Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Module--
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 16:32:11 -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/05/t1352151162ykprc00x02zfqli.htm/, Retrieved Mon, 06 Feb 2023 00:15:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186312, Retrieved Mon, 06 Feb 2023 00:15:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact97
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [ws7] [2012-11-04 16:42:51] [4a361cce6d88b3d167c89c496ff59bf0]
- RM      [Multiple Regression] [Ws7] [2012-11-05 21:32:11] [413ae3079a2d7d57ac33b6b2184bdf0e] [Current]
Feedback Forum

Post a new message
Dataseries X:
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
559	110	449	7.1	10.9	10.2
546	104	442	7.2	11	10.3
536	100	437	7.3	11.2	10.3
528	96	431	7.4	11.3	10.4
530	98	433	7.4	11.4	10.5
582	122	460	7.5	11.5	10.5
599	134	465	7.4	11.5	10.6
584	133	451	7.4	11.6	10.6




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 10 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186312&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186312&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186312&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 time10 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Multiple Linear Regression - Estimated Regression Equation
totaal[t] = + 1.25102743123547 + 0.993824229382807`<25J`[t] + 1.00177511231163`>25J`[t] -0.119519418446503belgie[t] -0.0904039875483088eurogebied[t] + 0.0521462742998931eu[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
totaal[t] =  +  1.25102743123547 +  0.993824229382807`<25J`[t] +  1.00177511231163`>25J`[t] -0.119519418446503belgie[t] -0.0904039875483088eurogebied[t] +  0.0521462742998931eu[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186312&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]totaal[t] =  +  1.25102743123547 +  0.993824229382807`<25J`[t] +  1.00177511231163`>25J`[t] -0.119519418446503belgie[t] -0.0904039875483088eurogebied[t] +  0.0521462742998931eu[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186312&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186312&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] = + 1.25102743123547 + 0.993824229382807`<25J`[t] + 1.00177511231163`>25J`[t] -0.119519418446503belgie[t] -0.0904039875483088eurogebied[t] + 0.0521462742998931eu[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.251027431235470.6524781.91730.0572450.028623
`<25J`0.9938242293828070.003169313.627400
`>25J`1.001775112311630.002749364.437300
belgie-0.1195194184465030.107818-1.10850.2695480.134774
eurogebied-0.09040398754830880.20354-0.44420.6576190.32881
eu0.05214627429989310.207940.25080.8023580.401179

\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) & 1.25102743123547 & 0.652478 & 1.9173 & 0.057245 & 0.028623 \tabularnewline
`<25J` & 0.993824229382807 & 0.003169 & 313.6274 & 0 & 0 \tabularnewline
`>25J` & 1.00177511231163 & 0.002749 & 364.4373 & 0 & 0 \tabularnewline
belgie & -0.119519418446503 & 0.107818 & -1.1085 & 0.269548 & 0.134774 \tabularnewline
eurogebied & -0.0904039875483088 & 0.20354 & -0.4442 & 0.657619 & 0.32881 \tabularnewline
eu & 0.0521462742998931 & 0.20794 & 0.2508 & 0.802358 & 0.401179 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186312&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]1.25102743123547[/C][C]0.652478[/C][C]1.9173[/C][C]0.057245[/C][C]0.028623[/C][/ROW]
[ROW][C]`<25J`[/C][C]0.993824229382807[/C][C]0.003169[/C][C]313.6274[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`>25J`[/C][C]1.00177511231163[/C][C]0.002749[/C][C]364.4373[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]belgie[/C][C]-0.119519418446503[/C][C]0.107818[/C][C]-1.1085[/C][C]0.269548[/C][C]0.134774[/C][/ROW]
[ROW][C]eurogebied[/C][C]-0.0904039875483088[/C][C]0.20354[/C][C]-0.4442[/C][C]0.657619[/C][C]0.32881[/C][/ROW]
[ROW][C]eu[/C][C]0.0521462742998931[/C][C]0.20794[/C][C]0.2508[/C][C]0.802358[/C][C]0.401179[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186312&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186312&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)1.251027431235470.6524781.91730.0572450.028623
`<25J`0.9938242293828070.003169313.627400
`>25J`1.001775112311630.002749364.437300
belgie-0.1195194184465030.107818-1.10850.2695480.134774
eurogebied-0.09040398754830880.20354-0.44420.6576190.32881
eu0.05214627429989310.207940.25080.8023580.401179







Multiple Linear Regression - Regression Statistics
Multiple R0.999940423292632
R-squared0.999880850134648
Adjusted R-squared0.999876564168269
F-TEST (value)233291.809031408
F-TEST (DF numerator)5
F-TEST (DF denominator)139
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.50341422455435
Sum Squared Residuals35.2261975262285

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999940423292632 \tabularnewline
R-squared & 0.999880850134648 \tabularnewline
Adjusted R-squared & 0.999876564168269 \tabularnewline
F-TEST (value) & 233291.809031408 \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 & 0.50341422455435 \tabularnewline
Sum Squared Residuals & 35.2261975262285 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186312&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999940423292632[/C][/ROW]
[ROW][C]R-squared[/C][C]0.999880850134648[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.999876564168269[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]233291.809031408[/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]0.50341422455435[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]35.2261975262285[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186312&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186312&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 R0.999940423292632
R-squared0.999880850134648
Adjusted R-squared0.999876564168269
F-TEST (value)233291.809031408
F-TEST (DF numerator)5
F-TEST (DF denominator)139
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.50341422455435
Sum Squared Residuals35.2261975262285







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1501501.961174087868-0.961174087867868
2485485.002379837339-0.00237983733893331
3464464.064514132856-0.0645141328564004
4460460.104994928495-0.104994928494615
5467467.15036499712-0.150364997120161
6460460.17374380157-0.173743801569872
7448448.201237267229-0.201237267229252
8443443.192310470768-0.192310470768267
9436436.219639099231-0.219639099230981
10431431.238617245375-0.238617245375124
11484484.110124710787-0.110124710787306
12510509.0245521362180.975447863781942
13513512.9878458770020.0121541229981904
14503502.9649017093380.0350982906620005
15471471.008672103039-0.008672103038878
16471471.011532411226-0.0115324112258603
17476476.025447312623-0.0254473126230187
18475474.0419239654610.958076034538619
19470470.058676165001-0.058676165001329
20461461.082629856432-0.0826298564317625
21455455.090741256607-0.0907412566066105
22456455.0907412566070.909258743393389
23517516.8629841306890.137015869310556
24525525.791500429277-0.791500429277056
25523522.7731336346710.226866365328494
26519518.8296402488560.170359751144423
27509508.8624547314990.137545268500796
28512511.8836818342920.116318165708262
29519518.8763288483880.123671151612173
30517516.883589814880.116410185119621
31510509.9109184433430.0890815566569192
32509509.930770033214-0.930770033213723
33501499.9726761495151.02732385048499
34507506.9522088881550.0477911118448778
35569569.746005646635-0.74600564663489
36580579.7091901050750.290809894924565
37578577.6925984227820.307401577218483
38565565.759846303085-0.759846303085019
39547547.786410770164-0.786410770164427
40555554.800050124860.199949875139999
41562561.7926971389560.207302861043908
42561560.8147746754310.185225324569069
43555555.885459178064-0.885459178063843
44544543.9544562678460.0455437321541697
45537537.001687721082-0.00168772108200801
46543542.963492346650.0365076533499898
47594593.7120326387980.287967361201908
48611610.6347957762020.36520422379756
49613612.6462968837550.353703116245475
50611610.7740130798110.225986920188908
51594593.8382084847360.161791515263921
52595595.873562241075-0.873562241074632
53591590.9134814929150.0865185070845685
54589589.923607087546-0.923607087545618
55584583.9407588864750.0592411135246963
56573572.9609870656910.039012934308515
57567566.9819646359460.0180353640539828
58569568.9895159194850.0104840805149006
59621620.7784234049780.22157659502241
60629628.7328430113650.267156988635107
61628627.7310678990530.26893210094674
62612611.799341500630.200658499369957
63595595.857185847347-0.857185847346866
64597596.9025411456270.0974588543725546
65593592.9429707063630.0570292936371271
66590589.957372906610.0426270933895265
67580579.9911528523920.00884714760804069
68574574.001443284219-0.00144328421882253
69573573.018306193264-0.0183061932636766
70573573.038084965349-0.0380849653489946
71620619.8948166452870.105183354712594
72626625.8816659052740.118334094726439
73620619.8827918856570.117208114342562
74588586.9473021946311.05269780536917
75566565.9922699208740.00773007912632591
76557558.058543314807-1.05854331480672
77561561.054528912708-0.0545289127076374
78549549.084758633866-0.0847586338658535
79532532.106112793466-0.10611279346592
80526526.111188597863-0.111188597862802
81511511.152170035535-0.152170035534762
82499499.201315535446-0.201315535446108
83555555.078148337793-0.0781483377931809
84565564.0703229347140.929677065285787
85542542.094753474601-0.0947534746007814
86527527.110566225166-0.110566225166448
87510510.149035719138-0.149035719138325
88514514.158872423884-0.15887242388368
