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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 14:15:07 -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/t1352142983pi9gw2oikl85k4u.htm/, Retrieved Mon, 06 Feb 2023 00:42:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186236, Retrieved Mon, 06 Feb 2023 00:42:17 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact74
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [ws7] [2012-11-05 19:15:07] [9fce0523ac0e7dfdcafaec3da59cfa0a] [Current]
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Dataseries X:
2000	1	501	134	368	6.70	8.50	8.70
2000	2	485	124	361	6.80	8.40	8.60
2000	3	464	113	351	6.70	8.40	8.60
2000	4	460	109	351	6.60	8.30	8.50
2001	5	467	109	358	6.40	8.20	8.50
2001	6	460	106	354	6.30	8.20	8.50
2001	7	448	101	347	6.30	8.10	8.50
2001	8	443	98	345	6.50	8.10	8.50
2001	9	436	93	343	6.50	8.10	8.50
2001	10	431	91	340	6.40	8.10	8.50
2001	11	484	122	362	6.20	8.10	8.50
2001	12	510	139	370	6.20	8.10	8.60
2001	13	513	140	373	6.50	8.10	8.60
2001	14	503	132	371	7.00	8.20	8.60
2001	15	471	117	354	7.20	8.20	8.70
2001	16	471	114	357	7.30	8.30	8.70
2002	17	476	113	363	7.40	8.20	8.70
2002	18	475	110	364	7.40	8.30	8.80
2002	19	470	107	363	7.40	8.30	8.80
2002	20	461	103	358	7.30	8.40	8.90
2002	21	455	98	357	7.40	8.50	8.90
2002	22	456	98	357	7.40	8.50	8.90
2002	23	517	137	380	7.60	8.60	9.00
2002	24	525	148	378	7.60	8.60	9.00
2002	25	523	147	376	7.70	8.70	9.00
2002	26	519	139	380	7.70	8.70	9.00
2002	27	509	130	379	7.80	8.80	9.00
2002	28	512	128	384	7.80	8.80	9.00
2003	29	519	127	392	8.00	8.90	9.10
2003	30	517	123	394	8.10	9.00	9.10
2003	31	510	118	392	8.10	9.00	9.10
2003	32	509	114	396	8.20	9.00	9.10
2003	33	501	108	392	8.10	9.00	9.10
2003	34	507	111	396	8.10	9.10	9.10
2003	35	569	151	419	8.10	9.10	9.10
2003	36	580	159	421	8.10	9.00	9.10
2003	37	578	158	420	8.20	9.10	9.10
2003	38	565	148	418	8.20	9.00	9.10
2003	39	547	138	410	8.30	9.10	9.10
2003	40	555	137	418	8.40	9.10	9.20
2004	41	562	136	426	8.60	9.20	9.30
2004	42	561	133	428	8.60	9.20	9.30
2004	43	555	126	430	8.40	9.20	9.30
2004	44	544	120	424	8.00	9.20	9.20
2004	45	537	114	423	7.90	9.20	9.20
2004	46	543	116	427	8.10	9.30	9.20
2004	47	594	153	441	8.50	9.30	9.20
2004	48	611	162	449	8.80	9.30	9.20
2004	49	613	161	452	8.80	9.30	9.20
2004	50	611	149	462	8.50	9.30	9.20
2004	51	594	139	455	8.30	9.40	9.20
2004	52	595	135	461	8.30	9.40	9.20
2005	53	591	130	461	8.30	9.30	9.20
2005	54	589	127	463	8.40	9.30	9.20
2005	55	584	122	462	8.50	9.30	9.20
2005	56	573	117	456	8.50	9.30	9.20
2005	57	567	112	455	8.60	9.20	9.10
2005	58	569	113	456	8.50	9.20	9.10
2005	59	621	149	472	8.60	9.20	9.00
2005	60	629	157	472	8.60	9.10	8.90
2005	61	628	157	471	8.60	9.10	8.90
2005	62	612	147	465	8.50	9.10	9.00
2005	63	595	137	459	8.40	9.10	8.90
2005	64	597	132	465	8.40	9.00	8.80
2006	65	593	125	468	8.50	8.90	8.70
2006	66	590	123	467	8.50	8.80	8.60
2006	67	580	117	463	8.50	8.70	8.50
2006	68	574	114	460	8.60	8.60	8.50
2006	69	573	111	462	8.60	8.60	8.40
2006	70	573	112	461	8.40	8.50	8.30
2006	71	620	144	476	8.20	8.40	8.20
2006	72	626	150	476	8.00	8.40	8.20
2006	73	620	149	471	8.00	8.30	8.10
2006	74	588	134	453	8.00	8.20	8.00
2006	75	566	123	443	8.00	8.20	7.90
2006	76	557	116	442	7.90	8.00	7.80
2007	77	561	117	444	7.90	7.90	7.60
2007	78	549	111	438	7.90	7.80	7.50
2007	79	532	105	427	7.90	7.70	7.40
2007	80	526	102	424	8.00	7.60	7.30
2007	81	511	95	416	7.90	7.60	7.30
2007	82	499	93	406	7.40	7.60	7.20
2007	83	555	124	431	7.20	7.60	7.20
2007	84	565	130	434	7.00	7.60	7.20
2007	85	542	124	418	6.90	7.50	7.10
2007	86	527	115	412	7.10	7.50	7.00
2007	87	510	106	404	7.20	7.40	7.00
2007	88	514	105	409	7.20	7.40	6.90
2008	89	517	105	412	7.10	7.40	6.90
2008	90	508	101	406	6.90	7.30	6.80
2008	91	493	95	398	6.80	7.30	6.80
2008	92	490	93	397	6.80	7.40	6.80
2008	93	469	84	385	6.80	7.50	6.90
2008	94	478	87	390	6.90	7.60	7.00
2008	95	528	116	413	7.10	7.60	7.00
2008	96	534	120	413	7.20	7.70	7.10
2008	97	518	117	401	7.20	7.70	7.20
2008	98	506	109	397	7.10	7.90	7.30
2008	99	502	105	397	7.10	8.10	7.50
2008	100	516	107	409	7.20	8.40	7.70
2009	101	528	109	419	7.50	8.70	8.10
2009	102	533	109	424	7.70	9.00	8.40
2009	103	536	108	428	7.80	9.30	8.60
2009	104	537	107	430	7.70	9.40	8.80
2009	105	524	99	424	7.70	9.50	8.90
2009	106	536	103	433	7.80	9.60	9.10
2009	107	587	131	456	8.00	9.80	9.20
2009	108	597	137	459	8.10	9.80	9.30
2009	109	581	135	446	8.10	9.90	9.40
2009	110	564	124	441	8.00	10.00	9.40
2009	111	558	118	439	8.10	10.00	9.50
2010	112	575	121	454	8.20	10.10	9.50
2010	113	580	121	460	8.40	10.10	9.70
2010	114	575	118	457	8.50	10.10	9.70
2010	115	563	113	451	8.50	10.10	9.70
2010	116	552	107	444	8.50	10.20	9.70
2010	117	537	100	437	8.50	10.20	9.70
2010	118	545	102	443	8.50	10.10	9.60
2010	119	601	130	471	8.40	10.10	9.60
2010	120	604	136	469	8.30	10.10	9.60
2010	121	586	133	454	8.20	10.10	9.60
2010	122	564	120	444	8.10	10.10	9.60
2010	123	549	112	436	7.90	10.10	9.60
2010	124	551	109	442	7.60	10.10	9.60
2011	125	556	110	446	7.30	10.00	9.50
2011	126	548	106	442	7.10	9.90	9.50
2011	127	540	102	438	7.00	9.90	9.40
2011	128	531	98	433	7.10	9.90	9.40
2011	129	521	92	428	7.10	9.90	9.50
2011	130	519	92	426	7.10	10.00	9.50
2011	131	572	120	452	7.30	10.10	9.60
2011	132	581	127	455	7.30	10.20	9.70
2011	133	563	124	439	7.30	10.30	9.80
2011	134	548	114	434	7.20	10.50	9.90
2011	135	539	108	431	7.20	10.60	10.00
2011	136	541	106	435	7.10	10.70	10.00
2012	137	562	111	450	7.10	10.80	10.10
2012	138	559	110	449	7.10	10.90	10.20
2012	139	546	104	442	7.20	11.00	10.30
2012	140	536	100	437	7.30	11.20	10.30
2012	141	528	96	431	7.40	11.30	10.40
2012	142	530	98	433	7.40	11.40	10.50
2012	143	582	122	460	7.50	11.50	10.50
2012	144	599	134	465	7.40	11.50	10.60
2012	145	584	133	451	7.40	11.60	10.60




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 9 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186236&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186236&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186236&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 time9 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Totale_werkloosheid[t] = -84.8598455362373 + 0.0431272408509262Jaartal[t] + 0.00110852737564431t + 0.995878671716741Jonger_dan_25[t] + 1.0002341118164Vanaf_25[t] -0.0766601847092077`Belgi\303\253`[t] -0.461901000716775Euroraad[t] + 0.385382469325766`EU-27`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Totale_werkloosheid[t] =  -84.8598455362373 +  0.0431272408509262Jaartal[t] +  0.00110852737564431t +  0.995878671716741Jonger_dan_25[t] +  1.0002341118164Vanaf_25[t] -0.0766601847092077`Belgi\303\253`[t] -0.461901000716775Euroraad[t] +  0.385382469325766`EU-27`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186236&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Totale_werkloosheid[t] =  -84.8598455362373 +  0.0431272408509262Jaartal[t] +  0.00110852737564431t +  0.995878671716741Jonger_dan_25[t] +  1.0002341118164Vanaf_25[t] -0.0766601847092077`Belgi\303\253`[t] -0.461901000716775Euroraad[t] +  0.385382469325766`EU-27`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186236&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186236&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
Totale_werkloosheid[t] = -84.8598455362373 + 0.0431272408509262Jaartal[t] + 0.00110852737564431t + 0.995878671716741Jonger_dan_25[t] + 1.0002341118164Vanaf_25[t] -0.0766601847092077`Belgi\303\253`[t] -0.461901000716775Euroraad[t] + 0.385382469325766`EU-27`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-84.8598455362373336.990516-0.25180.801560.40078
Jaartal0.04312724085092620.1685330.25590.7984140.399207
t0.001108527375644310.0142540.07780.9381260.469063
Jonger_dan_250.9958786717167410.00393253.403400
Vanaf_251.00023411181640.003058327.134200
`Belgi\303\253`-0.07666018470920770.113462-0.67560.5004040.250202
Euroraad-0.4619010007167750.372345-1.24050.2169030.108452
`EU-27`0.3853824693257660.3508661.09840.2739670.136983

\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) & -84.8598455362373 & 336.990516 & -0.2518 & 0.80156 & 0.40078 \tabularnewline
Jaartal & 0.0431272408509262 & 0.168533 & 0.2559 & 0.798414 & 0.399207 \tabularnewline
t & 0.00110852737564431 & 0.014254 & 0.0778 & 0.938126 & 0.469063 \tabularnewline
Jonger_dan_25 & 0.995878671716741 & 0.00393 & 253.4034 & 0 & 0 \tabularnewline
Vanaf_25 & 1.0002341118164 & 0.003058 & 327.1342 & 0 & 0 \tabularnewline
`Belgi\303\253` & -0.0766601847092077 & 0.113462 & -0.6756 & 0.500404 & 0.250202 \tabularnewline
Euroraad & -0.461901000716775 & 0.372345 & -1.2405 & 0.216903 & 0.108452 \tabularnewline
`EU-27` & 0.385382469325766 & 0.350866 & 1.0984 & 0.273967 & 0.136983 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186236&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]-84.8598455362373[/C][C]336.990516[/C][C]-0.2518[/C][C]0.80156[/C][C]0.40078[/C][/ROW]
[ROW][C]Jaartal[/C][C]0.0431272408509262[/C][C]0.168533[/C][C]0.2559[/C][C]0.798414[/C][C]0.399207[/C][/ROW]
[ROW][C]t[/C][C]0.00110852737564431[/C][C]0.014254[/C][C]0.0778[/C][C]0.938126[/C][C]0.469063[/C][/ROW]
[ROW][C]Jonger_dan_25[/C][C]0.995878671716741[/C][C]0.00393[/C][C]253.4034[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Vanaf_25[/C][C]1.0002341118164[/C][C]0.003058[/C][C]327.1342[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Belgi\303\253`[/C][C]-0.0766601847092077[/C][C]0.113462[/C][C]-0.6756[/C][C]0.500404[/C][C]0.250202[/C][/ROW]
[ROW][C]Euroraad[/C][C]-0.461901000716775[/C][C]0.372345[/C][C]-1.2405[/C][C]0.216903[/C][C]0.108452[/C][/ROW]
