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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 22 Dec 2011 08:11:33 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/22/t1324559547mv6q023z5o25cht.htm/, Retrieved Fri, 03 May 2024 10:55:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=159411, Retrieved Fri, 03 May 2024 10:55:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact90

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2011-12-20 11:35:37] [97a82ed57455ec27012f2e899dc4f1a4]
-    D    [Multiple Regression] [Multiple Regressi...] [2011-12-22 13:11:33] [2fa2d22b72a9c62ab85a23406d5dc0a0] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1818	279055	73	504	95	3	96	42	159	130	140824	32033	186099	165	165
1412	209884	73	502	68	4	75	38	149	143	110459	20654	113854	135	132
2049	233446	82	709	64	16	70	46	178	118	105079	16346	99776	121	121
2733	222117	106	1154	139	2	134	42	164	146	112098	35926	106194	148	145
1330	179751	54	402	51	1	72	30	100	73	43929	10621	100792	73	71
631	70849	28	179	46	3	8	35	129	89	76173	10024	47552	49	47
5185	568125	131	2452	118	0	169	40	156	146	187326	43068	250931	185	177
381	33186	19	111	46	0	1	18	67	22	22807	1271	6853	5	5
2150	227332	62	763	79	7	88	38	148	132	144408	34416	115466	125	124
1978	258676	47	647	76	0	98	37	132	92	66485	20318	110896	93	92
2388	341549	117	922	82	0	101	46	169	147	79089	24409	169351	154	149
2352	260484	129	728	66	7	122	60	230	203	81625	20648	94853	98	93
2029	202918	79	760	60	10	57	37	122	113	68788	12347	72591	70	70
3010	367799	83	1185	117	4	139	55	191	171	103297	21857	101345	148	148
2265	269455	88	724	50	10	87	44	162	87	69446	11034	113713	100	100
5081	394578	186	1751	133	0	176	63	237	208	114948	33433	165354	150	142
2362	335567	76	845	63	8	114	40	156	153	167949	35902	164263	197	194
3537	423110	171	1382	100	4	121	43	157	97	125081	22355	135213	114	113
1477	182016	58	514	44	3	103	32	123	95	125818	31219	111669	169	162
2397	267365	88	692	65	8	135	52	203	197	136588	21983	134163	200	186
2545	279428	72	847	103	0	123	49	187	160	112431	40085	140303	148	147
3126	506616	109	1386	103	1	97	41	152	148	103037	18507	150773	140	137
1618	206722	47	533	62	5	74	25	89	84	82317	16278	111848	74	71
1787	200004	58	636	70	9	103	57	227	227	118906	24662	102509	128	123
3791	257139	132	1370	159	1	158	45	165	154	83515	31452	96785	140	134
3037	256931	135	1070	78	0	113	42	162	151	104581	32580	116136	116	115
3072	296850	132	1149	101	5	102	45	174	142	103129	22883	158376	147	138
2224	307100	89	715	73	0	132	43	154	148	83243	27652	153990	132	125
1744	184160	58	639	58	0	62	36	129	110	37110	9845	64057	70	66
3218	393860	79	1213	147	0	150	45	174	149	113344	20190	230054	144	137
2599	309877	86	1065	54	3	138	50	195	179	139165	46201	184531	155	152
2116	252512	82	728	84	6	50	50	186	149	86652	10971	114198	165	159
2483	367819	101	880	56	1	141	51	197	187	112302	34811	198299	161	159
1187	115602	46	410	45	4	48	42	157	153	69652	3029	33750	31	31
3566	430118	103	1293	87	4	141	44	168	163	119442	38941	189723	199	185
2764	273950	56	1186	87	0	83	42	159	127	69867	4958	100826	78	78
3753	428028	126	1348	77	0	112	44	161	151	101629	32344	188355	121	117
2061	251349	91	689	72	2	79	40	153	100	70168	19433	104470	112	109
947	115658	33	284	36	1	33	17	55	46	31081	12558	58391	41	41
3699	388812	207	1304	51	2	149	43	166	156	103925	36524	164808	158	149
3397	343783	84	1556	44	10	126	41	151	128	92622	26041	134097	123	123
1902	198635	74	742	75	10	81	41	148	111	79011	16637	80238	104	103
1922	214344	81	676	87	5	84	40	129	119	93487	28395	133252	94	87
1819	182398	65	487	97	6	68	49	181	148	64520	16747	54518	73	71
2598	157164	84	1051	90	1	50	52	93	65	93473	9105	121850	52	51
5568	459440	155	2088	860	2	101	42	150	134	114360	11941	79367	71	70
918	78800	42	330	57	2	20	26	82	66	33032	7935	56968	21	21
2404	217575	82	691	99	0	101	59	229	201	96125	19499	106314	155	155
4144	368086	122	1410	120	10	150	50	193	177	151911	22938	191889	174	172
2536	210554	66	1057	76	3	116	50	176	156	89256	25314	104864	136	133
2164	244640	79	688	56	0	99	47	179	158	95676	28527	160792	128	125
496	24188	24	218	20	0	8	4	12	7	5950	2694	15049	7	7
2688	399093	331	862	94	8	88	51	181	175	149695	20867	191179	165	158
744	65029	17	255	21	5	21	18	67	61	32551	3597	25109	21	21
1161	101097	64	454	70	3	30	14	52	41	31701	5296	45824	35	35
3215	297973	62	1185	133	1	97	41	148	133	100087	32982	129711	137	133
2954	369627	90	785	86	5	163	61	230	228	169707	38975	210012	174	169
3968	367127	204	1208	224	6	132	40	148	140	150491	42721	194679	257	256
2798	374143	150	1096	65	0	161	44	160	155	120192	41455	197680	207	190
2324	270099	88	887	86	12	89	40	155	141	95893	23923	81180	103	100
4076	391871	150	1335	70	10	160	51	198	181	151715	26719	197765	171	171
3293	315924	121	1190	148	12	139	29	104	75	176225	53405	214738	279	267
3132	291391	124	1257	72	11	104	43	169	97	59900	12526	96252	83	80
2808	286417	92	1008	59	8	100	42	163	142	104767	26584	124527	130	126
1745	276201	78	658	67	3	66	41	151	136	114799	37062	153242	131	132
2109	267432	71	542	58	0	163	30	116	87	72128	25696	145707	126	121
2140	215924	140	651	60	6	93	39	153	140	143592	24634	113963	158	156
2945	252767	156	877	105	10	85	51	195	169	89626	27269	134904	138	133
2536	260919	86	913	84	2	150	40	149	129	131072	25270	114268	200	199
1737	182961	73	637	63	5	143	29	106	92	126817	24634	94333	104	98
2679	256967	73	900	67	13	107	47	179	160	81351	17828	102204	111	109
893	73566	32	385	39	6	22	23	88	67	22618	3007	23824	26	25
2389	272362	93	784	60	7	85	48	185	179	88977	20065	111563	115	113
2061	216802	60	872	94	2	86	38	133	90	92059	24648	91313	127	126
2220	228835	68	779	67	4	131	42	164	144	81897	21588	89770	140	137
2369	371391	90	1001	96	4	140	46	169	144	108146	25217	100125	121	121
3207	393845	102	1253	54	3	156	40	153	144	126372	30927	165278	183	178
1974	220401	109	586	54	6	81	45	166	134	249771	18487	181712	68	63
2487	225825	69	755	62	2	137	42	164	146	71154	18050	80906	112	109
2126	217623	70	737	71	0	102	41	146	121	71571	17696	75881	103	101
2075	199011	52	760	50	1	72	37	141	112	55918	17326	83963	63	61
4228	483074	131	1272	117	1	161	47	183	145	160141	39361	175721	166	157
1370	145943	70	653	45	5	30	26	99	99	38692	9648	68580	38	38
2448	295224	108	703	61	2	120	48	134	96	102812	26759	136323	163	159
870	80953	25	437	31	0	49	8	28	27	56622	7905	55792	59	58
2169	171206	60	905	175	0	63	27	101	77	15986	4527	25157	27	27
1573	179344	61	459	70	6	76	38	139	137	123534	41517	100922	108	108
4045	415550	221	1586	284	1	85	41	159	151	108535	21261	118845	88	83
3116	369093	128	1053	95	0	146	61	222	126	93879	36099	170492	92	88
3098	180679	106	1051	72	1	165	45	171	159	144551	39039	81716	170	164
2605	298696	103	843	63	1	89	41	154	101	56750	13841	115750	98	96
2404	292260	84	732	75	3	168	42	154	144	127654	23841	105590	205	192
1931	199481	67	632	90	10	48	35	129	102	65594	8589	92795	96	94
3146	282361	77	1128	89	1	149	36	140	135	59938	15049	82390	107	107
2598	329281	89	971	138	4	75	40	156	147	146975	39038	135599	150	144
2058	231266	47	693	68	5	103	40	156	155	165904	36774	127667	138	136
2193	297995	67	738	80	7	116	38	138	138	169265	40076	163073	177	171
2262	305984	88	820	65	0	165	43	153	113	183500	43840	211381	213	210
4197	416463	162	1369	130	12	155	65	251	248	165986	43146	189944	208	193
4022	412530	117	1493	85	13	165	33	126	116	184923	50099	226168	307	297
2841	297080	141	893	83	9	121	51	198	176	140358	40312	117495	125	125
2493	318283	70	902	89	0	156	45	168	140	149959	32616	195894	208	204
2208	202726	195	719	116	0	81	36	138	59	57224	11338	80684	73	70
602	43287	14	214	43	4	13	19	71	64	43750	7409	19630	49	49
2434	223456	86	795	87	4	113	25	90	40	48029	18213	88634	82	82
2513	258249	158	874	80	0	112	44	167	98	104978	45873	139292	206	205
2900	299566	57	1275	132	0	133	45	172	139	100046	39844	128602	112	111
2786	321797	95	1079	59	0	169	44	162	135	101047	28317	135848	139	135
1343	170299	87	426	50	0	28	35	129	97	197426	24797	178377	60	59
3313	169545	100	976	87	0	121	46	179	142	160902	7471	106330	70	70
2106	354041	77	677	62	5	82	44	163	155	147172	27259	178303	112	108
2331	303273	90	696	70	1	148	45	164	115	109432	23201	116938	142	141
398	23623	11	156	9	0	12	1	0	0	1168	238	5841	11	11
2217	195880	76	779	54	0	146	40	155	103	83248	28830	106020	130	130
530	61857	25	192	25	4	23	11	32	30	25162	3913	24610	31	28
1946	207339	53	606	113	0	84	51	189	130	45724	9935	74151	132	101
3199	431443	122	1234	63	1	163	38	140	102	110529	27738	232241	219	216
387	21054	16	146	2	0	4	0	0	0	855	338	6622	4	4
2137	252805	52	866	67	5	81	30	111	77	101382	13326	127097	102	97
492	31961	22	200	22	0	18	8	25	9	14116	3988	13155	39	39
3808	354622	123	1343	157	3	118	43	159	150	89506	24347	160501	125	119
2183	251240	76	735	79	7	76	48	183	163	135356	27111	91502	121	118
1789	187003	95	522	113	14	55	49	184	148	116066	3938	24469	42	41
1863	172481	56	716	50	3	57	32	119	94	144244	17416	88229	111	107
568	38214	34	276	52	0	16	8	27	21	8773	1888	13983	16	16
2504	256082	52	836	113	3	93	43	163	151	102153	18700	80716	70	69
2819	358276	84	1031	115	0	137	52	198	187	117440	36809	157384	162	160
1463	211775	66	511	78	0	50	53	205	171	104128	24959	122975	173	158
3927	445926	89	1708	135	4	152	49	191	170	134238	37343	191469	171	161
2554	348017	99	884	120	0	163	48	187	145	134047	21849	231257	172	165
3506	441946	133	1201	122	3	142	56	210	198	279488	49809	258287	254	246
1458	208962	41	547	54	0	77	45	166	152	79756	21654	122531	90	89
1213	105332	44	422	63	0	42	40	145	112	66089	8728	61394	50	49
3060	315219	358	1002	162	4	94	48	187	173	102070	20920	86480	113	107
4494	460249	196	1558	162	5	126	50	186	177	146760	27195	195791	187	182
1844	160740	60	569	107	16	63	43	164	153	154771	1037	18284	16	16
4365	412099	138	1812	146	5	127	46	172	161	165933	42570	147581	175	173
2037	173802	83	755	77	5	59	40	147	115	64593	17672	72558	90	90
1981	284582	52	648	87	2	117	45	167	147	92280	34245	147341	140	140
2564	283913	100	905	192	1	110	46	158	124	67150	16786	114651	145	142
1996	234262	120	661	75	0	44	37	144	57	128692	20954	100187	141	126
4097	386740	124	1611	131	9	95	45	169	144	124089	16378	130332	125	123
2339	246963	92	811	67	1	128	39	145	126	125386	31852	134218	241	239
2035	173260	63	716	37	3	41	21	79	78	37238	2805	10901	16	15
3241	346748	108	1034	61	11	146	50	194	153	140015	38086	145758	175	170
1974	176654	58	732	127	5	147	55	212	196	150047	21166	75767	132	123
2770	264767	92	1060	58	2	121	40	148	130	154451	34672	134969	154	151
2748	314070	112	852	71	1	185	48	171	159	156349	36171	169216	198	194
2	1	0	0	0	9	0	0	0	0	0	0	0	0	0
207	14688	10	85	0	0	4	0	0	0	6023	2065	7953	5	5
5	98	1	0	0	0	0	0	0	0	0	0	0	0	0
8	455	2	0	0	0	0	0	0	0	0	0	0	0	0
0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
2414	284420	91	809	72	2	85	46	141	94	84601	19354	105406	125	122
3462	410509	164	1134	123	3	157	52	204	129	68946	22124	174586	174	173
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
4	203	4	0	0	0	0	0	0	0	0	0	0	0	0
151	7199	5	74	0	0	7	0	0	0	1644	556	4245	6	6
474	46660	20	259	7	0	12	5	15	13	6179	2089	21509	13	13
141	17547	5	69	3	0	0	1	4	4	3926	2658	7670	3	3
1145	121550	46	309	106	0	37	48	172	89	52789	1813	15673	35	35
29	969	2	0	0	0	0	0	0	0	0	0	0	0	0
2066	242258	73	690	53	2	62	34	125	71	100350	17372	75882	80	72

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'AstonUniversity' @ aston.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 & 7 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159411&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159411&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159411&T=0

As an alternative you can also use a QR Code:  
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 time7 seconds
R Server'AstonUniversity' @ aston.wessa.net






