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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 computationSat, 17 Dec 2011 09:41:46 -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/17/t1324133027fzor37098iu5wom.htm/, Retrieved Fri, 19 Apr 2024 11:03:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=156353, Retrieved Fri, 19 Apr 2024 11:03:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact99

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [] [2010-12-05 17:44:33] [b98453cac15ba1066b407e146608df68]
- RMPD  [Kendall tau Correlation Matrix] [Workshop 10 Kenda...] [2011-12-09 18:02:48] [de8512d9b386046939a89973b76869e3]
- RM D    [Multiple Regression] [Workshop 10 MLR] [2011-12-09 18:14:48] [de8512d9b386046939a89973b76869e3]
- R  D      [Multiple Regression] [Workshop 10 MLR] [2011-12-09 18:19:17] [de8512d9b386046939a89973b76869e3]
-   PD          [Multiple Regression] [] [2011-12-17 14:41:46] [43f1c1fe5c2aaa4d7bd6f731e1a494da] [Current]
-   PD            [Multiple Regression] [] [2011-12-17 16:19:05] [1dc3906a3b5a6ec06dc921f387100c9e]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1724	270018	69	476	90	3	96	38	144	116	140824	32033	186099	165	165
1209	179444	64	429	63	4	71	34	133	127	110459	20654	113854	135	132
1844	222373	69	673	59	16	70	42	162	106	105079	16346	99776	121	121
2683	218443	104	1137	135	2	134	38	148	133	112098	35926	106194	148	145
1149	162874	51	348	48	1	61	27	88	64	43929	10621	100792	73	71
631	70849	28	179	46	3	8	35	129	89	76173	10024	47552	49	47
4513	498732	117	2201	109	0	149	33	128	122	187326	43068	250931	185	177
381	33186	19	111	46	0	1	18	67	22	22807	1271	6853	5	5
1997	207822	57	735	75	7	74	34	132	117	144408	34416	115466	125	124
1758	213274	44	595	72	0	82	33	120	82	66485	20318	110896	93	92
2064	296074	107	776	79	0	92	43	158	139	79089	24409	169351	154	149
2128	237633	114	660	61	7	117	55	210	184	81625	20648	94853	98	93
1659	164107	67	633	60	8	50	37	122	113	68788	12347	72591	70	70
2934	358752	77	1163	114	4	139	52	179	162	103297	21857	101345	148	148
1944	222781	83	622	46	10	73	43	162	87	69446	11034	113713	100	100
4763	369889	176	1650	127	0	168	59	223	199	114948	33433	165354	150	142
2122	305704	68	746	58	6	113	36	140	139	167949	35902	164263	197	194
2956	322896	157	1157	90	4	98	39	144	92	125081	22355	135213	114	113
1438	176082	55	507	41	3	103	29	111	85	125818	31219	111669	169	162
2320	263411	85	683	62	8	135	49	191	185	136588	21983	134163	200	186
2471	271965	69	828	99	0	123	45	171	148	112431	40085	140303	148	147
2769	425544	103	1203	101	1	86	39	144	144	103037	18507	150773	140	137
1442	179306	41	461	62	5	66	25	89	84	82317	16278	111848	74	71
1717	189897	54	601	65	9	103	52	208	208	118906	24662	102509	128	123
3202	219420	114	1196	150	1	138	41	153	144	83515	31452	96785	140	134
2592	207280	115	960	72	0	86	38	146	139	104581	32580	116136	116	115
2824	267198	126	1061	91	5	99	41	158	127	103129	22883	158376	147	138
1968	270750	85	617	73	0	117	43	154	148	83243	27652	153990	132	125
1495	155915	51	559	53	0	57	32	117	99	37110	9845	64057	70	66
2733	327474	64	1026	140	0	125	41	158	135	113344	20190	230054	144	137
2272	278019	74	908	49	3	123	45	175	165	139165	46201	184531	155	152
1830	204039	76	615	83	6	44	49	182	146	86652	10971	114198	165	159
2090	318563	83	779	53	1	133	48	185	178	112302	34811	198299	161	159
945	97717	36	310	40	4	43	37	141	137	69652	3029	33750	31	31
3092	369331	93	1198	72	4	132	39	151	148	119442	38941	189723	199	185
2764	273950	56	1186	87	0	83	42	159	127	69867	4958	100826	78	78
3657	422946	119	1317	74	0	112	43	158	148	101629	32344	188355	121	117
1842	215710	82	611	67	2	79	36	139	89	70168	19433	104470	112	109
934	115469	32	276	36	1	33	17	55	46	31081	12558	58391	41	41
3315	336047	190	1174	45	2	124	39	151	143	103925	36524	164808	158	149
3246	324178	79	1490	42	10	123	39	145	122	92622	26041	134097	123	123
1578	166266	65	623	75	9	67	41	148	111	79011	16637	80238	104	103
1735	195153	73	635	82	5	75	36	115	108	93487	28395	133252	94	87
1714	173510	60	470	85	6	68	42	157	126	64520	16747	54518	73	71
2496	153778	82	1022	82	1	50	45	73	45	93473	9105	121850	52	51
5501	455168	151	2068	848	2	101	41	147	131	114360	11941	79367	71	70
918	78800	42	330	57	2	20	26	82	66	33032	7935	56968	21	21
2228	208051	76	648	80	0	101	52	201	180	96125	19499	106314	155	155
3942	334657	116	1342	116	10	142	47	181	165	151911	22938	191889	174	172
2081	175523	54	868	68	3	99	45	164	146	89256	25314	104864	136	133
1816	213060	73	559	48	0	94	40	158	137	95671	28524	160791	128	125
496	24188	24	218	20	0	8	4	12	7	5950	2694	15049	7	7
2531	372238	308	833	81	8	85	44	163	157	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
3027	279012	58	1108	125	1	97	37	134	120	100087	32982	129711	137	133
2433	302218	84	642	80	5	122	56	210	208	169707	38975	210012	174	169
3574	323485	179	1079	220	5	115	39	145	137	150491	42721	194679	257	256
2606	339837	138	1046	63	0	148	42	153	150	120192	41455	197680	207	190
2175	252529	83	822	77	12	89	36	139	127	95893	23923	81180	103	100
3937	370483	139	1298	65	10	154	46	178	161	151715	26719	197765	171	171
3161	303406	117	1143	146	12	139	28	101	73	176225	53405	214738	279	267
2670	233632	106	1073	72	11	77	43	169	97	59900	12526	96252	83	80
2610	264889	88	931	59	8	92	42	163	142	104767	26584	124527	130	126
1426	228595	66	557	58	2	52	37	139	125	114799	37062	153242	131	132
1646	216027	65	436	58	0	96	30	116	87	72128	25696	145707	126	121
1855	187965	127	562	54	6	79	35	137	128	143592	24634	113963	158	156
2712	237323	144	824	89	9	85	44	167	148	89626	27269	134904	138	133
2277	232765	78	834	78	2	135	36	135	116	131072	25270	114268	200	199
1675	175699	68	621	62	5	143	28	102	89	126817	24634	94333	104	98
2537	239314	68	865	64	13	99	45	173	154	81351	17828	102204	111	109
893	73566	32	385	39	6	22	23	88	67	22618	3007	23824	26	25
2189	242585	83	716	58	7	78	45	175	171	88977	20065	111563	115	113
1694	187167	52	705	94	2	79	38	133	90	92059	24648	91313	127	126
1948	191920	62	683	61	1	127	38	148	133	81897	21588	89770	140	137
2314	359644	84	982	95	4	140	45	166	143	108146	25217	100125	121	121
2645	341637	90	1056	48	3	130	36	140	133	126372	30927	165278	183	178
1804	206059	106	522	50	6	78	41	154	125	249771	18487	181712	68	63
2250	201783	61	690	58	2	133	38	148	134	71154	18050	80906	112	109
1787	182231	64	644	67	0	83	37	134	110	71571	17696	75881	103	101
1678	153613	46	622	41	1	62	28	109	89	55918	17326	83963	63	61
3843	447353	115	1216	114	0	147	45	175	138	160141	39361	175721	166	157
1369	145943	69	653	45	5	30	26	99	99	38692	9648	68580	38	38
2306	280343	103	656	57	2	117	44	122	92	102812	26759	136323	163	159
870	80953	25	437	31	0	49	8	28	27	56622	7905	55792	59	58
1966	150216	54	822	175	0	52	27	101	77	15986	4527	25157	27	27
1337	156923	57	390	68	6	71	36	132	130	123534	41517	100922	108	108
3727	365370	195	1467	278	1	72	37	143	137	108535	21261	118845	88	83
2616	318651	112	907	91	0	130	57	206	122	93879	36099	170492	92	88
3085	179797	104	1044	72	1	165	45	171	159	144551	39039	81716	170	164
2312	251466	89	786	58	1	76	37	138	85	56750	13841	115750	98	96
2136	254506	75	655	71	3	160	38	141	131	127654	23841	105590	205	192
1808	185890	62	590	86	9	48	31	114	90	65594	8589	92795	96	94
2992	263577	74	1072	89	1	132	36	140	135	59938	15049	82390	107	107
2474	314255	80	947	134	4	73	36	140	132	146975	39038	135599	150	144
1624	189252	36	555	64	3	83	36	140	139	143372	30391	111542	123	123
1606	222504	50	552	72	5	94	35	127	127	168553	39932	162519	176	170
2091	285198	78	771	61	0	158	39	141	104	183500	43840	211381	213	210
3845	368878	147	1263	126	12	151	60	231	229	165986	43146	189944	208	193
3705	397681	108	1415	73	13	165	30	114	106	184923	50099	226168	307	297
2676	287015	134	846	83	8	117	51	198	176	140358	40312	117495	125	125
2295	285330	64	838	85	0	145	41	155	130	149959	32616	195894	208	204
1997	186856	177	640	116	0	73	36	138	59	57224	11338	80684	73	70
602	43287	14	214	43	4	13	19	71	64	43750	7409	19630	49	49
2146	185468	80	716	85	4	89	23	84	36	48029	18213	88634	82	82
2131	219475	139	749	72	0	92	40	151	88	104978	45873	139292	206	205
2549	259692	47	1140	110	0	128	40	155	125	100046	39844	128602	112	111
2649	301614	88	1030	55	0	169	40	150	124	101047	28317	135848	139	135
1110	121726	67	356	44	0	28	30	112	83	197426	24797	178377	60	59
3102	154165	88	906	79	0	116	41	161	127	160902	7471	106330	70	70
1860	306952	67	606	58	4	76	40	149	143	147172	27259	178303	112	108
2295	297982	87	684	70	0	147	45	164	115	109432	23201	116938	142	141
398	23623	11	156	9	0	12	1	0	0	1168	238	5841	11	11
2205	195817	73	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
1596	163766	48	457	107	0	83	45	169	119	45724	9935	74151	132	101
2949	384053	114	1162	63	1	135	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
3397	311281	113	1211	153	3	106	39	146	137	89506	24347	160501	125	119
2089	240153	65	696	79	7	76	48	183	163	135356	27111	91502	121	118
1638	174892	88	485	112	13	55	48	181	146	116066	3938	24469	42	41
1685	152043	53	670	47	3	44	29	107	84	144244	17416	88229	111	107
568	38214	34	276	52	0	16	8	27	21	8773	1888	13983	16	16
1907	198094	40	659	113	2	66	43	163	151	102153	18700	80716	70	69
2758	353021	81	1010	115	0	137	52	198	187	117440	36809	157384	162	160
1288	196269	58	445	64	0	50	53	205	171	104128	24959	122975	173	158
3554	403932	80	1564	134	4	137	48	187	167	134238	37343	191469	171	161
2387	316105	97	820	120	0	154	48	187	145	134047	21849	231257	172	165
3328	396725	123	1151	111	3	137	50	186	175	279488	49809	258287	254	246
1250	187992	35	473	49	0	71	40	151	137	79756	21654	122531	90	89
1121	102424	42	401	55	0	42	36	131	100	66089	8728	61394	50	49
2862	283950	329	947	149	4	84	40	155	150	102070	20920	86480	113	107
4023	401260	164	1429	155	4	103	46	172	163	146760	27195	195791	187	182
1721	137843	52	534	104	15	63	40	152	141	154771	1037	18284	16	16
4060	383703	130	1698	146	0	127	46	172	161	165933	42570	147581	175	173
1830	157429	76	689	76	4	55	39	143	112	64593	17672	72558	90	90
1627	236370	46	528	83	1	104	41	151	135	92280	34245	147341	140	140
2535	282399	94	897	192	1	110	46	158	124	67150	16786	114651	145	142
1807	217478	112	610	69	0	38	32	125	45	128692	20954	100187	141	126
3873	366774	116	1548	117	9	95	39	145	120	124089	16378	130332	125	123
2181	236660	88	759	67	1	121	39	145	126	125386	31852	134218	241	239
2035	173260	63	716	37	3	41	21	79	78	37238	2805	10901	16	15
2960	323545	99	955	56	11	145	45	174	136	140015	38086	145758	175	170
1915	168994	57	720	122	5	147	50	192	179	150047	21166	75767	132	123
2604	246745	86	1011	52	2	116	36	132	118	154451	34672	134969	154	151
2633	301703	105	818	64	1	185	44	159	147	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
2030	233143	83	699	58	2	65	37	133	88	84601	19354	105406	125	122
3161	368328	145	1049	115	3	130	47	185	115	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
1047	116678	42	285	89	0	37	43	152	76	52789	1813	15673	35	35
29	969	2	0	0	0	0	0	0	0	0	0	0	0	0
1757	199726	65	580	47	2	48	32	117	66	100350	17372	75882	80	72

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'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 & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156353&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156353&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156353&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
totsize[t] = + 2307.62466618815 + 7.56541345402203pageviews[t] -0.136073044053713timeinrfc[t] + 6.65623958417471logins[t] -10.9886298491094compendiumviewsinfo[t] + 76.0490323880027compendiumviewspr[t] + 2288.72188427968sharedcompendiums[t] + 2.35843761982934bloggedcomputations[t] + 327.877361851027compendiumsreviewed[t] -77.8978066473687feedbackmessagesp1[t] + 204.405506531731feedbackmessagesp120[t] + 1.11009133506433totrevisions[t] + 0.44886094377429totseconds[t] + 27.190121718513tothyperlinks[t] + 7.23814799402061totblogs[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
