Free Statistics

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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 02 Nov 2012 19:08:24 -0400
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/02/t1351897755nan706jyib1x3f9.htm/, Retrieved Mon, 27 Jun 2022 04:44:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185684, Retrieved Mon, 27 Jun 2022 04:44:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact55
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Interactie-effect...] [2012-11-02 23:08:24] [18a55f974a2e8651a7d8da0218fcbdb6] [Current]
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Dataseries X:
14	501	1002000	11	22000	20	40000	91,81	183620	77585	155170000	1303,2	2606400	2000
14	485	970000	11	22000	19	38000	91,98	183960	77585	155170000	-58,7	-117400	2000
15	464	928000	11	22000	18	36000	91,72	183440	77585	155170000	-378,9	-757800	2000
13	460	920460	11	22011	13	26013	90,27	180630,27	78302	156682302	175,6	351375,6	2001
8	467	934467	11	22011	17	34017	91,89	183871,89	78302	156682302	233,7	467633,7	2001
7	460	920460	9	18009	17	34017	92,07	184232,07	78302	156682302	706,8	1414306,8	2001
3	448	896448	8	16008	13	26013	92,92	185932,92	78224	156526224	-23,6	-47223,6	2001
3	443	886443	6	12006	14	28014	93,34	186773,34	78224	156526224	420,9	842220,9	2001
4	436	872436	7	14007	13	26013	93,6	187293,6	78224	156526224	722,1	1444922,1	2001
4	431	862431	8	16008	17	34017	92,41	184912,41	78178	156434178	1401,3	2804001,3	2001
0	484	968484	6	12006	17	34017	93,6	187293,6	78178	156434178	-94,9	-189894,9	2001
-4	510	1020510	5	10005	15	30015	93,77	187633,77	78178	156434178	1043,6	2088243,6	2001
-14	513	1026513	2	4002	9	18009	93,6	187293,6	77988	156053988	1300,1	2601500,1	2001
-18	503	1006503	3	6003	10	20010	93,6	187293,6	77988	156053988	721,1	1442921,1	2001
-8	471	942471	3	6003	9	18009	93,51	187113,51	77988	156053988	-45,6	-91245,6	2001
-1	471	942942	7	14014	14	28028	92,66	185505,32	77876	155907752	787,5	1576575	2002
1	476	952952	8	16016	18	36036	94,2	188588,4	77876	155907752	694,3	1389988,6	2002
2	475	950950	7	14014	18	36036	94,37	188928,74	77876	155907752	1054,7	2111509,4	2002
0	470	940940	7	14014	12	24024	94,45	189088,9	78432	157020864	821,9	1645443,8	2002
1	461	922922	6	12012	16	32032	94,62	189429,24	78432	157020864	1100,7	2203601,4	2002
0	455	910910	6	12012	12	24024	94,37	188928,74	78432	157020864	862,4	1726524,8	2002
-1	456	912912	7	14014	19	38038	93,43	187046,86	79025	158208050	1656,1	3315512,2	2002
-3	517	1035034	5	10010	13	26026	94,79	189769,58	79025	158208050	-174	-348348	2002
-3	525	1051050	5	10010	12	24024	94,88	189949,76	79025	158208050	1337,6	2677875,2	2002
-3	523	1047046	5	10010	13	26026	94,79	189769,58	79407	158972814	1394,9	2792589,8	2002
-4	519	1039038	4	8008	11	22022	94,62	189429,24	79407	158972814	915,7	1833231,4	2002
-8	509	1019018	4	8008	10	20020	94,71	189609,42	79407	158972814	-481,1	-963162,2	2002
-9	512	1025536	4	8012	16	32048	93,77	187821,31	79644	159526932	167,9	336303,7	2003
-13	519	1039557	1	2003	12	24036	95,73	191747,19	79644	159526932	208,2	417024,6	2003
-18	517	1035551	-1	-2003	6	12018	95,99	192267,97	79644	159526932	382,2	765546,6	2003
-11	510	1021530	3	6009	8	16024	95,82	191927,46	79381	159000143	1004	2011012	2003
-9	509	1019527	4	8012	6	12018	95,47	191226,41	79381	159000143	864,7	1731994,1	2003
-10	501	1003503	3	6009	8	16024	95,82	191927,46	79381	159000143	1052,9	2108958,7	2003
-13	507	1015521	2	4006	8	16024	94,71	189704,13	79536	159310608	1417,6	2839452,8	2003
-11	569	1139707	1	2003	9	18027	96,33	192948,99	79536	159310608	-197,7	-395993,1	2003
-5	580	1161740	4	8012	13	26039	96,5	193289,5	79536	159310608	1262,1	2527986,3	2003
-15	578	1157734	3	6009	8	16024	96,16	192608,48	79813	159865439	1147,2	2297841,6	2003
-6	565	1131695	5	10015	11	22033	96,33	192948,99	79813	159865439	700,2	1402500,6	2003
-6	547	1095641	6	12018	8	16024	96,33	192948,99	79813	159865439	45,3	90735,9	2003
-3	555	1112220	6	12024	10	20040	95,05	190480,2	80332	160985328	458,5	918834	2004
-1	562	1126248	6	12024	15	30060	96,84	194067,36	80332	160985328	610,2	1222840,8	2004
-3	561	1124244	6	12024	12	24048	96,92	194227,68	80332	160985328	786,4	1575945,6	2004
-4	555	1112220	6	12024	13	26052	97,44	195269,76	81434	163193736	787,2	1577548,8	2004
-6	544	1090176	5	10020	12	24048	97,78	195951,12	81434	163193736	1040	2084160	2004
0	537	1076148	6	12024	15	30060	97,69	195770,76	81434	163193736	324,1	649496,4	2004
-4	543	1088172	5	10020	13	26052	96,67	193726,68	82167	164662668	1343	2691372	2004
-2	594	1190376	6	12024	13	26052	98,29	196973,16	82167	164662668	-501,2	-1004404,8	2004
-2	611	1224444	5	10020	16	32064	98,2	196792,8	82167	164662668	800,4	1604001,6	2004
-6	613	1228452	7	14028	14	28056	98,71	197814,84	82816	165963264	916,7	1837066,8	2004
-7	611	1224444	4	8016	12	24048	98,54	197474,16	82816	165963264	695,8	1394383,2	2004
-6	594	1190376	5	10020	15	30060	98,2	196792,8	82816	165963264	28	56112	2004
-6	595	1192975	6	12030	14	28070	96,92	194324,6	83000	166415000	495,6	993678	2005
-3	591	1184955	6	12030	19	38095	99,06	198615,3	83000	166415000	366,2	734231	2005
-2	589	1180945	5	10025	16	32080	99,65	199798,25	83000	166415000	633	1269165	2005
-5	584	1170920	3	6015	16	32080	99,82	200139,1	83251	166918255	848,3	1700841,5	2005
-11	573	1148865	2	4010	11	22055	99,99	200479,95	83251	166918255	472,2	946761	2005
-11	567	1136835	3	6015	13	26065	100,33	201161,65	83251	166918255	357,8	717389	2005
-11	569	1140845	3	6015	12	24060	99,31	199116,55	83591	167599955	824,3	1652721,5	2005
-10	621	1245105	2	4010	11	22055	101,1	202705,5	83591	167599955	-880,1	-1764600,5	2005
-14	629	1261145	0	0	6	12030	101,1	202705,5	83591	167599955	1066,8	2138934	2005
-8	628	1259140	4	8020	9	18045	100,93	202364,65	83910	168239550	1052,8	2110864	2005
-9	612	1227060	4	8020	6	12030	100,85	202204,25	83910	168239550	-32,1	-64360,5	2005
-5	595	1192975	5	10025	15	30075	100,93	202364,65	83910	168239550	-1331,4	-2669457	2005
-1	597	1197582	6	12036	17	34102	99,6	199797,6	84599	169705594	-767,1	-1538802,6	2006
-2	593	1189558	6	12036	13	26078	101,88	204371,28	84599	169705594	-236,7	-474820,2	2006
-5	590	1183540	5	10030	12	24072	101,81	204230,86	84599	169705594	-184,9	-370909,4	2006
-4	580	1163480	5	10030	13	26078	102,38	205374,28	85275	171061650	-143,4	-287660,4	2006
-6	574	1151444	3	6018	10	20060	102,74	206096,44	85275	171061650	493,9	990763,4	2006
-2	573	1149438	5	10030	14	28084	102,82	206256,92	85275	171061650	549,7	1102698,2	2006
-2	573	1149438	5	10030	13	26078	101,72	204050,32	85608	171729648	982,7	1971296,2	2006
-2	620	1243720	5	10030	10	20060	103,47	207560,82	85608	171729648	-856,3	-1717737,8	2006
-2	626	1255756	3	6018	11	22066	102,98	206577,88	85608	171729648	967	1939802	2006
2	620	1243720	6	12036	12	24072	102,68	205976,08	86303	173123818	659,4	1322756,4	2006
1	588	1179528	6	12036	7	14042	102,9	206417,4	86303	173123818	577,2	1157863,2	2006
-8	566	1135396	4	8024	11	22066	103,03	206678,18	86303	173123818	-213,1	-427478,6	2006
-1	557	1117899	6	12042	9	18063	101,29	203289,03	87115	174839805	17,7	35523,9	2007
1	561	1125927	5	10035	13	26091	103,69	208105,83	87115	174839805	390,1	782930,7	2007
-1	549	1101843	4	8028	12	24084	103,68	208085,76	87115	174839805	509,3	1022165,1	2007
2	532	1067724	5	10035	5	10035	104,2	209129,4	87931	176477517	410	822870	2007
2	526	1055682	5	10035	13	26091	104,08	208888,56	87931	176477517	212,5	426487,5	2007
1	511	1025577	4	8028	11	22077	104,16	209049,12	87931	176477517	818	1641726	2007
-1	499	1001493	3	6021	8	16056	103,05	206821,35	88164	176945148	422,7	848358,9	2007
-2	555	1113885	2	4014	8	16056	104,66	210052,62	88164	176945148	-158	-317106	2007
-2	565	1133955	3	6021	8	16056	104,46	209651,22	88164	176945148	427,2	857390,4	2007
-1	542	1087794	2	4014	8	16056	104,95	210634,65	88792	178205544	243,4	488503,8	2007
-8	527	1057689	-1	-2007	0	0	105,85	212440,95	88792	178205544	-419,3	-841535,1	2007
-4	510	1023570	0	0	3	6021	106,23	213203,61	88792	178205544	-1459,8	-2929818,6	2007
-6	514	1032112	-2	-4016	0	0	104,86	210558,88	89263	179240104	-1389,8	-2790718,4	2008
-3	517	1038136	1	2008	-1	-2008	107,44	215739,52	89263	179240104	-2,1	-4216,8	2008
-3	508	1020064	-2	-4016	-1	-2008	108,23	217325,84	89263	179240104	-938,6	-1884708,8	2008
-7	493	989944	-2	-4016	-4	-8032	108,45	217767,6	89881	180481048	-839,9	-1686519,2	2008
-9	490	983920	-2	-4016	1	2008	109,39	219655,12	89881	180481048	-297,6	-597580,8	2008
-11	469	941752	-6	-12048	-1	-2008	110,15	221181,2	89881	180481048	-376,3	-755610,4	2008
-13	478	959824	-4	-8032	0	0	109,13	219133,04	90120	180960960	-79,4	-159435,2	2008
-11	528	1060224	-2	-4016	-1	-2008	110,28	221442,24	90120	180960960	-2091,3	-4199330,4	2008
-9	534	1072272	0	0	6	12048	110,17	221221,36	90120	180960960	-1023	-2054184	2008
-17	518	1040144	-5	-10040	0	0	109,99	220859,92	89703	180123624	-765,6	-1537324,8	2008
-22	506	1016048	-4	-8032	-3	-6024	109,26	219394,08	89703	180123624	-1592,3	-3197338,4	2008
-25	502	1008016	-5	-10040	-3	-6024	109,11	219092,88	89703	180123624	-1588,8	-3190310,4	2008
-20	516	1036644	-1	-2009	4	8036	107,06	215083,54	87818	176426362	-1318	-2647862	2009
-24	528	1060752	-2	-4018	1	2009	109,53	220045,77	87818	176426362	-402,4	-808421,6	2009
-24	533	1070797	-4	-8036	0	0	108,92	218820,28	87818	176426362	-814,5	-1636330,5	2009
-22	536	1076824	-1	-2009	-4	-8036	109,24	219463,16	86273	173322457	-98,4	-197685,6	2009
-19	537	1078833	1	2009	-2	-4018	109,12	219222,08	86273	173322457	-305,9	-614553,1	2009
-18	524	1052716	1	2009	3	6027	109	218981	86273	173322457	-18,4	-36965,6	2009
-17	536	1076824	-2	-4018	2	4018	107,23	215425,07	86316	173408844	610,3	1226092,7	2009
-11	587	1179283	1	2009	5	10045	109,49	219965,41	86316	173408844	-917,3	-1842855,7	2009
-11	597	1199373	1	2009	6	12054	109,04	219061,36	86316	173408844	88,4	177595,6	2009
-12	581	1167229	3	6027	6	12054	109,02	219021,18	87234	175253106	-740,2	-1487061,8	2009
-10	564	1133076	3	6027	3	6027	109,23	219443,07	87234	175253106	29,3	58863,7	2009
-15	558	1121022	1	2009	4	8036	109,46	219905,14	87234	175253106	-893,2	-1794438,8	2009
-15	575	1155750	1	2010	7	14070	107,9	216879	87885	176648850	-1030,2	-2070702	2010
-15	580	1165800	0	0	5	10050	110,42	221944,2	87885	176648850	-403,4	-810834	2010
-13	575	1155750	2	4020	6	12060	110,98	223069,8	87885	176648850	-46,9	-94269	2010
-8	563	1131630	2	4020	1	2010	111,48	224074,8	88003	176886030	-321,2	-645612	2010
-13	552	1109520	-1	-2010	3	6030	111,88	224878,8	88003	176886030	-239,9	-482199	2010
-9	537	1079370	1	2010	6	12060	111,89	224898,9	88003	176886030	640,9	1288209	2010
-7	545	1095450	0	0	0	0	109,85	220798,5	88910	178709100	511,6	1028316	2010
-4	601	1208010	1	2010	3	6030	112,1	225321	88910	178709100	-665,1	-1336851	2010
-4	604	1214040	1	2010	4	8040	112,24	225602,4	88910	178709100	657,7	1321977	2010
-2	586	1177860	3	6030	7	14070	112,39	225903,9	89397	179687970	-207,7	-417477	2010
0	564	1133640	2	4020	6	12060	112,52	226165,2	89397	179687970	-885,2	-1779252	2010