Multiple Linear Regression - Estimated Regression Equation
k[t] = + 1701.4566256034 + 3.66049125852276a[t] -0.126422702886036b[t] + 4.96022863964021c[t] -4.64533631302219d[t] + 81.00798698278e[t] + 2272.69963918158f[t] -58.4205949104749g[t] + 192.28039636048h[t] -3.89643230014327i[t] + 187.56178990687j[t] + 1.11766124548519l[t] + 0.474200625398477m[t] + 79.2635781264385n[t] -36.0315116216371o[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
k[t] =  +  1701.4566256034 +  3.66049125852276a[t] -0.126422702886036b[t] +  4.96022863964021c[t] -4.64533631302219d[t] +  81.00798698278e[t] +  2272.69963918158f[t] -58.4205949104749g[t] +  192.28039636048h[t] -3.89643230014327i[t] +  187.56178990687j[t] +  1.11766124548519l[t] +  0.474200625398477m[t] +  79.2635781264385n[t] -36.0315116216371o[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159411&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]k[t] =  +  1701.4566256034 +  3.66049125852276a[t] -0.126422702886036b[t] +  4.96022863964021c[t] -4.64533631302219d[t] +  81.00798698278e[t] +  2272.69963918158f[t] -58.4205949104749g[t] +  192.28039636048h[t] -3.89643230014327i[t] +  187.56178990687j[t] +  1.11766124548519l[t] +  0.474200625398477m[t] +  79.2635781264385n[t] -36.0315116216371o[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159411&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159411&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
k[t] = + 1701.4566256034 + 3.66049125852276a[t] -0.126422702886036b[t] + 4.96022863964021c[t] -4.64533631302219d[t] + 81.00798698278e[t] + 2272.69963918158f[t] -58.4205949104749g[t] + 192.28039636048h[t] -3.89643230014327i[t] + 187.56178990687j[t] + 1.11766124548519l[t] + 0.474200625398477m[t] + 79.2635781264385n[t] -36.0315116216371o[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1701.45662560345738.6108040.29650.7672670.383633
a3.6604912585227611.1752640.32760.7437090.371855
b-0.1264227028860360.0604-2.09310.0380350.019018
c4.9602286396402164.0999980.07740.9384230.469211
d-4.6453363130221922.775784-0.2040.8386630.419332
e81.0079869827839.4517122.05330.0417880.020894
f2272.69963918158628.7354123.61470.0004110.000205
g-58.4205949104749105.263812-0.5550.5797320.289866
h192.28039636048790.8865960.24310.8082470.404123
i-3.89643230014327244.317932-0.01590.9872970.493649
j187.56178990687120.8660281.55180.1228280.061414
l1.117661245485190.3462173.22820.0015320.000766
m0.4742006253984770.0932225.08681e-061e-06
n79.2635781264385566.7912420.13980.888970.444485
o-36.0315116216371589.276201-0.06110.9513250.475663

\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) & 1701.4566256034 & 5738.610804 & 0.2965 & 0.767267 & 0.383633 \tabularnewline
a & 3.66049125852276 & 11.175264 & 0.3276 & 0.743709 & 0.371855 \tabularnewline
b & -0.126422702886036 & 0.0604 & -2.0931 & 0.038035 & 0.019018 \tabularnewline
c & 4.96022863964021 & 64.099998 & 0.0774 & 0.938423 & 0.469211 \tabularnewline
d & -4.64533631302219 & 22.775784 & -0.204 & 0.838663 & 0.419332 \tabularnewline
e & 81.00798698278 & 39.451712 & 2.0533 & 0.041788 & 0.020894 \tabularnewline
f & 2272.69963918158 & 628.735412 & 3.6147 & 0.000411 & 0.000205 \tabularnewline
g & -58.4205949104749 & 105.263812 & -0.555 & 0.579732 & 0.289866 \tabularnewline
h & 192.28039636048 & 790.886596 & 0.2431 & 0.808247 & 0.404123 \tabularnewline
i & -3.89643230014327 & 244.317932 & -0.0159 & 0.987297 & 0.493649 \tabularnewline
j & 187.56178990687 & 120.866028 & 1.5518 & 0.122828 & 0.061414 \tabularnewline
l & 1.11766124548519 & 0.346217 & 3.2282 & 0.001532 & 0.000766 \tabularnewline
m & 0.474200625398477 & 0.093222 & 5.0868 & 1e-06 & 1e-06 \tabularnewline
n & 79.2635781264385 & 566.791242 & 0.1398 & 0.88897 & 0.444485 \tabularnewline
o & -36.0315116216371 & 589.276201 & -0.0611 & 0.951325 & 0.475663 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159411&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]1701.4566256034[/C][C]5738.610804[/C][C]0.2965[/C][C]0.767267[/C][C]0.383633[/C][/ROW]
[ROW][C]a[/C][C]3.66049125852276[/C][C]11.175264[/C][C]0.3276[/C][C]0.743709[/C][C]0.371855[/C][/ROW]
[ROW][C]b[/C][C]-0.126422702886036[/C][C]0.0604[/C][C]-2.0931[/C][C]0.038035[/C][C]0.019018[/C][/ROW]
[ROW][C]c[/C][C]4.96022863964021[/C][C]64.099998[/C][C]0.0774[/C][C]0.938423[/C][C]0.469211[/C][/ROW]
[ROW][C]d[/C][C]-4.64533631302219[/C][C]22.775784[/C][C]-0.204[/C][C]0.838663[/C][C]0.419332[/C][/ROW]
[ROW][C]e[/C][C]81.00798698278[/C][C]39.451712[/C][C]2.0533[/C][C]0.041788[/C][C]0.020894[/C][/ROW]
[ROW][C]f[/C][C]2272.69963918158[/C][C]628.735412[/C][C]3.6147[/C][C]0.000411[/C][C]0.000205[/C][/ROW]
[ROW][C]g[/C][C]-58.4205949104749[/C][C]105.263812[/C][C]-0.555[/C][C]0.579732[/C][C]0.289866[/C][/ROW]
[ROW][C]h[/C][C]192.28039636048[/C][C]790.886596[/C][C]0.2431[/C][C]0.808247[/C][C]0.404123[/C][/ROW]
[ROW][C]i[/C][C]-3.89643230014327[/C][C]244.317932[/C][C]-0.0159[/C][C]0.987297[/C][C]0.493649[/C][/ROW]
[ROW][C]j[/C][C]187.56178990687[/C][C]120.866028[/C][C]1.5518[/C][C]0.122828[/C][C]0.061414[/C][/ROW]
[ROW][C]l[/C][C]1.11766124548519[/C][C]0.346217[/C][C]3.2282[/C][C]0.001532[/C][C]0.000766[/C][/ROW]
[ROW][C]m[/C][C]0.474200625398477[/C][C]0.093222[/C][C]5.0868[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]n[/C][C]79.2635781264385[/C][C]566.791242[/C][C]0.1398[/C][C]0.88897[/C][C]0.444485[/C][/ROW]
[ROW][C]o[/C][C]-36.0315116216371[/C][C]589.276201[/C][C]-0.0611[/C][C]0.951325[/C][C]0.475663[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159411&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159411&T=2

As an alternative you can also use a QR Code:  
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)1701.45662560345738.6108040.29650.7672670.383633
a3.6604912585227611.1752640.32760.7437090.371855
b-0.1264227028860360.0604-2.09310.0380350.019018
c4.9602286396402164.0999980.07740.9384230.469211
d-4.6453363130221922.775784-0.2040.8386630.419332
e81.0079869827839.4517122.05330.0417880.020894
f2272.69963918158628.7354123.61470.0004110.000205
g-58.4205949104749105.263812-0.5550.5797320.289866
h192.28039636048790.8865960.24310.8082470.404123
i-3.89643230014327244.317932-0.01590.9872970.493649
j187.56178990687120.8660281.55180.1228280.061414
l1.117661245485190.3462173.22820.0015320.000766
m0.4742006253984770.0932225.08681e-061e-06
n79.2635781264385566.7912420.13980.888970.444485
o-36.0315116216371589.276201-0.06110.9513250.475663






Multiple Linear Regression - Regression Statistics
Multiple R0.869758591930346
R-squared0.756480008236659
Adjusted R-squared0.73359893518507
F-TEST (value)33.0613868733801
F-TEST (DF numerator)14
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation26930.190930093
Sum Squared Residuals108060042346.158

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.869758591930346 \tabularnewline
R-squared & 0.756480008236659 \tabularnewline
Adjusted R-squared & 0.73359893518507 \tabularnewline
F-TEST (value) & 33.0613868733801 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 149 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 26930.190930093 \tabularnewline
Sum Squared Residuals & 108060042346.158 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159411&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.869758591930346[/C][/ROW]
[ROW][C]R-squared[/C][C]0.756480008236659[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.73359893518507[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]33.0613868733801[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]149[/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]26930.190930093[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]108060042346.158[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159411&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159411&T=3

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.869758591930346
R-squared0.756480008236659
Adjusted R-squared0.73359893518507
F-TEST (value)33.0613868733801
F-TEST (DF numerator)14
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation26930.190930093
Sum Squared Residuals108060042346.158






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1140824143026.54257343-2202.54257342974
2110459105149.5621119735309.43788802726
3105079115358.224826678-10279.2248266782
4112098118604.746192511-6506.74619251066
54392966408.6467489824-22479.6467489824
67617363302.009447586512870.9905524135
7187326147683.14275105739642.8572489426
82280714359.94900655138447.0509934487
9144408124909.90361213419498.0963878658
106648577106.3138138922-10621.3138138922
1179089114485.907062715-35396.9070627152
1281625109955.426518742-28330.4265187418
136878883676.2533662768-14888.2533662768
1410329792365.83677315310931.1632268471
156944689420.5682116406-19974.5682116406
16114948136451.315175473-21503.3151754732
17167949143426.09977745624522.9002225437
1812508185626.658163845539454.3418361545
19125818105258.46442400920559.5355759913
20136588132954.7691552233633.23084477717
21112431129742.581638191-17311.5816381913
2210303781540.51649470821496.483505292
238231786064.1798988467-3747.17989884675
24118906134937.976553357-16031.976553357
2583515107494.416149381-23979.4161493812
26104581108055.177199534-3474.17719953368
27103129126286.820069255-23157.8200692552
2883243111653.814335682-28410.8143356822
293711054799.9246513935-17689.9246513935
30113344135646.536393499-22302.5363934988
31139165159027.9936749-19862.9936749002
3286652102668.674178985-16016.6741789846
33112302143360.313359496-31058.3133594957
346965256578.146207962113073.8537920379
35119442143759.304350885-24317.3043508852
366986762154.55356354547712.44643645462
37101629122382.149804223-20753.1498042227
387016882543.5775969268-12375.5775969268
393108147831.1231102358-16750.1231102358
40103925124037.691205678-20112.6912056776
4192622112107.524146908-19485.5241469079
427901193844.2713143948-14833.2713143948
4393487121153.003980788-27666.0039807882
446452085155.7267927535-20635.7267927535
459347385588.59426731267884.40573268743
46114360110092.5604056894267.43959431129
473303255619.1274690877-22587.1274690877
4896125109371.572394542-13246.5723945422
49151911154361.879920984-2450.87992098383
5089256108175.42188791-18919.4218879098
5195676126390.853138088-30714.8531380876
52595013203.4511035883-7253.45110358826
53149695142668.6420102237026.35798977697
543255138417.3764868873-5866.37648688733
553170141456.2132865671-9755.21328656706
56100087114673.360826266-14586.3608262658
57169707175840.981843124-6133.98184312407
58150491173868.263749024-23377.2637490235
59120192142699.076643052-22507.0766430521
6095893104745.424453114-8852.42445311391
61151715154692.250070153-2977.25007015329
62176225193278.595041834-17053.5950418336
635990085002.0802768543-25102.0802768543
64104767117264.770542813-12497.7705428127
65114799131412.199151908-16613.1991519078
667212893697.7399304917-21569.7399304917
67143592114609.04644570128982.9535542988
6889626144832.723038219-55206.7230382191
6913107299188.565689170631883.4343108294
7012681789841.532607145836975.4673928542
7181351115535.662245758-34184.6622457578
722261842014.8231373449-19396.8231373449
7388977111091.661135085-22114.6611350846
749205985264.18432504616794.8156749539
758189791784.926767148-9887.92676714805
7610814683995.807530285724150.1924697143
77126372115550.38249444610821.6175055544
78249771135249.810507889114521.189492111
797115478965.3216738935-7811.32167389345
807157168982.95335093692588.04664906308
815591872529.2471523575-16611.2471523575
82160141123533.0034679736607.9965320298
833869266964.7260995624-28272.7260995624
84102812101541.942488771270.05751123039
855662236766.167414000619855.8325859994
861598631982.8937420904-15996.8937420904
87123534129212.709255907-5678.70925590717
8810853597704.456361111210830.5436388888
8993879121167.758972299-27288.7589722993
90144551112065.59187348432485.4081265165
915675073140.9992893769-16390.9992893769
9212765494180.902380020733473.0976199793
936559491344.8050404177-25750.8050404177
945993865652.601508749-5714.60150874896
95146975130690.35869929216284.6413007078
96165904131700.29148238534203.7085176147
97169265147213.8550643222051.1449356801
98183500150969.79013063232530.2098693676
99165986193468.957068222-27482.9570682218
100184923189193.77807912-4270.77807912017
101140358139424.482881239933.51711876063
102149959137581.84504708512377.1549529147
1035722458093.772603808-869.772603808017
1044375044410.9454112099-660.94541120992
1054802966521.7788935046-18492.7788935046
106104978127368.312772154-22390.3127721542
107100046116174.768984969-16128.7689849693
10810104796953.79366521444093.2063347856
109197426125306.83991073272119.1600892678
11016090284914.55984667375987.440153327
111147172130385.31649772716786.6835022729
11210943285544.871584733723887.1284152662
11311683233.4057876338-2065.4057876338
1148324892179.5973993541-8931.59739935413
1152516229934.8402171652-4772.84021716518
1164572470849.7385740574-25125.7385740574
117110529128192.515972845-17663.5159728449
1188553476.69561532768-2621.69561532768
11910138285389.095533683215992.9044663168
1201411614882.7664308975-766.766430897503
12189506122546.842821445-33040.8428214454
122135356110880.4654918324475.5345081703
12311606674733.855879264741332.1441207353
12414424480772.100133463463471.8998665366
125877315918.1611755719-7145.16117557193
12610215383598.952979690818554.0470203092
127117440130814.159189171-13374.1591891708
128104128117330.096480275-13202.0964802755
129134238144202.774949575-9964.77494957512
130134047141103.504633951-7056.50463395101
131279488198654.47018617980833.529813821
13279756100907.561017756-21151.5610177563
1336608962931.84173516193157.15826483808
13410207097336.18505319224733.81494680784
135146760144417.6361634712342.36383652897
13615477173982.193967848480788.8060321516
137165933137190.91885804428742.0811419556
1386459384981.417505735-20388.417505735
13992280124750.392443343-32470.3924433425
1406715093878.1741933222-26728.1741933222
14112869275230.231220627153461.7687793729
142124089107041.25292796617047.7470720338
143125386116256.7224945489129.277505452
1443723819045.077654761918192.9223452381
145140015143859.108695958-3844.10869595816
146150047108672.76444882341374.2355511775
147154451117093.0569702937357.9430297104
148156349133627.91088355422721.0891164459
149022162.9479380518-22162.9479380518
15060236318.79635452015-295.796354520146
15101712.32988565282-1712.32988565282
15201683.13868313771-1683.13868313771
15303974.15626478498-3974.15626478498
15401701.4566256034-1701.4566256034
1558460179739.48411488124861.51588511885
15668946114105.652240873-45159.6522408732
15701701.4566256034-1701.4566256034
15801710.27569651018-1710.27569651018
15916443509.96956556011-1865.96956556011
160617912737.3686273209-6558.36862732093
16139267611.0440376696-3685.04403766959
1625278931967.946324993820821.0536750062
16301695.02773028327-1695.02773028327
16410035059524.167697194940825.8323028051

