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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 computationTue, 20 Nov 2012 12:10:23 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/20/t13534315401rj3ku06eudzz5x.htm/, Retrieved Fri, 29 Mar 2024 07:34:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=191196, Retrieved Fri, 29 Mar 2024 07:34:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact88
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [] [2012-11-05 20:03:26] [15cbcfe738d59edaf37329746b028204]
- R  D      [Multiple Regression] [Workshop 7 : mult...] [2012-11-20 17:10:23] [2715be8b6e97cdb2c6fe0b5c822d3851] [Current]
- R  D        [Multiple Regression] [Paper- multiple r...] [2012-12-22 14:28:03] [c5937bf2e8e0a7b2aa466d1286878951]
- R  D        [Multiple Regression] [Paper- Multiple r...] [2012-12-22 14:46:38] [c5937bf2e8e0a7b2aa466d1286878951]
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Dataseries X:
493	116	377	7,4	9,1	9
481	111	370	7,2	9,1	9
462	104	358	7	9	9
457	100	357	7	8,9	8,9
442	93	349	6,8	8,8	8,9
439	91	348	6,8	8,7	8,8
488	119	369	6,7	8,7	8,8
521	139	381	6,7	8,6	8,7
501	134	368	6,7	8,5	8,7
485	124	361	6,8	8,4	8,6
464	113	351	6,7	8,4	8,6
460	109	351	6,6	8,3	8,5
467	109	358	6,4	8,2	8,5
460	106	354	6,3	8,2	8,5
448	101	347	6,3	8,1	8,5
443	98	345	6,5	8,1	8,5
436	93	343	6,5	8,1	8,5
431	91	340	6,4	8,1	8,5
484	122	362	6,2	8,1	8,5
510	139	370	6,2	8,1	8,6
513	140	373	6,5	8,1	8,6
503	132	371	7	8,2	8,6
471	117	354	7,2	8,2	8,7
471	114	357	7,3	8,3	8,7
476	113	363	7,4	8,2	8,7
475	110	364	7,4	8,3	8,8
470	107	363	7,4	8,3	8,8
461	103	358	7,3	8,4	8,9
455	98	357	7,4	8,5	8,9
456	98	357	7,4	8,5	8,9
517	137	380	7,6	8,6	9
525	148	378	7,6	8,6	9
523	147	376	7,7	8,7	9
519	139	380	7,7	8,7	9
509	130	379	7,8	8,8	9
512	128	384	7,8	8,8	9
519	127	392	8	8,9	9,1
517	123	394	8,1	9	9,1
510	118	392	8,1	9	9,1
509	114	396	8,2	9	9,1
501	108	392	8,1	9	9,1
507	111	396	8,1	9,1	9,1
569	151	419	8,1	9,1	9,1
580	159	421	8,1	9	9,1
578	158	420	8,2	9,1	9,1
565	148	418	8,2	9	9,1
547	138	410	8,3	9,1	9,1
555	137	418	8,4	9,1	9,2
562	136	426	8,6	9,2	9,3
561	133	428	8,6	9,2	9,3
555	126	430	8,4	9,2	9,3
544	120	424	8	9,2	9,2
537	114	423	7,9	9,2	9,2
543	116	427	8,1	9,3	9,2
594	153	441	8,5	9,3	9,2
611	162	449	8,8	9,3	9,2
613	161	452	8,8	9,3	9,2
611	149	462	8,5	9,3	9,2
594	139	455	8,3	9,4	9,2
595	135	461	8,3	9,4	9,2
591	130	461	8,3	9,3	9,2
589	127	463	8,4	9,3	9,2
584	122	462	8,5	9,3	9,2
573	117	456	8,5	9,3	9,2
567	112	455	8,6	9,2	9,1
569	113	456	8,5	9,2	9,1
621	149	472	8,6	9,2	9
629	157	472	8,6	9,1	8,9
628	157	471	8,6	9,1	8,9
612	147	465	8,5	9,1	9
595	137	459	8,4	9,1	8,9
597	132	465	8,4	9	8,8
593	125	468	8,5	8,9	8,7
590	123	467	8,5	8,8	8,6
580	117	463	8,5	8,7	8,5
574	114	460	8,6	8,6	8,5
573	111	462	8,6	8,6	8,4
573	112	461	8,4	8,5	8,3
620	144	476	8,2	8,4	8,2
626	150	476	8	8,4	8,2
620	149	471	8	8,3	8,1
588	134	453	8	8,2	8
566	123	443	8	8,2	7,9
557	116	442	7,9	8	7,8
561	117	444	7,9	7,9	7,6
549	111	438	7,9	7,8	7,5
532	105	427	7,9	7,7	7,4
526	102	424	8	7,6	7,3
511	95	416	7,9	7,6	7,3
499	93	406	7,4	7,6	7,2
555	124	431	7,2	7,6	7,2
565	130	434	7	7,6	7,2
542	124	418	6,9	7,5	7,1
527	115	412	7,1	7,5	7
510	106	404	7,2	7,4	7
514	105	409	7,2	7,4	6,9
517	105	412	7,1	7,4	6,9
508	101	406	6,9	7,3	6,8
493	95	398	6,8	7,3	6,8
490	93	397	6,8	7,4	6,8
469	84	385	6,8	7,5	6,9
478	87	390	6,9	7,6	7
528	116	413	7,1	7,6	7
534	120	413	7,2	7,7	7,1
518	117	401	7,2	7,7	7,2
506	109	397	7,1	7,9	7,3
502	105	397	7,1	8,1	7,5
516	107	409	7,2	8,4	7,7
528	109	419	7,5	8,7	8,1
533	109	424	7,7	9	8,4
536	108	428	7,8	9,3	8,6
537	107	430	7,7	9,4	8,8
524	99	424	7,7	9,5	8,9
536	103	433	7,8	9,6	9,1
587	131	456	8	9,8	9,2
597	137	459	8,1	9,8	9,3
581	135	446	8,1	9,9	9,4
564	124	441	8	10	9,4
558	118	439	8,1	10	9,5
575	121	454	8,2	10,1	9,5
580	121	460	8,4	10,1	9,7
575	118	457	8,5	10,1	9,7
563	113	451	8,5	10,1	9,7
552	107	444	8,5	10,2	9,7
537	100	437	8,5	10,2	9,7
545	102	443	8,5	10,1	9,6
601	130	471	8,4	10,1	9,6
604	136	469	8,3	10,1	9,6
586	133	454	8,2	10,1	9,6
564	120	444	8,1	10,1	9,6
549	112	436	7,9	10,1	9,6
551	109	442	7,6	10,1	9,6
556	110	446	7,3	10	9,5
548	106	442	7,1	9,9	9,5
540	102	438	7	9,9	9,4
531	98	433	7,1	9,9	9,4
521	92	428	7,1	9,9	9,5
519	92	426	7,1	10	9,5
572	120	452	7,3	10,1	9,6
581	127	455	7,3	10,2	9,7
563	124	439	7,3	10,3	9,8
548	114	434	7,2	10,5	9,9
539	108	431	7,2	10,6	10
541	106	435	7,1	10,7	10
562	111	450	7,1	10,8	10,1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net

\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 & 11 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191196&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191196&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191196&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 time11 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Totaal_werklozen[t] = + 1.25805258726478 + 0.994661922800537Jonger_dan_25_jaar[t] + 1.00172411751936Vanaf_25_jaar[t] -0.159747721315807Belgie[t] -0.0302394170281228Eurogebied[t] + 0.0176099625292626EU_27[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Totaal_werklozen[t] =  +  1.25805258726478 +  0.994661922800537Jonger_dan_25_jaar[t] +  1.00172411751936Vanaf_25_jaar[t] -0.159747721315807Belgie[t] -0.0302394170281228Eurogebied[t] +  0.0176099625292626EU_27[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191196&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Totaal_werklozen[t] =  +  1.25805258726478 +  0.994661922800537Jonger_dan_25_jaar[t] +  1.00172411751936Vanaf_25_jaar[t] -0.159747721315807Belgie[t] -0.0302394170281228Eurogebied[t] +  0.0176099625292626EU_27[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191196&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191196&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_werklozen[t] = + 1.25805258726478 + 0.994661922800537Jonger_dan_25_jaar[t] + 1.00172411751936Vanaf_25_jaar[t] -0.159747721315807Belgie[t] -0.0302394170281228Eurogebied[t] + 0.0176099625292626EU_27[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.258052587264780.6105112.06070.0412010.0206
Jonger_dan_25_jaar0.9946619228005370.003107320.141500
Vanaf_25_jaar1.001724117519360.00261383.863600
Belgie-0.1597477213158070.105403-1.51560.1318940.065947
Eurogebied-0.03023941702812280.211592-0.14290.8865650.443283
EU_270.01760996252926260.2081590.08460.9327020.466351

\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) & 1.25805258726478 & 0.610511 & 2.0607 & 0.041201 & 0.0206 \tabularnewline
Jonger_dan_25_jaar & 0.994661922800537 & 0.003107 & 320.1415 & 0 & 0 \tabularnewline
Vanaf_25_jaar & 1.00172411751936 & 0.00261 & 383.8636 & 0 & 0 \tabularnewline
Belgie & -0.159747721315807 & 0.105403 & -1.5156 & 0.131894 & 0.065947 \tabularnewline
Eurogebied & -0.0302394170281228 & 0.211592 & -0.1429 & 0.886565 & 0.443283 \tabularnewline
EU_27 & 0.0176099625292626 & 0.208159 & 0.0846 & 0.932702 & 0.466351 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191196&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]1.25805258726478[/C][C]0.610511[/C][C]2.0607[/C][C]0.041201[/C][C]0.0206[/C][/ROW]
[ROW][C]Jonger_dan_25_jaar[/C][C]0.994661922800537[/C][C]0.003107[/C][C]320.1415[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Vanaf_25_jaar[/C][C]1.00172411751936[/C][C]0.00261[/C][C]383.8636[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Belgie[/C][C]-0.159747721315807[/C][C]0.105403[/C][C]-1.5156[/C][C]0.131894[/C][C]0.065947[/C][/ROW]
[ROW][C]Eurogebied[/C][C]-0.0302394170281228[/C][C]0.211592[/C][C]-0.1429[/C][C]0.886565[/C][C]0.443283[/C][/ROW]
[ROW][C]EU_27[/C][C]0.0176099625292626[/C][C]0.208159[/C][C]0.0846[/C][C]0.932702[/C][C]0.466351[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191196&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191196&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)1.258052587264780.6105112.06070.0412010.0206
Jonger_dan_25_jaar0.9946619228005370.003107320.141500
Vanaf_25_jaar1.001724117519360.00261383.863600
Belgie-0.1597477213158070.105403-1.51560.1318940.065947
Eurogebied-0.03023941702812280.211592-0.14290.8865650.443283
EU_270.01760996252926260.2081590.08460.9327020.466351







Multiple Linear Regression - Regression Statistics
Multiple R0.999949566855604
R-squared0.99989913625471
Adjusted R-squared0.999895508062433
F-TEST (value)275591.550838126
F-TEST (DF numerator)5
F-TEST (DF denominator)139
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.48812243024861
Sum Squared Residuals33.1186274607415

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999949566855604 \tabularnewline
R-squared & 0.99989913625471 \tabularnewline
Adjusted R-squared & 0.999895508062433 \tabularnewline
F-TEST (value) & 275591.550838126 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 139 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.48812243024861 \tabularnewline
Sum Squared Residuals & 33.1186274607415 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191196&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999949566855604[/C][/ROW]
[ROW][C]R-squared[/C][C]0.99989913625471[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.999895508062433[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]275591.550838126[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]139[/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]0.48812243024861[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]33.1186274607415[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191196&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191196&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 R0.999949566855604
