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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 22 Dec 2011 07:51:01 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/22/t13245584675p4cz90ioi0fvh1.htm/, Retrieved Fri, 03 May 2024 09:10:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=159387, Retrieved Fri, 03 May 2024 09:10:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact88

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-01 16:06:53] [84ec9e690346b814992f2f0baa963a63]
-    D    [Multiple Regression] [] [2011-12-22 12:51:01] [5d1d3e74ee2cfe51b36819d331f689c0] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1845	162687	95	595	115	0	48	21	82	73	20465	6200	23975	39	37
1796	201906	62	545	76	1	58	20	80	56	33629	10265	85634	46	43
192	7215	18	72	1	0	0	0	0	0	1423	603	1929	0	0
2444	146367	97	679	155	0	67	27	84	63	25629	8874	36294	54	54
3567	257045	139	1201	125	0	83	31	124	116	54002	20323	72255	93	86
6917	524450	265	1967	278	1	136	36	140	138	151036	26258	189748	198	181
1840	188294	58	595	89	1	65	23	88	71	33287	10165	61834	42	42
1740	195674	60	496	59	0	86	30	115	107	31172	8247	68167	59	59
2078	177020	44	670	87	0	62	30	109	50	28113	8683	38462	49	46
3097	325899	98	1039	130	1	71	27	108	81	57803	16957	101219	83	77
1946	121844	75	634	158	2	50	24	63	58	49830	8058	43270	49	49
2369	203938	71	743	120	0	88	30	118	91	52143	20488	76183	83	79
1883	107394	105	681	87	0	50	22	71	41	21055	7945	31476	39	37
3198	220751	120	1086	264	4	79	28	112	100	47007	13448	62157	93	92
1490	172905	62	419	51	4	56	18	63	61	28735	5389	46261	31	31
1573	156326	88	474	85	3	54	22	86	74	59147	6185	50063	29	28
1807	145178	58	442	100	0	81	37	148	147	78950	24369	64483	104	103
1309	89171	61	373	72	5	13	15	54	45	13497	70	2341	2	2
2820	172624	88	899	147	0	74	34	134	110	46154	17327	48149	46	48
757	32443	26	235	49	0	14	18	57	41	53249	3878	12743	27	25
1162	87927	62	399	40	0	31	15	59	37	10726	3149	18743	16	16
2817	241285	102	850	99	0	99	30	113	84	83700	20517	97057	108	106
1760	195820	72	642	127	1	38	25	96	67	40400	2570	17675	36	35
2315	146946	56	717	164	1	59	34	96	69	33797	5162	33106	33	33
1994	159763	89	619	41	1	54	21	78	58	36205	5299	53311	46	45
1806	207078	34	657	160	0	63	21	80	60	30165	7233	42754	65	64
2152	212394	166	691	92	0	66	25	93	88	58534	15657	59056	80	73
1457	201536	95	366	59	0	90	31	109	75	44663	15329	101621	81	78
3000	394662	121	994	89	0	72	31	115	98	92556	14881	118120	69	63
2234	217808	44	929	90	0	61	20	79	67	40078	16318	79572	69	69
1684	182286	44	490	76	0	61	28	103	84	34711	9556	42744	37	36
1626	181740	47	553	116	2	61	22	71	62	31076	10462	65931	45	41
2257	137978	107	738	92	4	53	17	66	35	74608	7192	38575	62	59
3373	255929	130	1028	361	0	118	25	100	74	58092	4362	28795	33	33
2571	236489	55	844	85	1	73	25	100	93	42009	14349	94440	77	76
1	0	1	0	0	0	0	0	0	0	0	0	0	0	0
2142	230761	64	1000	63	0	54	31	121	87	36022	10881	38229	34	27
1878	132807	54	629	138	3	54	14	51	39	23333	8022	31972	44	44
2190	157118	49	532	270	9	46	35	119	101	53349	13073	40071	43	43
2186	253254	68	811	64	0	83	34	136	135	92596	26641	132480	117	104
2532	269329	71	837	96	2	106	22	84	76	49598	14426	62797	125	120
1823	161273	61	682	62	0	44	34	136	118	44093	15604	40429	49	44
1095	107181	33	400	35	2	27	23	84	76	84205	9184	45545	76	71
2162	195891	79	804	59	1	64	24	92	65	63369	5989	57568	81	78
1365	139667	51	419	56	2	71	26	103	97	60132	11270	39019	111	106
1244	171101	98	334	41	2	44	23	85	70	37403	13958	53866	61	61
756	81407	33	216	49	1	23	35	106	63	24460	7162	38345	56	53
2417	247563	104	786	121	0	78	24	96	96	46456	13275	50210	54	51
2327	239807	90	752	113	1	60	31	124	112	66616	21224	80947	47	46
2786	172743	59	964	190	8	73	30	106	82	41554	10615	43461	55	55
658	48188	28	205	37	0	12	22	82	39	22346	2102	14812	14	14
2012	169355	70	506	52	0	104	23	87	69	30874	12396	37819	44	44
2602	315622	76	830	89	0	83	27	97	93	68701	18717	102738	115	113
2071	241518	79	694	73	0	57	30	107	76	35728	9724	54509	57	55
1911	195583	59	691	49	1	67	33	126	117	29010	9863	62956	48	46
1775	159913	57	547	77	8	44	12	43	31	23110	8374	55411	40	39
1918	220241	69	538	58	0	53	26	96	65	38844	8030	50611	51	51
1046	101694	25	329	75	1	26	26	100	78	27084	7509	26692	32	31
1178	151985	67	421	32	0	67	23	91	87	35139	14146	60056	36	36
2890	202536	99	972	59	10	36	38	136	85	57476	7768	25155	47	47
1836	173505	64	541	71	6	56	32	128	119	33277	13823	42840	51	53
2254	150518	83	836	91	0	52	21	83	65	31141	7230	39358	37	38
1392	141491	64	376	87	11	54	22	74	60	61281	10170	47241	52	52
1325	125612	38	467	48	3	57	26	96	67	25820	7573	49611	42	37
1317	166049	36	430	63	0	27	28	102	94	23284	5753	41833	11	11
1525	124197	42	483	41	0	58	33	122	100	35378	9791	48930	47	45
2335	195043	71	504	86	8	76	36	144	135	74990	19365	110600	59	59
2897	138708	65	887	152	2	93	25	90	71	29653	9422	52235	82	82
1118	116552	40	271	49	0	59	25	97	78	64622	12310	53986	49	49
340	31970	15	101	40	0	5	21	78	42	4157	1283	4105	6	6
2977	258158	115	1097	135	3	57	19	72	42	29245	6372	59331	83	81
1449	151184	78	469	83	1	42	12	45	8	50008	5413	47796	56	56
1550	135926	68	528	62	2	88	30	120	86	52338	10837	38302	114	105
1684	119629	72	475	91	1	53	21	59	41	13310	3394	14063	46	46
2728	171518	71	698	95	0	81	39	150	131	92901	12964	54414	46	46
1574	108949	45	425	82	2	35	32	117	91	10956	3495	9903	2	2
2413	183471	60	709	112	1	102	28	123	102	34241	11580	53987	51	51
2563	159966	98	824	70	0	71	29	114	91	75043	9970	88937	96	95
1079	93786	34	336	78	0	28	21	75	46	21152	4911	21928	20	18
1235	84971	72	395	105	0	34	31	114	60	42249	10138	29487	57	55
966	88797	75	228	49	0	54	26	94	69	42005	14697	35334	49	48
2246	304603	65	830	60	0	49	29	116	95	41152	8464	57596	51	48
1076	75101	30	334	49	1	30	23	86	17	14399	4204	29750	40	39
1637	145043	40	524	132	0	57	25	90	61	28263	10226	41029	40	40
1208	95827	48	393	49	0	54	22	87	55	17215	3456	12416	36	36
1865	173924	58	574	71	0	38	26	99	55	48140	8895	51158	64	60
2726	241957	237	672	102	0	63	33	132	124	62897	22557	79935	117	114
1208	115367	115	284	74	0	58	24	96	73	22883	6900	26552	40	39
1419	118408	64	450	49	7	46	24	91	73	41622	8620	25807	46	45
1609	164078	53	653	74	0	46	21	77	67	40715	7820	50620	61	59
1864	158931	41	684	59	5	51	28	104	66	65897	12112	61467	59	59
2412	184139	82	706	91	1	87	28	100	77	76542	13178	65292	94	93
1238	152856	58	417	68	0	39	25	94	83	37477	7028	55516	36	35
1462	144014	59	549	81	0	28	15	60	55	53216	6616	42006	51	47
973	62535	42	394	33	0	26	13	46	27	40911	9570	26273	39	36
2319	245196	117	730	166	0	52	36	135	115	57021	14612	90248	62	59
1890	199841	71	571	97	0	96	27	99	85	73116	11219	61476	79	79
223	19349	12	67	15	0	13	1	2	0	3895	786	9604	14	14
2526	247280	108	877	105	3	43	24	96	83	46609	11252	45108	45	42
2072	159408	83	856	61	0	42	31	109	90	29351	9289	47232	43	41
778	72128	30	306	11	0	30	4	15	4	2325	593	3439	8	8
1194	104253	26	382	45	0	59	21	68	60	31747	6562	30553	41	41
1424	151090	57	435	89	0	73	27	102	74	32665	8208	24751	25	24
1327	137382	65	336	67	1	39	23	84	52	19249	7488	34458	22	22
839	87448	42	227	27	1	36	12	46	24	15292	4574	24649	18	18
596	27676	22	194	59	0	2	16	59	17	5842	522	2342	3	1
1671	165507	50	410	127	0	102	29	116	105	33994	12840	52739	54	53
1167	132148	37	273	48	1	30	26	29	20	13018	1350	6245	6	6
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
1105	95778	33	343	58	0	46	25	91	51	98177	10623	35381	50	49
1148	109001	67	376	57	0	25	21	76	76	37941	5322	19595	33	33
1485	158833	46	495	60	0	59	24	86	61	31032	7987	50848	54	50
1526	147690	63	448	77	1	60	21	84	70	32683	10566	39443	63	64
962	89887	63	313	71	0	36	21	65	38	34545	1900	27023	56	53
78	3616	5	14	5	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
1184	199005	45	410	70	0	45	23	84	81	27525	10698	61022	49	48
1671	160930	92	606	76	0	79	33	114	78	66856	14884	63528	90	90
2142	177948	102	593	124	2	30	32	132	76	28549	6852	34835	51	46
1015	136061	39	312	56	0	43	23	92	89	38610	6873	37172	29	29
778	43410	19	292	63	0	7	1	3	3	2781	4	13	1	1
1856	184277	74	547	92	1	80	29	109	87	41211	9188	62548	68	64
1056	108858	43	302	58	0	32	20	81	55	22698	5141	31334	29	29
2234	141744	58	632	64	8	81	33	121	73	41194	4260	20839	27	27
731	60493	40	174	29	3	3	12	48	32	32689	443	5084	4	4
285	19764	12	75	19	1	10	2	8	4	5752	2416	9927	10	10
1872	177559	56	572	64	3	47	21	80	70	26757	9831	53229	47	47
1181	140281	35	389	79	0	35	28	107	102	22527	5953	29877	44	44
1725	164249	54	562	104	0	54	35	140	109	44810	9435	37310	53	51
256	11796	9	79	22	0	1	2	8	1	0	0	0	0	0
98	10674	9	33	7	0	0	0	0	0	0	0	0	0	0
1435	151322	59	487	37	0	46	18	56	39	100674	7642	50067	40	38
41	6836	3	11	5	0	0	1	4	0	0	0	0	0	0
1930	174712	67	664	48	6	51	21	70	45	57786	6837	47708	57	57
42	5118	3	6	1	0	5	0	0	0	0	0	0	0	0
528	40248	16	183	34	1	8	4	14	7	5444	775	6012	6	6
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
1121	127628	50	342	53	0	38	29	104	86	28470	8191	27749	24	22
1305	88837	38	269	44	0	21	26	89	52	61849	1661	47555	34	34
81	7131	4	27	0	1	0	0	0	0	0	0	0	0	0
262	9056	15	99	18	0	0	4	12	1	2179	548	1336	10	10
1099	87957	26	305	52	1	18	19	60	49	8019	3080	11017	16	16
1290	144470	53	327	56	0	53	22	84	72	39644	13400	55184	93	93
1248	111408	20	459	50	1	17	22	88	56	23494	8181	43485	28	22

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'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 & 7 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159387&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159387&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159387&T=0

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


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

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






Multiple Linear Regression - Estimated Regression Equation
