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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 19 Dec 2010 10:14:36 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/19/t129275362136m7llb7omex99n.htm/, Retrieved Sat, 04 May 2024 21:22:19 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112246, Retrieved Sat, 04 May 2024 21:22:19 +0000

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No (this computation is public)

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Estimated Impact

146

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [paper: Multiple R...] [2010-12-19 10:14:36] [6f3869f9d1e39c73f93153f1f7803f84] [Current]

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Dataseries X:

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591	0	595	594	611	613	562	519
589	0	591	595	594	611	561	517
584	0	589	591	595	594	555	510
573	0	584	589	591	595	544	509
567	0	573	584	589	591	537	501
569	0	567	573	584	589	543	507
621	0	569	567	573	584	594	569
629	0	621	569	567	573	611	580
628	0	629	621	569	567	613	578
612	0	628	629	621	569	611	565
595	0	612	628	629	621	594	547
597	0	595	612	628	629	595	555
593	0	597	595	612	628	591	562
590	0	593	597	595	612	589	561
580	0	590	593	597	595	584	555
574	0	580	590	593	597	573	544
573	0	574	580	590	593	567	537
573	0	573	574	580	590	569	543
620	0	573	573	574	580	621	594
626	0	620	573	573	574	629	611
620	0	626	620	573	573	628	613
588	0	620	626	620	573	612	611
566	0	588	620	626	620	595	594
557	0	566	588	620	626	597	595
561	0	557	566	588	620	593	591
549	0	561	557	566	588	590	589
532	0	549	561	557	566	580	584
526	0	532	549	561	557	574	573
511	0	526	532	549	561	573	567
499	0	511	526	532	549	573	569
555	1	499	511	526	532	620	621
565	1	555	499	511	526	626	629
542	1	565	555	499	511	620	628
527	1	542	565	555	499	588	612
510	1	527	542	565	555	566	595
514	1	510	527	542	565	557	597
517	1	514	510	527	542	561	593
508	1	517	514	510	527	549	590
493	1	508	517	514	510	532	580
490	1	493	508	517	514	526	574
469	1	490	493	508	517	511	573
478	1	469	490	493	508	499	573
528	1	478	469	490	493	555	620
534	1	528	478	469	490	565	626
518	1	534	528	478	469	542	620
506	1	518	534	528	478	527	588
502	1	506	518	534	528	510	566
516	1	502	506	518	534	514	557
528	1	516	502	506	518	517	561
533	1	528	516	502	506	508	549
536	1	533	528	516	502	493	532
537	1	536	533	528	516	490	526
524	1	537	536	533	528	469	511
536	1	524	537	536	533	478	499
587	1	536	524	537	536	528	555
597	1	587	536	524	537	534	565
581	1	597	587	536	524	518	542
564	1	581	597	587	536	506	527
558	1	564	581	597	587	502	510
575	1	558	564	581	597	516	514

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 5 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112246&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[ROW][C]R Framework error message[/C][C]The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112246&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112246&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Multiple Linear Regression - Estimated Regression Equation
Totaal[t] = + 121.350385460641 + 15.4495309520155crisis[t] + 1.07950932409880`t-1`[t] -0.50927530639777`t-2`[t] + 0.0759563844468494`t-3`[t] + 0.0672167107393819`t-4`[t] + 0.440072107331956`t-12`[t] -0.382095098205888`t-24 `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Totaal[t] =  +  121.350385460641 +  15.4495309520155crisis[t] +  1.07950932409880`t-1`[t] -0.50927530639777`t-2`[t] +  0.0759563844468494`t-3`[t] +  0.0672167107393819`t-4`[t] +  0.440072107331956`t-12`[t] -0.382095098205888`t-24
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112246&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Totaal[t] =  +  121.350385460641 +  15.4495309520155crisis[t] +  1.07950932409880`t-1`[t] -0.50927530639777`t-2`[t] +  0.0759563844468494`t-3`[t] +  0.0672167107393819`t-4`[t] +  0.440072107331956`t-12`[t] -0.382095098205888`t-24
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112246&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112246&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
Totaal[t] = + 121.350385460641 + 15.4495309520155crisis[t] + 1.07950932409880`t-1`[t] -0.50927530639777`t-2`[t] + 0.0759563844468494`t-3`[t] + 0.0672167107393819`t-4`[t] + 0.440072107331956`t-12`[t] -0.382095098205888`t-24 `[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	121.350385460641	87.277162	1.3904	0.170329	0.085165
crisis	15.4495309520155	8.971025	1.7222	0.090985	0.045492
`t-1`	1.07950932409880	0.142087	7.5975	0	0
`t-2`	-0.50927530639777	0.20721	-2.4578	0.01735	0.008675
`t-3`	0.0759563844468494	0.205224	0.3701	0.7128	0.3564
`t-4`	0.0672167107393819	0.14843	0.4529	0.652539	0.326269
`t-12`	0.440072107331956	0.15909	2.7662	0.007834	0.003917
`t-24 `	-0.382095098205888	0.152642	-2.5032	0.015486	0.007743