[ROW][C]`EU-27`[/C][C]0.385382469325766[/C][C]0.350866[/C][C]1.0984[/C][C]0.273967[/C][C]0.136983[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186236&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186236&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)-84.8598455362373336.990516-0.25180.801560.40078
Jaartal0.04312724085092620.1685330.25590.7984140.399207
t0.001108527375644310.0142540.07780.9381260.469063
Jonger_dan_250.9958786717167410.00393253.403400
Vanaf_251.00023411181640.003058327.134200
`Belgi\303\253`-0.07666018470920770.113462-0.67560.5004040.250202
Euroraad-0.4619010007167750.372345-1.24050.2169030.108452
`EU-27`0.3853824693257660.3508661.09840.2739670.136983







Multiple Linear Regression - Regression Statistics
Multiple R0.999941101403851
R-squared0.999882206276746
Adjusted R-squared0.999876187619353
F-TEST (value)166130.440903983
F-TEST (DF numerator)7
F-TEST (DF denominator)137
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.504181488531057
Sum Squared Residuals34.8252593527028

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999941101403851 \tabularnewline
R-squared & 0.999882206276746 \tabularnewline
Adjusted R-squared & 0.999876187619353 \tabularnewline
F-TEST (value) & 166130.440903983 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 137 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.504181488531057 \tabularnewline
Sum Squared Residuals & 34.8252593527028 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186236&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999941101403851[/C][/ROW]
[ROW][C]R-squared[/C][C]0.999882206276746[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.999876187619353[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]166130.440903983[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]137[/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]0.504181488531057[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]34.8252593527028[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186236&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186236&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 R0.999941101403851
R-squared0.999882206276746
Adjusted R-squared0.999876187619353
F-TEST (value)166130.440903983
F-TEST (DF numerator)7
F-TEST (DF denominator)137
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.504181488531057
Sum Squared Residuals34.8252593527028







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1501501.842685590958-0.842685590957651
2485484.8833544531190.11664554688051
3464463.9351224919180.0648775080820712
4460459.9680342040370.0319657959633518
5467467.075430891991-0.075430891991465
6460460.095632975422-0.0956329754222524
7448448.161899461571-0.161899461571094
8443443.159571713222-0.159571713221867
9436436.180818658381-0.180818658381019
10431431.197133525345-0.197133525344912
11484484.090963372842-0.0909633728421026
12510509.0624204608660.937579539133899
13513513.037111939995-0.0371119399949131
14503502.9862026775780.0137973224224519
15471471.068357438314-0.0683574383140762
16471471.028676167446-0.0286761674460778
17476476.116962016455-0.116962016455046
18475474.1230167873580.876983212642237
19470470.136255187767-0.136255187766779
20461461.152692634525-0.152692634525302
21455455.120317572958-0.120317572958251
22456455.1214261003340.878573899666117
23517516.9442035063590.0557964936413786
24525525.899509198986-0.89950919898563
25523522.8504147124690.149585287530868
26519518.8854303133760.114569686623559
27509508.8695405649420.130459435057582
28512511.8800623079670.119937692033439
29519518.9073084089270.0926915910733787
30517516.8715143545260.128485645474504
31510509.8927612996850.107238700315361
32509509.903625568988-0.903625568987983
33501499.9361916372691.06380836273149
34507506.8796825269880.12031747301171
35569569.721322494811-0.721322494810696
36580579.7361187196250.263881280375261
37578577.6872583449250.31274165507535
38565565.775302031572-0.775302031571768
39547547.761894828706-0.761894828706228
40555554.7998698073580.200130192642036
41562561.8271159083180.172884091681977
42561560.8410566441760.158943355823761
43555555.886814730109-0.886814730109328
44544543.9033723832370.0966276167627479
45537536.9366407869670.063359213033029
46543542.8689209680280.131079031971839
47594593.6901538404690.309846159530895
48611610.6330452524140.366954747586191
49613612.6389774435220.361022556478091
50611610.7148810838730.285118916126611
51594593.7247060482370.275293951762998
52595595.743704559644-0.743704559644074
53591590.8547370693590.145262930641387
54589589.861011786746-0.861011786745898
55584583.8748268252510.125173174749471
56573572.8951373231440.104862676855917
57567566.9166042147880.083395785212198
58569568.9214915441670.0785084558324952
59621620.7317737770050.268226222995335
60629628.7075635312530.292436468746663
61628627.7084379468130.291562053187403
62612611.7955593515260.204440648474046
63595595.805604262374-0.805604262374159
64597596.8363759552040.163624044796435
65593592.910149191530.089850808469674
66590589.9269181167950.0730818832048146
67580579.9594700197440.0405299802560961
68574574.010764278121-0.0107642781208959
69573572.9861667670460.0138332329534815
70573573.005903744403-0.00590374440346041
71620619.9016253340420.0983746659583022
72626625.893337928660.106662071340366
73620619.9050490783760.0949509216243459
74588586.9714153704441.02858462955584
75566565.9769791438390.0230208561608848
76557558.068210829063-1.06821082906287
77561561.077907098845-0.0779070988454993
78549549.109990778161-0.109990778161418
79532532.140903898395-0.140903898395353
80526526.15365990984-0.153659909839763
81511511.189410859138-0.189410859137972
82499499.196212770338-0.19621277033819
83555555.090744953285-0.0907449532845708
84565564.0831598833520.9168401166483
85542542.120568462975-0.120568462974569
86527527.103493990127-0.103493990126742
87510510.178345659121-0.178345659121301
88514514.14620782693-0.146207826929612
89517517.198811949076-0.198811949076292
90508507.2379850087680.762014991232465
91493493.269614629782-0.269614629782482
92490490.232541601837-0.232541601836569
93469469.260280888826-0.26028088882568
94478477.2348781188240.765121881176492
95528529.10652066082-1.10652066081993
96534533.0758260034530.924173996547485
97518518.125027420814-0.125027420813761
98506506.11199419245-0.111994192450033
99502502.11428432668-0.114284326680504
100516516.040799714466-0.0407997144655875
101528528.071718576392-0.0717185763921203
102533533.035710066491-0.0357100664906006
103536535.9727165445940.0272834554057111
104537537.01696703615-0.0169670361503801
105524523.0419896657550.95801033424538
106536536.051940261667-0.051940261667347
107587586.8738621787360.126137821263754
108597595.8818173003231.11818269967681
109581580.8804731775130.119526822486909
110564564.887221675322-0.887221675321845
111558556.9434621772261.05653782277409
112575574.9249895193060.0750104806939534
113580580.989247174503-0.989247174503379
114575574.9943513328090.00564866719130818
115563564.014661830702-1.01466183070225
116552550.9926694449911.007330555009
117537537.020988487635-0.0209884876346739
118545545.022910882481-0.0229108824812839
119601600.9228433672560.0771566327442979
120604604.90642171977-0.906421719769917
121586586.92404857322-0.924048573220314
122564563.9840592685850.0159407314147161
123549548.0315975646380.968402435362332
124551551.069472803174-0.0694728031742269
125556556.141173598935-0.141173598934993
126548548.219353129192-0.219353129191603
127540540.205138293973-0.205138293973038
128531531.213895556929-0.213895556928814
129521520.2770997418550.722900258145392
130519518.2315499455260.768450054474219
131572572.100364298116-0.100364298115545
132581582.065674009819-1.06567400981847
133563563.067748879842-0.0677488798424472
134548548.062724196229-0.062724196228838
135539539.080206504716-0.0802065047157384
136541541.051970054323-0.0519700543227361
137562561.071459005240.928540994760142
138559559.068802895943-0.0688028959432661
139546546.077682738694-0.0776827386936672
140536536.994059801506-0.994059801506085
141528526.9949310995061.00506890049363
142530530.980613340809-0.980613340809178
143582581.8352748898870.164725110113281
144599598.8343023023490.165697697651263
145584583.7900644925060.209935507493585

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 501 & 501.842685590958 & -0.842685590957651 \tabularnewline
2 & 485 & 484.883354453119 & 0.11664554688051 \tabularnewline
3 & 464 & 463.935122491918 & 0.0648775080820712 \tabularnewline
4 & 460 & 459.968034204037 & 0.0319657959633518 \tabularnewline
5 & 467 & 467.075430891991 & -0.075430891991465 \tabularnewline
6 & 460 & 460.095632975422 & -0.0956329754222524 \tabularnewline
7 & 448 & 448.161899461571 & -0.161899461571094 \tabularnewline
8 & 443 & 443.159571713222 & -0.159571713221867 \tabularnewline
9 & 436 & 436.180818658381 & -0.180818658381019 \tabularnewline
10 & 431 & 431.197133525345 & -0.197133525344912 \tabularnewline
11 & 484 & 484.090963372842 & -0.0909633728421026 \tabularnewline
12 & 510 & 509.062420460866 & 0.937579539133899 \tabularnewline
13 & 513 & 513.037111939995 & -0.0371119399949131 \tabularnewline
14 & 503 & 502.986202677578 & 0.0137973224224519 \tabularnewline
15 & 471 & 471.068357438314 & -0.0683574383140762 \tabularnewline
16 & 471 & 471.028676167446 & -0.0286761674460778 \tabularnewline
17 & 476 & 476.116962016455 & -0.116962016455046 \tabularnewline
18 & 475 & 474.123016787358 & 0.876983212642237 \tabularnewline
19 & 470 & 470.136255187767 & -0.136255187766779 \tabularnewline
20 & 461 & 461.152692634525 & -0.152692634525302 \tabularnewline
21 & 455 & 455.120317572958 & -0.120317572958251 \tabularnewline
22 & 456 & 455.121426100334 & 0.878573899666117 \tabularnewline