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 140824 & 143026.54257343 & -2202.54257342974 \tabularnewline
2 & 110459 & 105149.562111973 & 5309.43788802726 \tabularnewline
3 & 105079 & 115358.224826678 & -10279.2248266782 \tabularnewline
4 & 112098 & 118604.746192511 & -6506.74619251066 \tabularnewline
5 & 43929 & 66408.6467489824 & -22479.6467489824 \tabularnewline
6 & 76173 & 63302.0094475865 & 12870.9905524135 \tabularnewline
7 & 187326 & 147683.142751057 & 39642.8572489426 \tabularnewline
8 & 22807 & 14359.9490065513 & 8447.0509934487 \tabularnewline
9 & 144408 & 124909.903612134 & 19498.0963878658 \tabularnewline
10 & 66485 & 77106.3138138922 & -10621.3138138922 \tabularnewline
11 & 79089 & 114485.907062715 & -35396.9070627152 \tabularnewline
12 & 81625 & 109955.426518742 & -28330.4265187418 \tabularnewline
13 & 68788 & 83676.2533662768 & -14888.2533662768 \tabularnewline
14 & 103297 & 92365.836773153 & 10931.1632268471 \tabularnewline
15 & 69446 & 89420.5682116406 & -19974.5682116406 \tabularnewline
16 & 114948 & 136451.315175473 & -21503.3151754732 \tabularnewline
17 & 167949 & 143426.099777456 & 24522.9002225437 \tabularnewline
18 & 125081 & 85626.6581638455 & 39454.3418361545 \tabularnewline
19 & 125818 & 105258.464424009 & 20559.5355759913 \tabularnewline
20 & 136588 & 132954.769155223 & 3633.23084477717 \tabularnewline
21 & 112431 & 129742.581638191 & -17311.5816381913 \tabularnewline
22 & 103037 & 81540.516494708 & 21496.483505292 \tabularnewline
23 & 82317 & 86064.1798988467 & -3747.17989884675 \tabularnewline
24 & 118906 & 134937.976553357 & -16031.976553357 \tabularnewline
25 & 83515 & 107494.416149381 & -23979.4161493812 \tabularnewline
26 & 104581 & 108055.177199534 & -3474.17719953368 \tabularnewline
27 & 103129 & 126286.820069255 & -23157.8200692552 \tabularnewline
28 & 83243 & 111653.814335682 & -28410.8143356822 \tabularnewline
29 & 37110 & 54799.9246513935 & -17689.9246513935 \tabularnewline
30 & 113344 & 135646.536393499 & -22302.5363934988 \tabularnewline
31 & 139165 & 159027.9936749 & -19862.9936749002 \tabularnewline
32 & 86652 & 102668.674178985 & -16016.6741789846 \tabularnewline
33 & 112302 & 143360.313359496 & -31058.3133594957 \tabularnewline
34 & 69652 & 56578.1462079621 & 13073.8537920379 \tabularnewline
35 & 119442 & 143759.304350885 & -24317.3043508852 \tabularnewline
36 & 69867 & 62154.5535635454 & 7712.44643645462 \tabularnewline
37 & 101629 & 122382.149804223 & -20753.1498042227 \tabularnewline
38 & 70168 & 82543.5775969268 & -12375.5775969268 \tabularnewline
39 & 31081 & 47831.1231102358 & -16750.1231102358 \tabularnewline
40 & 103925 & 124037.691205678 & -20112.6912056776 \tabularnewline
41 & 92622 & 112107.524146908 & -19485.5241469079 \tabularnewline
42 & 79011 & 93844.2713143948 & -14833.2713143948 \tabularnewline
43 & 93487 & 121153.003980788 & -27666.0039807882 \tabularnewline
44 & 64520 & 85155.7267927535 & -20635.7267927535 \tabularnewline
45 & 93473 & 85588.5942673126 & 7884.40573268743 \tabularnewline
46 & 114360 & 110092.560405689 & 4267.43959431129 \tabularnewline
47 & 33032 & 55619.1274690877 & -22587.1274690877 \tabularnewline
48 & 96125 & 109371.572394542 & -13246.5723945422 \tabularnewline
49 & 151911 & 154361.879920984 & -2450.87992098383 \tabularnewline
50 & 89256 & 108175.42188791 & -18919.4218879098 \tabularnewline
51 & 95676 & 126390.853138088 & -30714.8531380876 \tabularnewline
52 & 5950 & 13203.4511035883 & -7253.45110358826 \tabularnewline
53 & 149695 & 142668.642010223 & 7026.35798977697 \tabularnewline
54 & 32551 & 38417.3764868873 & -5866.37648688733 \tabularnewline
55 & 31701 & 41456.2132865671 & -9755.21328656706 \tabularnewline
56 & 100087 & 114673.360826266 & -14586.3608262658 \tabularnewline
57 & 169707 & 175840.981843124 & -6133.98184312407 \tabularnewline
58 & 150491 & 173868.263749024 & -23377.2637490235 \tabularnewline
59 & 120192 & 142699.076643052 & -22507.0766430521 \tabularnewline
60 & 95893 & 104745.424453114 & -8852.42445311391 \tabularnewline
61 & 151715 & 154692.250070153 & -2977.25007015329 \tabularnewline
62 & 176225 & 193278.595041834 & -17053.5950418336 \tabularnewline
63 & 59900 & 85002.0802768543 & -25102.0802768543 \tabularnewline
64 & 104767 & 117264.770542813 & -12497.7705428127 \tabularnewline
65 & 114799 & 131412.199151908 & -16613.1991519078 \tabularnewline
66 & 72128 & 93697.7399304917 & -21569.7399304917 \tabularnewline
67 & 143592 & 114609.046445701 & 28982.9535542988 \tabularnewline
68 & 89626 & 144832.723038219 & -55206.7230382191 \tabularnewline
69 & 131072 & 99188.5656891706 & 31883.4343108294 \tabularnewline
70 & 126817 & 89841.5326071458 & 36975.4673928542 \tabularnewline
71 & 81351 & 115535.662245758 & -34184.6622457578 \tabularnewline
72 & 22618 & 42014.8231373449 & -19396.8231373449 \tabularnewline
73 & 88977 & 111091.661135085 & -22114.6611350846 \tabularnewline
74 & 92059 & 85264.1843250461 & 6794.8156749539 \tabularnewline
75 & 81897 & 91784.926767148 & -9887.92676714805 \tabularnewline
76 & 108146 & 83995.8075302857 & 24150.1924697143 \tabularnewline
77 & 126372 & 115550.382494446 & 10821.6175055544 \tabularnewline
78 & 249771 & 135249.810507889 & 114521.189492111 \tabularnewline
79 & 71154 & 78965.3216738935 & -7811.32167389345 \tabularnewline
80 & 71571 & 68982.9533509369 & 2588.04664906308 \tabularnewline
81 & 55918 & 72529.2471523575 & -16611.2471523575 \tabularnewline
82 & 160141 & 123533.00346797 & 36607.9965320298 \tabularnewline
83 & 38692 & 66964.7260995624 & -28272.7260995624 \tabularnewline
84 & 102812 & 101541.94248877 & 1270.05751123039 \tabularnewline
85 & 56622 & 36766.1674140006 & 19855.8325859994 \tabularnewline
86 & 15986 & 31982.8937420904 & -15996.8937420904 \tabularnewline
87 & 123534 & 129212.709255907 & -5678.70925590717 \tabularnewline
88 & 108535 & 97704.4563611112 & 10830.5436388888 \tabularnewline
89 & 93879 & 121167.758972299 & -27288.7589722993 \tabularnewline
90 & 144551 & 112065.591873484 & 32485.4081265165 \tabularnewline
91 & 56750 & 73140.9992893769 & -16390.9992893769 \tabularnewline
92 & 127654 & 94180.9023800207 & 33473.0976199793 \tabularnewline
93 & 65594 & 91344.8050404177 & -25750.8050404177 \tabularnewline
94 & 59938 & 65652.601508749 & -5714.60150874896 \tabularnewline
95 & 146975 & 130690.358699292 & 16284.6413007078 \tabularnewline
96 & 165904 & 131700.291482385 & 34203.7085176147 \tabularnewline
97 & 169265 & 147213.85506432 & 22051.1449356801 \tabularnewline
98 & 183500 & 150969.790130632 & 32530.2098693676 \tabularnewline
99 & 165986 & 193468.957068222 & -27482.9570682218 \tabularnewline
100 & 184923 & 189193.77807912 & -4270.77807912017 \tabularnewline
101 & 140358 & 139424.482881239 & 933.51711876063 \tabularnewline
102 & 149959 & 137581.845047085 & 12377.1549529147 \tabularnewline
103 & 57224 & 58093.772603808 & -869.772603808017 \tabularnewline
104 & 43750 & 44410.9454112099 & -660.94541120992 \tabularnewline
105 & 48029 & 66521.7788935046 & -18492.7788935046 \tabularnewline
106 & 104978 & 127368.312772154 & -22390.3127721542 \tabularnewline
107 & 100046 & 116174.768984969 & -16128.7689849693 \tabularnewline
108 & 101047 & 96953.7936652144 & 4093.2063347856 \tabularnewline
109 & 197426 & 125306.839910732 & 72119.1600892678 \tabularnewline
110 & 160902 & 84914.559846673 & 75987.440153327 \tabularnewline
111 & 147172 & 130385.316497727 & 16786.6835022729 \tabularnewline
112 & 109432 & 85544.8715847337 & 23887.1284152662 \tabularnewline
113 & 1168 & 3233.4057876338 & -2065.4057876338 \tabularnewline
114 & 83248 & 92179.5973993541 & -8931.59739935413 \tabularnewline
115 & 25162 & 29934.8402171652 & -4772.84021716518 \tabularnewline
116 & 45724 & 70849.7385740574 & -25125.7385740574 \tabularnewline
117 & 110529 & 128192.515972845 & -17663.5159728449 \tabularnewline
118 & 855 & 3476.69561532768 & -2621.69561532768 \tabularnewline
119 & 101382 & 85389.0955336832 & 15992.9044663168 \tabularnewline
120 & 14116 & 14882.7664308975 & -766.766430897503 \tabularnewline
121 & 89506 & 122546.842821445 & -33040.8428214454 \tabularnewline
122 & 135356 & 110880.46549183 & 24475.5345081703 \tabularnewline
123 & 116066 & 74733.8558792647 & 41332.1441207353 \tabularnewline
124 & 144244 & 80772.1001334634 & 63471.8998665366 \tabularnewline
125 & 8773 & 15918.1611755719 & -7145.16117557193 \tabularnewline
126 & 102153 & 83598.9529796908 & 18554.0470203092 \tabularnewline
127 & 117440 & 130814.159189171 & -13374.1591891708 \tabularnewline
128 & 104128 & 117330.096480275 & -13202.0964802755 \tabularnewline
129 & 134238 & 144202.774949575 & -9964.77494957512 \tabularnewline
130 & 134047 & 141103.504633951 & -7056.50463395101 \tabularnewline
131 & 279488 & 198654.470186179 & 80833.529813821 \tabularnewline
132 & 79756 & 100907.561017756 & -21151.5610177563 \tabularnewline
133 & 66089 & 62931.8417351619 & 3157.15826483808 \tabularnewline
134 & 102070 & 97336.1850531922 & 4733.81494680784 \tabularnewline
135 & 146760 & 144417.636163471 & 2342.36383652897 \tabularnewline
136 & 154771 & 73982.1939678484 & 80788.8060321516 \tabularnewline
137 & 165933 & 137190.918858044 & 28742.0811419556 \tabularnewline
138 & 64593 & 84981.417505735 & -20388.417505735 \tabularnewline
139 & 92280 & 124750.392443343 & -32470.3924433425 \tabularnewline
140 & 67150 & 93878.1741933222 & -26728.1741933222 \tabularnewline
141 & 128692 & 75230.2312206271 & 53461.7687793729 \tabularnewline
142 & 124089 & 107041.252927966 & 17047.7470720338 \tabularnewline
143 & 125386 & 116256.722494548 & 9129.277505452 \tabularnewline
144 & 37238 & 19045.0776547619 & 18192.9223452381 \tabularnewline
145 & 140015 & 143859.108695958 & -3844.10869595816 \tabularnewline
146 & 150047 & 108672.764448823 & 41374.2355511775 \tabularnewline
147 & 154451 & 117093.05697029 & 37357.9430297104 \tabularnewline
148 & 156349 & 133627.910883554 & 22721.0891164459 \tabularnewline
149 & 0 & 22162.9479380518 & -22162.9479380518 \tabularnewline
150 & 6023 & 6318.79635452015 & -295.796354520146 \tabularnewline
151 & 0 & 1712.32988565282 & -1712.32988565282 \tabularnewline
152 & 0 & 1683.13868313771 & -1683.13868313771 \tabularnewline
153 & 0 & 3974.15626478498 & -3974.15626478498 \tabularnewline
154 & 0 & 1701.4566256034 & -1701.4566256034 \tabularnewline
155 & 84601 & 79739.4841148812 & 4861.51588511885 \tabularnewline
156 & 68946 & 114105.652240873 & -45159.6522408732 \tabularnewline
157 & 0 & 1701.4566256034 & -1701.4566256034 \tabularnewline
158 & 0 & 1710.27569651018 & -1710.27569651018 \tabularnewline
159 & 1644 & 3509.96956556011 & -1865.96956556011 \tabularnewline
160 & 6179 & 12737.3686273209 & -6558.36862732093 \tabularnewline
161 & 3926 & 7611.0440376696 & -3685.04403766959 \tabularnewline
162 & 52789 & 31967.9463249938 & 20821.0536750062 \tabularnewline
163 & 0 & 1695.02773028327 & -1695.02773028327 \tabularnewline
164 & 100350 & 59524.1676971949 & 40825.8323028051 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159411&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]140824[/C][C]143026.54257343[/C][C]-2202.54257342974[/C][/ROW]
[ROW][C]2[/C][C]110459[/C][C]105149.562111973[/C][C]5309.43788802726[/C][/ROW]
[ROW][C]3[/C][C]105079[/C][C]115358.224826678[/C][C]-10279.2248266782[/C][/ROW]
[ROW][C]4[/C][C]112098[/C][C]118604.746192511[/C][C]-6506.74619251066[/C][/ROW]
[ROW][C]5[/C][C]43929[/C][C]66408.6467489824[/C][C]-22479.6467489824[/C][/ROW]
[ROW][C]6[/C][C]76173[/C][C]63302.0094475865[/C][C]12870.9905524135[/C][/ROW]
[ROW][C]7[/C][C]187326[/C][C]147683.142751057[/C][C]39642.8572489426[/C][/ROW]
[ROW][C]8[/C][C]22807[/C][C]14359.9490065513[/C][C]8447.0509934487[/C][/ROW]
[ROW][C]9[/C][C]144408[/C][C]124909.903612134[/C][C]19498.0963878658[/C][/ROW]
[ROW][C]10[/C][C]66485[/C][C]77106.3138138922[/C][C]-10621.3138138922[/C][/ROW]
[ROW][C]11[/C][C]79089[/C][C]114485.907062715[/C][C]-35396.9070627152[/C][/ROW]
[ROW][C]12[/C][C]81625[/C][C]109955.426518742[/C][C]-28330.4265187418[/C][/ROW]
[ROW][C]13[/C][C]68788[/C][C]83676.2533662768[/C][C]-14888.2533662768[/C][/ROW]
[ROW][C]14[/C][C]103297[/C][C]92365.836773153[/C][C]10931.1632268471[/C][/ROW]
[ROW][C]15[/C][C]69446[/C][C]89420.5682116406[/C][C]-19974.5682116406[/C][/ROW]
