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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 20 Dec 2011 04:15:17 -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/t1324373372mm5i3rs9x6plm1a.htm/, Retrieved Mon, 06 May 2024 09:11:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=157830, Retrieved Mon, 06 May 2024 09:11:16 +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 09:15:17] [bed8b29df92274246a1a4d59b25859b7] [Current]
- RMP     [Recursive Partitioning (Regression Trees)] [] [2011-12-20 10:36:15] [97a82ed57455ec27012f2e899dc4f1a4]
- R P       [Recursive Partitioning (Regression Trees)] [] [2011-12-21 22:02:04] [74be16979710d4c4e7c6647856088456]
- RMPD      [Histogram] [] [2011-12-21 22:51:18] [97a82ed57455ec27012f2e899dc4f1a4]
- R PD        [Histogram] [] [2011-12-21 23:16:39] [97a82ed57455ec27012f2e899dc4f1a4]
-   PD      [Recursive Partitioning (Regression Trees)] [Regression trees RCF] [2011-12-22 15:05:47] [f033824ca1b38a5ddbb2c3414ea3bb75]
-   PD      [Recursive Partitioning (Regression Trees)] [Regression trees ...] [2011-12-22 15:35:50] [f033824ca1b38a5ddbb2c3414ea3bb75]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4746	517243	123	2302	109	0	149	33	128	122	187326	43068	250931	185	177
381	33186	19	111	46	0	1	18	67	22	22807	1271	6853	5	5
1758	213274	44	595	72	0	82	33	120	82	66485	20318	110896	93	92
2133	307153	110	800	82	0	92	46	169	147	79089	24409	169351	154	149
4919	384524	179	1696	129	0	175	59	223	199	114948	33433	165354	150	142
2522	278265	70	837	103	0	123	49	187	160	112431	40085	140303	148	147
2914	238006	126	1032	74	0	113	38	146	139	104581	32580	116136	116	115
1973	270787	87	618	73	0	117	43	154	148	83243	27652	153990	132	125
1495	155915	51	559	53	0	57	32	117	99	37110	9845	64057	70	66
2910	359764	71	1100	140	0	138	41	158	135	113344	20190	230054	144	137
2764	273950	56	1186	87	0	83	42	159	127	69867	4958	100826	78	78
3697	425253	121	1329	77	0	112	44	161	151	101629	32344	188355	121	117
2254	208277	77	658	80	0	101	52	201	180	96125	19499	106314	155	155
1886	224649	75	593	48	0	95	40	158	137	95676	28527	160792	128	125
496	24188	24	218	20	0	8	4	12	7	5950	2694	15049	7	7
2712	367112	144	1076	63	0	161	42	153	150	120192	41455	197680	207	190
1720	225460	67	447	58	0	107	30	116	87	72128	25696	145707	126	121
1925	195793	66	684	67	0	95	37	134	110	71571	17696	75881	103	101
4147	470376	127	1253	114	0	161	45	175	138	160141	39361	175721	166	157
870	80953	25	437	31	0	49	8	28	27	56622	7905	55792	59	58
1966	150216	54	822	175	0	52	27	101	77	15986	4527	25157	27	27
2836	342522	118	954	91	0	146	57	206	122	93879	36099	170492	92	88
2196	297452	82	800	61	0	164	39	141	104	183500	43840	211381	213	210
2356	296670	67	860	85	0	150	41	155	130	149959	32616	195894	208	204
2007	186856	187	640	116	0	73	36	138	59	57224	11338	80684	73	70
2412	250254	148	830	80	0	105	44	167	98	104978	45873	139292	206	205
2626	268391	50	1174	110	0	129	40	155	125	100046	39844	128602	112	111
2753	314131	91	1067	59	0	169	44	162	135	101047	28317	135848	139	135
1239	153059	77	408	44	0	28	30	112	83	197426	24797	178377	60	59
3174	157578	93	929	79	0	118	41	161	127	160902	7471	106330	70	70
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
1604	163871	50	458	107	0	83	45	169	119	45724	9935	74151	132	101
387	21054	16	146	2	0	4	0	0	0	855	338	6622	4	4
492	31961	22	200	22	0	18	8	25	9	14116	3988	13155	39	39
568	38214	34	276	52	0	16	8	27	21	8773	1888	13983	16	16
2792	357602	82	1021	115	0	137	52	198	187	117440	36809	157384	162	160
1395	198104	61	481	78	0	50	53	205	171	104128	24959	122975	173	158
2517	338606	99	873	120	0	157	48	187	145	134047	21849	231257	172	165
1250	187992	35	473	49	0	71	40	151	137	79756	21654	122531	90	89
1121	102424	42	401	55	0	42	36	131	100	66089	8728	61394	50	49
1817	217665	114	613	69	0	38	32	125	45	128692	20954	100187	141	126
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	0	0	0	0	0	0	0	0	0	0
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
1059	116969	45	290	89	0	37	43	152	76	52789	1813	15673	35	35
29	969	2	0	0	0	0	0	0	0	0	0	0	0	0
1247	171533	52	380	48	1	67	27	88	64	43929	10621	100792	73	71
2889	442703	103	1270	101	1	87	39	144	144	103037	18507	150773	140	137
3462	238434	122	1265	159	1	144	45	165	154	83515	31452	96785	140	134
2227	336277	91	812	53	1	140	48	185	178	112302	34811	198299	161	159
947	115658	33	284	36	1	33	17	55	46	31081	12558	58391	41	41
2496	153778	82	1022	82	1	50	45	73	45	93473	9105	121850	52	51
3034	279128	59	1112	125	1	97	37	134	120	100087	32982	129711	137	133
1678	153613	46	622	41	1	62	28	109	89	55918	17326	83963	63	61
3872	384720	209	1513	279	1	81	37	143	137	108535	21261	118845	88	83
3088	179811	105	1045	72	1	165	45	171	159	144551	39039	81716	170	164
2467	273486	97	821	62	1	83	40	150	97	56750	13841	115750	98	96
3093	276134	75	1101	89	1	149	36	140	135	59938	15049	82390	107	107
3153	428191	120	1213	63	1	163	38	140	102	110529	27738	232241	219	216
2537	282410	95	897	192	1	110	46	158	124	67150	16786	114651	145	142
2339	246963	92	811	67	1	128	39	145	126	125386	31852	134218	241	239
2689	305217	108	829	70	1	185	47	168	156	156349	36171	169216	198	194
0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
2683	218443	104	1137	135	2	134	38	148	133	112098	35926	106194	148	145
1918	227636	83	640	67	2	79	36	139	89	70168	19433	104470	112	109
3547	366277	199	1250	50	2	135	42	163	153	103925	36524	164808	158	149
5517	455401	152	2072	849	2	101	41	147	131	114360	11941	79367	71	70
918	78800	42	330	57	2	20	26	82	66	33032	7935	56968	21	21
1450	229585	67	562	58	2	52	37	139	125	114799	37062	153242	131	132
2328	238482	83	853	78	2	137	36	135	116	131072	25270	114268	200	199
1802	194855	53	747	94	2	83	38	133	90	92059	24648	91313	127	126
2327	208804	64	706	58	2	133	38	148	134	71154	18050	80906	112	109
2383	287359	106	688	57	2	118	44	122	92	102812	26759	136323	163	159
2190	224597	43	752	113	2	81	43	163	151	102153	18700	80716	70	69
1922	278077	49	632	83	2	117	41	151	135	92280	34245	147341	140	140
2648	253330	86	1023	52	2	119	36	132	118	154451	34672	134969	154	151
2150	251752	86	747	58	2	72	37	133	88	84601	19354	105406	125	122
1823	206501	69	591	53	2	52	34	125	71	100350	17372	75882	80	72
1748	272545	70	483	90	3	96	38	144	116	140824	32033	186099	165	165
631	70849	28	179	46	3	8	35	129	89	76173	10024	47552	49	47
1438	176082	55	507	41	3	103	29	111	85	125818	31219	111669	169	162
2332	283441	78	921	54	3	123	50	195	179	139165	46201	184531	155	152
2119	176357	57	885	68	3	99	45	164	146	89256	25314	104864	136	133
1161	101097	64	454	70	3	30	14	52	41	31701	5296	45824	35	35
2846	350641	94	1130	48	3	142	36	140	133	126372	30927	165278	183	178
2213	262811	78	680	73	3	161	40	148	138	127654	23841	105590	205	192
3540	329583	117	1260	153	3	114	39	146	137	89506	24347	160501	125	119
1686	152043	54	670	47	3	44	29	107	84	144244	17416	88229	111	107
3388	410575	127	1169	111	3	141	50	186	175	279488	49809	258287	254	246
2035	173260	63	716	37	3	41	21	79	78	37238	2805	10901	16	15
3444	409163	161	1128	123	3	157	52	204	129	68946	22124	174586	174	173
1209	179444	64	429	63	4	71	34	133	127	110459	20654	113854	135	132
2976	364569	80	1176	117	4	139	55	191	171	103297	21857	101345	148	148
3067	343085	162	1196	90	4	100	39	144	92	125081	22355	135213	114	113
1017	101014	39	343	40	4	43	37	141	137	69652	3029	33750	31	31
3371	396684	98	1259	87	4	138	44	168	163	119442	38941	189723	199	185
2062	209049	64	732	61	4	131	38	148	133	81897	21588	89770	140	137
2349	363250	88	995	96	4	140	46	169	144	108146	25217	100125	121	121
2559	328875	82	966	138	4	75	40	156	147	146975	39038	135599	150	144
1662	189654	39	565	64	4	83	36	140	139	161100	34974	119595	132	132
602	43287	14	214	43	4	13	19	71	64	43750	7409	19630	49	49
2147	185468	81	716	85	4	89	23	84	36	48029	18213	88634	82	82
1913	316505	71	627	60	4	76	42	157	149	147172	27259	178303	112	108
530	61857	25	192	25	4	23	11	32	30	25162	3913	24610	31	28
3641	417399	81	1600	134	4	142	48	187	167	134238	37343	191469	171	161
2956	301282	343	972	153	4	94	43	167	159	102070	20920	86480	113	107
4191	425430	179	1482	155	4	115	46	172	163	146760	27195	195791	187	182
1867	162422	77	707	76	4	55	39	143	112	64593	17672	72558	90	90
1447	180393	41	462	62	5	66	25	89	84	82317	16278	111848	74	71
2828	267268	127	1062	91	5	99	41	158	127	103129	22883	158376	147	138
1848	206196	75	668	82	5	78	36	115	108	93487	28395	133252	94	87
744	65029	17	255	21	5	21	18	67	61	32551	3597	25109	21	21
2592	328024	85	678	86	5	130	61	230	228	169707	38975	210012	174	169
3748	344696	192	1128	222	5	132	39	145	137	150491	42721	194679	257	256
1685	175816	70	625	62	5	143	28	102	89	126817	24634	94333	104	98
1369	145943	69	653	45	5	30	26	99	99	38692	9648	68580	38	38
1901	259191	57	652	72	5	110	35	127	127	168553	39932	162519	176	170
2137	252805	52	866	67	5	81	30	111	77	101382	13326	127097	102	97
4213	395382	133	1755	146	5	127	46	172	161	165933	42570	147581	175	173
1915	168994	57	720	122	5	147	50	192	179	150047	21166	75767	132	123
1911	216853	78	643	84	6	44	50	186	149	86652	10971	114198	165	159
1813	182286	64	486	97	6	68	49	181	148	64520	16747	54518	73	71
1995	203663	133	605	54	6	93	35	137	128	143592	24634	113963	158	156
893	73566	32	385	39	6	22	23	88	67	22618	3007	23824	26	25
1926	217036	108	575	50	6	80	41	154	125	249771	18487	181712	68	63
1569	179317	60	458	70	6	76	38	139	137	123534	41517	100922	108	108
2065	217320	59	740	75	7	83	34	132	117	144408	34416	115466	125	124
2155	239766	118	672	61	7	117	55	210	184	81625	20648	94853	98	93
2197	242622	86	718	58	7	78	45	175	171	88977	20065	111563	115	113
2107	243164	67	702	79	7	76	48	183	163	135356	27111	91502	121	118
2243	325587	69	811	58	8	114	36	140	139	167949	35902	164263	197	194
2347	266736	87	689	64	8	135	51	199	193	136588	21983	134163	200	186
2590	380576	322	840	81	8	88	44	163	157	149695	20867	191179	165	158
2611	264889	89	931	59	8	92	42	163	142	104767	26584	124527	130	126
2751	291970	137	868	83	8	121	51	198	176	140358	40312	117495	125	125
1717	189897	54	601	65	9	103	52	208	208	118906	24662	102509	128	123
2752	239986	146	838	89	9	85	44	167	148	89626	27269	134904	138	133
4081	384177	122	1606	131	9	95	45	169	144	124089	16378	130332	125	123
2	1	0	0	0	9	0	0	0	0	0	0	0	0	0
1877	185384	73	718	60	10	53	37	122	113	68788	12347	72591	70	70
2156	251622	85	691	50	10	80	44	162	87	69446	11034	113713	100	100
3281	329639	81	1499	44	10	123	41	151	128	92622	26041	134097	123	123
1781	186409	69	699	75	10	76	41	148	111	79011	16637	80238	104	103
4092	358127	120	1380	120	10	149	50	193	177	151911	22938	191889	174	172
4032	385286	143	1324	70	10	159	51	198	181	151715	26719	197765	171	171
1840	190312	65	600	86	10	48	31	114	90	65594	8589	92795	96	94
2981	272297	117	1197	72	11	102	43	169	97	59900	12526	96252	83	80