\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) & 121.350385460641 & 87.277162 & 1.3904 & 0.170329 & 0.085165 \tabularnewline
crisis & 15.4495309520155 & 8.971025 & 1.7222 & 0.090985 & 0.045492 \tabularnewline
`t-1` & 1.07950932409880 & 0.142087 & 7.5975 & 0 & 0 \tabularnewline
`t-2` & -0.50927530639777 & 0.20721 & -2.4578 & 0.01735 & 0.008675 \tabularnewline
`t-3` & 0.0759563844468494 & 0.205224 & 0.3701 & 0.7128 & 0.3564 \tabularnewline
`t-4` & 0.0672167107393819 & 0.14843 & 0.4529 & 0.652539 & 0.326269 \tabularnewline
`t-12` & 0.440072107331956 & 0.15909 & 2.7662 & 0.007834 & 0.003917 \tabularnewline
`t-24
` & -0.382095098205888 & 0.152642 & -2.5032 & 0.015486 & 0.007743 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112246&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]121.350385460641[/C][C]87.277162[/C][C]1.3904[/C][C]0.170329[/C][C]0.085165[/C][/ROW]
[ROW][C]crisis[/C][C]15.4495309520155[/C][C]8.971025[/C][C]1.7222[/C][C]0.090985[/C][C]0.045492[/C][/ROW]
[ROW][C]`t-1`[/C][C]1.07950932409880[/C][C]0.142087[/C][C]7.5975[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`t-2`[/C][C]-0.50927530639777[/C][C]0.20721[/C][C]-2.4578[/C][C]0.01735[/C][C]0.008675[/C][/ROW]
[ROW][C]`t-3`[/C][C]0.0759563844468494[/C][C]0.205224[/C][C]0.3701[/C][C]0.7128[/C][C]0.3564[/C][/ROW]
[ROW][C]`t-4`[/C][C]0.0672167107393819[/C][C]0.14843[/C][C]0.4529[/C][C]0.652539[/C][C]0.326269[/C][/ROW]
[ROW][C]`t-12`[/C][C]0.440072107331956[/C][C]0.15909[/C][C]2.7662[/C][C]0.007834[/C][C]0.003917[/C][/ROW]
[ROW][C]`t-24
`[/C][C]-0.382095098205888[/C][C]0.152642[/C][C]-2.5032[/C][C]0.015486[/C][C]0.007743[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112246&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112246&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
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	121.350385460641	87.277162	1.3904	0.170329	0.085165
crisis	15.4495309520155	8.971025	1.7222	0.090985	0.045492
`t-1`	1.07950932409880	0.142087	7.5975	0	0
`t-2`	-0.50927530639777	0.20721	-2.4578	0.01735	0.008675
`t-3`	0.0759563844468494	0.205224	0.3701	0.7128	0.3564
`t-4`	0.0672167107393819	0.14843	0.4529	0.652539	0.326269
`t-12`	0.440072107331956	0.15909	2.7662	0.007834	0.003917
`t-24 `	-0.382095098205888	0.152642	-2.5032	0.015486	0.007743

Multiple Linear Regression - Regression Statistics
Multiple R	0.92733413415113
R-squared	0.859948596361827
Adjusted R-squared	0.84109552279515
F-TEST (value)	45.6131777834778
F-TEST (DF numerator)	7
F-TEST (DF denominator)	52
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	16.2044643843294
Sum Squared Residuals	13654.402631116

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.92733413415113 \tabularnewline
R-squared & 0.859948596361827 \tabularnewline
Adjusted R-squared & 0.84109552279515 \tabularnewline
F-TEST (value) & 45.6131777834778 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 52 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 16.2044643843294 \tabularnewline
Sum Squared Residuals & 13654.402631116 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112246&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.92733413415113[/C][/ROW]
[ROW][C]R-squared[/C][C]0.859948596361827[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.84109552279515[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]45.6131777834778[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]52[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]16.2044643843294[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]13654.402631116[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112246&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112246&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 R	0.92733413415113
R-squared	0.859948596361827
Adjusted R-squared	0.84109552279515
F-TEST (value)	45.6131777834778
F-TEST (DF numerator)	7
F-TEST (DF denominator)	52
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	16.2044643843294
Sum Squared Residuals	13654.402631116

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	591	597.775264231122	-6.7752642311221
2	589	591.846377760334	-2.84637776033359
3	584	590.691965683054	-6.69196568305394
4	573	581.617662765862	-8.6176627658618
5	567	571.844913155236	-4.84491315523601
6	569	570.803532292062	-1.80353229206206
7	621	573.600380381199	47.3996196188013
8	629	630.799372241105	-1.79937224110505
9	628	614.346077816744	13.6539221832555
10	612	617.363623516191	-5.36362351619113
11	595	604.100155614093	-9.10015561409346
12	597	591.741990629931	5.25800937006879
13	593	596.841216508233	-3.84121650823289
14	590	588.639853575158	1.36014642484243
15	580	586.539865567352	-6.539865567352
16	574	576.465459028862	-2.46545902886191
17	573	574.618653195398	-1.61865319539820
18	573	574.221155358428	-1.22115535842797
19	620	576.999424823512	43.0005751764878
20	626	624.282066596428	1.71793340357167
21	620	605.551704125843	14.4482958741572
22	588	593.311982890966	-5.31198289096582
23	566	564.45265091448	1.54734908552033
24	557	557.445866663248	-0.445866663247895
25	561	555.86852688387	5.13147311613013
26	549	560.39204061077	-11.39204061077
27	532	540.748206817415	-8.74820681741515
28	526	529.769340561915	-3.76934056191475
29	511	533.159853537583	-22.1598535375827
30	499	517.160816253607	-18.1608162536066
31	555	526.511386461048	28.4886135389518
32	565	591.11623811456	-26.1162381145599
33	542	569.213849377034	-27.2138493770340
34	527	534.770552995607	-7.7705529956065
35	510	531.628975135344	-21.6289751353441
36	514	515.116777324348	-1.11677732434796
37	517	528.695833537948	-11.6958335379481
38	508	523.4631710946	-15.4631710946005
39	493	507.720627871151	-14.7206278711515
40	490	496.260339708791	-6.26033970879102
41	469	493.959997492884	-24.9599974928842
42	478	465.792966154662	12.2070338453380
43	528	491.652780086386	36.3472199136141
44	534	541.356184812228	-7.35618481222837
45	518	513.812444092028	4.18755590797234
46	506	503.513374219666	2.48662578033381
47	502	503.449107412381	-1.44910741238136
48	516	509.629516219227	6.37048378077309
49	528	524.584639926181	3.41536007381878
50	533	529.922963671621	3.07703632837886
51	536	529.898264214161	6.10173578583897
52	537	533.415280485422	3.58471951457841
53	524	530.643258560551	-6.64325856055116
54	536	525.210104892365	10.7898951076353
55	587	545.668682148454	41.3313178515456
56	597	592.511619375583	4.4883806244168
57	581	579.118364905459	1.88163509454067
58	564	561.884399976668	2.11560002333245
59	558	560.604090701701	-2.60409070170147
60	575	566.874209031939	8.1257909680612