23 & 517 & 516.944203506359 & 0.0557964936413786 \tabularnewline
24 & 525 & 525.899509198986 & -0.89950919898563 \tabularnewline
25 & 523 & 522.850414712469 & 0.149585287530868 \tabularnewline
26 & 519 & 518.885430313376 & 0.114569686623559 \tabularnewline
27 & 509 & 508.869540564942 & 0.130459435057582 \tabularnewline
28 & 512 & 511.880062307967 & 0.119937692033439 \tabularnewline
29 & 519 & 518.907308408927 & 0.0926915910733787 \tabularnewline
30 & 517 & 516.871514354526 & 0.128485645474504 \tabularnewline
31 & 510 & 509.892761299685 & 0.107238700315361 \tabularnewline
32 & 509 & 509.903625568988 & -0.903625568987983 \tabularnewline
33 & 501 & 499.936191637269 & 1.06380836273149 \tabularnewline
34 & 507 & 506.879682526988 & 0.12031747301171 \tabularnewline
35 & 569 & 569.721322494811 & -0.721322494810696 \tabularnewline
36 & 580 & 579.736118719625 & 0.263881280375261 \tabularnewline
37 & 578 & 577.687258344925 & 0.31274165507535 \tabularnewline
38 & 565 & 565.775302031572 & -0.775302031571768 \tabularnewline
39 & 547 & 547.761894828706 & -0.761894828706228 \tabularnewline
40 & 555 & 554.799869807358 & 0.200130192642036 \tabularnewline
41 & 562 & 561.827115908318 & 0.172884091681977 \tabularnewline
42 & 561 & 560.841056644176 & 0.158943355823761 \tabularnewline
43 & 555 & 555.886814730109 & -0.886814730109328 \tabularnewline
44 & 544 & 543.903372383237 & 0.0966276167627479 \tabularnewline
45 & 537 & 536.936640786967 & 0.063359213033029 \tabularnewline
46 & 543 & 542.868920968028 & 0.131079031971839 \tabularnewline
47 & 594 & 593.690153840469 & 0.309846159530895 \tabularnewline
48 & 611 & 610.633045252414 & 0.366954747586191 \tabularnewline
49 & 613 & 612.638977443522 & 0.361022556478091 \tabularnewline
50 & 611 & 610.714881083873 & 0.285118916126611 \tabularnewline
51 & 594 & 593.724706048237 & 0.275293951762998 \tabularnewline
52 & 595 & 595.743704559644 & -0.743704559644074 \tabularnewline
53 & 591 & 590.854737069359 & 0.145262930641387 \tabularnewline
54 & 589 & 589.861011786746 & -0.861011786745898 \tabularnewline
55 & 584 & 583.874826825251 & 0.125173174749471 \tabularnewline
56 & 573 & 572.895137323144 & 0.104862676855917 \tabularnewline
57 & 567 & 566.916604214788 & 0.083395785212198 \tabularnewline
58 & 569 & 568.921491544167 & 0.0785084558324952 \tabularnewline
59 & 621 & 620.731773777005 & 0.268226222995335 \tabularnewline
60 & 629 & 628.707563531253 & 0.292436468746663 \tabularnewline
61 & 628 & 627.708437946813 & 0.291562053187403 \tabularnewline
62 & 612 & 611.795559351526 & 0.204440648474046 \tabularnewline
63 & 595 & 595.805604262374 & -0.805604262374159 \tabularnewline
64 & 597 & 596.836375955204 & 0.163624044796435 \tabularnewline
65 & 593 & 592.91014919153 & 0.089850808469674 \tabularnewline
66 & 590 & 589.926918116795 & 0.0730818832048146 \tabularnewline
67 & 580 & 579.959470019744 & 0.0405299802560961 \tabularnewline
68 & 574 & 574.010764278121 & -0.0107642781208959 \tabularnewline
69 & 573 & 572.986166767046 & 0.0138332329534815 \tabularnewline
70 & 573 & 573.005903744403 & -0.00590374440346041 \tabularnewline
71 & 620 & 619.901625334042 & 0.0983746659583022 \tabularnewline
72 & 626 & 625.89333792866 & 0.106662071340366 \tabularnewline
73 & 620 & 619.905049078376 & 0.0949509216243459 \tabularnewline
74 & 588 & 586.971415370444 & 1.02858462955584 \tabularnewline
75 & 566 & 565.976979143839 & 0.0230208561608848 \tabularnewline
76 & 557 & 558.068210829063 & -1.06821082906287 \tabularnewline
77 & 561 & 561.077907098845 & -0.0779070988454993 \tabularnewline
78 & 549 & 549.109990778161 & -0.109990778161418 \tabularnewline
79 & 532 & 532.140903898395 & -0.140903898395353 \tabularnewline
80 & 526 & 526.15365990984 & -0.153659909839763 \tabularnewline
81 & 511 & 511.189410859138 & -0.189410859137972 \tabularnewline
82 & 499 & 499.196212770338 & -0.19621277033819 \tabularnewline
83 & 555 & 555.090744953285 & -0.0907449532845708 \tabularnewline
84 & 565 & 564.083159883352 & 0.9168401166483 \tabularnewline
85 & 542 & 542.120568462975 & -0.120568462974569 \tabularnewline
86 & 527 & 527.103493990127 & -0.103493990126742 \tabularnewline
87 & 510 & 510.178345659121 & -0.178345659121301 \tabularnewline
88 & 514 & 514.14620782693 & -0.146207826929612 \tabularnewline
89 & 517 & 517.198811949076 & -0.198811949076292 \tabularnewline
90 & 508 & 507.237985008768 & 0.762014991232465 \tabularnewline
91 & 493 & 493.269614629782 & -0.269614629782482 \tabularnewline
92 & 490 & 490.232541601837 & -0.232541601836569 \tabularnewline
93 & 469 & 469.260280888826 & -0.26028088882568 \tabularnewline
94 & 478 & 477.234878118824 & 0.765121881176492 \tabularnewline
95 & 528 & 529.10652066082 & -1.10652066081993 \tabularnewline
96 & 534 & 533.075826003453 & 0.924173996547485 \tabularnewline
97 & 518 & 518.125027420814 & -0.125027420813761 \tabularnewline
98 & 506 & 506.11199419245 & -0.111994192450033 \tabularnewline
99 & 502 & 502.11428432668 & -0.114284326680504 \tabularnewline
100 & 516 & 516.040799714466 & -0.0407997144655875 \tabularnewline
101 & 528 & 528.071718576392 & -0.0717185763921203 \tabularnewline
102 & 533 & 533.035710066491 & -0.0357100664906006 \tabularnewline
103 & 536 & 535.972716544594 & 0.0272834554057111 \tabularnewline
104 & 537 & 537.01696703615 & -0.0169670361503801 \tabularnewline
105 & 524 & 523.041989665755 & 0.95801033424538 \tabularnewline
106 & 536 & 536.051940261667 & -0.051940261667347 \tabularnewline
107 & 587 & 586.873862178736 & 0.126137821263754 \tabularnewline
108 & 597 & 595.881817300323 & 1.11818269967681 \tabularnewline
109 & 581 & 580.880473177513 & 0.119526822486909 \tabularnewline
110 & 564 & 564.887221675322 & -0.887221675321845 \tabularnewline
111 & 558 & 556.943462177226 & 1.05653782277409 \tabularnewline
112 & 575 & 574.924989519306 & 0.0750104806939534 \tabularnewline
113 & 580 & 580.989247174503 & -0.989247174503379 \tabularnewline
114 & 575 & 574.994351332809 & 0.00564866719130818 \tabularnewline
115 & 563 & 564.014661830702 & -1.01466183070225 \tabularnewline
116 & 552 & 550.992669444991 & 1.007330555009 \tabularnewline
117 & 537 & 537.020988487635 & -0.0209884876346739 \tabularnewline
118 & 545 & 545.022910882481 & -0.0229108824812839 \tabularnewline
119 & 601 & 600.922843367256 & 0.0771566327442979 \tabularnewline
120 & 604 & 604.90642171977 & -0.906421719769917 \tabularnewline
121 & 586 & 586.92404857322 & -0.924048573220314 \tabularnewline
122 & 564 & 563.984059268585 & 0.0159407314147161 \tabularnewline
123 & 549 & 548.031597564638 & 0.968402435362332 \tabularnewline
124 & 551 & 551.069472803174 & -0.0694728031742269 \tabularnewline
125 & 556 & 556.141173598935 & -0.141173598934993 \tabularnewline
126 & 548 & 548.219353129192 & -0.219353129191603 \tabularnewline
127 & 540 & 540.205138293973 & -0.205138293973038 \tabularnewline
128 & 531 & 531.213895556929 & -0.213895556928814 \tabularnewline
129 & 521 & 520.277099741855 & 0.722900258145392 \tabularnewline
130 & 519 & 518.231549945526 & 0.768450054474219 \tabularnewline
131 & 572 & 572.100364298116 & -0.100364298115545 \tabularnewline
132 & 581 & 582.065674009819 & -1.06567400981847 \tabularnewline
133 & 563 & 563.067748879842 & -0.0677488798424472 \tabularnewline
134 & 548 & 548.062724196229 & -0.062724196228838 \tabularnewline
135 & 539 & 539.080206504716 & -0.0802065047157384 \tabularnewline
136 & 541 & 541.051970054323 & -0.0519700543227361 \tabularnewline
137 & 562 & 561.07145900524 & 0.928540994760142 \tabularnewline
138 & 559 & 559.068802895943 & -0.0688028959432661 \tabularnewline
139 & 546 & 546.077682738694 & -0.0776827386936672 \tabularnewline
140 & 536 & 536.994059801506 & -0.994059801506085 \tabularnewline
141 & 528 & 526.994931099506 & 1.00506890049363 \tabularnewline
142 & 530 & 530.980613340809 & -0.980613340809178 \tabularnewline
143 & 582 & 581.835274889887 & 0.164725110113281 \tabularnewline
144 & 599 & 598.834302302349 & 0.165697697651263 \tabularnewline
145 & 584 & 583.790064492506 & 0.209935507493585 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186236&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]501.842685590958[/C][C]-0.842685590957651[/C][/ROW]
[ROW][C]2[/C][C]485[/C][C]484.883354453119[/C][C]0.11664554688051[/C][/ROW]
[ROW][C]3[/C][C]464[/C][C]463.935122491918[/C][C]0.0648775080820712[/C][/ROW]
[ROW][C]4[/C][C]460[/C][C]459.968034204037[/C][C]0.0319657959633518[/C][/ROW]
[ROW][C]5[/C][C]467[/C][C]467.075430891991[/C][C]-0.075430891991465[/C][/ROW]
[ROW][C]6[/C][C]460[/C][C]460.095632975422[/C][C]-0.0956329754222524[/C][/ROW]
[ROW][C]7[/C][C]448[/C][C]448.161899461571[/C][C]-0.161899461571094[/C][/ROW]
[ROW][C]8[/C][C]443[/C][C]443.159571713222[/C][C]-0.159571713221867[/C][/ROW]
[ROW][C]9[/C][C]436[/C][C]436.180818658381[/C][C]-0.180818658381019[/C][/ROW]
[ROW][C]10[/C][C]431[/C][C]431.197133525345[/C][C]-0.197133525344912[/C][/ROW]
[ROW][C]11[/C][C]484[/C][C]484.090963372842[/C][C]-0.0909633728421026[/C][/ROW]
[ROW][C]12[/C][C]510[/C][C]509.062420460866[/C][C]0.937579539133899[/C][/ROW]
[ROW][C]13[/C][C]513[/C][C]513.037111939995[/C][C]-0.0371119399949131[/C][/ROW]
[ROW][C]14[/C][C]503[/C][C]502.986202677578[/C][C]0.0137973224224519[/C][/ROW]
[ROW][C]15[/C][C]471[/C][C]471.068357438314[/C][C]-0.0683574383140762[/C][/ROW]
[ROW][C]16[/C][C]471[/C][C]471.028676167446[/C][C]-0.0286761674460778[/C][/ROW]
[ROW][C]17[/C][C]476[/C][C]476.116962016455[/C][C]-0.116962016455046[/C][/ROW]
[ROW][C]18[/C][C]475[/C][C]474.123016787358[/C][C]0.876983212642237[/C][/ROW]
[ROW][C]19[/C][C]470[/C][C]470.136255187767[/C][C]-0.136255187766779[/C][/ROW]
