106383 & 51.3191489361703 \tabularnewline
15 & 629 & 569.68085106383 & 59.3191489361703 \tabularnewline
16 & 628 & 569.68085106383 & 58.3191489361703 \tabularnewline
17 & 612 & 569.68085106383 & 42.3191489361703 \tabularnewline
18 & 595 & 569.68085106383 & 25.3191489361703 \tabularnewline
19 & 597 & 569.68085106383 & 27.3191489361703 \tabularnewline
20 & 593 & 569.68085106383 & 23.3191489361703 \tabularnewline
21 & 590 & 569.68085106383 & 20.3191489361703 \tabularnewline
22 & 580 & 569.68085106383 & 10.3191489361703 \tabularnewline
23 & 574 & 569.68085106383 & 4.31914893617026 \tabularnewline
24 & 573 & 569.68085106383 & 3.31914893617026 \tabularnewline
25 & 573 & 569.68085106383 & 3.31914893617026 \tabularnewline
26 & 620 & 569.68085106383 & 50.3191489361703 \tabularnewline
27 & 626 & 569.68085106383 & 56.3191489361703 \tabularnewline
28 & 620 & 569.68085106383 & 50.3191489361703 \tabularnewline
29 & 588 & 569.68085106383 & 18.3191489361703 \tabularnewline
30 & 566 & 569.68085106383 & -3.68085106382974 \tabularnewline
31 & 557 & 569.68085106383 & -12.6808510638297 \tabularnewline
32 & 561 & 569.68085106383 & -8.68085106382974 \tabularnewline
33 & 549 & 569.68085106383 & -20.6808510638297 \tabularnewline
34 & 532 & 569.68085106383 & -37.6808510638297 \tabularnewline
35 & 526 & 569.68085106383 & -43.6808510638297 \tabularnewline
36 & 511 & 569.68085106383 & -58.6808510638297 \tabularnewline
37 & 499 & 569.68085106383 & -70.6808510638297 \tabularnewline
38 & 555 & 569.68085106383 & -14.6808510638297 \tabularnewline
39 & 565 & 569.68085106383 & -4.68085106382974 \tabularnewline
40 & 542 & 569.68085106383 & -27.6808510638297 \tabularnewline
41 & 527 & 569.68085106383 & -42.6808510638297 \tabularnewline
42 & 510 & 569.68085106383 & -59.6808510638297 \tabularnewline
43 & 514 & 569.68085106383 & -55.6808510638297 \tabularnewline
44 & 517 & 569.68085106383 & -52.6808510638297 \tabularnewline
45 & 508 & 569.68085106383 & -61.6808510638297 \tabularnewline
46 & 493 & 569.68085106383 & -76.6808510638297 \tabularnewline
47 & 490 & 569.68085106383 & -79.6808510638297 \tabularnewline
48 & 469 & 517.5 & -48.5 \tabularnewline
49 & 478 & 517.5 & -39.5 \tabularnewline
50 & 528 & 517.5 & 10.5 \tabularnewline
51 & 534 & 517.5 & 16.5 \tabularnewline
52 & 518 & 517.5 & 0.500000000000005 \tabularnewline
53 & 506 & 517.5 & -11.5 \tabularnewline
54 & 502 & 517.5 & -15.5 \tabularnewline
55 & 516 & 517.5 & -1.49999999999999 \tabularnewline
56 & 528 & 517.5 & 10.5 \tabularnewline
57 & 533 & 517.5 & 15.5 \tabularnewline
58 & 536 & 517.5 & 18.5 \tabularnewline
59 & 537 & 517.5 & 19.5 \tabularnewline
60 & 524 & 517.5 & 6.5 \tabularnewline
61 & 536 & 517.5 & 18.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58370&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]569.680851063832[/C][C]-26.6808510638318[/C][/ROW]
[ROW][C]2[/C][C]594[/C][C]569.68085106383[/C][C]24.3191489361702[/C][/ROW]
[ROW][C]3[/C][C]611[/C][C]569.68085106383[/C][C]41.3191489361703[/C][/ROW]
[ROW][C]4[/C][C]613[/C][C]569.68085106383[/C][C]43.3191489361703[/C][/ROW]
[ROW][C]5[/C][C]611[/C][C]569.68085106383[/C][C]41.3191489361703[/C][/ROW]
[ROW][C]6[/C][C]594[/C][C]569.68085106383[/C][C]24.3191489361703[/C][/ROW]