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 591 & 597.775264231122 & -6.7752642311221 \tabularnewline
2 & 589 & 591.846377760334 & -2.84637776033359 \tabularnewline
3 & 584 & 590.691965683054 & -6.69196568305394 \tabularnewline
4 & 573 & 581.617662765862 & -8.6176627658618 \tabularnewline
5 & 567 & 571.844913155236 & -4.84491315523601 \tabularnewline
6 & 569 & 570.803532292062 & -1.80353229206206 \tabularnewline
7 & 621 & 573.600380381199 & 47.3996196188013 \tabularnewline
8 & 629 & 630.799372241105 & -1.79937224110505 \tabularnewline
9 & 628 & 614.346077816744 & 13.6539221832555 \tabularnewline
10 & 612 & 617.363623516191 & -5.36362351619113 \tabularnewline
11 & 595 & 604.100155614093 & -9.10015561409346 \tabularnewline
12 & 597 & 591.741990629931 & 5.25800937006879 \tabularnewline
13 & 593 & 596.841216508233 & -3.84121650823289 \tabularnewline
14 & 590 & 588.639853575158 & 1.36014642484243 \tabularnewline
15 & 580 & 586.539865567352 & -6.539865567352 \tabularnewline
16 & 574 & 576.465459028862 & -2.46545902886191 \tabularnewline
17 & 573 & 574.618653195398 & -1.61865319539820 \tabularnewline
18 & 573 & 574.221155358428 & -1.22115535842797 \tabularnewline
19 & 620 & 576.999424823512 & 43.0005751764878 \tabularnewline
20 & 626 & 624.282066596428 & 1.71793340357167 \tabularnewline
21 & 620 & 605.551704125843 & 14.4482958741572 \tabularnewline
22 & 588 & 593.311982890966 & -5.31198289096582 \tabularnewline
23 & 566 & 564.45265091448 & 1.54734908552033 \tabularnewline
24 & 557 & 557.445866663248 & -0.445866663247895 \tabularnewline
25 & 561 & 555.86852688387 & 5.13147311613013 \tabularnewline
26 & 549 & 560.39204061077 & -11.39204061077 \tabularnewline
27 & 532 & 540.748206817415 & -8.74820681741515 \tabularnewline
28 & 526 & 529.769340561915 & -3.76934056191475 \tabularnewline
29 & 511 & 533.159853537583 & -22.1598535375827 \tabularnewline
30 & 499 & 517.160816253607 & -18.1608162536066 \tabularnewline
31 & 555 & 526.511386461048 & 28.4886135389518 \tabularnewline
32 & 565 & 591.11623811456 & -26.1162381145599 \tabularnewline
33 & 542 & 569.213849377034 & -27.2138493770340 \tabularnewline
34 & 527 & 534.770552995607 & -7.7705529956065 \tabularnewline
35 & 510 & 531.628975135344 & -21.6289751353441 \tabularnewline
36 & 514 & 515.116777324348 & -1.11677732434796 \tabularnewline
37 & 517 & 528.695833537948 & -11.6958335379481 \tabularnewline
38 & 508 & 523.4631710946 & -15.4631710946005 \tabularnewline
39 & 493 & 507.720627871151 & -14.7206278711515 \tabularnewline
40 & 490 & 496.260339708791 & -6.26033970879102 \tabularnewline
41 & 469 & 493.959997492884 & -24.9599974928842 \tabularnewline
42 & 478 & 465.792966154662 & 12.2070338453380 \tabularnewline
43 & 528 & 491.652780086386 & 36.3472199136141 \tabularnewline
44 & 534 & 541.356184812228 & -7.35618481222837 \tabularnewline
45 & 518 & 513.812444092028 & 4.18755590797234 \tabularnewline
46 & 506 & 503.513374219666 & 2.48662578033381 \tabularnewline
47 & 502 & 503.449107412381 & -1.44910741238136 \tabularnewline
48 & 516 & 509.629516219227 & 6.37048378077309 \tabularnewline
49 & 528 & 524.584639926181 & 3.41536007381878 \tabularnewline
50 & 533 & 529.922963671621 & 3.07703632837886 \tabularnewline
51 & 536 & 529.898264214161 & 6.10173578583897 \tabularnewline
52 & 537 & 533.415280485422 & 3.58471951457841 \tabularnewline
53 & 524 & 530.643258560551 & -6.64325856055116 \tabularnewline
54 & 536 & 525.210104892365 & 10.7898951076353 \tabularnewline
55 & 587 & 545.668682148454 & 41.3313178515456 \tabularnewline
56 & 597 & 592.511619375583 & 4.4883806244168 \tabularnewline
57 & 581 & 579.118364905459 & 1.88163509454067 \tabularnewline
58 & 564 & 561.884399976668 & 2.11560002333245 \tabularnewline
59 & 558 & 560.604090701701 & -2.60409070170147 \tabularnewline