2[t] = + 6860.46739580395 -2.74305077717542`1`[t] + 280.013304272678`3`[t] + 128.223863419206`4`[t] -14.4959618943275`5`[t] -187.131861055584`6`[t] + 333.49962968309`7`[t] + 890.56389149714`8`[t] -325.750120382976`9`[t] + 469.541026455697`10`[t] -0.175402817919373`11`[t] -1.86792219988296`12`[t] + 1.30417569933872`13`[t] + 944.205820968608`14`[t] -1204.4034670031`15`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
2[t] =  +  6860.46739580395 -2.74305077717542`1`[t] +  280.013304272678`3`[t] +  128.223863419206`4`[t] -14.4959618943275`5`[t] -187.131861055584`6`[t] +  333.49962968309`7`[t] +  890.56389149714`8`[t] -325.750120382976`9`[t] +  469.541026455697`10`[t] -0.175402817919373`11`[t] -1.86792219988296`12`[t] +  1.30417569933872`13`[t] +  944.205820968608`14`[t] -1204.4034670031`15`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159387&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]2[t] =  +  6860.46739580395 -2.74305077717542`1`[t] +  280.013304272678`3`[t] +  128.223863419206`4`[t] -14.4959618943275`5`[t] -187.131861055584`6`[t] +  333.49962968309`7`[t] +  890.56389149714`8`[t] -325.750120382976`9`[t] +  469.541026455697`10`[t] -0.175402817919373`11`[t] -1.86792219988296`12`[t] +  1.30417569933872`13`[t] +  944.205820968608`14`[t] -1204.4034670031`15`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159387&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159387&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
2[t] = + 6860.46739580395 -2.74305077717542`1`[t] + 280.013304272678`3`[t] + 128.223863419206`4`[t] -14.4959618943275`5`[t] -187.131861055584`6`[t] + 333.49962968309`7`[t] + 890.56389149714`8`[t] -325.750120382976`9`[t] + 469.541026455697`10`[t] -0.175402817919373`11`[t] -1.86792219988296`12`[t] + 1.30417569933872`13`[t] + 944.205820968608`14`[t] -1204.4034670031`15`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6860.467395803956203.806341.10580.270850.135425
`1`-2.7430507771754214.887017-0.18430.85410.42705
`3`280.013304272678113.7039562.46270.015110.007555
`4`128.22386341920633.7093433.80380.0002190.000109
`5`-14.495961894327570.947741-0.20430.8384260.419213
`6`-187.1318610555841146.401223-0.16320.870590.435295
`7`333.49962968309173.1508011.92610.0562960.028148
`8`890.563891497141039.0851040.85710.3929980.196499
`9`-325.750120382976331.956168-0.98130.3282790.16414
`10`469.541026455697212.284432.21180.0287380.014369
`11`-0.1754028179193730.16003-1.09610.2750930.137547
`12`-1.867922199882960.975011-1.91580.0576040.028802
`13`1.304175699338720.1866916.985700
`14`944.2058209686081223.0540010.7720.4415220.220761
`15`-1204.40346700311264.093519-0.95280.3424830.171242

\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) & 6860.46739580395 & 6203.80634 & 1.1058 & 0.27085 & 0.135425 \tabularnewline
`1` & -2.74305077717542 & 14.887017 & -0.1843 & 0.8541 & 0.42705 \tabularnewline
`3` & 280.013304272678 & 113.703956 & 2.4627 & 0.01511 & 0.007555 \tabularnewline
`4` & 128.223863419206 & 33.709343 & 3.8038 & 0.000219 & 0.000109 \tabularnewline
`5` & -14.4959618943275 & 70.947741 & -0.2043 & 0.838426 & 0.419213 \tabularnewline
`6` & -187.131861055584 & 1146.401223 & -0.1632 & 0.87059 & 0.435295 \tabularnewline
`7` & 333.49962968309 & 173.150801 & 1.9261 & 0.056296 & 0.028148 \tabularnewline
`8` & 890.56389149714 & 1039.085104 & 0.8571 & 0.392998 & 0.196499 \tabularnewline
`9` & -325.750120382976 & 331.956168 & -0.9813 & 0.328279 & 0.16414 \tabularnewline
`10` & 469.541026455697 & 212.28443 & 2.2118 & 0.028738 & 0.014369 \tabularnewline
`11` & -0.175402817919373 & 0.16003 & -1.0961 & 0.275093 & 0.137547 \tabularnewline
`12` & -1.86792219988296 & 0.975011 & -1.9158 & 0.057604 & 0.028802 \tabularnewline
`13` & 1.30417569933872 & 0.186691 & 6.9857 & 0 & 0 \tabularnewline
`14` & 944.205820968608 & 1223.054001 & 0.772 & 0.441522 & 0.220761 \tabularnewline
`15` & -1204.4034670031 & 1264.093519 & -0.9528 & 0.342483 & 0.171242 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159387&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]6860.46739580395[/C][C]6203.80634[/C][C]1.1058[/C][C]0.27085[/C][C]0.135425[/C][/ROW]
[ROW][C]`1`[/C][C]-2.74305077717542[/C][C]14.887017[/C][C]-0.1843[/C][C]0.8541[/C][C]0.42705[/C][/ROW]
[ROW][C]`3`[/C][C]280.013304272678[/C][C]113.703956[/C][C]2.4627[/C][C]0.01511[/C][C]0.007555[/C][/ROW]
[ROW][C]`4`[/C][C]128.223863419206[/C][C]33.709343[/C][C]3.8038[/C][C]0.000219[/C][C]0.000109[/C][/ROW]
[ROW][C]`5`[/C][C]-14.4959618943275[/C][C]70.947741[/C][C]-0.2043[/C][C]0.838426[/C][C]0.419213[/C][/ROW]
[ROW][C]`6`[/C][C]-187.131861055584[/C][C]1146.401223[/C][C]-0.1632[/C][C]0.87059[/C][C]0.435295[/C][/ROW]
[ROW][C]`7`[/C][C]333.49962968309[/C][C]173.150801[/C][C]1.9261[/C][C]0.056296[/C][C]0.028148[/C][/ROW]
[ROW][C]`8`[/C][C]890.56389149714[/C][C]1039.085104[/C][C]0.8571[/C][C]0.392998[/C][C]0.196499[/C][/ROW]
[ROW][C]`9`[/C][C]-325.750120382976[/C][C]331.956168[/C][C]-0.9813[/C][C]0.328279[/C][C]0.16414[/C][/ROW]
[ROW][C]`10`[/C][C]469.541026455697[/C][C]212.28443[/C][C]2.2118[/C][C]0.028738[/C][C]0.014369[/C][/ROW]
[ROW][C]`11`[/C][C]-0.175402817919373[/C][C]0.16003[/C][C]-1.0961[/C][C]0.275093[/C][C]0.137547[/C][/ROW]
[ROW][C]`12`[/C][C]-1.86792219988296[/C][C]0.975011[/C][C]-1.9158[/C][C]0.057604[/C][C]0.028802[/C][/ROW]
[ROW][C]`13`[/C][C]1.30417569933872[/C][C]0.186691[/C][C]6.9857[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`14`[/C][C]944.205820968608[/C][C]1223.054001[/C][C]0.772[/C][C]0.441522[/C][C]0.220761[/C][/ROW]
[ROW][C]`15`[/C][C]-1204.4034670031[/C][C]1264.093519[/C][C]-0.9528[/C][C]0.342483[/C][C]0.171242[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159387&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159387&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)6860.467395803956203.806341.10580.270850.135425
`1`-2.7430507771754214.887017-0.18430.85410.42705
`3`280.013304272678113.7039562.46270.015110.007555
`4`128.22386341920633.7093433.80380.0002190.000109
`5`-14.495961894327570.947741-0.20430.8384260.419213
`6`-187.1318610555841146.401223-0.16320.870590.435295
`7`333.49962968309173.1508011.92610.0562960.028148
`8`890.563891497141039.0851040.85710.3929980.196499
`9`-325.750120382976331.956168-0.98130.3282790.16414
`10`469.541026455697212.284432.21180.0287380.014369
`11`-0.1754028179193730.16003-1.09610.2750930.137547
`12`-1.867922199882960.975011-1.91580.0576040.028802
`13`1.304175699338720.1866916.985700
`14`944.2058209686081223.0540010.7720.4415220.220761
`15`-1204.40346700311264.093519-0.95280.3424830.171242






Multiple Linear Regression - Regression Statistics
Multiple R0.945895233829262
R-squared0.894717793380914
Adjusted R-squared0.883291817468765
F-TEST (value)78.3055907224163
F-TEST (DF numerator)14
F-TEST (DF denominator)129
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation27651.3675754845
Sum Squared Residuals98633158614.4973

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.945895233829262 \tabularnewline
R-squared & 0.894717793380914 \tabularnewline
Adjusted R-squared & 0.883291817468765 \tabularnewline
F-TEST (value) & 78.3055907224163 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 129 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 27651.3675754845 \tabularnewline
Sum Squared Residuals & 98633158614.4973 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159387&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.945895233829262[/C][/ROW]
[ROW][C]R-squared[/C][C]0.894717793380914[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.883291817468765[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]78.3055907224163[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]129[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]27651.3675754845[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]98633158614.4973[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159387&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159387&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 R0.945895233829262
R-squared0.894717793380914
Adjusted R-squared0.883291817468765
F-TEST (value)78.3055907224163
F-TEST (DF numerator)14
F-TEST (DF denominator)129
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation27651.3675754845
Sum Squared Residuals98633158614.4973






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1162687153659.7495628029027.25043719788
2201906203529.555640774-1623.55564077391
3721521731.4629553786-14516.4629553786
4146367172954.385316306-26587.3853163056
5257045288576.185840706-31531.1858407061
6524450547577.949898857-23127.9498988566
7188294184589.835894453704.16410554985
8195674202487.315446587-6813.31544658737
9177020153368.47867273323651.5213272671
10325899283359.24952723642539.7504727643
11121844165802.387291739-43958.3872917388
12203938209289.750544389-5351.7505443888
13107394164323.897510954-56929.8975109544
14220751252822.484005242-32071.4840052415
15172905152357.91050536120547.0894946388
16156326167526.259926675-11200.2599266751
17145178153019.306129343-7841.30612934314
188917187466.06486360531704.93513639472
19172624207821.550962069-35197.5509620686
203244358287.9542098422-25844.9542098422
2187927105983.603218739-18056.6032187394
22241285245496.363151792-4211.36315179174
23195820140611.33745818655208.6625418137
24146946175664.275026793-28718.2750267931
25159763185950.448949947-26187.4489499472
26207078156424.04179899850653.9582010023
27212394215129.939998727-2735.93999872671
28201536211472.484105327-9936.4841053266
29394662318141.39859330176520.6014066986
30217808223059.83697357-5251.8369735704
31182286150842.71034328131443.289656719
32181740184433.369223291-2693.36922329114
33137978162196.359171072-24218.3591710722
34255929235008.89334924620920.1066507536
35236489249876.566506596-13387.5665065961
3607137.73764929946-7137.73764929946
37230761216065.30264886314695.6973511367
38132807138267.689804943-5460.68980494294
39157118139655.14931194117462.8506880588
