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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 08:53:21 -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/t1324562031pk1es9qsn0zt76o.htm/, Retrieved Fri, 03 May 2024 12:37:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=159458, Retrieved Fri, 03 May 2024 12:37:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact89

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [MultiplRegrAll] [2011-12-22 13:53:21] [3208276753335f0b81f0071d45b8bac9] [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
2415	284420	92	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 time8 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159458&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159458&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159458&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 time8 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
LARGPEER[t] = -6.50594877680909 -0.00290226551640432P[t] + 9.49030259323816e-06TRFC[t] -0.0324595075960939LOG[t] + 0.00855206587632857CCV[t] + 0.0110878652354355CCVComp[t] + 0.677716321632255Shared[t] + 0.0324115915672004B[t] -0.794772837361118NRC[t] + 0.993030248767129SUBMESP[t] + 8.47991752073166e-05ComNrW[t] + 0.0003999514071329CRRev[t] -3.10123632076686e-05COMTime[t] + 0.110494189465079CompHyp[t] -0.151347475243327CompBlog[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
LARGPEER[t] =  -6.50594877680909 -0.00290226551640432P[t] +  9.49030259323816e-06TRFC[t] -0.0324595075960939LOG[t] +  0.00855206587632857CCV[t] +  0.0110878652354355CCVComp[t] +  0.677716321632255Shared[t] +  0.0324115915672004B[t] -0.794772837361118NRC[t] +  0.993030248767129SUBMESP[t] +  8.47991752073166e-05ComNrW[t] +  0.0003999514071329CRRev[t] -3.10123632076686e-05COMTime[t] +  0.110494189465079CompHyp[t] -0.151347475243327CompBlog[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159458&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]LARGPEER[t] =  -6.50594877680909 -0.00290226551640432P[t] +  9.49030259323816e-06TRFC[t] -0.0324595075960939LOG[t] +  0.00855206587632857CCV[t] +  0.0110878652354355CCVComp[t] +  0.677716321632255Shared[t] +  0.0324115915672004B[t] -0.794772837361118NRC[t] +  0.993030248767129SUBMESP[t] +  8.47991752073166e-05ComNrW[t] +  0.0003999514071329CRRev[t] -3.10123632076686e-05COMTime[t] +  0.110494189465079CompHyp[t] -0.151347475243327CompBlog[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159458&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159458&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
LARGPEER[t] = -6.50594877680909 -0.00290226551640432P[t] + 9.49030259323816e-06TRFC[t] -0.0324595075960939LOG[t] + 0.00855206587632857CCV[t] + 0.0110878652354355CCVComp[t] + 0.677716321632255Shared[t] + 0.0324115915672004B[t] -0.794772837361118NRC[t] + 0.993030248767129SUBMESP[t] + 8.47991752073166e-05ComNrW[t] + 0.0003999514071329CRRev[t] -3.10123632076686e-05COMTime[t] + 0.110494189465079CompHyp[t] -0.151347475243327CompBlog[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-6.505948776809093.822702-1.70190.0908550.045428
P-0.002902265516404320.007513-0.38630.6998350.349918
TRFC9.49030259323816e-064.1e-050.23040.8181340.409067
LOG-0.03245950759609390.043023-0.75450.4517540.225877
CCV0.008552065876328570.0153010.55890.5770430.288521
CCVComp0.01108786523543550.0268840.41240.6806140.340307
Shared0.6777163216322550.4373871.54950.1233920.061696
B0.03241159156720040.0708030.45780.6477830.323892
NRC-0.7947728373611180.527884-1.50560.1342920.067146
SUBMESP0.9930302487671290.1427146.958200
ComNrW8.47991752073166e-055.5e-051.55180.122820.06141
CRRev0.00039995140713290.0002391.67660.0957250.047862
COMTime-3.10123632076686e-056.8e-05-0.4570.6483260.324163
CompHyp0.1104941894650790.3810180.290.7722210.386111
CompBlog-0.1513474752433270.396029-0.38220.7028860.351443

\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) & -6.50594877680909 & 3.822702 & -1.7019 & 0.090855 & 0.045428 \tabularnewline
P & -0.00290226551640432 & 0.007513 & -0.3863 & 0.699835 & 0.349918 \tabularnewline
TRFC & 9.49030259323816e-06 & 4.1e-05 & 0.2304 & 0.818134 & 0.409067 \tabularnewline
LOG & -0.0324595075960939 & 0.043023 & -0.7545 & 0.451754 & 0.225877 \tabularnewline
CCV & 0.00855206587632857 & 0.015301 & 0.5589 & 0.577043 & 0.288521 \tabularnewline
CCVComp & 0.0110878652354355 & 0.026884 & 0.4124 & 0.680614 & 0.340307 \tabularnewline
Shared & 0.677716321632255 & 0.437387 & 1.5495 & 0.123392 & 0.061696 \tabularnewline
B & 0.0324115915672004 & 0.070803 & 0.4578 & 0.647783 & 0.323892 \tabularnewline
NRC & -0.794772837361118 & 0.527884 & -1.5056 & 0.134292 & 0.067146 \tabularnewline
SUBMESP & 0.993030248767129 & 0.142714 & 6.9582 & 0 & 0 \tabularnewline
ComNrW & 8.47991752073166e-05 & 5.5e-05 & 1.5518 & 0.12282 & 0.06141 \tabularnewline
CRRev & 0.0003999514071329 & 0.000239 & 1.6766 & 0.095725 & 0.047862 \tabularnewline
COMTime & -3.10123632076686e-05 & 6.8e-05 & -0.457 & 0.648326 & 0.324163 \tabularnewline
CompHyp & 0.110494189465079 & 0.381018 & 0.29 & 0.772221 & 0.386111 \tabularnewline
CompBlog & -0.151347475243327 & 0.396029 & -0.3822 & 0.702886 & 0.351443 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159458&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]-6.50594877680909[/C][C]3.822702[/C][C]-1.7019[/C][C]0.090855[/C][C]0.045428[/C][/ROW]
[ROW][C]P[/C][C]-0.00290226551640432[/C][C]0.007513[/C][C]-0.3863[/C][C]0.699835[/C][C]0.349918[/C][/ROW]
[ROW][C]TRFC[/C][C]9.49030259323816e-06[/C][C]4.1e-05[/C][C]0.2304[/C][C]0.818134[/C][C]0.409067[/C][/ROW]
[ROW][C]LOG[/C][C]-0.0324595075960939[/C][C]0.043023[/C][C]-0.7545[/C][C]0.451754[/C][C]0.225877[/C][/ROW]
[ROW][C]CCV[/C][C]0.00855206587632857[/C][C]0.015301[/C][C]0.5589[/C][C]0.577043[/C][C]0.288521[/C][/ROW]
[ROW][C]CCVComp[/C][C]0.0110878652354355[/C][C]0.026884[/C][C]0.4124[/C][C]0.680614[/C][C]0.340307[/C][/ROW]
[ROW][C]Shared[/C][C]0.677716321632255[/C][C]0.437387[/C][C]1.5495[/C][C]0.123392[/C][C]0.061696[/C][/ROW]
[ROW][C]B[/C][C]0.0324115915672004[/C][C]0.070803[/C][C]0.4578[/C][C]0.647783[/C][C]0.323892[/C][/ROW]
[ROW][C]NRC[/C][C]-0.794772837361118[/C][C]0.527884[/C][C]-1.5056[/C][C]0.134292[/C][C]0.067146[/C][/ROW]
[ROW][C]SUBMESP[/C][C]0.993030248767129[/C][C]0.142714[/C][C]6.9582[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]ComNrW[/C][C]8.47991752073166e-05[/C][C]5.5e-05[/C][C]1.5518[/C][C]0.12282[/C][C]0.06141[/C][/ROW]
[ROW][C]CRRev[/C][C]0.0003999514071329[/C][C]0.000239[/C][C]1.6766[/C][C]0.095725[/C][C]0.047862[/C][/ROW]
[ROW][C]COMTime[/C][C]-3.10123632076686e-05[/C][C]6.8e-05[/C][C]-0.457[/C][C]0.648326[/C][C]0.324163[/C][/ROW]
[ROW][C]CompHyp[/C][C]0.110494189465079[/C][C]0.381018[/C][C]0.29[/C][C]0.772221[/C][C]0.386111[/C][/ROW]
[ROW][C]CompBlog[/C][C]-0.151347475243327[/C][C]0.396029[/C][C]-0.3822[/C][C]0.702886[/C][C]0.351443[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159458&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159458&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)-6.505948776809093.822702-1.70190.0908550.045428
P-0.002902265516404320.007513-0.38630.6998350.349918
TRFC9.49030259323816e-064.1e-050.23040.8181340.409067
LOG-0.03245950759609390.043023-0.75450.4517540.225877
CCV0.008552065876328570.0153010.55890.5770430.288521
CCVComp0.01108786523543550.0268840.41240.6806140.340307
Shared0.6777163216322550.4373871.54950.1233920.061696
B0.03241159156720040.0708030.45780.6477830.323892
NRC-0.7947728373611180.527884-1.50560.1342920.067146
SUBMESP0.9930302487671290.1427146.958200
ComNrW8.47991752073166e-055.5e-051.55180.122820.06141
CRRev0.00039995140713290.0002391.67660.0957250.047862
COMTime-3.10123632076686e-056.8e-05-0.4570.6483260.324163
CompHyp0.1104941894650790.3810180.290.7722210.386111
CompBlog-0.1513474752433270.396029-0.38220.7028860.351443






Multiple Linear Regression - Regression Statistics
Multiple R0.94993312814588
R-squared0.902372947949017
Adjusted R-squared0.893199936346911
F-TEST (value)98.3725942025263
F-TEST (DF numerator)14
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation18.1073973129211
Sum Squared Residuals48853.7977797494

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.94993312814588 \tabularnewline
R-squared & 0.902372947949017 \tabularnewline
Adjusted R-squared & 0.893199936346911 \tabularnewline
F-TEST (value) & 98.3725942025263 \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 & 18.1073973129211 \tabularnewline
Sum Squared Residuals & 48853.7977797494 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159458&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.94993312814588[/C][/ROW]
[ROW][C]R-squared[/C][C]0.902372947949017[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.893199936346911[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]98.3725942025263[/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]18.1073973129211[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]48853.7977797494[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159458&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159458&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 R0.94993312814588
R-squared0.902372947949017
Adjusted R-squared0.893199936346911
F-TEST (value)98.3725942025263
F-TEST (DF numerator)14
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation18.1073973129211
Sum Squared Residuals48853.7977797494






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1130135.757345929288-5.75734592928829
2143126.00275243773616.9972475622638
3118154.59692823755-36.59692823755
4146145.8036955608150.196304439185029
57374.2293521763506-1.2293521763506
689103.338094521794-14.3380945217938
7146149.443841219146-3.44384121914575
82247.8309097720073-25.8309097720073
9132136.63787142953-4.63787142952972
1092106.585215618072-14.5852156180716
11147135.01987660975711.9801233902434
12203190.310665703712.6893342962996
13113100.15978512089312.8402148791074
14171158.47044702448612.529552975514
1587131.555338653122-44.5553386531216
16208196.95994969952611.040050300474
17153143.4328517723359.56714822766485
1897133.83300827435-36.8330082743498
1995109.864754210269-14.8647542102691
20197173.07342542200223.9265745779975
21160160.867662185664-0.867662185663872
22148127.06129632431320.9387036756873
238476.23060362931087.76939637068917
24227196.38727260161230.6127273983883
25154139.85886458795514.1411354120452
26151137.61421911244613.3857808875542
