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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 06:39:03 -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/t1258725121wioxg3shjq8uftc.htm/, Retrieved Thu, 28 Mar 2024 17:55:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58158, Retrieved Thu, 28 Mar 2024 17:55:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact159
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] [model 1] [2009-11-19 08:35:38] [3445d50c581a74ea3ff7b84cc82fcfeb]
-    D        [Multiple Regression] [] [2009-11-20 13:39:03] [27b6e36591879260e4dc6bb7e89a38fd] [Current]
-    D          [Multiple Regression] [] [2009-12-15 16:03:21] [e149fd9094b67af26551857fa83a9d9d]
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Dataseries X:
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	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 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=58158&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=58158&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58158&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
WlhBe[t] = + 563.25 -24.7115384615384X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
WlhBe[t] =  +  563.25 -24.7115384615384X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58158&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]WlhBe[t] =  +  563.25 -24.7115384615384X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58158&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58158&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
WlhBe[t] = + 563.25 -24.7115384615384X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)563.255.93765494.860700
X-24.711538461538412.861988-1.92130.0595310.029766

\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) & 563.25 & 5.937654 & 94.8607 & 0 & 0 \tabularnewline
X & -24.7115384615384 & 12.861988 & -1.9213 & 0.059531 & 0.029766 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58158&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]563.25[/C][C]5.937654[/C][C]94.8607[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-24.7115384615384[/C][C]12.861988[/C][C]-1.9213[/C][C]0.059531[/C][C]0.029766[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58158&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58158&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)563.255.93765494.860700
X-24.711538461538412.861988-1.92130.0595310.029766







Multiple Linear Regression - Regression Statistics
Multiple R0.242654261833831
R-squared0.0588810907861214
Adjusted R-squared0.0429299228333438
F-TEST (value)3.69133413681274
F-TEST (DF numerator)1
F-TEST (DF denominator)59
p-value0.0595310492716236
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation41.1372713944857
Sum Squared Residuals99844.2307692306

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.242654261833831 \tabularnewline
R-squared & 0.0588810907861214 \tabularnewline
Adjusted R-squared & 0.0429299228333438 \tabularnewline
F-TEST (value) & 3.69133413681274 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 0.0595310492716236 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 41.1372713944857 \tabularnewline
Sum Squared Residuals & 99844.2307692306 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58158&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.242654261833831[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0588810907861214[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0429299228333438[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.69133413681274[/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]0.0595310492716236[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]41.1372713944857[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]99844.2307692306[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58158&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58158&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 R0.242654261833831