60 & 575 & 566.874209031939 & 8.1257909680612 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112246&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]591[/C][C]597.775264231122[/C][C]-6.7752642311221[/C][/ROW]
[ROW][C]2[/C][C]589[/C][C]591.846377760334[/C][C]-2.84637776033359[/C][/ROW]
[ROW][C]3[/C][C]584[/C][C]590.691965683054[/C][C]-6.69196568305394[/C][/ROW]
[ROW][C]4[/C][C]573[/C][C]581.617662765862[/C][C]-8.6176627658618[/C][/ROW]
[ROW][C]5[/C][C]567[/C][C]571.844913155236[/C][C]-4.84491315523601[/C][/ROW]
[ROW][C]6[/C][C]569[/C][C]570.803532292062[/C][C]-1.80353229206206[/C][/ROW]
[ROW][C]7[/C][C]621[/C][C]573.600380381199[/C][C]47.3996196188013[/C][/ROW]
[ROW][C]8[/C][C]629[/C][C]630.799372241105[/C][C]-1.79937224110505[/C][/ROW]
[ROW][C]9[/C][C]628[/C][C]614.346077816744[/C][C]13.6539221832555[/C][/ROW]
[ROW][C]10[/C][C]612[/C][C]617.363623516191[/C][C]-5.36362351619113[/C][/ROW]
[ROW][C]11[/C][C]595[/C][C]604.100155614093[/C][C]-9.10015561409346[/C][/ROW]
[ROW][C]12[/C][C]597[/C][C]591.741990629931[/C][C]5.25800937006879[/C][/ROW]
[ROW][C]13[/C][C]593[/C][C]596.841216508233[/C][C]-3.84121650823289[/C][/ROW]
[ROW][C]14[/C][C]590[/C][C]588.639853575158[/C][C]1.36014642484243[/C][/ROW]
[ROW][C]15[/C][C]580[/C][C]586.539865567352[/C][C]-6.539865567352[/C][/ROW]
[ROW][C]16[/C][C]574[/C][C]576.465459028862[/C][C]-2.46545902886191[/C][/ROW]
[ROW][C]17[/C][C]573[/C][C]574.618653195398[/C][C]-1.61865319539820[/C][/ROW]
[ROW][C]18[/C][C]573[/C][C]574.221155358428[/C][C]-1.22115535842797[/C][/ROW]
[ROW][C]19[/C][C]620[/C][C]576.999424823512[/C][C]43.0005751764878[/C][/ROW]
[ROW][C]20[/C][C]626[/C][C]624.282066596428[/C][C]1.71793340357167[/C][/ROW]
[ROW][C]21[/C][C]620[/C][C]605.551704125843[/C][C]14.4482958741572[/C][/ROW]
[ROW][C]22[/C][C]588[/C][C]593.311982890966[/C][C]-5.31198289096582[/C][/ROW]
[ROW][C]23[/C][C]566[/C][C]564.45265091448[/C][C]1.54734908552033[/C][/ROW]
[ROW][C]24[/C][C]557[/C][C]557.445866663248[/C][C]-0.445866663247895[/C][/ROW]
[ROW][C]25[/C][C]561[/C][C]555.86852688387[/C][C]5.13147311613013[/C][/ROW]
[ROW][C]26[/C][C]549[/C][C]560.39204061077[/C][C]-11.39204061077[/C][/ROW]
[ROW][C]27[/C][C]532[/C][C]540.748206817415[/C][C]-8.74820681741515[/C][/ROW]
[ROW][C]28[/C][C]526[/C][C]529.769340561915[/C][C]-3.76934056191475[/C][/ROW]
[ROW][C]29[/C][C]511[/C][C]533.159853537583[/C][C]-22.1598535375827[/C][/ROW]
[ROW][C]30[/C][C]499[/C][C]517.160816253607[/C][C]-18.1608162536066[/C][/ROW]
[ROW][C]31[/C][C]555[/C][C]526.511386461048[/C][C]28.4886135389518[/C][/ROW]
[ROW][C]32[/C][C]565[/C][C]591.11623811456[/C][C]-26.1162381145599[/C][/ROW]
[ROW][C]33[/C][C]542[/C][C]569.213849377034[/C][C]-27.2138493770340[/C][/ROW]
[ROW][C]34[/C][C]527[/C][C]534.770552995607[/C][C]-7.7705529956065[/C][/ROW]
[ROW][C]35[/C][C]510[/C][C]531.628975135344[/C][C]-21.6289751353441[/C][/ROW]
[ROW][C]36[/C][C]514[/C][C]515.116777324348[/C][C]-1.11677732434796[/C][/ROW]
[ROW][C]37[/C][C]517[/C][C]528.695833537948[/C][C]-11.6958335379481[/C][/ROW]
[ROW][C]38[/C][C]508[/C][C]523.4631710946[/C][C]-15.4631710946005[/C][/ROW]
[ROW][C]39[/C][C]493[/C][C]507.720627871151[/C][C]-14.7206278711515[/C][/ROW]
[ROW][C]40[/C][C]490[/C][C]496.260339708791[/C][C]-6.26033970879102[/C][/ROW]
[ROW][C]41[/C][C]469[/C][C]493.959997492884[/C][C]-24.9599974928842[/C][/ROW]
[ROW][C]42[/C][C]478[/C][C]465.792966154662[/C][C]12.2070338453380[/C][/ROW]
[ROW][C]43[/C][C]528[/C][C]491.652780086386[/C][C]36.3472199136141[/C][/ROW]
[ROW][C]44[/C][C]534[/C][C]541.356184812228[/C][C]-7.35618481222837[/C][/ROW]
[ROW][C]45[/C][C]518[/C][C]513.812444092028[/C][C]4.18755590797234[/C][/ROW]
[ROW][C]46[/C][C]506[/C][C]503.513374219666[/C][C]2.48662578033381[/C][/ROW]