[ROW][C]20[/C][C]461[/C][C]461.152692634525[/C][C]-0.152692634525302[/C][/ROW]
[ROW][C]21[/C][C]455[/C][C]455.120317572958[/C][C]-0.120317572958251[/C][/ROW]
[ROW][C]22[/C][C]456[/C][C]455.121426100334[/C][C]0.878573899666117[/C][/ROW]
[ROW][C]23[/C][C]517[/C][C]516.944203506359[/C][C]0.0557964936413786[/C][/ROW]
[ROW][C]24[/C][C]525[/C][C]525.899509198986[/C][C]-0.89950919898563[/C][/ROW]
[ROW][C]25[/C][C]523[/C][C]522.850414712469[/C][C]0.149585287530868[/C][/ROW]
[ROW][C]26[/C][C]519[/C][C]518.885430313376[/C][C]0.114569686623559[/C][/ROW]
[ROW][C]27[/C][C]509[/C][C]508.869540564942[/C][C]0.130459435057582[/C][/ROW]
[ROW][C]28[/C][C]512[/C][C]511.880062307967[/C][C]0.119937692033439[/C][/ROW]
[ROW][C]29[/C][C]519[/C][C]518.907308408927[/C][C]0.0926915910733787[/C][/ROW]
[ROW][C]30[/C][C]517[/C][C]516.871514354526[/C][C]0.128485645474504[/C][/ROW]
[ROW][C]31[/C][C]510[/C][C]509.892761299685[/C][C]0.107238700315361[/C][/ROW]
[ROW][C]32[/C][C]509[/C][C]509.903625568988[/C][C]-0.903625568987983[/C][/ROW]
[ROW][C]33[/C][C]501[/C][C]499.936191637269[/C][C]1.06380836273149[/C][/ROW]
[ROW][C]34[/C][C]507[/C][C]506.879682526988[/C][C]0.12031747301171[/C][/ROW]
[ROW][C]35[/C][C]569[/C][C]569.721322494811[/C][C]-0.721322494810696[/C][/ROW]
[ROW][C]36[/C][C]580[/C][C]579.736118719625[/C][C]0.263881280375261[/C][/ROW]
[ROW][C]37[/C][C]578[/C][C]577.687258344925[/C][C]0.31274165507535[/C][/ROW]
[ROW][C]38[/C][C]565[/C][C]565.775302031572[/C][C]-0.775302031571768[/C][/ROW]
[ROW][C]39[/C][C]547[/C][C]547.761894828706[/C][C]-0.761894828706228[/C][/ROW]
[ROW][C]40[/C][C]555[/C][C]554.799869807358[/C][C]0.200130192642036[/C][/ROW]
[ROW][C]41[/C][C]562[/C][C]561.827115908318[/C][C]0.172884091681977[/C][/ROW]
[ROW][C]42[/C][C]561[/C][C]560.841056644176[/C][C]0.158943355823761[/C][/ROW]
[ROW][C]43[/C][C]555[/C][C]555.886814730109[/C][C]-0.886814730109328[/C][/ROW]
[ROW][C]44[/C][C]544[/C][C]543.903372383237[/C][C]0.0966276167627479[/C][/ROW]
[ROW][C]45[/C][C]537[/C][C]536.936640786967[/C][C]0.063359213033029[/C][/ROW]
[ROW][C]46[/C][C]543[/C][C]542.868920968028[/C][C]0.131079031971839[/C][/ROW]
[ROW][C]47[/C][C]594[/C][C]593.690153840469[/C][C]0.309846159530895[/C][/ROW]
[ROW][C]48[/C][C]611[/C][C]610.633045252414[/C][C]0.366954747586191[/C][/ROW]
[ROW][C]49[/C][C]613[/C][C]612.638977443522[/C][C]0.361022556478091[/C][/ROW]
[ROW][C]50[/C][C]611[/C][C]610.714881083873[/C][C]0.285118916126611[/C][/ROW]
[ROW][C]51[/C][C]594[/C][C]593.724706048237[/C][C]0.275293951762998[/C][/ROW]
[ROW][C]52[/C][C]595[/C][C]595.743704559644[/C][C]-0.743704559644074[/C][/ROW]
[ROW][C]53[/C][C]591[/C][C]590.854737069359[/C][C]0.145262930641387[/C][/ROW]
[ROW][C]54[/C][C]589[/C][C]589.861011786746[/C][C]-0.861011786745898[/C][/ROW]
[ROW][C]55[/C][C]584[/C][C]583.874826825251[/C][C]0.125173174749471[/C][/ROW]
[ROW][C]56[/C][C]573[/C][C]572.895137323144[/C][C]0.104862676855917[/C][/ROW]
[ROW][C]57[/C][C]567[/C][C]566.916604214788[/C][C]0.083395785212198[/C][/ROW]
[ROW][C]58[/C][C]569[/C][C]568.921491544167[/C][C]0.0785084558324952[/C][/ROW]
[ROW][C]59[/C][C]621[/C][C]620.731773777005[/C][C]0.268226222995335[/C][/ROW]
[ROW][C]60[/C][C]629[/C][C]628.707563531253[/C][C]0.292436468746663[/C][/ROW]
[ROW][C]61[/C][C]628[/C][C]627.708437946813[/C][C]0.291562053187403[/C][/ROW]
[ROW][C]62[/C][C]612[/C][C]611.795559351526[/C][C]0.204440648474046[/C][/ROW]
[ROW][C]63[/C][C]595[/C][C]595.805604262374[/C][C]-0.805604262374159[/C][/ROW]
[ROW][C]64[/C][C]597[/C][C]596.836375955204[/C][C]0.163624044796435[/C][/ROW]
[ROW][C]65[/C][C]593[/C][C]592.91014919153[/C][C]0.089850808469674[/C][/ROW]
[ROW][C]66[/C][C]590[/C][C]589.926918116795[/C][C]0.0730818832048146[/C][/ROW]
[ROW][C]67[/C][C]580[/C][C]579.959470019744[/C][C]0.0405299802560961[/C][/ROW]
[ROW][C]68[/C][C]574[/C][C]574.010764278121[/C][C]-0.0107642781208959[/C][/ROW]
[ROW][C]69[/C][C]573[/C][C]572.986166767046[/C][C]0.0138332329534815[/C][/ROW]
[ROW][C]70[/C][C]573[/C][C]573.005903744403[/C][C]-0.00590374440346041[/C][/ROW]
[ROW][C]71[/C][C]620[/C][C]619.901625334042[/C][C]0.0983746659583022[/C][/ROW]
[ROW][C]72[/C][C]626[/C][C]625.89333792866[/C][C]0.106662071340366[/C][/ROW]
[ROW][C]73[/C][C]620[/C][C]619.905049078376[/C][C]0.0949509216243459[/C][/ROW]
[ROW][C]74[/C][C]588[/C][C]586.971415370444[/C][C]1.02858462955584[/C][/ROW]
[ROW][C]75[/C][C]566[/C][C]565.976979143839[/C][C]0.0230208561608848[/C][/ROW]
[ROW][C]76[/C][C]557[/C][C]558.068210829063[/C][C]-1.06821082906287[/C][/ROW]
[ROW][C]77[/C][C]561[/C][C]561.077907098845[/C][C]-0.0779070988454993[/C][/ROW]
[ROW][C]78[/C][C]549[/C][C]549.109990778161[/C][C]-0.109990778161418[/C][/ROW]
[ROW][C]79[/C][C]532[/C][C]532.140903898395[/C][C]-0.140903898395353[/C][/ROW]
[ROW][C]80[/C][C]526[/C][C]526.15365990984[/C][C]-0.153659909839763[/C][/ROW]
[ROW][C]81[/C][C]511[/C][C]511.189410859138[/C][C]-0.189410859137972[/C][/ROW]
[ROW][C]82[/C][C]499[/C][C]499.196212770338[/C][C]-0.19621277033819[/C][/ROW]
[ROW][C]83[/C][C]555[/C][C]555.090744953285[/C][C]-0.0907449532845708[/C][/ROW]
[ROW][C]84[/C][C]565[/C][C]564.083159883352[/C][C]0.9168401166483[/C][/ROW]
[ROW][C]85[/C][C]542[/C][C]542.120568462975[/C][C]-0.120568462974569[/C][/ROW]
[ROW][C]86[/C][C]527[/C][C]527.103493990127[/C][C]-0.103493990126742[/C][/ROW]
[ROW][C]87[/C][C]510[/C][C]510.178345659121[/C][C]-0.178345659121301[/C][/ROW]
[ROW][C]88[/C][C]514[/C][C]514.14620782693[/C][C]-0.146207826929612[/C][/ROW]
[ROW][C]89[/C][C]517[/C][C]517.198811949076[/C][C]-0.198811949076292[/C][/ROW]
[ROW][C]90[/C][C]508[/C][C]507.237985008768[/C][C]0.762014991232465[/C][/ROW]
[ROW][C]91[/C][C]493[/C][C]493.269614629782[/C][C]-0.269614629782482[/C][/ROW]
[ROW][C]92[/C][C]490[/C][C]490.232541601837[/C][C]-0.232541601836569[/C][/ROW]
[ROW][C]93[/C][C]469[/C][C]469.260280888826[/C][C]-0.26028088882568[/C][/ROW]
[ROW][C]94[/C][C]478[/C][C]477.234878118824[/C][C]0.765121881176492[/C][/ROW]
[ROW][C]95[/C][C]528[/C][C]529.10652066082[/C][C]-1.10652066081993[/C][/ROW]
[ROW][C]96[/C][C]534[/C][C]533.075826003453[/C][C]0.924173996547485[/C][/ROW]
[ROW][C]97[/C][C]518[/C][C]518.125027420814[/C][C]-0.125027420813761[/C][/ROW]
[ROW][C]98[/C][C]506[/C][C]506.11199419245[/C][C]-0.111994192450033[/C][/ROW]
[ROW][C]99[/C][C]502[/C][C]502.11428432668[/C][C]-0.114284326680504[/C][/ROW]
[ROW][C]100[/C][C]516[/C][C]516.040799714466[/C][C]-0.0407997144655875[/C][/ROW]
[ROW][C]101[/C][C]528[/C][C]528.071718576392[/C][C]-0.0717185763921203[/C][/ROW]
[ROW][C]102[/C][C]533[/C][C]533.035710066491[/C][C]-0.0357100664906006[/C][/ROW]
[ROW][C]103[/C][C]536[/C][C]535.972716544594[/C][C]0.0272834554057111[/C][/ROW]
[ROW][C]104[/C][C]537[/C][C]537.01696703615[/C][C]-0.0169670361503801[/C][/ROW]
[ROW][C]105[/C][C]524[/C][C]523.041989665755[/C][C]0.95801033424538[/C][/ROW]
[ROW][C]106[/C][C]536[/C][C]536.051940261667[/C][C]-0.051940261667347[/C][/ROW]
[ROW][C]107[/C][C]587[/C][C]586.873862178736[/C][C]0.126137821263754[/C][/ROW]
[ROW][C]108[/C][C]597[/C][C]595.881817300323[/C][C]1.11818269967681[/C][/ROW]
[ROW][C]109[/C][C]581[/C][C]580.880473177513[/C][C]0.119526822486909[/C][/ROW]
[ROW][C]110[/C][C]564[/C][C]564.887221675322[/C][C]-0.887221675321845[/C][/ROW]
[ROW][C]111[/C][C]558[/C][C]556.943462177226[/C][C]1.05653782277409[/C][/ROW]
[ROW][C]112[/C][C]575[/C][C]574.924989519306[/C][C]0.0750104806939534[/C][/ROW]
[ROW][C]113[/C][C]580[/C][C]580.989247174503[/C][C]-0.989247174503379[/C][/ROW]
[ROW][C]114[/C][C]575[/C][C]574.994351332809[/C][C]0.00564866719130818[/C][/ROW]
[ROW][C]115[/C][C]563[/C][C]564.014661830702[/C][C]-1.01466183070225[/C][/ROW]
[ROW][C]116[/C][C]552[/C][C]550.992669444991[/C][C]1.007330555009[/C][/ROW]
[ROW][C]117[/C][C]537[/C][C]537.020988487635[/C][C]-0.0209884876346739[/C][/ROW]
[ROW][C]118[/C][C]545[/C][C]545.022910882481[/C][C]-0.0229108824812839[/C][/ROW]
[ROW][C]119[/C][C]601[/C][C]600.922843367256[/C][C]0.0771566327442979[/C][/ROW]
[ROW][C]120[/C][C]604[/C][C]604.90642171977[/C][C]-0.906421719769917[/C][/ROW]
[ROW][C]121[/C][C]586[/C][C]586.92404857322[/C][C]-0.924048573220314[/C][/ROW]
[ROW][C]122[/C][C]564[/C][C]563.984059268585[/C][C]0.0159407314147161[/C][/ROW]
[ROW][C]123[/C][C]549[/C][C]548.031597564638[/C][C]0.968402435362332[/C][/ROW]
[ROW][C]124[/C][C]551[/C][C]551.069472803174[/C][C]-0.0694728031742269[/C][/ROW]
[ROW][C]125[/C][C]556[/C][C]556.141173598935[/C][C]-0.141173598934993[/C][/ROW]
[ROW][C]126[/C][C]548[/C][C]548.219353129192[/C][C]-0.219353129191603[/C][/ROW]
[ROW][C]127[/C][C]540[/C][C]540.205138293973[/C][C]-0.205138293973038[/C][/ROW]
[ROW][C]128[/C][C]531[/C][C]531.213895556929[/C][C]-0.213895556928814[/C][/ROW]
[ROW][C]129[/C][C]521[/C][C]520.277099741855[/C][C]0.722900258145392[/C][/ROW]
[ROW][C]130[/C][C]519[/C][C]518.231549945526[/C][C]0.768450054474219[/C][/ROW]
[ROW][C]131[/C][C]572[/C][C]572.100364298116[/C][C]-0.100364298115545[/C][/ROW]
[ROW][C]132[/C][C]581[/C][C]582.065674009819[/C][C]-1.06567400981847[/C][/ROW]
[ROW][C]133[/C][C]563[/C][C]563.067748879842[/C][C]-0.0677488798424472[/C][/ROW]
[ROW][C]134[/C][C]548[/C][C]548.062724196229[/C][C]-0.062724196228838[/C][/ROW]
[ROW][C]135[/C][C]539[/C][C]539.080206504716[/C][C]-0.0802065047157384[/C][/ROW]
[ROW][C]136[/C][C]541[/C][C]541.051970054323[/C][C]-0.0519700543227361[/C][/ROW]
[ROW][C]137[/C][C]562[/C][C]561.07145900524[/C][C]0.928540994760142[/C][/ROW]
[ROW][C]138[/C][C]559[/C][C]559.068802895943[/C][C]-0.0688028959432661[/C][/ROW]
[ROW][C]139[/C][C]546[/C][C]546.077682738694[/C][C]-0.0776827386936672[/C][/ROW]
[ROW][C]140[/C][C]536[/C][C]536.994059801506[/C][C]-0.994059801506085[/C][/ROW]
[ROW][C]141[/C][C]528[/C][C]526.994931099506[/C][C]1.00506890049363[/C][/ROW]