-2	549	1103490	0	0	6	12060	113,16	227451,6	89397	179687970	-1595,8	-3207558	2010
-3	551	1108061	0	0	6	12066	111,84	224910,24	89813	180613943	-1374,9	-2764923,9	2011
1	556	1118116	3	6033	6	12066	114,33	229917,63	89813	180613943	-316,6	-636682,6	2011
-2	548	1102028	-2	-4022	2	4022	114,82	230903,02	89813	180613943	-283,4	-569917,4	2011
-1	540	1085940	0	0	2	4022	115,2	231667,2	90539	182073929	-175,8	-353533,8	2011
1	531	1067841	1	2011	2	4022	115,4	232069,4	90539	182073929	-694,2	-1396036,2	2011
-3	521	1047731	-1	-2011	3	6033	115,74	232753,14	90539	182073929	-249,9	-502548,9	2011
-4	519	1043709	-2	-4022	-1	-2011	114,19	229636,09	90688	182373568	268,2	539350,2	2011
-9	572	1150292	-1	-2011	-4	-8044	115,94	233155,34	90688	182373568	-2105,1	-4233356,1	2011
-9	581	1168391	-1	-2011	4	8044	116,03	233336,33	90688	182373568	-762,8	-1533990,8	2011
-7	563	1132193	1	2011	5	10055	116,24	233758,64	90691	182379601	-117,1	-235488,1	2011
-14	548	1102028	-2	-4022	3	6033	116,66	234603,26	90691	182379601	-1094,4	-2200838,4	2011
-12	539	1083929	-5	-10055	-1	-2011	116,79	234864,69	90691	182379601	-2095,2	-4213447,2	2011
-16	541	1088492	-5	-10060	-4	-8048	115,48	232345,76	90645	182377740	-1587,6	-3194251,2	2012
-20	562	1130744	-6	-12072	0	0	118,16	237737,92	90645	182377740	-528	-1062336	2012
-12	559	1124708	-4	-8048	-1	-2012	118,38	238180,56	90645	182377740	-324,2	-652290,4	2012
-12	546	1098552	-3	-6036	-1	-2012	118,51	238442,12	90861	182812332	-276,1	-555513,2	2012
-10	536	1078432	-3	-6036	3	6036	118,42	238261,04	90861	182812332	-139,1	-279869,2	2012
-10	528	1062336	-1	-2012	2	4024	118,24	237898,88	90861	182812332	268	539216	2012
-13	530	1066360	-2	-4024	-4	-8048	116,47	234337,64	90401	181886812	570,5	1147846	2012
-16	582	1170984	-3	-6036	-3	-6036	118,96	239347,52	90401	181886812	-316,5	-636798	2012




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185684&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185684&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185684&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'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
i[t] = + 51381.1102672671 + 3.05661109468523w[t] -0.00152616435008931w_t[t] + 99.8573045141556f[t] -0.0487896954155831f_t[t] -20.1585665970961s[t] + 0.0101743426250989s_t[t] + 240.842761054923c[t] -0.120079724699806c_t[t] -0.894033936104984b[t] + 0.000446713155338788b_t[t] -0.215670296892744h[t] + 0.000107828721621598h_t[t] -25.706391475291t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
i[t] =  +  51381.1102672671 +  3.05661109468523w[t] -0.00152616435008931w_t[t] +  99.8573045141556f[t] -0.0487896954155831f_t[t] -20.1585665970961s[t] +  0.0101743426250989s_t[t] +  240.842761054923c[t] -0.120079724699806c_t[t] -0.894033936104984b[t] +  0.000446713155338788b_t[t] -0.215670296892744h[t] +  0.000107828721621598h_t[t] -25.706391475291t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185684&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]i[t] =  +  51381.1102672671 +  3.05661109468523w[t] -0.00152616435008931w_t[t] +  99.8573045141556f[t] -0.0487896954155831f_t[t] -20.1585665970961s[t] +  0.0101743426250989s_t[t] +  240.842761054923c[t] -0.120079724699806c_t[t] -0.894033936104984b[t] +  0.000446713155338788b_t[t] -0.215670296892744h[t] +  0.000107828721621598h_t[t] -25.706391475291t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185684&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185684&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
i[t] = + 51381.1102672671 + 3.05661109468523w[t] -0.00152616435008931w_t[t] + 99.8573045141556f[t] -0.0487896954155831f_t[t] -20.1585665970961s[t] + 0.0101743426250989s_t[t] + 240.842761054923c[t] -0.120079724699806c_t[t] -0.894033936104984b[t] + 0.000446713155338788b_t[t] -0.215670296892744h[t] + 0.000107828721621598h_t[t] -25.706391475291t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)51381.110267267110465.5527694.90953e-061e-06
w3.056611094685236.2589050.48840.6261230.313061
w_t-0.001526164350089310.003122-0.48880.6257850.312892
f99.8573045141556103.9886390.96030.3387150.169358
f_t-0.04878969541558310.051836-0.94120.3483410.174171
s-20.158566597096168.166736-0.29570.7679160.383958
s_t0.01017434262509890.0339760.29950.7650710.382536
c240.842761054923141.8002151.69850.0918310.045915
c_t-0.1200797246998060.070639-1.69990.0915610.04578
b-0.8940339361049840.26325-3.39610.0009090.000454
b_t0.0004467131553387880.0001313.40510.0008820.000441
h-0.2156702968927440.265308-0.81290.417770.208885
h_t0.0001078287216215980.0001320.81550.4163060.208153
t-25.7063914752915.217794-4.92673e-061e-06

\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) & 51381.1102672671 & 10465.552769 & 4.9095 & 3e-06 & 1e-06 \tabularnewline
w & 3.05661109468523 & 6.258905 & 0.4884 & 0.626123 & 0.313061 \tabularnewline
w_t & -0.00152616435008931 & 0.003122 & -0.4888 & 0.625785 & 0.312892 \tabularnewline
f & 99.8573045141556 & 103.988639 & 0.9603 & 0.338715 & 0.169358 \tabularnewline
f_t & -0.0487896954155831 & 0.051836 & -0.9412 & 0.348341 & 0.174171 \tabularnewline
s & -20.1585665970961 & 68.166736 & -0.2957 & 0.767916 & 0.383958 \tabularnewline
s_t & 0.0101743426250989 & 0.033976 & 0.2995 & 0.765071 & 0.382536 \tabularnewline
c & 240.842761054923 & 141.800215 & 1.6985 & 0.091831 & 0.045915 \tabularnewline
c_t & -0.120079724699806 & 0.070639 & -1.6999 & 0.091561 & 0.04578 \tabularnewline
b & -0.894033936104984 & 0.26325 & -3.3961 & 0.000909 & 0.000454 \tabularnewline
b_t & 0.000446713155338788 & 0.000131 & 3.4051 & 0.000882 & 0.000441 \tabularnewline
h & -0.215670296892744 & 0.265308 & -0.8129 & 0.41777 & 0.208885 \tabularnewline
h_t & 0.000107828721621598 & 0.000132 & 0.8155 & 0.416306 & 0.208153 \tabularnewline
t & -25.706391475291 & 5.217794 & -4.9267 & 3e-06 & 1e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185684&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]51381.1102672671[/C][C]10465.552769[/C][C]4.9095[/C][C]3e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]w[/C][C]3.05661109468523[/C][C]6.258905[/C][C]0.4884[/C][C]0.626123[/C][C]0.313061[/C][/ROW]
[ROW][C]w_t[/C][C]-0.00152616435008931[/C][C]0.003122[/C][C]-0.4888[/C][C]0.625785[/C][C]0.312892[/C][/ROW]
[ROW][C]f[/C][C]99.8573045141556[/C][C]103.988639[/C][C]0.9603[/C][C]0.338715[/C][C]0.169358[/C][/ROW]
[ROW][C]f_t[/C][C]-0.0487896954155831[/C][C]0.051836[/C][C]-0.9412[/C][C]0.348341[/C][C]0.174171[/C][/ROW]
[ROW][C]s[/C][C]-20.1585665970961[/C][C]68.166736[/C][C]-0.2957[/C][C]0.767916[/C][C]0.383958[/C][/ROW]
[ROW][C]s_t[/C][C]0.0101743426250989[/C][C]0.033976[/C][C]0.2995[/C][C]0.765071[/C][C]0.382536[/C][/ROW]
[ROW][C]c[/C][C]240.842761054923[/C][C]141.800215[/C][C]1.6985[/C][C]0.091831[/C][C]0.045915[/C][/ROW]
[ROW][C]c_t[/C][C]-0.120079724699806[/C][C]0.070639[/C][C]-1.6999[/C][C]0.091561[/C][C]0.04578[/C][/ROW]
[ROW][C]b[/C][C]-0.894033936104984[/C][C]0.26325[/C][C]-3.3961[/C][C]0.000909[/C][C]0.000454[/C][/ROW]
[ROW][C]b_t[/C][C]0.000446713155338788[/C][C]0.000131[/C][C]3.4051[/C][C]0.000882[/C][C]0.000441[/C][/ROW]
[ROW][C]h[/C][C]-0.215670296892744[/C][C]0.265308[/C][C]-0.8129[/C][C]0.41777[/C][C]0.208885[/C][/ROW]
[ROW][C]h_t[/C][C]0.000107828721621598[/C][C]0.000132[/C][C]0.8155[/C][C]0.416306[/C][C]0.208153[/C][/ROW]
[ROW][C]t[/C][C]-25.706391475291[/C][C]5.217794[/C][C]-4.9267[/C][C]3e-06[/C][C]1e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185684&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185684&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)51381.110267267110465.5527694.90953e-061e-06
w3.056611094685236.2589050.48840.6261230.313061
w_t-0.001526164350089310.003122-0.48880.6257850.312892
f99.8573045141556103.9886390.96030.3387150.169358
f_t-0.04878969541558310.051836-0.94120.3483410.174171
s-20.158566597096168.166736-0.29570.7679160.383958
s_t0.01017434262509890.0339760.29950.7650710.382536
c240.842761054923141.8002151.69850.0918310.045915
c_t-0.1200797246998060.070639-1.69990.0915610.04578
b-0.8940339361049840.26325-3.39610.0009090.000454
b_t0.0004467131553387880.0001313.40510.0008820.000441
h-0.2156702968927440.265308-0.81290.417770.208885
h_t0.0001078287216215980.0001320.81550.4163060.208153
t-25.7063914752915.217794-4.92673e-061e-06







Multiple Linear Regression - Regression Statistics
Multiple R0.906606241824317
R-squared0.821934877714812
Adjusted R-squared0.803990330507777
F-TEST (value)45.8041581228978
F-TEST (DF numerator)13
F-TEST (DF denominator)129
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.31867276504668
Sum Squared Residuals1420.75297086867

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.906606241824317 \tabularnewline
R-squared & 0.821934877714812 \tabularnewline
Adjusted R-squared & 0.803990330507777 \tabularnewline
F-TEST (value) & 45.8041581228978 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 129 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.31867276504668 \tabularnewline
Sum Squared Residuals & 1420.75297086867 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185684&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.906606241824317[/C][/ROW]
[ROW][C]R-squared[/C][C]0.821934877714812[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.803990330507777[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]45.8041581228978[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]129[/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]3.31867276504668[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1420.75297086867[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185684&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185684&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 R0.906606241824317
R-squared0.821934877714812
Adjusted R-squared0.803990330507777
F-TEST (value)45.8041581228978
F-TEST (DF numerator)13
F-TEST (DF denominator)129
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.31867276504668
Sum Squared Residuals1420.75297086867







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11414.9076933202381-0.907693320238097
21414.7827247218733-0.782724721873284
31514.32913049233520.670869507664754
4139.272835160900843.72716483909916
5811.0112545341795-3.01125453417947
676.68002740204270.319972597957296
734.03858517528349-1.03858517528349
830.04562287547581922.95437712452418
942.23020704989191.7697929501081
1044.6483849056313-0.648384905631303
1100.864361423369056-0.864361423369056
12-4-1.48980802382724-2.51019197617276
13-14-9.41148426096705-4.58851573903295
14-18-7.06462014596193-10.9353798540381
15-8-7.47662076820828-0.523379231791718
16-1-0.818001515868475-0.181998484131525
1712.8739055422644-1.8739055422644
1820.8407675465535291.15923245344647
190-0.2810420681931050.281042068193105
201-1.498700026513572.49870002651357
210-2.507065970274792.50706597027479
