Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 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]
Feedback Forum

Post a new message
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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
Totaal[t] = -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] -0.0972881677544726`Belgi\303\253`[t] -0.0248614013962234`Belgi\303\253_s`[t] -0.174261086271905Eurogebied[t] -0.616968998076365Eurogebied_s[t] + 0.123190347467024`EU-27`[t] + 0.548103385829031`EU-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] -0.0972881677544726`Belgi\303\253`[t] -0.0248614013962234`Belgi\303\253_s`[t] -0.174261086271905Eurogebied[t] -0.616968998076365Eurogebied_s[t] +  0.123190347467024`EU-27`[t] +  0.548103385829031`EU-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] -0.0972881677544726`Belgi\303\253`[t] -0.0248614013962234`Belgi\303\253_s`[t] -0.174261086271905Eurogebied[t] -0.616968998076365Eurogebied_s[t] +  0.123190347467024`EU-27`[t] +  0.548103385829031`EU-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] -0.0972881677544726`Belgi\303\253`[t] -0.0248614013962234`Belgi\303\253_s`[t] -0.174261086271905Eurogebied[t] -0.616968998076365Eurogebied_s[t] + 0.123190347467024`EU-27`[t] + 0.548103385829031`EU-27_s\r`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-235.158663233414372.210094-0.63180.5286330.264317
jaartal0.1181107113692270.1861040.63460.5267730.263386
S_t0.00898205201170470.0078951.13770.2573390.12867
s0.6301500367391591.3698640.460.6462780.323139
t-0.009697238417683670.016237-0.59720.5513830.275691
jongerdan25jaar0.9923406618136980.004534218.882500
`<25jaar_s`0.01003319574360650.0092991.0790.2825910.141295
vanaf25jaar1.0027121188280.004638216.190300
vanaf25_s-0.004019780535668760.006187-0.64970.5170420.258521
`Belgi\303\253`-0.09728816775447260.170447-0.57080.5691330.284567
`Belgi\303\253_s`-0.02486140139622340.241232-0.10310.9180740.459037
Eurogebied-0.1742610862719050.548075-0.3180.7510320.375516
Eurogebied_s-0.6169689980763650.771011-0.80020.425050.212525
`EU-27`0.1231903474670240.5221940.23590.8138740.406937
`EU-27_s\r`0.5481033858290310.7244950.75650.4506990.22535

\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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-235.158663233414372.210094-0.63180.5286330.264317
jaartal0.1181107113692270.1861040.63460.5267730.263386
S_t0.00898205201170470.0078951.13770.2573390.12867
s0.6301500367391591.3698640.460.6462780.323139
t-0.009697238417683670.016237-0.59720.5513830.275691
jongerdan25jaar0.9923406618136980.004534218.882500
`<25jaar_s`0.01003319574360650.0092991.0790.2825910.141295
vanaf25jaar1.0027121188280.004638216.190300
vanaf25_s-0.004019780535668760.006187-0.64970.5170420.258521
`Belgi\303\253`-0.09728816775447260.170447-0.57080.5691330.284567
`Belgi\303\253_s`-0.02486140139622340.241232-0.10310.9180740.459037
Eurogebied-0.1742610862719050.548075-0.3180.7510320.375516
Eurogebied_s-0.6169689980763650.771011-0.80020.425050.212525
`EU-27`0.1231903474670240.5221940.23590.8138740.406937
`EU-27_s\r`0.5481033858290310.7244950.75650.4506990.22535







Multiple Linear Regression - Regression Statistics
Multiple R0.999943307872238
R-squared0.999886618958474
Adjusted R-squared0.999874408692463
F-TEST (value)81889.0119264686
F-TEST (DF numerator)14
F-TEST (DF denominator)130
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.507790591618823
Sum Squared Residuals33.5206670417573

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999943307872238 \tabularnewline
R-squared & 0.999886618958474 \tabularnewline
Adjusted R-squared & 0.999874408692463 \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]Adjusted R-squared[/C][C]0.999874408692463[/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 R0.999943307872238
R-squared0.999886618958474
Adjusted R-squared0.999874408692463
F-TEST (value)81889.0119264686
F-TEST (DF numerator)14
F-TEST (DF denominator)130
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.507790591618823
Sum Squared Residuals33.5206670417573