[ROW][C]7[/C][C]595[/C][C]569.68085106383[/C][C]25.3191489361703[/C][/ROW]
[ROW][C]8[/C][C]591[/C][C]569.68085106383[/C][C]21.3191489361703[/C][/ROW]
[ROW][C]9[/C][C]589[/C][C]569.68085106383[/C][C]19.3191489361703[/C][/ROW]
[ROW][C]10[/C][C]584[/C][C]569.68085106383[/C][C]14.3191489361703[/C][/ROW]
[ROW][C]11[/C][C]573[/C][C]569.68085106383[/C][C]3.31914893617026[/C][/ROW]
[ROW][C]12[/C][C]567[/C][C]569.68085106383[/C][C]-2.68085106382974[/C][/ROW]
[ROW][C]13[/C][C]569[/C][C]569.68085106383[/C][C]-0.680851063829743[/C][/ROW]
[ROW][C]14[/C][C]621[/C][C]569.68085106383[/C][C]51.3191489361703[/C][/ROW]
[ROW][C]15[/C][C]629[/C][C]569.68085106383[/C][C]59.3191489361703[/C][/ROW]
[ROW][C]16[/C][C]628[/C][C]569.68085106383[/C][C]58.3191489361703[/C][/ROW]
[ROW][C]17[/C][C]612[/C][C]569.68085106383[/C][C]42.3191489361703[/C][/ROW]
[ROW][C]18[/C][C]595[/C][C]569.68085106383[/C][C]25.3191489361703[/C][/ROW]
[ROW][C]19[/C][C]597[/C][C]569.68085106383[/C][C]27.3191489361703[/C][/ROW]
[ROW][C]20[/C][C]593[/C][C]569.68085106383[/C][C]23.3191489361703[/C][/ROW]
[ROW][C]21[/C][C]590[/C][C]569.68085106383[/C][C]20.3191489361703[/C][/ROW]
[ROW][C]22[/C][C]580[/C][C]569.68085106383[/C][C]10.3191489361703[/C][/ROW]
[ROW][C]23[/C][C]574[/C][C]569.68085106383[/C][C]4.31914893617026[/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]569.68085106383[/C][C]3.31914893617026[/C][/ROW]
[ROW][C]26[/C][C]620[/C][C]569.68085106383[/C][C]50.3191489361703[/C][/ROW]
[ROW][C]27[/C][C]626[/C][C]569.68085106383[/C][C]56.3191489361703[/C][/ROW]
[ROW][C]28[/C][C]620[/C][C]569.68085106383[/C][C]50.3191489361703[/C][/ROW]
[ROW][C]29[/C][C]588[/C][C]569.68085106383[/C][C]18.3191489361703[/C][/ROW]
[ROW][C]30[/C][C]566[/C][C]569.68085106383[/C][C]-3.68085106382974[/C][/ROW]
[ROW][C]31[/C][C]557[/C][C]569.68085106383[/C][C]-12.6808510638297[/C][/ROW]
[ROW][C]32[/C][C]561[/C][C]569.68085106383[/C][C]-8.68085106382974[/C][/ROW]
[ROW][C]33[/C][C]549[/C][C]569.68085106383[/C][C]-20.6808510638297[/C][/ROW]
[ROW][C]34[/C][C]532[/C][C]569.68085106383[/C][C]-37.6808510638297[/C][/ROW]
[ROW][C]35[/C][C]526[/C][C]569.68085106383[/C][C]-43.6808510638297[/C][/ROW]
[ROW][C]36[/C][C]511[/C][C]569.68085106383[/C][C]-58.6808510638297[/C][/ROW]
[ROW][C]37[/C][C]499[/C][C]569.68085106383[/C][C]-70.6808510638297[/C][/ROW]
[ROW][C]38[/C][C]555[/C][C]569.68085106383[/C][C]-14.6808510638297[/C][/ROW]
[ROW][C]39[/C][C]565[/C][C]569.68085106383[/C][C]-4.68085106382974[/C][/ROW]
[ROW][C]40[/C][C]542[/C][C]569.68085106383[/C][C]-27.6808510638297[/C][/ROW]
[ROW][C]41[/C][C]527[/C][C]569.68085106383[/C][C]-42.6808510638297[/C][/ROW]
[ROW][C]42[/C][C]510[/C][C]569.68085106383[/C][C]-59.6808510638297[/C][/ROW]
[ROW][C]43[/C][C]514[/C][C]569.68085106383[/C][C]-55.6808510638297[/C][/ROW]
[ROW][C]44[/C][C]517[/C][C]569.68085106383[/C][C]-52.6808510638297[/C][/ROW]
[ROW][C]45[/C][C]508[/C][C]569.68085106383[/C][C]-61.6808510638297[/C][/ROW]
[ROW][C]46[/C][C]493[/C][C]569.68085106383[/C][C]-76.6808510638297[/C][/ROW]
[ROW][C]47[/C][C]490[/C][C]569.68085106383[/C][C]-79.6808510638297[/C][/ROW]
[ROW][C]48[/C][C]469[/C][C]517.5[/C][C]-48.5[/C][/ROW]
[ROW][C]49[/C][C]478[/C][C]517.5[/C][C]-39.5[/C][/ROW]