totsize[t] =  +  2307.62466618815 +  7.56541345402203pageviews[t] -0.136073044053713timeinrfc[t] +  6.65623958417471logins[t] -10.9886298491094compendiumviewsinfo[t] +  76.0490323880027compendiumviewspr[t] +  2288.72188427968sharedcompendiums[t] +  2.35843761982934bloggedcomputations[t] +  327.877361851027compendiumsreviewed[t] -77.8978066473687feedbackmessagesp1[t] +  204.405506531731feedbackmessagesp120[t] +  1.11009133506433totrevisions[t] +  0.44886094377429totseconds[t] +  27.190121718513tothyperlinks[t] +  7.23814799402061totblogs[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156353&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]totsize[t] =  +  2307.62466618815 +  7.56541345402203pageviews[t] -0.136073044053713timeinrfc[t] +  6.65623958417471logins[t] -10.9886298491094compendiumviewsinfo[t] +  76.0490323880027compendiumviewspr[t] +  2288.72188427968sharedcompendiums[t] +  2.35843761982934bloggedcomputations[t] +  327.877361851027compendiumsreviewed[t] -77.8978066473687feedbackmessagesp1[t] +  204.405506531731feedbackmessagesp120[t] +  1.11009133506433totrevisions[t] +  0.44886094377429totseconds[t] +  27.190121718513tothyperlinks[t] +  7.23814799402061totblogs[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156353&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156353&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
totsize[t] = + 2307.62466618815 + 7.56541345402203pageviews[t] -0.136073044053713timeinrfc[t] + 6.65623958417471logins[t] -10.9886298491094compendiumviewsinfo[t] + 76.0490323880027compendiumviewspr[t] + 2288.72188427968sharedcompendiums[t] + 2.35843761982934bloggedcomputations[t] + 327.877361851027compendiumsreviewed[t] -77.8978066473687feedbackmessagesp1[t] + 204.405506531731feedbackmessagesp120[t] + 1.11009133506433totrevisions[t] + 0.44886094377429totseconds[t] + 27.190121718513tothyperlinks[t] + 7.23814799402061totblogs[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2307.624666188155872.1245970.3930.6948960.347448
pageviews7.5654134540220312.1357510.62340.5339760.266988
timeinrfc-0.1360730440537130.068659-1.98190.0493350.024668
logins6.6562395841747171.4522240.09320.9259040.462952
compendiumviewsinfo-10.988629849109425.068197-0.43830.6617680.330884
compendiumviewspr76.049032388002741.3183861.84060.0676750.033838
sharedcompendiums2288.72188427968639.3595623.57970.0004650.000232
bloggedcomputations2.35843761982934111.969630.02110.9832230.491612
compendiumsreviewed327.877361851027880.4911630.37240.7101390.35507
feedbackmessagesp1-77.8978066473687265.411163-0.29350.7695490.384775
feedbackmessagesp120204.405506531731128.5674151.58990.1139840.056992
totrevisions1.110091335064330.3558523.11950.0021750.001088
totseconds0.448860943774290.0915264.90422e-061e-06
tothyperlinks27.190121718513573.8795860.04740.9622740.481137
totblogs7.23814799402061595.0226380.01220.9903110.495155

\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) & 2307.62466618815 & 5872.124597 & 0.393 & 0.694896 & 0.347448 \tabularnewline
pageviews & 7.56541345402203 & 12.135751 & 0.6234 & 0.533976 & 0.266988 \tabularnewline
timeinrfc & -0.136073044053713 & 0.068659 & -1.9819 & 0.049335 & 0.024668 \tabularnewline
logins & 6.65623958417471 & 71.452224 & 0.0932 & 0.925904 & 0.462952 \tabularnewline
compendiumviewsinfo & -10.9886298491094 & 25.068197 & -0.4383 & 0.661768 & 0.330884 \tabularnewline
compendiumviewspr & 76.0490323880027 & 41.318386 & 1.8406 & 0.067675 & 0.033838 \tabularnewline
sharedcompendiums & 2288.72188427968 & 639.359562 & 3.5797 & 0.000465 & 0.000232 \tabularnewline
bloggedcomputations & 2.35843761982934 & 111.96963 & 0.0211 & 0.983223 & 0.491612 \tabularnewline
compendiumsreviewed & 327.877361851027 & 880.491163 & 0.3724 & 0.710139 & 0.35507 \tabularnewline
feedbackmessagesp1 & -77.8978066473687 & 265.411163 & -0.2935 & 0.769549 & 0.384775 \tabularnewline
feedbackmessagesp120 & 204.405506531731 & 128.567415 & 1.5899 & 0.113984 & 0.056992 \tabularnewline
totrevisions & 1.11009133506433 & 0.355852 & 3.1195 & 0.002175 & 0.001088 \tabularnewline
totseconds & 0.44886094377429 & 0.091526 & 4.9042 & 2e-06 & 1e-06 \tabularnewline
tothyperlinks & 27.190121718513 & 573.879586 & 0.0474 & 0.962274 & 0.481137 \tabularnewline
totblogs & 7.23814799402061 & 595.022638 & 0.0122 & 0.990311 & 0.495155 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156353&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]2307.62466618815[/C][C]5872.124597[/C][C]0.393[/C][C]0.694896[/C][C]0.347448[/C][/ROW]
[ROW][C]pageviews[/C][C]7.56541345402203[/C][C]12.135751[/C][C]0.6234[/C][C]0.533976[/C][C]0.266988[/C][/ROW]
[ROW][C]timeinrfc[/C][C]-0.136073044053713[/C][C]0.068659[/C][C]-1.9819[/C][C]0.049335[/C][C]0.024668[/C][/ROW]
[ROW][C]logins[/C][C]6.65623958417471[/C][C]71.452224[/C][C]0.0932[/C][C]0.925904[/C][C]0.462952[/C][/ROW]
[ROW][C]compendiumviewsinfo[/C][C]-10.9886298491094[/C][C]25.068197[/C][C]-0.4383[/C][C]0.661768[/C][C]0.330884[/C][/ROW]
[ROW][C]compendiumviewspr[/C][C]76.0490323880027[/C][C]41.318386[/C][C]1.8406[/C][C]0.067675[/C][C]0.033838[/C][/ROW]
[ROW][C]sharedcompendiums[/C][C]2288.72188427968[/C][C]639.359562[/C][C]3.5797[/C][C]0.000465[/C][C]0.000232[/C][/ROW]
[ROW][C]bloggedcomputations[/C][C]2.35843761982934[/C][C]111.96963[/C][C]0.0211[/C][C]0.983223[/C][C]0.491612[/C][/ROW]
[ROW][C]compendiumsreviewed[/C][C]327.877361851027[/C][C]880.491163[/C][C]0.3724[/C][C]0.710139[/C][C]0.35507[/C][/ROW]
[ROW][C]feedbackmessagesp1[/C][C]-77.8978066473687[/C][C]265.411163[/C][C]-0.2935[/C][C]0.769549[/C][C]0.384775[/C][/ROW]
[ROW][C]feedbackmessagesp120[/C][C]204.405506531731[/C][C]128.567415[/C][C]1.5899[/C][C]0.113984[/C][C]0.056992[/C][/ROW]
[ROW][C]totrevisions[/C][C]1.11009133506433[/C][C]0.355852[/C][C]3.1195[/C][C]0.002175[/C][C]0.001088[/C][/ROW]
[ROW][C]totseconds[/C][C]0.44886094377429[/C][C]0.091526[/C][C]4.9042[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]tothyperlinks[/C][C]27.190121718513[/C][C]573.879586[/C][C]0.0474[/C][C]0.962274[/C][C]0.481137[/C][/ROW]
[ROW][C]totblogs[/C][C]7.23814799402061[/C][C]595.022638[/C][C]0.0122[/C][C]0.990311[/C][C]0.495155[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156353&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156353&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)2307.624666188155872.1245970.3930.6948960.347448
pageviews7.5654134540220312.1357510.62340.5339760.266988
timeinrfc-0.1360730440537130.068659-1.98190.0493350.024668
logins6.6562395841747171.4522240.09320.9259040.462952
compendiumviewsinfo-10.988629849109425.068197-0.43830.6617680.330884
compendiumviewspr76.049032388002741.3183861.84060.0676750.033838
sharedcompendiums2288.72188427968639.3595623.57970.0004650.000232
bloggedcomputations2.35843761982934111.969630.02110.9832230.491612
compendiumsreviewed327.877361851027880.4911630.37240.7101390.35507
feedbackmessagesp1-77.8978066473687265.411163-0.29350.7695490.384775
feedbackmessagesp120204.405506531731128.5674151.58990.1139840.056992
totrevisions1.110091335064330.3558523.11950.0021750.001088
totseconds0.448860943774290.0915264.90422e-061e-06
tothyperlinks27.190121718513573.8795860.04740.9622740.481137
totblogs7.23814799402061595.0226380.01220.9903110.495155






Multiple Linear Regression - Regression Statistics
Multiple R0.864136555339632
R-squared0.746731986274245
Adjusted R-squared0.722934991695986
F-TEST (value)31.3792560576732
F-TEST (DF numerator)14
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation27377.1295558262
Sum Squared Residuals111676576184.757

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.864136555339632 \tabularnewline
R-squared & 0.746731986274245 \tabularnewline
Adjusted R-squared & 0.722934991695986 \tabularnewline
F-TEST (value) & 31.3792560576732 \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 & 27377.1295558262 \tabularnewline
Sum Squared Residuals & 111676576184.757 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156353&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.864136555339632[/C][/ROW]
[ROW][C]R-squared[/C][C]0.746731986274245[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.722934991695986[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]31.3792560576732[/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]27377.1295558262[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]111676576184.757[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156353&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156353&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 R0.864136555339632
R-squared0.746731986274245
Adjusted R-squared0.722934991695986
F-TEST (value)31.3792560576732
F-TEST (DF numerator)14
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation27377.1295558262
Sum Squared Residuals111676576184.757






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1140824137499.7949189563324.20508104445
2110459102267.4846340888191.51536591233
3105079110250.053723095-5171.0537230955
4112098116977.242136827-4879.24213682722
54392966046.1828233547-22117.1828233547
67617359806.740816092116366.2591839079
7187326146360.28094703440965.7190529661
82280712920.34381327779886.65618672234
9144408122448.94166985121959.0583301492
106648579770.1186999969-13285.1186999969
1179089114625.10868209-35536.1086820904
1281625108633.757183847-27008.7571838474
136878883434.2043810813-14646.2043810813
1410329792641.195091181910655.8049088181
156944692970.9402044497-23524.9402044497
16114948140195.690946552-25247.6909465524
17167949137084.79972771730864.2002722835
1812508195095.908162626829985.0918373732
19125818103032.3880454122785.6119545899
20136588130830.5144692935757.48553070691
21112431127422.958178076-14991.9581780763
2210303787013.09844607416023.901553926
238231789574.0263141825-7257.02631418248
24118906130120.661423037-11214.6614230365
2583515112406.010711621-28891.010711621
26104581111387.451420797-6806.45142079661
27103129123672.664528461-20543.6645284609
2883243116632.580177453-33389.5801774528
293711054440.6812154429-17330.6812154429
30113344137828.715136622-24484.7151366222
31139165157342.923938408-18177.9239384082
3286652103091.026134966-16439.0261349657
33112302144288.559873409-31986.5598734089
346965254021.92766997715630.072330023
35119442144254.139380031-24812.139380031
366986760884.15625461698982.84374538305
37101629121264.385711751-19635.3857117514
387016882048.6436924925-11880.6436924925
393108148199.6703266461-17118.6703266461
40103925128466.171968722-24541.1719687219
4192622113040.198498553-20418.1984985534
427901194329.1587663622-15318.1587663622
4393487119682.576900464-26195.576900464
446452080112.7759065382-15592.7759065382
459347383074.26111381710398.738886183
46114360109661.3316158824698.66838411781
473303254873.0732717182-21841.0732717182
4896125103448.009790273-7323.00979027328
49151911157270.311973303-5359.31197330314
5089256108912.773808121-19656.7738081214
5195671122302.945577102-26631.9455771017
52595013866.878142313-7916.87814231303
53149695136798.92105744912896.0789425512
543255138626.6370469812-6075.6370469812
553170141604.8655080997-9903.86550809973
56100087113221.170160159-13134.1701601585
57169707178915.498260378-9208.49826037813
58150491176253.604156179-25762.6041561791
59120192144611.610839359-24419.6108393593
6095893102905.095382694-7012.09538269396
61151715154978.073615522-3263.0736155219
62176225193475.987077612-17250.9870776116
635990091170.0244187413-31270.0244187413
64104767119329.866063994-14562.8660639943
65114799126718.109003381-11919.1090033811
6672128102456.024649324-30328.0246493236
67143592114351.85796850529240.1420314954
6889626137211.771495913-47585.7714959127
69131072101262.64181045529809.3581895455
7012681793848.966053294232968.0339467058
7181351116968.124099773-35617.1240997727
722261841086.6416017874-18468.6416017874
7388977111530.237007848-22553.237007848
749205987375.51116577314683.48883422694
758189788238.1372797858-6341.13727978576
7610814685508.782677946922637.2173220531
77126372118515.3821074667856.61789253408
78249771131992.800682286117778.199317714
797115482506.1598792132-11352.1598792132
807157171084.2398610452486.760138954774
815591871081.4684030937-15163.4684030937
82160141124477.27117359335663.7288264067
833869264876.4343342026-26184.4343342026
84102812104477.807970044-1665.80797004387
855662237514.451957673219107.5480423268
861598635470.0575770732-19484.0575770732
87123534129434.196409108-5900.19640910776
8810853598500.377795618310034.6222043817
8993879124063.173704374-30184.1737043736
90144551118314.69920723626236.3007927635
915675073851.2214320464-17101.2214320464
9212765498858.088351511128795.9116484889
