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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 14:41:37 -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/t1352146684cmzjfk45og5zvkb.htm/, Retrieved Wed, 01 Feb 2023 12:00:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186277, Retrieved Wed, 01 Feb 2023 12:00:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact88
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [ws7t] [2012-11-05 19:41:37] [fa3197be492c9e61892dda11815d51ad] [Current]
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Dataseries X:
1	2000	501	134	368	6.70	8.50	8.70
2	2000	485	124	361	6.80	8.40	8.60
3	2000	464	113	351	6.70	8.40	8.60
4	2000	460	109	351	6.60	8.30	8.50
5	2001	467	109	358	6.40	8.20	8.50
6	2001	460	106	354	6.30	8.20	8.50
7	2001	448	101	347	6.30	8.10	8.50
8	2001	443	98	345	6.50	8.10	8.50
9	2001	436	93	343	6.50	8.10	8.50
10	2001	431	91	340	6.40	8.10	8.50
11	2001	484	122	362	6.20	8.10	8.50
12	2001	510	139	370	6.20	8.10	8.60
13	2001	513	140	373	6.50	8.10	8.60
14	2001	503	132	371	7.00	8.20	8.60
15	2001	471	117	354	7.20	8.20	8.70
16	2001	471	114	357	7.30	8.30	8.70
17	2002	476	113	363	7.40	8.20	8.70
18	2002	475	110	364	7.40	8.30	8.80
19	2002	470	107	363	7.40	8.30	8.80
20	2002	461	103	358	7.30	8.40	8.90
21	2002	455	98	357	7.40	8.50	8.90
22	2002	456	98	357	7.40	8.50	8.90
23	2002	517	137	380	7.60	8.60	9.00
24	2002	525	148	378	7.60	8.60	9.00
25	2002	523	147	376	7.70	8.70	9.00
26	2002	519	139	380	7.70	8.70	9.00
27	2002	509	130	379	7.80	8.80	9.00
28	2002	512	128	384	7.80	8.80	9.00
29	2003	519	127	392	8.00	8.90	9.10
30	2003	517	123	394	8.10	9.00	9.10
31	2003	510	118	392	8.10	9.00	9.10
32	2003	509	114	396	8.20	9.00	9.10
33	2003	501	108	392	8.10	9.00	9.10
34	2003	507	111	396	8.10	9.10	9.10
35	2003	569	151	419	8.10	9.10	9.10
36	2003	580	159	421	8.10	9.00	9.10
37	2003	578	158	420	8.20	9.10	9.10
38	2003	565	148	418	8.20	9.00	9.10
39	2003	547	138	410	8.30	9.10	9.10
40	2003	555	137	418	8.40	9.10	9.20
41	2004	562	136	426	8.60	9.20	9.30
42	2004	561	133	428	8.60	9.20	9.30
43	2004	555	126	430	8.40	9.20	9.30
44	2004	544	120	424	8.00	9.20	9.20
45	2004	537	114	423	7.90	9.20	9.20
46	2004	543	116	427	8.10	9.30	9.20
47	2004	594	153	441	8.50	9.30	9.20
48	2004	611	162	449	8.80	9.30	9.20
49	2004	613	161	452	8.80	9.30	9.20
50	2004	611	149	462	8.50	9.30	9.20
51	2004	594	139	455	8.30	9.40	9.20
52	2004	595	135	461	8.30	9.40	9.20
53	2005	591	130	461	8.30	9.30	9.20
54	2005	589	127	463	8.40	9.30	9.20
55	2005	584	122	462	8.50	9.30	9.20
56	2005	573	117	456	8.50	9.30	9.20
57	2005	567	112	455	8.60	9.20	9.10
58	2005	569	113	456	8.50	9.20	9.10
59	2005	621	149	472	8.60	9.20	9.00
60	2005	629	157	472	8.60	9.10	8.90
61	2005	628	157	471	8.60	9.10	8.90
62	2005	612	147	465	8.50	9.10	9.00
63	2005	595	137	459	8.40	9.10	8.90
64	2005	597	132	465	8.40	9.00	8.80
65	2006	593	125	468	8.50	8.90	8.70
66	2006	590	123	467	8.50	8.80	8.60
67	2006	580	117	463	8.50	8.70	8.50
68	2006	574	114	460	8.60	8.60	8.50
69	2006	573	111	462	8.60	8.60	8.40
70	2006	573	112	461	8.40	8.50	8.30
71	2006	620	144	476	8.20	8.40	8.20
72	2006	626	150	476	8.00	8.40	8.20
73	2006	620	149	471	8.00	8.30	8.10
74	2006	588	134	453	8.00	8.20	8.00
75	2006	566	123	443	8.00	8.20	7.90
76	2006	557	116	442	7.90	8.00	7.80
77	2007	561	117	444	7.90	7.90	7.60
78	2007	549	111	438	7.90	7.80	7.50
79	2007	532	105	427	7.90	7.70	7.40
80	2007	526	102	424	8.00	7.60	7.30
81	2007	511	95	416	7.90	7.60	7.30
82	2007	499	93	406	7.40	7.60	7.20
83	2007	555	124	431	7.20	7.60	7.20
84	2007	565	130	434	7.00	7.60	7.20
85	2007	542	124	418	6.90	7.50	7.10
86	2007	527	115	412	7.10	7.50	7.00
87	2007	510	106	404	7.20	7.40	7.00
88	2007	514	105	409	7.20	7.40	6.90
89	2008	517	105	412	7.10	7.40	6.90
90	2008	508	101	406	6.90	7.30	6.80
91	2008	493	95	398	6.80	7.30	6.80
92	2008	490	93	397	6.80	7.40	6.80
93	2008	469	84	385	6.80	7.50	6.90
94	2008	478	87	390	6.90	7.60	7.00
95	2008	528	116	413	7.10	7.60	7.00
96	2008	534	120	413	7.20	7.70	7.10
97	2008	518	117	401	7.20	7.70	7.20
98	2008	506	109	397	7.10	7.90	7.30
99	2008	502	105	397	7.10	8.10	7.50
100	2008	516	107	409	7.20	8.40	7.70
101	2009	528	109	419	7.50	8.70	8.10
102	2009	533	109	424	7.70	9.00	8.40
103	2009	536	108	428	7.80	9.30	8.60
104	2009	537	107	430	7.70	9.40	8.80
105	2009	524	99	424	7.70	9.50	8.90
106	2009	536	103	433	7.80	9.60	9.10
107	2009	587	131	456	8.00	9.80	9.20
108	2009	597	137	459	8.10	9.80	9.30
109	2009	581	135	446	8.10	9.90	9.40
110	2009	564	124	441	8.00	10.00	9.40
111	2009	558	118	439	8.10	10.00	9.50
112	2010	575	121	454	8.20	10.10	9.50
113	2010	580	121	460	8.40	10.10	9.70
114	2010	575	118	457	8.50	10.10	9.70
115	2010	563	113	451	8.50	10.10	9.70
116	2010	552	107	444	8.50	10.20	9.70
117	2010	537	100	437	8.50	10.20	9.70
118	2010	545	102	443	8.50	10.10	9.60
119	2010	601	130	471	8.40	10.10	9.60
120	2010	604	136	469	8.30	10.10	9.60
121	2010	586	133	454	8.20	10.10	9.60
122	2010	564	120	444	8.10	10.10	9.60
123	2010	549	112	436	7.90	10.10	9.60
124	2010	551	109	442	7.60	10.10	9.60
125	2011	556	110	446	7.30	10.00	9.50
126	2011	548	106	442	7.10	9.90	9.50
127	2011	540	102	438	7.00	9.90	9.40
128	2011	531	98	433	7.10	9.90	9.40
129	2011	521	92	428	7.10	9.90	9.50
130	2011	519	92	426	7.10	10.00	9.50
131	2011	572	120	452	7.30	10.10	9.60
132	2011	581	127	455	7.30	10.20	9.70
133	2011	563	124	439	7.30	10.30	9.80
134	2011	548	114	434	7.20	10.50	9.90
135	2011	539	108	431	7.20	10.60	10.00
136	2011	541	106	435	7.10	10.70	10.00
137	2012	562	111	450	7.10	10.80	10.10
138	2012	559	110	449	7.10	10.90	10.20
139	2012	546	104	442	7.20	11.00	10.30
140	2012	536	100	437	7.30	11.20	10.30
141	2012	528	96	431	7.40	11.30	10.40
142	2012	530	98	433	7.40	11.40	10.50
143	2012	582	122	460	7.50	11.50	10.50
144	2012	599	134	465	7.40	11.50	10.60
145	2012	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 time10 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=186277&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]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=186277&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186277&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
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
Totaal[t] = -84.8598455362288 + 0.00110852737564472t + 0.0431272408509219jaartal[t] + 0.995878671716741jongerdan25jaar[t] + 1.0002341118164vanaf25jaar[t] -0.0766601847092071`Belgi\303\253`[t] -0.461901000716776Eurogebied[t] + 0.385382469325768`EU-27\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Totaal[t] =  -84.8598455362288 +  0.00110852737564472t +  0.0431272408509219jaartal[t] +  0.995878671716741jongerdan25jaar[t] +  1.0002341118164vanaf25jaar[t] -0.0766601847092071`Belgi\303\253`[t] -0.461901000716776Eurogebied[t] +  0.385382469325768`EU-27\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186277&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Totaal[t] =  -84.8598455362288 +  0.00110852737564472t +  0.0431272408509219jaartal[t] +  0.995878671716741jongerdan25jaar[t] +  1.0002341118164vanaf25jaar[t] -0.0766601847092071`Belgi\303\253`[t] -0.461901000716776Eurogebied[t] +  0.385382469325768`EU-27\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186277&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186277&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] = -84.8598455362288 + 0.00110852737564472t + 0.0431272408509219jaartal[t] + 0.995878671716741jongerdan25jaar[t] + 1.0002341118164vanaf25jaar[t] -0.0766601847092071`Belgi\303\253`[t] -0.461901000716776Eurogebied[t] + 0.385382469325768`EU-27\r`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-84.8598455362288336.990516-0.25180.801560.40078
t0.001108527375644720.0142540.07780.9381260.469063
jaartal0.04312724085092190.1685330.25590.7984140.399207
jongerdan25jaar0.9958786717167410.00393253.403400
vanaf25jaar1.00023411181640.003058327.134200
`Belgi\303\253`-0.07666018470920710.113462-0.67560.5004040.250202
Eurogebied-0.4619010007167760.372345-1.24050.2169030.108452
`EU-27\r`0.3853824693257680.3508661.09840.2739670.136983

\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) & -84.8598455362288 & 336.990516 & -0.2518 & 0.80156 & 0.40078 \tabularnewline
t & 0.00110852737564472 & 0.014254 & 0.0778 & 0.938126 & 0.469063 \tabularnewline
jaartal & 0.0431272408509219 & 0.168533 & 0.2559 & 0.798414 & 0.399207 \tabularnewline
jongerdan25jaar & 0.995878671716741 & 0.00393 & 253.4034 & 0 & 0 \tabularnewline
vanaf25jaar & 1.0002341118164 & 0.003058 & 327.1342 & 0 & 0 \tabularnewline
`Belgi\303\253` & -0.0766601847092071 & 0.113462 & -0.6756 & 0.500404 & 0.250202 \tabularnewline
Eurogebied & -0.461901000716776 & 0.372345 & -1.2405 & 0.216903 & 0.108452 \tabularnewline
`EU-27\r` & 0.385382469325768 & 0.350866 & 1.0984 & 0.273967 & 0.136983 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186277&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]-84.8598455362288[/C][C]336.990516[/C][C]-0.2518[/C][C]0.80156[/C][C]0.40078[/C][/ROW]
[ROW][C]t[/C][C]0.00110852737564472[/C][C]0.014254[/C][C]0.0778[/C][C]0.938126[/C][C]0.469063[/C][/ROW]
[ROW][C]jaartal[/C][C]0.0431272408509219[/C][C]0.168533[/C][C]0.2559[/C][C]0.798414[/C][C]0.399207[/C][/ROW]