R-squared0.99989913625471
Adjusted R-squared0.999895508062433
F-TEST (value)275591.550838126
F-TEST (DF numerator)5
F-TEST (DF denominator)139
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.48812243024861
Sum Squared Residuals33.1186274607415







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1493492.9900057669960.00999423300358567
2481481.036576874621-0.0365768746211799
3462462.088227490751-0.088227490751074
4457457.109118627479-0.109118627479444
5442442.167665713687-0.167665713686772
6439439.177880696016-0.177880696016228
7488488.080595774469-0.080595774469399
8521519.9957865861621.00421341383765
9501502.003087386111-1.00308738611081
10485485.029687508788-0.0296875087882139
11464464.08713995492-0.0871399549202874
12460460.1257299813-0.125729981299612
13467467.172772289901-0.172772289901108
14460460.197864823554-0.197864823553632
15448448.215510328618-0.215510328618235
16443443.196126780915-0.196126780914748
17436436.219368931873-0.219368931873335
18431431.240847505846-0.240847505845768
19484484.145247242352-0.145247242351496
20510509.0700538663680.929946133631572
21513513.021963825332-0.0219638253323059
22503502.9783224055290.0216775944714245
23471470.9988950176810.00110498231883641
24471471.001082888003-0.00108288800324184
25476476.00381483989-0.00381483989009308
26475474.0202902435580.979709756442046
27470470.034580357637-0.0345803576369908
28461461.06202390552-0.0620239055197399
29455455.067991460163-0.0679914601633029
30456455.0679914601630.932008539836697
31517516.8662486626160.133751337383537
32525525.804081578384-0.804081578383649
33523522.786972706710.213027293289996
34519518.8365737943830.163426205616844
35509508.8638936578250.136106342175435
36512511.883190399820.116809600179718
37519518.8691089274620.130891072538418
38517516.8749107574640.125089242536241
39510509.8981529084220.101847091577642
40509509.910426915166-0.910426915166068
41501499.9515336804171.04846631958301
42507506.9393919771930.0606080228067766
43569569.76552359216-0.765523592159977
44580579.7292911513060.270708848694189
45578577.7139063971520.286093602848484
46565565.76686287581-0.766862875810238
47547547.787451993816-0.7874519938156
48555554.7923692352910.207630764708712
49562561.7782877629330.221712237067418
50561560.797750229570.202249770430309
51555555.870514549268-0.870514549267814
52544543.9543363996220.0456636003781708
53537537.000615517431-0.000615517430826621
54543542.9618623471430.0381376528566306
55594593.7245920475080.275407952492049
56611610.6424179764730.357582023527074
57613612.652928406230.34707159376953
58611610.7821508242120.217849175787636
59594593.8523883761320.147611623868174
60595595.884085390046-0.884085390045837
61591590.9137997177460.0862002822540332
62589589.917287412252-0.91728741225149
63584583.9262789085980.0737210914021351
64573572.9426245894790.0573754105209769
65567566.9528790312750.0471209687247226
66569568.9652398437270.0347601562732398
67621620.7829191764710.217080823528655
68629628.7414775043260.258522495674476
69628627.7397533868060.260246613193835
70612611.8005252220690.199474777930859
71595595.857775064826-0.857775064826268
72597596.896073101390.103926898610366
73593592.9239001676620.0760998323377368
74590589.9341151499920.0658848500082872
75580579.9605100885610.0394899114390652
76574573.9584011371720.0415988628275222
77573572.9761026075570.0238973924433451
78573573.002252902551-0.00225290255087449
79620619.8905086846720.109491315328492
80626625.8904297657380.109570234262105
81620619.888410200790.111589799209559
82588586.9387101888841.0612898111162
83566565.9784268666310.0215731333686393
84557558.034330948793-1.03433094879252
85561561.031943055829-0.0319430558287356
86549549.054889759359-0.0548897593592419
87532532.069215875293-0.0692158752929444
88526526.065345927652-0.0653459276515623
89511511.104894300025-0.104894300024506
90499499.176442143635-0.176442143634814
91555555.086014232699-0.0860142326986132
92565564.0911076663230.908892333676925
93542542.112787966792-0.112787966791563
94527527.116775415955-0.116775415954483
95510510.138074340166-0.138074340166003
96514514.150272008709-0.150272008709338
97517517.171419133399-0.171419133398999
98508507.2156392267940.784360773206259
99493493.249849521967-0.249849521967222
100490490.255777617144-0.255777617143978
101469469.281867956257-0.281867956256935
102478477.2572365946740.742763405326119
103528529.110137514572-1.11013751457157
104534533.0715474881920.928452511807751
105518518.068633305811-0.0686333058112465
106506506.116129338308-0.116129338308392
107502502.134955756206-0.134955756206471
108516516.123444407306-0.1234444073057
109528528.080057271609-0.0800572716088993
110533533.052939478593-0.0529394785928807
111536536.043649421136-0.0436494211356145
112537537.068908556308-0.0689085563084191
113524523.1000055233380.89999447666192
114536536.078693550886-0.0786935508859344
115587586.932645660830.0673543391696196
116597595.8915757743131.10842422568697
117581580.878575455510.121424544489613
118564564.941624547536-0.941624547536446
119558556.9559909998161.04400900018415
120575574.9468398171730.0531601828265303
121580580.928756970532-0.92875697053232
122575574.9236240774410.0763759225589417
123563563.939969758322-0.939969758322209
124552550.9569054571811.04309454281935
125537536.9822031749410.0177968250586253
126545544.9831346711080.0168653288915049
127601600.8979185721970.102081427802805
128604604.878416646093-0.878416646093268
129586586.884543887033-0.884543887032844
130564563.9526724875640.047327512436156
131549548.0135337092680.986466290732173
132551551.087816962377-0.0878169623771218
133556556.1385626171-0.138562617099725
134548548.187991941786-0.187991941786115
135540540.216661556385-0.21666155638518
136531531.213418505455-0.213418505454659
137521520.2385873773080.761412622692448
138519518.2321152005660.767884799433978
139572572.094263604771-0.0942636047713698
140581582.060806471483-1.06080647148332
141563563.047971877322-0.0479718773220614
142548548.104419946699-0.104419946698775
143539539.130013111888-0.130013111887587
144541541.160536566793-0.160536566792726
145562561.1584449981360.841555001864079

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 493 & 492.990005766996 & 0.00999423300358567 \tabularnewline
2 & 481 & 481.036576874621 & -0.0365768746211799 \tabularnewline
3 & 462 & 462.088227490751 & -0.088227490751074 \tabularnewline
4 & 457 & 457.109118627479 & -0.109118627479444 \tabularnewline
5 & 442 & 442.167665713687 & -0.167665713686772 \tabularnewline
6 & 439 & 439.177880696016 & -0.177880696016228 \tabularnewline
7 & 488 & 488.080595774469 & -0.080595774469399 \tabularnewline
8 & 521 & 519.995786586162 & 1.00421341383765 \tabularnewline
9 & 501 & 502.003087386111 & -1.00308738611081 \tabularnewline
10 & 485 & 485.029687508788 & -0.0296875087882139 \tabularnewline
11 & 464 & 464.08713995492 & -0.0871399549202874 \tabularnewline
12 & 460 & 460.1257299813 & -0.125729981299612 \tabularnewline
13 & 467 & 467.172772289901 & -0.172772289901108 \tabularnewline
14 & 460 & 460.197864823554 & -0.197864823553632 \tabularnewline
15 & 448 & 448.215510328618 & -0.215510328618235 \tabularnewline
16 & 443 & 443.196126780915 & -0.196126780914748 \tabularnewline
17 & 436 & 436.219368931873 & -0.219368931873335 \tabularnewline
18 & 431 & 431.240847505846 & -0.240847505845768 \tabularnewline
19 & 484 & 484.145247242352 & -0.145247242351496 \tabularnewline
20 & 510 & 509.070053866368 & 0.929946133631572 \tabularnewline
21 & 513 & 513.021963825332 & -0.0219638253323059 \tabularnewline
22 & 503 & 502.978322405529 & 0.0216775944714245 \tabularnewline
23 & 471 & 470.998895017681 & 0.00110498231883641 \tabularnewline
24 & 471 & 471.001082888003 & -0.00108288800324184 \tabularnewline
25 & 476 & 476.00381483989 & -0.00381483989009308 \tabularnewline
26 & 475 & 474.020290243558 & 0.979709756442046 \tabularnewline
27 & 470 & 470.034580357637 & -0.0345803576369908 \tabularnewline
28 & 461 & 461.06202390552 & -0.0620239055197399 \tabularnewline
29 & 455 & 455.067991460163 & -0.0679914601633029 \tabularnewline
30 & 456 & 455.067991460163 & 0.932008539836697 \tabularnewline
31 & 517 & 516.866248662616 & 0.133751337383537 \tabularnewline
32 & 525 & 525.804081578384 & -0.804081578383649 \tabularnewline
33 & 523 & 522.78697270671 & 0.213027293289996 \tabularnewline
34 & 519 & 518.836573794383 & 0.163426205616844 \tabularnewline
35 & 509 & 508.863893657825 & 0.136106342175435 \tabularnewline
36 & 512 & 511.88319039982 & 0.116809600179718 \tabularnewline
37 & 519 & 518.869108927462 & 0.130891072538418 \tabularnewline
38 & 517 & 516.874910757464 & 0.125089242536241 \tabularnewline
39 & 510 & 509.898152908422 & 0.101847091577642 \tabularnewline
40 & 509 & 509.910426915166 & -0.910426915166068 \tabularnewline
41 & 501 & 499.951533680417 & 1.04846631958301 \tabularnewline
42 & 507 & 506.939391977193 & 0.0606080228067766 \tabularnewline
43 & 569 & 569.76552359216 & -0.765523592159977 \tabularnewline
44 & 580 & 579.729291151306 & 0.270708848694189 \tabularnewline
45 & 578 & 577.713906397152 & 0.286093602848484 \tabularnewline
46 & 565 & 565.76686287581 & -0.766862875810238 \tabularnewline
47 & 547 & 547.787451993816 & -0.7874519938156 \tabularnewline
48 & 555 & 554.792369235291 & 0.207630764708712 \tabularnewline
49 & 562 & 561.778287762933 & 0.221712237067418 \tabularnewline
50 & 561 & 560.79775022957 & 0.202249770430309 \tabularnewline
51 & 555 & 555.870514549268 & -0.870514549267814 \tabularnewline
52 & 544 & 543.954336399622 & 0.0456636003781708 \tabularnewline