936559486030.1952230523-20436.1952230523
945993873022.6450476665-13084.6450476665
95146975125107.78479552621867.215204474
96143372112260.31732212831111.6826778724
97168553146423.53431590322129.4656840967
98183500150298.10671819333201.8932818072
99165986194399.140643276-28413.1406432763
100184923187340.385625681-2417.38562568133
101140358139056.8046357171301.1953642827
102149959138077.75547809611881.2445219035
1035722459536.2494045436-2312.24940454358
1044375043672.756157818277.2438421818241
1054802972981.3173287825-24952.3173287825
106104978126823.283582681-21845.2835826812
107100046115103.106480123-15057.1064801227
10810104799100.20388439471946.7961156053
109197426121817.10073508875608.8992649118
11016090287001.118817549373900.8811824507
111147172127001.78660435720170.2133956428
11210943286467.503857630622964.4961423694
11311684768.52746212273-3600.52746212273
1148324894883.2356714279-11635.2356714279
1152516230749.0734825354-5587.07348253543
1164572470274.9803603446-24550.9803603446
117110529132703.704589576-22174.704589576
1188554519.53061379518-3664.53061379518
11910138283881.203956356117500.7960436439
1201411615534.7551810749-1418.75518107493
12189506124594.787899215-35088.7878992152
122135356110538.53959916924817.4604008314
12311606672833.820491943543232.1795080565
12414424478972.961605496265271.0383945038
125877316326.0402814987-7553.04028149872
12610215387789.322555984514363.6774440155
127117440130563.249771056-13123.2497710558
128104128110940.892543634-6812.89254363408
129134238145764.585691153-11526.5856911528
130134047143214.59721547-9167.59721547049
131279488194893.46186839184594.5381316092
1327975696597.6301731674-16841.6301731675
1336608958006.12463956248082.87536043758
13410207095379.20075178816690.79924821187
135146760144192.6872665872567.31273341318
13615477173442.4847410681328.51525894
137165933128524.49446945337408.5055305468
1386459382555.3025437049-17962.3025437049
13992280124049.028737998-31769.0287379976
1406715094165.0278758171-27015.0278758171
14112869268694.668353338459997.3316466616
142124089102174.20018088121914.7998191186
143125386117653.2148698867732.78513011414
1443723821680.966241375415557.0337586246
145140015143309.161964954-3294.16196495418
146150047107346.24077207942700.7592279212
147154451116693.68499015237757.3150098476
148156349135459.96361514320889.0363848565
149022921.1163785693-22921.1163785693
15060237051.25803056398-1028.25803056398
15102338.77281472518-2338.77281472518
15202319.54721794424-2319.54721794424
15304596.34655046783-4596.34655046783
15402307.62466618815-2307.62466618815
1558460180791.84026892223809.15973107776
15668946114872.420957275-45926.4209572749
15702307.62466618815-2307.62466618815
15802336.88845039803-2336.88845039803
15916444436.23901292121-2792.23901292121
160617912941.4661860771-6762.46618607711
16139267820.46627256696-3894.46627256695
1625278926400.971596245626388.0284037544
16302408.47935583509-2408.47935583509
16410035061657.059699121838692.9403008782

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 140824 & 137499.794918956 & 3324.20508104445 \tabularnewline
2 & 110459 & 102267.484634088 & 8191.51536591233 \tabularnewline
3 & 105079 & 110250.053723095 & -5171.0537230955 \tabularnewline
4 & 112098 & 116977.242136827 & -4879.24213682722 \tabularnewline
5 & 43929 & 66046.1828233547 & -22117.1828233547 \tabularnewline
6 & 76173 & 59806.7408160921 & 16366.2591839079 \tabularnewline
7 & 187326 & 146360.280947034 & 40965.7190529661 \tabularnewline
8 & 22807 & 12920.3438132777 & 9886.65618672234 \tabularnewline
9 & 144408 & 122448.941669851 & 21959.0583301492 \tabularnewline
10 & 66485 & 79770.1186999969 & -13285.1186999969 \tabularnewline
11 & 79089 & 114625.10868209 & -35536.1086820904 \tabularnewline
12 & 81625 & 108633.757183847 & -27008.7571838474 \tabularnewline
13 & 68788 & 83434.2043810813 & -14646.2043810813 \tabularnewline
14 & 103297 & 92641.1950911819 & 10655.8049088181 \tabularnewline
15 & 69446 & 92970.9402044497 & -23524.9402044497 \tabularnewline
16 & 114948 & 140195.690946552 & -25247.6909465524 \tabularnewline
17 & 167949 & 137084.799727717 & 30864.2002722835 \tabularnewline
18 & 125081 & 95095.9081626268 & 29985.0918373732 \tabularnewline
19 & 125818 & 103032.38804541 & 22785.6119545899 \tabularnewline
20 & 136588 & 130830.514469293 & 5757.48553070691 \tabularnewline
21 & 112431 & 127422.958178076 & -14991.9581780763 \tabularnewline
22 & 103037 & 87013.098446074 & 16023.901553926 \tabularnewline
23 & 82317 & 89574.0263141825 & -7257.02631418248 \tabularnewline
24 & 118906 & 130120.661423037 & -11214.6614230365 \tabularnewline
25 & 83515 & 112406.010711621 & -28891.010711621 \tabularnewline
26 & 104581 & 111387.451420797 & -6806.45142079661 \tabularnewline
27 & 103129 & 123672.664528461 & -20543.6645284609 \tabularnewline
28 & 83243 & 116632.580177453 & -33389.5801774528 \tabularnewline
29 & 37110 & 54440.6812154429 & -17330.6812154429 \tabularnewline
30 & 113344 & 137828.715136622 & -24484.7151366222 \tabularnewline
31 & 139165 & 157342.923938408 & -18177.9239384082 \tabularnewline
32 & 86652 & 103091.026134966 & -16439.0261349657 \tabularnewline
33 & 112302 & 144288.559873409 & -31986.5598734089 \tabularnewline
34 & 69652 & 54021.927669977 & 15630.072330023 \tabularnewline
35 & 119442 & 144254.139380031 & -24812.139380031 \tabularnewline
36 & 69867 & 60884.1562546169 & 8982.84374538305 \tabularnewline
37 & 101629 & 121264.385711751 & -19635.3857117514 \tabularnewline
38 & 70168 & 82048.6436924925 & -11880.6436924925 \tabularnewline
39 & 31081 & 48199.6703266461 & -17118.6703266461 \tabularnewline
40 & 103925 & 128466.171968722 & -24541.1719687219 \tabularnewline
41 & 92622 & 113040.198498553 & -20418.1984985534 \tabularnewline
42 & 79011 & 94329.1587663622 & -15318.1587663622 \tabularnewline
43 & 93487 & 119682.576900464 & -26195.576900464 \tabularnewline
44 & 64520 & 80112.7759065382 & -15592.7759065382 \tabularnewline
45 & 93473 & 83074.261113817 & 10398.738886183 \tabularnewline
46 & 114360 & 109661.331615882 & 4698.66838411781 \tabularnewline
47 & 33032 & 54873.0732717182 & -21841.0732717182 \tabularnewline
48 & 96125 & 103448.009790273 & -7323.00979027328 \tabularnewline
49 & 151911 & 157270.311973303 & -5359.31197330314 \tabularnewline
50 & 89256 & 108912.773808121 & -19656.7738081214 \tabularnewline
51 & 95671 & 122302.945577102 & -26631.9455771017 \tabularnewline
52 & 5950 & 13866.878142313 & -7916.87814231303 \tabularnewline
53 & 149695 & 136798.921057449 & 12896.0789425512 \tabularnewline
54 & 32551 & 38626.6370469812 & -6075.6370469812 \tabularnewline
55 & 31701 & 41604.8655080997 & -9903.86550809973 \tabularnewline
56 & 100087 & 113221.170160159 & -13134.1701601585 \tabularnewline
57 & 169707 & 178915.498260378 & -9208.49826037813 \tabularnewline
58 & 150491 & 176253.604156179 & -25762.6041561791 \tabularnewline
59 & 120192 & 144611.610839359 & -24419.6108393593 \tabularnewline
60 & 95893 & 102905.095382694 & -7012.09538269396 \tabularnewline
61 & 151715 & 154978.073615522 & -3263.0736155219 \tabularnewline
62 & 176225 & 193475.987077612 & -17250.9870776116 \tabularnewline
63 & 59900 & 91170.0244187413 & -31270.0244187413 \tabularnewline
64 & 104767 & 119329.866063994 & -14562.8660639943 \tabularnewline
65 & 114799 & 126718.109003381 & -11919.1090033811 \tabularnewline
66 & 72128 & 102456.024649324 & -30328.0246493236 \tabularnewline
67 & 143592 & 114351.857968505 & 29240.1420314954 \tabularnewline
68 & 89626 & 137211.771495913 & -47585.7714959127 \tabularnewline
69 & 131072 & 101262.641810455 & 29809.3581895455 \tabularnewline
70 & 126817 & 93848.9660532942 & 32968.0339467058 \tabularnewline
71 & 81351 & 116968.124099773 & -35617.1240997727 \tabularnewline
72 & 22618 & 41086.6416017874 & -18468.6416017874 \tabularnewline
73 & 88977 & 111530.237007848 & -22553.237007848 \tabularnewline
74 & 92059 & 87375.5111657731 & 4683.48883422694 \tabularnewline
75 & 81897 & 88238.1372797858 & -6341.13727978576 \tabularnewline
76 & 108146 & 85508.7826779469 & 22637.2173220531 \tabularnewline
77 & 126372 & 118515.382107466 & 7856.61789253408 \tabularnewline
78 & 249771 & 131992.800682286 & 117778.199317714 \tabularnewline
79 & 71154 & 82506.1598792132 & -11352.1598792132 \tabularnewline
80 & 71571 & 71084.2398610452 & 486.760138954774 \tabularnewline
81 & 55918 & 71081.4684030937 & -15163.4684030937 \tabularnewline
82 & 160141 & 124477.271173593 & 35663.7288264067 \tabularnewline
83 & 38692 & 64876.4343342026 & -26184.4343342026 \tabularnewline
84 & 102812 & 104477.807970044 & -1665.80797004387 \tabularnewline
85 & 56622 & 37514.4519576732 & 19107.5480423268 \tabularnewline
86 & 15986 & 35470.0575770732 & -19484.0575770732 \tabularnewline
87 & 123534 & 129434.196409108 & -5900.19640910776 \tabularnewline
88 & 108535 & 98500.3777956183 & 10034.6222043817 \tabularnewline
89 & 93879 & 124063.173704374 & -30184.1737043736 \tabularnewline
90 & 144551 & 118314.699207236 & 26236.3007927635 \tabularnewline
91 & 56750 & 73851.2214320464 & -17101.2214320464 \tabularnewline
92 & 127654 & 98858.0883515111 & 28795.9116484889 \tabularnewline
93 & 65594 & 86030.1952230523 & -20436.1952230523 \tabularnewline
94 & 59938 & 73022.6450476665 & -13084.6450476665 \tabularnewline
95 & 146975 & 125107.784795526 & 21867.215204474 \tabularnewline
96 & 143372 & 112260.317322128 & 31111.6826778724 \tabularnewline
97 & 168553 & 146423.534315903 & 22129.4656840967 \tabularnewline
98 & 183500 & 150298.106718193 & 33201.8932818072 \tabularnewline
99 & 165986 & 194399.140643276 & -28413.1406432763 \tabularnewline
100 & 184923 & 187340.385625681 & -2417.38562568133 \tabularnewline
101 & 140358 & 139056.804635717 & 1301.1953642827 \tabularnewline
102 & 149959 & 138077.755478096 & 11881.2445219035 \tabularnewline
103 & 57224 & 59536.2494045436 & -2312.24940454358 \tabularnewline
104 & 43750 & 43672.7561578182 & 77.2438421818241 \tabularnewline
105 & 48029 & 72981.3173287825 & -24952.3173287825 \tabularnewline
106 & 104978 & 126823.283582681 & -21845.2835826812 \tabularnewline
107 & 100046 & 115103.106480123 & -15057.1064801227 \tabularnewline
108 & 101047 & 99100.2038843947 & 1946.7961156053 \tabularnewline
109 & 197426 & 121817.100735088 & 75608.8992649118 \tabularnewline
110 & 160902 & 87001.1188175493 & 73900.8811824507 \tabularnewline
111 & 147172 & 127001.786604357 & 20170.2133956428 \tabularnewline
112 & 109432 & 86467.5038576306 & 22964.4961423694 \tabularnewline
113 & 1168 & 4768.52746212273 & -3600.52746212273 \tabularnewline
114 & 83248 & 94883.2356714279 & -11635.2356714279 \tabularnewline
115 & 25162 & 30749.0734825354 & -5587.07348253543 \tabularnewline
116 & 45724 & 70274.9803603446 & -24550.9803603446 \tabularnewline
117 & 110529 & 132703.704589576 & -22174.704589576 \tabularnewline
118 & 855 & 4519.53061379518 & -3664.53061379518 \tabularnewline
119 & 101382 & 83881.2039563561 & 17500.7960436439 \tabularnewline
120 & 14116 & 15534.7551810749 & -1418.75518107493 \tabularnewline
121 & 89506 & 124594.787899215 & -35088.7878992152 \tabularnewline
122 & 135356 & 110538.539599169 & 24817.4604008314 \tabularnewline
123 & 116066 & 72833.8204919435 & 43232.1795080565 \tabularnewline
124 & 144244 & 78972.9616054962 & 65271.0383945038 \tabularnewline
125 & 8773 & 16326.0402814987 & -7553.04028149872 \tabularnewline
126 & 102153 & 87789.3225559845 & 14363.6774440155 \tabularnewline
127 & 117440 & 130563.249771056 & -13123.2497710558 \tabularnewline
128 & 104128 & 110940.892543634 & -6812.89254363408 \tabularnewline
129 & 134238 & 145764.585691153 & -11526.5856911528 \tabularnewline
130 & 134047 & 143214.59721547 & -9167.59721547049 \tabularnewline
131 & 279488 & 194893.461868391 & 84594.5381316092 \tabularnewline
132 & 79756 & 96597.6301731674 & -16841.6301731675 \tabularnewline
133 & 66089 & 58006.1246395624 & 8082.87536043758 \tabularnewline
134 & 102070 & 95379.2007517881 & 6690.79924821187 \tabularnewline
135 & 146760 & 144192.687266587 & 2567.31273341318 \tabularnewline
136 & 154771 & 73442.48474106 & 81328.51525894 \tabularnewline
137 & 165933 & 128524.494469453 & 37408.5055305468 \tabularnewline
138 & 64593 & 82555.3025437049 & -17962.3025437049 \tabularnewline
