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Author's title
Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 17:53:07 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t1352156119oekavrosx1ykvmu.htm/, Retrieved Wed, 01 Feb 2023 15:41:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186354, Retrieved Wed, 01 Feb 2023 15:41:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact103
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2012-11-05 07:44:33] [d2c1a12335a0e7c18f8727e39be21dbc]
- R PD    [Multiple Regression] [ws7du] [2012-11-05 22:53:07] [fa3197be492c9e61892dda11815d51ad] [Current]
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Dataseries X:
2000	0	0	1	501	134	0	368	0	6.70	0.00	8.50	0.00	8.70	0
2000	2	1	2	485	124	124	361	361	6.80	6.80	8.40	8.40	8.60	8.6
2000	3	1	3	464	113	113	351	351	6.70	6.70	8.40	8.40	8.60	8.6
2000	4	1	4	460	109	109	351	351	6.60	6.60	8.30	8.30	8.50	8.5
2001	5	1	5	467	109	109	358	358	6.40	6.40	8.20	8.20	8.50	8.5
2001	6	1	6	460	106	106	354	354	6.30	6.30	8.20	8.20	8.50	8.5
2001	7	1	7	448	101	101	347	347	6.30	6.30	8.10	8.10	8.50	8.5
2001	8	1	8	443	98	98	345	345	6.50	6.50	8.10	8.10	8.50	8.5
2001	0	0	9	436	93	0	343	0	6.50	0.00	8.10	0.00	8.50	0
2001	0	0	10	431	91	0	340	0	6.40	0.00	8.10	0.00	8.50	0
2001	0	0	11	484	122	0	362	0	6.20	0.00	8.10	0.00	8.50	0
2001	0	0	12	510	139	0	370	0	6.20	0.00	8.10	0.00	8.60	0
2001	0	0	13	513	140	0	373	0	6.50	0.00	8.10	0.00	8.60	0
2001	14	1	14	503	132	132	371	371	7.00	7.00	8.20	8.20	8.60	8.6
2001	15	1	15	471	117	117	354	354	7.20	7.20	8.20	8.20	8.70	8.7
2001	16	1	16	471	114	114	357	357	7.30	7.30	8.30	8.30	8.70	8.7
2002	17	1	17	476	113	113	363	363	7.40	7.40	8.20	8.20	8.70	8.7
2002	18	1	18	475	110	110	364	364	7.40	7.40	8.30	8.30	8.80	8.8
2002	19	1	19	470	107	107	363	363	7.40	7.40	8.30	8.30	8.80	8.8
2002	20	1	20	461	103	103	358	358	7.30	7.30	8.40	8.40	8.90	8.9
2002	0	0	21	455	98	0	357	0	7.40	0.00	8.50	0.00	8.90	0
2002	0	0	22	456	98	0	357	0	7.40	0.00	8.50	0.00	8.90	0
2002	0	0	23	517	137	0	380	0	7.60	0.00	8.60	0.00	9.00	0
2002	0	0	24	525	148	0	378	0	7.60	0.00	8.60	0.00	9.00	0
2002	0	0	25	523	147	0	376	0	7.70	0.00	8.70	0.00	9.00	0
2002	26	1	26	519	139	139	380	380	7.70	7.70	8.70	8.70	9.00	9
2002	27	1	27	509	130	130	379	379	7.80	7.80	8.80	8.80	9.00	9
2002	28	1	28	512	128	128	384	384	7.80	7.80	8.80	8.80	9.00	9
2003	29	1	29	519	127	127	392	392	8.00	8.00	8.90	8.90	9.10	9.1
2003	30	1	30	517	123	123	394	394	8.10	8.10	9.00	9.00	9.10	9.1
2003	31	1	31	510	118	118	392	392	8.10	8.10	9.00	9.00	9.10	9.1
2003	32	1	32	509	114	114	396	396	8.20	8.20	9.00	9.00	9.10	9.1
2003	0	0	33	501	108	0	392	0	8.10	0.00	9.00	0.00	9.10	0
2003	0	0	34	507	111	0	396	0	8.10	0.00	9.10	0.00	9.10	0
2003	0	0	35	569	151	0	419	0	8.10	0.00	9.10	0.00	9.10	0
2003	0	0	36	580	159	0	421	0	8.10	0.00	9.00	0.00	9.10	0
2003	0	0	37	578	158	0	420	0	8.20	0.00	9.10	0.00	9.10	0
2003	38	1	38	565	148	148	418	418	8.20	8.20	9.00	9.00	9.10	9.1
2003	39	1	39	547	138	138	410	410	8.30	8.30	9.10	9.10	9.10	9.1
2003	40	1	40	555	137	137	418	418	8.40	8.40	9.10	9.10	9.20	9.2
2004	41	1	41	562	136	136	426	426	8.60	8.60	9.20	9.20	9.30	9.3
2004	42	1	42	561	133	133	428	428	8.60	8.60	9.20	9.20	9.30	9.3
2004	43	1	43	555	126	126	430	430	8.40	8.40	9.20	9.20	9.30	9.3
2004	44	1	44	544	120	120	424	424	8.00	8.00	9.20	9.20	9.20	9.2
2004	0	0	45	537	114	0	423	0	7.90	0.00	9.20	0.00	9.20	0
2004	0	0	46	543	116	0	427	0	8.10	0.00	9.30	0.00	9.20	0
2004	0	0	47	594	153	0	441	0	8.50	0.00	9.30	0.00	9.20	0
2004	0	0	48	611	162	0	449	0	8.80	0.00	9.30	0.00	9.20	0
2004	0	0	49	613	161	0	452	0	8.80	0.00	9.30	0.00	9.20	0
2004	50	1	50	611	149	149	462	462	8.50	8.50	9.30	9.30	9.20	9.2
2004	51	1	51	594	139	139	455	455	8.30	8.30	9.40	9.40	9.20	9.2
2004	52	1	52	595	135	135	461	461	8.30	8.30	9.40	9.40	9.20	9.2
2005	53	1	53	591	130	130	461	461	8.30	8.30	9.30	9.30	9.20	9.2
2005	54	1	54	589	127	127	463	463	8.40	8.40	9.30	9.30	9.20	9.2
2005	55	1	55	584	122	122	462	462	8.50	8.50	9.30	9.30	9.20	9.2
2005	56	1	56	573	117	117	456	456	8.50	8.50	9.30	9.30	9.20	9.2
2005	0	0	57	567	112	0	455	0	8.60	0.00	9.20	0.00	9.10	0
2005	0	0	58	569	113	0	456	0	8.50	0.00	9.20	0.00	9.10	0
2005	0	0	59	621	149	0	472	0	8.60	0.00	9.20	0.00	9.00	0
2005	0	0	60	629	157	0	472	0	8.60	0.00	9.10	0.00	8.90	0
2005	0	0	61	628	157	0	471	0	8.60	0.00	9.10	0.00	8.90	0
2005	62	1	62	612	147	147	465	465	8.50	8.50	9.10	9.10	9.00	9
2005	63	1	63	595	137	137	459	459	8.40	8.40	9.10	9.10	8.90	8.9
2005	64	1	64	597	132	132	465	465	8.40	8.40	9.00	9.00	8.80	8.8
2006	65	1	65	593	125	125	468	468	8.50	8.50	8.90	8.90	8.70	8.7
2006	66	1	66	590	123	123	467	467	8.50	8.50	8.80	8.80	8.60	8.6
2006	67	1	67	580	117	117	463	463	8.50	8.50	8.70	8.70	8.50	8.5
2006	68	1	68	574	114	114	460	460	8.60	8.60	8.60	8.60	8.50	8.5
2006	0	0	69	573	111	0	462	0	8.60	0.00	8.60	0.00	8.40	0
2006	0	0	70	573	112	0	461	0	8.40	0.00	8.50	0.00	8.30	0
2006	0	0	71	620	144	0	476	0	8.20	0.00	8.40	0.00	8.20	0
2006	0	0	72	626	150	0	476	0	8.00	0.00	8.40	0.00	8.20	0
2006	0	0	73	620	149	0	471	0	8.00	0.00	8.30	0.00	8.10	0
2006	74	1	74	588	134	134	453	453	8.00	8.00	8.20	8.20	8.00	8
2006	75	1	75	566	123	123	443	443	8.00	8.00	8.20	8.20	7.90	7.9
2006	76	1	76	557	116	116	442	442	7.90	7.90	8.00	8.00	7.80	7.8
2007	77	1	77	561	117	117	444	444	7.90	7.90	7.90	7.90	7.60	7.6
2007	78	1	78	549	111	111	438	438	7.90	7.90	7.80	7.80	7.50	7.5
2007	79	1	79	532	105	105	427	427	7.90	7.90	7.70	7.70	7.40	7.4
2007	80	1	80	526	102	102	424	424	8.00	8.00	7.60	7.60	7.30	7.3
2007	0	0	81	511	95	0	416	0	7.90	0.00	7.60	0.00	7.30	0
2007	0	0	82	499	93	0	406	0	7.40	0.00	7.60	0.00	7.20	0
2007	0	0	83	555	124	0	431	0	7.20	0.00	7.60	0.00	7.20	0
2007	0	0	84	565	130	0	434	0	7.00	0.00	7.60	0.00	7.20	0
2007	0	0	85	542	124	0	418	0	6.90	0.00	7.50	0.00	7.10	0
2007	86	1	86	527	115	115	412	412	7.10	7.10	7.50	7.50	7.00	7
2007	87	1	87	510	106	106	404	404	7.20	7.20	7.40	7.40	7.00	7
2007	88	1	88	514	105	105	409	409	7.20	7.20	7.40	7.40	6.90	6.9
2008	89	1	89	517	105	105	412	412	7.10	7.10	7.40	7.40	6.90	6.9
2008	90	1	90	508	101	101	406	406	6.90	6.90	7.30	7.30	6.80	6.8
2008	91	1	91	493	95	95	398	398	6.80	6.80	7.30	7.30	6.80	6.8
2008	92	1	92	490	93	93	397	397	6.80	6.80	7.40	7.40	6.80	6.8
2008	0	0	93	469	84	0	385	0	6.80	0.00	7.50	0.00	6.90	0
2008	0	0	94	478	87	0	390	0	6.90	0.00	7.60	0.00	7.00	0
2008	0	0	95	528	116	0	413	0	7.10	0.00	7.60	0.00	7.00	0
2008	0	0	96	534	120	0	413	0	7.20	0.00	7.70	0.00	7.10	0
2008	0	0	97	518	117	0	401	0	7.20	0.00	7.70	0.00	7.20	0
2008	98	1	98	506	109	109	397	397	7.10	7.10	7.90	7.90	7.30	7.3
2008	99	1	99	502	105	105	397	397	7.10	7.10	8.10	8.10	7.50	7.5
2008	100	1	100	516	107	107	409	409	7.20	7.20	8.40	8.40	7.70	7.7
2009	101	1	101	528	109	109	419	419	7.50	7.50	8.70	8.70	8.10	8.1
2009	102	1	102	533	109	109	424	424	7.70	7.70	9.00	9.00	8.40	8.4
2009	103	1	103	536	108	108	428	428	7.80	7.80	9.30	9.30	8.60	8.6
2009	104	1	104	537	107	107	430	430	7.70	7.70	9.40	9.40	8.80	8.8
2009	0	0	105	524	99	0	424	0	7.70	0.00	9.50	0.00	8.90	0
2009	0	0	106	536	103	0	433	0	7.80	0.00	9.60	0.00	9.10	0
2009	0	0	107	587	131	0	456	0	8.00	0.00	9.80	0.00	9.20	0
2009	0	0	108	597	137	0	459	0	8.10	0.00	9.80	0.00	9.30	0
2009	0	0	109	581	135	0	446	0	8.10	0.00	9.90	0.00	9.40	0
2009	110	1	110	564	124	124	441	441	8.00	8.00	10.00	10.00	9.40	9.4
2009	111	1	111	558	118	118	439	439	8.10	8.10	10.00	10.00	9.50	9.5
2010	112	1	112	575	121	121	454	454	8.20	8.20	10.10	10.10	9.50	9.5
2010	113	1	113	580	121	121	460	460	8.40	8.40	10.10	10.10	9.70	9.7
2010	114	1	114	575	118	118	457	457	8.50	8.50	10.10	10.10	9.70	9.7
2010	115	1	115	563	113	113	451	451	8.50	8.50	10.10	10.10	9.70	9.7
2010	116	1	116	552	107	107	444	444	8.50	8.50	10.20	10.20	9.70	9.7
2010	0	0	117	537	100	0	437	0	8.50	0.00	10.20	0.00	9.70	0
2010	0	0	118	545	102	0	443	0	8.50	0.00	10.10	0.00	9.60	0
2010	0	0	119	601	130	0	471	0	8.40	0.00	10.10	0.00	9.60	0
2010	0	0	120	604	136	0	469	0	8.30	0.00	10.10	0.00	9.60	0
2010	0	0	121	586	133	0	454	0	8.20	0.00	10.10	0.00	9.60	0
2010	122	1	122	564	120	120	444	444	8.10	8.10	10.10	10.10	9.60	9.6
2010	123	1	123	549	112	112	436	436	7.90	7.90	10.10	10.10	9.60	9.6
2010	124	1	124	551	109	109	442	442	7.60	7.60	10.10	10.10	9.60	9.6
2011	125	1	125	556	110	110	446	446	7.30	7.30	10.00	10.00	9.50	9.5
2011	126	1	126	548	106	106	442	442	7.10	7.10	9.90	9.90	9.50	9.5
2011	127	1	127	540	102	102	438	438	7.00	7.00	9.90	9.90	9.40	9.4
2011	128	1	128	531	98	98	433	433	7.10	7.10	9.90	9.90	9.40	9.4
2011	0	0	129	521	92	0	428	0	7.10	0.00	9.90	0.00	9.50	0
2011	0	0	130	519	92	0	426	0	7.10	0.00	10.00	0.00	9.50	0
2011	0	0	131	572	120	0	452	0	7.30	0.00	10.10	0.00	9.60	0
2011	0	0	132	581	127	0	455	0	7.30	0.00	10.20	0.00	9.70	0
2011	0	0	133	563	124	0	439	0	7.30	0.00	10.30	0.00	9.80	0
2011	134	1	134	548	114	114	434	434	7.20	7.20	10.50	10.50	9.90	9.9
2011	135	1	135	539	108	108	431	431	7.20	7.20	10.60	10.60	10.00	10
2011	136	1	136	541	106	106	435	435	7.10	7.10	10.70	10.70	10.00	10
2012	137	1	137	562	111	111	450	450	7.10	7.10	10.80	10.80	10.10	10.1
2012	138	1	138	559	110	110	449	449	7.10	7.10	10.90	10.90	10.20	10.2
2012	139	1	139	546	104	104	442	442	7.20	7.20	11.00	11.00	10.30	10.3
2012	140	1	140	536	100	100	437	437	7.30	7.30	11.20	11.20	10.30	10.3
2012	0	0	141	528	96	0	431	0	7.40	0.00	11.30	0.00	10.40	0
2012	0	0	142	530	98	0	433	0	7.40	0.00	11.40	0.00	10.50	0
2012	0	0	143	582	122	0	460	0	7.50	0.00	11.50	0.00	10.50	0
2012	0	0	144	599	134	0	465	0	7.40	0.00	11.50	0.00	10.60	0
2012	0	0	145	584	133	0	451	0	7.40	0.00	11.60	0.00	10.60	0