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1501501.96347674406-0.963476744059784
2485484.8099479572010.190052042798564
3464463.8084119116570.19158808834315
4460459.8224098870420.177590112958041
5467467.034204702316-0.03420470231647
6460460.043813546984-0.0438135469842987
7448448.11950571318-0.119505713180325
8443443.089854363688-0.0898543636876219
9436436.314763441592-0.314763441592201
10431431.321977339839-0.321977339838558
11484484.153964865412-0.153964865412427
12510509.0480748631980.95192513680169
13513513.009668192752-0.00966819275199646
14503503.05920677812-0.0592067781201836
15471471.087813436884-0.0878134368844562
16471470.9847157273340.0152842726663334
17476476.158799476013-0.158799476013351
18475474.1376614201230.86233857987744
19470470.131132322752-0.131132322752345
20461461.127681336465-0.127681336465337
21455455.208192618109-0.208192618108734
22456455.1984953796910.801504620308957
23517516.927897977620.0721020223797681
24525525.828523781497-0.828523781497222
25523522.7939067182070.206093281792799
26519518.9609810528890.0390189471114124
27509508.8488708448250.151129155175356
28512511.8368696347660.163130365234276
29519518.9050064595750.0949935404250384
30517516.8008425541750.199157445825467
31510509.7908734033970.209126596602631
32509509.763217183016-0.763217183016435
33501500.0976730545120.902326945487889
34507507.05842016822-0.0584201682203387
35569569.804728135395-0.804728135394604
36580579.756606537770.243393462230318
37578577.7247015933080.275298406692333
38565565.81086866396-0.810868663960223
39547547.705538230293-0.705538230292649
40555554.7469023090830.253097690917435
41562561.8150391338920.184960866108205
42561560.8045870513990.195412948601422
43555555.809069452506-0.809069452506276
44544543.7836875453330.216312454666855
45537537.134461010463-0.134461010462582
46543543.083409828806-0.0834098288062141
47594593.7893714739860.210628526014422
48611610.7032506921890.296749307811154
49613612.7093491484410.29065085155852
50611610.6583493581580.341650641842431
51594593.5883561335280.411643866472481
52595595.570299546646-0.570299546646333
53591590.7549487922580.245051207742132
54589589.73228175285-0.732281752849575
55584583.708789983450.291210016550327
56573572.7040514795030.295948520496808
57567567.157890587513-0.157890587513426
58569569.152974946513-0.15297494651289
59621620.8888875831140.111112416885804
60629628.8230227130870.176977286913427
61628627.8106133558410.1893866441591
62612611.7831944026280.216805597372133
63595595.71167219448-0.711672194480334
64597596.7032353851470.296764614852959
65593592.7998696002760.20013039972369
66590589.8077079955690.192292004431403
67580579.8099739457550.19002605424531
68574573.872968223320.127031776680442
69573573.204602016389-0.204602016388986
70573573.209098028388-0.209098028388389
71620620.01954845786-0.0195484578604203
72626625.9833528238760.0166471761241799
73620619.9728614033850.0271385966150842
74588586.9794427965651.02055720343485
75566565.8985624207760.101437579224108
76557557.985869493632-0.985869493631561
77561561.047887814512-0.0478878145123859
78549549.052769088114-0.0527690881138106
79532532.064188670254-0.0641886702535674
80526526.060053574489-0.0600535744888332
81511511.310991233125-0.310991233125077
82499499.325816531931-0.32581653193052
83555555.165940413988-0.165940413988407
84565564.1378811364880.862118863512187
85542542.145581916596-0.145581916595849
86527527.089984045472-0.0899840454715638
87510510.145273486231-0.145273486230942
88514514.0685167604-0.0685167603997262
89517517.194204257155-0.194204257155016
90508507.2282631597010.771736840298811
91493493.235981078528-0.235981078527801
92490490.15270283028-0.152702830280044
93469469.388079074034-0.388079074033684
94478477.3541285245410.645871475458841
95528529.165231578214-1.16523157821387
96534533.1100610963950.889938903604967
97518518.103115481347-0.103115481346935
98506506.08978038649-0.0897803864897348
99502502.055582499644-0.0555824996440885
100516515.92859785230.0714021476996164
101528528.03216807257-0.0321680725702868
102533532.964503758480.035496241519837
103536535.8408588321260.159141167874127