[ROW][C]16[/C][C]114948[/C][C]136451.315175473[/C][C]-21503.3151754732[/C][/ROW]
[ROW][C]17[/C][C]167949[/C][C]143426.099777456[/C][C]24522.9002225437[/C][/ROW]
[ROW][C]18[/C][C]125081[/C][C]85626.6581638455[/C][C]39454.3418361545[/C][/ROW]
[ROW][C]19[/C][C]125818[/C][C]105258.464424009[/C][C]20559.5355759913[/C][/ROW]
[ROW][C]20[/C][C]136588[/C][C]132954.769155223[/C][C]3633.23084477717[/C][/ROW]
[ROW][C]21[/C][C]112431[/C][C]129742.581638191[/C][C]-17311.5816381913[/C][/ROW]
[ROW][C]22[/C][C]103037[/C][C]81540.516494708[/C][C]21496.483505292[/C][/ROW]
[ROW][C]23[/C][C]82317[/C][C]86064.1798988467[/C][C]-3747.17989884675[/C][/ROW]
[ROW][C]24[/C][C]118906[/C][C]134937.976553357[/C][C]-16031.976553357[/C][/ROW]
[ROW][C]25[/C][C]83515[/C][C]107494.416149381[/C][C]-23979.4161493812[/C][/ROW]
[ROW][C]26[/C][C]104581[/C][C]108055.177199534[/C][C]-3474.17719953368[/C][/ROW]
[ROW][C]27[/C][C]103129[/C][C]126286.820069255[/C][C]-23157.8200692552[/C][/ROW]
[ROW][C]28[/C][C]83243[/C][C]111653.814335682[/C][C]-28410.8143356822[/C][/ROW]
[ROW][C]29[/C][C]37110[/C][C]54799.9246513935[/C][C]-17689.9246513935[/C][/ROW]
[ROW][C]30[/C][C]113344[/C][C]135646.536393499[/C][C]-22302.5363934988[/C][/ROW]
[ROW][C]31[/C][C]139165[/C][C]159027.9936749[/C][C]-19862.9936749002[/C][/ROW]
[ROW][C]32[/C][C]86652[/C][C]102668.674178985[/C][C]-16016.6741789846[/C][/ROW]
[ROW][C]33[/C][C]112302[/C][C]143360.313359496[/C][C]-31058.3133594957[/C][/ROW]
[ROW][C]34[/C][C]69652[/C][C]56578.1462079621[/C][C]13073.8537920379[/C][/ROW]
[ROW][C]35[/C][C]119442[/C][C]143759.304350885[/C][C]-24317.3043508852[/C][/ROW]
[ROW][C]36[/C][C]69867[/C][C]62154.5535635454[/C][C]7712.44643645462[/C][/ROW]
[ROW][C]37[/C][C]101629[/C][C]122382.149804223[/C][C]-20753.1498042227[/C][/ROW]
[ROW][C]38[/C][C]70168[/C][C]82543.5775969268[/C][C]-12375.5775969268[/C][/ROW]
[ROW][C]39[/C][C]31081[/C][C]47831.1231102358[/C][C]-16750.1231102358[/C][/ROW]
[ROW][C]40[/C][C]103925[/C][C]124037.691205678[/C][C]-20112.6912056776[/C][/ROW]
[ROW][C]41[/C][C]92622[/C][C]112107.524146908[/C][C]-19485.5241469079[/C][/ROW]
[ROW][C]42[/C][C]79011[/C][C]93844.2713143948[/C][C]-14833.2713143948[/C][/ROW]
[ROW][C]43[/C][C]93487[/C][C]121153.003980788[/C][C]-27666.0039807882[/C][/ROW]
[ROW][C]44[/C][C]64520[/C][C]85155.7267927535[/C][C]-20635.7267927535[/C][/ROW]
[ROW][C]45[/C][C]93473[/C][C]85588.5942673126[/C][C]7884.40573268743[/C][/ROW]
[ROW][C]46[/C][C]114360[/C][C]110092.560405689[/C][C]4267.43959431129[/C][/ROW]
[ROW][C]47[/C][C]33032[/C][C]55619.1274690877[/C][C]-22587.1274690877[/C][/ROW]
[ROW][C]48[/C][C]96125[/C][C]109371.572394542[/C][C]-13246.5723945422[/C][/ROW]
[ROW][C]49[/C][C]151911[/C][C]154361.879920984[/C][C]-2450.87992098383[/C][/ROW]
[ROW][C]50[/C][C]89256[/C][C]108175.42188791[/C][C]-18919.4218879098[/C][/ROW]
[ROW][C]51[/C][C]95676[/C][C]126390.853138088[/C][C]-30714.8531380876[/C][/ROW]
[ROW][C]52[/C][C]5950[/C][C]13203.4511035883[/C][C]-7253.45110358826[/C][/ROW]
[ROW][C]53[/C][C]149695[/C][C]142668.642010223[/C][C]7026.35798977697[/C][/ROW]
[ROW][C]54[/C][C]32551[/C][C]38417.3764868873[/C][C]-5866.37648688733[/C][/ROW]
[ROW][C]55[/C][C]31701[/C][C]41456.2132865671[/C][C]-9755.21328656706[/C][/ROW]
[ROW][C]56[/C][C]100087[/C][C]114673.360826266[/C][C]-14586.3608262658[/C][/ROW]
[ROW][C]57[/C][C]169707[/C][C]175840.981843124[/C][C]-6133.98184312407[/C][/ROW]
[ROW][C]58[/C][C]150491[/C][C]173868.263749024[/C][C]-23377.2637490235[/C][/ROW]
[ROW][C]59[/C][C]120192[/C][C]142699.076643052[/C][C]-22507.0766430521[/C][/ROW]
[ROW][C]60[/C][C]95893[/C][C]104745.424453114[/C][C]-8852.42445311391[/C][/ROW]
[ROW][C]61[/C][C]151715[/C][C]154692.250070153[/C][C]-2977.25007015329[/C][/ROW]
[ROW][C]62[/C][C]176225[/C][C]193278.595041834[/C][C]-17053.5950418336[/C][/ROW]
[ROW][C]63[/C][C]59900[/C][C]85002.0802768543[/C][C]-25102.0802768543[/C][/ROW]
[ROW][C]64[/C][C]104767[/C][C]117264.770542813[/C][C]-12497.7705428127[/C][/ROW]
[ROW][C]65[/C][C]114799[/C][C]131412.199151908[/C][C]-16613.1991519078[/C][/ROW]
[ROW][C]66[/C][C]72128[/C][C]93697.7399304917[/C][C]-21569.7399304917[/C][/ROW]
[ROW][C]67[/C][C]143592[/C][C]114609.046445701[/C][C]28982.9535542988[/C][/ROW]
[ROW][C]68[/C][C]89626[/C][C]144832.723038219[/C][C]-55206.7230382191[/C][/ROW]
[ROW][C]69[/C][C]131072[/C][C]99188.5656891706[/C][C]31883.4343108294[/C][/ROW]
[ROW][C]70[/C][C]126817[/C][C]89841.5326071458[/C][C]36975.4673928542[/C][/ROW]
[ROW][C]71[/C][C]81351[/C][C]115535.662245758[/C][C]-34184.6622457578[/C][/ROW]
[ROW][C]72[/C][C]22618[/C][C]42014.8231373449[/C][C]-19396.8231373449[/C][/ROW]
[ROW][C]73[/C][C]88977[/C][C]111091.661135085[/C][C]-22114.6611350846[/C][/ROW]
[ROW][C]74[/C][C]92059[/C][C]85264.1843250461[/C][C]6794.8156749539[/C][/ROW]
[ROW][C]75[/C][C]81897[/C][C]91784.926767148[/C][C]-9887.92676714805[/C][/ROW]
[ROW][C]76[/C][C]108146[/C][C]83995.8075302857[/C][C]24150.1924697143[/C][/ROW]
[ROW][C]77[/C][C]126372[/C][C]115550.382494446[/C][C]10821.6175055544[/C][/ROW]
[ROW][C]78[/C][C]249771[/C][C]135249.810507889[/C][C]114521.189492111[/C][/ROW]
[ROW][C]79[/C][C]71154[/C][C]78965.3216738935[/C][C]-7811.32167389345[/C][/ROW]
[ROW][C]80[/C][C]71571[/C][C]68982.9533509369[/C][C]2588.04664906308[/C][/ROW]
[ROW][C]81[/C][C]55918[/C][C]72529.2471523575[/C][C]-16611.2471523575[/C][/ROW]
[ROW][C]82[/C][C]160141[/C][C]123533.00346797[/C][C]36607.9965320298[/C][/ROW]
[ROW][C]83[/C][C]38692[/C][C]66964.7260995624[/C][C]-28272.7260995624[/C][/ROW]
[ROW][C]84[/C][C]102812[/C][C]101541.94248877[/C][C]1270.05751123039[/C][/ROW]
[ROW][C]85[/C][C]56622[/C][C]36766.1674140006[/C][C]19855.8325859994[/C][/ROW]
[ROW][C]86[/C][C]15986[/C][C]31982.8937420904[/C][C]-15996.8937420904[/C][/ROW]
[ROW][C]87[/C][C]123534[/C][C]129212.709255907[/C][C]-5678.70925590717[/C][/ROW]
[ROW][C]88[/C][C]108535[/C][C]97704.4563611112[/C][C]10830.5436388888[/C][/ROW]
[ROW][C]89[/C][C]93879[/C][C]121167.758972299[/C][C]-27288.7589722993[/C][/ROW]
[ROW][C]90[/C][C]144551[/C][C]112065.591873484[/C][C]32485.4081265165[/C][/ROW]
[ROW][C]91[/C][C]56750[/C][C]73140.9992893769[/C][C]-16390.9992893769[/C][/ROW]
[ROW][C]92[/C][C]127654[/C][C]94180.9023800207[/C][C]33473.0976199793[/C][/ROW]
[ROW][C]93[/C][C]65594[/C][C]91344.8050404177[/C][C]-25750.8050404177[/C][/ROW]
[ROW][C]94[/C][C]59938[/C][C]65652.601508749[/C][C]-5714.60150874896[/C][/ROW]
[ROW][C]95[/C][C]146975[/C][C]130690.358699292[/C][C]16284.6413007078[/C][/ROW]
[ROW][C]96[/C][C]165904[/C][C]131700.291482385[/C][C]34203.7085176147[/C][/ROW]
[ROW][C]97[/C][C]169265[/C][C]147213.85506432[/C][C]22051.1449356801[/C][/ROW]
[ROW][C]98[/C][C]183500[/C][C]150969.790130632[/C][C]32530.2098693676[/C][/ROW]
[ROW][C]99[/C][C]165986[/C][C]193468.957068222[/C][C]-27482.9570682218[/C][/ROW]
[ROW][C]100[/C][C]184923[/C][C]189193.77807912[/C][C]-4270.77807912017[/C][/ROW]
[ROW][C]101[/C][C]140358[/C][C]139424.482881239[/C][C]933.51711876063[/C][/ROW]
[ROW][C]102[/C][C]149959[/C][C]137581.845047085[/C][C]12377.1549529147[/C][/ROW]
[ROW][C]103[/C][C]57224[/C][C]58093.772603808[/C][C]-869.772603808017[/C][/ROW]
[ROW][C]104[/C][C]43750[/C][C]44410.9454112099[/C][C]-660.94541120992[/C][/ROW]
[ROW][C]105[/C][C]48029[/C][C]66521.7788935046[/C][C]-18492.7788935046[/C][/ROW]
[ROW][C]106[/C][C]104978[/C][C]127368.312772154[/C][C]-22390.3127721542[/C][/ROW]
[ROW][C]107[/C][C]100046[/C][C]116174.768984969[/C][C]-16128.7689849693[/C][/ROW]
[ROW][C]108[/C][C]101047[/C][C]96953.7936652144[/C][C]4093.2063347856[/C][/ROW]
[ROW][C]109[/C][C]197426[/C][C]125306.839910732[/C][C]72119.1600892678[/C][/ROW]
[ROW][C]110[/C][C]160902[/C][C]84914.559846673[/C][C]75987.440153327[/C][/ROW]
[ROW][C]111[/C][C]147172[/C][C]130385.316497727[/C][C]16786.6835022729[/C][/ROW]
[ROW][C]112[/C][C]109432[/C][C]85544.8715847337[/C][C]23887.1284152662[/C][/ROW]
[ROW][C]113[/C][C]1168[/C][C]3233.4057876338[/C][C]-2065.4057876338[/C][/ROW]
[ROW][C]114[/C][C]83248[/C][C]92179.5973993541[/C][C]-8931.59739935413[/C][/ROW]
[ROW][C]115[/C][C]25162[/C][C]29934.8402171652[/C][C]-4772.84021716518[/C][/ROW]
[ROW][C]116[/C][C]45724[/C][C]70849.7385740574[/C][C]-25125.7385740574[/C][/ROW]
[ROW][C]117[/C][C]110529[/C][C]128192.515972845[/C][C]-17663.5159728449[/C][/ROW]
[ROW][C]118[/C][C]855[/C][C]3476.69561532768[/C][C]-2621.69561532768[/C][/ROW]
[ROW][C]119[/C][C]101382[/C][C]85389.0955336832[/C][C]15992.9044663168[/C][/ROW]
[ROW][C]120[/C][C]14116[/C][C]14882.7664308975[/C][C]-766.766430897503[/C][/ROW]
[ROW][C]121[/C][C]89506[/C][C]122546.842821445[/C][C]-33040.8428214454[/C][/ROW]
[ROW][C]122[/C][C]135356[/C][C]110880.46549183[/C][C]24475.5345081703[/C][/ROW]
[ROW][C]123[/C][C]116066[/C][C]74733.8558792647[/C][C]41332.1441207353[/C][/ROW]
[ROW][C]124[/C][C]144244[/C][C]80772.1001334634[/C][C]63471.8998665366[/C][/ROW]
[ROW][C]125[/C][C]8773[/C][C]15918.1611755719[/C][C]-7145.16117557193[/C][/ROW]
[ROW][C]126[/C][C]102153[/C][C]83598.9529796908[/C][C]18554.0470203092[/C][/ROW]
[ROW][C]127[/C][C]117440[/C][C]130814.159189171[/C][C]-13374.1591891708[/C][/ROW]
[ROW][C]128[/C][C]104128[/C][C]117330.096480275[/C][C]-13202.0964802755[/C][/ROW]
[ROW][C]129[/C][C]134238[/C][C]144202.774949575[/C][C]-9964.77494957512[/C][/ROW]
[ROW][C]130[/C][C]134047[/C][C]141103.504633951[/C][C]-7056.50463395101[/C][/ROW]
[ROW][C]131[/C][C]279488[/C][C]198654.470186179[/C][C]80833.529813821[/C][/ROW]
[ROW][C]132[/C][C]79756[/C][C]100907.561017756[/C][C]-21151.5610177563[/C][/ROW]
[ROW][C]133[/C][C]66089[/C][C]62931.8417351619[/C][C]3157.15826483808[/C][/ROW]
[ROW][C]134[/C][C]102070[/C][C]97336.1850531922[/C][C]4733.81494680784[/C][/ROW]
[ROW][C]135[/C][C]146760[/C][C]144417.636163471[/C][C]2342.36383652897[/C][/ROW]
[ROW][C]136[/C][C]154771[/C][C]73982.1939678484[/C][C]80788.8060321516[/C][/ROW]
[ROW][C]137[/C][C]165933[/C][C]137190.918858044[/C][C]28742.0811419556[/C][/ROW]
[ROW][C]138[/C][C]64593[/C][C]84981.417505735[/C][C]-20388.417505735[/C][/ROW]
[ROW][C]139[/C][C]92280[/C][C]124750.392443343[/C][C]-32470.3924433425[/C][/ROW]
[ROW][C]140[/C][C]67150[/C][C]93878.1741933222[/C][C]-26728.1741933222[/C][/ROW]
[ROW][C]141[/C][C]128692[/C][C]75230.2312206271[/C][C]53461.7687793729[/C][/ROW]
[ROW][C]142[/C][C]124089[/C][C]107041.252927966[/C][C]17047.7470720338[/C][/ROW]
[ROW][C]143[/C][C]125386[/C][C]116256.722494548[/C][C]9129.277505452[/C][/ROW]
[ROW][C]144[/C][C]37238[/C][C]19045.0776547619[/C][C]18192.9223452381[/C][/ROW]
[ROW][C]145[/C][C]140015[/C][C]143859.108695958[/C][C]-3844.10869595816[/C][/ROW]
[ROW][C]146[/C][C]150047[/C][C]108672.764448823[/C][C]41374.2355511775[/C][/ROW]
[ROW][C]147[/C][C]154451[/C][C]117093.05697029[/C][C]37357.9430297104[/C][/ROW]
[ROW][C]148[/C][C]156349[/C][C]133627.910883554[/C][C]22721.0891164459[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]22162.9479380518[/C][C]-22162.9479380518[/C][/ROW]
[ROW][C]150[/C][C]6023[/C][C]6318.79635452015[/C][C]-295.796354520146[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]1712.32988565282[/C][C]-1712.32988565282[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]1683.13868313771[/C][C]-1683.13868313771[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]3974.15626478498[/C][C]-3974.15626478498[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]1701.4566256034[/C][C]-1701.4566256034[/C][/ROW]
[ROW][C]155[/C][C]84601[/C][C]79739.4841148812[/C][C]4861.51588511885[/C][/ROW]
[ROW][C]156[/C][C]68946[/C][C]114105.652240873[/C][C]-45159.6522408732[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]1701.4566256034[/C][C]-1701.4566256034[/C][/ROW]
[ROW][C]158[/C][C]0[/C][C]1710.27569651018[/C][C]-1710.27569651018[/C][/ROW]
[ROW][C]159[/C][C]1644[/C][C]3509.96956556011[/C][C]-1865.96956556011[/C][/ROW]
[ROW][C]160[/C][C]6179[/C][C]12737.3686273209[/C][C]-6558.36862732093[/C][/ROW]
[ROW][C]161[/C][C]3926[/C][C]7611.0440376696[/C][C]-3685.04403766959[/C][/ROW]
[ROW][C]162[/C][C]52789[/C][C]31967.9463249938[/C][C]20821.0536750062[/C][/ROW]
[ROW][C]163[/C][C]0[/C][C]1695.02773028327[/C][C]-1695.02773028327[/C][/ROW]
[ROW][C]164[/C][C]100350[/C][C]59524.1676971949[/C][C]40825.8323028051[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159411&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159411&T=4