3000	325961	102	960	60	11	145	49	190	149	140015	38086	145758	175	170
2265	261528	84	864	86	12	89	40	155	141	95893	23923	81180	103	100
3165	304468	117	1145	146	12	139	28	101	73	176225	53405	214738	279	267
4037	393266	154	1326	130	12	152	65	251	248	165986	43146	189944	208	193
2539	239337	70	865	64	13	99	45	173	154	81351	17828	102204	111	109
3726	399801	109	1421	74	13	165	31	118	110	184923	50099	226168	307	297
1695	175582	91	503	112	13	55	48	181	146	116066	3938	24469	42	41
1759	143860	54	546	106	15	63	42	160	149	154771	1037	18284	16	16
1845	222373	70	673	59	16	70	42	162	106	105079	16346	99776	121	121

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'AstonUniversity' @ aston.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157830&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157830&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157830&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 time6 seconds
R Server'AstonUniversity' @ aston.wessa.net






Multiple Linear Regression - Estimated Regression Equation
f[t] = + 0.840733354435314 + 0.00202525578178962a[t] + 5.29020327172241e-06b[t] -0.00147704415270482c[t] -0.00149678889553491d[t] -0.0132757523340236e[t] -0.0415140024839815g[t] -0.0634972563783954h[t] + 0.0171901371840117i[t] + 0.0230724066056562j[t] + 3.36599150208752e-05k[t] -8.23680845564617e-05l[t] -3.51820878651381e-05m[t] -0.0652817618576815n[t] + 0.0932765969301084o[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
f[t] =  +  0.840733354435314 +  0.00202525578178962a[t] +  5.29020327172241e-06b[t] -0.00147704415270482c[t] -0.00149678889553491d[t] -0.0132757523340236e[t] -0.0415140024839815g[t] -0.0634972563783954h[t] +  0.0171901371840117i[t] +  0.0230724066056562j[t] +  3.36599150208752e-05k[t] -8.23680845564617e-05l[t] -3.51820878651381e-05m[t] -0.0652817618576815n[t] +  0.0932765969301084o[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157830&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]f[t] =  +  0.840733354435314 +  0.00202525578178962a[t] +  5.29020327172241e-06b[t] -0.00147704415270482c[t] -0.00149678889553491d[t] -0.0132757523340236e[t] -0.0415140024839815g[t] -0.0634972563783954h[t] +  0.0171901371840117i[t] +  0.0230724066056562j[t] +  3.36599150208752e-05k[t] -8.23680845564617e-05l[t] -3.51820878651381e-05m[t] -0.0652817618576815n[t] +  0.0932765969301084o[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157830&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157830&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
f[t] = + 0.840733354435314 + 0.00202525578178962a[t] + 5.29020327172241e-06b[t] -0.00147704415270482c[t] -0.00149678889553491d[t] -0.0132757523340236e[t] -0.0415140024839815g[t] -0.0634972563783954h[t] + 0.0171901371840117i[t] + 0.0230724066056562j[t] + 3.36599150208752e-05k[t] -8.23680845564617e-05l[t] -3.51820878651381e-05m[t] -0.0652817618576815n[t] + 0.0932765969301084o[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.8407333544353140.7092331.18540.2377420.118871
a0.002025255781789620.0014261.42010.1576590.078829
b5.29020327172241e-068e-060.62830.5307950.265397
c-0.001477044152704820.008298-0.1780.8589570.429478
d-0.001496788895534910.002947-0.50790.6122450.306123
e-0.01327575233402360.004953-2.68030.0081840.004092
g-0.04151400248398150.013057-3.17960.0017940.000897
h-0.06349725637839540.107002-0.59340.5537990.2769
i0.01719013718401170.0323790.53090.596280.29814
j0.02307240660565620.0155641.48240.1403510.070176
k3.36599150208752e-051e-053.50180.000610.000305
l-8.23680845564617e-054.4e-05-1.87090.0633220.031661
m-3.51820878651381e-051.2e-05-2.88720.0044650.002233
n-0.06528176185768150.069988-0.93280.3524540.176227
o0.09327659693010840.0725771.28520.2007160.100358

\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) & 0.840733354435314 & 0.709233 & 1.1854 & 0.237742 & 0.118871 \tabularnewline
a & 0.00202525578178962 & 0.001426 & 1.4201 & 0.157659 & 0.078829 \tabularnewline
b & 5.29020327172241e-06 & 8e-06 & 0.6283 & 0.530795 & 0.265397 \tabularnewline
c & -0.00147704415270482 & 0.008298 & -0.178 & 0.858957 & 0.429478 \tabularnewline
d & -0.00149678889553491 & 0.002947 & -0.5079 & 0.612245 & 0.306123 \tabularnewline
e & -0.0132757523340236 & 0.004953 & -2.6803 & 0.008184 & 0.004092 \tabularnewline
g & -0.0415140024839815 & 0.013057 & -3.1796 & 0.001794 & 0.000897 \tabularnewline
h & -0.0634972563783954 & 0.107002 & -0.5934 & 0.553799 & 0.2769 \tabularnewline
i & 0.0171901371840117 & 0.032379 & 0.5309 & 0.59628 & 0.29814 \tabularnewline
j & 0.0230724066056562 & 0.015564 & 1.4824 & 0.140351 & 0.070176 \tabularnewline
k & 3.36599150208752e-05 & 1e-05 & 3.5018 & 0.00061 & 0.000305 \tabularnewline
l & -8.23680845564617e-05 & 4.4e-05 & -1.8709 & 0.063322 & 0.031661 \tabularnewline
m & -3.51820878651381e-05 & 1.2e-05 & -2.8872 & 0.004465 & 0.002233 \tabularnewline
n & -0.0652817618576815 & 0.069988 & -0.9328 & 0.352454 & 0.176227 \tabularnewline
o & 0.0932765969301084 & 0.072577 & 1.2852 & 0.200716 & 0.100358 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157830&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]0.840733354435314[/C][C]0.709233[/C][C]1.1854[/C][C]0.237742[/C][C]0.118871[/C][/ROW]
[ROW][C]a[/C][C]0.00202525578178962[/C][C]0.001426[/C][C]1.4201[/C][C]0.157659[/C][C]0.078829[/C][/ROW]
[ROW][C]b[/C][C]5.29020327172241e-06[/C][C]8e-06[/C][C]0.6283[/C][C]0.530795[/C][C]0.265397[/C][/ROW]
[ROW][C]c[/C][C]-0.00147704415270482[/C][C]0.008298[/C][C]-0.178[/C][C]0.858957[/C][C]0.429478[/C][/ROW]
[ROW][C]d[/C][C]-0.00149678889553491[/C][C]0.002947[/C][C]-0.5079[/C][C]0.612245[/C][C]0.306123[/C][/ROW]
[ROW][C]e[/C][C]-0.0132757523340236[/C][C]0.004953[/C][C]-2.6803[/C][C]0.008184[/C][C]0.004092[/C][/ROW]
[ROW][C]g[/C][C]-0.0415140024839815[/C][C]0.013057[/C][C]-3.1796[/C][C]0.001794[/C][C]0.000897[/C][/ROW]
[ROW][C]h[/C][C]-0.0634972563783954[/C][C]0.107002[/C][C]-0.5934[/C][C]0.553799[/C][C]0.2769[/C][/ROW]
[ROW][C]i[/C][C]0.0171901371840117[/C][C]0.032379[/C][C]0.5309[/C][C]0.59628[/C][C]0.29814[/C][/ROW]
[ROW][C]j[/C][C]0.0230724066056562[/C][C]0.015564[/C][C]1.4824[/C][C]0.140351[/C][C]0.070176[/C][/ROW]
[ROW][C]k[/C][C]3.36599150208752e-05[/C][C]1e-05[/C][C]3.5018[/C][C]0.00061[/C][C]0.000305[/C][/ROW]
[ROW][C]l[/C][C]-8.23680845564617e-05[/C][C]4.4e-05[/C][C]-1.8709[/C][C]0.063322[/C][C]0.031661[/C][/ROW]
[ROW][C]m[/C][C]-3.51820878651381e-05[/C][C]1.2e-05[/C][C]-2.8872[/C][C]0.004465[/C][C]0.002233[/C][/ROW]
[ROW][C]n[/C][C]-0.0652817618576815[/C][C]0.069988[/C][C]-0.9328[/C][C]0.352454[/C][C]0.176227[/C][/ROW]
[ROW][C]o[/C][C]0.0932765969301084[/C][C]0.072577[/C][C]1.2852[/C][C]0.200716[/C][C]0.100358[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157830&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157830&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)0.8407333544353140.7092331.18540.2377420.118871
a0.002025255781789620.0014261.42010.1576590.078829
b5.29020327172241e-068e-060.62830.5307950.265397
c-0.001477044152704820.008298-0.1780.8589570.429478
d-0.001496788895534910.002947-0.50790.6122450.306123
e-0.01327575233402360.004953-2.68030.0081840.004092
g-0.04151400248398150.013057-3.17960.0017940.000897
h-0.06349725637839540.107002-0.59340.5537990.2769
i0.01719013718401170.0323790.53090.596280.29814
j0.02307240660565620.0155641.48240.1403510.070176
k3.36599150208752e-051e-053.50180.000610.000305
l-8.23680845564617e-054.4e-05-1.87090.0633220.031661
m-3.51820878651381e-051.2e-05-2.88720.0044650.002233
n-0.06528176185768150.069988-0.93280.3524540.176227
o0.09327659693010840.0725771.28520.2007160.100358






Multiple Linear Regression - Regression Statistics
Multiple R0.533240375387996
R-squared0.284345297943931
Adjusted R-squared0.21710257426081
F-TEST (value)4.22864039957542
F-TEST (DF numerator)14
F-TEST (DF denominator)149
p-value3.05977850512118e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.34247401335983
Sum Squared Residuals1664.64774696788

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.533240375387996 \tabularnewline
R-squared & 0.284345297943931 \tabularnewline
Adjusted R-squared & 0.21710257426081 \tabularnewline
F-TEST (value) & 4.22864039957542 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 149 \tabularnewline
p-value & 3.05977850512118e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.34247401335983 \tabularnewline
Sum Squared Residuals & 1664.64774696788 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157830&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.533240375387996[/C][/ROW]
[ROW][C]R-squared[/C][C]0.284345297943931[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.21710257426081[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.22864039957542[/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]3.05977850512118e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.34247401335983[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1664.64774696788[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157830&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157830&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 R0.533240375387996
R-squared0.284345297943931
Adjusted R-squared0.21710257426081
F-TEST (value)4.22864039957542
F-TEST (DF numerator)14
F-TEST (DF denominator)149
p-value3.05977850512118e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.34247401335983
Sum Squared Residuals1664.64774696788






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
103.21125556620894-3.21125556620894
202.01975517239198-2.01975517239198
301.24617342441244-1.24617342441244
402.43190930869502-2.43190930869502
504.48602196161646-4.48602196161646
602.98195156141504-2.98195156141504
703.80592866340661-3.80592866340661
800.869912347839022-0.869912347839022
902.74594635095854-2.74594635095854
100-0.03731635374554760.0373163537455476
1105.00543970707883-5.00543970707883
1203.33994567545045-3.33994567545045
1306.68860205166436-6.68860205166436
1402.12367779194032-2.12367779194032
1500.772528500233317-0.772528500233317
1600.241542487598328-0.241542487598328
170-0.1206965624807080.120696562480708
1803.29133692881071-3.29133692881071
1904.57615592094598-4.57615592094598
2001.34081880935418-1.34081880935418
2101.65929883937473-1.65929883937473
220-1.347630040080641.34763004008064
2301.08387993329522-1.08387993329522
2402.41938660012886-2.41938660012886
2501.45430324739705-1.45430324739705
2603.03370200195603-3.03370200195603
2700.544132334299346-0.544132334299346
2801.4607324214162-1.4607324214162
2903.53777768124159-3.53777768124159
3006.74091909270319-6.74091909270319
