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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 23 Dec 2011 17:24:40 -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/23/t1324679101lcf4sk7g137ncqz.htm/, Retrieved Mon, 29 Apr 2024 22:32:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=160732, Retrieved Mon, 29 Apr 2024 22:32:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact78

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  [Multiple Regression] [] [2011-12-09 11:34:14] [14511500b645ce5186c706473940fe45]
- R PD      [Multiple Regression] [] [2011-12-23 22:24:40] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1818	279055	73	504	95	3	96	42	159	130	140824	32033	186099	165	165
1439	212450	76	513	68	4	75	38	149	143	110459	20654	113854	135	132
2059	233939	83	710	64	16	70	46	178	118	105079	16346	99776	121	121
2733	222117	106	1154	139	2	134	42	164	146	112098	35926	106194	148	145
1399	189911	56	415	51	1	83	30	100	73	43929	10621	100792	73	71
631	70849	28	179	46	3	8	35	129	89	76173	10024	47552	49	47
5460	605767	135	2563	118	0	173	40	156	146	187326	43068	250931	185	177
381	33186	19	111	46	0	1	18	67	22	22807	1271	6853	5	5
2150	227332	62	763	79	7	88	38	148	132	144408	34416	115466	125	124
2042	267925	49	661	76	0	104	37	132	92	66485	20318	110896	93	92
2541	372083	123	983	82	0	114	46	169	147	79089	24409	169351	154	149
2449	279589	133	754	66	7	132	60	230	203	81625	20648	94853	98	93
2100	212638	87	785	60	10	57	37	122	113	68788	12347	72591	70	70
3020	368577	85	1186	117	4	139	55	191	171	103297	21857	101345	148	148
2265	269455	88	724	50	10	87	44	162	87	69446	11034	113713	100	100
5169	402600	197	1783	133	0	176	63	237	208	114948	33433	165354	150	142
2375	335735	78	850	63	8	114	40	156	153	167949	35902	164263	197	194
3564	432711	173	1390	100	4	121	43	157	97	125081	22355	135213	114	113
1516	185822	59	527	44	3	103	32	123	95	125818	31219	111669	169	162
2398	267365	89	692	65	8	135	52	203	197	136588	21983	134163	200	186
2551	279452	74	849	103	0	123	49	187	160	112431	40085	140303	148	147
3253	527853	112	1443	103	1	110	41	152	148	103037	18507	150773	140	137
1761	227252	50	587	62	5	84	25	89	84	82317	16278	111848	74	71
1787	200004	58	636	70	9	103	57	227	227	118906	24662	102509	128	123
3796	257239	134	1371	159	1	158	45	165	154	83515	31452	96785	140	134
3113	271341	139	1094	78	0	116	42	162	151	104581	32580	116136	116	115
3230	324969	134	1201	101	5	114	45	174	142	103129	22883	158376	147	138
2494	349753	93	795	73	0	181	43	154	148	83243	27652	153990	132	125
1856	200723	63	674	58	0	76	36	129	110	37110	9845	64057	70	66
3257	399591	80	1230	147	0	155	45	174	149	113344	20190	230054	144	137
2692	327660	89	1111	54	3	143	50	195	179	139165	46201	184531	155	152
2187	269239	83	758	84	6	50	50	186	149	86652	10971	114198	165	159
2593	397689	107	911	56	1	145	51	197	187	112302	34811	198299	161	159
1293	130446	49	456	45	4	56	42	157	153	69652	3029	33750	31	31
3567	430118	104	1293	87	4	141	44	168	163	119442	38941	189723	199	185
2764	273950	56	1186	87	0	83	42	159	127	69867	4958	100826	78	78
3766	429837	129	1353	77	0	112	44	161	151	101629	32344	188355	121	117
2075	254312	93	695	72	2	79	40	153	100	70168	19433	104470	112	109
995	120351	35	306	36	1	33	17	55	46	31081	12558	58391	41	41
3750	395658	212	1319	51	2	152	43	166	156	103925	36524	164808	158	149
3437	349119	87	1582	44	10	126	41	151	128	92622	26041	134097	123	123
2071	220517	86	788	75	10	97	41	148	111	79011	16637	80238	104	103
2038	234780	85	768	87	5	84	40	129	119	93487	28395	133252	94	87
1841	191784	71	491	97	6	68	49	181	148	64520	16747	54518	73	71
2964	186112	94	1211	90	1	64	52	93	65	93473	9105	121850	52	51
5578	459665	158	2091	860	2	101	42	150	134	114360	11941	79367	71	70
918	78800	42	330	57	2	20	26	82	66	33032	7935	56968	21	21
2704	259887	87	770	99	1	107	59	229	201	96125	19499	106314	155	155
4145	368086	123	1410	120	10	150	50	193	177	151911	22938	191889	174	172
2841	230299	70	1187	76	3	129	50	176	156	89256	25314	104864	136	133
2222	256033	82	706	56	0	102	47	179	158	95676	28527	160792	128	125
496	24188	24	218	20	0	8	4	12	7	5950	2694	15049	7	7
2699	400109	334	865	94	8	88	51	181	175	149695	20867	191179	165	158
744	65029	17	255	21	5	21	18	67	61	32551	3597	25109	21	21
1161	101097	64	454	70	3	30	14	52	41	31701	5296	45824	35	35
3405	319020	68	1245	133	1	106	41	148	133	100087	32982	129711	137	133
2970	375638	91	790	86	5	166	61	230	228	169707	38975	210012	174	169
4002	375011	207	1233	224	6	132	40	148	140	150491	42721	194679	257	256
2919	387748	156	1118	65	0	161	44	160	155	120192	41455	197680	207	190
2399	280106	90	919	86	12	90	40	155	141	95893	23923	81180	103	100
4121	400971	153	1352	70	10	160	51	198	181	151715	26719	197765	171	171
3330	322780	124	1212	148	12	139	29	104	75	176225	53405	214738	279	267
3132	291391	124	1257	72	11	104	43	169	97	59900	12526	96252	83	80
2868	295075	93	1030	59	8	103	42	163	142	104767	26584	124527	130	126
1778	280018	81	669	67	3	66	41	151	136	114799	37062	153242	131	132
2109	267432	71	542	58	0	163	30	116	87	72128	25696	145707	126	121
2148	217181	141	652	60	6	93	39	153	140	143592	24634	113963	158	156
3009	258166	159	894	105	10	85	51	195	169	89626	27269	134904	138	133
2624	277891	90	935	84	2	159	40	149	129	131072	25270	114268	200	199
1781	192894	74	659	63	5	146	29	106	92	126817	24634	94333	104	98
2735	271853	75	917	67	13	122	47	179	160	81351	17828	102204	111	109
893	73566	32	385	39	6	22	23	88	67	22618	3007	23824	26	25
2411	276269	96	789	60	7	85	48	185	179	88977	20065	111563	115	113
2291	242619	64	949	94	2	105	38	133	90	92059	24648	91313	127	126
2235	230030	71	784	67	5	131	42	164	144	81897	21588	89770	140	137
2370	371391	91	1001	96	4	140	46	169	144	108146	25217	100125	121	121
3242	398698	107	1268	54	3	156	40	153	144	126372	30927	165278	183	178
2080	243355	114	616	54	6	89	45	166	134	249771	18487	181712	68	63
2537	233519	73	775	62	2	137	42	164	146	71154	18050	80906	112	109
2149	219936	74	746	71	0	102	41	146	121	71571	17696	75881	103	101
2150	206169	54	795	50	1	74	37	141	112	55918	17326	83963	63	61
4232	483429	133	1273	117	3	161	47	183	145	160141	39361	175721	166	157
1380	146100	72	657	45	5	30	26	99	99	38692	9648	68580	38	38
2449	295224	109	703	61	2	120	48	134	96	102812	26759	136323	163	159
870	80953	25	437	31	0	49	8	28	27	56622	7905	55792	59	58
2700	217384	63	1060	175	0	121	27	101	77	15986	4527	25157	27	27
1574	179344	62	459	70	6	76	38	139	137	123534	41517	100922	108	108
4055	416097	223	1591	284	1	85	41	159	151	108535	21261	118845	88	83
3327	395041	131	1102	95	4	152	61	222	126	93879	36099	170492	92	88
3098	180679	106	1051	72	1	165	45	171	159	144551	39039	81716	170	164
2673	311447	105	850	63	1	89	41	154	101	56750	13841	115750	98	96
2404	292260	84	732	75	3	168	42	154	144	127654	23841	105590	205	192
1932	199481	68	632	90	10	48	35	129	102	65594	8589	92795	96	94
3147	282361	78	1128	89	1	149	36	140	135	59938	15049	82390	107	107
2598	329281	89	971	138	4	75	40	156	147	146975	39038	135599	150	144
2108	234577	48	711	68	5	107	40	156	155	165904	36774	127667	138	136
2240	310685	68	750	80	7	121	38	138	138	169265	40076	163073	177	171
2506	352078	91	904	65	0	184	43	153	113	183500	43840	211381	213	210
4198	416463	163	1369	130	12	155	65	251	248	165986	43146	189944	208	193
4165	429565	120	1538	85	13	165	33	126	116	184923	50099	226168	307	297
2842	297080	142	893	83	9	121	51	198	176	140358	40312	117495	125	125
2562	331792	71	926	89	0	176	45	168	140	149959	32616	195894	208	204
2564	242507	205	833	116	0	92	36	138	59	57224	11338	80684	73	70
602	43287	14	214	43	4	13	19	71	64	43750	7409	19630	49	49
2579	238089	87	833	87	4	120	25	90	40	48029	18213	88634	82	82
2665	285479	164	931	80	0	124	44	167	98	104978	45873	139292	206	205
3004	310383	63	1304	132	0	133	45	172	139	100046	39844	128602	112	111
2786	321797	95	1079	59	0	169	44	162	135	101047	28317	135848	139	135
1477	193926	96	490	50	0	39	35	129	97	197426	24797	178377	60	59
3358	175737	106	992	87	0	126	46	179	142	160902	7471	106330	70	70
2107	354041	78	677	62	5	82	44	163	155	147172	27259	178303	112	108
2338	303566	92	698	70	1	148	45	164	115	109432	23201	116938	142	141
400	23668	13	156	9	0	12	1	0	0	1168	238	5841	11	11
2233	196743	79	785	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
2033	217543	54	641	113	0	87	51	189	130	45724	9935	74151	132	101
3246	440711	128	1251	63	1	164	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
3838	360436	125	1351	157	3	118	43	159	150	89506	24347	160501	125	119
2193	251948	77	740	79	7	76	48	183	163	135356	27111	91502	121	118
1796	187320	97	524	113	14	55	49	184	148	116066	3938	24469	42	41
1907	180842	58	724	50	3	62	32	119	94	144244	17416	88229	111	107
568	38214	34	276	52	0	16	8	27	21	8773	1888	13983	16	16
2647	289296	58	869	113	3	98	43	163	151	102153	18700	80716	70	69
2819	358276	84	1031	115	0	137	52	198	187	117440	36809	157384	162	160
1464	211775	67	511	78	0	50	53	205	171	104128	24959	122975	173	158
3946	447335	90	1716	135	4	152	49	191	170	134238	37343	191469	171	161
2554	348017	99	884	120	0	163	48	187	145	134047	21849	231257	172	165
3506	441946	133	1201	122	3	142	56	210	198	279488	49809	258287	254	246
1552	215177	43	575	54	0	80	45	166	152	79756	21654	122531	90	89
1476	140328	48	507	63	0	65	40	145	112	66089	8728	61394	50	49
3105	318092	366	1033	162	4	94	48	187	173	102070	20920	86480	113	107
4541	466139	198	1574	162	5	128	50	186	177	146760	27195	195791	187	182
1876	162406	63	576	107	16	63	43	164	153	154771	1037	18284	16	16
4469	417354	142	1834	146	6	127	46	172	161	165933	42570	147581	175	173
2113	178322	86	790	77	5	60	40	147	115	64593	17672	72558	90	90
2046	292443	54	668	87	2	118	45	167	147	92280	34245	147341	140	140
2564	283913	100	905	192	1	110	46	158	124	67150	16786	114651	145	142
2209	253950	131	723	75	2	48	37	144	57	128692	20954	100187	141	126
4123	389698	126	1619	131	9	96	45	169	144	124089	16378	130332	125	123
2340	246963	93	811	67	1	128	39	145	126	125386	31852	134218	241	239
2035	173260	63	716	37	3	41	21	79	78	37238	2805	10901	16	15
3241	346748	108	1034	61	11	146	50	194	153	140015	38086	145758	175	170
2056	188437	62	782	127	5	147	55	212	196	150047	21166	75767	132	123
2872	279125	98	1103	58	2	121	40	148	130	154451	34672	134969	154	151
2749	314070	113	852	71	1	185	48	171	159	156349	36171	169216	198	194
2	1	0	0	0	9	0	0	0	0	0	0	0	0	0
207	14688	10	85	0	0	4	0	0	0	6023	2065	7953	5	5
5	98	1	0	0	0	0	0	0	0	0	0	0	0	0
8	455	2	0	0	0	0	0	0	0	0	0	0	0	0
0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
2449	291847	95	816	72	2	85	46	141	94	84601	19354	105406	125	122
3497	415839	170	1145	123	3	164	52	204	129	68946	22124	174586	174	173
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
4	203	4	0	0	0	0	0	0	0	0	0	0	0	0
151	7199	5	74	0	0	7	0	0	0	1644	556	4245	6	6
475	46660	21	259	7	0	12	5	15	13	6179	2089	21509	13	13
141	17547	5	69	3	0	0	1	4	4	3926	2658	7670	3	3
1145	121550	46	309	106	0	37	48	172	89	52789	1813	15673	35	35
29	969	2	0	0	0	0	0	0	0	0	0	0	0	0
2080	242774	75	695	53	2	62	34	125	71	100350	17372	75882	80	72

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160732&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160732&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160732&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'Herman Ole Andreas Wold' @ wold.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TimeRFC[t] = -4359.40975614201 + 6.25418149991897Pageviews[t] + 163.607473986435Logins[t] + 102.478902619754CCviews[t] + 87.7265787754009Cviews[t] + 668.875683300692Shares[t] + 157.443166803841BloggedC[t] -1033.3026007572RVC[t] + 551.175668230589Feedbackm[t] -11.3184148150977`Feedback+120`[t] -0.232327279517693Characters[t] -0.388281134672322Revisions[t] + 0.993301801971848Seconds[t] + 98.3101462996Hyperlinks[t] -84.2635724873418Blogs[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TimeRFC[t] =  -4359.40975614201 +  6.25418149991897Pageviews[t] +  163.607473986435Logins[t] +  102.478902619754CCviews[t] +  87.7265787754009Cviews[t] +  668.875683300692Shares[t] +  157.443166803841BloggedC[t] -1033.3026007572RVC[t] +  551.175668230589Feedbackm[t] -11.3184148150977`Feedback+120`[t] -0.232327279517693Characters[t] -0.388281134672322Revisions[t] +  0.993301801971848Seconds[t] +  98.3101462996Hyperlinks[t] -84.2635724873418Blogs[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160732&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TimeRFC[t] =  -4359.40975614201 +  6.25418149991897Pageviews[t] +  163.607473986435Logins[t] +  102.478902619754CCviews[t] +  87.7265787754009Cviews[t] +  668.875683300692Shares[t] +  157.443166803841BloggedC[t] -1033.3026007572RVC[t] +  551.175668230589Feedbackm[t] -11.3184148150977`Feedback+120`[t] -0.232327279517693Characters[t] -0.388281134672322Revisions[t] +  0.993301801971848Seconds[t] +  98.3101462996Hyperlinks[t] -84.2635724873418Blogs[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160732&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160732&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