[ROW][C]50[/C][C]528[/C][C]517.5[/C][C]10.5[/C][/ROW]
[ROW][C]51[/C][C]534[/C][C]517.5[/C][C]16.5[/C][/ROW]
[ROW][C]52[/C][C]518[/C][C]517.5[/C][C]0.500000000000005[/C][/ROW]
[ROW][C]53[/C][C]506[/C][C]517.5[/C][C]-11.5[/C][/ROW]
[ROW][C]54[/C][C]502[/C][C]517.5[/C][C]-15.5[/C][/ROW]
[ROW][C]55[/C][C]516[/C][C]517.5[/C][C]-1.49999999999999[/C][/ROW]
[ROW][C]56[/C][C]528[/C][C]517.5[/C][C]10.5[/C][/ROW]
[ROW][C]57[/C][C]533[/C][C]517.5[/C][C]15.5[/C][/ROW]
[ROW][C]58[/C][C]536[/C][C]517.5[/C][C]18.5[/C][/ROW]
[ROW][C]59[/C][C]537[/C][C]517.5[/C][C]19.5[/C][/ROW]
[ROW][C]60[/C][C]524[/C][C]517.5[/C][C]6.5[/C][/ROW]
[ROW][C]61[/C][C]536[/C][C]517.5[/C][C]18.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58370&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58370&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
1543569.680851063832-26.6808510638318
2594569.6808510638324.3191489361702
3611569.6808510638341.3191489361703
4613569.6808510638343.3191489361703
5611569.6808510638341.3191489361703
6594569.6808510638324.3191489361703
7595569.6808510638325.3191489361703
8591569.6808510638321.3191489361703
9589569.6808510638319.3191489361703
10584569.6808510638314.3191489361703
11573569.680851063833.31914893617026
12567569.68085106383-2.68085106382974
13569569.68085106383-0.680851063829743
14621569.6808510638351.3191489361703
15629569.6808510638359.3191489361703
16628569.6808510638358.3191489361703
17612569.6808510638342.3191489361703
18595569.6808510638325.3191489361703
19597569.6808510638327.3191489361703
20593569.6808510638323.3191489361703
21590569.6808510638320.3191489361703
22580569.6808510638310.3191489361703
23574569.680851063834.31914893617026
24573569.680851063833.31914893617026
25573569.680851063833.31914893617026
26620569.6808510638350.3191489361703
27626569.6808510638356.3191489361703
28620569.6808510638350.3191489361703
29588569.6808510638318.3191489361703
30566569.68085106383-3.68085106382974
31557569.68085106383-12.6808510638297
32561569.68085106383-8.68085106382974
33549569.68085106383-20.6808510638297
34532569.68085106383-37.6808510638297
35526569.68085106383-43.6808510638297
36511569.68085106383-58.6808510638297
37499569.68085106383-70.6808510638297
38555569.68085106383-14.6808510638297
39565569.68085106383-4.68085106382974
40542569.68085106383-27.6808510638297
41527569.68085106383-42.6808510638297
42510569.68085106383-59.6808510638297
43514569.68085106383-55.6808510638297
44517569.68085106383-52.6808510638297
45508569.68085106383-61.6808510638297
46493569.68085106383-76.6808510638297
47490569.68085106383-79.6808510638297
48469517.5-48.5
49478517.5-39.5
50528517.510.5
51534517.516.5
52518517.50.500000000000005
53506517.5-11.5
54502517.5-15.5
55516517.5-1.49999999999999
56528517.510.5
57533517.515.5
58536517.518.5
59537517.519.5
60524517.56.5
61536517.518.5







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.5501329655007360.8997340689985290.449867034499264
60.3814582538378930.7629165076757860.618541746162107
70.2468132332443250.493626466488650.753186766755675
80.1499671962090820.2999343924181650.850032803790918
90.08658670273705630.1731734054741130.913413297262944
100.04941825167527210.09883650335054410.950581748324728