3103.06826514692178-3.06826514692178
3200.963017804096269-0.963017804096269
3301.12838754648976-1.12838754648976
3401.03967409599353-1.03967409599353
3501.18105825108493-1.18105825108493
3601.54013271852295-1.54013271852295
3700.911771977880967-0.911771977880967
3803.67254165891961-3.67254165891961
3905.46327157209943-5.46327157209943
4000.416491994151872-0.416491994151872
4102.24221923689669-2.24221923689669
4203.44227111965645-3.44227111965645
4304.87691070881004-4.87691070881004
4400.922424903469715-0.922424903469715
4500.849901029112187-0.849901029112187
4600.856388354872856-0.856388354872856
4700.840733354435315-0.840733354435315
4800.840733354435315-0.840733354435315
4900.844000112215814-0.844000112215814
5000.804046827145677-0.804046827145677
5100.922664052586148-0.922664052586148
5200.893535972611891-0.893535972611891
5304.07814520851776-4.07814520851776
5400.901637890772103-0.901637890772103
5510.3991633173716750.600836682628325
5615.62824153585938-4.62824153585938
5712.6581673572316-1.6581673572316
5811.76057108865149-0.760571088651487
5911.08139648931166-0.0813964893116568
6010.801751999141960.19824800085804
6113.29780777298674-2.29780777298674
6212.16209525746818-1.16209525746818
6314.06556695168147-3.06556695168147
6415.24624052298338-4.24624052298338
6513.1743513383053-2.1743513383053
6613.55002194794125-2.55002194794125
6711.35867281025756-0.35867281025756
6812.15392868957211-1.15392868957211
6915.68956610824233-4.68956610824233
7012.8942785089958-1.8942785089958
7110.8407333544353150.159266645564685
7222.36074517503053-0.360745175030526
7322.77807447034701-0.778074470347012
7423.46918512251995-1.46918512251995
7520.5472465070815251.45275349291847
7621.297155393528610.702844606471389
7723.22773299982885-1.22773299982885
7825.22196539889488-3.22196539889488
7922.8989833955065-0.898983395506498
8023.35588986051332-1.35588986051332
8122.42183622498118-0.421836224981178
8224.89264124393381-2.89264124393381
8321.355040241943260.644959758056735
8424.58659736378772-2.58659736378772
8524.25461716572471-2.25461716572471
8624.17490787032215-2.17490787032216
8732.728501451951590.271498548048406
8834.54049830974329-1.54049830974329
8932.865058930338380.134941069661615
9032.49937809809940.500621901900603
9133.73152196594513-0.731521965945127
9231.727444313578911.27255568642109
9333.83460320780272-0.834603207802719
9434.25983401831978-1.25983401831978
9532.425715174630380.574284825369622
9636.51343206061875-3.51343206061875
9737.25235512934462-4.25235512934462
9835.33841035181945-2.33841035181945
9932.218672616310440.781327383689562
10044.28628180972419-0.286281809724188
10145.5851465805794-1.5851465805794
10244.32310801514944-0.323108015149438
10345.55843002113824-1.55843002113824
10443.111305845745630.888694154254374
10543.343896547316840.656103452683162
10643.548571735753630.451428264246368
10745.38456418775827-1.38456418775827
10845.36878571782431-1.36878571782431
10943.871568108736640.128431891263357
11040.2634650424676553.73653495753234
11144.06198142889025-0.0619814288902534
11241.420939734195662.57906026580434
11342.81479242464141.1852075753586
11445.60884173212325-1.60884173212325
11546.64158372646256-2.64158372646256
11644.26822731618109-0.268227316181093
11751.577808361015333.42219163898467
11853.219991965908151.78000803409185
11950.5180895348057444.48191046519426
12053.054260279980961.94573972001904
12155.00559573444941-0.00559573444940814
12254.831814496166450.16818550383355
12350.6863869965401584.31361300345984
12452.956989999405722.04301000059428
12553.404395399398841.59560460060116
12652.893147337069042.10685266293096
12756.78064139282288-1.78064139282288
12854.481989417028020.518010582971983
12967.35386371204088-1.35386371204088
13064.6902792567721.309720743228
13166.3961667542904-0.396166754290396
13262.892070927522693.10792907247731
13365.741192894738130.258807105261873
13463.457180963785482.54281903621452
13574.715827304126412.28417269587359
13673.976119060123773.02388093987623
13775.91028916486731.0897108351327
13877.11081113048991-0.110811130489912
13985.767191736164122.23280826383588
14086.386522231649581.61347776835042
14185.828553491056122.17144650894388
14285.035734651924432.96426534807557
14385.325824852783312.67417514721669
14495.758725784801953.24127421519805
14594.416620205262524.58337979473748
14697.796613696643191.20338630335681
14790.8447891562021688.15521084379783
148104.50266678452245.4973332154776
149103.619320733627386.38067926637262
150103.819617237153026.18038276284698
151103.921119837506466.07888016249354
152106.385248320616043.61475167938396
153106.398456084124083.60154391587592
154104.249960533799615.75003946620039
155113.218430507190477.78156949280953
156114.710262914821396.28973708517861
157124.940320957879897.05967904212011
158121.5777478423201410.4222521576799
159126.532696098226385.46730390177362
160135.155950023703197.84404997629681
161134.782219753314598.21778024668541
162137.781116181582725.21888381841728
163158.69472472860336.3052752713967
164165.5849481663765810.4150518336234

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 3.21125556620894 & -3.21125556620894 \tabularnewline
2 & 0 & 2.01975517239198 & -2.01975517239198 \tabularnewline
3 & 0 & 1.24617342441244 & -1.24617342441244 \tabularnewline
4 & 0 & 2.43190930869502 & -2.43190930869502 \tabularnewline
5 & 0 & 4.48602196161646 & -4.48602196161646 \tabularnewline
6 & 0 & 2.98195156141504 & -2.98195156141504 \tabularnewline
7 & 0 & 3.80592866340661 & -3.80592866340661 \tabularnewline
8 & 0 & 0.869912347839022 & -0.869912347839022 \tabularnewline
9 & 0 & 2.74594635095854 & -2.74594635095854 \tabularnewline
10 & 0 & -0.0373163537455476 & 0.0373163537455476 \tabularnewline
11 & 0 & 5.00543970707883 & -5.00543970707883 \tabularnewline
12 & 0 & 3.33994567545045 & -3.33994567545045 \tabularnewline
13 & 0 & 6.68860205166436 & -6.68860205166436 \tabularnewline
14 & 0 & 2.12367779194032 & -2.12367779194032 \tabularnewline
15 & 0 & 0.772528500233317 & -0.772528500233317 \tabularnewline
16 & 0 & 0.241542487598328 & -0.241542487598328 \tabularnewline
17 & 0 & -0.120696562480708 & 0.120696562480708 \tabularnewline
18 & 0 & 3.29133692881071 & -3.29133692881071 \tabularnewline
19 & 0 & 4.57615592094598 & -4.57615592094598 \tabularnewline
20 & 0 & 1.34081880935418 & -1.34081880935418 \tabularnewline
21 & 0 & 1.65929883937473 & -1.65929883937473 \tabularnewline
22 & 0 & -1.34763004008064 & 1.34763004008064 \tabularnewline
23 & 0 & 1.08387993329522 & -1.08387993329522 \tabularnewline
24 & 0 & 2.41938660012886 & -2.41938660012886 \tabularnewline
25 & 0 & 1.45430324739705 & -1.45430324739705 \tabularnewline
26 & 0 & 3.03370200195603 & -3.03370200195603 \tabularnewline
27 & 0 & 0.544132334299346 & -0.544132334299346 \tabularnewline
28 & 0 & 1.4607324214162 & -1.4607324214162 \tabularnewline
29 & 0 & 3.53777768124159 & -3.53777768124159 \tabularnewline
30 & 0 & 6.74091909270319 & -6.74091909270319 \tabularnewline
31 & 0 & 3.06826514692178 & -3.06826514692178 \tabularnewline
32 & 0 & 0.963017804096269 & -0.963017804096269 \tabularnewline
33 & 0 & 1.12838754648976 & -1.12838754648976 \tabularnewline
34 & 0 & 1.03967409599353 & -1.03967409599353 \tabularnewline
35 & 0 & 1.18105825108493 & -1.18105825108493 \tabularnewline
36 & 0 & 1.54013271852295 & -1.54013271852295 \tabularnewline
37 & 0 & 0.911771977880967 & -0.911771977880967 \tabularnewline
38 & 0 & 3.67254165891961 & -3.67254165891961 \tabularnewline
39 & 0 & 5.46327157209943 & -5.46327157209943 \tabularnewline
40 & 0 & 0.416491994151872 & -0.416491994151872 \tabularnewline
41 & 0 & 2.24221923689669 & -2.24221923689669 \tabularnewline
42 & 0 & 3.44227111965645 & -3.44227111965645 \tabularnewline
43 & 0 & 4.87691070881004 & -4.87691070881004 \tabularnewline
44 & 0 & 0.922424903469715 & -0.922424903469715 \tabularnewline
45 & 0 & 0.849901029112187 & -0.849901029112187 \tabularnewline
46 & 0 & 0.856388354872856 & -0.856388354872856 \tabularnewline
47 & 0 & 0.840733354435315 & -0.840733354435315 \tabularnewline
48 & 0 & 0.840733354435315 & -0.840733354435315 \tabularnewline
49 & 0 & 0.844000112215814 & -0.844000112215814 \tabularnewline
50 & 0 & 0.804046827145677 & -0.804046827145677 \tabularnewline
51 & 0 & 0.922664052586148 & -0.922664052586148 \tabularnewline
52 & 0 & 0.893535972611891 & -0.893535972611891 \tabularnewline
53 & 0 & 4.07814520851776 & -4.07814520851776 \tabularnewline
54 & 0 & 0.901637890772103 & -0.901637890772103 \tabularnewline
55 & 1 & 0.399163317371675 & 0.600836682628325 \tabularnewline
56 & 1 & 5.62824153585938 & -4.62824153585938 \tabularnewline
57 & 1 & 2.6581673572316 & -1.6581673572316 \tabularnewline
58 & 1 & 1.76057108865149 & -0.760571088651487 \tabularnewline
59 & 1 & 1.08139648931166 & -0.0813964893116568 \tabularnewline
60 & 1 & 0.80175199914196 & 0.19824800085804 \tabularnewline
61 & 1 & 3.29780777298674 & -2.29780777298674 \tabularnewline
62 & 1 & 2.16209525746818 & -1.16209525746818 \tabularnewline
63 & 1 & 4.06556695168147 & -3.06556695168147 \tabularnewline
64 & 1 & 5.24624052298338 & -4.24624052298338 \tabularnewline
65 & 1 & 3.1743513383053 & -2.1743513383053 \tabularnewline
66 & 1 & 3.55002194794125 & -2.55002194794125 \tabularnewline
67 & 1 & 1.35867281025756 & -0.35867281025756 \tabularnewline
68 & 1 & 2.15392868957211 & -1.15392868957211 \tabularnewline
69 & 1 & 5.68956610824233 & -4.68956610824233 \tabularnewline
70 & 1 & 2.8942785089958 & -1.8942785089958 \tabularnewline
71 & 1 & 0.840733354435315 & 0.159266645564685 \tabularnewline
72 & 2 & 2.36074517503053 & -0.360745175030526 \tabularnewline
73 & 2 & 2.77807447034701 & -0.778074470347012 \tabularnewline
74 & 2 & 3.46918512251995 & -1.46918512251995 \tabularnewline
75 & 2 & 0.547246507081525 & 1.45275349291847 \tabularnewline
76 & 2 & 1.29715539352861 & 0.702844606471389 \tabularnewline
77 & 2 & 3.22773299982885 & -1.22773299982885 \tabularnewline
78 & 2 & 5.22196539889488 & -3.22196539889488 \tabularnewline
79 & 2 & 2.8989833955065 & -0.898983395506498 \tabularnewline
80 & 2 & 3.35588986051332 & -1.35588986051332 \tabularnewline
81 & 2 & 2.42183622498118 & -0.421836224981178 \tabularnewline
82 & 2 & 4.89264124393381 & -2.89264124393381 \tabularnewline
83 & 2 & 1.35504024194326 & 0.644959758056735 \tabularnewline
84 & 2 & 4.58659736378772 & -2.58659736378772 \tabularnewline
85 & 2 & 4.25461716572471 & -2.25461716572471 \tabularnewline
86 & 2 & 4.17490787032215 & -2.17490787032216 \tabularnewline
87 & 3 & 2.72850145195159 & 0.271498548048406 \tabularnewline
88 & 3 & 4.54049830974329 & -1.54049830974329 \tabularnewline