40253254291998.941011467-38744.9410114672
41269329208368.33766047360960.662339527
42161273170665.360941141-9392.3609411407
43107181115035.642915556-7854.64291555568
44195891203683.201077263-7792.20107726347
45139667125191.89963837614475.1003616235
46171101134829.27524766336271.724752337
478140796100.2278375042-14693.2278375042
48247563211672.2929304435890.7070695596
49239807223305.60335985216501.3966401478
50172743205384.097380464-32641.097380464
514818861669.2048251488-13481.2048251488
52169355153598.64090400915756.3590959912
53315622249397.20637462666224.7936253742
54241518192022.45398997249495.5460100279
55195583219982.65932337-24399.6593233699
56159913154753.4246712835159.57532871722
57220241160064.97749179560176.0225082055
5810169496690.02936861575003.97063138428
59151985166312.027209255-14327.0272092552
60202536186003.54641786116532.4535821393
61173505156850.4057577116654.5942422903
62150518190852.215823862-40334.215823862
63141491125858.56173239615632.4382676038
64125612155961.651670779-30349.6516707789
65166049149267.15961382716781.8403861726
66124197161218.447621367-37021.4476213673
67195043235668.201686885-40625.2016868848
68138708209557.723839749-70849.7238397492
69116552119328.188945403-2776.18894540319
703197037847.1166893815-5877.1166893815
71258158242394.46529232815763.5347076721
72151184126145.55029851925038.4497014811
73135926147140.959520536-11214.9595205358
74119629115908.6372913933720.36270860744
75171518200259.812146063-28741.8121460628
76108949116806.46970676-7857.46970676044
77183471202422.941348226-18951.9413482256
78159966247439.590063997-87473.5900639969
799378693500.4929945637285.507005436339
8084971102429.704594378-17458.7045943779
818879796393.6926168802-7596.69261688022
82304603215873.87107856388729.1289214367
837510183911.6803450397-8810.68034503975
84145043138486.8675327126556.13246728791
859582799105.490043029-3278.49004302908
86173924149784.80062232324139.1993776767
87241957240309.4069139071647.59308609338
88115367123334.828304083-7967.82830408258
89118408117407.2621044631000.73789553701
90164078171169.846736003-7091.84673600254
91158931168827.446463829-9896.446463829
92184139193611.910057018-9472.91005701834
93152856160349.522249671-7493.5222496707
94144014142206.1661648861807.83383511413
956253586613.48656924-24078.48656924
96245196251765.694088642-6569.69408864159
97199841182928.59825008116912.4017499193
981934929288.2005281114-9939.2005281114
99247280205495.58898378341784.4110162172
100159408211979.49555601-52571.4955560095
1017212863650.96604689748477.03395310257
102104253114947.139412243-10694.1394122431
103151090129230.25971385221859.7402861476
104137382115740.34667889621641.653321104
1058744880062.06635153187385.93364846818
1062767641767.8662817687-14091.8662817687
107165507164354.2888463051152.71115369542
10813214883024.015698462249123.9843015378
10906860.46739580396-6860.46739580396
1109577885392.367937535210385.6320624648
111109001108198.440879513802.559120487402
112158833156665.679561172167.32043883005
113147690129047.65188587118642.3481141287
11489887103020.515865014-13133.5158650138
11536169769.2302349449-6153.2302349449
11606860.46739580396-6860.46739580396
117199005157157.76058686741847.2394131334
118160930179768.788039165-18838.7880391651
119177948154976.65312731222971.3468726876
120136061122157.01038560213903.9896143984
1214341049492.6729215284-6082.67292152839
122184277193264.432890587-8987.4328905871
123108858101544.5099427527313.49005724811
124141744151818.246330044-10074.2463300438
1256049347489.982282435413003.0177175646
1261976427804.138080959-8040.13808095898
127177559164579.20323975112979.7967602491
128140281124246.61058470616034.3894152943
129164249154350.8064854699898.19351453145
1301179618467.3076598125-6671.30765981246
1311067413241.6839176684-2567.68391766836
132151322138158.77276910213163.2272308978
13368368513.58832486266-1677.58832486266
134174712162168.01647738912543.9835226113
135511810007.644543017-4889.64454301698
1364024841510.8457799067-1262.84577990671
13706860.46739580396-6860.46739580396
138127628117932.0684786519695.9315213492
13988837112580.462451629-23743.4624516288
140713111033.2459512064-3902.24595120642
141905620632.5961367156-11576.5961367156
1428795778724.95308269479232.04691730535
143144470118779.32018955125690.6798104486
144111408127120.99734771-15712.9973477096

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 162687 & 153659.749562802 & 9027.25043719788 \tabularnewline
2 & 201906 & 203529.555640774 & -1623.55564077391 \tabularnewline
3 & 7215 & 21731.4629553786 & -14516.4629553786 \tabularnewline
4 & 146367 & 172954.385316306 & -26587.3853163056 \tabularnewline
5 & 257045 & 288576.185840706 & -31531.1858407061 \tabularnewline
6 & 524450 & 547577.949898857 & -23127.9498988566 \tabularnewline
7 & 188294 & 184589.83589445 & 3704.16410554985 \tabularnewline
8 & 195674 & 202487.315446587 & -6813.31544658737 \tabularnewline
9 & 177020 & 153368.478672733 & 23651.5213272671 \tabularnewline
10 & 325899 & 283359.249527236 & 42539.7504727643 \tabularnewline
11 & 121844 & 165802.387291739 & -43958.3872917388 \tabularnewline
12 & 203938 & 209289.750544389 & -5351.7505443888 \tabularnewline
13 & 107394 & 164323.897510954 & -56929.8975109544 \tabularnewline
14 & 220751 & 252822.484005242 & -32071.4840052415 \tabularnewline
15 & 172905 & 152357.910505361 & 20547.0894946388 \tabularnewline
16 & 156326 & 167526.259926675 & -11200.2599266751 \tabularnewline
17 & 145178 & 153019.306129343 & -7841.30612934314 \tabularnewline
18 & 89171 & 87466.0648636053 & 1704.93513639472 \tabularnewline
19 & 172624 & 207821.550962069 & -35197.5509620686 \tabularnewline
20 & 32443 & 58287.9542098422 & -25844.9542098422 \tabularnewline
21 & 87927 & 105983.603218739 & -18056.6032187394 \tabularnewline
22 & 241285 & 245496.363151792 & -4211.36315179174 \tabularnewline
23 & 195820 & 140611.337458186 & 55208.6625418137 \tabularnewline
24 & 146946 & 175664.275026793 & -28718.2750267931 \tabularnewline
25 & 159763 & 185950.448949947 & -26187.4489499472 \tabularnewline
26 & 207078 & 156424.041798998 & 50653.9582010023 \tabularnewline
27 & 212394 & 215129.939998727 & -2735.93999872671 \tabularnewline
28 & 201536 & 211472.484105327 & -9936.4841053266 \tabularnewline
29 & 394662 & 318141.398593301 & 76520.6014066986 \tabularnewline
30 & 217808 & 223059.83697357 & -5251.8369735704 \tabularnewline
31 & 182286 & 150842.710343281 & 31443.289656719 \tabularnewline
32 & 181740 & 184433.369223291 & -2693.36922329114 \tabularnewline
33 & 137978 & 162196.359171072 & -24218.3591710722 \tabularnewline
34 & 255929 & 235008.893349246 & 20920.1066507536 \tabularnewline
35 & 236489 & 249876.566506596 & -13387.5665065961 \tabularnewline
36 & 0 & 7137.73764929946 & -7137.73764929946 \tabularnewline
37 & 230761 & 216065.302648863 & 14695.6973511367 \tabularnewline
38 & 132807 & 138267.689804943 & -5460.68980494294 \tabularnewline
39 & 157118 & 139655.149311941 & 17462.8506880588 \tabularnewline
40 & 253254 & 291998.941011467 & -38744.9410114672 \tabularnewline
41 & 269329 & 208368.337660473 & 60960.662339527 \tabularnewline
42 & 161273 & 170665.360941141 & -9392.3609411407 \tabularnewline
43 & 107181 & 115035.642915556 & -7854.64291555568 \tabularnewline
44 & 195891 & 203683.201077263 & -7792.20107726347 \tabularnewline
45 & 139667 & 125191.899638376 & 14475.1003616235 \tabularnewline
46 & 171101 & 134829.275247663 & 36271.724752337 \tabularnewline
47 & 81407 & 96100.2278375042 & -14693.2278375042 \tabularnewline
48 & 247563 & 211672.29293044 & 35890.7070695596 \tabularnewline
49 & 239807 & 223305.603359852 & 16501.3966401478 \tabularnewline
50 & 172743 & 205384.097380464 & -32641.097380464 \tabularnewline
51 & 48188 & 61669.2048251488 & -13481.2048251488 \tabularnewline
52 & 169355 & 153598.640904009 & 15756.3590959912 \tabularnewline
53 & 315622 & 249397.206374626 & 66224.7936253742 \tabularnewline
54 & 241518 & 192022.453989972 & 49495.5460100279 \tabularnewline
55 & 195583 & 219982.65932337 & -24399.6593233699 \tabularnewline
56 & 159913 & 154753.424671283 & 5159.57532871722 \tabularnewline
57 & 220241 & 160064.977491795 & 60176.0225082055 \tabularnewline
58 & 101694 & 96690.0293686157 & 5003.97063138428 \tabularnewline
59 & 151985 & 166312.027209255 & -14327.0272092552 \tabularnewline
60 & 202536 & 186003.546417861 & 16532.4535821393 \tabularnewline
61 & 173505 & 156850.40575771 & 16654.5942422903 \tabularnewline
62 & 150518 & 190852.215823862 & -40334.215823862 \tabularnewline
63 & 141491 & 125858.561732396 & 15632.4382676038 \tabularnewline
64 & 125612 & 155961.651670779 & -30349.6516707789 \tabularnewline
65 & 166049 & 149267.159613827 & 16781.8403861726 \tabularnewline
66 & 124197 & 161218.447621367 & -37021.4476213673 \tabularnewline
67 & 195043 & 235668.201686885 & -40625.2016868848 \tabularnewline
68 & 138708 & 209557.723839749 & -70849.7238397492 \tabularnewline
69 & 116552 & 119328.188945403 & -2776.18894540319 \tabularnewline
70 & 31970 & 37847.1166893815 & -5877.1166893815 \tabularnewline
71 & 258158 & 242394.465292328 & 15763.5347076721 \tabularnewline
72 & 151184 & 126145.550298519 & 25038.4497014811 \tabularnewline
73 & 135926 & 147140.959520536 & -11214.9595205358 \tabularnewline
74 & 119629 & 115908.637291393 & 3720.36270860744 \tabularnewline
75 & 171518 & 200259.812146063 & -28741.8121460628 \tabularnewline
76 & 108949 & 116806.46970676 & -7857.46970676044 \tabularnewline
77 & 183471 & 202422.941348226 & -18951.9413482256 \tabularnewline
78 & 159966 & 247439.590063997 & -87473.5900639969 \tabularnewline
79 & 93786 & 93500.4929945637 & 285.507005436339 \tabularnewline
80 & 84971 & 102429.704594378 & -17458.7045943779 \tabularnewline
81 & 88797 & 96393.6926168802 & -7596.69261688022 \tabularnewline
