FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 20 Dec 2011 06:35:37 -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/20/t13243817306kaczlx56ek63ym.htm/, Retrieved Mon, 06 May 2024 09:57:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=157934, Retrieved Mon, 06 May 2024 09:57:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact156

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-12-20 11:35:37] [bed8b29df92274246a1a4d59b25859b7] [Current]
-    D    [Multiple Regression] [Multiple Regressi...] [2011-12-22 13:11:33] [f033824ca1b38a5ddbb2c3414ea3bb75]
- RMPD    [Kendall tau Correlation Matrix] [Pearson correlati...] [2011-12-22 14:06:44] [f033824ca1b38a5ddbb2c3414ea3bb75]
- RMPD    [Kendall tau Correlation Matrix] [Kendall tau corre...] [2011-12-22 14:27:43] [f033824ca1b38a5ddbb2c3414ea3bb75]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1785	276257	71	492	93	3	96	41	155	126	140824	32033	186099	165	165
1227	180480	66	436	63	4	71	34	133	127	110459	20654	113854	135	132
1944	229345	76	697	62	16	70	44	170	111	105079	16346	99776	121	121
2683	218443	104	1137	135	2	134	38	148	133	112098	35926	106194	148	145
1247	171533	52	380	48	1	67	27	88	64	43929	10621	100792	73	71
631	70849	28	179	46	3	8	35	129	89	76173	10024	47552	49	47
4845	536497	125	2354	109	0	162	33	128	122	187326	43068	250931	185	177
381	33186	19	111	46	0	1	18	67	22	22807	1271	6853	5	5
2066	217320	60	740	75	7	83	34	132	117	144408	34416	115466	125	124
1759	213274	45	595	72	0	82	33	120	82	66485	20318	110896	93	92
2157	310843	112	809	82	0	92	46	169	147	79089	24409	169351	154	149
2211	242788	122	693	63	7	117	57	218	192	81625	20648	94853	98	93
1967	195022	76	738	60	10	56	37	122	113	68788	12347	72591	70	70
3005	367785	81	1184	117	4	139	55	191	171	103297	21857	101345	148	148
2228	261990	86	713	50	10	83	44	162	87	69446	11034	113713	100	100
5009	392509	181	1729	132	0	176	62	234	207	114948	33433	165354	150	142
2353	335528	75	844	63	8	114	40	156	153	167949	35902	164263	197	194
3278	376673	163	1298	90	4	105	39	144	92	125081	22355	135213	114	113
1473	181980	56	514	44	3	103	32	123	95	125818	31219	111669	169	162
2347	266736	87	689	64	8	135	51	199	193	136588	21983	134163	200	186
2523	278265	71	837	103	0	123	49	187	160	112431	40085	140303	148	147
2987	461287	104	1330	101	1	87	39	144	144	103037	18507	150773	140	137
1523	195786	42	491	62	5	74	25	89	84	82317	16278	111848	74	71
1761	197058	55	622	69	9	103	56	223	223	118906	24662	102509	128	123
3660	250284	126	1332	159	1	154	45	165	154	83515	31452	96785	140	134
2949	250092	131	1046	74	0	113	38	146	139	104581	32580	116136	116	115
2914	274121	129	1082	101	5	99	45	174	142	103129	22883	158376	147	138
2009	278027	88	636	73	0	117	43	154	148	83243	27652	153990	132	125
1555	165597	52	586	53	0	59	32	117	99	37110	9845	64057	70	66
3050	371452	73	1170	140	0	142	41	158	135	113344	20190	230054	144	137
2438	296386	81	976	54	3	134	50	195	179	139165	46201	184531	155	152
2092	248320	81	721	84	6	50	50	186	149	86652	10971	114198	165	159
2404	351980	95	863	56	1	141	51	197	187	112302	34811	198299	161	159
1018	101014	40	343	40	4	43	37	141	137	69652	3029	33750	31	31
3462	412722	101	1278	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
3710	425531	122	1334	77	0	112	44	161	151	101629	32344	188355	121	117
1947	231912	84	652	67	2	79	36	139	89	70168	19433	104470	112	109
947	115658	33	284	36	1	33	17	55	46	31081	12558	58391	41	41
3609	376008	202	1273	51	2	137	43	166	156	103925	36524	164808	158	149
3324	335039	82	1518	44	10	125	41	151	128	92622	26041	134097	123	123
1842	194127	71	715	75	10	80	41	148	111	79011	16637	80238	104	103
1869	206947	78	671	85	5	78	38	123	114	93487	28395	133252	94	87
1813	182286	64	486	97	6	68	49	181	148	64520	16747	54518	73	71
2496	153778	82	1022	82	1	50	45	73	45	93473	9105	121850	52	51
5557	457592	153	2084	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
2254	208277	77	658	80	0	101	52	201	180	96125	19499	106314	155	155
4103	359144	121	1385	120	10	149	50	193	177	151911	22938	191889	174	172
2241	184648	60	930	68	3	108	45	164	146	89256	25314	104864	136	133
1943	234078	77	620	48	0	96	40	158	137	95676	28527	160792	128	125
496	24188	24	218	20	0	8	4	12	7	5950	2694	15049	7	7
2594	380576	326	840	81	8	88	44	163	157	149695	20867	191179	165	158
744	65029	17	255	21	5	21	18	67	61	32551	3597	25109	21	21
1161	101097	64	454	70	3	30	14	52	41	31701	5296	45824	35	35
3121	288327	60	1149	128	1	97	38	137	123	100087	32982	129711	137	133
2621	334829	87	684	86	5	134	61	230	228	169707	38975	210012	174	169
3906	359400	199	1190	222	6	132	39	145	137	150491	42721	194679	257	256
2722	369577	146	1079	63	0	161	42	153	150	120192	41455	197680	207	190
2312	269961	86	883	86	12	89	40	155	141	95893	23923	81180	103	100
4061	389738	147	1331	70	10	160	51	198	181	151715	26719	197765	171	171
3195	309474	119	1159	146	12	139	28	101	73	176225	53405	214738	279	267
3041	282769	118	1217	72	11	104	43	169	97	59900	12526	96252	83	80
2644	269324	90	946	59	8	92	42	163	142	104767	26584	124527	130	126
1496	234773	69	579	58	2	54	37	139	125	114799	37062	153242	131	132
1835	237561	68	474	58	0	119	30	116	87	72128	25696	145707	126	121
2090	214916	137	630	60	6	93	39	153	140	143592	24634	113963	158	156
2772	240376	151	843	89	10	85	44	167	148	89626	27269	134904	138	133
2440	247447	84	893	78	2	145	36	135	116	131072	25270	114268	200	199
1718	181550	70	633	62	5	143	28	102	89	126817	24634	94333	104	98
2566	242152	70	873	67	13	99	47	179	160	81351	17828	102204	111	109
893	73566	32	385	39	6	22	23	88	67	22618	3007	23824	26	25
2227	246125	86	729	60	7	78	48	185	179	88977	20065	111563	115	113
1878	199565	56	774	94	2	83	38	133	90	92059	24648	91313	127	126
2177	222676	65	769	67	4	131	42	164	144	81897	21588	89770	140	137
2352	363558	88	996	96	4	140	46	169	144	108146	25217	100125	121	121
2981	365442	96	1194	51	3	148	37	143	135	126372	30927	165278	183	178
1926	217036	108	575	50	6	80	41	154	125	249771	18487	181712	68	63
2400	213466	66	725	62	2	133	42	164	146	71154	18050	80906	112	109
2034	204477	67	706	71	0	99	41	146	121	71571	17696	75881	103	101
1800	169080	48	665	49	1	62	36	137	109	55918	17326	83963	63	61
4168	478396	128	1259	114	0	161	45	175	138	160141	39361	175721	166	157
1369	145943	69	653	45	5	30	26	99	99	38692	9648	68580	38	38
2403	288626	106	694	57	2	120	44	122	92	102812	26759	136323	163	159
870	80953	25	437	31	0	49	8	28	27	56622	7905	55792	59	58
1968	150221	55	822	175	0	52	27	101	77	15986	4527	25157	27	27
1569	179317	60	458	70	6	76	38	139	137	123534	41517	100922	108	108
3962	395368	213	1545	282	1	85	38	147	140	108535	21261	118845	88	83
2928	349460	120	987	91	0	146	57	206	122	93879	36099	170492	92	88
3098	180679	106	1051	72	1	165	45	171	159	144551	39039	81716	170	164
2557	286578	100	838	62	1	87	40	150	97	56750	13841	115750	98	96
2329	274477	81	703	75	3	165	42	154	144	127654	23841	105590	205	192
1858	195623	65	613	86	10	48	31	114	90	65594	8589	92795	96	94
3146	282361	77	1128	89	1	149	36	140	135	59938	15049	82390	107	107
2582	329121	86	967	138	4	75	40	156	147	146975	39038	135599	150	144
1824	198658	42	617	68	4	84	40	156	155	165904	36774	127667	138	136
1938	264817	60	654	72	5	112	35	127	127	169265	40076	163073	177	171
2210	300481	83	805	61	0	165	39	141	104	183500	43840	211381	213	210
4119	403404	156	1355	130	12	155	65	251	248	165986	43146	189944	208	193
3844	406613	114	1456	85	13	165	33	126	116	184923	50099	226168	307	297
2791	294371	138	878	83	8	121	51	198	176	140358	40312	117495	125	125
2425	313267	67	887	86	0	156	42	159	133	149959	32616	195894	208	204
2052	189276	188	663	116	0	73	36	138	59	57224	11338	80684	73	70
602	43287	14	214	43	4	13	19	71	64	43750	7409	19630	49	49
2195	189520	83	733	87	4	89	25	90	40	48029	18213	88634	82	82
2413	250254	149	830	80	0	105	44	167	98	104978	45873	139292	206	205
2632	270832	54	1174	110	0	129	40	155	125	100046	39844	128602	112	111
2759	314153	93	1068	59	0	169	44	162	135	101047	28317	135848	139	135
1268	160308	81	413	44	0	28	30	112	83	197426	24797	178377	60	59
3233	162843	97	946	85	0	118	45	175	138	160902	7471	106330	70	70
2025	344925	74	657	60	5	82	42	157	149	147172	27259	178303	112	108
2310	300526	89	690	70	0	147	45	164	115	109432	23201	116938	142	141
398	23623	11	156	9	0	12	1	0	0	1168	238	5841	11	11
2205	195817	73	779	54	0	146	40	155	103	83248	28830	106020	130	130
530	61857	25	192	25	4	23	11	32	30	25162	3913	24610	31	28
1611	163931	51	461	107	0	83	45	169	119	45724	9935	74151	132	101
3153	428191	120	1213	63	1	163	38	140	102	110529	27738	232241	219	216
387	21054	16	146	2	0	4	0	0	0	855	338	6622	4	4
2137	252805	52	866	67	5	81	30	111	77	101382	13326	127097	102	97
492	31961	22	200	22	0	18	8	25	9	14116	3988	13155	39	39
3674	335888	119	1290	155	3	115	41	152	143	89506	24347	160501	125	119
2136	246100	68	715	79	7	76	48	183	163	135356	27111	91502	121	118
1748	180591	93	514	112	13	55	48	181	146	116066	3938	24469	42	41
1790	163400	54	697	50	3	53	32	119	94	144244	17416	88229	111	107
568	38214	34	276	52	0	16	8	27	21	8773	1888	13983	16	16
2191	224597	44	752	113	3	81	43	163	151	102153	18700	80716	70	69
2792	357602	82	1021	115	0	137	52	198	187	117440	36809	157384	162	160
1420	202565	63	494	78	0	50	53	205	171	104128	24959	122975	173	158
3718	424398	85	1626	135	4	142	49	191	170	134238	37343	191469	171	161
2554	348017	99	884	120	0	163	48	187	145	134047	21849	231257	172	165
3460	421610	129	1187	122	3	141	56	210	198	279488	49809	258287	254	246
1284	192170	38	488	49	0	71	40	151	137	79756	21654	122531	90	89
1130	102510	43	403	56	0	42	36	131	100	66089	8728	61394	50	49
2974	302158	347	977	155	4	94	44	171	162	102070	20920	86480	113	107
4346	444599	182	1525	155	5	118	46	172	163	146760	27195	195791	187	182
1785	148707	57	551	107	15	63	43	164	153	154771	1037	18284	16	16
4350	407736	135	1807	146	5	127	46	172	161	165933	42570	147581	175	173
1920	164406	79	723	76	5	55	39	143	112	64593	17672	72558	90	90
1923	278077	50	632	83	2	117	41	151	135	92280	34245	147341	140	140
2541	282461	96	898	192	1	110	46	158	124	67150	16786	114651	145	142
1845	219544	116	621	69	0	39	32	125	45	128692	20954	100187	141	126
4081	384177	122	1606	131	9	95	45	169	144	124089	16378	130332	125	123
2339	246963	92	811	67	1	128	39	145	126	125386	31852	134218	241	239
2035	173260	63	716	37	3	41	21	79	78	37238	2805	10901	16	15
3108	336715	104	1001	60	11	146	49	190	149	140015	38086	145758	175	170
1974	176654	58	732	127	5	147	55	212	196	150047	21166	75767	132	123
2651	253341	87	1024	52	2	119	36	132	118	154451	34672	134969	154	151
2702	307133	109	831	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
2255	260901	88	773	67	2	75	43	133	88	84601	19354	105406	125	122
3447	409280	162	1128	123	3	157	52	204	129	68946	22124	174586	174	173
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
4	203	4	0	0	0	0	0	0	0	0	0	0	0	0
151	7199	5	74	0	0	7	0	0	0	1644	556	4245	6	6
474	46660	20	259	7	0	12	5	15	13	6179	2089	21509	13	13
141	17547	5	69	3	0	0	1	4	4	3926	2658	7670	3	3
1111	118589	45	301	102	0	37	45	160	82	52789	1813	15673	35	35
29	969	2	0	0	0	0	0	0	0	0	0	0	0	0
2011	233108	70	668	53	2	61	34	125	71	100350	17372	75882	80	72

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157934&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157934&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157934&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
k[t] = + 2243.08833737666 + 5.45519276103028a[t] -0.139811012652016b[t] + 11.2197364524116c[t] -5.38094603990477d[t] + 76.6170622386854e[t] + 2173.33685671233f[t] -47.7878250022556g[t] + 176.492318120445h[t] -15.8186478513051i[t] + 192.784762038083j[t] + 1.10656314300898l[t] + 0.480100444908951m[t] + 36.7514845743641n[t] + 4.53996782818319o[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