89517517.176149702663-0.17614970266322
90508507.2179317662760.782068233723647
91493493.252737433331-0.252737433331119
92490490.254273463499-0.254273463499046
93469469.284728279989-0.284728279989383
94478477.2592988165260.740701183473541
95528529.097125168106-1.09712516810605
96534533.0566443724680.94335562753221
97518518.05908496401-0.0590849640098026
98506506.100476451466-0.100476451465801
99502502.117527991285-0.11752799128489
100516516.097833914541-0.0978339145409005
101528528.061114984344-0.061114984344326
102533533.034609348239-0.0346093482386506
103536536.019241684853-0.0192416848531983
104537537.042308478043-0.0423084780434488
105524523.0772381977860.922761802213626
106536536.057948040383-0.0579480403827707
107587586.88908399250.11091600750011
108597595.8506173913171.14938260868304
109581580.8360667011750.163933298824692
110564564.898036159496-0.898036159496104
111558556.9248032441611.07519675583866
112575574.9119102763850.0880897236152704
113580580.909086321425-0.909086321425183
114575574.9103363544970.0896636455027791
115563563.930564533713-0.930564533713409
116552550.946152972481.05384702751967
117537536.9769575806190.0230424193807341
118545544.979082484580.0209175154204943
119601600.8678159938680.132184006131612
120604604.839163087387-0.839163087386618
121586586.843015656408-0.843015656408399
122564563.917501493160.0824985068397358
123549547.9766106432941.02338935670594
124551551.041644454549-0.0416444545493731
125556556.082250730037-0.082250730037493
126548548.132797645704-0.132797645703873
127540540.157137593341-0.157137593340795
128531531.161013172407-0.16101317240676
129521520.1944068619820.805593138018239
130519518.1818162386040.81818376139633
131572572.027317926411-0.0273179264105021
132581581.9855870977-0.985587097700198
133563562.9718868412410.0281131587591395
134548548.02385475762-0.023854757619615
135539539.051758273063-0.0517582730630412
136541541.074121806634-0.0741218066337642
137562561.0660438668970.933956133102586
138559559.066618753878-0.0666187538781284
139546546.07546987823-0.075469878230393
140536537.061264659787-1.0612646597867
141528527.0595393552160.940460644783808
142530531.04691226728-1.04691226728023
143582581.9256294642820.0743705357178721
144599598.8775623477090.122437652291408
145584583.8498461472080.150153852791857

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 501 & 501.961174087868 & -0.961174087867868 \tabularnewline
2 & 485 & 485.002379837339 & -0.00237983733893331 \tabularnewline
3 & 464 & 464.064514132856 & -0.0645141328564004 \tabularnewline
4 & 460 & 460.104994928495 & -0.104994928494615 \tabularnewline
5 & 467 & 467.15036499712 & -0.150364997120161 \tabularnewline
6 & 460 & 460.17374380157 & -0.173743801569872 \tabularnewline
7 & 448 & 448.201237267229 & -0.201237267229252 \tabularnewline
8 & 443 & 443.192310470768 & -0.192310470768267 \tabularnewline
9 & 436 & 436.219639099231 & -0.219639099230981 \tabularnewline
10 & 431 & 431.238617245375 & -0.238617245375124 \tabularnewline
11 & 484 & 484.110124710787 & -0.110124710787306 \tabularnewline
12 & 510 & 509.024552136218 & 0.975447863781942 \tabularnewline
13 & 513 & 512.987845877002 & 0.0121541229981904 \tabularnewline
14 & 503 & 502.964901709338 & 0.0350982906620005 \tabularnewline
15 & 471 & 471.008672103039 & -0.008672103038878 \tabularnewline
16 & 471 & 471.011532411226 & -0.0115324112258603 \tabularnewline
17 & 476 & 476.025447312623 & -0.0254473126230187 \tabularnewline
18 & 475 & 474.041923965461 & 0.958076034538619 \tabularnewline
19 & 470 & 470.058676165001 & -0.058676165001329 \tabularnewline
20 & 461 & 461.082629856432 & -0.0826298564317625 \tabularnewline
21 & 455 & 455.090741256607 & -0.0907412566066105 \tabularnewline
22 & 456 & 455.090741256607 & 0.909258743393389 \tabularnewline
23 & 517 & 516.862984130689 & 0.137015869310556 \tabularnewline
24 & 525 & 525.791500429277 & -0.791500429277056 \tabularnewline
25 & 523 & 522.773133634671 & 0.226866365328494 \tabularnewline
26 & 519 & 518.829640248856 & 0.170359751144423 \tabularnewline
27 & 509 & 508.862454731499 & 0.137545268500796 \tabularnewline
28 & 512 & 511.883681834292 & 0.116318165708262 \tabularnewline
29 & 519 & 518.876328848388 & 0.123671151612173 \tabularnewline
30 & 517 & 516.88358981488 & 0.116410185119621 \tabularnewline
31 & 510 & 509.910918443343 & 0.0890815566569192 \tabularnewline
32 & 509 & 509.930770033214 & -0.930770033213723 \tabularnewline
33 & 501 & 499.972676149515 & 1.02732385048499 \tabularnewline
34 & 507 & 506.952208888155 & 0.0477911118448778 \tabularnewline
35 & 569 & 569.746005646635 & -0.74600564663489 \tabularnewline
36 & 580 & 579.709190105075 & 0.290809894924565 \tabularnewline
37 & 578 & 577.692598422782 & 0.307401577218483 \tabularnewline
38 & 565 & 565.759846303085 & -0.759846303085019 \tabularnewline
39 & 547 & 547.786410770164 & -0.786410770164427 \tabularnewline
40 & 555 & 554.80005012486 & 0.199949875139999 \tabularnewline
41 & 562 & 561.792697138956 & 0.207302861043908 \tabularnewline
42 & 561 & 560.814774675431 & 0.185225324569069 \tabularnewline
43 & 555 & 555.885459178064 & -0.885459178063843 \tabularnewline
44 & 544 & 543.954456267846 & 0.0455437321541697 \tabularnewline
45 & 537 & 537.001687721082 & -0.00168772108200801 \tabularnewline
46 & 543 & 542.96349234665 & 0.0365076533499898 \tabularnewline
47 & 594 & 593.712032638798 & 0.287967361201908 \tabularnewline
48 & 611 & 610.634795776202 & 0.36520422379756 \tabularnewline
49 & 613 & 612.646296883755 & 0.353703116245475 \tabularnewline
50 & 611 & 610.774013079811 & 0.225986920188908 \tabularnewline
51 & 594 & 593.838208484736 & 0.161791515263921 \tabularnewline
52 & 595 & 595.873562241075 & -0.873562241074632 \tabularnewline
53 & 591 & 590.913481492915 & 0.0865185070845685 \tabularnewline
54 & 589 & 589.923607087546 & -0.923607087545618 \tabularnewline
55 & 584 & 583.940758886475 & 0.0592411135246963 \tabularnewline
56 & 573 & 572.960987065691 & 0.039012934308515 \tabularnewline
57 & 567 & 566.981964635946 & 0.0180353640539828 \tabularnewline
58 & 569 & 568.989515919485 & 0.0104840805149006 \tabularnewline
59 & 621 & 620.778423404978 & 0.22157659502241 \tabularnewline
60 & 629 & 628.732843011365 & 0.267156988635107 \tabularnewline
61 & 628 & 627.731067899053 & 0.26893210094674 \tabularnewline
62 & 612 & 611.79934150063 & 0.200658499369957 \tabularnewline
63 & 595 & 595.857185847347 & -0.857185847346866 \tabularnewline
64 & 597 & 596.902541145627 & 0.0974588543725546 \tabularnewline
65 & 593 & 592.942970706363 & 0.0570292936371271 \tabularnewline
66 & 590 & 589.95737290661 & 0.0426270933895265 \tabularnewline
67 & 580 & 579.991152852392 & 0.00884714760804069 \tabularnewline
68 & 574 & 574.001443284219 & -0.00144328421882253 \tabularnewline
69 & 573 & 573.018306193264 & -0.0183061932636766 \tabularnewline
70 & 573 & 573.038084965349 & -0.0380849653489946 \tabularnewline
71 & 620 & 619.894816645287 & 0.105183354712594 \tabularnewline
72 & 626 & 625.881665905274 & 0.118334094726439 \tabularnewline
73 & 620 & 619.882791885657 & 0.117208114342562 \tabularnewline
74 & 588 & 586.947302194631 & 1.05269780536917 \tabularnewline
75 & 566 & 565.992269920874 & 0.00773007912632591 \tabularnewline
76 & 557 & 558.058543314807 & -1.05854331480672 \tabularnewline
77 & 561 & 561.054528912708 & -0.0545289127076374 \tabularnewline
78 & 549 & 549.084758633866 & -0.0847586338658535 \tabularnewline
79 & 532 & 532.106112793466 & -0.10611279346592 \tabularnewline
80 & 526 & 526.111188597863 & -0.111188597862802 \tabularnewline
81 & 511 & 511.152170035535 & -0.152170035534762 \tabularnewline
82 & 499 & 499.201315535446 & -0.201315535446108 \tabularnewline
83 & 555 & 555.078148337793 & -0.0781483377931809 \tabularnewline
84 & 565 & 564.070322934714 & 0.929677065285787 \tabularnewline
85 & 542 & 542.094753474601 & -0.0947534746007814 \tabularnewline
86 & 527 & 527.110566225166 & -0.110566225166448 \tabularnewline
87 & 510 & 510.149035719138 & -0.149035719138325 \tabularnewline
88 & 514 & 514.158872423884 & -0.15887242388368 \tabularnewline
89 & 517 & 517.176149702663 & -0.17614970266322 \tabularnewline
90 & 508 & 507.217931766276 & 0.782068233723647 \tabularnewline
91 & 493 & 493.252737433331 & -0.252737433331119 \tabularnewline
92 & 490 & 490.254273463499 & -0.254273463499046 \tabularnewline
93 & 469 & 469.284728279989 & -0.284728279989383 \tabularnewline
94 & 478 & 477.259298816526 & 0.740701183473541 \tabularnewline
95 & 528 & 529.097125168106 & -1.09712516810605 \tabularnewline
96 & 534 & 533.056644372468 & 0.94335562753221 \tabularnewline
97 & 518 & 518.05908496401 & -0.0590849640098026 \tabularnewline
98 & 506 & 506.100476451466 & -0.100476451465801 \tabularnewline
99 & 502 & 502.117527991285 & -0.11752799128489 \tabularnewline
100 & 516 & 516.097833914541 & -0.0978339145409005 \tabularnewline
101 & 528 & 528.061114984344 & -0.061114984344326 \tabularnewline
102 & 533 & 533.034609348239 & -0.0346093482386506 \tabularnewline
103 & 536 & 536.019241684853 & -0.0192416848531983 \tabularnewline
104 & 537 & 537.042308478043 & -0.0423084780434488 \tabularnewline
105 & 524 & 523.077238197786 & 0.922761802213626 \tabularnewline
106 & 536 & 536.057948040383 & -0.0579480403827707 \tabularnewline
107 & 587 & 586.8890839925 & 0.11091600750011 \tabularnewline
108 & 597 & 595.850617391317 & 1.14938260868304 \tabularnewline
109 & 581 & 580.836066701175 & 0.163933298824692 \tabularnewline
110 & 564 & 564.898036159496 & -0.898036159496104 \tabularnewline
111 & 558 & 556.924803244161 & 1.07519675583866 \tabularnewline
112 & 575 & 574.911910276385 & 0.0880897236152704 \tabularnewline