53 & 537 & 537.000615517431 & -0.000615517430826621 \tabularnewline
54 & 543 & 542.961862347143 & 0.0381376528566306 \tabularnewline
55 & 594 & 593.724592047508 & 0.275407952492049 \tabularnewline
56 & 611 & 610.642417976473 & 0.357582023527074 \tabularnewline
57 & 613 & 612.65292840623 & 0.34707159376953 \tabularnewline
58 & 611 & 610.782150824212 & 0.217849175787636 \tabularnewline
59 & 594 & 593.852388376132 & 0.147611623868174 \tabularnewline
60 & 595 & 595.884085390046 & -0.884085390045837 \tabularnewline
61 & 591 & 590.913799717746 & 0.0862002822540332 \tabularnewline
62 & 589 & 589.917287412252 & -0.91728741225149 \tabularnewline
63 & 584 & 583.926278908598 & 0.0737210914021351 \tabularnewline
64 & 573 & 572.942624589479 & 0.0573754105209769 \tabularnewline
65 & 567 & 566.952879031275 & 0.0471209687247226 \tabularnewline
66 & 569 & 568.965239843727 & 0.0347601562732398 \tabularnewline
67 & 621 & 620.782919176471 & 0.217080823528655 \tabularnewline
68 & 629 & 628.741477504326 & 0.258522495674476 \tabularnewline
69 & 628 & 627.739753386806 & 0.260246613193835 \tabularnewline
70 & 612 & 611.800525222069 & 0.199474777930859 \tabularnewline
71 & 595 & 595.857775064826 & -0.857775064826268 \tabularnewline
72 & 597 & 596.89607310139 & 0.103926898610366 \tabularnewline
73 & 593 & 592.923900167662 & 0.0760998323377368 \tabularnewline
74 & 590 & 589.934115149992 & 0.0658848500082872 \tabularnewline
75 & 580 & 579.960510088561 & 0.0394899114390652 \tabularnewline
76 & 574 & 573.958401137172 & 0.0415988628275222 \tabularnewline
77 & 573 & 572.976102607557 & 0.0238973924433451 \tabularnewline
78 & 573 & 573.002252902551 & -0.00225290255087449 \tabularnewline
79 & 620 & 619.890508684672 & 0.109491315328492 \tabularnewline
80 & 626 & 625.890429765738 & 0.109570234262105 \tabularnewline
81 & 620 & 619.88841020079 & 0.111589799209559 \tabularnewline
82 & 588 & 586.938710188884 & 1.0612898111162 \tabularnewline
83 & 566 & 565.978426866631 & 0.0215731333686393 \tabularnewline
84 & 557 & 558.034330948793 & -1.03433094879252 \tabularnewline
85 & 561 & 561.031943055829 & -0.0319430558287356 \tabularnewline
86 & 549 & 549.054889759359 & -0.0548897593592419 \tabularnewline
87 & 532 & 532.069215875293 & -0.0692158752929444 \tabularnewline
88 & 526 & 526.065345927652 & -0.0653459276515623 \tabularnewline
89 & 511 & 511.104894300025 & -0.104894300024506 \tabularnewline
90 & 499 & 499.176442143635 & -0.176442143634814 \tabularnewline
91 & 555 & 555.086014232699 & -0.0860142326986132 \tabularnewline
92 & 565 & 564.091107666323 & 0.908892333676925 \tabularnewline
93 & 542 & 542.112787966792 & -0.112787966791563 \tabularnewline
94 & 527 & 527.116775415955 & -0.116775415954483 \tabularnewline
95 & 510 & 510.138074340166 & -0.138074340166003 \tabularnewline
96 & 514 & 514.150272008709 & -0.150272008709338 \tabularnewline
97 & 517 & 517.171419133399 & -0.171419133398999 \tabularnewline
98 & 508 & 507.215639226794 & 0.784360773206259 \tabularnewline
99 & 493 & 493.249849521967 & -0.249849521967222 \tabularnewline
100 & 490 & 490.255777617144 & -0.255777617143978 \tabularnewline
101 & 469 & 469.281867956257 & -0.281867956256935 \tabularnewline
102 & 478 & 477.257236594674 & 0.742763405326119 \tabularnewline
103 & 528 & 529.110137514572 & -1.11013751457157 \tabularnewline
104 & 534 & 533.071547488192 & 0.928452511807751 \tabularnewline
105 & 518 & 518.068633305811 & -0.0686333058112465 \tabularnewline
106 & 506 & 506.116129338308 & -0.116129338308392 \tabularnewline
107 & 502 & 502.134955756206 & -0.134955756206471 \tabularnewline
108 & 516 & 516.123444407306 & -0.1234444073057 \tabularnewline
109 & 528 & 528.080057271609 & -0.0800572716088993 \tabularnewline
110 & 533 & 533.052939478593 & -0.0529394785928807 \tabularnewline
111 & 536 & 536.043649421136 & -0.0436494211356145 \tabularnewline
112 & 537 & 537.068908556308 & -0.0689085563084191 \tabularnewline
113 & 524 & 523.100005523338 & 0.89999447666192 \tabularnewline
114 & 536 & 536.078693550886 & -0.0786935508859344 \tabularnewline
115 & 587 & 586.93264566083 & 0.0673543391696196 \tabularnewline
116 & 597 & 595.891575774313 & 1.10842422568697 \tabularnewline
117 & 581 & 580.87857545551 & 0.121424544489613 \tabularnewline
118 & 564 & 564.941624547536 & -0.941624547536446 \tabularnewline
119 & 558 & 556.955990999816 & 1.04400900018415 \tabularnewline
120 & 575 & 574.946839817173 & 0.0531601828265303 \tabularnewline
121 & 580 & 580.928756970532 & -0.92875697053232 \tabularnewline
122 & 575 & 574.923624077441 & 0.0763759225589417 \tabularnewline
123 & 563 & 563.939969758322 & -0.939969758322209 \tabularnewline
124 & 552 & 550.956905457181 & 1.04309454281935 \tabularnewline
125 & 537 & 536.982203174941 & 0.0177968250586253 \tabularnewline
126 & 545 & 544.983134671108 & 0.0168653288915049 \tabularnewline
127 & 601 & 600.897918572197 & 0.102081427802805 \tabularnewline
128 & 604 & 604.878416646093 & -0.878416646093268 \tabularnewline
129 & 586 & 586.884543887033 & -0.884543887032844 \tabularnewline
130 & 564 & 563.952672487564 & 0.047327512436156 \tabularnewline
131 & 549 & 548.013533709268 & 0.986466290732173 \tabularnewline
132 & 551 & 551.087816962377 & -0.0878169623771218 \tabularnewline
133 & 556 & 556.1385626171 & -0.138562617099725 \tabularnewline
134 & 548 & 548.187991941786 & -0.187991941786115 \tabularnewline
135 & 540 & 540.216661556385 & -0.21666155638518 \tabularnewline
136 & 531 & 531.213418505455 & -0.213418505454659 \tabularnewline
137 & 521 & 520.238587377308 & 0.761412622692448 \tabularnewline
138 & 519 & 518.232115200566 & 0.767884799433978 \tabularnewline
139 & 572 & 572.094263604771 & -0.0942636047713698 \tabularnewline
140 & 581 & 582.060806471483 & -1.06080647148332 \tabularnewline
141 & 563 & 563.047971877322 & -0.0479718773220614 \tabularnewline
142 & 548 & 548.104419946699 & -0.104419946698775 \tabularnewline
143 & 539 & 539.130013111888 & -0.130013111887587 \tabularnewline
144 & 541 & 541.160536566793 & -0.160536566792726 \tabularnewline
145 & 562 & 561.158444998136 & 0.841555001864079 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191196&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]493[/C][C]492.990005766996[/C][C]0.00999423300358567[/C][/ROW]
[ROW][C]2[/C][C]481[/C][C]481.036576874621[/C][C]-0.0365768746211799[/C][/ROW]
[ROW][C]3[/C][C]462[/C][C]462.088227490751[/C][C]-0.088227490751074[/C][/ROW]
[ROW][C]4[/C][C]457[/C][C]457.109118627479[/C][C]-0.109118627479444[/C][/ROW]
[ROW][C]5[/C][C]442[/C][C]442.167665713687[/C][C]-0.167665713686772[/C][/ROW]
[ROW][C]6[/C][C]439[/C][C]439.177880696016[/C][C]-0.177880696016228[/C][/ROW]
[ROW][C]7[/C][C]488[/C][C]488.080595774469[/C][C]-0.080595774469399[/C][/ROW]
[ROW][C]8[/C][C]521[/C][C]519.995786586162[/C][C]1.00421341383765[/C][/ROW]
[ROW][C]9[/C][C]501[/C][C]502.003087386111[/C][C]-1.00308738611081[/C][/ROW]
[ROW][C]10[/C][C]485[/C][C]485.029687508788[/C][C]-0.0296875087882139[/C][/ROW]
[ROW][C]11[/C][C]464[/C][C]464.08713995492[/C][C]-0.0871399549202874[/C][/ROW]
[ROW][C]12[/C][C]460[/C][C]460.1257299813[/C][C]-0.125729981299612[/C][/ROW]
[ROW][C]13[/C][C]467[/C][C]467.172772289901[/C][C]-0.172772289901108[/C][/ROW]
[ROW][C]14[/C][C]460[/C][C]460.197864823554[/C][C]-0.197864823553632[/C][/ROW]
[ROW][C]15[/C][C]448[/C][C]448.215510328618[/C][C]-0.215510328618235[/C][/ROW]
[ROW][C]16[/C][C]443[/C][C]443.196126780915[/C][C]-0.196126780914748[/C][/ROW]
[ROW][C]17[/C][C]436[/C][C]436.219368931873[/C][C]-0.219368931873335[/C][/ROW]
[ROW][C]18[/C][C]431[/C][C]431.240847505846[/C][C]-0.240847505845768[/C][/ROW]
[ROW][C]19[/C][C]484[/C][C]484.145247242352[/C][C]-0.145247242351496[/C][/ROW]
[ROW][C]20[/C][C]510[/C][C]509.070053866368[/C][C]0.929946133631572[/C][/ROW]
[ROW][C]21[/C][C]513[/C][C]513.021963825332[/C][C]-0.0219638253323059[/C][/ROW]
[ROW][C]22[/C][C]503[/C][C]502.978322405529[/C][C]0.0216775944714245[/C][/ROW]
[ROW][C]23[/C][C]471[/C][C]470.998895017681[/C][C]0.00110498231883641[/C][/ROW]
[ROW][C]24[/C][C]471[/C][C]471.001082888003[/C][C]-0.00108288800324184[/C][/ROW]
[ROW][C]25[/C][C]476[/C][C]476.00381483989[/C][C]-0.00381483989009308[/C][/ROW]
[ROW][C]26[/C][C]475[/C][C]474.020290243558[/C][C]0.979709756442046[/C][/ROW]
[ROW][C]27[/C][C]470[/C][C]470.034580357637[/C][C]-0.0345803576369908[/C][/ROW]
[ROW][C]28[/C][C]461[/C][C]461.06202390552[/C][C]-0.0620239055197399[/C][/ROW]
[ROW][C]29[/C][C]455[/C][C]455.067991460163[/C][C]-0.0679914601633029[/C][/ROW]
[ROW][C]30[/C][C]456[/C][C]455.067991460163[/C][C]0.932008539836697[/C][/ROW]
[ROW][C]31[/C][C]517[/C][C]516.866248662616[/C][C]0.133751337383537[/C][/ROW]
[ROW][C]32[/C][C]525[/C][C]525.804081578384[/C][C]-0.804081578383649[/C][/ROW]
[ROW][C]33[/C][C]523[/C][C]522.78697270671[/C][C]0.213027293289996[/C][/ROW]
[ROW][C]34[/C][C]519[/C][C]518.836573794383[/C][C]0.163426205616844[/C][/ROW]
[ROW][C]35[/C][C]509[/C][C]508.863893657825[/C][C]0.136106342175435[/C][/ROW]
[ROW][C]36[/C][C]512[/C][C]511.88319039982[/C][C]0.116809600179718[/C][/ROW]
[ROW][C]37[/C][C]519[/C][C]518.869108927462[/C][C]0.130891072538418[/C][/ROW]
[ROW][C]38[/C][C]517[/C][C]516.874910757464[/C][C]0.125089242536241[/C][/ROW]
[ROW][C]39[/C][C]510[/C][C]509.898152908422[/C][C]0.101847091577642[/C][/ROW]
[ROW][C]40[/C][C]509[/C][C]509.910426915166[/C][C]-0.910426915166068[/C][/ROW]
[ROW][C]41[/C][C]501[/C][C]499.951533680417[/C][C]1.04846631958301[/C][/ROW]
[ROW][C]42[/C][C]507[/C][C]506.939391977193[/C][C]0.0606080228067766[/C][/ROW]
[ROW][C]43[/C][C]569[/C][C]569.76552359216[/C][C]-0.765523592159977[/C][/ROW]