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1140824143026.54257343-2202.54257342974
2110459105149.5621119735309.43788802726
3105079115358.224826678-10279.2248266782
4112098118604.746192511-6506.74619251066
54392966408.6467489824-22479.6467489824
67617363302.009447586512870.9905524135
7187326147683.14275105739642.8572489426
82280714359.94900655138447.0509934487
9144408124909.90361213419498.0963878658
106648577106.3138138922-10621.3138138922
1179089114485.907062715-35396.9070627152
1281625109955.426518742-28330.4265187418
136878883676.2533662768-14888.2533662768
1410329792365.83677315310931.1632268471
156944689420.5682116406-19974.5682116406
16114948136451.315175473-21503.3151754732
17167949143426.09977745624522.9002225437
1812508185626.658163845539454.3418361545
19125818105258.46442400920559.5355759913
20136588132954.7691552233633.23084477717
21112431129742.581638191-17311.5816381913
2210303781540.51649470821496.483505292
238231786064.1798988467-3747.17989884675
24118906134937.976553357-16031.976553357
2583515107494.416149381-23979.4161493812
26104581108055.177199534-3474.17719953368
27103129126286.820069255-23157.8200692552
2883243111653.814335682-28410.8143356822
293711054799.9246513935-17689.9246513935
30113344135646.536393499-22302.5363934988
31139165159027.9936749-19862.9936749002
3286652102668.674178985-16016.6741789846
33112302143360.313359496-31058.3133594957
346965256578.146207962113073.8537920379
35119442143759.304350885-24317.3043508852
366986762154.55356354547712.44643645462
37101629122382.149804223-20753.1498042227
387016882543.5775969268-12375.5775969268
393108147831.1231102358-16750.1231102358
40103925124037.691205678-20112.6912056776
4192622112107.524146908-19485.5241469079
427901193844.2713143948-14833.2713143948
4393487121153.003980788-27666.0039807882
446452085155.7267927535-20635.7267927535
459347385588.59426731267884.40573268743
46114360110092.5604056894267.43959431129
473303255619.1274690877-22587.1274690877
4896125109371.572394542-13246.5723945422
49151911154361.879920984-2450.87992098383
5089256108175.42188791-18919.4218879098
5195676126390.853138088-30714.8531380876
52595013203.4511035883-7253.45110358826
53149695142668.6420102237026.35798977697
543255138417.3764868873-5866.37648688733
553170141456.2132865671-9755.21328656706
56100087114673.360826266-14586.3608262658
57169707175840.981843124-6133.98184312407
58150491173868.263749024-23377.2637490235
59120192142699.076643052-22507.0766430521
6095893104745.424453114-8852.42445311391
61151715154692.250070153-2977.25007015329
62176225193278.595041834-17053.5950418336
635990085002.0802768543-25102.0802768543
64104767117264.770542813-12497.7705428127
65114799131412.199151908-16613.1991519078
667212893697.7399304917-21569.7399304917
67143592114609.04644570128982.9535542988
6889626144832.723038219-55206.7230382191
6913107299188.565689170631883.4343108294
7012681789841.532607145836975.4673928542
7181351115535.662245758-34184.6622457578
722261842014.8231373449-19396.8231373449
7388977111091.661135085-22114.6611350846
749205985264.18432504616794.8156749539
758189791784.926767148-9887.92676714805
7610814683995.807530285724150.1924697143
77126372115550.38249444610821.6175055544
78249771135249.810507889114521.189492111
797115478965.3216738935-7811.32167389345
807157168982.95335093692588.04664906308
815591872529.2471523575-16611.2471523575
82160141123533.0034679736607.9965320298
833869266964.7260995624-28272.7260995624
84102812101541.942488771270.05751123039
855662236766.167414000619855.8325859994
861598631982.8937420904-15996.8937420904
87123534129212.709255907-5678.70925590717
8810853597704.456361111210830.5436388888
8993879121167.758972299-27288.7589722993
90144551112065.59187348432485.4081265165
915675073140.9992893769-16390.9992893769
9212765494180.902380020733473.0976199793
936559491344.8050404177-25750.8050404177
945993865652.601508749-5714.60150874896
95146975130690.35869929216284.6413007078
96165904131700.29148238534203.7085176147
97169265147213.8550643222051.1449356801
98183500150969.79013063232530.2098693676
99165986193468.957068222-27482.9570682218
100184923189193.77807912-4270.77807912017
101140358139424.482881239933.51711876063
102149959137581.84504708512377.1549529147
1035722458093.772603808-869.772603808017
1044375044410.9454112099-660.94541120992
1054802966521.7788935046-18492.7788935046
106104978127368.312772154-22390.3127721542
107100046116174.768984969-16128.7689849693
10810104796953.79366521444093.2063347856
109197426125306.83991073272119.1600892678
11016090284914.55984667375987.440153327
111147172130385.31649772716786.6835022729
11210943285544.871584733723887.1284152662
11311683233.4057876338-2065.4057876338
1148324892179.5973993541-8931.59739935413
1152516229934.8402171652-4772.84021716518
1164572470849.7385740574-25125.7385740574
117110529128192.515972845-17663.5159728449
1188553476.69561532768-2621.69561532768
11910138285389.095533683215992.9044663168
1201411614882.7664308975-766.766430897503
12189506122546.842821445-33040.8428214454
122135356110880.4654918324475.5345081703
12311606674733.855879264741332.1441207353
12414424480772.100133463463471.8998665366
125877315918.1611755719-7145.16117557193
12610215383598.952979690818554.0470203092
127117440130814.159189171-13374.1591891708
128104128117330.096480275-13202.0964802755
129134238144202.774949575-9964.77494957512
130134047141103.504633951-7056.50463395101
131279488198654.47018617980833.529813821
13279756100907.561017756-21151.5610177563
1336608962931.84173516193157.15826483808
13410207097336.18505319224733.81494680784
135146760144417.6361634712342.36383652897
13615477173982.193967848480788.8060321516
137165933137190.91885804428742.0811419556
1386459384981.417505735-20388.417505735
13992280124750.392443343-32470.3924433425
1406715093878.1741933222-26728.1741933222
14112869275230.231220627153461.7687793729
142124089107041.25292796617047.7470720338
143125386116256.7224945489129.277505452
1443723819045.077654761918192.9223452381
145140015143859.108695958-3844.10869595816
146150047108672.76444882341374.2355511775
147154451117093.0569702937357.9430297104
148156349133627.91088355422721.0891164459
149022162.9479380518-22162.9479380518
15060236318.79635452015-295.796354520146
15101712.32988565282-1712.32988565282
15201683.13868313771-1683.13868313771
15303974.15626478498-3974.15626478498
15401701.4566256034-1701.4566256034
1558460179739.48411488124861.51588511885
15668946114105.652240873-45159.6522408732
15701701.4566256034-1701.4566256034
15801710.27569651018-1710.27569651018
15916443509.96956556011-1865.96956556011
160617912737.3686273209-6558.36862732093
16139267611.0440376696-3685.04403766959
1625278931967.946324993820821.0536750062
16301695.02773028327-1695.02773028327
16410035059524.167697194940825.8323028051