113 & 580 & 580.909086321425 & -0.909086321425183 \tabularnewline
114 & 575 & 574.910336354497 & 0.0896636455027791 \tabularnewline
115 & 563 & 563.930564533713 & -0.930564533713409 \tabularnewline
116 & 552 & 550.94615297248 & 1.05384702751967 \tabularnewline
117 & 537 & 536.976957580619 & 0.0230424193807341 \tabularnewline
118 & 545 & 544.97908248458 & 0.0209175154204943 \tabularnewline
119 & 601 & 600.867815993868 & 0.132184006131612 \tabularnewline
120 & 604 & 604.839163087387 & -0.839163087386618 \tabularnewline
121 & 586 & 586.843015656408 & -0.843015656408399 \tabularnewline
122 & 564 & 563.91750149316 & 0.0824985068397358 \tabularnewline
123 & 549 & 547.976610643294 & 1.02338935670594 \tabularnewline
124 & 551 & 551.041644454549 & -0.0416444545493731 \tabularnewline
125 & 556 & 556.082250730037 & -0.082250730037493 \tabularnewline
126 & 548 & 548.132797645704 & -0.132797645703873 \tabularnewline
127 & 540 & 540.157137593341 & -0.157137593340795 \tabularnewline
128 & 531 & 531.161013172407 & -0.16101317240676 \tabularnewline
129 & 521 & 520.194406861982 & 0.805593138018239 \tabularnewline
130 & 519 & 518.181816238604 & 0.81818376139633 \tabularnewline
131 & 572 & 572.027317926411 & -0.0273179264105021 \tabularnewline
132 & 581 & 581.9855870977 & -0.985587097700198 \tabularnewline
133 & 563 & 562.971886841241 & 0.0281131587591395 \tabularnewline
134 & 548 & 548.02385475762 & -0.023854757619615 \tabularnewline
135 & 539 & 539.051758273063 & -0.0517582730630412 \tabularnewline
136 & 541 & 541.074121806634 & -0.0741218066337642 \tabularnewline
137 & 562 & 561.066043866897 & 0.933956133102586 \tabularnewline
138 & 559 & 559.066618753878 & -0.0666187538781284 \tabularnewline
139 & 546 & 546.07546987823 & -0.075469878230393 \tabularnewline
140 & 536 & 537.061264659787 & -1.0612646597867 \tabularnewline
141 & 528 & 527.059539355216 & 0.940460644783808 \tabularnewline
142 & 530 & 531.04691226728 & -1.04691226728023 \tabularnewline
143 & 582 & 581.925629464282 & 0.0743705357178721 \tabularnewline
144 & 599 & 598.877562347709 & 0.122437652291408 \tabularnewline
145 & 584 & 583.849846147208 & 0.150153852791857 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186312&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]501[/C][C]501.961174087868[/C][C]-0.961174087867868[/C][/ROW]
[ROW][C]2[/C][C]485[/C][C]485.002379837339[/C][C]-0.00237983733893331[/C][/ROW]
[ROW][C]3[/C][C]464[/C][C]464.064514132856[/C][C]-0.0645141328564004[/C][/ROW]
[ROW][C]4[/C][C]460[/C][C]460.104994928495[/C][C]-0.104994928494615[/C][/ROW]
[ROW][C]5[/C][C]467[/C][C]467.15036499712[/C][C]-0.150364997120161[/C][/ROW]
[ROW][C]6[/C][C]460[/C][C]460.17374380157[/C][C]-0.173743801569872[/C][/ROW]
[ROW][C]7[/C][C]448[/C][C]448.201237267229[/C][C]-0.201237267229252[/C][/ROW]
[ROW][C]8[/C][C]443[/C][C]443.192310470768[/C][C]-0.192310470768267[/C][/ROW]
[ROW][C]9[/C][C]436[/C][C]436.219639099231[/C][C]-0.219639099230981[/C][/ROW]
[ROW][C]10[/C][C]431[/C][C]431.238617245375[/C][C]-0.238617245375124[/C][/ROW]
[ROW][C]11[/C][C]484[/C][C]484.110124710787[/C][C]-0.110124710787306[/C][/ROW]
[ROW][C]12[/C][C]510[/C][C]509.024552136218[/C][C]0.975447863781942[/C][/ROW]
[ROW][C]13[/C][C]513[/C][C]512.987845877002[/C][C]0.0121541229981904[/C][/ROW]
[ROW][C]14[/C][C]503[/C][C]502.964901709338[/C][C]0.0350982906620005[/C][/ROW]
[ROW][C]15[/C][C]471[/C][C]471.008672103039[/C][C]-0.008672103038878[/C][/ROW]
[ROW][C]16[/C][C]471[/C][C]471.011532411226[/C][C]-0.0115324112258603[/C][/ROW]
[ROW][C]17[/C][C]476[/C][C]476.025447312623[/C][C]-0.0254473126230187[/C][/ROW]
[ROW][C]18[/C][C]475[/C][C]474.041923965461[/C][C]0.958076034538619[/C][/ROW]
[ROW][C]19[/C][C]470[/C][C]470.058676165001[/C][C]-0.058676165001329[/C][/ROW]
[ROW][C]20[/C][C]461[/C][C]461.082629856432[/C][C]-0.0826298564317625[/C][/ROW]
[ROW][C]21[/C][C]455[/C][C]455.090741256607[/C][C]-0.0907412566066105[/C][/ROW]
[ROW][C]22[/C][C]456[/C][C]455.090741256607[/C][C]0.909258743393389[/C][/ROW]
[ROW][C]23[/C][C]517[/C][C]516.862984130689[/C][C]0.137015869310556[/C][/ROW]
[ROW][C]24[/C][C]525[/C][C]525.791500429277[/C][C]-0.791500429277056[/C][/ROW]
[ROW][C]25[/C][C]523[/C][C]522.773133634671[/C][C]0.226866365328494[/C][/ROW]
[ROW][C]26[/C][C]519[/C][C]518.829640248856[/C][C]0.170359751144423[/C][/ROW]
[ROW][C]27[/C][C]509[/C][C]508.862454731499[/C][C]0.137545268500796[/C][/ROW]
[ROW][C]28[/C][C]512[/C][C]511.883681834292[/C][C]0.116318165708262[/C][/ROW]
[ROW][C]29[/C][C]519[/C][C]518.876328848388[/C][C]0.123671151612173[/C][/ROW]
[ROW][C]30[/C][C]517[/C][C]516.88358981488[/C][C]0.116410185119621[/C][/ROW]
[ROW][C]31[/C][C]510[/C][C]509.910918443343[/C][C]0.0890815566569192[/C][/ROW]
[ROW][C]32[/C][C]509[/C][C]509.930770033214[/C][C]-0.930770033213723[/C][/ROW]
[ROW][C]33[/C][C]501[/C][C]499.972676149515[/C][C]1.02732385048499[/C][/ROW]
[ROW][C]34[/C][C]507[/C][C]506.952208888155[/C][C]0.0477911118448778[/C][/ROW]
[ROW][C]35[/C][C]569[/C][C]569.746005646635[/C][C]-0.74600564663489[/C][/ROW]
[ROW][C]36[/C][C]580[/C][C]579.709190105075[/C][C]0.290809894924565[/C][/ROW]
[ROW][C]37[/C][C]578[/C][C]577.692598422782[/C][C]0.307401577218483[/C][/ROW]
[ROW][C]38[/C][C]565[/C][C]565.759846303085[/C][C]-0.759846303085019[/C][/ROW]
[ROW][C]39[/C][C]547[/C][C]547.786410770164[/C][C]-0.786410770164427[/C][/ROW]
[ROW][C]40[/C][C]555[/C][C]554.80005012486[/C][C]0.199949875139999[/C][/ROW]
[ROW][C]41[/C][C]562[/C][C]561.792697138956[/C][C]0.207302861043908[/C][/ROW]
[ROW][C]42[/C][C]561[/C][C]560.814774675431[/C][C]0.185225324569069[/C][/ROW]
[ROW][C]43[/C][C]555[/C][C]555.885459178064[/C][C]-0.885459178063843[/C][/ROW]
[ROW][C]44[/C][C]544[/C][C]543.954456267846[/C][C]0.0455437321541697[/C][/ROW]
[ROW][C]45[/C][C]537[/C][C]537.001687721082[/C][C]-0.00168772108200801[/C][/ROW]
[ROW][C]46[/C][C]543[/C][C]542.96349234665[/C][C]0.0365076533499898[/C][/ROW]
[ROW][C]47[/C][C]594[/C][C]593.712032638798[/C][C]0.287967361201908[/C][/ROW]
[ROW][C]48[/C][C]611[/C][C]610.634795776202[/C][C]0.36520422379756[/C][/ROW]
[ROW][C]49[/C][C]613[/C][C]612.646296883755[/C][C]0.353703116245475[/C][/ROW]
[ROW][C]50[/C][C]611[/C][C]610.774013079811[/C][C]0.225986920188908[/C][/ROW]
[ROW][C]51[/C][C]594[/C][C]593.838208484736[/C][C]0.161791515263921[/C][/ROW]
[ROW][C]52[/C][C]595[/C][C]595.873562241075[/C][C]-0.873562241074632[/C][/ROW]
[ROW][C]53[/C][C]591[/C][C]590.913481492915[/C][C]0.0865185070845685[/C][/ROW]
[ROW][C]54[/C][C]589[/C][C]589.923607087546[/C][C]-0.923607087545618[/C][/ROW]
[ROW][C]55[/C][C]584[/C][C]583.940758886475[/C][C]0.0592411135246963[/C][/ROW]
[ROW][C]56[/C][C]573[/C][C]572.960987065691[/C][C]0.039012934308515[/C][/ROW]
[ROW][C]57[/C][C]567[/C][C]566.981964635946[/C][C]0.0180353640539828[/C][/ROW]
[ROW][C]58[/C][C]569[/C][C]568.989515919485[/C][C]0.0104840805149006[/C][/ROW]
[ROW][C]59[/C][C]621[/C][C]620.778423404978[/C][C]0.22157659502241[/C][/ROW]
[ROW][C]60[/C][C]629[/C][C]628.732843011365[/C][C]0.267156988635107[/C][/ROW]
[ROW][C]61[/C][C]628[/C][C]627.731067899053[/C][C]0.26893210094674[/C][/ROW]
[ROW][C]62[/C][C]612[/C][C]611.79934150063[/C][C]0.200658499369957[/C][/ROW]
[ROW][C]63[/C][C]595[/C][C]595.857185847347[/C][C]-0.857185847346866[/C][/ROW]
[ROW][C]64[/C][C]597[/C][C]596.902541145627[/C][C]0.0974588543725546[/C][/ROW]
[ROW][C]65[/C][C]593[/C][C]592.942970706363[/C][C]0.0570292936371271[/C][/ROW]
[ROW][C]66[/C][C]590[/C][C]589.95737290661[/C][C]0.0426270933895265[/C][/ROW]
[ROW][C]67[/C][C]580[/C][C]579.991152852392[/C][C]0.00884714760804069[/C][/ROW]
[ROW][C]68[/C][C]574[/C][C]574.001443284219[/C][C]-0.00144328421882253[/C][/ROW]
[ROW][C]69[/C][C]573[/C][C]573.018306193264[/C][C]-0.0183061932636766[/C][/ROW]
[ROW][C]70[/C][C]573[/C][C]573.038084965349[/C][C]-0.0380849653489946[/C][/ROW]
[ROW][C]71[/C][C]620[/C][C]619.894816645287[/C][C]0.105183354712594[/C][/ROW]
[ROW][C]72[/C][C]626[/C][C]625.881665905274[/C][C]0.118334094726439[/C][/ROW]
[ROW][C]73[/C][C]620[/C][C]619.882791885657[/C][C]0.117208114342562[/C][/ROW]
[ROW][C]74[/C][C]588[/C][C]586.947302194631[/C][C]1.05269780536917[/C][/ROW]
[ROW][C]75[/C][C]566[/C][C]565.992269920874[/C][C]0.00773007912632591[/C][/ROW]
[ROW][C]76[/C][C]557[/C][C]558.058543314807[/C][C]-1.05854331480672[/C][/ROW]
[ROW][C]77[/C][C]561[/C][C]561.054528912708[/C][C]-0.0545289127076374[/C][/ROW]
[ROW][C]78[/C][C]549[/C][C]549.084758633866[/C][C]-0.0847586338658535[/C][/ROW]
[ROW][C]79[/C][C]532[/C][C]532.106112793466[/C][C]-0.10611279346592[/C][/ROW]
[ROW][C]80[/C][C]526[/C][C]526.111188597863[/C][C]-0.111188597862802[/C][/ROW]
[ROW][C]81[/C][C]511[/C][C]511.152170035535[/C][C]-0.152170035534762[/C][/ROW]
[ROW][C]82[/C][C]499[/C][C]499.201315535446[/C][C]-0.201315535446108[/C][/ROW]
[ROW][C]83[/C][C]555[/C][C]555.078148337793[/C][C]-0.0781483377931809[/C][/ROW]
[ROW][C]84[/C][C]565[/C][C]564.070322934714[/C][C]0.929677065285787[/C][/ROW]
[ROW][C]85[/C][C]542[/C][C]542.094753474601[/C][C]-0.0947534746007814[/C][/ROW]
[ROW][C]86[/C][C]527[/C][C]527.110566225166[/C][C]-0.110566225166448[/C][/ROW]
[ROW][C]87[/C][C]510[/C][C]510.149035719138[/C][C]-0.149035719138325[/C][/ROW]
[ROW][C]88[/C][C]514[/C][C]514.158872423884[/C][C]-0.15887242388368[/C][/ROW]
[ROW][C]89[/C][C]517[/C][C]517.176149702663[/C][C]-0.17614970266322[/C][/ROW]
[ROW][C]90[/C][C]508[/C][C]507.217931766276[/C][C]0.782068233723647[/C][/ROW]
[ROW][C]91[/C][C]493[/C][C]493.252737433331[/C][C]-0.252737433331119[/C][/ROW]
[ROW][C]92[/C][C]490[/C][C]490.254273463499[/C][C]-0.254273463499046[/C][/ROW]
[ROW][C]93[/C][C]469[/C][C]469.284728279989[/C][C]-0.284728279989383[/C][/ROW]
[ROW][C]94[/C][C]478[/C][C]477.259298816526[/C][C]0.740701183473541[/C][/ROW]