R-squared0.0588810907861214
Adjusted R-squared0.0429299228333438
F-TEST (value)3.69133413681274
F-TEST (DF numerator)1
F-TEST (DF denominator)59
p-value0.0595310492716236
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation41.1372713944857
Sum Squared Residuals99844.2307692306







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1613563.25000000000149.7499999999988
2611563.2547.75
3594563.2530.75
4595563.2531.75
5591563.2527.75
6589563.2525.7500000000000
7584563.2520.7500000000000
8573563.259.75000000000002
9567563.253.75000000000002
10569563.255.75000000000002
11621563.2557.75
12629563.2565.75
13628563.2564.75
14612563.2548.75
15595563.2531.75
16597563.2533.75
17593563.2529.75
18590563.2526.7500000000000
19580563.2516.7500000000000
20574563.2510.7500000000000
21573563.259.75000000000002
22573563.259.75000000000002
23620563.2556.75
24626563.2562.75
25620563.2556.75
26588563.2524.7500000000000
27566563.252.75000000000002
28557563.25-6.24999999999998
29561563.25-2.24999999999998
30549563.25-14.2500000000000
31532563.25-31.25
32526563.25-37.25
33511563.25-52.25
34499563.25-64.25
35555563.25-8.24999999999997
36565563.251.75000000000002
37542563.25-21.2500000000000
38527563.25-36.25
39510563.25-53.25
40514563.25-49.25
41517563.25-46.25
42508563.25-55.25
43493563.25-70.25
44490563.25-73.25
45469563.25-94.25
46478563.25-85.25
47528563.25-35.25
48534563.25-29.25
49518538.538461538462-20.5384615384615
50506538.538461538462-32.5384615384615
51502538.538461538462-36.5384615384615
52516538.538461538462-22.5384615384615
53528538.538461538462-10.5384615384615
54533538.538461538462-5.53846153846154
55536538.538461538462-2.53846153846154
56537538.538461538462-1.53846153846154
57524538.538461538462-14.5384615384615
58536538.538461538462-2.53846153846154
59587538.53846153846248.4615384615385
60597538.53846153846258.4615384615385
61581538.53846153846242.4615384615385

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 613 & 563.250000000001 & 49.7499999999988 \tabularnewline
2 & 611 & 563.25 & 47.75 \tabularnewline
3 & 594 & 563.25 & 30.75 \tabularnewline
4 & 595 & 563.25 & 31.75 \tabularnewline
5 & 591 & 563.25 & 27.75 \tabularnewline
6 & 589 & 563.25 & 25.7500000000000 \tabularnewline
7 & 584 & 563.25 & 20.7500000000000 \tabularnewline
8 & 573 & 563.25 & 9.75000000000002 \tabularnewline
9 & 567 & 563.25 & 3.75000000000002 \tabularnewline
10 & 569 & 563.25 & 5.75000000000002 \tabularnewline
11 & 621 & 563.25 & 57.75 \tabularnewline
12 & 629 & 563.25 & 65.75 \tabularnewline
13 & 628 & 563.25 & 64.75 \tabularnewline
14 & 612 & 563.25 & 48.75 \tabularnewline
15 & 595 & 563.25 & 31.75 \tabularnewline
16 & 597 & 563.25 & 33.75 \tabularnewline
17 & 593 & 563.25 & 29.75 \tabularnewline
18 & 590 & 563.25 & 26.7500000000000 \tabularnewline
19 & 580 & 563.25 & 16.7500000000000 \tabularnewline
20 & 574 & 563.25 & 10.7500000000000 \tabularnewline
21 & 573 & 563.25 & 9.75000000000002 \tabularnewline
22 & 573 & 563.25 & 9.75000000000002 \tabularnewline
23 & 620 & 563.25 & 56.75 \tabularnewline
24 & 626 & 563.25 & 62.75 \tabularnewline
25 & 620 & 563.25 & 56.75 \tabularnewline
26 & 588 & 563.25 & 24.7500000000000 \tabularnewline
27 & 566 & 563.25 & 2.75000000000002 \tabularnewline
28 & 557 & 563.25 & -6.24999999999998 \tabularnewline
29 & 561 & 563.25 & -2.24999999999998 \tabularnewline
30 & 549 & 563.25 & -14.2500000000000 \tabularnewline
31 & 532 & 563.25 & -31.25 \tabularnewline
32 & 526 & 563.25 & -37.25 \tabularnewline
33 & 511 & 563.25 & -52.25 \tabularnewline
34 & 499 & 563.25 & -64.25 \tabularnewline
35 & 555 & 563.25 & -8.24999999999997 \tabularnewline
36 & 565 & 563.25 & 1.75000000000002 \tabularnewline
37 & 542 & 563.25 & -21.2500000000000 \tabularnewline