22-11.06165197743161-2.06165197743161
23-3-4.255250845605671.25525084560567
24-3-4.10943574443111.1094357444311
25-3-3.820515641430430.820515641430427
26-4-6.599224326462882.59922432646288
27-8-7.06538496338572-0.934615036614276
28-9-8.20000505910236-0.799994940897635
29-13-14.83353770949981.83353770949976
30-18-20.2818358889832.28183588898303
31-11-11.36672366618810.366723666188052
32-9-9.832512810663310.832512810663309
33-10-11.34886884929521.34886884929525
34-13-13.6139731371270.613973137127041
35-11-15.52162144502444.52162144502441
36-5-7.739294347203262.73929434720326
37-15-10.9160850826742-4.08391491732582
38-6-6.071151977727440.0711519777274354
39-6-4.79963588441074-1.20036411558926
40-3-6.488910550405223.48891055040522
41-1-4.920748668845513.92074866884551
42-3-5.521402186290492.52140218629049
43-4-3.87425321522332-0.125746784776683
44-6-5.99297471040701-0.00702528959298606
450-3.522861645629683.52286164562968
46-4-4.994491095683660.99449109568366
47-2-3.447549549550271.44754954955027
48-2-4.342435051351232.34243505135123
49-60.275309974856734-6.27530997485673
50-7-6.55788910638623-0.442110893613767
51-6-4.10017361629865-1.89982638370135
52-6-3.36948920141286-2.63051079858714
53-3-2.04181169385884-0.958188306141162
54-2-4.602718294251732.60271829425173
55-5-8.118390098702823.11839009870282
56-11-11.50431687407520.504316874075239
57-11-9.00029751268589-1.99970248731411
58-11-8.53422204142273-2.46577795857727
59-10-11.73189010867031.73189010867034
60-14-16.00692584925982.00692584925983
61-8-6.64753380309063-1.35246619690937
62-9-7.89453508446117-1.10546491553883
63-5-4.31190881996681-0.688091180033192
64-1-1.484131448242670.484131448242673
65-2-2.217695429090110.217695429090114
66-5-4.40396285751786-0.596037142482136
67-4-2.69780755332138-1.30219244667862
68-6-7.001661325867341.00166132586733
69-2-1.98936639095042-0.0106336090495766
70-2-1.23488073206651-0.765119267933492
71-2-3.448666628944561.44866662894456
72-2-6.022700221556594.02270022155659
7321.469827855905650.530172144094347
7410.3096900147028830.690309985297117
75-8-3.05473681883696-4.94526318116304
76-12.34799106527062-3.34799106527062
7711.33026824254509-0.330268242545089
78-1-0.700634958214385-0.299365041785615
7921.415550027958470.584449972041533
8023.4170019336953-1.4170019336953
8111.49061879320462-0.490618793204625
82-1-0.684710957619696-0.315289042380304
83-2-3.663554694107561.66355469410756
84-2-1.32553953541592-0.674460464584076
85-1-1.745966378393130.745966378393129
86-8-10.18303521530032.1830352153003
87-4-8.185569304993344.18556930499334
88-6-10.67369997103254.67369997103253
89-3-4.842472887645971.84247288764597
90-3-11.44882195598728.44882195598722
91-7-10.28855985594923.28855985594918
92-9-8.70706510656994-0.292934893430064
93-11-16.91165629631085.91165629631081
94-13-11.6922286734922-1.30777132650781
95-11-10.6134854893168-0.386514510683193
96-9-4.04693930472647-4.95306069527353
97-17-15.9553737461644-1.04462625383562
98-22-15.287256602638-6.71274339736204
99-25-17.0985719033547-7.90142809664533
100-20-12.7237959559649-7.27620404403509
101-24-15.6259121014592-8.37408789854076
102-24-19.7846894876889-4.21531051231109
103-22-20.1375746033238-1.86242539667616
104-19-16.0570536673192-2.94294633268083
105-18-14.20272478632-3.79727521367998
106-17-18.66206416313951.66206416313951
107-11-15.14366341774914.14366341774905
108-11-13.81461069465192.8146106946519
109-12-7.63832780206654-4.36167219793346
110-10-7.66926687710202-2.33073312289798
111-15-11.983267299973-3.01673270002695
112-15-8.9582240769282-6.0417759230718
113-15-12.0231111409047-2.97688885909534
114-13-8.00628422373739-4.99371577626261
115-8-9.429413132884961.42941313288496
116-13-14.21534190944951.2153419094495
117-9-8.66177444920507-0.338225550794926
118-7-7.872315542976320.872315542976319
119-4-8.239588778958824.23958877895882
120-4-6.643756907436312.64375690743631
121-2-1.11058029380749-0.889419706192508
1220-3.740019992459133.74001999245913
123-2-8.243652586364486.24365258636448
124-3-5.685204755819832.68520475581983
1251-0.8699248803970591.86992488039706
126-2-10.95761712570058.95761712570048
127-1-4.364836351879413.36483635187941
1281-3.246792831885494.24679283188549
129-3-5.997648116190442.99764811619044
130-4-6.684289905949912.68428990594991
131-9-10.41220173819111.41220173819112
132-9-6.59096999137001-2.40903000862999
133-7-1.94477666140994-5.05522333859006
134-14-8.99935607268074-5.00064392731926
135-12-16.57571852999514.57571852999505
136-16-16.14864301076370.148643010763669
137-20-17.5599450979496-2.44005490205041
138-12-14.35078201468182.35078201468178
139-12-11.4861741380959-0.513825861904129
140-10-9.85331778068667-0.146682219313325
141-10-6.01049299020037-3.98950700979963
142-13-10.0620452388507-2.93795476114935
143-16-15.1947777304554-0.80522226954458

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 14.9076933202381 & -0.907693320238097 \tabularnewline
2 & 14 & 14.7827247218733 & -0.782724721873284 \tabularnewline
3 & 15 & 14.3291304923352 & 0.670869507664754 \tabularnewline
4 & 13 & 9.27283516090084 & 3.72716483909916 \tabularnewline
5 & 8 & 11.0112545341795 & -3.01125453417947 \tabularnewline
6 & 7 & 6.6800274020427 & 0.319972597957296 \tabularnewline
7 & 3 & 4.03858517528349 & -1.03858517528349 \tabularnewline
8 & 3 & 0.0456228754758192 & 2.95437712452418 \tabularnewline
9 & 4 & 2.2302070498919 & 1.7697929501081 \tabularnewline
10 & 4 & 4.6483849056313 & -0.648384905631303 \tabularnewline
11 & 0 & 0.864361423369056 & -0.864361423369056 \tabularnewline
12 & -4 & -1.48980802382724 & -2.51019197617276 \tabularnewline
13 & -14 & -9.41148426096705 & -4.58851573903295 \tabularnewline
14 & -18 & -7.06462014596193 & -10.9353798540381 \tabularnewline
15 & -8 & -7.47662076820828 & -0.523379231791718 \tabularnewline
16 & -1 & -0.818001515868475 & -0.181998484131525 \tabularnewline
17 & 1 & 2.8739055422644 & -1.8739055422644 \tabularnewline
18 & 2 & 0.840767546553529 & 1.15923245344647 \tabularnewline
19 & 0 & -0.281042068193105 & 0.281042068193105 \tabularnewline
20 & 1 & -1.49870002651357 & 2.49870002651357 \tabularnewline
21 & 0 & -2.50706597027479 & 2.50706597027479 \tabularnewline
22 & -1 & 1.06165197743161 & -2.06165197743161 \tabularnewline
23 & -3 & -4.25525084560567 & 1.25525084560567 \tabularnewline
24 & -3 & -4.1094357444311 & 1.1094357444311 \tabularnewline
25 & -3 & -3.82051564143043 & 0.820515641430427 \tabularnewline
26 & -4 & -6.59922432646288 & 2.59922432646288 \tabularnewline
27 & -8 & -7.06538496338572 & -0.934615036614276 \tabularnewline
28 & -9 & -8.20000505910236 & -0.799994940897635 \tabularnewline
29 & -13 & -14.8335377094998 & 1.83353770949976 \tabularnewline
30 & -18 & -20.281835888983 & 2.28183588898303 \tabularnewline
31 & -11 & -11.3667236661881 & 0.366723666188052 \tabularnewline
32 & -9 & -9.83251281066331 & 0.832512810663309 \tabularnewline
33 & -10 & -11.3488688492952 & 1.34886884929525 \tabularnewline
34 & -13 & -13.613973137127 & 0.613973137127041 \tabularnewline
35 & -11 & -15.5216214450244 & 4.52162144502441 \tabularnewline
36 & -5 & -7.73929434720326 & 2.73929434720326 \tabularnewline
37 & -15 & -10.9160850826742 & -4.08391491732582 \tabularnewline
38 & -6 & -6.07115197772744 & 0.0711519777274354 \tabularnewline
39 & -6 & -4.79963588441074 & -1.20036411558926 \tabularnewline
40 & -3 & -6.48891055040522 & 3.48891055040522 \tabularnewline
41 & -1 & -4.92074866884551 & 3.92074866884551 \tabularnewline
42 & -3 & -5.52140218629049 & 2.52140218629049 \tabularnewline
43 & -4 & -3.87425321522332 & -0.125746784776683 \tabularnewline
44 & -6 & -5.99297471040701 & -0.00702528959298606 \tabularnewline
45 & 0 & -3.52286164562968 & 3.52286164562968 \tabularnewline
46 & -4 & -4.99449109568366 & 0.99449109568366 \tabularnewline
47 & -2 & -3.44754954955027 & 1.44754954955027 \tabularnewline
48 & -2 & -4.34243505135123 & 2.34243505135123 \tabularnewline
49 & -6 & 0.275309974856734 & -6.27530997485673 \tabularnewline
50 & -7 & -6.55788910638623 & -0.442110893613767 \tabularnewline
51 & -6 & -4.10017361629865 & -1.89982638370135 \tabularnewline
52 & -6 & -3.36948920141286 & -2.63051079858714 \tabularnewline
53 & -3 & -2.04181169385884 & -0.958188306141162 \tabularnewline
54 & -2 & -4.60271829425173 & 2.60271829425173 \tabularnewline
55 & -5 & -8.11839009870282 & 3.11839009870282 \tabularnewline
56 & -11 & -11.5043168740752 & 0.504316874075239 \tabularnewline
57 & -11 & -9.00029751268589 & -1.99970248731411 \tabularnewline
58 & -11 & -8.53422204142273 & -2.46577795857727 \tabularnewline
59 & -10 & -11.7318901086703 & 1.73189010867034 \tabularnewline
60 & -14 & -16.0069258492598 & 2.00692584925983 \tabularnewline
61 & -8 & -6.64753380309063 & -1.35246619690937 \tabularnewline
62 & -9 & -7.89453508446117 & -1.10546491553883 \tabularnewline
63 & -5 & -4.31190881996681 & -0.688091180033192 \tabularnewline
64 & -1 & -1.48413144824267 & 0.484131448242673 \tabularnewline
65 & -2 & -2.21769542909011 & 0.217695429090114 \tabularnewline
66 & -5 & -4.40396285751786 & -0.596037142482136 \tabularnewline
67 & -4 & -2.69780755332138 & -1.30219244667862 \tabularnewline
68 & -6 & -7.00166132586734 & 1.00166132586733 \tabularnewline
69 & -2 & -1.98936639095042 & -0.0106336090495766 \tabularnewline
70 & -2 & -1.23488073206651 & -0.765119267933492 \tabularnewline
71 & -2 & -3.44866662894456 & 1.44866662894456 \tabularnewline
72 & -2 & -6.02270022155659 & 4.02270022155659 \tabularnewline
73 & 2 & 1.46982785590565 & 0.530172144094347 \tabularnewline
74 & 1 & 0.309690014702883 & 0.690309985297117 \tabularnewline
75 & -8 & -3.05473681883696 & -4.94526318116304 \tabularnewline
76 & -1 & 2.34799106527062 & -3.34799106527062 \tabularnewline
77 & 1 & 1.33026824254509 & -0.330268242545089 \tabularnewline
78 & -1 & -0.700634958214385 & -0.299365041785615 \tabularnewline
79 & 2 & 1.41555002795847 & 0.584449972041533 \tabularnewline
80 & 2 & 3.4170019336953 & -1.4170019336953 \tabularnewline
81 & 1 & 1.49061879320462 & -0.490618793204625 \tabularnewline
82 & -1 & -0.684710957619696 & -0.315289042380304 \tabularnewline
83 & -2 & -3.66355469410756 & 1.66355469410756 \tabularnewline
84 & -2 & -1.32553953541592 & -0.674460464584076 \tabularnewline
85 & -1 & -1.74596637839313 & 0.745966378393129 \tabularnewline
86 & -8 & -10.1830352153003 & 2.1830352153003 \tabularnewline
87 & -4 & -8.18556930499334 & 4.18556930499334 \tabularnewline
88 & -6 & -10.6736999710325 & 4.67369997103253 \tabularnewline
89 & -3 & -4.84247288764597 & 1.84247288764597 \tabularnewline
90 & -3 & -11.4488219559872 & 8.44882195598722 \tabularnewline
91 & -7 & -10.2885598559492 & 3.28855985594918 \tabularnewline
92 & -9 & -8.70706510656994 & -0.292934893430064 \tabularnewline
93 & -11 & -16.9116562963108 & 5.91165629631081 \tabularnewline
94 & -13 & -11.6922286734922 & -1.30777132650781 \tabularnewline