[ROW][C]95[/C][C]528[/C][C]529.097125168106[/C][C]-1.09712516810605[/C][/ROW]
[ROW][C]96[/C][C]534[/C][C]533.056644372468[/C][C]0.94335562753221[/C][/ROW]
[ROW][C]97[/C][C]518[/C][C]518.05908496401[/C][C]-0.0590849640098026[/C][/ROW]
[ROW][C]98[/C][C]506[/C][C]506.100476451466[/C][C]-0.100476451465801[/C][/ROW]
[ROW][C]99[/C][C]502[/C][C]502.117527991285[/C][C]-0.11752799128489[/C][/ROW]
[ROW][C]100[/C][C]516[/C][C]516.097833914541[/C][C]-0.0978339145409005[/C][/ROW]
[ROW][C]101[/C][C]528[/C][C]528.061114984344[/C][C]-0.061114984344326[/C][/ROW]
[ROW][C]102[/C][C]533[/C][C]533.034609348239[/C][C]-0.0346093482386506[/C][/ROW]
[ROW][C]103[/C][C]536[/C][C]536.019241684853[/C][C]-0.0192416848531983[/C][/ROW]
[ROW][C]104[/C][C]537[/C][C]537.042308478043[/C][C]-0.0423084780434488[/C][/ROW]
[ROW][C]105[/C][C]524[/C][C]523.077238197786[/C][C]0.922761802213626[/C][/ROW]
[ROW][C]106[/C][C]536[/C][C]536.057948040383[/C][C]-0.0579480403827707[/C][/ROW]
[ROW][C]107[/C][C]587[/C][C]586.8890839925[/C][C]0.11091600750011[/C][/ROW]
[ROW][C]108[/C][C]597[/C][C]595.850617391317[/C][C]1.14938260868304[/C][/ROW]
[ROW][C]109[/C][C]581[/C][C]580.836066701175[/C][C]0.163933298824692[/C][/ROW]
[ROW][C]110[/C][C]564[/C][C]564.898036159496[/C][C]-0.898036159496104[/C][/ROW]
[ROW][C]111[/C][C]558[/C][C]556.924803244161[/C][C]1.07519675583866[/C][/ROW]
[ROW][C]112[/C][C]575[/C][C]574.911910276385[/C][C]0.0880897236152704[/C][/ROW]
[ROW][C]113[/C][C]580[/C][C]580.909086321425[/C][C]-0.909086321425183[/C][/ROW]
[ROW][C]114[/C][C]575[/C][C]574.910336354497[/C][C]0.0896636455027791[/C][/ROW]
[ROW][C]115[/C][C]563[/C][C]563.930564533713[/C][C]-0.930564533713409[/C][/ROW]
[ROW][C]116[/C][C]552[/C][C]550.94615297248[/C][C]1.05384702751967[/C][/ROW]
[ROW][C]117[/C][C]537[/C][C]536.976957580619[/C][C]0.0230424193807341[/C][/ROW]
[ROW][C]118[/C][C]545[/C][C]544.97908248458[/C][C]0.0209175154204943[/C][/ROW]
[ROW][C]119[/C][C]601[/C][C]600.867815993868[/C][C]0.132184006131612[/C][/ROW]
[ROW][C]120[/C][C]604[/C][C]604.839163087387[/C][C]-0.839163087386618[/C][/ROW]
[ROW][C]121[/C][C]586[/C][C]586.843015656408[/C][C]-0.843015656408399[/C][/ROW]
[ROW][C]122[/C][C]564[/C][C]563.91750149316[/C][C]0.0824985068397358[/C][/ROW]
[ROW][C]123[/C][C]549[/C][C]547.976610643294[/C][C]1.02338935670594[/C][/ROW]
[ROW][C]124[/C][C]551[/C][C]551.041644454549[/C][C]-0.0416444545493731[/C][/ROW]
[ROW][C]125[/C][C]556[/C][C]556.082250730037[/C][C]-0.082250730037493[/C][/ROW]
[ROW][C]126[/C][C]548[/C][C]548.132797645704[/C][C]-0.132797645703873[/C][/ROW]
[ROW][C]127[/C][C]540[/C][C]540.157137593341[/C][C]-0.157137593340795[/C][/ROW]
[ROW][C]128[/C][C]531[/C][C]531.161013172407[/C][C]-0.16101317240676[/C][/ROW]
[ROW][C]129[/C][C]521[/C][C]520.194406861982[/C][C]0.805593138018239[/C][/ROW]
[ROW][C]130[/C][C]519[/C][C]518.181816238604[/C][C]0.81818376139633[/C][/ROW]
[ROW][C]131[/C][C]572[/C][C]572.027317926411[/C][C]-0.0273179264105021[/C][/ROW]
[ROW][C]132[/C][C]581[/C][C]581.9855870977[/C][C]-0.985587097700198[/C][/ROW]
[ROW][C]133[/C][C]563[/C][C]562.971886841241[/C][C]0.0281131587591395[/C][/ROW]
[ROW][C]134[/C][C]548[/C][C]548.02385475762[/C][C]-0.023854757619615[/C][/ROW]
[ROW][C]135[/C][C]539[/C][C]539.051758273063[/C][C]-0.0517582730630412[/C][/ROW]
[ROW][C]136[/C][C]541[/C][C]541.074121806634[/C][C]-0.0741218066337642[/C][/ROW]
[ROW][C]137[/C][C]562[/C][C]561.066043866897[/C][C]0.933956133102586[/C][/ROW]
[ROW][C]138[/C][C]559[/C][C]559.066618753878[/C][C]-0.0666187538781284[/C][/ROW]
[ROW][C]139[/C][C]546[/C][C]546.07546987823[/C][C]-0.075469878230393[/C][/ROW]
[ROW][C]140[/C][C]536[/C][C]537.061264659787[/C][C]-1.0612646597867[/C][/ROW]
[ROW][C]141[/C][C]528[/C][C]527.059539355216[/C][C]0.940460644783808[/C][/ROW]
[ROW][C]142[/C][C]530[/C][C]531.04691226728[/C][C]-1.04691226728023[/C][/ROW]
[ROW][C]143[/C][C]582[/C][C]581.925629464282[/C][C]0.0743705357178721[/C][/ROW]
[ROW][C]144[/C][C]599[/C][C]598.877562347709[/C][C]0.122437652291408[/C][/ROW]
[ROW][C]145[/C][C]584[/C][C]583.849846147208[/C][C]0.150153852791857[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186312&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186312&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
1501501.961174087868-0.961174087867868
2485485.002379837339-0.00237983733893331
3464464.064514132856-0.0645141328564004
4460460.104994928495-0.104994928494615
5467467.15036499712-0.150364997120161
6460460.17374380157-0.173743801569872
7448448.201237267229-0.201237267229252
8443443.192310470768-0.192310470768267
9436436.219639099231-0.219639099230981
10431431.238617245375-0.238617245375124
11484484.110124710787-0.110124710787306
12510509.0245521362180.975447863781942
13513512.9878458770020.0121541229981904
14503502.9649017093380.0350982906620005
15471471.008672103039-0.008672103038878
16471471.011532411226-0.0115324112258603
17476476.025447312623-0.0254473126230187
18475474.0419239654610.958076034538619
19470470.058676165001-0.058676165001329
20461461.082629856432-0.0826298564317625
21455455.090741256607-0.0907412566066105
22456455.0907412566070.909258743393389
23517516.8629841306890.137015869310556
24525525.791500429277-0.791500429277056
25523522.7731336346710.226866365328494
26519518.8296402488560.170359751144423
27509508.8624547314990.137545268500796
28512511.8836818342920.116318165708262
29519518.8763288483880.123671151612173
30517516.883589814880.116410185119621
31510509.9109184433430.0890815566569192
32509509.930770033214-0.930770033213723
33501499.9726761495151.02732385048499
34507506.9522088881550.0477911118448778
35569569.746005646635-0.74600564663489
36580579.7091901050750.290809894924565
37578577.6925984227820.307401577218483
38565565.759846303085-0.759846303085019
39547547.786410770164-0.786410770164427
40555554.800050124860.199949875139999
41562561.7926971389560.207302861043908
42561560.8147746754310.185225324569069
43555555.885459178064-0.885459178063843
44544543.9544562678460.0455437321541697
45537537.001687721082-0.00168772108200801
46543542.963492346650.0365076533499898
47594593.7120326387980.287967361201908
48611610.6347957762020.36520422379756
49613612.6462968837550.353703116245475
50611610.7740130798110.225986920188908
51594593.8382084847360.161791515263921
52595595.873562241075-0.873562241074632
53591590.9134814929150.0865185070845685
54589589.923607087546-0.923607087545618
55584583.9407588864750.0592411135246963
56573572.9609870656910.039012934308515
57567566.9819646359460.0180353640539828
58569568.9895159194850.0104840805149006
59621620.7784234049780.22157659502241
60629628.7328430113650.267156988635107
61628627.7310678990530.26893210094674
62612611.799341500630.200658499369957
63595595.857185847347-0.857185847346866
64597596.9025411456270.0974588543725546
65593592.9429707063630.0570292936371271
66590589.957372906610.0426270933895265
67580579.9911528523920.00884714760804069
68574574.001443284219-0.00144328421882253
69573573.018306193264-0.0183061932636766
70573573.038084965349-0.0380849653489946
71620619.8948166452870.105183354712594
72626625.8816659052740.118334094726439
73620619.8827918856570.117208114342562
74588586.9473021946311.05269780536917
75566565.9922699208740.00773007912632591
76557558.058543314807-1.05854331480672
77561561.054528912708-0.0545289127076374
78549549.084758633866-0.0847586338658535
79532532.106112793466-0.10611279346592
80526526.111188597863-0.111188597862802
81511511.152170035535-0.152170035534762
82499499.201315535446-0.201315535446108
83555555.078148337793-0.0781483377931809
84565564.0703229347140.929677065285787
85542542.094753474601-0.0947534746007814
86527527.110566225166-0.110566225166448
87510510.149035719138-0.149035719138325
88514514.158872423884-0.15887242388368
89517517.176149702663-0.17614970266322
90508507.2179317662760.782068233723647
91493493.252737433331-0.252737433331119
92490490.254273463499-0.254273463499046
93469469.284728279989-0.284728279989383
94478477.2592988165260.740701183473541
95528529.097125168106-1.09712516810605
96534533.0566443724680.94335562753221
97518518.05908496401-0.0590849640098026
98506506.100476451466-0.100476451465801
99502502.117527991285-0.11752799128489
100516516.097833914541-0.0978339145409005
101528528.061114984344-0.061114984344326
102533533.034609348239-0.0346093482386506
103536536.019241684853-0.0192416848531983
104537537.042308478043-0.0423084780434488
105524523.0772381977860.922761802213626
106536536.057948040383-0.0579480403827707
107587586.88908399250.11091600750011
108597595.8506173913171.14938260868304
109581580.8360667011750.163933298824692
110564564.898036159496-0.898036159496104
111558556.9248032441611.07519675583866
112575574.9119102763850.0880897236152704
113580580.909086321425-0.909086321425183
114575574.9103363544970.0896636455027791
115563563.930564533713-0.930564533713409
116552550.946152972481.05384702751967
117537536.9769575806190.0230424193807341
118545544.979082484580.0209175154204943
119601600.8678159938680.132184006131612
120604604.839163087387-0.839163087386618
121586586.843015656408-0.843015656408399
122564563.917501493160.0824985068397358
123549547.9766106432941.02338935670594
124551551.041644454549-0.0416444545493731
125556556.082250730037-0.082250730037493
126548548.132797645704-0.132797645703873
127540540.157137593341-0.157137593340795
128531531.161013172407-0.16101317240676
129521520.1944068619820.805593138018239
130519518.1818162386040.81818376139633
131572572.027317926411-0.0273179264105021
132581581.9855870977-0.985587097700198
133563562.9718868412410.0281131587591395
134548548.02385475762-0.023854757619615
135539539.051758273063-0.0517582730630412
136541541.074121806634-0.0741218066337642