[ROW][C]jongerdan25jaar[/C][C]0.995878671716741[/C][C]0.00393[/C][C]253.4034[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]vanaf25jaar[/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]-0.0766601847092071[/C][C]0.113462[/C][C]-0.6756[/C][C]0.500404[/C][C]0.250202[/C][/ROW]
[ROW][C]Eurogebied[/C][C]-0.461901000716776[/C][C]0.372345[/C][C]-1.2405[/C][C]0.216903[/C][C]0.108452[/C][/ROW]
[ROW][C]`EU-27\r`[/C][C]0.385382469325768[/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=186277&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186277&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)-84.8598455362288336.990516-0.25180.801560.40078
t0.001108527375644720.0142540.07780.9381260.469063
jaartal0.04312724085092190.1685330.25590.7984140.399207
jongerdan25jaar0.9958786717167410.00393253.403400
vanaf25jaar1.00023411181640.003058327.134200
`Belgi\303\253`-0.07666018470920710.113462-0.67560.5004040.250202
Eurogebied-0.4619010007167760.372345-1.24050.2169030.108452
`EU-27\r`0.3853824693257680.3508661.09840.2739670.136983







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 Residuals34.8252593527027

\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 & 34.8252593527027 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186277&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]34.8252593527027[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186277&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186277&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 Residuals34.8252593527027







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1501501.842685590958-0.842685590957632
2485484.8833544531190.116645546880518
3464463.9351224919180.0648775080820763
4460459.9680342040370.0319657959633536
5467467.075430891992-0.0754308919914907
6460460.095632975422-0.0956329754222533
7448448.161899461571-0.161899461571095
8443443.159571713222-0.159571713221877
9436436.180818658381-0.180818658381022
10431431.197133525345-0.197133525344919
11484484.090963372842-0.0909633728420994
12510509.0624204608660.937579539133901
13513513.037111939995-0.037111939994909
14503502.9862026775780.0137973224224533
15471471.068357438314-0.0683574383140691
16471471.028676167446-0.0286761674460842
17476476.116962016455-0.116962016455051
18475474.1230167873580.876983212642239
19470470.136255187767-0.136255187766788
20461461.152692634525-0.152692634525302
21455455.120317572958-0.120317572958242
22456455.1214261003340.878573899666104
23517516.9442035063590.055796493641379
24525525.899509198986-0.899509198985624
25523522.8504147124690.149585287530861
26519518.8854303133760.114569686623559
27509508.8695405649420.130459435057583
28512511.8800623079670.119937692033441
29519518.9073084089270.0926915910733805
30517516.8715143545260.128485645474508
31510509.8927612996850.107238700315362
32509509.903625568988-0.903625568987984
33501499.9361916372691.06380836273149
34507506.8796825269880.120317473011711
35569569.721322494811-0.721322494810697
36580579.7361187196250.26388128037526
37578577.6872583449250.312741655075352
38565565.775302031572-0.775302031571767
39547547.761894828706-0.761894828706227
40555554.7998698073580.200130192642039
41562561.8271159083180.172884091681981
42561560.8410566441760.158943355823764
43555555.886814730109-0.886814730109324
44544543.9033723832370.0966276167627559
45537536.9366407869670.0633592130330377
46543542.8689209680280.131079031971837
47594593.6901538404690.309846159530917
48611610.6330452524140.366954747586178
49613612.6389774435220.36102255647809
50611610.7148810838730.285118916126623
51594593.7247060482370.275293951762989
52595595.743704559644-0.743704559644064
53591590.8547370693590.14526293064139
54589589.861011786746-0.861011786745899
55584583.874826825250.125173174749477
56573572.8951373231440.104862676855925
57567566.9166042147880.0833957852121922
58569568.9214915441680.0785084558324827
59621620.7317737770050.26822622299532
60629628.7075635312530.29243646874664
61628627.7084379468130.291562053187388
62612611.7955593515260.204440648474038
63595595.805604262374-0.805604262374154
64597596.8363759552040.163624044796427
65593592.910149191530.0898508084696743
66590589.9269181167950.0730818832048224
67580579.9594700197440.0405299802560979
68574574.010764278121-0.010764278120887
69573572.9861667670460.0138332329534836
70573573.005903744403-0.00590374440345748
71620619.9016253340420.0983746659582861
72626625.893337928660.10666207134036
73620619.9050490783760.0949509216243334
74588586.9714153704441.02858462955585
75566565.9769791438390.023020856160887
76557558.068210829063-1.06821082906286
77561561.077907098845-0.0779070988455016
78549549.109990778161-0.109990778161412
79532532.140903898395-0.14090389839535
80526526.15365990984-0.153659909839761
81511511.189410859138-0.18941085913797
82499499.196212770338-0.196212770338188
83555555.090744953285-0.090744953284569
84565564.0831598833520.91684011664831
85542542.120568462975-0.120568462974566
86527527.103493990127-0.103493990126747
87510510.178345659121-0.178345659121301
88514514.14620782693-0.146207826929605
89517517.198811949076-0.19881194907629
90508507.2379850087680.762014991232466
91493493.269614629782-0.26961462978248
92490490.232541601837-0.232541601836574
93469469.260280888826-0.260280888825686
94478477.2348781188240.765121881176488
95528529.10652066082-1.10652066081993
96534533.0758260034530.924173996547484
97518518.125027420814-0.125027420813761
98506506.11199419245-0.111994192450032
99502502.11428432668-0.114284326680503
100516516.040799714466-0.0407997144656011
101528528.071718576392-0.0717185763921175
102533533.035710066491-0.0357100664906003
103536535.9727165445940.0272834554057086
104537537.01696703615-0.0169670361503836
105524523.0419896657550.95801033424538
106536536.051940261667-0.0519402616673513
107587586.8738621787360.126137821263758
108597595.8818173003231.11818269967682
109581580.8804731775130.119526822486912
110564564.887221675322-0.887221675321847
111558556.9434621772261.05653782277409
112575574.9249895193060.0750104806939537
113580580.989247174503-0.98924717450338
114575574.9943513328090.00564866719130946
115563564.014661830702-1.01466183070225
116552550.9926694449911.007330555009
117537537.020988487635-0.0209884876346796
118545545.022910882481-0.0229108824812848
119601600.9228433672560.0771566327443079
120604604.90642171977-0.906421719769919
121586586.92404857322-0.924048573220318
122564563.9840592685850.015940731414714
123549548.0315975646380.968402435362333
124551551.069472803174-0.0694728031742298
125556556.141173598935-0.141173598934989
126548548.219353129192-0.219353129191604
127540540.205138293973-0.20513829397304
128531531.213895556929-0.213895556928815
129521520.2770997418550.722900258145392
130519518.2315499455260.768450054474221
131572572.100364298116-0.100364298115542
132581582.065674009819-1.06567400981846
133563563.067748879842-0.0677488798424492
134548548.062724196229-0.0627241962288378
135539539.080206504716-0.080206504715741
136541541.051970054323-0.0519700543227406
137562561.071459005240.928540994760145
138559559.068802895943-0.0688028959432636
139546546.077682738694-0.0776827386936581
140536536.994059801506-0.994059801506085
141528526.9949310995061.00506890049364
142530530.980613340809-0.980613340809183
143582581.8352748898870.164725110113281
144599598.8343023023490.165697697651263
145584583.7900644925060.209935507493581

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 501 & 501.842685590958 & -0.842685590957632 \tabularnewline
2 & 485 & 484.883354453119 & 0.116645546880518 \tabularnewline
3 & 464 & 463.935122491918 & 0.0648775080820763 \tabularnewline
4 & 460 & 459.968034204037 & 0.0319657959633536 \tabularnewline
5 & 467 & 467.075430891992 & -0.0754308919914907 \tabularnewline
6 & 460 & 460.095632975422 & -0.0956329754222533 \tabularnewline
7 & 448 & 448.161899461571 & -0.161899461571095 \tabularnewline
8 & 443 & 443.159571713222 & -0.159571713221877 \tabularnewline
9 & 436 & 436.180818658381 & -0.180818658381022 \tabularnewline
10 & 431 & 431.197133525345 & -0.197133525344919 \tabularnewline
11 & 484 & 484.090963372842 & -0.0909633728420994 \tabularnewline
12 & 510 & 509.062420460866 & 0.937579539133901 \tabularnewline
13 & 513 & 513.037111939995 & -0.037111939994909 \tabularnewline
14 & 503 & 502.986202677578 & 0.0137973224224533 \tabularnewline
15 & 471 & 471.068357438314 & -0.0683574383140691 \tabularnewline
16 & 471 & 471.028676167446 & -0.0286761674460842 \tabularnewline
17 & 476 & 476.116962016455 & -0.116962016455051 \tabularnewline
18 & 475 & 474.123016787358 & 0.876983212642239 \tabularnewline
19 & 470 & 470.136255187767 & -0.136255187766788 \tabularnewline
20 & 461 & 461.152692634525 & -0.152692634525302 \tabularnewline
21 & 455 & 455.120317572958 & -0.120317572958242 \tabularnewline
22 & 456 & 455.121426100334 & 0.878573899666104 \tabularnewline
23 & 517 & 516.944203506359 & 0.055796493641379 \tabularnewline
24 & 525 & 525.899509198986 & -0.899509198985624 \tabularnewline
25 & 523 & 522.850414712469 & 0.149585287530861 \tabularnewline
26 & 519 & 518.885430313376 & 0.114569686623559 \tabularnewline
27 & 509 & 508.869540564942 & 0.130459435057583 \tabularnewline