95 & -11 & -10.6134854893168 & -0.386514510683193 \tabularnewline
96 & -9 & -4.04693930472647 & -4.95306069527353 \tabularnewline
97 & -17 & -15.9553737461644 & -1.04462625383562 \tabularnewline
98 & -22 & -15.287256602638 & -6.71274339736204 \tabularnewline
99 & -25 & -17.0985719033547 & -7.90142809664533 \tabularnewline
100 & -20 & -12.7237959559649 & -7.27620404403509 \tabularnewline
101 & -24 & -15.6259121014592 & -8.37408789854076 \tabularnewline
102 & -24 & -19.7846894876889 & -4.21531051231109 \tabularnewline
103 & -22 & -20.1375746033238 & -1.86242539667616 \tabularnewline
104 & -19 & -16.0570536673192 & -2.94294633268083 \tabularnewline
105 & -18 & -14.20272478632 & -3.79727521367998 \tabularnewline
106 & -17 & -18.6620641631395 & 1.66206416313951 \tabularnewline
107 & -11 & -15.1436634177491 & 4.14366341774905 \tabularnewline
108 & -11 & -13.8146106946519 & 2.8146106946519 \tabularnewline
109 & -12 & -7.63832780206654 & -4.36167219793346 \tabularnewline
110 & -10 & -7.66926687710202 & -2.33073312289798 \tabularnewline
111 & -15 & -11.983267299973 & -3.01673270002695 \tabularnewline
112 & -15 & -8.9582240769282 & -6.0417759230718 \tabularnewline
113 & -15 & -12.0231111409047 & -2.97688885909534 \tabularnewline
114 & -13 & -8.00628422373739 & -4.99371577626261 \tabularnewline
115 & -8 & -9.42941313288496 & 1.42941313288496 \tabularnewline
116 & -13 & -14.2153419094495 & 1.2153419094495 \tabularnewline
117 & -9 & -8.66177444920507 & -0.338225550794926 \tabularnewline
118 & -7 & -7.87231554297632 & 0.872315542976319 \tabularnewline
119 & -4 & -8.23958877895882 & 4.23958877895882 \tabularnewline
120 & -4 & -6.64375690743631 & 2.64375690743631 \tabularnewline
121 & -2 & -1.11058029380749 & -0.889419706192508 \tabularnewline
122 & 0 & -3.74001999245913 & 3.74001999245913 \tabularnewline
123 & -2 & -8.24365258636448 & 6.24365258636448 \tabularnewline
124 & -3 & -5.68520475581983 & 2.68520475581983 \tabularnewline
125 & 1 & -0.869924880397059 & 1.86992488039706 \tabularnewline
126 & -2 & -10.9576171257005 & 8.95761712570048 \tabularnewline
127 & -1 & -4.36483635187941 & 3.36483635187941 \tabularnewline
128 & 1 & -3.24679283188549 & 4.24679283188549 \tabularnewline
129 & -3 & -5.99764811619044 & 2.99764811619044 \tabularnewline
130 & -4 & -6.68428990594991 & 2.68428990594991 \tabularnewline
131 & -9 & -10.4122017381911 & 1.41220173819112 \tabularnewline
132 & -9 & -6.59096999137001 & -2.40903000862999 \tabularnewline
133 & -7 & -1.94477666140994 & -5.05522333859006 \tabularnewline
134 & -14 & -8.99935607268074 & -5.00064392731926 \tabularnewline
135 & -12 & -16.5757185299951 & 4.57571852999505 \tabularnewline
136 & -16 & -16.1486430107637 & 0.148643010763669 \tabularnewline
137 & -20 & -17.5599450979496 & -2.44005490205041 \tabularnewline
138 & -12 & -14.3507820146818 & 2.35078201468178 \tabularnewline
139 & -12 & -11.4861741380959 & -0.513825861904129 \tabularnewline
140 & -10 & -9.85331778068667 & -0.146682219313325 \tabularnewline
141 & -10 & -6.01049299020037 & -3.98950700979963 \tabularnewline
142 & -13 & -10.0620452388507 & -2.93795476114935 \tabularnewline
143 & -16 & -15.1947777304554 & -0.80522226954458 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185684&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]14[/C][C]14.9076933202381[/C][C]-0.907693320238097[/C][/ROW]
[ROW][C]2[/C][C]14[/C][C]14.7827247218733[/C][C]-0.782724721873284[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]14.3291304923352[/C][C]0.670869507664754[/C][/ROW]
[ROW][C]4[/C][C]13[/C][C]9.27283516090084[/C][C]3.72716483909916[/C][/ROW]
[ROW][C]5[/C][C]8[/C][C]11.0112545341795[/C][C]-3.01125453417947[/C][/ROW]
[ROW][C]6[/C][C]7[/C][C]6.6800274020427[/C][C]0.319972597957296[/C][/ROW]
[ROW][C]7[/C][C]3[/C][C]4.03858517528349[/C][C]-1.03858517528349[/C][/ROW]
[ROW][C]8[/C][C]3[/C][C]0.0456228754758192[/C][C]2.95437712452418[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]2.2302070498919[/C][C]1.7697929501081[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]4.6483849056313[/C][C]-0.648384905631303[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.864361423369056[/C][C]-0.864361423369056[/C][/ROW]
[ROW][C]12[/C][C]-4[/C][C]-1.48980802382724[/C][C]-2.51019197617276[/C][/ROW]
[ROW][C]13[/C][C]-14[/C][C]-9.41148426096705[/C][C]-4.58851573903295[/C][/ROW]
[ROW][C]14[/C][C]-18[/C][C]-7.06462014596193[/C][C]-10.9353798540381[/C][/ROW]
[ROW][C]15[/C][C]-8[/C][C]-7.47662076820828[/C][C]-0.523379231791718[/C][/ROW]
[ROW][C]16[/C][C]-1[/C][C]-0.818001515868475[/C][C]-0.181998484131525[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]2.8739055422644[/C][C]-1.8739055422644[/C][/ROW]
[ROW][C]18[/C][C]2[/C][C]0.840767546553529[/C][C]1.15923245344647[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]-0.281042068193105[/C][C]0.281042068193105[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]-1.49870002651357[/C][C]2.49870002651357[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]-2.50706597027479[/C][C]2.50706597027479[/C][/ROW]
[ROW][C]22[/C][C]-1[/C][C]1.06165197743161[/C][C]-2.06165197743161[/C][/ROW]
[ROW][C]23[/C][C]-3[/C][C]-4.25525084560567[/C][C]1.25525084560567[/C][/ROW]
[ROW][C]24[/C][C]-3[/C][C]-4.1094357444311[/C][C]1.1094357444311[/C][/ROW]
[ROW][C]25[/C][C]-3[/C][C]-3.82051564143043[/C][C]0.820515641430427[/C][/ROW]
[ROW][C]26[/C][C]-4[/C][C]-6.59922432646288[/C][C]2.59922432646288[/C][/ROW]
[ROW][C]27[/C][C]-8[/C][C]-7.06538496338572[/C][C]-0.934615036614276[/C][/ROW]
[ROW][C]28[/C][C]-9[/C][C]-8.20000505910236[/C][C]-0.799994940897635[/C][/ROW]
[ROW][C]29[/C][C]-13[/C][C]-14.8335377094998[/C][C]1.83353770949976[/C][/ROW]
[ROW][C]30[/C][C]-18[/C][C]-20.281835888983[/C][C]2.28183588898303[/C][/ROW]
[ROW][C]31[/C][C]-11[/C][C]-11.3667236661881[/C][C]0.366723666188052[/C][/ROW]
[ROW][C]32[/C][C]-9[/C][C]-9.83251281066331[/C][C]0.832512810663309[/C][/ROW]
[ROW][C]33[/C][C]-10[/C][C]-11.3488688492952[/C][C]1.34886884929525[/C][/ROW]
[ROW][C]34[/C][C]-13[/C][C]-13.613973137127[/C][C]0.613973137127041[/C][/ROW]
[ROW][C]35[/C][C]-11[/C][C]-15.5216214450244[/C][C]4.52162144502441[/C][/ROW]
[ROW][C]36[/C][C]-5[/C][C]-7.73929434720326[/C][C]2.73929434720326[/C][/ROW]
[ROW][C]37[/C][C]-15[/C][C]-10.9160850826742[/C][C]-4.08391491732582[/C][/ROW]
[ROW][C]38[/C][C]-6[/C][C]-6.07115197772744[/C][C]0.0711519777274354[/C][/ROW]
[ROW][C]39[/C][C]-6[/C][C]-4.79963588441074[/C][C]-1.20036411558926[/C][/ROW]
[ROW][C]40[/C][C]-3[/C][C]-6.48891055040522[/C][C]3.48891055040522[/C][/ROW]
[ROW][C]41[/C][C]-1[/C][C]-4.92074866884551[/C][C]3.92074866884551[/C][/ROW]
[ROW][C]42[/C][C]-3[/C][C]-5.52140218629049[/C][C]2.52140218629049[/C][/ROW]
[ROW][C]43[/C][C]-4[/C][C]-3.87425321522332[/C][C]-0.125746784776683[/C][/ROW]
[ROW][C]44[/C][C]-6[/C][C]-5.99297471040701[/C][C]-0.00702528959298606[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]-3.52286164562968[/C][C]3.52286164562968[/C][/ROW]
[ROW][C]46[/C][C]-4[/C][C]-4.99449109568366[/C][C]0.99449109568366[/C][/ROW]
[ROW][C]47[/C][C]-2[/C][C]-3.44754954955027[/C][C]1.44754954955027[/C][/ROW]
[ROW][C]48[/C][C]-2[/C][C]-4.34243505135123[/C][C]2.34243505135123[/C][/ROW]
[ROW][C]49[/C][C]-6[/C][C]0.275309974856734[/C][C]-6.27530997485673[/C][/ROW]
[ROW][C]50[/C][C]-7[/C][C]-6.55788910638623[/C][C]-0.442110893613767[/C][/ROW]
[ROW][C]51[/C][C]-6[/C][C]-4.10017361629865[/C][C]-1.89982638370135[/C][/ROW]
[ROW][C]52[/C][C]-6[/C][C]-3.36948920141286[/C][C]-2.63051079858714[/C][/ROW]
[ROW][C]53[/C][C]-3[/C][C]-2.04181169385884[/C][C]-0.958188306141162[/C][/ROW]
[ROW][C]54[/C][C]-2[/C][C]-4.60271829425173[/C][C]2.60271829425173[/C][/ROW]
[ROW][C]55[/C][C]-5[/C][C]-8.11839009870282[/C][C]3.11839009870282[/C][/ROW]
[ROW][C]56[/C][C]-11[/C][C]-11.5043168740752[/C][C]0.504316874075239[/C][/ROW]
[ROW][C]57[/C][C]-11[/C][C]-9.00029751268589[/C][C]-1.99970248731411[/C][/ROW]
[ROW][C]58[/C][C]-11[/C][C]-8.53422204142273[/C][C]-2.46577795857727[/C][/ROW]
[ROW][C]59[/C][C]-10[/C][C]-11.7318901086703[/C][C]1.73189010867034[/C][/ROW]
[ROW][C]60[/C][C]-14[/C][C]-16.0069258492598[/C][C]2.00692584925983[/C][/ROW]
[ROW][C]61[/C][C]-8[/C][C]-6.64753380309063[/C][C]-1.35246619690937[/C][/ROW]
[ROW][C]62[/C][C]-9[/C][C]-7.89453508446117[/C][C]-1.10546491553883[/C][/ROW]
[ROW][C]63[/C][C]-5[/C][C]-4.31190881996681[/C][C]-0.688091180033192[/C][/ROW]
[ROW][C]64[/C][C]-1[/C][C]-1.48413144824267[/C][C]0.484131448242673[/C][/ROW]
[ROW][C]65[/C][C]-2[/C][C]-2.21769542909011[/C][C]0.217695429090114[/C][/ROW]
[ROW][C]66[/C][C]-5[/C][C]-4.40396285751786[/C][C]-0.596037142482136[/C][/ROW]
[ROW][C]67[/C][C]-4[/C][C]-2.69780755332138[/C][C]-1.30219244667862[/C][/ROW]
[ROW][C]68[/C][C]-6[/C][C]-7.00166132586734[/C][C]1.00166132586733[/C][/ROW]
[ROW][C]69[/C][C]-2[/C][C]-1.98936639095042[/C][C]-0.0106336090495766[/C][/ROW]
[ROW][C]70[/C][C]-2[/C][C]-1.23488073206651[/C][C]-0.765119267933492[/C][/ROW]
[ROW][C]71[/C][C]-2[/C][C]-3.44866662894456[/C][C]1.44866662894456[/C][/ROW]
[ROW][C]72[/C][C]-2[/C][C]-6.02270022155659[/C][C]4.02270022155659[/C][/ROW]
[ROW][C]73[/C][C]2[/C][C]1.46982785590565[/C][C]0.530172144094347[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]0.309690014702883[/C][C]0.690309985297117[/C][/ROW]
[ROW][C]75[/C][C]-8[/C][C]-3.05473681883696[/C][C]-4.94526318116304[/C][/ROW]
[ROW][C]76[/C][C]-1[/C][C]2.34799106527062[/C][C]-3.34799106527062[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]1.33026824254509[/C][C]-0.330268242545089[/C][/ROW]
[ROW][C]78[/C][C]-1[/C][C]-0.700634958214385[/C][C]-0.299365041785615[/C][/ROW]
[ROW][C]79[/C][C]2[/C][C]1.41555002795847[/C][C]0.584449972041533[/C][/ROW]
[ROW][C]80[/C][C]2[/C][C]3.4170019336953[/C][C]-1.4170019336953[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]1.49061879320462[/C][C]-0.490618793204625[/C][/ROW]
[ROW][C]82[/C][C]-1[/C][C]-0.684710957619696[/C][C]-0.315289042380304[/C][/ROW]
[ROW][C]83[/C][C]-2[/C][C]-3.66355469410756[/C][C]1.66355469410756[/C][/ROW]
[ROW][C]84[/C][C]-2[/C][C]-1.32553953541592[/C][C]-0.674460464584076[/C][/ROW]
[ROW][C]85[/C][C]-1[/C][C]-1.74596637839313[/C][C]0.745966378393129[/C][/ROW]
[ROW][C]86[/C][C]-8[/C][C]-10.1830352153003[/C][C]2.1830352153003[/C][/ROW]
[ROW][C]87[/C][C]-4[/C][C]-8.18556930499334[/C][C]4.18556930499334[/C][/ROW]
[ROW][C]88[/C][C]-6[/C][C]-10.6736999710325[/C][C]4.67369997103253[/C][/ROW]
[ROW][C]89[/C][C]-3[/C][C]-4.84247288764597[/C][C]1.84247288764597[/C][/ROW]
[ROW][C]90[/C][C]-3[/C][C]-11.4488219559872[/C][C]8.44882195598722[/C][/ROW]
[ROW][C]91[/C][C]-7[/C][C]-10.2885598559492[/C][C]3.28855985594918[/C][/ROW]
[ROW][C]92[/C][C]-9[/C][C]-8.70706510656994[/C][C]-0.292934893430064[/C][/ROW]
[ROW][C]93[/C][C]-11[/C][C]-16.9116562963108[/C][C]5.91165629631081[/C][/ROW]
[ROW][C]94[/C][C]-13[/C][C]-11.6922286734922[/C][C]-1.30777132650781[/C][/ROW]