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.410882159660120.821764319320240.58911784033988
190.3246115671629170.6492231343258340.675388432837083
200.2203848113939570.4407696227879140.779615188606043
210.1818532806911950.363706561382390.818146719308805
220.2090381158675260.4180762317350520.790961884132474
230.1335802627123820.2671605254247650.866419737287618
240.0897876015283240.1795752030566480.910212398471676
250.07301264498826370.1460252899765270.926987355011736
260.04307860085505470.08615720171010940.956921399144945
270.02992886569551860.05985773139103730.970071134304481
280.01952880398288860.03905760796577720.980471196017111
290.01378009005761410.02756018011522810.986219909942386
300.01047213905461320.02094427810922640.989527860945387
310.006713014197374090.01342602839474820.993286985802626
320.004093930361057830.008187860722115650.995906069638942
330.002672772093314310.005345544186628620.997327227906686
340.005384698335591910.01076939667118380.994615301664408
350.006989955263532480.0139799105270650.993010044736467
360.004369987034011230.008739974068022460.995630012965989
370.002661994799657750.00532398959931550.997338005200342
380.001822169500077750.00364433900015550.998177830499922
390.001725013852676570.003450027705353140.998274986147323
400.001109043511232910.002218087022465830.998890956488767
410.002049780513104830.004099561026209660.997950219486895
420.001269342435313240.002538684870626490.998730657564687
430.001155585053333950.00231117010666790.998844414946666
440.0007820705736470480.00156414114729410.999217929426353
450.002860396307788990.005720792615577980.99713960369221
460.001802577195755920.003605154391511840.998197422804244
470.001445985892658270.002891971785316530.998554014107342
480.0009049780561649550.001809956112329910.999095021943835
490.0006410261528841560.001282052305768310.999358973847116
500.0005666579355564740.001133315871112950.999433342064444
510.0004689202931396990.0009378405862793990.99953107970686
520.0003087836251879650.0006175672503759290.999691216374812
530.0002172403619441390.0004344807238882770.999782759638056
540.0001250108796131470.0002500217592262940.999874989120387
557.92384051452432e-050.0001584768102904860.999920761594855
565.22806664282168e-050.0001045613328564340.999947719333572
577.18832178381399e-050.000143766435676280.999928116782162
588.4908573958807e-050.0001698171479176140.99991509142604
596.40491076634652e-050.000128098215326930.999935950892337
603.81701826397705e-057.63403652795409e-050.99996182981736
612.51304411607614e-055.02608823215228e-050.99997486955884
621.5172472072977e-053.03449441459541e-050.999984827527927
631.22237583079564e-052.44475166159128e-050.999987776241692
647.21331301930665e-061.44266260386133e-050.99999278668698
655.26060844160651e-061.0521216883213e-050.999994739391558
663.42831548512127e-066.85663097024253e-060.999996571684515
677.61742521059617e-061.52348504211923e-050.99999238257479
684.20287506424036e-058.40575012848072e-050.999957971249358
694.13313385943422e-058.26626771886844e-050.999958668661406
700.0001265456701997250.0002530913403994490.9998734543298
710.000193242099747030.0003864841994940590.999806757900253
720.0001584107579467280.0003168215158934560.999841589242053
730.0001536651613276320.0003073303226552630.999846334838672
749.6581645039407e-050.0001931632900788140.99990341835496
756.9302461461744e-050.0001386049229234880.999930697538538
767.48852881798918e-050.0001497705763597840.99992511471182
774.79900880372639e-059.59801760745279e-050.999952009911963
780.1757860696183090.3515721392366190.82421393038169
790.1565969194333750.3131938388667490.843403080566625
800.1317930156487860.2635860312975710.868206984351214
810.1201066555274060.2402133110548130.879893344472594
820.19536259093770.39072518187540.8046374090623
830.2148411677170860.4296823354341720.785158832282914
840.1822763708921230.3645527417842460.817723629107877
850.1666472617941850.333294523588370.833352738205815
860.1428437183308620.2856874366617250.857156281669138
870.1295452524026850.259090504805370.870454747597315
880.1279158742792990.2558317485585970.872084125720701
890.1348571992129790.2697143984259580.865142800787021
900.1519820457042050.3039640914084110.848017954295795
910.1395805613374960.2791611226749920.860419438662504
920.1697820877784840.3395641755569680.830217912221516
930.1962195488694980.3924390977389950.803780451130502
940.1662433737312590.3324867474625180.83375662626874
950.1688127942508610.3376255885017230.831187205749139
960.1868586249510790.3737172499021580.813141375048921
970.1772177502729740.3544355005459490.822782249727026
980.1857103517429040.3714207034858080.814289648257096
990.2904654544052030.5809309088104050.709534545594797
1000.2596317899210480.5192635798420960.740368210078952
1010.2584368050423350.516873610084670.741563194957665
1020.2328468211174480.4656936422348970.767153178882552
1030.1959462821942460.3918925643884910.804053717805754
1040.1689944558708150.3379889117416290.831005544129185
1050.152725295793530.3054505915870610.84727470420647
1060.1711802618134410.3423605236268820.828819738186559
1070.1471231955321290.2942463910642580.852876804467871
1080.1234263124267960.2468526248535910.876573687573204
1090.2292198898777230.4584397797554470.770780110122277
1100.3842731242313550.7685462484627090.615726875768645
1110.3445784884088150.6891569768176290.655421511591185
1120.352970463143010.705940926286020.64702953685699
1130.3047379242470070.6094758484940130.695262075752993
1140.2744400489708660.5488800979417320.725559951029134
1150.2350758241723370.4701516483446740.764924175827663
1160.347351216435940.694702432871880.65264878356406
1170.3202929747078970.6405859494157940.679707025292103
1180.2713237956498480.5426475912996950.728676204350152
1190.2382850719792980.4765701439585970.761714928020702
1200.1955226668282810.3910453336565620.804477333171719
1210.4333429723241590.8666859446483190.566657027675841
1220.402689538794180.805379077588360.59731046120582
1230.4343401570443110.8686803140886230.565659842955689
1240.5904404852575620.8191190294848770.409559514742438
1250.529799980529630.940400038940740.47020001947037
1260.4820920592755940.9641841185511880.517907940724406
1270.4198478935218480.8396957870436960.580152106478152
1280.6397357510648110.7205284978703790.360264248935189
1290.6329345292754960.7341309414490080.367065470724504
1300.5866245015374360.8267509969251280.413375498462564
1310.9197520089306450.160495982138710.080247991069355
1320.8928418241036880.2143163517926250.107158175896312
1330.8603982585002430.2792034829995140.139601741499757
1340.8273083618205310.3453832763589380.172691638179469
1350.7832447135449350.4335105729101310.216755286455065
1360.999878526782890.0002429464342186190.000121473217109309
1370.999965631633456.87367330975475e-053.43683665487737e-05
1380.9999846074130983.07851738034566e-051.53925869017283e-05
1390.9999434852921270.0001130294157454255.65147078727124e-05
1400.999865506604250.0002689867915006460.000134493395750323
1410.999876218525770.0002475629484618330.000123781474230916
1420.9998269446624520.0003461106750963760.000173055337548188
1430.99993830686740.0001233862651993886.16931325996938e-05
1440.9999999999913431.73144264757195e-118.65721323785975e-12
1450.9999999995259819.480375559471e-104.7401877797355e-10
14616.19029551997312e-463.09514775998656e-46