28 & 512 & 511.880062307967 & 0.119937692033441 \tabularnewline
29 & 519 & 518.907308408927 & 0.0926915910733805 \tabularnewline
30 & 517 & 516.871514354526 & 0.128485645474508 \tabularnewline
31 & 510 & 509.892761299685 & 0.107238700315362 \tabularnewline
32 & 509 & 509.903625568988 & -0.903625568987984 \tabularnewline
33 & 501 & 499.936191637269 & 1.06380836273149 \tabularnewline
34 & 507 & 506.879682526988 & 0.120317473011711 \tabularnewline
35 & 569 & 569.721322494811 & -0.721322494810697 \tabularnewline
36 & 580 & 579.736118719625 & 0.26388128037526 \tabularnewline
37 & 578 & 577.687258344925 & 0.312741655075352 \tabularnewline
38 & 565 & 565.775302031572 & -0.775302031571767 \tabularnewline
39 & 547 & 547.761894828706 & -0.761894828706227 \tabularnewline
40 & 555 & 554.799869807358 & 0.200130192642039 \tabularnewline
41 & 562 & 561.827115908318 & 0.172884091681981 \tabularnewline
42 & 561 & 560.841056644176 & 0.158943355823764 \tabularnewline
43 & 555 & 555.886814730109 & -0.886814730109324 \tabularnewline
44 & 544 & 543.903372383237 & 0.0966276167627559 \tabularnewline
45 & 537 & 536.936640786967 & 0.0633592130330377 \tabularnewline
46 & 543 & 542.868920968028 & 0.131079031971837 \tabularnewline
47 & 594 & 593.690153840469 & 0.309846159530917 \tabularnewline
48 & 611 & 610.633045252414 & 0.366954747586178 \tabularnewline
49 & 613 & 612.638977443522 & 0.36102255647809 \tabularnewline
50 & 611 & 610.714881083873 & 0.285118916126623 \tabularnewline
51 & 594 & 593.724706048237 & 0.275293951762989 \tabularnewline
52 & 595 & 595.743704559644 & -0.743704559644064 \tabularnewline
53 & 591 & 590.854737069359 & 0.14526293064139 \tabularnewline
54 & 589 & 589.861011786746 & -0.861011786745899 \tabularnewline
55 & 584 & 583.87482682525 & 0.125173174749477 \tabularnewline
56 & 573 & 572.895137323144 & 0.104862676855925 \tabularnewline
57 & 567 & 566.916604214788 & 0.0833957852121922 \tabularnewline
58 & 569 & 568.921491544168 & 0.0785084558324827 \tabularnewline
59 & 621 & 620.731773777005 & 0.26822622299532 \tabularnewline
60 & 629 & 628.707563531253 & 0.29243646874664 \tabularnewline
61 & 628 & 627.708437946813 & 0.291562053187388 \tabularnewline
62 & 612 & 611.795559351526 & 0.204440648474038 \tabularnewline
63 & 595 & 595.805604262374 & -0.805604262374154 \tabularnewline
64 & 597 & 596.836375955204 & 0.163624044796427 \tabularnewline
65 & 593 & 592.91014919153 & 0.0898508084696743 \tabularnewline
66 & 590 & 589.926918116795 & 0.0730818832048224 \tabularnewline
67 & 580 & 579.959470019744 & 0.0405299802560979 \tabularnewline
68 & 574 & 574.010764278121 & -0.010764278120887 \tabularnewline
69 & 573 & 572.986166767046 & 0.0138332329534836 \tabularnewline
70 & 573 & 573.005903744403 & -0.00590374440345748 \tabularnewline
71 & 620 & 619.901625334042 & 0.0983746659582861 \tabularnewline
72 & 626 & 625.89333792866 & 0.10666207134036 \tabularnewline
73 & 620 & 619.905049078376 & 0.0949509216243334 \tabularnewline
74 & 588 & 586.971415370444 & 1.02858462955585 \tabularnewline
75 & 566 & 565.976979143839 & 0.023020856160887 \tabularnewline
76 & 557 & 558.068210829063 & -1.06821082906286 \tabularnewline
77 & 561 & 561.077907098845 & -0.0779070988455016 \tabularnewline
78 & 549 & 549.109990778161 & -0.109990778161412 \tabularnewline
79 & 532 & 532.140903898395 & -0.14090389839535 \tabularnewline
80 & 526 & 526.15365990984 & -0.153659909839761 \tabularnewline
81 & 511 & 511.189410859138 & -0.18941085913797 \tabularnewline
82 & 499 & 499.196212770338 & -0.196212770338188 \tabularnewline
83 & 555 & 555.090744953285 & -0.090744953284569 \tabularnewline
84 & 565 & 564.083159883352 & 0.91684011664831 \tabularnewline
85 & 542 & 542.120568462975 & -0.120568462974566 \tabularnewline
86 & 527 & 527.103493990127 & -0.103493990126747 \tabularnewline
87 & 510 & 510.178345659121 & -0.178345659121301 \tabularnewline
88 & 514 & 514.14620782693 & -0.146207826929605 \tabularnewline
89 & 517 & 517.198811949076 & -0.19881194907629 \tabularnewline
90 & 508 & 507.237985008768 & 0.762014991232466 \tabularnewline
91 & 493 & 493.269614629782 & -0.26961462978248 \tabularnewline
92 & 490 & 490.232541601837 & -0.232541601836574 \tabularnewline
93 & 469 & 469.260280888826 & -0.260280888825686 \tabularnewline
94 & 478 & 477.234878118824 & 0.765121881176488 \tabularnewline
95 & 528 & 529.10652066082 & -1.10652066081993 \tabularnewline
96 & 534 & 533.075826003453 & 0.924173996547484 \tabularnewline
97 & 518 & 518.125027420814 & -0.125027420813761 \tabularnewline
98 & 506 & 506.11199419245 & -0.111994192450032 \tabularnewline
99 & 502 & 502.11428432668 & -0.114284326680503 \tabularnewline
100 & 516 & 516.040799714466 & -0.0407997144656011 \tabularnewline
101 & 528 & 528.071718576392 & -0.0717185763921175 \tabularnewline
102 & 533 & 533.035710066491 & -0.0357100664906003 \tabularnewline
103 & 536 & 535.972716544594 & 0.0272834554057086 \tabularnewline
104 & 537 & 537.01696703615 & -0.0169670361503836 \tabularnewline
105 & 524 & 523.041989665755 & 0.95801033424538 \tabularnewline
106 & 536 & 536.051940261667 & -0.0519402616673513 \tabularnewline
107 & 587 & 586.873862178736 & 0.126137821263758 \tabularnewline
108 & 597 & 595.881817300323 & 1.11818269967682 \tabularnewline
109 & 581 & 580.880473177513 & 0.119526822486912 \tabularnewline
110 & 564 & 564.887221675322 & -0.887221675321847 \tabularnewline
111 & 558 & 556.943462177226 & 1.05653782277409 \tabularnewline
112 & 575 & 574.924989519306 & 0.0750104806939537 \tabularnewline
113 & 580 & 580.989247174503 & -0.98924717450338 \tabularnewline
114 & 575 & 574.994351332809 & 0.00564866719130946 \tabularnewline
115 & 563 & 564.014661830702 & -1.01466183070225 \tabularnewline
116 & 552 & 550.992669444991 & 1.007330555009 \tabularnewline
117 & 537 & 537.020988487635 & -0.0209884876346796 \tabularnewline
118 & 545 & 545.022910882481 & -0.0229108824812848 \tabularnewline
119 & 601 & 600.922843367256 & 0.0771566327443079 \tabularnewline
120 & 604 & 604.90642171977 & -0.906421719769919 \tabularnewline
121 & 586 & 586.92404857322 & -0.924048573220318 \tabularnewline
122 & 564 & 563.984059268585 & 0.015940731414714 \tabularnewline
123 & 549 & 548.031597564638 & 0.968402435362333 \tabularnewline
124 & 551 & 551.069472803174 & -0.0694728031742298 \tabularnewline
125 & 556 & 556.141173598935 & -0.141173598934989 \tabularnewline
126 & 548 & 548.219353129192 & -0.219353129191604 \tabularnewline
127 & 540 & 540.205138293973 & -0.20513829397304 \tabularnewline
128 & 531 & 531.213895556929 & -0.213895556928815 \tabularnewline
129 & 521 & 520.277099741855 & 0.722900258145392 \tabularnewline
130 & 519 & 518.231549945526 & 0.768450054474221 \tabularnewline
131 & 572 & 572.100364298116 & -0.100364298115542 \tabularnewline
132 & 581 & 582.065674009819 & -1.06567400981846 \tabularnewline
133 & 563 & 563.067748879842 & -0.0677488798424492 \tabularnewline
134 & 548 & 548.062724196229 & -0.0627241962288378 \tabularnewline
135 & 539 & 539.080206504716 & -0.080206504715741 \tabularnewline
136 & 541 & 541.051970054323 & -0.0519700543227406 \tabularnewline
137 & 562 & 561.07145900524 & 0.928540994760145 \tabularnewline
138 & 559 & 559.068802895943 & -0.0688028959432636 \tabularnewline
139 & 546 & 546.077682738694 & -0.0776827386936581 \tabularnewline
140 & 536 & 536.994059801506 & -0.994059801506085 \tabularnewline
141 & 528 & 526.994931099506 & 1.00506890049364 \tabularnewline
142 & 530 & 530.980613340809 & -0.980613340809183 \tabularnewline
143 & 582 & 581.835274889887 & 0.164725110113281 \tabularnewline
144 & 599 & 598.834302302349 & 0.165697697651263 \tabularnewline
145 & 584 & 583.790064492506 & 0.209935507493581 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186277&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]-0.842685590957632[/C][/ROW]
[ROW][C]2[/C][C]485[/C][C]484.883354453119[/C][C]0.116645546880518[/C][/ROW]
[ROW][C]3[/C][C]464[/C][C]463.935122491918[/C][C]0.0648775080820763[/C][/ROW]
[ROW][C]4[/C][C]460[/C][C]459.968034204037[/C][C]0.0319657959633536[/C][/ROW]
[ROW][C]5[/C][C]467[/C][C]467.075430891992[/C][C]-0.0754308919914907[/C][/ROW]
[ROW][C]6[/C][C]460[/C][C]460.095632975422[/C][C]-0.0956329754222533[/C][/ROW]
[ROW][C]7[/C][C]448[/C][C]448.161899461571[/C][C]-0.161899461571095[/C][/ROW]
[ROW][C]8[/C][C]443[/C][C]443.159571713222[/C][C]-0.159571713221877[/C][/ROW]
[ROW][C]9[/C][C]436[/C][C]436.180818658381[/C][C]-0.180818658381022[/C][/ROW]
[ROW][C]10[/C][C]431[/C][C]431.197133525345[/C][C]-0.197133525344919[/C][/ROW]
[ROW][C]11[/C][C]484[/C][C]484.090963372842[/C][C]-0.0909633728420994[/C][/ROW]
[ROW][C]12[/C][C]510[/C][C]509.062420460866[/C][C]0.937579539133901[/C][/ROW]
[ROW][C]13[/C][C]513[/C][C]513.037111939995[/C][C]-0.037111939994909[/C][/ROW]
[ROW][C]14[/C][C]503[/C][C]502.986202677578[/C][C]0.0137973224224533[/C][/ROW]
[ROW][C]15[/C][C]471[/C][C]471.068357438314[/C][C]-0.0683574383140691[/C][/ROW]
[ROW][C]16[/C][C]471[/C][C]471.028676167446[/C][C]-0.0286761674460842[/C][/ROW]
[ROW][C]17[/C][C]476[/C][C]476.116962016455[/C][C]-0.116962016455051[/C][/ROW]
[ROW][C]18[/C][C]475[/C][C]474.123016787358[/C][C]0.876983212642239[/C][/ROW]