[ROW][C]44[/C][C]580[/C][C]579.729291151306[/C][C]0.270708848694189[/C][/ROW]
[ROW][C]45[/C][C]578[/C][C]577.713906397152[/C][C]0.286093602848484[/C][/ROW]
[ROW][C]46[/C][C]565[/C][C]565.76686287581[/C][C]-0.766862875810238[/C][/ROW]
[ROW][C]47[/C][C]547[/C][C]547.787451993816[/C][C]-0.7874519938156[/C][/ROW]
[ROW][C]48[/C][C]555[/C][C]554.792369235291[/C][C]0.207630764708712[/C][/ROW]
[ROW][C]49[/C][C]562[/C][C]561.778287762933[/C][C]0.221712237067418[/C][/ROW]
[ROW][C]50[/C][C]561[/C][C]560.79775022957[/C][C]0.202249770430309[/C][/ROW]
[ROW][C]51[/C][C]555[/C][C]555.870514549268[/C][C]-0.870514549267814[/C][/ROW]
[ROW][C]52[/C][C]544[/C][C]543.954336399622[/C][C]0.0456636003781708[/C][/ROW]
[ROW][C]53[/C][C]537[/C][C]537.000615517431[/C][C]-0.000615517430826621[/C][/ROW]
[ROW][C]54[/C][C]543[/C][C]542.961862347143[/C][C]0.0381376528566306[/C][/ROW]
[ROW][C]55[/C][C]594[/C][C]593.724592047508[/C][C]0.275407952492049[/C][/ROW]
[ROW][C]56[/C][C]611[/C][C]610.642417976473[/C][C]0.357582023527074[/C][/ROW]
[ROW][C]57[/C][C]613[/C][C]612.65292840623[/C][C]0.34707159376953[/C][/ROW]
[ROW][C]58[/C][C]611[/C][C]610.782150824212[/C][C]0.217849175787636[/C][/ROW]
[ROW][C]59[/C][C]594[/C][C]593.852388376132[/C][C]0.147611623868174[/C][/ROW]
[ROW][C]60[/C][C]595[/C][C]595.884085390046[/C][C]-0.884085390045837[/C][/ROW]
[ROW][C]61[/C][C]591[/C][C]590.913799717746[/C][C]0.0862002822540332[/C][/ROW]
[ROW][C]62[/C][C]589[/C][C]589.917287412252[/C][C]-0.91728741225149[/C][/ROW]
[ROW][C]63[/C][C]584[/C][C]583.926278908598[/C][C]0.0737210914021351[/C][/ROW]
[ROW][C]64[/C][C]573[/C][C]572.942624589479[/C][C]0.0573754105209769[/C][/ROW]
[ROW][C]65[/C][C]567[/C][C]566.952879031275[/C][C]0.0471209687247226[/C][/ROW]
[ROW][C]66[/C][C]569[/C][C]568.965239843727[/C][C]0.0347601562732398[/C][/ROW]
[ROW][C]67[/C][C]621[/C][C]620.782919176471[/C][C]0.217080823528655[/C][/ROW]
[ROW][C]68[/C][C]629[/C][C]628.741477504326[/C][C]0.258522495674476[/C][/ROW]
[ROW][C]69[/C][C]628[/C][C]627.739753386806[/C][C]0.260246613193835[/C][/ROW]
[ROW][C]70[/C][C]612[/C][C]611.800525222069[/C][C]0.199474777930859[/C][/ROW]
[ROW][C]71[/C][C]595[/C][C]595.857775064826[/C][C]-0.857775064826268[/C][/ROW]
[ROW][C]72[/C][C]597[/C][C]596.89607310139[/C][C]0.103926898610366[/C][/ROW]
[ROW][C]73[/C][C]593[/C][C]592.923900167662[/C][C]0.0760998323377368[/C][/ROW]
[ROW][C]74[/C][C]590[/C][C]589.934115149992[/C][C]0.0658848500082872[/C][/ROW]
[ROW][C]75[/C][C]580[/C][C]579.960510088561[/C][C]0.0394899114390652[/C][/ROW]
[ROW][C]76[/C][C]574[/C][C]573.958401137172[/C][C]0.0415988628275222[/C][/ROW]
[ROW][C]77[/C][C]573[/C][C]572.976102607557[/C][C]0.0238973924433451[/C][/ROW]
[ROW][C]78[/C][C]573[/C][C]573.002252902551[/C][C]-0.00225290255087449[/C][/ROW]
[ROW][C]79[/C][C]620[/C][C]619.890508684672[/C][C]0.109491315328492[/C][/ROW]
[ROW][C]80[/C][C]626[/C][C]625.890429765738[/C][C]0.109570234262105[/C][/ROW]
[ROW][C]81[/C][C]620[/C][C]619.88841020079[/C][C]0.111589799209559[/C][/ROW]
[ROW][C]82[/C][C]588[/C][C]586.938710188884[/C][C]1.0612898111162[/C][/ROW]
[ROW][C]83[/C][C]566[/C][C]565.978426866631[/C][C]0.0215731333686393[/C][/ROW]
[ROW][C]84[/C][C]557[/C][C]558.034330948793[/C][C]-1.03433094879252[/C][/ROW]
[ROW][C]85[/C][C]561[/C][C]561.031943055829[/C][C]-0.0319430558287356[/C][/ROW]
[ROW][C]86[/C][C]549[/C][C]549.054889759359[/C][C]-0.0548897593592419[/C][/ROW]
[ROW][C]87[/C][C]532[/C][C]532.069215875293[/C][C]-0.0692158752929444[/C][/ROW]
[ROW][C]88[/C][C]526[/C][C]526.065345927652[/C][C]-0.0653459276515623[/C][/ROW]
[ROW][C]89[/C][C]511[/C][C]511.104894300025[/C][C]-0.104894300024506[/C][/ROW]
[ROW][C]90[/C][C]499[/C][C]499.176442143635[/C][C]-0.176442143634814[/C][/ROW]
[ROW][C]91[/C][C]555[/C][C]555.086014232699[/C][C]-0.0860142326986132[/C][/ROW]
[ROW][C]92[/C][C]565[/C][C]564.091107666323[/C][C]0.908892333676925[/C][/ROW]
[ROW][C]93[/C][C]542[/C][C]542.112787966792[/C][C]-0.112787966791563[/C][/ROW]
[ROW][C]94[/C][C]527[/C][C]527.116775415955[/C][C]-0.116775415954483[/C][/ROW]
[ROW][C]95[/C][C]510[/C][C]510.138074340166[/C][C]-0.138074340166003[/C][/ROW]
[ROW][C]96[/C][C]514[/C][C]514.150272008709[/C][C]-0.150272008709338[/C][/ROW]
[ROW][C]97[/C][C]517[/C][C]517.171419133399[/C][C]-0.171419133398999[/C][/ROW]
[ROW][C]98[/C][C]508[/C][C]507.215639226794[/C][C]0.784360773206259[/C][/ROW]
[ROW][C]99[/C][C]493[/C][C]493.249849521967[/C][C]-0.249849521967222[/C][/ROW]
[ROW][C]100[/C][C]490[/C][C]490.255777617144[/C][C]-0.255777617143978[/C][/ROW]
[ROW][C]101[/C][C]469[/C][C]469.281867956257[/C][C]-0.281867956256935[/C][/ROW]
[ROW][C]102[/C][C]478[/C][C]477.257236594674[/C][C]0.742763405326119[/C][/ROW]
[ROW][C]103[/C][C]528[/C][C]529.110137514572[/C][C]-1.11013751457157[/C][/ROW]
[ROW][C]104[/C][C]534[/C][C]533.071547488192[/C][C]0.928452511807751[/C][/ROW]
[ROW][C]105[/C][C]518[/C][C]518.068633305811[/C][C]-0.0686333058112465[/C][/ROW]
[ROW][C]106[/C][C]506[/C][C]506.116129338308[/C][C]-0.116129338308392[/C][/ROW]
[ROW][C]107[/C][C]502[/C][C]502.134955756206[/C][C]-0.134955756206471[/C][/ROW]
[ROW][C]108[/C][C]516[/C][C]516.123444407306[/C][C]-0.1234444073057[/C][/ROW]
[ROW][C]109[/C][C]528[/C][C]528.080057271609[/C][C]-0.0800572716088993[/C][/ROW]
[ROW][C]110[/C][C]533[/C][C]533.052939478593[/C][C]-0.0529394785928807[/C][/ROW]
[ROW][C]111[/C][C]536[/C][C]536.043649421136[/C][C]-0.0436494211356145[/C][/ROW]
[ROW][C]112[/C][C]537[/C][C]537.068908556308[/C][C]-0.0689085563084191[/C][/ROW]
[ROW][C]113[/C][C]524[/C][C]523.100005523338[/C][C]0.89999447666192[/C][/ROW]
[ROW][C]114[/C][C]536[/C][C]536.078693550886[/C][C]-0.0786935508859344[/C][/ROW]
[ROW][C]115[/C][C]587[/C][C]586.93264566083[/C][C]0.0673543391696196[/C][/ROW]
[ROW][C]116[/C][C]597[/C][C]595.891575774313[/C][C]1.10842422568697[/C][/ROW]
[ROW][C]117[/C][C]581[/C][C]580.87857545551[/C][C]0.121424544489613[/C][/ROW]
[ROW][C]118[/C][C]564[/C][C]564.941624547536[/C][C]-0.941624547536446[/C][/ROW]
[ROW][C]119[/C][C]558[/C][C]556.955990999816[/C][C]1.04400900018415[/C][/ROW]
[ROW][C]120[/C][C]575[/C][C]574.946839817173[/C][C]0.0531601828265303[/C][/ROW]
[ROW][C]121[/C][C]580[/C][C]580.928756970532[/C][C]-0.92875697053232[/C][/ROW]
[ROW][C]122[/C][C]575[/C][C]574.923624077441[/C][C]0.0763759225589417[/C][/ROW]
[ROW][C]123[/C][C]563[/C][C]563.939969758322[/C][C]-0.939969758322209[/C][/ROW]
[ROW][C]124[/C][C]552[/C][C]550.956905457181[/C][C]1.04309454281935[/C][/ROW]
[ROW][C]125[/C][C]537[/C][C]536.982203174941[/C][C]0.0177968250586253[/C][/ROW]
[ROW][C]126[/C][C]545[/C][C]544.983134671108[/C][C]0.0168653288915049[/C][/ROW]
[ROW][C]127[/C][C]601[/C][C]600.897918572197[/C][C]0.102081427802805[/C][/ROW]
[ROW][C]128[/C][C]604[/C][C]604.878416646093[/C][C]-0.878416646093268[/C][/ROW]
[ROW][C]129[/C][C]586[/C][C]586.884543887033[/C][C]-0.884543887032844[/C][/ROW]
[ROW][C]130[/C][C]564[/C][C]563.952672487564[/C][C]0.047327512436156[/C][/ROW]
[ROW][C]131[/C][C]549[/C][C]548.013533709268[/C][C]0.986466290732173[/C][/ROW]
[ROW][C]132[/C][C]551[/C][C]551.087816962377[/C][C]-0.0878169623771218[/C][/ROW]
[ROW][C]133[/C][C]556[/C][C]556.1385626171[/C][C]-0.138562617099725[/C][/ROW]
[ROW][C]134[/C][C]548[/C][C]548.187991941786[/C][C]-0.187991941786115[/C][/ROW]
[ROW][C]135[/C][C]540[/C][C]540.216661556385[/C][C]-0.21666155638518[/C][/ROW]
[ROW][C]136[/C][C]531[/C][C]531.213418505455[/C][C]-0.213418505454659[/C][/ROW]
[ROW][C]137[/C][C]521[/C][C]520.238587377308[/C][C]0.761412622692448[/C][/ROW]
[ROW][C]138[/C][C]519[/C][C]518.232115200566[/C][C]0.767884799433978[/C][/ROW]
[ROW][C]139[/C][C]572[/C][C]572.094263604771[/C][C]-0.0942636047713698[/C][/ROW]
[ROW][C]140[/C][C]581[/C][C]582.060806471483[/C][C]-1.06080647148332[/C][/ROW]
[ROW][C]141[/C][C]563[/C][C]563.047971877322[/C][C]-0.0479718773220614[/C][/ROW]
[ROW][C]142[/C][C]548[/C][C]548.104419946699[/C][C]-0.104419946698775[/C][/ROW]
[ROW][C]143[/C][C]539[/C][C]539.130013111888[/C][C]-0.130013111887587[/C][/ROW]
[ROW][C]144[/C][C]541[/C][C]541.160536566793[/C][C]-0.160536566792726[/C][/ROW]
[ROW][C]145[/C][C]562[/C][C]561.158444998136[/C][C]0.841555001864079[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191196&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191196&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
1493492.9900057669960.00999423300358567
2481481.036576874621-0.0365768746211799
3462462.088227490751-0.088227490751074
4457457.109118627479-0.109118627479444
5442442.167665713687-0.167665713686772
6439439.177880696016-0.177880696016228
7488488.080595774469-0.080595774469399
8521519.9957865861621.00421341383765
9501502.003087386111-1.00308738611081
10485485.029687508788-0.0296875087882139
11464464.08713995492-0.0871399549202874
12460460.1257299813-0.125729981299612
13467467.172772289901-0.172772289901108
14460460.197864823554-0.197864823553632
15448448.215510328618-0.215510328618235
16443443.196126780915-0.196126780914748
17436436.219368931873-0.219368931873335
18431431.240847505846-0.240847505845768
19484484.145247242352-0.145247242351496
20510509.0700538663680.929946133631572
21513513.021963825332-0.0219638253323059
22503502.9783224055290.0216775944714245
23471470.9988950176810.00110498231883641
24471471.001082888003-0.00108288800324184
25476476.00381483989-0.00381483989009308
26475474.0202902435580.979709756442046
27470470.034580357637-0.0345803576369908
28461461.06202390552-0.0620239055197399
29455455.067991460163-0.0679914601633029
30456455.0679914601630.932008539836697
31517516.8662486626160.133751337383537
32525525.804081578384-0.804081578383649
