## Free Statistics

of Irreproducible Research!

Author's title
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
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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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 10 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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 10 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] -0.0766601847092071Belgi\303\253[t] -0.461901000716776Eurogebied[t] + 0.385382469325768EU-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] -0.0766601847092071Belgi\303\253[t] -0.461901000716776Eurogebied[t] +  0.385382469325768EU-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] -0.0766601847092071Belgi\303\253[t] -0.461901000716776Eurogebied[t] +  0.385382469325768EU-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] -0.0766601847092071Belgi\303\253[t] -0.461901000716776Eurogebied[t] + 0.385382469325768EU-27\r[t] + e[t]

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) -84.8598455362288 336.990516 -0.2518 0.80156 0.40078 t 0.00110852737564472 0.014254 0.0778 0.938126 0.469063 jaartal 0.0431272408509219 0.168533 0.2559 0.798414 0.399207 jongerdan25jaar 0.995878671716741 0.00393 253.4034 0 0 vanaf25jaar 1.0002341118164 0.003058 327.1342 0 0 Belgi\303\253 -0.0766601847092071 0.113462 -0.6756 0.500404 0.250202 Eurogebied -0.461901000716776 0.372345 -1.2405 0.216903 0.108452 EU-27\r 0.385382469325768 0.350866 1.0984 0.273967 0.136983

\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 Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) -84.8598455362288 336.990516 -0.2518 0.80156 0.40078 t 0.00110852737564472 0.014254 0.0778 0.938126 0.469063 jaartal 0.0431272408509219 0.168533 0.2559 0.798414 0.399207 jongerdan25jaar 0.995878671716741 0.00393 253.4034 0 0 vanaf25jaar 1.0002341118164 0.003058 327.1342 0 0 Belgi\303\253 -0.0766601847092071 0.113462 -0.6756 0.500404 0.250202 Eurogebied -0.461901000716776 0.372345 -1.2405 0.216903 0.108452 EU-27\r 0.385382469325768 0.350866 1.0984 0.273967 0.136983

 Multiple Linear Regression - Regression Statistics Multiple R 0.999941101403851 R-squared 0.999882206276746 Adjusted R-squared 0.999876187619353 F-TEST (value) 166130.440903983 F-TEST (DF numerator) 7 F-TEST (DF denominator) 137 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.504181488531057 Sum Squared Residuals 34.8252593527027

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999941101403851 \tabularnewline
R-squared & 0.999882206276746 \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]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 R 0.999941101403851 R-squared 0.999882206276746 Adjusted R-squared 0.999876187619353 F-TEST (value) 166130.440903983 F-TEST (DF numerator) 7 F-TEST (DF denominator) 137 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.504181488531057 Sum Squared Residuals 34.8252593527027