[ROW][C]19[/C][C]470[/C][C]470.136255187767[/C][C]-0.136255187766788[/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]-0.120317572958242[/C][/ROW]
[ROW][C]22[/C][C]456[/C][C]455.121426100334[/C][C]0.878573899666104[/C][/ROW]
[ROW][C]23[/C][C]517[/C][C]516.944203506359[/C][C]0.055796493641379[/C][/ROW]
[ROW][C]24[/C][C]525[/C][C]525.899509198986[/C][C]-0.899509198985624[/C][/ROW]
[ROW][C]25[/C][C]523[/C][C]522.850414712469[/C][C]0.149585287530861[/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]0.130459435057583[/C][/ROW]
[ROW][C]28[/C][C]512[/C][C]511.880062307967[/C][C]0.119937692033441[/C][/ROW]
[ROW][C]29[/C][C]519[/C][C]518.907308408927[/C][C]0.0926915910733805[/C][/ROW]
[ROW][C]30[/C][C]517[/C][C]516.871514354526[/C][C]0.128485645474508[/C][/ROW]
[ROW][C]31[/C][C]510[/C][C]509.892761299685[/C][C]0.107238700315362[/C][/ROW]
[ROW][C]32[/C][C]509[/C][C]509.903625568988[/C][C]-0.903625568987984[/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]0.120317473011711[/C][/ROW]
[ROW][C]35[/C][C]569[/C][C]569.721322494811[/C][C]-0.721322494810697[/C][/ROW]
[ROW][C]36[/C][C]580[/C][C]579.736118719625[/C][C]0.26388128037526[/C][/ROW]
[ROW][C]37[/C][C]578[/C][C]577.687258344925[/C][C]0.312741655075352[/C][/ROW]
[ROW][C]38[/C][C]565[/C][C]565.775302031572[/C][C]-0.775302031571767[/C][/ROW]
[ROW][C]39[/C][C]547[/C][C]547.761894828706[/C][C]-0.761894828706227[/C][/ROW]
[ROW][C]40[/C][C]555[/C][C]554.799869807358[/C][C]0.200130192642039[/C][/ROW]
[ROW][C]41[/C][C]562[/C][C]561.827115908318[/C][C]0.172884091681981[/C][/ROW]
[ROW][C]42[/C][C]561[/C][C]560.841056644176[/C][C]0.158943355823764[/C][/ROW]
[ROW][C]43[/C][C]555[/C][C]555.886814730109[/C][C]-0.886814730109324[/C][/ROW]
[ROW][C]44[/C][C]544[/C][C]543.903372383237[/C][C]0.0966276167627559[/C][/ROW]
[ROW][C]45[/C][C]537[/C][C]536.936640786967[/C][C]0.0633592130330377[/C][/ROW]
[ROW][C]46[/C][C]543[/C][C]542.868920968028[/C][C]0.131079031971837[/C][/ROW]
[ROW][C]47[/C][C]594[/C][C]593.690153840469[/C][C]0.309846159530917[/C][/ROW]
[ROW][C]48[/C][C]611[/C][C]610.633045252414[/C][C]0.366954747586178[/C][/ROW]
[ROW][C]49[/C][C]613[/C][C]612.638977443522[/C][C]0.36102255647809[/C][/ROW]
[ROW][C]50[/C][C]611[/C][C]610.714881083873[/C][C]0.285118916126623[/C][/ROW]
[ROW][C]51[/C][C]594[/C][C]593.724706048237[/C][C]0.275293951762989[/C][/ROW]
[ROW][C]52[/C][C]595[/C][C]595.743704559644[/C][C]-0.743704559644064[/C][/ROW]
[ROW][C]53[/C][C]591[/C][C]590.854737069359[/C][C]0.14526293064139[/C][/ROW]
[ROW][C]54[/C][C]589[/C][C]589.861011786746[/C][C]-0.861011786745899[/C][/ROW]
[ROW][C]55[/C][C]584[/C][C]583.87482682525[/C][C]0.125173174749477[/C][/ROW]
[ROW][C]56[/C][C]573[/C][C]572.895137323144[/C][C]0.104862676855925[/C][/ROW]
[ROW][C]57[/C][C]567[/C][C]566.916604214788[/C][C]0.0833957852121922[/C][/ROW]
[ROW][C]58[/C][C]569[/C][C]568.921491544168[/C][C]0.0785084558324827[/C][/ROW]
[ROW][C]59[/C][C]621[/C][C]620.731773777005[/C][C]0.26822622299532[/C][/ROW]
[ROW][C]60[/C][C]629[/C][C]628.707563531253[/C][C]0.29243646874664[/C][/ROW]
[ROW][C]61[/C][C]628[/C][C]627.708437946813[/C][C]0.291562053187388[/C][/ROW]
[ROW][C]62[/C][C]612[/C][C]611.795559351526[/C][C]0.204440648474038[/C][/ROW]
[ROW][C]63[/C][C]595[/C][C]595.805604262374[/C][C]-0.805604262374154[/C][/ROW]
[ROW][C]64[/C][C]597[/C][C]596.836375955204[/C][C]0.163624044796427[/C][/ROW]
[ROW][C]65[/C][C]593[/C][C]592.91014919153[/C][C]0.0898508084696743[/C][/ROW]
[ROW][C]66[/C][C]590[/C][C]589.926918116795[/C][C]0.0730818832048224[/C][/ROW]
[ROW][C]67[/C][C]580[/C][C]579.959470019744[/C][C]0.0405299802560979[/C][/ROW]
[ROW][C]68[/C][C]574[/C][C]574.010764278121[/C][C]-0.010764278120887[/C][/ROW]
[ROW][C]69[/C][C]573[/C][C]572.986166767046[/C][C]0.0138332329534836[/C][/ROW]
[ROW][C]70[/C][C]573[/C][C]573.005903744403[/C][C]-0.00590374440345748[/C][/ROW]
[ROW][C]71[/C][C]620[/C][C]619.901625334042[/C][C]0.0983746659582861[/C][/ROW]
[ROW][C]72[/C][C]626[/C][C]625.89333792866[/C][C]0.10666207134036[/C][/ROW]
[ROW][C]73[/C][C]620[/C][C]619.905049078376[/C][C]0.0949509216243334[/C][/ROW]
[ROW][C]74[/C][C]588[/C][C]586.971415370444[/C][C]1.02858462955585[/C][/ROW]
[ROW][C]75[/C][C]566[/C][C]565.976979143839[/C][C]0.023020856160887[/C][/ROW]
[ROW][C]76[/C][C]557[/C][C]558.068210829063[/C][C]-1.06821082906286[/C][/ROW]
[ROW][C]77[/C][C]561[/C][C]561.077907098845[/C][C]-0.0779070988455016[/C][/ROW]
[ROW][C]78[/C][C]549[/C][C]549.109990778161[/C][C]-0.109990778161412[/C][/ROW]
[ROW][C]79[/C][C]532[/C][C]532.140903898395[/C][C]-0.14090389839535[/C][/ROW]
[ROW][C]80[/C][C]526[/C][C]526.15365990984[/C][C]-0.153659909839761[/C][/ROW]
[ROW][C]81[/C][C]511[/C][C]511.189410859138[/C][C]-0.18941085913797[/C][/ROW]
[ROW][C]82[/C][C]499[/C][C]499.196212770338[/C][C]-0.196212770338188[/C][/ROW]
[ROW][C]83[/C][C]555[/C][C]555.090744953285[/C][C]-0.090744953284569[/C][/ROW]
[ROW][C]84[/C][C]565[/C][C]564.083159883352[/C][C]0.91684011664831[/C][/ROW]
[ROW][C]85[/C][C]542[/C][C]542.120568462975[/C][C]-0.120568462974566[/C][/ROW]
[ROW][C]86[/C][C]527[/C][C]527.103493990127[/C][C]-0.103493990126747[/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]-0.146207826929605[/C][/ROW]
[ROW][C]89[/C][C]517[/C][C]517.198811949076[/C][C]-0.19881194907629[/C][/ROW]
[ROW][C]90[/C][C]508[/C][C]507.237985008768[/C][C]0.762014991232466[/C][/ROW]
[ROW][C]91[/C][C]493[/C][C]493.269614629782[/C][C]-0.26961462978248[/C][/ROW]
[ROW][C]92[/C][C]490[/C][C]490.232541601837[/C][C]-0.232541601836574[/C][/ROW]
[ROW][C]93[/C][C]469[/C][C]469.260280888826[/C][C]-0.260280888825686[/C][/ROW]
[ROW][C]94[/C][C]478[/C][C]477.234878118824[/C][C]0.765121881176488[/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]0.924173996547484[/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]-0.111994192450032[/C][/ROW]
[ROW][C]99[/C][C]502[/C][C]502.11428432668[/C][C]-0.114284326680503[/C][/ROW]
[ROW][C]100[/C][C]516[/C][C]516.040799714466[/C][C]-0.0407997144656011[/C][/ROW]
[ROW][C]101[/C][C]528[/C][C]528.071718576392[/C][C]-0.0717185763921175[/C][/ROW]
[ROW][C]102[/C][C]533[/C][C]533.035710066491[/C][C]-0.0357100664906003[/C][/ROW]
[ROW][C]103[/C][C]536[/C][C]535.972716544594[/C][C]0.0272834554057086[/C][/ROW]
[ROW][C]104[/C][C]537[/C][C]537.01696703615[/C][C]-0.0169670361503836[/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]-0.0519402616673513[/C][/ROW]
[ROW][C]107[/C][C]587[/C][C]586.873862178736[/C][C]0.126137821263758[/C][/ROW]
[ROW][C]108[/C][C]597[/C][C]595.881817300323[/C][C]1.11818269967682[/C][/ROW]
[ROW][C]109[/C][C]581[/C][C]580.880473177513[/C][C]0.119526822486912[/C][/ROW]
[ROW][C]110[/C][C]564[/C][C]564.887221675322[/C][C]-0.887221675321847[/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]0.0750104806939537[/C][/ROW]
[ROW][C]113[/C][C]580[/C][C]580.989247174503[/C][C]-0.98924717450338[/C][/ROW]
[ROW][C]114[/C][C]575[/C][C]574.994351332809[/C][C]0.00564866719130946[/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]-0.0209884876346796[/C][/ROW]
[ROW][C]118[/C][C]545[/C][C]545.022910882481[/C][C]-0.0229108824812848[/C][/ROW]
[ROW][C]119[/C][C]601[/C][C]600.922843367256[/C][C]0.0771566327443079[/C][/ROW]
[ROW][C]120[/C][C]604[/C][C]604.90642171977[/C][C]-0.906421719769919[/C][/ROW]
[ROW][C]121[/C][C]586[/C][C]586.92404857322[/C][C]-0.924048573220318[/C][/ROW]
[ROW][C]122[/C][C]564[/C][C]563.984059268585[/C][C]0.015940731414714[/C][/ROW]
[ROW][C]123[/C][C]549[/C][C]548.031597564638[/C][C]0.968402435362333[/C][/ROW]
[ROW][C]124[/C][C]551[/C][C]551.069472803174[/C][C]-0.0694728031742298[/C][/ROW]
[ROW][C]125[/C][C]556[/C][C]556.141173598935[/C][C]-0.141173598934989[/C][/ROW]
[ROW][C]126[/C][C]548[/C][C]548.219353129192[/C][C]-0.219353129191604[/C][/ROW]
[ROW][C]127[/C][C]540[/C][C]540.205138293973[/C][C]-0.20513829397304[/C][/ROW]
[ROW][C]128[/C][C]531[/C][C]531.213895556929[/C][C]-0.213895556928815[/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]0.768450054474221[/C][/ROW]
[ROW][C]131[/C][C]572[/C][C]572.100364298116[/C][C]-0.100364298115542[/C][/ROW]
[ROW][C]132[/C][C]581[/C][C]582.065674009819[/C][C]-1.06567400981846[/C][/ROW]
[ROW][C]133[/C][C]563[/C][C]563.067748879842[/C][C]-0.0677488798424492[/C][/ROW]
[ROW][C]134[/C][C]548[/C][C]548.062724196229[/C][C]-0.0627241962288378[/C][/ROW]
[ROW][C]135[/C][C]539[/C][C]539.080206504716[/C][C]-0.080206504715741[/C][/ROW]
[ROW][C]136[/C][C]541[/C][C]541.051970054323[/C][C]-0.0519700543227406[/C][/ROW]
[ROW][C]137[/C][C]562[/C][C]561.07145900524[/C][C]0.928540994760145[/C][/ROW]
[ROW][C]138[/C][C]559[/C][C]559.068802895943[/C][C]-0.0688028959432636[/C][/ROW]
[ROW][C]139[/C][C]546[/C][C]546.077682738694[/C][C]-0.0776827386936581[/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]1.00506890049364[/C][/ROW]
[ROW][C]142[/C][C]530[/C][C]530.980613340809[/C][C]-0.980613340809183[/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]0.209935507493581[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186277&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186277&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
1501501.842685590958-0.842685590957632
2485484.8833544531190.116645546880518
3464463.9351224919180.0648775080820763