38 & 527 & 563.25 & -36.25 \tabularnewline
39 & 510 & 563.25 & -53.25 \tabularnewline
40 & 514 & 563.25 & -49.25 \tabularnewline
41 & 517 & 563.25 & -46.25 \tabularnewline
42 & 508 & 563.25 & -55.25 \tabularnewline
43 & 493 & 563.25 & -70.25 \tabularnewline
44 & 490 & 563.25 & -73.25 \tabularnewline
45 & 469 & 563.25 & -94.25 \tabularnewline
46 & 478 & 563.25 & -85.25 \tabularnewline
47 & 528 & 563.25 & -35.25 \tabularnewline
48 & 534 & 563.25 & -29.25 \tabularnewline
49 & 518 & 538.538461538462 & -20.5384615384615 \tabularnewline
50 & 506 & 538.538461538462 & -32.5384615384615 \tabularnewline
51 & 502 & 538.538461538462 & -36.5384615384615 \tabularnewline
52 & 516 & 538.538461538462 & -22.5384615384615 \tabularnewline
53 & 528 & 538.538461538462 & -10.5384615384615 \tabularnewline
54 & 533 & 538.538461538462 & -5.53846153846154 \tabularnewline
55 & 536 & 538.538461538462 & -2.53846153846154 \tabularnewline
56 & 537 & 538.538461538462 & -1.53846153846154 \tabularnewline
57 & 524 & 538.538461538462 & -14.5384615384615 \tabularnewline
58 & 536 & 538.538461538462 & -2.53846153846154 \tabularnewline
59 & 587 & 538.538461538462 & 48.4615384615385 \tabularnewline
60 & 597 & 538.538461538462 & 58.4615384615385 \tabularnewline
61 & 581 & 538.538461538462 & 42.4615384615385 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58158&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]613[/C][C]563.250000000001[/C][C]49.7499999999988[/C][/ROW]
[ROW][C]2[/C][C]611[/C][C]563.25[/C][C]47.75[/C][/ROW]
[ROW][C]3[/C][C]594[/C][C]563.25[/C][C]30.75[/C][/ROW]
[ROW][C]4[/C][C]595[/C][C]563.25[/C][C]31.75[/C][/ROW]
[ROW][C]5[/C][C]591[/C][C]563.25[/C][C]27.75[/C][/ROW]
[ROW][C]6[/C][C]589[/C][C]563.25[/C][C]25.7500000000000[/C][/ROW]
[ROW][C]7[/C][C]584[/C][C]563.25[/C][C]20.7500000000000[/C][/ROW]
[ROW][C]8[/C][C]573[/C][C]563.25[/C][C]9.75000000000002[/C][/ROW]
[ROW][C]9[/C][C]567[/C][C]563.25[/C][C]3.75000000000002[/C][/ROW]
[ROW][C]10[/C][C]569[/C][C]563.25[/C][C]5.75000000000002[/C][/ROW]
[ROW][C]11[/C][C]621[/C][C]563.25[/C][C]57.75[/C][/ROW]
[ROW][C]12[/C][C]629[/C][C]563.25[/C][C]65.75[/C][/ROW]
[ROW][C]13[/C][C]628[/C][C]563.25[/C][C]64.75[/C][/ROW]
[ROW][C]14[/C][C]612[/C][C]563.25[/C][C]48.75[/C][/ROW]
[ROW][C]15[/C][C]595[/C][C]563.25[/C][C]31.75[/C][/ROW]
[ROW][C]16[/C][C]597[/C][C]563.25[/C][C]33.75[/C][/ROW]
[ROW][C]17[/C][C]593[/C][C]563.25[/C][C]29.75[/C][/ROW]
[ROW][C]18[/C][C]590[/C][C]563.25[/C][C]26.7500000000000[/C][/ROW]
[ROW][C]19[/C][C]580[/C][C]563.25[/C][C]16.7500000000000[/C][/ROW]
[ROW][C]20[/C][C]574[/C][C]563.25[/C][C]10.7500000000000[/C][/ROW]
[ROW][C]21[/C][C]573[/C][C]563.25[/C][C]9.75000000000002[/C][/ROW]
[ROW][C]22[/C][C]573[/C][C]563.25[/C][C]9.75000000000002[/C][/ROW]
[ROW][C]23[/C][C]620[/C][C]563.25[/C][C]56.75[/C][/ROW]
[ROW][C]24[/C][C]626[/C][C]563.25[/C][C]62.75[/C][/ROW]
[ROW][C]25[/C][C]620[/C][C]563.25[/C][C]56.75[/C][/ROW]
[ROW][C]26[/C][C]588[/C][C]563.25[/C][C]24.7500000000000[/C][/ROW]
[ROW][C]27[/C][C]566[/C][C]563.25[/C][C]2.75000000000002[/C][/ROW]
[ROW][C]28[/C][C]557[/C][C]563.25[/C][C]-6.24999999999998[/C][/ROW]
[ROW][C]29[/C][C]561[/C][C]563.25[/C][C]-2.24999999999998[/C][/ROW]
[ROW][C]30[/C][C]549[/C][C]563.25[/C][C]-14.2500000000000[/C][/ROW]
[ROW][C]31[/C][C]532[/C][C]563.25[/C][C]-31.25[/C][/ROW]
[ROW][C]32[/C][C]526[/C][C]563.25[/C][C]-37.25[/C][/ROW]
[ROW][C]33[/C][C]511[/C][C]563.25[/C][C]-52.25[/C][/ROW]
[ROW][C]34[/C][C]499[/C][C]563.25[/C][C]-64.25[/C][/ROW]
[ROW][C]35[/C][C]555[/C][C]563.25[/C][C]-8.24999999999997[/C][/ROW]
[ROW][C]36[/C][C]565[/C][C]563.25[/C][C]1.75000000000002[/C][/ROW]
[ROW][C]37[/C][C]542[/C][C]563.25[/C][C]-21.2500000000000[/C][/ROW]
[ROW][C]38[/C][C]527[/C][C]563.25[/C][C]-36.25[/C][/ROW]
[ROW][C]39[/C][C]510[/C][C]563.25[/C][C]-53.25[/C][/ROW]
[ROW][C]40[/C][C]514[/C][C]563.25[/C][C]-49.25[/C][/ROW]