89 & 3 & 2.86505893033838 & 0.134941069661615 \tabularnewline
90 & 3 & 2.4993780980994 & 0.500621901900603 \tabularnewline
91 & 3 & 3.73152196594513 & -0.731521965945127 \tabularnewline
92 & 3 & 1.72744431357891 & 1.27255568642109 \tabularnewline
93 & 3 & 3.83460320780272 & -0.834603207802719 \tabularnewline
94 & 3 & 4.25983401831978 & -1.25983401831978 \tabularnewline
95 & 3 & 2.42571517463038 & 0.574284825369622 \tabularnewline
96 & 3 & 6.51343206061875 & -3.51343206061875 \tabularnewline
97 & 3 & 7.25235512934462 & -4.25235512934462 \tabularnewline
98 & 3 & 5.33841035181945 & -2.33841035181945 \tabularnewline
99 & 3 & 2.21867261631044 & 0.781327383689562 \tabularnewline
100 & 4 & 4.28628180972419 & -0.286281809724188 \tabularnewline
101 & 4 & 5.5851465805794 & -1.5851465805794 \tabularnewline
102 & 4 & 4.32310801514944 & -0.323108015149438 \tabularnewline
103 & 4 & 5.55843002113824 & -1.55843002113824 \tabularnewline
104 & 4 & 3.11130584574563 & 0.888694154254374 \tabularnewline
105 & 4 & 3.34389654731684 & 0.656103452683162 \tabularnewline
106 & 4 & 3.54857173575363 & 0.451428264246368 \tabularnewline
107 & 4 & 5.38456418775827 & -1.38456418775827 \tabularnewline
108 & 4 & 5.36878571782431 & -1.36878571782431 \tabularnewline
109 & 4 & 3.87156810873664 & 0.128431891263357 \tabularnewline
110 & 4 & 0.263465042467655 & 3.73653495753234 \tabularnewline
111 & 4 & 4.06198142889025 & -0.0619814288902534 \tabularnewline
112 & 4 & 1.42093973419566 & 2.57906026580434 \tabularnewline
113 & 4 & 2.8147924246414 & 1.1852075753586 \tabularnewline
114 & 4 & 5.60884173212325 & -1.60884173212325 \tabularnewline
115 & 4 & 6.64158372646256 & -2.64158372646256 \tabularnewline
116 & 4 & 4.26822731618109 & -0.268227316181093 \tabularnewline
117 & 5 & 1.57780836101533 & 3.42219163898467 \tabularnewline
118 & 5 & 3.21999196590815 & 1.78000803409185 \tabularnewline
119 & 5 & 0.518089534805744 & 4.48191046519426 \tabularnewline
120 & 5 & 3.05426027998096 & 1.94573972001904 \tabularnewline
121 & 5 & 5.00559573444941 & -0.00559573444940814 \tabularnewline
122 & 5 & 4.83181449616645 & 0.16818550383355 \tabularnewline
123 & 5 & 0.686386996540158 & 4.31361300345984 \tabularnewline
124 & 5 & 2.95698999940572 & 2.04301000059428 \tabularnewline
125 & 5 & 3.40439539939884 & 1.59560460060116 \tabularnewline
126 & 5 & 2.89314733706904 & 2.10685266293096 \tabularnewline
127 & 5 & 6.78064139282288 & -1.78064139282288 \tabularnewline
128 & 5 & 4.48198941702802 & 0.518010582971983 \tabularnewline
129 & 6 & 7.35386371204088 & -1.35386371204088 \tabularnewline
130 & 6 & 4.690279256772 & 1.309720743228 \tabularnewline
131 & 6 & 6.3961667542904 & -0.396166754290396 \tabularnewline
132 & 6 & 2.89207092752269 & 3.10792907247731 \tabularnewline
133 & 6 & 5.74119289473813 & 0.258807105261873 \tabularnewline
134 & 6 & 3.45718096378548 & 2.54281903621452 \tabularnewline
135 & 7 & 4.71582730412641 & 2.28417269587359 \tabularnewline
136 & 7 & 3.97611906012377 & 3.02388093987623 \tabularnewline
137 & 7 & 5.9102891648673 & 1.0897108351327 \tabularnewline
138 & 7 & 7.11081113048991 & -0.110811130489912 \tabularnewline
139 & 8 & 5.76719173616412 & 2.23280826383588 \tabularnewline
140 & 8 & 6.38652223164958 & 1.61347776835042 \tabularnewline
141 & 8 & 5.82855349105612 & 2.17144650894388 \tabularnewline
142 & 8 & 5.03573465192443 & 2.96426534807557 \tabularnewline
143 & 8 & 5.32582485278331 & 2.67417514721669 \tabularnewline
144 & 9 & 5.75872578480195 & 3.24127421519805 \tabularnewline
145 & 9 & 4.41662020526252 & 4.58337979473748 \tabularnewline
146 & 9 & 7.79661369664319 & 1.20338630335681 \tabularnewline
147 & 9 & 0.844789156202168 & 8.15521084379783 \tabularnewline
148 & 10 & 4.5026667845224 & 5.4973332154776 \tabularnewline
149 & 10 & 3.61932073362738 & 6.38067926637262 \tabularnewline
150 & 10 & 3.81961723715302 & 6.18038276284698 \tabularnewline
151 & 10 & 3.92111983750646 & 6.07888016249354 \tabularnewline
152 & 10 & 6.38524832061604 & 3.61475167938396 \tabularnewline
153 & 10 & 6.39845608412408 & 3.60154391587592 \tabularnewline
154 & 10 & 4.24996053379961 & 5.75003946620039 \tabularnewline
155 & 11 & 3.21843050719047 & 7.78156949280953 \tabularnewline
156 & 11 & 4.71026291482139 & 6.28973708517861 \tabularnewline
157 & 12 & 4.94032095787989 & 7.05967904212011 \tabularnewline
158 & 12 & 1.57774784232014 & 10.4222521576799 \tabularnewline
159 & 12 & 6.53269609822638 & 5.46730390177362 \tabularnewline
160 & 13 & 5.15595002370319 & 7.84404997629681 \tabularnewline
161 & 13 & 4.78221975331459 & 8.21778024668541 \tabularnewline
162 & 13 & 7.78111618158272 & 5.21888381841728 \tabularnewline
163 & 15 & 8.6947247286033 & 6.3052752713967 \tabularnewline
164 & 16 & 5.58494816637658 & 10.4150518336234 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157830&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]0[/C][C]3.21125556620894[/C][C]-3.21125556620894[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]2.01975517239198[/C][C]-2.01975517239198[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]1.24617342441244[/C][C]-1.24617342441244[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]2.43190930869502[/C][C]-2.43190930869502[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]4.48602196161646[/C][C]-4.48602196161646[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]2.98195156141504[/C][C]-2.98195156141504[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]3.80592866340661[/C][C]-3.80592866340661[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.869912347839022[/C][C]-0.869912347839022[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]2.74594635095854[/C][C]-2.74594635095854[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]-0.0373163537455476[/C][C]0.0373163537455476[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]5.00543970707883[/C][C]-5.00543970707883[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]3.33994567545045[/C][C]-3.33994567545045[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]6.68860205166436[/C][C]-6.68860205166436[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]2.12367779194032[/C][C]-2.12367779194032[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.772528500233317[/C][C]-0.772528500233317[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.241542487598328[/C][C]-0.241542487598328[/C][/ROW]
[ROW][C]17[/C][C]0[/C][C]-0.120696562480708[/C][C]0.120696562480708[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]3.29133692881071[/C][C]-3.29133692881071[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]4.57615592094598[/C][C]-4.57615592094598[/C][/ROW]
[ROW][C]20[/C][C]0[/C][C]1.34081880935418[/C][C]-1.34081880935418[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]1.65929883937473[/C][C]-1.65929883937473[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]-1.34763004008064[/C][C]1.34763004008064[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]1.08387993329522[/C][C]-1.08387993329522[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]2.41938660012886[/C][C]-2.41938660012886[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]1.45430324739705[/C][C]-1.45430324739705[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]3.03370200195603[/C][C]-3.03370200195603[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]0.544132334299346[/C][C]-0.544132334299346[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]1.4607324214162[/C][C]-1.4607324214162[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]3.53777768124159[/C][C]-3.53777768124159[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]6.74091909270319[/C][C]-6.74091909270319[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]3.06826514692178[/C][C]-3.06826514692178[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.963017804096269[/C][C]-0.963017804096269[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]1.12838754648976[/C][C]-1.12838754648976[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]1.03967409599353[/C][C]-1.03967409599353[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]1.18105825108493[/C][C]-1.18105825108493[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]1.54013271852295[/C][C]-1.54013271852295[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.911771977880967[/C][C]-0.911771977880967[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]3.67254165891961[/C][C]-3.67254165891961[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]5.46327157209943[/C][C]-5.46327157209943[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.416491994151872[/C][C]-0.416491994151872[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]2.24221923689669[/C][C]-2.24221923689669[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]3.44227111965645[/C][C]-3.44227111965645[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]4.87691070881004[/C][C]-4.87691070881004[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.922424903469715[/C][C]-0.922424903469715[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.849901029112187[/C][C]-0.849901029112187[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0.856388354872856[/C][C]-0.856388354872856[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.840733354435315[/C][C]-0.840733354435315[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0.840733354435315[/C][C]-0.840733354435315[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.844000112215814[/C][C]-0.844000112215814[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.804046827145677[/C][C]-0.804046827145677[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.922664052586148[/C][C]-0.922664052586148[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0.893535972611891[/C][C]-0.893535972611891[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]4.07814520851776[/C][C]-4.07814520851776[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.901637890772103[/C][C]-0.901637890772103[/C][/ROW]
[ROW][C]55[/C][C]1[/C][C]0.399163317371675[/C][C]0.600836682628325[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]5.62824153585938[/C][C]-4.62824153585938[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]2.6581673572316[/C][C]-1.6581673572316[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]1.76057108865149[/C][C]-0.760571088651487[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]1.08139648931166[/C][C]-0.0813964893116568[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.80175199914196[/C][C]0.19824800085804[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]3.29780777298674[/C][C]-2.29780777298674[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]2.16209525746818[/C][C]-1.16209525746818[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]4.06556695168147[/C][C]-3.06556695168147[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]5.24624052298338[/C][C]-4.24624052298338[/C][/ROW]