[ROW][C]47[/C][C]502[/C][C]503.449107412381[/C][C]-1.44910741238136[/C][/ROW]
[ROW][C]48[/C][C]516[/C][C]509.629516219227[/C][C]6.37048378077309[/C][/ROW]
[ROW][C]49[/C][C]528[/C][C]524.584639926181[/C][C]3.41536007381878[/C][/ROW]
[ROW][C]50[/C][C]533[/C][C]529.922963671621[/C][C]3.07703632837886[/C][/ROW]
[ROW][C]51[/C][C]536[/C][C]529.898264214161[/C][C]6.10173578583897[/C][/ROW]
[ROW][C]52[/C][C]537[/C][C]533.415280485422[/C][C]3.58471951457841[/C][/ROW]
[ROW][C]53[/C][C]524[/C][C]530.643258560551[/C][C]-6.64325856055116[/C][/ROW]
[ROW][C]54[/C][C]536[/C][C]525.210104892365[/C][C]10.7898951076353[/C][/ROW]
[ROW][C]55[/C][C]587[/C][C]545.668682148454[/C][C]41.3313178515456[/C][/ROW]
[ROW][C]56[/C][C]597[/C][C]592.511619375583[/C][C]4.4883806244168[/C][/ROW]
[ROW][C]57[/C][C]581[/C][C]579.118364905459[/C][C]1.88163509454067[/C][/ROW]
[ROW][C]58[/C][C]564[/C][C]561.884399976668[/C][C]2.11560002333245[/C][/ROW]
[ROW][C]59[/C][C]558[/C][C]560.604090701701[/C][C]-2.60409070170147[/C][/ROW]
[ROW][C]60[/C][C]575[/C][C]566.874209031939[/C][C]8.1257909680612[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112246&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112246&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 Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	591	597.775264231122	-6.7752642311221
2	589	591.846377760334	-2.84637776033359
3	584	590.691965683054	-6.69196568305394
4	573	581.617662765862	-8.6176627658618
5	567	571.844913155236	-4.84491315523601
6	569	570.803532292062	-1.80353229206206
7	621	573.600380381199	47.3996196188013
8	629	630.799372241105	-1.79937224110505
9	628	614.346077816744	13.6539221832555
10	612	617.363623516191	-5.36362351619113
11	595	604.100155614093	-9.10015561409346
12	597	591.741990629931	5.25800937006879
13	593	596.841216508233	-3.84121650823289
14	590	588.639853575158	1.36014642484243
15	580	586.539865567352	-6.539865567352
16	574	576.465459028862	-2.46545902886191
17	573	574.618653195398	-1.61865319539820
18	573	574.221155358428	-1.22115535842797
19	620	576.999424823512	43.0005751764878
20	626	624.282066596428	1.71793340357167
21	620	605.551704125843	14.4482958741572
22	588	593.311982890966	-5.31198289096582
23	566	564.45265091448	1.54734908552033
24	557	557.445866663248	-0.445866663247895
25	561	555.86852688387	5.13147311613013
26	549	560.39204061077	-11.39204061077
27	532	540.748206817415	-8.74820681741515
28	526	529.769340561915	-3.76934056191475
29	511	533.159853537583	-22.1598535375827
30	499	517.160816253607	-18.1608162536066
31	555	526.511386461048	28.4886135389518
32	565	591.11623811456	-26.1162381145599
33	542	569.213849377034	-27.2138493770340
34	527	534.770552995607	-7.7705529956065
35	510	531.628975135344	-21.6289751353441
36	514	515.116777324348	-1.11677732434796
37	517	528.695833537948	-11.6958335379481
38	508	523.4631710946	-15.4631710946005
39	493	507.720627871151	-14.7206278711515
40	490	496.260339708791	-6.26033970879102
41	469	493.959997492884	-24.9599974928842
42	478	465.792966154662	12.2070338453380
43	528	491.652780086386	36.3472199136141
44	534	541.356184812228	-7.35618481222837
45	518	513.812444092028	4.18755590797234
46	506	503.513374219666	2.48662578033381
47	502	503.449107412381	-1.44910741238136
48	516	509.629516219227	6.37048378077309
49	528	524.584639926181	3.41536007381878
50	533	529.922963671621	3.07703632837886
51	536	529.898264214161	6.10173578583897
52	537	533.415280485422	3.58471951457841
53	524	530.643258560551	-6.64325856055116
54	536	525.210104892365	10.7898951076353
55	587	545.668682148454	41.3313178515456
56	597	592.511619375583	4.4883806244168
57	581	579.118364905459	1.88163509454067
58	564	561.884399976668	2.11560002333245
59	558	560.604090701701	-2.60409070170147
60	575	566.874209031939	8.1257909680612