TimeRFC[t] = -4359.40975614201 + 6.25418149991897Pageviews[t] + 163.607473986435Logins[t] + 102.478902619754CCviews[t] + 87.7265787754009Cviews[t] + 668.875683300692Shares[t] + 157.443166803841BloggedC[t] -1033.3026007572RVC[t] + 551.175668230589Feedbackm[t] -11.3184148150977`Feedback+120`[t] -0.232327279517693Characters[t] -0.388281134672322Revisions[t] + 0.993301801971848Seconds[t] + 98.3101462996Hyperlinks[t] -84.2635724873418Blogs[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-4359.409756142017643.15339-0.57040.5692870.284644
Pageviews6.2541814999189714.7846280.4230.6728910.336446
Logins163.60747398643583.074621.96940.0507610.025381
CCviews102.47890261975429.1153233.51980.0005730.000287
Cviews87.726578775400952.4932161.67120.0967820.048391
Shares668.875683300692879.4676550.76050.448130.224065
BloggedC157.443166803841131.7198571.19530.2338730.116937
RVC-1033.30260075721052.393908-0.98190.3277610.16388
Feedbackm551.175668230589323.7104461.70270.0907130.045356
`Feedback+120`-11.3184148150977161.933557-0.06990.9443710.472185
Characters-0.2323272795176930.107801-2.15520.0327560.016378
Revisions-0.3882811346723220.474106-0.8190.4141080.207054
Seconds0.9933018019718480.1107898.965700
Hyperlinks98.3101462996755.6471510.13010.8966620.448331
Blogs-84.2635724873418785.29574-0.10730.9146940.457347

\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) & -4359.40975614201 & 7643.15339 & -0.5704 & 0.569287 & 0.284644 \tabularnewline
Pageviews & 6.25418149991897 & 14.784628 & 0.423 & 0.672891 & 0.336446 \tabularnewline
Logins & 163.607473986435 & 83.07462 & 1.9694 & 0.050761 & 0.025381 \tabularnewline
CCviews & 102.478902619754 & 29.115323 & 3.5198 & 0.000573 & 0.000287 \tabularnewline
Cviews & 87.7265787754009 & 52.493216 & 1.6712 & 0.096782 & 0.048391 \tabularnewline
Shares & 668.875683300692 & 879.467655 & 0.7605 & 0.44813 & 0.224065 \tabularnewline
BloggedC & 157.443166803841 & 131.719857 & 1.1953 & 0.233873 & 0.116937 \tabularnewline
RVC & -1033.3026007572 & 1052.393908 & -0.9819 & 0.327761 & 0.16388 \tabularnewline
Feedbackm & 551.175668230589 & 323.710446 & 1.7027 & 0.090713 & 0.045356 \tabularnewline
`Feedback+120` & -11.3184148150977 & 161.933557 & -0.0699 & 0.944371 & 0.472185 \tabularnewline
Characters & -0.232327279517693 & 0.107801 & -2.1552 & 0.032756 & 0.016378 \tabularnewline
Revisions & -0.388281134672322 & 0.474106 & -0.819 & 0.414108 & 0.207054 \tabularnewline
Seconds & 0.993301801971848 & 0.110789 & 8.9657 & 0 & 0 \tabularnewline
Hyperlinks & 98.3101462996 & 755.647151 & 0.1301 & 0.896662 & 0.448331 \tabularnewline
Blogs & -84.2635724873418 & 785.29574 & -0.1073 & 0.914694 & 0.457347 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160732&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]-4359.40975614201[/C][C]7643.15339[/C][C]-0.5704[/C][C]0.569287[/C][C]0.284644[/C][/ROW]
[ROW][C]Pageviews[/C][C]6.25418149991897[/C][C]14.784628[/C][C]0.423[/C][C]0.672891[/C][C]0.336446[/C][/ROW]
[ROW][C]Logins[/C][C]163.607473986435[/C][C]83.07462[/C][C]1.9694[/C][C]0.050761[/C][C]0.025381[/C][/ROW]
[ROW][C]CCviews[/C][C]102.478902619754[/C][C]29.115323[/C][C]3.5198[/C][C]0.000573[/C][C]0.000287[/C][/ROW]
[ROW][C]Cviews[/C][C]87.7265787754009[/C][C]52.493216[/C][C]1.6712[/C][C]0.096782[/C][C]0.048391[/C][/ROW]
[ROW][C]Shares[/C][C]668.875683300692[/C][C]879.467655[/C][C]0.7605[/C][C]0.44813[/C][C]0.224065[/C][/ROW]
[ROW][C]BloggedC[/C][C]157.443166803841[/C][C]131.719857[/C][C]1.1953[/C][C]0.233873[/C][C]0.116937[/C][/ROW]
[ROW][C]RVC[/C][C]-1033.3026007572[/C][C]1052.393908[/C][C]-0.9819[/C][C]0.327761[/C][C]0.16388[/C][/ROW]
[ROW][C]Feedbackm[/C][C]551.175668230589[/C][C]323.710446[/C][C]1.7027[/C][C]0.090713[/C][C]0.045356[/C][/ROW]
[ROW][C]`Feedback+120`[/C][C]-11.3184148150977[/C][C]161.933557[/C][C]-0.0699[/C][C]0.944371[/C][C]0.472185[/C][/ROW]
[ROW][C]Characters[/C][C]-0.232327279517693[/C][C]0.107801[/C][C]-2.1552[/C][C]0.032756[/C][C]0.016378[/C][/ROW]
[ROW][C]Revisions[/C][C]-0.388281134672322[/C][C]0.474106[/C][C]-0.819[/C][C]0.414108[/C][C]0.207054[/C][/ROW]
[ROW][C]Seconds[/C][C]0.993301801971848[/C][C]0.110789[/C][C]8.9657[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Hyperlinks[/C][C]98.3101462996[/C][C]755.647151[/C][C]0.1301[/C][C]0.896662[/C][C]0.448331[/C][/ROW]
[ROW][C]Blogs[/C][C]-84.2635724873418[/C][C]785.29574[/C][C]-0.1073[/C][C]0.914694[/C][C]0.457347[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160732&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160732&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)-4359.409756142017643.15339-0.57040.5692870.284644
Pageviews6.2541814999189714.7846280.4230.6728910.336446
Logins163.60747398643583.074621.96940.0507610.025381
CCviews102.47890261975429.1153233.51980.0005730.000287
Cviews87.726578775400952.4932161.67120.0967820.048391
Shares668.875683300692879.4676550.76050.448130.224065
BloggedC157.443166803841131.7198571.19530.2338730.116937
RVC-1033.30260075721052.393908-0.98190.3277610.16388
Feedbackm551.175668230589323.7104461.70270.0907130.045356
`Feedback+120`-11.3184148150977161.933557-0.06990.9443710.472185
Characters-0.2323272795176930.107801-2.15520.0327560.016378
Revisions-0.3882811346723220.474106-0.8190.4141080.207054
Seconds0.9933018019718480.1107898.965700
Hyperlinks98.3101462996755.6471510.13010.8966620.448331
Blogs-84.2635724873418785.29574-0.10730.9146940.457347






Multiple Linear Regression - Regression Statistics
Multiple R0.962474492617398
R-squared0.926357148939117
Adjusted R-squared0.91943768642333
F-TEST (value)133.877038400818
F-TEST (DF numerator)14
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation35936.9369606596
Sum Squared Residuals192428052279.049

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.962474492617398 \tabularnewline
R-squared & 0.926357148939117 \tabularnewline
Adjusted R-squared & 0.91943768642333 \tabularnewline
F-TEST (value) & 133.877038400818 \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 & 35936.9369606596 \tabularnewline
Sum Squared Residuals & 192428052279.049 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160732&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.962474492617398[/C][/ROW]
[ROW][C]R-squared[/C][C]0.926357148939117[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.91943768642333[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]133.877038400818[/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]35936.9369606596[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]192428052279.049[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160732&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160732&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 R0.962474492617398
R-squared0.926357148939117
Adjusted R-squared0.91943768642333
F-TEST (value)133.877038400818
F-TEST (DF numerator)14
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation35936.9369606596
Sum Squared Residuals192428052279.049






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1279055280840.519196133-1785.51919613265
2212450212894.756528441-444.756528440597
3233939241484.455558861-7545.45555886148
4222117296128.619888664-74011.6198886644
5189911184564.8751214175346.12487858264
67084990243.1384680487-19394.1384680487
7605767587396.58077402218370.4192259785
83318635865.453746068-2679.45374606798
9227332233323.813907603-5991.81390760289
10267925228898.37316913939026.6268308609
11372083344437.06507350927645.9349264907
12279589272749.7536523056839.24634769492
13212638204425.6218769088212.37812309154
14368577301566.09775434167010.9022456586
15269455259949.068459019505.93154099037
16402600472813.54325279-70213.5432527898
17335735295332.10128682240402.8987131779
18432711358436.86270941974274.137290581
19185822197050.101809443-11228.1018094435
20267365281334.493373472-13969.4933734716
21279452289576.313503623-10124.3135036233
22527853369806.346203674158046.653796326
23227252206215.8004718221036.1995281795
24200004240350.121155645-40346.1211556445
25257239330992.980305312-73753.9803053119
26271341299376.510103079-28035.5101030793
27324969366087.84574966-41118.8457496595
28349753306926.07826393942826.9217360608
29200723188841.92139742611881.0786025738
30399591437120.416004006-37529.4160040055
31327660359392.518929858-31732.5189298585
32269239260863.3487558098375.65124419084
33397689364692.92342444832996.0765755521
34130446131919.778450839-1473.77845083862
35430118394821.61725010635296.382749894
36273950290219.095832627-16269.0958326272
37429837397866.9224454931970.0775545102
38254312238763.19459822315548.8054017772
39120351106677.63049084113673.3695091588
40395658392344.4940374793313.50596252093
41349119366588.287738637-17469.2877386374
42220517226338.517117194-5821.5171171942
43234780255146.378694284-20366.3786942837
44191784173626.97536455518157.0246354445
45186112265688.455679933-79576.4556799326
46459665449816.6650118589848.33498814224
4778800115268.18172717-36468.1817271699
48259887272749.654433163-12862.6544331626
49368086428740.838811167-60654.8388111675
50230299294823.003502102-64524.0035021018
51256033293043.198699584-37010.1986995844
522418843043.5381811927-18855.5381811927
53400109378271.19679920621837.8032007937
546502971615.6517121427-6586.65171214273
55101097123086.968867802-21989.9688678025
56319020317420.7258110131599.27418898677
57375638365154.79574313710483.2042568631
58375011409515.192781291-34504.1927812911
59387748382685.6876835335062.31231646695
60280106242560.68157630337545.3184236969
61400971430624.010831785-29653.0108317849
62322780386912.796880158-64132.7968801581
63291391320244.747317293-28853.7473172932
64295075297119.281748033-2044.28174803269
65280018259079.4654501720938.5345498301
66267432258881.967830358550.03216964981
67217181237987.314393979-20806.3143939789
68258166319189.405354671-61023.4053546706
69277891271799.9748269086091.02517309174
70192894202334.841795533-9440.84179553314
71271853278479.679537519-6626.6795375187
727356698484.4533771435-24918.4533771435
73276269265089.93148213711179.0685178634
74242619238441.4424374564177.55756254381
75230030240768.532341724-10738.5323417242
76371391271296.303274711100094.696725289
77398698361833.55518294636864.4448170544
78243355273334.922970588-29979.9229705879
79233519235210.661585416-1691.66158541612
80219936210150.6831630979785.3168369031
81206169219049.945884123-12880.9458841229
82483429387747.29768955795681.7023104433
83146100177894.171693584-31794.1716935843
84295224253348.1437431241875.85625688
8580953107357.443235686-26404.4432356864
86217384212658.6350688724725.36493112797
87179344177525.6915453381818.30845466206
88416097389293.51673395226803.483266048
89395041378809.02692671316231.9730732866
90180679254300.770816955-73621.770816955
91311447276185.4504195335261.5495804696
92292260244265.48579840247994.514201598
93199481214654.87308849-15173.8730884902
94282361277626.0151101544734.98488984648
95329281283533.81246947545747.1875305253
96234577234689.35395413-112.353954129771
97310685273707.02144249436977.9785575056
98352078345688.9704351546389.02956484626
99416463438606.895983677-22143.8959836766
100429565442494.044910949-12929.0449109486
101297080285157.44999681311922.5500031868
102331792348544.238589324-16752.2385893239
103242507257162.111705514-14655.1117055142
1044328758043.1516578822-14756.1516578822
105238089234852.2238384033236.7761615974
106285479305696.343246972-20217.3432469725
107310383328302.088812605-17919.0888126049
108321797316019.7667558415777.23324415885