27142146.116502933629-4.11650293362885
28148126.02848496472821.9715150352719
2911098.747567691003411.2524323089966
30149144.9444443015594.0555556984411
31179174.8935450268864.10645497311443
32149147.258367466731.74163253326997
33187166.00948348327120.9905165167291
34153125.2550010264627.7449989735404
35163148.85402820912414.1459717908764
36127126.1574464228730.84255357712671
37151134.86887224823616.1311277517635
38100124.054464553908-24.0544645539077
394640.62525091199615.37474908800395
40156141.51612268907414.4838773109259
41128135.274631661468-7.27463166146832
42111125.187855244399-14.1878552443993
43119108.8588556481710.1411443518299
44148147.9401477005840.0598522994155424
456553.843062314050611.1569376859494
46134133.5214563471650.478543652835425
476659.78661862666536.21338137333474
48201183.03391990290317.9660800970968
49177167.2436100693499.75638993065089
50156146.0387066422339.96129335776697
51158146.84347730624711.1565226937526
5273.417068946860023.58293105313998
53175144.05885503647830.9411449635216
546152.6723762660728.327623733928
554139.13697776440171.86302223559831
56133127.4562848451065.54371515489417
57228198.87610552365229.1238944763478
58140128.63947106931711.3605289306833
59155138.04011410730916.959885892691
60141139.5695673171891.43043268281138
61181171.1897527712739.81024722872705
6275107.751717607565-32.7517176075649
6397143.332883289736-46.3328832897358
64142142.446476106823-0.446476106823179
65136130.7256640198075.27433598019304
668797.0003390924168-10.0003390924168
67140131.381286180198.61871381980998
68169163.0278292258325.97217077416771
69129126.6043094093192.39569059068113
709298.5422958182424-6.54229581824244
71160153.5314463969096.46855360309093
726769.6439642876202-2.64396428762021
73179154.27212942334924.7278705766514
7490111.93225149482-21.9322514948201
75144138.3832719538335.61672804616727
76144146.565849925668-2.56584992566811
77144134.4003424944329.59965750556776
78134148.616372589235-14.6163725892353
79146135.21844971305610.781550286944
80121116.7980598876094.20194011239076
81112115.145020080773-3.14502008077292
82145162.45056873509-17.4505687350896
839980.181636464710918.8183635352891
8496101.674964809096-5.67496480909569
852722.01554648170354.98445351829651
867778.7183413255911-1.7183413255911
87137127.2501301786349.74986982136644
88151135.15891557744615.8410844225543
89126184.519151634798-58.5191516347981
90159151.9327231072847.06727689271612
91101120.293755209045-19.2937552090448
92144131.35843329476512.6415667052347
93102105.128197459908-3.12819745990763
94135115.27199112277519.7280088772248
95147142.9392752533014.06072474669859
96155144.20173514301310.7982648569872
97138129.3234370240488.6765629759519
98113136.107671174653-23.1076711746528
99248223.11700941681724.8829905831825
100116126.37764213555-10.3776421355505
101176177.431040502059-1.43104050205884
102140143.62242075272-3.6224207527201
10359105.523743006453-46.5237430064529
1046456.60948123435647.39051876564363
1054074.6578129248527-34.6578129248527
10698141.045411908502-43.0454119085019
107139153.791919951258-14.7919199512584
108135137.246896650865-2.24689665086516
10997112.604886211224-15.6048862112239
110142147.143174589569-5.1431745895689
111155141.54247236329213.4575276367077
112115135.263543669836-20.2635436698363
1130-7.202131777980467.20213177798046
114103130.564540848479-27.5645408484789
1153022.26449938913687.73550061086322
116130149.251370153576-19.2513701535764
117102115.151076851331-13.1510768513313
1180-6.709335249574376.70933524957437
1197795.1242667363701-18.1242667363701
120913.4514609476565-4.45146094765652
121150132.76724912732317.2327508726767
122163160.0145323078632.98546769213732
123148156.848407657241-8.84840765724054
12494103.734257264885-9.73425726488468
1252115.42551520548815.57448479451193
126151139.03776603160911.9622339683914
127187169.29482728949717.7051727105031
128171167.620501459853.37949854015024
129170172.814370841922-2.81437084192187
130145154.858634722363-9.85863472236252
131198191.9297005514536.07029944854681
132152134.86384848804717.1361515119527
133112112.712062833861-0.712062833861452
134173150.28680775186722.713192248133
135177156.38192493488620.6180750651135
136153147.6583417978345.34165820216633
137161158.7923194090732.20768059092707
138115119.95178837317-4.95178837316995
139147141.7155350246795.2844649753215
140124123.3343883526410.665611647359104
14157120.22600235506-63.2260023550597
142144145.941720916999-1.94172091699906
143126121.2279062491394.77209375086115
1447862.281240729235215.7187592707648
145153174.681137640081-21.681137640081
146196184.99910408323711.0008959167634
147130132.085665913325-2.08566591332502
148159146.26387559078912.7361244092105
1490-0.4122969228489890.412296922848989
1500-5.549609949898215.54960994989821
1510-6.551989562333046.55198956233304
1520-6.589767828452556.58976782845255
1530-5.828232455176815.82823245517681
1540-6.505948776809076.50594877680907
15594108.47636434328-14.4763643432799
156129163.776145733372-34.776145733372
1570-6.505948776809076.50594877680907
1580-6.645469337832646.64546933783264
1590-6.193418073658476.19341807365847
160135.676468455374477.32353154462553
1614-2.074667395232996.07466739523299
16289130.786257481395-41.7862574813949
1630-6.645837388764136.64583738876413
16471105.434356467136-34.4343564671357

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 130 & 135.757345929288 & -5.75734592928829 \tabularnewline
2 & 143 & 126.002752437736 & 16.9972475622638 \tabularnewline
3 & 118 & 154.59692823755 & -36.59692823755 \tabularnewline
4 & 146 & 145.803695560815 & 0.196304439185029 \tabularnewline
5 & 73 & 74.2293521763506 & -1.2293521763506 \tabularnewline
6 & 89 & 103.338094521794 & -14.3380945217938 \tabularnewline
7 & 146 & 149.443841219146 & -3.44384121914575 \tabularnewline
8 & 22 & 47.8309097720073 & -25.8309097720073 \tabularnewline
9 & 132 & 136.63787142953 & -4.63787142952972 \tabularnewline
10 & 92 & 106.585215618072 & -14.5852156180716 \tabularnewline
11 & 147 & 135.019876609757 & 11.9801233902434 \tabularnewline
12 & 203 & 190.3106657037 & 12.6893342962996 \tabularnewline
13 & 113 & 100.159785120893 & 12.8402148791074 \tabularnewline
14 & 171 & 158.470447024486 & 12.529552975514 \tabularnewline
15 & 87 & 131.555338653122 & -44.5553386531216 \tabularnewline
16 & 208 & 196.959949699526 & 11.040050300474 \tabularnewline
17 & 153 & 143.432851772335 & 9.56714822766485 \tabularnewline
18 & 97 & 133.83300827435 & -36.8330082743498 \tabularnewline
19 & 95 & 109.864754210269 & -14.8647542102691 \tabularnewline
20 & 197 & 173.073425422002 & 23.9265745779975 \tabularnewline
21 & 160 & 160.867662185664 & -0.867662185663872 \tabularnewline
22 & 148 & 127.061296324313 & 20.9387036756873 \tabularnewline
23 & 84 & 76.2306036293108 & 7.76939637068917 \tabularnewline
24 & 227 & 196.387272601612 & 30.6127273983883 \tabularnewline
25 & 154 & 139.858864587955 & 14.1411354120452 \tabularnewline
26 & 151 & 137.614219112446 & 13.3857808875542 \tabularnewline
27 & 142 & 146.116502933629 & -4.11650293362885 \tabularnewline
28 & 148 & 126.028484964728 & 21.9715150352719 \tabularnewline
29 & 110 & 98.7475676910034 & 11.2524323089966 \tabularnewline
30 & 149 & 144.944444301559 & 4.0555556984411 \tabularnewline
31 & 179 & 174.893545026886 & 4.10645497311443 \tabularnewline
32 & 149 & 147.25836746673 & 1.74163253326997 \tabularnewline
33 & 187 & 166.009483483271 & 20.9905165167291 \tabularnewline
34 & 153 & 125.25500102646 & 27.7449989735404 \tabularnewline
35 & 163 & 148.854028209124 & 14.1459717908764 \tabularnewline
36 & 127 & 126.157446422873 & 0.84255357712671 \tabularnewline
37 & 151 & 134.868872248236 & 16.1311277517635 \tabularnewline
38 & 100 & 124.054464553908 & -24.0544645539077 \tabularnewline
39 & 46 & 40.6252509119961 & 5.37474908800395 \tabularnewline
40 & 156 & 141.516122689074 & 14.4838773109259 \tabularnewline
41 & 128 & 135.274631661468 & -7.27463166146832 \tabularnewline
42 & 111 & 125.187855244399 & -14.1878552443993 \tabularnewline
43 & 119 & 108.85885564817 & 10.1411443518299 \tabularnewline
44 & 148 & 147.940147700584 & 0.0598522994155424 \tabularnewline
45 & 65 & 53.8430623140506 & 11.1569376859494 \tabularnewline
46 & 134 & 133.521456347165 & 0.478543652835425 \tabularnewline
47 & 66 & 59.7866186266653 & 6.21338137333474 \tabularnewline
48 & 201 & 183.033919902903 & 17.9660800970968 \tabularnewline
49 & 177 & 167.243610069349 & 9.75638993065089 \tabularnewline
50 & 156 & 146.038706642233 & 9.96129335776697 \tabularnewline
51 & 158 & 146.843477306247 & 11.1565226937526 \tabularnewline
52 & 7 & 3.41706894686002 & 3.58293105313998 \tabularnewline
53 & 175 & 144.058855036478 & 30.9411449635216 \tabularnewline
54 & 61 & 52.672376266072 & 8.327623733928 \tabularnewline
55 & 41 & 39.1369777644017 & 1.86302223559831 \tabularnewline
56 & 133 & 127.456284845106 & 5.54371515489417 \tabularnewline
57 & 228 & 198.876105523652 & 29.1238944763478 \tabularnewline
58 & 140 & 128.639471069317 & 11.3605289306833 \tabularnewline
59 & 155 & 138.040114107309 & 16.959885892691 \tabularnewline
60 & 141 & 139.569567317189 & 1.43043268281138 \tabularnewline
61 & 181 & 171.189752771273 & 9.81024722872705 \tabularnewline
62 & 75 & 107.751717607565 & -32.7517176075649 \tabularnewline
63 & 97 & 143.332883289736 & -46.3328832897358 \tabularnewline
64 & 142 & 142.446476106823 & -0.446476106823179 \tabularnewline
65 & 136 & 130.725664019807 & 5.27433598019304 \tabularnewline
66 & 87 & 97.0003390924168 & -10.0003390924168 \tabularnewline
67 & 140 & 131.38128618019 & 8.61871381980998 \tabularnewline
68 & 169 & 163.027829225832 & 5.97217077416771 \tabularnewline
69 & 129 & 126.604309409319 & 2.39569059068113 \tabularnewline
70 & 92 & 98.5422958182424 & -6.54229581824244 \tabularnewline
71 & 160 & 153.531446396909 & 6.46855360309093 \tabularnewline
72 & 67 & 69.6439642876202 & -2.64396428762021 \tabularnewline
73 & 179 & 154.272129423349 & 24.7278705766514 \tabularnewline
74 & 90 & 111.93225149482 & -21.9322514948201 \tabularnewline
75 & 144 & 138.383271953833 & 5.61672804616727 \tabularnewline
76 & 144 & 146.565849925668 & -2.56584992566811 \tabularnewline
77 & 144 & 134.400342494432 & 9.59965750556776 \tabularnewline
78 & 134 & 148.616372589235 & -14.6163725892353 \tabularnewline
79 & 146 & 135.218449713056 & 10.781550286944 \tabularnewline
80 & 121 & 116.798059887609 & 4.20194011239076 \tabularnewline
81 & 112 & 115.145020080773 & -3.14502008077292 \tabularnewline