110.03362186576372100.06724373152744190.96637813423628
120.0260098552725220.0520197105450440.973990144727478
130.01766987521558130.03533975043116260.982330124784419
140.02493706058765780.04987412117531560.975062939412342
150.04558199080464730.09116398160929460.954418009195353
160.06960559149092890.1392111829818580.930394408509071
170.06328616410176650.1265723282035330.936713835898233
180.04555541837543210.09111083675086430.954444581624568
190.03360068505179080.06720137010358160.96639931494821
200.02436486044886280.04872972089772560.975635139551137
210.01767869157411850.03535738314823690.982321308425882
220.01338832976016580.02677665952033150.986611670239834
230.01094389978664380.02188779957328750.989056100213356
240.008975168273456160.01795033654691230.991024831726544
250.007280296016300140.01456059203260030.9927197039837
260.01754039992802610.03508079985605220.982459600071974
270.06899966293985580.1379993258797120.931000337060144
280.2244101087859430.4488202175718870.775589891214057
290.3167310876103180.6334621752206370.683268912389682
300.3928483772368620.7856967544737240.607151622763138
310.4809951318206440.9619902636412880.519004868179356
320.5763105446930190.8473789106139620.423689455306981
330.6673628668231710.6652742663536570.332637133176829
340.7633922155049130.4732155689901740.236607784495087
350.8317159358353880.3365681283292230.168284064164612
360.9015915877033320.1968168245933360.0984084122966678
370.9556939735911530.08861205281769430.0443060264088472
380.9639150761906920.07216984761861560.0360849238093078
390.986095858429380.02780828314123980.0139041415706199
400.9905921839334360.01881563213312740.00940781606656369
410.9916065769248030.01678684615039480.0083934230751974
420.99143341210370.01713317579260070.00856658789630036
430.9904620232842220.01907595343155630.00953797671577814
440.9897779813121450.02044403737570920.0102220186878546
450.988354307546820.02329138490635850.0116456924531792
460.986025613926030.02794877214793760.0139743860739688
470.9821390724424740.03572185511505190.0178609275575259
480.9961602773517760.007679445296447740.00383972264822387
490.9997372081165720.0005255837668555980.000262791883427799
500.999254497363430.001491005273140260.000745502636570132
510.9982694807395150.003461038520970080.00173051926048504
520.9951424706863930.009715058627214290.00485752931360714
530.9937708134467710.01245837310645800.00622918655322901
540.9983546326048710.003290734790257080.00164536739512854
550.9988414557041540.002317088591692180.00115854429584609
560.993892674880760.01221465023847790.00610732511923893

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.550132965500736 & 0.899734068998529 & 0.449867034499264 \tabularnewline
6 & 0.381458253837893 & 0.762916507675786 & 0.618541746162107 \tabularnewline
7 & 0.246813233244325 & 0.49362646648865 & 0.753186766755675 \tabularnewline
8 & 0.149967196209082 & 0.299934392418165 & 0.850032803790918 \tabularnewline
9 & 0.0865867027370563 & 0.173173405474113 & 0.913413297262944 \tabularnewline
10 & 0.0494182516752721 & 0.0988365033505441 & 0.950581748324728 \tabularnewline
11 & 0.0336218657637210 & 0.0672437315274419 & 0.96637813423628 \tabularnewline