104537536.9025051598870.0974948401132872
105524523.1910046572990.808995342700547
106536536.172562279679-0.172562279679336
107587586.9687914890310.0312085109693502
108597595.923864795951.07613520404959
109581580.8891216152610.110878384739172
110564564.815578659764-0.815578659764141
111558556.8581500678441.14184993215579
112575574.8717142745140.128285725485552
113580580.972981950692-0.97298195069154
114575574.9568532198220.0431467801784193
115563563.952114715875-0.952114715875089
116552550.8671870076441.13281299235587
117537537.119085697614-0.119085697613901
118545545.115449569672-0.115449569672104
119601600.9769590059970.0230409940025485
120604604.925610317581-0.925610317581398
121586586.907938128078-0.907938128078042
122564563.9546095002190.0453904997812817
123549547.9697946608461.03020533915422
124551550.9907568022670.00924319773290566
125556556.153934043807-0.153934043807399
126548548.252506996268-0.252506996267838
127540540.192612610049-0.192612610048777
128531531.176725345037-0.176725345036842
129521520.3215388752540.678461124746242
130519518.2889912905530.71100870944712
131572572.110782965015-0.110782965015384
132581582.050499641897-1.0504996418971
133563563.01527944291-0.0152794429098155
134548548.057802144934-0.0578021449341769
135539539.034773163202-0.0347731632021486
136541540.9571715633310.0428284366688538
137562561.0548278153610.945172184639332
138559559.041052798-0.0410527979998336
139546546.011039506183-0.011039506183411
140536536.836906224302-0.836906224301831
141528527.1385010709630.861498929037115
142530531.113802319948-1.11380231994811
143582581.9663532480130.0336467519874244
144599598.9003523970210.0996476029785776
145584583.8429187245710.157081275429178

\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 IndexActualsInterpolationForecastResidualsPrediction Error
1501501.96347674406-0.963476744059784
2485484.8099479572010.190052042798564
3464463.8084119116570.19158808834315
4460459.8224098870420.177590112958041
5467467.034204702316-0.03420470231647
6460460.043813546984-0.0438135469842987
7448448.11950571318-0.119505713180325
8443443.089854363688-0.0898543636876219
9436436.314763441592-0.314763441592201
10431431.321977339839-0.321977339838558
11484484.153964865412-0.153964865412427
12510509.0480748631980.95192513680169
13513513.009668192752-0.00966819275199646
14503503.05920677812-0.0592067781201836
15471471.087813436884-0.0878134368844562
16471470.9847157273340.0152842726663334
17476476.158799476013-0.158799476013351
18475474.1376614201230.86233857987744
19470470.131132322752-0.131132322752345
20461461.127681336465-0.127681336465337
21455455.208192618109-0.208192618108734
22456455.1984953796910.801504620308957
23517516.927897977620.0721020223797681
24525525.828523781497-0.828523781497222
25523522.7939067182070.206093281792799
26519518.9609810528890.0390189471114124
27509508.8488708448250.151129155175356
28512511.8368696347660.163130365234276
29519518.9050064595750.0949935404250384
30517516.8008425541750.199157445825467
31510509.7908734033970.209126596602631
32509509.763217183016-0.763217183016435
33501500.0976730545120.902326945487889
34507507.05842016822-0.0584201682203387
35569569.804728135395-0.804728135394604
36580579.756606537770.243393462230318
37578577.7247015933080.275298406692333
38565565.81086866396-0.810868663960223
39547547.705538230293-0.705538230292649
40555554.7469023090830.253097690917435
41562561.8150391338920.184960866108205
42561560.8045870513990.195412948601422
43555555.809069452506-0.809069452506276
44544543.7836875453330.216312454666855
45537537.134461010463-0.134461010462582
46543543.083409828806-0.0834098288062141
47594593.7893714739860.210628526014422
48611610.7032506921890.296749307811154
49613612.7093491484410.29065085155852
50611610.6583493581580.341650641842431
51594593.5883561335280.411643866472481
52595595.570299546646-0.570299546646333
53591590.7549487922580.245051207742132
54589589.73228175285-0.732281752849575
55584583.708789983450.291210016550327
56573572.7040514795030.295948520496808
57567567.157890587513-0.157890587513426
58569569.152974946513-0.15297494651289