82 & 145 & 162.45056873509 & -17.4505687350896 \tabularnewline
83 & 99 & 80.1816364647109 & 18.8183635352891 \tabularnewline
84 & 96 & 101.674964809096 & -5.67496480909569 \tabularnewline
85 & 27 & 22.0155464817035 & 4.98445351829651 \tabularnewline
86 & 77 & 78.7183413255911 & -1.7183413255911 \tabularnewline
87 & 137 & 127.250130178634 & 9.74986982136644 \tabularnewline
88 & 151 & 135.158915577446 & 15.8410844225543 \tabularnewline
89 & 126 & 184.519151634798 & -58.5191516347981 \tabularnewline
90 & 159 & 151.932723107284 & 7.06727689271612 \tabularnewline
91 & 101 & 120.293755209045 & -19.2937552090448 \tabularnewline
92 & 144 & 131.358433294765 & 12.6415667052347 \tabularnewline
93 & 102 & 105.128197459908 & -3.12819745990763 \tabularnewline
94 & 135 & 115.271991122775 & 19.7280088772248 \tabularnewline
95 & 147 & 142.939275253301 & 4.06072474669859 \tabularnewline
96 & 155 & 144.201735143013 & 10.7982648569872 \tabularnewline
97 & 138 & 129.323437024048 & 8.6765629759519 \tabularnewline
98 & 113 & 136.107671174653 & -23.1076711746528 \tabularnewline
99 & 248 & 223.117009416817 & 24.8829905831825 \tabularnewline
100 & 116 & 126.37764213555 & -10.3776421355505 \tabularnewline
101 & 176 & 177.431040502059 & -1.43104050205884 \tabularnewline
102 & 140 & 143.62242075272 & -3.6224207527201 \tabularnewline
103 & 59 & 105.523743006453 & -46.5237430064529 \tabularnewline
104 & 64 & 56.6094812343564 & 7.39051876564363 \tabularnewline
105 & 40 & 74.6578129248527 & -34.6578129248527 \tabularnewline
106 & 98 & 141.045411908502 & -43.0454119085019 \tabularnewline
107 & 139 & 153.791919951258 & -14.7919199512584 \tabularnewline
108 & 135 & 137.246896650865 & -2.24689665086516 \tabularnewline
109 & 97 & 112.604886211224 & -15.6048862112239 \tabularnewline
110 & 142 & 147.143174589569 & -5.1431745895689 \tabularnewline
111 & 155 & 141.542472363292 & 13.4575276367077 \tabularnewline
112 & 115 & 135.263543669836 & -20.2635436698363 \tabularnewline
113 & 0 & -7.20213177798046 & 7.20213177798046 \tabularnewline
114 & 103 & 130.564540848479 & -27.5645408484789 \tabularnewline
115 & 30 & 22.2644993891368 & 7.73550061086322 \tabularnewline
116 & 130 & 149.251370153576 & -19.2513701535764 \tabularnewline
117 & 102 & 115.151076851331 & -13.1510768513313 \tabularnewline
118 & 0 & -6.70933524957437 & 6.70933524957437 \tabularnewline
119 & 77 & 95.1242667363701 & -18.1242667363701 \tabularnewline
120 & 9 & 13.4514609476565 & -4.45146094765652 \tabularnewline
121 & 150 & 132.767249127323 & 17.2327508726767 \tabularnewline
122 & 163 & 160.014532307863 & 2.98546769213732 \tabularnewline
123 & 148 & 156.848407657241 & -8.84840765724054 \tabularnewline
124 & 94 & 103.734257264885 & -9.73425726488468 \tabularnewline
125 & 21 & 15.4255152054881 & 5.57448479451193 \tabularnewline
126 & 151 & 139.037766031609 & 11.9622339683914 \tabularnewline
127 & 187 & 169.294827289497 & 17.7051727105031 \tabularnewline
128 & 171 & 167.62050145985 & 3.37949854015024 \tabularnewline
129 & 170 & 172.814370841922 & -2.81437084192187 \tabularnewline
130 & 145 & 154.858634722363 & -9.85863472236252 \tabularnewline
131 & 198 & 191.929700551453 & 6.07029944854681 \tabularnewline
132 & 152 & 134.863848488047 & 17.1361515119527 \tabularnewline
133 & 112 & 112.712062833861 & -0.712062833861452 \tabularnewline
134 & 173 & 150.286807751867 & 22.713192248133 \tabularnewline
135 & 177 & 156.381924934886 & 20.6180750651135 \tabularnewline
136 & 153 & 147.658341797834 & 5.34165820216633 \tabularnewline
137 & 161 & 158.792319409073 & 2.20768059092707 \tabularnewline
138 & 115 & 119.95178837317 & -4.95178837316995 \tabularnewline
139 & 147 & 141.715535024679 & 5.2844649753215 \tabularnewline
140 & 124 & 123.334388352641 & 0.665611647359104 \tabularnewline
141 & 57 & 120.22600235506 & -63.2260023550597 \tabularnewline
142 & 144 & 145.941720916999 & -1.94172091699906 \tabularnewline
143 & 126 & 121.227906249139 & 4.77209375086115 \tabularnewline
144 & 78 & 62.2812407292352 & 15.7187592707648 \tabularnewline
145 & 153 & 174.681137640081 & -21.681137640081 \tabularnewline
146 & 196 & 184.999104083237 & 11.0008959167634 \tabularnewline
147 & 130 & 132.085665913325 & -2.08566591332502 \tabularnewline
148 & 159 & 146.263875590789 & 12.7361244092105 \tabularnewline
149 & 0 & -0.412296922848989 & 0.412296922848989 \tabularnewline
150 & 0 & -5.54960994989821 & 5.54960994989821 \tabularnewline
151 & 0 & -6.55198956233304 & 6.55198956233304 \tabularnewline
152 & 0 & -6.58976782845255 & 6.58976782845255 \tabularnewline
153 & 0 & -5.82823245517681 & 5.82823245517681 \tabularnewline
154 & 0 & -6.50594877680907 & 6.50594877680907 \tabularnewline
155 & 94 & 108.47636434328 & -14.4763643432799 \tabularnewline
156 & 129 & 163.776145733372 & -34.776145733372 \tabularnewline
157 & 0 & -6.50594877680907 & 6.50594877680907 \tabularnewline
158 & 0 & -6.64546933783264 & 6.64546933783264 \tabularnewline
159 & 0 & -6.19341807365847 & 6.19341807365847 \tabularnewline
160 & 13 & 5.67646845537447 & 7.32353154462553 \tabularnewline
161 & 4 & -2.07466739523299 & 6.07466739523299 \tabularnewline
162 & 89 & 130.786257481395 & -41.7862574813949 \tabularnewline
163 & 0 & -6.64583738876413 & 6.64583738876413 \tabularnewline
164 & 71 & 105.434356467136 & -34.4343564671357 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159458&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]130[/C][C]135.757345929288[/C][C]-5.75734592928829[/C][/ROW]
[ROW][C]2[/C][C]143[/C][C]126.002752437736[/C][C]16.9972475622638[/C][/ROW]
[ROW][C]3[/C][C]118[/C][C]154.59692823755[/C][C]-36.59692823755[/C][/ROW]
[ROW][C]4[/C][C]146[/C][C]145.803695560815[/C][C]0.196304439185029[/C][/ROW]
[ROW][C]5[/C][C]73[/C][C]74.2293521763506[/C][C]-1.2293521763506[/C][/ROW]
[ROW][C]6[/C][C]89[/C][C]103.338094521794[/C][C]-14.3380945217938[/C][/ROW]
[ROW][C]7[/C][C]146[/C][C]149.443841219146[/C][C]-3.44384121914575[/C][/ROW]
[ROW][C]8[/C][C]22[/C][C]47.8309097720073[/C][C]-25.8309097720073[/C][/ROW]
[ROW][C]9[/C][C]132[/C][C]136.63787142953[/C][C]-4.63787142952972[/C][/ROW]
[ROW][C]10[/C][C]92[/C][C]106.585215618072[/C][C]-14.5852156180716[/C][/ROW]
[ROW][C]11[/C][C]147[/C][C]135.019876609757[/C][C]11.9801233902434[/C][/ROW]
[ROW][C]12[/C][C]203[/C][C]190.3106657037[/C][C]12.6893342962996[/C][/ROW]
[ROW][C]13[/C][C]113[/C][C]100.159785120893[/C][C]12.8402148791074[/C][/ROW]
[ROW][C]14[/C][C]171[/C][C]158.470447024486[/C][C]12.529552975514[/C][/ROW]
[ROW][C]15[/C][C]87[/C][C]131.555338653122[/C][C]-44.5553386531216[/C][/ROW]
[ROW][C]16[/C][C]208[/C][C]196.959949699526[/C][C]11.040050300474[/C][/ROW]
[ROW][C]17[/C][C]153[/C][C]143.432851772335[/C][C]9.56714822766485[/C][/ROW]
[ROW][C]18[/C][C]97[/C][C]133.83300827435[/C][C]-36.8330082743498[/C][/ROW]
[ROW][C]19[/C][C]95[/C][C]109.864754210269[/C][C]-14.8647542102691[/C][/ROW]
[ROW][C]20[/C][C]197[/C][C]173.073425422002[/C][C]23.9265745779975[/C][/ROW]
[ROW][C]21[/C][C]160[/C][C]160.867662185664[/C][C]-0.867662185663872[/C][/ROW]
[ROW][C]22[/C][C]148[/C][C]127.061296324313[/C][C]20.9387036756873[/C][/ROW]
[ROW][C]23[/C][C]84[/C][C]76.2306036293108[/C][C]7.76939637068917[/C][/ROW]
[ROW][C]24[/C][C]227[/C][C]196.387272601612[/C][C]30.6127273983883[/C][/ROW]
[ROW][C]25[/C][C]154[/C][C]139.858864587955[/C][C]14.1411354120452[/C][/ROW]
[ROW][C]26[/C][C]151[/C][C]137.614219112446[/C][C]13.3857808875542[/C][/ROW]
[ROW][C]27[/C][C]142[/C][C]146.116502933629[/C][C]-4.11650293362885[/C][/ROW]
[ROW][C]28[/C][C]148[/C][C]126.028484964728[/C][C]21.9715150352719[/C][/ROW]
[ROW][C]29[/C][C]110[/C][C]98.7475676910034[/C][C]11.2524323089966[/C][/ROW]
[ROW][C]30[/C][C]149[/C][C]144.944444301559[/C][C]4.0555556984411[/C][/ROW]
[ROW][C]31[/C][C]179[/C][C]174.893545026886[/C][C]4.10645497311443[/C][/ROW]
[ROW][C]32[/C][C]149[/C][C]147.25836746673[/C][C]1.74163253326997[/C][/ROW]
[ROW][C]33[/C][C]187[/C][C]166.009483483271[/C][C]20.9905165167291[/C][/ROW]
[ROW][C]34[/C][C]153[/C][C]125.25500102646[/C][C]27.7449989735404[/C][/ROW]
[ROW][C]35[/C][C]163[/C][C]148.854028209124[/C][C]14.1459717908764[/C][/ROW]
[ROW][C]36[/C][C]127[/C][C]126.157446422873[/C][C]0.84255357712671[/C][/ROW]
[ROW][C]37[/C][C]151[/C][C]134.868872248236[/C][C]16.1311277517635[/C][/ROW]
[ROW][C]38[/C][C]100[/C][C]124.054464553908[/C][C]-24.0544645539077[/C][/ROW]
[ROW][C]39[/C][C]46[/C][C]40.6252509119961[/C][C]5.37474908800395[/C][/ROW]
[ROW][C]40[/C][C]156[/C][C]141.516122689074[/C][C]14.4838773109259[/C][/ROW]
[ROW][C]41[/C][C]128[/C][C]135.274631661468[/C][C]-7.27463166146832[/C][/ROW]
[ROW][C]42[/C][C]111[/C][C]125.187855244399[/C][C]-14.1878552443993[/C][/ROW]
[ROW][C]43[/C][C]119[/C][C]108.85885564817[/C][C]10.1411443518299[/C][/ROW]
[ROW][C]44[/C][C]148[/C][C]147.940147700584[/C][C]0.0598522994155424[/C][/ROW]
[ROW][C]45[/C][C]65[/C][C]53.8430623140506[/C][C]11.1569376859494[/C][/ROW]
[ROW][C]46[/C][C]134[/C][C]133.521456347165[/C][C]0.478543652835425[/C][/ROW]
[ROW][C]47[/C][C]66[/C][C]59.7866186266653[/C][C]6.21338137333474[/C][/ROW]
[ROW][C]48[/C][C]201[/C][C]183.033919902903[/C][C]17.9660800970968[/C][/ROW]
[ROW][C]49[/C][C]177[/C][C]167.243610069349[/C][C]9.75638993065089[/C][/ROW]
[ROW][C]50[/C][C]156[/C][C]146.038706642233[/C][C]9.96129335776697[/C][/ROW]
[ROW][C]51[/C][C]158[/C][C]146.843477306247[/C][C]11.1565226937526[/C][/ROW]
[ROW][C]52[/C][C]7[/C][C]3.41706894686002[/C][C]3.58293105313998[/C][/ROW]
[ROW][C]53[/C][C]175[/C][C]144.058855036478[/C][C]30.9411449635216[/C][/ROW]
[ROW][C]54[/C][C]61[/C][C]52.672376266072[/C][C]8.327623733928[/C][/ROW]
[ROW][C]55[/C][C]41[/C][C]39.1369777644017[/C][C]1.86302223559831[/C][/ROW]
[ROW][C]56[/C][C]133[/C][C]127.456284845106[/C][C]5.54371515489417[/C][/ROW]
[ROW][C]57[/C][C]228[/C][C]198.876105523652[/C][C]29.1238944763478[/C][/ROW]
[ROW][C]58[/C][C]140[/C][C]128.639471069317[/C][C]11.3605289306833[/C][/ROW]
[ROW][C]59[/C][C]155[/C][C]138.040114107309[/C][C]16.959885892691[/C][/ROW]
[ROW][C]60[/C][C]141[/C][C]139.569567317189[/C][C]1.43043268281138[/C][/ROW]
[ROW][C]61[/C][C]181[/C][C]171.189752771273[/C][C]9.81024722872705[/C][/ROW]
[ROW][C]62[/C][C]75[/C][C]107.751717607565[/C][C]-32.7517176075649[/C][/ROW]
[ROW][C]63[/C][C]97[/C][C]143.332883289736[/C][C]-46.3328832897358[/C][/ROW]
[ROW][C]64[/C][C]142[/C][C]142.446476106823[/C][C]-0.446476106823179[/C][/ROW]
[ROW][C]65[/C][C]136[/C][C]130.725664019807[/C][C]5.27433598019304[/C][/ROW]