k[t] =  +  2243.08833737666 +  5.45519276103028a[t] -0.139811012652016b[t] +  11.2197364524116c[t] -5.38094603990477d[t] +  76.6170622386854e[t] +  2173.33685671233f[t] -47.7878250022556g[t] +  176.492318120445h[t] -15.8186478513051i[t] +  192.784762038083j[t] +  1.10656314300898l[t] +  0.480100444908951m[t] +  36.7514845743641n[t] +  4.53996782818319o[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157934&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]k[t] =  +  2243.08833737666 +  5.45519276103028a[t] -0.139811012652016b[t] +  11.2197364524116c[t] -5.38094603990477d[t] +  76.6170622386854e[t] +  2173.33685671233f[t] -47.7878250022556g[t] +  176.492318120445h[t] -15.8186478513051i[t] +  192.784762038083j[t] +  1.10656314300898l[t] +  0.480100444908951m[t] +  36.7514845743641n[t] +  4.53996782818319o[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157934&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157934&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
k[t] = + 2243.08833737666 + 5.45519276103028a[t] -0.139811012652016b[t] + 11.2197364524116c[t] -5.38094603990477d[t] + 76.6170622386854e[t] + 2173.33685671233f[t] -47.7878250022556g[t] + 176.492318120445h[t] -15.8186478513051i[t] + 192.784762038083j[t] + 1.10656314300898l[t] + 0.480100444908951m[t] + 36.7514845743641n[t] + 4.53996782818319o[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2243.088337376665818.7614790.38550.7004220.350211
a5.4551927610302811.5379140.47280.6370440.318522
b-0.1398110126520160.065476-2.13530.0343710.017186
c11.219736452411666.7190270.16820.8666820.433341
d-5.3809460399047723.658648-0.22740.8203930.410196
e76.617062238685440.1991011.90590.0585840.029292
f2173.33685671233645.3942063.36750.0009660.000483
g-47.7878250022556109.815925-0.43520.6640740.332037
h176.492318120445854.9465110.20640.8367320.418366
i-15.8186478513051260.251402-0.06080.9516140.475807
j192.784762038083126.3758831.52550.1292580.064629
l1.106563143008980.3512623.15020.0019720.000986
m0.4801004449089510.094055.10471e-060
n36.7514845743641574.0329160.0640.9490370.474519
o4.53996782818319596.5935390.00760.9939380.496969

\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) & 2243.08833737666 & 5818.761479 & 0.3855 & 0.700422 & 0.350211 \tabularnewline
a & 5.45519276103028 & 11.537914 & 0.4728 & 0.637044 & 0.318522 \tabularnewline
b & -0.139811012652016 & 0.065476 & -2.1353 & 0.034371 & 0.017186 \tabularnewline
c & 11.2197364524116 & 66.719027 & 0.1682 & 0.866682 & 0.433341 \tabularnewline
d & -5.38094603990477 & 23.658648 & -0.2274 & 0.820393 & 0.410196 \tabularnewline
e & 76.6170622386854 & 40.199101 & 1.9059 & 0.058584 & 0.029292 \tabularnewline
f & 2173.33685671233 & 645.394206 & 3.3675 & 0.000966 & 0.000483 \tabularnewline
g & -47.7878250022556 & 109.815925 & -0.4352 & 0.664074 & 0.332037 \tabularnewline
h & 176.492318120445 & 854.946511 & 0.2064 & 0.836732 & 0.418366 \tabularnewline
i & -15.8186478513051 & 260.251402 & -0.0608 & 0.951614 & 0.475807 \tabularnewline
j & 192.784762038083 & 126.375883 & 1.5255 & 0.129258 & 0.064629 \tabularnewline
l & 1.10656314300898 & 0.351262 & 3.1502 & 0.001972 & 0.000986 \tabularnewline
m & 0.480100444908951 & 0.09405 & 5.1047 & 1e-06 & 0 \tabularnewline
n & 36.7514845743641 & 574.032916 & 0.064 & 0.949037 & 0.474519 \tabularnewline
o & 4.53996782818319 & 596.593539 & 0.0076 & 0.993938 & 0.496969 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157934&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]2243.08833737666[/C][C]5818.761479[/C][C]0.3855[/C][C]0.700422[/C][C]0.350211[/C][/ROW]
[ROW][C]a[/C][C]5.45519276103028[/C][C]11.537914[/C][C]0.4728[/C][C]0.637044[/C][C]0.318522[/C][/ROW]
[ROW][C]b[/C][C]-0.139811012652016[/C][C]0.065476[/C][C]-2.1353[/C][C]0.034371[/C][C]0.017186[/C][/ROW]
[ROW][C]c[/C][C]11.2197364524116[/C][C]66.719027[/C][C]0.1682[/C][C]0.866682[/C][C]0.433341[/C][/ROW]
[ROW][C]d[/C][C]-5.38094603990477[/C][C]23.658648[/C][C]-0.2274[/C][C]0.820393[/C][C]0.410196[/C][/ROW]
[ROW][C]e[/C][C]76.6170622386854[/C][C]40.199101[/C][C]1.9059[/C][C]0.058584[/C][C]0.029292[/C][/ROW]
[ROW][C]f[/C][C]2173.33685671233[/C][C]645.394206[/C][C]3.3675[/C][C]0.000966[/C][C]0.000483[/C][/ROW]
[ROW][C]g[/C][C]-47.7878250022556[/C][C]109.815925[/C][C]-0.4352[/C][C]0.664074[/C][C]0.332037[/C][/ROW]
[ROW][C]h[/C][C]176.492318120445[/C][C]854.946511[/C][C]0.2064[/C][C]0.836732[/C][C]0.418366[/C][/ROW]
[ROW][C]i[/C][C]-15.8186478513051[/C][C]260.251402[/C][C]-0.0608[/C][C]0.951614[/C][C]0.475807[/C][/ROW]
[ROW][C]j[/C][C]192.784762038083[/C][C]126.375883[/C][C]1.5255[/C][C]0.129258[/C][C]0.064629[/C][/ROW]
[ROW][C]l[/C][C]1.10656314300898[/C][C]0.351262[/C][C]3.1502[/C][C]0.001972[/C][C]0.000986[/C][/ROW]
[ROW][C]m[/C][C]0.480100444908951[/C][C]0.09405[/C][C]5.1047[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]n[/C][C]36.7514845743641[/C][C]574.032916[/C][C]0.064[/C][C]0.949037[/C][C]0.474519[/C][/ROW]
[ROW][C]o[/C][C]4.53996782818319[/C][C]596.593539[/C][C]0.0076[/C][C]0.993938[/C][C]0.496969[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157934&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157934&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)2243.088337376665818.7614790.38550.7004220.350211
a5.4551927610302811.5379140.47280.6370440.318522
b-0.1398110126520160.065476-2.13530.0343710.017186
c11.219736452411666.7190270.16820.8666820.433341
d-5.3809460399047723.658648-0.22740.8203930.410196
e76.617062238685440.1991011.90590.0585840.029292
f2173.33685671233645.3942063.36750.0009660.000483
g-47.7878250022556109.815925-0.43520.6640740.332037
h176.492318120445854.9465110.20640.8367320.418366
i-15.8186478513051260.251402-0.06080.9516140.475807
j192.784762038083126.3758831.52550.1292580.064629
l1.106563143008980.3512623.15020.0019720.000986
m0.4801004449089510.094055.10471e-060
n36.7514845743641574.0329160.0640.9490370.474519
o4.53996782818319596.5935390.00760.9939380.496969






Multiple Linear Regression - Regression Statistics
Multiple R0.866234189155795
R-squared0.750361670462398
Adjusted R-squared0.726905720036047
F-TEST (value)31.990247967927
F-TEST (DF numerator)14
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation27266.3971527563
Sum Squared Residuals110775005640.084

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.866234189155795 \tabularnewline
R-squared & 0.750361670462398 \tabularnewline
Adjusted R-squared & 0.726905720036047 \tabularnewline
F-TEST (value) & 31.990247967927 \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 & 27266.3971527563 \tabularnewline
Sum Squared Residuals & 110775005640.084 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157934&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.866234189155795[/C][/ROW]
[ROW][C]R-squared[/C][C]0.750361670462398[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.726905720036047[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]31.990247967927[/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]27266.3971527563[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]110775005640.084[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157934&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157934&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 R0.866234189155795
R-squared0.750361670462398
Adjusted R-squared0.726905720036047
F-TEST (value)31.990247967927
F-TEST (DF numerator)14
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation27266.3971527563
Sum Squared Residuals110775005640.084






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1140824141244.792634536-420.792634536205
2110459103682.7760088516776.22399114943
3105079111525.956081124-6446.95608112384
4112098116515.369719644-4417.36971964361
54392965111.1132701883-21182.1132701883
67617362023.496563514614149.5034364854
7187326146062.00630012541263.9936998751
82280714035.53998430978771.46001569032
9144408121958.46076176822449.5392382324
106648580216.0637961275-13731.0637961275
1179089117776.638057482-38687.6380574816
1281625108486.370013256-26861.3700132564
136878884032.1921173499-15244.1921173498
1410329791373.319098070311923.6809019297
156944689402.2258114248-19956.2258114248
16114948138808.038235944-23860.0382359437
17167949142033.78476575125915.2152342492
1812508189574.92647351735506.073526483
19125818104788.51341532121029.486584679
20136588130853.9520573865734.04794261414
21112431129766.742288996-17335.7422889963
2210303784807.912997354318229.0870026457
238231787039.3607907639-4722.36079076385
24118906132607.70044292-13701.7004429204
2583515110503.665559955-26988.6655599552
26104581107264.098717046-2683.09871704605
27103129129265.562781261-26136.5627812613
2883243115531.545500344-32288.5455003444
293711053647.8573206447-16537.8573206447
30113344134879.705983513-21535.7059835133
31139165160368.326359707-21203.3263597065
3286652101412.822246257-14760.8222462571
33112302144593.055744935-32291.0557449353
346965253526.386966753716125.6130332463
35119442145165.434681664-25723.4346816641
366986762460.67033079587406.3296692042
37101629123253.298906755-21624.2989067548
387016881163.0898548977-10995.0898548977
393108148056.6666876601-16975.6666876601
40103925127545.867226319-23620.8672263193
4192622113215.69825982-20593.6982598199
427901193272.4033925839-14261.4033925839
4393487120404.881803629-26917.881803629
446452084001.0082053719-19481.0082053719
459347382027.447312138611445.5526878614
46114360109612.4926034624747.50739653798
473303255700.9806214818-22668.9806214818
4896125103763.484444043-7638.48444404324
49151911156703.958045213-4792.95804521297
5089256108343.422928523-19087.4229285229
5195676121740.348887823-26064.3488878227
52595014174.1729577866-8224.17295778657
53149695138821.64608479110873.353915209
543255138279.8269564065-5728.82695640648
553170142024.9570304191-10323.9570304191
56100087113454.656648749-13367.6566487494
57169707180275.934232752-10568.9342327522
58150491175220.741852352-24729.7418523523
59120192141545.385033484-21353.3850334842
6095893103218.206040999-7325.20604099889
61151715156181.956038599-4466.95603859933
62176225193201.852449864-16976.8524498644
635990085627.575671315-25727.575671315
64104767119203.92538724-14436.9253872397
65114799129874.955457168-15075.9554571679
667212899809.4810705286-27681.4810705286
67143592114877.24963380228714.7503661976
6889626139679.989314448-50053.9893144481
6913107298146.743181871532925.2568181285
7012681789696.535654603737120.4643453963
7181351116808.567695766-35457.5676957657
722261841511.6078024493-18893.6078024493
7388977113663.662492758-24686.6624927578
749205986939.20796774715119.7920322529
758189792478.0171616601-10581.0171616601
7610814683407.320471720924738.6795282791
77126372116819.7772550789552.22274492218
78249771132951.674792053116819.325207947
797115481463.6093746467-10309.6093746467
807157170922.2067250129648.793274987078
815591875623.5112737017-19705.5112737017
82160141120308.19467108139832.8053289189
833869266726.9604756732-28034.9604756732
84102812100776.6340097992035.36599020087
855662237772.537000338518849.4629996615
861598635307.5277002668-19321.5277002668
87123534128484.630526733-4950.63052673342
8810853597939.744992685210595.2550073148
8993879121288.964453357-27409.9644533565
90144551114534.3098391130016.6901608905
915675073816.1174728649-17066.1174728649
9212765496299.33501399831354.664986002
936559487516.6257315636-21922.6257315636
945993867385.4148951421-7447.41489514209
95146975129154.5673452717820.4326547303
96165904133607.59815835832296.4018416423
97169265142547.16008840926717.8399115908
98183500149156.42378587234343.5762141277
99165986194173.585981873-28187.5859818734
100184923189535.374647155-4612.37464715537
101140358137077.4017438193280.59825618092
102149959136035.55209906513923.4479009346
1035722460743.430218881-3519.43021888103
1044375044061.9423558509-311.94235585086
1054802972606.1500073506-24577.1500073506
106104978128889.27812614-23911.2781261404
107100046114445.521594498-14399.5215944977
10810104798618.33516729482428.6648327052
109197426122542.49524721874883.5047527823
11016090287968.711542115672933.2884578844
111147172127933.34179015819238.6582098416
11210943283644.174368436925787.8256315631
11311684209.91570455844-3041.91570455844
1148324893316.9842097837-10068.9842097836
1152516229873.4867485436-4711.48674854359
1164572470548.8265211496-24824.8265211496
117110529128985.778101527-18456.7781015272
1188554485.056659535-3630.056659535