[ROW][C]65[/C][C]1[/C][C]3.1743513383053[/C][C]-2.1743513383053[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]3.55002194794125[/C][C]-2.55002194794125[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]1.35867281025756[/C][C]-0.35867281025756[/C][/ROW]
[ROW][C]68[/C][C]1[/C][C]2.15392868957211[/C][C]-1.15392868957211[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]5.68956610824233[/C][C]-4.68956610824233[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]2.8942785089958[/C][C]-1.8942785089958[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]0.840733354435315[/C][C]0.159266645564685[/C][/ROW]
[ROW][C]72[/C][C]2[/C][C]2.36074517503053[/C][C]-0.360745175030526[/C][/ROW]
[ROW][C]73[/C][C]2[/C][C]2.77807447034701[/C][C]-0.778074470347012[/C][/ROW]
[ROW][C]74[/C][C]2[/C][C]3.46918512251995[/C][C]-1.46918512251995[/C][/ROW]
[ROW][C]75[/C][C]2[/C][C]0.547246507081525[/C][C]1.45275349291847[/C][/ROW]
[ROW][C]76[/C][C]2[/C][C]1.29715539352861[/C][C]0.702844606471389[/C][/ROW]
[ROW][C]77[/C][C]2[/C][C]3.22773299982885[/C][C]-1.22773299982885[/C][/ROW]
[ROW][C]78[/C][C]2[/C][C]5.22196539889488[/C][C]-3.22196539889488[/C][/ROW]
[ROW][C]79[/C][C]2[/C][C]2.8989833955065[/C][C]-0.898983395506498[/C][/ROW]
[ROW][C]80[/C][C]2[/C][C]3.35588986051332[/C][C]-1.35588986051332[/C][/ROW]
[ROW][C]81[/C][C]2[/C][C]2.42183622498118[/C][C]-0.421836224981178[/C][/ROW]
[ROW][C]82[/C][C]2[/C][C]4.89264124393381[/C][C]-2.89264124393381[/C][/ROW]
[ROW][C]83[/C][C]2[/C][C]1.35504024194326[/C][C]0.644959758056735[/C][/ROW]
[ROW][C]84[/C][C]2[/C][C]4.58659736378772[/C][C]-2.58659736378772[/C][/ROW]
[ROW][C]85[/C][C]2[/C][C]4.25461716572471[/C][C]-2.25461716572471[/C][/ROW]
[ROW][C]86[/C][C]2[/C][C]4.17490787032215[/C][C]-2.17490787032216[/C][/ROW]
[ROW][C]87[/C][C]3[/C][C]2.72850145195159[/C][C]0.271498548048406[/C][/ROW]
[ROW][C]88[/C][C]3[/C][C]4.54049830974329[/C][C]-1.54049830974329[/C][/ROW]
[ROW][C]89[/C][C]3[/C][C]2.86505893033838[/C][C]0.134941069661615[/C][/ROW]
[ROW][C]90[/C][C]3[/C][C]2.4993780980994[/C][C]0.500621901900603[/C][/ROW]
[ROW][C]91[/C][C]3[/C][C]3.73152196594513[/C][C]-0.731521965945127[/C][/ROW]
[ROW][C]92[/C][C]3[/C][C]1.72744431357891[/C][C]1.27255568642109[/C][/ROW]
[ROW][C]93[/C][C]3[/C][C]3.83460320780272[/C][C]-0.834603207802719[/C][/ROW]
[ROW][C]94[/C][C]3[/C][C]4.25983401831978[/C][C]-1.25983401831978[/C][/ROW]
[ROW][C]95[/C][C]3[/C][C]2.42571517463038[/C][C]0.574284825369622[/C][/ROW]
[ROW][C]96[/C][C]3[/C][C]6.51343206061875[/C][C]-3.51343206061875[/C][/ROW]
[ROW][C]97[/C][C]3[/C][C]7.25235512934462[/C][C]-4.25235512934462[/C][/ROW]
[ROW][C]98[/C][C]3[/C][C]5.33841035181945[/C][C]-2.33841035181945[/C][/ROW]
[ROW][C]99[/C][C]3[/C][C]2.21867261631044[/C][C]0.781327383689562[/C][/ROW]
[ROW][C]100[/C][C]4[/C][C]4.28628180972419[/C][C]-0.286281809724188[/C][/ROW]
[ROW][C]101[/C][C]4[/C][C]5.5851465805794[/C][C]-1.5851465805794[/C][/ROW]
[ROW][C]102[/C][C]4[/C][C]4.32310801514944[/C][C]-0.323108015149438[/C][/ROW]
[ROW][C]103[/C][C]4[/C][C]5.55843002113824[/C][C]-1.55843002113824[/C][/ROW]
[ROW][C]104[/C][C]4[/C][C]3.11130584574563[/C][C]0.888694154254374[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]3.34389654731684[/C][C]0.656103452683162[/C][/ROW]
[ROW][C]106[/C][C]4[/C][C]3.54857173575363[/C][C]0.451428264246368[/C][/ROW]
[ROW][C]107[/C][C]4[/C][C]5.38456418775827[/C][C]-1.38456418775827[/C][/ROW]
[ROW][C]108[/C][C]4[/C][C]5.36878571782431[/C][C]-1.36878571782431[/C][/ROW]
[ROW][C]109[/C][C]4[/C][C]3.87156810873664[/C][C]0.128431891263357[/C][/ROW]
[ROW][C]110[/C][C]4[/C][C]0.263465042467655[/C][C]3.73653495753234[/C][/ROW]
[ROW][C]111[/C][C]4[/C][C]4.06198142889025[/C][C]-0.0619814288902534[/C][/ROW]
[ROW][C]112[/C][C]4[/C][C]1.42093973419566[/C][C]2.57906026580434[/C][/ROW]
[ROW][C]113[/C][C]4[/C][C]2.8147924246414[/C][C]1.1852075753586[/C][/ROW]
[ROW][C]114[/C][C]4[/C][C]5.60884173212325[/C][C]-1.60884173212325[/C][/ROW]
[ROW][C]115[/C][C]4[/C][C]6.64158372646256[/C][C]-2.64158372646256[/C][/ROW]
[ROW][C]116[/C][C]4[/C][C]4.26822731618109[/C][C]-0.268227316181093[/C][/ROW]
[ROW][C]117[/C][C]5[/C][C]1.57780836101533[/C][C]3.42219163898467[/C][/ROW]
[ROW][C]118[/C][C]5[/C][C]3.21999196590815[/C][C]1.78000803409185[/C][/ROW]
[ROW][C]119[/C][C]5[/C][C]0.518089534805744[/C][C]4.48191046519426[/C][/ROW]
[ROW][C]120[/C][C]5[/C][C]3.05426027998096[/C][C]1.94573972001904[/C][/ROW]
[ROW][C]121[/C][C]5[/C][C]5.00559573444941[/C][C]-0.00559573444940814[/C][/ROW]
[ROW][C]122[/C][C]5[/C][C]4.83181449616645[/C][C]0.16818550383355[/C][/ROW]
[ROW][C]123[/C][C]5[/C][C]0.686386996540158[/C][C]4.31361300345984[/C][/ROW]
[ROW][C]124[/C][C]5[/C][C]2.95698999940572[/C][C]2.04301000059428[/C][/ROW]
[ROW][C]125[/C][C]5[/C][C]3.40439539939884[/C][C]1.59560460060116[/C][/ROW]
[ROW][C]126[/C][C]5[/C][C]2.89314733706904[/C][C]2.10685266293096[/C][/ROW]
[ROW][C]127[/C][C]5[/C][C]6.78064139282288[/C][C]-1.78064139282288[/C][/ROW]
[ROW][C]128[/C][C]5[/C][C]4.48198941702802[/C][C]0.518010582971983[/C][/ROW]
[ROW][C]129[/C][C]6[/C][C]7.35386371204088[/C][C]-1.35386371204088[/C][/ROW]
[ROW][C]130[/C][C]6[/C][C]4.690279256772[/C][C]1.309720743228[/C][/ROW]
[ROW][C]131[/C][C]6[/C][C]6.3961667542904[/C][C]-0.396166754290396[/C][/ROW]
[ROW][C]132[/C][C]6[/C][C]2.89207092752269[/C][C]3.10792907247731[/C][/ROW]
[ROW][C]133[/C][C]6[/C][C]5.74119289473813[/C][C]0.258807105261873[/C][/ROW]
[ROW][C]134[/C][C]6[/C][C]3.45718096378548[/C][C]2.54281903621452[/C][/ROW]
[ROW][C]135[/C][C]7[/C][C]4.71582730412641[/C][C]2.28417269587359[/C][/ROW]
[ROW][C]136[/C][C]7[/C][C]3.97611906012377[/C][C]3.02388093987623[/C][/ROW]
[ROW][C]137[/C][C]7[/C][C]5.9102891648673[/C][C]1.0897108351327[/C][/ROW]
[ROW][C]138[/C][C]7[/C][C]7.11081113048991[/C][C]-0.110811130489912[/C][/ROW]
[ROW][C]139[/C][C]8[/C][C]5.76719173616412[/C][C]2.23280826383588[/C][/ROW]
[ROW][C]140[/C][C]8[/C][C]6.38652223164958[/C][C]1.61347776835042[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]5.82855349105612[/C][C]2.17144650894388[/C][/ROW]
[ROW][C]142[/C][C]8[/C][C]5.03573465192443[/C][C]2.96426534807557[/C][/ROW]
[ROW][C]143[/C][C]8[/C][C]5.32582485278331[/C][C]2.67417514721669[/C][/ROW]
[ROW][C]144[/C][C]9[/C][C]5.75872578480195[/C][C]3.24127421519805[/C][/ROW]
[ROW][C]145[/C][C]9[/C][C]4.41662020526252[/C][C]4.58337979473748[/C][/ROW]
[ROW][C]146[/C][C]9[/C][C]7.79661369664319[/C][C]1.20338630335681[/C][/ROW]
[ROW][C]147[/C][C]9[/C][C]0.844789156202168[/C][C]8.15521084379783[/C][/ROW]
[ROW][C]148[/C][C]10[/C][C]4.5026667845224[/C][C]5.4973332154776[/C][/ROW]
[ROW][C]149[/C][C]10[/C][C]3.61932073362738[/C][C]6.38067926637262[/C][/ROW]
[ROW][C]150[/C][C]10[/C][C]3.81961723715302[/C][C]6.18038276284698[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]3.92111983750646[/C][C]6.07888016249354[/C][/ROW]
[ROW][C]152[/C][C]10[/C][C]6.38524832061604[/C][C]3.61475167938396[/C][/ROW]
[ROW][C]153[/C][C]10[/C][C]6.39845608412408[/C][C]3.60154391587592[/C][/ROW]
[ROW][C]154[/C][C]10[/C][C]4.24996053379961[/C][C]5.75003946620039[/C][/ROW]
[ROW][C]155[/C][C]11[/C][C]3.21843050719047[/C][C]7.78156949280953[/C][/ROW]
[ROW][C]156[/C][C]11[/C][C]4.71026291482139[/C][C]6.28973708517861[/C][/ROW]
[ROW][C]157[/C][C]12[/C][C]4.94032095787989[/C][C]7.05967904212011[/C][/ROW]
[ROW][C]158[/C][C]12[/C][C]1.57774784232014[/C][C]10.4222521576799[/C][/ROW]
[ROW][C]159[/C][C]12[/C][C]6.53269609822638[/C][C]5.46730390177362[/C][/ROW]
[ROW][C]160[/C][C]13[/C][C]5.15595002370319[/C][C]7.84404997629681[/C][/ROW]
[ROW][C]161[/C][C]13[/C][C]4.78221975331459[/C][C]8.21778024668541[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]7.78111618158272[/C][C]5.21888381841728[/C][/ROW]
[ROW][C]163[/C][C]15[/C][C]8.6947247286033[/C][C]6.3052752713967[/C][/ROW]
[ROW][C]164[/C][C]16[/C][C]5.58494816637658[/C][C]10.4150518336234[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157830&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157830&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
103.21125556620894-3.21125556620894
202.01975517239198-2.01975517239198
301.24617342441244-1.24617342441244
402.43190930869502-2.43190930869502
504.48602196161646-4.48602196161646
602.98195156141504-2.98195156141504
703.80592866340661-3.80592866340661
800.869912347839022-0.869912347839022
902.74594635095854-2.74594635095854
100-0.03731635374554760.0373163537455476
1105.00543970707883-5.00543970707883
1203.33994567545045-3.33994567545045
1306.68860205166436-6.68860205166436
1402.12367779194032-2.12367779194032
1500.772528500233317-0.772528500233317
1600.241542487598328-0.241542487598328
170-0.1206965624807080.120696562480708
1803.29133692881071-3.29133692881071
1904.57615592094598-4.57615592094598
2001.34081880935418-1.34081880935418
2101.65929883937473-1.65929883937473
220-1.347630040080641.34763004008064
2301.08387993329522-1.08387993329522
2402.41938660012886-2.41938660012886
2501.45430324739705-1.45430324739705
2603.03370200195603-3.03370200195603
2700.544132334299346-0.544132334299346
2801.4607324214162-1.4607324214162
2903.53777768124159-3.53777768124159
3006.74091909270319-6.74091909270319
3103.06826514692178-3.06826514692178
3200.963017804096269-0.963017804096269
3301.12838754648976-1.12838754648976
3401.03967409599353-1.03967409599353
3501.18105825108493-1.18105825108493
3601.54013271852295-1.54013271852295
3700.911771977880967-0.911771977880967
3803.67254165891961-3.67254165891961
3905.46327157209943-5.46327157209943
4000.416491994151872-0.416491994151872
4102.24221923689669-2.24221923689669
4203.44227111965645-3.44227111965645
4304.87691070881004-4.87691070881004
4400.922424903469715-0.922424903469715
4500.849901029112187-0.849901029112187
4600.856388354872856-0.856388354872856
4700.840733354435315-0.840733354435315
4800.840733354435315-0.840733354435315
4900.844000112215814-0.844000112215814
5000.804046827145677-0.804046827145677
5100.922664052586148-0.922664052586148
5200.893535972611891-0.893535972611891
5304.07814520851776-4.07814520851776
5400.901637890772103-0.901637890772103
5510.3991633173716750.600836682628325
5615.62824153585938-4.62824153585938
5712.6581673572316-1.6581673572316
5811.76057108865149-0.760571088651487
5911.08139648931166-0.0813964893116568
6010.801751999141960.19824800085804
6113.29780777298674-2.29780777298674
6212.16209525746818-1.16209525746818