 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=186354&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=186354&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186354&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] = -235.158663233414 + 0.118110711369227jaartal[t] + 0.0089820520117047S_t[t] + 0.630150036739159s[t] -0.00969723841768367t + 0.992340661813698jongerdan25jaar[t] + 0.0100331957436065<25jaar_s[t] + 1.002712118828vanaf25jaar[t] -0.00401978053566876vanaf25_s[t] -0.0972881677544726Belgi\303\253[t] -0.0248614013962234Belgi\303\253_s[t] -0.174261086271905Eurogebied[t] -0.616968998076365Eurogebied_s[t] + 0.123190347467024EU-27[t] + 0.548103385829031EU-27_s\r[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Totaal[t] =  -235.158663233414 +  0.118110711369227jaartal[t] +  0.0089820520117047S_t[t] +  0.630150036739159s[t] -0.00969723841768367t +  0.992340661813698jongerdan25jaar[t] +  0.0100331957436065<25jaar_s[t] +  1.002712118828vanaf25jaar[t] -0.00401978053566876vanaf25_s[t] -0.0972881677544726Belgi\303\253[t] -0.0248614013962234Belgi\303\253_s[t] -0.174261086271905Eurogebied[t] -0.616968998076365Eurogebied_s[t] +  0.123190347467024EU-27[t] +  0.548103385829031EU-27_s\r[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186354&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Totaal[t] =  -235.158663233414 +  0.118110711369227jaartal[t] +  0.0089820520117047S_t[t] +  0.630150036739159s[t] -0.00969723841768367t +  0.992340661813698jongerdan25jaar[t] +  0.0100331957436065<25jaar_s[t] +  1.002712118828vanaf25jaar[t] -0.00401978053566876vanaf25_s[t] -0.0972881677544726Belgi\303\253[t] -0.0248614013962234Belgi\303\253_s[t] -0.174261086271905Eurogebied[t] -0.616968998076365Eurogebied_s[t] +  0.123190347467024EU-27[t] +  0.548103385829031EU-27_s\r[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186354&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186354&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] = -235.158663233414 + 0.118110711369227jaartal[t] + 0.0089820520117047S_t[t] + 0.630150036739159s[t] -0.00969723841768367t + 0.992340661813698jongerdan25jaar[t] + 0.0100331957436065<25jaar_s[t] + 1.002712118828vanaf25jaar[t] -0.00401978053566876vanaf25_s[t] -0.0972881677544726Belgi\303\253[t] -0.0248614013962234Belgi\303\253_s[t] -0.174261086271905Eurogebied[t] -0.616968998076365Eurogebied_s[t] + 0.123190347467024EU-27[t] + 0.548103385829031EU-27_s\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) -235.158663233414 372.210094 -0.6318 0.528633 0.264317 jaartal 0.118110711369227 0.186104 0.6346 0.526773 0.263386 S_t 0.0089820520117047 0.007895 1.1377 0.257339 0.12867 s 0.630150036739159 1.369864 0.46 0.646278 0.323139 t -0.00969723841768367 0.016237 -0.5972 0.551383 0.275691 jongerdan25jaar 0.992340661813698 0.004534 218.8825 0 0 <25jaar_s 0.0100331957436065 0.009299 1.079 0.282591 0.141295 vanaf25jaar 1.002712118828 0.004638 216.1903 0 0 vanaf25_s -0.00401978053566876 0.006187 -0.6497 0.517042 0.258521 Belgi\303\253 -0.0972881677544726 0.170447 -0.5708 0.569133 0.284567 Belgi\303\253_s -0.0248614013962234 0.241232 -0.1031 0.918074 0.459037 Eurogebied -0.174261086271905 0.548075 -0.318 0.751032 0.375516 Eurogebied_s -0.616968998076365 0.771011 -0.8002 0.42505 0.212525 EU-27 0.123190347467024 0.522194 0.2359 0.813874 0.406937 EU-27_s\r 0.548103385829031 0.724495 0.7565 0.450699 0.22535

\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) & -235.158663233414 & 372.210094 & -0.6318 & 0.528633 & 0.264317 \tabularnewline
jaartal & 0.118110711369227 & 0.186104 & 0.6346 & 0.526773 & 0.263386 \tabularnewline
S_t & 0.0089820520117047 & 0.007895 & 1.1377 & 0.257339 & 0.12867 \tabularnewline
s & 0.630150036739159 & 1.369864 & 0.46 & 0.646278 & 0.323139 \tabularnewline
t & -0.00969723841768367 & 0.016237 & -0.5972 & 0.551383 & 0.275691 \tabularnewline
jongerdan25jaar & 0.992340661813698 & 0.004534 & 218.8825 & 0 & 0 \tabularnewline
<25jaar_s & 0.0100331957436065 & 0.009299 & 1.079 & 0.282591 & 0.141295 \tabularnewline
vanaf25jaar & 1.002712118828 & 0.004638 & 216.1903 & 0 & 0 \tabularnewline
vanaf25_s & -0.00401978053566876 & 0.006187 & -0.6497 & 0.517042 & 0.258521 \tabularnewline
Belgi\303\253 & -0.0972881677544726 & 0.170447 & -0.5708 & 0.569133 & 0.284567 \tabularnewline
Belgi\303\253_s & -0.0248614013962234 & 0.241232 & -0.1031 & 0.918074 & 0.459037 \tabularnewline
Eurogebied & -0.174261086271905 & 0.548075 & -0.318 & 0.751032 & 0.375516 \tabularnewline
Eurogebied_s & -0.616968998076365 & 0.771011 & -0.8002 & 0.42505 & 0.212525 \tabularnewline
EU-27 & 0.123190347467024 & 0.522194 & 0.2359 & 0.813874 & 0.406937 \tabularnewline
EU-27_s\r & 0.548103385829031 & 0.724495 & 0.7565 & 0.450699 & 0.22535 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186354&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]-235.158663233414[/C][C]372.210094[/C][C]-0.6318[/C][C]0.528633[/C][C]0.264317[/C][/ROW]
[ROW][C]jaartal[/C][C]0.118110711369227[/C][C]0.186104[/C][C]0.6346[/C][C]0.526773[/C][C]0.263386[/C][/ROW]
[ROW][C]S_t[/C][C]0.0089820520117047[/C][C]0.007895[/C][C]1.1377[/C][C]0.257339[/C][C]0.12867[/C][/ROW]
[ROW][C]s[/C][C]0.630150036739159[/C][C]1.369864[/C][C]0.46[/C][C]0.646278[/C][C]0.323139[/C][/ROW]
[ROW][C]t[/C][C]-0.00969723841768367[/C][C]0.016237[/C][C]-0.5972[/C][C]0.551383[/C][C]0.275691[/C][/ROW]
[ROW][C]jongerdan25jaar[/C][C]0.992340661813698[/C][C]0.004534[/C][C]218.8825[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]<25jaar_s[/C][C]0.0100331957436065[/C][C]0.009299[/C][C]1.079[/C][C]0.282591[/C][C]0.141295[/C][/ROW]
[ROW][C]vanaf25jaar[/C][C]1.002712118828[/C][C]0.004638[/C][C]216.1903[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]vanaf25_s[/C][C]-0.00401978053566876[/C][C]0.006187[/C][C]-0.6497[/C][C]0.517042[/C][C]0.258521[/C][/ROW]
[ROW][C]Belgi\303\253[/C][C]-0.0972881677544726[/C][C]0.170447[/C][C]-0.5708[/C][C]0.569133[/C][C]0.284567[/C][/ROW]
[ROW][C]Belgi\303\253_s[/C][C]-0.0248614013962234[/C][C]0.241232[/C][C]-0.1031[/C][C]0.918074[/C][C]0.459037[/C][/ROW]
[ROW][C]Eurogebied[/C][C]-0.174261086271905[/C][C]0.548075[/C][C]-0.318[/C][C]0.751032[/C][C]0.375516[/C][/ROW]
[ROW][C]Eurogebied_s[/C][C]-0.616968998076365[/C][C]0.771011[/C][C]-0.8002[/C][C]0.42505[/C][C]0.212525[/C][/ROW]
[ROW][C]EU-27[/C][C]0.123190347467024[/C][C]0.522194[/C][C]0.2359[/C][C]0.813874[/C][C]0.406937[/C][/ROW]
[ROW][C]EU-27_s\r[/C][C]0.548103385829031[/C][C]0.724495[/C][C]0.7565[/C][C]0.450699[/C][C]0.22535[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186354&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186354&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) -235.158663233414 372.210094 -0.6318 0.528633 0.264317 jaartal 0.118110711369227 0.186104 0.6346 0.526773 0.263386 S_t 0.0089820520117047 0.007895 1.1377 0.257339 0.12867 s 0.630150036739159 1.369864 0.46 0.646278 0.323139 t -0.00969723841768367 0.016237 -0.5972 0.551383 0.275691 jongerdan25jaar 0.992340661813698 0.004534 218.8825 0 0 <25jaar_s 0.0100331957436065 0.009299 1.079 0.282591 0.141295 vanaf25jaar 1.002712118828 0.004638 216.1903 0 0 vanaf25_s -0.00401978053566876 0.006187 -0.6497 0.517042 0.258521 Belgi\303\253 -0.0972881677544726 0.170447 -0.5708 0.569133 0.284567 Belgi\303\253_s -0.0248614013962234 0.241232 -0.1031 0.918074 0.459037 Eurogebied -0.174261086271905 0.548075 -0.318 0.751032 0.375516 Eurogebied_s -0.616968998076365 0.771011 -0.8002 0.42505 0.212525 EU-27 0.123190347467024 0.522194 0.2359 0.813874 0.406937 EU-27_s\r 0.548103385829031 0.724495 0.7565 0.450699 0.22535

 Multiple Linear Regression - Regression Statistics Multiple R 0.999943307872238 R-squared 0.999886618958474 Adjusted R-squared 0.999874408692463 F-TEST (value) 81889.0119264686 F-TEST (DF numerator) 14 F-TEST (DF denominator) 130 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.507790591618823 Sum Squared Residuals 33.5206670417573

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999943307872238 \tabularnewline
R-squared & 0.999886618958474 \tabularnewline
F-TEST (value) & 81889.0119264686 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 130 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.507790591618823 \tabularnewline
Sum Squared Residuals & 33.5206670417573 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186354&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999943307872238[/C][/ROW]
[ROW][C]R-squared[/C][C]0.999886618958474[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]81889.0119264686[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]130[/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]0.507790591618823[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]33.5206670417573[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186354&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186354&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 0.999943307872238 R-squared 0.999886618958474 Adjusted R-squared 0.999874408692463 F-TEST (value) 81889.0119264686 F-TEST (DF numerator) 14 F-TEST (DF denominator) 130 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.507790591618823 Sum Squared Residuals 33.5206670417573

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 501 501.96347674406 -0.963476744059784 2 485 484.809947957201 0.190052042798564 3 464 463.808411911657 0.19158808834315 4 460 459.822409887042 0.177590112958041 5 467 467.034204702316 -0.03420470231647 6 460 460.043813546984 -0.0438135469842987 7 448 448.11950571318 -0.119505713180325 8 443 443.089854363688 -0.0898543636876219 9 436 436.314763441592 -0.314763441592201 10 431 431.321977339839 -0.321977339838558 11 484 484.153964865412 -0.153964865412427 12 510 509.048074863198 0.95192513680169 13 513 513.009668192752 -0.00966819275199646 14 503 503.05920677812 -0.0592067781201836 15 471 471.087813436884 -0.0878134368844562 16 471 470.984715727334 0.0152842726663334 17 476 476.158799476013 -0.158799476013351 18 475 474.137661420123 0.86233857987744 19 470 470.131132322752 -0.131132322752345 20 461 461.127681336465 -0.127681336465337 21 455 455.208192618109 -0.208192618108734 22 456 455.198495379691 0.801504620308957 23 517 516.92789797762 0.0721020223797681 24 525 525.828523781497 -0.828523781497222 25 523 522.793906718207 0.206093281792799 26 519 518.960981052889 0.0390189471114124 27 509 508.848870844825 0.151129155175356 28 512 511.836869634766 0.163130365234276 29 519 518.905006459575 0.0949935404250384 30 517 516.800842554175 0.199157445825467 31 510 509.790873403397 0.209126596602631 32 509 509.763217183016 -0.763217183016435 33 501 500.097673054512 0.902326945487889 34 507 507.05842016822 -0.0584201682203387 35 569 569.804728135395 -0.804728135394604 36 580 579.75660653777 0.243393462230318 37 578 577.724701593308 0.275298406692333 38 565 565.81086866396 -0.810868663960223 39 547 547.705538230293 -0.705538230292649 40 555 554.746902309083 0.253097690917435 41 562 561.815039133892 0.184960866108205 42 561 560.804587051399 0.195412948601422 43 555 555.809069452506 -0.809069452506276 44 544 543.783687545333 0.216312454666855 45 537 537.134461010463 -0.134461010462582 46 543 543.083409828806 -0.0834098288062141 47 594 593.789371473986 0.210628526014422 48 611 610.703250692189 0.296749307811154 49 613 612.709349148441 0.29065085155852 50 611 610.658349358158 0.341650641842431 51 594 593.588356133528 0.411643866472481 52 595 595.570299546646 -0.570299546646333 53 591 590.754948792258 0.245051207742132 54 589 589.73228175285 -0.732281752849575 55 584 583.70878998345 0.291210016550327 56 573 572.704051479503 0.295948520496808 57 567 567.157890587513 -0.157890587513426 58 569 569.152974946513 -0.15297494651289 59 621 620.888887583114 0.111112416885804 60 629 628.823022713087 0.176977286913427 61 628 627.810613355841 0.1893866441591 62 612 611.783194402628 0.216805597372133 63 595 595.71167219448 -0.711672194480334 64 597 596.703235385147 0.296764614852959 65 593 592.799869600276 0.20013039972369 66 590 589.807707995569 0.192292004431403 67 580 579.809973945755 0.19002605424531 68 574 573.87296822332 0.127031776680442 69 573 573.204602016389 -0.204602016388986 70 573 573.209098028388 -0.209098028388389 71 620 620.01954845786 -0.0195484578604203 72 626 625.983352823876 0.0166471761241799 73 620 619.972861403385 0.0271385966150842 74 588 586.979442796565 1.02055720343485 75 566 565.898562420776 0.101437579224108 76 557 557.985869493632 -0.985869493631561 77 561 561.047887814512 -0.0478878145123859 78 549 549.052769088114 -0.0527690881138106 79 532 532.064188670254 -0.0641886702535674 80 526 526.060053574489 -0.0600535744888332 81 511 511.310991233125 -0.310991233125077 82 499 499.325816531931 -0.32581653193052 83 555 555.165940413988 -0.165940413988407 84 565 564.137881136488 0.862118863512187 85 542 542.145581916596 -0.145581916595849 86 527 527.089984045472 -0.0899840454715638 87 510 510.145273486231 -0.145273486230942 88 514 514.0685167604 -0.0685167603997262 89 517 517.194204257155 -0.194204257155016 90 508 507.228263159701 0.771736840298811 91 493 493.235981078528 -0.235981078527801 92 490 490.15270283028 -0.152702830280044 93 469 469.388079074034 -0.388079074033684 94 478 477.354128524541 0.645871475458841 95 528 529.165231578214 -1.16523157821387 96 534 533.110061096395 0.889938903604967 97 518 518.103115481347 -0.103115481346935 98 506 506.08978038649 -0.0897803864897348 99 502 502.055582499644 -0.0555824996440885 100 516 515.9285978523 0.0714021476996164 101 528 528.03216807257 -0.0321680725702868 102 533 532.96450375848 0.035496241519837 103 536 535.840858832126 0.159141167874127 104 537 536.902505159887 0.0974948401132872 105 524 523.191004657299 0.808995342700547 106 536 536.172562279679 -0.172562279679336 107 587 586.968791489031 0.0312085109693502 108 597 595.92386479595 1.07613520404959 109 581 580.889121615261 0.110878384739172 110 564 564.815578659764 -0.815578659764141 111 558 556.858150067844 1.14184993215579 112 575 574.871714274514 0.128285725485552 113 580 580.972981950692 -0.97298195069154 114 575 574.956853219822 0.0431467801784193 115 563 563.952114715875 -0.952114715875089 116 552 550.867187007644 1.13281299235587 117 537 537.119085697614 -0.119085697613901 118 545 545.115449569672 -0.115449569672104 119 601 600.976959005997 0.0230409940025485 120 604 604.925610317581 -0.925610317581398 121 586 586.907938128078 -0.907938128078042 122 564 563.954609500219 0.0453904997812817 123 549 547.969794660846 1.03020533915422 124 551 550.990756802267 0.00924319773290566 125 556 556.153934043807 -0.153934043807399 126 548 548.252506996268 -0.252506996267838 127 540 540.192612610049 -0.192612610048777 128 531 531.176725345037 -0.176725345036842 129 521 520.321538875254 0.678461124746242 130 519 518.288991290553 0.71100870944712 131 572 572.110782965015 -0.110782965015384 132 581 582.050499641897 -1.0504996418971 133 563 563.01527944291 -0.0152794429098155 134 548 548.057802144934 -0.0578021449341769 135 539 539.034773163202 -0.0347731632021486 136 541 540.957171563331 0.0428284366688538 137 562 561.054827815361 0.945172184639332 138 559 559.041052798 -0.0410527979998336 139 546 546.011039506183 -0.011039506183411 140 536 536.836906224302 -0.836906224301831 141 528 527.138501070963 0.861498929037115 142 530 531.113802319948 -1.11380231994811 143 582 581.966353248013 0.0336467519874244 144 599 598.900352397021 0.0996476029785776 145 584 583.842918724571 0.157081275429178

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 501 & 501.96347674406 & -0.963476744059784 \tabularnewline
2 & 485 & 484.809947957201 & 0.190052042798564 \tabularnewline
3 & 464 & 463.808411911657 & 0.19158808834315 \tabularnewline
4 & 460 & 459.822409887042 & 0.177590112958041 \tabularnewline
5 & 467 & 467.034204702316 & -0.03420470231647 \tabularnewline
6 & 460 & 460.043813546984 & -0.0438135469842987 \tabularnewline
7 & 448 & 448.11950571318 & -0.119505713180325 \tabularnewline
8 & 443 & 443.089854363688 & -0.0898543636876219 \tabularnewline
9 & 436 & 436.314763441592 & -0.314763441592201 \tabularnewline
10 & 431 & 431.321977339839 & -0.321977339838558 \tabularnewline
11 & 484 & 484.153964865412 & -0.153964865412427 \tabularnewline
12 & 510 & 509.048074863198 & 0.95192513680169 \tabularnewline
13 & 513 & 513.009668192752 & -0.00966819275199646 \tabularnewline
14 & 503 & 503.05920677812 & -0.0592067781201836 \tabularnewline
15 & 471 & 471.087813436884 & -0.0878134368844562 \tabularnewline
16 & 471 & 470.984715727334 & 0.0152842726663334 \tabularnewline
17 & 476 & 476.158799476013 & -0.158799476013351 \tabularnewline
18 & 475 & 474.137661420123 & 0.86233857987744 \tabularnewline
19 & 470 & 470.131132322752 & -0.131132322752345 \tabularnewline
20 & 461 & 461.127681336465 & -0.127681336465337 \tabularnewline
21 & 455 & 455.208192618109 & -0.208192618108734 \tabularnewline
22 & 456 & 455.198495379691 & 0.801504620308957 \tabularnewline
23 & 517 & 516.92789797762 & 0.0721020223797681 \tabularnewline
24 & 525 & 525.828523781497 & -0.828523781497222 \tabularnewline
25 & 523 & 522.793906718207 & 0.206093281792799 \tabularnewline
26 & 519 & 518.960981052889 & 0.0390189471114124 \tabularnewline
27 & 509 & 508.848870844825 & 0.151129155175356 \tabularnewline
28 & 512 & 511.836869634766 & 0.163130365234276 \tabularnewline
29 & 519 & 518.905006459575 & 0.0949935404250384 \tabularnewline
30 & 517 & 516.800842554175 & 0.199157445825467 \tabularnewline
31 & 510 & 509.790873403397 & 0.209126596602631 \tabularnewline
32 & 509 & 509.763217183016 & -0.763217183016435 \tabularnewline
33 & 501 & 500.097673054512 & 0.902326945487889 \tabularnewline
34 & 507 & 507.05842016822 & -0.0584201682203387 \tabularnewline
35 & 569 & 569.804728135395 & -0.804728135394604 \tabularnewline
36 & 580 & 579.75660653777 & 0.243393462230318 \tabularnewline
37 & 578 & 577.724701593308 & 0.275298406692333 \tabularnewline
38 & 565 & 565.81086866396 & -0.810868663960223 \tabularnewline
39 & 547 & 547.705538230293 & -0.705538230292649 \tabularnewline
40 & 555 & 554.746902309083 & 0.253097690917435 \tabularnewline
41 & 562 & 561.815039133892 & 0.184960866108205 \tabularnewline
42 & 561 & 560.804587051399 & 0.195412948601422 \tabularnewline
43 & 555 & 555.809069452506 & -0.809069452506276 \tabularnewline
44 & 544 & 543.783687545333 & 0.216312454666855 \tabularnewline
45 & 537 & 537.134461010463 & -0.134461010462582 \tabularnewline
46 & 543 & 543.083409828806 & -0.0834098288062141 \tabularnewline
47 & 594 & 593.789371473986 & 0.210628526014422 \tabularnewline
48 & 611 & 610.703250692189 & 0.296749307811154 \tabularnewline
49 & 613 & 612.709349148441 & 0.29065085155852 \tabularnewline
50 & 611 & 610.658349358158 & 0.341650641842431 \tabularnewline
51 & 594 & 593.588356133528 & 0.411643866472481 \tabularnewline
52 & 595 & 595.570299546646 & -0.570299546646333 \tabularnewline
53 & 591 & 590.754948792258 & 0.245051207742132 \tabularnewline
54 & 589 & 589.73228175285 & -0.732281752849575 \tabularnewline
55 & 584 & 583.70878998345 & 0.291210016550327 \tabularnewline
56 & 573 & 572.704051479503 & 0.295948520496808 \tabularnewline
57 & 567 & 567.157890587513 & -0.157890587513426 \tabularnewline
58 & 569 & 569.152974946513 & -0.15297494651289 \tabularnewline
59 & 621 & 620.888887583114 & 0.111112416885804 \tabularnewline
60 & 629 & 628.823022713087 & 0.176977286913427 \tabularnewline
61 & 628 & 627.810613355841 & 0.1893866441591 \tabularnewline
62 & 612 & 611.783194402628 & 0.216805597372133 \tabularnewline
63 & 595 & 595.71167219448 & -0.711672194480334 \tabularnewline
64 & 597 & 596.703235385147 & 0.296764614852959 \tabularnewline
65 & 593 & 592.799869600276 & 0.20013039972369 \tabularnewline
66 & 590 & 589.807707995569 & 0.192292004431403 \tabularnewline
67 & 580 & 579.809973945755 & 0.19002605424531 \tabularnewline
68 & 574 & 573.87296822332 & 0.127031776680442 \tabularnewline
69 & 573 & 573.204602016389 & -0.204602016388986 \tabularnewline
70 & 573 & 573.209098028388 & -0.209098028388389 \tabularnewline
71 & 620 & 620.01954845786 & -0.0195484578604203 \tabularnewline
72 & 626 & 625.983352823876 & 0.0166471761241799 \tabularnewline
73 & 620 & 619.972861403385 & 0.0271385966150842 \tabularnewline
74 & 588 & 586.979442796565 & 1.02055720343485 \tabularnewline
75 & 566 & 565.898562420776 & 0.101437579224108 \tabularnewline
76 & 557 & 557.985869493632 & -0.985869493631561 \tabularnewline
77 & 561 & 561.047887814512 & -0.0478878145123859 \tabularnewline
78 & 549 & 549.052769088114 & -0.0527690881138106 \tabularnewline
79 & 532 & 532.064188670254 & -0.0641886702535674 \tabularnewline
80 & 526 & 526.060053574489 & -0.0600535744888332 \tabularnewline
81 & 511 & 511.310991233125 & -0.310991233125077 \tabularnewline
82 & 499 & 499.325816531931 & -0.32581653193052 \tabularnewline
83 & 555 & 555.165940413988 & -0.165940413988407 \tabularnewline
84 & 565 & 564.137881136488 & 0.862118863512187 \tabularnewline
85 & 542 & 542.145581916596 & -0.145581916595849 \tabularnewline
86 & 527 & 527.089984045472 & -0.0899840454715638 \tabularnewline
87 & 510 & 510.145273486231 & -0.145273486230942 \tabularnewline
88 & 514 & 514.0685167604 & -0.0685167603997262 \tabularnewline
89 & 517 & 517.194204257155 & -0.194204257155016 \tabularnewline
90 & 508 & 507.228263159701 & 0.771736840298811 \tabularnewline
91 & 493 & 493.235981078528 & -0.235981078527801 \tabularnewline
92 & 490 & 490.15270283028 & -0.152702830280044 \tabularnewline
93 & 469 & 469.388079074034 & -0.388079074033684 \tabularnewline
94 & 478 & 477.354128524541 & 0.645871475458841 \tabularnewline
95 & 528 & 529.165231578214 & -1.16523157821387 \tabularnewline
96 & 534 & 533.110061096395 & 0.889938903604967 \tabularnewline
97 & 518 & 518.103115481347 & -0.103115481346935 \tabularnewline
98 & 506 & 506.08978038649 & -0.0897803864897348 \tabularnewline
99 & 502 & 502.055582499644 & -0.0555824996440885 \tabularnewline
100 & 516 & 515.9285978523 & 0.0714021476996164 \tabularnewline
101 & 528 & 528.03216807257 & -0.0321680725702868 \tabularnewline
102 & 533 & 532.96450375848 & 0.035496241519837 \tabularnewline
103 & 536 & 535.840858832126 & 0.159141167874127 \tabularnewline
104 & 537 & 536.902505159887 & 0.0974948401132872 \tabularnewline
105 & 524 & 523.191004657299 & 0.808995342700547 \tabularnewline
106 & 536 & 536.172562279679 & -0.172562279679336 \tabularnewline
107 & 587 & 586.968791489031 & 0.0312085109693502 \tabularnewline
108 & 597 & 595.92386479595 & 1.07613520404959 \tabularnewline
109 & 581 & 580.889121615261 & 0.110878384739172 \tabularnewline
110 & 564 & 564.815578659764 & -0.815578659764141 \tabularnewline
111 & 558 & 556.858150067844 & 1.14184993215579 \tabularnewline
112 & 575 & 574.871714274514 & 0.128285725485552 \tabularnewline
113 & 580 & 580.972981950692 & -0.97298195069154 \tabularnewline
114 & 575 & 574.956853219822 & 0.0431467801784193 \tabularnewline