4460459.9680342040370.0319657959633536
5467467.075430891992-0.0754308919914907
6460460.095632975422-0.0956329754222533
7448448.161899461571-0.161899461571095
8443443.159571713222-0.159571713221877
9436436.180818658381-0.180818658381022
10431431.197133525345-0.197133525344919
11484484.090963372842-0.0909633728420994
12510509.0624204608660.937579539133901
13513513.037111939995-0.037111939994909
14503502.9862026775780.0137973224224533
15471471.068357438314-0.0683574383140691
16471471.028676167446-0.0286761674460842
17476476.116962016455-0.116962016455051
18475474.1230167873580.876983212642239
19470470.136255187767-0.136255187766788
20461461.152692634525-0.152692634525302
21455455.120317572958-0.120317572958242
22456455.1214261003340.878573899666104
23517516.9442035063590.055796493641379
24525525.899509198986-0.899509198985624
25523522.8504147124690.149585287530861
26519518.8854303133760.114569686623559
27509508.8695405649420.130459435057583
28512511.8800623079670.119937692033441
29519518.9073084089270.0926915910733805
30517516.8715143545260.128485645474508
31510509.8927612996850.107238700315362
32509509.903625568988-0.903625568987984
33501499.9361916372691.06380836273149
34507506.8796825269880.120317473011711
35569569.721322494811-0.721322494810697
36580579.7361187196250.26388128037526
37578577.6872583449250.312741655075352
38565565.775302031572-0.775302031571767
39547547.761894828706-0.761894828706227
40555554.7998698073580.200130192642039
41562561.8271159083180.172884091681981
42561560.8410566441760.158943355823764
43555555.886814730109-0.886814730109324
44544543.9033723832370.0966276167627559
45537536.9366407869670.0633592130330377
46543542.8689209680280.131079031971837
47594593.6901538404690.309846159530917
48611610.6330452524140.366954747586178
49613612.6389774435220.36102255647809
50611610.7148810838730.285118916126623
51594593.7247060482370.275293951762989
52595595.743704559644-0.743704559644064
53591590.8547370693590.14526293064139
54589589.861011786746-0.861011786745899
55584583.874826825250.125173174749477
56573572.8951373231440.104862676855925
57567566.9166042147880.0833957852121922
58569568.9214915441680.0785084558324827
59621620.7317737770050.26822622299532
60629628.7075635312530.29243646874664
61628627.7084379468130.291562053187388
62612611.7955593515260.204440648474038
63595595.805604262374-0.805604262374154
64597596.8363759552040.163624044796427
65593592.910149191530.0898508084696743
66590589.9269181167950.0730818832048224
67580579.9594700197440.0405299802560979
68574574.010764278121-0.010764278120887
69573572.9861667670460.0138332329534836
70573573.005903744403-0.00590374440345748
71620619.9016253340420.0983746659582861
72626625.893337928660.10666207134036
73620619.9050490783760.0949509216243334
74588586.9714153704441.02858462955585
75566565.9769791438390.023020856160887
76557558.068210829063-1.06821082906286
77561561.077907098845-0.0779070988455016
78549549.109990778161-0.109990778161412
79532532.140903898395-0.14090389839535
80526526.15365990984-0.153659909839761
81511511.189410859138-0.18941085913797
82499499.196212770338-0.196212770338188
83555555.090744953285-0.090744953284569
84565564.0831598833520.91684011664831
85542542.120568462975-0.120568462974566
86527527.103493990127-0.103493990126747
87510510.178345659121-0.178345659121301
88514514.14620782693-0.146207826929605
89517517.198811949076-0.19881194907629
90508507.2379850087680.762014991232466
91493493.269614629782-0.26961462978248
92490490.232541601837-0.232541601836574
93469469.260280888826-0.260280888825686
94478477.2348781188240.765121881176488
95528529.10652066082-1.10652066081993
96534533.0758260034530.924173996547484
97518518.125027420814-0.125027420813761
98506506.11199419245-0.111994192450032
99502502.11428432668-0.114284326680503
100516516.040799714466-0.0407997144656011
101528528.071718576392-0.0717185763921175
102533533.035710066491-0.0357100664906003
103536535.9727165445940.0272834554057086
104537537.01696703615-0.0169670361503836
105524523.0419896657550.95801033424538
106536536.051940261667-0.0519402616673513
107587586.8738621787360.126137821263758
108597595.8818173003231.11818269967682
109581580.8804731775130.119526822486912
110564564.887221675322-0.887221675321847
111558556.9434621772261.05653782277409
112575574.9249895193060.0750104806939537
113580580.989247174503-0.98924717450338
114575574.9943513328090.00564866719130946
115563564.014661830702-1.01466183070225
116552550.9926694449911.007330555009
117537537.020988487635-0.0209884876346796
118545545.022910882481-0.0229108824812848
119601600.9228433672560.0771566327443079
120604604.90642171977-0.906421719769919
121586586.92404857322-0.924048573220318
122564563.9840592685850.015940731414714
123549548.0315975646380.968402435362333
124551551.069472803174-0.0694728031742298
125556556.141173598935-0.141173598934989
126548548.219353129192-0.219353129191604
127540540.205138293973-0.20513829397304
128531531.213895556929-0.213895556928815
129521520.2770997418550.722900258145392
130519518.2315499455260.768450054474221
131572572.100364298116-0.100364298115542
132581582.065674009819-1.06567400981846
133563563.067748879842-0.0677488798424492
134548548.062724196229-0.0627241962288378
135539539.080206504716-0.080206504715741
136541541.051970054323-0.0519700543227406
137562561.071459005240.928540994760145
138559559.068802895943-0.0688028959432636
139546546.077682738694-0.0776827386936581
140536536.994059801506-0.994059801506085
141528526.9949310995061.00506890049364
142530530.980613340809-0.980613340809183
143582581.8352748898870.164725110113281
144599598.8343023023490.165697697651263
145584583.7900644925060.209935507493581







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.04412303669198250.08824607338396510.955876963308018
120.3573185287769940.7146370575539880.642681471223006
130.296862431465620.593724862931240.70313756853438
140.1934032779443620.3868065558887240.806596722055638
150.1157020033701090.2314040067402170.884297996629891
160.1047585832800930.2095171665601850.895241416719907
170.06638089151336730.1327617830267350.933619108486633
180.2389261689953150.477852337990630.761073831004685
190.2425732440580590.4851464881161190.757426755941941
200.2040925087507980.4081850175015960.795907491249202
210.1472493216351430.2944986432702870.852750678364857
220.2223422870978120.4446845741956230.777657712902188
230.1841944157659180.3683888315318370.815805584234082
240.182130224040440.364260448080880.81786977595956
250.24177085280760.4835417056151990.7582291471924
260.1867755289541810.3735510579083620.813224471045819
270.1452078132544280.2904156265088560.854792186745572
280.1207206855235950.2414413710471890.879279314476405
290.08830501962530930.1766100392506190.911694980374691
300.06328757517732120.1265751503546420.936712424822679
310.04528657327025030.09057314654050070.95471342672975
320.1479019044239760.2958038088479520.852098095576024
330.2637105373899260.5274210747798520.736289462610074
340.2188830132941550.4377660265883090.781116986705845
350.2636168465857490.5272336931714980.736383153414251
360.2346543119526810.4693086239053610.765345688047319
370.2133129031439480.4266258062878960.786687096856052
380.3093186181237970.6186372362475940.690681381876203
390.3645665794745530.7291331589491060.635433420525447
400.3134188357163030.6268376714326060.686581164283697
410.2658351750332280.5316703500664560.734164824966772
420.2208755723978940.4417511447957880.779124427602106
430.3618166032008330.7236332064016660.638183396799167
440.3121495474477110.6242990948954210.687850452552289
450.264929614429610.5298592288592210.73507038557039
460.2236140440020570.4472280880041150.776385955997943
470.208649204422010.417298408844020.79135079557799
480.1959938037978860.3919876075957730.804006196202114
490.1734772636517610.3469545273035230.826522736348239
500.1456633975622350.291326795124470.854336602437765
510.1194010095299550.238802019059910.880598990470045
520.1682530020600230.3365060041200450.831746997939977
530.1371067244016890.2742134488033770.862893275598311
540.2123592539862160.4247185079724330.787640746013784
550.1800091922090980.3600183844181970.819990807790902
560.1489512915814520.2979025831629030.851048708418549
570.1210496123947560.2420992247895130.878950387605244
580.09737334936669390.1947466987333880.902626650633306
590.07725372271616370.1545074454323270.922746277283836
600.06040426939512050.1208085387902410.93959573060488
610.04681669122515610.09363338245031210.953183308774844
620.03572979530359810.07145959060719630.964270204696402
630.08535678609217820.1707135721843560.914643213907822
640.0680704160516340.1361408321032680.931929583948366
650.05379991533065250.1075998306613050.946200084669347
660.04196118091337770.08392236182675530.958038819086622
670.03242963731940170.06485927463880330.967570362680598
680.02515213588467320.05030427176934640.974847864115327
690.01872857485818440.03745714971636890.981271425141816
700.01382772019294090.02765544038588180.986172279807059
710.009941312975660940.01988262595132190.990058687024339
720.007050676873190160.01410135374638030.99294932312681
730.004935062454447030.009870124908894060.995064937545553