11910138284946.394172844616435.6058271554
1201411615545.1799314352-1429.17993143517
12189506124150.666347481-34644.6663474814
122135356109951.25177777825404.7482222217
12311606670603.899399230145462.1006007699
12414424481919.199162864762324.8008371353
125877316611.1257862832-7838.12578628322
12610215387000.012561068515152.9874389315
127117440130235.396669232-12795.3966692322
128104128116115.887744113-11987.8877441132
129134238146307.369504705-12069.369504705
130134047141019.242417181-6972.24241718149
131279488200666.95915872378821.040841277
1327975698124.8082941995-18368.8082941995
1336608959426.18276587156662.81723412851
13410207096533.70284629485536.29715370521
135146760143343.8226252733416.17737472699
13615477171729.106178102683041.8938218974
137165933138355.41719930427577.5828006962
1386459385108.0446973681-20515.0446973681
13992280121417.190245537-29137.1902455369
1406715093602.7483987886-26452.7483987886
14112869272382.504211078156309.4957889219
142124089107455.48910186916633.5108981308
143125386116839.6513538478546.34864615299
1443723819857.86140974317380.138590257
145140015143122.453511281-3107.45351128071
146150047107943.83275683342103.1672431669
147154451115919.94255794838531.0574420524
148156349135409.36888036820939.6311196316
149021813.890622297-21813.890622297
15060237092.1837268875-1069.1837268875
15102267.88255839435-2267.88255839435
15202245.55534161309-2245.55534161309
15304416.42519408902-4416.42519408902
15402243.08833737669-2243.08833737669
1558460180411.5613157994189.43868420103
15668946114314.82008338-45368.8200833796
15702243.08833737669-2243.08833737669
15802281.4064186621-2281.4064186621
15916444284.74107507049-2640.74107507049
160617913425.1266319718-7246.12663197182
16139268105.51764854644-4179.51764854644
1625278928851.308752857523937.6912471425
16302288.25152909158-2288.25152909158
16410035059937.964996369540412.0350036305

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 140824 & 141244.792634536 & -420.792634536205 \tabularnewline
2 & 110459 & 103682.776008851 & 6776.22399114943 \tabularnewline
3 & 105079 & 111525.956081124 & -6446.95608112384 \tabularnewline
4 & 112098 & 116515.369719644 & -4417.36971964361 \tabularnewline
5 & 43929 & 65111.1132701883 & -21182.1132701883 \tabularnewline
6 & 76173 & 62023.4965635146 & 14149.5034364854 \tabularnewline
7 & 187326 & 146062.006300125 & 41263.9936998751 \tabularnewline
8 & 22807 & 14035.5399843097 & 8771.46001569032 \tabularnewline
9 & 144408 & 121958.460761768 & 22449.5392382324 \tabularnewline
10 & 66485 & 80216.0637961275 & -13731.0637961275 \tabularnewline
11 & 79089 & 117776.638057482 & -38687.6380574816 \tabularnewline
12 & 81625 & 108486.370013256 & -26861.3700132564 \tabularnewline
13 & 68788 & 84032.1921173499 & -15244.1921173498 \tabularnewline
14 & 103297 & 91373.3190980703 & 11923.6809019297 \tabularnewline
15 & 69446 & 89402.2258114248 & -19956.2258114248 \tabularnewline
16 & 114948 & 138808.038235944 & -23860.0382359437 \tabularnewline
17 & 167949 & 142033.784765751 & 25915.2152342492 \tabularnewline
18 & 125081 & 89574.926473517 & 35506.073526483 \tabularnewline
19 & 125818 & 104788.513415321 & 21029.486584679 \tabularnewline
20 & 136588 & 130853.952057386 & 5734.04794261414 \tabularnewline
21 & 112431 & 129766.742288996 & -17335.7422889963 \tabularnewline
22 & 103037 & 84807.9129973543 & 18229.0870026457 \tabularnewline
23 & 82317 & 87039.3607907639 & -4722.36079076385 \tabularnewline
24 & 118906 & 132607.70044292 & -13701.7004429204 \tabularnewline
25 & 83515 & 110503.665559955 & -26988.6655599552 \tabularnewline
26 & 104581 & 107264.098717046 & -2683.09871704605 \tabularnewline
27 & 103129 & 129265.562781261 & -26136.5627812613 \tabularnewline
28 & 83243 & 115531.545500344 & -32288.5455003444 \tabularnewline
29 & 37110 & 53647.8573206447 & -16537.8573206447 \tabularnewline
30 & 113344 & 134879.705983513 & -21535.7059835133 \tabularnewline
31 & 139165 & 160368.326359707 & -21203.3263597065 \tabularnewline
32 & 86652 & 101412.822246257 & -14760.8222462571 \tabularnewline
33 & 112302 & 144593.055744935 & -32291.0557449353 \tabularnewline
34 & 69652 & 53526.3869667537 & 16125.6130332463 \tabularnewline
35 & 119442 & 145165.434681664 & -25723.4346816641 \tabularnewline
36 & 69867 & 62460.6703307958 & 7406.3296692042 \tabularnewline
37 & 101629 & 123253.298906755 & -21624.2989067548 \tabularnewline
38 & 70168 & 81163.0898548977 & -10995.0898548977 \tabularnewline
39 & 31081 & 48056.6666876601 & -16975.6666876601 \tabularnewline
40 & 103925 & 127545.867226319 & -23620.8672263193 \tabularnewline
41 & 92622 & 113215.69825982 & -20593.6982598199 \tabularnewline
42 & 79011 & 93272.4033925839 & -14261.4033925839 \tabularnewline
43 & 93487 & 120404.881803629 & -26917.881803629 \tabularnewline
44 & 64520 & 84001.0082053719 & -19481.0082053719 \tabularnewline
45 & 93473 & 82027.4473121386 & 11445.5526878614 \tabularnewline
46 & 114360 & 109612.492603462 & 4747.50739653798 \tabularnewline
47 & 33032 & 55700.9806214818 & -22668.9806214818 \tabularnewline
48 & 96125 & 103763.484444043 & -7638.48444404324 \tabularnewline
49 & 151911 & 156703.958045213 & -4792.95804521297 \tabularnewline
50 & 89256 & 108343.422928523 & -19087.4229285229 \tabularnewline
51 & 95676 & 121740.348887823 & -26064.3488878227 \tabularnewline
52 & 5950 & 14174.1729577866 & -8224.17295778657 \tabularnewline
53 & 149695 & 138821.646084791 & 10873.353915209 \tabularnewline
54 & 32551 & 38279.8269564065 & -5728.82695640648 \tabularnewline
55 & 31701 & 42024.9570304191 & -10323.9570304191 \tabularnewline
56 & 100087 & 113454.656648749 & -13367.6566487494 \tabularnewline
57 & 169707 & 180275.934232752 & -10568.9342327522 \tabularnewline
58 & 150491 & 175220.741852352 & -24729.7418523523 \tabularnewline
59 & 120192 & 141545.385033484 & -21353.3850334842 \tabularnewline
60 & 95893 & 103218.206040999 & -7325.20604099889 \tabularnewline
61 & 151715 & 156181.956038599 & -4466.95603859933 \tabularnewline
62 & 176225 & 193201.852449864 & -16976.8524498644 \tabularnewline
63 & 59900 & 85627.575671315 & -25727.575671315 \tabularnewline
64 & 104767 & 119203.92538724 & -14436.9253872397 \tabularnewline
65 & 114799 & 129874.955457168 & -15075.9554571679 \tabularnewline
66 & 72128 & 99809.4810705286 & -27681.4810705286 \tabularnewline
67 & 143592 & 114877.249633802 & 28714.7503661976 \tabularnewline
68 & 89626 & 139679.989314448 & -50053.9893144481 \tabularnewline
69 & 131072 & 98146.7431818715 & 32925.2568181285 \tabularnewline
70 & 126817 & 89696.5356546037 & 37120.4643453963 \tabularnewline
71 & 81351 & 116808.567695766 & -35457.5676957657 \tabularnewline
72 & 22618 & 41511.6078024493 & -18893.6078024493 \tabularnewline
73 & 88977 & 113663.662492758 & -24686.6624927578 \tabularnewline
74 & 92059 & 86939.2079677471 & 5119.7920322529 \tabularnewline
75 & 81897 & 92478.0171616601 & -10581.0171616601 \tabularnewline
76 & 108146 & 83407.3204717209 & 24738.6795282791 \tabularnewline
77 & 126372 & 116819.777255078 & 9552.22274492218 \tabularnewline
78 & 249771 & 132951.674792053 & 116819.325207947 \tabularnewline
79 & 71154 & 81463.6093746467 & -10309.6093746467 \tabularnewline
80 & 71571 & 70922.2067250129 & 648.793274987078 \tabularnewline
81 & 55918 & 75623.5112737017 & -19705.5112737017 \tabularnewline
82 & 160141 & 120308.194671081 & 39832.8053289189 \tabularnewline
83 & 38692 & 66726.9604756732 & -28034.9604756732 \tabularnewline
84 & 102812 & 100776.634009799 & 2035.36599020087 \tabularnewline
85 & 56622 & 37772.5370003385 & 18849.4629996615 \tabularnewline
86 & 15986 & 35307.5277002668 & -19321.5277002668 \tabularnewline
87 & 123534 & 128484.630526733 & -4950.63052673342 \tabularnewline
88 & 108535 & 97939.7449926852 & 10595.2550073148 \tabularnewline
89 & 93879 & 121288.964453357 & -27409.9644533565 \tabularnewline
90 & 144551 & 114534.30983911 & 30016.6901608905 \tabularnewline
91 & 56750 & 73816.1174728649 & -17066.1174728649 \tabularnewline
92 & 127654 & 96299.335013998 & 31354.664986002 \tabularnewline
93 & 65594 & 87516.6257315636 & -21922.6257315636 \tabularnewline
94 & 59938 & 67385.4148951421 & -7447.41489514209 \tabularnewline
95 & 146975 & 129154.56734527 & 17820.4326547303 \tabularnewline
96 & 165904 & 133607.598158358 & 32296.4018416423 \tabularnewline
97 & 169265 & 142547.160088409 & 26717.8399115908 \tabularnewline
98 & 183500 & 149156.423785872 & 34343.5762141277 \tabularnewline
99 & 165986 & 194173.585981873 & -28187.5859818734 \tabularnewline
100 & 184923 & 189535.374647155 & -4612.37464715537 \tabularnewline
101 & 140358 & 137077.401743819 & 3280.59825618092 \tabularnewline
102 & 149959 & 136035.552099065 & 13923.4479009346 \tabularnewline
103 & 57224 & 60743.430218881 & -3519.43021888103 \tabularnewline
104 & 43750 & 44061.9423558509 & -311.94235585086 \tabularnewline
105 & 48029 & 72606.1500073506 & -24577.1500073506 \tabularnewline
106 & 104978 & 128889.27812614 & -23911.2781261404 \tabularnewline
107 & 100046 & 114445.521594498 & -14399.5215944977 \tabularnewline
108 & 101047 & 98618.3351672948 & 2428.6648327052 \tabularnewline
109 & 197426 & 122542.495247218 & 74883.5047527823 \tabularnewline
110 & 160902 & 87968.7115421156 & 72933.2884578844 \tabularnewline
111 & 147172 & 127933.341790158 & 19238.6582098416 \tabularnewline
112 & 109432 & 83644.1743684369 & 25787.8256315631 \tabularnewline
113 & 1168 & 4209.91570455844 & -3041.91570455844 \tabularnewline
114 & 83248 & 93316.9842097837 & -10068.9842097836 \tabularnewline
115 & 25162 & 29873.4867485436 & -4711.48674854359 \tabularnewline
116 & 45724 & 70548.8265211496 & -24824.8265211496 \tabularnewline
117 & 110529 & 128985.778101527 & -18456.7781015272 \tabularnewline
118 & 855 & 4485.056659535 & -3630.056659535 \tabularnewline
119 & 101382 & 84946.3941728446 & 16435.6058271554 \tabularnewline
120 & 14116 & 15545.1799314352 & -1429.17993143517 \tabularnewline
121 & 89506 & 124150.666347481 & -34644.6663474814 \tabularnewline
122 & 135356 & 109951.251777778 & 25404.7482222217 \tabularnewline
123 & 116066 & 70603.8993992301 & 45462.1006007699 \tabularnewline
124 & 144244 & 81919.1991628647 & 62324.8008371353 \tabularnewline
125 & 8773 & 16611.1257862832 & -7838.12578628322 \tabularnewline
126 & 102153 & 87000.0125610685 & 15152.9874389315 \tabularnewline
127 & 117440 & 130235.396669232 & -12795.3966692322 \tabularnewline
128 & 104128 & 116115.887744113 & -11987.8877441132 \tabularnewline
129 & 134238 & 146307.369504705 & -12069.369504705 \tabularnewline
130 & 134047 & 141019.242417181 & -6972.24241718149 \tabularnewline
131 & 279488 & 200666.959158723 & 78821.040841277 \tabularnewline
132 & 79756 & 98124.8082941995 & -18368.8082941995 \tabularnewline
133 & 66089 & 59426.1827658715 & 6662.81723412851 \tabularnewline
134 & 102070 & 96533.7028462948 & 5536.29715370521 \tabularnewline
135 & 146760 & 143343.822625273 & 3416.17737472699 \tabularnewline
136 & 154771 & 71729.1061781026 & 83041.8938218974 \tabularnewline
137 & 165933 & 138355.417199304 & 27577.5828006962 \tabularnewline
138 & 64593 & 85108.0446973681 & -20515.0446973681 \tabularnewline
139 & 92280 & 121417.190245537 & -29137.1902455369 \tabularnewline
140 & 67150 & 93602.7483987886 & -26452.7483987886 \tabularnewline
141 & 128692 & 72382.5042110781 & 56309.4957889219 \tabularnewline
142 & 124089 & 107455.489101869 & 16633.5108981308 \tabularnewline
143 & 125386 & 116839.651353847 & 8546.34864615299 \tabularnewline
144 & 37238 & 19857.861409743 & 17380.138590257 \tabularnewline
145 & 140015 & 143122.453511281 & -3107.45351128071 \tabularnewline
146 & 150047 & 107943.832756833 & 42103.1672431669 \tabularnewline
147 & 154451 & 115919.942557948 & 38531.0574420524 \tabularnewline
148 & 156349 & 135409.368880368 & 20939.6311196316 \tabularnewline
149 & 0 & 21813.890622297 & -21813.890622297 \tabularnewline
150 & 6023 & 7092.1837268875 & -1069.1837268875 \tabularnewline
151 & 0 & 2267.88255839435 & -2267.88255839435 \tabularnewline
152 & 0 & 2245.55534161309 & -2245.55534161309 \tabularnewline