109193926237777.393236941-43851.3932369407
110175737278953.22529182-103216.22529182
111354041289516.21780182364524.7821981768
112303566253349.98167766250216.018322338
1132366823494.0423889222173.957611077825
114196743250236.3571241-53493.3571241002
1156185754911.27172611396945.72827388611
116217543220132.289858983-2589.28985898286
117440711431410.823337269300.17666274024
1182105422749.7756557597-1695.77565575968
119252805256916.662204667-4111.66220466693
1203196141774.5639362426-9813.56393624258
121360436385848.390735834-25412.3907358344
122251948221656.05127147430291.9487285262
123187320149973.04976609137346.9502339085
124180842188126.124306301-7284.12430630056
1253821457841.107137135-19627.107137135
126289296232037.5969765357258.4030234695
127358276334809.805130523466.1948695004
128211775231092.90480637-19317.9048063697
129447335449810.977703807-2475.97770380743
130348017399510.36142459-51493.3614245903
131441946429638.10368775312307.8963122474
132215177228037.787875591-12860.7878755911
133140328160789.178000154-20461.1780001542
134318092320158.068573008-2066.06857300812
135466139457167.8186433348971.18135666647
136162406132971.41058215929434.5894178408
137417354411173.0038021166180.99619788396
138178322213287.031684699-34965.0316846993
139292443270744.27845078921698.721549211
140283913287816.070416193-3903.0704161925
141253950225673.1129749328276.8870250696
142389698381796.1228920387901.87710796149
143246963268870.705787972-21907.7057879723
144173260126114.84173471347145.1582652874
145346748329113.84628618317634.1537138168
146188437229003.989123461-40566.9891234615
147279125294052.628457343-14927.628457343
148314070318342.518769883-4272.51876988285
14911672.97975656411-1671.97975656411
1501468813680.61429661011007.38570338991
15198-4164.531374655934262.53137465593
152455-3982.161356169744437.16135616974
1530-3690.534072841273690.53407284127
1540-4359.409756141964359.40975614196
155291847239817.89704112452029.1029588757
156415839409865.3655677725973.63443222814
1570-4359.409756141964359.40975614196
158203-3679.963134196533882.96313419653
15971999791.56521560585-2592.56521560585
1604666053347.3702364031-6687.37023640307
1611754711517.41402248826029.58597751183
162121550104405.68924696517144.3107530346
163969-3850.823544671434819.82354467143
164242774187964.78411912354809.2158808769

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 279055 & 280840.519196133 & -1785.51919613265 \tabularnewline
2 & 212450 & 212894.756528441 & -444.756528440597 \tabularnewline
3 & 233939 & 241484.455558861 & -7545.45555886148 \tabularnewline
4 & 222117 & 296128.619888664 & -74011.6198886644 \tabularnewline
5 & 189911 & 184564.875121417 & 5346.12487858264 \tabularnewline
6 & 70849 & 90243.1384680487 & -19394.1384680487 \tabularnewline
7 & 605767 & 587396.580774022 & 18370.4192259785 \tabularnewline
8 & 33186 & 35865.453746068 & -2679.45374606798 \tabularnewline
9 & 227332 & 233323.813907603 & -5991.81390760289 \tabularnewline
10 & 267925 & 228898.373169139 & 39026.6268308609 \tabularnewline
11 & 372083 & 344437.065073509 & 27645.9349264907 \tabularnewline
12 & 279589 & 272749.753652305 & 6839.24634769492 \tabularnewline
13 & 212638 & 204425.621876908 & 8212.37812309154 \tabularnewline
14 & 368577 & 301566.097754341 & 67010.9022456586 \tabularnewline
15 & 269455 & 259949.06845901 & 9505.93154099037 \tabularnewline
16 & 402600 & 472813.54325279 & -70213.5432527898 \tabularnewline
17 & 335735 & 295332.101286822 & 40402.8987131779 \tabularnewline
18 & 432711 & 358436.862709419 & 74274.137290581 \tabularnewline
19 & 185822 & 197050.101809443 & -11228.1018094435 \tabularnewline
20 & 267365 & 281334.493373472 & -13969.4933734716 \tabularnewline
21 & 279452 & 289576.313503623 & -10124.3135036233 \tabularnewline
22 & 527853 & 369806.346203674 & 158046.653796326 \tabularnewline
23 & 227252 & 206215.80047182 & 21036.1995281795 \tabularnewline
24 & 200004 & 240350.121155645 & -40346.1211556445 \tabularnewline
25 & 257239 & 330992.980305312 & -73753.9803053119 \tabularnewline
26 & 271341 & 299376.510103079 & -28035.5101030793 \tabularnewline
27 & 324969 & 366087.84574966 & -41118.8457496595 \tabularnewline
28 & 349753 & 306926.078263939 & 42826.9217360608 \tabularnewline
29 & 200723 & 188841.921397426 & 11881.0786025738 \tabularnewline
30 & 399591 & 437120.416004006 & -37529.4160040055 \tabularnewline
31 & 327660 & 359392.518929858 & -31732.5189298585 \tabularnewline
32 & 269239 & 260863.348755809 & 8375.65124419084 \tabularnewline
33 & 397689 & 364692.923424448 & 32996.0765755521 \tabularnewline
34 & 130446 & 131919.778450839 & -1473.77845083862 \tabularnewline
35 & 430118 & 394821.617250106 & 35296.382749894 \tabularnewline
36 & 273950 & 290219.095832627 & -16269.0958326272 \tabularnewline
37 & 429837 & 397866.92244549 & 31970.0775545102 \tabularnewline
38 & 254312 & 238763.194598223 & 15548.8054017772 \tabularnewline
39 & 120351 & 106677.630490841 & 13673.3695091588 \tabularnewline
40 & 395658 & 392344.494037479 & 3313.50596252093 \tabularnewline
41 & 349119 & 366588.287738637 & -17469.2877386374 \tabularnewline
42 & 220517 & 226338.517117194 & -5821.5171171942 \tabularnewline
43 & 234780 & 255146.378694284 & -20366.3786942837 \tabularnewline
44 & 191784 & 173626.975364555 & 18157.0246354445 \tabularnewline
45 & 186112 & 265688.455679933 & -79576.4556799326 \tabularnewline
46 & 459665 & 449816.665011858 & 9848.33498814224 \tabularnewline
47 & 78800 & 115268.18172717 & -36468.1817271699 \tabularnewline
48 & 259887 & 272749.654433163 & -12862.6544331626 \tabularnewline
49 & 368086 & 428740.838811167 & -60654.8388111675 \tabularnewline
50 & 230299 & 294823.003502102 & -64524.0035021018 \tabularnewline
51 & 256033 & 293043.198699584 & -37010.1986995844 \tabularnewline
52 & 24188 & 43043.5381811927 & -18855.5381811927 \tabularnewline
53 & 400109 & 378271.196799206 & 21837.8032007937 \tabularnewline
54 & 65029 & 71615.6517121427 & -6586.65171214273 \tabularnewline
55 & 101097 & 123086.968867802 & -21989.9688678025 \tabularnewline
56 & 319020 & 317420.725811013 & 1599.27418898677 \tabularnewline
57 & 375638 & 365154.795743137 & 10483.2042568631 \tabularnewline
58 & 375011 & 409515.192781291 & -34504.1927812911 \tabularnewline
59 & 387748 & 382685.687683533 & 5062.31231646695 \tabularnewline
60 & 280106 & 242560.681576303 & 37545.3184236969 \tabularnewline
61 & 400971 & 430624.010831785 & -29653.0108317849 \tabularnewline
62 & 322780 & 386912.796880158 & -64132.7968801581 \tabularnewline
63 & 291391 & 320244.747317293 & -28853.7473172932 \tabularnewline
64 & 295075 & 297119.281748033 & -2044.28174803269 \tabularnewline
65 & 280018 & 259079.46545017 & 20938.5345498301 \tabularnewline
66 & 267432 & 258881.96783035 & 8550.03216964981 \tabularnewline
67 & 217181 & 237987.314393979 & -20806.3143939789 \tabularnewline
68 & 258166 & 319189.405354671 & -61023.4053546706 \tabularnewline
69 & 277891 & 271799.974826908 & 6091.02517309174 \tabularnewline
70 & 192894 & 202334.841795533 & -9440.84179553314 \tabularnewline
71 & 271853 & 278479.679537519 & -6626.6795375187 \tabularnewline
72 & 73566 & 98484.4533771435 & -24918.4533771435 \tabularnewline
73 & 276269 & 265089.931482137 & 11179.0685178634 \tabularnewline
74 & 242619 & 238441.442437456 & 4177.55756254381 \tabularnewline
75 & 230030 & 240768.532341724 & -10738.5323417242 \tabularnewline
76 & 371391 & 271296.303274711 & 100094.696725289 \tabularnewline
77 & 398698 & 361833.555182946 & 36864.4448170544 \tabularnewline
78 & 243355 & 273334.922970588 & -29979.9229705879 \tabularnewline
79 & 233519 & 235210.661585416 & -1691.66158541612 \tabularnewline
80 & 219936 & 210150.683163097 & 9785.3168369031 \tabularnewline
81 & 206169 & 219049.945884123 & -12880.9458841229 \tabularnewline
82 & 483429 & 387747.297689557 & 95681.7023104433 \tabularnewline
83 & 146100 & 177894.171693584 & -31794.1716935843 \tabularnewline
84 & 295224 & 253348.14374312 & 41875.85625688 \tabularnewline
85 & 80953 & 107357.443235686 & -26404.4432356864 \tabularnewline
86 & 217384 & 212658.635068872 & 4725.36493112797 \tabularnewline
87 & 179344 & 177525.691545338 & 1818.30845466206 \tabularnewline
88 & 416097 & 389293.516733952 & 26803.483266048 \tabularnewline
89 & 395041 & 378809.026926713 & 16231.9730732866 \tabularnewline
90 & 180679 & 254300.770816955 & -73621.770816955 \tabularnewline
91 & 311447 & 276185.45041953 & 35261.5495804696 \tabularnewline
92 & 292260 & 244265.485798402 & 47994.514201598 \tabularnewline
93 & 199481 & 214654.87308849 & -15173.8730884902 \tabularnewline
94 & 282361 & 277626.015110154 & 4734.98488984648 \tabularnewline
95 & 329281 & 283533.812469475 & 45747.1875305253 \tabularnewline
96 & 234577 & 234689.35395413 & -112.353954129771 \tabularnewline
97 & 310685 & 273707.021442494 & 36977.9785575056 \tabularnewline
98 & 352078 & 345688.970435154 & 6389.02956484626 \tabularnewline
99 & 416463 & 438606.895983677 & -22143.8959836766 \tabularnewline
100 & 429565 & 442494.044910949 & -12929.0449109486 \tabularnewline
101 & 297080 & 285157.449996813 & 11922.5500031868 \tabularnewline
102 & 331792 & 348544.238589324 & -16752.2385893239 \tabularnewline
103 & 242507 & 257162.111705514 & -14655.1117055142 \tabularnewline
104 & 43287 & 58043.1516578822 & -14756.1516578822 \tabularnewline
105 & 238089 & 234852.223838403 & 3236.7761615974 \tabularnewline
106 & 285479 & 305696.343246972 & -20217.3432469725 \tabularnewline
107 & 310383 & 328302.088812605 & -17919.0888126049 \tabularnewline
108 & 321797 & 316019.766755841 & 5777.23324415885 \tabularnewline
109 & 193926 & 237777.393236941 & -43851.3932369407 \tabularnewline
110 & 175737 & 278953.22529182 & -103216.22529182 \tabularnewline
111 & 354041 & 289516.217801823 & 64524.7821981768 \tabularnewline
112 & 303566 & 253349.981677662 & 50216.018322338 \tabularnewline
113 & 23668 & 23494.0423889222 & 173.957611077825 \tabularnewline
114 & 196743 & 250236.3571241 & -53493.3571241002 \tabularnewline
115 & 61857 & 54911.2717261139 & 6945.72827388611 \tabularnewline
116 & 217543 & 220132.289858983 & -2589.28985898286 \tabularnewline
117 & 440711 & 431410.82333726 & 9300.17666274024 \tabularnewline
118 & 21054 & 22749.7756557597 & -1695.77565575968 \tabularnewline
119 & 252805 & 256916.662204667 & -4111.66220466693 \tabularnewline
120 & 31961 & 41774.5639362426 & -9813.56393624258 \tabularnewline
121 & 360436 & 385848.390735834 & -25412.3907358344 \tabularnewline
122 & 251948 & 221656.051271474 & 30291.9487285262 \tabularnewline
123 & 187320 & 149973.049766091 & 37346.9502339085 \tabularnewline
124 & 180842 & 188126.124306301 & -7284.12430630056 \tabularnewline
125 & 38214 & 57841.107137135 & -19627.107137135 \tabularnewline
126 & 289296 & 232037.59697653 & 57258.4030234695 \tabularnewline
127 & 358276 & 334809.8051305 & 23466.1948695004 \tabularnewline
128 & 211775 & 231092.90480637 & -19317.9048063697 \tabularnewline
129 & 447335 & 449810.977703807 & -2475.97770380743 \tabularnewline
130 & 348017 & 399510.36142459 & -51493.3614245903 \tabularnewline
131 & 441946 & 429638.103687753 & 12307.8963122474 \tabularnewline
132 & 215177 & 228037.787875591 & -12860.7878755911 \tabularnewline
133 & 140328 & 160789.178000154 & -20461.1780001542 \tabularnewline
134 & 318092 & 320158.068573008 & -2066.06857300812 \tabularnewline
135 & 466139 & 457167.818643334 & 8971.18135666647 \tabularnewline
136 & 162406 & 132971.410582159 & 29434.5894178408 \tabularnewline
137 & 417354 & 411173.003802116 & 6180.99619788396 \tabularnewline
138 & 178322 & 213287.031684699 & -34965.0316846993 \tabularnewline
139 & 292443 & 270744.278450789 & 21698.721549211 \tabularnewline
140 & 283913 & 287816.070416193 & -3903.0704161925 \tabularnewline
141 & 253950 & 225673.11297493 & 28276.8870250696 \tabularnewline
142 & 389698 & 381796.122892038 & 7901.87710796149 \tabularnewline
143 & 246963 & 268870.705787972 & -21907.7057879723 \tabularnewline
144 & 173260 & 126114.841734713 & 47145.1582652874 \tabularnewline
145 & 346748 & 329113.846286183 & 17634.1537138168 \tabularnewline
146 & 188437 & 229003.989123461 & -40566.9891234615 \tabularnewline
147 & 279125 & 294052.628457343 & -14927.628457343 \tabularnewline