59621620.8888875831140.111112416885804
60629628.8230227130870.176977286913427
61628627.8106133558410.1893866441591
62612611.7831944026280.216805597372133
63595595.71167219448-0.711672194480334
64597596.7032353851470.296764614852959
65593592.7998696002760.20013039972369
66590589.8077079955690.192292004431403
67580579.8099739457550.19002605424531
68574573.872968223320.127031776680442
69573573.204602016389-0.204602016388986
70573573.209098028388-0.209098028388389
71620620.01954845786-0.0195484578604203
72626625.9833528238760.0166471761241799
73620619.9728614033850.0271385966150842
74588586.9794427965651.02055720343485
75566565.8985624207760.101437579224108
76557557.985869493632-0.985869493631561
77561561.047887814512-0.0478878145123859
78549549.052769088114-0.0527690881138106
79532532.064188670254-0.0641886702535674
80526526.060053574489-0.0600535744888332
81511511.310991233125-0.310991233125077
82499499.325816531931-0.32581653193052
83555555.165940413988-0.165940413988407
84565564.1378811364880.862118863512187
85542542.145581916596-0.145581916595849
86527527.089984045472-0.0899840454715638
87510510.145273486231-0.145273486230942
88514514.0685167604-0.0685167603997262
89517517.194204257155-0.194204257155016
90508507.2282631597010.771736840298811
91493493.235981078528-0.235981078527801
92490490.15270283028-0.152702830280044
93469469.388079074034-0.388079074033684
94478477.3541285245410.645871475458841
95528529.165231578214-1.16523157821387
96534533.1100610963950.889938903604967
97518518.103115481347-0.103115481346935
98506506.08978038649-0.0897803864897348
99502502.055582499644-0.0555824996440885
100516515.92859785230.0714021476996164
101528528.03216807257-0.0321680725702868
102533532.964503758480.035496241519837
103536535.8408588321260.159141167874127
104537536.9025051598870.0974948401132872
105524523.1910046572990.808995342700547
106536536.172562279679-0.172562279679336
107587586.9687914890310.0312085109693502
108597595.923864795951.07613520404959
109581580.8891216152610.110878384739172
110564564.815578659764-0.815578659764141
111558556.8581500678441.14184993215579
112575574.8717142745140.128285725485552
113580580.972981950692-0.97298195069154
114575574.9568532198220.0431467801784193
115563563.952114715875-0.952114715875089
116552550.8671870076441.13281299235587
117537537.119085697614-0.119085697613901
118545545.115449569672-0.115449569672104
119601600.9769590059970.0230409940025485
120604604.925610317581-0.925610317581398
121586586.907938128078-0.907938128078042
122564563.9546095002190.0453904997812817
123549547.9697946608461.03020533915422
124551550.9907568022670.00924319773290566
125556556.153934043807-0.153934043807399
126548548.252506996268-0.252506996267838
127540540.192612610049-0.192612610048777
128531531.176725345037-0.176725345036842
129521520.3215388752540.678461124746242
130519518.2889912905530.71100870944712
131572572.110782965015-0.110782965015384
132581582.050499641897-1.0504996418971
133563563.01527944291-0.0152794429098155
134548548.057802144934-0.0578021449341769
135539539.034773163202-0.0347731632021486
136541540.9571715633310.0428284366688538
137562561.0548278153610.945172184639332
138559559.041052798-0.0410527979998336
139546546.011039506183-0.011039506183411
140536536.836906224302-0.836906224301831
141528527.1385010709630.861498929037115
142530531.113802319948-1.11380231994811
143582581.9663532480130.0336467519874244
144599598.9003523970210.0996476029785776
145584583.8429187245710.157081275429178







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.02614510626654680.05229021253309350.973854893733453
190.2832710629535250.5665421259070490.716728937046475
200.2168047824774540.4336095649549090.783195217522546
210.1266512224947840.2533024449895680.873348777505216
220.2720923233105780.5441846466211560.727907676689422
230.1835663041627030.3671326083254070.816433695837297
240.1488926462074010.2977852924148020.851107353792599
250.3257520179456140.6515040358912270.674247982054386
260.2780229545060250.5560459090120510.721977045493975
270.2061614229839560.4123228459679120.793838577016044
280.1526586165963130.3053172331926260.847341383403687