153 & 0 & 4416.42519408902 & -4416.42519408902 \tabularnewline
154 & 0 & 2243.08833737669 & -2243.08833737669 \tabularnewline
155 & 84601 & 80411.561315799 & 4189.43868420103 \tabularnewline
156 & 68946 & 114314.82008338 & -45368.8200833796 \tabularnewline
157 & 0 & 2243.08833737669 & -2243.08833737669 \tabularnewline
158 & 0 & 2281.4064186621 & -2281.4064186621 \tabularnewline
159 & 1644 & 4284.74107507049 & -2640.74107507049 \tabularnewline
160 & 6179 & 13425.1266319718 & -7246.12663197182 \tabularnewline
161 & 3926 & 8105.51764854644 & -4179.51764854644 \tabularnewline
162 & 52789 & 28851.3087528575 & 23937.6912471425 \tabularnewline
163 & 0 & 2288.25152909158 & -2288.25152909158 \tabularnewline
164 & 100350 & 59937.9649963695 & 40412.0350036305 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157934&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]140824[/C][C]141244.792634536[/C][C]-420.792634536205[/C][/ROW]
[ROW][C]2[/C][C]110459[/C][C]103682.776008851[/C][C]6776.22399114943[/C][/ROW]
[ROW][C]3[/C][C]105079[/C][C]111525.956081124[/C][C]-6446.95608112384[/C][/ROW]
[ROW][C]4[/C][C]112098[/C][C]116515.369719644[/C][C]-4417.36971964361[/C][/ROW]
[ROW][C]5[/C][C]43929[/C][C]65111.1132701883[/C][C]-21182.1132701883[/C][/ROW]
[ROW][C]6[/C][C]76173[/C][C]62023.4965635146[/C][C]14149.5034364854[/C][/ROW]
[ROW][C]7[/C][C]187326[/C][C]146062.006300125[/C][C]41263.9936998751[/C][/ROW]
[ROW][C]8[/C][C]22807[/C][C]14035.5399843097[/C][C]8771.46001569032[/C][/ROW]
[ROW][C]9[/C][C]144408[/C][C]121958.460761768[/C][C]22449.5392382324[/C][/ROW]
[ROW][C]10[/C][C]66485[/C][C]80216.0637961275[/C][C]-13731.0637961275[/C][/ROW]
[ROW][C]11[/C][C]79089[/C][C]117776.638057482[/C][C]-38687.6380574816[/C][/ROW]
[ROW][C]12[/C][C]81625[/C][C]108486.370013256[/C][C]-26861.3700132564[/C][/ROW]
[ROW][C]13[/C][C]68788[/C][C]84032.1921173499[/C][C]-15244.1921173498[/C][/ROW]
[ROW][C]14[/C][C]103297[/C][C]91373.3190980703[/C][C]11923.6809019297[/C][/ROW]
[ROW][C]15[/C][C]69446[/C][C]89402.2258114248[/C][C]-19956.2258114248[/C][/ROW]
[ROW][C]16[/C][C]114948[/C][C]138808.038235944[/C][C]-23860.0382359437[/C][/ROW]
[ROW][C]17[/C][C]167949[/C][C]142033.784765751[/C][C]25915.2152342492[/C][/ROW]
[ROW][C]18[/C][C]125081[/C][C]89574.926473517[/C][C]35506.073526483[/C][/ROW]
[ROW][C]19[/C][C]125818[/C][C]104788.513415321[/C][C]21029.486584679[/C][/ROW]
[ROW][C]20[/C][C]136588[/C][C]130853.952057386[/C][C]5734.04794261414[/C][/ROW]
[ROW][C]21[/C][C]112431[/C][C]129766.742288996[/C][C]-17335.7422889963[/C][/ROW]
[ROW][C]22[/C][C]103037[/C][C]84807.9129973543[/C][C]18229.0870026457[/C][/ROW]
[ROW][C]23[/C][C]82317[/C][C]87039.3607907639[/C][C]-4722.36079076385[/C][/ROW]
[ROW][C]24[/C][C]118906[/C][C]132607.70044292[/C][C]-13701.7004429204[/C][/ROW]
[ROW][C]25[/C][C]83515[/C][C]110503.665559955[/C][C]-26988.6655599552[/C][/ROW]
[ROW][C]26[/C][C]104581[/C][C]107264.098717046[/C][C]-2683.09871704605[/C][/ROW]
[ROW][C]27[/C][C]103129[/C][C]129265.562781261[/C][C]-26136.5627812613[/C][/ROW]
[ROW][C]28[/C][C]83243[/C][C]115531.545500344[/C][C]-32288.5455003444[/C][/ROW]
[ROW][C]29[/C][C]37110[/C][C]53647.8573206447[/C][C]-16537.8573206447[/C][/ROW]
[ROW][C]30[/C][C]113344[/C][C]134879.705983513[/C][C]-21535.7059835133[/C][/ROW]
[ROW][C]31[/C][C]139165[/C][C]160368.326359707[/C][C]-21203.3263597065[/C][/ROW]
[ROW][C]32[/C][C]86652[/C][C]101412.822246257[/C][C]-14760.8222462571[/C][/ROW]
[ROW][C]33[/C][C]112302[/C][C]144593.055744935[/C][C]-32291.0557449353[/C][/ROW]
[ROW][C]34[/C][C]69652[/C][C]53526.3869667537[/C][C]16125.6130332463[/C][/ROW]
[ROW][C]35[/C][C]119442[/C][C]145165.434681664[/C][C]-25723.4346816641[/C][/ROW]
[ROW][C]36[/C][C]69867[/C][C]62460.6703307958[/C][C]7406.3296692042[/C][/ROW]
[ROW][C]37[/C][C]101629[/C][C]123253.298906755[/C][C]-21624.2989067548[/C][/ROW]
[ROW][C]38[/C][C]70168[/C][C]81163.0898548977[/C][C]-10995.0898548977[/C][/ROW]
[ROW][C]39[/C][C]31081[/C][C]48056.6666876601[/C][C]-16975.6666876601[/C][/ROW]
[ROW][C]40[/C][C]103925[/C][C]127545.867226319[/C][C]-23620.8672263193[/C][/ROW]
[ROW][C]41[/C][C]92622[/C][C]113215.69825982[/C][C]-20593.6982598199[/C][/ROW]
[ROW][C]42[/C][C]79011[/C][C]93272.4033925839[/C][C]-14261.4033925839[/C][/ROW]
[ROW][C]43[/C][C]93487[/C][C]120404.881803629[/C][C]-26917.881803629[/C][/ROW]
[ROW][C]44[/C][C]64520[/C][C]84001.0082053719[/C][C]-19481.0082053719[/C][/ROW]
[ROW][C]45[/C][C]93473[/C][C]82027.4473121386[/C][C]11445.5526878614[/C][/ROW]
[ROW][C]46[/C][C]114360[/C][C]109612.492603462[/C][C]4747.50739653798[/C][/ROW]
[ROW][C]47[/C][C]33032[/C][C]55700.9806214818[/C][C]-22668.9806214818[/C][/ROW]
[ROW][C]48[/C][C]96125[/C][C]103763.484444043[/C][C]-7638.48444404324[/C][/ROW]
[ROW][C]49[/C][C]151911[/C][C]156703.958045213[/C][C]-4792.95804521297[/C][/ROW]
[ROW][C]50[/C][C]89256[/C][C]108343.422928523[/C][C]-19087.4229285229[/C][/ROW]
[ROW][C]51[/C][C]95676[/C][C]121740.348887823[/C][C]-26064.3488878227[/C][/ROW]
[ROW][C]52[/C][C]5950[/C][C]14174.1729577866[/C][C]-8224.17295778657[/C][/ROW]
[ROW][C]53[/C][C]149695[/C][C]138821.646084791[/C][C]10873.353915209[/C][/ROW]
[ROW][C]54[/C][C]32551[/C][C]38279.8269564065[/C][C]-5728.82695640648[/C][/ROW]
[ROW][C]55[/C][C]31701[/C][C]42024.9570304191[/C][C]-10323.9570304191[/C][/ROW]
[ROW][C]56[/C][C]100087[/C][C]113454.656648749[/C][C]-13367.6566487494[/C][/ROW]
[ROW][C]57[/C][C]169707[/C][C]180275.934232752[/C][C]-10568.9342327522[/C][/ROW]
[ROW][C]58[/C][C]150491[/C][C]175220.741852352[/C][C]-24729.7418523523[/C][/ROW]
[ROW][C]59[/C][C]120192[/C][C]141545.385033484[/C][C]-21353.3850334842[/C][/ROW]
[ROW][C]60[/C][C]95893[/C][C]103218.206040999[/C][C]-7325.20604099889[/C][/ROW]
[ROW][C]61[/C][C]151715[/C][C]156181.956038599[/C][C]-4466.95603859933[/C][/ROW]
[ROW][C]62[/C][C]176225[/C][C]193201.852449864[/C][C]-16976.8524498644[/C][/ROW]
[ROW][C]63[/C][C]59900[/C][C]85627.575671315[/C][C]-25727.575671315[/C][/ROW]
[ROW][C]64[/C][C]104767[/C][C]119203.92538724[/C][C]-14436.9253872397[/C][/ROW]
[ROW][C]65[/C][C]114799[/C][C]129874.955457168[/C][C]-15075.9554571679[/C][/ROW]
[ROW][C]66[/C][C]72128[/C][C]99809.4810705286[/C][C]-27681.4810705286[/C][/ROW]
[ROW][C]67[/C][C]143592[/C][C]114877.249633802[/C][C]28714.7503661976[/C][/ROW]
[ROW][C]68[/C][C]89626[/C][C]139679.989314448[/C][C]-50053.9893144481[/C][/ROW]
[ROW][C]69[/C][C]131072[/C][C]98146.7431818715[/C][C]32925.2568181285[/C][/ROW]
[ROW][C]70[/C][C]126817[/C][C]89696.5356546037[/C][C]37120.4643453963[/C][/ROW]
[ROW][C]71[/C][C]81351[/C][C]116808.567695766[/C][C]-35457.5676957657[/C][/ROW]
[ROW][C]72[/C][C]22618[/C][C]41511.6078024493[/C][C]-18893.6078024493[/C][/ROW]
[ROW][C]73[/C][C]88977[/C][C]113663.662492758[/C][C]-24686.6624927578[/C][/ROW]
[ROW][C]74[/C][C]92059[/C][C]86939.2079677471[/C][C]5119.7920322529[/C][/ROW]
[ROW][C]75[/C][C]81897[/C][C]92478.0171616601[/C][C]-10581.0171616601[/C][/ROW]
[ROW][C]76[/C][C]108146[/C][C]83407.3204717209[/C][C]24738.6795282791[/C][/ROW]
[ROW][C]77[/C][C]126372[/C][C]116819.777255078[/C][C]9552.22274492218[/C][/ROW]
[ROW][C]78[/C][C]249771[/C][C]132951.674792053[/C][C]116819.325207947[/C][/ROW]
[ROW][C]79[/C][C]71154[/C][C]81463.6093746467[/C][C]-10309.6093746467[/C][/ROW]
[ROW][C]80[/C][C]71571[/C][C]70922.2067250129[/C][C]648.793274987078[/C][/ROW]
[ROW][C]81[/C][C]55918[/C][C]75623.5112737017[/C][C]-19705.5112737017[/C][/ROW]
[ROW][C]82[/C][C]160141[/C][C]120308.194671081[/C][C]39832.8053289189[/C][/ROW]
[ROW][C]83[/C][C]38692[/C][C]66726.9604756732[/C][C]-28034.9604756732[/C][/ROW]
[ROW][C]84[/C][C]102812[/C][C]100776.634009799[/C][C]2035.36599020087[/C][/ROW]
[ROW][C]85[/C][C]56622[/C][C]37772.5370003385[/C][C]18849.4629996615[/C][/ROW]
[ROW][C]86[/C][C]15986[/C][C]35307.5277002668[/C][C]-19321.5277002668[/C][/ROW]
[ROW][C]87[/C][C]123534[/C][C]128484.630526733[/C][C]-4950.63052673342[/C][/ROW]
[ROW][C]88[/C][C]108535[/C][C]97939.7449926852[/C][C]10595.2550073148[/C][/ROW]
[ROW][C]89[/C][C]93879[/C][C]121288.964453357[/C][C]-27409.9644533565[/C][/ROW]
[ROW][C]90[/C][C]144551[/C][C]114534.30983911[/C][C]30016.6901608905[/C][/ROW]
[ROW][C]91[/C][C]56750[/C][C]73816.1174728649[/C][C]-17066.1174728649[/C][/ROW]
[ROW][C]92[/C][C]127654[/C][C]96299.335013998[/C][C]31354.664986002[/C][/ROW]
[ROW][C]93[/C][C]65594[/C][C]87516.6257315636[/C][C]-21922.6257315636[/C][/ROW]
[ROW][C]94[/C][C]59938[/C][C]67385.4148951421[/C][C]-7447.41489514209[/C][/ROW]
[ROW][C]95[/C][C]146975[/C][C]129154.56734527[/C][C]17820.4326547303[/C][/ROW]
[ROW][C]96[/C][C]165904[/C][C]133607.598158358[/C][C]32296.4018416423[/C][/ROW]
[ROW][C]97[/C][C]169265[/C][C]142547.160088409[/C][C]26717.8399115908[/C][/ROW]
[ROW][C]98[/C][C]183500[/C][C]149156.423785872[/C][C]34343.5762141277[/C][/ROW]
[ROW][C]99[/C][C]165986[/C][C]194173.585981873[/C][C]-28187.5859818734[/C][/ROW]
[ROW][C]100[/C][C]184923[/C][C]189535.374647155[/C][C]-4612.37464715537[/C][/ROW]
[ROW][C]101[/C][C]140358[/C][C]137077.401743819[/C][C]3280.59825618092[/C][/ROW]
[ROW][C]102[/C][C]149959[/C][C]136035.552099065[/C][C]13923.4479009346[/C][/ROW]
[ROW][C]103[/C][C]57224[/C][C]60743.430218881[/C][C]-3519.43021888103[/C][/ROW]
[ROW][C]104[/C][C]43750[/C][C]44061.9423558509[/C][C]-311.94235585086[/C][/ROW]
[ROW][C]105[/C][C]48029[/C][C]72606.1500073506[/C][C]-24577.1500073506[/C][/ROW]
[ROW][C]106[/C][C]104978[/C][C]128889.27812614[/C][C]-23911.2781261404[/C][/ROW]
[ROW][C]107[/C][C]100046[/C][C]114445.521594498[/C][C]-14399.5215944977[/C][/ROW]
[ROW][C]108[/C][C]101047[/C][C]98618.3351672948[/C][C]2428.6648327052[/C][/ROW]
[ROW][C]109[/C][C]197426[/C][C]122542.495247218[/C][C]74883.5047527823[/C][/ROW]
[ROW][C]110[/C][C]160902[/C][C]87968.7115421156[/C][C]72933.2884578844[/C][/ROW]
[ROW][C]111[/C][C]147172[/C][C]127933.341790158[/C][C]19238.6582098416[/C][/ROW]
[ROW][C]112[/C][C]109432[/C][C]83644.1743684369[/C][C]25787.8256315631[/C][/ROW]
[ROW][C]113[/C][C]1168[/C][C]4209.91570455844[/C][C]-3041.91570455844[/C][/ROW]
[ROW][C]114[/C][C]83248[/C][C]93316.9842097837[/C][C]-10068.9842097836[/C][/ROW]
[ROW][C]115[/C][C]25162[/C][C]29873.4867485436[/C][C]-4711.48674854359[/C][/ROW]
[ROW][C]116[/C][C]45724[/C][C]70548.8265211496[/C][C]-24824.8265211496[/C][/ROW]
[ROW][C]117[/C][C]110529[/C][C]128985.778101527[/C][C]-18456.7781015272[/C][/ROW]
[ROW][C]118[/C][C]855[/C][C]4485.056659535[/C][C]-3630.056659535[/C][/ROW]
[ROW][C]119[/C][C]101382[/C][C]84946.3941728446[/C][C]16435.6058271554[/C][/ROW]
[ROW][C]120[/C][C]14116[/C][C]15545.1799314352[/C][C]-1429.17993143517[/C][/ROW]
[ROW][C]121[/C][C]89506[/C][C]124150.666347481[/C][C]-34644.6663474814[/C][/ROW]
[ROW][C]122[/C][C]135356[/C][C]109951.251777778[/C][C]25404.7482222217[/C][/ROW]
[ROW][C]123[/C][C]116066[/C][C]70603.8993992301[/C][C]45462.1006007699[/C][/ROW]
[ROW][C]124[/C][C]144244[/C][C]81919.1991628647[/C][C]62324.8008371353[/C][/ROW]
[ROW][C]125[/C][C]8773[/C][C]16611.1257862832[/C][C]-7838.12578628322[/C][/ROW]
[ROW][C]126[/C][C]102153[/C][C]87000.0125610685[/C][C]15152.9874389315[/C][/ROW]
[ROW][C]127[/C][C]117440[/C][C]130235.396669232[/C][C]-12795.3966692322[/C][/ROW]
[ROW][C]128[/C][C]104128[/C][C]116115.887744113[/C][C]-11987.8877441132[/C][/ROW]
[ROW][C]129[/C][C]134238[/C][C]146307.369504705[/C][C]-12069.369504705[/C][/ROW]
[ROW][C]130[/C][C]134047[/C][C]141019.242417181[/C][C]-6972.24241718149[/C][/ROW]
[ROW][C]131[/C][C]279488[/C][C]200666.959158723[/C][C]78821.040841277[/C][/ROW]
[ROW][C]132[/C][C]79756[/C][C]98124.8082941995[/C][C]-18368.8082941995[/C][/ROW]
[ROW][C]133[/C][C]66089[/C][C]59426.1827658715[/C][C]6662.81723412851[/C][/ROW]
[ROW][C]134[/C][C]102070[/C][C]96533.7028462948[/C][C]5536.29715370521[/C][/ROW]
[ROW][C]135[/C][C]146760[/C][C]143343.822625273[/C][C]3416.17737472699[/C][/ROW]
[ROW][C]136[/C][C]154771[/C][C]71729.1061781026[/C][C]83041.8938218974[/C][/ROW]
[ROW][C]137[/C][C]165933[/C][C]138355.417199304[/C][C]27577.5828006962[/C][/ROW]
[ROW][C]138[/C][C]64593[/C][C]85108.0446973681[/C][C]-20515.0446973681[/C][/ROW]