[ROW][C]41[/C][C]517[/C][C]563.25[/C][C]-46.25[/C][/ROW]
[ROW][C]42[/C][C]508[/C][C]563.25[/C][C]-55.25[/C][/ROW]
[ROW][C]43[/C][C]493[/C][C]563.25[/C][C]-70.25[/C][/ROW]
[ROW][C]44[/C][C]490[/C][C]563.25[/C][C]-73.25[/C][/ROW]
[ROW][C]45[/C][C]469[/C][C]563.25[/C][C]-94.25[/C][/ROW]
[ROW][C]46[/C][C]478[/C][C]563.25[/C][C]-85.25[/C][/ROW]
[ROW][C]47[/C][C]528[/C][C]563.25[/C][C]-35.25[/C][/ROW]
[ROW][C]48[/C][C]534[/C][C]563.25[/C][C]-29.25[/C][/ROW]
[ROW][C]49[/C][C]518[/C][C]538.538461538462[/C][C]-20.5384615384615[/C][/ROW]
[ROW][C]50[/C][C]506[/C][C]538.538461538462[/C][C]-32.5384615384615[/C][/ROW]
[ROW][C]51[/C][C]502[/C][C]538.538461538462[/C][C]-36.5384615384615[/C][/ROW]
[ROW][C]52[/C][C]516[/C][C]538.538461538462[/C][C]-22.5384615384615[/C][/ROW]
[ROW][C]53[/C][C]528[/C][C]538.538461538462[/C][C]-10.5384615384615[/C][/ROW]
[ROW][C]54[/C][C]533[/C][C]538.538461538462[/C][C]-5.53846153846154[/C][/ROW]
[ROW][C]55[/C][C]536[/C][C]538.538461538462[/C][C]-2.53846153846154[/C][/ROW]
[ROW][C]56[/C][C]537[/C][C]538.538461538462[/C][C]-1.53846153846154[/C][/ROW]
[ROW][C]57[/C][C]524[/C][C]538.538461538462[/C][C]-14.5384615384615[/C][/ROW]
[ROW][C]58[/C][C]536[/C][C]538.538461538462[/C][C]-2.53846153846154[/C][/ROW]
[ROW][C]59[/C][C]587[/C][C]538.538461538462[/C][C]48.4615384615385[/C][/ROW]
[ROW][C]60[/C][C]597[/C][C]538.538461538462[/C][C]58.4615384615385[/C][/ROW]
[ROW][C]61[/C][C]581[/C][C]538.538461538462[/C][C]42.4615384615385[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58158&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58158&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
1613563.25000000000149.7499999999988
2611563.2547.75
3594563.2530.75
4595563.2531.75
5591563.2527.75
6589563.2525.7500000000000
7584563.2520.7500000000000
8573563.259.75000000000002
9567563.253.75000000000002
10569563.255.75000000000002
11621563.2557.75
12629563.2565.75
13628563.2564.75
14612563.2548.75
15595563.2531.75
16597563.2533.75
17593563.2529.75
18590563.2526.7500000000000
19580563.2516.7500000000000
20574563.2510.7500000000000
21573563.259.75000000000002
22573563.259.75000000000002
23620563.2556.75
24626563.2562.75
25620563.2556.75
26588563.2524.7500000000000
27566563.252.75000000000002
28557563.25-6.24999999999998
29561563.25-2.24999999999998
30549563.25-14.2500000000000
31532563.25-31.25
32526563.25-37.25
33511563.25-52.25
34499563.25-64.25
35555563.25-8.24999999999997
36565563.251.75000000000002
37542563.25-21.2500000000000
38527563.25-36.25
39510563.25-53.25
40514563.25-49.25
41517563.25-46.25
42508563.25-55.25
43493563.25-70.25
44490563.25-73.25
45469563.25-94.25
46478563.25-85.25
47528563.25-35.25
48534563.25-29.25
49518538.538461538462-20.5384615384615
50506538.538461538462-32.5384615384615
51502538.538461538462-36.5384615384615
52516538.538461538462-22.5384615384615
53528538.538461538462-10.5384615384615
54533538.538461538462-5.53846153846154
55536538.538461538462-2.53846153846154
56537538.538461538462-1.53846153846154
57524538.538461538462-14.5384615384615
58536538.538461538462-2.53846153846154
59587538.53846153846248.4615384615385
60597538.53846153846258.4615384615385
61581538.53846153846242.4615384615385







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.03168334872908060.06336669745816130.96831665127092
60.01176275956305400.02352551912610800.988237240436946
70.005670913167334920.01134182633466980.994329086832665
80.005926294325763660.01185258865152730.994073705674236
90.006546373211905720.01309274642381140.993453626788094
100.004597343506980050.00919468701396010.99540265649302
110.007351136851975530.01470227370395110.992648863148024
120.01463692274752770.02927384549505540.985363077252472
130.02117312611368380.04234625222736750.978826873886316
140.0156653221402570.0313306442805140.984334677859743
150.009199528421835090.01839905684367020.990800471578165
160.005454681987198310.01090936397439660.994545318012802