[ROW][C]66[/C][C]87[/C][C]97.0003390924168[/C][C]-10.0003390924168[/C][/ROW]
[ROW][C]67[/C][C]140[/C][C]131.38128618019[/C][C]8.61871381980998[/C][/ROW]
[ROW][C]68[/C][C]169[/C][C]163.027829225832[/C][C]5.97217077416771[/C][/ROW]
[ROW][C]69[/C][C]129[/C][C]126.604309409319[/C][C]2.39569059068113[/C][/ROW]
[ROW][C]70[/C][C]92[/C][C]98.5422958182424[/C][C]-6.54229581824244[/C][/ROW]
[ROW][C]71[/C][C]160[/C][C]153.531446396909[/C][C]6.46855360309093[/C][/ROW]
[ROW][C]72[/C][C]67[/C][C]69.6439642876202[/C][C]-2.64396428762021[/C][/ROW]
[ROW][C]73[/C][C]179[/C][C]154.272129423349[/C][C]24.7278705766514[/C][/ROW]
[ROW][C]74[/C][C]90[/C][C]111.93225149482[/C][C]-21.9322514948201[/C][/ROW]
[ROW][C]75[/C][C]144[/C][C]138.383271953833[/C][C]5.61672804616727[/C][/ROW]
[ROW][C]76[/C][C]144[/C][C]146.565849925668[/C][C]-2.56584992566811[/C][/ROW]
[ROW][C]77[/C][C]144[/C][C]134.400342494432[/C][C]9.59965750556776[/C][/ROW]
[ROW][C]78[/C][C]134[/C][C]148.616372589235[/C][C]-14.6163725892353[/C][/ROW]
[ROW][C]79[/C][C]146[/C][C]135.218449713056[/C][C]10.781550286944[/C][/ROW]
[ROW][C]80[/C][C]121[/C][C]116.798059887609[/C][C]4.20194011239076[/C][/ROW]
[ROW][C]81[/C][C]112[/C][C]115.145020080773[/C][C]-3.14502008077292[/C][/ROW]
[ROW][C]82[/C][C]145[/C][C]162.45056873509[/C][C]-17.4505687350896[/C][/ROW]
[ROW][C]83[/C][C]99[/C][C]80.1816364647109[/C][C]18.8183635352891[/C][/ROW]
[ROW][C]84[/C][C]96[/C][C]101.674964809096[/C][C]-5.67496480909569[/C][/ROW]
[ROW][C]85[/C][C]27[/C][C]22.0155464817035[/C][C]4.98445351829651[/C][/ROW]
[ROW][C]86[/C][C]77[/C][C]78.7183413255911[/C][C]-1.7183413255911[/C][/ROW]
[ROW][C]87[/C][C]137[/C][C]127.250130178634[/C][C]9.74986982136644[/C][/ROW]
[ROW][C]88[/C][C]151[/C][C]135.158915577446[/C][C]15.8410844225543[/C][/ROW]
[ROW][C]89[/C][C]126[/C][C]184.519151634798[/C][C]-58.5191516347981[/C][/ROW]
[ROW][C]90[/C][C]159[/C][C]151.932723107284[/C][C]7.06727689271612[/C][/ROW]
[ROW][C]91[/C][C]101[/C][C]120.293755209045[/C][C]-19.2937552090448[/C][/ROW]
[ROW][C]92[/C][C]144[/C][C]131.358433294765[/C][C]12.6415667052347[/C][/ROW]
[ROW][C]93[/C][C]102[/C][C]105.128197459908[/C][C]-3.12819745990763[/C][/ROW]
[ROW][C]94[/C][C]135[/C][C]115.271991122775[/C][C]19.7280088772248[/C][/ROW]
[ROW][C]95[/C][C]147[/C][C]142.939275253301[/C][C]4.06072474669859[/C][/ROW]
[ROW][C]96[/C][C]155[/C][C]144.201735143013[/C][C]10.7982648569872[/C][/ROW]
[ROW][C]97[/C][C]138[/C][C]129.323437024048[/C][C]8.6765629759519[/C][/ROW]
[ROW][C]98[/C][C]113[/C][C]136.107671174653[/C][C]-23.1076711746528[/C][/ROW]
[ROW][C]99[/C][C]248[/C][C]223.117009416817[/C][C]24.8829905831825[/C][/ROW]
[ROW][C]100[/C][C]116[/C][C]126.37764213555[/C][C]-10.3776421355505[/C][/ROW]
[ROW][C]101[/C][C]176[/C][C]177.431040502059[/C][C]-1.43104050205884[/C][/ROW]
[ROW][C]102[/C][C]140[/C][C]143.62242075272[/C][C]-3.6224207527201[/C][/ROW]
[ROW][C]103[/C][C]59[/C][C]105.523743006453[/C][C]-46.5237430064529[/C][/ROW]
[ROW][C]104[/C][C]64[/C][C]56.6094812343564[/C][C]7.39051876564363[/C][/ROW]
[ROW][C]105[/C][C]40[/C][C]74.6578129248527[/C][C]-34.6578129248527[/C][/ROW]
[ROW][C]106[/C][C]98[/C][C]141.045411908502[/C][C]-43.0454119085019[/C][/ROW]
[ROW][C]107[/C][C]139[/C][C]153.791919951258[/C][C]-14.7919199512584[/C][/ROW]
[ROW][C]108[/C][C]135[/C][C]137.246896650865[/C][C]-2.24689665086516[/C][/ROW]
[ROW][C]109[/C][C]97[/C][C]112.604886211224[/C][C]-15.6048862112239[/C][/ROW]
[ROW][C]110[/C][C]142[/C][C]147.143174589569[/C][C]-5.1431745895689[/C][/ROW]
[ROW][C]111[/C][C]155[/C][C]141.542472363292[/C][C]13.4575276367077[/C][/ROW]
[ROW][C]112[/C][C]115[/C][C]135.263543669836[/C][C]-20.2635436698363[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]-7.20213177798046[/C][C]7.20213177798046[/C][/ROW]
[ROW][C]114[/C][C]103[/C][C]130.564540848479[/C][C]-27.5645408484789[/C][/ROW]
[ROW][C]115[/C][C]30[/C][C]22.2644993891368[/C][C]7.73550061086322[/C][/ROW]
[ROW][C]116[/C][C]130[/C][C]149.251370153576[/C][C]-19.2513701535764[/C][/ROW]
[ROW][C]117[/C][C]102[/C][C]115.151076851331[/C][C]-13.1510768513313[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]-6.70933524957437[/C][C]6.70933524957437[/C][/ROW]
[ROW][C]119[/C][C]77[/C][C]95.1242667363701[/C][C]-18.1242667363701[/C][/ROW]
[ROW][C]120[/C][C]9[/C][C]13.4514609476565[/C][C]-4.45146094765652[/C][/ROW]
[ROW][C]121[/C][C]150[/C][C]132.767249127323[/C][C]17.2327508726767[/C][/ROW]
[ROW][C]122[/C][C]163[/C][C]160.014532307863[/C][C]2.98546769213732[/C][/ROW]
[ROW][C]123[/C][C]148[/C][C]156.848407657241[/C][C]-8.84840765724054[/C][/ROW]
[ROW][C]124[/C][C]94[/C][C]103.734257264885[/C][C]-9.73425726488468[/C][/ROW]
[ROW][C]125[/C][C]21[/C][C]15.4255152054881[/C][C]5.57448479451193[/C][/ROW]
[ROW][C]126[/C][C]151[/C][C]139.037766031609[/C][C]11.9622339683914[/C][/ROW]
[ROW][C]127[/C][C]187[/C][C]169.294827289497[/C][C]17.7051727105031[/C][/ROW]
[ROW][C]128[/C][C]171[/C][C]167.62050145985[/C][C]3.37949854015024[/C][/ROW]
[ROW][C]129[/C][C]170[/C][C]172.814370841922[/C][C]-2.81437084192187[/C][/ROW]
[ROW][C]130[/C][C]145[/C][C]154.858634722363[/C][C]-9.85863472236252[/C][/ROW]
[ROW][C]131[/C][C]198[/C][C]191.929700551453[/C][C]6.07029944854681[/C][/ROW]
[ROW][C]132[/C][C]152[/C][C]134.863848488047[/C][C]17.1361515119527[/C][/ROW]
[ROW][C]133[/C][C]112[/C][C]112.712062833861[/C][C]-0.712062833861452[/C][/ROW]
[ROW][C]134[/C][C]173[/C][C]150.286807751867[/C][C]22.713192248133[/C][/ROW]
[ROW][C]135[/C][C]177[/C][C]156.381924934886[/C][C]20.6180750651135[/C][/ROW]
[ROW][C]136[/C][C]153[/C][C]147.658341797834[/C][C]5.34165820216633[/C][/ROW]
[ROW][C]137[/C][C]161[/C][C]158.792319409073[/C][C]2.20768059092707[/C][/ROW]
[ROW][C]138[/C][C]115[/C][C]119.95178837317[/C][C]-4.95178837316995[/C][/ROW]
[ROW][C]139[/C][C]147[/C][C]141.715535024679[/C][C]5.2844649753215[/C][/ROW]
[ROW][C]140[/C][C]124[/C][C]123.334388352641[/C][C]0.665611647359104[/C][/ROW]
[ROW][C]141[/C][C]57[/C][C]120.22600235506[/C][C]-63.2260023550597[/C][/ROW]
[ROW][C]142[/C][C]144[/C][C]145.941720916999[/C][C]-1.94172091699906[/C][/ROW]
[ROW][C]143[/C][C]126[/C][C]121.227906249139[/C][C]4.77209375086115[/C][/ROW]
[ROW][C]144[/C][C]78[/C][C]62.2812407292352[/C][C]15.7187592707648[/C][/ROW]
[ROW][C]145[/C][C]153[/C][C]174.681137640081[/C][C]-21.681137640081[/C][/ROW]
[ROW][C]146[/C][C]196[/C][C]184.999104083237[/C][C]11.0008959167634[/C][/ROW]
[ROW][C]147[/C][C]130[/C][C]132.085665913325[/C][C]-2.08566591332502[/C][/ROW]
[ROW][C]148[/C][C]159[/C][C]146.263875590789[/C][C]12.7361244092105[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]-0.412296922848989[/C][C]0.412296922848989[/C][/ROW]
[ROW][C]150[/C][C]0[/C][C]-5.54960994989821[/C][C]5.54960994989821[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]-6.55198956233304[/C][C]6.55198956233304[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]-6.58976782845255[/C][C]6.58976782845255[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]-5.82823245517681[/C][C]5.82823245517681[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]-6.50594877680907[/C][C]6.50594877680907[/C][/ROW]
[ROW][C]155[/C][C]94[/C][C]108.47636434328[/C][C]-14.4763643432799[/C][/ROW]
[ROW][C]156[/C][C]129[/C][C]163.776145733372[/C][C]-34.776145733372[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]-6.50594877680907[/C][C]6.50594877680907[/C][/ROW]
[ROW][C]158[/C][C]0[/C][C]-6.64546933783264[/C][C]6.64546933783264[/C][/ROW]
[ROW][C]159[/C][C]0[/C][C]-6.19341807365847[/C][C]6.19341807365847[/C][/ROW]
[ROW][C]160[/C][C]13[/C][C]5.67646845537447[/C][C]7.32353154462553[/C][/ROW]
[ROW][C]161[/C][C]4[/C][C]-2.07466739523299[/C][C]6.07466739523299[/C][/ROW]
[ROW][C]162[/C][C]89[/C][C]130.786257481395[/C][C]-41.7862574813949[/C][/ROW]
[ROW][C]163[/C][C]0[/C][C]-6.64583738876413[/C][C]6.64583738876413[/C][/ROW]
[ROW][C]164[/C][C]71[/C][C]105.434356467136[/C][C]-34.4343564671357[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159458&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159458&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
1130135.757345929288-5.75734592928829
2143126.00275243773616.9972475622638
3118154.59692823755-36.59692823755
4146145.8036955608150.196304439185029
57374.2293521763506-1.2293521763506
689103.338094521794-14.3380945217938
7146149.443841219146-3.44384121914575
82247.8309097720073-25.8309097720073
9132136.63787142953-4.63787142952972
1092106.585215618072-14.5852156180716
11147135.01987660975711.9801233902434
12203190.310665703712.6893342962996
13113100.15978512089312.8402148791074
14171158.47044702448612.529552975514
1587131.555338653122-44.5553386531216
16208196.95994969952611.040050300474
17153143.4328517723359.56714822766485
1897133.83300827435-36.8330082743498
1995109.864754210269-14.8647542102691
20197173.07342542200223.9265745779975
21160160.867662185664-0.867662185663872
22148127.06129632431320.9387036756873
238476.23060362931087.76939637068917
24227196.38727260161230.6127273983883
25154139.85886458795514.1411354120452
26151137.61421911244613.3857808875542
27142146.116502933629-4.11650293362885
28148126.02848496472821.9715150352719
2911098.747567691003411.2524323089966
30149144.9444443015594.0555556984411
31179174.8935450268864.10645497311443
32149147.258367466731.74163253326997
33187166.00948348327120.9905165167291
34153125.2550010264627.7449989735404
35163148.85402820912414.1459717908764
36127126.1574464228730.84255357712671
37151134.86887224823616.1311277517635
38100124.054464553908-24.0544645539077
394640.62525091199615.37474908800395
40156141.51612268907414.4838773109259
41128135.274631661468-7.27463166146832
42111125.187855244399-14.1878552443993
43119108.8588556481710.1411443518299
44148147.9401477005840.0598522994155424
456553.843062314050611.1569376859494
46134133.5214563471650.478543652835425
476659.78661862666536.21338137333474
48201183.03391990290317.9660800970968
49177167.2436100693499.75638993065089
50156146.0387066422339.96129335776697
51158146.84347730624711.1565226937526
5273.417068946860023.58293105313998
53175144.05885503647830.9411449635216
546152.6723762660728.327623733928
554139.13697776440171.86302223559831
56133127.4562848451065.54371515489417
57228198.87610552365229.1238944763478
58140128.63947106931711.3605289306833
59155138.04011410730916.959885892691
60141139.5695673171891.43043268281138
61181171.1897527712739.81024722872705
6275107.751717607565-32.7517176075649
6397143.332883289736-46.3328832897358
64142142.446476106823-0.446476106823179
65136130.7256640198075.27433598019304