12 & 0.026009855272522 & 0.052019710545044 & 0.973990144727478 \tabularnewline
13 & 0.0176698752155813 & 0.0353397504311626 & 0.982330124784419 \tabularnewline
14 & 0.0249370605876578 & 0.0498741211753156 & 0.975062939412342 \tabularnewline
15 & 0.0455819908046473 & 0.0911639816092946 & 0.954418009195353 \tabularnewline
16 & 0.0696055914909289 & 0.139211182981858 & 0.930394408509071 \tabularnewline
17 & 0.0632861641017665 & 0.126572328203533 & 0.936713835898233 \tabularnewline
18 & 0.0455554183754321 & 0.0911108367508643 & 0.954444581624568 \tabularnewline
19 & 0.0336006850517908 & 0.0672013701035816 & 0.96639931494821 \tabularnewline
20 & 0.0243648604488628 & 0.0487297208977256 & 0.975635139551137 \tabularnewline
21 & 0.0176786915741185 & 0.0353573831482369 & 0.982321308425882 \tabularnewline
22 & 0.0133883297601658 & 0.0267766595203315 & 0.986611670239834 \tabularnewline
23 & 0.0109438997866438 & 0.0218877995732875 & 0.989056100213356 \tabularnewline
24 & 0.00897516827345616 & 0.0179503365469123 & 0.991024831726544 \tabularnewline
25 & 0.00728029601630014 & 0.0145605920326003 & 0.9927197039837 \tabularnewline
26 & 0.0175403999280261 & 0.0350807998560522 & 0.982459600071974 \tabularnewline
27 & 0.0689996629398558 & 0.137999325879712 & 0.931000337060144 \tabularnewline
28 & 0.224410108785943 & 0.448820217571887 & 0.775589891214057 \tabularnewline
29 & 0.316731087610318 & 0.633462175220637 & 0.683268912389682 \tabularnewline
30 & 0.392848377236862 & 0.785696754473724 & 0.607151622763138 \tabularnewline
31 & 0.480995131820644 & 0.961990263641288 & 0.519004868179356 \tabularnewline
32 & 0.576310544693019 & 0.847378910613962 & 0.423689455306981 \tabularnewline
33 & 0.667362866823171 & 0.665274266353657 & 0.332637133176829 \tabularnewline
34 & 0.763392215504913 & 0.473215568990174 & 0.236607784495087 \tabularnewline
35 & 0.831715935835388 & 0.336568128329223 & 0.168284064164612 \tabularnewline
36 & 0.901591587703332 & 0.196816824593336 & 0.0984084122966678 \tabularnewline
37 & 0.955693973591153 & 0.0886120528176943 & 0.0443060264088472 \tabularnewline
38 & 0.963915076190692 & 0.0721698476186156 & 0.0360849238093078 \tabularnewline
39 & 0.98609585842938 & 0.0278082831412398 & 0.0139041415706199 \tabularnewline
40 & 0.990592183933436 & 0.0188156321331274 & 0.00940781606656369 \tabularnewline
41 & 0.991606576924803 & 0.0167868461503948 & 0.0083934230751974 \tabularnewline
42 & 0.9914334121037 & 0.0171331757926007 & 0.00856658789630036 \tabularnewline
43 & 0.990462023284222 & 0.0190759534315563 & 0.00953797671577814 \tabularnewline
44 & 0.989777981312145 & 0.0204440373757092 & 0.0102220186878546 \tabularnewline
45 & 0.98835430754682 & 0.0232913849063585 & 0.0116456924531792 \tabularnewline
46 & 0.98602561392603 & 0.0279487721479376 & 0.0139743860739688 \tabularnewline
47 & 0.982139072442474 & 0.0357218551150519 & 0.0178609275575259 \tabularnewline
48 & 0.996160277351776 & 0.00767944529644774 & 0.00383972264822387 \tabularnewline
49 & 0.999737208116572 & 0.000525583766855598 & 0.000262791883427799 \tabularnewline
50 & 0.99925449736343 & 0.00149100527314026 & 0.000745502636570132 \tabularnewline
51 & 0.998269480739515 & 0.00346103852097008 & 0.00173051926048504 \tabularnewline