[ROW][C]139[/C][C]92280[/C][C]121417.190245537[/C][C]-29137.1902455369[/C][/ROW]
[ROW][C]140[/C][C]67150[/C][C]93602.7483987886[/C][C]-26452.7483987886[/C][/ROW]
[ROW][C]141[/C][C]128692[/C][C]72382.5042110781[/C][C]56309.4957889219[/C][/ROW]
[ROW][C]142[/C][C]124089[/C][C]107455.489101869[/C][C]16633.5108981308[/C][/ROW]
[ROW][C]143[/C][C]125386[/C][C]116839.651353847[/C][C]8546.34864615299[/C][/ROW]
[ROW][C]144[/C][C]37238[/C][C]19857.861409743[/C][C]17380.138590257[/C][/ROW]
[ROW][C]145[/C][C]140015[/C][C]143122.453511281[/C][C]-3107.45351128071[/C][/ROW]
[ROW][C]146[/C][C]150047[/C][C]107943.832756833[/C][C]42103.1672431669[/C][/ROW]
[ROW][C]147[/C][C]154451[/C][C]115919.942557948[/C][C]38531.0574420524[/C][/ROW]
[ROW][C]148[/C][C]156349[/C][C]135409.368880368[/C][C]20939.6311196316[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]21813.890622297[/C][C]-21813.890622297[/C][/ROW]
[ROW][C]150[/C][C]6023[/C][C]7092.1837268875[/C][C]-1069.1837268875[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]2267.88255839435[/C][C]-2267.88255839435[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]2245.55534161309[/C][C]-2245.55534161309[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]4416.42519408902[/C][C]-4416.42519408902[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]2243.08833737669[/C][C]-2243.08833737669[/C][/ROW]
[ROW][C]155[/C][C]84601[/C][C]80411.561315799[/C][C]4189.43868420103[/C][/ROW]
[ROW][C]156[/C][C]68946[/C][C]114314.82008338[/C][C]-45368.8200833796[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]2243.08833737669[/C][C]-2243.08833737669[/C][/ROW]
[ROW][C]158[/C][C]0[/C][C]2281.4064186621[/C][C]-2281.4064186621[/C][/ROW]
[ROW][C]159[/C][C]1644[/C][C]4284.74107507049[/C][C]-2640.74107507049[/C][/ROW]
[ROW][C]160[/C][C]6179[/C][C]13425.1266319718[/C][C]-7246.12663197182[/C][/ROW]
[ROW][C]161[/C][C]3926[/C][C]8105.51764854644[/C][C]-4179.51764854644[/C][/ROW]
[ROW][C]162[/C][C]52789[/C][C]28851.3087528575[/C][C]23937.6912471425[/C][/ROW]
[ROW][C]163[/C][C]0[/C][C]2288.25152909158[/C][C]-2288.25152909158[/C][/ROW]
[ROW][C]164[/C][C]100350[/C][C]59937.9649963695[/C][C]40412.0350036305[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157934&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157934&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
1140824141244.792634536-420.792634536205
2110459103682.7760088516776.22399114943
3105079111525.956081124-6446.95608112384
4112098116515.369719644-4417.36971964361
54392965111.1132701883-21182.1132701883
67617362023.496563514614149.5034364854
7187326146062.00630012541263.9936998751
82280714035.53998430978771.46001569032
9144408121958.46076176822449.5392382324
106648580216.0637961275-13731.0637961275
1179089117776.638057482-38687.6380574816
1281625108486.370013256-26861.3700132564
136878884032.1921173499-15244.1921173498
1410329791373.319098070311923.6809019297
156944689402.2258114248-19956.2258114248
16114948138808.038235944-23860.0382359437
17167949142033.78476575125915.2152342492
1812508189574.92647351735506.073526483
19125818104788.51341532121029.486584679
20136588130853.9520573865734.04794261414
21112431129766.742288996-17335.7422889963
2210303784807.912997354318229.0870026457
238231787039.3607907639-4722.36079076385
24118906132607.70044292-13701.7004429204
2583515110503.665559955-26988.6655599552
26104581107264.098717046-2683.09871704605
27103129129265.562781261-26136.5627812613
2883243115531.545500344-32288.5455003444
293711053647.8573206447-16537.8573206447
30113344134879.705983513-21535.7059835133
31139165160368.326359707-21203.3263597065
3286652101412.822246257-14760.8222462571
33112302144593.055744935-32291.0557449353
346965253526.386966753716125.6130332463
35119442145165.434681664-25723.4346816641
366986762460.67033079587406.3296692042
37101629123253.298906755-21624.2989067548
387016881163.0898548977-10995.0898548977
393108148056.6666876601-16975.6666876601
40103925127545.867226319-23620.8672263193
4192622113215.69825982-20593.6982598199
427901193272.4033925839-14261.4033925839
4393487120404.881803629-26917.881803629
446452084001.0082053719-19481.0082053719
459347382027.447312138611445.5526878614
46114360109612.4926034624747.50739653798
473303255700.9806214818-22668.9806214818
4896125103763.484444043-7638.48444404324
49151911156703.958045213-4792.95804521297
5089256108343.422928523-19087.4229285229
5195676121740.348887823-26064.3488878227
52595014174.1729577866-8224.17295778657
53149695138821.64608479110873.353915209
543255138279.8269564065-5728.82695640648
553170142024.9570304191-10323.9570304191
56100087113454.656648749-13367.6566487494
57169707180275.934232752-10568.9342327522
58150491175220.741852352-24729.7418523523
59120192141545.385033484-21353.3850334842
6095893103218.206040999-7325.20604099889
61151715156181.956038599-4466.95603859933
62176225193201.852449864-16976.8524498644
635990085627.575671315-25727.575671315
64104767119203.92538724-14436.9253872397
65114799129874.955457168-15075.9554571679
667212899809.4810705286-27681.4810705286
67143592114877.24963380228714.7503661976
6889626139679.989314448-50053.9893144481
6913107298146.743181871532925.2568181285
7012681789696.535654603737120.4643453963
7181351116808.567695766-35457.5676957657
722261841511.6078024493-18893.6078024493
7388977113663.662492758-24686.6624927578
749205986939.20796774715119.7920322529
758189792478.0171616601-10581.0171616601
7610814683407.320471720924738.6795282791
77126372116819.7772550789552.22274492218
78249771132951.674792053116819.325207947
797115481463.6093746467-10309.6093746467
807157170922.2067250129648.793274987078
815591875623.5112737017-19705.5112737017
82160141120308.19467108139832.8053289189
833869266726.9604756732-28034.9604756732
84102812100776.6340097992035.36599020087
855662237772.537000338518849.4629996615
861598635307.5277002668-19321.5277002668
87123534128484.630526733-4950.63052673342
8810853597939.744992685210595.2550073148
8993879121288.964453357-27409.9644533565
90144551114534.3098391130016.6901608905
915675073816.1174728649-17066.1174728649
9212765496299.33501399831354.664986002
936559487516.6257315636-21922.6257315636
945993867385.4148951421-7447.41489514209
95146975129154.5673452717820.4326547303
96165904133607.59815835832296.4018416423
97169265142547.16008840926717.8399115908
98183500149156.42378587234343.5762141277
99165986194173.585981873-28187.5859818734
100184923189535.374647155-4612.37464715537
101140358137077.4017438193280.59825618092
102149959136035.55209906513923.4479009346
1035722460743.430218881-3519.43021888103
1044375044061.9423558509-311.94235585086
1054802972606.1500073506-24577.1500073506
106104978128889.27812614-23911.2781261404
107100046114445.521594498-14399.5215944977
10810104798618.33516729482428.6648327052
109197426122542.49524721874883.5047527823
11016090287968.711542115672933.2884578844
111147172127933.34179015819238.6582098416
11210943283644.174368436925787.8256315631
11311684209.91570455844-3041.91570455844
1148324893316.9842097837-10068.9842097836
1152516229873.4867485436-4711.48674854359
1164572470548.8265211496-24824.8265211496
117110529128985.778101527-18456.7781015272
1188554485.056659535-3630.056659535
11910138284946.394172844616435.6058271554
1201411615545.1799314352-1429.17993143517
12189506124150.666347481-34644.6663474814
122135356109951.25177777825404.7482222217
12311606670603.899399230145462.1006007699
12414424481919.199162864762324.8008371353
125877316611.1257862832-7838.12578628322
12610215387000.012561068515152.9874389315
127117440130235.396669232-12795.3966692322
128104128116115.887744113-11987.8877441132
129134238146307.369504705-12069.369504705
130134047141019.242417181-6972.24241718149
131279488200666.95915872378821.040841277
1327975698124.8082941995-18368.8082941995
1336608959426.18276587156662.81723412851
13410207096533.70284629485536.29715370521
135146760143343.8226252733416.17737472699
13615477171729.106178102683041.8938218974
137165933138355.41719930427577.5828006962
1386459385108.0446973681-20515.0446973681
13992280121417.190245537-29137.1902455369
1406715093602.7483987886-26452.7483987886
14112869272382.504211078156309.4957889219
142124089107455.48910186916633.5108981308
143125386116839.6513538478546.34864615299
1443723819857.86140974317380.138590257
145140015143122.453511281-3107.45351128071
146150047107943.83275683342103.1672431669
147154451115919.94255794838531.0574420524
148156349135409.36888036820939.6311196316
149021813.890622297-21813.890622297
15060237092.1837268875-1069.1837268875
15102267.88255839435-2267.88255839435
15202245.55534161309-2245.55534161309
15304416.42519408902-4416.42519408902
15402243.08833737669-2243.08833737669
1558460180411.5613157994189.43868420103
15668946114314.82008338-45368.8200833796
15702243.08833737669-2243.08833737669
15802281.4064186621-2281.4064186621
15916444284.74107507049-2640.74107507049
160617913425.1266319718-7246.12663197182
16139268105.51764854644-4179.51764854644
1625278928851.308752857523937.6912471425
16302288.25152909158-2288.25152909158
16410035059937.964996369540412.0350036305






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.390771982577460.781543965154920.60922801742254
190.3064049974111240.6128099948222480.693595002588876
200.2122546997791230.4245093995582470.787745300220877
210.1636966218992250.3273932437984490.836303378100775
220.1835153629514770.3670307259029550.816484637048522
230.1137560998291020.2275121996582050.886243900170898
240.07237098088828650.1447419617765730.927629019111713
250.05306668673469490.106133373469390.946933313265305
260.03139001807071390.06278003614142770.968609981929286
270.01925312433874160.03850624867748320.980746875661258
280.01205825861670140.02411651723340270.987941741383299
290.009663199983489240.01932639996697850.990336800016511
300.007325360674634130.01465072134926830.992674639325366
310.004491100565590420.008982201131180850.99550889943441
320.002555602109264790.005111204218529570.997444397890735
330.001617749887054520.003235499774109040.998382250112945
340.002570239056723340.005140478113446680.997429760943277
350.004108733207578290.008217466415156580.995891266792422
360.002357284009976030.004714568019952050.997642715990024
370.001416570577100590.002833141154201170.998583429422899
380.001013205751807450.002026411503614910.998986794248193
390.001011501670179590.002023003340359180.99898849832982
400.0006864517015531760.001372903403106350.999313548298447
410.001388933994425120.002777867988850250.998611066005575
420.0008289733370456180.001657946674091240.999171026662954
430.0007622470796454840.001524494159290970.999237752920355
440.0004741323194154220.0009482646388308440.999525867680585
450.002616386834678520.005232773669357040.997383613165321
460.001632344686477650.00326468937295530.998367655313522
470.00136708537443930.002734170748878610.998632914625561
480.0008285569285194130.001657113857038830.999171443071481
490.0005216054528852980.00104321090577060.999478394547115
500.0004663589122830750.000932717824566150.999533641087717
510.0003330971746541280.0006661943493082560.999666902825346
520.0002479585541859180.0004959171083718350.999752041445814
530.0001871704136821220.0003743408273642450.999812829586318
540.0001075994294441050.000215198858888210.999892400570556
557.33135148818994e-050.0001466270297637990.999926686485118
564.69327270491355e-059.3865454098271e-050.999953067272951
575.23572511295037e-050.0001047145022590070.99994764274887
588.62253991217547e-050.0001724507982435090.999913774600878
596.14619361404234e-050.0001229238722808470.99993853806386
603.58954196505269e-057.17908393010539e-050.999964104580349
612.38724310291507e-054.77448620583015e-050.999976127568971
621.49454606649108e-052.98909213298216e-050.999985054539335
631.14330646770132e-052.28661293540264e-050.999988566935323
646.99772284616822e-061.39954456923364e-050.999993002277154
655.01630363035751e-061.0032607260715e-050.99999498369637
663.65530548627983e-067.31061097255965e-060.999996344694514
676.98990015653883e-061.39798003130777e-050.999993010099843