[ROW][C]142[/C][C]530[/C][C]530.980613340809[/C][C]-0.980613340809178[/C][/ROW]
[ROW][C]143[/C][C]582[/C][C]581.835274889887[/C][C]0.164725110113281[/C][/ROW]
[ROW][C]144[/C][C]599[/C][C]598.834302302349[/C][C]0.165697697651263[/C][/ROW]
[ROW][C]145[/C][C]584[/C][C]583.790064492506[/C][C]0.209935507493585[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186236&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186236&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
1501501.842685590958-0.842685590957651
2485484.8833544531190.11664554688051
3464463.9351224919180.0648775080820712
4460459.9680342040370.0319657959633518
5467467.075430891991-0.075430891991465
6460460.095632975422-0.0956329754222524
7448448.161899461571-0.161899461571094
8443443.159571713222-0.159571713221867
9436436.180818658381-0.180818658381019
10431431.197133525345-0.197133525344912
11484484.090963372842-0.0909633728421026
12510509.0624204608660.937579539133899
13513513.037111939995-0.0371119399949131
14503502.9862026775780.0137973224224519
15471471.068357438314-0.0683574383140762
16471471.028676167446-0.0286761674460778
17476476.116962016455-0.116962016455046
18475474.1230167873580.876983212642237
19470470.136255187767-0.136255187766779
20461461.152692634525-0.152692634525302
21455455.120317572958-0.120317572958251
22456455.1214261003340.878573899666117
23517516.9442035063590.0557964936413786
24525525.899509198986-0.89950919898563
25523522.8504147124690.149585287530868
26519518.8854303133760.114569686623559
27509508.8695405649420.130459435057582
28512511.8800623079670.119937692033439
29519518.9073084089270.0926915910733787
30517516.8715143545260.128485645474504
31510509.8927612996850.107238700315361
32509509.903625568988-0.903625568987983
33501499.9361916372691.06380836273149
34507506.8796825269880.12031747301171
35569569.721322494811-0.721322494810696
36580579.7361187196250.263881280375261
37578577.6872583449250.31274165507535
38565565.775302031572-0.775302031571768
39547547.761894828706-0.761894828706228
40555554.7998698073580.200130192642036
41562561.8271159083180.172884091681977
42561560.8410566441760.158943355823761
43555555.886814730109-0.886814730109328
44544543.9033723832370.0966276167627479
45537536.9366407869670.063359213033029
46543542.8689209680280.131079031971839
47594593.6901538404690.309846159530895
48611610.6330452524140.366954747586191
49613612.6389774435220.361022556478091
50611610.7148810838730.285118916126611
51594593.7247060482370.275293951762998
52595595.743704559644-0.743704559644074
53591590.8547370693590.145262930641387
54589589.861011786746-0.861011786745898
55584583.8748268252510.125173174749471
56573572.8951373231440.104862676855917
57567566.9166042147880.083395785212198
58569568.9214915441670.0785084558324952
59621620.7317737770050.268226222995335
60629628.7075635312530.292436468746663
61628627.7084379468130.291562053187403
62612611.7955593515260.204440648474046
63595595.805604262374-0.805604262374159
64597596.8363759552040.163624044796435
65593592.910149191530.089850808469674
66590589.9269181167950.0730818832048146
67580579.9594700197440.0405299802560961
68574574.010764278121-0.0107642781208959
69573572.9861667670460.0138332329534815
70573573.005903744403-0.00590374440346041
71620619.9016253340420.0983746659583022
72626625.893337928660.106662071340366
73620619.9050490783760.0949509216243459
74588586.9714153704441.02858462955584
75566565.9769791438390.0230208561608848
76557558.068210829063-1.06821082906287
77561561.077907098845-0.0779070988454993
78549549.109990778161-0.109990778161418
79532532.140903898395-0.140903898395353
80526526.15365990984-0.153659909839763
81511511.189410859138-0.189410859137972
82499499.196212770338-0.19621277033819
83555555.090744953285-0.0907449532845708
84565564.0831598833520.9168401166483
85542542.120568462975-0.120568462974569
86527527.103493990127-0.103493990126742
87510510.178345659121-0.178345659121301
88514514.14620782693-0.146207826929612
89517517.198811949076-0.198811949076292
90508507.2379850087680.762014991232465
91493493.269614629782-0.269614629782482
92490490.232541601837-0.232541601836569
93469469.260280888826-0.26028088882568
94478477.2348781188240.765121881176492
95528529.10652066082-1.10652066081993
96534533.0758260034530.924173996547485
97518518.125027420814-0.125027420813761
98506506.11199419245-0.111994192450033
99502502.11428432668-0.114284326680504
100516516.040799714466-0.0407997144655875
101528528.071718576392-0.0717185763921203
102533533.035710066491-0.0357100664906006
103536535.9727165445940.0272834554057111
104537537.01696703615-0.0169670361503801
105524523.0419896657550.95801033424538
106536536.051940261667-0.051940261667347
107587586.8738621787360.126137821263754
108597595.8818173003231.11818269967681
109581580.8804731775130.119526822486909
110564564.887221675322-0.887221675321845
111558556.9434621772261.05653782277409
112575574.9249895193060.0750104806939534
113580580.989247174503-0.989247174503379
114575574.9943513328090.00564866719130818
115563564.014661830702-1.01466183070225
116552550.9926694449911.007330555009
117537537.020988487635-0.0209884876346739
118545545.022910882481-0.0229108824812839
119601600.9228433672560.0771566327442979
120604604.90642171977-0.906421719769917
121586586.92404857322-0.924048573220314
122564563.9840592685850.0159407314147161
123549548.0315975646380.968402435362332
124551551.069472803174-0.0694728031742269
125556556.141173598935-0.141173598934993
126548548.219353129192-0.219353129191603
127540540.205138293973-0.205138293973038
128531531.213895556929-0.213895556928814
129521520.2770997418550.722900258145392
130519518.2315499455260.768450054474219
131572572.100364298116-0.100364298115545
132581582.065674009819-1.06567400981847
133563563.067748879842-0.0677488798424472
134548548.062724196229-0.062724196228838
135539539.080206504716-0.0802065047157384
136541541.051970054323-0.0519700543227361
137562561.071459005240.928540994760142
138559559.068802895943-0.0688028959432661
139546546.077682738694-0.0776827386936672
140536536.994059801506-0.994059801506085
141528526.9949310995061.00506890049363
142530530.980613340809-0.980613340809178
143582581.8352748898870.164725110113281
144599598.8343023023490.165697697651263
145584583.7900644925060.209935507493585







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.04412303669198170.08824607338396340.955876963308018
120.3573185287769980.7146370575539970.642681471223001
130.2968624314656150.593724862931230.703137568534385
140.1934032779443630.3868065558887260.806596722055637
150.1157020033701050.231404006740210.884297996629895
160.1047585832800930.2095171665601860.895241416719907
170.06638089151336650.1327617830267330.933619108486634
180.2389261689953140.4778523379906270.761073831004686
190.2425732440580660.4851464881161320.757426755941934
200.2040925087507940.4081850175015880.795907491249206
210.1472493216351370.2944986432702750.852750678364863
220.2223422870978190.4446845741956370.777657712902181
230.1841944157659180.3683888315318370.815805584234081
240.182130224040440.364260448080880.81786977595956
250.24177085280760.4835417056152010.7582291471924
260.1867755289541840.3735510579083690.813224471045816
270.1452078132544220.2904156265088440.854792186745578
280.1207206855235940.2414413710471870.879279314476406
290.088305019625310.176610039250620.91169498037469
300.06328757517732030.1265751503546410.93671242482268
310.0452865732702490.0905731465404980.954713426729751
320.1479019044239790.2958038088479570.852098095576021
330.2637105373899310.5274210747798610.736289462610069
340.2188830132941530.4377660265883060.781116986705847
350.2636168465857450.5272336931714890.736383153414255
360.2346543119526840.4693086239053670.765345688047316
370.2133129031439460.4266258062878920.786687096856054
380.3093186181238040.6186372362476080.690681381876196
390.364566579474550.72913315894910.63543342052545
400.3134188357163060.6268376714326130.686581164283694
410.2658351750332280.5316703500664550.734164824966772
420.2208755723978910.4417511447957830.779124427602109
430.3618166032008290.7236332064016580.638183396799171
440.3121495474477110.6242990948954230.687850452552289
450.2649296144296040.5298592288592090.735070385570396
460.2236140440020610.4472280880041220.776385955997939
470.208649204422010.417298408844020.79135079557799
480.1959938037978890.3919876075957770.804006196202111
490.173477263651760.346954527303520.82652273634824
500.1456633975622330.2913267951244670.854336602437767
510.1194010095299590.2388020190599180.880598990470041
520.1682530020600180.3365060041200360.831746997939982
530.1371067244016940.2742134488033880.862893275598306
540.2123592539862180.4247185079724360.787640746013782
550.1800091922091020.3600183844182040.819990807790898
560.1489512915814580.2979025831629160.851048708418542
570.1210496123947580.2420992247895170.878950387605241
580.09737334936669320.1947466987333860.902626650633307
590.0772537227161670.1545074454323340.922746277283833
600.06040426939512050.1208085387902410.93959573060488
610.04681669122515730.09363338245031450.953183308774843
620.03572979530359720.07145959060719450.964270204696403
630.08535678609217680.1707135721843540.914643213907823
640.06807041605163490.136140832103270.931929583948365
650.05379991533065450.1075998306613090.946200084669345
660.04196118091337530.08392236182675070.958038819086625
670.0324296373194010.06485927463880210.967570362680599
680.02515213588467290.05030427176934580.974847864115327
690.01872857485818390.03745714971636780.981271425141816
700.01382772019294020.02765544038588040.98617227980706
710.009941312975660160.01988262595132030.99005868702434