52 & 0.995142470686393 & 0.00971505862721429 & 0.00485752931360714 \tabularnewline
53 & 0.993770813446771 & 0.0124583731064580 & 0.00622918655322901 \tabularnewline
54 & 0.998354632604871 & 0.00329073479025708 & 0.00164536739512854 \tabularnewline
55 & 0.998841455704154 & 0.00231708859169218 & 0.00115854429584609 \tabularnewline
56 & 0.99389267488076 & 0.0122146502384779 & 0.00610732511923893 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58370&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]5[/C][C]0.550132965500736[/C][C]0.899734068998529[/C][C]0.449867034499264[/C][/ROW]
[ROW][C]6[/C][C]0.381458253837893[/C][C]0.762916507675786[/C][C]0.618541746162107[/C][/ROW]
[ROW][C]7[/C][C]0.246813233244325[/C][C]0.49362646648865[/C][C]0.753186766755675[/C][/ROW]
[ROW][C]8[/C][C]0.149967196209082[/C][C]0.299934392418165[/C][C]0.850032803790918[/C][/ROW]
[ROW][C]9[/C][C]0.0865867027370563[/C][C]0.173173405474113[/C][C]0.913413297262944[/C][/ROW]
[ROW][C]10[/C][C]0.0494182516752721[/C][C]0.0988365033505441[/C][C]0.950581748324728[/C][/ROW]
[ROW][C]11[/C][C]0.0336218657637210[/C][C]0.0672437315274419[/C][C]0.96637813423628[/C][/ROW]
[ROW][C]12[/C][C]0.026009855272522[/C][C]0.052019710545044[/C][C]0.973990144727478[/C][/ROW]
[ROW][C]13[/C][C]0.0176698752155813[/C][C]0.0353397504311626[/C][C]0.982330124784419[/C][/ROW]
[ROW][C]14[/C][C]0.0249370605876578[/C][C]0.0498741211753156[/C][C]0.975062939412342[/C][/ROW]
[ROW][C]15[/C][C]0.0455819908046473[/C][C]0.0911639816092946[/C][C]0.954418009195353[/C][/ROW]
[ROW][C]16[/C][C]0.0696055914909289[/C][C]0.139211182981858[/C][C]0.930394408509071[/C][/ROW]
[ROW][C]17[/C][C]0.0632861641017665[/C][C]0.126572328203533[/C][C]0.936713835898233[/C][/ROW]
[ROW][C]18[/C][C]0.0455554183754321[/C][C]0.0911108367508643[/C][C]0.954444581624568[/C][/ROW]
[ROW][C]19[/C][C]0.0336006850517908[/C][C]0.0672013701035816[/C][C]0.96639931494821[/C][/ROW]
[ROW][C]20[/C][C]0.0243648604488628[/C][C]0.0487297208977256[/C][C]0.975635139551137[/C][/ROW]
[ROW][C]21[/C][C]0.0176786915741185[/C][C]0.0353573831482369[/C][C]0.982321308425882[/C][/ROW]
[ROW][C]22[/C][C]0.0133883297601658[/C][C]0.0267766595203315[/C][C]0.986611670239834[/C][/ROW]
[ROW][C]23[/C][C]0.0109438997866438[/C][C]0.0218877995732875[/C][C]0.989056100213356[/C][/ROW]
[ROW][C]24[/C][C]0.00897516827345616[/C][C]0.0179503365469123[/C][C]0.991024831726544[/C][/ROW]
[ROW][C]25[/C][C]0.00728029601630014[/C][C]0.0145605920326003[/C][C]0.9927197039837[/C][/ROW]
[ROW][C]26[/C][C]0.0175403999280261[/C][C]0.0350807998560522[/C][C]0.982459600071974[/C][/ROW]
[ROW][C]27[/C][C]0.0689996629398558[/C][C]0.137999325879712[/C][C]0.931000337060144[/C][/ROW]
[ROW][C]28[/C][C]0.224410108785943[/C][C]0.448820217571887[/C][C]0.775589891214057[/C][/ROW]
[ROW][C]29[/C][C]0.316731087610318[/C][C]0.633462175220637[/C][C]0.683268912389682[/C][/ROW]
[ROW][C]30[/C][C]0.392848377236862[/C][C]0.785696754473724[/C][C]0.607151622763138[/C][/ROW]
[ROW][C]31[/C][C]0.480995131820644[/C][C]0.961990263641288[/C][C]0.519004868179356[/C][/ROW]
[ROW][C]32[/C][C]0.576310544693019[/C][C]0.847378910613962[/C][C]0.423689455306981[/C][/ROW]