290.1115744427300150.223148885460030.888425557269985
300.07791039287893230.1558207857578650.922089607121068
310.05419373524494270.1083874704898850.945806264755057
320.110896485175640.2217929703512790.88910351482436
330.08640613372046870.1728122674409370.913593866279531
340.1226637632655860.2453275265311730.877336236734414
350.1531662536692730.3063325073385470.846833746330727
360.1556237518082140.3112475036164280.844376248191786
370.1706362278282160.3412724556564330.829363772171784
380.1543967118541820.3087934237083640.845603288145818
390.130602272794660.2612045455893210.86939772720534
400.1546177160175410.3092354320350830.845382283982459
410.1243769223433990.2487538446867980.875623077656601
420.09766862110243160.1953372422048630.902331378897568
430.1324829280377170.2649658560754340.867517071962283
440.1643626535475270.3287253070950540.835637346452473
450.2685795015023750.537159003004750.731420498497625
460.235834576708180.471669153416360.76416542329182
470.1939334546277490.3878669092554990.806066545372251
480.1551338466578740.3102676933157470.844866153342126
490.1224017366598730.2448034733197450.877598263340127
500.1371719994633690.2743439989267370.862828000536631
510.1357808552524720.2715617105049450.864219144747528
520.1410630547733730.2821261095467450.858936945226627
530.125142372473160.2502847449463210.87485762752684
540.1421758901783540.2843517803567070.857824109821646
550.1350818765159240.2701637530318470.864918123484076
560.1229172530492110.2458345060984210.877082746950789
570.1343786552734370.2687573105468750.865621344726563
580.1235552196551790.2471104393103580.876444780344821
590.09850539958308360.1970107991661670.901494600416916
600.07702147726896730.1540429545379350.922978522731033
610.0601148476553010.1202296953106020.939885152344699
620.05013280718662680.1002656143732540.949867192813373
630.06117924903920290.1223584980784060.938820750960797
640.05582243507973420.1116448701594680.944177564920266
650.04318744486713740.08637488973427480.956812555132863
660.0322575860681640.06451517213632810.967742413931836
670.02384601905942790.04769203811885580.976153980940572
680.01748526516543860.03497053033087720.982514734834561
690.0138958636586050.027791727317210.986104136341395
700.01055095137638350.02110190275276710.989449048623616
710.007401578469367670.01480315693873530.992598421530632
720.005237909986278470.01047581997255690.994762090013722
730.003725310788675090.007450621577350170.996274689211325
740.007784417316142950.01556883463228590.992215582683857
750.006875960206515530.01375192041303110.993124039793485
760.01911434130880110.03822868261760210.980885658691199
770.01420673814152170.02841347628304340.985793261858478
780.01018451098384790.02036902196769590.989815489016152
790.007093204604512950.01418640920902590.992906795395487
800.004864744837822280.009729489675644570.995135255162178
810.003486354965906720.006972709931813440.996513645034093
820.003109016990824060.006218033981648130.996890983009176
830.002442406945509350.00488481389101870.997557593054491
840.003350716897518440.006701433795036880.996649283102482
850.003024818989708420.006049637979416850.996975181010292
860.002048214394161250.00409642878832250.997951785605839
870.001380123485051790.002760246970103580.998619876514948
880.0009201143507557010.00184022870151140.999079885649244
890.0005938972208695450.001187794441739090.99940610277913
900.001271112548773090.002542225097546190.998728887451227
910.0008463124974017850.001692624994803570.999153687502598
920.0005299883969904670.001059976793980930.99947001160301
930.0006210571027554590.001242114205510920.999378942897244
940.000549898633270040.001099797266540080.99945010136673
950.005270922163996580.01054184432799320.994729077836003
960.007377455830120360.01475491166024070.99262254416988
970.005622433171371730.01124486634274350.994377566828628
980.003843759528061480.007687519056122950.996156240471939
990.002739516353648320.005479032707296650.997260483646352
1000.001850604232351310.003701208464702620.998149395767649
1010.001186115528557340.002372231057114680.998813884471443