139 & 92280 & 124049.028737998 & -31769.0287379976 \tabularnewline
140 & 67150 & 94165.0278758171 & -27015.0278758171 \tabularnewline
141 & 128692 & 68694.6683533384 & 59997.3316466616 \tabularnewline
142 & 124089 & 102174.200180881 & 21914.7998191186 \tabularnewline
143 & 125386 & 117653.214869886 & 7732.78513011414 \tabularnewline
144 & 37238 & 21680.9662413754 & 15557.0337586246 \tabularnewline
145 & 140015 & 143309.161964954 & -3294.16196495418 \tabularnewline
146 & 150047 & 107346.240772079 & 42700.7592279212 \tabularnewline
147 & 154451 & 116693.684990152 & 37757.3150098476 \tabularnewline
148 & 156349 & 135459.963615143 & 20889.0363848565 \tabularnewline
149 & 0 & 22921.1163785693 & -22921.1163785693 \tabularnewline
150 & 6023 & 7051.25803056398 & -1028.25803056398 \tabularnewline
151 & 0 & 2338.77281472518 & -2338.77281472518 \tabularnewline
152 & 0 & 2319.54721794424 & -2319.54721794424 \tabularnewline
153 & 0 & 4596.34655046783 & -4596.34655046783 \tabularnewline
154 & 0 & 2307.62466618815 & -2307.62466618815 \tabularnewline
155 & 84601 & 80791.8402689222 & 3809.15973107776 \tabularnewline
156 & 68946 & 114872.420957275 & -45926.4209572749 \tabularnewline
157 & 0 & 2307.62466618815 & -2307.62466618815 \tabularnewline
158 & 0 & 2336.88845039803 & -2336.88845039803 \tabularnewline
159 & 1644 & 4436.23901292121 & -2792.23901292121 \tabularnewline
160 & 6179 & 12941.4661860771 & -6762.46618607711 \tabularnewline
161 & 3926 & 7820.46627256696 & -3894.46627256695 \tabularnewline
162 & 52789 & 26400.9715962456 & 26388.0284037544 \tabularnewline
163 & 0 & 2408.47935583509 & -2408.47935583509 \tabularnewline
164 & 100350 & 61657.0596991218 & 38692.9403008782 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156353&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]140824[/C][C]137499.794918956[/C][C]3324.20508104445[/C][/ROW]
[ROW][C]2[/C][C]110459[/C][C]102267.484634088[/C][C]8191.51536591233[/C][/ROW]
[ROW][C]3[/C][C]105079[/C][C]110250.053723095[/C][C]-5171.0537230955[/C][/ROW]
[ROW][C]4[/C][C]112098[/C][C]116977.242136827[/C][C]-4879.24213682722[/C][/ROW]
[ROW][C]5[/C][C]43929[/C][C]66046.1828233547[/C][C]-22117.1828233547[/C][/ROW]
[ROW][C]6[/C][C]76173[/C][C]59806.7408160921[/C][C]16366.2591839079[/C][/ROW]
[ROW][C]7[/C][C]187326[/C][C]146360.280947034[/C][C]40965.7190529661[/C][/ROW]
[ROW][C]8[/C][C]22807[/C][C]12920.3438132777[/C][C]9886.65618672234[/C][/ROW]
[ROW][C]9[/C][C]144408[/C][C]122448.941669851[/C][C]21959.0583301492[/C][/ROW]
[ROW][C]10[/C][C]66485[/C][C]79770.1186999969[/C][C]-13285.1186999969[/C][/ROW]
[ROW][C]11[/C][C]79089[/C][C]114625.10868209[/C][C]-35536.1086820904[/C][/ROW]
[ROW][C]12[/C][C]81625[/C][C]108633.757183847[/C][C]-27008.7571838474[/C][/ROW]
[ROW][C]13[/C][C]68788[/C][C]83434.2043810813[/C][C]-14646.2043810813[/C][/ROW]
[ROW][C]14[/C][C]103297[/C][C]92641.1950911819[/C][C]10655.8049088181[/C][/ROW]
[ROW][C]15[/C][C]69446[/C][C]92970.9402044497[/C][C]-23524.9402044497[/C][/ROW]
[ROW][C]16[/C][C]114948[/C][C]140195.690946552[/C][C]-25247.6909465524[/C][/ROW]
[ROW][C]17[/C][C]167949[/C][C]137084.799727717[/C][C]30864.2002722835[/C][/ROW]
[ROW][C]18[/C][C]125081[/C][C]95095.9081626268[/C][C]29985.0918373732[/C][/ROW]
[ROW][C]19[/C][C]125818[/C][C]103032.38804541[/C][C]22785.6119545899[/C][/ROW]
[ROW][C]20[/C][C]136588[/C][C]130830.514469293[/C][C]5757.48553070691[/C][/ROW]
[ROW][C]21[/C][C]112431[/C][C]127422.958178076[/C][C]-14991.9581780763[/C][/ROW]
[ROW][C]22[/C][C]103037[/C][C]87013.098446074[/C][C]16023.901553926[/C][/ROW]
[ROW][C]23[/C][C]82317[/C][C]89574.0263141825[/C][C]-7257.02631418248[/C][/ROW]
[ROW][C]24[/C][C]118906[/C][C]130120.661423037[/C][C]-11214.6614230365[/C][/ROW]
[ROW][C]25[/C][C]83515[/C][C]112406.010711621[/C][C]-28891.010711621[/C][/ROW]
[ROW][C]26[/C][C]104581[/C][C]111387.451420797[/C][C]-6806.45142079661[/C][/ROW]
[ROW][C]27[/C][C]103129[/C][C]123672.664528461[/C][C]-20543.6645284609[/C][/ROW]
[ROW][C]28[/C][C]83243[/C][C]116632.580177453[/C][C]-33389.5801774528[/C][/ROW]
[ROW][C]29[/C][C]37110[/C][C]54440.6812154429[/C][C]-17330.6812154429[/C][/ROW]
[ROW][C]30[/C][C]113344[/C][C]137828.715136622[/C][C]-24484.7151366222[/C][/ROW]
[ROW][C]31[/C][C]139165[/C][C]157342.923938408[/C][C]-18177.9239384082[/C][/ROW]
[ROW][C]32[/C][C]86652[/C][C]103091.026134966[/C][C]-16439.0261349657[/C][/ROW]
[ROW][C]33[/C][C]112302[/C][C]144288.559873409[/C][C]-31986.5598734089[/C][/ROW]
[ROW][C]34[/C][C]69652[/C][C]54021.927669977[/C][C]15630.072330023[/C][/ROW]
[ROW][C]35[/C][C]119442[/C][C]144254.139380031[/C][C]-24812.139380031[/C][/ROW]
[ROW][C]36[/C][C]69867[/C][C]60884.1562546169[/C][C]8982.84374538305[/C][/ROW]
[ROW][C]37[/C][C]101629[/C][C]121264.385711751[/C][C]-19635.3857117514[/C][/ROW]
[ROW][C]38[/C][C]70168[/C][C]82048.6436924925[/C][C]-11880.6436924925[/C][/ROW]
[ROW][C]39[/C][C]31081[/C][C]48199.6703266461[/C][C]-17118.6703266461[/C][/ROW]
[ROW][C]40[/C][C]103925[/C][C]128466.171968722[/C][C]-24541.1719687219[/C][/ROW]
[ROW][C]41[/C][C]92622[/C][C]113040.198498553[/C][C]-20418.1984985534[/C][/ROW]
[ROW][C]42[/C][C]79011[/C][C]94329.1587663622[/C][C]-15318.1587663622[/C][/ROW]
[ROW][C]43[/C][C]93487[/C][C]119682.576900464[/C][C]-26195.576900464[/C][/ROW]
[ROW][C]44[/C][C]64520[/C][C]80112.7759065382[/C][C]-15592.7759065382[/C][/ROW]
[ROW][C]45[/C][C]93473[/C][C]83074.261113817[/C][C]10398.738886183[/C][/ROW]
[ROW][C]46[/C][C]114360[/C][C]109661.331615882[/C][C]4698.66838411781[/C][/ROW]
[ROW][C]47[/C][C]33032[/C][C]54873.0732717182[/C][C]-21841.0732717182[/C][/ROW]
[ROW][C]48[/C][C]96125[/C][C]103448.009790273[/C][C]-7323.00979027328[/C][/ROW]
[ROW][C]49[/C][C]151911[/C][C]157270.311973303[/C][C]-5359.31197330314[/C][/ROW]
[ROW][C]50[/C][C]89256[/C][C]108912.773808121[/C][C]-19656.7738081214[/C][/ROW]
[ROW][C]51[/C][C]95671[/C][C]122302.945577102[/C][C]-26631.9455771017[/C][/ROW]
[ROW][C]52[/C][C]5950[/C][C]13866.878142313[/C][C]-7916.87814231303[/C][/ROW]
[ROW][C]53[/C][C]149695[/C][C]136798.921057449[/C][C]12896.0789425512[/C][/ROW]
[ROW][C]54[/C][C]32551[/C][C]38626.6370469812[/C][C]-6075.6370469812[/C][/ROW]
[ROW][C]55[/C][C]31701[/C][C]41604.8655080997[/C][C]-9903.86550809973[/C][/ROW]
[ROW][C]56[/C][C]100087[/C][C]113221.170160159[/C][C]-13134.1701601585[/C][/ROW]
[ROW][C]57[/C][C]169707[/C][C]178915.498260378[/C][C]-9208.49826037813[/C][/ROW]
[ROW][C]58[/C][C]150491[/C][C]176253.604156179[/C][C]-25762.6041561791[/C][/ROW]
[ROW][C]59[/C][C]120192[/C][C]144611.610839359[/C][C]-24419.6108393593[/C][/ROW]
[ROW][C]60[/C][C]95893[/C][C]102905.095382694[/C][C]-7012.09538269396[/C][/ROW]
[ROW][C]61[/C][C]151715[/C][C]154978.073615522[/C][C]-3263.0736155219[/C][/ROW]
[ROW][C]62[/C][C]176225[/C][C]193475.987077612[/C][C]-17250.9870776116[/C][/ROW]
[ROW][C]63[/C][C]59900[/C][C]91170.0244187413[/C][C]-31270.0244187413[/C][/ROW]
[ROW][C]64[/C][C]104767[/C][C]119329.866063994[/C][C]-14562.8660639943[/C][/ROW]
[ROW][C]65[/C][C]114799[/C][C]126718.109003381[/C][C]-11919.1090033811[/C][/ROW]
[ROW][C]66[/C][C]72128[/C][C]102456.024649324[/C][C]-30328.0246493236[/C][/ROW]
[ROW][C]67[/C][C]143592[/C][C]114351.857968505[/C][C]29240.1420314954[/C][/ROW]
[ROW][C]68[/C][C]89626[/C][C]137211.771495913[/C][C]-47585.7714959127[/C][/ROW]
[ROW][C]69[/C][C]131072[/C][C]101262.641810455[/C][C]29809.3581895455[/C][/ROW]
[ROW][C]70[/C][C]126817[/C][C]93848.9660532942[/C][C]32968.0339467058[/C][/ROW]
[ROW][C]71[/C][C]81351[/C][C]116968.124099773[/C][C]-35617.1240997727[/C][/ROW]
[ROW][C]72[/C][C]22618[/C][C]41086.6416017874[/C][C]-18468.6416017874[/C][/ROW]
[ROW][C]73[/C][C]88977[/C][C]111530.237007848[/C][C]-22553.237007848[/C][/ROW]
[ROW][C]74[/C][C]92059[/C][C]87375.5111657731[/C][C]4683.48883422694[/C][/ROW]
[ROW][C]75[/C][C]81897[/C][C]88238.1372797858[/C][C]-6341.13727978576[/C][/ROW]
[ROW][C]76[/C][C]108146[/C][C]85508.7826779469[/C][C]22637.2173220531[/C][/ROW]
[ROW][C]77[/C][C]126372[/C][C]118515.382107466[/C][C]7856.61789253408[/C][/ROW]
[ROW][C]78[/C][C]249771[/C][C]131992.800682286[/C][C]117778.199317714[/C][/ROW]
[ROW][C]79[/C][C]71154[/C][C]82506.1598792132[/C][C]-11352.1598792132[/C][/ROW]
[ROW][C]80[/C][C]71571[/C][C]71084.2398610452[/C][C]486.760138954774[/C][/ROW]
[ROW][C]81[/C][C]55918[/C][C]71081.4684030937[/C][C]-15163.4684030937[/C][/ROW]
[ROW][C]82[/C][C]160141[/C][C]124477.271173593[/C][C]35663.7288264067[/C][/ROW]
[ROW][C]83[/C][C]38692[/C][C]64876.4343342026[/C][C]-26184.4343342026[/C][/ROW]
[ROW][C]84[/C][C]102812[/C][C]104477.807970044[/C][C]-1665.80797004387[/C][/ROW]
[ROW][C]85[/C][C]56622[/C][C]37514.4519576732[/C][C]19107.5480423268[/C][/ROW]
[ROW][C]86[/C][C]15986[/C][C]35470.0575770732[/C][C]-19484.0575770732[/C][/ROW]
[ROW][C]87[/C][C]123534[/C][C]129434.196409108[/C][C]-5900.19640910776[/C][/ROW]
[ROW][C]88[/C][C]108535[/C][C]98500.3777956183[/C][C]10034.6222043817[/C][/ROW]
[ROW][C]89[/C][C]93879[/C][C]124063.173704374[/C][C]-30184.1737043736[/C][/ROW]
[ROW][C]90[/C][C]144551[/C][C]118314.699207236[/C][C]26236.3007927635[/C][/ROW]
[ROW][C]91[/C][C]56750[/C][C]73851.2214320464[/C][C]-17101.2214320464[/C][/ROW]
[ROW][C]92[/C][C]127654[/C][C]98858.0883515111[/C][C]28795.9116484889[/C][/ROW]
[ROW][C]93[/C][C]65594[/C][C]86030.1952230523[/C][C]-20436.1952230523[/C][/ROW]
[ROW][C]94[/C][C]59938[/C][C]73022.6450476665[/C][C]-13084.6450476665[/C][/ROW]
[ROW][C]95[/C][C]146975[/C][C]125107.784795526[/C][C]21867.215204474[/C][/ROW]
[ROW][C]96[/C][C]143372[/C][C]112260.317322128[/C][C]31111.6826778724[/C][/ROW]
[ROW][C]97[/C][C]168553[/C][C]146423.534315903[/C][C]22129.4656840967[/C][/ROW]
[ROW][C]98[/C][C]183500[/C][C]150298.106718193[/C][C]33201.8932818072[/C][/ROW]
[ROW][C]99[/C][C]165986[/C][C]194399.140643276[/C][C]-28413.1406432763[/C][/ROW]
[ROW][C]100[/C][C]184923[/C][C]187340.385625681[/C][C]-2417.38562568133[/C][/ROW]
[ROW][C]101[/C][C]140358[/C][C]139056.804635717[/C][C]1301.1953642827[/C][/ROW]
[ROW][C]102[/C][C]149959[/C][C]138077.755478096[/C][C]11881.2445219035[/C][/ROW]
[ROW][C]103[/C][C]57224[/C][C]59536.2494045436[/C][C]-2312.24940454358[/C][/ROW]
[ROW][C]104[/C][C]43750[/C][C]43672.7561578182[/C][C]77.2438421818241[/C][/ROW]
[ROW][C]105[/C][C]48029[/C][C]72981.3173287825[/C][C]-24952.3173287825[/C][/ROW]
[ROW][C]106[/C][C]104978[/C][C]126823.283582681[/C][C]-21845.2835826812[/C][/ROW]
[ROW][C]107[/C][C]100046[/C][C]115103.106480123[/C][C]-15057.1064801227[/C][/ROW]
[ROW][C]108[/C][C]101047[/C][C]99100.2038843947[/C][C]1946.7961156053[/C][/ROW]
[ROW][C]109[/C][C]197426[/C][C]121817.100735088[/C][C]75608.8992649118[/C][/ROW]
[ROW][C]110[/C][C]160902[/C][C]87001.1188175493[/C][C]73900.8811824507[/C][/ROW]
[ROW][C]111[/C][C]147172[/C][C]127001.786604357[/C][C]20170.2133956428[/C][/ROW]
[ROW][C]112[/C][C]109432[/C][C]86467.5038576306[/C][C]22964.4961423694[/C][/ROW]
[ROW][C]113[/C][C]1168[/C][C]4768.52746212273[/C][C]-3600.52746212273[/C][/ROW]
[ROW][C]114[/C][C]83248[/C][C]94883.2356714279[/C][C]-11635.2356714279[/C][/ROW]
[ROW][C]115[/C][C]25162[/C][C]30749.0734825354[/C][C]-5587.07348253543[/C][/ROW]
[ROW][C]116[/C][C]45724[/C][C]70274.9803603446[/C][C]-24550.9803603446[/C][/ROW]
[ROW][C]117[/C][C]110529[/C][C]132703.704589576[/C][C]-22174.704589576[/C][/ROW]
[ROW][C]118[/C][C]855[/C][C]4519.53061379518[/C][C]-3664.53061379518[/C][/ROW]
[ROW][C]119[/C][C]101382[/C][C]83881.2039563561[/C][C]17500.7960436439[/C][/ROW]
[ROW][C]120[/C][C]14116[/C][C]15534.7551810749[/C][C]-1418.75518107493[/C][/ROW]
[ROW][C]121[/C][C]89506[/C][C]124594.787899215[/C][C]-35088.7878992152[/C][/ROW]
[ROW][C]122[/C][C]135356[/C][C]110538.539599169[/C][C]24817.4604008314[/C][/ROW]
[ROW][C]123[/C][C]116066[/C][C]72833.8204919435[/C][C]43232.1795080565[/C][/ROW]
[ROW][C]124[/C][C]144244[/C][C]78972.9616054962[/C][C]65271.0383945038[/C][/ROW]
[ROW][C]125[/C][C]8773[/C][C]16326.0402814987[/C][C]-7553.04028149872[/C][/ROW]
[ROW][C]126[/C][C]102153[/C][C]87789.3225559845[/C][C]14363.6774440155[/C][/ROW]
[ROW][C]127[/C][C]117440[/C][C]130563.249771056[/C][C]-13123.2497710558[/C][/ROW]
[ROW][C]128[/C][C]104128[/C][C]110940.892543634[/C][C]-6812.89254363408[/C][/ROW]
[ROW][C]129[/C][C]134238[/C][C]145764.585691153[/C][C]-11526.5856911528[/C][/ROW]