82 & 304603 & 215873.871078563 & 88729.1289214367 \tabularnewline
83 & 75101 & 83911.6803450397 & -8810.68034503975 \tabularnewline
84 & 145043 & 138486.867532712 & 6556.13246728791 \tabularnewline
85 & 95827 & 99105.490043029 & -3278.49004302908 \tabularnewline
86 & 173924 & 149784.800622323 & 24139.1993776767 \tabularnewline
87 & 241957 & 240309.406913907 & 1647.59308609338 \tabularnewline
88 & 115367 & 123334.828304083 & -7967.82830408258 \tabularnewline
89 & 118408 & 117407.262104463 & 1000.73789553701 \tabularnewline
90 & 164078 & 171169.846736003 & -7091.84673600254 \tabularnewline
91 & 158931 & 168827.446463829 & -9896.446463829 \tabularnewline
92 & 184139 & 193611.910057018 & -9472.91005701834 \tabularnewline
93 & 152856 & 160349.522249671 & -7493.5222496707 \tabularnewline
94 & 144014 & 142206.166164886 & 1807.83383511413 \tabularnewline
95 & 62535 & 86613.48656924 & -24078.48656924 \tabularnewline
96 & 245196 & 251765.694088642 & -6569.69408864159 \tabularnewline
97 & 199841 & 182928.598250081 & 16912.4017499193 \tabularnewline
98 & 19349 & 29288.2005281114 & -9939.2005281114 \tabularnewline
99 & 247280 & 205495.588983783 & 41784.4110162172 \tabularnewline
100 & 159408 & 211979.49555601 & -52571.4955560095 \tabularnewline
101 & 72128 & 63650.9660468974 & 8477.03395310257 \tabularnewline
102 & 104253 & 114947.139412243 & -10694.1394122431 \tabularnewline
103 & 151090 & 129230.259713852 & 21859.7402861476 \tabularnewline
104 & 137382 & 115740.346678896 & 21641.653321104 \tabularnewline
105 & 87448 & 80062.0663515318 & 7385.93364846818 \tabularnewline
106 & 27676 & 41767.8662817687 & -14091.8662817687 \tabularnewline
107 & 165507 & 164354.288846305 & 1152.71115369542 \tabularnewline
108 & 132148 & 83024.0156984622 & 49123.9843015378 \tabularnewline
109 & 0 & 6860.46739580396 & -6860.46739580396 \tabularnewline
110 & 95778 & 85392.3679375352 & 10385.6320624648 \tabularnewline
111 & 109001 & 108198.440879513 & 802.559120487402 \tabularnewline
112 & 158833 & 156665.67956117 & 2167.32043883005 \tabularnewline
113 & 147690 & 129047.651885871 & 18642.3481141287 \tabularnewline
114 & 89887 & 103020.515865014 & -13133.5158650138 \tabularnewline
115 & 3616 & 9769.2302349449 & -6153.2302349449 \tabularnewline
116 & 0 & 6860.46739580396 & -6860.46739580396 \tabularnewline
117 & 199005 & 157157.760586867 & 41847.2394131334 \tabularnewline
118 & 160930 & 179768.788039165 & -18838.7880391651 \tabularnewline
119 & 177948 & 154976.653127312 & 22971.3468726876 \tabularnewline
120 & 136061 & 122157.010385602 & 13903.9896143984 \tabularnewline
121 & 43410 & 49492.6729215284 & -6082.67292152839 \tabularnewline
122 & 184277 & 193264.432890587 & -8987.4328905871 \tabularnewline
123 & 108858 & 101544.509942752 & 7313.49005724811 \tabularnewline
124 & 141744 & 151818.246330044 & -10074.2463300438 \tabularnewline
125 & 60493 & 47489.9822824354 & 13003.0177175646 \tabularnewline
126 & 19764 & 27804.138080959 & -8040.13808095898 \tabularnewline
127 & 177559 & 164579.203239751 & 12979.7967602491 \tabularnewline
128 & 140281 & 124246.610584706 & 16034.3894152943 \tabularnewline
129 & 164249 & 154350.806485469 & 9898.19351453145 \tabularnewline
130 & 11796 & 18467.3076598125 & -6671.30765981246 \tabularnewline
131 & 10674 & 13241.6839176684 & -2567.68391766836 \tabularnewline
132 & 151322 & 138158.772769102 & 13163.2272308978 \tabularnewline
133 & 6836 & 8513.58832486266 & -1677.58832486266 \tabularnewline
134 & 174712 & 162168.016477389 & 12543.9835226113 \tabularnewline
135 & 5118 & 10007.644543017 & -4889.64454301698 \tabularnewline
136 & 40248 & 41510.8457799067 & -1262.84577990671 \tabularnewline
137 & 0 & 6860.46739580396 & -6860.46739580396 \tabularnewline
138 & 127628 & 117932.068478651 & 9695.9315213492 \tabularnewline
139 & 88837 & 112580.462451629 & -23743.4624516288 \tabularnewline
140 & 7131 & 11033.2459512064 & -3902.24595120642 \tabularnewline
141 & 9056 & 20632.5961367156 & -11576.5961367156 \tabularnewline
142 & 87957 & 78724.9530826947 & 9232.04691730535 \tabularnewline
143 & 144470 & 118779.320189551 & 25690.6798104486 \tabularnewline
144 & 111408 & 127120.99734771 & -15712.9973477096 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159387&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]162687[/C][C]153659.749562802[/C][C]9027.25043719788[/C][/ROW]
[ROW][C]2[/C][C]201906[/C][C]203529.555640774[/C][C]-1623.55564077391[/C][/ROW]
[ROW][C]3[/C][C]7215[/C][C]21731.4629553786[/C][C]-14516.4629553786[/C][/ROW]
[ROW][C]4[/C][C]146367[/C][C]172954.385316306[/C][C]-26587.3853163056[/C][/ROW]
[ROW][C]5[/C][C]257045[/C][C]288576.185840706[/C][C]-31531.1858407061[/C][/ROW]
[ROW][C]6[/C][C]524450[/C][C]547577.949898857[/C][C]-23127.9498988566[/C][/ROW]
[ROW][C]7[/C][C]188294[/C][C]184589.83589445[/C][C]3704.16410554985[/C][/ROW]
[ROW][C]8[/C][C]195674[/C][C]202487.315446587[/C][C]-6813.31544658737[/C][/ROW]
[ROW][C]9[/C][C]177020[/C][C]153368.478672733[/C][C]23651.5213272671[/C][/ROW]
[ROW][C]10[/C][C]325899[/C][C]283359.249527236[/C][C]42539.7504727643[/C][/ROW]
[ROW][C]11[/C][C]121844[/C][C]165802.387291739[/C][C]-43958.3872917388[/C][/ROW]
[ROW][C]12[/C][C]203938[/C][C]209289.750544389[/C][C]-5351.7505443888[/C][/ROW]
[ROW][C]13[/C][C]107394[/C][C]164323.897510954[/C][C]-56929.8975109544[/C][/ROW]
[ROW][C]14[/C][C]220751[/C][C]252822.484005242[/C][C]-32071.4840052415[/C][/ROW]
[ROW][C]15[/C][C]172905[/C][C]152357.910505361[/C][C]20547.0894946388[/C][/ROW]
[ROW][C]16[/C][C]156326[/C][C]167526.259926675[/C][C]-11200.2599266751[/C][/ROW]
[ROW][C]17[/C][C]145178[/C][C]153019.306129343[/C][C]-7841.30612934314[/C][/ROW]
[ROW][C]18[/C][C]89171[/C][C]87466.0648636053[/C][C]1704.93513639472[/C][/ROW]
[ROW][C]19[/C][C]172624[/C][C]207821.550962069[/C][C]-35197.5509620686[/C][/ROW]
[ROW][C]20[/C][C]32443[/C][C]58287.9542098422[/C][C]-25844.9542098422[/C][/ROW]
[ROW][C]21[/C][C]87927[/C][C]105983.603218739[/C][C]-18056.6032187394[/C][/ROW]
[ROW][C]22[/C][C]241285[/C][C]245496.363151792[/C][C]-4211.36315179174[/C][/ROW]
[ROW][C]23[/C][C]195820[/C][C]140611.337458186[/C][C]55208.6625418137[/C][/ROW]
[ROW][C]24[/C][C]146946[/C][C]175664.275026793[/C][C]-28718.2750267931[/C][/ROW]
[ROW][C]25[/C][C]159763[/C][C]185950.448949947[/C][C]-26187.4489499472[/C][/ROW]
[ROW][C]26[/C][C]207078[/C][C]156424.041798998[/C][C]50653.9582010023[/C][/ROW]
[ROW][C]27[/C][C]212394[/C][C]215129.939998727[/C][C]-2735.93999872671[/C][/ROW]
[ROW][C]28[/C][C]201536[/C][C]211472.484105327[/C][C]-9936.4841053266[/C][/ROW]
[ROW][C]29[/C][C]394662[/C][C]318141.398593301[/C][C]76520.6014066986[/C][/ROW]
[ROW][C]30[/C][C]217808[/C][C]223059.83697357[/C][C]-5251.8369735704[/C][/ROW]
[ROW][C]31[/C][C]182286[/C][C]150842.710343281[/C][C]31443.289656719[/C][/ROW]
[ROW][C]32[/C][C]181740[/C][C]184433.369223291[/C][C]-2693.36922329114[/C][/ROW]
[ROW][C]33[/C][C]137978[/C][C]162196.359171072[/C][C]-24218.3591710722[/C][/ROW]
[ROW][C]34[/C][C]255929[/C][C]235008.893349246[/C][C]20920.1066507536[/C][/ROW]
[ROW][C]35[/C][C]236489[/C][C]249876.566506596[/C][C]-13387.5665065961[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]7137.73764929946[/C][C]-7137.73764929946[/C][/ROW]
[ROW][C]37[/C][C]230761[/C][C]216065.302648863[/C][C]14695.6973511367[/C][/ROW]
[ROW][C]38[/C][C]132807[/C][C]138267.689804943[/C][C]-5460.68980494294[/C][/ROW]
[ROW][C]39[/C][C]157118[/C][C]139655.149311941[/C][C]17462.8506880588[/C][/ROW]
[ROW][C]40[/C][C]253254[/C][C]291998.941011467[/C][C]-38744.9410114672[/C][/ROW]
[ROW][C]41[/C][C]269329[/C][C]208368.337660473[/C][C]60960.662339527[/C][/ROW]
[ROW][C]42[/C][C]161273[/C][C]170665.360941141[/C][C]-9392.3609411407[/C][/ROW]
[ROW][C]43[/C][C]107181[/C][C]115035.642915556[/C][C]-7854.64291555568[/C][/ROW]
[ROW][C]44[/C][C]195891[/C][C]203683.201077263[/C][C]-7792.20107726347[/C][/ROW]
[ROW][C]45[/C][C]139667[/C][C]125191.899638376[/C][C]14475.1003616235[/C][/ROW]
[ROW][C]46[/C][C]171101[/C][C]134829.275247663[/C][C]36271.724752337[/C][/ROW]
[ROW][C]47[/C][C]81407[/C][C]96100.2278375042[/C][C]-14693.2278375042[/C][/ROW]
[ROW][C]48[/C][C]247563[/C][C]211672.29293044[/C][C]35890.7070695596[/C][/ROW]
[ROW][C]49[/C][C]239807[/C][C]223305.603359852[/C][C]16501.3966401478[/C][/ROW]
[ROW][C]50[/C][C]172743[/C][C]205384.097380464[/C][C]-32641.097380464[/C][/ROW]
[ROW][C]51[/C][C]48188[/C][C]61669.2048251488[/C][C]-13481.2048251488[/C][/ROW]
[ROW][C]52[/C][C]169355[/C][C]153598.640904009[/C][C]15756.3590959912[/C][/ROW]
[ROW][C]53[/C][C]315622[/C][C]249397.206374626[/C][C]66224.7936253742[/C][/ROW]
[ROW][C]54[/C][C]241518[/C][C]192022.453989972[/C][C]49495.5460100279[/C][/ROW]
[ROW][C]55[/C][C]195583[/C][C]219982.65932337[/C][C]-24399.6593233699[/C][/ROW]
[ROW][C]56[/C][C]159913[/C][C]154753.424671283[/C][C]5159.57532871722[/C][/ROW]
[ROW][C]57[/C][C]220241[/C][C]160064.977491795[/C][C]60176.0225082055[/C][/ROW]
[ROW][C]58[/C][C]101694[/C][C]96690.0293686157[/C][C]5003.97063138428[/C][/ROW]
[ROW][C]59[/C][C]151985[/C][C]166312.027209255[/C][C]-14327.0272092552[/C][/ROW]
[ROW][C]60[/C][C]202536[/C][C]186003.546417861[/C][C]16532.4535821393[/C][/ROW]
[ROW][C]61[/C][C]173505[/C][C]156850.40575771[/C][C]16654.5942422903[/C][/ROW]
[ROW][C]62[/C][C]150518[/C][C]190852.215823862[/C][C]-40334.215823862[/C][/ROW]
[ROW][C]63[/C][C]141491[/C][C]125858.561732396[/C][C]15632.4382676038[/C][/ROW]
[ROW][C]64[/C][C]125612[/C][C]155961.651670779[/C][C]-30349.6516707789[/C][/ROW]
[ROW][C]65[/C][C]166049[/C][C]149267.159613827[/C][C]16781.8403861726[/C][/ROW]
[ROW][C]66[/C][C]124197[/C][C]161218.447621367[/C][C]-37021.4476213673[/C][/ROW]
[ROW][C]67[/C][C]195043[/C][C]235668.201686885[/C][C]-40625.2016868848[/C][/ROW]
[ROW][C]68[/C][C]138708[/C][C]209557.723839749[/C][C]-70849.7238397492[/C][/ROW]