682.91108819308891e-055.82217638617781e-050.999970889118069
693.03670102037689e-056.07340204075378e-050.999969632989796
709.75160310606974e-050.0001950320621213950.999902483968939
710.0001554248753135330.0003108497506270660.999844575124686
720.0001254523051078760.0002509046102157530.999874547694892
730.0001390421273184370.0002780842546368740.999860957872682
748.59347948230096e-050.0001718695896460190.999914065205177
756.2202806609967e-050.0001244056132199340.99993779719339
766.60134614043979e-050.0001320269228087960.999933986538596
774.12138612158876e-058.24277224317751e-050.999958786138784
780.1802036584758790.3604073169517570.819796341524121
790.1629544878233460.3259089756466910.837045512176654
800.137273985129550.27454797025910.86272601487045
810.1312636309602220.2625272619204440.868736369039778
820.2191989752666870.4383979505333750.780801024733313
830.2437305807348180.4874611614696360.756269419265182
840.2085321760358410.4170643520716820.791467823964159
850.1879601597857470.3759203195714940.812039840214253
860.1646585638861230.3293171277722460.835341436113877
870.1499547727218550.2999095454437090.850045227278145
880.1521112733186410.3042225466372810.847888726681359
890.1605398870269480.3210797740538950.839460112973052
900.1766352591841730.3532705183683460.823364740815827
910.1663637090227950.332727418045590.833636290977205
920.1879422690863480.3758845381726960.812057730913652
930.1976313814881180.3952627629762370.802368618511882
940.1701763390558670.3403526781117340.829823660944133
950.1813588246049590.3627176492099190.818641175395041
960.195217159318450.39043431863690.80478284068155
970.1932419577681170.3864839155362340.806758042231883
980.2143695411071280.4287390822142560.785630458892872
990.3477055268111760.6954110536223530.652294473188824
1000.3068616892972610.6137233785945210.693138310702739
1010.2950856607910160.5901713215820320.704914339208984
1020.2808011967190560.5616023934381120.719198803280944
1030.2393909975229360.4787819950458720.760609002477064
1040.2081400278992930.4162800557985860.791859972100707
1050.2061476343267540.4122952686535080.793852365673246
1060.2321926466631080.4643852933262160.767807353336892
1070.2018237277633810.4036474555267620.798176272236619
1080.169464852993760.3389297059875190.83053514700624
1090.3084468714924910.6168937429849830.691553128507509
1100.4391451694742020.8782903389484040.560854830525798
1110.4013900807442840.8027801614885680.598609919255716
1120.4241937633355130.8483875266710270.575806236664487
1130.3736273840305320.7472547680610650.626372615969468
1140.3480760797137830.6961521594275670.651923920286217
1150.3022180689679440.6044361379358890.697781931032056
1160.4217049314530830.8434098629061670.578295068546917
1170.3971577188112880.7943154376225750.602842281188712
1180.3441929143973470.6883858287946930.655807085602653
1190.3109807460709150.621961492141830.689019253929085
1200.2613281700918790.5226563401837590.738671829908121
1210.6192046857703840.7615906284592330.380795314229616
1220.5902748218208430.8194503563583140.409725178179157
1230.658507497133590.6829850057328210.34149250286641
1240.7719092655865270.4561814688269460.228090734413473
1250.723296775910750.55340644817850.27670322408925
1260.6921682718821030.6156634562357940.307831728117897
1270.639732965345820.7205340693083610.36026703465418
1280.8227970551702640.3544058896594730.177202944829736
1290.7984585978245820.4030828043508360.201541402175418
1300.7639174176231280.4721651647537440.236082582376872
1310.9374725658145990.1250548683708030.0625274341854014
1320.9112086861891990.1775826276216010.0887913138108005
1330.8820387604523810.2359224790952370.117961239547619
1340.8384466712583260.3231066574833480.161553328741674
1350.8023879617271110.3952240765457770.197612038272889
1360.9999095354801920.0001809290396168449.0464519808422e-05
1370.9999488305558780.0001023388882435225.11694441217611e-05
1380.9999957251151848.54976963135552e-064.27488481567776e-06
1390.99998867022932.26595414008855e-051.13297707004427e-05
1400.9999730932304325.38135391354912e-052.69067695677456e-05
1410.9998974935105930.0002050129788147930.000102506489407396
1420.9998326567980350.0003346864039294180.000167343201964709
1430.999975683873624.86322527599045e-052.43161263799523e-05
1440.9999999996723596.55281864688328e-103.27640932344164e-10
1450.9999999993677381.26452378770268e-096.32261893851341e-10
14614.48151559315196e-462.24075779657598e-46

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.39077198257746 & 0.78154396515492 & 0.60922801742254 \tabularnewline
19 & 0.306404997411124 & 0.612809994822248 & 0.693595002588876 \tabularnewline
20 & 0.212254699779123 & 0.424509399558247 & 0.787745300220877 \tabularnewline
21 & 0.163696621899225 & 0.327393243798449 & 0.836303378100775 \tabularnewline
22 & 0.183515362951477 & 0.367030725902955 & 0.816484637048522 \tabularnewline
23 & 0.113756099829102 & 0.227512199658205 & 0.886243900170898 \tabularnewline
24 & 0.0723709808882865 & 0.144741961776573 & 0.927629019111713 \tabularnewline
25 & 0.0530666867346949 & 0.10613337346939 & 0.946933313265305 \tabularnewline
26 & 0.0313900180707139 & 0.0627800361414277 & 0.968609981929286 \tabularnewline
27 & 0.0192531243387416 & 0.0385062486774832 & 0.980746875661258 \tabularnewline
28 & 0.0120582586167014 & 0.0241165172334027 & 0.987941741383299 \tabularnewline
29 & 0.00966319998348924 & 0.0193263999669785 & 0.990336800016511 \tabularnewline
30 & 0.00732536067463413 & 0.0146507213492683 & 0.992674639325366 \tabularnewline
31 & 0.00449110056559042 & 0.00898220113118085 & 0.99550889943441 \tabularnewline
32 & 0.00255560210926479 & 0.00511120421852957 & 0.997444397890735 \tabularnewline
33 & 0.00161774988705452 & 0.00323549977410904 & 0.998382250112945 \tabularnewline
34 & 0.00257023905672334 & 0.00514047811344668 & 0.997429760943277 \tabularnewline
35 & 0.00410873320757829 & 0.00821746641515658 & 0.995891266792422 \tabularnewline
36 & 0.00235728400997603 & 0.00471456801995205 & 0.997642715990024 \tabularnewline
37 & 0.00141657057710059 & 0.00283314115420117 & 0.998583429422899 \tabularnewline
38 & 0.00101320575180745 & 0.00202641150361491 & 0.998986794248193 \tabularnewline
39 & 0.00101150167017959 & 0.00202300334035918 & 0.99898849832982 \tabularnewline
40 & 0.000686451701553176 & 0.00137290340310635 & 0.999313548298447 \tabularnewline
41 & 0.00138893399442512 & 0.00277786798885025 & 0.998611066005575 \tabularnewline
42 & 0.000828973337045618 & 0.00165794667409124 & 0.999171026662954 \tabularnewline
43 & 0.000762247079645484 & 0.00152449415929097 & 0.999237752920355 \tabularnewline
44 & 0.000474132319415422 & 0.000948264638830844 & 0.999525867680585 \tabularnewline
45 & 0.00261638683467852 & 0.00523277366935704 & 0.997383613165321 \tabularnewline
46 & 0.00163234468647765 & 0.0032646893729553 & 0.998367655313522 \tabularnewline
47 & 0.0013670853744393 & 0.00273417074887861 & 0.998632914625561 \tabularnewline
48 & 0.000828556928519413 & 0.00165711385703883 & 0.999171443071481 \tabularnewline
49 & 0.000521605452885298 & 0.0010432109057706 & 0.999478394547115 \tabularnewline
50 & 0.000466358912283075 & 0.00093271782456615 & 0.999533641087717 \tabularnewline
51 & 0.000333097174654128 & 0.000666194349308256 & 0.999666902825346 \tabularnewline
52 & 0.000247958554185918 & 0.000495917108371835 & 0.999752041445814 \tabularnewline
53 & 0.000187170413682122 & 0.000374340827364245 & 0.999812829586318 \tabularnewline
54 & 0.000107599429444105 & 0.00021519885888821 & 0.999892400570556 \tabularnewline
55 & 7.33135148818994e-05 & 0.000146627029763799 & 0.999926686485118 \tabularnewline
56 & 4.69327270491355e-05 & 9.3865454098271e-05 & 0.999953067272951 \tabularnewline
57 & 5.23572511295037e-05 & 0.000104714502259007 & 0.99994764274887 \tabularnewline
58 & 8.62253991217547e-05 & 0.000172450798243509 & 0.999913774600878 \tabularnewline
59 & 6.14619361404234e-05 & 0.000122923872280847 & 0.99993853806386 \tabularnewline
60 & 3.58954196505269e-05 & 7.17908393010539e-05 & 0.999964104580349 \tabularnewline
61 & 2.38724310291507e-05 & 4.77448620583015e-05 & 0.999976127568971 \tabularnewline
62 & 1.49454606649108e-05 & 2.98909213298216e-05 & 0.999985054539335 \tabularnewline
63 & 1.14330646770132e-05 & 2.28661293540264e-05 & 0.999988566935323 \tabularnewline
64 & 6.99772284616822e-06 & 1.39954456923364e-05 & 0.999993002277154 \tabularnewline
65 & 5.01630363035751e-06 & 1.0032607260715e-05 & 0.99999498369637 \tabularnewline
66 & 3.65530548627983e-06 & 7.31061097255965e-06 & 0.999996344694514 \tabularnewline
67 & 6.98990015653883e-06 & 1.39798003130777e-05 & 0.999993010099843 \tabularnewline
68 & 2.91108819308891e-05 & 5.82217638617781e-05 & 0.999970889118069 \tabularnewline
69 & 3.03670102037689e-05 & 6.07340204075378e-05 & 0.999969632989796 \tabularnewline
70 & 9.75160310606974e-05 & 0.000195032062121395 & 0.999902483968939 \tabularnewline
71 & 0.000155424875313533 & 0.000310849750627066 & 0.999844575124686 \tabularnewline
72 & 0.000125452305107876 & 0.000250904610215753 & 0.999874547694892 \tabularnewline
73 & 0.000139042127318437 & 0.000278084254636874 & 0.999860957872682 \tabularnewline
74 & 8.59347948230096e-05 & 0.000171869589646019 & 0.999914065205177 \tabularnewline
75 & 6.2202806609967e-05 & 0.000124405613219934 & 0.99993779719339 \tabularnewline
76 & 6.60134614043979e-05 & 0.000132026922808796 & 0.999933986538596 \tabularnewline
77 & 4.12138612158876e-05 & 8.24277224317751e-05 & 0.999958786138784 \tabularnewline
78 & 0.180203658475879 & 0.360407316951757 & 0.819796341524121 \tabularnewline
79 & 0.162954487823346 & 0.325908975646691 & 0.837045512176654 \tabularnewline
80 & 0.13727398512955 & 0.2745479702591 & 0.86272601487045 \tabularnewline
81 & 0.131263630960222 & 0.262527261920444 & 0.868736369039778 \tabularnewline
82 & 0.219198975266687 & 0.438397950533375 & 0.780801024733313 \tabularnewline
83 & 0.243730580734818 & 0.487461161469636 & 0.756269419265182 \tabularnewline
84 & 0.208532176035841 & 0.417064352071682 & 0.791467823964159 \tabularnewline
85 & 0.187960159785747 & 0.375920319571494 & 0.812039840214253 \tabularnewline
86 & 0.164658563886123 & 0.329317127772246 & 0.835341436113877 \tabularnewline
87 & 0.149954772721855 & 0.299909545443709 & 0.850045227278145 \tabularnewline
88 & 0.152111273318641 & 0.304222546637281 & 0.847888726681359 \tabularnewline
89 & 0.160539887026948 & 0.321079774053895 & 0.839460112973052 \tabularnewline
90 & 0.176635259184173 & 0.353270518368346 & 0.823364740815827 \tabularnewline
91 & 0.166363709022795 & 0.33272741804559 & 0.833636290977205 \tabularnewline
92 & 0.187942269086348 & 0.375884538172696 & 0.812057730913652 \tabularnewline
93 & 0.197631381488118 & 0.395262762976237 & 0.802368618511882 \tabularnewline
94 & 0.170176339055867 & 0.340352678111734 & 0.829823660944133 \tabularnewline
95 & 0.181358824604959 & 0.362717649209919 & 0.818641175395041 \tabularnewline
96 & 0.19521715931845 & 0.3904343186369 & 0.80478284068155 \tabularnewline
97 & 0.193241957768117 & 0.386483915536234 & 0.806758042231883 \tabularnewline
98 & 0.214369541107128 & 0.428739082214256 & 0.785630458892872 \tabularnewline
99 & 0.347705526811176 & 0.695411053622353 & 0.652294473188824 \tabularnewline
100 & 0.306861689297261 & 0.613723378594521 & 0.693138310702739 \tabularnewline
101 & 0.295085660791016 & 0.590171321582032 & 0.704914339208984 \tabularnewline
102 & 0.280801196719056 & 0.561602393438112 & 0.719198803280944 \tabularnewline
103 & 0.239390997522936 & 0.478781995045872 & 0.760609002477064 \tabularnewline
104 & 0.208140027899293 & 0.416280055798586 & 0.791859972100707 \tabularnewline
105 & 0.206147634326754 & 0.412295268653508 & 0.793852365673246 \tabularnewline
106 & 0.232192646663108 & 0.464385293326216 & 0.767807353336892 \tabularnewline
107 & 0.201823727763381 & 0.403647455526762 & 0.798176272236619 \tabularnewline
108 & 0.16946485299376 & 0.338929705987519 & 0.83053514700624 \tabularnewline
109 & 0.308446871492491 & 0.616893742984983 & 0.691553128507509 \tabularnewline