[ROW][C]33[/C][C]0.667362866823171[/C][C]0.665274266353657[/C][C]0.332637133176829[/C][/ROW]
[ROW][C]34[/C][C]0.763392215504913[/C][C]0.473215568990174[/C][C]0.236607784495087[/C][/ROW]
[ROW][C]35[/C][C]0.831715935835388[/C][C]0.336568128329223[/C][C]0.168284064164612[/C][/ROW]
[ROW][C]36[/C][C]0.901591587703332[/C][C]0.196816824593336[/C][C]0.0984084122966678[/C][/ROW]
[ROW][C]37[/C][C]0.955693973591153[/C][C]0.0886120528176943[/C][C]0.0443060264088472[/C][/ROW]
[ROW][C]38[/C][C]0.963915076190692[/C][C]0.0721698476186156[/C][C]0.0360849238093078[/C][/ROW]
[ROW][C]39[/C][C]0.98609585842938[/C][C]0.0278082831412398[/C][C]0.0139041415706199[/C][/ROW]
[ROW][C]40[/C][C]0.990592183933436[/C][C]0.0188156321331274[/C][C]0.00940781606656369[/C][/ROW]
[ROW][C]41[/C][C]0.991606576924803[/C][C]0.0167868461503948[/C][C]0.0083934230751974[/C][/ROW]
[ROW][C]42[/C][C]0.9914334121037[/C][C]0.0171331757926007[/C][C]0.00856658789630036[/C][/ROW]
[ROW][C]43[/C][C]0.990462023284222[/C][C]0.0190759534315563[/C][C]0.00953797671577814[/C][/ROW]
[ROW][C]44[/C][C]0.989777981312145[/C][C]0.0204440373757092[/C][C]0.0102220186878546[/C][/ROW]
[ROW][C]45[/C][C]0.98835430754682[/C][C]0.0232913849063585[/C][C]0.0116456924531792[/C][/ROW]
[ROW][C]46[/C][C]0.98602561392603[/C][C]0.0279487721479376[/C][C]0.0139743860739688[/C][/ROW]
[ROW][C]47[/C][C]0.982139072442474[/C][C]0.0357218551150519[/C][C]0.0178609275575259[/C][/ROW]
[ROW][C]48[/C][C]0.996160277351776[/C][C]0.00767944529644774[/C][C]0.00383972264822387[/C][/ROW]
[ROW][C]49[/C][C]0.999737208116572[/C][C]0.000525583766855598[/C][C]0.000262791883427799[/C][/ROW]
[ROW][C]50[/C][C]0.99925449736343[/C][C]0.00149100527314026[/C][C]0.000745502636570132[/C][/ROW]
[ROW][C]51[/C][C]0.998269480739515[/C][C]0.00346103852097008[/C][C]0.00173051926048504[/C][/ROW]
[ROW][C]52[/C][C]0.995142470686393[/C][C]0.00971505862721429[/C][C]0.00485752931360714[/C][/ROW]
[ROW][C]53[/C][C]0.993770813446771[/C][C]0.0124583731064580[/C][C]0.00622918655322901[/C][/ROW]
[ROW][C]54[/C][C]0.998354632604871[/C][C]0.00329073479025708[/C][C]0.00164536739512854[/C][/ROW]
[ROW][C]55[/C][C]0.998841455704154[/C][C]0.00231708859169218[/C][C]0.00115854429584609[/C][/ROW]
[ROW][C]56[/C][C]0.99389267488076[/C][C]0.0122146502384779[/C][C]0.00610732511923893[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58370&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58370&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
50.5501329655007360.8997340689985290.449867034499264
60.3814582538378930.7629165076757860.618541746162107
70.2468132332443250.493626466488650.753186766755675
80.1499671962090820.2999343924181650.850032803790918
90.08658670273705630.1731734054741130.913413297262944
100.04941825167527210.09883650335054410.950581748324728
110.03362186576372100.06724373152744190.96637813423628
120.0260098552725220.0520197105450440.973990144727478
130.01766987521558130.03533975043116260.982330124784419
140.02493706058765780.04987412117531560.975062939412342
150.04558199080464730.09116398160929460.954418009195353
160.06960559149092890.1392111829818580.930394408509071
170.06328616410176650.1265723282035330.936713835898233
180.04555541837543210.09111083675086430.954444581624568