668797.0003390924168-10.0003390924168
67140131.381286180198.61871381980998
68169163.0278292258325.97217077416771
69129126.6043094093192.39569059068113
709298.5422958182424-6.54229581824244
71160153.5314463969096.46855360309093
726769.6439642876202-2.64396428762021
73179154.27212942334924.7278705766514
7490111.93225149482-21.9322514948201
75144138.3832719538335.61672804616727
76144146.565849925668-2.56584992566811
77144134.4003424944329.59965750556776
78134148.616372589235-14.6163725892353
79146135.21844971305610.781550286944
80121116.7980598876094.20194011239076
81112115.145020080773-3.14502008077292
82145162.45056873509-17.4505687350896
839980.181636464710918.8183635352891
8496101.674964809096-5.67496480909569
852722.01554648170354.98445351829651
867778.7183413255911-1.7183413255911
87137127.2501301786349.74986982136644
88151135.15891557744615.8410844225543
89126184.519151634798-58.5191516347981
90159151.9327231072847.06727689271612
91101120.293755209045-19.2937552090448
92144131.35843329476512.6415667052347
93102105.128197459908-3.12819745990763
94135115.27199112277519.7280088772248
95147142.9392752533014.06072474669859
96155144.20173514301310.7982648569872
97138129.3234370240488.6765629759519
98113136.107671174653-23.1076711746528
99248223.11700941681724.8829905831825
100116126.37764213555-10.3776421355505
101176177.431040502059-1.43104050205884
102140143.62242075272-3.6224207527201
10359105.523743006453-46.5237430064529
1046456.60948123435647.39051876564363
1054074.6578129248527-34.6578129248527
10698141.045411908502-43.0454119085019
107139153.791919951258-14.7919199512584
108135137.246896650865-2.24689665086516
10997112.604886211224-15.6048862112239
110142147.143174589569-5.1431745895689
111155141.54247236329213.4575276367077
112115135.263543669836-20.2635436698363
1130-7.202131777980467.20213177798046
114103130.564540848479-27.5645408484789
1153022.26449938913687.73550061086322
116130149.251370153576-19.2513701535764
117102115.151076851331-13.1510768513313
1180-6.709335249574376.70933524957437
1197795.1242667363701-18.1242667363701
120913.4514609476565-4.45146094765652
121150132.76724912732317.2327508726767
122163160.0145323078632.98546769213732
123148156.848407657241-8.84840765724054
12494103.734257264885-9.73425726488468
1252115.42551520548815.57448479451193
126151139.03776603160911.9622339683914
127187169.29482728949717.7051727105031
128171167.620501459853.37949854015024
129170172.814370841922-2.81437084192187
130145154.858634722363-9.85863472236252
131198191.9297005514536.07029944854681
132152134.86384848804717.1361515119527
133112112.712062833861-0.712062833861452
134173150.28680775186722.713192248133
135177156.38192493488620.6180750651135
136153147.6583417978345.34165820216633
137161158.7923194090732.20768059092707
138115119.95178837317-4.95178837316995
139147141.7155350246795.2844649753215
140124123.3343883526410.665611647359104
14157120.22600235506-63.2260023550597
142144145.941720916999-1.94172091699906
143126121.2279062491394.77209375086115
1447862.281240729235215.7187592707648
145153174.681137640081-21.681137640081
146196184.99910408323711.0008959167634
147130132.085665913325-2.08566591332502
148159146.26387559078912.7361244092105
1490-0.4122969228489890.412296922848989
1500-5.549609949898215.54960994989821
1510-6.551989562333046.55198956233304
1520-6.589767828452556.58976782845255
1530-5.828232455176815.82823245517681
1540-6.505948776809076.50594877680907
15594108.47636434328-14.4763643432799
156129163.776145733372-34.776145733372
1570-6.505948776809076.50594877680907
1580-6.645469337832646.64546933783264
1590-6.193418073658476.19341807365847
160135.676468455374477.32353154462553
1614-2.074667395232996.07466739523299
16289130.786257481395-41.7862574813949
1630-6.645837388764136.64583738876413
16471105.434356467136-34.4343564671357






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.6183611628892980.7632776742214040.381638837110702
190.912747472893720.174505054212560.0872525271062802
200.8773350375544430.2453299248911140.122664962445557
210.8139641923289440.3720716153421120.186035807671056
220.7609405273439830.4781189453120350.239059472656017
230.6863510326414710.6272979347170580.313648967358529
240.6980088868314170.6039822263371670.301991113168583
250.6163581018433560.7672837963132880.383641898156644
260.8647406853608630.2705186292782740.135259314639137
270.8295526087273530.3408947825452940.170447391272647
280.7851419628586870.4297160742826270.214858037141313
290.737329168455460.5253416630890810.26267083154454
300.675294675408230.649410649183540.32470532459177
310.6104617282344510.7790765435310990.389538271765549
320.5402913685571730.9194172628856540.459708631442827
330.5473471858393770.9053056283212450.452652814160622
340.651505029699750.69698994060050.34849497030025
350.5948537642780230.8102924714439540.405146235721977
360.5309229985709350.938154002858130.469077001429065
370.5248713475619160.9502573048761690.475128652438084
380.544872030727870.910255938544260.45512796927213
390.5531411613352640.8937176773294720.446858838664736
400.5316881611200430.9366236777599130.468311838879957
410.4727240811252440.9454481622504880.527275918874756
420.4276056190254040.8552112380508080.572394380974596
430.3817637160229940.7635274320459880.618236283977006
440.3266985109829540.6533970219659080.673301489017046
450.2990315317005440.5980630634010890.700968468299456
460.2747915234599470.5495830469198940.725208476540053
470.25009367684630.5001873536925990.7499063231537
480.2353647132603930.4707294265207870.764635286739607
490.2632449668431250.526489933686250.736755033156875
500.2306472670223270.4612945340446540.769352732977673
510.2003541330121980.4007082660243960.799645866987802
520.1965267074428180.3930534148856360.803473292557182
530.2564279858893280.5128559717786560.743572014110672
540.2531142644896980.5062285289793960.746885735510302
550.2207282885282660.4414565770565320.779271711471734
560.1883077228422640.3766154456845280.811692277157736
570.2075279821584180.4150559643168370.792472017841582
580.1952357897196450.3904715794392910.804764210280355
590.2114915785397820.4229831570795630.788508421460218
600.181313597537920.362627195075840.81868640246208
610.1613028530472920.3226057060945850.838697146952708
620.232860084918250.46572016983650.76713991508175
630.4158671322971380.8317342645942760.584132867702862
640.3691724952280510.7383449904561020.630827504771949
650.3305270343348170.6610540686696350.669472965665183
660.321251851377810.6425037027556190.67874814862219
670.2811370471362080.5622740942724160.718862952863792
680.2516637052153390.5033274104306770.748336294784661
690.21377935292180.4275587058435990.7862206470782
700.1897731008162350.3795462016324690.810226899183765
710.180416954951010.360833909902020.81958304504899
720.1549151039618860.3098302079237720.845084896038114
730.1950089229240870.3900178458481740.804991077075913
740.2187842086982540.4375684173965080.781215791301746
750.1883063012636060.3766126025272130.811693698736394
760.1591977975067010.3183955950134030.840802202493299
770.1417066367322410.2834132734644830.858293363267759
780.1612339155823140.3224678311646270.838766084417686
790.1468635307270050.2937270614540110.853136469272995
800.1271733559445440.2543467118890880.872826644055456
810.1070606200115360.2141212400230730.892939379988464
820.1219457913948910.2438915827897830.878054208605109
830.1501923651241780.3003847302483570.849807634875822
840.1391395900742270.2782791801484540.860860409925773
850.1210931983702280.2421863967404550.878906801629772
860.1029271493212810.2058542986425630.897072850678719
870.09129119747120860.1825823949424170.908708802528791
880.07460805174666950.1492161034933390.92539194825333
890.3764771618325550.7529543236651090.623522838167445
900.3421984192385940.6843968384771880.657801580761406
910.3382847315078480.6765694630156960.661715268492152
920.3189639318891290.6379278637782580.681036068110871
930.276759912156790.5535198243135790.72324008784321
940.3018293877627070.6036587755254130.698170612237293
950.2611095804881470.5222191609762940.738890419511853
960.2344854800207140.4689709600414290.765514519979286
970.2041693136142850.408338627228570.795830686385715
980.2298747303728430.4597494607456850.770125269627157
990.3038253279732790.6076506559465570.696174672026721
1000.2701423033706290.5402846067412580.729857696629371
1010.2338188252249020.4676376504498050.766181174775098
1020.2001076309145430.4002152618290870.799892369085456
1030.4319997402012230.8639994804024470.568000259798777
1040.3914766301664910.7829532603329810.608523369833509
1050.538075706787610.9238485864247790.46192429321239
1060.7384690705180450.5230618589639110.261530929481955
1070.7841446353252320.4317107293495350.215855364674768
1080.7444108443761380.5111783112477240.255589155623862
1090.8053315268236930.3893369463526130.194668473176307
1100.7683147571247510.4633704857504980.231685242875249
1110.7570313402498870.4859373195002260.242968659750113
1120.7304703597416540.5390592805166920.269529640258346
1130.6922503298034740.6154993403930520.307749670196526
1140.8131562367541570.3736875264916860.186843763245843
1150.7787303129540560.4425393740918890.221269687045944
1160.8488442863874170.3023114272251660.151155713612583
1170.8153355405723170.3693289188553660.184664459427683
1180.7778294956773410.4443410086453180.222170504322659
1190.7541980044412720.4916039911174560.245801995558728
1200.7142771577491820.5714456845016360.285722842250818
1210.6857116983322010.6285766033355990.314288301667799
1220.6395643361571990.7208713276856020.360435663842801
1230.5844233880214790.8311532239570420.415576611978521
1240.5403628503388440.9192742993223120.459637149661156
1250.4897479347819080.9794958695638160.510252065218092
1260.4710201935322760.9420403870645530.528979806467724
1270.4915483306268510.9830966612537020.508451669373149
1280.8641718714020780.2716562571958440.135828128597922
1290.8444347068042240.3111305863915520.155565293195776
1300.8504695095254630.2990609809490740.149530490474537
1310.8036081449735480.3927837100529040.196391855026452
1320.937784872382280.1244302552354410.0622151276177203
1330.9260703518529930.1478592962940130.0739296481470066
1340.9754947532113180.04901049357736410.024505246788682
1350.9960075851270090.007984829745980950.00399241487299047
1360.9987225996199870.00255480076002570.00127740038001285
1370.9974861096265510.005027780746897030.00251389037344851
1380.998822598216910.002354803566180180.00117740178309009