[ROW][C]95[/C][C]-11[/C][C]-10.6134854893168[/C][C]-0.386514510683193[/C][/ROW]
[ROW][C]96[/C][C]-9[/C][C]-4.04693930472647[/C][C]-4.95306069527353[/C][/ROW]
[ROW][C]97[/C][C]-17[/C][C]-15.9553737461644[/C][C]-1.04462625383562[/C][/ROW]
[ROW][C]98[/C][C]-22[/C][C]-15.287256602638[/C][C]-6.71274339736204[/C][/ROW]
[ROW][C]99[/C][C]-25[/C][C]-17.0985719033547[/C][C]-7.90142809664533[/C][/ROW]
[ROW][C]100[/C][C]-20[/C][C]-12.7237959559649[/C][C]-7.27620404403509[/C][/ROW]
[ROW][C]101[/C][C]-24[/C][C]-15.6259121014592[/C][C]-8.37408789854076[/C][/ROW]
[ROW][C]102[/C][C]-24[/C][C]-19.7846894876889[/C][C]-4.21531051231109[/C][/ROW]
[ROW][C]103[/C][C]-22[/C][C]-20.1375746033238[/C][C]-1.86242539667616[/C][/ROW]
[ROW][C]104[/C][C]-19[/C][C]-16.0570536673192[/C][C]-2.94294633268083[/C][/ROW]
[ROW][C]105[/C][C]-18[/C][C]-14.20272478632[/C][C]-3.79727521367998[/C][/ROW]
[ROW][C]106[/C][C]-17[/C][C]-18.6620641631395[/C][C]1.66206416313951[/C][/ROW]
[ROW][C]107[/C][C]-11[/C][C]-15.1436634177491[/C][C]4.14366341774905[/C][/ROW]
[ROW][C]108[/C][C]-11[/C][C]-13.8146106946519[/C][C]2.8146106946519[/C][/ROW]
[ROW][C]109[/C][C]-12[/C][C]-7.63832780206654[/C][C]-4.36167219793346[/C][/ROW]
[ROW][C]110[/C][C]-10[/C][C]-7.66926687710202[/C][C]-2.33073312289798[/C][/ROW]
[ROW][C]111[/C][C]-15[/C][C]-11.983267299973[/C][C]-3.01673270002695[/C][/ROW]
[ROW][C]112[/C][C]-15[/C][C]-8.9582240769282[/C][C]-6.0417759230718[/C][/ROW]
[ROW][C]113[/C][C]-15[/C][C]-12.0231111409047[/C][C]-2.97688885909534[/C][/ROW]
[ROW][C]114[/C][C]-13[/C][C]-8.00628422373739[/C][C]-4.99371577626261[/C][/ROW]
[ROW][C]115[/C][C]-8[/C][C]-9.42941313288496[/C][C]1.42941313288496[/C][/ROW]
[ROW][C]116[/C][C]-13[/C][C]-14.2153419094495[/C][C]1.2153419094495[/C][/ROW]
[ROW][C]117[/C][C]-9[/C][C]-8.66177444920507[/C][C]-0.338225550794926[/C][/ROW]
[ROW][C]118[/C][C]-7[/C][C]-7.87231554297632[/C][C]0.872315542976319[/C][/ROW]
[ROW][C]119[/C][C]-4[/C][C]-8.23958877895882[/C][C]4.23958877895882[/C][/ROW]
[ROW][C]120[/C][C]-4[/C][C]-6.64375690743631[/C][C]2.64375690743631[/C][/ROW]
[ROW][C]121[/C][C]-2[/C][C]-1.11058029380749[/C][C]-0.889419706192508[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]-3.74001999245913[/C][C]3.74001999245913[/C][/ROW]
[ROW][C]123[/C][C]-2[/C][C]-8.24365258636448[/C][C]6.24365258636448[/C][/ROW]
[ROW][C]124[/C][C]-3[/C][C]-5.68520475581983[/C][C]2.68520475581983[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]-0.869924880397059[/C][C]1.86992488039706[/C][/ROW]
[ROW][C]126[/C][C]-2[/C][C]-10.9576171257005[/C][C]8.95761712570048[/C][/ROW]
[ROW][C]127[/C][C]-1[/C][C]-4.36483635187941[/C][C]3.36483635187941[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]-3.24679283188549[/C][C]4.24679283188549[/C][/ROW]
[ROW][C]129[/C][C]-3[/C][C]-5.99764811619044[/C][C]2.99764811619044[/C][/ROW]
[ROW][C]130[/C][C]-4[/C][C]-6.68428990594991[/C][C]2.68428990594991[/C][/ROW]
[ROW][C]131[/C][C]-9[/C][C]-10.4122017381911[/C][C]1.41220173819112[/C][/ROW]
[ROW][C]132[/C][C]-9[/C][C]-6.59096999137001[/C][C]-2.40903000862999[/C][/ROW]
[ROW][C]133[/C][C]-7[/C][C]-1.94477666140994[/C][C]-5.05522333859006[/C][/ROW]
[ROW][C]134[/C][C]-14[/C][C]-8.99935607268074[/C][C]-5.00064392731926[/C][/ROW]
[ROW][C]135[/C][C]-12[/C][C]-16.5757185299951[/C][C]4.57571852999505[/C][/ROW]
[ROW][C]136[/C][C]-16[/C][C]-16.1486430107637[/C][C]0.148643010763669[/C][/ROW]
[ROW][C]137[/C][C]-20[/C][C]-17.5599450979496[/C][C]-2.44005490205041[/C][/ROW]
[ROW][C]138[/C][C]-12[/C][C]-14.3507820146818[/C][C]2.35078201468178[/C][/ROW]
[ROW][C]139[/C][C]-12[/C][C]-11.4861741380959[/C][C]-0.513825861904129[/C][/ROW]
[ROW][C]140[/C][C]-10[/C][C]-9.85331778068667[/C][C]-0.146682219313325[/C][/ROW]
[ROW][C]141[/C][C]-10[/C][C]-6.01049299020037[/C][C]-3.98950700979963[/C][/ROW]
[ROW][C]142[/C][C]-13[/C][C]-10.0620452388507[/C][C]-2.93795476114935[/C][/ROW]
[ROW][C]143[/C][C]-16[/C][C]-15.1947777304554[/C][C]-0.80522226954458[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185684&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185684&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
11414.9076933202381-0.907693320238097
21414.7827247218733-0.782724721873284
31514.32913049233520.670869507664754
4139.272835160900843.72716483909916
5811.0112545341795-3.01125453417947
676.68002740204270.319972597957296
734.03858517528349-1.03858517528349
830.04562287547581922.95437712452418
942.23020704989191.7697929501081
1044.6483849056313-0.648384905631303
1100.864361423369056-0.864361423369056
12-4-1.48980802382724-2.51019197617276
13-14-9.41148426096705-4.58851573903295
14-18-7.06462014596193-10.9353798540381
15-8-7.47662076820828-0.523379231791718
16-1-0.818001515868475-0.181998484131525
1712.8739055422644-1.8739055422644
1820.8407675465535291.15923245344647
190-0.2810420681931050.281042068193105
201-1.498700026513572.49870002651357
210-2.507065970274792.50706597027479
22-11.06165197743161-2.06165197743161
23-3-4.255250845605671.25525084560567
24-3-4.10943574443111.1094357444311
25-3-3.820515641430430.820515641430427
26-4-6.599224326462882.59922432646288
27-8-7.06538496338572-0.934615036614276
28-9-8.20000505910236-0.799994940897635
29-13-14.83353770949981.83353770949976
30-18-20.2818358889832.28183588898303
31-11-11.36672366618810.366723666188052
32-9-9.832512810663310.832512810663309
33-10-11.34886884929521.34886884929525
34-13-13.6139731371270.613973137127041
35-11-15.52162144502444.52162144502441
36-5-7.739294347203262.73929434720326
37-15-10.9160850826742-4.08391491732582
38-6-6.071151977727440.0711519777274354
39-6-4.79963588441074-1.20036411558926
40-3-6.488910550405223.48891055040522
41-1-4.920748668845513.92074866884551
42-3-5.521402186290492.52140218629049
43-4-3.87425321522332-0.125746784776683
44-6-5.99297471040701-0.00702528959298606
450-3.522861645629683.52286164562968
46-4-4.994491095683660.99449109568366
47-2-3.447549549550271.44754954955027
48-2-4.342435051351232.34243505135123
49-60.275309974856734-6.27530997485673
50-7-6.55788910638623-0.442110893613767
51-6-4.10017361629865-1.89982638370135
52-6-3.36948920141286-2.63051079858714
53-3-2.04181169385884-0.958188306141162
54-2-4.602718294251732.60271829425173
55-5-8.118390098702823.11839009870282
56-11-11.50431687407520.504316874075239
57-11-9.00029751268589-1.99970248731411
58-11-8.53422204142273-2.46577795857727
59-10-11.73189010867031.73189010867034
60-14-16.00692584925982.00692584925983
61-8-6.64753380309063-1.35246619690937
62-9-7.89453508446117-1.10546491553883
63-5-4.31190881996681-0.688091180033192
64-1-1.484131448242670.484131448242673
65-2-2.217695429090110.217695429090114
66-5-4.40396285751786-0.596037142482136
67-4-2.69780755332138-1.30219244667862
68-6-7.001661325867341.00166132586733
69-2-1.98936639095042-0.0106336090495766
70-2-1.23488073206651-0.765119267933492
71-2-3.448666628944561.44866662894456
72-2-6.022700221556594.02270022155659
7321.469827855905650.530172144094347
7410.3096900147028830.690309985297117
75-8-3.05473681883696-4.94526318116304
76-12.34799106527062-3.34799106527062
7711.33026824254509-0.330268242545089
78-1-0.700634958214385-0.299365041785615
7921.415550027958470.584449972041533
8023.4170019336953-1.4170019336953
8111.49061879320462-0.490618793204625
82-1-0.684710957619696-0.315289042380304
83-2-3.663554694107561.66355469410756
84-2-1.32553953541592-0.674460464584076
85-1-1.745966378393130.745966378393129
86-8-10.18303521530032.1830352153003
87-4-8.185569304993344.18556930499334
88-6-10.67369997103254.67369997103253
89-3-4.842472887645971.84247288764597
90-3-11.44882195598728.44882195598722
91-7-10.28855985594923.28855985594918
92-9-8.70706510656994-0.292934893430064
93-11-16.91165629631085.91165629631081
94-13-11.6922286734922-1.30777132650781
95-11-10.6134854893168-0.386514510683193
96-9-4.04693930472647-4.95306069527353
97-17-15.9553737461644-1.04462625383562
98-22-15.287256602638-6.71274339736204
99-25-17.0985719033547-7.90142809664533
100-20-12.7237959559649-7.27620404403509
101-24-15.6259121014592-8.37408789854076
102-24-19.7846894876889-4.21531051231109
103-22-20.1375746033238-1.86242539667616
104-19-16.0570536673192-2.94294633268083
105-18-14.20272478632-3.79727521367998
106-17-18.66206416313951.66206416313951
107-11-15.14366341774914.14366341774905
108-11-13.81461069465192.8146106946519
109-12-7.63832780206654-4.36167219793346
110-10-7.66926687710202-2.33073312289798
111-15-11.983267299973-3.01673270002695
112-15-8.9582240769282-6.0417759230718
113-15-12.0231111409047-2.97688885909534
114-13-8.00628422373739-4.99371577626261
115-8-9.429413132884961.42941313288496
116-13-14.21534190944951.2153419094495
117-9-8.66177444920507-0.338225550794926
118-7-7.872315542976320.872315542976319
119-4-8.239588778958824.23958877895882
120-4-6.643756907436312.64375690743631
121-2-1.11058029380749-0.889419706192508
1220-3.740019992459133.74001999245913
123-2-8.243652586364486.24365258636448
124-3-5.685204755819832.68520475581983
1251-0.8699248803970591.86992488039706
126-2-10.95761712570058.95761712570048
127-1-4.364836351879413.36483635187941
1281-3.246792831885494.24679283188549
129-3-5.997648116190442.99764811619044
130-4-6.684289905949912.68428990594991
131-9-10.41220173819111.41220173819112
132-9-6.59096999137001-2.40903000862999
133-7-1.94477666140994-5.05522333859006
134-14-8.99935607268074-5.00064392731926
135-12-16.57571852999514.57571852999505
136-16-16.14864301076370.148643010763669
137-20-17.5599450979496-2.44005490205041
138-12-14.35078201468182.35078201468178
139-12-11.4861741380959-0.513825861904129
140-10-9.85331778068667-0.146682219313325
141-10-6.01049299020037-3.98950700979963
142-13-10.0620452388507-2.93795476114935
143-16-15.1947777304554-0.80522226954458







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.6337164426511110.7325671146977770.366283557348889
180.4681931818384470.9363863636768940.531806818161553
190.6807207273548810.6385585452902380.319279272645119
200.5253524649613130.9492950700773740.474647535038687
210.4926145490464790.9852290980929590.507385450953521
220.4328417640490170.8656835280980340.567158235950983
230.3333054465976080.6666108931952160.666694553402392
240.2665917113494340.5331834226988670.733408288650566
250.2000609587629380.4001219175258770.799939041237062
260.1407718412450120.2815436824900230.859228158754988
270.1535858532850320.3071717065700640.846414146714968
280.1260137699445950.252027539889190.873986230055405
290.1899168706040880.3798337412081760.810083129395912
300.1707715499116250.3415430998232510.829228450088375
310.1440368878641040.2880737757282090.855963112135896
320.1047032579757570.2094065159515130.895296742024243
330.07369470304934440.1473894060986890.926305296950656
340.05094285842219670.1018857168443930.949057141577803
350.03586913440777060.07173826881554120.964130865592229