[ROW][C]69[/C][C]116552[/C][C]119328.188945403[/C][C]-2776.18894540319[/C][/ROW]
[ROW][C]70[/C][C]31970[/C][C]37847.1166893815[/C][C]-5877.1166893815[/C][/ROW]
[ROW][C]71[/C][C]258158[/C][C]242394.465292328[/C][C]15763.5347076721[/C][/ROW]
[ROW][C]72[/C][C]151184[/C][C]126145.550298519[/C][C]25038.4497014811[/C][/ROW]
[ROW][C]73[/C][C]135926[/C][C]147140.959520536[/C][C]-11214.9595205358[/C][/ROW]
[ROW][C]74[/C][C]119629[/C][C]115908.637291393[/C][C]3720.36270860744[/C][/ROW]
[ROW][C]75[/C][C]171518[/C][C]200259.812146063[/C][C]-28741.8121460628[/C][/ROW]
[ROW][C]76[/C][C]108949[/C][C]116806.46970676[/C][C]-7857.46970676044[/C][/ROW]
[ROW][C]77[/C][C]183471[/C][C]202422.941348226[/C][C]-18951.9413482256[/C][/ROW]
[ROW][C]78[/C][C]159966[/C][C]247439.590063997[/C][C]-87473.5900639969[/C][/ROW]
[ROW][C]79[/C][C]93786[/C][C]93500.4929945637[/C][C]285.507005436339[/C][/ROW]
[ROW][C]80[/C][C]84971[/C][C]102429.704594378[/C][C]-17458.7045943779[/C][/ROW]
[ROW][C]81[/C][C]88797[/C][C]96393.6926168802[/C][C]-7596.69261688022[/C][/ROW]
[ROW][C]82[/C][C]304603[/C][C]215873.871078563[/C][C]88729.1289214367[/C][/ROW]
[ROW][C]83[/C][C]75101[/C][C]83911.6803450397[/C][C]-8810.68034503975[/C][/ROW]
[ROW][C]84[/C][C]145043[/C][C]138486.867532712[/C][C]6556.13246728791[/C][/ROW]
[ROW][C]85[/C][C]95827[/C][C]99105.490043029[/C][C]-3278.49004302908[/C][/ROW]
[ROW][C]86[/C][C]173924[/C][C]149784.800622323[/C][C]24139.1993776767[/C][/ROW]
[ROW][C]87[/C][C]241957[/C][C]240309.406913907[/C][C]1647.59308609338[/C][/ROW]
[ROW][C]88[/C][C]115367[/C][C]123334.828304083[/C][C]-7967.82830408258[/C][/ROW]
[ROW][C]89[/C][C]118408[/C][C]117407.262104463[/C][C]1000.73789553701[/C][/ROW]
[ROW][C]90[/C][C]164078[/C][C]171169.846736003[/C][C]-7091.84673600254[/C][/ROW]
[ROW][C]91[/C][C]158931[/C][C]168827.446463829[/C][C]-9896.446463829[/C][/ROW]
[ROW][C]92[/C][C]184139[/C][C]193611.910057018[/C][C]-9472.91005701834[/C][/ROW]
[ROW][C]93[/C][C]152856[/C][C]160349.522249671[/C][C]-7493.5222496707[/C][/ROW]
[ROW][C]94[/C][C]144014[/C][C]142206.166164886[/C][C]1807.83383511413[/C][/ROW]
[ROW][C]95[/C][C]62535[/C][C]86613.48656924[/C][C]-24078.48656924[/C][/ROW]
[ROW][C]96[/C][C]245196[/C][C]251765.694088642[/C][C]-6569.69408864159[/C][/ROW]
[ROW][C]97[/C][C]199841[/C][C]182928.598250081[/C][C]16912.4017499193[/C][/ROW]
[ROW][C]98[/C][C]19349[/C][C]29288.2005281114[/C][C]-9939.2005281114[/C][/ROW]
[ROW][C]99[/C][C]247280[/C][C]205495.588983783[/C][C]41784.4110162172[/C][/ROW]
[ROW][C]100[/C][C]159408[/C][C]211979.49555601[/C][C]-52571.4955560095[/C][/ROW]
[ROW][C]101[/C][C]72128[/C][C]63650.9660468974[/C][C]8477.03395310257[/C][/ROW]
[ROW][C]102[/C][C]104253[/C][C]114947.139412243[/C][C]-10694.1394122431[/C][/ROW]
[ROW][C]103[/C][C]151090[/C][C]129230.259713852[/C][C]21859.7402861476[/C][/ROW]
[ROW][C]104[/C][C]137382[/C][C]115740.346678896[/C][C]21641.653321104[/C][/ROW]
[ROW][C]105[/C][C]87448[/C][C]80062.0663515318[/C][C]7385.93364846818[/C][/ROW]
[ROW][C]106[/C][C]27676[/C][C]41767.8662817687[/C][C]-14091.8662817687[/C][/ROW]
[ROW][C]107[/C][C]165507[/C][C]164354.288846305[/C][C]1152.71115369542[/C][/ROW]
[ROW][C]108[/C][C]132148[/C][C]83024.0156984622[/C][C]49123.9843015378[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]6860.46739580396[/C][C]-6860.46739580396[/C][/ROW]
[ROW][C]110[/C][C]95778[/C][C]85392.3679375352[/C][C]10385.6320624648[/C][/ROW]
[ROW][C]111[/C][C]109001[/C][C]108198.440879513[/C][C]802.559120487402[/C][/ROW]
[ROW][C]112[/C][C]158833[/C][C]156665.67956117[/C][C]2167.32043883005[/C][/ROW]
[ROW][C]113[/C][C]147690[/C][C]129047.651885871[/C][C]18642.3481141287[/C][/ROW]
[ROW][C]114[/C][C]89887[/C][C]103020.515865014[/C][C]-13133.5158650138[/C][/ROW]
[ROW][C]115[/C][C]3616[/C][C]9769.2302349449[/C][C]-6153.2302349449[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]6860.46739580396[/C][C]-6860.46739580396[/C][/ROW]
[ROW][C]117[/C][C]199005[/C][C]157157.760586867[/C][C]41847.2394131334[/C][/ROW]
[ROW][C]118[/C][C]160930[/C][C]179768.788039165[/C][C]-18838.7880391651[/C][/ROW]
[ROW][C]119[/C][C]177948[/C][C]154976.653127312[/C][C]22971.3468726876[/C][/ROW]
[ROW][C]120[/C][C]136061[/C][C]122157.010385602[/C][C]13903.9896143984[/C][/ROW]
[ROW][C]121[/C][C]43410[/C][C]49492.6729215284[/C][C]-6082.67292152839[/C][/ROW]
[ROW][C]122[/C][C]184277[/C][C]193264.432890587[/C][C]-8987.4328905871[/C][/ROW]
[ROW][C]123[/C][C]108858[/C][C]101544.509942752[/C][C]7313.49005724811[/C][/ROW]
[ROW][C]124[/C][C]141744[/C][C]151818.246330044[/C][C]-10074.2463300438[/C][/ROW]
[ROW][C]125[/C][C]60493[/C][C]47489.9822824354[/C][C]13003.0177175646[/C][/ROW]
[ROW][C]126[/C][C]19764[/C][C]27804.138080959[/C][C]-8040.13808095898[/C][/ROW]
[ROW][C]127[/C][C]177559[/C][C]164579.203239751[/C][C]12979.7967602491[/C][/ROW]
[ROW][C]128[/C][C]140281[/C][C]124246.610584706[/C][C]16034.3894152943[/C][/ROW]
[ROW][C]129[/C][C]164249[/C][C]154350.806485469[/C][C]9898.19351453145[/C][/ROW]
[ROW][C]130[/C][C]11796[/C][C]18467.3076598125[/C][C]-6671.30765981246[/C][/ROW]
[ROW][C]131[/C][C]10674[/C][C]13241.6839176684[/C][C]-2567.68391766836[/C][/ROW]
[ROW][C]132[/C][C]151322[/C][C]138158.772769102[/C][C]13163.2272308978[/C][/ROW]
[ROW][C]133[/C][C]6836[/C][C]8513.58832486266[/C][C]-1677.58832486266[/C][/ROW]
[ROW][C]134[/C][C]174712[/C][C]162168.016477389[/C][C]12543.9835226113[/C][/ROW]
[ROW][C]135[/C][C]5118[/C][C]10007.644543017[/C][C]-4889.64454301698[/C][/ROW]
[ROW][C]136[/C][C]40248[/C][C]41510.8457799067[/C][C]-1262.84577990671[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]6860.46739580396[/C][C]-6860.46739580396[/C][/ROW]
[ROW][C]138[/C][C]127628[/C][C]117932.068478651[/C][C]9695.9315213492[/C][/ROW]
[ROW][C]139[/C][C]88837[/C][C]112580.462451629[/C][C]-23743.4624516288[/C][/ROW]
[ROW][C]140[/C][C]7131[/C][C]11033.2459512064[/C][C]-3902.24595120642[/C][/ROW]
[ROW][C]141[/C][C]9056[/C][C]20632.5961367156[/C][C]-11576.5961367156[/C][/ROW]
[ROW][C]142[/C][C]87957[/C][C]78724.9530826947[/C][C]9232.04691730535[/C][/ROW]
[ROW][C]143[/C][C]144470[/C][C]118779.320189551[/C][C]25690.6798104486[/C][/ROW]
[ROW][C]144[/C][C]111408[/C][C]127120.99734771[/C][C]-15712.9973477096[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159387&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159387&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
1162687153659.7495628029027.25043719788
2201906203529.555640774-1623.55564077391
3721521731.4629553786-14516.4629553786
4146367172954.385316306-26587.3853163056
5257045288576.185840706-31531.1858407061
6524450547577.949898857-23127.9498988566
7188294184589.835894453704.16410554985
8195674202487.315446587-6813.31544658737
9177020153368.47867273323651.5213272671
10325899283359.24952723642539.7504727643
11121844165802.387291739-43958.3872917388
12203938209289.750544389-5351.7505443888
13107394164323.897510954-56929.8975109544
14220751252822.484005242-32071.4840052415
15172905152357.91050536120547.0894946388
16156326167526.259926675-11200.2599266751
17145178153019.306129343-7841.30612934314
188917187466.06486360531704.93513639472
19172624207821.550962069-35197.5509620686
203244358287.9542098422-25844.9542098422
2187927105983.603218739-18056.6032187394
22241285245496.363151792-4211.36315179174
23195820140611.33745818655208.6625418137
24146946175664.275026793-28718.2750267931
25159763185950.448949947-26187.4489499472
26207078156424.04179899850653.9582010023
27212394215129.939998727-2735.93999872671
28201536211472.484105327-9936.4841053266
29394662318141.39859330176520.6014066986
30217808223059.83697357-5251.8369735704
31182286150842.71034328131443.289656719
32181740184433.369223291-2693.36922329114
33137978162196.359171072-24218.3591710722
34255929235008.89334924620920.1066507536
35236489249876.566506596-13387.5665065961
3607137.73764929946-7137.73764929946
37230761216065.30264886314695.6973511367
38132807138267.689804943-5460.68980494294
39157118139655.14931194117462.8506880588
40253254291998.941011467-38744.9410114672
41269329208368.33766047360960.662339527
42161273170665.360941141-9392.3609411407
43107181115035.642915556-7854.64291555568
44195891203683.201077263-7792.20107726347
45139667125191.89963837614475.1003616235
46171101134829.27524766336271.724752337
478140796100.2278375042-14693.2278375042
48247563211672.2929304435890.7070695596
49239807223305.60335985216501.3966401478
50172743205384.097380464-32641.097380464
514818861669.2048251488-13481.2048251488
52169355153598.64090400915756.3590959912
53315622249397.20637462666224.7936253742
54241518192022.45398997249495.5460100279
55195583219982.65932337-24399.6593233699
56159913154753.4246712835159.57532871722
57220241160064.97749179560176.0225082055
5810169496690.02936861575003.97063138428
59151985166312.027209255-14327.0272092552
60202536186003.54641786116532.4535821393
61173505156850.4057577116654.5942422903
62150518190852.215823862-40334.215823862
63141491125858.56173239615632.4382676038
64125612155961.651670779-30349.6516707789
65166049149267.15961382716781.8403861726
66124197161218.447621367-37021.4476213673
67195043235668.201686885-40625.2016868848
68138708209557.723839749-70849.7238397492
69116552119328.188945403-2776.18894540319
703197037847.1166893815-5877.1166893815
71258158242394.46529232815763.5347076721
72151184126145.55029851925038.4497014811
73135926147140.959520536-11214.9595205358
74119629115908.6372913933720.36270860744
75171518200259.812146063-28741.8121460628
76108949116806.46970676-7857.46970676044
77183471202422.941348226-18951.9413482256
78159966247439.590063997-87473.5900639969
799378693500.4929945637285.507005436339
8084971102429.704594378-17458.7045943779
818879796393.6926168802-7596.69261688022
82304603215873.87107856388729.1289214367
837510183911.6803450397-8810.68034503975
84145043138486.8675327126556.13246728791