[ROW][C]130[/C][C]134047[/C][C]143214.59721547[/C][C]-9167.59721547049[/C][/ROW]
[ROW][C]131[/C][C]279488[/C][C]194893.461868391[/C][C]84594.5381316092[/C][/ROW]
[ROW][C]132[/C][C]79756[/C][C]96597.6301731674[/C][C]-16841.6301731675[/C][/ROW]
[ROW][C]133[/C][C]66089[/C][C]58006.1246395624[/C][C]8082.87536043758[/C][/ROW]
[ROW][C]134[/C][C]102070[/C][C]95379.2007517881[/C][C]6690.79924821187[/C][/ROW]
[ROW][C]135[/C][C]146760[/C][C]144192.687266587[/C][C]2567.31273341318[/C][/ROW]
[ROW][C]136[/C][C]154771[/C][C]73442.48474106[/C][C]81328.51525894[/C][/ROW]
[ROW][C]137[/C][C]165933[/C][C]128524.494469453[/C][C]37408.5055305468[/C][/ROW]
[ROW][C]138[/C][C]64593[/C][C]82555.3025437049[/C][C]-17962.3025437049[/C][/ROW]
[ROW][C]139[/C][C]92280[/C][C]124049.028737998[/C][C]-31769.0287379976[/C][/ROW]
[ROW][C]140[/C][C]67150[/C][C]94165.0278758171[/C][C]-27015.0278758171[/C][/ROW]
[ROW][C]141[/C][C]128692[/C][C]68694.6683533384[/C][C]59997.3316466616[/C][/ROW]
[ROW][C]142[/C][C]124089[/C][C]102174.200180881[/C][C]21914.7998191186[/C][/ROW]
[ROW][C]143[/C][C]125386[/C][C]117653.214869886[/C][C]7732.78513011414[/C][/ROW]
[ROW][C]144[/C][C]37238[/C][C]21680.9662413754[/C][C]15557.0337586246[/C][/ROW]
[ROW][C]145[/C][C]140015[/C][C]143309.161964954[/C][C]-3294.16196495418[/C][/ROW]
[ROW][C]146[/C][C]150047[/C][C]107346.240772079[/C][C]42700.7592279212[/C][/ROW]
[ROW][C]147[/C][C]154451[/C][C]116693.684990152[/C][C]37757.3150098476[/C][/ROW]
[ROW][C]148[/C][C]156349[/C][C]135459.963615143[/C][C]20889.0363848565[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]22921.1163785693[/C][C]-22921.1163785693[/C][/ROW]
[ROW][C]150[/C][C]6023[/C][C]7051.25803056398[/C][C]-1028.25803056398[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]2338.77281472518[/C][C]-2338.77281472518[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]2319.54721794424[/C][C]-2319.54721794424[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]4596.34655046783[/C][C]-4596.34655046783[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]2307.62466618815[/C][C]-2307.62466618815[/C][/ROW]
[ROW][C]155[/C][C]84601[/C][C]80791.8402689222[/C][C]3809.15973107776[/C][/ROW]
[ROW][C]156[/C][C]68946[/C][C]114872.420957275[/C][C]-45926.4209572749[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]2307.62466618815[/C][C]-2307.62466618815[/C][/ROW]
[ROW][C]158[/C][C]0[/C][C]2336.88845039803[/C][C]-2336.88845039803[/C][/ROW]
[ROW][C]159[/C][C]1644[/C][C]4436.23901292121[/C][C]-2792.23901292121[/C][/ROW]
[ROW][C]160[/C][C]6179[/C][C]12941.4661860771[/C][C]-6762.46618607711[/C][/ROW]
[ROW][C]161[/C][C]3926[/C][C]7820.46627256696[/C][C]-3894.46627256695[/C][/ROW]
[ROW][C]162[/C][C]52789[/C][C]26400.9715962456[/C][C]26388.0284037544[/C][/ROW]
[ROW][C]163[/C][C]0[/C][C]2408.47935583509[/C][C]-2408.47935583509[/C][/ROW]
[ROW][C]164[/C][C]100350[/C][C]61657.0596991218[/C][C]38692.9403008782[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156353&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156353&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
1140824137499.7949189563324.20508104445
2110459102267.4846340888191.51536591233
3105079110250.053723095-5171.0537230955
4112098116977.242136827-4879.24213682722
54392966046.1828233547-22117.1828233547
67617359806.740816092116366.2591839079
7187326146360.28094703440965.7190529661
82280712920.34381327779886.65618672234
9144408122448.94166985121959.0583301492
106648579770.1186999969-13285.1186999969
1179089114625.10868209-35536.1086820904
1281625108633.757183847-27008.7571838474
136878883434.2043810813-14646.2043810813
1410329792641.195091181910655.8049088181
156944692970.9402044497-23524.9402044497
16114948140195.690946552-25247.6909465524
17167949137084.79972771730864.2002722835
1812508195095.908162626829985.0918373732
19125818103032.3880454122785.6119545899
20136588130830.5144692935757.48553070691
21112431127422.958178076-14991.9581780763
2210303787013.09844607416023.901553926
238231789574.0263141825-7257.02631418248
24118906130120.661423037-11214.6614230365
2583515112406.010711621-28891.010711621
26104581111387.451420797-6806.45142079661
27103129123672.664528461-20543.6645284609
2883243116632.580177453-33389.5801774528
293711054440.6812154429-17330.6812154429
30113344137828.715136622-24484.7151366222
31139165157342.923938408-18177.9239384082
3286652103091.026134966-16439.0261349657
33112302144288.559873409-31986.5598734089
346965254021.92766997715630.072330023
35119442144254.139380031-24812.139380031
366986760884.15625461698982.84374538305
37101629121264.385711751-19635.3857117514
387016882048.6436924925-11880.6436924925
393108148199.6703266461-17118.6703266461
40103925128466.171968722-24541.1719687219
4192622113040.198498553-20418.1984985534
427901194329.1587663622-15318.1587663622
4393487119682.576900464-26195.576900464
446452080112.7759065382-15592.7759065382
459347383074.26111381710398.738886183
46114360109661.3316158824698.66838411781
473303254873.0732717182-21841.0732717182
4896125103448.009790273-7323.00979027328
49151911157270.311973303-5359.31197330314
5089256108912.773808121-19656.7738081214
5195671122302.945577102-26631.9455771017
52595013866.878142313-7916.87814231303
53149695136798.92105744912896.0789425512
543255138626.6370469812-6075.6370469812
553170141604.8655080997-9903.86550809973
56100087113221.170160159-13134.1701601585
57169707178915.498260378-9208.49826037813
58150491176253.604156179-25762.6041561791
59120192144611.610839359-24419.6108393593
6095893102905.095382694-7012.09538269396
61151715154978.073615522-3263.0736155219
62176225193475.987077612-17250.9870776116
635990091170.0244187413-31270.0244187413
64104767119329.866063994-14562.8660639943
65114799126718.109003381-11919.1090033811
6672128102456.024649324-30328.0246493236
67143592114351.85796850529240.1420314954
6889626137211.771495913-47585.7714959127
69131072101262.64181045529809.3581895455
7012681793848.966053294232968.0339467058
7181351116968.124099773-35617.1240997727
722261841086.6416017874-18468.6416017874
7388977111530.237007848-22553.237007848
749205987375.51116577314683.48883422694
758189788238.1372797858-6341.13727978576
7610814685508.782677946922637.2173220531
77126372118515.3821074667856.61789253408
78249771131992.800682286117778.199317714
797115482506.1598792132-11352.1598792132
807157171084.2398610452486.760138954774
815591871081.4684030937-15163.4684030937
82160141124477.27117359335663.7288264067
833869264876.4343342026-26184.4343342026
84102812104477.807970044-1665.80797004387
855662237514.451957673219107.5480423268
861598635470.0575770732-19484.0575770732
87123534129434.196409108-5900.19640910776
8810853598500.377795618310034.6222043817
8993879124063.173704374-30184.1737043736
90144551118314.69920723626236.3007927635
915675073851.2214320464-17101.2214320464
9212765498858.088351511128795.9116484889
936559486030.1952230523-20436.1952230523
945993873022.6450476665-13084.6450476665
95146975125107.78479552621867.215204474
96143372112260.31732212831111.6826778724
97168553146423.53431590322129.4656840967
98183500150298.10671819333201.8932818072
99165986194399.140643276-28413.1406432763
100184923187340.385625681-2417.38562568133
101140358139056.8046357171301.1953642827
102149959138077.75547809611881.2445219035
1035722459536.2494045436-2312.24940454358
1044375043672.756157818277.2438421818241
1054802972981.3173287825-24952.3173287825
106104978126823.283582681-21845.2835826812
107100046115103.106480123-15057.1064801227
10810104799100.20388439471946.7961156053
109197426121817.10073508875608.8992649118
11016090287001.118817549373900.8811824507
111147172127001.78660435720170.2133956428
11210943286467.503857630622964.4961423694
11311684768.52746212273-3600.52746212273
1148324894883.2356714279-11635.2356714279
1152516230749.0734825354-5587.07348253543
1164572470274.9803603446-24550.9803603446
117110529132703.704589576-22174.704589576
1188554519.53061379518-3664.53061379518
11910138283881.203956356117500.7960436439
1201411615534.7551810749-1418.75518107493
12189506124594.787899215-35088.7878992152
122135356110538.53959916924817.4604008314
12311606672833.820491943543232.1795080565
12414424478972.961605496265271.0383945038
125877316326.0402814987-7553.04028149872
12610215387789.322555984514363.6774440155
127117440130563.249771056-13123.2497710558
128104128110940.892543634-6812.89254363408
129134238145764.585691153-11526.5856911528
130134047143214.59721547-9167.59721547049
131279488194893.46186839184594.5381316092
1327975696597.6301731674-16841.6301731675
1336608958006.12463956248082.87536043758
13410207095379.20075178816690.79924821187
135146760144192.6872665872567.31273341318
13615477173442.4847410681328.51525894
137165933128524.49446945337408.5055305468
1386459382555.3025437049-17962.3025437049
13992280124049.028737998-31769.0287379976
1406715094165.0278758171-27015.0278758171
14112869268694.668353338459997.3316466616
142124089102174.20018088121914.7998191186
143125386117653.2148698867732.78513011414
1443723821680.966241375415557.0337586246
145140015143309.161964954-3294.16196495418
146150047107346.24077207942700.7592279212
147154451116693.68499015237757.3150098476
148156349135459.96361514320889.0363848565
149022921.1163785693-22921.1163785693
15060237051.25803056398-1028.25803056398
15102338.77281472518-2338.77281472518
15202319.54721794424-2319.54721794424
15304596.34655046783-4596.34655046783
15402307.62466618815-2307.62466618815
1558460180791.84026892223809.15973107776
15668946114872.420957275-45926.4209572749
15702307.62466618815-2307.62466618815
15802336.88845039803-2336.88845039803
15916444436.23901292121-2792.23901292121
160617912941.4661860771-6762.46618607711
16139267820.46627256696-3894.46627256695
1625278926400.971596245626388.0284037544
16302408.47935583509-2408.47935583509
16410035061657.059699121838692.9403008782






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.4315703555802960.8631407111605920.568429644419704
190.322636197070940.6452723941418810.67736380292906
200.2273376594848550.454675318969710.772662340515145
210.1624662462726060.3249324925452130.837533753727394
220.1781403258322330.3562806516644660.821859674167767
230.1097490013006540.2194980026013070.890250998699346
240.0719913692408730.1439827384817460.928008630759127
250.05858587659493720.1171717531898740.941414123405063
260.03825641912595270.07651283825190530.961743580874047
270.0244615301599740.04892306031994790.975538469840026
280.01584005759955110.03168011519910210.984159942400449
290.01165924128291830.02331848256583660.988340758717082
300.01088478103416890.02176956206833790.989115218965831
310.006999195442459340.01399839088491870.993000804557541
320.00421853928172240.008437078563444790.995781460718278
330.002541298697600970.005082597395201940.997458701302399
340.003821341270527080.007642682541054160.996178658729473
350.00729869279717810.01459738559435620.992701307202822
360.00434926951585840.00869853903171680.995650730484142
370.002982762382999830.005965524765999650.997017237617
380.001980756719801490.003961513439602980.998019243280199
390.002034548933546420.004069097867092840.997965451066454
400.001418543106443540.002837086212887070.998581456893556
410.002468142433537930.004936284867075860.997531857566462
420.001510060198811170.003020120397622340.998489939801189
430.00121768306065250.0024353661213050.998782316939347
440.0007954079801530910.001590815960306180.999204592019847
450.003018900855456180.006037801710912350.996981099144544
460.001906525010703430.003813050021406860.998093474989297
470.00150155855442950.0030031171088590.998498441445571
480.0009084217194550470.001816843438910090.999091578280545
490.0005645036250077810.001129007250015560.999435496374992
500.000495279717038030.000990559434076060.999504720282962
510.0003473962991405460.0006947925982810920.999652603700859
520.0002427111210060640.0004854222420121280.999757288878994
530.0002036408053434370.0004072816106868740.999796359194657
540.0001175556613719320.0002351113227438640.999882444338628
557.60799182937448e-050.000152159836587490.999923920081706
564.84634202059586e-059.69268404119172e-050.999951536579794
574.43048155311918e-058.86096310623835e-050.999955695184469
588.11117058178312e-050.0001622234116356620.999918888294182