148 & 314070 & 318342.518769883 & -4272.51876988285 \tabularnewline
149 & 1 & 1672.97975656411 & -1671.97975656411 \tabularnewline
150 & 14688 & 13680.6142966101 & 1007.38570338991 \tabularnewline
151 & 98 & -4164.53137465593 & 4262.53137465593 \tabularnewline
152 & 455 & -3982.16135616974 & 4437.16135616974 \tabularnewline
153 & 0 & -3690.53407284127 & 3690.53407284127 \tabularnewline
154 & 0 & -4359.40975614196 & 4359.40975614196 \tabularnewline
155 & 291847 & 239817.897041124 & 52029.1029588757 \tabularnewline
156 & 415839 & 409865.365567772 & 5973.63443222814 \tabularnewline
157 & 0 & -4359.40975614196 & 4359.40975614196 \tabularnewline
158 & 203 & -3679.96313419653 & 3882.96313419653 \tabularnewline
159 & 7199 & 9791.56521560585 & -2592.56521560585 \tabularnewline
160 & 46660 & 53347.3702364031 & -6687.37023640307 \tabularnewline
161 & 17547 & 11517.4140224882 & 6029.58597751183 \tabularnewline
162 & 121550 & 104405.689246965 & 17144.3107530346 \tabularnewline
163 & 969 & -3850.82354467143 & 4819.82354467143 \tabularnewline
164 & 242774 & 187964.784119123 & 54809.2158808769 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160732&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]279055[/C][C]280840.519196133[/C][C]-1785.51919613265[/C][/ROW]
[ROW][C]2[/C][C]212450[/C][C]212894.756528441[/C][C]-444.756528440597[/C][/ROW]
[ROW][C]3[/C][C]233939[/C][C]241484.455558861[/C][C]-7545.45555886148[/C][/ROW]
[ROW][C]4[/C][C]222117[/C][C]296128.619888664[/C][C]-74011.6198886644[/C][/ROW]
[ROW][C]5[/C][C]189911[/C][C]184564.875121417[/C][C]5346.12487858264[/C][/ROW]
[ROW][C]6[/C][C]70849[/C][C]90243.1384680487[/C][C]-19394.1384680487[/C][/ROW]
[ROW][C]7[/C][C]605767[/C][C]587396.580774022[/C][C]18370.4192259785[/C][/ROW]
[ROW][C]8[/C][C]33186[/C][C]35865.453746068[/C][C]-2679.45374606798[/C][/ROW]
[ROW][C]9[/C][C]227332[/C][C]233323.813907603[/C][C]-5991.81390760289[/C][/ROW]
[ROW][C]10[/C][C]267925[/C][C]228898.373169139[/C][C]39026.6268308609[/C][/ROW]
[ROW][C]11[/C][C]372083[/C][C]344437.065073509[/C][C]27645.9349264907[/C][/ROW]
[ROW][C]12[/C][C]279589[/C][C]272749.753652305[/C][C]6839.24634769492[/C][/ROW]
[ROW][C]13[/C][C]212638[/C][C]204425.621876908[/C][C]8212.37812309154[/C][/ROW]
[ROW][C]14[/C][C]368577[/C][C]301566.097754341[/C][C]67010.9022456586[/C][/ROW]
[ROW][C]15[/C][C]269455[/C][C]259949.06845901[/C][C]9505.93154099037[/C][/ROW]
[ROW][C]16[/C][C]402600[/C][C]472813.54325279[/C][C]-70213.5432527898[/C][/ROW]
[ROW][C]17[/C][C]335735[/C][C]295332.101286822[/C][C]40402.8987131779[/C][/ROW]
[ROW][C]18[/C][C]432711[/C][C]358436.862709419[/C][C]74274.137290581[/C][/ROW]
[ROW][C]19[/C][C]185822[/C][C]197050.101809443[/C][C]-11228.1018094435[/C][/ROW]
[ROW][C]20[/C][C]267365[/C][C]281334.493373472[/C][C]-13969.4933734716[/C][/ROW]
[ROW][C]21[/C][C]279452[/C][C]289576.313503623[/C][C]-10124.3135036233[/C][/ROW]
[ROW][C]22[/C][C]527853[/C][C]369806.346203674[/C][C]158046.653796326[/C][/ROW]
[ROW][C]23[/C][C]227252[/C][C]206215.80047182[/C][C]21036.1995281795[/C][/ROW]
[ROW][C]24[/C][C]200004[/C][C]240350.121155645[/C][C]-40346.1211556445[/C][/ROW]
[ROW][C]25[/C][C]257239[/C][C]330992.980305312[/C][C]-73753.9803053119[/C][/ROW]
[ROW][C]26[/C][C]271341[/C][C]299376.510103079[/C][C]-28035.5101030793[/C][/ROW]
[ROW][C]27[/C][C]324969[/C][C]366087.84574966[/C][C]-41118.8457496595[/C][/ROW]
[ROW][C]28[/C][C]349753[/C][C]306926.078263939[/C][C]42826.9217360608[/C][/ROW]
[ROW][C]29[/C][C]200723[/C][C]188841.921397426[/C][C]11881.0786025738[/C][/ROW]
[ROW][C]30[/C][C]399591[/C][C]437120.416004006[/C][C]-37529.4160040055[/C][/ROW]
[ROW][C]31[/C][C]327660[/C][C]359392.518929858[/C][C]-31732.5189298585[/C][/ROW]
[ROW][C]32[/C][C]269239[/C][C]260863.348755809[/C][C]8375.65124419084[/C][/ROW]
[ROW][C]33[/C][C]397689[/C][C]364692.923424448[/C][C]32996.0765755521[/C][/ROW]
[ROW][C]34[/C][C]130446[/C][C]131919.778450839[/C][C]-1473.77845083862[/C][/ROW]
[ROW][C]35[/C][C]430118[/C][C]394821.617250106[/C][C]35296.382749894[/C][/ROW]
[ROW][C]36[/C][C]273950[/C][C]290219.095832627[/C][C]-16269.0958326272[/C][/ROW]
[ROW][C]37[/C][C]429837[/C][C]397866.92244549[/C][C]31970.0775545102[/C][/ROW]
[ROW][C]38[/C][C]254312[/C][C]238763.194598223[/C][C]15548.8054017772[/C][/ROW]
[ROW][C]39[/C][C]120351[/C][C]106677.630490841[/C][C]13673.3695091588[/C][/ROW]
[ROW][C]40[/C][C]395658[/C][C]392344.494037479[/C][C]3313.50596252093[/C][/ROW]
[ROW][C]41[/C][C]349119[/C][C]366588.287738637[/C][C]-17469.2877386374[/C][/ROW]
[ROW][C]42[/C][C]220517[/C][C]226338.517117194[/C][C]-5821.5171171942[/C][/ROW]
[ROW][C]43[/C][C]234780[/C][C]255146.378694284[/C][C]-20366.3786942837[/C][/ROW]
[ROW][C]44[/C][C]191784[/C][C]173626.975364555[/C][C]18157.0246354445[/C][/ROW]
[ROW][C]45[/C][C]186112[/C][C]265688.455679933[/C][C]-79576.4556799326[/C][/ROW]
[ROW][C]46[/C][C]459665[/C][C]449816.665011858[/C][C]9848.33498814224[/C][/ROW]
[ROW][C]47[/C][C]78800[/C][C]115268.18172717[/C][C]-36468.1817271699[/C][/ROW]
[ROW][C]48[/C][C]259887[/C][C]272749.654433163[/C][C]-12862.6544331626[/C][/ROW]
[ROW][C]49[/C][C]368086[/C][C]428740.838811167[/C][C]-60654.8388111675[/C][/ROW]
[ROW][C]50[/C][C]230299[/C][C]294823.003502102[/C][C]-64524.0035021018[/C][/ROW]
[ROW][C]51[/C][C]256033[/C][C]293043.198699584[/C][C]-37010.1986995844[/C][/ROW]
[ROW][C]52[/C][C]24188[/C][C]43043.5381811927[/C][C]-18855.5381811927[/C][/ROW]
[ROW][C]53[/C][C]400109[/C][C]378271.196799206[/C][C]21837.8032007937[/C][/ROW]
[ROW][C]54[/C][C]65029[/C][C]71615.6517121427[/C][C]-6586.65171214273[/C][/ROW]
[ROW][C]55[/C][C]101097[/C][C]123086.968867802[/C][C]-21989.9688678025[/C][/ROW]
[ROW][C]56[/C][C]319020[/C][C]317420.725811013[/C][C]1599.27418898677[/C][/ROW]
[ROW][C]57[/C][C]375638[/C][C]365154.795743137[/C][C]10483.2042568631[/C][/ROW]
[ROW][C]58[/C][C]375011[/C][C]409515.192781291[/C][C]-34504.1927812911[/C][/ROW]
[ROW][C]59[/C][C]387748[/C][C]382685.687683533[/C][C]5062.31231646695[/C][/ROW]
[ROW][C]60[/C][C]280106[/C][C]242560.681576303[/C][C]37545.3184236969[/C][/ROW]
[ROW][C]61[/C][C]400971[/C][C]430624.010831785[/C][C]-29653.0108317849[/C][/ROW]
[ROW][C]62[/C][C]322780[/C][C]386912.796880158[/C][C]-64132.7968801581[/C][/ROW]
[ROW][C]63[/C][C]291391[/C][C]320244.747317293[/C][C]-28853.7473172932[/C][/ROW]
[ROW][C]64[/C][C]295075[/C][C]297119.281748033[/C][C]-2044.28174803269[/C][/ROW]
[ROW][C]65[/C][C]280018[/C][C]259079.46545017[/C][C]20938.5345498301[/C][/ROW]
[ROW][C]66[/C][C]267432[/C][C]258881.96783035[/C][C]8550.03216964981[/C][/ROW]
[ROW][C]67[/C][C]217181[/C][C]237987.314393979[/C][C]-20806.3143939789[/C][/ROW]
[ROW][C]68[/C][C]258166[/C][C]319189.405354671[/C][C]-61023.4053546706[/C][/ROW]
[ROW][C]69[/C][C]277891[/C][C]271799.974826908[/C][C]6091.02517309174[/C][/ROW]
[ROW][C]70[/C][C]192894[/C][C]202334.841795533[/C][C]-9440.84179553314[/C][/ROW]
[ROW][C]71[/C][C]271853[/C][C]278479.679537519[/C][C]-6626.6795375187[/C][/ROW]
[ROW][C]72[/C][C]73566[/C][C]98484.4533771435[/C][C]-24918.4533771435[/C][/ROW]
[ROW][C]73[/C][C]276269[/C][C]265089.931482137[/C][C]11179.0685178634[/C][/ROW]
[ROW][C]74[/C][C]242619[/C][C]238441.442437456[/C][C]4177.55756254381[/C][/ROW]
[ROW][C]75[/C][C]230030[/C][C]240768.532341724[/C][C]-10738.5323417242[/C][/ROW]
[ROW][C]76[/C][C]371391[/C][C]271296.303274711[/C][C]100094.696725289[/C][/ROW]
[ROW][C]77[/C][C]398698[/C][C]361833.555182946[/C][C]36864.4448170544[/C][/ROW]
[ROW][C]78[/C][C]243355[/C][C]273334.922970588[/C][C]-29979.9229705879[/C][/ROW]
[ROW][C]79[/C][C]233519[/C][C]235210.661585416[/C][C]-1691.66158541612[/C][/ROW]
[ROW][C]80[/C][C]219936[/C][C]210150.683163097[/C][C]9785.3168369031[/C][/ROW]
[ROW][C]81[/C][C]206169[/C][C]219049.945884123[/C][C]-12880.9458841229[/C][/ROW]
[ROW][C]82[/C][C]483429[/C][C]387747.297689557[/C][C]95681.7023104433[/C][/ROW]
[ROW][C]83[/C][C]146100[/C][C]177894.171693584[/C][C]-31794.1716935843[/C][/ROW]
[ROW][C]84[/C][C]295224[/C][C]253348.14374312[/C][C]41875.85625688[/C][/ROW]
[ROW][C]85[/C][C]80953[/C][C]107357.443235686[/C][C]-26404.4432356864[/C][/ROW]
[ROW][C]86[/C][C]217384[/C][C]212658.635068872[/C][C]4725.36493112797[/C][/ROW]
[ROW][C]87[/C][C]179344[/C][C]177525.691545338[/C][C]1818.30845466206[/C][/ROW]
[ROW][C]88[/C][C]416097[/C][C]389293.516733952[/C][C]26803.483266048[/C][/ROW]
[ROW][C]89[/C][C]395041[/C][C]378809.026926713[/C][C]16231.9730732866[/C][/ROW]
[ROW][C]90[/C][C]180679[/C][C]254300.770816955[/C][C]-73621.770816955[/C][/ROW]
[ROW][C]91[/C][C]311447[/C][C]276185.45041953[/C][C]35261.5495804696[/C][/ROW]
[ROW][C]92[/C][C]292260[/C][C]244265.485798402[/C][C]47994.514201598[/C][/ROW]
[ROW][C]93[/C][C]199481[/C][C]214654.87308849[/C][C]-15173.8730884902[/C][/ROW]
[ROW][C]94[/C][C]282361[/C][C]277626.015110154[/C][C]4734.98488984648[/C][/ROW]
[ROW][C]95[/C][C]329281[/C][C]283533.812469475[/C][C]45747.1875305253[/C][/ROW]
[ROW][C]96[/C][C]234577[/C][C]234689.35395413[/C][C]-112.353954129771[/C][/ROW]
[ROW][C]97[/C][C]310685[/C][C]273707.021442494[/C][C]36977.9785575056[/C][/ROW]
[ROW][C]98[/C][C]352078[/C][C]345688.970435154[/C][C]6389.02956484626[/C][/ROW]
[ROW][C]99[/C][C]416463[/C][C]438606.895983677[/C][C]-22143.8959836766[/C][/ROW]
[ROW][C]100[/C][C]429565[/C][C]442494.044910949[/C][C]-12929.0449109486[/C][/ROW]
[ROW][C]101[/C][C]297080[/C][C]285157.449996813[/C][C]11922.5500031868[/C][/ROW]
[ROW][C]102[/C][C]331792[/C][C]348544.238589324[/C][C]-16752.2385893239[/C][/ROW]
[ROW][C]103[/C][C]242507[/C][C]257162.111705514[/C][C]-14655.1117055142[/C][/ROW]
[ROW][C]104[/C][C]43287[/C][C]58043.1516578822[/C][C]-14756.1516578822[/C][/ROW]
[ROW][C]105[/C][C]238089[/C][C]234852.223838403[/C][C]3236.7761615974[/C][/ROW]
[ROW][C]106[/C][C]285479[/C][C]305696.343246972[/C][C]-20217.3432469725[/C][/ROW]
[ROW][C]107[/C][C]310383[/C][C]328302.088812605[/C][C]-17919.0888126049[/C][/ROW]
[ROW][C]108[/C][C]321797[/C][C]316019.766755841[/C][C]5777.23324415885[/C][/ROW]
[ROW][C]109[/C][C]193926[/C][C]237777.393236941[/C][C]-43851.3932369407[/C][/ROW]
[ROW][C]110[/C][C]175737[/C][C]278953.22529182[/C][C]-103216.22529182[/C][/ROW]
[ROW][C]111[/C][C]354041[/C][C]289516.217801823[/C][C]64524.7821981768[/C][/ROW]
[ROW][C]112[/C][C]303566[/C][C]253349.981677662[/C][C]50216.018322338[/C][/ROW]
[ROW][C]113[/C][C]23668[/C][C]23494.0423889222[/C][C]173.957611077825[/C][/ROW]
[ROW][C]114[/C][C]196743[/C][C]250236.3571241[/C][C]-53493.3571241002[/C][/ROW]
[ROW][C]115[/C][C]61857[/C][C]54911.2717261139[/C][C]6945.72827388611[/C][/ROW]
[ROW][C]116[/C][C]217543[/C][C]220132.289858983[/C][C]-2589.28985898286[/C][/ROW]
[ROW][C]117[/C][C]440711[/C][C]431410.82333726[/C][C]9300.17666274024[/C][/ROW]
[ROW][C]118[/C][C]21054[/C][C]22749.7756557597[/C][C]-1695.77565575968[/C][/ROW]
[ROW][C]119[/C][C]252805[/C][C]256916.662204667[/C][C]-4111.66220466693[/C][/ROW]
[ROW][C]120[/C][C]31961[/C][C]41774.5639362426[/C][C]-9813.56393624258[/C][/ROW]
[ROW][C]121[/C][C]360436[/C][C]385848.390735834[/C][C]-25412.3907358344[/C][/ROW]
[ROW][C]122[/C][C]251948[/C][C]221656.051271474[/C][C]30291.9487285262[/C][/ROW]
[ROW][C]123[/C][C]187320[/C][C]149973.049766091[/C][C]37346.9502339085[/C][/ROW]
[ROW][C]124[/C][C]180842[/C][C]188126.124306301[/C][C]-7284.12430630056[/C][/ROW]
[ROW][C]125[/C][C]38214[/C][C]57841.107137135[/C][C]-19627.107137135[/C][/ROW]
[ROW][C]126[/C][C]289296[/C][C]232037.59697653[/C][C]57258.4030234695[/C][/ROW]
[ROW][C]127[/C][C]358276[/C][C]334809.8051305[/C][C]23466.1948695004[/C][/ROW]
[ROW][C]128[/C][C]211775[/C][C]231092.90480637[/C][C]-19317.9048063697[/C][/ROW]
[ROW][C]129[/C][C]447335[/C][C]449810.977703807[/C][C]-2475.97770380743[/C][/ROW]
[ROW][C]130[/C][C]348017[/C][C]399510.36142459[/C][C]-51493.3614245903[/C][/ROW]
[ROW][C]131[/C][C]441946[/C][C]429638.103687753[/C][C]12307.8963122474[/C][/ROW]
[ROW][C]132[/C][C]215177[/C][C]228037.787875591[/C][C]-12860.7878755911[/C][/ROW]
[ROW][C]133[/C][C]140328[/C][C]160789.178000154[/C][C]-20461.1780001542[/C][/ROW]
[ROW][C]134[/C][C]318092[/C][C]320158.068573008[/C][C]-2066.06857300812[/C][/ROW]
[ROW][C]135[/C][C]466139[/C][C]457167.818643334[/C][C]8971.18135666647[/C][/ROW]
[ROW][C]136[/C][C]162406[/C][C]132971.410582159[/C][C]29434.5894178408[/C][/ROW]