1390.9999956129992858.77400143043363e-064.38700071521682e-06
1400.9999863800597722.72398804553444e-051.36199402276722e-05
1410.9999990033463491.99330730271526e-069.96653651357631e-07
1420.9999976532665794.69346684108233e-062.34673342054116e-06
1430.9999833038203783.33923592429822e-051.66961796214911e-05
1440.9999999999232581.53483870818822e-107.6741935409411e-11
1450.9999999947880841.04238329990004e-085.21191649950018e-09
14611.65326927899847e-468.26634639499237e-47

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.618361162889298 & 0.763277674221404 & 0.381638837110702 \tabularnewline
19 & 0.91274747289372 & 0.17450505421256 & 0.0872525271062802 \tabularnewline
20 & 0.877335037554443 & 0.245329924891114 & 0.122664962445557 \tabularnewline
21 & 0.813964192328944 & 0.372071615342112 & 0.186035807671056 \tabularnewline
22 & 0.760940527343983 & 0.478118945312035 & 0.239059472656017 \tabularnewline
23 & 0.686351032641471 & 0.627297934717058 & 0.313648967358529 \tabularnewline
24 & 0.698008886831417 & 0.603982226337167 & 0.301991113168583 \tabularnewline
25 & 0.616358101843356 & 0.767283796313288 & 0.383641898156644 \tabularnewline
26 & 0.864740685360863 & 0.270518629278274 & 0.135259314639137 \tabularnewline
27 & 0.829552608727353 & 0.340894782545294 & 0.170447391272647 \tabularnewline
28 & 0.785141962858687 & 0.429716074282627 & 0.214858037141313 \tabularnewline
29 & 0.73732916845546 & 0.525341663089081 & 0.26267083154454 \tabularnewline
30 & 0.67529467540823 & 0.64941064918354 & 0.32470532459177 \tabularnewline
31 & 0.610461728234451 & 0.779076543531099 & 0.389538271765549 \tabularnewline
32 & 0.540291368557173 & 0.919417262885654 & 0.459708631442827 \tabularnewline
33 & 0.547347185839377 & 0.905305628321245 & 0.452652814160622 \tabularnewline
34 & 0.65150502969975 & 0.6969899406005 & 0.34849497030025 \tabularnewline
35 & 0.594853764278023 & 0.810292471443954 & 0.405146235721977 \tabularnewline
36 & 0.530922998570935 & 0.93815400285813 & 0.469077001429065 \tabularnewline
37 & 0.524871347561916 & 0.950257304876169 & 0.475128652438084 \tabularnewline
38 & 0.54487203072787 & 0.91025593854426 & 0.45512796927213 \tabularnewline
39 & 0.553141161335264 & 0.893717677329472 & 0.446858838664736 \tabularnewline
40 & 0.531688161120043 & 0.936623677759913 & 0.468311838879957 \tabularnewline
41 & 0.472724081125244 & 0.945448162250488 & 0.527275918874756 \tabularnewline
42 & 0.427605619025404 & 0.855211238050808 & 0.572394380974596 \tabularnewline
43 & 0.381763716022994 & 0.763527432045988 & 0.618236283977006 \tabularnewline
44 & 0.326698510982954 & 0.653397021965908 & 0.673301489017046 \tabularnewline
45 & 0.299031531700544 & 0.598063063401089 & 0.700968468299456 \tabularnewline
46 & 0.274791523459947 & 0.549583046919894 & 0.725208476540053 \tabularnewline
47 & 0.2500936768463 & 0.500187353692599 & 0.7499063231537 \tabularnewline
48 & 0.235364713260393 & 0.470729426520787 & 0.764635286739607 \tabularnewline
49 & 0.263244966843125 & 0.52648993368625 & 0.736755033156875 \tabularnewline
50 & 0.230647267022327 & 0.461294534044654 & 0.769352732977673 \tabularnewline
51 & 0.200354133012198 & 0.400708266024396 & 0.799645866987802 \tabularnewline
52 & 0.196526707442818 & 0.393053414885636 & 0.803473292557182 \tabularnewline
53 & 0.256427985889328 & 0.512855971778656 & 0.743572014110672 \tabularnewline
54 & 0.253114264489698 & 0.506228528979396 & 0.746885735510302 \tabularnewline
55 & 0.220728288528266 & 0.441456577056532 & 0.779271711471734 \tabularnewline
56 & 0.188307722842264 & 0.376615445684528 & 0.811692277157736 \tabularnewline
57 & 0.207527982158418 & 0.415055964316837 & 0.792472017841582 \tabularnewline
58 & 0.195235789719645 & 0.390471579439291 & 0.804764210280355 \tabularnewline
59 & 0.211491578539782 & 0.422983157079563 & 0.788508421460218 \tabularnewline
60 & 0.18131359753792 & 0.36262719507584 & 0.81868640246208 \tabularnewline
61 & 0.161302853047292 & 0.322605706094585 & 0.838697146952708 \tabularnewline
62 & 0.23286008491825 & 0.4657201698365 & 0.76713991508175 \tabularnewline
63 & 0.415867132297138 & 0.831734264594276 & 0.584132867702862 \tabularnewline
64 & 0.369172495228051 & 0.738344990456102 & 0.630827504771949 \tabularnewline
65 & 0.330527034334817 & 0.661054068669635 & 0.669472965665183 \tabularnewline
66 & 0.32125185137781 & 0.642503702755619 & 0.67874814862219 \tabularnewline
67 & 0.281137047136208 & 0.562274094272416 & 0.718862952863792 \tabularnewline
68 & 0.251663705215339 & 0.503327410430677 & 0.748336294784661 \tabularnewline
69 & 0.2137793529218 & 0.427558705843599 & 0.7862206470782 \tabularnewline
70 & 0.189773100816235 & 0.379546201632469 & 0.810226899183765 \tabularnewline
71 & 0.18041695495101 & 0.36083390990202 & 0.81958304504899 \tabularnewline
72 & 0.154915103961886 & 0.309830207923772 & 0.845084896038114 \tabularnewline
73 & 0.195008922924087 & 0.390017845848174 & 0.804991077075913 \tabularnewline
74 & 0.218784208698254 & 0.437568417396508 & 0.781215791301746 \tabularnewline
75 & 0.188306301263606 & 0.376612602527213 & 0.811693698736394 \tabularnewline
76 & 0.159197797506701 & 0.318395595013403 & 0.840802202493299 \tabularnewline
77 & 0.141706636732241 & 0.283413273464483 & 0.858293363267759 \tabularnewline
78 & 0.161233915582314 & 0.322467831164627 & 0.838766084417686 \tabularnewline
79 & 0.146863530727005 & 0.293727061454011 & 0.853136469272995 \tabularnewline
80 & 0.127173355944544 & 0.254346711889088 & 0.872826644055456 \tabularnewline
81 & 0.107060620011536 & 0.214121240023073 & 0.892939379988464 \tabularnewline
82 & 0.121945791394891 & 0.243891582789783 & 0.878054208605109 \tabularnewline
83 & 0.150192365124178 & 0.300384730248357 & 0.849807634875822 \tabularnewline
84 & 0.139139590074227 & 0.278279180148454 & 0.860860409925773 \tabularnewline
85 & 0.121093198370228 & 0.242186396740455 & 0.878906801629772 \tabularnewline
86 & 0.102927149321281 & 0.205854298642563 & 0.897072850678719 \tabularnewline
87 & 0.0912911974712086 & 0.182582394942417 & 0.908708802528791 \tabularnewline
88 & 0.0746080517466695 & 0.149216103493339 & 0.92539194825333 \tabularnewline
89 & 0.376477161832555 & 0.752954323665109 & 0.623522838167445 \tabularnewline
90 & 0.342198419238594 & 0.684396838477188 & 0.657801580761406 \tabularnewline
91 & 0.338284731507848 & 0.676569463015696 & 0.661715268492152 \tabularnewline
92 & 0.318963931889129 & 0.637927863778258 & 0.681036068110871 \tabularnewline
93 & 0.27675991215679 & 0.553519824313579 & 0.72324008784321 \tabularnewline
94 & 0.301829387762707 & 0.603658775525413 & 0.698170612237293 \tabularnewline
95 & 0.261109580488147 & 0.522219160976294 & 0.738890419511853 \tabularnewline
96 & 0.234485480020714 & 0.468970960041429 & 0.765514519979286 \tabularnewline
97 & 0.204169313614285 & 0.40833862722857 & 0.795830686385715 \tabularnewline
98 & 0.229874730372843 & 0.459749460745685 & 0.770125269627157 \tabularnewline
99 & 0.303825327973279 & 0.607650655946557 & 0.696174672026721 \tabularnewline
100 & 0.270142303370629 & 0.540284606741258 & 0.729857696629371 \tabularnewline
101 & 0.233818825224902 & 0.467637650449805 & 0.766181174775098 \tabularnewline
102 & 0.200107630914543 & 0.400215261829087 & 0.799892369085456 \tabularnewline
103 & 0.431999740201223 & 0.863999480402447 & 0.568000259798777 \tabularnewline
104 & 0.391476630166491 & 0.782953260332981 & 0.608523369833509 \tabularnewline
105 & 0.53807570678761 & 0.923848586424779 & 0.46192429321239 \tabularnewline
106 & 0.738469070518045 & 0.523061858963911 & 0.261530929481955 \tabularnewline
107 & 0.784144635325232 & 0.431710729349535 & 0.215855364674768 \tabularnewline
108 & 0.744410844376138 & 0.511178311247724 & 0.255589155623862 \tabularnewline
109 & 0.805331526823693 & 0.389336946352613 & 0.194668473176307 \tabularnewline
110 & 0.768314757124751 & 0.463370485750498 & 0.231685242875249 \tabularnewline
111 & 0.757031340249887 & 0.485937319500226 & 0.242968659750113 \tabularnewline
112 & 0.730470359741654 & 0.539059280516692 & 0.269529640258346 \tabularnewline
113 & 0.692250329803474 & 0.615499340393052 & 0.307749670196526 \tabularnewline
114 & 0.813156236754157 & 0.373687526491686 & 0.186843763245843 \tabularnewline
115 & 0.778730312954056 & 0.442539374091889 & 0.221269687045944 \tabularnewline
116 & 0.848844286387417 & 0.302311427225166 & 0.151155713612583 \tabularnewline
117 & 0.815335540572317 & 0.369328918855366 & 0.184664459427683 \tabularnewline
118 & 0.777829495677341 & 0.444341008645318 & 0.222170504322659 \tabularnewline
119 & 0.754198004441272 & 0.491603991117456 & 0.245801995558728 \tabularnewline
120 & 0.714277157749182 & 0.571445684501636 & 0.285722842250818 \tabularnewline
121 & 0.685711698332201 & 0.628576603335599 & 0.314288301667799 \tabularnewline
122 & 0.639564336157199 & 0.720871327685602 & 0.360435663842801 \tabularnewline
123 & 0.584423388021479 & 0.831153223957042 & 0.415576611978521 \tabularnewline
124 & 0.540362850338844 & 0.919274299322312 & 0.459637149661156 \tabularnewline
125 & 0.489747934781908 & 0.979495869563816 & 0.510252065218092 \tabularnewline
126 & 0.471020193532276 & 0.942040387064553 & 0.528979806467724 \tabularnewline
127 & 0.491548330626851 & 0.983096661253702 & 0.508451669373149 \tabularnewline
128 & 0.864171871402078 & 0.271656257195844 & 0.135828128597922 \tabularnewline
129 & 0.844434706804224 & 0.311130586391552 & 0.155565293195776 \tabularnewline
130 & 0.850469509525463 & 0.299060980949074 & 0.149530490474537 \tabularnewline
131 & 0.803608144973548 & 0.392783710052904 & 0.196391855026452 \tabularnewline
132 & 0.93778487238228 & 0.124430255235441 & 0.0622151276177203 \tabularnewline
133 & 0.926070351852993 & 0.147859296294013 & 0.0739296481470066 \tabularnewline
134 & 0.975494753211318 & 0.0490104935773641 & 0.024505246788682 \tabularnewline
135 & 0.996007585127009 & 0.00798482974598095 & 0.00399241487299047 \tabularnewline
136 & 0.998722599619987 & 0.0025548007600257 & 0.00127740038001285 \tabularnewline
137 & 0.997486109626551 & 0.00502778074689703 & 0.00251389037344851 \tabularnewline
138 & 0.99882259821691 & 0.00235480356618018 & 0.00117740178309009 \tabularnewline
139 & 0.999995612999285 & 8.77400143043363e-06 & 4.38700071521682e-06 \tabularnewline
140 & 0.999986380059772 & 2.72398804553444e-05 & 1.36199402276722e-05 \tabularnewline
141 & 0.999999003346349 & 1.99330730271526e-06 & 9.96653651357631e-07 \tabularnewline
142 & 0.999997653266579 & 4.69346684108233e-06 & 2.34673342054116e-06 \tabularnewline