137562561.0660438668970.933956133102586
138559559.066618753878-0.0666187538781284
139546546.07546987823-0.075469878230393
140536537.061264659787-1.0612646597867
141528527.0595393552160.940460644783808
142530531.04691226728-1.04691226728023
143582581.9256294642820.0743705357178721
144599598.8775623477090.122437652291408
145584583.8498461472080.150153852791857







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.1318092675865390.2636185351730770.868190732413461
100.05177159397872710.1035431879574540.948228406021273
110.01962502328845280.03925004657690550.980374976711547
120.1681930937973680.3363861875947350.831806906202632
130.1505594805136930.3011189610273850.849440519486307
140.1030512096831240.2061024193662480.896948790316876
150.06449734153411030.1289946830682210.93550265846589
160.06515478274845450.1303095654969090.934845217251545
170.04633519657365060.09267039314730120.953664803426349
180.1728293576326180.3456587152652360.827170642367382
190.1563928972793960.3127857945587920.843607102720604
200.119270777483440.238541554966880.88072922251656
210.08182820218009890.1636564043601980.918171797819901
220.1910510347220770.3821020694441550.808948965277922
230.1466106346859780.2932212693719560.853389365314022
240.2205111937573890.4410223875147770.779488806242612
250.2267473264061870.4534946528123740.773252673593813
260.1808307457435070.3616614914870130.819169254256493
270.1396202435028470.2792404870056930.860379756497153
280.1048597103067850.2097194206135710.895140289693215
290.08039498400304420.1607899680060880.919605015996956
300.05844598257717310.1168919651543460.941554017422827
310.04173818689077770.08347637378155540.958261813109222
320.1192287001132040.2384574002264090.880771299886796
330.2574589303648920.5149178607297840.742541069635108
340.2104285986509260.4208571973018510.789571401349074
350.2446735863900110.4893471727800230.755326413609989
360.2240439175842170.4480878351684330.775956082415783
370.2118313656236960.4236627312473920.788168634376304
380.2785252184411260.5570504368822520.721474781558874
390.3139534104009380.6279068208018750.686046589599062
400.2696313073776230.5392626147552470.730368692622377
410.2254317830393130.4508635660786270.774568216960687
420.1857659621465250.371531924293050.814234037853475
430.3238130339830960.6476260679661910.676186966016904
440.280217731209770.5604354624195410.71978226879023
450.2372180646935680.4744361293871370.762781935306431
460.2019887261307630.4039774522615260.798011273869237
470.1974529050261760.3949058100523520.802547094973824
480.1897524932045580.3795049864091170.810247506795442
490.1696636760289120.3393273520578230.830336323971088
500.1405238415486920.2810476830973830.859476158451308
510.1157888031007610.2315776062015230.884211196899239
520.1734302010101710.3468604020203420.826569798989829
530.1426211623091690.2852423246183370.857378837690831
540.2175214862101890.4350429724203780.782478513789811
550.1842530089961920.3685060179923840.815746991003808
560.1533529896625680.3067059793251360.846647010337432
570.1252197588329430.2504395176658870.874780241167057
580.1009734354060050.2019468708120090.899026564593995
590.08468302417284840.1693660483456970.915316975827152
600.06943959157385210.1388791831477040.930560408426148
610.05600154164842930.1120030832968590.943998458351571
620.04371914986323660.08743829972647320.956280850136763
630.07367417997270230.1473483599454050.926325820027298
640.05815256765246860.1163051353049370.941847432347531
650.04499450728135870.08998901456271730.955005492718641
660.03441951120631210.06883902241262410.965580488793688
670.02606854684027350.05213709368054710.973931453159727
680.01981520142502410.03963040285004820.980184798574976
690.01464707114347160.02929414228694310.985352928856528
700.01065209004574820.02130418009149640.989347909954252
710.007692609086176450.01538521817235290.992307390913824
720.005509810566286590.01101962113257320.994490189433713
730.003892204664485950.007784409328971910.996107795335514
740.01122358728423540.02244717456847080.988776412715765
750.008236572733190340.01647314546638070.99176342726681
760.02684060375969760.05368120751939530.973159396240302
770.02004145485476620.04008290970953240.979958545145234
780.0147680869925050.02953617398501010.985231913007495
790.0107817477703230.02156349554064590.989218252229677
800.007792176054785050.01558435210957010.992207823945215
810.00565758724077310.01131517448154620.994342412759227
820.004201897198551550.00840379439710310.995798102801448
830.00293970806417440.005879416128348790.997060291935826
840.009502356748681090.01900471349736220.990497643251319
850.006771659119869090.01354331823973820.993228340880131
860.004755517833862320.009511035667724650.995244482166138
870.003382325539930940.006764651079861880.996617674460069
880.002338660319699580.004677320639399160.9976613396803
890.00159576673741660.00319153347483320.998404233262583
900.003369004588623330.006738009177246670.996630995411377
910.002415261802482630.004830523604965250.997584738197517
920.001702477400033340.003404954800066680.998297522599967
930.001353937152350280.002707874304700550.99864606284765
940.00230429833857060.00460859667714120.997695701661429
950.007330324141167530.01466064828233510.992669675858832
960.01711384889289270.03422769778578550.982886151107107
970.01241601090777940.02483202181555880.987583989092221
980.009057778083915770.01811555616783150.990942221916084
990.006876891863729040.01375378372745810.993123108136271
1000.005135398006726520.0102707960134530.994864601993274
1010.003862990117209410.007725980234418820.996137009882791
1020.002955115937924260.005910231875848530.997044884062076
1030.002386459492767290.004772918985534580.997613540507233
1040.002089237212108690.004178474424217390.997910762787891
1050.003006003885393850.00601200777078770.996993996114606
1060.002272064229738880.004544128459477770.997727935770261
1070.001485157692357690.002970315384715380.998514842307642
1080.007481027772108770.01496205554421750.992518972227891
1090.005404003342981010.0108080066859620.994595996657019
1100.01446153056742290.02892306113484590.985538469432577
1110.03033996938139290.06067993876278580.969660030618607
1120.02197945358015010.04395890716030030.97802054641985
1130.03126667667969210.06253335335938420.968733323320308
1140.02313384155419650.04626768310839290.976866158445803
1150.04885179003263370.09770358006526730.951148209967366
1160.09209201624643240.1841840324928650.907907983753568
1170.06930538625216940.1386107725043390.930694613747831
1180.05066235726115180.1013247145223040.949337642738848
1190.04604086648971720.09208173297943430.953959133510283
1200.04563588438408930.09127176876817870.954364115615911
1210.06990606182141160.1398121236428230.930093938178588
1220.05416910979450520.108338219589010.945830890205495
1230.09352627821587850.1870525564317570.906473721784122
1240.06988853359618620.1397770671923720.930111466403814
1250.0484002046987060.09680040939741210.951599795301294
1260.03402579461945390.06805158923890780.965974205380546
1270.02621921853978270.05243843707956540.973780781460217
1280.02151206434808840.04302412869617690.978487935651912
1290.0248214306101680.0496428612203360.975178569389832
1300.03742780056518430.07485560113036850.962572199434816
1310.02797221050248220.05594442100496440.972027789497518
1320.05295289422526330.1059057884505270.947047105774737
1330.03192723171309430.06385446342618850.968072768286906
1340.01822335952791260.03644671905582520.981776640472087
1350.01683088495864660.03366176991729330.983169115041353
1360.2929787491460210.5859574982920430.707021250853979

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.131809267586539 & 0.263618535173077 & 0.868190732413461 \tabularnewline
10 & 0.0517715939787271 & 0.103543187957454 & 0.948228406021273 \tabularnewline
11 & 0.0196250232884528 & 0.0392500465769055 & 0.980374976711547 \tabularnewline
12 & 0.168193093797368 & 0.336386187594735 & 0.831806906202632 \tabularnewline
13 & 0.150559480513693 & 0.301118961027385 & 0.849440519486307 \tabularnewline
14 & 0.103051209683124 & 0.206102419366248 & 0.896948790316876 \tabularnewline
15 & 0.0644973415341103 & 0.128994683068221 & 0.93550265846589 \tabularnewline
16 & 0.0651547827484545 & 0.130309565496909 & 0.934845217251545 \tabularnewline
17 & 0.0463351965736506 & 0.0926703931473012 & 0.953664803426349 \tabularnewline
18 & 0.172829357632618 & 0.345658715265236 & 0.827170642367382 \tabularnewline
19 & 0.156392897279396 & 0.312785794558792 & 0.843607102720604 \tabularnewline
20 & 0.11927077748344 & 0.23854155496688 & 0.88072922251656 \tabularnewline
21 & 0.0818282021800989 & 0.163656404360198 & 0.918171797819901 \tabularnewline
22 & 0.191051034722077 & 0.382102069444155 & 0.808948965277922 \tabularnewline
23 & 0.146610634685978 & 0.293221269371956 & 0.853389365314022 \tabularnewline
24 & 0.220511193757389 & 0.441022387514777 & 0.779488806242612 \tabularnewline
25 & 0.226747326406187 & 0.453494652812374 & 0.773252673593813 \tabularnewline
26 & 0.180830745743507 & 0.361661491487013 & 0.819169254256493 \tabularnewline
27 & 0.139620243502847 & 0.279240487005693 & 0.860379756497153 \tabularnewline
28 & 0.104859710306785 & 0.209719420613571 & 0.895140289693215 \tabularnewline
29 & 0.0803949840030442 & 0.160789968006088 & 0.919605015996956 \tabularnewline
30 & 0.0584459825771731 & 0.116891965154346 & 0.941554017422827 \tabularnewline
31 & 0.0417381868907777 & 0.0834763737815554 & 0.958261813109222 \tabularnewline
32 & 0.119228700113204 & 0.238457400226409 & 0.880771299886796 \tabularnewline
33 & 0.257458930364892 & 0.514917860729784 & 0.742541069635108 \tabularnewline