[ROW][C]137[/C][C]417354[/C][C]411173.003802116[/C][C]6180.99619788396[/C][/ROW]
[ROW][C]138[/C][C]178322[/C][C]213287.031684699[/C][C]-34965.0316846993[/C][/ROW]
[ROW][C]139[/C][C]292443[/C][C]270744.278450789[/C][C]21698.721549211[/C][/ROW]
[ROW][C]140[/C][C]283913[/C][C]287816.070416193[/C][C]-3903.0704161925[/C][/ROW]
[ROW][C]141[/C][C]253950[/C][C]225673.11297493[/C][C]28276.8870250696[/C][/ROW]
[ROW][C]142[/C][C]389698[/C][C]381796.122892038[/C][C]7901.87710796149[/C][/ROW]
[ROW][C]143[/C][C]246963[/C][C]268870.705787972[/C][C]-21907.7057879723[/C][/ROW]
[ROW][C]144[/C][C]173260[/C][C]126114.841734713[/C][C]47145.1582652874[/C][/ROW]
[ROW][C]145[/C][C]346748[/C][C]329113.846286183[/C][C]17634.1537138168[/C][/ROW]
[ROW][C]146[/C][C]188437[/C][C]229003.989123461[/C][C]-40566.9891234615[/C][/ROW]
[ROW][C]147[/C][C]279125[/C][C]294052.628457343[/C][C]-14927.628457343[/C][/ROW]
[ROW][C]148[/C][C]314070[/C][C]318342.518769883[/C][C]-4272.51876988285[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]1672.97975656411[/C][C]-1671.97975656411[/C][/ROW]
[ROW][C]150[/C][C]14688[/C][C]13680.6142966101[/C][C]1007.38570338991[/C][/ROW]
[ROW][C]151[/C][C]98[/C][C]-4164.53137465593[/C][C]4262.53137465593[/C][/ROW]
[ROW][C]152[/C][C]455[/C][C]-3982.16135616974[/C][C]4437.16135616974[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]-3690.53407284127[/C][C]3690.53407284127[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]-4359.40975614196[/C][C]4359.40975614196[/C][/ROW]
[ROW][C]155[/C][C]291847[/C][C]239817.897041124[/C][C]52029.1029588757[/C][/ROW]
[ROW][C]156[/C][C]415839[/C][C]409865.365567772[/C][C]5973.63443222814[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]-4359.40975614196[/C][C]4359.40975614196[/C][/ROW]
[ROW][C]158[/C][C]203[/C][C]-3679.96313419653[/C][C]3882.96313419653[/C][/ROW]
[ROW][C]159[/C][C]7199[/C][C]9791.56521560585[/C][C]-2592.56521560585[/C][/ROW]
[ROW][C]160[/C][C]46660[/C][C]53347.3702364031[/C][C]-6687.37023640307[/C][/ROW]
[ROW][C]161[/C][C]17547[/C][C]11517.4140224882[/C][C]6029.58597751183[/C][/ROW]
[ROW][C]162[/C][C]121550[/C][C]104405.689246965[/C][C]17144.3107530346[/C][/ROW]
[ROW][C]163[/C][C]969[/C][C]-3850.82354467143[/C][C]4819.82354467143[/C][/ROW]
[ROW][C]164[/C][C]242774[/C][C]187964.784119123[/C][C]54809.2158808769[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160732&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160732&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
1279055280840.519196133-1785.51919613265
2212450212894.756528441-444.756528440597
3233939241484.455558861-7545.45555886148
4222117296128.619888664-74011.6198886644
5189911184564.8751214175346.12487858264
67084990243.1384680487-19394.1384680487
7605767587396.58077402218370.4192259785
83318635865.453746068-2679.45374606798
9227332233323.813907603-5991.81390760289
10267925228898.37316913939026.6268308609
11372083344437.06507350927645.9349264907
12279589272749.7536523056839.24634769492
13212638204425.6218769088212.37812309154
14368577301566.09775434167010.9022456586
15269455259949.068459019505.93154099037
16402600472813.54325279-70213.5432527898
17335735295332.10128682240402.8987131779
18432711358436.86270941974274.137290581
19185822197050.101809443-11228.1018094435
20267365281334.493373472-13969.4933734716
21279452289576.313503623-10124.3135036233
22527853369806.346203674158046.653796326
23227252206215.8004718221036.1995281795
24200004240350.121155645-40346.1211556445
25257239330992.980305312-73753.9803053119
26271341299376.510103079-28035.5101030793
27324969366087.84574966-41118.8457496595
28349753306926.07826393942826.9217360608
29200723188841.92139742611881.0786025738
30399591437120.416004006-37529.4160040055
31327660359392.518929858-31732.5189298585
32269239260863.3487558098375.65124419084
33397689364692.92342444832996.0765755521
34130446131919.778450839-1473.77845083862
35430118394821.61725010635296.382749894
36273950290219.095832627-16269.0958326272
37429837397866.9224454931970.0775545102
38254312238763.19459822315548.8054017772
39120351106677.63049084113673.3695091588
40395658392344.4940374793313.50596252093
41349119366588.287738637-17469.2877386374
42220517226338.517117194-5821.5171171942
43234780255146.378694284-20366.3786942837
44191784173626.97536455518157.0246354445
45186112265688.455679933-79576.4556799326
46459665449816.6650118589848.33498814224
4778800115268.18172717-36468.1817271699
48259887272749.654433163-12862.6544331626
49368086428740.838811167-60654.8388111675
50230299294823.003502102-64524.0035021018
51256033293043.198699584-37010.1986995844
522418843043.5381811927-18855.5381811927
53400109378271.19679920621837.8032007937
546502971615.6517121427-6586.65171214273
55101097123086.968867802-21989.9688678025
56319020317420.7258110131599.27418898677
57375638365154.79574313710483.2042568631
58375011409515.192781291-34504.1927812911
59387748382685.6876835335062.31231646695
60280106242560.68157630337545.3184236969
61400971430624.010831785-29653.0108317849
62322780386912.796880158-64132.7968801581
63291391320244.747317293-28853.7473172932
64295075297119.281748033-2044.28174803269
65280018259079.4654501720938.5345498301
66267432258881.967830358550.03216964981
67217181237987.314393979-20806.3143939789
68258166319189.405354671-61023.4053546706
69277891271799.9748269086091.02517309174
70192894202334.841795533-9440.84179553314
71271853278479.679537519-6626.6795375187
727356698484.4533771435-24918.4533771435
73276269265089.93148213711179.0685178634
74242619238441.4424374564177.55756254381
75230030240768.532341724-10738.5323417242
76371391271296.303274711100094.696725289
77398698361833.55518294636864.4448170544
78243355273334.922970588-29979.9229705879
79233519235210.661585416-1691.66158541612
80219936210150.6831630979785.3168369031
81206169219049.945884123-12880.9458841229
82483429387747.29768955795681.7023104433
83146100177894.171693584-31794.1716935843
84295224253348.1437431241875.85625688
8580953107357.443235686-26404.4432356864
86217384212658.6350688724725.36493112797
87179344177525.6915453381818.30845466206
88416097389293.51673395226803.483266048
89395041378809.02692671316231.9730732866
90180679254300.770816955-73621.770816955
91311447276185.4504195335261.5495804696
92292260244265.48579840247994.514201598
93199481214654.87308849-15173.8730884902
94282361277626.0151101544734.98488984648
95329281283533.81246947545747.1875305253
96234577234689.35395413-112.353954129771
97310685273707.02144249436977.9785575056
98352078345688.9704351546389.02956484626
99416463438606.895983677-22143.8959836766
100429565442494.044910949-12929.0449109486
101297080285157.44999681311922.5500031868
102331792348544.238589324-16752.2385893239
103242507257162.111705514-14655.1117055142
1044328758043.1516578822-14756.1516578822
105238089234852.2238384033236.7761615974
106285479305696.343246972-20217.3432469725
107310383328302.088812605-17919.0888126049
108321797316019.7667558415777.23324415885
109193926237777.393236941-43851.3932369407
110175737278953.22529182-103216.22529182
111354041289516.21780182364524.7821981768
112303566253349.98167766250216.018322338
1132366823494.0423889222173.957611077825
114196743250236.3571241-53493.3571241002
1156185754911.27172611396945.72827388611
116217543220132.289858983-2589.28985898286
117440711431410.823337269300.17666274024
1182105422749.7756557597-1695.77565575968
119252805256916.662204667-4111.66220466693
1203196141774.5639362426-9813.56393624258
121360436385848.390735834-25412.3907358344
122251948221656.05127147430291.9487285262
123187320149973.04976609137346.9502339085
124180842188126.124306301-7284.12430630056
1253821457841.107137135-19627.107137135
126289296232037.5969765357258.4030234695
127358276334809.805130523466.1948695004
128211775231092.90480637-19317.9048063697
129447335449810.977703807-2475.97770380743
130348017399510.36142459-51493.3614245903
131441946429638.10368775312307.8963122474
132215177228037.787875591-12860.7878755911
133140328160789.178000154-20461.1780001542
134318092320158.068573008-2066.06857300812
135466139457167.8186433348971.18135666647
136162406132971.41058215929434.5894178408
137417354411173.0038021166180.99619788396
138178322213287.031684699-34965.0316846993
139292443270744.27845078921698.721549211
140283913287816.070416193-3903.0704161925
141253950225673.1129749328276.8870250696
142389698381796.1228920387901.87710796149
143246963268870.705787972-21907.7057879723
144173260126114.84173471347145.1582652874
145346748329113.84628618317634.1537138168
146188437229003.989123461-40566.9891234615
147279125294052.628457343-14927.628457343
148314070318342.518769883-4272.51876988285
14911672.97975656411-1671.97975656411
1501468813680.61429661011007.38570338991
15198-4164.531374655934262.53137465593
152455-3982.161356169744437.16135616974
1530-3690.534072841273690.53407284127
1540-4359.409756141964359.40975614196
155291847239817.89704112452029.1029588757
156415839409865.3655677725973.63443222814
1570-4359.409756141964359.40975614196
158203-3679.963134196533882.96313419653
15971999791.56521560585-2592.56521560585
1604666053347.3702364031-6687.37023640307
1611754711517.41402248826029.58597751183
162121550104405.68924696517144.3107530346
163969-3850.823544671434819.82354467143
164242774187964.78411912354809.2158808769






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.3221677405167310.6443354810334620.677832259483269
190.1785542866761690.3571085733523370.821445713323831
200.4223137007753380.8446274015506770.577686299224662
210.3406440093097340.6812880186194680.659355990690266
220.8362361190332670.3275277619334670.163763880966733
230.8056181384202350.3887637231595290.194381861579765
240.7928982550753290.4142034898493430.207101744924671
250.7640060370045430.4719879259909140.235993962995457
260.8144128482773750.3711743034452510.185587151722625
270.7635896436130770.4728207127738460.236410356386923
280.7201410071710790.5597179856578420.279858992828921
290.6510645539178510.6978708921642980.348935446082149
300.6980004153012650.6039991693974710.301999584698735
310.6578392833766110.6843214332467780.342160716623389
320.591802153063370.8163956938732590.40819784693663
330.5371285781373440.9257428437253120.462871421862656
340.6682687730302210.6634624539395580.331731226969779
350.8876017850397250.224796429920550.112398214960275
360.8996183360802690.2007633278394630.100381663919731
370.8811313925131920.2377372149736150.118868607486808
380.8681633656243990.2636732687512030.131836634375601
390.8347036232799710.3305927534400570.165296376720029
400.8018253187436590.3963493625126810.198174681256341
410.8408392764857310.3183214470285380.159160723514269
420.803544849724370.392910300551260.19645515027563
430.7866225235405690.4267549529188630.213377476459431
440.8542425317072560.2915149365854880.145757468292744
450.9803187118167650.03936257636646940.0196812881832347
460.9819519667793470.03609606644130580.0180480332206529
470.9838618941420510.03227621171589770.0161381058579488
480.9803212227622040.03935755447559250.0196787772377963
490.9925304433584370.01493911328312670.00746955664156333
500.9965293017426770.006941396514645790.0034706982573229
510.9963092945616220.00738141087675630.00369070543837815
520.9964668241647910.007066351670418610.00353317583520931
530.9962620245571060.007475950885788760.00373797544289438
540.9946402181919420.0107195636161160.00535978180805799
550.9942385488908490.01152290221830220.00576145110915108
560.9926164931762540.01476701364749120.00738350682374559
570.9908630251437410.0182739497125170.00913697485625851
580.9921070164355580.01578596712888450.00789298356444223
590.9889463534244810.02210729315103750.0110536465755188
600.9905503237222090.01889935255558160.0094496762777908
610.9890353233220650.0219293533558690.0109646766779345
620.9945096409869430.01098071802611320.00549035901305662
630.993132546874490.01373490625101990.00686745312550996
640.9905248689117420.01895026217651560.00947513108825781
650.9879276945716710.02414461085665850.0120723054283293
660.9838061915055370.03238761698892660.0161938084944633
670.9792920140363130.04141597192737450.0207079859636873
680.9885471413193860.02290571736122710.0114528586806136