360.02361802050344250.04723604100688510.976381979496557
370.04818607101315170.09637214202630340.951813928986848
380.03497077777019670.06994155554039340.965029222229803
390.02526932279892340.05053864559784680.974730677201077
400.06163367072483810.1232673414496760.938366329275162
410.04665969318070420.09331938636140850.953340306819296
420.03331164029246340.06662328058492680.966688359707537
430.02496627178998730.04993254357997450.975033728210013
440.01727724302009680.03455448604019360.982722756979903
450.01455110884528540.02910221769057080.985448891154715
460.01059093092770740.02118186185541480.989409069072293
470.008543944021864950.01708788804372990.991456055978135
480.005778002062387010.0115560041247740.994221997937613
490.01496420291132410.02992840582264830.985035797088676
500.01037924647882370.02075849295764740.989620753521176
510.008930332380815030.01786066476163010.991069667619185
520.006029177740004950.01205835548000990.993970822259995
530.004263299046830530.008526598093661060.995736700953169
540.003376708338345630.006753416676691270.996623291661654
550.002351904495886790.004703808991773590.997648095504113
560.001804027715301860.003608055430603710.998195972284698
570.001732316697459160.003464633394918320.998267683302541
580.001154165373694780.002308330747389560.998845834626305
590.000751637138837590.001503274277675180.999248362861162
600.0004610526811383950.0009221053622767890.999538947318862
610.0003415115139349330.0006830230278698660.999658488486065
620.0002608810867465010.0005217621734930020.999739118913254
630.0001579374904384020.0003158749808768050.999842062509562
640.0002265476892120070.0004530953784240140.999773452310788
650.0002131901351141170.0004263802702282330.999786809864886
660.0001305626342713550.000261125268542710.999869437365729
678.50481411121924e-050.0001700962822243850.999914951858888
686.65195684965238e-050.0001330391369930480.999933480431503
694.43895763497847e-058.87791526995694e-050.99995561042365
703.41689617940813e-056.83379235881626e-050.999965831038206
713.85435127452557e-057.70870254905113e-050.999961456487255
724.79902979987787e-059.59805959975573e-050.999952009702001
734.81055706177123e-059.62111412354247e-050.999951894429382
744.31965410711234e-058.63930821422468e-050.999956803458929
753.32871124966947e-056.65742249933894e-050.999966712887503
762.50752014563646e-055.01504029127293e-050.999974924798544
771.66311432620102e-053.32622865240203e-050.999983368856738
789.97508726887931e-061.99501745377586e-050.999990024912731
797.98426892601397e-061.59685378520279e-050.999992015731074
804.96171164867864e-069.92342329735729e-060.999995038288351
812.75841984881638e-065.51683969763275e-060.999997241580151
821.69717898760979e-063.39435797521957e-060.999998302821012
831.15490439014992e-062.30980878029983e-060.99999884509561
847.11595747424123e-071.42319149484825e-060.999999288404253
855.24628874158455e-071.04925774831691e-060.999999475371126
862.87433886717748e-075.74867773435496e-070.999999712566113
871.0580263328253e-062.11605266565061e-060.999998941973667
882.53880719492228e-065.07761438984456e-060.999997461192805
891.6608348859802e-063.32166977196039e-060.999998339165114
903.55581913640508e-057.11163827281016e-050.999964441808636
910.0001210529834836080.0002421059669672160.999878947016516
920.000201657104280490.000403314208560980.99979834289572
930.0006163681506229190.001232736301245840.999383631849377
940.001714058735455930.003428117470911860.998285941264544
950.002280433334480850.004560866668961690.997719566665519
960.0020616248643810.0041232497287620.997938375135619
970.002288258086193090.004576516172386190.997711741913807
980.009536399625984740.01907279925196950.990463600374015
990.02620315667939480.05240631335878960.973796843320605
1000.03302831140767170.06605662281534330.966971688592328
1010.06800892483915290.1360178496783060.931991075160847
1020.07590543136416450.1518108627283290.924094568635836
1030.06307617976880880.1261523595376180.936923820231191
1040.05055561938270090.1011112387654020.949444380617299
1050.03978527766072430.07957055532144860.960214722339276
1060.0360145044676080.0720290089352160.963985495532392
1070.08523921698047310.1704784339609460.914760783019527
1080.2803237242837550.5606474485675110.719676275716245
1090.2284152124471630.4568304248943270.771584787552837
1100.1854596352002240.3709192704004480.814540364799776
1110.362650703814330.7253014076286610.63734929618567
1120.326632045250780.6532640905015590.67336795474922
1130.2806343794051790.5612687588103580.719365620594821
1140.3177526874137820.6355053748275640.682247312586218
1150.3060772313345220.6121544626690430.693922768665478
1160.2731910950807220.5463821901614440.726808904919278
1170.2997998609705660.5995997219411330.700200139029434
1180.7108923879342680.5782152241314640.289107612065732
1190.66488009340090.67023981319820.3351199065991
1200.6902486476953840.6195027046092320.309751352304616
1210.592882109503180.8142357809936410.40711789049682
1220.5554568919090890.8890862161818220.444543108090911
1230.5393083952284380.9213832095431230.460691604771562
1240.4222882662475230.8445765324950460.577711733752477
1250.5081150101568520.9837699796862970.491884989843148
1260.5453285715695740.9093428568608520.454671428430426

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.633716442651111 & 0.732567114697777 & 0.366283557348889 \tabularnewline
18 & 0.468193181838447 & 0.936386363676894 & 0.531806818161553 \tabularnewline
19 & 0.680720727354881 & 0.638558545290238 & 0.319279272645119 \tabularnewline
20 & 0.525352464961313 & 0.949295070077374 & 0.474647535038687 \tabularnewline
21 & 0.492614549046479 & 0.985229098092959 & 0.507385450953521 \tabularnewline
22 & 0.432841764049017 & 0.865683528098034 & 0.567158235950983 \tabularnewline
23 & 0.333305446597608 & 0.666610893195216 & 0.666694553402392 \tabularnewline
24 & 0.266591711349434 & 0.533183422698867 & 0.733408288650566 \tabularnewline
25 & 0.200060958762938 & 0.400121917525877 & 0.799939041237062 \tabularnewline
26 & 0.140771841245012 & 0.281543682490023 & 0.859228158754988 \tabularnewline
27 & 0.153585853285032 & 0.307171706570064 & 0.846414146714968 \tabularnewline
28 & 0.126013769944595 & 0.25202753988919 & 0.873986230055405 \tabularnewline
29 & 0.189916870604088 & 0.379833741208176 & 0.810083129395912 \tabularnewline
30 & 0.170771549911625 & 0.341543099823251 & 0.829228450088375 \tabularnewline
31 & 0.144036887864104 & 0.288073775728209 & 0.855963112135896 \tabularnewline
32 & 0.104703257975757 & 0.209406515951513 & 0.895296742024243 \tabularnewline
33 & 0.0736947030493444 & 0.147389406098689 & 0.926305296950656 \tabularnewline
34 & 0.0509428584221967 & 0.101885716844393 & 0.949057141577803 \tabularnewline
35 & 0.0358691344077706 & 0.0717382688155412 & 0.964130865592229 \tabularnewline
36 & 0.0236180205034425 & 0.0472360410068851 & 0.976381979496557 \tabularnewline
37 & 0.0481860710131517 & 0.0963721420263034 & 0.951813928986848 \tabularnewline
38 & 0.0349707777701967 & 0.0699415555403934 & 0.965029222229803 \tabularnewline
39 & 0.0252693227989234 & 0.0505386455978468 & 0.974730677201077 \tabularnewline
40 & 0.0616336707248381 & 0.123267341449676 & 0.938366329275162 \tabularnewline
41 & 0.0466596931807042 & 0.0933193863614085 & 0.953340306819296 \tabularnewline
42 & 0.0333116402924634 & 0.0666232805849268 & 0.966688359707537 \tabularnewline
43 & 0.0249662717899873 & 0.0499325435799745 & 0.975033728210013 \tabularnewline
44 & 0.0172772430200968 & 0.0345544860401936 & 0.982722756979903 \tabularnewline
45 & 0.0145511088452854 & 0.0291022176905708 & 0.985448891154715 \tabularnewline
46 & 0.0105909309277074 & 0.0211818618554148 & 0.989409069072293 \tabularnewline
47 & 0.00854394402186495 & 0.0170878880437299 & 0.991456055978135 \tabularnewline
48 & 0.00577800206238701 & 0.011556004124774 & 0.994221997937613 \tabularnewline
49 & 0.0149642029113241 & 0.0299284058226483 & 0.985035797088676 \tabularnewline
50 & 0.0103792464788237 & 0.0207584929576474 & 0.989620753521176 \tabularnewline
51 & 0.00893033238081503 & 0.0178606647616301 & 0.991069667619185 \tabularnewline
52 & 0.00602917774000495 & 0.0120583554800099 & 0.993970822259995 \tabularnewline
53 & 0.00426329904683053 & 0.00852659809366106 & 0.995736700953169 \tabularnewline
54 & 0.00337670833834563 & 0.00675341667669127 & 0.996623291661654 \tabularnewline
55 & 0.00235190449588679 & 0.00470380899177359 & 0.997648095504113 \tabularnewline
56 & 0.00180402771530186 & 0.00360805543060371 & 0.998195972284698 \tabularnewline
57 & 0.00173231669745916 & 0.00346463339491832 & 0.998267683302541 \tabularnewline
58 & 0.00115416537369478 & 0.00230833074738956 & 0.998845834626305 \tabularnewline
59 & 0.00075163713883759 & 0.00150327427767518 & 0.999248362861162 \tabularnewline
60 & 0.000461052681138395 & 0.000922105362276789 & 0.999538947318862 \tabularnewline
61 & 0.000341511513934933 & 0.000683023027869866 & 0.999658488486065 \tabularnewline
62 & 0.000260881086746501 & 0.000521762173493002 & 0.999739118913254 \tabularnewline
63 & 0.000157937490438402 & 0.000315874980876805 & 0.999842062509562 \tabularnewline
64 & 0.000226547689212007 & 0.000453095378424014 & 0.999773452310788 \tabularnewline
65 & 0.000213190135114117 & 0.000426380270228233 & 0.999786809864886 \tabularnewline
66 & 0.000130562634271355 & 0.00026112526854271 & 0.999869437365729 \tabularnewline
67 & 8.50481411121924e-05 & 0.000170096282224385 & 0.999914951858888 \tabularnewline
68 & 6.65195684965238e-05 & 0.000133039136993048 & 0.999933480431503 \tabularnewline
69 & 4.43895763497847e-05 & 8.87791526995694e-05 & 0.99995561042365 \tabularnewline
70 & 3.41689617940813e-05 & 6.83379235881626e-05 & 0.999965831038206 \tabularnewline
71 & 3.85435127452557e-05 & 7.70870254905113e-05 & 0.999961456487255 \tabularnewline
72 & 4.79902979987787e-05 & 9.59805959975573e-05 & 0.999952009702001 \tabularnewline
73 & 4.81055706177123e-05 & 9.62111412354247e-05 & 0.999951894429382 \tabularnewline
74 & 4.31965410711234e-05 & 8.63930821422468e-05 & 0.999956803458929 \tabularnewline
75 & 3.32871124966947e-05 & 6.65742249933894e-05 & 0.999966712887503 \tabularnewline
76 & 2.50752014563646e-05 & 5.01504029127293e-05 & 0.999974924798544 \tabularnewline
77 & 1.66311432620102e-05 & 3.32622865240203e-05 & 0.999983368856738 \tabularnewline
78 & 9.97508726887931e-06 & 1.99501745377586e-05 & 0.999990024912731 \tabularnewline
79 & 7.98426892601397e-06 & 1.59685378520279e-05 & 0.999992015731074 \tabularnewline
80 & 4.96171164867864e-06 & 9.92342329735729e-06 & 0.999995038288351 \tabularnewline
81 & 2.75841984881638e-06 & 5.51683969763275e-06 & 0.999997241580151 \tabularnewline
82 & 1.69717898760979e-06 & 3.39435797521957e-06 & 0.999998302821012 \tabularnewline
83 & 1.15490439014992e-06 & 2.30980878029983e-06 & 0.99999884509561 \tabularnewline