110 & 0.439145169474202 & 0.878290338948404 & 0.560854830525798 \tabularnewline
111 & 0.401390080744284 & 0.802780161488568 & 0.598609919255716 \tabularnewline
112 & 0.424193763335513 & 0.848387526671027 & 0.575806236664487 \tabularnewline
113 & 0.373627384030532 & 0.747254768061065 & 0.626372615969468 \tabularnewline
114 & 0.348076079713783 & 0.696152159427567 & 0.651923920286217 \tabularnewline
115 & 0.302218068967944 & 0.604436137935889 & 0.697781931032056 \tabularnewline
116 & 0.421704931453083 & 0.843409862906167 & 0.578295068546917 \tabularnewline
117 & 0.397157718811288 & 0.794315437622575 & 0.602842281188712 \tabularnewline
118 & 0.344192914397347 & 0.688385828794693 & 0.655807085602653 \tabularnewline
119 & 0.310980746070915 & 0.62196149214183 & 0.689019253929085 \tabularnewline
120 & 0.261328170091879 & 0.522656340183759 & 0.738671829908121 \tabularnewline
121 & 0.619204685770384 & 0.761590628459233 & 0.380795314229616 \tabularnewline
122 & 0.590274821820843 & 0.819450356358314 & 0.409725178179157 \tabularnewline
123 & 0.65850749713359 & 0.682985005732821 & 0.34149250286641 \tabularnewline
124 & 0.771909265586527 & 0.456181468826946 & 0.228090734413473 \tabularnewline
125 & 0.72329677591075 & 0.5534064481785 & 0.27670322408925 \tabularnewline
126 & 0.692168271882103 & 0.615663456235794 & 0.307831728117897 \tabularnewline
127 & 0.63973296534582 & 0.720534069308361 & 0.36026703465418 \tabularnewline
128 & 0.822797055170264 & 0.354405889659473 & 0.177202944829736 \tabularnewline
129 & 0.798458597824582 & 0.403082804350836 & 0.201541402175418 \tabularnewline
130 & 0.763917417623128 & 0.472165164753744 & 0.236082582376872 \tabularnewline
131 & 0.937472565814599 & 0.125054868370803 & 0.0625274341854014 \tabularnewline
132 & 0.911208686189199 & 0.177582627621601 & 0.0887913138108005 \tabularnewline
133 & 0.882038760452381 & 0.235922479095237 & 0.117961239547619 \tabularnewline
134 & 0.838446671258326 & 0.323106657483348 & 0.161553328741674 \tabularnewline
135 & 0.802387961727111 & 0.395224076545777 & 0.197612038272889 \tabularnewline
136 & 0.999909535480192 & 0.000180929039616844 & 9.0464519808422e-05 \tabularnewline
137 & 0.999948830555878 & 0.000102338888243522 & 5.11694441217611e-05 \tabularnewline
138 & 0.999995725115184 & 8.54976963135552e-06 & 4.27488481567776e-06 \tabularnewline
139 & 0.9999886702293 & 2.26595414008855e-05 & 1.13297707004427e-05 \tabularnewline
140 & 0.999973093230432 & 5.38135391354912e-05 & 2.69067695677456e-05 \tabularnewline
141 & 0.999897493510593 & 0.000205012978814793 & 0.000102506489407396 \tabularnewline
142 & 0.999832656798035 & 0.000334686403929418 & 0.000167343201964709 \tabularnewline
143 & 0.99997568387362 & 4.86322527599045e-05 & 2.43161263799523e-05 \tabularnewline
144 & 0.999999999672359 & 6.55281864688328e-10 & 3.27640932344164e-10 \tabularnewline
145 & 0.999999999367738 & 1.26452378770268e-09 & 6.32261893851341e-10 \tabularnewline
146 & 1 & 4.48151559315196e-46 & 2.24075779657598e-46 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157934&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]0.39077198257746[/C][C]0.78154396515492[/C][C]0.60922801742254[/C][/ROW]
[ROW][C]19[/C][C]0.306404997411124[/C][C]0.612809994822248[/C][C]0.693595002588876[/C][/ROW]
[ROW][C]20[/C][C]0.212254699779123[/C][C]0.424509399558247[/C][C]0.787745300220877[/C][/ROW]
[ROW][C]21[/C][C]0.163696621899225[/C][C]0.327393243798449[/C][C]0.836303378100775[/C][/ROW]
[ROW][C]22[/C][C]0.183515362951477[/C][C]0.367030725902955[/C][C]0.816484637048522[/C][/ROW]
[ROW][C]23[/C][C]0.113756099829102[/C][C]0.227512199658205[/C][C]0.886243900170898[/C][/ROW]
[ROW][C]24[/C][C]0.0723709808882865[/C][C]0.144741961776573[/C][C]0.927629019111713[/C][/ROW]
[ROW][C]25[/C][C]0.0530666867346949[/C][C]0.10613337346939[/C][C]0.946933313265305[/C][/ROW]
[ROW][C]26[/C][C]0.0313900180707139[/C][C]0.0627800361414277[/C][C]0.968609981929286[/C][/ROW]
[ROW][C]27[/C][C]0.0192531243387416[/C][C]0.0385062486774832[/C][C]0.980746875661258[/C][/ROW]
[ROW][C]28[/C][C]0.0120582586167014[/C][C]0.0241165172334027[/C][C]0.987941741383299[/C][/ROW]
[ROW][C]29[/C][C]0.00966319998348924[/C][C]0.0193263999669785[/C][C]0.990336800016511[/C][/ROW]
[ROW][C]30[/C][C]0.00732536067463413[/C][C]0.0146507213492683[/C][C]0.992674639325366[/C][/ROW]
[ROW][C]31[/C][C]0.00449110056559042[/C][C]0.00898220113118085[/C][C]0.99550889943441[/C][/ROW]
[ROW][C]32[/C][C]0.00255560210926479[/C][C]0.00511120421852957[/C][C]0.997444397890735[/C][/ROW]
[ROW][C]33[/C][C]0.00161774988705452[/C][C]0.00323549977410904[/C][C]0.998382250112945[/C][/ROW]
[ROW][C]34[/C][C]0.00257023905672334[/C][C]0.00514047811344668[/C][C]0.997429760943277[/C][/ROW]
[ROW][C]35[/C][C]0.00410873320757829[/C][C]0.00821746641515658[/C][C]0.995891266792422[/C][/ROW]
[ROW][C]36[/C][C]0.00235728400997603[/C][C]0.00471456801995205[/C][C]0.997642715990024[/C][/ROW]
[ROW][C]37[/C][C]0.00141657057710059[/C][C]0.00283314115420117[/C][C]0.998583429422899[/C][/ROW]
[ROW][C]38[/C][C]0.00101320575180745[/C][C]0.00202641150361491[/C][C]0.998986794248193[/C][/ROW]
[ROW][C]39[/C][C]0.00101150167017959[/C][C]0.00202300334035918[/C][C]0.99898849832982[/C][/ROW]
[ROW][C]40[/C][C]0.000686451701553176[/C][C]0.00137290340310635[/C][C]0.999313548298447[/C][/ROW]
[ROW][C]41[/C][C]0.00138893399442512[/C][C]0.00277786798885025[/C][C]0.998611066005575[/C][/ROW]
[ROW][C]42[/C][C]0.000828973337045618[/C][C]0.00165794667409124[/C][C]0.999171026662954[/C][/ROW]
[ROW][C]43[/C][C]0.000762247079645484[/C][C]0.00152449415929097[/C][C]0.999237752920355[/C][/ROW]
[ROW][C]44[/C][C]0.000474132319415422[/C][C]0.000948264638830844[/C][C]0.999525867680585[/C][/ROW]
[ROW][C]45[/C][C]0.00261638683467852[/C][C]0.00523277366935704[/C][C]0.997383613165321[/C][/ROW]
[ROW][C]46[/C][C]0.00163234468647765[/C][C]0.0032646893729553[/C][C]0.998367655313522[/C][/ROW]
[ROW][C]47[/C][C]0.0013670853744393[/C][C]0.00273417074887861[/C][C]0.998632914625561[/C][/ROW]
[ROW][C]48[/C][C]0.000828556928519413[/C][C]0.00165711385703883[/C][C]0.999171443071481[/C][/ROW]
[ROW][C]49[/C][C]0.000521605452885298[/C][C]0.0010432109057706[/C][C]0.999478394547115[/C][/ROW]
[ROW][C]50[/C][C]0.000466358912283075[/C][C]0.00093271782456615[/C][C]0.999533641087717[/C][/ROW]
[ROW][C]51[/C][C]0.000333097174654128[/C][C]0.000666194349308256[/C][C]0.999666902825346[/C][/ROW]
[ROW][C]52[/C][C]0.000247958554185918[/C][C]0.000495917108371835[/C][C]0.999752041445814[/C][/ROW]
[ROW][C]53[/C][C]0.000187170413682122[/C][C]0.000374340827364245[/C][C]0.999812829586318[/C][/ROW]
[ROW][C]54[/C][C]0.000107599429444105[/C][C]0.00021519885888821[/C][C]0.999892400570556[/C][/ROW]
[ROW][C]55[/C][C]7.33135148818994e-05[/C][C]0.000146627029763799[/C][C]0.999926686485118[/C][/ROW]
[ROW][C]56[/C][C]4.69327270491355e-05[/C][C]9.3865454098271e-05[/C][C]0.999953067272951[/C][/ROW]
[ROW][C]57[/C][C]5.23572511295037e-05[/C][C]0.000104714502259007[/C][C]0.99994764274887[/C][/ROW]
[ROW][C]58[/C][C]8.62253991217547e-05[/C][C]0.000172450798243509[/C][C]0.999913774600878[/C][/ROW]
[ROW][C]59[/C][C]6.14619361404234e-05[/C][C]0.000122923872280847[/C][C]0.99993853806386[/C][/ROW]
[ROW][C]60[/C][C]3.58954196505269e-05[/C][C]7.17908393010539e-05[/C][C]0.999964104580349[/C][/ROW]
[ROW][C]61[/C][C]2.38724310291507e-05[/C][C]4.77448620583015e-05[/C][C]0.999976127568971[/C][/ROW]
[ROW][C]62[/C][C]1.49454606649108e-05[/C][C]2.98909213298216e-05[/C][C]0.999985054539335[/C][/ROW]
[ROW][C]63[/C][C]1.14330646770132e-05[/C][C]2.28661293540264e-05[/C][C]0.999988566935323[/C][/ROW]
[ROW][C]64[/C][C]6.99772284616822e-06[/C][C]1.39954456923364e-05[/C][C]0.999993002277154[/C][/ROW]
[ROW][C]65[/C][C]5.01630363035751e-06[/C][C]1.0032607260715e-05[/C][C]0.99999498369637[/C][/ROW]
[ROW][C]66[/C][C]3.65530548627983e-06[/C][C]7.31061097255965e-06[/C][C]0.999996344694514[/C][/ROW]
[ROW][C]67[/C][C]6.98990015653883e-06[/C][C]1.39798003130777e-05[/C][C]0.999993010099843[/C][/ROW]
[ROW][C]68[/C][C]2.91108819308891e-05[/C][C]5.82217638617781e-05[/C][C]0.999970889118069[/C][/ROW]
[ROW][C]69[/C][C]3.03670102037689e-05[/C][C]6.07340204075378e-05[/C][C]0.999969632989796[/C][/ROW]
[ROW][C]70[/C][C]9.75160310606974e-05[/C][C]0.000195032062121395[/C][C]0.999902483968939[/C][/ROW]
[ROW][C]71[/C][C]0.000155424875313533[/C][C]0.000310849750627066[/C][C]0.999844575124686[/C][/ROW]
[ROW][C]72[/C][C]0.000125452305107876[/C][C]0.000250904610215753[/C][C]0.999874547694892[/C][/ROW]
[ROW][C]73[/C][C]0.000139042127318437[/C][C]0.000278084254636874[/C][C]0.999860957872682[/C][/ROW]
[ROW][C]74[/C][C]8.59347948230096e-05[/C][C]0.000171869589646019[/C][C]0.999914065205177[/C][/ROW]
[ROW][C]75[/C][C]6.2202806609967e-05[/C][C]0.000124405613219934[/C][C]0.99993779719339[/C][/ROW]
[ROW][C]76[/C][C]6.60134614043979e-05[/C][C]0.000132026922808796[/C][C]0.999933986538596[/C][/ROW]
[ROW][C]77[/C][C]4.12138612158876e-05[/C][C]8.24277224317751e-05[/C][C]0.999958786138784[/C][/ROW]
[ROW][C]78[/C][C]0.180203658475879[/C][C]0.360407316951757[/C][C]0.819796341524121[/C][/ROW]
[ROW][C]79[/C][C]0.162954487823346[/C][C]0.325908975646691[/C][C]0.837045512176654[/C][/ROW]
[ROW][C]80[/C][C]0.13727398512955[/C][C]0.2745479702591[/C][C]0.86272601487045[/C][/ROW]
[ROW][C]81[/C][C]0.131263630960222[/C][C]0.262527261920444[/C][C]0.868736369039778[/C][/ROW]
[ROW][C]82[/C][C]0.219198975266687[/C][C]0.438397950533375[/C][C]0.780801024733313[/C][/ROW]
[ROW][C]83[/C][C]0.243730580734818[/C][C]0.487461161469636[/C][C]0.756269419265182[/C][/ROW]
[ROW][C]84[/C][C]0.208532176035841[/C][C]0.417064352071682[/C][C]0.791467823964159[/C][/ROW]
[ROW][C]85[/C][C]0.187960159785747[/C][C]0.375920319571494[/C][C]0.812039840214253[/C][/ROW]
[ROW][C]86[/C][C]0.164658563886123[/C][C]0.329317127772246[/C][C]0.835341436113877[/C][/ROW]
[ROW][C]87[/C][C]0.149954772721855[/C][C]0.299909545443709[/C][C]0.850045227278145[/C][/ROW]
[ROW][C]88[/C][C]0.152111273318641[/C][C]0.304222546637281[/C][C]0.847888726681359[/C][/ROW]
[ROW][C]89[/C][C]0.160539887026948[/C][C]0.321079774053895[/C][C]0.839460112973052[/C][/ROW]
[ROW][C]90[/C][C]0.176635259184173[/C][C]0.353270518368346[/C][C]0.823364740815827[/C][/ROW]
[ROW][C]91[/C][C]0.166363709022795[/C][C]0.33272741804559[/C][C]0.833636290977205[/C][/ROW]
[ROW][C]92[/C][C]0.187942269086348[/C][C]0.375884538172696[/C][C]0.812057730913652[/C][/ROW]
[ROW][C]93[/C][C]0.197631381488118[/C][C]0.395262762976237[/C][C]0.802368618511882[/C][/ROW]
[ROW][C]94[/C][C]0.170176339055867[/C][C]0.340352678111734[/C][C]0.829823660944133[/C][/ROW]
[ROW][C]95[/C][C]0.181358824604959[/C][C]0.362717649209919[/C][C]0.818641175395041[/C][/ROW]
[ROW][C]96[/C][C]0.19521715931845[/C][C]0.3904343186369[/C][C]0.80478284068155[/C][/ROW]
[ROW][C]97[/C][C]0.193241957768117[/C][C]0.386483915536234[/C][C]0.806758042231883[/C][/ROW]
[ROW][C]98[/C][C]0.214369541107128[/C][C]0.428739082214256[/C][C]0.785630458892872[/C][/ROW]
[ROW][C]99[/C][C]0.347705526811176[/C][C]0.695411053622353[/C][C]0.652294473188824[/C][/ROW]
[ROW][C]100[/C][C]0.306861689297261[/C][C]0.613723378594521[/C][C]0.693138310702739[/C][/ROW]
[ROW][C]101[/C][C]0.295085660791016[/C][C]0.590171321582032[/C][C]0.704914339208984[/C][/ROW]
[ROW][C]102[/C][C]0.280801196719056[/C][C]0.561602393438112[/C][C]0.719198803280944[/C][/ROW]
[ROW][C]103[/C][C]0.239390997522936[/C][C]0.478781995045872[/C][C]0.760609002477064[/C][/ROW]
[ROW][C]104[/C][C]0.208140027899293[/C][C]0.416280055798586[/C][C]0.791859972100707[/C][/ROW]
[ROW][C]105[/C][C]0.206147634326754[/C][C]0.412295268653508[/C][C]0.793852365673246[/C][/ROW]
[ROW][C]106[/C][C]0.232192646663108[/C][C]0.464385293326216[/C][C]0.767807353336892[/C][/ROW]
[ROW][C]107[/C][C]0.201823727763381[/C][C]0.403647455526762[/C][C]0.798176272236619[/C][/ROW]
[ROW][C]108[/C][C]0.16946485299376[/C][C]0.338929705987519[/C][C]0.83053514700624[/C][/ROW]
[ROW][C]109[/C][C]0.308446871492491[/C][C]0.616893742984983[/C][C]0.691553128507509[/C][/ROW]
[ROW][C]110[/C][C]0.439145169474202[/C][C]0.878290338948404[/C][C]0.560854830525798[/C][/ROW]