690.9846989279954580.03060214400908440.0153010720045422
700.9797251094499180.04054978110016340.0202748905500817
710.9744338203700490.05113235925990260.0255661796299513
720.9709614751162830.05807704976743410.029038524883717
730.9631451423765680.07370971524686450.0368548576234323
740.952624335789610.09475132842077990.04737566421039
750.9400660415781560.1198679168436890.0599339584218445
760.9930461972249580.01390760555008350.00695380277504173
770.9949455228645230.0101089542709540.00505447713547698
780.9937615505562910.0124768988874180.00623844944370898
790.9912556127693630.01748877446127330.00874438723063666
800.9882447667466150.02351046650677090.0117552332533854
810.9841577007825210.03168459843495750.0158422992174787
820.9988313408849560.002337318230088170.00116865911504409
830.9987144450530290.00257110989394180.0012855549469709
840.9987219873478050.002556025304389830.00127801265219492
850.9984243403299950.00315131934000980.0015756596700049
860.9976957238752170.004608552249566790.0023042761247834
870.9972278868649020.005544226270195490.00277211313509774
880.997023404920760.005953190158478980.00297659507923949
890.9958828496685050.008234300662990640.00411715033149532
900.9990147789671140.001970442065771560.00098522103288578
910.9990464602061140.001907079587771350.000953539793885675
920.9993913864864080.001217227027183860.000608613513591929
930.9993101300074020.001379739985195040.000689869992597522
940.999163202624930.001673594750139680.00083679737506984
950.9994763339617760.001047332076447540.000523666038223772
960.9991797543594750.001640491281048990.000820245640524496
970.999053711253420.001892577493160240.00094628874658012
980.9986480755838730.002703848832254640.00135192441612732
990.9991089423884780.001782115223044650.000891057611522327
1000.9991806133112380.001638773377524850.000819386688762423
1010.9989287665057010.002142466988598120.00107123349429906
1020.9985754747304610.00284905053907750.00142452526953875
1030.9979136674604090.004172665079182140.00208633253959107
1040.9973418001702960.005316399659407330.00265819982970367
1050.9962472842129270.007505431574146950.00375271578707347
1060.9961078813140830.007784237371833690.00389211868591684
1070.9945346631435580.01093067371288350.00546533685644177
1080.9929898846423940.01402023071521250.00701011535760627
1090.9962034277079880.007593144584023930.00379657229201196
1100.9999750049764114.99900471780703e-052.49950235890351e-05
1110.999987142723742.57145525194184e-051.28572762597092e-05
1120.9999945899074931.08201850131281e-055.41009250656404e-06
1130.9999891045586322.17908827366884e-051.08954413683442e-05
1140.9999983029977553.39400449016684e-061.69700224508342e-06
1150.9999964326257547.1347484929144e-063.5673742464572e-06
1160.999992435344841.51293103197665e-057.56465515988323e-06
1170.9999979536456354.09270873041592e-062.04635436520796e-06
1180.9999956548393138.69032137397898e-064.34516068698949e-06
1190.9999918310712211.63378575571027e-058.16892877855135e-06
1200.9999836887647953.26224704098903e-051.63112352049451e-05
1210.99999977131264.57374800178785e-072.28687400089392e-07
1220.9999995878393118.24321378200635e-074.12160689100318e-07
1230.9999996504415826.99116836706213e-073.49558418353106e-07
1240.9999990755737151.84885257048072e-069.24426285240359e-07
1250.9999981556042963.68879140883683e-061.84439570441841e-06
1260.9999966732440656.653511869376e-063.326755934688e-06
1270.9999978095876424.38082471693335e-062.19041235846667e-06
1280.9999945025239291.09949521425304e-055.49747607126521e-06
1290.9999973123223085.37535538427799e-062.68767769213899e-06
1300.9999950257536889.94849262432893e-064.97424631216447e-06
1310.9999877503570352.44992859290534e-051.22496429645267e-05
1320.9999853352719882.93294560237353e-051.46647280118677e-05
1330.999978398310594.32033788202463e-052.16016894101232e-05
1340.9999930793571051.38412857889929e-056.92064289449646e-06
1350.9999844212254763.11575490469438e-051.55787745234719e-05
1360.9999969682528576.06349428566393e-063.03174714283197e-06
1370.9999996704514556.5909709003329e-073.29548545016645e-07
1380.9999999575075438.49849131862218e-084.24924565931109e-08
1390.999999980378893.92422203133309e-081.96211101566655e-08
1400.9999998396888373.20622325921176e-071.60311162960588e-07
1410.9999999980305563.93888768702723e-091.96944384351362e-09
1420.999999997549584.90084070641116e-092.45042035320558e-09
1430.9999999634525577.30948852021104e-083.65474426010552e-08
1440.9999999999998742.52594390357832e-131.26297195178916e-13
1450.9999999999416461.1670699832217e-105.83534991610853e-11
1460.9999999735778855.28442300958561e-082.6422115047928e-08

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.322167740516731 & 0.644335481033462 & 0.677832259483269 \tabularnewline
19 & 0.178554286676169 & 0.357108573352337 & 0.821445713323831 \tabularnewline
20 & 0.422313700775338 & 0.844627401550677 & 0.577686299224662 \tabularnewline
21 & 0.340644009309734 & 0.681288018619468 & 0.659355990690266 \tabularnewline
22 & 0.836236119033267 & 0.327527761933467 & 0.163763880966733 \tabularnewline
23 & 0.805618138420235 & 0.388763723159529 & 0.194381861579765 \tabularnewline
24 & 0.792898255075329 & 0.414203489849343 & 0.207101744924671 \tabularnewline
25 & 0.764006037004543 & 0.471987925990914 & 0.235993962995457 \tabularnewline
26 & 0.814412848277375 & 0.371174303445251 & 0.185587151722625 \tabularnewline
27 & 0.763589643613077 & 0.472820712773846 & 0.236410356386923 \tabularnewline
28 & 0.720141007171079 & 0.559717985657842 & 0.279858992828921 \tabularnewline
29 & 0.651064553917851 & 0.697870892164298 & 0.348935446082149 \tabularnewline
30 & 0.698000415301265 & 0.603999169397471 & 0.301999584698735 \tabularnewline
31 & 0.657839283376611 & 0.684321433246778 & 0.342160716623389 \tabularnewline
32 & 0.59180215306337 & 0.816395693873259 & 0.40819784693663 \tabularnewline
33 & 0.537128578137344 & 0.925742843725312 & 0.462871421862656 \tabularnewline
34 & 0.668268773030221 & 0.663462453939558 & 0.331731226969779 \tabularnewline
35 & 0.887601785039725 & 0.22479642992055 & 0.112398214960275 \tabularnewline
36 & 0.899618336080269 & 0.200763327839463 & 0.100381663919731 \tabularnewline
37 & 0.881131392513192 & 0.237737214973615 & 0.118868607486808 \tabularnewline
38 & 0.868163365624399 & 0.263673268751203 & 0.131836634375601 \tabularnewline
39 & 0.834703623279971 & 0.330592753440057 & 0.165296376720029 \tabularnewline
40 & 0.801825318743659 & 0.396349362512681 & 0.198174681256341 \tabularnewline
41 & 0.840839276485731 & 0.318321447028538 & 0.159160723514269 \tabularnewline
42 & 0.80354484972437 & 0.39291030055126 & 0.19645515027563 \tabularnewline
43 & 0.786622523540569 & 0.426754952918863 & 0.213377476459431 \tabularnewline
44 & 0.854242531707256 & 0.291514936585488 & 0.145757468292744 \tabularnewline
45 & 0.980318711816765 & 0.0393625763664694 & 0.0196812881832347 \tabularnewline
46 & 0.981951966779347 & 0.0360960664413058 & 0.0180480332206529 \tabularnewline
47 & 0.983861894142051 & 0.0322762117158977 & 0.0161381058579488 \tabularnewline
48 & 0.980321222762204 & 0.0393575544755925 & 0.0196787772377963 \tabularnewline
49 & 0.992530443358437 & 0.0149391132831267 & 0.00746955664156333 \tabularnewline
50 & 0.996529301742677 & 0.00694139651464579 & 0.0034706982573229 \tabularnewline
51 & 0.996309294561622 & 0.0073814108767563 & 0.00369070543837815 \tabularnewline
52 & 0.996466824164791 & 0.00706635167041861 & 0.00353317583520931 \tabularnewline
53 & 0.996262024557106 & 0.00747595088578876 & 0.00373797544289438 \tabularnewline
54 & 0.994640218191942 & 0.010719563616116 & 0.00535978180805799 \tabularnewline
55 & 0.994238548890849 & 0.0115229022183022 & 0.00576145110915108 \tabularnewline
56 & 0.992616493176254 & 0.0147670136474912 & 0.00738350682374559 \tabularnewline
57 & 0.990863025143741 & 0.018273949712517 & 0.00913697485625851 \tabularnewline
58 & 0.992107016435558 & 0.0157859671288845 & 0.00789298356444223 \tabularnewline
59 & 0.988946353424481 & 0.0221072931510375 & 0.0110536465755188 \tabularnewline
60 & 0.990550323722209 & 0.0188993525555816 & 0.0094496762777908 \tabularnewline
61 & 0.989035323322065 & 0.021929353355869 & 0.0109646766779345 \tabularnewline
62 & 0.994509640986943 & 0.0109807180261132 & 0.00549035901305662 \tabularnewline
63 & 0.99313254687449 & 0.0137349062510199 & 0.00686745312550996 \tabularnewline
64 & 0.990524868911742 & 0.0189502621765156 & 0.00947513108825781 \tabularnewline
65 & 0.987927694571671 & 0.0241446108566585 & 0.0120723054283293 \tabularnewline
66 & 0.983806191505537 & 0.0323876169889266 & 0.0161938084944633 \tabularnewline
67 & 0.979292014036313 & 0.0414159719273745 & 0.0207079859636873 \tabularnewline
68 & 0.988547141319386 & 0.0229057173612271 & 0.0114528586806136 \tabularnewline
69 & 0.984698927995458 & 0.0306021440090844 & 0.0153010720045422 \tabularnewline
70 & 0.979725109449918 & 0.0405497811001634 & 0.0202748905500817 \tabularnewline
71 & 0.974433820370049 & 0.0511323592599026 & 0.0255661796299513 \tabularnewline
72 & 0.970961475116283 & 0.0580770497674341 & 0.029038524883717 \tabularnewline
73 & 0.963145142376568 & 0.0737097152468645 & 0.0368548576234323 \tabularnewline
74 & 0.95262433578961 & 0.0947513284207799 & 0.04737566421039 \tabularnewline
75 & 0.940066041578156 & 0.119867916843689 & 0.0599339584218445 \tabularnewline
76 & 0.993046197224958 & 0.0139076055500835 & 0.00695380277504173 \tabularnewline
77 & 0.994945522864523 & 0.010108954270954 & 0.00505447713547698 \tabularnewline
78 & 0.993761550556291 & 0.012476898887418 & 0.00623844944370898 \tabularnewline
79 & 0.991255612769363 & 0.0174887744612733 & 0.00874438723063666 \tabularnewline
80 & 0.988244766746615 & 0.0235104665067709 & 0.0117552332533854 \tabularnewline
81 & 0.984157700782521 & 0.0316845984349575 & 0.0158422992174787 \tabularnewline
82 & 0.998831340884956 & 0.00233731823008817 & 0.00116865911504409 \tabularnewline
83 & 0.998714445053029 & 0.0025711098939418 & 0.0012855549469709 \tabularnewline
84 & 0.998721987347805 & 0.00255602530438983 & 0.00127801265219492 \tabularnewline
85 & 0.998424340329995 & 0.0031513193400098 & 0.0015756596700049 \tabularnewline
86 & 0.997695723875217 & 0.00460855224956679 & 0.0023042761247834 \tabularnewline
87 & 0.997227886864902 & 0.00554422627019549 & 0.00277211313509774 \tabularnewline
88 & 0.99702340492076 & 0.00595319015847898 & 0.00297659507923949 \tabularnewline
89 & 0.995882849668505 & 0.00823430066299064 & 0.00411715033149532 \tabularnewline
90 & 0.999014778967114 & 0.00197044206577156 & 0.00098522103288578 \tabularnewline
91 & 0.999046460206114 & 0.00190707958777135 & 0.000953539793885675 \tabularnewline
92 & 0.999391386486408 & 0.00121722702718386 & 0.000608613513591929 \tabularnewline
93 & 0.999310130007402 & 0.00137973998519504 & 0.000689869992597522 \tabularnewline
94 & 0.99916320262493 & 0.00167359475013968 & 0.00083679737506984 \tabularnewline
95 & 0.999476333961776 & 0.00104733207644754 & 0.000523666038223772 \tabularnewline
96 & 0.999179754359475 & 0.00164049128104899 & 0.000820245640524496 \tabularnewline
97 & 0.99905371125342 & 0.00189257749316024 & 0.00094628874658012 \tabularnewline
98 & 0.998648075583873 & 0.00270384883225464 & 0.00135192441612732 \tabularnewline
99 & 0.999108942388478 & 0.00178211522304465 & 0.000891057611522327 \tabularnewline
100 & 0.999180613311238 & 0.00163877337752485 & 0.000819386688762423 \tabularnewline
101 & 0.998928766505701 & 0.00214246698859812 & 0.00107123349429906 \tabularnewline
102 & 0.998575474730461 & 0.0028490505390775 & 0.00142452526953875 \tabularnewline
103 & 0.997913667460409 & 0.00417266507918214 & 0.00208633253959107 \tabularnewline
104 & 0.997341800170296 & 0.00531639965940733 & 0.00265819982970367 \tabularnewline
105 & 0.996247284212927 & 0.00750543157414695 & 0.00375271578707347 \tabularnewline
106 & 0.996107881314083 & 0.00778423737183369 & 0.00389211868591684 \tabularnewline
107 & 0.994534663143558 & 0.0109306737128835 & 0.00546533685644177 \tabularnewline
108 & 0.992989884642394 & 0.0140202307152125 & 0.00701011535760627 \tabularnewline
109 & 0.996203427707988 & 0.00759314458402393 & 0.00379657229201196 \tabularnewline