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

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

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.0441230366919825 0.0882460733839651 0.955876963308018 12 0.357318528776994 0.714637057553988 0.642681471223006 13 0.29686243146562 0.59372486293124 0.70313756853438 14 0.193403277944362 0.386806555888724 0.806596722055638 15 0.115702003370109 0.231404006740217 0.884297996629891 16 0.104758583280093 0.209517166560185 0.895241416719907 17 0.0663808915133673 0.132761783026735 0.933619108486633 18 0.238926168995315 0.47785233799063 0.761073831004685 19 0.242573244058059 0.485146488116119 0.757426755941941 20 0.204092508750798 0.408185017501596 0.795907491249202 21 0.147249321635143 0.294498643270287 0.852750678364857 22 0.222342287097812 0.444684574195623 0.777657712902188 23 0.184194415765918 0.368388831531837 0.815805584234082 24 0.18213022404044 0.36426044808088 0.81786977595956 25 0.2417708528076 0.483541705615199 0.7582291471924 26 0.186775528954181 0.373551057908362 0.813224471045819 27 0.145207813254428 0.290415626508856 0.854792186745572 28 0.120720685523595 0.241441371047189 0.879279314476405 29 0.0883050196253093 0.176610039250619 0.911694980374691 30 0.0632875751773212 0.126575150354642 0.936712424822679 31 0.0452865732702503 0.0905731465405007 0.95471342672975 32 0.147901904423976 0.295803808847952 0.852098095576024 33 0.263710537389926 0.527421074779852 0.736289462610074 34 0.218883013294155 0.437766026588309 0.781116986705845 35 0.263616846585749 0.527233693171498 0.736383153414251 36 0.234654311952681 0.469308623905361 0.765345688047319 37 0.213312903143948 0.426625806287896 0.786687096856052 38 0.309318618123797 0.618637236247594 0.690681381876203 39 0.364566579474553 0.729133158949106 0.635433420525447 40 0.313418835716303 0.626837671432606 0.686581164283697 41 0.265835175033228 0.531670350066456 0.734164824966772 42 0.220875572397894 0.441751144795788 0.779124427602106 43 0.361816603200833 0.723633206401666 0.638183396799167 44 0.312149547447711 0.624299094895421 0.687850452552289 45 0.26492961442961 0.529859228859221 0.73507038557039 46 0.223614044002057 0.447228088004115 0.776385955997943 47 0.20864920442201 0.41729840884402 0.79135079557799 48 0.195993803797886 0.391987607595773 0.804006196202114 49 0.173477263651761 0.346954527303523 0.826522736348239 50 0.145663397562235 0.29132679512447 0.854336602437765 51 0.119401009529955 0.23880201905991 0.880598990470045 52 0.168253002060023 0.336506004120045 0.831746997939977 53 0.137106724401689 0.274213448803377 0.862893275598311 54 0.212359253986216 0.424718507972433 0.787640746013784 55 0.180009192209098 0.360018384418197 0.819990807790902 56 0.148951291581452 0.297902583162903 0.851048708418549 57 0.121049612394756 0.242099224789513 0.878950387605244 58 0.0973733493666939 0.194746698733388 0.902626650633306 59 0.0772537227161637 0.154507445432327 0.922746277283836 60 0.0604042693951205 0.120808538790241 0.93959573060488 61 0.0468166912251561 0.0936333824503121 0.953183308774844 62 0.0357297953035981 0.0714595906071963 0.964270204696402 63 0.0853567860921782 0.170713572184356 0.914643213907822 64 0.068070416051634 0.136140832103268 0.931929583948366 65 0.0537999153306525 0.107599830661305 0.946200084669347 66 0.0419611809133777 0.0839223618267553 0.958038819086622 67 0.0324296373194017 0.0648592746388033 0.967570362680598 68 0.0251521358846732 0.0503042717693464 0.974847864115327 69 0.0187285748581844 0.0374571497163689 0.981271425141816 70 0.0138277201929409 0.0276554403858818 0.986172279807059 71 0.00994131297566094 0.0198826259513219 0.990058687024339 72 0.00705067687319016 0.0141013537463803 0.99294932312681 73 