33523522.786972706710.213027293289996
34519518.8365737943830.163426205616844
35509508.8638936578250.136106342175435
36512511.883190399820.116809600179718
37519518.8691089274620.130891072538418
38517516.8749107574640.125089242536241
39510509.8981529084220.101847091577642
40509509.910426915166-0.910426915166068
41501499.9515336804171.04846631958301
42507506.9393919771930.0606080228067766
43569569.76552359216-0.765523592159977
44580579.7292911513060.270708848694189
45578577.7139063971520.286093602848484
46565565.76686287581-0.766862875810238
47547547.787451993816-0.7874519938156
48555554.7923692352910.207630764708712
49562561.7782877629330.221712237067418
50561560.797750229570.202249770430309
51555555.870514549268-0.870514549267814
52544543.9543363996220.0456636003781708
53537537.000615517431-0.000615517430826621
54543542.9618623471430.0381376528566306
55594593.7245920475080.275407952492049
56611610.6424179764730.357582023527074
57613612.652928406230.34707159376953
58611610.7821508242120.217849175787636
59594593.8523883761320.147611623868174
60595595.884085390046-0.884085390045837
61591590.9137997177460.0862002822540332
62589589.917287412252-0.91728741225149
63584583.9262789085980.0737210914021351
64573572.9426245894790.0573754105209769
65567566.9528790312750.0471209687247226
66569568.9652398437270.0347601562732398
67621620.7829191764710.217080823528655
68629628.7414775043260.258522495674476
69628627.7397533868060.260246613193835
70612611.8005252220690.199474777930859
71595595.857775064826-0.857775064826268
72597596.896073101390.103926898610366
73593592.9239001676620.0760998323377368
74590589.9341151499920.0658848500082872
75580579.9605100885610.0394899114390652
76574573.9584011371720.0415988628275222
77573572.9761026075570.0238973924433451
78573573.002252902551-0.00225290255087449
79620619.8905086846720.109491315328492
80626625.8904297657380.109570234262105
81620619.888410200790.111589799209559
82588586.9387101888841.0612898111162
83566565.9784268666310.0215731333686393
84557558.034330948793-1.03433094879252
85561561.031943055829-0.0319430558287356
86549549.054889759359-0.0548897593592419
87532532.069215875293-0.0692158752929444
88526526.065345927652-0.0653459276515623
89511511.104894300025-0.104894300024506
90499499.176442143635-0.176442143634814
91555555.086014232699-0.0860142326986132
92565564.0911076663230.908892333676925
93542542.112787966792-0.112787966791563
94527527.116775415955-0.116775415954483
95510510.138074340166-0.138074340166003
96514514.150272008709-0.150272008709338
97517517.171419133399-0.171419133398999
98508507.2156392267940.784360773206259
99493493.249849521967-0.249849521967222
100490490.255777617144-0.255777617143978
101469469.281867956257-0.281867956256935
102478477.2572365946740.742763405326119
103528529.110137514572-1.11013751457157
104534533.0715474881920.928452511807751
105518518.068633305811-0.0686333058112465
106506506.116129338308-0.116129338308392
107502502.134955756206-0.134955756206471
108516516.123444407306-0.1234444073057
109528528.080057271609-0.0800572716088993
110533533.052939478593-0.0529394785928807
111536536.043649421136-0.0436494211356145
112537537.068908556308-0.0689085563084191
113524523.1000055233380.89999447666192
114536536.078693550886-0.0786935508859344
115587586.932645660830.0673543391696196
116597595.8915757743131.10842422568697
117581580.878575455510.121424544489613
118564564.941624547536-0.941624547536446
119558556.9559909998161.04400900018415
120575574.9468398171730.0531601828265303
121580580.928756970532-0.92875697053232
122575574.9236240774410.0763759225589417
123563563.939969758322-0.939969758322209
124552550.9569054571811.04309454281935
125537536.9822031749410.0177968250586253
126545544.9831346711080.0168653288915049
127601600.8979185721970.102081427802805
128604604.878416646093-0.878416646093268
129586586.884543887033-0.884543887032844
130564563.9526724875640.047327512436156
131549548.0135337092680.986466290732173
132551551.087816962377-0.0878169623771218
133556556.1385626171-0.138562617099725
134548548.187991941786-0.187991941786115
135540540.216661556385-0.21666155638518
136531531.213418505455-0.213418505454659
137521520.2385873773080.761412622692448
138519518.2321152005660.767884799433978
139572572.094263604771-0.0942636047713698
140581582.060806471483-1.06080647148332
141563563.047971877322-0.0479718773220614
142548548.104419946699-0.104419946698775
143539539.130013111888-0.130013111887587
144541541.160536566793-0.160536566792726
145562561.1584449981360.841555001864079







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.5723750939202520.8552498121594960.427624906079748
100.5454046174690170.9091907650619670.454595382530983
110.398605782715420.797211565430840.60139421728458
120.3989413280975470.7978826561950940.601058671902453
130.2960120222754310.5920240445508610.703987977724569
140.2154441123266930.4308882246533850.784555887673307
150.204924272873950.40984854574790.79507572712605
160.1771581936290580.3543163872581160.822841806370942
170.1261946523075990.2523893046151970.873805347692401
180.08617003951132330.1723400790226470.913829960488677
190.05531009229457280.1106201845891460.944689907705427
200.1761927187856580.3523854375713160.823807281214342
210.1479343168745610.2958686337491220.852065683125439
220.1059317347392720.2118634694785450.894068265260728
230.0837044971194530.1674089942389060.916295502880547
240.05907881781356610.1181576356271320.940921182186434
250.03991559090364530.07983118180729050.960084409096355
260.07386230707790920.1477246141558180.926137692922091
270.06837670113235430.1367534022647090.931623298867646
280.05458203637319430.1091640727463890.945417963626806
290.0393906705321080.0787813410642160.960609329467892
300.08717782966778450.1743556593355690.912822170332215
310.07174645075410660.1434929015082130.928253549245893
320.1204366575742620.2408733151485240.879563342425738
330.1211950396454030.2423900792908070.878804960354597
340.09265036004999210.1853007200999840.907349639950008
350.06955967099950650.1391193419990130.930440329000494
360.05596673827372670.1119334765474530.944033261726273
370.05042676513773330.1008535302754670.949573234862267
380.04119754214069180.08239508428138350.958802457859308
390.03208640310882880.06417280621765750.967913596891171
400.1138074679416520.2276149358833050.886192532058348
410.2178055504261070.4356111008522140.782194449573893
420.1815717239487240.3631434478974480.818428276051276
430.2674418402926730.5348836805853460.732558159707327
440.2336138360032230.4672276720064460.766386163996777
450.2089644649258770.4179289298517540.791035535074123
460.2865172779409990.5730345558819990.713482722059001
470.3380927158239120.6761854316478250.661907284176088
480.2948490218475510.5896980436951020.705150978152449
490.2525554618136210.5051109236272430.747444538186379
500.2138899033895160.4277798067790320.786110096610484
510.3483085789354850.696617157870970.651691421064515
520.3032111703913650.606422340782730.696788829608635
530.2601687607188120.5203375214376240.739831239281188
540.2227946497025480.4455892994050960.777205350297452
550.2063082524541690.4126165049083370.793691747545831
560.1956608941996930.3913217883993860.804339105800307
570.1792977717194960.3585955434389930.820702228280504
580.152682414358890.305364828717780.84731758564111
590.1269669793580920.2539339587161850.873033020641908
600.189805640850960.379611281701920.81019435914904
610.159454292612390.3189085852247790.84054570738761
620.227578711155360.4551574223107190.77242128884464
630.1972228076833510.3944456153667010.802777192316649
640.1676157544962740.3352315089925470.832384245503726
650.140637992796220.2812759855924410.85936200720378
660.1159964369306660.2319928738613330.884003563069334
670.09995274610709170.1999054922141830.900047253892908
680.08553513401487480.171070268029750.914464865985125
690.0725042740542230.1450085481084460.927495725945777
700.05986474225686530.1197294845137310.940135257743135
710.09205322193850880.1841064438770180.907946778061491
720.07474471969634530.1494894393926910.925255280303655
730.05930730800816360.1186146160163270.940692691991836
740.04627596912963010.09255193825926020.95372403087037
750.03551868880901010.07103737761802020.96448131119099
760.02690351703527770.05380703407055540.973096482964722
770.02010568856815980.04021137713631960.97989431143184
780.01482160401598360.02964320803196730.985178395984016
790.01100042046125750.0220008409225150.988999579538743
800.008142525931532850.01628505186306570.991857474068467
810.006035095923968290.01207019184793660.993964904076032
820.02068874777791630.04137749555583260.979311252222084
830.01630297810485830.03260595620971650.983697021895142
840.0424110295413420.0848220590826840.957588970458658
850.03252800903533380.06505601807066770.967471990964666
860.02452405765638910.04904811531277820.975475942343611
870.01821164487914080.03642328975828150.981788355120859
880.0133380321162280.02667606423245610.986661967883772
890.009770970057637410.01954194011527480.990229029942363
900.007365449388240540.01473089877648110.992634550611759
910.005171575976681990.0103431519533640.994828424023318
920.01808138177742950.03616276355485890.981918618222571
930.01391503204434710.02783006408869430.986084967955653
940.01010971859072440.02021943718144870.989890281409276
950.007211533288264850.01442306657652970.992788466711735
960.005058069340785630.01011613868157130.994941930659214
970.003494529043661280.006989058087322560.996505470956339
980.007929982627661240.01585996525532250.992070017372339
990.00573199271118650.0114639854223730.994268007288813