143 & 0.999983303820378 & 3.33923592429822e-05 & 1.66961796214911e-05 \tabularnewline
144 & 0.999999999923258 & 1.53483870818822e-10 & 7.6741935409411e-11 \tabularnewline
145 & 0.999999994788084 & 1.04238329990004e-08 & 5.21191649950018e-09 \tabularnewline
146 & 1 & 1.65326927899847e-46 & 8.26634639499237e-47 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159458&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]0.618361162889298[/C][C]0.763277674221404[/C][C]0.381638837110702[/C][/ROW]
[ROW][C]19[/C][C]0.91274747289372[/C][C]0.17450505421256[/C][C]0.0872525271062802[/C][/ROW]
[ROW][C]20[/C][C]0.877335037554443[/C][C]0.245329924891114[/C][C]0.122664962445557[/C][/ROW]
[ROW][C]21[/C][C]0.813964192328944[/C][C]0.372071615342112[/C][C]0.186035807671056[/C][/ROW]
[ROW][C]22[/C][C]0.760940527343983[/C][C]0.478118945312035[/C][C]0.239059472656017[/C][/ROW]
[ROW][C]23[/C][C]0.686351032641471[/C][C]0.627297934717058[/C][C]0.313648967358529[/C][/ROW]
[ROW][C]24[/C][C]0.698008886831417[/C][C]0.603982226337167[/C][C]0.301991113168583[/C][/ROW]
[ROW][C]25[/C][C]0.616358101843356[/C][C]0.767283796313288[/C][C]0.383641898156644[/C][/ROW]
[ROW][C]26[/C][C]0.864740685360863[/C][C]0.270518629278274[/C][C]0.135259314639137[/C][/ROW]
[ROW][C]27[/C][C]0.829552608727353[/C][C]0.340894782545294[/C][C]0.170447391272647[/C][/ROW]
[ROW][C]28[/C][C]0.785141962858687[/C][C]0.429716074282627[/C][C]0.214858037141313[/C][/ROW]
[ROW][C]29[/C][C]0.73732916845546[/C][C]0.525341663089081[/C][C]0.26267083154454[/C][/ROW]
[ROW][C]30[/C][C]0.67529467540823[/C][C]0.64941064918354[/C][C]0.32470532459177[/C][/ROW]
[ROW][C]31[/C][C]0.610461728234451[/C][C]0.779076543531099[/C][C]0.389538271765549[/C][/ROW]
[ROW][C]32[/C][C]0.540291368557173[/C][C]0.919417262885654[/C][C]0.459708631442827[/C][/ROW]
[ROW][C]33[/C][C]0.547347185839377[/C][C]0.905305628321245[/C][C]0.452652814160622[/C][/ROW]
[ROW][C]34[/C][C]0.65150502969975[/C][C]0.6969899406005[/C][C]0.34849497030025[/C][/ROW]
[ROW][C]35[/C][C]0.594853764278023[/C][C]0.810292471443954[/C][C]0.405146235721977[/C][/ROW]
[ROW][C]36[/C][C]0.530922998570935[/C][C]0.93815400285813[/C][C]0.469077001429065[/C][/ROW]
[ROW][C]37[/C][C]0.524871347561916[/C][C]0.950257304876169[/C][C]0.475128652438084[/C][/ROW]
[ROW][C]38[/C][C]0.54487203072787[/C][C]0.91025593854426[/C][C]0.45512796927213[/C][/ROW]
[ROW][C]39[/C][C]0.553141161335264[/C][C]0.893717677329472[/C][C]0.446858838664736[/C][/ROW]
[ROW][C]40[/C][C]0.531688161120043[/C][C]0.936623677759913[/C][C]0.468311838879957[/C][/ROW]
[ROW][C]41[/C][C]0.472724081125244[/C][C]0.945448162250488[/C][C]0.527275918874756[/C][/ROW]
[ROW][C]42[/C][C]0.427605619025404[/C][C]0.855211238050808[/C][C]0.572394380974596[/C][/ROW]
[ROW][C]43[/C][C]0.381763716022994[/C][C]0.763527432045988[/C][C]0.618236283977006[/C][/ROW]
[ROW][C]44[/C][C]0.326698510982954[/C][C]0.653397021965908[/C][C]0.673301489017046[/C][/ROW]
[ROW][C]45[/C][C]0.299031531700544[/C][C]0.598063063401089[/C][C]0.700968468299456[/C][/ROW]
[ROW][C]46[/C][C]0.274791523459947[/C][C]0.549583046919894[/C][C]0.725208476540053[/C][/ROW]
[ROW][C]47[/C][C]0.2500936768463[/C][C]0.500187353692599[/C][C]0.7499063231537[/C][/ROW]
[ROW][C]48[/C][C]0.235364713260393[/C][C]0.470729426520787[/C][C]0.764635286739607[/C][/ROW]
[ROW][C]49[/C][C]0.263244966843125[/C][C]0.52648993368625[/C][C]0.736755033156875[/C][/ROW]
[ROW][C]50[/C][C]0.230647267022327[/C][C]0.461294534044654[/C][C]0.769352732977673[/C][/ROW]
[ROW][C]51[/C][C]0.200354133012198[/C][C]0.400708266024396[/C][C]0.799645866987802[/C][/ROW]
[ROW][C]52[/C][C]0.196526707442818[/C][C]0.393053414885636[/C][C]0.803473292557182[/C][/ROW]
[ROW][C]53[/C][C]0.256427985889328[/C][C]0.512855971778656[/C][C]0.743572014110672[/C][/ROW]
[ROW][C]54[/C][C]0.253114264489698[/C][C]0.506228528979396[/C][C]0.746885735510302[/C][/ROW]
[ROW][C]55[/C][C]0.220728288528266[/C][C]0.441456577056532[/C][C]0.779271711471734[/C][/ROW]
[ROW][C]56[/C][C]0.188307722842264[/C][C]0.376615445684528[/C][C]0.811692277157736[/C][/ROW]
[ROW][C]57[/C][C]0.207527982158418[/C][C]0.415055964316837[/C][C]0.792472017841582[/C][/ROW]
[ROW][C]58[/C][C]0.195235789719645[/C][C]0.390471579439291[/C][C]0.804764210280355[/C][/ROW]
[ROW][C]59[/C][C]0.211491578539782[/C][C]0.422983157079563[/C][C]0.788508421460218[/C][/ROW]
[ROW][C]60[/C][C]0.18131359753792[/C][C]0.36262719507584[/C][C]0.81868640246208[/C][/ROW]
[ROW][C]61[/C][C]0.161302853047292[/C][C]0.322605706094585[/C][C]0.838697146952708[/C][/ROW]
[ROW][C]62[/C][C]0.23286008491825[/C][C]0.4657201698365[/C][C]0.76713991508175[/C][/ROW]
[ROW][C]63[/C][C]0.415867132297138[/C][C]0.831734264594276[/C][C]0.584132867702862[/C][/ROW]
[ROW][C]64[/C][C]0.369172495228051[/C][C]0.738344990456102[/C][C]0.630827504771949[/C][/ROW]
[ROW][C]65[/C][C]0.330527034334817[/C][C]0.661054068669635[/C][C]0.669472965665183[/C][/ROW]
[ROW][C]66[/C][C]0.32125185137781[/C][C]0.642503702755619[/C][C]0.67874814862219[/C][/ROW]
[ROW][C]67[/C][C]0.281137047136208[/C][C]0.562274094272416[/C][C]0.718862952863792[/C][/ROW]
[ROW][C]68[/C][C]0.251663705215339[/C][C]0.503327410430677[/C][C]0.748336294784661[/C][/ROW]
[ROW][C]69[/C][C]0.2137793529218[/C][C]0.427558705843599[/C][C]0.7862206470782[/C][/ROW]
[ROW][C]70[/C][C]0.189773100816235[/C][C]0.379546201632469[/C][C]0.810226899183765[/C][/ROW]
[ROW][C]71[/C][C]0.18041695495101[/C][C]0.36083390990202[/C][C]0.81958304504899[/C][/ROW]
[ROW][C]72[/C][C]0.154915103961886[/C][C]0.309830207923772[/C][C]0.845084896038114[/C][/ROW]
[ROW][C]73[/C][C]0.195008922924087[/C][C]0.390017845848174[/C][C]0.804991077075913[/C][/ROW]
[ROW][C]74[/C][C]0.218784208698254[/C][C]0.437568417396508[/C][C]0.781215791301746[/C][/ROW]
[ROW][C]75[/C][C]0.188306301263606[/C][C]0.376612602527213[/C][C]0.811693698736394[/C][/ROW]
[ROW][C]76[/C][C]0.159197797506701[/C][C]0.318395595013403[/C][C]0.840802202493299[/C][/ROW]
[ROW][C]77[/C][C]0.141706636732241[/C][C]0.283413273464483[/C][C]0.858293363267759[/C][/ROW]
[ROW][C]78[/C][C]0.161233915582314[/C][C]0.322467831164627[/C][C]0.838766084417686[/C][/ROW]
[ROW][C]79[/C][C]0.146863530727005[/C][C]0.293727061454011[/C][C]0.853136469272995[/C][/ROW]
[ROW][C]80[/C][C]0.127173355944544[/C][C]0.254346711889088[/C][C]0.872826644055456[/C][/ROW]
[ROW][C]81[/C][C]0.107060620011536[/C][C]0.214121240023073[/C][C]0.892939379988464[/C][/ROW]
[ROW][C]82[/C][C]0.121945791394891[/C][C]0.243891582789783[/C][C]0.878054208605109[/C][/ROW]
[ROW][C]83[/C][C]0.150192365124178[/C][C]0.300384730248357[/C][C]0.849807634875822[/C][/ROW]
[ROW][C]84[/C][C]0.139139590074227[/C][C]0.278279180148454[/C][C]0.860860409925773[/C][/ROW]
[ROW][C]85[/C][C]0.121093198370228[/C][C]0.242186396740455[/C][C]0.878906801629772[/C][/ROW]
[ROW][C]86[/C][C]0.102927149321281[/C][C]0.205854298642563[/C][C]0.897072850678719[/C][/ROW]
[ROW][C]87[/C][C]0.0912911974712086[/C][C]0.182582394942417[/C][C]0.908708802528791[/C][/ROW]
[ROW][C]88[/C][C]0.0746080517466695[/C][C]0.149216103493339[/C][C]0.92539194825333[/C][/ROW]
[ROW][C]89[/C][C]0.376477161832555[/C][C]0.752954323665109[/C][C]0.623522838167445[/C][/ROW]
[ROW][C]90[/C][C]0.342198419238594[/C][C]0.684396838477188[/C][C]0.657801580761406[/C][/ROW]
[ROW][C]91[/C][C]0.338284731507848[/C][C]0.676569463015696[/C][C]0.661715268492152[/C][/ROW]
[ROW][C]92[/C][C]0.318963931889129[/C][C]0.637927863778258[/C][C]0.681036068110871[/C][/ROW]
[ROW][C]93[/C][C]0.27675991215679[/C][C]0.553519824313579[/C][C]0.72324008784321[/C][/ROW]
[ROW][C]94[/C][C]0.301829387762707[/C][C]0.603658775525413[/C][C]0.698170612237293[/C][/ROW]
[ROW][C]95[/C][C]0.261109580488147[/C][C]0.522219160976294[/C][C]0.738890419511853[/C][/ROW]
[ROW][C]96[/C][C]0.234485480020714[/C][C]0.468970960041429[/C][C]0.765514519979286[/C][/ROW]
[ROW][C]97[/C][C]0.204169313614285[/C][C]0.40833862722857[/C][C]0.795830686385715[/C][/ROW]
[ROW][C]98[/C][C]0.229874730372843[/C][C]0.459749460745685[/C][C]0.770125269627157[/C][/ROW]
[ROW][C]99[/C][C]0.303825327973279[/C][C]0.607650655946557[/C][C]0.696174672026721[/C][/ROW]
[ROW][C]100[/C][C]0.270142303370629[/C][C]0.540284606741258[/C][C]0.729857696629371[/C][/ROW]
[ROW][C]101[/C][C]0.233818825224902[/C][C]0.467637650449805[/C][C]0.766181174775098[/C][/ROW]
[ROW][C]102[/C][C]0.200107630914543[/C][C]0.400215261829087[/C][C]0.799892369085456[/C][/ROW]
[ROW][C]103[/C][C]0.431999740201223[/C][C]0.863999480402447[/C][C]0.568000259798777[/C][/ROW]
[ROW][C]104[/C][C]0.391476630166491[/C][C]0.782953260332981[/C][C]0.608523369833509[/C][/ROW]
[ROW][C]105[/C][C]0.53807570678761[/C][C]0.923848586424779[/C][C]0.46192429321239[/C][/ROW]
[ROW][C]106[/C][C]0.738469070518045[/C][C]0.523061858963911[/C][C]0.261530929481955[/C][/ROW]
[ROW][C]107[/C][C]0.784144635325232[/C][C]0.431710729349535[/C][C]0.215855364674768[/C][/ROW]
[ROW][C]108[/C][C]0.744410844376138[/C][C]0.511178311247724[/C][C]0.255589155623862[/C][/ROW]
[ROW][C]109[/C][C]0.805331526823693[/C][C]0.389336946352613[/C][C]0.194668473176307[/C][/ROW]
[ROW][C]110[/C][C]0.768314757124751[/C][C]0.463370485750498[/C][C]0.231685242875249[/C][/ROW]
[ROW][C]111[/C][C]0.757031340249887[/C][C]0.485937319500226[/C][C]0.242968659750113[/C][/ROW]
[ROW][C]112[/C][C]0.730470359741654[/C][C]0.539059280516692[/C][C]0.269529640258346[/C][/ROW]
[ROW][C]113[/C][C]0.692250329803474[/C][C]0.615499340393052[/C][C]0.307749670196526[/C][/ROW]
[ROW][C]114[/C][C]0.813156236754157[/C][C]0.373687526491686[/C][C]0.186843763245843[/C][/ROW]
[ROW][C]115[/C][C]0.778730312954056[/C][C]0.442539374091889[/C][C]0.221269687045944[/C][/ROW]
[ROW][C]116[/C][C]0.848844286387417[/C][C]0.302311427225166[/C][C]0.151155713612583[/C][/ROW]
[ROW][C]117[/C][C]0.815335540572317[/C][C]0.369328918855366[/C][C]0.184664459427683[/C][/ROW]
[ROW][C]118[/C][C]0.777829495677341[/C][C]0.444341008645318[/C][C]0.222170504322659[/C][/ROW]
[ROW][C]119[/C][C]0.754198004441272[/C][C]0.491603991117456[/C][C]0.245801995558728[/C][/ROW]
[ROW][C]120[/C][C]0.714277157749182[/C][C]0.571445684501636[/C][C]0.285722842250818[/C][/ROW]
[ROW][C]121[/C][C]0.685711698332201[/C][C]0.628576603335599[/C][C]0.314288301667799[/C][/ROW]
[ROW][C]122[/C][C]0.639564336157199[/C][C]0.720871327685602[/C][C]0.360435663842801[/C][/ROW]
[ROW][C]123[/C][C]0.584423388021479[/C][C]0.831153223957042[/C][C]0.415576611978521[/C][/ROW]
[ROW][C]124[/C][C]0.540362850338844[/C][C]0.919274299322312[/C][C]0.459637149661156[/C][/ROW]
[ROW][C]125[/C][C]0.489747934781908[/C][C]0.979495869563816[/C][C]0.510252065218092[/C][/ROW]