170.003234279199313380.006468558398626750.996765720800687
180.001958630972236630.003917261944473270.998041369027763
190.001396780579792720.002793561159585440.998603219420207
200.001171970406391050.00234394081278210.99882802959361
210.0009810876424704990.001962175284941000.99901891235753
220.0007981594811689320.001596318962337860.999201840518831
230.001808799976235310.003617599952470630.998191200023765
240.007290057913705950.01458011582741190.992709942086294
250.02463460365190670.04926920730381350.975365396348093
260.03346748838585690.06693497677171380.966532511614143
270.04980659595295220.09961319190590450.950193404047048
280.08049259197988340.1609851839597670.919507408020117
290.1159471309713830.2318942619427670.884052869028617
300.1765616788644490.3531233577288980.82343832113555
310.2911130905184020.5822261810368030.708886909481598
320.4134227237098220.8268454474196440.586577276290178
330.5753977265032960.8492045469934070.424602273496704
340.7359438425135630.5281123149728740.264056157486437
350.7553251123451670.4893497753096650.244674887654833
360.8196562974688320.3606874050623360.180343702531168
370.840765533332040.3184689333359180.159234466667959
380.852292412082470.295415175835060.14770758791753
390.8664612315965920.2670775368068160.133538768403408
400.8681978737475860.2636042525048290.131802126252414
410.8642957941011250.2714084117977490.135704205898875
420.8588549212496820.2822901575006370.141145078750318
430.86260539868370.27478920263260.1373946013163
440.8630035325106050.2739929349787900.136996467489395
450.9122009070860940.1755981858278120.0877990929139062
460.9443114334957640.1113771330084710.0556885665042356
470.9150958394392290.1698083211215420.0849041605607708
480.8715934987854710.2568130024290580.128406501214529
490.8272275936627150.3455448126745710.172772406337285
500.812363774280290.3752724514394220.187636225719711
510.8299516454426860.3400967091146290.170048354557315
520.8104366902319450.3791266195361090.189563309768055
530.7539992820130590.4920014359738830.246000717986941
540.674425090188050.6511498196238990.325574909811949
550.5752814770369420.8494370459261170.424718522963058
560.4683111518162710.9366223036325420.531688848183729

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.0316833487290806 & 0.0633666974581613 & 0.96831665127092 \tabularnewline
6 & 0.0117627595630540 & 0.0235255191261080 & 0.988237240436946 \tabularnewline
7 & 0.00567091316733492 & 0.0113418263346698 & 0.994329086832665 \tabularnewline
8 & 0.00592629432576366 & 0.0118525886515273 & 0.994073705674236 \tabularnewline
9 & 0.00654637321190572 & 0.0130927464238114 & 0.993453626788094 \tabularnewline
10 & 0.00459734350698005 & 0.0091946870139601 & 0.99540265649302 \tabularnewline
11 & 0.00735113685197553 & 0.0147022737039511 & 0.992648863148024 \tabularnewline
12 & 0.0146369227475277 & 0.0292738454950554 & 0.985363077252472 \tabularnewline
13 & 0.0211731261136838 & 0.0423462522273675 & 0.978826873886316 \tabularnewline
14 & 0.015665322140257 & 0.031330644280514 & 0.984334677859743 \tabularnewline
15 & 0.00919952842183509 & 0.0183990568436702 & 0.990800471578165 \tabularnewline
16 & 0.00545468198719831 & 0.0109093639743966 & 0.994545318012802 \tabularnewline
17 & 0.00323427919931338 & 0.00646855839862675 & 0.996765720800687 \tabularnewline
18 & 0.00195863097223663 & 0.00391726194447327 & 0.998041369027763 \tabularnewline
19 & 0.00139678057979272 & 0.00279356115958544 & 0.998603219420207 \tabularnewline
20 & 0.00117197040639105 & 0.0023439408127821 & 0.99882802959361 \tabularnewline
21 & 0.000981087642470499 & 0.00196217528494100 & 0.99901891235753 \tabularnewline
22 & 0.000798159481168932 & 0.00159631896233786 & 0.999201840518831 \tabularnewline
23 & 0.00180879997623531 & 0.00361759995247063 & 0.998191200023765 \tabularnewline
24 & 0.00729005791370595 & 0.0145801158274119 & 0.992709942086294 \tabularnewline