115 & 563 & 563.952114715875 & -0.952114715875089 \tabularnewline
116 & 552 & 550.867187007644 & 1.13281299235587 \tabularnewline
117 & 537 & 537.119085697614 & -0.119085697613901 \tabularnewline
118 & 545 & 545.115449569672 & -0.115449569672104 \tabularnewline
119 & 601 & 600.976959005997 & 0.0230409940025485 \tabularnewline
120 & 604 & 604.925610317581 & -0.925610317581398 \tabularnewline
121 & 586 & 586.907938128078 & -0.907938128078042 \tabularnewline
122 & 564 & 563.954609500219 & 0.0453904997812817 \tabularnewline
123 & 549 & 547.969794660846 & 1.03020533915422 \tabularnewline
124 & 551 & 550.990756802267 & 0.00924319773290566 \tabularnewline
125 & 556 & 556.153934043807 & -0.153934043807399 \tabularnewline
126 & 548 & 548.252506996268 & -0.252506996267838 \tabularnewline
127 & 540 & 540.192612610049 & -0.192612610048777 \tabularnewline
128 & 531 & 531.176725345037 & -0.176725345036842 \tabularnewline
129 & 521 & 520.321538875254 & 0.678461124746242 \tabularnewline
130 & 519 & 518.288991290553 & 0.71100870944712 \tabularnewline
131 & 572 & 572.110782965015 & -0.110782965015384 \tabularnewline
132 & 581 & 582.050499641897 & -1.0504996418971 \tabularnewline
133 & 563 & 563.01527944291 & -0.0152794429098155 \tabularnewline
134 & 548 & 548.057802144934 & -0.0578021449341769 \tabularnewline
135 & 539 & 539.034773163202 & -0.0347731632021486 \tabularnewline
136 & 541 & 540.957171563331 & 0.0428284366688538 \tabularnewline
137 & 562 & 561.054827815361 & 0.945172184639332 \tabularnewline
138 & 559 & 559.041052798 & -0.0410527979998336 \tabularnewline
139 & 546 & 546.011039506183 & -0.011039506183411 \tabularnewline
140 & 536 & 536.836906224302 & -0.836906224301831 \tabularnewline
141 & 528 & 527.138501070963 & 0.861498929037115 \tabularnewline
142 & 530 & 531.113802319948 & -1.11380231994811 \tabularnewline
143 & 582 & 581.966353248013 & 0.0336467519874244 \tabularnewline
144 & 599 & 598.900352397021 & 0.0996476029785776 \tabularnewline
145 & 584 & 583.842918724571 & 0.157081275429178 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186354&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]501.96347674406[/C][C]-0.963476744059784[/C][/ROW]
[ROW][C]2[/C][C]485[/C][C]484.809947957201[/C][C]0.190052042798564[/C][/ROW]
[ROW][C]3[/C][C]464[/C][C]463.808411911657[/C][C]0.19158808834315[/C][/ROW]
[ROW][C]4[/C][C]460[/C][C]459.822409887042[/C][C]0.177590112958041[/C][/ROW]
[ROW][C]5[/C][C]467[/C][C]467.034204702316[/C][C]-0.03420470231647[/C][/ROW]
[ROW][C]6[/C][C]460[/C][C]460.043813546984[/C][C]-0.0438135469842987[/C][/ROW]
[ROW][C]7[/C][C]448[/C][C]448.11950571318[/C][C]-0.119505713180325[/C][/ROW]
[ROW][C]8[/C][C]443[/C][C]443.089854363688[/C][C]-0.0898543636876219[/C][/ROW]
[ROW][C]9[/C][C]436[/C][C]436.314763441592[/C][C]-0.314763441592201[/C][/ROW]
[ROW][C]10[/C][C]431[/C][C]431.321977339839[/C][C]-0.321977339838558[/C][/ROW]
[ROW][C]11[/C][C]484[/C][C]484.153964865412[/C][C]-0.153964865412427[/C][/ROW]
[ROW][C]12[/C][C]510[/C][C]509.048074863198[/C][C]0.95192513680169[/C][/ROW]
[ROW][C]13[/C][C]513[/C][C]513.009668192752[/C][C]-0.00966819275199646[/C][/ROW]
[ROW][C]14[/C][C]503[/C][C]503.05920677812[/C][C]-0.0592067781201836[/C][/ROW]
[ROW][C]15[/C][C]471[/C][C]471.087813436884[/C][C]-0.0878134368844562[/C][/ROW]
[ROW][C]16[/C][C]471[/C][C]470.984715727334[/C][C]0.0152842726663334[/C][/ROW]
[ROW][C]17[/C][C]476[/C][C]476.158799476013[/C][C]-0.158799476013351[/C][/ROW]
[ROW][C]18[/C][C]475[/C][C]474.137661420123[/C][C]0.86233857987744[/C][/ROW]
[ROW][C]19[/C][C]470[/C][C]470.131132322752[/C][C]-0.131132322752345[/C][/ROW]
[ROW][C]20[/C][C]461[/C][C]461.127681336465[/C][C]-0.127681336465337[/C][/ROW]
[ROW][C]21[/C][C]455[/C][C]455.208192618109[/C][C]-0.208192618108734[/C][/ROW]
[ROW][C]22[/C][C]456[/C][C]455.198495379691[/C][C]0.801504620308957[/C][/ROW]
[ROW][C]23[/C][C]517[/C][C]516.92789797762[/C][C]0.0721020223797681[/C][/ROW]
[ROW][C]24[/C][C]525[/C][C]525.828523781497[/C][C]-0.828523781497222[/C][/ROW]
[ROW][C]25[/C][C]523[/C][C]522.793906718207[/C][C]0.206093281792799[/C][/ROW]
[ROW][C]26[/C][C]519[/C][C]518.960981052889[/C][C]0.0390189471114124[/C][/ROW]
[ROW][C]27[/C][C]509[/C][C]508.848870844825[/C][C]0.151129155175356[/C][/ROW]
[ROW][C]28[/C][C]512[/C][C]511.836869634766[/C][C]0.163130365234276[/C][/ROW]
[ROW][C]29[/C][C]519[/C][C]518.905006459575[/C][C]0.0949935404250384[/C][/ROW]
[ROW][C]30[/C][C]517[/C][C]516.800842554175[/C][C]0.199157445825467[/C][/ROW]
[ROW][C]31[/C][C]510[/C][C]509.790873403397[/C][C]0.209126596602631[/C][/ROW]
[ROW][C]32[/C][C]509[/C][C]509.763217183016[/C][C]-0.763217183016435[/C][/ROW]
[ROW][C]33[/C][C]501[/C][C]500.097673054512[/C][C]0.902326945487889[/C][/ROW]
[ROW][C]34[/C][C]507[/C][C]507.05842016822[/C][C]-0.0584201682203387[/C][/ROW]
[ROW][C]35[/C][C]569[/C][C]569.804728135395[/C][C]-0.804728135394604[/C][/ROW]
[ROW][C]36[/C][C]580[/C][C]579.75660653777[/C][C]0.243393462230318[/C][/ROW]
[ROW][C]37[/C][C]578[/C][C]577.724701593308[/C][C]0.275298406692333[/C][/ROW]
[ROW][C]38[/C][C]565[/C][C]565.81086866396[/C][C]-0.810868663960223[/C][/ROW]
[ROW][C]39[/C][C]547[/C][C]547.705538230293[/C][C]-0.705538230292649[/C][/ROW]
[ROW][C]40[/C][C]555[/C][C]554.746902309083[/C][C]0.253097690917435[/C][/ROW]
[ROW][C]41[/C][C]562[/C][C]561.815039133892[/C][C]0.184960866108205[/C][/ROW]
[ROW][C]42[/C][C]561[/C][C]560.804587051399[/C][C]0.195412948601422[/C][/ROW]
[ROW][C]43[/C][C]555[/C][C]555.809069452506[/C][C]-0.809069452506276[/C][/ROW]
[ROW][C]44[/C][C]544[/C][C]543.783687545333[/C][C]0.216312454666855[/C][/ROW]
[ROW][C]45[/C][C]537[/C][C]537.134461010463[/C][C]-0.134461010462582[/C][/ROW]
[ROW][C]46[/C][C]543[/C][C]543.083409828806[/C][C]-0.0834098288062141[/C][/ROW]
[ROW][C]47[/C][C]594[/C][C]593.789371473986[/C][C]0.210628526014422[/C][/ROW]
[ROW][C]48[/C][C]611[/C][C]610.703250692189[/C][C]0.296749307811154[/C][/ROW]
[ROW][C]49[/C][C]613[/C][C]612.709349148441[/C][C]0.29065085155852[/C][/ROW]
[ROW][C]50[/C][C]611[/C][C]610.658349358158[/C][C]0.341650641842431[/C][/ROW]
[ROW][C]51[/C][C]594[/C][C]593.588356133528[/C][C]0.411643866472481[/C][/ROW]
[ROW][C]52[/C][C]595[/C][C]595.570299546646[/C][C]-0.570299546646333[/C][/ROW]
[ROW][C]53[/C][C]591[/C][C]590.754948792258[/C][C]0.245051207742132[/C][/ROW]
[ROW][C]54[/C][C]589[/C][C]589.73228175285[/C][C]-0.732281752849575[/C][/ROW]
[ROW][C]55[/C][C]584[/C][C]583.70878998345[/C][C]0.291210016550327[/C][/ROW]
[ROW][C]56[/C][C]573[/C][C]572.704051479503[/C][C]0.295948520496808[/C][/ROW]
[ROW][C]57[/C][C]567[/C][C]567.157890587513[/C][C]-0.157890587513426[/C][/ROW]
[ROW][C]58[/C][C]569[/C][C]569.152974946513[/C][C]-0.15297494651289[/C][/ROW]
[ROW][C]59[/C][C]621[/C][C]620.888887583114[/C][C]0.111112416885804[/C][/ROW]
[ROW][C]60[/C][C]629[/C][C]628.823022713087[/C][C]0.176977286913427[/C][/ROW]
[ROW][C]61[/C][C]628[/C][C]627.810613355841[/C][C]0.1893866441591[/C][/ROW]
[ROW][C]62[/C][C]612[/C][C]611.783194402628[/C][C]0.216805597372133[/C][/ROW]
[ROW][C]63[/C][C]595[/C][C]595.71167219448[/C][C]-0.711672194480334[/C][/ROW]
[ROW][C]64[/C][C]597[/C][C]596.703235385147[/C][C]0.296764614852959[/C][/ROW]
[ROW][C]65[/C][C]593[/C][C]592.799869600276[/C][C]0.20013039972369[/C][/ROW]
[ROW][C]66[/C][C]590[/C][C]589.807707995569[/C][C]0.192292004431403[/C][/ROW]
[ROW][C]67[/C][C]580[/C][C]579.809973945755[/C][C]0.19002605424531[/C][/ROW]
[ROW][C]68[/C][C]574[/C][C]573.87296822332[/C][C]0.127031776680442[/C][/ROW]
[ROW][C]69[/C][C]573[/C][C]573.204602016389[/C][C]-0.204602016388986[/C][/ROW]
[ROW][C]70[/C][C]573[/C][C]573.209098028388[/C][C]-0.209098028388389[/C][/ROW]
[ROW][C]71[/C][C]620[/C][C]620.01954845786[/C][C]-0.0195484578604203[/C][/ROW]
[ROW][C]72[/C][C]626[/C][C]625.983352823876[/C][C]0.0166471761241799[/C][/ROW]
[ROW][C]73[/C][C]620[/C][C]619.972861403385[/C][C]0.0271385966150842[/C][/ROW]
[ROW][C]74[/C][C]588[/C][C]586.979442796565[/C][C]1.02055720343485[/C][/ROW]
[ROW][C]75[/C][C]566[/C][C]565.898562420776[/C][C]0.101437579224108[/C][/ROW]
[ROW][C]76[/C][C]557[/C][C]557.985869493632[/C][C]-0.985869493631561[/C][/ROW]
[ROW][C]77[/C][C]561[/C][C]561.047887814512[/C][C]-0.0478878145123859[/C][/ROW]
[ROW][C]78[/C][C]549[/C][C]549.052769088114[/C][C]-0.0527690881138106[/C][/ROW]
[ROW][C]79[/C][C]532[/C][C]532.064188670254[/C][C]-0.0641886702535674[/C][/ROW]
[ROW][C]80[/C][C]526[/C][C]526.060053574489[/C][C]-0.0600535744888332[/C][/ROW]
[ROW][C]81[/C][C]511[/C][C]511.310991233125[/C][C]-0.310991233125077[/C][/ROW]
[ROW][C]82[/C][C]499[/C][C]499.325816531931[/C][C]-0.32581653193052[/C][/ROW]
[ROW][C]83[/C][C]555[/C][C]555.165940413988[/C][C]-0.165940413988407[/C][/ROW]
[ROW][C]84[/C][C]565[/C][C]564.137881136488[/C][C]0.862118863512187[/C][/ROW]
[ROW][C]85[/C][C]542[/C][C]542.145581916596[/C][C]-0.145581916595849[/C][/ROW]
[ROW][C]86[/C][C]527[/C][C]527.089984045472[/C][C]-0.0899840454715638[/C][/ROW]
[ROW][C]87[/C][C]510[/C][C]510.145273486231[/C][C]-0.145273486230942[/C][/ROW]
[ROW][C]88[/C][C]514[/C][C]514.0685167604[/C][C]-0.0685167603997262[/C][/ROW]
[ROW][C]89[/C][C]517[/C][C]517.194204257155[/C][C]-0.194204257155016[/C][/ROW]
[ROW][C]90[/C][C]508[/C][C]507.228263159701[/C][C]0.771736840298811[/C][/ROW]
[ROW][C]91[/C][C]493[/C][C]493.235981078528[/C][C]-0.235981078527801[/C][/ROW]
[ROW][C]92[/C][C]490[/C][C]490.15270283028[/C][C]-0.152702830280044[/C][/ROW]
[ROW][C]93[/C][C]469[/C][C]469.388079074034[/C][C]-0.388079074033684[/C][/ROW]
[ROW][C]94[/C][C]478[/C][C]477.354128524541[/C][C]0.645871475458841[/C][/ROW]
[ROW][C]95[/C][C]528[/C][C]529.165231578214[/C][C]-1.16523157821387[/C][/ROW]
[ROW][C]96[/C][C]534[/C][C]533.110061096395[/C][C]0.889938903604967[/C][/ROW]
[ROW][C]97[/C][C]518[/C][C]518.103115481347[/C][C]-0.103115481346935[/C][/ROW]
[ROW][C]98[/C][C]506[/C][C]506.08978038649[/C][C]-0.0897803864897348[/C][/ROW]
[ROW][C]99[/C][C]502[/C][C]502.055582499644[/C][C]-0.0555824996440885[/C][/ROW]
[ROW][C]100[/C][C]516[/C][C]515.9285978523[/C][C]0.0714021476996164[/C][/ROW]
[ROW][C]101[/C][C]528[/C][C]528.03216807257[/C][C]-0.0321680725702868[/C][/ROW]
[ROW][C]102[/C][C]533[/C][C]532.96450375848[/C][C]0.035496241519837[/C][/ROW]
[ROW][C]103[/C][C]536[/C][C]535.840858832126[/C][C]0.159141167874127[/C][/ROW]
[ROW][C]104[/C][C]537[/C][C]536.902505159887[/C][C]0.0974948401132872[/C][/ROW]
[ROW][C]105[/C][C]524[/C][C]523.191004657299[/C][C]0.808995342700547[/C][/ROW]
[ROW][C]106[/C][C]536[/C][C]536.172562279679[/C][C]-0.172562279679336[/C][/ROW]
[ROW][C]107[/C][C]587[/C][C]586.968791489031[/C][C]0.0312085109693502[/C][/ROW]
[ROW][C]108[/C][C]597[/C][C]595.92386479595[/C][C]1.07613520404959[/C][/ROW]
[ROW][C]109[/C][C]581[/C][C]580.889121615261[/C][C]0.110878384739172[/C][/ROW]
[ROW][C]110[/C][C]564[/C][C]564.815578659764[/C][C]-0.815578659764141[/C][/ROW]