740.009402186689671250.01880437337934250.990597813310329
750.007344688836883210.01468937767376640.992655311163117
760.03091561798503240.06183123597006480.969084382014968
770.02361971708580810.04723943417161610.976380282914192
780.01779372170838250.0355874434167650.982206278291618
790.01329573785552970.02659147571105940.98670426214447
800.00970264200542750.0194052840108550.990297357994573
810.007193210521810440.01438642104362090.99280678947819
820.005780055654616920.01156011130923380.994219944345383
830.004175624585143660.008351249170287330.995824375414856
840.008038854463172850.01607770892634570.991961145536827
850.006011697992209590.01202339598441920.99398830200779
860.004272900690534060.008545801381068130.995727099309466
870.003147211325430370.006294422650860740.99685278867457
880.002241610202628550.004483220405257090.997758389797371
890.001551744919436170.003103489838872340.998448255080564
900.002718126073936280.005436252147872550.997281873926064
910.002058964902391150.004117929804782290.997941035097609
920.001476402119308620.002952804238617250.998523597880691
930.001187015049880640.002374030099761270.998812984950119
940.001686901351685030.003373802703370060.998313098648315
950.005880614840750040.01176122968150010.99411938515925
960.01510412953049160.03020825906098320.984895870469508
970.01138764562819210.02277529125638420.988612354371808
980.008121491524288920.01624298304857780.991878508475711
990.005784187676067260.01156837535213450.994215812323933
1000.004194724726448880.008389449452897750.995805275273551
1010.002880462558652310.005760925117304630.997119537441348
1020.00193470628997530.003869412579950590.998065293710025
1030.001328628316893310.002657256633786630.998671371683107
1040.001060472161304550.002120944322609110.998939527838695
1050.001214544819096970.002429089638193940.998785455180903
1060.001013627707387020.002027255414774040.998986372292613
1070.0006436948588907790.001287389717781560.999356305141109
1080.002353457549859250.004706915099718510.997646542450141
1090.001750291047244360.003500582094488710.998249708952756
1100.007618437825595010.015236875651190.992381562174405
1110.01194806000206570.02389612000413140.988051939997934
1120.009377291617107060.01875458323421410.990622708382893
1130.01641556582440150.03283113164880290.983584434175598
1140.0119509657030820.0239019314061640.988049034296918
1150.02990974730114110.05981949460228220.970090252698859
1160.05743694655341880.1148738931068380.942563053446581
1170.04109488588679610.08218977177359220.958905114113204
1180.02881606271822930.05763212543645860.971183937281771
1190.02490790257039910.04981580514079820.975092097429601
1200.02600449449083430.05200898898166860.973995505509166
1210.04272532663151090.08545065326302190.957274673368489
1220.03276645043566650.06553290087133290.967233549564333
1230.05556984005847140.1111396801169430.944430159941529
1240.05289367031838410.1057873406367680.947106329681616
1250.04897823559511490.09795647119022980.951021764404885
1260.04948018220520180.09896036441040370.950519817794798
1270.03593469220590460.07186938441180920.964065307794095
1280.02237994364103830.04475988728207660.977620056358962
1290.01895735411901790.03791470823803580.981042645880982
1300.01688418978532160.03376837957064330.983115810214678
1310.01237573822557680.02475147645115370.987624261774423
1320.02256833857151930.04513667714303860.977431661428481
1330.193083192043770.386166384087540.80691680795623
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 & 0.0441230366919825 & 0.0882460733839651 & 0.955876963308018 \tabularnewline
12 & 0.357318528776994 & 0.714637057553988 & 0.642681471223006 \tabularnewline
13 & 0.29686243146562 & 0.59372486293124 & 0.70313756853438 \tabularnewline
14 & 0.193403277944362 & 0.386806555888724 & 0.806596722055638 \tabularnewline
15 & 0.115702003370109 & 0.231404006740217 & 0.884297996629891 \tabularnewline
16 & 0.104758583280093 & 0.209517166560185 & 0.895241416719907 \tabularnewline
17 & 0.0663808915133673 & 0.132761783026735 & 0.933619108486633 \tabularnewline
18 & 0.238926168995315 & 0.47785233799063 & 0.761073831004685 \tabularnewline
19 & 0.242573244058059 & 0.485146488116119 & 0.757426755941941 \tabularnewline
20 & 0.204092508750798 & 0.408185017501596 & 0.795907491249202 \tabularnewline
21 & 0.147249321635143 & 0.294498643270287 & 0.852750678364857 \tabularnewline
22 & 0.222342287097812 & 0.444684574195623 & 0.777657712902188 \tabularnewline
23 & 0.184194415765918 & 0.368388831531837 & 0.815805584234082 \tabularnewline
24 & 0.18213022404044 & 0.36426044808088 & 0.81786977595956 \tabularnewline
25 & 0.2417708528076 & 0.483541705615199 & 0.7582291471924 \tabularnewline
26 & 0.186775528954181 & 0.373551057908362 & 0.813224471045819 \tabularnewline
27 & 0.145207813254428 & 0.290415626508856 & 0.854792186745572 \tabularnewline
28 & 0.120720685523595 & 0.241441371047189 & 0.879279314476405 \tabularnewline
29 & 0.0883050196253093 & 0.176610039250619 & 0.911694980374691 \tabularnewline
30 & 0.0632875751773212 & 0.126575150354642 & 0.936712424822679 \tabularnewline
31 & 0.0452865732702503 & 0.0905731465405007 & 0.95471342672975 \tabularnewline
32 & 0.147901904423976 & 0.295803808847952 & 0.852098095576024 \tabularnewline
33 & 0.263710537389926 & 0.527421074779852 & 0.736289462610074 \tabularnewline
34 & 0.218883013294155 & 0.437766026588309 & 0.781116986705845 \tabularnewline
35 & 0.263616846585749 & 0.527233693171498 & 0.736383153414251 \tabularnewline
36 & 0.234654311952681 & 0.469308623905361 & 0.765345688047319 \tabularnewline
37 & 0.213312903143948 & 0.426625806287896 & 0.786687096856052 \tabularnewline
38 & 0.309318618123797 & 0.618637236247594 & 0.690681381876203 \tabularnewline
39 & 0.364566579474553 & 0.729133158949106 & 0.635433420525447 \tabularnewline
40 & 0.313418835716303 & 0.626837671432606 & 0.686581164283697 \tabularnewline
41 & 0.265835175033228 & 0.531670350066456 & 0.734164824966772 \tabularnewline
42 & 0.220875572397894 & 0.441751144795788 & 0.779124427602106 \tabularnewline
43 & 0.361816603200833 & 0.723633206401666 & 0.638183396799167 \tabularnewline
44 & 0.312149547447711 & 0.624299094895421 & 0.687850452552289 \tabularnewline
45 & 0.26492961442961 & 0.529859228859221 & 0.73507038557039 \tabularnewline
46 & 0.223614044002057 & 0.447228088004115 & 0.776385955997943 \tabularnewline
47 & 0.20864920442201 & 0.41729840884402 & 0.79135079557799 \tabularnewline
48 & 0.195993803797886 & 0.391987607595773 & 0.804006196202114 \tabularnewline
49 & 0.173477263651761 & 0.346954527303523 & 0.826522736348239 \tabularnewline
50 & 0.145663397562235 & 0.29132679512447 & 0.854336602437765 \tabularnewline
51 & 0.119401009529955 & 0.23880201905991 & 0.880598990470045 \tabularnewline
52 & 0.168253002060023 & 0.336506004120045 & 0.831746997939977 \tabularnewline
53 & 0.137106724401689 & 0.274213448803377 & 0.862893275598311 \tabularnewline
54 & 0.212359253986216 & 0.424718507972433 & 0.787640746013784 \tabularnewline
55 & 0.180009192209098 & 0.360018384418197 & 0.819990807790902 \tabularnewline
56 & 0.148951291581452 & 0.297902583162903 & 0.851048708418549 \tabularnewline
57 & 0.121049612394756 & 0.242099224789513 & 0.878950387605244 \tabularnewline
58 & 0.0973733493666939 & 0.194746698733388 & 0.902626650633306 \tabularnewline
59 & 0.0772537227161637 & 0.154507445432327 & 0.922746277283836 \tabularnewline
60 & 0.0604042693951205 & 0.120808538790241 & 0.93959573060488 \tabularnewline
61 & 0.0468166912251561 & 0.0936333824503121 & 0.953183308774844 \tabularnewline
62 & 0.0357297953035981 & 0.0714595906071963 & 0.964270204696402 \tabularnewline
63 & 0.0853567860921782 & 0.170713572184356 & 0.914643213907822 \tabularnewline
64 & 0.068070416051634 & 0.136140832103268 & 0.931929583948366 \tabularnewline
65 & 0.0537999153306525 & 0.107599830661305 & 0.946200084669347 \tabularnewline
66 & 0.0419611809133777 & 0.0839223618267553 & 0.958038819086622 \tabularnewline
67 & 0.0324296373194017 & 0.0648592746388033 & 0.967570362680598 \tabularnewline
68 & 0.0251521358846732 & 0.0503042717693464 & 0.974847864115327 \tabularnewline
69 & 0.0187285748581844 & 0.0374571497163689 & 0.981271425141816 \tabularnewline
70 & 0.0138277201929409 & 0.0276554403858818 & 0.986172279807059 \tabularnewline
71 & 0.00994131297566094 & 0.0198826259513219 & 0.990058687024339 \tabularnewline
72 & 0.00705067687319016 & 0.0141013537463803 & 0.99294932312681 \tabularnewline
73 & 0.00493506245444703 & 0.00987012490889406 & 0.995064937545553 \tabularnewline
74 & 0.00940218668967125 & 0.0188043733793425 & 0.990597813310329 \tabularnewline
75 & 0.00734468883688321 & 0.0146893776737664 & 0.992655311163117 \tabularnewline
76 & 0.0309156179850324 & 0.0618312359700648 & 0.969084382014968 \tabularnewline
77 & 0.0236197170858081 & 0.0472394341716161 & 0.976380282914192 \tabularnewline
78 & 0.0177937217083825 & 0.035587443416765 & 0.982206278291618 \tabularnewline
79 & 0.0132957378555297 & 0.0265914757110594 & 0.98670426214447 \tabularnewline
80 & 0.0097026420054275 & 0.019405284010855 & 0.990297357994573 \tabularnewline
81 & 0.00719321052181044 & 0.0143864210436209 & 0.99280678947819 \tabularnewline