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.41088215966012 & 0.82176431932024 & 0.58911784033988 \tabularnewline
19 & 0.324611567162917 & 0.649223134325834 & 0.675388432837083 \tabularnewline
20 & 0.220384811393957 & 0.440769622787914 & 0.779615188606043 \tabularnewline
21 & 0.181853280691195 & 0.36370656138239 & 0.818146719308805 \tabularnewline
22 & 0.209038115867526 & 0.418076231735052 & 0.790961884132474 \tabularnewline
23 & 0.133580262712382 & 0.267160525424765 & 0.866419737287618 \tabularnewline
24 & 0.089787601528324 & 0.179575203056648 & 0.910212398471676 \tabularnewline
25 & 0.0730126449882637 & 0.146025289976527 & 0.926987355011736 \tabularnewline
26 & 0.0430786008550547 & 0.0861572017101094 & 0.956921399144945 \tabularnewline
27 & 0.0299288656955186 & 0.0598577313910373 & 0.970071134304481 \tabularnewline
28 & 0.0195288039828886 & 0.0390576079657772 & 0.980471196017111 \tabularnewline
29 & 0.0137800900576141 & 0.0275601801152281 & 0.986219909942386 \tabularnewline
30 & 0.0104721390546132 & 0.0209442781092264 & 0.989527860945387 \tabularnewline
31 & 0.00671301419737409 & 0.0134260283947482 & 0.993286985802626 \tabularnewline
32 & 0.00409393036105783 & 0.00818786072211565 & 0.995906069638942 \tabularnewline
33 & 0.00267277209331431 & 0.00534554418662862 & 0.997327227906686 \tabularnewline
34 & 0.00538469833559191 & 0.0107693966711838 & 0.994615301664408 \tabularnewline
35 & 0.00698995526353248 & 0.013979910527065 & 0.993010044736467 \tabularnewline
36 & 0.00436998703401123 & 0.00873997406802246 & 0.995630012965989 \tabularnewline
37 & 0.00266199479965775 & 0.0053239895993155 & 0.997338005200342 \tabularnewline
38 & 0.00182216950007775 & 0.0036443390001555 & 0.998177830499922 \tabularnewline
39 & 0.00172501385267657 & 0.00345002770535314 & 0.998274986147323 \tabularnewline
40 & 0.00110904351123291 & 0.00221808702246583 & 0.998890956488767 \tabularnewline
41 & 0.00204978051310483 & 0.00409956102620966 & 0.997950219486895 \tabularnewline
42 & 0.00126934243531324 & 0.00253868487062649 & 0.998730657564687 \tabularnewline
43 & 0.00115558505333395 & 0.0023111701066679 & 0.998844414946666 \tabularnewline
44 & 0.000782070573647048 & 0.0015641411472941 & 0.999217929426353 \tabularnewline
45 & 0.00286039630778899 & 0.00572079261557798 & 0.99713960369221 \tabularnewline
46 & 0.00180257719575592 & 0.00360515439151184 & 0.998197422804244 \tabularnewline
47 & 0.00144598589265827 & 0.00289197178531653 & 0.998554014107342 \tabularnewline
48 & 0.000904978056164955 & 0.00180995611232991 & 0.999095021943835 \tabularnewline
49 & 0.000641026152884156 & 0.00128205230576831 & 0.999358973847116 \tabularnewline
50 & 0.000566657935556474 & 0.00113331587111295 & 0.999433342064444 \tabularnewline
51 & 0.000468920293139699 & 0.000937840586279399 & 0.99953107970686 \tabularnewline
52 & 0.000308783625187965 & 0.000617567250375929 & 0.999691216374812 \tabularnewline
53 & 0.000217240361944139 & 0.000434480723888277 & 0.999782759638056 \tabularnewline
54 & 0.000125010879613147 & 0.000250021759226294 & 0.999874989120387 \tabularnewline
55 & 7.92384051452432e-05 & 0.000158476810290486 & 0.999920761594855 \tabularnewline
56 & 5.22806664282168e-05 & 0.000104561332856434 & 0.999947719333572 \tabularnewline
57 & 7.18832178381399e-05 & 0.00014376643567628 & 0.999928116782162 \tabularnewline
58 & 8.4908573958807e-05 & 0.000169817147917614 & 0.99991509142604 \tabularnewline
59 & 6.40491076634652e-05 & 0.00012809821532693 & 0.999935950892337 \tabularnewline
60 & 3.81701826397705e-05 & 7.63403652795409e-05 & 0.99996182981736 \tabularnewline
61 & 2.51304411607614e-05 & 5.02608823215228e-05 & 0.99997486955884 \tabularnewline
62 & 1.5172472072977e-05 & 3.03449441459541e-05 & 0.999984827527927 \tabularnewline
63 & 1.22237583079564e-05 & 2.44475166159128e-05 & 0.999987776241692 \tabularnewline
64 & 7.21331301930665e-06 & 1.44266260386133e-05 & 0.99999278668698 \tabularnewline
65 & 5.26060844160651e-06 & 1.0521216883213e-05 & 0.999994739391558 \tabularnewline
66 & 3.42831548512127e-06 & 6.85663097024253e-06 & 0.999996571684515 \tabularnewline
67 & 7.61742521059617e-06 & 1.52348504211923e-05 & 0.99999238257479 \tabularnewline
68 & 4.20287506424036e-05 & 8.40575012848072e-05 & 0.999957971249358 \tabularnewline
69 & 4.13313385943422e-05 & 8.26626771886844e-05 & 0.999958668661406 \tabularnewline
70 & 0.000126545670199725 & 0.000253091340399449 & 0.9998734543298 \tabularnewline
71 & 0.00019324209974703 & 0.000386484199494059 & 0.999806757900253 \tabularnewline
72 & 0.000158410757946728 & 0.000316821515893456 & 0.999841589242053 \tabularnewline
73 & 0.000153665161327632 & 0.000307330322655263 & 0.999846334838672 \tabularnewline
74 & 9.6581645039407e-05 & 0.000193163290078814 & 0.99990341835496 \tabularnewline
75 & 6.9302461461744e-05 & 0.000138604922923488 & 0.999930697538538 \tabularnewline
76 & 7.48852881798918e-05 & 0.000149770576359784 & 0.99992511471182 \tabularnewline
77 & 4.79900880372639e-05 & 9.59801760745279e-05 & 0.999952009911963 \tabularnewline
78 & 0.175786069618309 & 0.351572139236619 & 0.82421393038169 \tabularnewline
79 & 0.156596919433375 & 0.313193838866749 & 0.843403080566625 \tabularnewline
80 & 0.131793015648786 & 0.263586031297571 & 0.868206984351214 \tabularnewline
81 & 0.120106655527406 & 0.240213311054813 & 0.879893344472594 \tabularnewline
82 & 0.1953625909377 & 0.3907251818754 & 0.8046374090623 \tabularnewline
83 & 0.214841167717086 & 0.429682335434172 & 0.785158832282914 \tabularnewline
84 & 0.182276370892123 & 0.364552741784246 & 0.817723629107877 \tabularnewline
85 & 0.166647261794185 & 0.33329452358837 & 0.833352738205815 \tabularnewline
86 & 0.142843718330862 & 0.285687436661725 & 0.857156281669138 \tabularnewline
87 & 0.129545252402685 & 0.25909050480537 & 0.870454747597315 \tabularnewline
88 & 0.127915874279299 & 0.255831748558597 & 0.872084125720701 \tabularnewline
89 & 0.134857199212979 & 0.269714398425958 & 0.865142800787021 \tabularnewline
90 & 0.151982045704205 & 0.303964091408411 & 0.848017954295795 \tabularnewline
91 & 0.139580561337496 & 0.279161122674992 & 0.860419438662504 \tabularnewline
92 & 0.169782087778484 & 0.339564175556968 & 0.830217912221516 \tabularnewline
93 & 0.196219548869498 & 0.392439097738995 & 0.803780451130502 \tabularnewline
94 & 0.166243373731259 & 0.332486747462518 & 0.83375662626874 \tabularnewline
95 & 0.168812794250861 & 0.337625588501723 & 0.831187205749139 \tabularnewline
96 & 0.186858624951079 & 0.373717249902158 & 0.813141375048921 \tabularnewline
97 & 0.177217750272974 & 0.354435500545949 & 0.822782249727026 \tabularnewline
98 & 0.185710351742904 & 0.371420703485808 & 0.814289648257096 \tabularnewline
99 & 0.290465454405203 & 0.580930908810405 & 0.709534545594797 \tabularnewline
100 & 0.259631789921048 & 0.519263579842096 & 0.740368210078952 \tabularnewline
101 & 0.258436805042335 & 0.51687361008467 & 0.741563194957665 \tabularnewline
102 & 0.232846821117448 & 0.465693642234897 & 0.767153178882552 \tabularnewline
103 & 0.195946282194246 & 0.391892564388491 & 0.804053717805754 \tabularnewline
104 & 0.168994455870815 & 0.337988911741629 & 0.831005544129185 \tabularnewline
105 & 0.15272529579353 & 0.305450591587061 & 0.84727470420647 \tabularnewline
106 & 0.171180261813441 & 0.342360523626882 & 0.828819738186559 \tabularnewline
107 & 0.147123195532129 & 0.294246391064258 & 0.852876804467871 \tabularnewline
108 & 0.123426312426796 & 0.246852624853591 & 0.876573687573204 \tabularnewline
109 & 0.229219889877723 & 0.458439779755447 & 0.770780110122277 \tabularnewline
110 & 0.384273124231355 & 0.768546248462709 & 0.615726875768645 \tabularnewline
111 & 0.344578488408815 & 0.689156976817629 & 0.655421511591185 \tabularnewline
112 & 0.35297046314301 & 0.70594092628602 & 0.64702953685699 \tabularnewline
113 & 0.304737924247007 & 0.609475848494013 & 0.695262075752993 \tabularnewline
114 & 0.274440048970866 & 0.548880097941732 & 0.725559951029134 \tabularnewline
115 & 0.235075824172337 & 0.470151648344674 & 0.764924175827663 \tabularnewline
116 & 0.34735121643594 & 0.69470243287188 & 0.65264878356406 \tabularnewline
117 & 0.320292974707897 & 0.640585949415794 & 0.679707025292103 \tabularnewline
118 & 0.271323795649848 & 0.542647591299695 & 0.728676204350152 \tabularnewline
119 & 0.238285071979298 & 0.476570143958597 & 0.761714928020702 \tabularnewline
120 & 0.195522666828281 & 0.391045333656562 & 0.804477333171719 \tabularnewline
121 & 0.433342972324159 & 0.866685944648319 & 0.566657027675841 \tabularnewline
122 & 0.40268953879418 & 0.80537907758836 & 0.59731046120582 \tabularnewline
123 & 0.434340157044311 & 0.868680314088623 & 0.565659842955689 \tabularnewline
124 & 0.590440485257562 & 0.819119029484877 & 0.409559514742438 \tabularnewline
125 & 0.52979998052963 & 0.94040003894074 & 0.47020001947037 \tabularnewline
126 & 0.482092059275594 & 0.964184118551188 & 0.517907940724406 \tabularnewline
127 & 0.419847893521848 & 0.839695787043696 & 0.580152106478152 \tabularnewline
128 & 0.639735751064811 & 0.720528497870379 & 0.360264248935189 \tabularnewline
129 & 0.632934529275496 & 0.734130941449008 & 0.367065470724504 \tabularnewline
130 & 0.586624501537436 & 0.826750996925128 & 0.413375498462564 \tabularnewline
131 & 0.919752008930645 & 0.16049598213871 & 0.080247991069355 \tabularnewline
132 & 0.892841824103688 & 0.214316351792625 & 0.107158175896312 \tabularnewline
133 & 0.860398258500243 & 0.279203482999514 & 0.139601741499757 \tabularnewline
134 & 0.827308361820531 & 0.345383276358938 & 0.172691638179469 \tabularnewline
135 & 0.783244713544935 & 0.433510572910131 & 0.216755286455065 \tabularnewline
136 & 0.99987852678289 & 0.000242946434218619 & 0.000121473217109309 \tabularnewline
137 & 0.99996563163345 & 6.87367330975475e-05 & 3.43683665487737e-05 \tabularnewline
138 & 0.999984607413098 & 3.07851738034566e-05 & 1.53925869017283e-05 \tabularnewline
139 & 0.999943485292127 & 0.000113029415745425 & 5.65147078727124e-05 \tabularnewline
140 & 0.99986550660425 & 0.000268986791500646 & 0.000134493395750323 \tabularnewline
141 & 0.99987621852577 & 0.000247562948461833 & 0.000123781474230916 \tabularnewline
142 & 0.999826944662452 & 0.000346110675096376 & 0.000173055337548188 \tabularnewline
143 & 0.9999383068674 & 0.000123386265199388 & 6.16931325996938e-05 \tabularnewline
144 & 0.999999999991343 & 1.73144264757195e-11 & 8.65721323785975e-12 \tabularnewline
145 & 0.999999999525981 & 9.480375559471e-10 & 4.7401877797355e-10 \tabularnewline
146 & 1 & 6.19029551997312e-46 & 3.09514775998656e-46 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159411&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]18[/C][C]0.41088215966012[/C][C]0.82176431932024[/C][C]0.58911784033988[/C][/ROW]
[ROW][C]19[/C][C]0.324611567162917[/C][C]0.649223134325834[/C][C]0.675388432837083[/C][/ROW]
[ROW][C]20[/C][C]0.220384811393957[/C][C]0.440769622787914[/C][C]0.779615188606043[/C][/ROW]
[ROW][C]21[/C][C]0.181853280691195[/C][C]0.36370656138239[/C][C]0.818146719308805[/C][/ROW]
[ROW][C]22[/C][C]0.209038115867526[/C][C]0.418076231735052[/C][C]0.790961884132474[/C][/ROW]
[ROW][C]23[/C][C]0.133580262712382[/C][C]0.267160525424765[/C][C]0.866419737287618[/C][/ROW]
[ROW][C]24[/C][C]0.089787601528324[/C][C]0.179575203056648[/C][C]0.910212398471676[/C][/ROW]
[ROW][C]25[/C][C]0.0730126449882637[/C][C]0.146025289976527[/C][C]0.926987355011736[/C][/ROW]
[ROW][C]26[/C][C]0.0430786008550547[/C][C]0.0861572017101094[/C][C]0.956921399144945[/C][/ROW]
[ROW][C]27[/C][C]0.0299288656955186[/C][C]0.0598577313910373[/C][C]0.970071134304481[/C][/ROW]
[ROW][C]28[/C][C]0.0195288039828886[/C][C]0.0390576079657772[/C][C]0.980471196017111[/C][/ROW]
[ROW][C]29[/C][C]0.0137800900576141[/C][C]0.0275601801152281[/C][C]0.986219909942386[/C][/ROW]
[ROW][C]30[/C][C]0.0104721390546132[/C][C]0.0209442781092264[/C][C]0.989527860945387[/C][/ROW]
[ROW][C]31[/C][C]0.00671301419737409[/C][C]0.0134260283947482[/C][C]0.993286985802626[/C][/ROW]
[ROW][C]32[/C][C]0.00409393036105783[/C][C]0.00818786072211565[/C][C]0.995906069638942[/C][/ROW]
[ROW][C]33[/C][C]0.00267277209331431[/C][C]0.00534554418662862[/C][C]0.997327227906686[/C][/ROW]
[ROW][C]34[/C][C]0.00538469833559191[/C][C]0.0107693966711838[/C][C]0.994615301664408[/C][/ROW]
[ROW][C]35[/C][C]0.00698995526353248[/C][C]0.013979910527065[/C][C]0.993010044736467[/C][/ROW]
[ROW][C]36[/C][C]0.00436998703401123[/C][C]0.00873997406802246[/C][C]0.995630012965989[/C][/ROW]
[ROW][C]37[/C][C]0.00266199479965775[/C][C]0.0053239895993155[/C][C]0.997338005200342[/C][/ROW]
[ROW][C]38[/C][C]0.00182216950007775[/C][C]0.0036443390001555[/C][C]0.998177830499922[/C][/ROW]
[ROW][C]39[/C][C]0.00172501385267657[/C][C]0.00345002770535314[/C][C]0.998274986147323[/C][/ROW]
[ROW][C]40[/C][C]0.00110904351123291[/C][C]0.00221808702246583[/C][C]0.998890956488767[/C][/ROW]
[ROW][C]41[/C][C]0.00204978051310483[/C][C]0.00409956102620966[/C][C]0.997950219486895[/C][/ROW]
[ROW][C]42[/C][C]0.00126934243531324[/C][C]0.00253868487062649[/C][C]0.998730657564687[/C][/ROW]
[ROW][C]43[/C][C]0.00115558505333395[/C][C]0.0023111701066679[/C][C]0.998844414946666[/C][/ROW]
[ROW][C]44[/C][C]0.000782070573647048[/C][C]0.0015641411472941[/C][C]0.999217929426353[/C][/ROW]
[ROW][C]45[/C][C]0.00286039630778899[/C][C]0.00572079261557798[/C][C]0.99713960369221[/C][/ROW]
[ROW][C]46[/C][C]0.00180257719575592[/C][C]0.00360515439151184[/C][C]0.998197422804244[/C][/ROW]
[ROW][C]47[/C][C]0.00144598589265827[/C][C]0.00289197178531653[/C][C]0.998554014107342[/C][/ROW]
[ROW][C]48[/C][C]0.000904978056164955[/C][C]0.00180995611232991[/C][C]0.999095021943835[/C][/ROW]
[ROW][C]49[/C][C]0.000641026152884156[/C][C]0.00128205230576831[/C][C]0.999358973847116[/C][/ROW]
[ROW][C]50[/C][C]0.000566657935556474[/C][C]0.00113331587111295[/C][C]0.999433342064444[/C][/ROW]
[ROW][C]51[/C][C]0.000468920293139699[/C][C]0.000937840586279399[/C][C]0.99953107970686[/C][/ROW]
[ROW][C]52[/C][C]0.000308783625187965[/C][C]0.000617567250375929[/C][C]0.999691216374812[/C][/ROW]
[ROW][C]53[/C][C]0.000217240361944139[/C][C]0.000434480723888277[/C][C]0.999782759638056[/C][/ROW]
[ROW][C]54[/C][C]0.000125010879613147[/C][C]0.000250021759226294[/C][C]0.999874989120387[/C][/ROW]
[ROW][C]55[/C][C]7.92384051452432e-05[/C][C]0.000158476810290486[/C][C]0.999920761594855[/C][/ROW]
[ROW][C]56[/C][C]5.22806664282168e-05[/C][C]0.000104561332856434[/C][C]0.999947719333572[/C][/ROW]
[ROW][C]57[/C][C]7.18832178381399e-05[/C][C]0.00014376643567628[/C][C]0.999928116782162[/C][/ROW]
[ROW][C]58[/C][C]8.4908573958807e-05[/C][C]0.000169817147917614[/C][C]0.99991509142604[/C][/ROW]
[ROW][C]59[/C][C]6.40491076634652e-05[/C][C]0.00012809821532693[/C][C]0.999935950892337[/C][/ROW]
[ROW][C]60[/C][C]3.81701826397705e-05[/C][C]7.63403652795409e-05[/C][C]0.99996182981736[/C][/ROW]
[ROW][C]61[/C][C]2.51304411607614e-05[/C][C]5.02608823215228e-05[/C][C]0.99997486955884[/C][/ROW]
[ROW][C]62[/C][C]1.5172472072977e-05[/C][C]3.03449441459541e-05[/C][C]0.999984827527927[/C][/ROW]
[ROW][C]63[/C][C]1.22237583079564e-05[/C][C]2.44475166159128e-05[/C][C]0.999987776241692[/C][/ROW]
[ROW][C]64[/C][C]7.21331301930665e-06[/C][C]1.44266260386133e-05[/C][C]0.99999278668698[/C][/ROW]
[ROW][C]65[/C][C]5.26060844160651e-06[/C][C]1.0521216883213e-05[/C][C]0.999994739391558[/C][/ROW]
[ROW][C]66[/C][C]3.42831548512127e-06[/C][C]6.85663097024253e-06[/C][C]0.999996571684515[/C][/ROW]
[ROW][C]67[/C][C]7.61742521059617e-06[/C][C]1.52348504211923e-05[/C][C]0.99999238257479[/C][/ROW]
[ROW][C]68[/C][C]4.20287506424036e-05[/C][C]8.40575012848072e-05[/C][C]0.999957971249358[/C][/ROW]
[ROW][C]69[/C][C]4.13313385943422e-05[/C][C]8.26626771886844e-05[/C][C]0.999958668661406[/C][/ROW]
[ROW][C]70[/C][C]0.000126545670199725[/C][C]0.000253091340399449[/C][C]0.9998734543298[/C][/ROW]
[ROW][C]71[/C][C]0.00019324209974703[/C][C]0.000386484199494059[/C][C]0.999806757900253[/C][/ROW]
[ROW][C]72[/C][C]0.000158410757946728[/C][C]0.000316821515893456[/C][C]0.999841589242053[/C][/ROW]
[ROW][C]73[/C][C]0.000153665161327632[/C][C]0.000307330322655263[/C][C]0.999846334838672[/C][/ROW]
[ROW][C]74[/C][C]9.6581645039407e-05[/C][C]0.000193163290078814[/C][C]0.99990341835496[/C][/ROW]
[ROW][C]75[/C][C]6.9302461461744e-05[/C][C]0.000138604922923488[/C][C]0.999930697538538[/C][/ROW]
[ROW][C]76[/C][C]7.48852881798918e-05[/C][C]0.000149770576359784[/C][C]0.99992511471182[/C][/ROW]
[ROW][C]77[/C][C]4.79900880372639e-05[/C][C]9.59801760745279e-05[/C][C]0.999952009911963[/C][/ROW]
[ROW][C]78[/C][C]0.175786069618309[/C][C]0.351572139236619[/C][C]0.82421393038169[/C][/ROW]
[ROW][C]79[/C][C]0.156596919433375[/C][C]0.313193838866749[/C][C]0.843403080566625[/C][/ROW]
[ROW][C]80[/C][C]0.131793015648786[/C][C]0.263586031297571[/C][C]0.868206984351214[/C][/ROW]
[ROW][C]81[/C][C]0.120106655527406[/C][C]0.240213311054813[/C][C]0.879893344472594[/C][/ROW]
[ROW][C]82[/C][C]0.1953625909377[/C][C]0.3907251818754[/C][C]0.8046374090623[/C][/ROW]
[ROW][C]83[/C][C]0.214841167717086[/C][C]0.429682335434172[/C][C]0.785158832282914[/C][/ROW]
[ROW][C]84[/C][C]0.182276370892123[/C][C]0.364552741784246[/C][C]0.817723629107877[/C][/ROW]
[ROW][C]85[/C][C]0.166647261794185[/C][C]0.33329452358837[/C][C]0.833352738205815[/C][/ROW]
[ROW][C]86[/C][C]0.142843718330862[/C][C]0.285687436661725[/C][C]0.857156281669138[/C][/ROW]
[ROW][C]87[/C][C]0.129545252402685[/C][C]0.25909050480537[/C][C]0.870454747597315[/C][/ROW]
[ROW][C]88[/C][C]0.127915874279299[/C][C]0.255831748558597[/C][C]0.872084125720701[/C][/ROW]
[ROW][C]89[/C][C]0.134857199212979[/C][C]0.269714398425958[/C][C]0.865142800787021[/C][/ROW]
[ROW][C]90[/C][C]0.151982045704205[/C][C]0.303964091408411[/C][C]0.848017954295795[/C][/ROW]
[ROW][C]91[/C][C]0.139580561337496[/C][C]0.279161122674992[/C][C]0.860419438662504[/C][/ROW]
[ROW][C]92[/C][C]0.169782087778484[/C][C]0.339564175556968[/C][C]0.830217912221516[/C][/ROW]
[ROW][C]93[/C][C]0.196219548869498[/C][C]0.392439097738995[/C][C]0.803780451130502[/C][/ROW]
[ROW][C]94[/C][C]0.166243373731259[/C][C]0.332486747462518[/C][C]0.83375662626874[/C][/ROW]
[ROW][C]95[/C][C]0.168812794250861[/C][C]0.337625588501723[/C][C]0.831187205749139[/C][/ROW]
[ROW][C]96[/C][C]0.186858624951079[/C][C]0.373717249902158[/C][C]0.813141375048921[/C][/ROW]
[ROW][C]97[/C][C]0.177217750272974[/C][C]0.354435500545949[/C][C]0.822782249727026[/C][/ROW]
[ROW][C]98[/C][C]0.185710351742904[/C][C]0.371420703485808[/C][C]0.814289648257096[/C][/ROW]
[ROW][C]99[/C][C]0.290465454405203[/C][C]0.580930908810405[/C][C]0.709534545594797[/C][/ROW]
[ROW][C]100[/C][C]0.259631789921048[/C][C]0.519263579842096[/C][C]0.740368210078952[/C][/ROW]
[ROW][C]101[/C][C]0.258436805042335[/C][C]0.51687361008467[/C][C]0.741563194957665[/C][/ROW]
[ROW][C]102[/C][C]0.232846821117448[/C][C]0.465693642234897[/C][C]0.767153178882552[/C][/ROW]
[ROW][C]103[/C][C]0.195946282194246[/C][C]0.391892564388491[/C][C]0.804053717805754[/C][/ROW]
[ROW][C]104[/C][C]0.168994455870815[/C][C]0.337988911741629[/C][C]0.831005544129185[/C][/ROW]
[ROW][C]105[/C][C]0.15272529579353[/C][C]0.305450591587061[/C][C]0.84727470420647[/C][/ROW]
[ROW][C]106[/C][C]0.171180261813441[/C][C]0.342360523626882[/C][C]0.828819738186559[/C][/ROW]
[ROW][C]107[/C][C]0.147123195532129[/C][C]0.294246391064258[/C][C]0.852876804467871[/C][/ROW]
[ROW][C]108[/C][C]0.123426312426796[/C][C]0.246852624853591[/C][C]0.876573687573204[/C][/ROW]
[ROW][C]109[/C][C]0.229219889877723[/C][C]0.458439779755447[/C][C]0.770780110122277[/C][/ROW]
[ROW][C]110[/C][C]0.384273124231355[/C][C]0.768546248462709[/C][C]0.615726875768645[/C][/ROW]
[ROW][C]111[/C][C]0.344578488408815[/C][C]0.689156976817629[/C][C]0.655421511591185[/C][/ROW]
[ROW][C]112[/C][C]0.35297046314301[/C][C]0.70594092628602[/C][C]0.64702953685699[/C][/ROW]
[ROW][C]113[/C][C]0.304737924247007[/C][C]0.609475848494013[/C][C]0.695262075752993[/C][/ROW]
[ROW][C]114[/C][C]0.274440048970866[/C][C]0.548880097941732[/C][C]0.725559951029134[/C][/ROW]
[ROW][C]115[/C][C]0.235075824172337[/C][C]0.470151648344674[/C][C]0.764924175827663[/C][/ROW]
[ROW][C]116[/C][C]0.34735121643594[/C][C]0.69470243287188[/C][C]0.65264878356406[/C][/ROW]
[ROW][C]117[/C][C]0.320292974707897[/C][C]0.640585949415794[/C][C]0.679707025292103[/C][/ROW]
[ROW][C]118[/C][C]0.271323795649848[/C][C]0.542647591299695[/C][C]0.728676204350152[/C][/ROW]
[ROW][C]119[/C][C]0.238285071979298[/C][C]0.476570143958597[/C][C]0.761714928020702[/C][/ROW]
[ROW][C]120[/C][C]0.195522666828281[/C][C]0.391045333656562[/C][C]0.804477333171719[/C][/ROW]
[ROW][C]121[/C][C]0.433342972324159[/C][C]0.866685944648319[/C][C]0.566657027675841[/C][/ROW]
[ROW][C]122[/C][C]0.40268953879418[/C][C]0.80537907758836[/C][C]0.59731046120582[/C][/ROW]
[ROW][C]123[/C][C]0.434340157044311[/C][C]0.868680314088623[/C][C]0.565659842955689[/C][/ROW]
[ROW][C]124[/C][C]0.590440485257562[/C][C]0.819119029484877[/C][C]0.409559514742438[/C][/ROW]
[ROW][C]125[/C][C]0.52979998052963[/C][C]0.94040003894074[/C][C]0.47020001947037[/C][/ROW]
[ROW][C]126[/C][C]0.482092059275594[/C][C]0.964184118551188[/C][C]0.517907940724406[/C][/ROW]
[ROW][C]127[/C][C]0.419847893521848[/C][C]0.839695787043696[/C][C]0.580152106478152[/C][/ROW]
[ROW][C]128[/C][C]0.639735751064811[/C][C]0.720528497870379[/C][C]0.360264248935189[/C][/ROW]
[ROW][C]129[/C][C]0.632934529275496[/C][C]0.734130941449008[/C][C]0.367065470724504[/C][/ROW]
[ROW][C]130[/C][C]0.586624501537436[/C][C]0.826750996925128[/C][C]0.413375498462564[/C][/ROW]
[ROW][C]131[/C][C]0.919752008930645[/C][C]0.16049598213871[/C][C]0.080247991069355[/C][/ROW]
[ROW][C]132[/C][C]0.892841824103688[/C][C]0.214316351792625[/C][C]0.107158175896312[/C][/ROW]
[ROW][C]133[/C][C]0.860398258500243[/C][C]0.279203482999514[/C][C]0.139601741499757[/C][/ROW]
[ROW][C]134[/C][C]0.827308361820531[/C][C]0.345383276358938[/C][C]0.172691638179469[/C][/ROW]
[ROW][C]135[/C][C]0.783244713544935[/C][C]0.433510572910131[/C][C]0.216755286455065[/C][/ROW]
[ROW][C]136[/C][C]0.99987852678289[/C][C]0.000242946434218619[/C][C]0.000121473217109309[/C][/ROW]
[ROW][C]137[/C][C]0.99996563163345[/C][C]6.87367330975475e-05[/C][C]3.43683665487737e-05[/C][/ROW]
[ROW][C]138[/C][C]0.999984607413098[/C][C]3.07851738034566e-05[/C][C]1.53925869017283e-05[/C][/ROW]
[ROW][C]139[/C][C]0.999943485292127[/C][C]0.000113029415745425[/C][C]5.65147078727124e-05[/C][/ROW]
[ROW][C]140[/C][C]0.99986550660425[/C][C]0.000268986791500646[/C][C]0.000134493395750323[/C][/ROW]
[ROW][C]141[/C][C]0.99987621852577[/C][C]0.000247562948461833[/C][C]0.000123781474230916[/C][/ROW]
[ROW][C]142[/C][C]0.999826944662452[/C][C]0.000346110675096376[/C][C]0.000173055337548188[/C][/ROW]
[ROW][C]143[/C][C]0.9999383068674[/C][C]0.000123386265199388[/C][C]6.16931325996938e-05[/C][/ROW]
[ROW][C]144[/C][C]0.999999999991343[/C][C]1.73144264757195e-11[/C][C]8.65721323785975e-12[/C][/ROW]
[ROW][C]145[/C][C]0.999999999525981[/C][C]9.480375559471e-10[/C][C]4.7401877797355e-10[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]6.19029551997312e-46[/C][C]3.09514775998656e-46[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159411&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159411&T=5