[ROW][C]111[/C][C]0.401390080744284[/C][C]0.802780161488568[/C][C]0.598609919255716[/C][/ROW]
[ROW][C]112[/C][C]0.424193763335513[/C][C]0.848387526671027[/C][C]0.575806236664487[/C][/ROW]
[ROW][C]113[/C][C]0.373627384030532[/C][C]0.747254768061065[/C][C]0.626372615969468[/C][/ROW]
[ROW][C]114[/C][C]0.348076079713783[/C][C]0.696152159427567[/C][C]0.651923920286217[/C][/ROW]
[ROW][C]115[/C][C]0.302218068967944[/C][C]0.604436137935889[/C][C]0.697781931032056[/C][/ROW]
[ROW][C]116[/C][C]0.421704931453083[/C][C]0.843409862906167[/C][C]0.578295068546917[/C][/ROW]
[ROW][C]117[/C][C]0.397157718811288[/C][C]0.794315437622575[/C][C]0.602842281188712[/C][/ROW]
[ROW][C]118[/C][C]0.344192914397347[/C][C]0.688385828794693[/C][C]0.655807085602653[/C][/ROW]
[ROW][C]119[/C][C]0.310980746070915[/C][C]0.62196149214183[/C][C]0.689019253929085[/C][/ROW]
[ROW][C]120[/C][C]0.261328170091879[/C][C]0.522656340183759[/C][C]0.738671829908121[/C][/ROW]
[ROW][C]121[/C][C]0.619204685770384[/C][C]0.761590628459233[/C][C]0.380795314229616[/C][/ROW]
[ROW][C]122[/C][C]0.590274821820843[/C][C]0.819450356358314[/C][C]0.409725178179157[/C][/ROW]
[ROW][C]123[/C][C]0.65850749713359[/C][C]0.682985005732821[/C][C]0.34149250286641[/C][/ROW]
[ROW][C]124[/C][C]0.771909265586527[/C][C]0.456181468826946[/C][C]0.228090734413473[/C][/ROW]
[ROW][C]125[/C][C]0.72329677591075[/C][C]0.5534064481785[/C][C]0.27670322408925[/C][/ROW]
[ROW][C]126[/C][C]0.692168271882103[/C][C]0.615663456235794[/C][C]0.307831728117897[/C][/ROW]
[ROW][C]127[/C][C]0.63973296534582[/C][C]0.720534069308361[/C][C]0.36026703465418[/C][/ROW]
[ROW][C]128[/C][C]0.822797055170264[/C][C]0.354405889659473[/C][C]0.177202944829736[/C][/ROW]
[ROW][C]129[/C][C]0.798458597824582[/C][C]0.403082804350836[/C][C]0.201541402175418[/C][/ROW]
[ROW][C]130[/C][C]0.763917417623128[/C][C]0.472165164753744[/C][C]0.236082582376872[/C][/ROW]
[ROW][C]131[/C][C]0.937472565814599[/C][C]0.125054868370803[/C][C]0.0625274341854014[/C][/ROW]
[ROW][C]132[/C][C]0.911208686189199[/C][C]0.177582627621601[/C][C]0.0887913138108005[/C][/ROW]
[ROW][C]133[/C][C]0.882038760452381[/C][C]0.235922479095237[/C][C]0.117961239547619[/C][/ROW]
[ROW][C]134[/C][C]0.838446671258326[/C][C]0.323106657483348[/C][C]0.161553328741674[/C][/ROW]
[ROW][C]135[/C][C]0.802387961727111[/C][C]0.395224076545777[/C][C]0.197612038272889[/C][/ROW]
[ROW][C]136[/C][C]0.999909535480192[/C][C]0.000180929039616844[/C][C]9.0464519808422e-05[/C][/ROW]
[ROW][C]137[/C][C]0.999948830555878[/C][C]0.000102338888243522[/C][C]5.11694441217611e-05[/C][/ROW]
[ROW][C]138[/C][C]0.999995725115184[/C][C]8.54976963135552e-06[/C][C]4.27488481567776e-06[/C][/ROW]
[ROW][C]139[/C][C]0.9999886702293[/C][C]2.26595414008855e-05[/C][C]1.13297707004427e-05[/C][/ROW]
[ROW][C]140[/C][C]0.999973093230432[/C][C]5.38135391354912e-05[/C][C]2.69067695677456e-05[/C][/ROW]
[ROW][C]141[/C][C]0.999897493510593[/C][C]0.000205012978814793[/C][C]0.000102506489407396[/C][/ROW]
[ROW][C]142[/C][C]0.999832656798035[/C][C]0.000334686403929418[/C][C]0.000167343201964709[/C][/ROW]
[ROW][C]143[/C][C]0.99997568387362[/C][C]4.86322527599045e-05[/C][C]2.43161263799523e-05[/C][/ROW]
[ROW][C]144[/C][C]0.999999999672359[/C][C]6.55281864688328e-10[/C][C]3.27640932344164e-10[/C][/ROW]
[ROW][C]145[/C][C]0.999999999367738[/C][C]1.26452378770268e-09[/C][C]6.32261893851341e-10[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]4.48151559315196e-46[/C][C]2.24075779657598e-46[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157934&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157934&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
180.390771982577460.781543965154920.60922801742254
190.3064049974111240.6128099948222480.693595002588876
200.2122546997791230.4245093995582470.787745300220877
210.1636966218992250.3273932437984490.836303378100775
220.1835153629514770.3670307259029550.816484637048522
230.1137560998291020.2275121996582050.886243900170898
240.07237098088828650.1447419617765730.927629019111713
250.05306668673469490.106133373469390.946933313265305
260.03139001807071390.06278003614142770.968609981929286
270.01925312433874160.03850624867748320.980746875661258
280.01205825861670140.02411651723340270.987941741383299
290.009663199983489240.01932639996697850.990336800016511
300.007325360674634130.01465072134926830.992674639325366
310.004491100565590420.008982201131180850.99550889943441
320.002555602109264790.005111204218529570.997444397890735
330.001617749887054520.003235499774109040.998382250112945
340.002570239056723340.005140478113446680.997429760943277
350.004108733207578290.008217466415156580.995891266792422
360.002357284009976030.004714568019952050.997642715990024
370.001416570577100590.002833141154201170.998583429422899
380.001013205751807450.002026411503614910.998986794248193
390.001011501670179590.002023003340359180.99898849832982
400.0006864517015531760.001372903403106350.999313548298447
410.001388933994425120.002777867988850250.998611066005575
420.0008289733370456180.001657946674091240.999171026662954
430.0007622470796454840.001524494159290970.999237752920355
440.0004741323194154220.0009482646388308440.999525867680585
450.002616386834678520.005232773669357040.997383613165321
460.001632344686477650.00326468937295530.998367655313522
470.00136708537443930.002734170748878610.998632914625561
480.0008285569285194130.001657113857038830.999171443071481
490.0005216054528852980.00104321090577060.999478394547115
500.0004663589122830750.000932717824566150.999533641087717
510.0003330971746541280.0006661943493082560.999666902825346
520.0002479585541859180.0004959171083718350.999752041445814
530.0001871704136821220.0003743408273642450.999812829586318
540.0001075994294441050.000215198858888210.999892400570556
557.33135148818994e-050.0001466270297637990.999926686485118
564.69327270491355e-059.3865454098271e-050.999953067272951
575.23572511295037e-050.0001047145022590070.99994764274887
588.62253991217547e-050.0001724507982435090.999913774600878
596.14619361404234e-050.0001229238722808470.99993853806386
603.58954196505269e-057.17908393010539e-050.999964104580349
612.38724310291507e-054.77448620583015e-050.999976127568971
621.49454606649108e-052.98909213298216e-050.999985054539335
631.14330646770132e-052.28661293540264e-050.999988566935323
646.99772284616822e-061.39954456923364e-050.999993002277154
655.01630363035751e-061.0032607260715e-050.99999498369637
663.65530548627983e-067.31061097255965e-060.999996344694514
676.98990015653883e-061.39798003130777e-050.999993010099843
682.91108819308891e-055.82217638617781e-050.999970889118069
693.03670102037689e-056.07340204075378e-050.999969632989796
709.75160310606974e-050.0001950320621213950.999902483968939
710.0001554248753135330.0003108497506270660.999844575124686
720.0001254523051078760.0002509046102157530.999874547694892
730.0001390421273184370.0002780842546368740.999860957872682
748.59347948230096e-050.0001718695896460190.999914065205177
756.2202806609967e-050.0001244056132199340.99993779719339
766.60134614043979e-050.0001320269228087960.999933986538596
774.12138612158876e-058.24277224317751e-050.999958786138784
780.1802036584758790.3604073169517570.819796341524121
790.1629544878233460.3259089756466910.837045512176654
800.137273985129550.27454797025910.86272601487045
810.1312636309602220.2625272619204440.868736369039778
820.2191989752666870.4383979505333750.780801024733313
830.2437305807348180.4874611614696360.756269419265182
840.2085321760358410.4170643520716820.791467823964159
850.1879601597857470.3759203195714940.812039840214253
860.1646585638861230.3293171277722460.835341436113877
870.1499547727218550.2999095454437090.850045227278145
880.1521112733186410.3042225466372810.847888726681359
890.1605398870269480.3210797740538950.839460112973052
900.1766352591841730.3532705183683460.823364740815827
910.1663637090227950.332727418045590.833636290977205
920.1879422690863480.3758845381726960.812057730913652
930.1976313814881180.3952627629762370.802368618511882
940.1701763390558670.3403526781117340.829823660944133
950.1813588246049590.3627176492099190.818641175395041
960.195217159318450.39043431863690.80478284068155
970.1932419577681170.3864839155362340.806758042231883
980.2143695411071280.4287390822142560.785630458892872
990.3477055268111760.6954110536223530.652294473188824
1000.3068616892972610.6137233785945210.693138310702739
1010.2950856607910160.5901713215820320.704914339208984
1020.2808011967190560.5616023934381120.719198803280944
1030.2393909975229360.4787819950458720.760609002477064
1040.2081400278992930.4162800557985860.791859972100707
1050.2061476343267540.4122952686535080.793852365673246
1060.2321926466631080.4643852933262160.767807353336892
1070.2018237277633810.4036474555267620.798176272236619
1080.169464852993760.3389297059875190.83053514700624
1090.3084468714924910.6168937429849830.691553128507509
1100.4391451694742020.8782903389484040.560854830525798
1110.4013900807442840.8027801614885680.598609919255716
1120.4241937633355130.8483875266710270.575806236664487
1130.3736273840305320.7472547680610650.626372615969468
1140.3480760797137830.6961521594275670.651923920286217
1150.3022180689679440.6044361379358890.697781931032056
1160.4217049314530830.8434098629061670.578295068546917
1170.3971577188112880.7943154376225750.602842281188712
1180.3441929143973470.6883858287946930.655807085602653
1190.3109807460709150.621961492141830.689019253929085
1200.2613281700918790.5226563401837590.738671829908121
1210.6192046857703840.7615906284592330.380795314229616
1220.5902748218208430.8194503563583140.409725178179157
1230.658507497133590.6829850057328210.34149250286641
1240.7719092655865270.4561814688269460.228090734413473
1250.723296775910750.55340644817850.27670322408925
1260.6921682718821030.6156634562357940.307831728117897
1270.639732965345820.7205340693083610.36026703465418
1280.8227970551702640.3544058896594730.177202944829736
1290.7984585978245820.4030828043508360.201541402175418
1300.7639174176231280.4721651647537440.236082582376872
1310.9374725658145990.1250548683708030.0625274341854014
1320.9112086861891990.1775826276216010.0887913138108005
1330.8820387604523810.2359224790952370.117961239547619
1340.8384466712583260.3231066574833480.161553328741674
1350.8023879617271110.3952240765457770.197612038272889
1360.9999095354801920.0001809290396168449.0464519808422e-05
1370.9999488305558780.0001023388882435225.11694441217611e-05
1380.9999957251151848.54976963135552e-064.27488481567776e-06
1390.99998867022932.26595414008855e-051.13297707004427e-05
1400.9999730932304325.38135391354912e-052.69067695677456e-05
1410.9998974935105930.0002050129788147930.000102506489407396
1420.9998326567980350.0003346864039294180.000167343201964709
1430.999975683873624.86322527599045e-052.43161263799523e-05
1440.9999999996723596.55281864688328e-103.27640932344164e-10
1450.9999999993677381.26452378770268e-096.32261893851341e-10
14614.48151559315196e-462.24075779657598e-46






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level580.449612403100775NOK
5% type I error level620.48062015503876NOK
10% type I error level630.488372093023256NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 58 & 0.449612403100775 & NOK \tabularnewline
5% type I error level & 62 & 0.48062015503876 & NOK \tabularnewline
10% type I error level & 63 & 0.488372093023256 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157934&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]58[/C][C]0.449612403100775[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]62[/C][C]0.48062015503876[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]63[/C][C]0.488372093023256[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157934&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157934&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level580.449612403100775NOK
5% type I error level620.48062015503876NOK
10% type I error level630.488372093023256NOK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

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