720.007050676873190250.01410135374638050.99294932312681
730.004935062454446980.009870124908893960.995064937545553
740.009402186689671160.01880437337934230.990597813310329
750.007344688836883250.01468937767376650.992655311163117
760.03091561798503340.06183123597006690.969084382014967
770.02361971708580740.04723943417161470.976380282914193
780.01779372170838210.03558744341676420.982206278291618
790.01329573785552970.02659147571105950.98670426214447
800.009702642005427640.01940528401085530.990297357994572
810.007193210521810310.01438642104362060.99280678947819
820.005780055654616510.0115601113092330.994219944345384
830.004175624585143910.008351249170287810.995824375414856
840.008038854463172860.01607770892634570.991961145536827
850.006011697992209150.01202339598441830.993988302007791
860.004272900690534170.008545801381068340.995727099309466
870.003147211325430440.006294422650860880.99685278867457
880.00224161020262850.004483220405257010.997758389797372
890.001551744919436160.003103489838872320.998448255080564
900.002718126073936190.005436252147872370.997281873926064
910.002058964902391260.004117929804782530.997941035097609
920.001476402119308480.002952804238616960.998523597880692
930.001187015049880630.002374030099761260.998812984950119
940.001686901351684960.003373802703369930.998313098648315
950.005880614840749920.01176122968149980.99411938515925
960.01510412953049150.03020825906098290.984895870469509
970.0113876456281930.0227752912563860.988612354371807
980.0081214915242890.0162429830485780.991878508475711
990.005784187676067220.01156837535213440.994215812323933
1000.004194724726448820.008389449452897630.995805275273551
1010.002880462558652240.005760925117304480.997119537441348
1020.001934706289975350.00386941257995070.998065293710025
1030.001328628316893340.002657256633786670.998671371683107
1040.001060472161304570.002120944322609140.998939527838695
1050.0012145448190970.002429089638193990.998785455180903
1060.001013627707387070.002027255414774140.998986372292613
1070.0006436948588907920.001287389717781580.999356305141109
1080.002353457549859250.004706915099718510.997646542450141
1090.001750291047244360.003500582094488710.998249708952756
1100.007618437825595090.01523687565119020.992381562174405
1110.01194806000206530.02389612000413070.988051939997935
1120.009377291617107030.01875458323421410.990622708382893
1130.01641556582440130.03283113164880260.983584434175599
1140.01195096570308250.0239019314061650.988049034296917
1150.02990974730114080.05981949460228160.970090252698859
1160.05743694655341970.1148738931068390.94256305344658
1170.04109488588679810.08218977177359620.958905114113202
1180.02881606271822910.05763212543645820.971183937281771
1190.02490790257039960.04981580514079910.9750920974296
1200.02600449449083480.05200898898166950.973995505509165
1210.04272532663151090.08545065326302170.957274673368489
1220.03276645043566520.06553290087133050.967233549564335
1230.05556984005847070.1111396801169410.944430159941529
1240.05289367031838360.1057873406367670.947106329681616
1250.0489782355951150.09795647119022990.951021764404885
1260.0494801822052020.09896036441040410.950519817794798
1270.03593469220590390.07186938441180780.964065307794096
1280.02237994364103750.0447598872820750.977620056358963
1290.01895735411901740.03791470823803490.981042645880983
1300.01688418978532180.03376837957064350.983115810214678
1310.01237573822557670.02475147645115350.987624261774423
1320.02256833857152020.04513667714304040.97743166142848
1330.1930831920437690.3861663840875380.806916807956231
1340.130176892936420.260353785872840.86982310706358

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.0441230366919817 & 0.0882460733839634 & 0.955876963308018 \tabularnewline
12 & 0.357318528776998 & 0.714637057553997 & 0.642681471223001 \tabularnewline
13 & 0.296862431465615 & 0.59372486293123 & 0.703137568534385 \tabularnewline
14 & 0.193403277944363 & 0.386806555888726 & 0.806596722055637 \tabularnewline
15 & 0.115702003370105 & 0.23140400674021 & 0.884297996629895 \tabularnewline
16 & 0.104758583280093 & 0.209517166560186 & 0.895241416719907 \tabularnewline
17 & 0.0663808915133665 & 0.132761783026733 & 0.933619108486634 \tabularnewline
18 & 0.238926168995314 & 0.477852337990627 & 0.761073831004686 \tabularnewline
19 & 0.242573244058066 & 0.485146488116132 & 0.757426755941934 \tabularnewline
20 & 0.204092508750794 & 0.408185017501588 & 0.795907491249206 \tabularnewline
21 & 0.147249321635137 & 0.294498643270275 & 0.852750678364863 \tabularnewline
22 & 0.222342287097819 & 0.444684574195637 & 0.777657712902181 \tabularnewline
23 & 0.184194415765918 & 0.368388831531837 & 0.815805584234081 \tabularnewline
24 & 0.18213022404044 & 0.36426044808088 & 0.81786977595956 \tabularnewline
25 & 0.2417708528076 & 0.483541705615201 & 0.7582291471924 \tabularnewline
26 & 0.186775528954184 & 0.373551057908369 & 0.813224471045816 \tabularnewline
27 & 0.145207813254422 & 0.290415626508844 & 0.854792186745578 \tabularnewline
28 & 0.120720685523594 & 0.241441371047187 & 0.879279314476406 \tabularnewline
29 & 0.08830501962531 & 0.17661003925062 & 0.91169498037469 \tabularnewline
30 & 0.0632875751773203 & 0.126575150354641 & 0.93671242482268 \tabularnewline
31 & 0.045286573270249 & 0.090573146540498 & 0.954713426729751 \tabularnewline
32 & 0.147901904423979 & 0.295803808847957 & 0.852098095576021 \tabularnewline
33 & 0.263710537389931 & 0.527421074779861 & 0.736289462610069 \tabularnewline
34 & 0.218883013294153 & 0.437766026588306 & 0.781116986705847 \tabularnewline
35 & 0.263616846585745 & 0.527233693171489 & 0.736383153414255 \tabularnewline
36 & 0.234654311952684 & 0.469308623905367 & 0.765345688047316 \tabularnewline
37 & 0.213312903143946 & 0.426625806287892 & 0.786687096856054 \tabularnewline
38 & 0.309318618123804 & 0.618637236247608 & 0.690681381876196 \tabularnewline
39 & 0.36456657947455 & 0.7291331589491 & 0.63543342052545 \tabularnewline
40 & 0.313418835716306 & 0.626837671432613 & 0.686581164283694 \tabularnewline
41 & 0.265835175033228 & 0.531670350066455 & 0.734164824966772 \tabularnewline
42 & 0.220875572397891 & 0.441751144795783 & 0.779124427602109 \tabularnewline
43 & 0.361816603200829 & 0.723633206401658 & 0.638183396799171 \tabularnewline
44 & 0.312149547447711 & 0.624299094895423 & 0.687850452552289 \tabularnewline
45 & 0.264929614429604 & 0.529859228859209 & 0.735070385570396 \tabularnewline
46 & 0.223614044002061 & 0.447228088004122 & 0.776385955997939 \tabularnewline
47 & 0.20864920442201 & 0.41729840884402 & 0.79135079557799 \tabularnewline
48 & 0.195993803797889 & 0.391987607595777 & 0.804006196202111 \tabularnewline
49 & 0.17347726365176 & 0.34695452730352 & 0.82652273634824 \tabularnewline
50 & 0.145663397562233 & 0.291326795124467 & 0.854336602437767 \tabularnewline
51 & 0.119401009529959 & 0.238802019059918 & 0.880598990470041 \tabularnewline
52 & 0.168253002060018 & 0.336506004120036 & 0.831746997939982 \tabularnewline
53 & 0.137106724401694 & 0.274213448803388 & 0.862893275598306 \tabularnewline
54 & 0.212359253986218 & 0.424718507972436 & 0.787640746013782 \tabularnewline
55 & 0.180009192209102 & 0.360018384418204 & 0.819990807790898 \tabularnewline
56 & 0.148951291581458 & 0.297902583162916 & 0.851048708418542 \tabularnewline
57 & 0.121049612394758 & 0.242099224789517 & 0.878950387605241 \tabularnewline
58 & 0.0973733493666932 & 0.194746698733386 & 0.902626650633307 \tabularnewline
59 & 0.077253722716167 & 0.154507445432334 & 0.922746277283833 \tabularnewline
60 & 0.0604042693951205 & 0.120808538790241 & 0.93959573060488 \tabularnewline
61 & 0.0468166912251573 & 0.0936333824503145 & 0.953183308774843 \tabularnewline
62 & 0.0357297953035972 & 0.0714595906071945 & 0.964270204696403 \tabularnewline
63 & 0.0853567860921768 & 0.170713572184354 & 0.914643213907823 \tabularnewline
64 & 0.0680704160516349 & 0.13614083210327 & 0.931929583948365 \tabularnewline
65 & 0.0537999153306545 & 0.107599830661309 & 0.946200084669345 \tabularnewline
66 & 0.0419611809133753 & 0.0839223618267507 & 0.958038819086625 \tabularnewline
67 & 0.032429637319401 & 0.0648592746388021 & 0.967570362680599 \tabularnewline
68 & 0.0251521358846729 & 0.0503042717693458 & 0.974847864115327 \tabularnewline
69 & 0.0187285748581839 & 0.0374571497163678 & 0.981271425141816 \tabularnewline
70 & 0.0138277201929402 & 0.0276554403858804 & 0.98617227980706 \tabularnewline
71 & 0.00994131297566016 & 0.0198826259513203 & 0.99005868702434 \tabularnewline
72 & 0.00705067687319025 & 0.0141013537463805 & 0.99294932312681 \tabularnewline
73 & 0.00493506245444698 & 0.00987012490889396 & 0.995064937545553 \tabularnewline
74 & 0.00940218668967116 & 0.0188043733793423 & 0.990597813310329 \tabularnewline
75 & 0.00734468883688325 & 0.0146893776737665 & 0.992655311163117 \tabularnewline
76 & 0.0309156179850334 & 0.0618312359700669 & 0.969084382014967 \tabularnewline
77 & 0.0236197170858074 & 0.0472394341716147 & 0.976380282914193 \tabularnewline
78 & 0.0177937217083821 & 0.0355874434167642 & 0.982206278291618 \tabularnewline
79 & 0.0132957378555297 & 0.0265914757110595 & 0.98670426214447 \tabularnewline
80 & 0.00970264200542764 & 0.0194052840108553 & 0.990297357994572 \tabularnewline
81 & 0.00719321052181031 & 0.0143864210436206 & 0.99280678947819 \tabularnewline
82 & 0.00578005565461651 & 0.011560111309233 & 0.994219944345384 \tabularnewline
83 & 0.00417562458514391 & 0.00835124917028781 & 0.995824375414856 \tabularnewline
84 & 0.00803885446317286 & 0.0160777089263457 & 0.991961145536827 \tabularnewline
85 & 0.00601169799220915 & 0.0120233959844183 & 0.993988302007791 \tabularnewline