[ROW][C]111[/C][C]558[/C][C]556.858150067844[/C][C]1.14184993215579[/C][/ROW]
[ROW][C]112[/C][C]575[/C][C]574.871714274514[/C][C]0.128285725485552[/C][/ROW]
[ROW][C]113[/C][C]580[/C][C]580.972981950692[/C][C]-0.97298195069154[/C][/ROW]
[ROW][C]114[/C][C]575[/C][C]574.956853219822[/C][C]0.0431467801784193[/C][/ROW]
[ROW][C]115[/C][C]563[/C][C]563.952114715875[/C][C]-0.952114715875089[/C][/ROW]
[ROW][C]116[/C][C]552[/C][C]550.867187007644[/C][C]1.13281299235587[/C][/ROW]
[ROW][C]117[/C][C]537[/C][C]537.119085697614[/C][C]-0.119085697613901[/C][/ROW]
[ROW][C]118[/C][C]545[/C][C]545.115449569672[/C][C]-0.115449569672104[/C][/ROW]
[ROW][C]119[/C][C]601[/C][C]600.976959005997[/C][C]0.0230409940025485[/C][/ROW]
[ROW][C]120[/C][C]604[/C][C]604.925610317581[/C][C]-0.925610317581398[/C][/ROW]
[ROW][C]121[/C][C]586[/C][C]586.907938128078[/C][C]-0.907938128078042[/C][/ROW]
[ROW][C]122[/C][C]564[/C][C]563.954609500219[/C][C]0.0453904997812817[/C][/ROW]
[ROW][C]123[/C][C]549[/C][C]547.969794660846[/C][C]1.03020533915422[/C][/ROW]
[ROW][C]124[/C][C]551[/C][C]550.990756802267[/C][C]0.00924319773290566[/C][/ROW]
[ROW][C]125[/C][C]556[/C][C]556.153934043807[/C][C]-0.153934043807399[/C][/ROW]
[ROW][C]126[/C][C]548[/C][C]548.252506996268[/C][C]-0.252506996267838[/C][/ROW]
[ROW][C]127[/C][C]540[/C][C]540.192612610049[/C][C]-0.192612610048777[/C][/ROW]
[ROW][C]128[/C][C]531[/C][C]531.176725345037[/C][C]-0.176725345036842[/C][/ROW]
[ROW][C]129[/C][C]521[/C][C]520.321538875254[/C][C]0.678461124746242[/C][/ROW]
[ROW][C]130[/C][C]519[/C][C]518.288991290553[/C][C]0.71100870944712[/C][/ROW]
[ROW][C]131[/C][C]572[/C][C]572.110782965015[/C][C]-0.110782965015384[/C][/ROW]
[ROW][C]132[/C][C]581[/C][C]582.050499641897[/C][C]-1.0504996418971[/C][/ROW]
[ROW][C]133[/C][C]563[/C][C]563.01527944291[/C][C]-0.0152794429098155[/C][/ROW]
[ROW][C]134[/C][C]548[/C][C]548.057802144934[/C][C]-0.0578021449341769[/C][/ROW]
[ROW][C]135[/C][C]539[/C][C]539.034773163202[/C][C]-0.0347731632021486[/C][/ROW]
[ROW][C]136[/C][C]541[/C][C]540.957171563331[/C][C]0.0428284366688538[/C][/ROW]
[ROW][C]137[/C][C]562[/C][C]561.054827815361[/C][C]0.945172184639332[/C][/ROW]
[ROW][C]138[/C][C]559[/C][C]559.041052798[/C][C]-0.0410527979998336[/C][/ROW]
[ROW][C]139[/C][C]546[/C][C]546.011039506183[/C][C]-0.011039506183411[/C][/ROW]
[ROW][C]140[/C][C]536[/C][C]536.836906224302[/C][C]-0.836906224301831[/C][/ROW]
[ROW][C]141[/C][C]528[/C][C]527.138501070963[/C][C]0.861498929037115[/C][/ROW]
[ROW][C]142[/C][C]530[/C][C]531.113802319948[/C][C]-1.11380231994811[/C][/ROW]
[ROW][C]143[/C][C]582[/C][C]581.966353248013[/C][C]0.0336467519874244[/C][/ROW]
[ROW][C]144[/C][C]599[/C][C]598.900352397021[/C][C]0.0996476029785776[/C][/ROW]
[ROW][C]145[/C][C]584[/C][C]583.842918724571[/C][C]0.157081275429178[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186354&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186354&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 1 501 501.96347674406 -0.963476744059784 2 485 484.809947957201 0.190052042798564 3 464 463.808411911657 0.19158808834315 4 460 459.822409887042 0.177590112958041 5 467 467.034204702316 -0.03420470231647 6 460 460.043813546984 -0.0438135469842987 7 448 448.11950571318 -0.119505713180325 8 443 443.089854363688 -0.0898543636876219 9 436 436.314763441592 -0.314763441592201 10 431 431.321977339839 -0.321977339838558 11 484 484.153964865412 -0.153964865412427 12 510 509.048074863198 0.95192513680169 13 513 513.009668192752 -0.00966819275199646 14 503 503.05920677812 -0.0592067781201836 15 471 471.087813436884 -0.0878134368844562 16 471 470.984715727334 0.0152842726663334 17 476 476.158799476013 -0.158799476013351 18 475 474.137661420123 0.86233857987744 19 470 470.131132322752 -0.131132322752345 20 461 461.127681336465 -0.127681336465337 21 455 455.208192618109 -0.208192618108734 22 456 455.198495379691 0.801504620308957 23 517 516.92789797762 0.0721020223797681 24 525 525.828523781497 -0.828523781497222 25 523 522.793906718207 0.206093281792799 26 519 518.960981052889 0.0390189471114124 27 509 508.848870844825 0.151129155175356 28 512 511.836869634766 0.163130365234276 29 519 518.905006459575 0.0949935404250384 30 517 516.800842554175 0.199157445825467 31 510 509.790873403397 0.209126596602631 32 509 509.763217183016 -0.763217183016435 33 501 500.097673054512 0.902326945487889 34 507 507.05842016822 -0.0584201682203387 35 569 569.804728135395 -0.804728135394604 36 580 579.75660653777 0.243393462230318 37 578 577.724701593308 0.275298406692333 38 565 565.81086866396 -0.810868663960223 39 547 547.705538230293 -0.705538230292649 40 555 554.746902309083 0.253097690917435 41 562 561.815039133892 0.184960866108205 42 561 560.804587051399 0.195412948601422 43 555 555.809069452506 -0.809069452506276 44 544 543.783687545333 0.216312454666855 45 537 537.134461010463 -0.134461010462582 46 543 543.083409828806 -0.0834098288062141 47 594 593.789371473986 0.210628526014422 48 611 610.703250692189 0.296749307811154 49 613 612.709349148441 0.29065085155852 50 611 610.658349358158 0.341650641842431 51 594 593.588356133528 0.411643866472481 52 595 595.570299546646 -0.570299546646333 53 591 590.754948792258 0.245051207742132 54 589 589.73228175285 -0.732281752849575 55 584 583.70878998345 0.291210016550327 56 573 572.704051479503 0.295948520496808 57 567 567.157890587513 -0.157890587513426 58 569 569.152974946513 -0.15297494651289 59 621 620.888887583114 0.111112416885804 60 629 628.823022713087 0.176977286913427 61 628 627.810613355841 0.1893866441591 62 612 611.783194402628 0.216805597372133 63 595 595.71167219448 -0.711672194480334 64 597 596.703235385147 0.296764614852959 65 593 592.799869600276 0.20013039972369 66 590 589.807707995569 0.192292004431403 67 580 579.809973945755 0.19002605424531 68 574 573.87296822332 0.127031776680442 69 573 573.204602016389 -0.204602016388986 70 573 573.209098028388 -0.209098028388389 71 620 620.01954845786 -0.0195484578604203 72 626 625.983352823876 0.0166471761241799 73 620 619.972861403385 0.0271385966150842 74 588 586.979442796565 1.02055720343485 75 566 565.898562420776 0.101437579224108 76 557 557.985869493632 -0.985869493631561 77 561 561.047887814512 -0.0478878145123859 78 549 549.052769088114 -0.0527690881138106 79 532 532.064188670254 -0.0641886702535674 80 526 526.060053574489 -0.0600535744888332 81 511 511.310991233125 -0.310991233125077 82 499 499.325816531931 -0.32581653193052 83 555 555.165940413988 -0.165940413988407 84 565 564.137881136488 0.862118863512187 85 542 542.145581916596 -0.145581916595849 86 527 527.089984045472 -0.0899840454715638 87 510 510.145273486231 -0.145273486230942 88 514 514.0685167604 -0.0685167603997262 89 517 517.194204257155 -0.194204257155016 90 508 507.228263159701 0.771736840298811 91 493 493.235981078528 -0.235981078527801 92 490 490.15270283028 -0.152702830280044 93 469 469.388079074034 -0.388079074033684 94 478 477.354128524541 0.645871475458841 95 528 529.165231578214 -1.16523157821387 96 534 533.110061096395 0.889938903604967 97 518 518.103115481347 -0.103115481346935 98 506 506.08978038649 -0.0897803864897348 99 502 502.055582499644 -0.0555824996440885 100 516 515.9285978523 0.0714021476996164 101 528 528.03216807257 -0.0321680725702868 102 533 532.96450375848 0.035496241519837 103 536 535.840858832126 0.159141167874127 104 537 536.902505159887 0.0974948401132872 105 524 523.191004657299 0.808995342700547 106 536 536.172562279679 -0.172562279679336 107 587 586.968791489031 0.0312085109693502 108 597 595.92386479595 1.07613520404959 109 581 580.889121615261 0.110878384739172 110 564 564.815578659764 -0.815578659764141 111 558 556.858150067844 1.14184993215579 112 575 574.871714274514 0.128285725485552 113 580 580.972981950692 -0.97298195069154 114 575 574.956853219822 0.0431467801784193 115 563 563.952114715875 -0.952114715875089 116 552 550.867187007644 1.13281299235587 117 537 537.119085697614 -0.119085697613901 118 545 545.115449569672 -0.115449569672104 119 601 600.976959005997 0.0230409940025485 120 604 604.925610317581 -0.925610317581398 121 586 586.907938128078 -0.907938128078042 122 564 563.954609500219 0.0453904997812817 123 549 547.969794660846 1.03020533915422 124 551 550.990756802267 0.00924319773290566 125 556 556.153934043807 -0.153934043807399 126 548 548.252506996268 -0.252506996267838 127 540 540.192612610049 -0.192612610048777 128 531 531.176725345037 -0.176725345036842 129 521 520.321538875254 0.678461124746242 130 519 518.288991290553 0.71100870944712 131 572 572.110782965015 -0.110782965015384 132 581 582.050499641897 -1.0504996418971 133 563 563.01527944291 -0.0152794429098155 134 548 548.057802144934 -0.0578021449341769 135 539 539.034773163202 -0.0347731632021486 136 541 540.957171563331 0.0428284366688538 137 562 561.054827815361 0.945172184639332 138 559 559.041052798 -0.0410527979998336 139 546 546.011039506183 -0.011039506183411 140 536 536.836906224302 -0.836906224301831 141 528 527.138501070963 0.861498929037115 142 530 531.113802319948 -1.11380231994811 143 582 581.966353248013 0.0336467519874244 144 599 598.900352397021 0.0996476029785776 145 584 583.842918724571 0.157081275429178