6314.06556695168147-3.06556695168147
6415.24624052298338-4.24624052298338
6513.1743513383053-2.1743513383053
6613.55002194794125-2.55002194794125
6711.35867281025756-0.35867281025756
6812.15392868957211-1.15392868957211
6915.68956610824233-4.68956610824233
7012.8942785089958-1.8942785089958
7110.8407333544353150.159266645564685
7222.36074517503053-0.360745175030526
7322.77807447034701-0.778074470347012
7423.46918512251995-1.46918512251995
7520.5472465070815251.45275349291847
7621.297155393528610.702844606471389
7723.22773299982885-1.22773299982885
7825.22196539889488-3.22196539889488
7922.8989833955065-0.898983395506498
8023.35588986051332-1.35588986051332
8122.42183622498118-0.421836224981178
8224.89264124393381-2.89264124393381
8321.355040241943260.644959758056735
8424.58659736378772-2.58659736378772
8524.25461716572471-2.25461716572471
8624.17490787032215-2.17490787032216
8732.728501451951590.271498548048406
8834.54049830974329-1.54049830974329
8932.865058930338380.134941069661615
9032.49937809809940.500621901900603
9133.73152196594513-0.731521965945127
9231.727444313578911.27255568642109
9333.83460320780272-0.834603207802719
9434.25983401831978-1.25983401831978
9532.425715174630380.574284825369622
9636.51343206061875-3.51343206061875
9737.25235512934462-4.25235512934462
9835.33841035181945-2.33841035181945
9932.218672616310440.781327383689562
10044.28628180972419-0.286281809724188
10145.5851465805794-1.5851465805794
10244.32310801514944-0.323108015149438
10345.55843002113824-1.55843002113824
10443.111305845745630.888694154254374
10543.343896547316840.656103452683162
10643.548571735753630.451428264246368
10745.38456418775827-1.38456418775827
10845.36878571782431-1.36878571782431
10943.871568108736640.128431891263357
11040.2634650424676553.73653495753234
11144.06198142889025-0.0619814288902534
11241.420939734195662.57906026580434
11342.81479242464141.1852075753586
11445.60884173212325-1.60884173212325
11546.64158372646256-2.64158372646256
11644.26822731618109-0.268227316181093
11751.577808361015333.42219163898467
11853.219991965908151.78000803409185
11950.5180895348057444.48191046519426
12053.054260279980961.94573972001904
12155.00559573444941-0.00559573444940814
12254.831814496166450.16818550383355
12350.6863869965401584.31361300345984
12452.956989999405722.04301000059428
12553.404395399398841.59560460060116
12652.893147337069042.10685266293096
12756.78064139282288-1.78064139282288
12854.481989417028020.518010582971983
12967.35386371204088-1.35386371204088
13064.6902792567721.309720743228
13166.3961667542904-0.396166754290396
13262.892070927522693.10792907247731
13365.741192894738130.258807105261873
13463.457180963785482.54281903621452
13574.715827304126412.28417269587359
13673.976119060123773.02388093987623
13775.91028916486731.0897108351327
13877.11081113048991-0.110811130489912
13985.767191736164122.23280826383588
14086.386522231649581.61347776835042
14185.828553491056122.17144650894388
14285.035734651924432.96426534807557
14385.325824852783312.67417514721669
14495.758725784801953.24127421519805
14594.416620205262524.58337979473748
14697.796613696643191.20338630335681
14790.8447891562021688.15521084379783
148104.50266678452245.4973332154776
149103.619320733627386.38067926637262
150103.819617237153026.18038276284698
151103.921119837506466.07888016249354
152106.385248320616043.61475167938396
153106.398456084124083.60154391587592
154104.249960533799615.75003946620039
155113.218430507190477.78156949280953
156114.710262914821396.28973708517861
157124.940320957879897.05967904212011
158121.5777478423201410.4222521576799
159126.532696098226385.46730390177362
160135.155950023703197.84404997629681
161134.782219753314598.21778024668541
162137.781116181582725.21888381841728
163158.69472472860336.3052752713967
164165.5849481663765810.4150518336234






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
18001
19001
20001
21001
22001
23001
24001
25001
26001
27001
28001
29001
30001
31001
32001
33001
34001
35001
36001
37001
38001
39001
40001
41001
42001
43001
44001
45001
46001
47001
48001
49001
50001
51001
52001
53001
54001
553.66581585302211e-477.33163170604422e-471
561.79680687103659e-433.59361374207317e-431
575.0821725532089e-411.01643451064178e-401
581.44038286371827e-382.88076572743655e-381
597.57975512860494e-381.51595102572099e-371
609.91581106695508e-391.98316221339102e-381
611.93784330613672e-383.87568661227344e-381
623.94411894930536e-377.88823789861071e-371
637.87395199036912e-381.57479039807382e-371
642.88949035620235e-375.77898071240469e-371
652.47630337469587e-364.95260674939175e-361
662.10251444460031e-364.20502888920063e-361
671.18524556549535e-362.3704911309907e-361
681.90254122845091e-373.80508245690182e-371
696.21483923451197e-381.24296784690239e-371
702.00125766094774e-384.00251532189547e-381
714.38779398064881e-388.77558796129761e-381
725.68863150432002e-361.137726300864e-351
732.21671349813694e-334.43342699627388e-331
742.07270262019163e-324.14540524038326e-321
754.17564468128133e-338.35128936256267e-331
767.08934173365102e-321.4178683467302e-311
772.77629401334534e-315.55258802669068e-311
788.6065833510853e-311.72131667021706e-301
792.59179589550282e-305.18359179100564e-301
802.94654918240902e-295.89309836481804e-291
811.16513258296962e-292.33026516593925e-291
824.2539685810073e-298.5079371620146e-291
834.18952162787451e-298.37904325574902e-291
841.20067056429682e-282.40134112859364e-281
853.95908472622674e-287.91816945245348e-281
864.60162726894314e-279.20325453788628e-271
873.55661208125293e-267.11322416250587e-261
889.70897715329111e-251.94179543065822e-241
898.66874725758767e-241.73374945151753e-231
904.31168699826923e-238.62337399653847e-231
912.95607730430233e-225.91215460860467e-221
922.78956672571346e-215.57913345142692e-211
939.94034636849252e-211.9880692736985e-201
943.31439406602226e-206.62878813204451e-201
951.42582629164664e-192.85165258329328e-191
961.9402445447548e-183.8804890895096e-181
975.13499575452404e-181.02699915090481e-171
981.0851582231423e-162.1703164462846e-161
998.14821428625863e-161.62964285725173e-151
1001.94670798529685e-153.8934159705937e-150.999999999999998
1013.31393232431629e-156.62786464863259e-150.999999999999997
1024.56897309819084e-149.13794619638167e-140.999999999999954
1031.1037482779449e-132.2074965558898e-130.99999999999989
1046.81290427128373e-131.36258085425675e-120.999999999999319
1053.8149186797202e-127.6298373594404e-120.999999999996185
1063.61022987929987e-127.22045975859974e-120.99999999999639
1072.84615040508877e-125.69230081017754e-120.999999999997154
1083.56378456639747e-127.12756913279493e-120.999999999996436
1091.11787077475864e-112.23574154951729e-110.999999999988821
1102.9049966501221e-105.8099933002442e-100.9999999997095
1112.52232168579923e-105.04464337159847e-100.999999999747768
1129.38106158123407e-101.87621231624681e-090.999999999061894
1131.28322786114548e-092.56645572229096e-090.999999998716772
1141.07402909569504e-092.14805819139007e-090.99999999892597
1153.25854873190365e-096.5170974638073e-090.999999996741451
1161.00985664048431e-082.01971328096861e-080.999999989901434
1173.05124024866065e-086.1024804973213e-080.999999969487598
1181.36397749699464e-072.72795499398928e-070.99999986360225
1192.58299013392472e-075.16598026784944e-070.999999741700987
1208.46867935812463e-071.69373587162493e-060.999999153132064
1216.28044249319498e-071.256088498639e-060.99999937195575
1227.67906641239225e-071.53581328247845e-060.999999232093359
1232.12394344198991e-064.24788688397982e-060.999997876056558
1242.20963403595199e-064.41926807190399e-060.999997790365964
1251.83868954097353e-063.67737908194707e-060.99999816131046
1261.28645526626618e-052.57291053253236e-050.999987135447337
1277.50360868077307e-050.0001500721736154610.999924963913192
1280.0008802617340563180.001760523468112640.999119738265944
1290.001283441062507090.002566882125014180.998716558937493
1300.004458897301999730.008917794603999450.995541102698
1310.01216084085093910.02432168170187820.98783915914906
1320.03098379838746250.0619675967749250.969016201612538
1330.02356908315427040.04713816630854080.97643091684573
1340.0188109016538450.037621803307690.981189098346155
1350.03310060970266120.06620121940532240.966899390297339
1360.03554972483610740.07109944967221490.964450275163893
1370.02917931360343280.05835862720686560.970820686396567
1380.0692124328520420.1384248657040840.930787567147958
1390.08321206539399390.1664241307879880.916787934606006
1400.2422998258714980.4845996517429960.757700174128502
1410.1772885160276440.3545770320552880.822711483972356
1420.2354817806238210.4709635612476430.764518219376179
1430.2075972918158560.4151945836317110.792402708184144
1440.6771350971334270.6457298057331470.322864902866573
1450.6151649272969150.769670145406170.384835072703085
1460.7198460628348510.5603078743302970.280153937165149

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0 & 0 & 1 \tabularnewline
19 & 0 & 0 & 1 \tabularnewline
20 & 0 & 0 & 1 \tabularnewline
21 & 0 & 0 & 1 \tabularnewline
22 & 0 & 0 & 1 \tabularnewline
23 & 0 & 0 & 1 \tabularnewline
24 & 0 & 0 & 1 \tabularnewline
25 & 0 & 0 & 1 \tabularnewline
26 & 0 & 0 & 1 \tabularnewline
27 & 0 & 0 & 1 \tabularnewline
28 & 0 & 0 & 1 \tabularnewline
29 & 0 & 0 & 1 \tabularnewline
30 & 0 & 0 & 1 \tabularnewline
31 & 0 & 0 & 1 \tabularnewline
32 & 0 & 0 & 1 \tabularnewline
33 & 0 & 0 & 1 \tabularnewline
34 & 0 & 0 & 1 \tabularnewline
35 & 0 & 0 & 1 \tabularnewline
36 & 0 & 0 & 1 \tabularnewline
37 & 0 & 0 & 1 \tabularnewline
38 & 0 & 0 & 1 \tabularnewline
39 & 0 & 0 & 1 \tabularnewline
40 & 0 & 0 & 1 \tabularnewline
41 & 0 & 0 & 1 \tabularnewline
42 & 0 & 0 & 1 \tabularnewline
43 & 0 & 0 & 1 \tabularnewline
44 & 0 & 0 & 1 \tabularnewline
45 & 0 & 0 & 1 \tabularnewline
46 & 0 & 0 & 1 \tabularnewline
47 & 0 & 0 & 1 \tabularnewline
48 & 0 & 0 & 1 \tabularnewline
49 & 0 & 0 & 1 \tabularnewline
50 & 0 & 0 & 1 \tabularnewline
51 & 0 & 0 & 1 \tabularnewline
52 & 0 & 0 & 1 \tabularnewline
53 & 0 & 0 & 1 \tabularnewline
54 & 0 & 0 & 1 \tabularnewline
55 & 3.66581585302211e-47 & 7.33163170604422e-47 & 1 \tabularnewline
56 & 1.79680687103659e-43 & 3.59361374207317e-43 & 1 \tabularnewline
57 & 5.0821725532089e-41 & 1.01643451064178e-40 & 1 \tabularnewline
58 & 1.44038286371827e-38 & 2.88076572743655e-38 & 1 \tabularnewline
59 & 7.57975512860494e-38 & 1.51595102572099e-37 & 1 \tabularnewline
60 & 9.91581106695508e-39 & 1.98316221339102e-38 & 1 \tabularnewline
61 & 1.93784330613672e-38 & 3.87568661227344e-38 & 1 \tabularnewline
62 & 3.94411894930536e-37 & 7.88823789861071e-37 & 1 \tabularnewline