1020.0007502134294275250.001500426858855050.999249786570573
1030.0004649504603361580.0009299009206723170.999535049539664
1040.0002745486038866320.0005490972077732640.999725451396113
1050.0001693124011351250.000338624802270250.999830687598865
1060.0004578463975761540.0009156927951523080.999542153602424
1070.0006677924880246680.001335584976049340.999332207511975
1080.000626703591397250.00125340718279450.999373296408603
1090.0004487179305256040.0008974358610512080.999551282069474
1100.001757742774430940.003515485548861890.998242257225569
1110.004666635975368570.009333271950737150.995333364024631
1120.003578305172246270.007156610344492540.996421694827754
1130.005959874882997640.01191974976599530.994040125117002
1140.003672254549124530.007344509098249060.996327745450875
1150.03781049641232380.07562099282464760.962189503587676
1160.05239051782274590.1047810356454920.947609482177254
1170.03808967009329690.07617934018659370.961910329906703
1180.03222596528774660.06445193057549310.967774034712253
1190.05734766995035890.1146953399007180.942652330049641
1200.05088834928907390.1017766985781480.949111650710926
1210.03915174097295960.07830348194591910.96084825902704
1220.07425395928192610.1485079185638520.925746040718074
1230.09063821359787360.1812764271957470.909361786402126
1240.05706652747521080.1141330549504220.942933472524789
1250.03750648206236560.07501296412473130.962493517937634
1260.08371909265532240.1674381853106450.916280907344678
1270.05008147924756230.1001629584951250.949918520752438

\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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.02614510626654680.05229021253309350.973854893733453
190.2832710629535250.5665421259070490.716728937046475
200.2168047824774540.4336095649549090.783195217522546
210.1266512224947840.2533024449895680.873348777505216
220.2720923233105780.5441846466211560.727907676689422
230.1835663041627030.3671326083254070.816433695837297
240.1488926462074010.2977852924148020.851107353792599
250.3257520179456140.6515040358912270.674247982054386
260.2780229545060250.5560459090120510.721977045493975
270.2061614229839560.4123228459679120.793838577016044
280.1526586165963130.3053172331926260.847341383403687
290.1115744427300150.223148885460030.888425557269985
300.07791039287893230.1558207857578650.922089607121068
310.05419373524494270.1083874704898850.945806264755057
320.110896485175640.2217929703512790.88910351482436
330.08640613372046870.1728122674409370.913593866279531
340.1226637632655860.2453275265311730.877336236734414
350.1531662536692730.3063325073385470.846833746330727
360.1556237518082140.3112475036164280.844376248191786
370.1706362278282160.3412724556564330.829363772171784
380.1543967118541820.3087934237083640.845603288145818
390.130602272794660.2612045455893210.86939772720534
400.1546177160175410.3092354320350830.845382283982459
410.1243769223433990.2487538446867980.875623077656601
420.09766862110243160.1953372422048630.902331378897568
430.1324829280377170.2649658560754340.867517071962283
440.1643626535475270.3287253070950540.835637346452473
450.2685795015023750.537159003004750.731420498497625
460.235834576708180.471669153416360.76416542329182
470.1939334546277490.3878669092554990.806066545372251
480.1551338466578740.3102676933157470.844866153342126
490.1224017366598730.2448034733197450.877598263340127
500.1371719994633690.2743439989267370.862828000536631
510.1357808552524720.2715617105049450.864219144747528
520.1410630547733730.2821261095467450.858936945226627
530.125142372473160.2502847449463210.87485762752684
540.1421758901783540.2843517803567070.857824109821646
550.1350818765159240.2701637530318470.864918123484076
560.1229172530492110.2458345060984210.877082746950789
570.1343786552734370.2687573105468750.865621344726563
580.1235552196551790.2471104393103580.876444780344821
590.09850539958308360.1970107991661670.901494600416916
600.07702147726896730.1540429545379350.922978522731033
610.0601148476553010.1202296953106020.939885152344699
620.05013280718662680.1002656143732540.949867192813373
630.06117924903920290.1223584980784060.938820750960797