1000.004196581403826870.008393162807653730.995803418596173
1010.003808881860533210.007617763721066430.996191118139467
1020.005165639036975220.01033127807395040.994834360963025
1030.01521964552528160.03043929105056330.984780354474718
1040.04149312691788010.08298625383576020.95850687308212
1050.03160119106009980.06320238212019970.9683988089399
1060.02308332248245630.04616664496491270.976916677517544
1070.01702621422434010.03405242844868020.98297378577566
1080.01268637227612560.02537274455225120.987313627723874
1090.009248331635189630.01849666327037930.99075166836481
1100.006960572096194110.01392114419238820.993039427903806
1110.00621697493508740.01243394987017480.993783025064913
1120.006184193245494610.01236838649098920.993815806754505
1130.007094436540357620.01418887308071520.992905563459642
1140.006420600624690040.01284120124938010.99357939937531
1150.004210677601151310.008421355202302620.995789322398849
1160.03986927157360050.07973854314720110.960130728426399
1170.03843506007495250.0768701201499050.961564939925047
1180.09766539902993420.1953307980598680.902334600970066
1190.2023504668368460.4047009336736910.797649533163154
1200.1580855304407850.316171060881570.841914469559215
1210.1851534988432120.3703069976864250.814846501156788
1220.1550528873601640.3101057747203270.844947112639836
1230.3266343840414320.6532687680828640.673365615958568
1240.417835146390660.835670292781320.58216485360934
1250.4401532657489430.8803065314978860.559846734251057
1260.6463356381530720.7073287236938560.353664361846928
1270.5835877068316670.8328245863366660.416412293168333
1280.5591205194139150.8817589611721690.440879480586085
1290.5860211091022930.8279577817954140.413978890897707
1300.5371595272307810.9256809455384390.462840472769219
1310.6421218671517590.7157562656964820.357878132848241
1320.5654033393278010.8691933213443980.434596660672199
1330.4553488376233830.9106976752467650.544651162376617
1340.3865839972908740.7731679945817490.613416002709126
1350.2620919171947550.524183834389510.737908082805245
1360.1933390292037480.3866780584074960.806660970796252

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.572375093920252 & 0.855249812159496 & 0.427624906079748 \tabularnewline
10 & 0.545404617469017 & 0.909190765061967 & 0.454595382530983 \tabularnewline
11 & 0.39860578271542 & 0.79721156543084 & 0.60139421728458 \tabularnewline
12 & 0.398941328097547 & 0.797882656195094 & 0.601058671902453 \tabularnewline
13 & 0.296012022275431 & 0.592024044550861 & 0.703987977724569 \tabularnewline
14 & 0.215444112326693 & 0.430888224653385 & 0.784555887673307 \tabularnewline
15 & 0.20492427287395 & 0.4098485457479 & 0.79507572712605 \tabularnewline
16 & 0.177158193629058 & 0.354316387258116 & 0.822841806370942 \tabularnewline
17 & 0.126194652307599 & 0.252389304615197 & 0.873805347692401 \tabularnewline
18 & 0.0861700395113233 & 0.172340079022647 & 0.913829960488677 \tabularnewline
19 & 0.0553100922945728 & 0.110620184589146 & 0.944689907705427 \tabularnewline
20 & 0.176192718785658 & 0.352385437571316 & 0.823807281214342 \tabularnewline
21 & 0.147934316874561 & 0.295868633749122 & 0.852065683125439 \tabularnewline
22 & 0.105931734739272 & 0.211863469478545 & 0.894068265260728 \tabularnewline
23 & 0.083704497119453 & 0.167408994238906 & 0.916295502880547 \tabularnewline
24 & 0.0590788178135661 & 0.118157635627132 & 0.940921182186434 \tabularnewline
25 & 0.0399155909036453 & 0.0798311818072905 & 0.960084409096355 \tabularnewline
26 & 0.0738623070779092 & 0.147724614155818 & 0.926137692922091 \tabularnewline
27 & 0.0683767011323543 & 0.136753402264709 & 0.931623298867646 \tabularnewline
28 & 0.0545820363731943 & 0.109164072746389 & 0.945417963626806 \tabularnewline
29 & 0.039390670532108 & 0.078781341064216 & 0.960609329467892 \tabularnewline
30 & 0.0871778296677845 & 0.174355659335569 & 0.912822170332215 \tabularnewline
31 & 0.0717464507541066 & 0.143492901508213 & 0.928253549245893 \tabularnewline
32 & 0.120436657574262 & 0.240873315148524 & 0.879563342425738 \tabularnewline
33 & 0.121195039645403 & 0.242390079290807 & 0.878804960354597 \tabularnewline
34 & 0.0926503600499921 & 0.185300720099984 & 0.907349639950008 \tabularnewline
35 & 0.0695596709995065 & 0.139119341999013 & 0.930440329000494 \tabularnewline
36 & 0.0559667382737267 & 0.111933476547453 & 0.944033261726273 \tabularnewline
37 & 0.0504267651377333 & 0.100853530275467 & 0.949573234862267 \tabularnewline
38 & 0.0411975421406918 & 0.0823950842813835 & 0.958802457859308 \tabularnewline
39 & 0.0320864031088288 & 0.0641728062176575 & 0.967913596891171 \tabularnewline
40 & 0.113807467941652 & 0.227614935883305 & 0.886192532058348 \tabularnewline
41 & 0.217805550426107 & 0.435611100852214 & 0.782194449573893 \tabularnewline
42 & 0.181571723948724 & 0.363143447897448 & 0.818428276051276 \tabularnewline
43 & 0.267441840292673 & 0.534883680585346 & 0.732558159707327 \tabularnewline
44 & 0.233613836003223 & 0.467227672006446 & 0.766386163996777 \tabularnewline
45 & 0.208964464925877 & 0.417928929851754 & 0.791035535074123 \tabularnewline
46 & 0.286517277940999 & 0.573034555881999 & 0.713482722059001 \tabularnewline
47 & 0.338092715823912 & 0.676185431647825 & 0.661907284176088 \tabularnewline
48 & 0.294849021847551 & 0.589698043695102 & 0.705150978152449 \tabularnewline
49 & 0.252555461813621 & 0.505110923627243 & 0.747444538186379 \tabularnewline
50 & 0.213889903389516 & 0.427779806779032 & 0.786110096610484 \tabularnewline
51 & 0.348308578935485 & 0.69661715787097 & 0.651691421064515 \tabularnewline
52 & 0.303211170391365 & 0.60642234078273 & 0.696788829608635 \tabularnewline
53 & 0.260168760718812 & 0.520337521437624 & 0.739831239281188 \tabularnewline
54 & 0.222794649702548 & 0.445589299405096 & 0.777205350297452 \tabularnewline
55 & 0.206308252454169 & 0.412616504908337 & 0.793691747545831 \tabularnewline
56 & 0.195660894199693 & 0.391321788399386 & 0.804339105800307 \tabularnewline
57 & 0.179297771719496 & 0.358595543438993 & 0.820702228280504 \tabularnewline
58 & 0.15268241435889 & 0.30536482871778 & 0.84731758564111 \tabularnewline
59 & 0.126966979358092 & 0.253933958716185 & 0.873033020641908 \tabularnewline
60 & 0.18980564085096 & 0.37961128170192 & 0.81019435914904 \tabularnewline
61 & 0.15945429261239 & 0.318908585224779 & 0.84054570738761 \tabularnewline
62 & 0.22757871115536 & 0.455157422310719 & 0.77242128884464 \tabularnewline
63 & 0.197222807683351 & 0.394445615366701 & 0.802777192316649 \tabularnewline
64 & 0.167615754496274 & 0.335231508992547 & 0.832384245503726 \tabularnewline
65 & 0.14063799279622 & 0.281275985592441 & 0.85936200720378 \tabularnewline
66 & 0.115996436930666 & 0.231992873861333 & 0.884003563069334 \tabularnewline
67 & 0.0999527461070917 & 0.199905492214183 & 0.900047253892908 \tabularnewline
68 & 0.0855351340148748 & 0.17107026802975 & 0.914464865985125 \tabularnewline
69 & 0.072504274054223 & 0.145008548108446 & 0.927495725945777 \tabularnewline
70 & 0.0598647422568653 & 0.119729484513731 & 0.940135257743135 \tabularnewline
71 & 0.0920532219385088 & 0.184106443877018 & 0.907946778061491 \tabularnewline
72 & 0.0747447196963453 & 0.149489439392691 & 0.925255280303655 \tabularnewline
73 & 0.0593073080081636 & 0.118614616016327 & 0.940692691991836 \tabularnewline
74 & 0.0462759691296301 & 0.0925519382592602 & 0.95372403087037 \tabularnewline
75 & 0.0355186888090101 & 0.0710373776180202 & 0.96448131119099 \tabularnewline
76 & 0.0269035170352777 & 0.0538070340705554 & 0.973096482964722 \tabularnewline
77 & 0.0201056885681598 & 0.0402113771363196 & 0.97989431143184 \tabularnewline
78 & 0.0148216040159836 & 0.0296432080319673 & 0.985178395984016 \tabularnewline
79 & 0.0110004204612575 & 0.022000840922515 & 0.988999579538743 \tabularnewline
80 & 0.00814252593153285 & 0.0162850518630657 & 0.991857474068467 \tabularnewline
81 & 0.00603509592396829 & 0.0120701918479366 & 0.993964904076032 \tabularnewline
82 & 0.0206887477779163 & 0.0413774955558326 & 0.979311252222084 \tabularnewline
83 & 0.0163029781048583 & 0.0326059562097165 & 0.983697021895142 \tabularnewline
84 & 0.042411029541342 & 0.084822059082684 & 0.957588970458658 \tabularnewline
85 & 0.0325280090353338 & 0.0650560180706677 & 0.967471990964666 \tabularnewline
86 & 0.0245240576563891 & 0.0490481153127782 & 0.975475942343611 \tabularnewline
87 & 0.0182116448791408 & 0.0364232897582815 & 0.981788355120859 \tabularnewline
88 & 0.013338032116228 & 0.0266760642324561 & 0.986661967883772 \tabularnewline
89 & 0.00977097005763741 & 0.0195419401152748 & 0.990229029942363 \tabularnewline
90 & 0.00736544938824054 & 0.0147308987764811 & 0.992634550611759 \tabularnewline
91 & 0.00517157597668199 & 0.010343151953364 & 0.994828424023318 \tabularnewline
92 & 0.0180813817774295 & 0.0361627635548589 & 0.981918618222571 \tabularnewline
93 & 0.0139150320443471 & 0.0278300640886943 & 0.986084967955653 \tabularnewline
94 & 0.0101097185907244 & 0.0202194371814487 & 0.989890281409276 \tabularnewline
95 & 0.00721153328826485 & 0.0144230665765297 & 0.992788466711735 \tabularnewline
96 & 0.00505806934078563 & 0.0101161386815713 & 0.994941930659214 \tabularnewline
97 & 0.00349452904366128 & 0.00698905808732256 & 0.996505470956339 \tabularnewline
98 & 0.00792998262766124 & 0.0158599652553225 & 0.992070017372339 \tabularnewline
99 & 0.0057319927111865 & 0.011463985422373 & 0.994268007288813 \tabularnewline
100 & 0.00419658140382687 & 0.00839316280765373 & 0.995803418596173 \tabularnewline
101 & 0.00380888186053321 & 0.00761776372106643 & 0.996191118139467 \tabularnewline