86 & 0.00427290069053417 & 0.00854580138106834 & 0.995727099309466 \tabularnewline
87 & 0.00314721132543044 & 0.00629442265086088 & 0.99685278867457 \tabularnewline
88 & 0.0022416102026285 & 0.00448322040525701 & 0.997758389797372 \tabularnewline
89 & 0.00155174491943616 & 0.00310348983887232 & 0.998448255080564 \tabularnewline
90 & 0.00271812607393619 & 0.00543625214787237 & 0.997281873926064 \tabularnewline
91 & 0.00205896490239126 & 0.00411792980478253 & 0.997941035097609 \tabularnewline
92 & 0.00147640211930848 & 0.00295280423861696 & 0.998523597880692 \tabularnewline
93 & 0.00118701504988063 & 0.00237403009976126 & 0.998812984950119 \tabularnewline
94 & 0.00168690135168496 & 0.00337380270336993 & 0.998313098648315 \tabularnewline
95 & 0.00588061484074992 & 0.0117612296814998 & 0.99411938515925 \tabularnewline
96 & 0.0151041295304915 & 0.0302082590609829 & 0.984895870469509 \tabularnewline
97 & 0.011387645628193 & 0.022775291256386 & 0.988612354371807 \tabularnewline
98 & 0.008121491524289 & 0.016242983048578 & 0.991878508475711 \tabularnewline
99 & 0.00578418767606722 & 0.0115683753521344 & 0.994215812323933 \tabularnewline
100 & 0.00419472472644882 & 0.00838944945289763 & 0.995805275273551 \tabularnewline
101 & 0.00288046255865224 & 0.00576092511730448 & 0.997119537441348 \tabularnewline
102 & 0.00193470628997535 & 0.0038694125799507 & 0.998065293710025 \tabularnewline
103 & 0.00132862831689334 & 0.00265725663378667 & 0.998671371683107 \tabularnewline
104 & 0.00106047216130457 & 0.00212094432260914 & 0.998939527838695 \tabularnewline
105 & 0.001214544819097 & 0.00242908963819399 & 0.998785455180903 \tabularnewline
106 & 0.00101362770738707 & 0.00202725541477414 & 0.998986372292613 \tabularnewline
107 & 0.000643694858890792 & 0.00128738971778158 & 0.999356305141109 \tabularnewline
108 & 0.00235345754985925 & 0.00470691509971851 & 0.997646542450141 \tabularnewline
109 & 0.00175029104724436 & 0.00350058209448871 & 0.998249708952756 \tabularnewline
110 & 0.00761843782559509 & 0.0152368756511902 & 0.992381562174405 \tabularnewline
111 & 0.0119480600020653 & 0.0238961200041307 & 0.988051939997935 \tabularnewline
112 & 0.00937729161710703 & 0.0187545832342141 & 0.990622708382893 \tabularnewline
113 & 0.0164155658244013 & 0.0328311316488026 & 0.983584434175599 \tabularnewline
114 & 0.0119509657030825 & 0.023901931406165 & 0.988049034296917 \tabularnewline
115 & 0.0299097473011408 & 0.0598194946022816 & 0.970090252698859 \tabularnewline
116 & 0.0574369465534197 & 0.114873893106839 & 0.94256305344658 \tabularnewline
117 & 0.0410948858867981 & 0.0821897717735962 & 0.958905114113202 \tabularnewline
118 & 0.0288160627182291 & 0.0576321254364582 & 0.971183937281771 \tabularnewline
119 & 0.0249079025703996 & 0.0498158051407991 & 0.9750920974296 \tabularnewline
120 & 0.0260044944908348 & 0.0520089889816695 & 0.973995505509165 \tabularnewline
121 & 0.0427253266315109 & 0.0854506532630217 & 0.957274673368489 \tabularnewline
122 & 0.0327664504356652 & 0.0655329008713305 & 0.967233549564335 \tabularnewline
123 & 0.0555698400584707 & 0.111139680116941 & 0.944430159941529 \tabularnewline
124 & 0.0528936703183836 & 0.105787340636767 & 0.947106329681616 \tabularnewline
125 & 0.048978235595115 & 0.0979564711902299 & 0.951021764404885 \tabularnewline
126 & 0.049480182205202 & 0.0989603644104041 & 0.950519817794798 \tabularnewline
127 & 0.0359346922059039 & 0.0718693844118078 & 0.964065307794096 \tabularnewline
128 & 0.0223799436410375 & 0.044759887282075 & 0.977620056358963 \tabularnewline
129 & 0.0189573541190174 & 0.0379147082380349 & 0.981042645880983 \tabularnewline
130 & 0.0168841897853218 & 0.0337683795706435 & 0.983115810214678 \tabularnewline
131 & 0.0123757382255767 & 0.0247514764511535 & 0.987624261774423 \tabularnewline
132 & 0.0225683385715202 & 0.0451366771430404 & 0.97743166142848 \tabularnewline
133 & 0.193083192043769 & 0.386166384087538 & 0.806916807956231 \tabularnewline
134 & 0.13017689293642 & 0.26035378587284 & 0.86982310706358 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186236&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.0441230366919817[/C][C]0.0882460733839634[/C][C]0.955876963308018[/C][/ROW]
[ROW][C]12[/C][C]0.357318528776998[/C][C]0.714637057553997[/C][C]0.642681471223001[/C][/ROW]
[ROW][C]13[/C][C]0.296862431465615[/C][C]0.59372486293123[/C][C]0.703137568534385[/C][/ROW]
[ROW][C]14[/C][C]0.193403277944363[/C][C]0.386806555888726[/C][C]0.806596722055637[/C][/ROW]
[ROW][C]15[/C][C]0.115702003370105[/C][C]0.23140400674021[/C][C]0.884297996629895[/C][/ROW]
[ROW][C]16[/C][C]0.104758583280093[/C][C]0.209517166560186[/C][C]0.895241416719907[/C][/ROW]
[ROW][C]17[/C][C]0.0663808915133665[/C][C]0.132761783026733[/C][C]0.933619108486634[/C][/ROW]
[ROW][C]18[/C][C]0.238926168995314[/C][C]0.477852337990627[/C][C]0.761073831004686[/C][/ROW]
[ROW][C]19[/C][C]0.242573244058066[/C][C]0.485146488116132[/C][C]0.757426755941934[/C][/ROW]
[ROW][C]20[/C][C]0.204092508750794[/C][C]0.408185017501588[/C][C]0.795907491249206[/C][/ROW]
[ROW][C]21[/C][C]0.147249321635137[/C][C]0.294498643270275[/C][C]0.852750678364863[/C][/ROW]
[ROW][C]22[/C][C]0.222342287097819[/C][C]0.444684574195637[/C][C]0.777657712902181[/C][/ROW]
[ROW][C]23[/C][C]0.184194415765918[/C][C]0.368388831531837[/C][C]0.815805584234081[/C][/ROW]
[ROW][C]24[/C][C]0.18213022404044[/C][C]0.36426044808088[/C][C]0.81786977595956[/C][/ROW]
[ROW][C]25[/C][C]0.2417708528076[/C][C]0.483541705615201[/C][C]0.7582291471924[/C][/ROW]
[ROW][C]26[/C][C]0.186775528954184[/C][C]0.373551057908369[/C][C]0.813224471045816[/C][/ROW]
[ROW][C]27[/C][C]0.145207813254422[/C][C]0.290415626508844[/C][C]0.854792186745578[/C][/ROW]
[ROW][C]28[/C][C]0.120720685523594[/C][C]0.241441371047187[/C][C]0.879279314476406[/C][/ROW]
[ROW][C]29[/C][C]0.08830501962531[/C][C]0.17661003925062[/C][C]0.91169498037469[/C][/ROW]
[ROW][C]30[/C][C]0.0632875751773203[/C][C]0.126575150354641[/C][C]0.93671242482268[/C][/ROW]
[ROW][C]31[/C][C]0.045286573270249[/C][C]0.090573146540498[/C][C]0.954713426729751[/C][/ROW]
[ROW][C]32[/C][C]0.147901904423979[/C][C]0.295803808847957[/C][C]0.852098095576021[/C][/ROW]
[ROW][C]33[/C][C]0.263710537389931[/C][C]0.527421074779861[/C][C]0.736289462610069[/C][/ROW]
[ROW][C]34[/C][C]0.218883013294153[/C][C]0.437766026588306[/C][C]0.781116986705847[/C][/ROW]
[ROW][C]35[/C][C]0.263616846585745[/C][C]0.527233693171489[/C][C]0.736383153414255[/C][/ROW]
[ROW][C]36[/C][C]0.234654311952684[/C][C]0.469308623905367[/C][C]0.765345688047316[/C][/ROW]
[ROW][C]37[/C][C]0.213312903143946[/C][C]0.426625806287892[/C][C]0.786687096856054[/C][/ROW]
[ROW][C]38[/C][C]0.309318618123804[/C][C]0.618637236247608[/C][C]0.690681381876196[/C][/ROW]
[ROW][C]39[/C][C]0.36456657947455[/C][C]0.7291331589491[/C][C]0.63543342052545[/C][/ROW]
[ROW][C]40[/C][C]0.313418835716306[/C][C]0.626837671432613[/C][C]0.686581164283694[/C][/ROW]
[ROW][C]41[/C][C]0.265835175033228[/C][C]0.531670350066455[/C][C]0.734164824966772[/C][/ROW]
[ROW][C]42[/C][C]0.220875572397891[/C][C]0.441751144795783[/C][C]0.779124427602109[/C][/ROW]
[ROW][C]43[/C][C]0.361816603200829[/C][C]0.723633206401658[/C][C]0.638183396799171[/C][/ROW]
[ROW][C]44[/C][C]0.312149547447711[/C][C]0.624299094895423[/C][C]0.687850452552289[/C][/ROW]
[ROW][C]45[/C][C]0.264929614429604[/C][C]0.529859228859209[/C][C]0.735070385570396[/C][/ROW]
[ROW][C]46[/C][C]0.223614044002061[/C][C]0.447228088004122[/C][C]0.776385955997939[/C][/ROW]
[ROW][C]47[/C][C]0.20864920442201[/C][C]0.41729840884402[/C][C]0.79135079557799[/C][/ROW]
[ROW][C]48[/C][C]0.195993803797889[/C][C]0.391987607595777[/C][C]0.804006196202111[/C][/ROW]
[ROW][C]49[/C][C]0.17347726365176[/C][C]0.34695452730352[/C][C]0.82652273634824[/C][/ROW]
[ROW][C]50[/C][C]0.145663397562233[/C][C]0.291326795124467[/C][C]0.854336602437767[/C][/ROW]
[ROW][C]51[/C][C]0.119401009529959[/C][C]0.238802019059918[/C][C]0.880598990470041[/C][/ROW]
[ROW][C]52[/C][C]0.168253002060018[/C][C]0.336506004120036[/C][C]0.831746997939982[/C][/ROW]
[ROW][C]53[/C][C]0.137106724401694[/C][C]0.274213448803388[/C][C]0.862893275598306[/C][/ROW]
[ROW][C]54[/C][C]0.212359253986218[/C][C]0.424718507972436[/C][C]0.787640746013782[/C][/ROW]
[ROW][C]55[/C][C]0.180009192209102[/C][C]0.360018384418204[/C][C]0.819990807790898[/C][/ROW]
[ROW][C]56[/C][C]0.148951291581458[/C][C]0.297902583162916[/C][C]0.851048708418542[/C][/ROW]
[ROW][C]57[/C][C]0.121049612394758[/C][C]0.242099224789517[/C][C]0.878950387605241[/C][/ROW]
[ROW][C]58[/C][C]0.0973733493666932[/C][C]0.194746698733386[/C][C]0.902626650633307[/C][/ROW]
[ROW][C]59[/C][C]0.077253722716167[/C][C]0.154507445432334[/C][C]0.922746277283833[/C][/ROW]
[ROW][C]60[/C][C]0.0604042693951205[/C][C]0.120808538790241[/C][C]0.93959573060488[/C][/ROW]
[ROW][C]61[/C][C]0.0468166912251573[/C][C]0.0936333824503145[/C][C]0.953183308774843[/C][/ROW]
[ROW][C]62[/C][C]0.0357297953035972[/C][C]0.0714595906071945[/C][C]0.964270204696403[/C][/ROW]
[ROW][C]63[/C][C]0.0853567860921768[/C][C]0.170713572184354[/C][C]0.914643213907823[/C][/ROW]
[ROW][C]64[/C][C]0.0680704160516349[/C][C]0.13614083210327[/C][C]0.931929583948365[/C][/ROW]
[ROW][C]65[/C][C]0.0537999153306545[/C][C]0.107599830661309[/C][C]0.946200084669345[/C][/ROW]
[ROW][C]66[/C][C]0.0419611809133753[/C][C]0.0839223618267507[/C][C]0.958038819086625[/C][/ROW]
[ROW][C]67[/C][C]0.032429637319401[/C][C]0.0648592746388021[/C][C]0.967570362680599[/C][/ROW]
[ROW][C]68[/C][C]0.0251521358846729[/C][C]0.0503042717693458[/C][C]0.974847864115327[/C][/ROW]
[ROW][C]69[/C][C]0.0187285748581839[/C][C]0.0374571497163678[/C][C]0.981271425141816[/C][/ROW]