0.00493506245444703 0.00987012490889406 0.995064937545553 74 0.00940218668967125 0.0188043733793425 0.990597813310329 75 0.00734468883688321 0.0146893776737664 0.992655311163117 76 0.0309156179850324 0.0618312359700648 0.969084382014968 77 0.0236197170858081 0.0472394341716161 0.976380282914192 78 0.0177937217083825 0.035587443416765 0.982206278291618 79 0.0132957378555297 0.0265914757110594 0.98670426214447 80 0.0097026420054275 0.019405284010855 0.990297357994573 81 0.00719321052181044 0.0143864210436209 0.99280678947819 82 0.00578005565461692 0.0115601113092338 0.994219944345383 83 0.00417562458514366 0.00835124917028733 0.995824375414856 84 0.00803885446317285 0.0160777089263457 0.991961145536827 85 0.00601169799220959 0.0120233959844192 0.99398830200779 86 0.00427290069053406 0.00854580138106813 0.995727099309466 87 0.00314721132543037 0.00629442265086074 0.99685278867457 88 0.00224161020262855 0.00448322040525709 0.997758389797371 89 0.00155174491943617 0.00310348983887234 0.998448255080564 90 0.00271812607393628 0.00543625214787255 0.997281873926064 91 0.00205896490239115 0.00411792980478229 0.997941035097609 92 0.00147640211930862 0.00295280423861725 0.998523597880691 93 0.00118701504988064 0.00237403009976127 0.998812984950119 94 0.00168690135168503 0.00337380270337006 0.998313098648315 95 0.00588061484075004 0.0117612296815001 0.99411938515925 96 0.0151041295304916 0.0302082590609832 0.984895870469508 97 0.0113876456281921 0.0227752912563842 0.988612354371808 98 0.00812149152428892 0.0162429830485778 0.991878508475711 99 0.00578418767606726 0.0115683753521345 0.994215812323933 100 0.00419472472644888 0.00838944945289775 0.995805275273551 101 0.00288046255865231 0.00576092511730463 0.997119537441348 102 0.0019347062899753 0.00386941257995059 0.998065293710025 103 0.00132862831689331 0.00265725663378663 0.998671371683107 104 0.00106047216130455 0.00212094432260911 0.998939527838695 105 0.00121454481909697 0.00242908963819394 0.998785455180903 106 0.00101362770738702 0.00202725541477404 0.998986372292613 107 0.000643694858890779 0.00128738971778156 0.999356305141109 108 0.00235345754985925 0.00470691509971851 0.997646542450141 109 0.00175029104724436 0.00350058209448871 0.998249708952756 110 0.00761843782559501 0.01523687565119 0.992381562174405 111 0.0119480600020657 0.0238961200041314 0.988051939997934 112 0.00937729161710706 0.0187545832342141 0.990622708382893 113 0.0164155658244015 0.0328311316488029 0.983584434175598 114 0.011950965703082 0.023901931406164 0.988049034296918 115 0.0299097473011411 0.0598194946022822 0.970090252698859 116 0.0574369465534188 0.114873893106838 0.942563053446581 117 0.0410948858867961 0.0821897717735922 0.958905114113204 118 0.0288160627182293 0.0576321254364586 0.971183937281771 119 0.0249079025703991 0.0498158051407982 0.975092097429601 120 0.0260044944908343 0.0520089889816686 0.973995505509166 121 0.0427253266315109 0.0854506532630219 0.957274673368489 122 0.0327664504356665 0.0655329008713329 0.967233549564333 123 0.0555698400584714 0.111139680116943 0.944430159941529 124 0.0528936703183841 0.105787340636768 0.947106329681616 125 0.0489782355951149 0.0979564711902298 0.951021764404885 126 0.0494801822052018 0.0989603644104037 0.950519817794798 127 0.0359346922059046 0.0718693844118092 0.964065307794095 128 0.0223799436410383 0.0447598872820766 0.977620056358962 129 0.0189573541190179 0.0379147082380358 0.981042645880982 130 0.0168841897853216 0.0337683795706433 0.983115810214678 131 0.0123757382255768 0.0247514764511537 0.987624261774423 132 0.0225683385715193 0.0451366771430386 0.977431661428481 133 0.19308319204377 0.38616638408754 0.80691680795623 134 0.13017689293642 0.26035378587284 0.86982310706358