859582799105.490043029-3278.49004302908
86173924149784.80062232324139.1993776767
87241957240309.4069139071647.59308609338
88115367123334.828304083-7967.82830408258
89118408117407.2621044631000.73789553701
90164078171169.846736003-7091.84673600254
91158931168827.446463829-9896.446463829
92184139193611.910057018-9472.91005701834
93152856160349.522249671-7493.5222496707
94144014142206.1661648861807.83383511413
956253586613.48656924-24078.48656924
96245196251765.694088642-6569.69408864159
97199841182928.59825008116912.4017499193
981934929288.2005281114-9939.2005281114
99247280205495.58898378341784.4110162172
100159408211979.49555601-52571.4955560095
1017212863650.96604689748477.03395310257
102104253114947.139412243-10694.1394122431
103151090129230.25971385221859.7402861476
104137382115740.34667889621641.653321104
1058744880062.06635153187385.93364846818
1062767641767.8662817687-14091.8662817687
107165507164354.2888463051152.71115369542
10813214883024.015698462249123.9843015378
10906860.46739580396-6860.46739580396
1109577885392.367937535210385.6320624648
111109001108198.440879513802.559120487402
112158833156665.679561172167.32043883005
113147690129047.65188587118642.3481141287
11489887103020.515865014-13133.5158650138
11536169769.2302349449-6153.2302349449
11606860.46739580396-6860.46739580396
117199005157157.76058686741847.2394131334
118160930179768.788039165-18838.7880391651
119177948154976.65312731222971.3468726876
120136061122157.01038560213903.9896143984
1214341049492.6729215284-6082.67292152839
122184277193264.432890587-8987.4328905871
123108858101544.5099427527313.49005724811
124141744151818.246330044-10074.2463300438
1256049347489.982282435413003.0177175646
1261976427804.138080959-8040.13808095898
127177559164579.20323975112979.7967602491
128140281124246.61058470616034.3894152943
129164249154350.8064854699898.19351453145
1301179618467.3076598125-6671.30765981246
1311067413241.6839176684-2567.68391766836
132151322138158.77276910213163.2272308978
13368368513.58832486266-1677.58832486266
134174712162168.01647738912543.9835226113
135511810007.644543017-4889.64454301698
1364024841510.8457799067-1262.84577990671
13706860.46739580396-6860.46739580396
138127628117932.0684786519695.9315213492
13988837112580.462451629-23743.4624516288
140713111033.2459512064-3902.24595120642
141905620632.5961367156-11576.5961367156
1428795778724.95308269479232.04691730535
143144470118779.32018955125690.6798104486
144111408127120.99734771-15712.9973477096






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.7670164984998620.4659670030002760.232983501500138
190.6757401651280690.6485196697438630.324259834871931
200.586245165904350.82750966819130.41375483409565
210.4585014360450950.917002872090190.541498563954905
220.4253945366993860.8507890733987730.574605463300614
230.6884620709776230.6230758580447540.311537929022377
240.617005293192390.765989413615220.38299470680761
250.5949052160801680.8101895678396650.405094783919832
260.5620560208120290.8758879583759410.437943979187971
270.6432100100070870.7135799799858260.356789989992913
280.563001857089310.8739962858213790.43699814291069
290.8310379462596960.3379241074806080.168962053740304
300.7849504872366360.4300990255267290.215049512763364
310.7672637877242030.4654724245515940.232736212275797
320.7244147150071550.5511705699856910.275585284992845
330.6871534261956510.6256931476086980.312846573804349
340.626267961896110.7474640762077790.37373203810389
350.5850213945582960.8299572108834070.414978605441704
360.5179317699803820.9641364600392350.482068230019618
370.5450411867905120.9099176264189770.454958813209488
380.5116033696705920.9767932606588160.488396630329408
390.4540728048575190.9081456097150380.545927195142481
400.6999876269446340.6000247461107320.300012373055366
410.8796640390376060.2406719219247870.120335960962394
420.8539613467303380.2920773065393250.146038653269663
430.832838745593230.3343225088135410.16716125440677
440.8201230190674330.3597539618651340.179876980932567
450.7822920714833850.435415857033230.217707928516615
460.8737329542782660.2525340914434680.126267045721734
470.848664610617610.3026707787647790.15133538938239
480.8738334766776950.252333046644610.126166523322305
490.8585514988776460.2828970022447080.141448501122354
500.8733094904844770.2533810190310450.126690509515523
510.8552818542941070.2894362914117860.144718145705893
520.8275408950763160.3449182098473690.172459104923684
530.9448022349222060.1103955301555870.0551977650777936
540.9709643482820070.0580713034359860.029035651717993
550.96960573244450.06078853511099690.0303942675554985
560.9598431463625940.08031370727481280.0401568536374064
570.987560369731890.02487926053622040.0124396302681102
580.9824757718395110.03504845632097780.0175242281604889
590.9770733654976570.04585326900468650.0229266345023432
600.970752637794310.0584947244113790.0292473622056895
610.9628525976998950.0742948046002110.0371474023001055
620.9733627184576330.05327456308473310.0266372815423665
630.9650866821126830.06982663577463450.0349133178873173
640.9679638124318280.06407237513634460.0320361875681723
650.9613614589843620.07727708203127520.0386385410156376
660.968866972557490.06226605488501810.031133027442509
670.9812383119912140.03752337601757190.0187616880087859
680.9979948612870360.004010277425927630.00200513871296382
690.997079941128760.005840117742481770.00292005887124088
700.995709561514490.008580876971020690.00429043848551035
710.9939174965859660.01216500682806810.00608250341403405
720.994372379619480.01125524076104050.00562762038052023
730.9928134913530350.014373017293930.00718650864696499
740.9901479598559720.01970408028805540.00985204014402768
750.9933089843909620.01338203121807560.00669101560903782
760.9929274892850640.01414502142987120.00707251071493559
770.9935700973774410.01285980524511820.00642990262255909
780.999948203065540.0001035938689184225.17969344592109e-05
790.9999092061974770.0001815876050461729.07938025230862e-05
800.9998575449607980.0002849100784035770.000142455039201788
810.9997622248813520.0004755502372952410.000237775118647621
820.999999855057922.89884161226424e-071.44942080613212e-07
830.9999997218413665.56317267977446e-072.78158633988723e-07
840.9999995010725449.97854912654709e-074.98927456327354e-07
850.9999990316905961.93661880728281e-069.68309403641405e-07
860.9999993326364771.33472704658654e-066.6736352329327e-07
870.9999993438334841.31233303204168e-066.56166516020838e-07
880.9999990502137851.89957243076924e-069.49786215384622e-07
890.9999987688166392.46236672266692e-061.23118336133346e-06
900.9999978684810014.2630379970946e-062.1315189985473e-06
910.9999956475918528.70481629541472e-064.35240814770736e-06
920.9999963408765857.31824683026238e-063.65912341513119e-06
930.9999924408525251.51182949489502e-057.55914747447512e-06
940.9999846982332783.06035334431535e-051.53017667215767e-05
950.9999918869877581.62260244849124e-058.1130122424562e-06
960.9999968060480796.38790384285875e-063.19395192142938e-06
970.9999943358115261.13283769488238e-055.6641884744119e-06
980.9999884118150022.31763699953055e-051.15881849976527e-05
990.9999844475319263.11049361474187e-051.55524680737093e-05
1000.9999995833312228.3333755613003e-074.16668778065015e-07
1010.9999997940288334.11942334714972e-072.05971167357486e-07
1020.9999995044729839.910540347159e-074.9552701735795e-07
1030.999999630918777.38162461353781e-073.6908123067689e-07
1040.9999991402894981.71942100489484e-068.5971050244742e-07
1050.999998995854512.00829097855987e-061.00414548927993e-06
1060.9999974338140665.13237186849584e-062.56618593424792e-06
1070.9999988237645312.35247093804895e-061.17623546902448e-06
1080.9999990857099671.82858006623429e-069.14290033117145e-07
1090.9999973750779495.24984410244332e-062.62492205122166e-06
1100.999992653358861.46932822808839e-057.34664114044196e-06
1110.9999953350681959.32986360965015e-064.66493180482508e-06
1120.999997310146075.37970785864059e-062.6898539293203e-06
1130.9999918809982291.62380035420719e-058.11900177103595e-06
1140.9999760038075574.7992384886706e-052.3996192443353e-05
1150.9999302036262080.0001395927475831176.97963737915583e-05
1160.9998037819534780.0003924360930434880.000196218046521744
1170.999997385593385.22881323804298e-062.61440661902149e-06
1180.9999995143236049.71352791302068e-074.85676395651034e-07
1190.9999986960699632.60786007364667e-061.30393003682333e-06
1200.9999929613091361.4077381727401e-057.03869086370049e-06
1210.9999746109186075.07781627860704e-052.53890813930352e-05
1220.9998678798061890.0002642403876226170.000132120193811308
1230.999957759344628.4481310761689e-054.22406553808445e-05
1240.9997881372499740.0004237255000521510.000211862750026076
1250.9986258081330.002748383734000770.00137419186700039
1260.9966559476243460.006688104751307960.00334405237565398

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.767016498499862 & 0.465967003000276 & 0.232983501500138 \tabularnewline
19 & 0.675740165128069 & 0.648519669743863 & 0.324259834871931 \tabularnewline
20 & 0.58624516590435 & 0.8275096681913 & 0.41375483409565 \tabularnewline
21 & 0.458501436045095 & 0.91700287209019 & 0.541498563954905 \tabularnewline
22 & 0.425394536699386 & 0.850789073398773 & 0.574605463300614 \tabularnewline
23 & 0.688462070977623 & 0.623075858044754 & 0.311537929022377 \tabularnewline
24 & 0.61700529319239 & 0.76598941361522 & 0.38299470680761 \tabularnewline
25 & 0.594905216080168 & 0.810189567839665 & 0.405094783919832 \tabularnewline
26 & 0.562056020812029 & 0.875887958375941 & 0.437943979187971 \tabularnewline
27 & 0.643210010007087 & 0.713579979985826 & 0.356789989992913 \tabularnewline
28 & 0.56300185708931 & 0.873996285821379 & 0.43699814291069 \tabularnewline
29 & 0.831037946259696 & 0.337924107480608 & 0.168962053740304 \tabularnewline