34 & 0.210428598650926 & 0.420857197301851 & 0.789571401349074 \tabularnewline
35 & 0.244673586390011 & 0.489347172780023 & 0.755326413609989 \tabularnewline
36 & 0.224043917584217 & 0.448087835168433 & 0.775956082415783 \tabularnewline
37 & 0.211831365623696 & 0.423662731247392 & 0.788168634376304 \tabularnewline
38 & 0.278525218441126 & 0.557050436882252 & 0.721474781558874 \tabularnewline
39 & 0.313953410400938 & 0.627906820801875 & 0.686046589599062 \tabularnewline
40 & 0.269631307377623 & 0.539262614755247 & 0.730368692622377 \tabularnewline
41 & 0.225431783039313 & 0.450863566078627 & 0.774568216960687 \tabularnewline
42 & 0.185765962146525 & 0.37153192429305 & 0.814234037853475 \tabularnewline
43 & 0.323813033983096 & 0.647626067966191 & 0.676186966016904 \tabularnewline
44 & 0.28021773120977 & 0.560435462419541 & 0.71978226879023 \tabularnewline
45 & 0.237218064693568 & 0.474436129387137 & 0.762781935306431 \tabularnewline
46 & 0.201988726130763 & 0.403977452261526 & 0.798011273869237 \tabularnewline
47 & 0.197452905026176 & 0.394905810052352 & 0.802547094973824 \tabularnewline
48 & 0.189752493204558 & 0.379504986409117 & 0.810247506795442 \tabularnewline
49 & 0.169663676028912 & 0.339327352057823 & 0.830336323971088 \tabularnewline
50 & 0.140523841548692 & 0.281047683097383 & 0.859476158451308 \tabularnewline
51 & 0.115788803100761 & 0.231577606201523 & 0.884211196899239 \tabularnewline
52 & 0.173430201010171 & 0.346860402020342 & 0.826569798989829 \tabularnewline
53 & 0.142621162309169 & 0.285242324618337 & 0.857378837690831 \tabularnewline
54 & 0.217521486210189 & 0.435042972420378 & 0.782478513789811 \tabularnewline
55 & 0.184253008996192 & 0.368506017992384 & 0.815746991003808 \tabularnewline
56 & 0.153352989662568 & 0.306705979325136 & 0.846647010337432 \tabularnewline
57 & 0.125219758832943 & 0.250439517665887 & 0.874780241167057 \tabularnewline
58 & 0.100973435406005 & 0.201946870812009 & 0.899026564593995 \tabularnewline
59 & 0.0846830241728484 & 0.169366048345697 & 0.915316975827152 \tabularnewline
60 & 0.0694395915738521 & 0.138879183147704 & 0.930560408426148 \tabularnewline
61 & 0.0560015416484293 & 0.112003083296859 & 0.943998458351571 \tabularnewline
62 & 0.0437191498632366 & 0.0874382997264732 & 0.956280850136763 \tabularnewline
63 & 0.0736741799727023 & 0.147348359945405 & 0.926325820027298 \tabularnewline
64 & 0.0581525676524686 & 0.116305135304937 & 0.941847432347531 \tabularnewline
65 & 0.0449945072813587 & 0.0899890145627173 & 0.955005492718641 \tabularnewline
66 & 0.0344195112063121 & 0.0688390224126241 & 0.965580488793688 \tabularnewline
67 & 0.0260685468402735 & 0.0521370936805471 & 0.973931453159727 \tabularnewline
68 & 0.0198152014250241 & 0.0396304028500482 & 0.980184798574976 \tabularnewline
69 & 0.0146470711434716 & 0.0292941422869431 & 0.985352928856528 \tabularnewline
70 & 0.0106520900457482 & 0.0213041800914964 & 0.989347909954252 \tabularnewline
71 & 0.00769260908617645 & 0.0153852181723529 & 0.992307390913824 \tabularnewline
72 & 0.00550981056628659 & 0.0110196211325732 & 0.994490189433713 \tabularnewline
73 & 0.00389220466448595 & 0.00778440932897191 & 0.996107795335514 \tabularnewline
74 & 0.0112235872842354 & 0.0224471745684708 & 0.988776412715765 \tabularnewline
75 & 0.00823657273319034 & 0.0164731454663807 & 0.99176342726681 \tabularnewline
76 & 0.0268406037596976 & 0.0536812075193953 & 0.973159396240302 \tabularnewline
77 & 0.0200414548547662 & 0.0400829097095324 & 0.979958545145234 \tabularnewline
78 & 0.014768086992505 & 0.0295361739850101 & 0.985231913007495 \tabularnewline
79 & 0.010781747770323 & 0.0215634955406459 & 0.989218252229677 \tabularnewline
80 & 0.00779217605478505 & 0.0155843521095701 & 0.992207823945215 \tabularnewline
81 & 0.0056575872407731 & 0.0113151744815462 & 0.994342412759227 \tabularnewline
82 & 0.00420189719855155 & 0.0084037943971031 & 0.995798102801448 \tabularnewline
83 & 0.0029397080641744 & 0.00587941612834879 & 0.997060291935826 \tabularnewline
84 & 0.00950235674868109 & 0.0190047134973622 & 0.990497643251319 \tabularnewline
85 & 0.00677165911986909 & 0.0135433182397382 & 0.993228340880131 \tabularnewline
86 & 0.00475551783386232 & 0.00951103566772465 & 0.995244482166138 \tabularnewline
87 & 0.00338232553993094 & 0.00676465107986188 & 0.996617674460069 \tabularnewline
88 & 0.00233866031969958 & 0.00467732063939916 & 0.9976613396803 \tabularnewline
89 & 0.0015957667374166 & 0.0031915334748332 & 0.998404233262583 \tabularnewline
90 & 0.00336900458862333 & 0.00673800917724667 & 0.996630995411377 \tabularnewline
91 & 0.00241526180248263 & 0.00483052360496525 & 0.997584738197517 \tabularnewline
92 & 0.00170247740003334 & 0.00340495480006668 & 0.998297522599967 \tabularnewline
93 & 0.00135393715235028 & 0.00270787430470055 & 0.99864606284765 \tabularnewline
94 & 0.0023042983385706 & 0.0046085966771412 & 0.997695701661429 \tabularnewline
95 & 0.00733032414116753 & 0.0146606482823351 & 0.992669675858832 \tabularnewline
96 & 0.0171138488928927 & 0.0342276977857855 & 0.982886151107107 \tabularnewline
97 & 0.0124160109077794 & 0.0248320218155588 & 0.987583989092221 \tabularnewline
98 & 0.00905777808391577 & 0.0181155561678315 & 0.990942221916084 \tabularnewline
99 & 0.00687689186372904 & 0.0137537837274581 & 0.993123108136271 \tabularnewline
100 & 0.00513539800672652 & 0.010270796013453 & 0.994864601993274 \tabularnewline
101 & 0.00386299011720941 & 0.00772598023441882 & 0.996137009882791 \tabularnewline
102 & 0.00295511593792426 & 0.00591023187584853 & 0.997044884062076 \tabularnewline
103 & 0.00238645949276729 & 0.00477291898553458 & 0.997613540507233 \tabularnewline
104 & 0.00208923721210869 & 0.00417847442421739 & 0.997910762787891 \tabularnewline
105 & 0.00300600388539385 & 0.0060120077707877 & 0.996993996114606 \tabularnewline
106 & 0.00227206422973888 & 0.00454412845947777 & 0.997727935770261 \tabularnewline
107 & 0.00148515769235769 & 0.00297031538471538 & 0.998514842307642 \tabularnewline
108 & 0.00748102777210877 & 0.0149620555442175 & 0.992518972227891 \tabularnewline
109 & 0.00540400334298101 & 0.010808006685962 & 0.994595996657019 \tabularnewline
110 & 0.0144615305674229 & 0.0289230611348459 & 0.985538469432577 \tabularnewline
111 & 0.0303399693813929 & 0.0606799387627858 & 0.969660030618607 \tabularnewline
112 & 0.0219794535801501 & 0.0439589071603003 & 0.97802054641985 \tabularnewline
113 & 0.0312666766796921 & 0.0625333533593842 & 0.968733323320308 \tabularnewline
114 & 0.0231338415541965 & 0.0462676831083929 & 0.976866158445803 \tabularnewline
115 & 0.0488517900326337 & 0.0977035800652673 & 0.951148209967366 \tabularnewline
116 & 0.0920920162464324 & 0.184184032492865 & 0.907907983753568 \tabularnewline
117 & 0.0693053862521694 & 0.138610772504339 & 0.930694613747831 \tabularnewline
118 & 0.0506623572611518 & 0.101324714522304 & 0.949337642738848 \tabularnewline
119 & 0.0460408664897172 & 0.0920817329794343 & 0.953959133510283 \tabularnewline
120 & 0.0456358843840893 & 0.0912717687681787 & 0.954364115615911 \tabularnewline
121 & 0.0699060618214116 & 0.139812123642823 & 0.930093938178588 \tabularnewline
122 & 0.0541691097945052 & 0.10833821958901 & 0.945830890205495 \tabularnewline
123 & 0.0935262782158785 & 0.187052556431757 & 0.906473721784122 \tabularnewline
124 & 0.0698885335961862 & 0.139777067192372 & 0.930111466403814 \tabularnewline
125 & 0.048400204698706 & 0.0968004093974121 & 0.951599795301294 \tabularnewline
126 & 0.0340257946194539 & 0.0680515892389078 & 0.965974205380546 \tabularnewline
127 & 0.0262192185397827 & 0.0524384370795654 & 0.973780781460217 \tabularnewline
128 & 0.0215120643480884 & 0.0430241286961769 & 0.978487935651912 \tabularnewline
129 & 0.024821430610168 & 0.049642861220336 & 0.975178569389832 \tabularnewline
130 & 0.0374278005651843 & 0.0748556011303685 & 0.962572199434816 \tabularnewline
131 & 0.0279722105024822 & 0.0559444210049644 & 0.972027789497518 \tabularnewline
132 & 0.0529528942252633 & 0.105905788450527 & 0.947047105774737 \tabularnewline
133 & 0.0319272317130943 & 0.0638544634261885 & 0.968072768286906 \tabularnewline
134 & 0.0182233595279126 & 0.0364467190558252 & 0.981776640472087 \tabularnewline
135 & 0.0168308849586466 & 0.0336617699172933 & 0.983169115041353 \tabularnewline
136 & 0.292978749146021 & 0.585957498292043 & 0.707021250853979 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186312&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]0.131809267586539[/C][C]0.263618535173077[/C][C]0.868190732413461[/C][/ROW]
[ROW][C]10[/C][C]0.0517715939787271[/C][C]0.103543187957454[/C][C]0.948228406021273[/C][/ROW]
[ROW][C]11[/C][C]0.0196250232884528[/C][C]0.0392500465769055[/C][C]0.980374976711547[/C][/ROW]
[ROW][C]12[/C][C]0.168193093797368[/C][C]0.336386187594735[/C][C]0.831806906202632[/C][/ROW]
[ROW][C]13[/C][C]0.150559480513693[/C][C]0.301118961027385[/C][C]0.849440519486307[/C][/ROW]
[ROW][C]14[/C][C]0.103051209683124[/C][C]0.206102419366248[/C][C]0.896948790316876[/C][/ROW]
[ROW][C]15[/C][C]0.0644973415341103[/C][C]0.128994683068221[/C][C]0.93550265846589[/C][/ROW]
[ROW][C]16[/C][C]0.0651547827484545[/C][C]0.130309565496909[/C][C]0.934845217251545[/C][/ROW]
[ROW][C]17[/C][C]0.0463351965736506[/C][C]0.0926703931473012[/C][C]0.953664803426349[/C][/ROW]
[ROW][C]18[/C][C]0.172829357632618[/C][C]0.345658715265236[/C][C]0.827170642367382[/C][/ROW]
[ROW][C]19[/C][C]0.156392897279396[/C][C]0.312785794558792[/C][C]0.843607102720604[/C][/ROW]
[ROW][C]20[/C][C]0.11927077748344[/C][C]0.23854155496688[/C][C]0.88072922251656[/C][/ROW]
[ROW][C]21[/C][C]0.0818282021800989[/C][C]0.163656404360198[/C][C]0.918171797819901[/C][/ROW]