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.410882159660120.821764319320240.58911784033988
190.3246115671629170.6492231343258340.675388432837083
200.2203848113939570.4407696227879140.779615188606043
210.1818532806911950.363706561382390.818146719308805
220.2090381158675260.4180762317350520.790961884132474
230.1335802627123820.2671605254247650.866419737287618
240.0897876015283240.1795752030566480.910212398471676
250.07301264498826370.1460252899765270.926987355011736
260.04307860085505470.08615720171010940.956921399144945
270.02992886569551860.05985773139103730.970071134304481
280.01952880398288860.03905760796577720.980471196017111
290.01378009005761410.02756018011522810.986219909942386
300.01047213905461320.02094427810922640.989527860945387
310.006713014197374090.01342602839474820.993286985802626
320.004093930361057830.008187860722115650.995906069638942
330.002672772093314310.005345544186628620.997327227906686
340.005384698335591910.01076939667118380.994615301664408
350.006989955263532480.0139799105270650.993010044736467
360.004369987034011230.008739974068022460.995630012965989
370.002661994799657750.00532398959931550.997338005200342
380.001822169500077750.00364433900015550.998177830499922
390.001725013852676570.003450027705353140.998274986147323
400.001109043511232910.002218087022465830.998890956488767
410.002049780513104830.004099561026209660.997950219486895
420.001269342435313240.002538684870626490.998730657564687
430.001155585053333950.00231117010666790.998844414946666
440.0007820705736470480.00156414114729410.999217929426353
450.002860396307788990.005720792615577980.99713960369221
460.001802577195755920.003605154391511840.998197422804244
470.001445985892658270.002891971785316530.998554014107342
480.0009049780561649550.001809956112329910.999095021943835
490.0006410261528841560.001282052305768310.999358973847116
500.0005666579355564740.001133315871112950.999433342064444
510.0004689202931396990.0009378405862793990.99953107970686
520.0003087836251879650.0006175672503759290.999691216374812
530.0002172403619441390.0004344807238882770.999782759638056
540.0001250108796131470.0002500217592262940.999874989120387
557.92384051452432e-050.0001584768102904860.999920761594855
565.22806664282168e-050.0001045613328564340.999947719333572
577.18832178381399e-050.000143766435676280.999928116782162
588.4908573958807e-050.0001698171479176140.99991509142604
596.40491076634652e-050.000128098215326930.999935950892337
603.81701826397705e-057.63403652795409e-050.99996182981736
612.51304411607614e-055.02608823215228e-050.99997486955884
621.5172472072977e-053.03449441459541e-050.999984827527927
631.22237583079564e-052.44475166159128e-050.999987776241692
647.21331301930665e-061.44266260386133e-050.99999278668698
655.26060844160651e-061.0521216883213e-050.999994739391558
663.42831548512127e-066.85663097024253e-060.999996571684515
677.61742521059617e-061.52348504211923e-050.99999238257479
684.20287506424036e-058.40575012848072e-050.999957971249358
694.13313385943422e-058.26626771886844e-050.999958668661406
700.0001265456701997250.0002530913403994490.9998734543298
710.000193242099747030.0003864841994940590.999806757900253
720.0001584107579467280.0003168215158934560.999841589242053
730.0001536651613276320.0003073303226552630.999846334838672
749.6581645039407e-050.0001931632900788140.99990341835496
756.9302461461744e-050.0001386049229234880.999930697538538
767.48852881798918e-050.0001497705763597840.99992511471182
774.79900880372639e-059.59801760745279e-050.999952009911963
780.1757860696183090.3515721392366190.82421393038169
790.1565969194333750.3131938388667490.843403080566625
800.1317930156487860.2635860312975710.868206984351214
810.1201066555274060.2402133110548130.879893344472594
820.19536259093770.39072518187540.8046374090623
830.2148411677170860.4296823354341720.785158832282914
840.1822763708921230.3645527417842460.817723629107877
850.1666472617941850.333294523588370.833352738205815
860.1428437183308620.2856874366617250.857156281669138
870.1295452524026850.259090504805370.870454747597315
880.1279158742792990.2558317485585970.872084125720701
890.1348571992129790.2697143984259580.865142800787021
900.1519820457042050.3039640914084110.848017954295795
910.1395805613374960.2791611226749920.860419438662504
920.1697820877784840.3395641755569680.830217912221516
930.1962195488694980.3924390977389950.803780451130502
940.1662433737312590.3324867474625180.83375662626874
950.1688127942508610.3376255885017230.831187205749139
960.1868586249510790.3737172499021580.813141375048921
970.1772177502729740.3544355005459490.822782249727026
980.1857103517429040.3714207034858080.814289648257096
990.2904654544052030.5809309088104050.709534545594797
1000.2596317899210480.5192635798420960.740368210078952
1010.2584368050423350.516873610084670.741563194957665
1020.2328468211174480.4656936422348970.767153178882552
1030.1959462821942460.3918925643884910.804053717805754
1040.1689944558708150.3379889117416290.831005544129185
1050.152725295793530.3054505915870610.84727470420647
1060.1711802618134410.3423605236268820.828819738186559
1070.1471231955321290.2942463910642580.852876804467871
1080.1234263124267960.2468526248535910.876573687573204
1090.2292198898777230.4584397797554470.770780110122277
1100.3842731242313550.7685462484627090.615726875768645
1110.3445784884088150.6891569768176290.655421511591185
1120.352970463143010.705940926286020.64702953685699
1130.3047379242470070.6094758484940130.695262075752993
1140.2744400489708660.5488800979417320.725559951029134
1150.2350758241723370.4701516483446740.764924175827663
1160.347351216435940.694702432871880.65264878356406
1170.3202929747078970.6405859494157940.679707025292103
1180.2713237956498480.5426475912996950.728676204350152
1190.2382850719792980.4765701439585970.761714928020702
1200.1955226668282810.3910453336565620.804477333171719
1210.4333429723241590.8666859446483190.566657027675841
1220.402689538794180.805379077588360.59731046120582
1230.4343401570443110.8686803140886230.565659842955689
1240.5904404852575620.8191190294848770.409559514742438
1250.529799980529630.940400038940740.47020001947037
1260.4820920592755940.9641841185511880.517907940724406
1270.4198478935218480.8396957870436960.580152106478152
1280.6397357510648110.7205284978703790.360264248935189
1290.6329345292754960.7341309414490080.367065470724504
1300.5866245015374360.8267509969251280.413375498462564
1310.9197520089306450.160495982138710.080247991069355
1320.8928418241036880.2143163517926250.107158175896312
1330.8603982585002430.2792034829995140.139601741499757
1340.8273083618205310.3453832763589380.172691638179469
1350.7832447135449350.4335105729101310.216755286455065
1360.999878526782890.0002429464342186190.000121473217109309
1370.999965631633456.87367330975475e-053.43683665487737e-05
1380.9999846074130983.07851738034566e-051.53925869017283e-05
1390.9999434852921270.0001130294157454255.65147078727124e-05
1400.999865506604250.0002689867915006460.000134493395750323
1410.999876218525770.0002475629484618330.000123781474230916
1420.9998269446624520.0003461106750963760.000173055337548188
1430.99993830686740.0001233862651993886.16931325996938e-05
1440.9999999999913431.73144264757195e-118.65721323785975e-12
1450.9999999995259819.480375559471e-104.7401877797355e-10
14616.19029551997312e-463.09514775998656e-46






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level550.426356589147287NOK
5% type I error level610.472868217054264NOK
10% type I error level630.488372093023256NOK

\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 & 55 & 0.426356589147287 & NOK \tabularnewline
5% type I error level & 61 & 0.472868217054264 & NOK \tabularnewline
10% type I error level & 63 & 0.488372093023256 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159411&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]55[/C][C]0.426356589147287[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]61[/C][C]0.472868217054264[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]63[/C][C]0.488372093023256[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159411&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159411&T=6

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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 level550.426356589147287NOK
5% type I error level610.472868217054264NOK
10% type I error level630.488372093023256NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 11 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 11 ; 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')
}