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.110948631231029	0.221897262462059	0.88905136876897
12	0.0482329476659703	0.0964658953319405	0.95176705233403
13	0.0195503049620977	0.0391006099241954	0.980449695037902
14	0.0127386497038309	0.0254772994076618	0.98726135029617
15	0.0157369617954466	0.0314739235908933	0.984263038204553
16	0.0116793007389843	0.0233586014779686	0.988320699261016
17	0.00813113124147431	0.0162622624829486	0.991868868758526
18	0.00603013024879473	0.0120602604975895	0.993969869751205
19	0.00940571175264478	0.0188114235052896	0.990594288247355
20	0.00439325279037848	0.00878650558075696	0.995606747209622
21	0.00433311760453439	0.00866623520906877	0.995666882395466
22	0.00484923706649171	0.00969847413298342	0.995150762933508
23	0.00241477010448375	0.0048295402089675	0.997585229895516
24	0.00207581136788206	0.00415162273576413	0.997924188632118
25	0.00260678792008841	0.00521357584017682	0.997393212079912
26	0.0162500038945677	0.0325000077891355	0.983749996105432
27	0.0340006347137202	0.0680012694274404	0.96599936528628
28	0.0372938171773005	0.074587634354601	0.9627061828227
29	0.088727377920656	0.177454755841312	0.911272622079344
30	0.110324888236256	0.220649776472511	0.889675111763744
31	0.175724342942134	0.351448685884267	0.824275657057866
32	0.252778693323834	0.505557386647669	0.747221306676166
33	0.255466219370446	0.510932438740893	0.744533780629554
34	0.266284582270009	0.532569164540019	0.73371541772999
35	0.278588352213623	0.557176704427245	0.721411647786377
36	0.302086138167179	0.604172276334357	0.697913861832821
37	0.295089748133549	0.590179496267099	0.70491025186645
38	0.320700926601763	0.641401853203526	0.679299073398237
39	0.407317993872806	0.814635987745613	0.592682006127194
40	0.497682566369999	0.995365132739997	0.502317433630001
41	0.69374149948125	0.6125170010375	0.30625850051875
42	0.747616617571168	0.504766764857664	0.252383382428832
43	0.87073909149684	0.258521817006321	0.129260908503161
44	0.937082320687497	0.125835358625006	0.0629176793125032
45	0.93038397448665	0.139232051026699	0.0696160255133496
46	0.900177813034472	0.199644373931057	0.0998221869655284
47	0.824322291370719	0.351355417258562	0.175677708629281
48	0.715682492581568	0.568635014836864	0.284317507418432
49	0.624371888259115	0.751256223481769	0.375628111740885