596.4634519439192e-050.0001292690388783840.999935365480561
603.77932529894022e-057.55865059788044e-050.999962206747011
612.47182740044196e-054.94365480088393e-050.999975281725996
621.5476647629556e-053.0953295259112e-050.99998452335237
631.562291665286e-053.124583330572e-050.999984377083347
649.72822045117446e-061.94564409023489e-050.999990271779549
656.33970051462809e-061.26794010292562e-050.999993660299485
664.98896577833692e-069.97793155667383e-060.999995011034222
678.30576997477823e-061.66115399495565e-050.999991694230025
682.6860945491255e-055.372189098251e-050.999973139054509
692.51395238908625e-055.0279047781725e-050.999974860476109
706.72700038073696e-050.0001345400076147390.999932729996193
710.0001085731996604030.0002171463993208060.99989142680034
728.71247825515836e-050.0001742495651031670.999912875217448
738.72616549217975e-050.0001745233098435950.999912738345078
745.28098983014564e-050.0001056197966029130.999947190101699
753.40438344701838e-056.80876689403677e-050.99996595616553
763.3264021099794e-056.6528042199588e-050.9999667359789
771.97877771066641e-053.95755542133281e-050.999980212222893
780.1415184437191790.2830368874383580.858481556280821
790.1241346430125170.2482692860250330.875865356987483
800.1026514435248110.2053028870496220.897348556475189
810.09170300609049080.1834060121809820.908296993909509
820.1497432218099160.2994864436198330.850256778190083
830.1611233468273010.3222466936546010.838876653172699
840.1372927095397940.2745854190795870.862707290460207
850.1218301366224410.2436602732448820.878169863377559
860.1045499267025180.2090998534050350.895450073297482
870.09290543277888470.1858108655577690.907094567221115
880.08893199765004550.1778639953000910.911068002349954
890.1065883541497370.2131767082994740.893411645850263
900.1180007426380410.2360014852760830.881999257361959
910.1106714342278020.2213428684556030.889328565772198
920.1244843853667860.2489687707335730.875515614633213
930.1253996686070530.2507993372141060.874600331392947
940.1070662335532660.2141324671065320.892933766446734
950.1321154972175250.264230994435050.867884502782475
960.1458609107544530.2917218215089070.854139089245547
970.1332942616548760.2665885233097520.866705738345124
980.1390876685988370.2781753371976740.860912331401163
990.2537712186899830.5075424373799670.746228781310017
1000.2187346305585010.4374692611170020.781265369441499
1010.2108217271517070.4216434543034140.789178272848293
1020.1944747747552350.388949549510470.805525225244765
1030.1616178613022490.3232357226044990.838382138697751
1040.1360864974892260.2721729949784520.863913502510774
1050.1392375183778830.2784750367557660.860762481622117
1060.1618993530876060.3237987061752120.838100646912394
1070.1418387715954980.2836775431909950.858161228404502
1080.1161331061093620.2322662122187230.883866893890639
1090.2179231958038630.4358463916077260.782076804196137
1100.3470592498826170.6941184997652340.652940750117383
1110.3171868105281050.634373621056210.682813189471895
1120.3333562882995980.6667125765991950.666643711700402
1130.2869244557944560.5738489115889130.713075544205544
1140.2755543999335980.5511087998671960.724445600066402
1150.2409330864892890.4818661729785780.759066913510711
1160.3920535133342820.7841070266685630.607946486665718
1170.3594964451396120.7189928902792240.640503554860388
1180.3083613321830730.6167226643661450.691638667816927
1190.274542831600420.549085663200840.72545716839958
1200.2301566977204470.4603133954408940.769843302279553
1210.5485945890224950.9028108219550090.451405410977505
1220.5194460023302880.9611079953394250.480553997669712
1230.6084218733561950.783156253287610.391578126643805
1240.7270626546485160.5458746907029680.272937345351484
1250.6751762207564350.649647558487130.324823779243565
1260.6243361798355960.7513276403288080.375663820164404
1270.5834447982100830.8331104035798340.416555201789917
1280.6547807161906870.6904385676186270.345219283809313
1290.6437232029794870.7125535940410270.356276797020513
1300.5864741200627180.8270517598745630.413525879937282
1310.9221911524008640.1556176951982720.0778088475991361
1320.8914973965818120.2170052068363750.108502603418188
1330.8566787154717210.2866425690565570.143321284528279
1340.8067551676591350.3864896646817290.193244832340865
1350.7605597762628140.4788804474743720.239440223737186
1360.9999623889848117.52220303785403e-053.76110151892702e-05
1370.9999437573010490.0001124853979028185.62426989514088e-05
1380.9999303259566760.0001393480866488646.96740433244319e-05
1390.9999815322715043.6935456991569e-051.84677284957845e-05
1400.9999999986466462.7067073228226e-091.3533536614113e-09
1410.9999999924825191.50349617241143e-087.51748086205714e-09
1420.99999989474622.10507600665515e-071.05253800332758e-07
1430.9999998746723482.50655304334608e-071.25327652167304e-07
1440.9999999999957168.56727791756119e-124.2836389587806e-12
1450.9999999992744841.45103156889615e-097.25515784448074e-10
14614.25152281819284e-452.12576140909642e-45

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.431570355580296 & 0.863140711160592 & 0.568429644419704 \tabularnewline
19 & 0.32263619707094 & 0.645272394141881 & 0.67736380292906 \tabularnewline
20 & 0.227337659484855 & 0.45467531896971 & 0.772662340515145 \tabularnewline
21 & 0.162466246272606 & 0.324932492545213 & 0.837533753727394 \tabularnewline
22 & 0.178140325832233 & 0.356280651664466 & 0.821859674167767 \tabularnewline
23 & 0.109749001300654 & 0.219498002601307 & 0.890250998699346 \tabularnewline
24 & 0.071991369240873 & 0.143982738481746 & 0.928008630759127 \tabularnewline
25 & 0.0585858765949372 & 0.117171753189874 & 0.941414123405063 \tabularnewline
26 & 0.0382564191259527 & 0.0765128382519053 & 0.961743580874047 \tabularnewline
27 & 0.024461530159974 & 0.0489230603199479 & 0.975538469840026 \tabularnewline
28 & 0.0158400575995511 & 0.0316801151991021 & 0.984159942400449 \tabularnewline
29 & 0.0116592412829183 & 0.0233184825658366 & 0.988340758717082 \tabularnewline
30 & 0.0108847810341689 & 0.0217695620683379 & 0.989115218965831 \tabularnewline
31 & 0.00699919544245934 & 0.0139983908849187 & 0.993000804557541 \tabularnewline
32 & 0.0042185392817224 & 0.00843707856344479 & 0.995781460718278 \tabularnewline
33 & 0.00254129869760097 & 0.00508259739520194 & 0.997458701302399 \tabularnewline
34 & 0.00382134127052708 & 0.00764268254105416 & 0.996178658729473 \tabularnewline
35 & 0.0072986927971781 & 0.0145973855943562 & 0.992701307202822 \tabularnewline
36 & 0.0043492695158584 & 0.0086985390317168 & 0.995650730484142 \tabularnewline
37 & 0.00298276238299983 & 0.00596552476599965 & 0.997017237617 \tabularnewline
38 & 0.00198075671980149 & 0.00396151343960298 & 0.998019243280199 \tabularnewline
39 & 0.00203454893354642 & 0.00406909786709284 & 0.997965451066454 \tabularnewline
40 & 0.00141854310644354 & 0.00283708621288707 & 0.998581456893556 \tabularnewline
41 & 0.00246814243353793 & 0.00493628486707586 & 0.997531857566462 \tabularnewline
42 & 0.00151006019881117 & 0.00302012039762234 & 0.998489939801189 \tabularnewline
43 & 0.0012176830606525 & 0.002435366121305 & 0.998782316939347 \tabularnewline
44 & 0.000795407980153091 & 0.00159081596030618 & 0.999204592019847 \tabularnewline
45 & 0.00301890085545618 & 0.00603780171091235 & 0.996981099144544 \tabularnewline
46 & 0.00190652501070343 & 0.00381305002140686 & 0.998093474989297 \tabularnewline
47 & 0.0015015585544295 & 0.003003117108859 & 0.998498441445571 \tabularnewline
48 & 0.000908421719455047 & 0.00181684343891009 & 0.999091578280545 \tabularnewline
49 & 0.000564503625007781 & 0.00112900725001556 & 0.999435496374992 \tabularnewline
50 & 0.00049527971703803 & 0.00099055943407606 & 0.999504720282962 \tabularnewline
51 & 0.000347396299140546 & 0.000694792598281092 & 0.999652603700859 \tabularnewline
52 & 0.000242711121006064 & 0.000485422242012128 & 0.999757288878994 \tabularnewline
53 & 0.000203640805343437 & 0.000407281610686874 & 0.999796359194657 \tabularnewline
54 & 0.000117555661371932 & 0.000235111322743864 & 0.999882444338628 \tabularnewline
55 & 7.60799182937448e-05 & 0.00015215983658749 & 0.999923920081706 \tabularnewline
56 & 4.84634202059586e-05 & 9.69268404119172e-05 & 0.999951536579794 \tabularnewline
57 & 4.43048155311918e-05 & 8.86096310623835e-05 & 0.999955695184469 \tabularnewline
58 & 8.11117058178312e-05 & 0.000162223411635662 & 0.999918888294182 \tabularnewline
59 & 6.4634519439192e-05 & 0.000129269038878384 & 0.999935365480561 \tabularnewline
60 & 3.77932529894022e-05 & 7.55865059788044e-05 & 0.999962206747011 \tabularnewline
61 & 2.47182740044196e-05 & 4.94365480088393e-05 & 0.999975281725996 \tabularnewline
62 & 1.5476647629556e-05 & 3.0953295259112e-05 & 0.99998452335237 \tabularnewline
63 & 1.562291665286e-05 & 3.124583330572e-05 & 0.999984377083347 \tabularnewline
64 & 9.72822045117446e-06 & 1.94564409023489e-05 & 0.999990271779549 \tabularnewline
65 & 6.33970051462809e-06 & 1.26794010292562e-05 & 0.999993660299485 \tabularnewline
66 & 4.98896577833692e-06 & 9.97793155667383e-06 & 0.999995011034222 \tabularnewline
67 & 8.30576997477823e-06 & 1.66115399495565e-05 & 0.999991694230025 \tabularnewline
68 & 2.6860945491255e-05 & 5.372189098251e-05 & 0.999973139054509 \tabularnewline
69 & 2.51395238908625e-05 & 5.0279047781725e-05 & 0.999974860476109 \tabularnewline
70 & 6.72700038073696e-05 & 0.000134540007614739 & 0.999932729996193 \tabularnewline
71 & 0.000108573199660403 & 0.000217146399320806 & 0.99989142680034 \tabularnewline
72 & 8.71247825515836e-05 & 0.000174249565103167 & 0.999912875217448 \tabularnewline
73 & 8.72616549217975e-05 & 0.000174523309843595 & 0.999912738345078 \tabularnewline
74 & 5.28098983014564e-05 & 0.000105619796602913 & 0.999947190101699 \tabularnewline
75 & 3.40438344701838e-05 & 6.80876689403677e-05 & 0.99996595616553 \tabularnewline
76 & 3.3264021099794e-05 & 6.6528042199588e-05 & 0.9999667359789 \tabularnewline
77 & 1.97877771066641e-05 & 3.95755542133281e-05 & 0.999980212222893 \tabularnewline
78 & 0.141518443719179 & 0.283036887438358 & 0.858481556280821 \tabularnewline
79 & 0.124134643012517 & 0.248269286025033 & 0.875865356987483 \tabularnewline
80 & 0.102651443524811 & 0.205302887049622 & 0.897348556475189 \tabularnewline
81 & 0.0917030060904908 & 0.183406012180982 & 0.908296993909509 \tabularnewline
82 & 0.149743221809916 & 0.299486443619833 & 0.850256778190083 \tabularnewline
83 & 0.161123346827301 & 0.322246693654601 & 0.838876653172699 \tabularnewline
84 & 0.137292709539794 & 0.274585419079587 & 0.862707290460207 \tabularnewline
85 & 0.121830136622441 & 0.243660273244882 & 0.878169863377559 \tabularnewline
86 & 0.104549926702518 & 0.209099853405035 & 0.895450073297482 \tabularnewline
87 & 0.0929054327788847 & 0.185810865557769 & 0.907094567221115 \tabularnewline
88 & 0.0889319976500455 & 0.177863995300091 & 0.911068002349954 \tabularnewline
89 & 0.106588354149737 & 0.213176708299474 & 0.893411645850263 \tabularnewline
90 & 0.118000742638041 & 0.236001485276083 & 0.881999257361959 \tabularnewline
91 & 0.110671434227802 & 0.221342868455603 & 0.889328565772198 \tabularnewline
92 & 0.124484385366786 & 0.248968770733573 & 0.875515614633213 \tabularnewline
93 & 0.125399668607053 & 0.250799337214106 & 0.874600331392947 \tabularnewline
94 & 0.107066233553266 & 0.214132467106532 & 0.892933766446734 \tabularnewline
95 & 0.132115497217525 & 0.26423099443505 & 0.867884502782475 \tabularnewline
96 & 0.145860910754453 & 0.291721821508907 & 0.854139089245547 \tabularnewline
97 & 0.133294261654876 & 0.266588523309752 & 0.866705738345124 \tabularnewline
98 & 0.139087668598837 & 0.278175337197674 & 0.860912331401163 \tabularnewline
99 & 0.253771218689983 & 0.507542437379967 & 0.746228781310017 \tabularnewline
100 & 0.218734630558501 & 0.437469261117002 & 0.781265369441499 \tabularnewline
101 & 0.210821727151707 & 0.421643454303414 & 0.789178272848293 \tabularnewline
102 & 0.194474774755235 & 0.38894954951047 & 0.805525225244765 \tabularnewline
103 & 0.161617861302249 & 0.323235722604499 & 0.838382138697751 \tabularnewline
104 & 0.136086497489226 & 0.272172994978452 & 0.863913502510774 \tabularnewline
105 & 0.139237518377883 & 0.278475036755766 & 0.860762481622117 \tabularnewline