\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-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.0441230366919825 0.0882460733839651 0.955876963308018 12 0.357318528776994 0.714637057553988 0.642681471223006 13 0.29686243146562 0.59372486293124 0.70313756853438 14 0.193403277944362 0.386806555888724 0.806596722055638 15 0.115702003370109 0.231404006740217 0.884297996629891 16 0.104758583280093 0.209517166560185 0.895241416719907 17 0.0663808915133673 0.132761783026735 0.933619108486633 18 0.238926168995315 0.47785233799063 0.761073831004685 19 0.242573244058059 0.485146488116119 0.757426755941941 20 0.204092508750798 0.408185017501596 0.795907491249202 21 0.147249321635143 0.294498643270287 0.852750678364857 22 0.222342287097812 0.444684574195623 0.777657712902188 23 0.184194415765918 0.368388831531837 0.815805584234082 24 0.18213022404044 0.36426044808088 0.81786977595956 25 0.2417708528076 0.483541705615199 0.7582291471924 26 0.186775528954181 0.373551057908362 0.813224471045819 27 0.145207813254428 0.290415626508856 0.854792186745572 28 0.120720685523595 0.241441371047189 0.879279314476405 29 0.0883050196253093 0.176610039250619 0.911694980374691 30 0.0632875751773212 0.126575150354642 0.936712424822679 31 0.0452865732702503 0.0905731465405007 0.95471342672975 32 0.147901904423976 0.295803808847952 0.852098095576024 33 0.263710537389926 0.527421074779852 0.736289462610074 34 0.218883013294155 0.437766026588309 0.781116986705845 35 0.263616846585749 0.527233693171498 0.736383153414251 36 0.234654311952681 0.469308623905361 0.765345688047319 37 0.213312903143948 0.426625806287896 0.786687096856052 38 0.309318618123797 0.618637236247594 0.690681381876203 39 0.364566579474553 0.729133158949106 0.635433420525447 40 0.313418835716303 0.626837671432606 0.686581164283697 41 0.265835175033228 0.531670350066456 0.734164824966772 42 0.220875572397894 0.441751144795788 0.779124427602106 43 0.361816603200833 0.723633206401666 0.638183396799167 44 0.312149547447711 0.624299094895421 0.687850452552289 45 0.26492961442961 0.529859228859221 0.73507038557039 46 0.223614044002057 0.447228088004115 0.776385955997943 47 0.20864920442201 0.41729840884402 0.79135079557799 48 0.195993803797886 0.391987607595773 0.804006196202114 49 0.173477263651761 0.346954527303523 0.826522736348239 50 0.145663397562235 0.29132679512447 0.854336602437765 51 0.119401009529955 0.23880201905991 0.880598990470045 52 0.168253002060023 0.336506004120045 0.831746997939977 53 0.137106724401689 0.274213448803377 0.862893275598311 54 0.212359253986216 0.424718507972433 0.787640746013784 55 0.180009192209098 0.360018384418197 0.819990807790902 56 0.148951291581452 0.297902583162903 0.851048708418549 57 0.121049612394756 0.242099224789513 0.878950387605244 58 0.0973733493666939 0.194746698733388 0.902626650633306 59 0.0772537227161637 0.154507445432327 0.922746277283836 60 0.0604042693951205 0.120808538790241 0.93959573060488 61 0.0468166912251561 0.0936333824503121 0.953183308774844 62 0.0357297953035981 0.0714595906071963 0.964270204696402 63 0.0853567860921782 0.170713572184356 0.914643213907822 64 0.068070416051634 0.136140832103268 0.931929583948366 65 0.0537999153306525 0.107599830661305 0.946200084669347 66 0.0419611809133777 0.0839223618267553 0.958038819086622 67 0.0324296373194017 0.0648592746388033 0.967570362680598 68 0.0251521358846732 0.0503042717693464 0.974847864115327 69 0.0187285748581844 0.0374571497163689 0.981271425141816 70 0.0138277201929409 0.0276554403858818 0.986172279807059 71 0.00994131297566094 0.0198826259513219 0.990058687024339 72 0.00705067687319016 0.0141013537463803 0.99294932312681 73 0.00493506245444703 0.00987012490889406 0.995064937545553 74 0.00940218668967125 0.0188043733793425 0.990597813310329 75 0.00734468883688321 0.0146893776737664 0.992655311163117 76 0.0309156179850324 0.0618312359700648 0.969084382014968 77 0.0236197170858081 0.0472394341716161 0.976380282914192 78 0.0177937217083825 0.035587443416765 0.982206278291618 79 0.0132957378555297 0.0265914757110594 0.98670426214447 80 0.0097026420054275 0.019405284010855 0.990297357994573 81 0.00719321052181044 0.0143864210436209 0.99280678947819 82 0.00578005565461692 0.0115601113092338 0.994219944345383 83 0.00417562458514366 0.00835124917028733 0.995824375414856 84 0.00803885446317285 0.0160777089263457 0.991961145536827 85 0.00601169799220959 0.0120233959844192 0.99398830200779 86 0.00427290069053406 0.00854580138106813 0.995727099309466 87 0.00314721132543037 0.00629442265086074 0.99685278867457 88 0.00224161020262855 0.00448322040525709 0.997758389797371 89 0.00155174491943617 0.00310348983887234 0.998448255080564 90 0.00271812607393628 0.00543625214787255 0.997281873926064 91 0.00205896490239115 0.00411792980478229 0.997941035097609 92 0.00147640211930862 0.00295280423861725 0.998523597880691 93 0.00118701504988064 0.00237403009976127 0.998812984950119 94 0.00168690135168503 0.00337380270337006 0.998313098648315 95 0.00588061484075004 0.0117612296815001 0.99411938515925 96 0.0151041295304916 0.0302082590609832 0.984895870469508 97 0.0113876456281921 0.0227752912563842 0.988612354371808 98 0.00812149152428892 0.0162429830485778 0.991878508475711 99 0.00578418767606726 0.0115683753521345 0.994215812323933 100 0.00419472472644888 0.00838944945289775 0.995805275273551 101 0.00288046255865231 0.00576092511730463 0.997119537441348 102 0.0019347062899753 0.00386941257995059 0.998065293710025 103 0.00132862831689331 0.00265725663378663 0.998671371683107 104 0.00106047216130455 0.00212094432260911 0.998939527838695 105 0.00121454481909697 0.00242908963819394 0.998785455180903 106 0.00101362770738702 0.00202725541477404 0.998986372292613 107 0.000643694858890779 0.00128738971778156 0.999356305141109 108 0.00235345754985925 0.00470691509971851 0.997646542450141 109 0.00175029104724436 0.00350058209448871 0.998249708952756 110 0.00761843782559501 0.01523687565119 0.992381562174405 111 0.0119480600020657 0.0238961200041314 0.988051939997934 112 0.00937729161710706 0.0187545832342141 0.990622708382893 113 0.0164155658244015 0.0328311316488029 0.983584434175598 114 0.011950965703082 0.023901931406164 0.988049034296918 115 0.0299097473011411 0.0598194946022822 0.970090252698859 116 0.0574369465534188 0.114873893106838 0.942563053446581 117 0.0410948858867961 0.0821897717735922 0.958905114113204 118 0.0288160627182293 0.0576321254364586 0.971183937281771 119 0.0249079025703991 0.0498158051407982 0.975092097429601 120 0.0260044944908343 0.0520089889816686 0.973995505509166 121 0.0427253266315109 0.0854506532630219 0.957274673368489 122 0.0327664504356665 0.0655329008713329 0.967233549564333 123 0.0555698400584714 0.111139680116943 0.944430159941529 124 0.0528936703183841 0.105787340636768 0.947106329681616 125 0.0489782355951149 0.0979564711902298 0.951021764404885 126 0.0494801822052018 0.0989603644104037 0.950519817794798 127 0.0359346922059046 0.0718693844118092 0.964065307794095 128 0.0223799436410383 0.0447598872820766 0.977620056358962 129 0.0189573541190179 0.0379147082380358 0.981042645880982 130 0.0168841897853216 0.0337683795706433 0.983115810214678 131 0.0123757382255768 0.0247514764511537 0.987624261774423 132 0.0225683385715193 0.0451366771430386 0.977431661428481 133 0.19308319204377 0.38616638408754 0.80691680795623 134 0.13017689293642 0.26035378587284 0.86982310706358