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 18 0.0261451062665468 0.0522902125330935 0.973854893733453 19 0.283271062953525 0.566542125907049 0.716728937046475 20 0.216804782477454 0.433609564954909 0.783195217522546 21 0.126651222494784 0.253302444989568 0.873348777505216 22 0.272092323310578 0.544184646621156 0.727907676689422 23 0.183566304162703 0.367132608325407 0.816433695837297 24 0.148892646207401 0.297785292414802 0.851107353792599 25 0.325752017945614 0.651504035891227 0.674247982054386 26 0.278022954506025 0.556045909012051 0.721977045493975 27 0.206161422983956 0.412322845967912 0.793838577016044 28 0.152658616596313 0.305317233192626 0.847341383403687 29 0.111574442730015 0.22314888546003 0.888425557269985 30 0.0779103928789323 0.155820785757865 0.922089607121068 31 0.0541937352449427 0.108387470489885 0.945806264755057 32 0.11089648517564 0.221792970351279 0.88910351482436 33 0.0864061337204687 0.172812267440937 0.913593866279531 34 0.122663763265586 0.245327526531173 0.877336236734414 35 0.153166253669273 0.306332507338547 0.846833746330727 36 0.155623751808214 0.311247503616428 0.844376248191786 37 0.170636227828216 0.341272455656433 0.829363772171784 38 0.154396711854182 0.308793423708364 0.845603288145818 39 0.13060227279466 0.261204545589321 0.86939772720534 40 0.154617716017541 0.309235432035083 0.845382283982459 41 0.124376922343399 0.248753844686798 0.875623077656601 42 0.0976686211024316 0.195337242204863 0.902331378897568 43 0.132482928037717 0.264965856075434 0.867517071962283 44 0.164362653547527 0.328725307095054 0.835637346452473 45 0.268579501502375 0.53715900300475 0.731420498497625 46 0.23583457670818 0.47166915341636 0.76416542329182 47 0.193933454627749 0.387866909255499 0.806066545372251 48 0.155133846657874 0.310267693315747 0.844866153342126 49 0.122401736659873 0.244803473319745 0.877598263340127 50 0.137171999463369 0.274343998926737 0.862828000536631 51 0.135780855252472 0.271561710504945 0.864219144747528 52 0.141063054773373 0.282126109546745 0.858936945226627 53 0.12514237247316 0.250284744946321 0.87485762752684 54 0.142175890178354 0.284351780356707 0.857824109821646 55 0.135081876515924 0.270163753031847 0.864918123484076 56 0.122917253049211 0.245834506098421 0.877082746950789 57 0.134378655273437 0.268757310546875 0.865621344726563 58 0.123555219655179 0.247110439310358 0.876444780344821 59 0.0985053995830836 0.197010799166167 0.901494600416916 60 0.0770214772689673 0.154042954537935 0.922978522731033 61 0.060114847655301 0.120229695310602 0.939885152344699 62 0.0501328071866268 0.100265614373254 0.949867192813373 63 0.0611792490392029 0.122358498078406 0.938820750960797 64 0.0558224350797342 0.111644870159468 0.944177564920266 65 0.0431874448671374 0.0863748897342748 0.956812555132863 66 0.032257586068164 0.0645151721363281 0.967742413931836 67 0.0238460190594279 0.0476920381188558 0.976153980940572 68 0.0174852651654386 0.0349705303308772 0.982514734834561 69 0.013895863658605 0.02779172731721 0.986104136341395 70 0.0105509513763835 0.0211019027527671 0.989449048623616 71 0.00740157846936767 0.0148031569387353 0.992598421530632 72 0.00523790998627847 0.0104758199725569 0.994762090013722 73 0.00372531078867509 0.00745062157735017 0.996274689211325 74 0.00778441731614295 0.0155688346322859 0.992215582683857 75 0.00687596020651553 0.0137519204130311 0.993124039793485 76 0.0191143413088011 0.0382286826176021 0.980885658691199 77 0.0142067381415217 0.0284134762830434 0.985793261858478 78 0.0101845109838479 0.0203690219676959 0.989815489016152 79 0.00709320460451295 0.0141864092090259 0.992906795395487 80 0.00486474483782228 0.00972948967564457 0.995135255162178 81 0.00348635496590672 0.00697270993181344 0.996513645034093 82 0.00310901699082406 0.00621803398164813 0.996890983009176 83 0.00244240694550935 0.0048848138910187 0.997557593054491 84 0.00335071689751844 0.00670143379503688 0.996649283102482 85 0.00302481898970842 0.00604963797941685 0.996975181010292 86 0.00204821439416125 0.0040964287883225 0.997951785605839 87 0.00138012348505179 0.00276024697010358 0.998619876514948 88 0.000920114350755701 0.0018402287015114 0.999079885649244 89 0.000593897220869545 0.00118779444173909 0.99940610277913 90 0.00127111254877309 0.00254222509754619 0.998728887451227 91 0.000846312497401785 0.00169262499480357 0.999153687502598 92 0.000529988396990467 0.00105997679398093 0.99947001160301 93 0.000621057102755459 0.00124211420551092 0.999378942897244 94 0.00054989863327004 0.00109979726654008 0.99945010136673 95 0.00527092216399658 0.0105418443279932 0.994729077836003 96 0.00737745583012036 0.0147549116602407 0.99262254416988 97 0.00562243317137173 0.0112448663427435 0.994377566828628 98 0.00384375952806148 0.00768751905612295 0.996156240471939 99 0.00273951635364832 0.00547903270729665 0.997260483646352 100 0.00185060423235131 0.00370120846470262 0.998149395767649 101 0.00118611552855734 0.00237223105711468 0.998813884471443 102 0.000750213429427525 0.00150042685885505 0.999249786570573 103 0.000464950460336158 0.000929900920672317 0.999535049539664 104 0.000274548603886632 0.000549097207773264 0.999725451396113 105 0.000169312401135125 0.00033862480227025 0.999830687598865 106 0.000457846397576154 0.000915692795152308 0.999542153602424 107 0.000667792488024668 0.00133558497604934 0.999332207511975 108 0.00062670359139725 0.0012534071827945 0.999373296408603 109 0.000448717930525604 0.000897435861051208 0.999551282069474 110 0.00175774277443094 0.00351548554886189 0.998242257225569 111 0.00466663597536857 0.00933327195073715 0.995333364024631 112 0.00357830517224627 0.00715661034449254 0.996421694827754 113 0.00595987488299764 0.0119197497659953 0.994040125117002 114 0.00367225454912453 0.00734450909824906 0.996327745450875 115 0.0378104964123238 0.0756209928246476 0.962189503587676 116 0.0523905178227459 0.104781035645492 0.947609482177254 117 0.0380896700932969 0.0761793401865937 0.961910329906703 118 0.0322259652877466 0.0644519305754931 0.967774034712253 119 0.0573476699503589 0.114695339900718 0.942652330049641 120 0.0508883492890739 0.101776698578148 0.949111650710926 121 0.0391517409729596 0.0783034819459191 0.96084825902704 122 0.0742539592819261 0.148507918563852 0.925746040718074 123 0.0906382135978736 0.181276427195747 0.909361786402126 124 0.0570665274752108 0.114133054950422 0.942933472524789 125 0.0375064820623656 0.0750129641247313 0.962493517937634 126 0.0837190926553224 0.167438185310645 0.916280907344678 127 0.0500814792475623 0.100162958495125 0.949918520752438

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.0261451062665468 & 0.0522902125330935 & 0.973854893733453 \tabularnewline
19 & 0.283271062953525 & 0.566542125907049 & 0.716728937046475 \tabularnewline
20 & 0.216804782477454 & 0.433609564954909 & 0.783195217522546 \tabularnewline
21 & 0.126651222494784 & 0.253302444989568 & 0.873348777505216 \tabularnewline
22 & 0.272092323310578 & 0.544184646621156 & 0.727907676689422 \tabularnewline
23 & 0.183566304162703 & 0.367132608325407 & 0.816433695837297 \tabularnewline
24 & 0.148892646207401 & 0.297785292414802 & 0.851107353792599 \tabularnewline
25 & 0.325752017945614 & 0.651504035891227 & 0.674247982054386 \tabularnewline
26 & 0.278022954506025 & 0.556045909012051 & 0.721977045493975 \tabularnewline
27 & 0.206161422983956 & 0.412322845967912 & 0.793838577016044 \tabularnewline
28 & 0.152658616596313 & 0.305317233192626 & 0.847341383403687 \tabularnewline
29 & 0.111574442730015 & 0.22314888546003 & 0.888425557269985 \tabularnewline
30 & 0.0779103928789323 & 0.155820785757865 & 0.922089607121068 \tabularnewline
31 & 0.0541937352449427 & 0.108387470489885 & 0.945806264755057 \tabularnewline
32 & 0.11089648517564 & 0.221792970351279 & 0.88910351482436 \tabularnewline
33 & 0.0864061337204687 & 0.172812267440937 & 0.913593866279531 \tabularnewline
34 & 0.122663763265586 & 0.245327526531173 & 0.877336236734414 \tabularnewline
35 & 0.153166253669273 & 0.306332507338547 & 0.846833746330727 \tabularnewline
36 & 0.155623751808214 & 0.311247503616428 & 0.844376248191786 \tabularnewline
37 & 0.170636227828216 & 0.341272455656433 & 0.829363772171784 \tabularnewline
38 & 0.154396711854182 & 0.308793423708364 & 0.845603288145818 \tabularnewline
39 & 0.13060227279466 & 0.261204545589321 & 0.86939772720534 \tabularnewline
40 & 0.154617716017541 & 0.309235432035083 & 0.845382283982459 \tabularnewline
41 & 0.124376922343399 & 0.248753844686798 & 0.875623077656601 \tabularnewline
42 & 0.0976686211024316 & 0.195337242204863 & 0.902331378897568 \tabularnewline
43 & 0.132482928037717 & 0.264965856075434 & 0.867517071962283 \tabularnewline
44 & 0.164362653547527 & 0.328725307095054 & 0.835637346452473 \tabularnewline
45 & 0.268579501502375 & 0.53715900300475 & 0.731420498497625 \tabularnewline
46 & 0.23583457670818 & 0.47166915341636 & 0.76416542329182 \tabularnewline
47 & 0.193933454627749 & 0.387866909255499 & 0.806066545372251 \tabularnewline
48 & 0.155133846657874 & 0.310267693315747 & 0.844866153342126 \tabularnewline
49 & 0.122401736659873 & 0.244803473319745 & 0.877598263340127 \tabularnewline
50 & 0.137171999463369 & 0.274343998926737 & 0.862828000536631 \tabularnewline
51 & 0.135780855252472 & 0.271561710504945 & 0.864219144747528 \tabularnewline
52 & 0.141063054773373 & 0.282126109546745 & 0.858936945226627 \tabularnewline
53 & 0.12514237247316 & 0.250284744946321 & 0.87485762752684 \tabularnewline
54 & 0.142175890178354 & 0.284351780356707 & 0.857824109821646 \tabularnewline
55 & 0.135081876515924 & 0.270163753031847 & 0.864918123484076 \tabularnewline
56 & 0.122917253049211 & 0.245834506098421 & 0.877082746950789 \tabularnewline
57 & 0.134378655273437 & 0.268757310546875 & 0.865621344726563 \tabularnewline
58 & 0.123555219655179 & 0.247110439310358 & 0.876444780344821 \tabularnewline
59 & 0.0985053995830836 & 0.197010799166167 & 0.901494600416916 \tabularnewline
60 & 0.0770214772689673 & 0.154042954537935 & 0.922978522731033 \tabularnewline
61 & 0.060114847655301 & 0.120229695310602 & 0.939885152344699 \tabularnewline
62 & 0.0501328071866268 & 0.100265614373254 & 0.949867192813373 \tabularnewline
63 & 0.0611792490392029 & 0.122358498078406 & 0.938820750960797 \tabularnewline
64 & 0.0558224350797342 & 0.111644870159468 & 0.944177564920266 \tabularnewline
65 & 0.0431874448671374 & 0.0863748897342748 & 0.956812555132863 \tabularnewline
66 & 0.032257586068164 & 0.0645151721363281 & 0.967742413931836 \tabularnewline
67 & 0.0238460190594279 & 0.0476920381188558 & 0.976153980940572 \tabularnewline
68 & 0.0174852651654386 & 0.0349705303308772 & 0.982514734834561 \tabularnewline
69 & 0.013895863658605 & 0.02779172731721 & 0.986104136341395 \tabularnewline
70 & 0.0105509513763835 & 0.0211019027527671 & 0.989449048623616 \tabularnewline
71 & 0.00740157846936767 & 0.0148031569387353 & 0.992598421530632 \tabularnewline
72 & 0.00523790998627847 & 0.0104758199725569 & 0.994762090013722 \tabularnewline
73 & 0.00372531078867509 & 0.00745062157735017 & 0.996274689211325 \tabularnewline
74 & 0.00778441731614295 & 0.0155688346322859 & 0.992215582683857 \tabularnewline
75 & 0.00687596020651553 & 0.0137519204130311 & 0.993124039793485 \tabularnewline
76 & 0.0191143413088011 & 0.0382286826176021 & 0.980885658691199 \tabularnewline
77 & 0.0142067381415217 & 0.0284134762830434 & 0.985793261858478 \tabularnewline
78 & 0.0101845109838479 & 0.0203690219676959 & 0.989815489016152 \tabularnewline
79 & 0.00709320460451295 & 0.0141864092090259 & 0.992906795395487 \tabularnewline
80 & 0.00486474483782228 & 0.00972948967564457 & 0.995135255162178 \tabularnewline
81 & 0.00348635496590672 & 0.00697270993181344 & 0.996513645034093 \tabularnewline
82 & 0.00310901699082406 & 0.00621803398164813 & 0.996890983009176 \tabularnewline
83 & 0.00244240694550935 & 0.0048848138910187 & 0.997557593054491 \tabularnewline
84 & 0.00335071689751844 & 0.00670143379503688 & 0.996649283102482 \tabularnewline
85 & 0.00302481898970842 & 0.00604963797941685 & 0.996975181010292 \tabularnewline
86 & 0.00204821439416125 & 0.0040964287883225 & 0.997951785605839 \tabularnewline
87 & 0.00138012348505179 & 0.00276024697010358 & 0.998619876514948 \tabularnewline
88 & 0.000920114350755701 & 0.0018402287015114 & 0.999079885649244 \tabularnewline