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.110948631231029 & 0.221897262462059 & 0.88905136876897 \tabularnewline
12 & 0.0482329476659703 & 0.0964658953319405 & 0.95176705233403 \tabularnewline
13 & 0.0195503049620977 & 0.0391006099241954 & 0.980449695037902 \tabularnewline
14 & 0.0127386497038309 & 0.0254772994076618 & 0.98726135029617 \tabularnewline
15 & 0.0157369617954466 & 0.0314739235908933 & 0.984263038204553 \tabularnewline
16 & 0.0116793007389843 & 0.0233586014779686 & 0.988320699261016 \tabularnewline
17 & 0.00813113124147431 & 0.0162622624829486 & 0.991868868758526 \tabularnewline
18 & 0.00603013024879473 & 0.0120602604975895 & 0.993969869751205 \tabularnewline
19 & 0.00940571175264478 & 0.0188114235052896 & 0.990594288247355 \tabularnewline
20 & 0.00439325279037848 & 0.00878650558075696 & 0.995606747209622 \tabularnewline
21 & 0.00433311760453439 & 0.00866623520906877 & 0.995666882395466 \tabularnewline
22 & 0.00484923706649171 & 0.00969847413298342 & 0.995150762933508 \tabularnewline
23 & 0.00241477010448375 & 0.0048295402089675 & 0.997585229895516 \tabularnewline
24 & 0.00207581136788206 & 0.00415162273576413 & 0.997924188632118 \tabularnewline
25 & 0.00260678792008841 & 0.00521357584017682 & 0.997393212079912 \tabularnewline
26 & 0.0162500038945677 & 0.0325000077891355 & 0.983749996105432 \tabularnewline
27 & 0.0340006347137202 & 0.0680012694274404 & 0.96599936528628 \tabularnewline
28 & 0.0372938171773005 & 0.074587634354601 & 0.9627061828227 \tabularnewline
29 & 0.088727377920656 & 0.177454755841312 & 0.911272622079344 \tabularnewline
30 & 0.110324888236256 & 0.220649776472511 & 0.889675111763744 \tabularnewline
31 & 0.175724342942134 & 0.351448685884267 & 0.824275657057866 \tabularnewline
32 & 0.252778693323834 & 0.505557386647669 & 0.747221306676166 \tabularnewline
33 & 0.255466219370446 & 0.510932438740893 & 0.744533780629554 \tabularnewline
34 & 0.266284582270009 & 0.532569164540019 & 0.73371541772999 \tabularnewline
35 & 0.278588352213623 & 0.557176704427245 & 0.721411647786377 \tabularnewline
36 & 0.302086138167179 & 0.604172276334357 & 0.697913861832821 \tabularnewline
37 & 0.295089748133549 & 0.590179496267099 & 0.70491025186645 \tabularnewline
38 & 0.320700926601763 & 0.641401853203526 & 0.679299073398237 \tabularnewline
39 & 0.407317993872806 & 0.814635987745613 & 0.592682006127194 \tabularnewline
40 & 0.497682566369999 & 0.995365132739997 & 0.502317433630001 \tabularnewline
41 & 0.69374149948125 & 0.6125170010375 & 0.30625850051875 \tabularnewline
42 & 0.747616617571168 & 0.504766764857664 & 0.252383382428832 \tabularnewline
43 & 0.87073909149684 & 0.258521817006321 & 0.129260908503161 \tabularnewline
44 & 0.937082320687497 & 0.125835358625006 & 0.0629176793125032 \tabularnewline
45 & 0.93038397448665 & 0.139232051026699 & 0.0696160255133496 \tabularnewline
46 & 0.900177813034472 & 0.199644373931057 & 0.0998221869655284 \tabularnewline
47 & 0.824322291370719 & 0.351355417258562 & 0.175677708629281 \tabularnewline
48 & 0.715682492581568 & 0.568635014836864 & 0.284317507418432 \tabularnewline
49 & 0.624371888259115 & 0.751256223481769 & 0.375628111740885 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112246&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]11[/C][C]0.110948631231029[/C][C]0.221897262462059[/C][C]0.88905136876897[/C][/ROW]
[ROW][C]12[/C][C]0.0482329476659703[/C][C]0.0964658953319405[/C][C]0.95176705233403[/C][/ROW]
[ROW][C]13[/C][C]0.0195503049620977[/C][C]0.0391006099241954[/C][C]0.980449695037902[/C][/ROW]
[ROW][C]14[/C][C]0.0127386497038309[/C][C]0.0254772994076618[/C][C]0.98726135029617[/C][/ROW]
[ROW][C]15[/C][C]0.0157369617954466[/C][C]0.0314739235908933[/C][C]0.984263038204553[/C][/ROW]
[ROW][C]16[/C][C]0.0116793007389843[/C][C]0.0233586014779686[/C][C]0.988320699261016[/C][/ROW]
[ROW][C]17[/C][C]0.00813113124147431[/C][C]0.0162622624829486[/C][C]0.991868868758526[/C][/ROW]
[ROW][C]18[/C][C]0.00603013024879473[/C][C]0.0120602604975895[/C][C]0.993969869751205[/C][/ROW]
[ROW][C]19[/C][C]0.00940571175264478[/C][C]0.0188114235052896[/C][C]0.990594288247355[/C][/ROW]
[ROW][C]20[/C][C]0.00439325279037848[/C][C]0.00878650558075696[/C][C]0.995606747209622[/C][/ROW]
[ROW][C]21[/C][C]0.00433311760453439[/C][C]0.00866623520906877[/C][C]0.995666882395466[/C][/ROW]
[ROW][C]22[/C][C]0.00484923706649171[/C][C]0.00969847413298342[/C][C]0.995150762933508[/C][/ROW]
[ROW][C]23[/C][C]0.00241477010448375[/C][C]0.0048295402089675[/C][C]0.997585229895516[/C][/ROW]
[ROW][C]24[/C][C]0.00207581136788206[/C][C]0.00415162273576413[/C][C]0.997924188632118[/C][/ROW]
[ROW][C]25[/C][C]0.00260678792008841[/C][C]0.00521357584017682[/C][C]0.997393212079912[/C][/ROW]
[ROW][C]26[/C][C]0.0162500038945677[/C][C]0.0325000077891355[/C][C]0.983749996105432[/C][/ROW]
[ROW][C]27[/C][C]0.0340006347137202[/C][C]0.0680012694274404[/C][C]0.96599936528628[/C][/ROW]
[ROW][C]28[/C][C]0.0372938171773005[/C][C]0.074587634354601[/C][C]0.9627061828227[/C][/ROW]
[ROW][C]29[/C][C]0.088727377920656[/C][C]0.177454755841312[/C][C]0.911272622079344[/C][/ROW]
[ROW][C]30[/C][C]0.110324888236256[/C][C]0.220649776472511[/C][C]0.889675111763744[/C][/ROW]
[ROW][C]31[/C][C]0.175724342942134[/C][C]0.351448685884267[/C][C]0.824275657057866[/C][/ROW]