25 & 0.0246346036519067 & 0.0492692073038135 & 0.975365396348093 \tabularnewline
26 & 0.0334674883858569 & 0.0669349767717138 & 0.966532511614143 \tabularnewline
27 & 0.0498065959529522 & 0.0996131919059045 & 0.950193404047048 \tabularnewline
28 & 0.0804925919798834 & 0.160985183959767 & 0.919507408020117 \tabularnewline
29 & 0.115947130971383 & 0.231894261942767 & 0.884052869028617 \tabularnewline
30 & 0.176561678864449 & 0.353123357728898 & 0.82343832113555 \tabularnewline
31 & 0.291113090518402 & 0.582226181036803 & 0.708886909481598 \tabularnewline
32 & 0.413422723709822 & 0.826845447419644 & 0.586577276290178 \tabularnewline
33 & 0.575397726503296 & 0.849204546993407 & 0.424602273496704 \tabularnewline
34 & 0.735943842513563 & 0.528112314972874 & 0.264056157486437 \tabularnewline
35 & 0.755325112345167 & 0.489349775309665 & 0.244674887654833 \tabularnewline
36 & 0.819656297468832 & 0.360687405062336 & 0.180343702531168 \tabularnewline
37 & 0.84076553333204 & 0.318468933335918 & 0.159234466667959 \tabularnewline
38 & 0.85229241208247 & 0.29541517583506 & 0.14770758791753 \tabularnewline
39 & 0.866461231596592 & 0.267077536806816 & 0.133538768403408 \tabularnewline
40 & 0.868197873747586 & 0.263604252504829 & 0.131802126252414 \tabularnewline
41 & 0.864295794101125 & 0.271408411797749 & 0.135704205898875 \tabularnewline
42 & 0.858854921249682 & 0.282290157500637 & 0.141145078750318 \tabularnewline
43 & 0.8626053986837 & 0.2747892026326 & 0.1373946013163 \tabularnewline
44 & 0.863003532510605 & 0.273992934978790 & 0.136996467489395 \tabularnewline
45 & 0.912200907086094 & 0.175598185827812 & 0.0877990929139062 \tabularnewline
46 & 0.944311433495764 & 0.111377133008471 & 0.0556885665042356 \tabularnewline
47 & 0.915095839439229 & 0.169808321121542 & 0.0849041605607708 \tabularnewline
48 & 0.871593498785471 & 0.256813002429058 & 0.128406501214529 \tabularnewline
49 & 0.827227593662715 & 0.345544812674571 & 0.172772406337285 \tabularnewline
50 & 0.81236377428029 & 0.375272451439422 & 0.187636225719711 \tabularnewline
51 & 0.829951645442686 & 0.340096709114629 & 0.170048354557315 \tabularnewline
52 & 0.810436690231945 & 0.379126619536109 & 0.189563309768055 \tabularnewline
53 & 0.753999282013059 & 0.492001435973883 & 0.246000717986941 \tabularnewline
54 & 0.67442509018805 & 0.651149819623899 & 0.325574909811949 \tabularnewline
55 & 0.575281477036942 & 0.849437045926117 & 0.424718522963058 \tabularnewline
56 & 0.468311151816271 & 0.936622303632542 & 0.531688848183729 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58158&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]0.0316833487290806[/C][C]0.0633666974581613[/C][C]0.96831665127092[/C][/ROW]
[ROW][C]6[/C][C]0.0117627595630540[/C][C]0.0235255191261080[/C][C]0.988237240436946[/C][/ROW]
[ROW][C]7[/C][C]0.00567091316733492[/C][C]0.0113418263346698[/C][C]0.994329086832665[/C][/ROW]
[ROW][C]8[/C][C]0.00592629432576366[/C][C]0.0118525886515273[/C][C]0.994073705674236[/C][/ROW]
[ROW][C]9[/C][C]0.00654637321190572[/C][C]0.0130927464238114[/C][C]0.993453626788094[/C][/ROW]
[ROW][C]10[/C][C]0.00459734350698005[/C][C]0.0091946870139601[/C][C]0.99540265649302[/C][/ROW]
[ROW][C]11[/C][C]0.00735113685197553[/C][C]0.0147022737039511[/C][C]0.992648863148024[/C][/ROW]
[ROW][C]12[/C][C]0.0146369227475277[/C][C]0.0292738454950554[/C][C]0.985363077252472[/C][/ROW]
[ROW][C]13[/C][C]0.0211731261136838[/C][C]0.0423462522273675[/C][C]0.978826873886316[/C][/ROW]
[ROW][C]14[/C][C]0.015665322140257[/C][C]0.031330644280514[/C][C]0.984334677859743[/C][/ROW]
[ROW][C]15[/C][C]0.00919952842183509[/C][C]0.0183990568436702[/C][C]0.990800471578165[/C][/ROW]
[ROW][C]16[/C][C]0.00545468198719831[/C][C]0.0109093639743966[/C][C]0.994545318012802[/C][/ROW]
[ROW][C]17[/C][C]0.00323427919931338[/C][C]0.00646855839862675[/C][C]0.996765720800687[/C][/ROW]