84 & 7.11595747424123e-07 & 1.42319149484825e-06 & 0.999999288404253 \tabularnewline
85 & 5.24628874158455e-07 & 1.04925774831691e-06 & 0.999999475371126 \tabularnewline
86 & 2.87433886717748e-07 & 5.74867773435496e-07 & 0.999999712566113 \tabularnewline
87 & 1.0580263328253e-06 & 2.11605266565061e-06 & 0.999998941973667 \tabularnewline
88 & 2.53880719492228e-06 & 5.07761438984456e-06 & 0.999997461192805 \tabularnewline
89 & 1.6608348859802e-06 & 3.32166977196039e-06 & 0.999998339165114 \tabularnewline
90 & 3.55581913640508e-05 & 7.11163827281016e-05 & 0.999964441808636 \tabularnewline
91 & 0.000121052983483608 & 0.000242105966967216 & 0.999878947016516 \tabularnewline
92 & 0.00020165710428049 & 0.00040331420856098 & 0.99979834289572 \tabularnewline
93 & 0.000616368150622919 & 0.00123273630124584 & 0.999383631849377 \tabularnewline
94 & 0.00171405873545593 & 0.00342811747091186 & 0.998285941264544 \tabularnewline
95 & 0.00228043333448085 & 0.00456086666896169 & 0.997719566665519 \tabularnewline
96 & 0.002061624864381 & 0.004123249728762 & 0.997938375135619 \tabularnewline
97 & 0.00228825808619309 & 0.00457651617238619 & 0.997711741913807 \tabularnewline
98 & 0.00953639962598474 & 0.0190727992519695 & 0.990463600374015 \tabularnewline
99 & 0.0262031566793948 & 0.0524063133587896 & 0.973796843320605 \tabularnewline
100 & 0.0330283114076717 & 0.0660566228153433 & 0.966971688592328 \tabularnewline
101 & 0.0680089248391529 & 0.136017849678306 & 0.931991075160847 \tabularnewline
102 & 0.0759054313641645 & 0.151810862728329 & 0.924094568635836 \tabularnewline
103 & 0.0630761797688088 & 0.126152359537618 & 0.936923820231191 \tabularnewline
104 & 0.0505556193827009 & 0.101111238765402 & 0.949444380617299 \tabularnewline
105 & 0.0397852776607243 & 0.0795705553214486 & 0.960214722339276 \tabularnewline
106 & 0.036014504467608 & 0.072029008935216 & 0.963985495532392 \tabularnewline
107 & 0.0852392169804731 & 0.170478433960946 & 0.914760783019527 \tabularnewline
108 & 0.280323724283755 & 0.560647448567511 & 0.719676275716245 \tabularnewline
109 & 0.228415212447163 & 0.456830424894327 & 0.771584787552837 \tabularnewline
110 & 0.185459635200224 & 0.370919270400448 & 0.814540364799776 \tabularnewline
111 & 0.36265070381433 & 0.725301407628661 & 0.63734929618567 \tabularnewline
112 & 0.32663204525078 & 0.653264090501559 & 0.67336795474922 \tabularnewline
113 & 0.280634379405179 & 0.561268758810358 & 0.719365620594821 \tabularnewline
114 & 0.317752687413782 & 0.635505374827564 & 0.682247312586218 \tabularnewline
115 & 0.306077231334522 & 0.612154462669043 & 0.693922768665478 \tabularnewline
116 & 0.273191095080722 & 0.546382190161444 & 0.726808904919278 \tabularnewline
117 & 0.299799860970566 & 0.599599721941133 & 0.700200139029434 \tabularnewline
118 & 0.710892387934268 & 0.578215224131464 & 0.289107612065732 \tabularnewline
119 & 0.6648800934009 & 0.6702398131982 & 0.3351199065991 \tabularnewline
120 & 0.690248647695384 & 0.619502704609232 & 0.309751352304616 \tabularnewline
121 & 0.59288210950318 & 0.814235780993641 & 0.40711789049682 \tabularnewline
122 & 0.555456891909089 & 0.889086216181822 & 0.444543108090911 \tabularnewline
123 & 0.539308395228438 & 0.921383209543123 & 0.460691604771562 \tabularnewline
124 & 0.422288266247523 & 0.844576532495046 & 0.577711733752477 \tabularnewline
125 & 0.508115010156852 & 0.983769979686297 & 0.491884989843148 \tabularnewline
126 & 0.545328571569574 & 0.909342856860852 & 0.454671428430426 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185684&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]17[/C][C]0.633716442651111[/C][C]0.732567114697777[/C][C]0.366283557348889[/C][/ROW]
[ROW][C]18[/C][C]0.468193181838447[/C][C]0.936386363676894[/C][C]0.531806818161553[/C][/ROW]
[ROW][C]19[/C][C]0.680720727354881[/C][C]0.638558545290238[/C][C]0.319279272645119[/C][/ROW]
[ROW][C]20[/C][C]0.525352464961313[/C][C]0.949295070077374[/C][C]0.474647535038687[/C][/ROW]
[ROW][C]21[/C][C]0.492614549046479[/C][C]0.985229098092959[/C][C]0.507385450953521[/C][/ROW]
[ROW][C]22[/C][C]0.432841764049017[/C][C]0.865683528098034[/C][C]0.567158235950983[/C][/ROW]
[ROW][C]23[/C][C]0.333305446597608[/C][C]0.666610893195216[/C][C]0.666694553402392[/C][/ROW]
[ROW][C]24[/C][C]0.266591711349434[/C][C]0.533183422698867[/C][C]0.733408288650566[/C][/ROW]
[ROW][C]25[/C][C]0.200060958762938[/C][C]0.400121917525877[/C][C]0.799939041237062[/C][/ROW]
[ROW][C]26[/C][C]0.140771841245012[/C][C]0.281543682490023[/C][C]0.859228158754988[/C][/ROW]
[ROW][C]27[/C][C]0.153585853285032[/C][C]0.307171706570064[/C][C]0.846414146714968[/C][/ROW]
[ROW][C]28[/C][C]0.126013769944595[/C][C]0.25202753988919[/C][C]0.873986230055405[/C][/ROW]
[ROW][C]29[/C][C]0.189916870604088[/C][C]0.379833741208176[/C][C]0.810083129395912[/C][/ROW]
[ROW][C]30[/C][C]0.170771549911625[/C][C]0.341543099823251[/C][C]0.829228450088375[/C][/ROW]
[ROW][C]31[/C][C]0.144036887864104[/C][C]0.288073775728209[/C][C]0.855963112135896[/C][/ROW]
[ROW][C]32[/C][C]0.104703257975757[/C][C]0.209406515951513[/C][C]0.895296742024243[/C][/ROW]
[ROW][C]33[/C][C]0.0736947030493444[/C][C]0.147389406098689[/C][C]0.926305296950656[/C][/ROW]
[ROW][C]34[/C][C]0.0509428584221967[/C][C]0.101885716844393[/C][C]0.949057141577803[/C][/ROW]
[ROW][C]35[/C][C]0.0358691344077706[/C][C]0.0717382688155412[/C][C]0.964130865592229[/C][/ROW]
[ROW][C]36[/C][C]0.0236180205034425[/C][C]0.0472360410068851[/C][C]0.976381979496557[/C][/ROW]
[ROW][C]37[/C][C]0.0481860710131517[/C][C]0.0963721420263034[/C][C]0.951813928986848[/C][/ROW]
[ROW][C]38[/C][C]0.0349707777701967[/C][C]0.0699415555403934[/C][C]0.965029222229803[/C][/ROW]
[ROW][C]39[/C][C]0.0252693227989234[/C][C]0.0505386455978468[/C][C]0.974730677201077[/C][/ROW]
[ROW][C]40[/C][C]0.0616336707248381[/C][C]0.123267341449676[/C][C]0.938366329275162[/C][/ROW]
[ROW][C]41[/C][C]0.0466596931807042[/C][C]0.0933193863614085[/C][C]0.953340306819296[/C][/ROW]
[ROW][C]42[/C][C]0.0333116402924634[/C][C]0.0666232805849268[/C][C]0.966688359707537[/C][/ROW]
[ROW][C]43[/C][C]0.0249662717899873[/C][C]0.0499325435799745[/C][C]0.975033728210013[/C][/ROW]
[ROW][C]44[/C][C]0.0172772430200968[/C][C]0.0345544860401936[/C][C]0.982722756979903[/C][/ROW]
[ROW][C]45[/C][C]0.0145511088452854[/C][C]0.0291022176905708[/C][C]0.985448891154715[/C][/ROW]
[ROW][C]46[/C][C]0.0105909309277074[/C][C]0.0211818618554148[/C][C]0.989409069072293[/C][/ROW]
[ROW][C]47[/C][C]0.00854394402186495[/C][C]0.0170878880437299[/C][C]0.991456055978135[/C][/ROW]
[ROW][C]48[/C][C]0.00577800206238701[/C][C]0.011556004124774[/C][C]0.994221997937613[/C][/ROW]
[ROW][C]49[/C][C]0.0149642029113241[/C][C]0.0299284058226483[/C][C]0.985035797088676[/C][/ROW]
[ROW][C]50[/C][C]0.0103792464788237[/C][C]0.0207584929576474[/C][C]0.989620753521176[/C][/ROW]
[ROW][C]51[/C][C]0.00893033238081503[/C][C]0.0178606647616301[/C][C]0.991069667619185[/C][/ROW]
[ROW][C]52[/C][C]0.00602917774000495[/C][C]0.0120583554800099[/C][C]0.993970822259995[/C][/ROW]
[ROW][C]53[/C][C]0.00426329904683053[/C][C]0.00852659809366106[/C][C]0.995736700953169[/C][/ROW]
[ROW][C]54[/C][C]0.00337670833834563[/C][C]0.00675341667669127[/C][C]0.996623291661654[/C][/ROW]
[ROW][C]55[/C][C]0.00235190449588679[/C][C]0.00470380899177359[/C][C]0.997648095504113[/C][/ROW]
[ROW][C]56[/C][C]0.00180402771530186[/C][C]0.00360805543060371[/C][C]0.998195972284698[/C][/ROW]
[ROW][C]57[/C][C]0.00173231669745916[/C][C]0.00346463339491832[/C][C]0.998267683302541[/C][/ROW]
[ROW][C]58[/C][C]0.00115416537369478[/C][C]0.00230833074738956[/C][C]0.998845834626305[/C][/ROW]
[ROW][C]59[/C][C]0.00075163713883759[/C][C]0.00150327427767518[/C][C]0.999248362861162[/C][/ROW]
[ROW][C]60[/C][C]0.000461052681138395[/C][C]0.000922105362276789[/C][C]0.999538947318862[/C][/ROW]
[ROW][C]61[/C][C]0.000341511513934933[/C][C]0.000683023027869866[/C][C]0.999658488486065[/C][/ROW]
[ROW][C]62[/C][C]0.000260881086746501[/C][C]0.000521762173493002[/C][C]0.999739118913254[/C][/ROW]
[ROW][C]63[/C][C]0.000157937490438402[/C][C]0.000315874980876805[/C][C]0.999842062509562[/C][/ROW]
[ROW][C]64[/C][C]0.000226547689212007[/C][C]0.000453095378424014[/C][C]0.999773452310788[/C][/ROW]
[ROW][C]65[/C][C]0.000213190135114117[/C][C]0.000426380270228233[/C][C]0.999786809864886[/C][/ROW]
[ROW][C]66[/C][C]0.000130562634271355[/C][C]0.00026112526854271[/C][C]0.999869437365729[/C][/ROW]
[ROW][C]67[/C][C]8.50481411121924e-05[/C][C]0.000170096282224385[/C][C]0.999914951858888[/C][/ROW]
[ROW][C]68[/C][C]6.65195684965238e-05[/C][C]0.000133039136993048[/C][C]0.999933480431503[/C][/ROW]
[ROW][C]69[/C][C]4.43895763497847e-05[/C][C]8.87791526995694e-05[/C][C]0.99995561042365[/C][/ROW]
[ROW][C]70[/C][C]3.41689617940813e-05[/C][C]6.83379235881626e-05[/C][C]0.999965831038206[/C][/ROW]
[ROW][C]71[/C][C]3.85435127452557e-05[/C][C]7.70870254905113e-05[/C][C]0.999961456487255[/C][/ROW]
[ROW][C]72[/C][C]4.79902979987787e-05[/C][C]9.59805959975573e-05[/C][C]0.999952009702001[/C][/ROW]
[ROW][C]73[/C][C]4.81055706177123e-05[/C][C]9.62111412354247e-05[/C][C]0.999951894429382[/C][/ROW]
[ROW][C]74[/C][C]4.31965410711234e-05[/C][C]8.63930821422468e-05[/C][C]0.999956803458929[/C][/ROW]
[ROW][C]75[/C][C]3.32871124966947e-05[/C][C]6.65742249933894e-05[/C][C]0.999966712887503[/C][/ROW]
[ROW][C]76[/C][C]2.50752014563646e-05[/C][C]5.01504029127293e-05[/C][C]0.999974924798544[/C][/ROW]
[ROW][C]77[/C][C]1.66311432620102e-05[/C][C]3.32622865240203e-05[/C][C]0.999983368856738[/C][/ROW]
[ROW][C]78[/C][C]9.97508726887931e-06[/C][C]1.99501745377586e-05[/C][C]0.999990024912731[/C][/ROW]
[ROW][C]79[/C][C]7.98426892601397e-06[/C][C]1.59685378520279e-05[/C][C]0.999992015731074[/C][/ROW]
[ROW][C]80[/C][C]4.96171164867864e-06[/C][C]9.92342329735729e-06[/C][C]0.999995038288351[/C][/ROW]
[ROW][C]81[/C][C]2.75841984881638e-06[/C][C]5.51683969763275e-06[/C][C]0.999997241580151[/C][/ROW]
[ROW][C]82[/C][C]1.69717898760979e-06[/C][C]3.39435797521957e-06[/C][C]0.999998302821012[/C][/ROW]
[ROW][C]83[/C][C]1.15490439014992e-06[/C][C]2.30980878029983e-06[/C][C]0.99999884509561[/C][/ROW]
[ROW][C]84[/C][C]7.11595747424123e-07[/C][C]1.42319149484825e-06[/C][C]0.999999288404253[/C][/ROW]
[ROW][C]85[/C][C]5.24628874158455e-07[/C][C]1.04925774831691e-06[/C][C]0.999999475371126[/C][/ROW]
[ROW][C]86[/C][C]2.87433886717748e-07[/C][C]5.74867773435496e-07[/C][C]0.999999712566113[/C][/ROW]
[ROW][C]87[/C][C]1.0580263328253e-06[/C][C]2.11605266565061e-06[/C][C]0.999998941973667[/C][/ROW]
[ROW][C]88[/C][C]2.53880719492228e-06[/C][C]5.07761438984456e-06[/C][C]0.999997461192805[/C][/ROW]
[ROW][C]89[/C][C]1.6608348859802e-06[/C][C]3.32166977196039e-06[/C][C]0.999998339165114[/C][/ROW]
[ROW][C]90[/C][C]3.55581913640508e-05[/C][C]7.11163827281016e-05[/C][C]0.999964441808636[/C][/ROW]
[ROW][C]91[/C][C]0.000121052983483608[/C][C]0.000242105966967216[/C][C]0.999878947016516[/C][/ROW]