63 & 7.87395199036912e-38 & 1.57479039807382e-37 & 1 \tabularnewline
64 & 2.88949035620235e-37 & 5.77898071240469e-37 & 1 \tabularnewline
65 & 2.47630337469587e-36 & 4.95260674939175e-36 & 1 \tabularnewline
66 & 2.10251444460031e-36 & 4.20502888920063e-36 & 1 \tabularnewline
67 & 1.18524556549535e-36 & 2.3704911309907e-36 & 1 \tabularnewline
68 & 1.90254122845091e-37 & 3.80508245690182e-37 & 1 \tabularnewline
69 & 6.21483923451197e-38 & 1.24296784690239e-37 & 1 \tabularnewline
70 & 2.00125766094774e-38 & 4.00251532189547e-38 & 1 \tabularnewline
71 & 4.38779398064881e-38 & 8.77558796129761e-38 & 1 \tabularnewline
72 & 5.68863150432002e-36 & 1.137726300864e-35 & 1 \tabularnewline
73 & 2.21671349813694e-33 & 4.43342699627388e-33 & 1 \tabularnewline
74 & 2.07270262019163e-32 & 4.14540524038326e-32 & 1 \tabularnewline
75 & 4.17564468128133e-33 & 8.35128936256267e-33 & 1 \tabularnewline
76 & 7.08934173365102e-32 & 1.4178683467302e-31 & 1 \tabularnewline
77 & 2.77629401334534e-31 & 5.55258802669068e-31 & 1 \tabularnewline
78 & 8.6065833510853e-31 & 1.72131667021706e-30 & 1 \tabularnewline
79 & 2.59179589550282e-30 & 5.18359179100564e-30 & 1 \tabularnewline
80 & 2.94654918240902e-29 & 5.89309836481804e-29 & 1 \tabularnewline
81 & 1.16513258296962e-29 & 2.33026516593925e-29 & 1 \tabularnewline
82 & 4.2539685810073e-29 & 8.5079371620146e-29 & 1 \tabularnewline
83 & 4.18952162787451e-29 & 8.37904325574902e-29 & 1 \tabularnewline
84 & 1.20067056429682e-28 & 2.40134112859364e-28 & 1 \tabularnewline
85 & 3.95908472622674e-28 & 7.91816945245348e-28 & 1 \tabularnewline
86 & 4.60162726894314e-27 & 9.20325453788628e-27 & 1 \tabularnewline
87 & 3.55661208125293e-26 & 7.11322416250587e-26 & 1 \tabularnewline
88 & 9.70897715329111e-25 & 1.94179543065822e-24 & 1 \tabularnewline
89 & 8.66874725758767e-24 & 1.73374945151753e-23 & 1 \tabularnewline
90 & 4.31168699826923e-23 & 8.62337399653847e-23 & 1 \tabularnewline
91 & 2.95607730430233e-22 & 5.91215460860467e-22 & 1 \tabularnewline
92 & 2.78956672571346e-21 & 5.57913345142692e-21 & 1 \tabularnewline
93 & 9.94034636849252e-21 & 1.9880692736985e-20 & 1 \tabularnewline
94 & 3.31439406602226e-20 & 6.62878813204451e-20 & 1 \tabularnewline
95 & 1.42582629164664e-19 & 2.85165258329328e-19 & 1 \tabularnewline
96 & 1.9402445447548e-18 & 3.8804890895096e-18 & 1 \tabularnewline
97 & 5.13499575452404e-18 & 1.02699915090481e-17 & 1 \tabularnewline
98 & 1.0851582231423e-16 & 2.1703164462846e-16 & 1 \tabularnewline
99 & 8.14821428625863e-16 & 1.62964285725173e-15 & 1 \tabularnewline
100 & 1.94670798529685e-15 & 3.8934159705937e-15 & 0.999999999999998 \tabularnewline
101 & 3.31393232431629e-15 & 6.62786464863259e-15 & 0.999999999999997 \tabularnewline
102 & 4.56897309819084e-14 & 9.13794619638167e-14 & 0.999999999999954 \tabularnewline
103 & 1.1037482779449e-13 & 2.2074965558898e-13 & 0.99999999999989 \tabularnewline
104 & 6.81290427128373e-13 & 1.36258085425675e-12 & 0.999999999999319 \tabularnewline
105 & 3.8149186797202e-12 & 7.6298373594404e-12 & 0.999999999996185 \tabularnewline
106 & 3.61022987929987e-12 & 7.22045975859974e-12 & 0.99999999999639 \tabularnewline
107 & 2.84615040508877e-12 & 5.69230081017754e-12 & 0.999999999997154 \tabularnewline
108 & 3.56378456639747e-12 & 7.12756913279493e-12 & 0.999999999996436 \tabularnewline
109 & 1.11787077475864e-11 & 2.23574154951729e-11 & 0.999999999988821 \tabularnewline
110 & 2.9049966501221e-10 & 5.8099933002442e-10 & 0.9999999997095 \tabularnewline
111 & 2.52232168579923e-10 & 5.04464337159847e-10 & 0.999999999747768 \tabularnewline
112 & 9.38106158123407e-10 & 1.87621231624681e-09 & 0.999999999061894 \tabularnewline
113 & 1.28322786114548e-09 & 2.56645572229096e-09 & 0.999999998716772 \tabularnewline
114 & 1.07402909569504e-09 & 2.14805819139007e-09 & 0.99999999892597 \tabularnewline
115 & 3.25854873190365e-09 & 6.5170974638073e-09 & 0.999999996741451 \tabularnewline
116 & 1.00985664048431e-08 & 2.01971328096861e-08 & 0.999999989901434 \tabularnewline
117 & 3.05124024866065e-08 & 6.1024804973213e-08 & 0.999999969487598 \tabularnewline
118 & 1.36397749699464e-07 & 2.72795499398928e-07 & 0.99999986360225 \tabularnewline
119 & 2.58299013392472e-07 & 5.16598026784944e-07 & 0.999999741700987 \tabularnewline
120 & 8.46867935812463e-07 & 1.69373587162493e-06 & 0.999999153132064 \tabularnewline
121 & 6.28044249319498e-07 & 1.256088498639e-06 & 0.99999937195575 \tabularnewline
122 & 7.67906641239225e-07 & 1.53581328247845e-06 & 0.999999232093359 \tabularnewline
123 & 2.12394344198991e-06 & 4.24788688397982e-06 & 0.999997876056558 \tabularnewline
124 & 2.20963403595199e-06 & 4.41926807190399e-06 & 0.999997790365964 \tabularnewline
125 & 1.83868954097353e-06 & 3.67737908194707e-06 & 0.99999816131046 \tabularnewline
126 & 1.28645526626618e-05 & 2.57291053253236e-05 & 0.999987135447337 \tabularnewline
127 & 7.50360868077307e-05 & 0.000150072173615461 & 0.999924963913192 \tabularnewline
128 & 0.000880261734056318 & 0.00176052346811264 & 0.999119738265944 \tabularnewline
129 & 0.00128344106250709 & 0.00256688212501418 & 0.998716558937493 \tabularnewline
130 & 0.00445889730199973 & 0.00891779460399945 & 0.995541102698 \tabularnewline
131 & 0.0121608408509391 & 0.0243216817018782 & 0.98783915914906 \tabularnewline
132 & 0.0309837983874625 & 0.061967596774925 & 0.969016201612538 \tabularnewline
133 & 0.0235690831542704 & 0.0471381663085408 & 0.97643091684573 \tabularnewline
134 & 0.018810901653845 & 0.03762180330769 & 0.981189098346155 \tabularnewline
135 & 0.0331006097026612 & 0.0662012194053224 & 0.966899390297339 \tabularnewline
136 & 0.0355497248361074 & 0.0710994496722149 & 0.964450275163893 \tabularnewline
137 & 0.0291793136034328 & 0.0583586272068656 & 0.970820686396567 \tabularnewline
138 & 0.069212432852042 & 0.138424865704084 & 0.930787567147958 \tabularnewline
139 & 0.0832120653939939 & 0.166424130787988 & 0.916787934606006 \tabularnewline
140 & 0.242299825871498 & 0.484599651742996 & 0.757700174128502 \tabularnewline
141 & 0.177288516027644 & 0.354577032055288 & 0.822711483972356 \tabularnewline
142 & 0.235481780623821 & 0.470963561247643 & 0.764518219376179 \tabularnewline
143 & 0.207597291815856 & 0.415194583631711 & 0.792402708184144 \tabularnewline
144 & 0.677135097133427 & 0.645729805733147 & 0.322864902866573 \tabularnewline
145 & 0.615164927296915 & 0.76967014540617 & 0.384835072703085 \tabularnewline
146 & 0.719846062834851 & 0.560307874330297 & 0.280153937165149 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157830&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[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]55[/C][C]3.66581585302211e-47[/C][C]7.33163170604422e-47[/C][C]1[/C][/ROW]
[ROW][C]56[/C][C]1.79680687103659e-43[/C][C]3.59361374207317e-43[/C][C]1[/C][/ROW]
[ROW][C]57[/C][C]5.0821725532089e-41[/C][C]1.01643451064178e-40[/C][C]1[/C][/ROW]
[ROW][C]58[/C][C]1.44038286371827e-38[/C][C]2.88076572743655e-38[/C][C]1[/C][/ROW]
[ROW][C]59[/C][C]7.57975512860494e-38[/C][C]1.51595102572099e-37[/C][C]1[/C][/ROW]
[ROW][C]60[/C][C]9.91581106695508e-39[/C][C]1.98316221339102e-38[/C][C]1[/C][/ROW]
[ROW][C]61[/C][C]1.93784330613672e-38[/C][C]3.87568661227344e-38[/C][C]1[/C][/ROW]
[ROW][C]62[/C][C]3.94411894930536e-37[/C][C]7.88823789861071e-37[/C][C]1[/C][/ROW]
[ROW][C]63[/C][C]7.87395199036912e-38[/C][C]1.57479039807382e-37[/C][C]1[/C][/ROW]
[ROW][C]64[/C][C]2.88949035620235e-37[/C][C]5.77898071240469e-37[/C][C]1[/C][/ROW]
[ROW][C]65[/C][C]2.47630337469587e-36[/C][C]4.95260674939175e-36[/C][C]1[/C][/ROW]
[ROW][C]66[/C][C]2.10251444460031e-36[/C][C]4.20502888920063e-36[/C][C]1[/C][/ROW]
[ROW][C]67[/C][C]1.18524556549535e-36[/C][C]2.3704911309907e-36[/C][C]1[/C][/ROW]
[ROW][C]68[/C][C]1.90254122845091e-37[/C][C]3.80508245690182e-37[/C][C]1[/C][/ROW]
[ROW][C]69[/C][C]6.21483923451197e-38[/C][C]1.24296784690239e-37[/C][C]1[/C][/ROW]
[ROW][C]70[/C][C]2.00125766094774e-38[/C][C]4.00251532189547e-38[/C][C]1[/C][/ROW]
[ROW][C]71[/C][C]4.38779398064881e-38[/C][C]8.77558796129761e-38[/C][C]1[/C][/ROW]
[ROW][C]72[/C][C]5.68863150432002e-36[/C][C]1.137726300864e-35[/C][C]1[/C][/ROW]
[ROW][C]73[/C][C]2.21671349813694e-33[/C][C]4.43342699627388e-33[/C][C]1[/C][/ROW]
[ROW][C]74[/C][C]2.07270262019163e-32[/C][C]4.14540524038326e-32[/C][C]1[/C][/ROW]
[ROW][C]75[/C][C]4.17564468128133e-33[/C][C]8.35128936256267e-33[/C][C]1[/C][/ROW]
[ROW][C]76[/C][C]7.08934173365102e-32[/C][C]1.4178683467302e-31[/C][C]1[/C][/ROW]
[ROW][C]77[/C][C]2.77629401334534e-31[/C][C]5.55258802669068e-31[/C][C]1[/C][/ROW]
[ROW][C]78[/C][C]8.6065833510853e-31[/C][C]1.72131667021706e-30[/C][C]1[/C][/ROW]
[ROW][C]79[/C][C]2.59179589550282e-30[/C][C]5.18359179100564e-30[/C][C]1[/C][/ROW]
[ROW][C]80[/C][C]2.94654918240902e-29[/C][C]5.89309836481804e-29[/C][C]1[/C][/ROW]
[ROW][C]81[/C][C]1.16513258296962e-29[/C][C]2.33026516593925e-29[/C][C]1[/C][/ROW]
[ROW][C]82[/C][C]4.2539685810073e-29[/C][C]8.5079371620146e-29[/C][C]1[/C][/ROW]
[ROW][C]83[/C][C]4.18952162787451e-29[/C][C]8.37904325574902e-29[/C][C]1[/C][/ROW]
[ROW][C]84[/C][C]1.20067056429682e-28[/C][C]2.40134112859364e-28[/C][C]1[/C][/ROW]
[ROW][C]85[/C][C]3.95908472622674e-28[/C][C]7.91816945245348e-28[/C][C]1[/C][/ROW]
[ROW][C]86[/C][C]4.60162726894314e-27[/C][C]9.20325453788628e-27[/C][C]1[/C][/ROW]
[ROW][C]87[/C][C]3.55661208125293e-26[/C][C]7.11322416250587e-26[/C][C]1[/C][/ROW]
[ROW][C]88[/C][C]9.70897715329111e-25[/C][C]1.94179543065822e-24[/C][C]1[/C][/ROW]
[ROW][C]89[/C][C]8.66874725758767e-24[/C][C]1.73374945151753e-23[/C][C]1[/C][/ROW]
[ROW][C]90[/C][C]4.31168699826923e-23[/C][C]8.62337399653847e-23[/C][C]1[/C][/ROW]
[ROW][C]91[/C][C]2.95607730430233e-22[/C][C]5.91215460860467e-22[/C][C]1[/C][/ROW]
[ROW][C]92[/C][C]2.78956672571346e-21[/C][C]5.57913345142692e-21[/C][C]1[/C][/ROW]
[ROW][C]93[/C][C]9.94034636849252e-21[/C][C]1.9880692736985e-20[/C][C]1[/C][/ROW]
[ROW][C]94[/C][C]3.31439406602226e-20[/C][C]6.62878813204451e-20[/C][C]1[/C][/ROW]
[ROW][C]95[/C][C]1.42582629164664e-19[/C][C]2.85165258329328e-19[/C][C]1[/C][/ROW]
[ROW][C]96[/C][C]1.9402445447548e-18[/C][C]3.8804890895096e-18[/C][C]1[/C][/ROW]
[ROW][C]97[/C][C]5.13499575452404e-18[/C][C]1.02699915090481e-17[/C][C]1[/C][/ROW]
[ROW][C]98[/C][C]1.0851582231423e-16[/C][C]2.1703164462846e-16[/C][C]1[/C][/ROW]
[ROW][C]99[/C][C]8.14821428625863e-16[/C][C]1.62964285725173e-15[/C][C]1[/C][/ROW]
[ROW][C]100[/C][C]1.94670798529685e-15[/C][C]3.8934159705937e-15[/C][C]0.999999999999998[/C][/ROW]
[ROW][C]101[/C][C]3.31393232431629e-15[/C][C]6.62786464863259e-15[/C][C]0.999999999999997[/C][/ROW]