89 & 0.000593897220869545 & 0.00118779444173909 & 0.99940610277913 \tabularnewline
90 & 0.00127111254877309 & 0.00254222509754619 & 0.998728887451227 \tabularnewline
91 & 0.000846312497401785 & 0.00169262499480357 & 0.999153687502598 \tabularnewline
92 & 0.000529988396990467 & 0.00105997679398093 & 0.99947001160301 \tabularnewline
93 & 0.000621057102755459 & 0.00124211420551092 & 0.999378942897244 \tabularnewline
94 & 0.00054989863327004 & 0.00109979726654008 & 0.99945010136673 \tabularnewline
95 & 0.00527092216399658 & 0.0105418443279932 & 0.994729077836003 \tabularnewline
96 & 0.00737745583012036 & 0.0147549116602407 & 0.99262254416988 \tabularnewline
97 & 0.00562243317137173 & 0.0112448663427435 & 0.994377566828628 \tabularnewline
98 & 0.00384375952806148 & 0.00768751905612295 & 0.996156240471939 \tabularnewline
99 & 0.00273951635364832 & 0.00547903270729665 & 0.997260483646352 \tabularnewline
100 & 0.00185060423235131 & 0.00370120846470262 & 0.998149395767649 \tabularnewline
101 & 0.00118611552855734 & 0.00237223105711468 & 0.998813884471443 \tabularnewline
102 & 0.000750213429427525 & 0.00150042685885505 & 0.999249786570573 \tabularnewline
103 & 0.000464950460336158 & 0.000929900920672317 & 0.999535049539664 \tabularnewline
104 & 0.000274548603886632 & 0.000549097207773264 & 0.999725451396113 \tabularnewline
105 & 0.000169312401135125 & 0.00033862480227025 & 0.999830687598865 \tabularnewline
106 & 0.000457846397576154 & 0.000915692795152308 & 0.999542153602424 \tabularnewline
107 & 0.000667792488024668 & 0.00133558497604934 & 0.999332207511975 \tabularnewline
108 & 0.00062670359139725 & 0.0012534071827945 & 0.999373296408603 \tabularnewline
109 & 0.000448717930525604 & 0.000897435861051208 & 0.999551282069474 \tabularnewline
110 & 0.00175774277443094 & 0.00351548554886189 & 0.998242257225569 \tabularnewline
111 & 0.00466663597536857 & 0.00933327195073715 & 0.995333364024631 \tabularnewline
112 & 0.00357830517224627 & 0.00715661034449254 & 0.996421694827754 \tabularnewline
113 & 0.00595987488299764 & 0.0119197497659953 & 0.994040125117002 \tabularnewline
114 & 0.00367225454912453 & 0.00734450909824906 & 0.996327745450875 \tabularnewline
115 & 0.0378104964123238 & 0.0756209928246476 & 0.962189503587676 \tabularnewline
116 & 0.0523905178227459 & 0.104781035645492 & 0.947609482177254 \tabularnewline
117 & 0.0380896700932969 & 0.0761793401865937 & 0.961910329906703 \tabularnewline
118 & 0.0322259652877466 & 0.0644519305754931 & 0.967774034712253 \tabularnewline
119 & 0.0573476699503589 & 0.114695339900718 & 0.942652330049641 \tabularnewline
120 & 0.0508883492890739 & 0.101776698578148 & 0.949111650710926 \tabularnewline
121 & 0.0391517409729596 & 0.0783034819459191 & 0.96084825902704 \tabularnewline
122 & 0.0742539592819261 & 0.148507918563852 & 0.925746040718074 \tabularnewline
123 & 0.0906382135978736 & 0.181276427195747 & 0.909361786402126 \tabularnewline
124 & 0.0570665274752108 & 0.114133054950422 & 0.942933472524789 \tabularnewline
125 & 0.0375064820623656 & 0.0750129641247313 & 0.962493517937634 \tabularnewline
126 & 0.0837190926553224 & 0.167438185310645 & 0.916280907344678 \tabularnewline
127 & 0.0500814792475623 & 0.100162958495125 & 0.949918520752438 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186354&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]18[/C][C]0.0261451062665468[/C][C]0.0522902125330935[/C][C]0.973854893733453[/C][/ROW]
[ROW][C]19[/C][C]0.283271062953525[/C][C]0.566542125907049[/C][C]0.716728937046475[/C][/ROW]
[ROW][C]20[/C][C]0.216804782477454[/C][C]0.433609564954909[/C][C]0.783195217522546[/C][/ROW]
[ROW][C]21[/C][C]0.126651222494784[/C][C]0.253302444989568[/C][C]0.873348777505216[/C][/ROW]
[ROW][C]22[/C][C]0.272092323310578[/C][C]0.544184646621156[/C][C]0.727907676689422[/C][/ROW]
[ROW][C]23[/C][C]0.183566304162703[/C][C]0.367132608325407[/C][C]0.816433695837297[/C][/ROW]
[ROW][C]24[/C][C]0.148892646207401[/C][C]0.297785292414802[/C][C]0.851107353792599[/C][/ROW]
[ROW][C]25[/C][C]0.325752017945614[/C][C]0.651504035891227[/C][C]0.674247982054386[/C][/ROW]
[ROW][C]26[/C][C]0.278022954506025[/C][C]0.556045909012051[/C][C]0.721977045493975[/C][/ROW]
[ROW][C]27[/C][C]0.206161422983956[/C][C]0.412322845967912[/C][C]0.793838577016044[/C][/ROW]
[ROW][C]28[/C][C]0.152658616596313[/C][C]0.305317233192626[/C][C]0.847341383403687[/C][/ROW]
[ROW][C]29[/C][C]0.111574442730015[/C][C]0.22314888546003[/C][C]0.888425557269985[/C][/ROW]
[ROW][C]30[/C][C]0.0779103928789323[/C][C]0.155820785757865[/C][C]0.922089607121068[/C][/ROW]
[ROW][C]31[/C][C]0.0541937352449427[/C][C]0.108387470489885[/C][C]0.945806264755057[/C][/ROW]
[ROW][C]32[/C][C]0.11089648517564[/C][C]0.221792970351279[/C][C]0.88910351482436[/C][/ROW]
[ROW][C]33[/C][C]0.0864061337204687[/C][C]0.172812267440937[/C][C]0.913593866279531[/C][/ROW]
[ROW][C]34[/C][C]0.122663763265586[/C][C]0.245327526531173[/C][C]0.877336236734414[/C][/ROW]
[ROW][C]35[/C][C]0.153166253669273[/C][C]0.306332507338547[/C][C]0.846833746330727[/C][/ROW]
[ROW][C]36[/C][C]0.155623751808214[/C][C]0.311247503616428[/C][C]0.844376248191786[/C][/ROW]
[ROW][C]37[/C][C]0.170636227828216[/C][C]0.341272455656433[/C][C]0.829363772171784[/C][/ROW]
[ROW][C]38[/C][C]0.154396711854182[/C][C]0.308793423708364[/C][C]0.845603288145818[/C][/ROW]
[ROW][C]39[/C][C]0.13060227279466[/C][C]0.261204545589321[/C][C]0.86939772720534[/C][/ROW]
[ROW][C]40[/C][C]0.154617716017541[/C][C]0.309235432035083[/C][C]0.845382283982459[/C][/ROW]
[ROW][C]41[/C][C]0.124376922343399[/C][C]0.248753844686798[/C][C]0.875623077656601[/C][/ROW]
[ROW][C]42[/C][C]0.0976686211024316[/C][C]0.195337242204863[/C][C]0.902331378897568[/C][/ROW]
[ROW][C]43[/C][C]0.132482928037717[/C][C]0.264965856075434[/C][C]0.867517071962283[/C][/ROW]
[ROW][C]44[/C][C]0.164362653547527[/C][C]0.328725307095054[/C][C]0.835637346452473[/C][/ROW]
[ROW][C]45[/C][C]0.268579501502375[/C][C]0.53715900300475[/C][C]0.731420498497625[/C][/ROW]
[ROW][C]46[/C][C]0.23583457670818[/C][C]0.47166915341636[/C][C]0.76416542329182[/C][/ROW]
[ROW][C]47[/C][C]0.193933454627749[/C][C]0.387866909255499[/C][C]0.806066545372251[/C][/ROW]
[ROW][C]48[/C][C]0.155133846657874[/C][C]0.310267693315747[/C][C]0.844866153342126[/C][/ROW]
[ROW][C]49[/C][C]0.122401736659873[/C][C]0.244803473319745[/C][C]0.877598263340127[/C][/ROW]
[ROW][C]50[/C][C]0.137171999463369[/C][C]0.274343998926737[/C][C]0.862828000536631[/C][/ROW]
[ROW][C]51[/C][C]0.135780855252472[/C][C]0.271561710504945[/C][C]0.864219144747528[/C][/ROW]
[ROW][C]52[/C][C]0.141063054773373[/C][C]0.282126109546745[/C][C]0.858936945226627[/C][/ROW]
[ROW][C]53[/C][C]0.12514237247316[/C][C]0.250284744946321[/C][C]0.87485762752684[/C][/ROW]
[ROW][C]54[/C][C]0.142175890178354[/C][C]0.284351780356707[/C][C]0.857824109821646[/C][/ROW]
[ROW][C]55[/C][C]0.135081876515924[/C][C]0.270163753031847[/C][C]0.864918123484076[/C][/ROW]
[ROW][C]56[/C][C]0.122917253049211[/C][C]0.245834506098421[/C][C]0.877082746950789[/C][/ROW]
[ROW][C]57[/C][C]0.134378655273437[/C][C]0.268757310546875[/C][C]0.865621344726563[/C][/ROW]
[ROW][C]58[/C][C]0.123555219655179[/C][C]0.247110439310358[/C][C]0.876444780344821[/C][/ROW]
[ROW][C]59[/C][C]0.0985053995830836[/C][C]0.197010799166167[/C][C]0.901494600416916[/C][/ROW]
[ROW][C]60[/C][C]0.0770214772689673[/C][C]0.154042954537935[/C][C]0.922978522731033[/C][/ROW]
[ROW][C]61[/C][C]0.060114847655301[/C][C]0.120229695310602[/C][C]0.939885152344699[/C][/ROW]
[ROW][C]62[/C][C]0.0501328071866268[/C][C]0.100265614373254[/C][C]0.949867192813373[/C][/ROW]
[ROW][C]63[/C][C]0.0611792490392029[/C][C]0.122358498078406[/C][C]0.938820750960797[/C][/ROW]
[ROW][C]64[/C][C]0.0558224350797342[/C][C]0.111644870159468[/C][C]0.944177564920266[/C][/ROW]
[ROW][C]65[/C][C]0.0431874448671374[/C][C]0.0863748897342748[/C][C]0.956812555132863[/C][/ROW]
[ROW][C]66[/C][C]0.032257586068164[/C][C]0.0645151721363281[/C][C]0.967742413931836[/C][/ROW]
[ROW][C]67[/C][C]0.0238460190594279[/C][C]0.0476920381188558[/C][C]0.976153980940572[/C][/ROW]
[ROW][C]68[/C][C]0.0174852651654386[/C][C]0.0349705303308772[/C][C]0.982514734834561[/C][/ROW]
[ROW][C]69[/C][C]0.013895863658605[/C][C]0.02779172731721[/C][C]0.986104136341395[/C][/ROW]
[ROW][C]70[/C][C]0.0105509513763835[/C][C]0.0211019027527671[/C][C]0.989449048623616[/C][/ROW]
[ROW][C]71[/C][C]0.00740157846936767[/C][C]0.0148031569387353[/C][C]0.992598421530632[/C][/ROW]
[ROW][C]72[/C][C]0.00523790998627847[/C][C]0.0104758199725569[/C][C]0.994762090013722[/C][/ROW]
[ROW][C]73[/C][C]0.00372531078867509[/C][C]0.00745062157735017[/C][C]0.996274689211325[/C][/ROW]
[ROW][C]74[/C][C]0.00778441731614295[/C][C]0.0155688346322859[/C][C]0.992215582683857[/C][/ROW]
[ROW][C]75[/C][C]0.00687596020651553[/C][C]0.0137519204130311[/C][C]0.993124039793485[/C][/ROW]
[ROW][C]76[/C][C]0.0191143413088011[/C][C]0.0382286826176021[/C][C]0.980885658691199[/C][/ROW]
[ROW][C]77[/C][C]0.0142067381415217[/C][C]0.0284134762830434[/C][C]0.985793261858478[/C][/ROW]
[ROW][C]78[/C][C]0.0101845109838479[/C][C]0.0203690219676959[/C][C]0.989815489016152[/C][/ROW]
[ROW][C]79[/C][C]0.00709320460451295[/C][C]0.0141864092090259[/C][C]0.992906795395487[/C][/ROW]
[ROW][C]80[/C][C]0.00486474483782228[/C][C]0.00972948967564457[/C][C]0.995135255162178[/C][/ROW]
[ROW][C]81[/C][C]0.00348635496590672[/C][C]0.00697270993181344[/C][C]0.996513645034093[/C][/ROW]
[ROW][C]82[/C][C]0.00310901699082406[/C][C]0.00621803398164813[/C][C]0.996890983009176[/C][/ROW]
[ROW][C]83[/C][C]0.00244240694550935[/C][C]0.0048848138910187[/C][C]0.997557593054491[/C][/ROW]
[ROW][C]84[/C][C]0.00335071689751844[/C][C]0.00670143379503688[/C][C]0.996649283102482[/C][/ROW]
[ROW][C]85[/C][C]0.00302481898970842[/C][C]0.00604963797941685[/C][C]0.996975181010292[/C][/ROW]
[ROW][C]86[/C][C]0.00204821439416125[/C][C]0.0040964287883225[/C][C]0.997951785605839[/C][/ROW]
[ROW][C]87[/C][C]0.00138012348505179[/C][C]0.00276024697010358[/C][C]0.998619876514948[/C][/ROW]
[ROW][C]88[/C][C]0.000920114350755701[/C][C]0.0018402287015114[/C][C]0.999079885649244[/C][/ROW]
[ROW][C]89[/C][C]0.000593897220869545[/C][C]0.00118779444173909[/C][C]0.99940610277913[/C][/ROW]
[ROW][C]90[/C][C]0.00127111254877309[/C][C]0.00254222509754619[/C][C]0.998728887451227[/C][/ROW]
[ROW][C]91[/C][C]0.000846312497401785[/C][C]0.00169262499480357[/C][C]0.999153687502598[/C][/ROW]
[ROW][C]92[/C][C]0.000529988396990467[/C][C]0.00105997679398093[/C][C]0.99947001160301[/C][/ROW]
[ROW][C]93[/C][C]0.000621057102755459[/C][C]0.00124211420551092[/C][C]0.999378942897244[/C][/ROW]
[ROW][C]94[/C][C]0.00054989863327004[/C][C]0.00109979726654008[/C][C]0.99945010136673[/C][/ROW]
[ROW][C]95[/C][C]0.00527092216399658[/C][C]0.0105418443279932[/C][C]0.994729077836003[/C][/ROW]
[ROW][C]96[/C][C]0.00737745583012036[/C][C]0.0147549116602407[/C][C]0.99262254416988[/C][/ROW]