102 & 0.00516563903697522 & 0.0103312780739504 & 0.994834360963025 \tabularnewline
103 & 0.0152196455252816 & 0.0304392910505633 & 0.984780354474718 \tabularnewline
104 & 0.0414931269178801 & 0.0829862538357602 & 0.95850687308212 \tabularnewline
105 & 0.0316011910600998 & 0.0632023821201997 & 0.9683988089399 \tabularnewline
106 & 0.0230833224824563 & 0.0461666449649127 & 0.976916677517544 \tabularnewline
107 & 0.0170262142243401 & 0.0340524284486802 & 0.98297378577566 \tabularnewline
108 & 0.0126863722761256 & 0.0253727445522512 & 0.987313627723874 \tabularnewline
109 & 0.00924833163518963 & 0.0184966632703793 & 0.99075166836481 \tabularnewline
110 & 0.00696057209619411 & 0.0139211441923882 & 0.993039427903806 \tabularnewline
111 & 0.0062169749350874 & 0.0124339498701748 & 0.993783025064913 \tabularnewline
112 & 0.00618419324549461 & 0.0123683864909892 & 0.993815806754505 \tabularnewline
113 & 0.00709443654035762 & 0.0141888730807152 & 0.992905563459642 \tabularnewline
114 & 0.00642060062469004 & 0.0128412012493801 & 0.99357939937531 \tabularnewline
115 & 0.00421067760115131 & 0.00842135520230262 & 0.995789322398849 \tabularnewline
116 & 0.0398692715736005 & 0.0797385431472011 & 0.960130728426399 \tabularnewline
117 & 0.0384350600749525 & 0.076870120149905 & 0.961564939925047 \tabularnewline
118 & 0.0976653990299342 & 0.195330798059868 & 0.902334600970066 \tabularnewline
119 & 0.202350466836846 & 0.404700933673691 & 0.797649533163154 \tabularnewline
120 & 0.158085530440785 & 0.31617106088157 & 0.841914469559215 \tabularnewline
121 & 0.185153498843212 & 0.370306997686425 & 0.814846501156788 \tabularnewline
122 & 0.155052887360164 & 0.310105774720327 & 0.844947112639836 \tabularnewline
123 & 0.326634384041432 & 0.653268768082864 & 0.673365615958568 \tabularnewline
124 & 0.41783514639066 & 0.83567029278132 & 0.58216485360934 \tabularnewline
125 & 0.440153265748943 & 0.880306531497886 & 0.559846734251057 \tabularnewline
126 & 0.646335638153072 & 0.707328723693856 & 0.353664361846928 \tabularnewline
127 & 0.583587706831667 & 0.832824586336666 & 0.416412293168333 \tabularnewline
128 & 0.559120519413915 & 0.881758961172169 & 0.440879480586085 \tabularnewline
129 & 0.586021109102293 & 0.827957781795414 & 0.413978890897707 \tabularnewline
130 & 0.537159527230781 & 0.925680945538439 & 0.462840472769219 \tabularnewline
131 & 0.642121867151759 & 0.715756265696482 & 0.357878132848241 \tabularnewline
132 & 0.565403339327801 & 0.869193321344398 & 0.434596660672199 \tabularnewline
133 & 0.455348837623383 & 0.910697675246765 & 0.544651162376617 \tabularnewline
134 & 0.386583997290874 & 0.773167994581749 & 0.613416002709126 \tabularnewline
135 & 0.262091917194755 & 0.52418383438951 & 0.737908082805245 \tabularnewline
136 & 0.193339029203748 & 0.386678058407496 & 0.806660970796252 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191196&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]9[/C][C]0.572375093920252[/C][C]0.855249812159496[/C][C]0.427624906079748[/C][/ROW]
[ROW][C]10[/C][C]0.545404617469017[/C][C]0.909190765061967[/C][C]0.454595382530983[/C][/ROW]
[ROW][C]11[/C][C]0.39860578271542[/C][C]0.79721156543084[/C][C]0.60139421728458[/C][/ROW]
[ROW][C]12[/C][C]0.398941328097547[/C][C]0.797882656195094[/C][C]0.601058671902453[/C][/ROW]
[ROW][C]13[/C][C]0.296012022275431[/C][C]0.592024044550861[/C][C]0.703987977724569[/C][/ROW]
[ROW][C]14[/C][C]0.215444112326693[/C][C]0.430888224653385[/C][C]0.784555887673307[/C][/ROW]
[ROW][C]15[/C][C]0.20492427287395[/C][C]0.4098485457479[/C][C]0.79507572712605[/C][/ROW]
[ROW][C]16[/C][C]0.177158193629058[/C][C]0.354316387258116[/C][C]0.822841806370942[/C][/ROW]
[ROW][C]17[/C][C]0.126194652307599[/C][C]0.252389304615197[/C][C]0.873805347692401[/C][/ROW]
[ROW][C]18[/C][C]0.0861700395113233[/C][C]0.172340079022647[/C][C]0.913829960488677[/C][/ROW]
[ROW][C]19[/C][C]0.0553100922945728[/C][C]0.110620184589146[/C][C]0.944689907705427[/C][/ROW]
[ROW][C]20[/C][C]0.176192718785658[/C][C]0.352385437571316[/C][C]0.823807281214342[/C][/ROW]
[ROW][C]21[/C][C]0.147934316874561[/C][C]0.295868633749122[/C][C]0.852065683125439[/C][/ROW]
[ROW][C]22[/C][C]0.105931734739272[/C][C]0.211863469478545[/C][C]0.894068265260728[/C][/ROW]
[ROW][C]23[/C][C]0.083704497119453[/C][C]0.167408994238906[/C][C]0.916295502880547[/C][/ROW]
[ROW][C]24[/C][C]0.0590788178135661[/C][C]0.118157635627132[/C][C]0.940921182186434[/C][/ROW]
[ROW][C]25[/C][C]0.0399155909036453[/C][C]0.0798311818072905[/C][C]0.960084409096355[/C][/ROW]
[ROW][C]26[/C][C]0.0738623070779092[/C][C]0.147724614155818[/C][C]0.926137692922091[/C][/ROW]
[ROW][C]27[/C][C]0.0683767011323543[/C][C]0.136753402264709[/C][C]0.931623298867646[/C][/ROW]
[ROW][C]28[/C][C]0.0545820363731943[/C][C]0.109164072746389[/C][C]0.945417963626806[/C][/ROW]
[ROW][C]29[/C][C]0.039390670532108[/C][C]0.078781341064216[/C][C]0.960609329467892[/C][/ROW]
[ROW][C]30[/C][C]0.0871778296677845[/C][C]0.174355659335569[/C][C]0.912822170332215[/C][/ROW]
[ROW][C]31[/C][C]0.0717464507541066[/C][C]0.143492901508213[/C][C]0.928253549245893[/C][/ROW]
[ROW][C]32[/C][C]0.120436657574262[/C][C]0.240873315148524[/C][C]0.879563342425738[/C][/ROW]
[ROW][C]33[/C][C]0.121195039645403[/C][C]0.242390079290807[/C][C]0.878804960354597[/C][/ROW]
[ROW][C]34[/C][C]0.0926503600499921[/C][C]0.185300720099984[/C][C]0.907349639950008[/C][/ROW]
[ROW][C]35[/C][C]0.0695596709995065[/C][C]0.139119341999013[/C][C]0.930440329000494[/C][/ROW]
[ROW][C]36[/C][C]0.0559667382737267[/C][C]0.111933476547453[/C][C]0.944033261726273[/C][/ROW]
[ROW][C]37[/C][C]0.0504267651377333[/C][C]0.100853530275467[/C][C]0.949573234862267[/C][/ROW]
[ROW][C]38[/C][C]0.0411975421406918[/C][C]0.0823950842813835[/C][C]0.958802457859308[/C][/ROW]
[ROW][C]39[/C][C]0.0320864031088288[/C][C]0.0641728062176575[/C][C]0.967913596891171[/C][/ROW]
[ROW][C]40[/C][C]0.113807467941652[/C][C]0.227614935883305[/C][C]0.886192532058348[/C][/ROW]
[ROW][C]41[/C][C]0.217805550426107[/C][C]0.435611100852214[/C][C]0.782194449573893[/C][/ROW]
[ROW][C]42[/C][C]0.181571723948724[/C][C]0.363143447897448[/C][C]0.818428276051276[/C][/ROW]
[ROW][C]43[/C][C]0.267441840292673[/C][C]0.534883680585346[/C][C]0.732558159707327[/C][/ROW]
[ROW][C]44[/C][C]0.233613836003223[/C][C]0.467227672006446[/C][C]0.766386163996777[/C][/ROW]
[ROW][C]45[/C][C]0.208964464925877[/C][C]0.417928929851754[/C][C]0.791035535074123[/C][/ROW]
[ROW][C]46[/C][C]0.286517277940999[/C][C]0.573034555881999[/C][C]0.713482722059001[/C][/ROW]
[ROW][C]47[/C][C]0.338092715823912[/C][C]0.676185431647825[/C][C]0.661907284176088[/C][/ROW]
[ROW][C]48[/C][C]0.294849021847551[/C][C]0.589698043695102[/C][C]0.705150978152449[/C][/ROW]
[ROW][C]49[/C][C]0.252555461813621[/C][C]0.505110923627243[/C][C]0.747444538186379[/C][/ROW]
[ROW][C]50[/C][C]0.213889903389516[/C][C]0.427779806779032[/C][C]0.786110096610484[/C][/ROW]
[ROW][C]51[/C][C]0.348308578935485[/C][C]0.69661715787097[/C][C]0.651691421064515[/C][/ROW]
[ROW][C]52[/C][C]0.303211170391365[/C][C]0.60642234078273[/C][C]0.696788829608635[/C][/ROW]
[ROW][C]53[/C][C]0.260168760718812[/C][C]0.520337521437624[/C][C]0.739831239281188[/C][/ROW]
[ROW][C]54[/C][C]0.222794649702548[/C][C]0.445589299405096[/C][C]0.777205350297452[/C][/ROW]
[ROW][C]55[/C][C]0.206308252454169[/C][C]0.412616504908337[/C][C]0.793691747545831[/C][/ROW]
[ROW][C]56[/C][C]0.195660894199693[/C][C]0.391321788399386[/C][C]0.804339105800307[/C][/ROW]
[ROW][C]57[/C][C]0.179297771719496[/C][C]0.358595543438993[/C][C]0.820702228280504[/C][/ROW]
[ROW][C]58[/C][C]0.15268241435889[/C][C]0.30536482871778[/C][C]0.84731758564111[/C][/ROW]
[ROW][C]59[/C][C]0.126966979358092[/C][C]0.253933958716185[/C][C]0.873033020641908[/C][/ROW]
[ROW][C]60[/C][C]0.18980564085096[/C][C]0.37961128170192[/C][C]0.81019435914904[/C][/ROW]
[ROW][C]61[/C][C]0.15945429261239[/C][C]0.318908585224779[/C][C]0.84054570738761[/C][/ROW]
[ROW][C]62[/C][C]0.22757871115536[/C][C]0.455157422310719[/C][C]0.77242128884464[/C][/ROW]
[ROW][C]63[/C][C]0.197222807683351[/C][C]0.394445615366701[/C][C]0.802777192316649[/C][/ROW]
[ROW][C]64[/C][C]0.167615754496274[/C][C]0.335231508992547[/C][C]0.832384245503726[/C][/ROW]
[ROW][C]65[/C][C]0.14063799279622[/C][C]0.281275985592441[/C][C]0.85936200720378[/C][/ROW]
[ROW][C]66[/C][C]0.115996436930666[/C][C]0.231992873861333[/C][C]0.884003563069334[/C][/ROW]
[ROW][C]67[/C][C]0.0999527461070917[/C][C]0.199905492214183[/C][C]0.900047253892908[/C][/ROW]
[ROW][C]68[/C][C]0.0855351340148748[/C][C]0.17107026802975[/C][C]0.914464865985125[/C][/ROW]
[ROW][C]69[/C][C]0.072504274054223[/C][C]0.145008548108446[/C][C]0.927495725945777[/C][/ROW]
[ROW][C]70[/C][C]0.0598647422568653[/C][C]0.119729484513731[/C][C]0.940135257743135[/C][/ROW]
[ROW][C]71[/C][C]0.0920532219385088[/C][C]0.184106443877018[/C][C]0.907946778061491[/C][/ROW]
[ROW][C]72[/C][C]0.0747447196963453[/C][C]0.149489439392691[/C][C]0.925255280303655[/C][/ROW]
[ROW][C]73[/C][C]0.0593073080081636[/C][C]0.118614616016327[/C][C]0.940692691991836[/C][/ROW]
[ROW][C]74[/C][C]0.0462759691296301[/C][C]0.0925519382592602[/C][C]0.95372403087037[/C][/ROW]
[ROW][C]75[/C][C]0.0355186888090101[/C][C]0.0710373776180202[/C][C]0.96448131119099[/C][/ROW]
[ROW][C]76[/C][C]0.0269035170352777[/C][C]0.0538070340705554[/C][C]0.973096482964722[/C][/ROW]
[ROW][C]77[/C][C]0.0201056885681598[/C][C]0.0402113771363196[/C][C]0.97989431143184[/C][/ROW]
[ROW][C]78[/C][C]0.0148216040159836[/C][C]0.0296432080319673[/C][C]0.985178395984016[/C][/ROW]
[ROW][C]79[/C][C]0.0110004204612575[/C][C]0.022000840922515[/C][C]0.988999579538743[/C][/ROW]