[ROW][C]22[/C][C]0.191051034722077[/C][C]0.382102069444155[/C][C]0.808948965277922[/C][/ROW]
[ROW][C]23[/C][C]0.146610634685978[/C][C]0.293221269371956[/C][C]0.853389365314022[/C][/ROW]
[ROW][C]24[/C][C]0.220511193757389[/C][C]0.441022387514777[/C][C]0.779488806242612[/C][/ROW]
[ROW][C]25[/C][C]0.226747326406187[/C][C]0.453494652812374[/C][C]0.773252673593813[/C][/ROW]
[ROW][C]26[/C][C]0.180830745743507[/C][C]0.361661491487013[/C][C]0.819169254256493[/C][/ROW]
[ROW][C]27[/C][C]0.139620243502847[/C][C]0.279240487005693[/C][C]0.860379756497153[/C][/ROW]
[ROW][C]28[/C][C]0.104859710306785[/C][C]0.209719420613571[/C][C]0.895140289693215[/C][/ROW]
[ROW][C]29[/C][C]0.0803949840030442[/C][C]0.160789968006088[/C][C]0.919605015996956[/C][/ROW]
[ROW][C]30[/C][C]0.0584459825771731[/C][C]0.116891965154346[/C][C]0.941554017422827[/C][/ROW]
[ROW][C]31[/C][C]0.0417381868907777[/C][C]0.0834763737815554[/C][C]0.958261813109222[/C][/ROW]
[ROW][C]32[/C][C]0.119228700113204[/C][C]0.238457400226409[/C][C]0.880771299886796[/C][/ROW]
[ROW][C]33[/C][C]0.257458930364892[/C][C]0.514917860729784[/C][C]0.742541069635108[/C][/ROW]
[ROW][C]34[/C][C]0.210428598650926[/C][C]0.420857197301851[/C][C]0.789571401349074[/C][/ROW]
[ROW][C]35[/C][C]0.244673586390011[/C][C]0.489347172780023[/C][C]0.755326413609989[/C][/ROW]
[ROW][C]36[/C][C]0.224043917584217[/C][C]0.448087835168433[/C][C]0.775956082415783[/C][/ROW]
[ROW][C]37[/C][C]0.211831365623696[/C][C]0.423662731247392[/C][C]0.788168634376304[/C][/ROW]
[ROW][C]38[/C][C]0.278525218441126[/C][C]0.557050436882252[/C][C]0.721474781558874[/C][/ROW]
[ROW][C]39[/C][C]0.313953410400938[/C][C]0.627906820801875[/C][C]0.686046589599062[/C][/ROW]
[ROW][C]40[/C][C]0.269631307377623[/C][C]0.539262614755247[/C][C]0.730368692622377[/C][/ROW]
[ROW][C]41[/C][C]0.225431783039313[/C][C]0.450863566078627[/C][C]0.774568216960687[/C][/ROW]
[ROW][C]42[/C][C]0.185765962146525[/C][C]0.37153192429305[/C][C]0.814234037853475[/C][/ROW]
[ROW][C]43[/C][C]0.323813033983096[/C][C]0.647626067966191[/C][C]0.676186966016904[/C][/ROW]
[ROW][C]44[/C][C]0.28021773120977[/C][C]0.560435462419541[/C][C]0.71978226879023[/C][/ROW]
[ROW][C]45[/C][C]0.237218064693568[/C][C]0.474436129387137[/C][C]0.762781935306431[/C][/ROW]
[ROW][C]46[/C][C]0.201988726130763[/C][C]0.403977452261526[/C][C]0.798011273869237[/C][/ROW]
[ROW][C]47[/C][C]0.197452905026176[/C][C]0.394905810052352[/C][C]0.802547094973824[/C][/ROW]
[ROW][C]48[/C][C]0.189752493204558[/C][C]0.379504986409117[/C][C]0.810247506795442[/C][/ROW]
[ROW][C]49[/C][C]0.169663676028912[/C][C]0.339327352057823[/C][C]0.830336323971088[/C][/ROW]
[ROW][C]50[/C][C]0.140523841548692[/C][C]0.281047683097383[/C][C]0.859476158451308[/C][/ROW]
[ROW][C]51[/C][C]0.115788803100761[/C][C]0.231577606201523[/C][C]0.884211196899239[/C][/ROW]
[ROW][C]52[/C][C]0.173430201010171[/C][C]0.346860402020342[/C][C]0.826569798989829[/C][/ROW]
[ROW][C]53[/C][C]0.142621162309169[/C][C]0.285242324618337[/C][C]0.857378837690831[/C][/ROW]
[ROW][C]54[/C][C]0.217521486210189[/C][C]0.435042972420378[/C][C]0.782478513789811[/C][/ROW]
[ROW][C]55[/C][C]0.184253008996192[/C][C]0.368506017992384[/C][C]0.815746991003808[/C][/ROW]
[ROW][C]56[/C][C]0.153352989662568[/C][C]0.306705979325136[/C][C]0.846647010337432[/C][/ROW]
[ROW][C]57[/C][C]0.125219758832943[/C][C]0.250439517665887[/C][C]0.874780241167057[/C][/ROW]
[ROW][C]58[/C][C]0.100973435406005[/C][C]0.201946870812009[/C][C]0.899026564593995[/C][/ROW]
[ROW][C]59[/C][C]0.0846830241728484[/C][C]0.169366048345697[/C][C]0.915316975827152[/C][/ROW]
[ROW][C]60[/C][C]0.0694395915738521[/C][C]0.138879183147704[/C][C]0.930560408426148[/C][/ROW]
[ROW][C]61[/C][C]0.0560015416484293[/C][C]0.112003083296859[/C][C]0.943998458351571[/C][/ROW]
[ROW][C]62[/C][C]0.0437191498632366[/C][C]0.0874382997264732[/C][C]0.956280850136763[/C][/ROW]
[ROW][C]63[/C][C]0.0736741799727023[/C][C]0.147348359945405[/C][C]0.926325820027298[/C][/ROW]
[ROW][C]64[/C][C]0.0581525676524686[/C][C]0.116305135304937[/C][C]0.941847432347531[/C][/ROW]
[ROW][C]65[/C][C]0.0449945072813587[/C][C]0.0899890145627173[/C][C]0.955005492718641[/C][/ROW]
[ROW][C]66[/C][C]0.0344195112063121[/C][C]0.0688390224126241[/C][C]0.965580488793688[/C][/ROW]
[ROW][C]67[/C][C]0.0260685468402735[/C][C]0.0521370936805471[/C][C]0.973931453159727[/C][/ROW]
[ROW][C]68[/C][C]0.0198152014250241[/C][C]0.0396304028500482[/C][C]0.980184798574976[/C][/ROW]
[ROW][C]69[/C][C]0.0146470711434716[/C][C]0.0292941422869431[/C][C]0.985352928856528[/C][/ROW]
[ROW][C]70[/C][C]0.0106520900457482[/C][C]0.0213041800914964[/C][C]0.989347909954252[/C][/ROW]
[ROW][C]71[/C][C]0.00769260908617645[/C][C]0.0153852181723529[/C][C]0.992307390913824[/C][/ROW]
[ROW][C]72[/C][C]0.00550981056628659[/C][C]0.0110196211325732[/C][C]0.994490189433713[/C][/ROW]
[ROW][C]73[/C][C]0.00389220466448595[/C][C]0.00778440932897191[/C][C]0.996107795335514[/C][/ROW]
[ROW][C]74[/C][C]0.0112235872842354[/C][C]0.0224471745684708[/C][C]0.988776412715765[/C][/ROW]
[ROW][C]75[/C][C]0.00823657273319034[/C][C]0.0164731454663807[/C][C]0.99176342726681[/C][/ROW]
[ROW][C]76[/C][C]0.0268406037596976[/C][C]0.0536812075193953[/C][C]0.973159396240302[/C][/ROW]
[ROW][C]77[/C][C]0.0200414548547662[/C][C]0.0400829097095324[/C][C]0.979958545145234[/C][/ROW]
[ROW][C]78[/C][C]0.014768086992505[/C][C]0.0295361739850101[/C][C]0.985231913007495[/C][/ROW]
[ROW][C]79[/C][C]0.010781747770323[/C][C]0.0215634955406459[/C][C]0.989218252229677[/C][/ROW]
[ROW][C]80[/C][C]0.00779217605478505[/C][C]0.0155843521095701[/C][C]0.992207823945215[/C][/ROW]
[ROW][C]81[/C][C]0.0056575872407731[/C][C]0.0113151744815462[/C][C]0.994342412759227[/C][/ROW]
[ROW][C]82[/C][C]0.00420189719855155[/C][C]0.0084037943971031[/C][C]0.995798102801448[/C][/ROW]
[ROW][C]83[/C][C]0.0029397080641744[/C][C]0.00587941612834879[/C][C]0.997060291935826[/C][/ROW]
[ROW][C]84[/C][C]0.00950235674868109[/C][C]0.0190047134973622[/C][C]0.990497643251319[/C][/ROW]
[ROW][C]85[/C][C]0.00677165911986909[/C][C]0.0135433182397382[/C][C]0.993228340880131[/C][/ROW]
[ROW][C]86[/C][C]0.00475551783386232[/C][C]0.00951103566772465[/C][C]0.995244482166138[/C][/ROW]
[ROW][C]87[/C][C]0.00338232553993094[/C][C]0.00676465107986188[/C][C]0.996617674460069[/C][/ROW]
[ROW][C]88[/C][C]0.00233866031969958[/C][C]0.00467732063939916[/C][C]0.9976613396803[/C][/ROW]
[ROW][C]89[/C][C]0.0015957667374166[/C][C]0.0031915334748332[/C][C]0.998404233262583[/C][/ROW]
[ROW][C]90[/C][C]0.00336900458862333[/C][C]0.00673800917724667[/C][C]0.996630995411377[/C][/ROW]
[ROW][C]91[/C][C]0.00241526180248263[/C][C]0.00483052360496525[/C][C]0.997584738197517[/C][/ROW]
[ROW][C]92[/C][C]0.00170247740003334[/C][C]0.00340495480006668[/C][C]0.998297522599967[/C][/ROW]
[ROW][C]93[/C][C]0.00135393715235028[/C][C]0.00270787430470055[/C][C]0.99864606284765[/C][/ROW]
[ROW][C]94[/C][C]0.0023042983385706[/C][C]0.0046085966771412[/C][C]0.997695701661429[/C][/ROW]
[ROW][C]95[/C][C]0.00733032414116753[/C][C]0.0146606482823351[/C][C]0.992669675858832[/C][/ROW]
[ROW][C]96[/C][C]0.0171138488928927[/C][C]0.0342276977857855[/C][C]0.982886151107107[/C][/ROW]
[ROW][C]97[/C][C]0.0124160109077794[/C][C]0.0248320218155588[/C][C]0.987583989092221[/C][/ROW]
[ROW][C]98[/C][C]0.00905777808391577[/C][C]0.0181155561678315[/C][C]0.990942221916084[/C][/ROW]
[ROW][C]99[/C][C]0.00687689186372904[/C][C]0.0137537837274581[/C][C]0.993123108136271[/C][/ROW]
[ROW][C]100[/C][C]0.00513539800672652[/C][C]0.010270796013453[/C][C]0.994864601993274[/C][/ROW]
[ROW][C]101[/C][C]0.00386299011720941[/C][C]0.00772598023441882[/C][C]0.996137009882791[/C][/ROW]
[ROW][C]102[/C][C]0.00295511593792426[/C][C]0.00591023187584853[/C][C]0.997044884062076[/C][/ROW]
[ROW][C]103[/C][C]0.00238645949276729[/C][C]0.00477291898553458[/C][C]0.997613540507233[/C][/ROW]
[ROW][C]104[/C][C]0.00208923721210869[/C][C]0.00417847442421739[/C][C]0.997910762787891[/C][/ROW]
[ROW][C]105[/C][C]0.00300600388539385[/C][C]0.0060120077707877[/C][C]0.996993996114606[/C][/ROW]
[ROW][C]106[/C][C]0.00227206422973888[/C][C]0.00454412845947777[/C][C]0.997727935770261[/C][/ROW]
[ROW][C]107[/C][C]0.00148515769235769[/C][C]0.00297031538471538[/C][C]0.998514842307642[/C][/ROW]
[ROW][C]108[/C][C]0.00748102777210877[/C][C]0.0149620555442175[/C][C]0.992518972227891[/C][/ROW]
[ROW][C]109[/C][C]0.00540400334298101[/C][C]0.010808006685962[/C][C]0.994595996657019[/C][/ROW]
[ROW][C]110[/C][C]0.0144615305674229[/C][C]0.0289230611348459[/C][C]0.985538469432577[/C][/ROW]
[ROW][C]111[/C][C]0.0303399693813929[/C][C]0.0606799387627858[/C][C]0.969660030618607[/C][/ROW]
[ROW][C]112[/C][C]0.0219794535801501[/C][C]0.0439589071603003[/C][C]0.97802054641985[/C][/ROW]
[ROW][C]113[/C][C]0.0312666766796921[/C][C]0.0625333533593842[/C][C]0.968733323320308[/C][/ROW]
[ROW][C]114[/C][C]0.0231338415541965[/C][C]0.0462676831083929[/C][C]0.976866158445803[/C][/ROW]
[ROW][C]115[/C][C]0.0488517900326337[/C][C]0.0977035800652673[/C][C]0.951148209967366[/C][/ROW]
[ROW][C]116[/C][C]0.0920920162464324[/C][C]0.184184032492865[/C][C]0.907907983753568[/C][/ROW]
[ROW][C]117[/C][C]0.0693053862521694[/C][C]0.138610772504339[/C][C]0.930694613747831[/C][/ROW]
[ROW][C]118[/C][C]0.0506623572611518[/C][C]0.101324714522304[/C][C]0.949337642738848[/C][/ROW]
[ROW][C]119[/C][C]0.0460408664897172[/C][C]0.0920817329794343[/C][C]0.953959133510283[/C][/ROW]
[ROW][C]120[/C][C]0.0456358843840893[/C][C]0.0912717687681787[/C][C]0.954364115615911[/C][/ROW]
[ROW][C]121[/C][C]0.0699060618214116[/C][C]0.139812123642823[/C][C]0.930093938178588[/C][/ROW]
[ROW][C]122[/C][C]0.0541691097945052[/C][C]0.10833821958901[/C][C]0.945830890205495[/C][/ROW]