82 & 0.00578005565461692 & 0.0115601113092338 & 0.994219944345383 \tabularnewline
83 & 0.00417562458514366 & 0.00835124917028733 & 0.995824375414856 \tabularnewline
84 & 0.00803885446317285 & 0.0160777089263457 & 0.991961145536827 \tabularnewline
85 & 0.00601169799220959 & 0.0120233959844192 & 0.99398830200779 \tabularnewline
86 & 0.00427290069053406 & 0.00854580138106813 & 0.995727099309466 \tabularnewline
87 & 0.00314721132543037 & 0.00629442265086074 & 0.99685278867457 \tabularnewline
88 & 0.00224161020262855 & 0.00448322040525709 & 0.997758389797371 \tabularnewline
89 & 0.00155174491943617 & 0.00310348983887234 & 0.998448255080564 \tabularnewline
90 & 0.00271812607393628 & 0.00543625214787255 & 0.997281873926064 \tabularnewline
91 & 0.00205896490239115 & 0.00411792980478229 & 0.997941035097609 \tabularnewline
92 & 0.00147640211930862 & 0.00295280423861725 & 0.998523597880691 \tabularnewline
93 & 0.00118701504988064 & 0.00237403009976127 & 0.998812984950119 \tabularnewline
94 & 0.00168690135168503 & 0.00337380270337006 & 0.998313098648315 \tabularnewline
95 & 0.00588061484075004 & 0.0117612296815001 & 0.99411938515925 \tabularnewline
96 & 0.0151041295304916 & 0.0302082590609832 & 0.984895870469508 \tabularnewline
97 & 0.0113876456281921 & 0.0227752912563842 & 0.988612354371808 \tabularnewline
98 & 0.00812149152428892 & 0.0162429830485778 & 0.991878508475711 \tabularnewline
99 & 0.00578418767606726 & 0.0115683753521345 & 0.994215812323933 \tabularnewline
100 & 0.00419472472644888 & 0.00838944945289775 & 0.995805275273551 \tabularnewline
101 & 0.00288046255865231 & 0.00576092511730463 & 0.997119537441348 \tabularnewline
102 & 0.0019347062899753 & 0.00386941257995059 & 0.998065293710025 \tabularnewline
103 & 0.00132862831689331 & 0.00265725663378663 & 0.998671371683107 \tabularnewline
104 & 0.00106047216130455 & 0.00212094432260911 & 0.998939527838695 \tabularnewline
105 & 0.00121454481909697 & 0.00242908963819394 & 0.998785455180903 \tabularnewline
106 & 0.00101362770738702 & 0.00202725541477404 & 0.998986372292613 \tabularnewline
107 & 0.000643694858890779 & 0.00128738971778156 & 0.999356305141109 \tabularnewline
108 & 0.00235345754985925 & 0.00470691509971851 & 0.997646542450141 \tabularnewline
109 & 0.00175029104724436 & 0.00350058209448871 & 0.998249708952756 \tabularnewline
110 & 0.00761843782559501 & 0.01523687565119 & 0.992381562174405 \tabularnewline
111 & 0.0119480600020657 & 0.0238961200041314 & 0.988051939997934 \tabularnewline
112 & 0.00937729161710706 & 0.0187545832342141 & 0.990622708382893 \tabularnewline
113 & 0.0164155658244015 & 0.0328311316488029 & 0.983584434175598 \tabularnewline
114 & 0.011950965703082 & 0.023901931406164 & 0.988049034296918 \tabularnewline
115 & 0.0299097473011411 & 0.0598194946022822 & 0.970090252698859 \tabularnewline
116 & 0.0574369465534188 & 0.114873893106838 & 0.942563053446581 \tabularnewline
117 & 0.0410948858867961 & 0.0821897717735922 & 0.958905114113204 \tabularnewline
118 & 0.0288160627182293 & 0.0576321254364586 & 0.971183937281771 \tabularnewline
119 & 0.0249079025703991 & 0.0498158051407982 & 0.975092097429601 \tabularnewline
120 & 0.0260044944908343 & 0.0520089889816686 & 0.973995505509166 \tabularnewline
121 & 0.0427253266315109 & 0.0854506532630219 & 0.957274673368489 \tabularnewline
122 & 0.0327664504356665 & 0.0655329008713329 & 0.967233549564333 \tabularnewline
123 & 0.0555698400584714 & 0.111139680116943 & 0.944430159941529 \tabularnewline
124 & 0.0528936703183841 & 0.105787340636768 & 0.947106329681616 \tabularnewline
125 & 0.0489782355951149 & 0.0979564711902298 & 0.951021764404885 \tabularnewline
126 & 0.0494801822052018 & 0.0989603644104037 & 0.950519817794798 \tabularnewline
127 & 0.0359346922059046 & 0.0718693844118092 & 0.964065307794095 \tabularnewline
128 & 0.0223799436410383 & 0.0447598872820766 & 0.977620056358962 \tabularnewline
129 & 0.0189573541190179 & 0.0379147082380358 & 0.981042645880982 \tabularnewline
130 & 0.0168841897853216 & 0.0337683795706433 & 0.983115810214678 \tabularnewline
131 & 0.0123757382255768 & 0.0247514764511537 & 0.987624261774423 \tabularnewline
132 & 0.0225683385715193 & 0.0451366771430386 & 0.977431661428481 \tabularnewline
133 & 0.19308319204377 & 0.38616638408754 & 0.80691680795623 \tabularnewline
134 & 0.13017689293642 & 0.26035378587284 & 0.86982310706358 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186277&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]0.0441230366919825[/C][C]0.0882460733839651[/C][C]0.955876963308018[/C][/ROW]
[ROW][C]12[/C][C]0.357318528776994[/C][C]0.714637057553988[/C][C]0.642681471223006[/C][/ROW]
[ROW][C]13[/C][C]0.29686243146562[/C][C]0.59372486293124[/C][C]0.70313756853438[/C][/ROW]
[ROW][C]14[/C][C]0.193403277944362[/C][C]0.386806555888724[/C][C]0.806596722055638[/C][/ROW]
[ROW][C]15[/C][C]0.115702003370109[/C][C]0.231404006740217[/C][C]0.884297996629891[/C][/ROW]
[ROW][C]16[/C][C]0.104758583280093[/C][C]0.209517166560185[/C][C]0.895241416719907[/C][/ROW]
[ROW][C]17[/C][C]0.0663808915133673[/C][C]0.132761783026735[/C][C]0.933619108486633[/C][/ROW]
[ROW][C]18[/C][C]0.238926168995315[/C][C]0.47785233799063[/C][C]0.761073831004685[/C][/ROW]
[ROW][C]19[/C][C]0.242573244058059[/C][C]0.485146488116119[/C][C]0.757426755941941[/C][/ROW]
[ROW][C]20[/C][C]0.204092508750798[/C][C]0.408185017501596[/C][C]0.795907491249202[/C][/ROW]
[ROW][C]21[/C][C]0.147249321635143[/C][C]0.294498643270287[/C][C]0.852750678364857[/C][/ROW]
[ROW][C]22[/C][C]0.222342287097812[/C][C]0.444684574195623[/C][C]0.777657712902188[/C][/ROW]
[ROW][C]23[/C][C]0.184194415765918[/C][C]0.368388831531837[/C][C]0.815805584234082[/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]0.483541705615199[/C][C]0.7582291471924[/C][/ROW]
[ROW][C]26[/C][C]0.186775528954181[/C][C]0.373551057908362[/C][C]0.813224471045819[/C][/ROW]
[ROW][C]27[/C][C]0.145207813254428[/C][C]0.290415626508856[/C][C]0.854792186745572[/C][/ROW]
[ROW][C]28[/C][C]0.120720685523595[/C][C]0.241441371047189[/C][C]0.879279314476405[/C][/ROW]
[ROW][C]29[/C][C]0.0883050196253093[/C][C]0.176610039250619[/C][C]0.911694980374691[/C][/ROW]
[ROW][C]30[/C][C]0.0632875751773212[/C][C]0.126575150354642[/C][C]0.936712424822679[/C][/ROW]
[ROW][C]31[/C][C]0.0452865732702503[/C][C]0.0905731465405007[/C][C]0.95471342672975[/C][/ROW]
[ROW][C]32[/C][C]0.147901904423976[/C][C]0.295803808847952[/C][C]0.852098095576024[/C][/ROW]
[ROW][C]33[/C][C]0.263710537389926[/C][C]0.527421074779852[/C][C]0.736289462610074[/C][/ROW]
[ROW][C]34[/C][C]0.218883013294155[/C][C]0.437766026588309[/C][C]0.781116986705845[/C][/ROW]
[ROW][C]35[/C][C]0.263616846585749[/C][C]0.527233693171498[/C][C]0.736383153414251[/C][/ROW]
[ROW][C]36[/C][C]0.234654311952681[/C][C]0.469308623905361[/C][C]0.765345688047319[/C][/ROW]
[ROW][C]37[/C][C]0.213312903143948[/C][C]0.426625806287896[/C][C]0.786687096856052[/C][/ROW]
[ROW][C]38[/C][C]0.309318618123797[/C][C]0.618637236247594[/C][C]0.690681381876203[/C][/ROW]
[ROW][C]39[/C][C]0.364566579474553[/C][C]0.729133158949106[/C][C]0.635433420525447[/C][/ROW]
[ROW][C]40[/C][C]0.313418835716303[/C][C]0.626837671432606[/C][C]0.686581164283697[/C][/ROW]
[ROW][C]41[/C][C]0.265835175033228[/C][C]0.531670350066456[/C][C]0.734164824966772[/C][/ROW]
[ROW][C]42[/C][C]0.220875572397894[/C][C]0.441751144795788[/C][C]0.779124427602106[/C][/ROW]
[ROW][C]43[/C][C]0.361816603200833[/C][C]0.723633206401666[/C][C]0.638183396799167[/C][/ROW]
[ROW][C]44[/C][C]0.312149547447711[/C][C]0.624299094895421[/C][C]0.687850452552289[/C][/ROW]
[ROW][C]45[/C][C]0.26492961442961[/C][C]0.529859228859221[/C][C]0.73507038557039[/C][/ROW]
[ROW][C]46[/C][C]0.223614044002057[/C][C]0.447228088004115[/C][C]0.776385955997943[/C][/ROW]
[ROW][C]47[/C][C]0.20864920442201[/C][C]0.41729840884402[/C][C]0.79135079557799[/C][/ROW]
[ROW][C]48[/C][C]0.195993803797886[/C][C]0.391987607595773[/C][C]0.804006196202114[/C][/ROW]
[ROW][C]49[/C][C]0.173477263651761[/C][C]0.346954527303523[/C][C]0.826522736348239[/C][/ROW]
[ROW][C]50[/C][C]0.145663397562235[/C][C]0.29132679512447[/C][C]0.854336602437765[/C][/ROW]
[ROW][C]51[/C][C]0.119401009529955[/C][C]0.23880201905991[/C][C]0.880598990470045[/C][/ROW]
[ROW][C]52[/C][C]0.168253002060023[/C][C]0.336506004120045[/C][C]0.831746997939977[/C][/ROW]
[ROW][C]53[/C][C]0.137106724401689[/C][C]0.274213448803377[/C][C]0.862893275598311[/C][/ROW]
[ROW][C]54[/C][C]0.212359253986216[/C][C]0.424718507972433[/C][C]0.787640746013784[/C][/ROW]
[ROW][C]55[/C][C]0.180009192209098[/C][C]0.360018384418197[/C][C]0.819990807790902[/C][/ROW]
[ROW][C]56[/C][C]0.148951291581452[/C][C]0.297902583162903[/C][C]0.851048708418549[/C][/ROW]
[ROW][C]57[/C][C]0.121049612394756[/C][C]0.242099224789513[/C][C]0.878950387605244[/C][/ROW]
[ROW][C]58[/C][C]0.0973733493666939[/C][C]0.194746698733388[/C][C]0.902626650633306[/C][/ROW]
[ROW][C]59[/C][C]0.0772537227161637[/C][C]0.154507445432327[/C][C]0.922746277283836[/C][/ROW]
[ROW][C]60[/C][C]0.0604042693951205[/C][C]0.120808538790241[/C][C]0.93959573060488[/C][/ROW]
[ROW][C]61[/C][C]0.0468166912251561[/C][C]0.0936333824503121[/C][C]0.953183308774844[/C][/ROW]