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 21 0.169354838709677 NOK 5% type I error level 51 0.411290322580645 NOK 10% type I error level 68 0.548387096774194 NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 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 tests OK/NOK 1% type I error level 21 0.169354838709677 NOK 5% type I error level 51 0.411290322580645 NOK 10% type I error level 68 0.548387096774194 NOK

library(lattice)library(lmtest)n25 <- 25 #minimum number of obs. for Goldfeld-Quandt testpar1 <- 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 <- x1if (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'}xk <- length(x[1,])df <- as.data.frame(x)(mylm <- lm(df))(mysum <- summary(mylm))if (n > n25) {kp3 <- k + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$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)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)zplot(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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')}}myeq <- paste(myeq, ' + e[t]')a<-table.row.start(a)a<-table.element(a, myeq)a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable1.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Variable',header=TRUE)a<-table.element(a,'Parameter',header=TRUE)a<-table.element(a,'S.D.',header=TRUE)a<-table.element(a,'T-STATH0: parameter = 0',header=TRUE)a<-table.element(a,'2-tail p-value',header=TRUE)a<-table.element(a,'1-tail p-value',header=TRUE)a<-table.row.end(a)for (i in 1:k){a<-table.row.start(a)a<-table.element(a,rownames(mysum$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$sigma)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Sum Squared Residuals',1,TRUE)a<-table.element(a, sum(myerror*myerror))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable3.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,x[i])a<-table.element(a,x[i]-mysum$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')}