[ROW][C]126[/C][C]0.471020193532276[/C][C]0.942040387064553[/C][C]0.528979806467724[/C][/ROW]
[ROW][C]127[/C][C]0.491548330626851[/C][C]0.983096661253702[/C][C]0.508451669373149[/C][/ROW]
[ROW][C]128[/C][C]0.864171871402078[/C][C]0.271656257195844[/C][C]0.135828128597922[/C][/ROW]
[ROW][C]129[/C][C]0.844434706804224[/C][C]0.311130586391552[/C][C]0.155565293195776[/C][/ROW]
[ROW][C]130[/C][C]0.850469509525463[/C][C]0.299060980949074[/C][C]0.149530490474537[/C][/ROW]
[ROW][C]131[/C][C]0.803608144973548[/C][C]0.392783710052904[/C][C]0.196391855026452[/C][/ROW]
[ROW][C]132[/C][C]0.93778487238228[/C][C]0.124430255235441[/C][C]0.0622151276177203[/C][/ROW]
[ROW][C]133[/C][C]0.926070351852993[/C][C]0.147859296294013[/C][C]0.0739296481470066[/C][/ROW]
[ROW][C]134[/C][C]0.975494753211318[/C][C]0.0490104935773641[/C][C]0.024505246788682[/C][/ROW]
[ROW][C]135[/C][C]0.996007585127009[/C][C]0.00798482974598095[/C][C]0.00399241487299047[/C][/ROW]
[ROW][C]136[/C][C]0.998722599619987[/C][C]0.0025548007600257[/C][C]0.00127740038001285[/C][/ROW]
[ROW][C]137[/C][C]0.997486109626551[/C][C]0.00502778074689703[/C][C]0.00251389037344851[/C][/ROW]
[ROW][C]138[/C][C]0.99882259821691[/C][C]0.00235480356618018[/C][C]0.00117740178309009[/C][/ROW]
[ROW][C]139[/C][C]0.999995612999285[/C][C]8.77400143043363e-06[/C][C]4.38700071521682e-06[/C][/ROW]
[ROW][C]140[/C][C]0.999986380059772[/C][C]2.72398804553444e-05[/C][C]1.36199402276722e-05[/C][/ROW]
[ROW][C]141[/C][C]0.999999003346349[/C][C]1.99330730271526e-06[/C][C]9.96653651357631e-07[/C][/ROW]
[ROW][C]142[/C][C]0.999997653266579[/C][C]4.69346684108233e-06[/C][C]2.34673342054116e-06[/C][/ROW]
[ROW][C]143[/C][C]0.999983303820378[/C][C]3.33923592429822e-05[/C][C]1.66961796214911e-05[/C][/ROW]
[ROW][C]144[/C][C]0.999999999923258[/C][C]1.53483870818822e-10[/C][C]7.6741935409411e-11[/C][/ROW]
[ROW][C]145[/C][C]0.999999994788084[/C][C]1.04238329990004e-08[/C][C]5.21191649950018e-09[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]1.65326927899847e-46[/C][C]8.26634639499237e-47[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159458&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159458&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
180.6183611628892980.7632776742214040.381638837110702
190.912747472893720.174505054212560.0872525271062802
200.8773350375544430.2453299248911140.122664962445557
210.8139641923289440.3720716153421120.186035807671056
220.7609405273439830.4781189453120350.239059472656017
230.6863510326414710.6272979347170580.313648967358529
240.6980088868314170.6039822263371670.301991113168583
250.6163581018433560.7672837963132880.383641898156644
260.8647406853608630.2705186292782740.135259314639137
270.8295526087273530.3408947825452940.170447391272647
280.7851419628586870.4297160742826270.214858037141313
290.737329168455460.5253416630890810.26267083154454
300.675294675408230.649410649183540.32470532459177
310.6104617282344510.7790765435310990.389538271765549
320.5402913685571730.9194172628856540.459708631442827
330.5473471858393770.9053056283212450.452652814160622
340.651505029699750.69698994060050.34849497030025
350.5948537642780230.8102924714439540.405146235721977
360.5309229985709350.938154002858130.469077001429065
370.5248713475619160.9502573048761690.475128652438084
380.544872030727870.910255938544260.45512796927213
390.5531411613352640.8937176773294720.446858838664736
400.5316881611200430.9366236777599130.468311838879957
410.4727240811252440.9454481622504880.527275918874756
420.4276056190254040.8552112380508080.572394380974596
430.3817637160229940.7635274320459880.618236283977006
440.3266985109829540.6533970219659080.673301489017046
450.2990315317005440.5980630634010890.700968468299456
460.2747915234599470.5495830469198940.725208476540053
470.25009367684630.5001873536925990.7499063231537
480.2353647132603930.4707294265207870.764635286739607
490.2632449668431250.526489933686250.736755033156875
500.2306472670223270.4612945340446540.769352732977673
510.2003541330121980.4007082660243960.799645866987802
520.1965267074428180.3930534148856360.803473292557182
530.2564279858893280.5128559717786560.743572014110672
540.2531142644896980.5062285289793960.746885735510302
550.2207282885282660.4414565770565320.779271711471734
560.1883077228422640.3766154456845280.811692277157736
570.2075279821584180.4150559643168370.792472017841582
580.1952357897196450.3904715794392910.804764210280355
590.2114915785397820.4229831570795630.788508421460218
600.181313597537920.362627195075840.81868640246208
610.1613028530472920.3226057060945850.838697146952708
620.232860084918250.46572016983650.76713991508175
630.4158671322971380.8317342645942760.584132867702862
640.3691724952280510.7383449904561020.630827504771949
650.3305270343348170.6610540686696350.669472965665183
660.321251851377810.6425037027556190.67874814862219
670.2811370471362080.5622740942724160.718862952863792
680.2516637052153390.5033274104306770.748336294784661
690.21377935292180.4275587058435990.7862206470782
700.1897731008162350.3795462016324690.810226899183765
710.180416954951010.360833909902020.81958304504899
720.1549151039618860.3098302079237720.845084896038114
730.1950089229240870.3900178458481740.804991077075913
740.2187842086982540.4375684173965080.781215791301746
750.1883063012636060.3766126025272130.811693698736394
760.1591977975067010.3183955950134030.840802202493299
770.1417066367322410.2834132734644830.858293363267759
780.1612339155823140.3224678311646270.838766084417686
790.1468635307270050.2937270614540110.853136469272995
800.1271733559445440.2543467118890880.872826644055456
810.1070606200115360.2141212400230730.892939379988464
820.1219457913948910.2438915827897830.878054208605109
830.1501923651241780.3003847302483570.849807634875822
840.1391395900742270.2782791801484540.860860409925773
850.1210931983702280.2421863967404550.878906801629772
860.1029271493212810.2058542986425630.897072850678719
870.09129119747120860.1825823949424170.908708802528791
880.07460805174666950.1492161034933390.92539194825333
890.3764771618325550.7529543236651090.623522838167445
900.3421984192385940.6843968384771880.657801580761406
910.3382847315078480.6765694630156960.661715268492152
920.3189639318891290.6379278637782580.681036068110871
930.276759912156790.5535198243135790.72324008784321
940.3018293877627070.6036587755254130.698170612237293
950.2611095804881470.5222191609762940.738890419511853
960.2344854800207140.4689709600414290.765514519979286
970.2041693136142850.408338627228570.795830686385715
980.2298747303728430.4597494607456850.770125269627157
990.3038253279732790.6076506559465570.696174672026721
1000.2701423033706290.5402846067412580.729857696629371
1010.2338188252249020.4676376504498050.766181174775098
1020.2001076309145430.4002152618290870.799892369085456
1030.4319997402012230.8639994804024470.568000259798777
1040.3914766301664910.7829532603329810.608523369833509
1050.538075706787610.9238485864247790.46192429321239
1060.7384690705180450.5230618589639110.261530929481955
1070.7841446353252320.4317107293495350.215855364674768
1080.7444108443761380.5111783112477240.255589155623862
1090.8053315268236930.3893369463526130.194668473176307
1100.7683147571247510.4633704857504980.231685242875249
1110.7570313402498870.4859373195002260.242968659750113
1120.7304703597416540.5390592805166920.269529640258346
1130.6922503298034740.6154993403930520.307749670196526
1140.8131562367541570.3736875264916860.186843763245843
1150.7787303129540560.4425393740918890.221269687045944
1160.8488442863874170.3023114272251660.151155713612583
1170.8153355405723170.3693289188553660.184664459427683
1180.7778294956773410.4443410086453180.222170504322659
1190.7541980044412720.4916039911174560.245801995558728
1200.7142771577491820.5714456845016360.285722842250818
1210.6857116983322010.6285766033355990.314288301667799
1220.6395643361571990.7208713276856020.360435663842801
1230.5844233880214790.8311532239570420.415576611978521
1240.5403628503388440.9192742993223120.459637149661156
1250.4897479347819080.9794958695638160.510252065218092
1260.4710201935322760.9420403870645530.528979806467724
1270.4915483306268510.9830966612537020.508451669373149
1280.8641718714020780.2716562571958440.135828128597922
1290.8444347068042240.3111305863915520.155565293195776
1300.8504695095254630.2990609809490740.149530490474537
1310.8036081449735480.3927837100529040.196391855026452
1320.937784872382280.1244302552354410.0622151276177203
1330.9260703518529930.1478592962940130.0739296481470066
1340.9754947532113180.04901049357736410.024505246788682
1350.9960075851270090.007984829745980950.00399241487299047
1360.9987225996199870.00255480076002570.00127740038001285
1370.9974861096265510.005027780746897030.00251389037344851
1380.998822598216910.002354803566180180.00117740178309009
1390.9999956129992858.77400143043363e-064.38700071521682e-06
1400.9999863800597722.72398804553444e-051.36199402276722e-05
1410.9999990033463491.99330730271526e-069.96653651357631e-07
1420.9999976532665794.69346684108233e-062.34673342054116e-06
1430.9999833038203783.33923592429822e-051.66961796214911e-05
1440.9999999999232581.53483870818822e-107.6741935409411e-11
1450.9999999947880841.04238329990004e-085.21191649950018e-09
14611.65326927899847e-468.26634639499237e-47






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level120.0930232558139535NOK
5% type I error level130.10077519379845NOK
10% type I error level130.10077519379845NOK

\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 & 12 & 0.0930232558139535 & NOK \tabularnewline
5% type I error level & 13 & 0.10077519379845 & NOK \tabularnewline
10% type I error level & 13 & 0.10077519379845 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159458&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]12[/C][C]0.0930232558139535[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]13[/C][C]0.10077519379845[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]13[/C][C]0.10077519379845[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159458&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159458&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 level120.0930232558139535NOK
5% type I error level130.10077519379845NOK
10% type I error level130.10077519379845NOK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 10 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 10 ; 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')
}