110 & 0.999975004976411 & 4.99900471780703e-05 & 2.49950235890351e-05 \tabularnewline
111 & 0.99998714272374 & 2.57145525194184e-05 & 1.28572762597092e-05 \tabularnewline
112 & 0.999994589907493 & 1.08201850131281e-05 & 5.41009250656404e-06 \tabularnewline
113 & 0.999989104558632 & 2.17908827366884e-05 & 1.08954413683442e-05 \tabularnewline
114 & 0.999998302997755 & 3.39400449016684e-06 & 1.69700224508342e-06 \tabularnewline
115 & 0.999996432625754 & 7.1347484929144e-06 & 3.5673742464572e-06 \tabularnewline
116 & 0.99999243534484 & 1.51293103197665e-05 & 7.56465515988323e-06 \tabularnewline
117 & 0.999997953645635 & 4.09270873041592e-06 & 2.04635436520796e-06 \tabularnewline
118 & 0.999995654839313 & 8.69032137397898e-06 & 4.34516068698949e-06 \tabularnewline
119 & 0.999991831071221 & 1.63378575571027e-05 & 8.16892877855135e-06 \tabularnewline
120 & 0.999983688764795 & 3.26224704098903e-05 & 1.63112352049451e-05 \tabularnewline
121 & 0.9999997713126 & 4.57374800178785e-07 & 2.28687400089392e-07 \tabularnewline
122 & 0.999999587839311 & 8.24321378200635e-07 & 4.12160689100318e-07 \tabularnewline
123 & 0.999999650441582 & 6.99116836706213e-07 & 3.49558418353106e-07 \tabularnewline
124 & 0.999999075573715 & 1.84885257048072e-06 & 9.24426285240359e-07 \tabularnewline
125 & 0.999998155604296 & 3.68879140883683e-06 & 1.84439570441841e-06 \tabularnewline
126 & 0.999996673244065 & 6.653511869376e-06 & 3.326755934688e-06 \tabularnewline
127 & 0.999997809587642 & 4.38082471693335e-06 & 2.19041235846667e-06 \tabularnewline
128 & 0.999994502523929 & 1.09949521425304e-05 & 5.49747607126521e-06 \tabularnewline
129 & 0.999997312322308 & 5.37535538427799e-06 & 2.68767769213899e-06 \tabularnewline
130 & 0.999995025753688 & 9.94849262432893e-06 & 4.97424631216447e-06 \tabularnewline
131 & 0.999987750357035 & 2.44992859290534e-05 & 1.22496429645267e-05 \tabularnewline
132 & 0.999985335271988 & 2.93294560237353e-05 & 1.46647280118677e-05 \tabularnewline
133 & 0.99997839831059 & 4.32033788202463e-05 & 2.16016894101232e-05 \tabularnewline
134 & 0.999993079357105 & 1.38412857889929e-05 & 6.92064289449646e-06 \tabularnewline
135 & 0.999984421225476 & 3.11575490469438e-05 & 1.55787745234719e-05 \tabularnewline
136 & 0.999996968252857 & 6.06349428566393e-06 & 3.03174714283197e-06 \tabularnewline
137 & 0.999999670451455 & 6.5909709003329e-07 & 3.29548545016645e-07 \tabularnewline
138 & 0.999999957507543 & 8.49849131862218e-08 & 4.24924565931109e-08 \tabularnewline
139 & 0.99999998037889 & 3.92422203133309e-08 & 1.96211101566655e-08 \tabularnewline
140 & 0.999999839688837 & 3.20622325921176e-07 & 1.60311162960588e-07 \tabularnewline
141 & 0.999999998030556 & 3.93888768702723e-09 & 1.96944384351362e-09 \tabularnewline
142 & 0.99999999754958 & 4.90084070641116e-09 & 2.45042035320558e-09 \tabularnewline
143 & 0.999999963452557 & 7.30948852021104e-08 & 3.65474426010552e-08 \tabularnewline
144 & 0.999999999999874 & 2.52594390357832e-13 & 1.26297195178916e-13 \tabularnewline
145 & 0.999999999941646 & 1.1670699832217e-10 & 5.83534991610853e-11 \tabularnewline
146 & 0.999999973577885 & 5.28442300958561e-08 & 2.6422115047928e-08 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160732&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]0.322167740516731[/C][C]0.644335481033462[/C][C]0.677832259483269[/C][/ROW]
[ROW][C]19[/C][C]0.178554286676169[/C][C]0.357108573352337[/C][C]0.821445713323831[/C][/ROW]
[ROW][C]20[/C][C]0.422313700775338[/C][C]0.844627401550677[/C][C]0.577686299224662[/C][/ROW]
[ROW][C]21[/C][C]0.340644009309734[/C][C]0.681288018619468[/C][C]0.659355990690266[/C][/ROW]
[ROW][C]22[/C][C]0.836236119033267[/C][C]0.327527761933467[/C][C]0.163763880966733[/C][/ROW]
[ROW][C]23[/C][C]0.805618138420235[/C][C]0.388763723159529[/C][C]0.194381861579765[/C][/ROW]
[ROW][C]24[/C][C]0.792898255075329[/C][C]0.414203489849343[/C][C]0.207101744924671[/C][/ROW]
[ROW][C]25[/C][C]0.764006037004543[/C][C]0.471987925990914[/C][C]0.235993962995457[/C][/ROW]
[ROW][C]26[/C][C]0.814412848277375[/C][C]0.371174303445251[/C][C]0.185587151722625[/C][/ROW]
[ROW][C]27[/C][C]0.763589643613077[/C][C]0.472820712773846[/C][C]0.236410356386923[/C][/ROW]
[ROW][C]28[/C][C]0.720141007171079[/C][C]0.559717985657842[/C][C]0.279858992828921[/C][/ROW]
[ROW][C]29[/C][C]0.651064553917851[/C][C]0.697870892164298[/C][C]0.348935446082149[/C][/ROW]
[ROW][C]30[/C][C]0.698000415301265[/C][C]0.603999169397471[/C][C]0.301999584698735[/C][/ROW]
[ROW][C]31[/C][C]0.657839283376611[/C][C]0.684321433246778[/C][C]0.342160716623389[/C][/ROW]
[ROW][C]32[/C][C]0.59180215306337[/C][C]0.816395693873259[/C][C]0.40819784693663[/C][/ROW]
[ROW][C]33[/C][C]0.537128578137344[/C][C]0.925742843725312[/C][C]0.462871421862656[/C][/ROW]
[ROW][C]34[/C][C]0.668268773030221[/C][C]0.663462453939558[/C][C]0.331731226969779[/C][/ROW]
[ROW][C]35[/C][C]0.887601785039725[/C][C]0.22479642992055[/C][C]0.112398214960275[/C][/ROW]
[ROW][C]36[/C][C]0.899618336080269[/C][C]0.200763327839463[/C][C]0.100381663919731[/C][/ROW]
[ROW][C]37[/C][C]0.881131392513192[/C][C]0.237737214973615[/C][C]0.118868607486808[/C][/ROW]
[ROW][C]38[/C][C]0.868163365624399[/C][C]0.263673268751203[/C][C]0.131836634375601[/C][/ROW]
[ROW][C]39[/C][C]0.834703623279971[/C][C]0.330592753440057[/C][C]0.165296376720029[/C][/ROW]
[ROW][C]40[/C][C]0.801825318743659[/C][C]0.396349362512681[/C][C]0.198174681256341[/C][/ROW]
[ROW][C]41[/C][C]0.840839276485731[/C][C]0.318321447028538[/C][C]0.159160723514269[/C][/ROW]
[ROW][C]42[/C][C]0.80354484972437[/C][C]0.39291030055126[/C][C]0.19645515027563[/C][/ROW]
[ROW][C]43[/C][C]0.786622523540569[/C][C]0.426754952918863[/C][C]0.213377476459431[/C][/ROW]
[ROW][C]44[/C][C]0.854242531707256[/C][C]0.291514936585488[/C][C]0.145757468292744[/C][/ROW]
[ROW][C]45[/C][C]0.980318711816765[/C][C]0.0393625763664694[/C][C]0.0196812881832347[/C][/ROW]
[ROW][C]46[/C][C]0.981951966779347[/C][C]0.0360960664413058[/C][C]0.0180480332206529[/C][/ROW]
[ROW][C]47[/C][C]0.983861894142051[/C][C]0.0322762117158977[/C][C]0.0161381058579488[/C][/ROW]
[ROW][C]48[/C][C]0.980321222762204[/C][C]0.0393575544755925[/C][C]0.0196787772377963[/C][/ROW]
[ROW][C]49[/C][C]0.992530443358437[/C][C]0.0149391132831267[/C][C]0.00746955664156333[/C][/ROW]
[ROW][C]50[/C][C]0.996529301742677[/C][C]0.00694139651464579[/C][C]0.0034706982573229[/C][/ROW]
[ROW][C]51[/C][C]0.996309294561622[/C][C]0.0073814108767563[/C][C]0.00369070543837815[/C][/ROW]
[ROW][C]52[/C][C]0.996466824164791[/C][C]0.00706635167041861[/C][C]0.00353317583520931[/C][/ROW]
[ROW][C]53[/C][C]0.996262024557106[/C][C]0.00747595088578876[/C][C]0.00373797544289438[/C][/ROW]
[ROW][C]54[/C][C]0.994640218191942[/C][C]0.010719563616116[/C][C]0.00535978180805799[/C][/ROW]
[ROW][C]55[/C][C]0.994238548890849[/C][C]0.0115229022183022[/C][C]0.00576145110915108[/C][/ROW]
[ROW][C]56[/C][C]0.992616493176254[/C][C]0.0147670136474912[/C][C]0.00738350682374559[/C][/ROW]
[ROW][C]57[/C][C]0.990863025143741[/C][C]0.018273949712517[/C][C]0.00913697485625851[/C][/ROW]
[ROW][C]58[/C][C]0.992107016435558[/C][C]0.0157859671288845[/C][C]0.00789298356444223[/C][/ROW]
[ROW][C]59[/C][C]0.988946353424481[/C][C]0.0221072931510375[/C][C]0.0110536465755188[/C][/ROW]
[ROW][C]60[/C][C]0.990550323722209[/C][C]0.0188993525555816[/C][C]0.0094496762777908[/C][/ROW]
[ROW][C]61[/C][C]0.989035323322065[/C][C]0.021929353355869[/C][C]0.0109646766779345[/C][/ROW]
[ROW][C]62[/C][C]0.994509640986943[/C][C]0.0109807180261132[/C][C]0.00549035901305662[/C][/ROW]
[ROW][C]63[/C][C]0.99313254687449[/C][C]0.0137349062510199[/C][C]0.00686745312550996[/C][/ROW]
[ROW][C]64[/C][C]0.990524868911742[/C][C]0.0189502621765156[/C][C]0.00947513108825781[/C][/ROW]
[ROW][C]65[/C][C]0.987927694571671[/C][C]0.0241446108566585[/C][C]0.0120723054283293[/C][/ROW]
[ROW][C]66[/C][C]0.983806191505537[/C][C]0.0323876169889266[/C][C]0.0161938084944633[/C][/ROW]
[ROW][C]67[/C][C]0.979292014036313[/C][C]0.0414159719273745[/C][C]0.0207079859636873[/C][/ROW]
[ROW][C]68[/C][C]0.988547141319386[/C][C]0.0229057173612271[/C][C]0.0114528586806136[/C][/ROW]
[ROW][C]69[/C][C]0.984698927995458[/C][C]0.0306021440090844[/C][C]0.0153010720045422[/C][/ROW]
[ROW][C]70[/C][C]0.979725109449918[/C][C]0.0405497811001634[/C][C]0.0202748905500817[/C][/ROW]
[ROW][C]71[/C][C]0.974433820370049[/C][C]0.0511323592599026[/C][C]0.0255661796299513[/C][/ROW]
[ROW][C]72[/C][C]0.970961475116283[/C][C]0.0580770497674341[/C][C]0.029038524883717[/C][/ROW]
[ROW][C]73[/C][C]0.963145142376568[/C][C]0.0737097152468645[/C][C]0.0368548576234323[/C][/ROW]
[ROW][C]74[/C][C]0.95262433578961[/C][C]0.0947513284207799[/C][C]0.04737566421039[/C][/ROW]
[ROW][C]75[/C][C]0.940066041578156[/C][C]0.119867916843689[/C][C]0.0599339584218445[/C][/ROW]
[ROW][C]76[/C][C]0.993046197224958[/C][C]0.0139076055500835[/C][C]0.00695380277504173[/C][/ROW]
[ROW][C]77[/C][C]0.994945522864523[/C][C]0.010108954270954[/C][C]0.00505447713547698[/C][/ROW]
[ROW][C]78[/C][C]0.993761550556291[/C][C]0.012476898887418[/C][C]0.00623844944370898[/C][/ROW]
[ROW][C]79[/C][C]0.991255612769363[/C][C]0.0174887744612733[/C][C]0.00874438723063666[/C][/ROW]
[ROW][C]80[/C][C]0.988244766746615[/C][C]0.0235104665067709[/C][C]0.0117552332533854[/C][/ROW]
[ROW][C]81[/C][C]0.984157700782521[/C][C]0.0316845984349575[/C][C]0.0158422992174787[/C][/ROW]
[ROW][C]82[/C][C]0.998831340884956[/C][C]0.00233731823008817[/C][C]0.00116865911504409[/C][/ROW]
[ROW][C]83[/C][C]0.998714445053029[/C][C]0.0025711098939418[/C][C]0.0012855549469709[/C][/ROW]
[ROW][C]84[/C][C]0.998721987347805[/C][C]0.00255602530438983[/C][C]0.00127801265219492[/C][/ROW]
[ROW][C]85[/C][C]0.998424340329995[/C][C]0.0031513193400098[/C][C]0.0015756596700049[/C][/ROW]
[ROW][C]86[/C][C]0.997695723875217[/C][C]0.00460855224956679[/C][C]0.0023042761247834[/C][/ROW]
[ROW][C]87[/C][C]0.997227886864902[/C][C]0.00554422627019549[/C][C]0.00277211313509774[/C][/ROW]
[ROW][C]88[/C][C]0.99702340492076[/C][C]0.00595319015847898[/C][C]0.00297659507923949[/C][/ROW]
[ROW][C]89[/C][C]0.995882849668505[/C][C]0.00823430066299064[/C][C]0.00411715033149532[/C][/ROW]
[ROW][C]90[/C][C]0.999014778967114[/C][C]0.00197044206577156[/C][C]0.00098522103288578[/C][/ROW]
[ROW][C]91[/C][C]0.999046460206114[/C][C]0.00190707958777135[/C][C]0.000953539793885675[/C][/ROW]
[ROW][C]92[/C][C]0.999391386486408[/C][C]0.00121722702718386[/C][C]0.000608613513591929[/C][/ROW]
[ROW][C]93[/C][C]0.999310130007402[/C][C]0.00137973998519504[/C][C]0.000689869992597522[/C][/ROW]
[ROW][C]94[/C][C]0.99916320262493[/C][C]0.00167359475013968[/C][C]0.00083679737506984[/C][/ROW]
[ROW][C]95[/C][C]0.999476333961776[/C][C]0.00104733207644754[/C][C]0.000523666038223772[/C][/ROW]
[ROW][C]96[/C][C]0.999179754359475[/C][C]0.00164049128104899[/C][C]0.000820245640524496[/C][/ROW]
[ROW][C]97[/C][C]0.99905371125342[/C][C]0.00189257749316024[/C][C]0.00094628874658012[/C][/ROW]
[ROW][C]98[/C][C]0.998648075583873[/C][C]0.00270384883225464[/C][C]0.00135192441612732[/C][/ROW]
[ROW][C]99[/C][C]0.999108942388478[/C][C]0.00178211522304465[/C][C]0.000891057611522327[/C][/ROW]
[ROW][C]100[/C][C]0.999180613311238[/C][C]0.00163877337752485[/C][C]0.000819386688762423[/C][/ROW]
[ROW][C]101[/C][C]0.998928766505701[/C][C]0.00214246698859812[/C][C]0.00107123349429906[/C][/ROW]
[ROW][C]102[/C][C]0.998575474730461[/C][C]0.0028490505390775[/C][C]0.00142452526953875[/C][/ROW]
[ROW][C]103[/C][C]0.997913667460409[/C][C]0.00417266507918214[/C][C]0.00208633253959107[/C][/ROW]
[ROW][C]104[/C][C]0.997341800170296[/C][C]0.00531639965940733[/C][C]0.00265819982970367[/C][/ROW]
[ROW][C]105[/C][C]0.996247284212927[/C][C]0.00750543157414695[/C][C]0.00375271578707347[/C][/ROW]
[ROW][C]106[/C][C]0.996107881314083[/C][C]0.00778423737183369[/C][C]0.00389211868591684[/C][/ROW]
[ROW][C]107[/C][C]0.994534663143558[/C][C]0.0109306737128835[/C][C]0.00546533685644177[/C][/ROW]
[ROW][C]108[/C][C]0.992989884642394[/C][C]0.0140202307152125[/C][C]0.00701011535760627[/C][/ROW]
[ROW][C]109[/C][C]0.996203427707988[/C][C]0.00759314458402393[/C][C]0.00379657229201196[/C][/ROW]
[ROW][C]110[/C][C]0.999975004976411[/C][C]4.99900471780703e-05[/C][C]2.49950235890351e-05[/C][/ROW]
[ROW][C]111[/C][C]0.99998714272374[/C][C]2.57145525194184e-05[/C][C]1.28572762597092e-05[/C][/ROW]