30 & 0.784950487236636 & 0.430099025526729 & 0.215049512763364 \tabularnewline
31 & 0.767263787724203 & 0.465472424551594 & 0.232736212275797 \tabularnewline
32 & 0.724414715007155 & 0.551170569985691 & 0.275585284992845 \tabularnewline
33 & 0.687153426195651 & 0.625693147608698 & 0.312846573804349 \tabularnewline
34 & 0.62626796189611 & 0.747464076207779 & 0.37373203810389 \tabularnewline
35 & 0.585021394558296 & 0.829957210883407 & 0.414978605441704 \tabularnewline
36 & 0.517931769980382 & 0.964136460039235 & 0.482068230019618 \tabularnewline
37 & 0.545041186790512 & 0.909917626418977 & 0.454958813209488 \tabularnewline
38 & 0.511603369670592 & 0.976793260658816 & 0.488396630329408 \tabularnewline
39 & 0.454072804857519 & 0.908145609715038 & 0.545927195142481 \tabularnewline
40 & 0.699987626944634 & 0.600024746110732 & 0.300012373055366 \tabularnewline
41 & 0.879664039037606 & 0.240671921924787 & 0.120335960962394 \tabularnewline
42 & 0.853961346730338 & 0.292077306539325 & 0.146038653269663 \tabularnewline
43 & 0.83283874559323 & 0.334322508813541 & 0.16716125440677 \tabularnewline
44 & 0.820123019067433 & 0.359753961865134 & 0.179876980932567 \tabularnewline
45 & 0.782292071483385 & 0.43541585703323 & 0.217707928516615 \tabularnewline
46 & 0.873732954278266 & 0.252534091443468 & 0.126267045721734 \tabularnewline
47 & 0.84866461061761 & 0.302670778764779 & 0.15133538938239 \tabularnewline
48 & 0.873833476677695 & 0.25233304664461 & 0.126166523322305 \tabularnewline
49 & 0.858551498877646 & 0.282897002244708 & 0.141448501122354 \tabularnewline
50 & 0.873309490484477 & 0.253381019031045 & 0.126690509515523 \tabularnewline
51 & 0.855281854294107 & 0.289436291411786 & 0.144718145705893 \tabularnewline
52 & 0.827540895076316 & 0.344918209847369 & 0.172459104923684 \tabularnewline
53 & 0.944802234922206 & 0.110395530155587 & 0.0551977650777936 \tabularnewline
54 & 0.970964348282007 & 0.058071303435986 & 0.029035651717993 \tabularnewline
55 & 0.9696057324445 & 0.0607885351109969 & 0.0303942675554985 \tabularnewline
56 & 0.959843146362594 & 0.0803137072748128 & 0.0401568536374064 \tabularnewline
57 & 0.98756036973189 & 0.0248792605362204 & 0.0124396302681102 \tabularnewline
58 & 0.982475771839511 & 0.0350484563209778 & 0.0175242281604889 \tabularnewline
59 & 0.977073365497657 & 0.0458532690046865 & 0.0229266345023432 \tabularnewline
60 & 0.97075263779431 & 0.058494724411379 & 0.0292473622056895 \tabularnewline
61 & 0.962852597699895 & 0.074294804600211 & 0.0371474023001055 \tabularnewline
62 & 0.973362718457633 & 0.0532745630847331 & 0.0266372815423665 \tabularnewline
63 & 0.965086682112683 & 0.0698266357746345 & 0.0349133178873173 \tabularnewline
64 & 0.967963812431828 & 0.0640723751363446 & 0.0320361875681723 \tabularnewline
65 & 0.961361458984362 & 0.0772770820312752 & 0.0386385410156376 \tabularnewline
66 & 0.96886697255749 & 0.0622660548850181 & 0.031133027442509 \tabularnewline
67 & 0.981238311991214 & 0.0375233760175719 & 0.0187616880087859 \tabularnewline
68 & 0.997994861287036 & 0.00401027742592763 & 0.00200513871296382 \tabularnewline
69 & 0.99707994112876 & 0.00584011774248177 & 0.00292005887124088 \tabularnewline
70 & 0.99570956151449 & 0.00858087697102069 & 0.00429043848551035 \tabularnewline
71 & 0.993917496585966 & 0.0121650068280681 & 0.00608250341403405 \tabularnewline
72 & 0.99437237961948 & 0.0112552407610405 & 0.00562762038052023 \tabularnewline
73 & 0.992813491353035 & 0.01437301729393 & 0.00718650864696499 \tabularnewline
74 & 0.990147959855972 & 0.0197040802880554 & 0.00985204014402768 \tabularnewline
75 & 0.993308984390962 & 0.0133820312180756 & 0.00669101560903782 \tabularnewline
76 & 0.992927489285064 & 0.0141450214298712 & 0.00707251071493559 \tabularnewline
77 & 0.993570097377441 & 0.0128598052451182 & 0.00642990262255909 \tabularnewline
78 & 0.99994820306554 & 0.000103593868918422 & 5.17969344592109e-05 \tabularnewline
79 & 0.999909206197477 & 0.000181587605046172 & 9.07938025230862e-05 \tabularnewline
80 & 0.999857544960798 & 0.000284910078403577 & 0.000142455039201788 \tabularnewline
81 & 0.999762224881352 & 0.000475550237295241 & 0.000237775118647621 \tabularnewline
82 & 0.99999985505792 & 2.89884161226424e-07 & 1.44942080613212e-07 \tabularnewline
83 & 0.999999721841366 & 5.56317267977446e-07 & 2.78158633988723e-07 \tabularnewline
84 & 0.999999501072544 & 9.97854912654709e-07 & 4.98927456327354e-07 \tabularnewline
85 & 0.999999031690596 & 1.93661880728281e-06 & 9.68309403641405e-07 \tabularnewline
86 & 0.999999332636477 & 1.33472704658654e-06 & 6.6736352329327e-07 \tabularnewline
87 & 0.999999343833484 & 1.31233303204168e-06 & 6.56166516020838e-07 \tabularnewline
88 & 0.999999050213785 & 1.89957243076924e-06 & 9.49786215384622e-07 \tabularnewline
89 & 0.999998768816639 & 2.46236672266692e-06 & 1.23118336133346e-06 \tabularnewline
90 & 0.999997868481001 & 4.2630379970946e-06 & 2.1315189985473e-06 \tabularnewline
91 & 0.999995647591852 & 8.70481629541472e-06 & 4.35240814770736e-06 \tabularnewline
92 & 0.999996340876585 & 7.31824683026238e-06 & 3.65912341513119e-06 \tabularnewline
93 & 0.999992440852525 & 1.51182949489502e-05 & 7.55914747447512e-06 \tabularnewline
94 & 0.999984698233278 & 3.06035334431535e-05 & 1.53017667215767e-05 \tabularnewline
95 & 0.999991886987758 & 1.62260244849124e-05 & 8.1130122424562e-06 \tabularnewline
96 & 0.999996806048079 & 6.38790384285875e-06 & 3.19395192142938e-06 \tabularnewline
97 & 0.999994335811526 & 1.13283769488238e-05 & 5.6641884744119e-06 \tabularnewline
98 & 0.999988411815002 & 2.31763699953055e-05 & 1.15881849976527e-05 \tabularnewline
99 & 0.999984447531926 & 3.11049361474187e-05 & 1.55524680737093e-05 \tabularnewline
100 & 0.999999583331222 & 8.3333755613003e-07 & 4.16668778065015e-07 \tabularnewline
101 & 0.999999794028833 & 4.11942334714972e-07 & 2.05971167357486e-07 \tabularnewline
102 & 0.999999504472983 & 9.910540347159e-07 & 4.9552701735795e-07 \tabularnewline
103 & 0.99999963091877 & 7.38162461353781e-07 & 3.6908123067689e-07 \tabularnewline
104 & 0.999999140289498 & 1.71942100489484e-06 & 8.5971050244742e-07 \tabularnewline
105 & 0.99999899585451 & 2.00829097855987e-06 & 1.00414548927993e-06 \tabularnewline
106 & 0.999997433814066 & 5.13237186849584e-06 & 2.56618593424792e-06 \tabularnewline
107 & 0.999998823764531 & 2.35247093804895e-06 & 1.17623546902448e-06 \tabularnewline
108 & 0.999999085709967 & 1.82858006623429e-06 & 9.14290033117145e-07 \tabularnewline
109 & 0.999997375077949 & 5.24984410244332e-06 & 2.62492205122166e-06 \tabularnewline
110 & 0.99999265335886 & 1.46932822808839e-05 & 7.34664114044196e-06 \tabularnewline
111 & 0.999995335068195 & 9.32986360965015e-06 & 4.66493180482508e-06 \tabularnewline
112 & 0.99999731014607 & 5.37970785864059e-06 & 2.6898539293203e-06 \tabularnewline
113 & 0.999991880998229 & 1.62380035420719e-05 & 8.11900177103595e-06 \tabularnewline
114 & 0.999976003807557 & 4.7992384886706e-05 & 2.3996192443353e-05 \tabularnewline
115 & 0.999930203626208 & 0.000139592747583117 & 6.97963737915583e-05 \tabularnewline
116 & 0.999803781953478 & 0.000392436093043488 & 0.000196218046521744 \tabularnewline
117 & 0.99999738559338 & 5.22881323804298e-06 & 2.61440661902149e-06 \tabularnewline
118 & 0.999999514323604 & 9.71352791302068e-07 & 4.85676395651034e-07 \tabularnewline
119 & 0.999998696069963 & 2.60786007364667e-06 & 1.30393003682333e-06 \tabularnewline
120 & 0.999992961309136 & 1.4077381727401e-05 & 7.03869086370049e-06 \tabularnewline
121 & 0.999974610918607 & 5.07781627860704e-05 & 2.53890813930352e-05 \tabularnewline
122 & 0.999867879806189 & 0.000264240387622617 & 0.000132120193811308 \tabularnewline
123 & 0.99995775934462 & 8.4481310761689e-05 & 4.22406553808445e-05 \tabularnewline
124 & 0.999788137249974 & 0.000423725500052151 & 0.000211862750026076 \tabularnewline
125 & 0.998625808133 & 0.00274838373400077 & 0.00137419186700039 \tabularnewline
126 & 0.996655947624346 & 0.00668810475130796 & 0.00334405237565398 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159387&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]0.767016498499862[/C][C]0.465967003000276[/C][C]0.232983501500138[/C][/ROW]
[ROW][C]19[/C][C]0.675740165128069[/C][C]0.648519669743863[/C][C]0.324259834871931[/C][/ROW]
[ROW][C]20[/C][C]0.58624516590435[/C][C]0.8275096681913[/C][C]0.41375483409565[/C][/ROW]
[ROW][C]21[/C][C]0.458501436045095[/C][C]0.91700287209019[/C][C]0.541498563954905[/C][/ROW]
[ROW][C]22[/C][C]0.425394536699386[/C][C]0.850789073398773[/C][C]0.574605463300614[/C][/ROW]
[ROW][C]23[/C][C]0.688462070977623[/C][C]0.623075858044754[/C][C]0.311537929022377[/C][/ROW]
[ROW][C]24[/C][C]0.61700529319239[/C][C]0.76598941361522[/C][C]0.38299470680761[/C][/ROW]
[ROW][C]25[/C][C]0.594905216080168[/C][C]0.810189567839665[/C][C]0.405094783919832[/C][/ROW]
[ROW][C]26[/C][C]0.562056020812029[/C][C]0.875887958375941[/C][C]0.437943979187971[/C][/ROW]
[ROW][C]27[/C][C]0.643210010007087[/C][C]0.713579979985826[/C][C]0.356789989992913[/C][/ROW]
[ROW][C]28[/C][C]0.56300185708931[/C][C]0.873996285821379[/C][C]0.43699814291069[/C][/ROW]
[ROW][C]29[/C][C]0.831037946259696[/C][C]0.337924107480608[/C][C]0.168962053740304[/C][/ROW]
[ROW][C]30[/C][C]0.784950487236636[/C][C]0.430099025526729[/C][C]0.215049512763364[/C][/ROW]
[ROW][C]31[/C][C]0.767263787724203[/C][C]0.465472424551594[/C][C]0.232736212275797[/C][/ROW]
[ROW][C]32[/C][C]0.724414715007155[/C][C]0.551170569985691[/C][C]0.275585284992845[/C][/ROW]
[ROW][C]33[/C][C]0.687153426195651[/C][C]0.625693147608698[/C][C]0.312846573804349[/C][/ROW]
[ROW][C]34[/C][C]0.62626796189611[/C][C]0.747464076207779[/C][C]0.37373203810389[/C][/ROW]
[ROW][C]35[/C][C]0.585021394558296[/C][C]0.829957210883407[/C][C]0.414978605441704[/C][/ROW]