640.05582243507973420.1116448701594680.944177564920266
650.04318744486713740.08637488973427480.956812555132863
660.0322575860681640.06451517213632810.967742413931836
670.02384601905942790.04769203811885580.976153980940572
680.01748526516543860.03497053033087720.982514734834561
690.0138958636586050.027791727317210.986104136341395
700.01055095137638350.02110190275276710.989449048623616
710.007401578469367670.01480315693873530.992598421530632
720.005237909986278470.01047581997255690.994762090013722
730.003725310788675090.007450621577350170.996274689211325
740.007784417316142950.01556883463228590.992215582683857
750.006875960206515530.01375192041303110.993124039793485
760.01911434130880110.03822868261760210.980885658691199
770.01420673814152170.02841347628304340.985793261858478
780.01018451098384790.02036902196769590.989815489016152
790.007093204604512950.01418640920902590.992906795395487
800.004864744837822280.009729489675644570.995135255162178
810.003486354965906720.006972709931813440.996513645034093
820.003109016990824060.006218033981648130.996890983009176
830.002442406945509350.00488481389101870.997557593054491
840.003350716897518440.006701433795036880.996649283102482
850.003024818989708420.006049637979416850.996975181010292
860.002048214394161250.00409642878832250.997951785605839
870.001380123485051790.002760246970103580.998619876514948
880.0009201143507557010.00184022870151140.999079885649244
890.0005938972208695450.001187794441739090.99940610277913
900.001271112548773090.002542225097546190.998728887451227
910.0008463124974017850.001692624994803570.999153687502598
920.0005299883969904670.001059976793980930.99947001160301
930.0006210571027554590.001242114205510920.999378942897244
940.000549898633270040.001099797266540080.99945010136673
950.005270922163996580.01054184432799320.994729077836003
960.007377455830120360.01475491166024070.99262254416988
970.005622433171371730.01124486634274350.994377566828628
980.003843759528061480.007687519056122950.996156240471939
990.002739516353648320.005479032707296650.997260483646352
1000.001850604232351310.003701208464702620.998149395767649
1010.001186115528557340.002372231057114680.998813884471443
1020.0007502134294275250.001500426858855050.999249786570573
1030.0004649504603361580.0009299009206723170.999535049539664
1040.0002745486038866320.0005490972077732640.999725451396113
1050.0001693124011351250.000338624802270250.999830687598865
1060.0004578463975761540.0009156927951523080.999542153602424
1070.0006677924880246680.001335584976049340.999332207511975
1080.000626703591397250.00125340718279450.999373296408603
1090.0004487179305256040.0008974358610512080.999551282069474
1100.001757742774430940.003515485548861890.998242257225569
1110.004666635975368570.009333271950737150.995333364024631
1120.003578305172246270.007156610344492540.996421694827754
1130.005959874882997640.01191974976599530.994040125117002
1140.003672254549124530.007344509098249060.996327745450875
1150.03781049641232380.07562099282464760.962189503587676
1160.05239051782274590.1047810356454920.947609482177254
1170.03808967009329690.07617934018659370.961910329906703
1180.03222596528774660.06445193057549310.967774034712253
1190.05734766995035890.1146953399007180.942652330049641
1200.05088834928907390.1017766985781480.949111650710926
1210.03915174097295960.07830348194591910.96084825902704
1220.07425395928192610.1485079185638520.925746040718074
1230.09063821359787360.1812764271957470.909361786402126
1240.05706652747521080.1141330549504220.942933472524789
1250.03750648206236560.07501296412473130.962493517937634
1260.08371909265532240.1674381853106450.916280907344678
1270.05008147924756230.1001629584951250.949918520752438







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level320.290909090909091NOK
5% type I error level480.436363636363636NOK
10% type I error level560.509090909090909NOK

\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 testsOK/NOK
1% type I error level320.290909090909091NOK
5% type I error level480.436363636363636NOK
10% type I error level560.509090909090909NOK



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