[ROW][C]18[/C][C]0.00195863097223663[/C][C]0.00391726194447327[/C][C]0.998041369027763[/C][/ROW]
[ROW][C]19[/C][C]0.00139678057979272[/C][C]0.00279356115958544[/C][C]0.998603219420207[/C][/ROW]
[ROW][C]20[/C][C]0.00117197040639105[/C][C]0.0023439408127821[/C][C]0.99882802959361[/C][/ROW]
[ROW][C]21[/C][C]0.000981087642470499[/C][C]0.00196217528494100[/C][C]0.99901891235753[/C][/ROW]
[ROW][C]22[/C][C]0.000798159481168932[/C][C]0.00159631896233786[/C][C]0.999201840518831[/C][/ROW]
[ROW][C]23[/C][C]0.00180879997623531[/C][C]0.00361759995247063[/C][C]0.998191200023765[/C][/ROW]
[ROW][C]24[/C][C]0.00729005791370595[/C][C]0.0145801158274119[/C][C]0.992709942086294[/C][/ROW]
[ROW][C]25[/C][C]0.0246346036519067[/C][C]0.0492692073038135[/C][C]0.975365396348093[/C][/ROW]
[ROW][C]26[/C][C]0.0334674883858569[/C][C]0.0669349767717138[/C][C]0.966532511614143[/C][/ROW]
[ROW][C]27[/C][C]0.0498065959529522[/C][C]0.0996131919059045[/C][C]0.950193404047048[/C][/ROW]
[ROW][C]28[/C][C]0.0804925919798834[/C][C]0.160985183959767[/C][C]0.919507408020117[/C][/ROW]
[ROW][C]29[/C][C]0.115947130971383[/C][C]0.231894261942767[/C][C]0.884052869028617[/C][/ROW]
[ROW][C]30[/C][C]0.176561678864449[/C][C]0.353123357728898[/C][C]0.82343832113555[/C][/ROW]
[ROW][C]31[/C][C]0.291113090518402[/C][C]0.582226181036803[/C][C]0.708886909481598[/C][/ROW]
[ROW][C]32[/C][C]0.413422723709822[/C][C]0.826845447419644[/C][C]0.586577276290178[/C][/ROW]
[ROW][C]33[/C][C]0.575397726503296[/C][C]0.849204546993407[/C][C]0.424602273496704[/C][/ROW]
[ROW][C]34[/C][C]0.735943842513563[/C][C]0.528112314972874[/C][C]0.264056157486437[/C][/ROW]
[ROW][C]35[/C][C]0.755325112345167[/C][C]0.489349775309665[/C][C]0.244674887654833[/C][/ROW]
[ROW][C]36[/C][C]0.819656297468832[/C][C]0.360687405062336[/C][C]0.180343702531168[/C][/ROW]
[ROW][C]37[/C][C]0.84076553333204[/C][C]0.318468933335918[/C][C]0.159234466667959[/C][/ROW]
[ROW][C]38[/C][C]0.85229241208247[/C][C]0.29541517583506[/C][C]0.14770758791753[/C][/ROW]
[ROW][C]39[/C][C]0.866461231596592[/C][C]0.267077536806816[/C][C]0.133538768403408[/C][/ROW]
[ROW][C]40[/C][C]0.868197873747586[/C][C]0.263604252504829[/C][C]0.131802126252414[/C][/ROW]
[ROW][C]41[/C][C]0.864295794101125[/C][C]0.271408411797749[/C][C]0.135704205898875[/C][/ROW]
[ROW][C]42[/C][C]0.858854921249682[/C][C]0.282290157500637[/C][C]0.141145078750318[/C][/ROW]
[ROW][C]43[/C][C]0.8626053986837[/C][C]0.2747892026326[/C][C]0.1373946013163[/C][/ROW]
[ROW][C]44[/C][C]0.863003532510605[/C][C]0.273992934978790[/C][C]0.136996467489395[/C][/ROW]
[ROW][C]45[/C][C]0.912200907086094[/C][C]0.175598185827812[/C][C]0.0877990929139062[/C][/ROW]
[ROW][C]46[/C][C]0.944311433495764[/C][C]0.111377133008471[/C][C]0.0556885665042356[/C][/ROW]
[ROW][C]47[/C][C]0.915095839439229[/C][C]0.169808321121542[/C][C]0.0849041605607708[/C][/ROW]
[ROW][C]48[/C][C]0.871593498785471[/C][C]0.256813002429058[/C][C]0.128406501214529[/C][/ROW]
[ROW][C]49[/C][C]0.827227593662715[/C][C]0.345544812674571[/C][C]0.172772406337285[/C][/ROW]
[ROW][C]50[/C][C]0.81236377428029[/C][C]0.375272451439422[/C][C]0.187636225719711[/C][/ROW]
[ROW][C]51[/C][C]0.829951645442686[/C][C]0.340096709114629[/C][C]0.170048354557315[/C][/ROW]
[ROW][C]52[/C][C]0.810436690231945[/C][C]0.379126619536109[/C][C]0.189563309768055[/C][/ROW]
[ROW][C]53[/C][C]0.753999282013059[/C][C]0.492001435973883[/C][C]0.246000717986941[/C][/ROW]
[ROW][C]54[/C][C]0.67442509018805[/C][C]0.651149819623899[/C][C]0.325574909811949[/C][/ROW]
[ROW][C]55[/C][C]0.575281477036942[/C][C]0.849437045926117[/C][C]0.424718522963058[/C][/ROW]
[ROW][C]56[/C][C]0.468311151816271[/C][C]0.936622303632542[/C][C]0.531688848183729[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58158&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58158&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
50.03168334872908060.06336669745816130.96831665127092