[ROW][C]112[/C][C]0.999994589907493[/C][C]1.08201850131281e-05[/C][C]5.41009250656404e-06[/C][/ROW]
[ROW][C]113[/C][C]0.999989104558632[/C][C]2.17908827366884e-05[/C][C]1.08954413683442e-05[/C][/ROW]
[ROW][C]114[/C][C]0.999998302997755[/C][C]3.39400449016684e-06[/C][C]1.69700224508342e-06[/C][/ROW]
[ROW][C]115[/C][C]0.999996432625754[/C][C]7.1347484929144e-06[/C][C]3.5673742464572e-06[/C][/ROW]
[ROW][C]116[/C][C]0.99999243534484[/C][C]1.51293103197665e-05[/C][C]7.56465515988323e-06[/C][/ROW]
[ROW][C]117[/C][C]0.999997953645635[/C][C]4.09270873041592e-06[/C][C]2.04635436520796e-06[/C][/ROW]
[ROW][C]118[/C][C]0.999995654839313[/C][C]8.69032137397898e-06[/C][C]4.34516068698949e-06[/C][/ROW]
[ROW][C]119[/C][C]0.999991831071221[/C][C]1.63378575571027e-05[/C][C]8.16892877855135e-06[/C][/ROW]
[ROW][C]120[/C][C]0.999983688764795[/C][C]3.26224704098903e-05[/C][C]1.63112352049451e-05[/C][/ROW]
[ROW][C]121[/C][C]0.9999997713126[/C][C]4.57374800178785e-07[/C][C]2.28687400089392e-07[/C][/ROW]
[ROW][C]122[/C][C]0.999999587839311[/C][C]8.24321378200635e-07[/C][C]4.12160689100318e-07[/C][/ROW]
[ROW][C]123[/C][C]0.999999650441582[/C][C]6.99116836706213e-07[/C][C]3.49558418353106e-07[/C][/ROW]
[ROW][C]124[/C][C]0.999999075573715[/C][C]1.84885257048072e-06[/C][C]9.24426285240359e-07[/C][/ROW]
[ROW][C]125[/C][C]0.999998155604296[/C][C]3.68879140883683e-06[/C][C]1.84439570441841e-06[/C][/ROW]
[ROW][C]126[/C][C]0.999996673244065[/C][C]6.653511869376e-06[/C][C]3.326755934688e-06[/C][/ROW]
[ROW][C]127[/C][C]0.999997809587642[/C][C]4.38082471693335e-06[/C][C]2.19041235846667e-06[/C][/ROW]
[ROW][C]128[/C][C]0.999994502523929[/C][C]1.09949521425304e-05[/C][C]5.49747607126521e-06[/C][/ROW]
[ROW][C]129[/C][C]0.999997312322308[/C][C]5.37535538427799e-06[/C][C]2.68767769213899e-06[/C][/ROW]
[ROW][C]130[/C][C]0.999995025753688[/C][C]9.94849262432893e-06[/C][C]4.97424631216447e-06[/C][/ROW]
[ROW][C]131[/C][C]0.999987750357035[/C][C]2.44992859290534e-05[/C][C]1.22496429645267e-05[/C][/ROW]
[ROW][C]132[/C][C]0.999985335271988[/C][C]2.93294560237353e-05[/C][C]1.46647280118677e-05[/C][/ROW]
[ROW][C]133[/C][C]0.99997839831059[/C][C]4.32033788202463e-05[/C][C]2.16016894101232e-05[/C][/ROW]
[ROW][C]134[/C][C]0.999993079357105[/C][C]1.38412857889929e-05[/C][C]6.92064289449646e-06[/C][/ROW]
[ROW][C]135[/C][C]0.999984421225476[/C][C]3.11575490469438e-05[/C][C]1.55787745234719e-05[/C][/ROW]
[ROW][C]136[/C][C]0.999996968252857[/C][C]6.06349428566393e-06[/C][C]3.03174714283197e-06[/C][/ROW]
[ROW][C]137[/C][C]0.999999670451455[/C][C]6.5909709003329e-07[/C][C]3.29548545016645e-07[/C][/ROW]
[ROW][C]138[/C][C]0.999999957507543[/C][C]8.49849131862218e-08[/C][C]4.24924565931109e-08[/C][/ROW]
[ROW][C]139[/C][C]0.99999998037889[/C][C]3.92422203133309e-08[/C][C]1.96211101566655e-08[/C][/ROW]
[ROW][C]140[/C][C]0.999999839688837[/C][C]3.20622325921176e-07[/C][C]1.60311162960588e-07[/C][/ROW]
[ROW][C]141[/C][C]0.999999998030556[/C][C]3.93888768702723e-09[/C][C]1.96944384351362e-09[/C][/ROW]
[ROW][C]142[/C][C]0.99999999754958[/C][C]4.90084070641116e-09[/C][C]2.45042035320558e-09[/C][/ROW]
[ROW][C]143[/C][C]0.999999963452557[/C][C]7.30948852021104e-08[/C][C]3.65474426010552e-08[/C][/ROW]
[ROW][C]144[/C][C]0.999999999999874[/C][C]2.52594390357832e-13[/C][C]1.26297195178916e-13[/C][/ROW]
[ROW][C]145[/C][C]0.999999999941646[/C][C]1.1670699832217e-10[/C][C]5.83534991610853e-11[/C][/ROW]
[ROW][C]146[/C][C]0.999999973577885[/C][C]5.28442300958561e-08[/C][C]2.6422115047928e-08[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160732&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160732&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
180.3221677405167310.6443354810334620.677832259483269
190.1785542866761690.3571085733523370.821445713323831
200.4223137007753380.8446274015506770.577686299224662
210.3406440093097340.6812880186194680.659355990690266
220.8362361190332670.3275277619334670.163763880966733
230.8056181384202350.3887637231595290.194381861579765
240.7928982550753290.4142034898493430.207101744924671
250.7640060370045430.4719879259909140.235993962995457
260.8144128482773750.3711743034452510.185587151722625
270.7635896436130770.4728207127738460.236410356386923
280.7201410071710790.5597179856578420.279858992828921
290.6510645539178510.6978708921642980.348935446082149
300.6980004153012650.6039991693974710.301999584698735
310.6578392833766110.6843214332467780.342160716623389
320.591802153063370.8163956938732590.40819784693663
330.5371285781373440.9257428437253120.462871421862656
340.6682687730302210.6634624539395580.331731226969779
350.8876017850397250.224796429920550.112398214960275
360.8996183360802690.2007633278394630.100381663919731
370.8811313925131920.2377372149736150.118868607486808
380.8681633656243990.2636732687512030.131836634375601
390.8347036232799710.3305927534400570.165296376720029
400.8018253187436590.3963493625126810.198174681256341
410.8408392764857310.3183214470285380.159160723514269
420.803544849724370.392910300551260.19645515027563
430.7866225235405690.4267549529188630.213377476459431
440.8542425317072560.2915149365854880.145757468292744
450.9803187118167650.03936257636646940.0196812881832347
460.9819519667793470.03609606644130580.0180480332206529
470.9838618941420510.03227621171589770.0161381058579488
480.9803212227622040.03935755447559250.0196787772377963
490.9925304433584370.01493911328312670.00746955664156333
500.9965293017426770.006941396514645790.0034706982573229
510.9963092945616220.00738141087675630.00369070543837815
520.9964668241647910.007066351670418610.00353317583520931
530.9962620245571060.007475950885788760.00373797544289438
540.9946402181919420.0107195636161160.00535978180805799
550.9942385488908490.01152290221830220.00576145110915108
560.9926164931762540.01476701364749120.00738350682374559
570.9908630251437410.0182739497125170.00913697485625851
580.9921070164355580.01578596712888450.00789298356444223
590.9889463534244810.02210729315103750.0110536465755188
600.9905503237222090.01889935255558160.0094496762777908
610.9890353233220650.0219293533558690.0109646766779345
620.9945096409869430.01098071802611320.00549035901305662
630.993132546874490.01373490625101990.00686745312550996
640.9905248689117420.01895026217651560.00947513108825781
650.9879276945716710.02414461085665850.0120723054283293
660.9838061915055370.03238761698892660.0161938084944633
670.9792920140363130.04141597192737450.0207079859636873
680.9885471413193860.02290571736122710.0114528586806136
690.9846989279954580.03060214400908440.0153010720045422
700.9797251094499180.04054978110016340.0202748905500817
710.9744338203700490.05113235925990260.0255661796299513
720.9709614751162830.05807704976743410.029038524883717
730.9631451423765680.07370971524686450.0368548576234323
740.952624335789610.09475132842077990.04737566421039
750.9400660415781560.1198679168436890.0599339584218445
760.9930461972249580.01390760555008350.00695380277504173
770.9949455228645230.0101089542709540.00505447713547698
780.9937615505562910.0124768988874180.00623844944370898
790.9912556127693630.01748877446127330.00874438723063666
800.9882447667466150.02351046650677090.0117552332533854
810.9841577007825210.03168459843495750.0158422992174787
820.9988313408849560.002337318230088170.00116865911504409
830.9987144450530290.00257110989394180.0012855549469709
840.9987219873478050.002556025304389830.00127801265219492
850.9984243403299950.00315131934000980.0015756596700049
860.9976957238752170.004608552249566790.0023042761247834
870.9972278868649020.005544226270195490.00277211313509774
880.997023404920760.005953190158478980.00297659507923949
890.9958828496685050.008234300662990640.00411715033149532
900.9990147789671140.001970442065771560.00098522103288578
910.9990464602061140.001907079587771350.000953539793885675
920.9993913864864080.001217227027183860.000608613513591929
930.9993101300074020.001379739985195040.000689869992597522
940.999163202624930.001673594750139680.00083679737506984
950.9994763339617760.001047332076447540.000523666038223772
960.9991797543594750.001640491281048990.000820245640524496
970.999053711253420.001892577493160240.00094628874658012
980.9986480755838730.002703848832254640.00135192441612732
990.9991089423884780.001782115223044650.000891057611522327
1000.9991806133112380.001638773377524850.000819386688762423
1010.9989287665057010.002142466988598120.00107123349429906
1020.9985754747304610.00284905053907750.00142452526953875
1030.9979136674604090.004172665079182140.00208633253959107
1040.9973418001702960.005316399659407330.00265819982970367
1050.9962472842129270.007505431574146950.00375271578707347
1060.9961078813140830.007784237371833690.00389211868591684
1070.9945346631435580.01093067371288350.00546533685644177
1080.9929898846423940.01402023071521250.00701011535760627
1090.9962034277079880.007593144584023930.00379657229201196
1100.9999750049764114.99900471780703e-052.49950235890351e-05
1110.999987142723742.57145525194184e-051.28572762597092e-05
1120.9999945899074931.08201850131281e-055.41009250656404e-06
1130.9999891045586322.17908827366884e-051.08954413683442e-05
1140.9999983029977553.39400449016684e-061.69700224508342e-06
1150.9999964326257547.1347484929144e-063.5673742464572e-06
1160.999992435344841.51293103197665e-057.56465515988323e-06
1170.9999979536456354.09270873041592e-062.04635436520796e-06
1180.9999956548393138.69032137397898e-064.34516068698949e-06
1190.9999918310712211.63378575571027e-058.16892877855135e-06
1200.9999836887647953.26224704098903e-051.63112352049451e-05
1210.99999977131264.57374800178785e-072.28687400089392e-07
1220.9999995878393118.24321378200635e-074.12160689100318e-07
1230.9999996504415826.99116836706213e-073.49558418353106e-07
1240.9999990755737151.84885257048072e-069.24426285240359e-07
1250.9999981556042963.68879140883683e-061.84439570441841e-06
1260.9999966732440656.653511869376e-063.326755934688e-06
1270.9999978095876424.38082471693335e-062.19041235846667e-06
1280.9999945025239291.09949521425304e-055.49747607126521e-06
1290.9999973123223085.37535538427799e-062.68767769213899e-06
1300.9999950257536889.94849262432893e-064.97424631216447e-06
1310.9999877503570352.44992859290534e-051.22496429645267e-05
1320.9999853352719882.93294560237353e-051.46647280118677e-05
1330.999978398310594.32033788202463e-052.16016894101232e-05
1340.9999930793571051.38412857889929e-056.92064289449646e-06
1350.9999844212254763.11575490469438e-051.55787745234719e-05
1360.9999969682528576.06349428566393e-063.03174714283197e-06
1370.9999996704514556.5909709003329e-073.29548545016645e-07
1380.9999999575075438.49849131862218e-084.24924565931109e-08
1390.999999980378893.92422203133309e-081.96211101566655e-08
1400.9999998396888373.20622325921176e-071.60311162960588e-07
1410.9999999980305563.93888768702723e-091.96944384351362e-09
1420.999999997549584.90084070641116e-092.45042035320558e-09
1430.9999999634525577.30948852021104e-083.65474426010552e-08
1440.9999999999998742.52594390357832e-131.26297195178916e-13
1450.9999999999416461.1670699832217e-105.83534991610853e-11
1460.9999999735778855.28442300958561e-082.6422115047928e-08






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level670.51937984496124NOK
5% type I error level970.751937984496124NOK
10% type I error level1010.782945736434108NOK

\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 & 67 & 0.51937984496124 & NOK \tabularnewline
5% type I error level & 97 & 0.751937984496124 & NOK \tabularnewline
10% type I error level & 101 & 0.782945736434108 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160732&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]67[/C][C]0.51937984496124[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]97[/C][C]0.751937984496124[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]101[/C][C]0.782945736434108[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160732&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160732&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 level670.51937984496124NOK
5% type I error level970.751937984496124NOK
10% type I error level1010.782945736434108NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}