[ROW][C]92[/C][C]0.00020165710428049[/C][C]0.00040331420856098[/C][C]0.99979834289572[/C][/ROW]
[ROW][C]93[/C][C]0.000616368150622919[/C][C]0.00123273630124584[/C][C]0.999383631849377[/C][/ROW]
[ROW][C]94[/C][C]0.00171405873545593[/C][C]0.00342811747091186[/C][C]0.998285941264544[/C][/ROW]
[ROW][C]95[/C][C]0.00228043333448085[/C][C]0.00456086666896169[/C][C]0.997719566665519[/C][/ROW]
[ROW][C]96[/C][C]0.002061624864381[/C][C]0.004123249728762[/C][C]0.997938375135619[/C][/ROW]
[ROW][C]97[/C][C]0.00228825808619309[/C][C]0.00457651617238619[/C][C]0.997711741913807[/C][/ROW]
[ROW][C]98[/C][C]0.00953639962598474[/C][C]0.0190727992519695[/C][C]0.990463600374015[/C][/ROW]
[ROW][C]99[/C][C]0.0262031566793948[/C][C]0.0524063133587896[/C][C]0.973796843320605[/C][/ROW]
[ROW][C]100[/C][C]0.0330283114076717[/C][C]0.0660566228153433[/C][C]0.966971688592328[/C][/ROW]
[ROW][C]101[/C][C]0.0680089248391529[/C][C]0.136017849678306[/C][C]0.931991075160847[/C][/ROW]
[ROW][C]102[/C][C]0.0759054313641645[/C][C]0.151810862728329[/C][C]0.924094568635836[/C][/ROW]
[ROW][C]103[/C][C]0.0630761797688088[/C][C]0.126152359537618[/C][C]0.936923820231191[/C][/ROW]
[ROW][C]104[/C][C]0.0505556193827009[/C][C]0.101111238765402[/C][C]0.949444380617299[/C][/ROW]
[ROW][C]105[/C][C]0.0397852776607243[/C][C]0.0795705553214486[/C][C]0.960214722339276[/C][/ROW]
[ROW][C]106[/C][C]0.036014504467608[/C][C]0.072029008935216[/C][C]0.963985495532392[/C][/ROW]
[ROW][C]107[/C][C]0.0852392169804731[/C][C]0.170478433960946[/C][C]0.914760783019527[/C][/ROW]
[ROW][C]108[/C][C]0.280323724283755[/C][C]0.560647448567511[/C][C]0.719676275716245[/C][/ROW]
[ROW][C]109[/C][C]0.228415212447163[/C][C]0.456830424894327[/C][C]0.771584787552837[/C][/ROW]
[ROW][C]110[/C][C]0.185459635200224[/C][C]0.370919270400448[/C][C]0.814540364799776[/C][/ROW]
[ROW][C]111[/C][C]0.36265070381433[/C][C]0.725301407628661[/C][C]0.63734929618567[/C][/ROW]
[ROW][C]112[/C][C]0.32663204525078[/C][C]0.653264090501559[/C][C]0.67336795474922[/C][/ROW]
[ROW][C]113[/C][C]0.280634379405179[/C][C]0.561268758810358[/C][C]0.719365620594821[/C][/ROW]
[ROW][C]114[/C][C]0.317752687413782[/C][C]0.635505374827564[/C][C]0.682247312586218[/C][/ROW]
[ROW][C]115[/C][C]0.306077231334522[/C][C]0.612154462669043[/C][C]0.693922768665478[/C][/ROW]
[ROW][C]116[/C][C]0.273191095080722[/C][C]0.546382190161444[/C][C]0.726808904919278[/C][/ROW]
[ROW][C]117[/C][C]0.299799860970566[/C][C]0.599599721941133[/C][C]0.700200139029434[/C][/ROW]
[ROW][C]118[/C][C]0.710892387934268[/C][C]0.578215224131464[/C][C]0.289107612065732[/C][/ROW]
[ROW][C]119[/C][C]0.6648800934009[/C][C]0.6702398131982[/C][C]0.3351199065991[/C][/ROW]
[ROW][C]120[/C][C]0.690248647695384[/C][C]0.619502704609232[/C][C]0.309751352304616[/C][/ROW]
[ROW][C]121[/C][C]0.59288210950318[/C][C]0.814235780993641[/C][C]0.40711789049682[/C][/ROW]
[ROW][C]122[/C][C]0.555456891909089[/C][C]0.889086216181822[/C][C]0.444543108090911[/C][/ROW]
[ROW][C]123[/C][C]0.539308395228438[/C][C]0.921383209543123[/C][C]0.460691604771562[/C][/ROW]
[ROW][C]124[/C][C]0.422288266247523[/C][C]0.844576532495046[/C][C]0.577711733752477[/C][/ROW]
[ROW][C]125[/C][C]0.508115010156852[/C][C]0.983769979686297[/C][C]0.491884989843148[/C][/ROW]
[ROW][C]126[/C][C]0.545328571569574[/C][C]0.909342856860852[/C][C]0.454671428430426[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185684&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185684&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
170.6337164426511110.7325671146977770.366283557348889
180.4681931818384470.9363863636768940.531806818161553
190.6807207273548810.6385585452902380.319279272645119
200.5253524649613130.9492950700773740.474647535038687
210.4926145490464790.9852290980929590.507385450953521
220.4328417640490170.8656835280980340.567158235950983
230.3333054465976080.6666108931952160.666694553402392
240.2665917113494340.5331834226988670.733408288650566
250.2000609587629380.4001219175258770.799939041237062
260.1407718412450120.2815436824900230.859228158754988
270.1535858532850320.3071717065700640.846414146714968
280.1260137699445950.252027539889190.873986230055405
290.1899168706040880.3798337412081760.810083129395912
300.1707715499116250.3415430998232510.829228450088375
310.1440368878641040.2880737757282090.855963112135896
320.1047032579757570.2094065159515130.895296742024243
330.07369470304934440.1473894060986890.926305296950656
340.05094285842219670.1018857168443930.949057141577803
350.03586913440777060.07173826881554120.964130865592229
360.02361802050344250.04723604100688510.976381979496557
370.04818607101315170.09637214202630340.951813928986848
380.03497077777019670.06994155554039340.965029222229803
390.02526932279892340.05053864559784680.974730677201077
400.06163367072483810.1232673414496760.938366329275162
410.04665969318070420.09331938636140850.953340306819296
420.03331164029246340.06662328058492680.966688359707537
430.02496627178998730.04993254357997450.975033728210013
440.01727724302009680.03455448604019360.982722756979903
450.01455110884528540.02910221769057080.985448891154715
460.01059093092770740.02118186185541480.989409069072293
470.008543944021864950.01708788804372990.991456055978135
480.005778002062387010.0115560041247740.994221997937613
490.01496420291132410.02992840582264830.985035797088676
500.01037924647882370.02075849295764740.989620753521176
510.008930332380815030.01786066476163010.991069667619185
520.006029177740004950.01205835548000990.993970822259995
530.004263299046830530.008526598093661060.995736700953169
540.003376708338345630.006753416676691270.996623291661654
550.002351904495886790.004703808991773590.997648095504113
560.001804027715301860.003608055430603710.998195972284698
570.001732316697459160.003464633394918320.998267683302541
580.001154165373694780.002308330747389560.998845834626305
590.000751637138837590.001503274277675180.999248362861162
600.0004610526811383950.0009221053622767890.999538947318862
610.0003415115139349330.0006830230278698660.999658488486065
620.0002608810867465010.0005217621734930020.999739118913254
630.0001579374904384020.0003158749808768050.999842062509562
640.0002265476892120070.0004530953784240140.999773452310788
650.0002131901351141170.0004263802702282330.999786809864886
660.0001305626342713550.000261125268542710.999869437365729
678.50481411121924e-050.0001700962822243850.999914951858888
686.65195684965238e-050.0001330391369930480.999933480431503
694.43895763497847e-058.87791526995694e-050.99995561042365
703.41689617940813e-056.83379235881626e-050.999965831038206
713.85435127452557e-057.70870254905113e-050.999961456487255
724.79902979987787e-059.59805959975573e-050.999952009702001
734.81055706177123e-059.62111412354247e-050.999951894429382
744.31965410711234e-058.63930821422468e-050.999956803458929
753.32871124966947e-056.65742249933894e-050.999966712887503
762.50752014563646e-055.01504029127293e-050.999974924798544
771.66311432620102e-053.32622865240203e-050.999983368856738
789.97508726887931e-061.99501745377586e-050.999990024912731
797.98426892601397e-061.59685378520279e-050.999992015731074
804.96171164867864e-069.92342329735729e-060.999995038288351
812.75841984881638e-065.51683969763275e-060.999997241580151
821.69717898760979e-063.39435797521957e-060.999998302821012
831.15490439014992e-062.30980878029983e-060.99999884509561
847.11595747424123e-071.42319149484825e-060.999999288404253
855.24628874158455e-071.04925774831691e-060.999999475371126
862.87433886717748e-075.74867773435496e-070.999999712566113
871.0580263328253e-062.11605266565061e-060.999998941973667
882.53880719492228e-065.07761438984456e-060.999997461192805
891.6608348859802e-063.32166977196039e-060.999998339165114
903.55581913640508e-057.11163827281016e-050.999964441808636
910.0001210529834836080.0002421059669672160.999878947016516
920.000201657104280490.000403314208560980.99979834289572
930.0006163681506229190.001232736301245840.999383631849377
940.001714058735455930.003428117470911860.998285941264544
950.002280433334480850.004560866668961690.997719566665519
960.0020616248643810.0041232497287620.997938375135619
970.002288258086193090.004576516172386190.997711741913807
980.009536399625984740.01907279925196950.990463600374015
990.02620315667939480.05240631335878960.973796843320605
1000.03302831140767170.06605662281534330.966971688592328
1010.06800892483915290.1360178496783060.931991075160847
1020.07590543136416450.1518108627283290.924094568635836
1030.06307617976880880.1261523595376180.936923820231191
1040.05055561938270090.1011112387654020.949444380617299
1050.03978527766072430.07957055532144860.960214722339276
1060.0360145044676080.0720290089352160.963985495532392
1070.08523921698047310.1704784339609460.914760783019527
1080.2803237242837550.5606474485675110.719676275716245
1090.2284152124471630.4568304248943270.771584787552837
1100.1854596352002240.3709192704004480.814540364799776
1110.362650703814330.7253014076286610.63734929618567
1120.326632045250780.6532640905015590.67336795474922
1130.2806343794051790.5612687588103580.719365620594821
1140.3177526874137820.6355053748275640.682247312586218
1150.3060772313345220.6121544626690430.693922768665478
1160.2731910950807220.5463821901614440.726808904919278
1170.2997998609705660.5995997219411330.700200139029434
1180.7108923879342680.5782152241314640.289107612065732
1190.66488009340090.67023981319820.3351199065991
1200.6902486476953840.6195027046092320.309751352304616
1210.592882109503180.8142357809936410.40711789049682
1220.5554568919090890.8890862161818220.444543108090911
1230.5393083952284380.9213832095431230.460691604771562
1240.4222882662475230.8445765324950460.577711733752477
1250.5081150101568520.9837699796862970.491884989843148
1260.5453285715695740.9093428568608520.454671428430426







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level450.409090909090909NOK
5% type I error level570.518181818181818NOK
10% type I error level670.609090909090909NOK

\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 & 45 & 0.409090909090909 & NOK \tabularnewline
5% type I error level & 57 & 0.518181818181818 & NOK \tabularnewline
10% type I error level & 67 & 0.609090909090909 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185684&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]45[/C][C]0.409090909090909[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]57[/C][C]0.518181818181818[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]67[/C][C]0.609090909090909[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185684&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185684&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 level450.409090909090909NOK
5% type I error level570.518181818181818NOK
10% type I error level670.609090909090909NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; 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')
}