60.01176275956305400.02352551912610800.988237240436946
70.005670913167334920.01134182633466980.994329086832665
80.005926294325763660.01185258865152730.994073705674236
90.006546373211905720.01309274642381140.993453626788094
100.004597343506980050.00919468701396010.99540265649302
110.007351136851975530.01470227370395110.992648863148024
120.01463692274752770.02927384549505540.985363077252472
130.02117312611368380.04234625222736750.978826873886316
140.0156653221402570.0313306442805140.984334677859743
150.009199528421835090.01839905684367020.990800471578165
160.005454681987198310.01090936397439660.994545318012802
170.003234279199313380.006468558398626750.996765720800687
180.001958630972236630.003917261944473270.998041369027763
190.001396780579792720.002793561159585440.998603219420207
200.001171970406391050.00234394081278210.99882802959361
210.0009810876424704990.001962175284941000.99901891235753
220.0007981594811689320.001596318962337860.999201840518831
230.001808799976235310.003617599952470630.998191200023765
240.007290057913705950.01458011582741190.992709942086294
250.02463460365190670.04926920730381350.975365396348093
260.03346748838585690.06693497677171380.966532511614143
270.04980659595295220.09961319190590450.950193404047048
280.08049259197988340.1609851839597670.919507408020117
290.1159471309713830.2318942619427670.884052869028617
300.1765616788644490.3531233577288980.82343832113555
310.2911130905184020.5822261810368030.708886909481598
320.4134227237098220.8268454474196440.586577276290178
330.5753977265032960.8492045469934070.424602273496704
340.7359438425135630.5281123149728740.264056157486437
350.7553251123451670.4893497753096650.244674887654833
360.8196562974688320.3606874050623360.180343702531168
370.840765533332040.3184689333359180.159234466667959
380.852292412082470.295415175835060.14770758791753
390.8664612315965920.2670775368068160.133538768403408
400.8681978737475860.2636042525048290.131802126252414
410.8642957941011250.2714084117977490.135704205898875
420.8588549212496820.2822901575006370.141145078750318
430.86260539868370.27478920263260.1373946013163
440.8630035325106050.2739929349787900.136996467489395
450.9122009070860940.1755981858278120.0877990929139062
460.9443114334957640.1113771330084710.0556885665042356
470.9150958394392290.1698083211215420.0849041605607708
480.8715934987854710.2568130024290580.128406501214529
490.8272275936627150.3455448126745710.172772406337285
500.812363774280290.3752724514394220.187636225719711
510.8299516454426860.3400967091146290.170048354557315
520.8104366902319450.3791266195361090.189563309768055
530.7539992820130590.4920014359738830.246000717986941
540.674425090188050.6511498196238990.325574909811949
550.5752814770369420.8494370459261170.424718522963058
560.4683111518162710.9366223036325420.531688848183729







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level80.153846153846154NOK
5% type I error level200.384615384615385NOK
10% type I error level230.442307692307692NOK

\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 & 8 & 0.153846153846154 & NOK \tabularnewline
5% type I error level & 20 & 0.384615384615385 & NOK \tabularnewline
10% type I error level & 23 & 0.442307692307692 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58158&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]8[/C][C]0.153846153846154[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]20[/C][C]0.384615384615385[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]23[/C][C]0.442307692307692[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58158&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58158&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 level80.153846153846154NOK
5% type I error level200.384615384615385NOK
10% type I error level230.442307692307692NOK



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')
}