[ROW][C]102[/C][C]4.56897309819084e-14[/C][C]9.13794619638167e-14[/C][C]0.999999999999954[/C][/ROW]
[ROW][C]103[/C][C]1.1037482779449e-13[/C][C]2.2074965558898e-13[/C][C]0.99999999999989[/C][/ROW]
[ROW][C]104[/C][C]6.81290427128373e-13[/C][C]1.36258085425675e-12[/C][C]0.999999999999319[/C][/ROW]
[ROW][C]105[/C][C]3.8149186797202e-12[/C][C]7.6298373594404e-12[/C][C]0.999999999996185[/C][/ROW]
[ROW][C]106[/C][C]3.61022987929987e-12[/C][C]7.22045975859974e-12[/C][C]0.99999999999639[/C][/ROW]
[ROW][C]107[/C][C]2.84615040508877e-12[/C][C]5.69230081017754e-12[/C][C]0.999999999997154[/C][/ROW]
[ROW][C]108[/C][C]3.56378456639747e-12[/C][C]7.12756913279493e-12[/C][C]0.999999999996436[/C][/ROW]
[ROW][C]109[/C][C]1.11787077475864e-11[/C][C]2.23574154951729e-11[/C][C]0.999999999988821[/C][/ROW]
[ROW][C]110[/C][C]2.9049966501221e-10[/C][C]5.8099933002442e-10[/C][C]0.9999999997095[/C][/ROW]
[ROW][C]111[/C][C]2.52232168579923e-10[/C][C]5.04464337159847e-10[/C][C]0.999999999747768[/C][/ROW]
[ROW][C]112[/C][C]9.38106158123407e-10[/C][C]1.87621231624681e-09[/C][C]0.999999999061894[/C][/ROW]
[ROW][C]113[/C][C]1.28322786114548e-09[/C][C]2.56645572229096e-09[/C][C]0.999999998716772[/C][/ROW]
[ROW][C]114[/C][C]1.07402909569504e-09[/C][C]2.14805819139007e-09[/C][C]0.99999999892597[/C][/ROW]
[ROW][C]115[/C][C]3.25854873190365e-09[/C][C]6.5170974638073e-09[/C][C]0.999999996741451[/C][/ROW]
[ROW][C]116[/C][C]1.00985664048431e-08[/C][C]2.01971328096861e-08[/C][C]0.999999989901434[/C][/ROW]
[ROW][C]117[/C][C]3.05124024866065e-08[/C][C]6.1024804973213e-08[/C][C]0.999999969487598[/C][/ROW]
[ROW][C]118[/C][C]1.36397749699464e-07[/C][C]2.72795499398928e-07[/C][C]0.99999986360225[/C][/ROW]
[ROW][C]119[/C][C]2.58299013392472e-07[/C][C]5.16598026784944e-07[/C][C]0.999999741700987[/C][/ROW]
[ROW][C]120[/C][C]8.46867935812463e-07[/C][C]1.69373587162493e-06[/C][C]0.999999153132064[/C][/ROW]
[ROW][C]121[/C][C]6.28044249319498e-07[/C][C]1.256088498639e-06[/C][C]0.99999937195575[/C][/ROW]
[ROW][C]122[/C][C]7.67906641239225e-07[/C][C]1.53581328247845e-06[/C][C]0.999999232093359[/C][/ROW]
[ROW][C]123[/C][C]2.12394344198991e-06[/C][C]4.24788688397982e-06[/C][C]0.999997876056558[/C][/ROW]
[ROW][C]124[/C][C]2.20963403595199e-06[/C][C]4.41926807190399e-06[/C][C]0.999997790365964[/C][/ROW]
[ROW][C]125[/C][C]1.83868954097353e-06[/C][C]3.67737908194707e-06[/C][C]0.99999816131046[/C][/ROW]
[ROW][C]126[/C][C]1.28645526626618e-05[/C][C]2.57291053253236e-05[/C][C]0.999987135447337[/C][/ROW]
[ROW][C]127[/C][C]7.50360868077307e-05[/C][C]0.000150072173615461[/C][C]0.999924963913192[/C][/ROW]
[ROW][C]128[/C][C]0.000880261734056318[/C][C]0.00176052346811264[/C][C]0.999119738265944[/C][/ROW]
[ROW][C]129[/C][C]0.00128344106250709[/C][C]0.00256688212501418[/C][C]0.998716558937493[/C][/ROW]
[ROW][C]130[/C][C]0.00445889730199973[/C][C]0.00891779460399945[/C][C]0.995541102698[/C][/ROW]
[ROW][C]131[/C][C]0.0121608408509391[/C][C]0.0243216817018782[/C][C]0.98783915914906[/C][/ROW]
[ROW][C]132[/C][C]0.0309837983874625[/C][C]0.061967596774925[/C][C]0.969016201612538[/C][/ROW]
[ROW][C]133[/C][C]0.0235690831542704[/C][C]0.0471381663085408[/C][C]0.97643091684573[/C][/ROW]
[ROW][C]134[/C][C]0.018810901653845[/C][C]0.03762180330769[/C][C]0.981189098346155[/C][/ROW]
[ROW][C]135[/C][C]0.0331006097026612[/C][C]0.0662012194053224[/C][C]0.966899390297339[/C][/ROW]
[ROW][C]136[/C][C]0.0355497248361074[/C][C]0.0710994496722149[/C][C]0.964450275163893[/C][/ROW]
[ROW][C]137[/C][C]0.0291793136034328[/C][C]0.0583586272068656[/C][C]0.970820686396567[/C][/ROW]
[ROW][C]138[/C][C]0.069212432852042[/C][C]0.138424865704084[/C][C]0.930787567147958[/C][/ROW]
[ROW][C]139[/C][C]0.0832120653939939[/C][C]0.166424130787988[/C][C]0.916787934606006[/C][/ROW]
[ROW][C]140[/C][C]0.242299825871498[/C][C]0.484599651742996[/C][C]0.757700174128502[/C][/ROW]
[ROW][C]141[/C][C]0.177288516027644[/C][C]0.354577032055288[/C][C]0.822711483972356[/C][/ROW]
[ROW][C]142[/C][C]0.235481780623821[/C][C]0.470963561247643[/C][C]0.764518219376179[/C][/ROW]
[ROW][C]143[/C][C]0.207597291815856[/C][C]0.415194583631711[/C][C]0.792402708184144[/C][/ROW]
[ROW][C]144[/C][C]0.677135097133427[/C][C]0.645729805733147[/C][C]0.322864902866573[/C][/ROW]
[ROW][C]145[/C][C]0.615164927296915[/C][C]0.76967014540617[/C][C]0.384835072703085[/C][/ROW]
[ROW][C]146[/C][C]0.719846062834851[/C][C]0.560307874330297[/C][C]0.280153937165149[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157830&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157830&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
18001
19001
20001
21001
22001
23001
24001
25001
26001
27001
28001
29001
30001
31001
32001
33001
34001
35001
36001
37001
38001
39001
40001
41001
42001
43001
44001
45001
46001
47001
48001
49001
50001
51001
52001
53001
54001
553.66581585302211e-477.33163170604422e-471
561.79680687103659e-433.59361374207317e-431
575.0821725532089e-411.01643451064178e-401
581.44038286371827e-382.88076572743655e-381
597.57975512860494e-381.51595102572099e-371
609.91581106695508e-391.98316221339102e-381
611.93784330613672e-383.87568661227344e-381
623.94411894930536e-377.88823789861071e-371
637.87395199036912e-381.57479039807382e-371
642.88949035620235e-375.77898071240469e-371
652.47630337469587e-364.95260674939175e-361
662.10251444460031e-364.20502888920063e-361
671.18524556549535e-362.3704911309907e-361
681.90254122845091e-373.80508245690182e-371
696.21483923451197e-381.24296784690239e-371
702.00125766094774e-384.00251532189547e-381
714.38779398064881e-388.77558796129761e-381
725.68863150432002e-361.137726300864e-351
732.21671349813694e-334.43342699627388e-331
742.07270262019163e-324.14540524038326e-321
754.17564468128133e-338.35128936256267e-331
767.08934173365102e-321.4178683467302e-311
772.77629401334534e-315.55258802669068e-311
788.6065833510853e-311.72131667021706e-301
792.59179589550282e-305.18359179100564e-301
802.94654918240902e-295.89309836481804e-291
811.16513258296962e-292.33026516593925e-291
824.2539685810073e-298.5079371620146e-291
834.18952162787451e-298.37904325574902e-291
841.20067056429682e-282.40134112859364e-281
853.95908472622674e-287.91816945245348e-281
864.60162726894314e-279.20325453788628e-271
873.55661208125293e-267.11322416250587e-261
889.70897715329111e-251.94179543065822e-241
898.66874725758767e-241.73374945151753e-231
904.31168699826923e-238.62337399653847e-231
912.95607730430233e-225.91215460860467e-221
922.78956672571346e-215.57913345142692e-211
939.94034636849252e-211.9880692736985e-201
943.31439406602226e-206.62878813204451e-201
951.42582629164664e-192.85165258329328e-191
961.9402445447548e-183.8804890895096e-181
975.13499575452404e-181.02699915090481e-171
981.0851582231423e-162.1703164462846e-161
998.14821428625863e-161.62964285725173e-151
1001.94670798529685e-153.8934159705937e-150.999999999999998
1013.31393232431629e-156.62786464863259e-150.999999999999997
1024.56897309819084e-149.13794619638167e-140.999999999999954
1031.1037482779449e-132.2074965558898e-130.99999999999989
1046.81290427128373e-131.36258085425675e-120.999999999999319
1053.8149186797202e-127.6298373594404e-120.999999999996185
1063.61022987929987e-127.22045975859974e-120.99999999999639
1072.84615040508877e-125.69230081017754e-120.999999999997154
1083.56378456639747e-127.12756913279493e-120.999999999996436
1091.11787077475864e-112.23574154951729e-110.999999999988821
1102.9049966501221e-105.8099933002442e-100.9999999997095
1112.52232168579923e-105.04464337159847e-100.999999999747768
1129.38106158123407e-101.87621231624681e-090.999999999061894
1131.28322786114548e-092.56645572229096e-090.999999998716772
1141.07402909569504e-092.14805819139007e-090.99999999892597
1153.25854873190365e-096.5170974638073e-090.999999996741451
1161.00985664048431e-082.01971328096861e-080.999999989901434
1173.05124024866065e-086.1024804973213e-080.999999969487598
1181.36397749699464e-072.72795499398928e-070.99999986360225
1192.58299013392472e-075.16598026784944e-070.999999741700987
1208.46867935812463e-071.69373587162493e-060.999999153132064
1216.28044249319498e-071.256088498639e-060.99999937195575
1227.67906641239225e-071.53581328247845e-060.999999232093359
1232.12394344198991e-064.24788688397982e-060.999997876056558
1242.20963403595199e-064.41926807190399e-060.999997790365964
1251.83868954097353e-063.67737908194707e-060.99999816131046
1261.28645526626618e-052.57291053253236e-050.999987135447337
1277.50360868077307e-050.0001500721736154610.999924963913192
1280.0008802617340563180.001760523468112640.999119738265944
1290.001283441062507090.002566882125014180.998716558937493
1300.004458897301999730.008917794603999450.995541102698
1310.01216084085093910.02432168170187820.98783915914906
1320.03098379838746250.0619675967749250.969016201612538
1330.02356908315427040.04713816630854080.97643091684573
1340.0188109016538450.037621803307690.981189098346155
1350.03310060970266120.06620121940532240.966899390297339
1360.03554972483610740.07109944967221490.964450275163893
1370.02917931360343280.05835862720686560.970820686396567
1380.0692124328520420.1384248657040840.930787567147958
1390.08321206539399390.1664241307879880.916787934606006
1400.2422998258714980.4845996517429960.757700174128502
1410.1772885160276440.3545770320552880.822711483972356
1420.2354817806238210.4709635612476430.764518219376179
1430.2075972918158560.4151945836317110.792402708184144
1440.6771350971334270.6457298057331470.322864902866573
1450.6151649272969150.769670145406170.384835072703085
1460.7198460628348510.5603078743302970.280153937165149






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1130.875968992248062NOK
5% type I error level1160.89922480620155NOK
10% type I error level1200.930232558139535NOK

\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 & 113 & 0.875968992248062 & NOK \tabularnewline
5% type I error level & 116 & 0.89922480620155 & NOK \tabularnewline
10% type I error level & 120 & 0.930232558139535 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157830&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]113[/C][C]0.875968992248062[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]116[/C][C]0.89922480620155[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]120[/C][C]0.930232558139535[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157830&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157830&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 level1130.875968992248062NOK
5% type I error level1160.89922480620155NOK
10% type I error level1200.930232558139535NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 6 ; 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')
}