[ROW][C]62[/C][C]0.0357297953035981[/C][C]0.0714595906071963[/C][C]0.964270204696402[/C][/ROW]
[ROW][C]63[/C][C]0.0853567860921782[/C][C]0.170713572184356[/C][C]0.914643213907822[/C][/ROW]
[ROW][C]64[/C][C]0.068070416051634[/C][C]0.136140832103268[/C][C]0.931929583948366[/C][/ROW]
[ROW][C]65[/C][C]0.0537999153306525[/C][C]0.107599830661305[/C][C]0.946200084669347[/C][/ROW]
[ROW][C]66[/C][C]0.0419611809133777[/C][C]0.0839223618267553[/C][C]0.958038819086622[/C][/ROW]
[ROW][C]67[/C][C]0.0324296373194017[/C][C]0.0648592746388033[/C][C]0.967570362680598[/C][/ROW]
[ROW][C]68[/C][C]0.0251521358846732[/C][C]0.0503042717693464[/C][C]0.974847864115327[/C][/ROW]
[ROW][C]69[/C][C]0.0187285748581844[/C][C]0.0374571497163689[/C][C]0.981271425141816[/C][/ROW]
[ROW][C]70[/C][C]0.0138277201929409[/C][C]0.0276554403858818[/C][C]0.986172279807059[/C][/ROW]
[ROW][C]71[/C][C]0.00994131297566094[/C][C]0.0198826259513219[/C][C]0.990058687024339[/C][/ROW]
[ROW][C]72[/C][C]0.00705067687319016[/C][C]0.0141013537463803[/C][C]0.99294932312681[/C][/ROW]
[ROW][C]73[/C][C]0.00493506245444703[/C][C]0.00987012490889406[/C][C]0.995064937545553[/C][/ROW]
[ROW][C]74[/C][C]0.00940218668967125[/C][C]0.0188043733793425[/C][C]0.990597813310329[/C][/ROW]
[ROW][C]75[/C][C]0.00734468883688321[/C][C]0.0146893776737664[/C][C]0.992655311163117[/C][/ROW]
[ROW][C]76[/C][C]0.0309156179850324[/C][C]0.0618312359700648[/C][C]0.969084382014968[/C][/ROW]
[ROW][C]77[/C][C]0.0236197170858081[/C][C]0.0472394341716161[/C][C]0.976380282914192[/C][/ROW]
[ROW][C]78[/C][C]0.0177937217083825[/C][C]0.035587443416765[/C][C]0.982206278291618[/C][/ROW]
[ROW][C]79[/C][C]0.0132957378555297[/C][C]0.0265914757110594[/C][C]0.98670426214447[/C][/ROW]
[ROW][C]80[/C][C]0.0097026420054275[/C][C]0.019405284010855[/C][C]0.990297357994573[/C][/ROW]
[ROW][C]81[/C][C]0.00719321052181044[/C][C]0.0143864210436209[/C][C]0.99280678947819[/C][/ROW]
[ROW][C]82[/C][C]0.00578005565461692[/C][C]0.0115601113092338[/C][C]0.994219944345383[/C][/ROW]
[ROW][C]83[/C][C]0.00417562458514366[/C][C]0.00835124917028733[/C][C]0.995824375414856[/C][/ROW]
[ROW][C]84[/C][C]0.00803885446317285[/C][C]0.0160777089263457[/C][C]0.991961145536827[/C][/ROW]
[ROW][C]85[/C][C]0.00601169799220959[/C][C]0.0120233959844192[/C][C]0.99398830200779[/C][/ROW]
[ROW][C]86[/C][C]0.00427290069053406[/C][C]0.00854580138106813[/C][C]0.995727099309466[/C][/ROW]
[ROW][C]87[/C][C]0.00314721132543037[/C][C]0.00629442265086074[/C][C]0.99685278867457[/C][/ROW]
[ROW][C]88[/C][C]0.00224161020262855[/C][C]0.00448322040525709[/C][C]0.997758389797371[/C][/ROW]
[ROW][C]89[/C][C]0.00155174491943617[/C][C]0.00310348983887234[/C][C]0.998448255080564[/C][/ROW]
[ROW][C]90[/C][C]0.00271812607393628[/C][C]0.00543625214787255[/C][C]0.997281873926064[/C][/ROW]
[ROW][C]91[/C][C]0.00205896490239115[/C][C]0.00411792980478229[/C][C]0.997941035097609[/C][/ROW]
[ROW][C]92[/C][C]0.00147640211930862[/C][C]0.00295280423861725[/C][C]0.998523597880691[/C][/ROW]
[ROW][C]93[/C][C]0.00118701504988064[/C][C]0.00237403009976127[/C][C]0.998812984950119[/C][/ROW]
[ROW][C]94[/C][C]0.00168690135168503[/C][C]0.00337380270337006[/C][C]0.998313098648315[/C][/ROW]
[ROW][C]95[/C][C]0.00588061484075004[/C][C]0.0117612296815001[/C][C]0.99411938515925[/C][/ROW]
[ROW][C]96[/C][C]0.0151041295304916[/C][C]0.0302082590609832[/C][C]0.984895870469508[/C][/ROW]
[ROW][C]97[/C][C]0.0113876456281921[/C][C]0.0227752912563842[/C][C]0.988612354371808[/C][/ROW]
[ROW][C]98[/C][C]0.00812149152428892[/C][C]0.0162429830485778[/C][C]0.991878508475711[/C][/ROW]
[ROW][C]99[/C][C]0.00578418767606726[/C][C]0.0115683753521345[/C][C]0.994215812323933[/C][/ROW]
[ROW][C]100[/C][C]0.00419472472644888[/C][C]0.00838944945289775[/C][C]0.995805275273551[/C][/ROW]
[ROW][C]101[/C][C]0.00288046255865231[/C][C]0.00576092511730463[/C][C]0.997119537441348[/C][/ROW]
[ROW][C]102[/C][C]0.0019347062899753[/C][C]0.00386941257995059[/C][C]0.998065293710025[/C][/ROW]
[ROW][C]103[/C][C]0.00132862831689331[/C][C]0.00265725663378663[/C][C]0.998671371683107[/C][/ROW]
[ROW][C]104[/C][C]0.00106047216130455[/C][C]0.00212094432260911[/C][C]0.998939527838695[/C][/ROW]
[ROW][C]105[/C][C]0.00121454481909697[/C][C]0.00242908963819394[/C][C]0.998785455180903[/C][/ROW]
[ROW][C]106[/C][C]0.00101362770738702[/C][C]0.00202725541477404[/C][C]0.998986372292613[/C][/ROW]
[ROW][C]107[/C][C]0.000643694858890779[/C][C]0.00128738971778156[/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]0.00761843782559501[/C][C]0.01523687565119[/C][C]0.992381562174405[/C][/ROW]
[ROW][C]111[/C][C]0.0119480600020657[/C][C]0.0238961200041314[/C][C]0.988051939997934[/C][/ROW]
[ROW][C]112[/C][C]0.00937729161710706[/C][C]0.0187545832342141[/C][C]0.990622708382893[/C][/ROW]
[ROW][C]113[/C][C]0.0164155658244015[/C][C]0.0328311316488029[/C][C]0.983584434175598[/C][/ROW]
[ROW][C]114[/C][C]0.011950965703082[/C][C]0.023901931406164[/C][C]0.988049034296918[/C][/ROW]
[ROW][C]115[/C][C]0.0299097473011411[/C][C]0.0598194946022822[/C][C]0.970090252698859[/C][/ROW]
[ROW][C]116[/C][C]0.0574369465534188[/C][C]0.114873893106838[/C][C]0.942563053446581[/C][/ROW]
[ROW][C]117[/C][C]0.0410948858867961[/C][C]0.0821897717735922[/C][C]0.958905114113204[/C][/ROW]
[ROW][C]118[/C][C]0.0288160627182293[/C][C]0.0576321254364586[/C][C]0.971183937281771[/C][/ROW]
[ROW][C]119[/C][C]0.0249079025703991[/C][C]0.0498158051407982[/C][C]0.975092097429601[/C][/ROW]
[ROW][C]120[/C][C]0.0260044944908343[/C][C]0.0520089889816686[/C][C]0.973995505509166[/C][/ROW]
[ROW][C]121[/C][C]0.0427253266315109[/C][C]0.0854506532630219[/C][C]0.957274673368489[/C][/ROW]
[ROW][C]122[/C][C]0.0327664504356665[/C][C]0.0655329008713329[/C][C]0.967233549564333[/C][/ROW]
[ROW][C]123[/C][C]0.0555698400584714[/C][C]0.111139680116943[/C][C]0.944430159941529[/C][/ROW]
[ROW][C]124[/C][C]0.0528936703183841[/C][C]0.105787340636768[/C][C]0.947106329681616[/C][/ROW]
[ROW][C]125[/C][C]0.0489782355951149[/C][C]0.0979564711902298[/C][C]0.951021764404885[/C][/ROW]
[ROW][C]126[/C][C]0.0494801822052018[/C][C]0.0989603644104037[/C][C]0.950519817794798[/C][/ROW]
[ROW][C]127[/C][C]0.0359346922059046[/C][C]0.0718693844118092[/C][C]0.964065307794095[/C][/ROW]
[ROW][C]128[/C][C]0.0223799436410383[/C][C]0.0447598872820766[/C][C]0.977620056358962[/C][/ROW]
[ROW][C]129[/C][C]0.0189573541190179[/C][C]0.0379147082380358[/C][C]0.981042645880982[/C][/ROW]
[ROW][C]130[/C][C]0.0168841897853216[/C][C]0.0337683795706433[/C][C]0.983115810214678[/C][/ROW]
[ROW][C]131[/C][C]0.0123757382255768[/C][C]0.0247514764511537[/C][C]0.987624261774423[/C][/ROW]
[ROW][C]132[/C][C]0.0225683385715193[/C][C]0.0451366771430386[/C][C]0.977431661428481[/C][/ROW]
[ROW][C]133[/C][C]0.19308319204377[/C][C]0.38616638408754[/C][C]0.80691680795623[/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=186277&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186277&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
110.04412303669198250.08824607338396510.955876963308018
120.3573185287769940.7146370575539880.642681471223006
130.296862431465620.593724862931240.70313756853438
140.1934032779443620.3868065558887240.806596722055638
150.1157020033701090.2314040067402170.884297996629891
160.1047585832800930.2095171665601850.895241416719907
170.06638089151336730.1327617830267350.933619108486633
180.2389261689953150.477852337990630.761073831004685
190.2425732440580590.4851464881161190.757426755941941
200.2040925087507980.4081850175015960.795907491249202
210.1472493216351430.2944986432702870.852750678364857
220.2223422870978120.4446845741956230.777657712902188
230.1841944157659180.3683888315318370.815805584234082
240.182130224040440.364260448080880.81786977595956
250.24177085280760.4835417056151990.7582291471924
260.1867755289541810.3735510579083620.813224471045819
270.1452078132544280.2904156265088560.854792186745572
280.1207206855235950.2414413710471890.879279314476405
290.08830501962530930.1766100392506190.911694980374691
300.06328757517732120.1265751503546420.936712424822679
310.04528657327025030.09057314654050070.95471342672975
320.1479019044239760.2958038088479520.852098095576024
330.2637105373899260.5274210747798520.736289462610074
340.2188830132941550.4377660265883090.781116986705845
350.2636168465857490.5272336931714980.736383153414251
360.2346543119526810.4693086239053610.765345688047319
370.2133129031439480.4266258062878960.786687096856052
380.3093186181237970.6186372362475940.690681381876203
390.3645665794745530.7291331589491060.635433420525447
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600.06040426939512050.1208085387902410.93959573060488
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700.01382772019294090.02765544038588180.986172279807059
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1000.004194724726448880.008389449452897750.995805275273551
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1330.193083192043770.386166384087540.80691680795623
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=186277&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=186277&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186277&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')
}