[ROW][C]32[/C][C]0.252778693323834[/C][C]0.505557386647669[/C][C]0.747221306676166[/C][/ROW]
[ROW][C]33[/C][C]0.255466219370446[/C][C]0.510932438740893[/C][C]0.744533780629554[/C][/ROW]
[ROW][C]34[/C][C]0.266284582270009[/C][C]0.532569164540019[/C][C]0.73371541772999[/C][/ROW]
[ROW][C]35[/C][C]0.278588352213623[/C][C]0.557176704427245[/C][C]0.721411647786377[/C][/ROW]
[ROW][C]36[/C][C]0.302086138167179[/C][C]0.604172276334357[/C][C]0.697913861832821[/C][/ROW]
[ROW][C]37[/C][C]0.295089748133549[/C][C]0.590179496267099[/C][C]0.70491025186645[/C][/ROW]
[ROW][C]38[/C][C]0.320700926601763[/C][C]0.641401853203526[/C][C]0.679299073398237[/C][/ROW]
[ROW][C]39[/C][C]0.407317993872806[/C][C]0.814635987745613[/C][C]0.592682006127194[/C][/ROW]
[ROW][C]40[/C][C]0.497682566369999[/C][C]0.995365132739997[/C][C]0.502317433630001[/C][/ROW]
[ROW][C]41[/C][C]0.69374149948125[/C][C]0.6125170010375[/C][C]0.30625850051875[/C][/ROW]
[ROW][C]42[/C][C]0.747616617571168[/C][C]0.504766764857664[/C][C]0.252383382428832[/C][/ROW]
[ROW][C]43[/C][C]0.87073909149684[/C][C]0.258521817006321[/C][C]0.129260908503161[/C][/ROW]
[ROW][C]44[/C][C]0.937082320687497[/C][C]0.125835358625006[/C][C]0.0629176793125032[/C][/ROW]
[ROW][C]45[/C][C]0.93038397448665[/C][C]0.139232051026699[/C][C]0.0696160255133496[/C][/ROW]
[ROW][C]46[/C][C]0.900177813034472[/C][C]0.199644373931057[/C][C]0.0998221869655284[/C][/ROW]
[ROW][C]47[/C][C]0.824322291370719[/C][C]0.351355417258562[/C][C]0.175677708629281[/C][/ROW]
[ROW][C]48[/C][C]0.715682492581568[/C][C]0.568635014836864[/C][C]0.284317507418432[/C][/ROW]
[ROW][C]49[/C][C]0.624371888259115[/C][C]0.751256223481769[/C][C]0.375628111740885[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112246&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112246&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-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.110948631231029	0.221897262462059	0.88905136876897
12	0.0482329476659703	0.0964658953319405	0.95176705233403
13	0.0195503049620977	0.0391006099241954	0.980449695037902
14	0.0127386497038309	0.0254772994076618	0.98726135029617
15	0.0157369617954466	0.0314739235908933	0.984263038204553
16	0.0116793007389843	0.0233586014779686	0.988320699261016
17	0.00813113124147431	0.0162622624829486	0.991868868758526
18	0.00603013024879473	0.0120602604975895	0.993969869751205
19	0.00940571175264478	0.0188114235052896	0.990594288247355
20	0.00439325279037848	0.00878650558075696	0.995606747209622
21	0.00433311760453439	0.00866623520906877	0.995666882395466
22	0.00484923706649171	0.00969847413298342	0.995150762933508
23	0.00241477010448375	0.0048295402089675	0.997585229895516
24	0.00207581136788206	0.00415162273576413	0.997924188632118
25	0.00260678792008841	0.00521357584017682	0.997393212079912
26	0.0162500038945677	0.0325000077891355	0.983749996105432
27	0.0340006347137202	0.0680012694274404	0.96599936528628
28	0.0372938171773005	0.074587634354601	0.9627061828227
29	0.088727377920656	0.177454755841312	0.911272622079344
30	0.110324888236256	0.220649776472511	0.889675111763744
31	0.175724342942134	0.351448685884267	0.824275657057866
32	0.252778693323834	0.505557386647669	0.747221306676166
33	0.255466219370446	0.510932438740893	0.744533780629554
34	0.266284582270009	0.532569164540019	0.73371541772999
35	0.278588352213623	0.557176704427245	0.721411647786377
36	0.302086138167179	0.604172276334357	0.697913861832821
37	0.295089748133549	0.590179496267099	0.70491025186645
38	0.320700926601763	0.641401853203526	0.679299073398237
39	0.407317993872806	0.814635987745613	0.592682006127194
40	0.497682566369999	0.995365132739997	0.502317433630001
41	0.69374149948125	0.6125170010375	0.30625850051875
42	0.747616617571168	0.504766764857664	0.252383382428832
43	0.87073909149684	0.258521817006321	0.129260908503161
44	0.937082320687497	0.125835358625006	0.0629176793125032
45	0.93038397448665	0.139232051026699	0.0696160255133496
46	0.900177813034472	0.199644373931057	0.0998221869655284
47	0.824322291370719	0.351355417258562	0.175677708629281
48	0.715682492581568	0.568635014836864	0.284317507418432
49	0.624371888259115	0.751256223481769	0.375628111740885

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	6	0.153846153846154	NOK
5% type I error level	14	0.358974358974359	NOK
10% type I error level	17	0.435897435897436	NOK

\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 & 6 & 0.153846153846154 & NOK \tabularnewline
5% type I error level & 14 & 0.358974358974359 & NOK \tabularnewline
10% type I error level & 17 & 0.435897435897436 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112246&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]6[/C][C]0.153846153846154[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]14[/C][C]0.358974358974359[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]17[/C][C]0.435897435897436[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112246&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112246&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 tests	OK/NOK
1% type I error level	6	0.153846153846154	NOK
5% type I error level	14	0.358974358974359	NOK
10% type I error level	17	0.435897435897436	NOK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code