106 & 0.161899353087606 & 0.323798706175212 & 0.838100646912394 \tabularnewline
107 & 0.141838771595498 & 0.283677543190995 & 0.858161228404502 \tabularnewline
108 & 0.116133106109362 & 0.232266212218723 & 0.883866893890639 \tabularnewline
109 & 0.217923195803863 & 0.435846391607726 & 0.782076804196137 \tabularnewline
110 & 0.347059249882617 & 0.694118499765234 & 0.652940750117383 \tabularnewline
111 & 0.317186810528105 & 0.63437362105621 & 0.682813189471895 \tabularnewline
112 & 0.333356288299598 & 0.666712576599195 & 0.666643711700402 \tabularnewline
113 & 0.286924455794456 & 0.573848911588913 & 0.713075544205544 \tabularnewline
114 & 0.275554399933598 & 0.551108799867196 & 0.724445600066402 \tabularnewline
115 & 0.240933086489289 & 0.481866172978578 & 0.759066913510711 \tabularnewline
116 & 0.392053513334282 & 0.784107026668563 & 0.607946486665718 \tabularnewline
117 & 0.359496445139612 & 0.718992890279224 & 0.640503554860388 \tabularnewline
118 & 0.308361332183073 & 0.616722664366145 & 0.691638667816927 \tabularnewline
119 & 0.27454283160042 & 0.54908566320084 & 0.72545716839958 \tabularnewline
120 & 0.230156697720447 & 0.460313395440894 & 0.769843302279553 \tabularnewline
121 & 0.548594589022495 & 0.902810821955009 & 0.451405410977505 \tabularnewline
122 & 0.519446002330288 & 0.961107995339425 & 0.480553997669712 \tabularnewline
123 & 0.608421873356195 & 0.78315625328761 & 0.391578126643805 \tabularnewline
124 & 0.727062654648516 & 0.545874690702968 & 0.272937345351484 \tabularnewline
125 & 0.675176220756435 & 0.64964755848713 & 0.324823779243565 \tabularnewline
126 & 0.624336179835596 & 0.751327640328808 & 0.375663820164404 \tabularnewline
127 & 0.583444798210083 & 0.833110403579834 & 0.416555201789917 \tabularnewline
128 & 0.654780716190687 & 0.690438567618627 & 0.345219283809313 \tabularnewline
129 & 0.643723202979487 & 0.712553594041027 & 0.356276797020513 \tabularnewline
130 & 0.586474120062718 & 0.827051759874563 & 0.413525879937282 \tabularnewline
131 & 0.922191152400864 & 0.155617695198272 & 0.0778088475991361 \tabularnewline
132 & 0.891497396581812 & 0.217005206836375 & 0.108502603418188 \tabularnewline
133 & 0.856678715471721 & 0.286642569056557 & 0.143321284528279 \tabularnewline
134 & 0.806755167659135 & 0.386489664681729 & 0.193244832340865 \tabularnewline
135 & 0.760559776262814 & 0.478880447474372 & 0.239440223737186 \tabularnewline
136 & 0.999962388984811 & 7.52220303785403e-05 & 3.76110151892702e-05 \tabularnewline
137 & 0.999943757301049 & 0.000112485397902818 & 5.62426989514088e-05 \tabularnewline
138 & 0.999930325956676 & 0.000139348086648864 & 6.96740433244319e-05 \tabularnewline
139 & 0.999981532271504 & 3.6935456991569e-05 & 1.84677284957845e-05 \tabularnewline
140 & 0.999999998646646 & 2.7067073228226e-09 & 1.3533536614113e-09 \tabularnewline
141 & 0.999999992482519 & 1.50349617241143e-08 & 7.51748086205714e-09 \tabularnewline
142 & 0.9999998947462 & 2.10507600665515e-07 & 1.05253800332758e-07 \tabularnewline
143 & 0.999999874672348 & 2.50655304334608e-07 & 1.25327652167304e-07 \tabularnewline
144 & 0.999999999995716 & 8.56727791756119e-12 & 4.2836389587806e-12 \tabularnewline
145 & 0.999999999274484 & 1.45103156889615e-09 & 7.25515784448074e-10 \tabularnewline
146 & 1 & 4.25152281819284e-45 & 2.12576140909642e-45 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156353&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]0.431570355580296[/C][C]0.863140711160592[/C][C]0.568429644419704[/C][/ROW]
[ROW][C]19[/C][C]0.32263619707094[/C][C]0.645272394141881[/C][C]0.67736380292906[/C][/ROW]
[ROW][C]20[/C][C]0.227337659484855[/C][C]0.45467531896971[/C][C]0.772662340515145[/C][/ROW]
[ROW][C]21[/C][C]0.162466246272606[/C][C]0.324932492545213[/C][C]0.837533753727394[/C][/ROW]
[ROW][C]22[/C][C]0.178140325832233[/C][C]0.356280651664466[/C][C]0.821859674167767[/C][/ROW]
[ROW][C]23[/C][C]0.109749001300654[/C][C]0.219498002601307[/C][C]0.890250998699346[/C][/ROW]
[ROW][C]24[/C][C]0.071991369240873[/C][C]0.143982738481746[/C][C]0.928008630759127[/C][/ROW]
[ROW][C]25[/C][C]0.0585858765949372[/C][C]0.117171753189874[/C][C]0.941414123405063[/C][/ROW]
[ROW][C]26[/C][C]0.0382564191259527[/C][C]0.0765128382519053[/C][C]0.961743580874047[/C][/ROW]
[ROW][C]27[/C][C]0.024461530159974[/C][C]0.0489230603199479[/C][C]0.975538469840026[/C][/ROW]
[ROW][C]28[/C][C]0.0158400575995511[/C][C]0.0316801151991021[/C][C]0.984159942400449[/C][/ROW]
[ROW][C]29[/C][C]0.0116592412829183[/C][C]0.0233184825658366[/C][C]0.988340758717082[/C][/ROW]
[ROW][C]30[/C][C]0.0108847810341689[/C][C]0.0217695620683379[/C][C]0.989115218965831[/C][/ROW]
[ROW][C]31[/C][C]0.00699919544245934[/C][C]0.0139983908849187[/C][C]0.993000804557541[/C][/ROW]
[ROW][C]32[/C][C]0.0042185392817224[/C][C]0.00843707856344479[/C][C]0.995781460718278[/C][/ROW]
[ROW][C]33[/C][C]0.00254129869760097[/C][C]0.00508259739520194[/C][C]0.997458701302399[/C][/ROW]
[ROW][C]34[/C][C]0.00382134127052708[/C][C]0.00764268254105416[/C][C]0.996178658729473[/C][/ROW]
[ROW][C]35[/C][C]0.0072986927971781[/C][C]0.0145973855943562[/C][C]0.992701307202822[/C][/ROW]
[ROW][C]36[/C][C]0.0043492695158584[/C][C]0.0086985390317168[/C][C]0.995650730484142[/C][/ROW]
[ROW][C]37[/C][C]0.00298276238299983[/C][C]0.00596552476599965[/C][C]0.997017237617[/C][/ROW]
[ROW][C]38[/C][C]0.00198075671980149[/C][C]0.00396151343960298[/C][C]0.998019243280199[/C][/ROW]
[ROW][C]39[/C][C]0.00203454893354642[/C][C]0.00406909786709284[/C][C]0.997965451066454[/C][/ROW]
[ROW][C]40[/C][C]0.00141854310644354[/C][C]0.00283708621288707[/C][C]0.998581456893556[/C][/ROW]
[ROW][C]41[/C][C]0.00246814243353793[/C][C]0.00493628486707586[/C][C]0.997531857566462[/C][/ROW]
[ROW][C]42[/C][C]0.00151006019881117[/C][C]0.00302012039762234[/C][C]0.998489939801189[/C][/ROW]
[ROW][C]43[/C][C]0.0012176830606525[/C][C]0.002435366121305[/C][C]0.998782316939347[/C][/ROW]
[ROW][C]44[/C][C]0.000795407980153091[/C][C]0.00159081596030618[/C][C]0.999204592019847[/C][/ROW]
[ROW][C]45[/C][C]0.00301890085545618[/C][C]0.00603780171091235[/C][C]0.996981099144544[/C][/ROW]
[ROW][C]46[/C][C]0.00190652501070343[/C][C]0.00381305002140686[/C][C]0.998093474989297[/C][/ROW]
[ROW][C]47[/C][C]0.0015015585544295[/C][C]0.003003117108859[/C][C]0.998498441445571[/C][/ROW]
[ROW][C]48[/C][C]0.000908421719455047[/C][C]0.00181684343891009[/C][C]0.999091578280545[/C][/ROW]
[ROW][C]49[/C][C]0.000564503625007781[/C][C]0.00112900725001556[/C][C]0.999435496374992[/C][/ROW]
[ROW][C]50[/C][C]0.00049527971703803[/C][C]0.00099055943407606[/C][C]0.999504720282962[/C][/ROW]
[ROW][C]51[/C][C]0.000347396299140546[/C][C]0.000694792598281092[/C][C]0.999652603700859[/C][/ROW]
[ROW][C]52[/C][C]0.000242711121006064[/C][C]0.000485422242012128[/C][C]0.999757288878994[/C][/ROW]
[ROW][C]53[/C][C]0.000203640805343437[/C][C]0.000407281610686874[/C][C]0.999796359194657[/C][/ROW]
[ROW][C]54[/C][C]0.000117555661371932[/C][C]0.000235111322743864[/C][C]0.999882444338628[/C][/ROW]
[ROW][C]55[/C][C]7.60799182937448e-05[/C][C]0.00015215983658749[/C][C]0.999923920081706[/C][/ROW]
[ROW][C]56[/C][C]4.84634202059586e-05[/C][C]9.69268404119172e-05[/C][C]0.999951536579794[/C][/ROW]
[ROW][C]57[/C][C]4.43048155311918e-05[/C][C]8.86096310623835e-05[/C][C]0.999955695184469[/C][/ROW]
[ROW][C]58[/C][C]8.11117058178312e-05[/C][C]0.000162223411635662[/C][C]0.999918888294182[/C][/ROW]
[ROW][C]59[/C][C]6.4634519439192e-05[/C][C]0.000129269038878384[/C][C]0.999935365480561[/C][/ROW]
[ROW][C]60[/C][C]3.77932529894022e-05[/C][C]7.55865059788044e-05[/C][C]0.999962206747011[/C][/ROW]
[ROW][C]61[/C][C]2.47182740044196e-05[/C][C]4.94365480088393e-05[/C][C]0.999975281725996[/C][/ROW]
[ROW][C]62[/C][C]1.5476647629556e-05[/C][C]3.0953295259112e-05[/C][C]0.99998452335237[/C][/ROW]
[ROW][C]63[/C][C]1.562291665286e-05[/C][C]3.124583330572e-05[/C][C]0.999984377083347[/C][/ROW]
[ROW][C]64[/C][C]9.72822045117446e-06[/C][C]1.94564409023489e-05[/C][C]0.999990271779549[/C][/ROW]
[ROW][C]65[/C][C]6.33970051462809e-06[/C][C]1.26794010292562e-05[/C][C]0.999993660299485[/C][/ROW]
[ROW][C]66[/C][C]4.98896577833692e-06[/C][C]9.97793155667383e-06[/C][C]0.999995011034222[/C][/ROW]
[ROW][C]67[/C][C]8.30576997477823e-06[/C][C]1.66115399495565e-05[/C][C]0.999991694230025[/C][/ROW]
[ROW][C]68[/C][C]2.6860945491255e-05[/C][C]5.372189098251e-05[/C][C]0.999973139054509[/C][/ROW]
[ROW][C]69[/C][C]2.51395238908625e-05[/C][C]5.0279047781725e-05[/C][C]0.999974860476109[/C][/ROW]
[ROW][C]70[/C][C]6.72700038073696e-05[/C][C]0.000134540007614739[/C][C]0.999932729996193[/C][/ROW]
[ROW][C]71[/C][C]0.000108573199660403[/C][C]0.000217146399320806[/C][C]0.99989142680034[/C][/ROW]
[ROW][C]72[/C][C]8.71247825515836e-05[/C][C]0.000174249565103167[/C][C]0.999912875217448[/C][/ROW]
[ROW][C]73[/C][C]8.72616549217975e-05[/C][C]0.000174523309843595[/C][C]0.999912738345078[/C][/ROW]
[ROW][C]74[/C][C]5.28098983014564e-05[/C][C]0.000105619796602913[/C][C]0.999947190101699[/C][/ROW]
[ROW][C]75[/C][C]3.40438344701838e-05[/C][C]6.80876689403677e-05[/C][C]0.99996595616553[/C][/ROW]
[ROW][C]76[/C][C]3.3264021099794e-05[/C][C]6.6528042199588e-05[/C][C]0.9999667359789[/C][/ROW]
[ROW][C]77[/C][C]1.97877771066641e-05[/C][C]3.95755542133281e-05[/C][C]0.999980212222893[/C][/ROW]
[ROW][C]78[/C][C]0.141518443719179[/C][C]0.283036887438358[/C][C]0.858481556280821[/C][/ROW]
[ROW][C]79[/C][C]0.124134643012517[/C][C]0.248269286025033[/C][C]0.875865356987483[/C][/ROW]
[ROW][C]80[/C][C]0.102651443524811[/C][C]0.205302887049622[/C][C]0.897348556475189[/C][/ROW]
[ROW][C]81[/C][C]0.0917030060904908[/C][C]0.183406012180982[/C][C]0.908296993909509[/C][/ROW]
[ROW][C]82[/C][C]0.149743221809916[/C][C]0.299486443619833[/C][C]0.850256778190083[/C][/ROW]
[ROW][C]83[/C][C]0.161123346827301[/C][C]0.322246693654601[/C][C]0.838876653172699[/C][/ROW]
[ROW][C]84[/C][C]0.137292709539794[/C][C]0.274585419079587[/C][C]0.862707290460207[/C][/ROW]
[ROW][C]85[/C][C]0.121830136622441[/C][C]0.243660273244882[/C][C]0.878169863377559[/C][/ROW]
[ROW][C]86[/C][C]0.104549926702518[/C][C]0.209099853405035[/C][C]0.895450073297482[/C][/ROW]
[ROW][C]87[/C][C]0.0929054327788847[/C][C]0.185810865557769[/C][C]0.907094567221115[/C][/ROW]
[ROW][C]88[/C][C]0.0889319976500455[/C][C]0.177863995300091[/C][C]0.911068002349954[/C][/ROW]
[ROW][C]89[/C][C]0.106588354149737[/C][C]0.213176708299474[/C][C]0.893411645850263[/C][/ROW]
[ROW][C]90[/C][C]0.118000742638041[/C][C]0.236001485276083[/C][C]0.881999257361959[/C][/ROW]
[ROW][C]91[/C][C]0.110671434227802[/C][C]0.221342868455603[/C][C]0.889328565772198[/C][/ROW]
[ROW][C]92[/C][C]0.124484385366786[/C][C]0.248968770733573[/C][C]0.875515614633213[/C][/ROW]
[ROW][C]93[/C][C]0.125399668607053[/C][C]0.250799337214106[/C][C]0.874600331392947[/C][/ROW]
[ROW][C]94[/C][C]0.107066233553266[/C][C]0.214132467106532[/C][C]0.892933766446734[/C][/ROW]
[ROW][C]95[/C][C]0.132115497217525[/C][C]0.26423099443505[/C][C]0.867884502782475[/C][/ROW]
[ROW][C]96[/C][C]0.145860910754453[/C][C]0.291721821508907[/C][C]0.854139089245547[/C][/ROW]
[ROW][C]97[/C][C]0.133294261654876[/C][C]0.266588523309752[/C][C]0.866705738345124[/C][/ROW]
[ROW][C]98[/C][C]0.139087668598837[/C][C]0.278175337197674[/C][C]0.860912331401163[/C][/ROW]
[ROW][C]99[/C][C]0.253771218689983[/C][C]0.507542437379967[/C][C]0.746228781310017[/C][/ROW]
[ROW][C]100[/C][C]0.218734630558501[/C][C]0.437469261117002[/C][C]0.781265369441499[/C][/ROW]
[ROW][C]101[/C][C]0.210821727151707[/C][C]0.421643454303414[/C][C]0.789178272848293[/C][/ROW]
[ROW][C]102[/C][C]0.194474774755235[/C][C]0.38894954951047[/C][C]0.805525225244765[/C][/ROW]
[ROW][C]103[/C][C]0.161617861302249[/C][C]0.323235722604499[/C][C]0.838382138697751[/C][/ROW]
[ROW][C]104[/C][C]0.136086497489226[/C][C]0.272172994978452[/C][C]0.863913502510774[/C][/ROW]
[ROW][C]105[/C][C]0.139237518377883[/C][C]0.278475036755766[/C][C]0.860762481622117[/C][/ROW]
[ROW][C]106[/C][C]0.161899353087606[/C][C]0.323798706175212[/C][C]0.838100646912394[/C][/ROW]
[ROW][C]107[/C][C]0.141838771595498[/C][C]0.283677543190995[/C][C]0.858161228404502[/C][/ROW]
[ROW][C]108[/C][C]0.116133106109362[/C][C]0.232266212218723[/C][C]0.883866893890639[/C][/ROW]
[ROW][C]109[/C][C]0.217923195803863[/C][C]0.435846391607726[/C][C]0.782076804196137[/C][/ROW]