[ROW][C]80[/C][C]0.00814252593153285[/C][C]0.0162850518630657[/C][C]0.991857474068467[/C][/ROW]
[ROW][C]81[/C][C]0.00603509592396829[/C][C]0.0120701918479366[/C][C]0.993964904076032[/C][/ROW]
[ROW][C]82[/C][C]0.0206887477779163[/C][C]0.0413774955558326[/C][C]0.979311252222084[/C][/ROW]
[ROW][C]83[/C][C]0.0163029781048583[/C][C]0.0326059562097165[/C][C]0.983697021895142[/C][/ROW]
[ROW][C]84[/C][C]0.042411029541342[/C][C]0.084822059082684[/C][C]0.957588970458658[/C][/ROW]
[ROW][C]85[/C][C]0.0325280090353338[/C][C]0.0650560180706677[/C][C]0.967471990964666[/C][/ROW]
[ROW][C]86[/C][C]0.0245240576563891[/C][C]0.0490481153127782[/C][C]0.975475942343611[/C][/ROW]
[ROW][C]87[/C][C]0.0182116448791408[/C][C]0.0364232897582815[/C][C]0.981788355120859[/C][/ROW]
[ROW][C]88[/C][C]0.013338032116228[/C][C]0.0266760642324561[/C][C]0.986661967883772[/C][/ROW]
[ROW][C]89[/C][C]0.00977097005763741[/C][C]0.0195419401152748[/C][C]0.990229029942363[/C][/ROW]
[ROW][C]90[/C][C]0.00736544938824054[/C][C]0.0147308987764811[/C][C]0.992634550611759[/C][/ROW]
[ROW][C]91[/C][C]0.00517157597668199[/C][C]0.010343151953364[/C][C]0.994828424023318[/C][/ROW]
[ROW][C]92[/C][C]0.0180813817774295[/C][C]0.0361627635548589[/C][C]0.981918618222571[/C][/ROW]
[ROW][C]93[/C][C]0.0139150320443471[/C][C]0.0278300640886943[/C][C]0.986084967955653[/C][/ROW]
[ROW][C]94[/C][C]0.0101097185907244[/C][C]0.0202194371814487[/C][C]0.989890281409276[/C][/ROW]
[ROW][C]95[/C][C]0.00721153328826485[/C][C]0.0144230665765297[/C][C]0.992788466711735[/C][/ROW]
[ROW][C]96[/C][C]0.00505806934078563[/C][C]0.0101161386815713[/C][C]0.994941930659214[/C][/ROW]
[ROW][C]97[/C][C]0.00349452904366128[/C][C]0.00698905808732256[/C][C]0.996505470956339[/C][/ROW]
[ROW][C]98[/C][C]0.00792998262766124[/C][C]0.0158599652553225[/C][C]0.992070017372339[/C][/ROW]
[ROW][C]99[/C][C]0.0057319927111865[/C][C]0.011463985422373[/C][C]0.994268007288813[/C][/ROW]
[ROW][C]100[/C][C]0.00419658140382687[/C][C]0.00839316280765373[/C][C]0.995803418596173[/C][/ROW]
[ROW][C]101[/C][C]0.00380888186053321[/C][C]0.00761776372106643[/C][C]0.996191118139467[/C][/ROW]
[ROW][C]102[/C][C]0.00516563903697522[/C][C]0.0103312780739504[/C][C]0.994834360963025[/C][/ROW]
[ROW][C]103[/C][C]0.0152196455252816[/C][C]0.0304392910505633[/C][C]0.984780354474718[/C][/ROW]
[ROW][C]104[/C][C]0.0414931269178801[/C][C]0.0829862538357602[/C][C]0.95850687308212[/C][/ROW]
[ROW][C]105[/C][C]0.0316011910600998[/C][C]0.0632023821201997[/C][C]0.9683988089399[/C][/ROW]
[ROW][C]106[/C][C]0.0230833224824563[/C][C]0.0461666449649127[/C][C]0.976916677517544[/C][/ROW]
[ROW][C]107[/C][C]0.0170262142243401[/C][C]0.0340524284486802[/C][C]0.98297378577566[/C][/ROW]
[ROW][C]108[/C][C]0.0126863722761256[/C][C]0.0253727445522512[/C][C]0.987313627723874[/C][/ROW]
[ROW][C]109[/C][C]0.00924833163518963[/C][C]0.0184966632703793[/C][C]0.99075166836481[/C][/ROW]
[ROW][C]110[/C][C]0.00696057209619411[/C][C]0.0139211441923882[/C][C]0.993039427903806[/C][/ROW]
[ROW][C]111[/C][C]0.0062169749350874[/C][C]0.0124339498701748[/C][C]0.993783025064913[/C][/ROW]
[ROW][C]112[/C][C]0.00618419324549461[/C][C]0.0123683864909892[/C][C]0.993815806754505[/C][/ROW]
[ROW][C]113[/C][C]0.00709443654035762[/C][C]0.0141888730807152[/C][C]0.992905563459642[/C][/ROW]
[ROW][C]114[/C][C]0.00642060062469004[/C][C]0.0128412012493801[/C][C]0.99357939937531[/C][/ROW]
[ROW][C]115[/C][C]0.00421067760115131[/C][C]0.00842135520230262[/C][C]0.995789322398849[/C][/ROW]
[ROW][C]116[/C][C]0.0398692715736005[/C][C]0.0797385431472011[/C][C]0.960130728426399[/C][/ROW]
[ROW][C]117[/C][C]0.0384350600749525[/C][C]0.076870120149905[/C][C]0.961564939925047[/C][/ROW]
[ROW][C]118[/C][C]0.0976653990299342[/C][C]0.195330798059868[/C][C]0.902334600970066[/C][/ROW]
[ROW][C]119[/C][C]0.202350466836846[/C][C]0.404700933673691[/C][C]0.797649533163154[/C][/ROW]
[ROW][C]120[/C][C]0.158085530440785[/C][C]0.31617106088157[/C][C]0.841914469559215[/C][/ROW]
[ROW][C]121[/C][C]0.185153498843212[/C][C]0.370306997686425[/C][C]0.814846501156788[/C][/ROW]
[ROW][C]122[/C][C]0.155052887360164[/C][C]0.310105774720327[/C][C]0.844947112639836[/C][/ROW]
[ROW][C]123[/C][C]0.326634384041432[/C][C]0.653268768082864[/C][C]0.673365615958568[/C][/ROW]
[ROW][C]124[/C][C]0.41783514639066[/C][C]0.83567029278132[/C][C]0.58216485360934[/C][/ROW]
[ROW][C]125[/C][C]0.440153265748943[/C][C]0.880306531497886[/C][C]0.559846734251057[/C][/ROW]
[ROW][C]126[/C][C]0.646335638153072[/C][C]0.707328723693856[/C][C]0.353664361846928[/C][/ROW]
[ROW][C]127[/C][C]0.583587706831667[/C][C]0.832824586336666[/C][C]0.416412293168333[/C][/ROW]
[ROW][C]128[/C][C]0.559120519413915[/C][C]0.881758961172169[/C][C]0.440879480586085[/C][/ROW]
[ROW][C]129[/C][C]0.586021109102293[/C][C]0.827957781795414[/C][C]0.413978890897707[/C][/ROW]
[ROW][C]130[/C][C]0.537159527230781[/C][C]0.925680945538439[/C][C]0.462840472769219[/C][/ROW]
[ROW][C]131[/C][C]0.642121867151759[/C][C]0.715756265696482[/C][C]0.357878132848241[/C][/ROW]
[ROW][C]132[/C][C]0.565403339327801[/C][C]0.869193321344398[/C][C]0.434596660672199[/C][/ROW]
[ROW][C]133[/C][C]0.455348837623383[/C][C]0.910697675246765[/C][C]0.544651162376617[/C][/ROW]
[ROW][C]134[/C][C]0.386583997290874[/C][C]0.773167994581749[/C][C]0.613416002709126[/C][/ROW]
[ROW][C]135[/C][C]0.262091917194755[/C][C]0.52418383438951[/C][C]0.737908082805245[/C][/ROW]
[ROW][C]136[/C][C]0.193339029203748[/C][C]0.386678058407496[/C][C]0.806660970796252[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191196&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191196&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
90.5723750939202520.8552498121594960.427624906079748
100.5454046174690170.9091907650619670.454595382530983
110.398605782715420.797211565430840.60139421728458
120.3989413280975470.7978826561950940.601058671902453
130.2960120222754310.5920240445508610.703987977724569
140.2154441123266930.4308882246533850.784555887673307
150.204924272873950.40984854574790.79507572712605
160.1771581936290580.3543163872581160.822841806370942
170.1261946523075990.2523893046151970.873805347692401
180.08617003951132330.1723400790226470.913829960488677
190.05531009229457280.1106201845891460.944689907705427
200.1761927187856580.3523854375713160.823807281214342
210.1479343168745610.2958686337491220.852065683125439
220.1059317347392720.2118634694785450.894068265260728
230.0837044971194530.1674089942389060.916295502880547
240.05907881781356610.1181576356271320.940921182186434
250.03991559090364530.07983118180729050.960084409096355
260.07386230707790920.1477246141558180.926137692922091
270.06837670113235430.1367534022647090.931623298867646
280.05458203637319430.1091640727463890.945417963626806
290.0393906705321080.0787813410642160.960609329467892
300.08717782966778450.1743556593355690.912822170332215
310.07174645075410660.1434929015082130.928253549245893
320.1204366575742620.2408733151485240.879563342425738
330.1211950396454030.2423900792908070.878804960354597
340.09265036004999210.1853007200999840.907349639950008
350.06955967099950650.1391193419990130.930440329000494
360.05596673827372670.1119334765474530.944033261726273
370.05042676513773330.1008535302754670.949573234862267
380.04119754214069180.08239508428138350.958802457859308
390.03208640310882880.06417280621765750.967913596891171
400.1138074679416520.2276149358833050.886192532058348
410.2178055504261070.4356111008522140.782194449573893
420.1815717239487240.3631434478974480.818428276051276
430.2674418402926730.5348836805853460.732558159707327
440.2336138360032230.4672276720064460.766386163996777
450.2089644649258770.4179289298517540.791035535074123
460.2865172779409990.5730345558819990.713482722059001
470.3380927158239120.6761854316478250.661907284176088
480.2948490218475510.5896980436951020.705150978152449
490.2525554618136210.5051109236272430.747444538186379
500.2138899033895160.4277798067790320.786110096610484
510.3483085789354850.696617157870970.651691421064515
520.3032111703913650.606422340782730.696788829608635
530.2601687607188120.5203375214376240.739831239281188
540.2227946497025480.4455892994050960.777205350297452
550.2063082524541690.4126165049083370.793691747545831
560.1956608941996930.3913217883993860.804339105800307
570.1792977717194960.3585955434389930.820702228280504
580.152682414358890.305364828717780.84731758564111
590.1269669793580920.2539339587161850.873033020641908
600.189805640850960.379611281701920.81019435914904
610.159454292612390.3189085852247790.84054570738761
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1360.1933390292037480.3866780584074960.806660970796252







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level40.03125NOK
5% type I error level350.2734375NOK
10% type I error level480.375NOK

\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 & 4 & 0.03125 & NOK \tabularnewline
5% type I error level & 35 & 0.2734375 & NOK \tabularnewline
10% type I error level & 48 & 0.375 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191196&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]4[/C][C]0.03125[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]35[/C][C]0.2734375[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]48[/C][C]0.375[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191196&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191196&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 level40.03125NOK
5% type I error level350.2734375NOK
10% type I error level480.375NOK



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')
}