[ROW][C]97[/C][C]0.00562243317137173[/C][C]0.0112448663427435[/C][C]0.994377566828628[/C][/ROW]
[ROW][C]98[/C][C]0.00384375952806148[/C][C]0.00768751905612295[/C][C]0.996156240471939[/C][/ROW]
[ROW][C]99[/C][C]0.00273951635364832[/C][C]0.00547903270729665[/C][C]0.997260483646352[/C][/ROW]
[ROW][C]100[/C][C]0.00185060423235131[/C][C]0.00370120846470262[/C][C]0.998149395767649[/C][/ROW]
[ROW][C]101[/C][C]0.00118611552855734[/C][C]0.00237223105711468[/C][C]0.998813884471443[/C][/ROW]
[ROW][C]102[/C][C]0.000750213429427525[/C][C]0.00150042685885505[/C][C]0.999249786570573[/C][/ROW]
[ROW][C]103[/C][C]0.000464950460336158[/C][C]0.000929900920672317[/C][C]0.999535049539664[/C][/ROW]
[ROW][C]104[/C][C]0.000274548603886632[/C][C]0.000549097207773264[/C][C]0.999725451396113[/C][/ROW]
[ROW][C]105[/C][C]0.000169312401135125[/C][C]0.00033862480227025[/C][C]0.999830687598865[/C][/ROW]
[ROW][C]106[/C][C]0.000457846397576154[/C][C]0.000915692795152308[/C][C]0.999542153602424[/C][/ROW]
[ROW][C]107[/C][C]0.000667792488024668[/C][C]0.00133558497604934[/C][C]0.999332207511975[/C][/ROW]
[ROW][C]108[/C][C]0.00062670359139725[/C][C]0.0012534071827945[/C][C]0.999373296408603[/C][/ROW]
[ROW][C]109[/C][C]0.000448717930525604[/C][C]0.000897435861051208[/C][C]0.999551282069474[/C][/ROW]
[ROW][C]110[/C][C]0.00175774277443094[/C][C]0.00351548554886189[/C][C]0.998242257225569[/C][/ROW]
[ROW][C]111[/C][C]0.00466663597536857[/C][C]0.00933327195073715[/C][C]0.995333364024631[/C][/ROW]
[ROW][C]112[/C][C]0.00357830517224627[/C][C]0.00715661034449254[/C][C]0.996421694827754[/C][/ROW]
[ROW][C]113[/C][C]0.00595987488299764[/C][C]0.0119197497659953[/C][C]0.994040125117002[/C][/ROW]
[ROW][C]114[/C][C]0.00367225454912453[/C][C]0.00734450909824906[/C][C]0.996327745450875[/C][/ROW]
[ROW][C]115[/C][C]0.0378104964123238[/C][C]0.0756209928246476[/C][C]0.962189503587676[/C][/ROW]
[ROW][C]116[/C][C]0.0523905178227459[/C][C]0.104781035645492[/C][C]0.947609482177254[/C][/ROW]
[ROW][C]117[/C][C]0.0380896700932969[/C][C]0.0761793401865937[/C][C]0.961910329906703[/C][/ROW]
[ROW][C]118[/C][C]0.0322259652877466[/C][C]0.0644519305754931[/C][C]0.967774034712253[/C][/ROW]
[ROW][C]119[/C][C]0.0573476699503589[/C][C]0.114695339900718[/C][C]0.942652330049641[/C][/ROW]
[ROW][C]120[/C][C]0.0508883492890739[/C][C]0.101776698578148[/C][C]0.949111650710926[/C][/ROW]
[ROW][C]121[/C][C]0.0391517409729596[/C][C]0.0783034819459191[/C][C]0.96084825902704[/C][/ROW]
[ROW][C]122[/C][C]0.0742539592819261[/C][C]0.148507918563852[/C][C]0.925746040718074[/C][/ROW]
[ROW][C]123[/C][C]0.0906382135978736[/C][C]0.181276427195747[/C][C]0.909361786402126[/C][/ROW]
[ROW][C]124[/C][C]0.0570665274752108[/C][C]0.114133054950422[/C][C]0.942933472524789[/C][/ROW]
[ROW][C]125[/C][C]0.0375064820623656[/C][C]0.0750129641247313[/C][C]0.962493517937634[/C][/ROW]
[ROW][C]126[/C][C]0.0837190926553224[/C][C]0.167438185310645[/C][C]0.916280907344678[/C][/ROW]
[ROW][C]127[/C][C]0.0500814792475623[/C][C]0.100162958495125[/C][C]0.949918520752438[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186354&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186354&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 18 0.0261451062665468 0.0522902125330935 0.973854893733453 19 0.283271062953525 0.566542125907049 0.716728937046475 20 0.216804782477454 0.433609564954909 0.783195217522546 21 0.126651222494784 0.253302444989568 0.873348777505216 22 0.272092323310578 0.544184646621156 0.727907676689422 23 0.183566304162703 0.367132608325407 0.816433695837297 24 0.148892646207401 0.297785292414802 0.851107353792599 25 0.325752017945614 0.651504035891227 0.674247982054386 26 0.278022954506025 0.556045909012051 0.721977045493975 27 0.206161422983956 0.412322845967912 0.793838577016044 28 0.152658616596313 0.305317233192626 0.847341383403687 29 0.111574442730015 0.22314888546003 0.888425557269985 30 0.0779103928789323 0.155820785757865 0.922089607121068 31 0.0541937352449427 0.108387470489885 0.945806264755057 32 0.11089648517564 0.221792970351279 0.88910351482436 33 0.0864061337204687 0.172812267440937 0.913593866279531 34 0.122663763265586 0.245327526531173 0.877336236734414 35 0.153166253669273 0.306332507338547 0.846833746330727 36 0.155623751808214 0.311247503616428 0.844376248191786 37 0.170636227828216 0.341272455656433 0.829363772171784 38 0.154396711854182 0.308793423708364 0.845603288145818 39 0.13060227279466 0.261204545589321 0.86939772720534 40 0.154617716017541 0.309235432035083 0.845382283982459 41 0.124376922343399 0.248753844686798 0.875623077656601 42 0.0976686211024316 0.195337242204863 0.902331378897568 43 0.132482928037717 0.264965856075434 0.867517071962283 44 0.164362653547527 0.328725307095054 0.835637346452473 45 0.268579501502375 0.53715900300475 0.731420498497625 46 0.23583457670818 0.47166915341636 0.76416542329182 47 0.193933454627749 0.387866909255499 0.806066545372251 48 0.155133846657874 0.310267693315747 0.844866153342126 49 0.122401736659873 0.244803473319745 0.877598263340127 50 0.137171999463369 0.274343998926737 0.862828000536631 51 0.135780855252472 0.271561710504945 0.864219144747528 52 0.141063054773373 0.282126109546745 0.858936945226627 53 0.12514237247316 0.250284744946321 0.87485762752684 54 0.142175890178354 0.284351780356707 0.857824109821646 55 0.135081876515924 0.270163753031847 0.864918123484076 56 0.122917253049211 0.245834506098421 0.877082746950789 57 0.134378655273437 0.268757310546875 0.865621344726563 58 0.123555219655179 0.247110439310358 0.876444780344821 59 0.0985053995830836 0.197010799166167 0.901494600416916 60 0.0770214772689673 0.154042954537935 0.922978522731033 61 0.060114847655301 0.120229695310602 0.939885152344699 62 0.0501328071866268 0.100265614373254 0.949867192813373 63 0.0611792490392029 0.122358498078406 0.938820750960797 64 0.0558224350797342 0.111644870159468 0.944177564920266 65 0.0431874448671374 0.0863748897342748 0.956812555132863 66 0.032257586068164 0.0645151721363281 0.967742413931836 67 0.0238460190594279 0.0476920381188558 0.976153980940572 68 0.0174852651654386 0.0349705303308772 0.982514734834561 69 0.013895863658605 0.02779172731721 0.986104136341395 70 0.0105509513763835 0.0211019027527671 0.989449048623616 71 0.00740157846936767 0.0148031569387353 0.992598421530632 72 0.00523790998627847 0.0104758199725569 0.994762090013722 73 0.00372531078867509 0.00745062157735017 0.996274689211325 74 0.00778441731614295 0.0155688346322859 0.992215582683857 75 0.00687596020651553 0.0137519204130311 0.993124039793485 76 0.0191143413088011 0.0382286826176021 0.980885658691199 77 0.0142067381415217 0.0284134762830434 0.985793261858478 78 0.0101845109838479 0.0203690219676959 0.989815489016152 79 0.00709320460451295 0.0141864092090259 0.992906795395487 80 0.00486474483782228 0.00972948967564457 0.995135255162178 81 0.00348635496590672 0.00697270993181344 0.996513645034093 82 0.00310901699082406 0.00621803398164813 0.996890983009176 83 0.00244240694550935 0.0048848138910187 0.997557593054491 84 0.00335071689751844 0.00670143379503688 0.996649283102482 85 0.00302481898970842 0.00604963797941685 0.996975181010292 86 0.00204821439416125 0.0040964287883225 0.997951785605839 87 0.00138012348505179 0.00276024697010358 0.998619876514948 88 0.000920114350755701 0.0018402287015114 0.999079885649244 89 0.000593897220869545 0.00118779444173909 0.99940610277913 90 0.00127111254877309 0.00254222509754619 0.998728887451227 91 0.000846312497401785 0.00169262499480357 0.999153687502598 92 0.000529988396990467 0.00105997679398093 0.99947001160301 93 0.000621057102755459 0.00124211420551092 0.999378942897244 94 0.00054989863327004 0.00109979726654008 0.99945010136673 95 0.00527092216399658 0.0105418443279932 0.994729077836003 96 0.00737745583012036 0.0147549116602407 0.99262254416988 97 0.00562243317137173 0.0112448663427435 0.994377566828628 98 0.00384375952806148 0.00768751905612295 0.996156240471939 99 0.00273951635364832 0.00547903270729665 0.997260483646352 100 0.00185060423235131 0.00370120846470262 0.998149395767649 101 0.00118611552855734 0.00237223105711468 0.998813884471443 102 0.000750213429427525 0.00150042685885505 0.999249786570573 103 0.000464950460336158 0.000929900920672317 0.999535049539664 104 0.000274548603886632 0.000549097207773264 0.999725451396113 105 0.000169312401135125 0.00033862480227025 0.999830687598865 106 0.000457846397576154 0.000915692795152308 0.999542153602424 107 0.000667792488024668 0.00133558497604934 0.999332207511975 108 0.00062670359139725 0.0012534071827945 0.999373296408603 109 0.000448717930525604 0.000897435861051208 0.999551282069474 110 0.00175774277443094 0.00351548554886189 0.998242257225569 111 0.00466663597536857 0.00933327195073715 0.995333364024631 112 0.00357830517224627 0.00715661034449254 0.996421694827754 113 0.00595987488299764 0.0119197497659953 0.994040125117002 114 0.00367225454912453 0.00734450909824906 0.996327745450875 115 0.0378104964123238 0.0756209928246476 0.962189503587676 116 0.0523905178227459 0.104781035645492 0.947609482177254 117 0.0380896700932969 0.0761793401865937 0.961910329906703 118 0.0322259652877466 0.0644519305754931 0.967774034712253 119 0.0573476699503589 0.114695339900718 0.942652330049641 120 0.0508883492890739 0.101776698578148 0.949111650710926 121 0.0391517409729596 0.0783034819459191 0.96084825902704 122 0.0742539592819261 0.148507918563852 0.925746040718074 123 0.0906382135978736 0.181276427195747 0.909361786402126 124 0.0570665274752108 0.114133054950422 0.942933472524789 125 0.0375064820623656 0.0750129641247313 0.962493517937634 126 0.0837190926553224 0.167438185310645 0.916280907344678 127 0.0500814792475623 0.100162958495125 0.949918520752438

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 32 0.290909090909091 NOK 5% type I error level 48 0.436363636363636 NOK 10% type I error level 56 0.509090909090909 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 & 32 & 0.290909090909091 & NOK \tabularnewline
5% type I error level & 48 & 0.436363636363636 & NOK \tabularnewline
10% type I error level & 56 & 0.509090909090909 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186354&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]32[/C][C]0.290909090909091[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]48[/C][C]0.436363636363636[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]56[/C][C]0.509090909090909[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186354&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186354&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 32 0.290909090909091 NOK 5% type I error level 48 0.436363636363636 NOK 10% type I error level 56 0.509090909090909 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')}