[ROW][C]123[/C][C]0.0935262782158785[/C][C]0.187052556431757[/C][C]0.906473721784122[/C][/ROW]
[ROW][C]124[/C][C]0.0698885335961862[/C][C]0.139777067192372[/C][C]0.930111466403814[/C][/ROW]
[ROW][C]125[/C][C]0.048400204698706[/C][C]0.0968004093974121[/C][C]0.951599795301294[/C][/ROW]
[ROW][C]126[/C][C]0.0340257946194539[/C][C]0.0680515892389078[/C][C]0.965974205380546[/C][/ROW]
[ROW][C]127[/C][C]0.0262192185397827[/C][C]0.0524384370795654[/C][C]0.973780781460217[/C][/ROW]
[ROW][C]128[/C][C]0.0215120643480884[/C][C]0.0430241286961769[/C][C]0.978487935651912[/C][/ROW]
[ROW][C]129[/C][C]0.024821430610168[/C][C]0.049642861220336[/C][C]0.975178569389832[/C][/ROW]
[ROW][C]130[/C][C]0.0374278005651843[/C][C]0.0748556011303685[/C][C]0.962572199434816[/C][/ROW]
[ROW][C]131[/C][C]0.0279722105024822[/C][C]0.0559444210049644[/C][C]0.972027789497518[/C][/ROW]
[ROW][C]132[/C][C]0.0529528942252633[/C][C]0.105905788450527[/C][C]0.947047105774737[/C][/ROW]
[ROW][C]133[/C][C]0.0319272317130943[/C][C]0.0638544634261885[/C][C]0.968072768286906[/C][/ROW]
[ROW][C]134[/C][C]0.0182233595279126[/C][C]0.0364467190558252[/C][C]0.981776640472087[/C][/ROW]
[ROW][C]135[/C][C]0.0168308849586466[/C][C]0.0336617699172933[/C][C]0.983169115041353[/C][/ROW]
[ROW][C]136[/C][C]0.292978749146021[/C][C]0.585957498292043[/C][C]0.707021250853979[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186312&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186312&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
90.1318092675865390.2636185351730770.868190732413461
100.05177159397872710.1035431879574540.948228406021273
110.01962502328845280.03925004657690550.980374976711547
120.1681930937973680.3363861875947350.831806906202632
130.1505594805136930.3011189610273850.849440519486307
140.1030512096831240.2061024193662480.896948790316876
150.06449734153411030.1289946830682210.93550265846589
160.06515478274845450.1303095654969090.934845217251545
170.04633519657365060.09267039314730120.953664803426349
180.1728293576326180.3456587152652360.827170642367382
190.1563928972793960.3127857945587920.843607102720604
200.119270777483440.238541554966880.88072922251656
210.08182820218009890.1636564043601980.918171797819901
220.1910510347220770.3821020694441550.808948965277922
230.1466106346859780.2932212693719560.853389365314022
240.2205111937573890.4410223875147770.779488806242612
250.2267473264061870.4534946528123740.773252673593813
260.1808307457435070.3616614914870130.819169254256493
270.1396202435028470.2792404870056930.860379756497153
280.1048597103067850.2097194206135710.895140289693215
290.08039498400304420.1607899680060880.919605015996956
300.05844598257717310.1168919651543460.941554017422827
310.04173818689077770.08347637378155540.958261813109222
320.1192287001132040.2384574002264090.880771299886796
330.2574589303648920.5149178607297840.742541069635108
340.2104285986509260.4208571973018510.789571401349074
350.2446735863900110.4893471727800230.755326413609989
360.2240439175842170.4480878351684330.775956082415783
370.2118313656236960.4236627312473920.788168634376304
380.2785252184411260.5570504368822520.721474781558874
390.3139534104009380.6279068208018750.686046589599062
400.2696313073776230.5392626147552470.730368692622377
410.2254317830393130.4508635660786270.774568216960687
420.1857659621465250.371531924293050.814234037853475
430.3238130339830960.6476260679661910.676186966016904
440.280217731209770.5604354624195410.71978226879023
450.2372180646935680.4744361293871370.762781935306431
460.2019887261307630.4039774522615260.798011273869237
470.1974529050261760.3949058100523520.802547094973824
480.1897524932045580.3795049864091170.810247506795442
490.1696636760289120.3393273520578230.830336323971088
500.1405238415486920.2810476830973830.859476158451308
510.1157888031007610.2315776062015230.884211196899239
520.1734302010101710.3468604020203420.826569798989829
530.1426211623091690.2852423246183370.857378837690831
540.2175214862101890.4350429724203780.782478513789811
550.1842530089961920.3685060179923840.815746991003808
560.1533529896625680.3067059793251360.846647010337432
570.1252197588329430.2504395176658870.874780241167057
580.1009734354060050.2019468708120090.899026564593995
590.08468302417284840.1693660483456970.915316975827152
600.06943959157385210.1388791831477040.930560408426148
610.05600154164842930.1120030832968590.943998458351571
620.04371914986323660.08743829972647320.956280850136763
630.07367417997270230.1473483599454050.926325820027298
640.05815256765246860.1163051353049370.941847432347531
650.04499450728135870.08998901456271730.955005492718641
660.03441951120631210.06883902241262410.965580488793688
670.02606854684027350.05213709368054710.973931453159727
680.01981520142502410.03963040285004820.980184798574976
690.01464707114347160.02929414228694310.985352928856528
700.01065209004574820.02130418009149640.989347909954252
710.007692609086176450.01538521817235290.992307390913824
720.005509810566286590.01101962113257320.994490189433713
730.003892204664485950.007784409328971910.996107795335514
740.01122358728423540.02244717456847080.988776412715765
750.008236572733190340.01647314546638070.99176342726681
760.02684060375969760.05368120751939530.973159396240302
770.02004145485476620.04008290970953240.979958545145234
780.0147680869925050.02953617398501010.985231913007495
790.0107817477703230.02156349554064590.989218252229677
800.007792176054785050.01558435210957010.992207823945215
810.00565758724077310.01131517448154620.994342412759227
820.004201897198551550.00840379439710310.995798102801448
830.00293970806417440.005879416128348790.997060291935826
840.009502356748681090.01900471349736220.990497643251319
850.006771659119869090.01354331823973820.993228340880131
860.004755517833862320.009511035667724650.995244482166138
870.003382325539930940.006764651079861880.996617674460069
880.002338660319699580.004677320639399160.9976613396803
890.00159576673741660.00319153347483320.998404233262583
900.003369004588623330.006738009177246670.996630995411377
910.002415261802482630.004830523604965250.997584738197517
920.001702477400033340.003404954800066680.998297522599967
930.001353937152350280.002707874304700550.99864606284765
940.00230429833857060.00460859667714120.997695701661429
950.007330324141167530.01466064828233510.992669675858832
960.01711384889289270.03422769778578550.982886151107107
970.01241601090777940.02483202181555880.987583989092221
980.009057778083915770.01811555616783150.990942221916084
990.006876891863729040.01375378372745810.993123108136271
1000.005135398006726520.0102707960134530.994864601993274
1010.003862990117209410.007725980234418820.996137009882791
1020.002955115937924260.005910231875848530.997044884062076
1030.002386459492767290.004772918985534580.997613540507233
1040.002089237212108690.004178474424217390.997910762787891
1050.003006003885393850.00601200777078770.996993996114606
1060.002272064229738880.004544128459477770.997727935770261
1070.001485157692357690.002970315384715380.998514842307642
1080.007481027772108770.01496205554421750.992518972227891
1090.005404003342981010.0108080066859620.994595996657019
1100.01446153056742290.02892306113484590.985538469432577
1110.03033996938139290.06067993876278580.969660030618607
1120.02197945358015010.04395890716030030.97802054641985
1130.03126667667969210.06253335335938420.968733323320308
1140.02313384155419650.04626768310839290.976866158445803
1150.04885179003263370.09770358006526730.951148209967366
1160.09209201624643240.1841840324928650.907907983753568
1170.06930538625216940.1386107725043390.930694613747831
1180.05066235726115180.1013247145223040.949337642738848
1190.04604086648971720.09208173297943430.953959133510283
1200.04563588438408930.09127176876817870.954364115615911
1210.06990606182141160.1398121236428230.930093938178588
1220.05416910979450520.108338219589010.945830890205495
1230.09352627821587850.1870525564317570.906473721784122
1240.06988853359618620.1397770671923720.930111466403814
1250.0484002046987060.09680040939741210.951599795301294
1260.03402579461945390.06805158923890780.965974205380546
1270.02621921853978270.05243843707956540.973780781460217
1280.02151206434808840.04302412869617690.978487935651912
1290.0248214306101680.0496428612203360.975178569389832
1300.03742780056518430.07485560113036850.962572199434816
1310.02797221050248220.05594442100496440.972027789497518
1320.05295289422526330.1059057884505270.947047105774737
1330.03192723171309430.06385446342618850.968072768286906
1340.01822335952791260.03644671905582520.981776640472087
1350.01683088495864660.03366176991729330.983169115041353
1360.2929787491460210.5859574982920430.707021250853979







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level190.1484375NOK
5% type I error level490.3828125NOK
10% type I error level670.5234375NOK

\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 & 19 & 0.1484375 & NOK \tabularnewline
5% type I error level & 49 & 0.3828125 & NOK \tabularnewline
10% type I error level & 67 & 0.5234375 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186312&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]19[/C][C]0.1484375[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]49[/C][C]0.3828125[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]67[/C][C]0.5234375[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186312&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186312&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 level190.1484375NOK
5% type I error level490.3828125NOK
10% type I error level670.5234375NOK



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 ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')
}