[ROW][C]70[/C][C]0.0138277201929402[/C][C]0.0276554403858804[/C][C]0.98617227980706[/C][/ROW]
[ROW][C]71[/C][C]0.00994131297566016[/C][C]0.0198826259513203[/C][C]0.99005868702434[/C][/ROW]
[ROW][C]72[/C][C]0.00705067687319025[/C][C]0.0141013537463805[/C][C]0.99294932312681[/C][/ROW]
[ROW][C]73[/C][C]0.00493506245444698[/C][C]0.00987012490889396[/C][C]0.995064937545553[/C][/ROW]
[ROW][C]74[/C][C]0.00940218668967116[/C][C]0.0188043733793423[/C][C]0.990597813310329[/C][/ROW]
[ROW][C]75[/C][C]0.00734468883688325[/C][C]0.0146893776737665[/C][C]0.992655311163117[/C][/ROW]
[ROW][C]76[/C][C]0.0309156179850334[/C][C]0.0618312359700669[/C][C]0.969084382014967[/C][/ROW]
[ROW][C]77[/C][C]0.0236197170858074[/C][C]0.0472394341716147[/C][C]0.976380282914193[/C][/ROW]
[ROW][C]78[/C][C]0.0177937217083821[/C][C]0.0355874434167642[/C][C]0.982206278291618[/C][/ROW]
[ROW][C]79[/C][C]0.0132957378555297[/C][C]0.0265914757110595[/C][C]0.98670426214447[/C][/ROW]
[ROW][C]80[/C][C]0.00970264200542764[/C][C]0.0194052840108553[/C][C]0.990297357994572[/C][/ROW]
[ROW][C]81[/C][C]0.00719321052181031[/C][C]0.0143864210436206[/C][C]0.99280678947819[/C][/ROW]
[ROW][C]82[/C][C]0.00578005565461651[/C][C]0.011560111309233[/C][C]0.994219944345384[/C][/ROW]
[ROW][C]83[/C][C]0.00417562458514391[/C][C]0.00835124917028781[/C][C]0.995824375414856[/C][/ROW]
[ROW][C]84[/C][C]0.00803885446317286[/C][C]0.0160777089263457[/C][C]0.991961145536827[/C][/ROW]
[ROW][C]85[/C][C]0.00601169799220915[/C][C]0.0120233959844183[/C][C]0.993988302007791[/C][/ROW]
[ROW][C]86[/C][C]0.00427290069053417[/C][C]0.00854580138106834[/C][C]0.995727099309466[/C][/ROW]
[ROW][C]87[/C][C]0.00314721132543044[/C][C]0.00629442265086088[/C][C]0.99685278867457[/C][/ROW]
[ROW][C]88[/C][C]0.0022416102026285[/C][C]0.00448322040525701[/C][C]0.997758389797372[/C][/ROW]
[ROW][C]89[/C][C]0.00155174491943616[/C][C]0.00310348983887232[/C][C]0.998448255080564[/C][/ROW]
[ROW][C]90[/C][C]0.00271812607393619[/C][C]0.00543625214787237[/C][C]0.997281873926064[/C][/ROW]
[ROW][C]91[/C][C]0.00205896490239126[/C][C]0.00411792980478253[/C][C]0.997941035097609[/C][/ROW]
[ROW][C]92[/C][C]0.00147640211930848[/C][C]0.00295280423861696[/C][C]0.998523597880692[/C][/ROW]
[ROW][C]93[/C][C]0.00118701504988063[/C][C]0.00237403009976126[/C][C]0.998812984950119[/C][/ROW]
[ROW][C]94[/C][C]0.00168690135168496[/C][C]0.00337380270336993[/C][C]0.998313098648315[/C][/ROW]
[ROW][C]95[/C][C]0.00588061484074992[/C][C]0.0117612296814998[/C][C]0.99411938515925[/C][/ROW]
[ROW][C]96[/C][C]0.0151041295304915[/C][C]0.0302082590609829[/C][C]0.984895870469509[/C][/ROW]
[ROW][C]97[/C][C]0.011387645628193[/C][C]0.022775291256386[/C][C]0.988612354371807[/C][/ROW]
[ROW][C]98[/C][C]0.008121491524289[/C][C]0.016242983048578[/C][C]0.991878508475711[/C][/ROW]
[ROW][C]99[/C][C]0.00578418767606722[/C][C]0.0115683753521344[/C][C]0.994215812323933[/C][/ROW]
[ROW][C]100[/C][C]0.00419472472644882[/C][C]0.00838944945289763[/C][C]0.995805275273551[/C][/ROW]
[ROW][C]101[/C][C]0.00288046255865224[/C][C]0.00576092511730448[/C][C]0.997119537441348[/C][/ROW]
[ROW][C]102[/C][C]0.00193470628997535[/C][C]0.0038694125799507[/C][C]0.998065293710025[/C][/ROW]
[ROW][C]103[/C][C]0.00132862831689334[/C][C]0.00265725663378667[/C][C]0.998671371683107[/C][/ROW]
[ROW][C]104[/C][C]0.00106047216130457[/C][C]0.00212094432260914[/C][C]0.998939527838695[/C][/ROW]
[ROW][C]105[/C][C]0.001214544819097[/C][C]0.00242908963819399[/C][C]0.998785455180903[/C][/ROW]
[ROW][C]106[/C][C]0.00101362770738707[/C][C]0.00202725541477414[/C][C]0.998986372292613[/C][/ROW]
[ROW][C]107[/C][C]0.000643694858890792[/C][C]0.00128738971778158[/C][C]0.999356305141109[/C][/ROW]
[ROW][C]108[/C][C]0.00235345754985925[/C][C]0.00470691509971851[/C][C]0.997646542450141[/C][/ROW]
[ROW][C]109[/C][C]0.00175029104724436[/C][C]0.00350058209448871[/C][C]0.998249708952756[/C][/ROW]
[ROW][C]110[/C][C]0.00761843782559509[/C][C]0.0152368756511902[/C][C]0.992381562174405[/C][/ROW]
[ROW][C]111[/C][C]0.0119480600020653[/C][C]0.0238961200041307[/C][C]0.988051939997935[/C][/ROW]
[ROW][C]112[/C][C]0.00937729161710703[/C][C]0.0187545832342141[/C][C]0.990622708382893[/C][/ROW]
[ROW][C]113[/C][C]0.0164155658244013[/C][C]0.0328311316488026[/C][C]0.983584434175599[/C][/ROW]
[ROW][C]114[/C][C]0.0119509657030825[/C][C]0.023901931406165[/C][C]0.988049034296917[/C][/ROW]
[ROW][C]115[/C][C]0.0299097473011408[/C][C]0.0598194946022816[/C][C]0.970090252698859[/C][/ROW]
[ROW][C]116[/C][C]0.0574369465534197[/C][C]0.114873893106839[/C][C]0.94256305344658[/C][/ROW]
[ROW][C]117[/C][C]0.0410948858867981[/C][C]0.0821897717735962[/C][C]0.958905114113202[/C][/ROW]
[ROW][C]118[/C][C]0.0288160627182291[/C][C]0.0576321254364582[/C][C]0.971183937281771[/C][/ROW]
[ROW][C]119[/C][C]0.0249079025703996[/C][C]0.0498158051407991[/C][C]0.9750920974296[/C][/ROW]
[ROW][C]120[/C][C]0.0260044944908348[/C][C]0.0520089889816695[/C][C]0.973995505509165[/C][/ROW]
[ROW][C]121[/C][C]0.0427253266315109[/C][C]0.0854506532630217[/C][C]0.957274673368489[/C][/ROW]
[ROW][C]122[/C][C]0.0327664504356652[/C][C]0.0655329008713305[/C][C]0.967233549564335[/C][/ROW]
[ROW][C]123[/C][C]0.0555698400584707[/C][C]0.111139680116941[/C][C]0.944430159941529[/C][/ROW]
[ROW][C]124[/C][C]0.0528936703183836[/C][C]0.105787340636767[/C][C]0.947106329681616[/C][/ROW]
[ROW][C]125[/C][C]0.048978235595115[/C][C]0.0979564711902299[/C][C]0.951021764404885[/C][/ROW]
[ROW][C]126[/C][C]0.049480182205202[/C][C]0.0989603644104041[/C][C]0.950519817794798[/C][/ROW]
[ROW][C]127[/C][C]0.0359346922059039[/C][C]0.0718693844118078[/C][C]0.964065307794096[/C][/ROW]
[ROW][C]128[/C][C]0.0223799436410375[/C][C]0.044759887282075[/C][C]0.977620056358963[/C][/ROW]
[ROW][C]129[/C][C]0.0189573541190174[/C][C]0.0379147082380349[/C][C]0.981042645880983[/C][/ROW]
[ROW][C]130[/C][C]0.0168841897853218[/C][C]0.0337683795706435[/C][C]0.983115810214678[/C][/ROW]
[ROW][C]131[/C][C]0.0123757382255767[/C][C]0.0247514764511535[/C][C]0.987624261774423[/C][/ROW]
[ROW][C]132[/C][C]0.0225683385715202[/C][C]0.0451366771430404[/C][C]0.97743166142848[/C][/ROW]
[ROW][C]133[/C][C]0.193083192043769[/C][C]0.386166384087538[/C][C]0.806916807956231[/C][/ROW]
[ROW][C]134[/C][C]0.13017689293642[/C][C]0.26035378587284[/C][C]0.86982310706358[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186236&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186236&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
110.04412303669198170.08824607338396340.955876963308018
120.3573185287769980.7146370575539970.642681471223001
130.2968624314656150.593724862931230.703137568534385
140.1934032779443630.3868065558887260.806596722055637
150.1157020033701050.231404006740210.884297996629895
160.1047585832800930.2095171665601860.895241416719907
170.06638089151336650.1327617830267330.933619108486634
180.2389261689953140.4778523379906270.761073831004686
190.2425732440580660.4851464881161320.757426755941934
200.2040925087507940.4081850175015880.795907491249206
210.1472493216351370.2944986432702750.852750678364863
220.2223422870978190.4446845741956370.777657712902181
230.1841944157659180.3683888315318370.815805584234081
240.182130224040440.364260448080880.81786977595956
250.24177085280760.4835417056152010.7582291471924
260.1867755289541840.3735510579083690.813224471045816
270.1452078132544220.2904156265088440.854792186745578
280.1207206855235940.2414413710471870.879279314476406
290.088305019625310.176610039250620.91169498037469
300.06328757517732030.1265751503546410.93671242482268
310.0452865732702490.0905731465404980.954713426729751
320.1479019044239790.2958038088479570.852098095576021
330.2637105373899310.5274210747798610.736289462610069
340.2188830132941530.4377660265883060.781116986705847
350.2636168465857450.5272336931714890.736383153414255
360.2346543119526840.4693086239053670.765345688047316
370.2133129031439460.4266258062878920.786687096856054
380.3093186181238040.6186372362476080.690681381876196
390.364566579474550.72913315894910.63543342052545
400.3134188357163060.6268376714326130.686581164283694
410.2658351750332280.5316703500664550.734164824966772
420.2208755723978910.4417511447957830.779124427602109
430.3618166032008290.7236332064016580.638183396799171
440.3121495474477110.6242990948954230.687850452552289
450.2649296144296040.5298592288592090.735070385570396
460.2236140440020610.4472280880041220.776385955997939
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1000.004194724726448820.008389449452897630.995805275273551
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1330.1930831920437690.3861663840875380.806916807956231
1340.130176892936420.260353785872840.86982310706358







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level210.169354838709677NOK
5% type I error level510.411290322580645NOK
10% type I error level680.548387096774194NOK

\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 & 21 & 0.169354838709677 & NOK \tabularnewline
5% type I error level & 51 & 0.411290322580645 & NOK \tabularnewline
10% type I error level & 68 & 0.548387096774194 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186236&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]21[/C][C]0.169354838709677[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]51[/C][C]0.411290322580645[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]68[/C][C]0.548387096774194[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186236&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186236&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 level210.169354838709677NOK
5% type I error level510.411290322580645NOK
10% type I error level680.548387096774194NOK



Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}