[ROW][C]110[/C][C]0.347059249882617[/C][C]0.694118499765234[/C][C]0.652940750117383[/C][/ROW]
[ROW][C]111[/C][C]0.317186810528105[/C][C]0.63437362105621[/C][C]0.682813189471895[/C][/ROW]
[ROW][C]112[/C][C]0.333356288299598[/C][C]0.666712576599195[/C][C]0.666643711700402[/C][/ROW]
[ROW][C]113[/C][C]0.286924455794456[/C][C]0.573848911588913[/C][C]0.713075544205544[/C][/ROW]
[ROW][C]114[/C][C]0.275554399933598[/C][C]0.551108799867196[/C][C]0.724445600066402[/C][/ROW]
[ROW][C]115[/C][C]0.240933086489289[/C][C]0.481866172978578[/C][C]0.759066913510711[/C][/ROW]
[ROW][C]116[/C][C]0.392053513334282[/C][C]0.784107026668563[/C][C]0.607946486665718[/C][/ROW]
[ROW][C]117[/C][C]0.359496445139612[/C][C]0.718992890279224[/C][C]0.640503554860388[/C][/ROW]
[ROW][C]118[/C][C]0.308361332183073[/C][C]0.616722664366145[/C][C]0.691638667816927[/C][/ROW]
[ROW][C]119[/C][C]0.27454283160042[/C][C]0.54908566320084[/C][C]0.72545716839958[/C][/ROW]
[ROW][C]120[/C][C]0.230156697720447[/C][C]0.460313395440894[/C][C]0.769843302279553[/C][/ROW]
[ROW][C]121[/C][C]0.548594589022495[/C][C]0.902810821955009[/C][C]0.451405410977505[/C][/ROW]
[ROW][C]122[/C][C]0.519446002330288[/C][C]0.961107995339425[/C][C]0.480553997669712[/C][/ROW]
[ROW][C]123[/C][C]0.608421873356195[/C][C]0.78315625328761[/C][C]0.391578126643805[/C][/ROW]
[ROW][C]124[/C][C]0.727062654648516[/C][C]0.545874690702968[/C][C]0.272937345351484[/C][/ROW]
[ROW][C]125[/C][C]0.675176220756435[/C][C]0.64964755848713[/C][C]0.324823779243565[/C][/ROW]
[ROW][C]126[/C][C]0.624336179835596[/C][C]0.751327640328808[/C][C]0.375663820164404[/C][/ROW]
[ROW][C]127[/C][C]0.583444798210083[/C][C]0.833110403579834[/C][C]0.416555201789917[/C][/ROW]
[ROW][C]128[/C][C]0.654780716190687[/C][C]0.690438567618627[/C][C]0.345219283809313[/C][/ROW]
[ROW][C]129[/C][C]0.643723202979487[/C][C]0.712553594041027[/C][C]0.356276797020513[/C][/ROW]
[ROW][C]130[/C][C]0.586474120062718[/C][C]0.827051759874563[/C][C]0.413525879937282[/C][/ROW]
[ROW][C]131[/C][C]0.922191152400864[/C][C]0.155617695198272[/C][C]0.0778088475991361[/C][/ROW]
[ROW][C]132[/C][C]0.891497396581812[/C][C]0.217005206836375[/C][C]0.108502603418188[/C][/ROW]
[ROW][C]133[/C][C]0.856678715471721[/C][C]0.286642569056557[/C][C]0.143321284528279[/C][/ROW]
[ROW][C]134[/C][C]0.806755167659135[/C][C]0.386489664681729[/C][C]0.193244832340865[/C][/ROW]
[ROW][C]135[/C][C]0.760559776262814[/C][C]0.478880447474372[/C][C]0.239440223737186[/C][/ROW]
[ROW][C]136[/C][C]0.999962388984811[/C][C]7.52220303785403e-05[/C][C]3.76110151892702e-05[/C][/ROW]
[ROW][C]137[/C][C]0.999943757301049[/C][C]0.000112485397902818[/C][C]5.62426989514088e-05[/C][/ROW]
[ROW][C]138[/C][C]0.999930325956676[/C][C]0.000139348086648864[/C][C]6.96740433244319e-05[/C][/ROW]
[ROW][C]139[/C][C]0.999981532271504[/C][C]3.6935456991569e-05[/C][C]1.84677284957845e-05[/C][/ROW]
[ROW][C]140[/C][C]0.999999998646646[/C][C]2.7067073228226e-09[/C][C]1.3533536614113e-09[/C][/ROW]
[ROW][C]141[/C][C]0.999999992482519[/C][C]1.50349617241143e-08[/C][C]7.51748086205714e-09[/C][/ROW]
[ROW][C]142[/C][C]0.9999998947462[/C][C]2.10507600665515e-07[/C][C]1.05253800332758e-07[/C][/ROW]
[ROW][C]143[/C][C]0.999999874672348[/C][C]2.50655304334608e-07[/C][C]1.25327652167304e-07[/C][/ROW]
[ROW][C]144[/C][C]0.999999999995716[/C][C]8.56727791756119e-12[/C][C]4.2836389587806e-12[/C][/ROW]
[ROW][C]145[/C][C]0.999999999274484[/C][C]1.45103156889615e-09[/C][C]7.25515784448074e-10[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]4.25152281819284e-45[/C][C]2.12576140909642e-45[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156353&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156353&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
180.4315703555802960.8631407111605920.568429644419704
190.322636197070940.6452723941418810.67736380292906
200.2273376594848550.454675318969710.772662340515145
210.1624662462726060.3249324925452130.837533753727394
220.1781403258322330.3562806516644660.821859674167767
230.1097490013006540.2194980026013070.890250998699346
240.0719913692408730.1439827384817460.928008630759127
250.05858587659493720.1171717531898740.941414123405063
260.03825641912595270.07651283825190530.961743580874047
270.0244615301599740.04892306031994790.975538469840026
280.01584005759955110.03168011519910210.984159942400449
290.01165924128291830.02331848256583660.988340758717082
300.01088478103416890.02176956206833790.989115218965831
310.006999195442459340.01399839088491870.993000804557541
320.00421853928172240.008437078563444790.995781460718278
330.002541298697600970.005082597395201940.997458701302399
340.003821341270527080.007642682541054160.996178658729473
350.00729869279717810.01459738559435620.992701307202822
360.00434926951585840.00869853903171680.995650730484142
370.002982762382999830.005965524765999650.997017237617
380.001980756719801490.003961513439602980.998019243280199
390.002034548933546420.004069097867092840.997965451066454
400.001418543106443540.002837086212887070.998581456893556
410.002468142433537930.004936284867075860.997531857566462
420.001510060198811170.003020120397622340.998489939801189
430.00121768306065250.0024353661213050.998782316939347
440.0007954079801530910.001590815960306180.999204592019847
450.003018900855456180.006037801710912350.996981099144544
460.001906525010703430.003813050021406860.998093474989297
470.00150155855442950.0030031171088590.998498441445571
480.0009084217194550470.001816843438910090.999091578280545
490.0005645036250077810.001129007250015560.999435496374992
500.000495279717038030.000990559434076060.999504720282962
510.0003473962991405460.0006947925982810920.999652603700859
520.0002427111210060640.0004854222420121280.999757288878994
530.0002036408053434370.0004072816106868740.999796359194657
540.0001175556613719320.0002351113227438640.999882444338628
557.60799182937448e-050.000152159836587490.999923920081706
564.84634202059586e-059.69268404119172e-050.999951536579794
574.43048155311918e-058.86096310623835e-050.999955695184469
588.11117058178312e-050.0001622234116356620.999918888294182
596.4634519439192e-050.0001292690388783840.999935365480561
603.77932529894022e-057.55865059788044e-050.999962206747011
612.47182740044196e-054.94365480088393e-050.999975281725996
621.5476647629556e-053.0953295259112e-050.99998452335237
631.562291665286e-053.124583330572e-050.999984377083347
649.72822045117446e-061.94564409023489e-050.999990271779549
656.33970051462809e-061.26794010292562e-050.999993660299485
664.98896577833692e-069.97793155667383e-060.999995011034222
678.30576997477823e-061.66115399495565e-050.999991694230025
682.6860945491255e-055.372189098251e-050.999973139054509
692.51395238908625e-055.0279047781725e-050.999974860476109
706.72700038073696e-050.0001345400076147390.999932729996193
710.0001085731996604030.0002171463993208060.99989142680034
728.71247825515836e-050.0001742495651031670.999912875217448
738.72616549217975e-050.0001745233098435950.999912738345078
745.28098983014564e-050.0001056197966029130.999947190101699
753.40438344701838e-056.80876689403677e-050.99996595616553
763.3264021099794e-056.6528042199588e-050.9999667359789
771.97877771066641e-053.95755542133281e-050.999980212222893
780.1415184437191790.2830368874383580.858481556280821
790.1241346430125170.2482692860250330.875865356987483
800.1026514435248110.2053028870496220.897348556475189
810.09170300609049080.1834060121809820.908296993909509
820.1497432218099160.2994864436198330.850256778190083
830.1611233468273010.3222466936546010.838876653172699
840.1372927095397940.2745854190795870.862707290460207
850.1218301366224410.2436602732448820.878169863377559
860.1045499267025180.2090998534050350.895450073297482
870.09290543277888470.1858108655577690.907094567221115
880.08893199765004550.1778639953000910.911068002349954
890.1065883541497370.2131767082994740.893411645850263
900.1180007426380410.2360014852760830.881999257361959
910.1106714342278020.2213428684556030.889328565772198
920.1244843853667860.2489687707335730.875515614633213
930.1253996686070530.2507993372141060.874600331392947
940.1070662335532660.2141324671065320.892933766446734
950.1321154972175250.264230994435050.867884502782475
960.1458609107544530.2917218215089070.854139089245547
970.1332942616548760.2665885233097520.866705738345124
980.1390876685988370.2781753371976740.860912331401163
990.2537712186899830.5075424373799670.746228781310017
1000.2187346305585010.4374692611170020.781265369441499
1010.2108217271517070.4216434543034140.789178272848293
1020.1944747747552350.388949549510470.805525225244765
1030.1616178613022490.3232357226044990.838382138697751
1040.1360864974892260.2721729949784520.863913502510774
1050.1392375183778830.2784750367557660.860762481622117
1060.1618993530876060.3237987061752120.838100646912394
1070.1418387715954980.2836775431909950.858161228404502
1080.1161331061093620.2322662122187230.883866893890639
1090.2179231958038630.4358463916077260.782076804196137
1100.3470592498826170.6941184997652340.652940750117383
1110.3171868105281050.634373621056210.682813189471895
1120.3333562882995980.6667125765991950.666643711700402
1130.2869244557944560.5738489115889130.713075544205544
1140.2755543999335980.5511087998671960.724445600066402
1150.2409330864892890.4818661729785780.759066913510711
1160.3920535133342820.7841070266685630.607946486665718
1170.3594964451396120.7189928902792240.640503554860388
1180.3083613321830730.6167226643661450.691638667816927
1190.274542831600420.549085663200840.72545716839958
1200.2301566977204470.4603133954408940.769843302279553
1210.5485945890224950.9028108219550090.451405410977505
1220.5194460023302880.9611079953394250.480553997669712
1230.6084218733561950.783156253287610.391578126643805
1240.7270626546485160.5458746907029680.272937345351484
1250.6751762207564350.649647558487130.324823779243565
1260.6243361798355960.7513276403288080.375663820164404
1270.5834447982100830.8331104035798340.416555201789917
1280.6547807161906870.6904385676186270.345219283809313
1290.6437232029794870.7125535940410270.356276797020513
1300.5864741200627180.8270517598745630.413525879937282
1310.9221911524008640.1556176951982720.0778088475991361
1320.8914973965818120.2170052068363750.108502603418188
1330.8566787154717210.2866425690565570.143321284528279
1340.8067551676591350.3864896646817290.193244832340865
1350.7605597762628140.4788804474743720.239440223737186
1360.9999623889848117.52220303785403e-053.76110151892702e-05
1370.9999437573010490.0001124853979028185.62426989514088e-05
1380.9999303259566760.0001393480866488646.96740433244319e-05
1390.9999815322715043.6935456991569e-051.84677284957845e-05
1400.9999999986466462.7067073228226e-091.3533536614113e-09
1410.9999999924825191.50349617241143e-087.51748086205714e-09
1420.99999989474622.10507600665515e-071.05253800332758e-07
1430.9999998746723482.50655304334608e-071.25327652167304e-07
1440.9999999999957168.56727791756119e-124.2836389587806e-12
1450.9999999992744841.45103156889615e-097.25515784448074e-10
14614.25152281819284e-452.12576140909642e-45






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level560.434108527131783NOK
5% type I error level620.48062015503876NOK
10% type I error level630.488372093023256NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 56 & 0.434108527131783 & NOK \tabularnewline
5% type I error level & 62 & 0.48062015503876 & NOK \tabularnewline
10% type I error level & 63 & 0.488372093023256 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156353&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]56[/C][C]0.434108527131783[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]62[/C][C]0.48062015503876[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]63[/C][C]0.488372093023256[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156353&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156353&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 level560.434108527131783NOK
5% type I error level620.48062015503876NOK
10% type I error level630.488372093023256NOK


Figures (Output of Computation)

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Input Parameters & R Code

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