190.03360068505179080.06720137010358160.96639931494821
200.02436486044886280.04872972089772560.975635139551137
210.01767869157411850.03535738314823690.982321308425882
220.01338832976016580.02677665952033150.986611670239834
230.01094389978664380.02188779957328750.989056100213356
240.008975168273456160.01795033654691230.991024831726544
250.007280296016300140.01456059203260030.9927197039837
260.01754039992802610.03508079985605220.982459600071974
270.06899966293985580.1379993258797120.931000337060144
280.2244101087859430.4488202175718870.775589891214057
290.3167310876103180.6334621752206370.683268912389682
300.3928483772368620.7856967544737240.607151622763138
310.4809951318206440.9619902636412880.519004868179356
320.5763105446930190.8473789106139620.423689455306981
330.6673628668231710.6652742663536570.332637133176829
340.7633922155049130.4732155689901740.236607784495087
350.8317159358353880.3365681283292230.168284064164612
360.9015915877033320.1968168245933360.0984084122966678
370.9556939735911530.08861205281769430.0443060264088472
380.9639150761906920.07216984761861560.0360849238093078
390.986095858429380.02780828314123980.0139041415706199
400.9905921839334360.01881563213312740.00940781606656369
410.9916065769248030.01678684615039480.0083934230751974
420.99143341210370.01713317579260070.00856658789630036
430.9904620232842220.01907595343155630.00953797671577814
440.9897779813121450.02044403737570920.0102220186878546
450.988354307546820.02329138490635850.0116456924531792
460.986025613926030.02794877214793760.0139743860739688
470.9821390724424740.03572185511505190.0178609275575259
480.9961602773517760.007679445296447740.00383972264822387
490.9997372081165720.0005255837668555980.000262791883427799
500.999254497363430.001491005273140260.000745502636570132
510.9982694807395150.003461038520970080.00173051926048504
520.9951424706863930.009715058627214290.00485752931360714
530.9937708134467710.01245837310645800.00622918655322901
540.9983546326048710.003290734790257080.00164536739512854
550.9988414557041540.002317088591692180.00115854429584609
560.993892674880760.01221465023847790.00610732511923893







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level70.134615384615385NOK
5% type I error level270.519230769230769NOK
10% type I error level350.673076923076923NOK

\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 & 7 & 0.134615384615385 & NOK \tabularnewline
5% type I error level & 27 & 0.519230769230769 & NOK \tabularnewline
10% type I error level & 35 & 0.673076923076923 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58370&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]7[/C][C]0.134615384615385[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]27[/C][C]0.519230769230769[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]35[/C][C]0.673076923076923[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58370&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58370&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 level70.134615384615385NOK
5% type I error level270.519230769230769NOK
10% type I error level350.673076923076923NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal 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')
}