[ROW][C]36[/C][C]0.517931769980382[/C][C]0.964136460039235[/C][C]0.482068230019618[/C][/ROW]
[ROW][C]37[/C][C]0.545041186790512[/C][C]0.909917626418977[/C][C]0.454958813209488[/C][/ROW]
[ROW][C]38[/C][C]0.511603369670592[/C][C]0.976793260658816[/C][C]0.488396630329408[/C][/ROW]
[ROW][C]39[/C][C]0.454072804857519[/C][C]0.908145609715038[/C][C]0.545927195142481[/C][/ROW]
[ROW][C]40[/C][C]0.699987626944634[/C][C]0.600024746110732[/C][C]0.300012373055366[/C][/ROW]
[ROW][C]41[/C][C]0.879664039037606[/C][C]0.240671921924787[/C][C]0.120335960962394[/C][/ROW]
[ROW][C]42[/C][C]0.853961346730338[/C][C]0.292077306539325[/C][C]0.146038653269663[/C][/ROW]
[ROW][C]43[/C][C]0.83283874559323[/C][C]0.334322508813541[/C][C]0.16716125440677[/C][/ROW]
[ROW][C]44[/C][C]0.820123019067433[/C][C]0.359753961865134[/C][C]0.179876980932567[/C][/ROW]
[ROW][C]45[/C][C]0.782292071483385[/C][C]0.43541585703323[/C][C]0.217707928516615[/C][/ROW]
[ROW][C]46[/C][C]0.873732954278266[/C][C]0.252534091443468[/C][C]0.126267045721734[/C][/ROW]
[ROW][C]47[/C][C]0.84866461061761[/C][C]0.302670778764779[/C][C]0.15133538938239[/C][/ROW]
[ROW][C]48[/C][C]0.873833476677695[/C][C]0.25233304664461[/C][C]0.126166523322305[/C][/ROW]
[ROW][C]49[/C][C]0.858551498877646[/C][C]0.282897002244708[/C][C]0.141448501122354[/C][/ROW]
[ROW][C]50[/C][C]0.873309490484477[/C][C]0.253381019031045[/C][C]0.126690509515523[/C][/ROW]
[ROW][C]51[/C][C]0.855281854294107[/C][C]0.289436291411786[/C][C]0.144718145705893[/C][/ROW]
[ROW][C]52[/C][C]0.827540895076316[/C][C]0.344918209847369[/C][C]0.172459104923684[/C][/ROW]
[ROW][C]53[/C][C]0.944802234922206[/C][C]0.110395530155587[/C][C]0.0551977650777936[/C][/ROW]
[ROW][C]54[/C][C]0.970964348282007[/C][C]0.058071303435986[/C][C]0.029035651717993[/C][/ROW]
[ROW][C]55[/C][C]0.9696057324445[/C][C]0.0607885351109969[/C][C]0.0303942675554985[/C][/ROW]
[ROW][C]56[/C][C]0.959843146362594[/C][C]0.0803137072748128[/C][C]0.0401568536374064[/C][/ROW]
[ROW][C]57[/C][C]0.98756036973189[/C][C]0.0248792605362204[/C][C]0.0124396302681102[/C][/ROW]
[ROW][C]58[/C][C]0.982475771839511[/C][C]0.0350484563209778[/C][C]0.0175242281604889[/C][/ROW]
[ROW][C]59[/C][C]0.977073365497657[/C][C]0.0458532690046865[/C][C]0.0229266345023432[/C][/ROW]
[ROW][C]60[/C][C]0.97075263779431[/C][C]0.058494724411379[/C][C]0.0292473622056895[/C][/ROW]
[ROW][C]61[/C][C]0.962852597699895[/C][C]0.074294804600211[/C][C]0.0371474023001055[/C][/ROW]
[ROW][C]62[/C][C]0.973362718457633[/C][C]0.0532745630847331[/C][C]0.0266372815423665[/C][/ROW]
[ROW][C]63[/C][C]0.965086682112683[/C][C]0.0698266357746345[/C][C]0.0349133178873173[/C][/ROW]
[ROW][C]64[/C][C]0.967963812431828[/C][C]0.0640723751363446[/C][C]0.0320361875681723[/C][/ROW]
[ROW][C]65[/C][C]0.961361458984362[/C][C]0.0772770820312752[/C][C]0.0386385410156376[/C][/ROW]
[ROW][C]66[/C][C]0.96886697255749[/C][C]0.0622660548850181[/C][C]0.031133027442509[/C][/ROW]
[ROW][C]67[/C][C]0.981238311991214[/C][C]0.0375233760175719[/C][C]0.0187616880087859[/C][/ROW]
[ROW][C]68[/C][C]0.997994861287036[/C][C]0.00401027742592763[/C][C]0.00200513871296382[/C][/ROW]
[ROW][C]69[/C][C]0.99707994112876[/C][C]0.00584011774248177[/C][C]0.00292005887124088[/C][/ROW]
[ROW][C]70[/C][C]0.99570956151449[/C][C]0.00858087697102069[/C][C]0.00429043848551035[/C][/ROW]
[ROW][C]71[/C][C]0.993917496585966[/C][C]0.0121650068280681[/C][C]0.00608250341403405[/C][/ROW]
[ROW][C]72[/C][C]0.99437237961948[/C][C]0.0112552407610405[/C][C]0.00562762038052023[/C][/ROW]
[ROW][C]73[/C][C]0.992813491353035[/C][C]0.01437301729393[/C][C]0.00718650864696499[/C][/ROW]
[ROW][C]74[/C][C]0.990147959855972[/C][C]0.0197040802880554[/C][C]0.00985204014402768[/C][/ROW]
[ROW][C]75[/C][C]0.993308984390962[/C][C]0.0133820312180756[/C][C]0.00669101560903782[/C][/ROW]
[ROW][C]76[/C][C]0.992927489285064[/C][C]0.0141450214298712[/C][C]0.00707251071493559[/C][/ROW]
[ROW][C]77[/C][C]0.993570097377441[/C][C]0.0128598052451182[/C][C]0.00642990262255909[/C][/ROW]
[ROW][C]78[/C][C]0.99994820306554[/C][C]0.000103593868918422[/C][C]5.17969344592109e-05[/C][/ROW]
[ROW][C]79[/C][C]0.999909206197477[/C][C]0.000181587605046172[/C][C]9.07938025230862e-05[/C][/ROW]
[ROW][C]80[/C][C]0.999857544960798[/C][C]0.000284910078403577[/C][C]0.000142455039201788[/C][/ROW]
[ROW][C]81[/C][C]0.999762224881352[/C][C]0.000475550237295241[/C][C]0.000237775118647621[/C][/ROW]
[ROW][C]82[/C][C]0.99999985505792[/C][C]2.89884161226424e-07[/C][C]1.44942080613212e-07[/C][/ROW]
[ROW][C]83[/C][C]0.999999721841366[/C][C]5.56317267977446e-07[/C][C]2.78158633988723e-07[/C][/ROW]
[ROW][C]84[/C][C]0.999999501072544[/C][C]9.97854912654709e-07[/C][C]4.98927456327354e-07[/C][/ROW]
[ROW][C]85[/C][C]0.999999031690596[/C][C]1.93661880728281e-06[/C][C]9.68309403641405e-07[/C][/ROW]
[ROW][C]86[/C][C]0.999999332636477[/C][C]1.33472704658654e-06[/C][C]6.6736352329327e-07[/C][/ROW]
[ROW][C]87[/C][C]0.999999343833484[/C][C]1.31233303204168e-06[/C][C]6.56166516020838e-07[/C][/ROW]
[ROW][C]88[/C][C]0.999999050213785[/C][C]1.89957243076924e-06[/C][C]9.49786215384622e-07[/C][/ROW]
[ROW][C]89[/C][C]0.999998768816639[/C][C]2.46236672266692e-06[/C][C]1.23118336133346e-06[/C][/ROW]
[ROW][C]90[/C][C]0.999997868481001[/C][C]4.2630379970946e-06[/C][C]2.1315189985473e-06[/C][/ROW]
[ROW][C]91[/C][C]0.999995647591852[/C][C]8.70481629541472e-06[/C][C]4.35240814770736e-06[/C][/ROW]
[ROW][C]92[/C][C]0.999996340876585[/C][C]7.31824683026238e-06[/C][C]3.65912341513119e-06[/C][/ROW]
[ROW][C]93[/C][C]0.999992440852525[/C][C]1.51182949489502e-05[/C][C]7.55914747447512e-06[/C][/ROW]
[ROW][C]94[/C][C]0.999984698233278[/C][C]3.06035334431535e-05[/C][C]1.53017667215767e-05[/C][/ROW]
[ROW][C]95[/C][C]0.999991886987758[/C][C]1.62260244849124e-05[/C][C]8.1130122424562e-06[/C][/ROW]
[ROW][C]96[/C][C]0.999996806048079[/C][C]6.38790384285875e-06[/C][C]3.19395192142938e-06[/C][/ROW]
[ROW][C]97[/C][C]0.999994335811526[/C][C]1.13283769488238e-05[/C][C]5.6641884744119e-06[/C][/ROW]
[ROW][C]98[/C][C]0.999988411815002[/C][C]2.31763699953055e-05[/C][C]1.15881849976527e-05[/C][/ROW]
[ROW][C]99[/C][C]0.999984447531926[/C][C]3.11049361474187e-05[/C][C]1.55524680737093e-05[/C][/ROW]
[ROW][C]100[/C][C]0.999999583331222[/C][C]8.3333755613003e-07[/C][C]4.16668778065015e-07[/C][/ROW]
[ROW][C]101[/C][C]0.999999794028833[/C][C]4.11942334714972e-07[/C][C]2.05971167357486e-07[/C][/ROW]
[ROW][C]102[/C][C]0.999999504472983[/C][C]9.910540347159e-07[/C][C]4.9552701735795e-07[/C][/ROW]
[ROW][C]103[/C][C]0.99999963091877[/C][C]7.38162461353781e-07[/C][C]3.6908123067689e-07[/C][/ROW]
[ROW][C]104[/C][C]0.999999140289498[/C][C]1.71942100489484e-06[/C][C]8.5971050244742e-07[/C][/ROW]
[ROW][C]105[/C][C]0.99999899585451[/C][C]2.00829097855987e-06[/C][C]1.00414548927993e-06[/C][/ROW]
[ROW][C]106[/C][C]0.999997433814066[/C][C]5.13237186849584e-06[/C][C]2.56618593424792e-06[/C][/ROW]
[ROW][C]107[/C][C]0.999998823764531[/C][C]2.35247093804895e-06[/C][C]1.17623546902448e-06[/C][/ROW]
[ROW][C]108[/C][C]0.999999085709967[/C][C]1.82858006623429e-06[/C][C]9.14290033117145e-07[/C][/ROW]
[ROW][C]109[/C][C]0.999997375077949[/C][C]5.24984410244332e-06[/C][C]2.62492205122166e-06[/C][/ROW]
[ROW][C]110[/C][C]0.99999265335886[/C][C]1.46932822808839e-05[/C][C]7.34664114044196e-06[/C][/ROW]
[ROW][C]111[/C][C]0.999995335068195[/C][C]9.32986360965015e-06[/C][C]4.66493180482508e-06[/C][/ROW]
[ROW][C]112[/C][C]0.99999731014607[/C][C]5.37970785864059e-06[/C][C]2.6898539293203e-06[/C][/ROW]
[ROW][C]113[/C][C]0.999991880998229[/C][C]1.62380035420719e-05[/C][C]8.11900177103595e-06[/C][/ROW]
[ROW][C]114[/C][C]0.999976003807557[/C][C]4.7992384886706e-05[/C][C]2.3996192443353e-05[/C][/ROW]
[ROW][C]115[/C][C]0.999930203626208[/C][C]0.000139592747583117[/C][C]6.97963737915583e-05[/C][/ROW]
[ROW][C]116[/C][C]0.999803781953478[/C][C]0.000392436093043488[/C][C]0.000196218046521744[/C][/ROW]
[ROW][C]117[/C][C]0.99999738559338[/C][C]5.22881323804298e-06[/C][C]2.61440661902149e-06[/C][/ROW]
[ROW][C]118[/C][C]0.999999514323604[/C][C]9.71352791302068e-07[/C][C]4.85676395651034e-07[/C][/ROW]
[ROW][C]119[/C][C]0.999998696069963[/C][C]2.60786007364667e-06[/C][C]1.30393003682333e-06[/C][/ROW]
[ROW][C]120[/C][C]0.999992961309136[/C][C]1.4077381727401e-05[/C][C]7.03869086370049e-06[/C][/ROW]
[ROW][C]121[/C][C]0.999974610918607[/C][C]5.07781627860704e-05[/C][C]2.53890813930352e-05[/C][/ROW]
[ROW][C]122[/C][C]0.999867879806189[/C][C]0.000264240387622617[/C][C]0.000132120193811308[/C][/ROW]
[ROW][C]123[/C][C]0.99995775934462[/C][C]8.4481310761689e-05[/C][C]4.22406553808445e-05[/C][/ROW]
[ROW][C]124[/C][C]0.999788137249974[/C][C]0.000423725500052151[/C][C]0.000211862750026076[/C][/ROW]
[ROW][C]125[/C][C]0.998625808133[/C][C]0.00274838373400077[/C][C]0.00137419186700039[/C][/ROW]
[ROW][C]126[/C][C]0.996655947624346[/C][C]0.00668810475130796[/C][C]0.00334405237565398[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159387&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159387&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.7670164984998620.4659670030002760.232983501500138
190.6757401651280690.6485196697438630.324259834871931
200.586245165904350.82750966819130.41375483409565
210.4585014360450950.917002872090190.541498563954905
220.4253945366993860.8507890733987730.574605463300614
230.6884620709776230.6230758580447540.311537929022377
240.617005293192390.765989413615220.38299470680761
250.5949052160801680.8101895678396650.405094783919832
260.5620560208120290.8758879583759410.437943979187971
270.6432100100070870.7135799799858260.356789989992913
280.563001857089310.8739962858213790.43699814291069
290.8310379462596960.3379241074806080.168962053740304
300.7849504872366360.4300990255267290.215049512763364
310.7672637877242030.4654724245515940.232736212275797
320.7244147150071550.5511705699856910.275585284992845
330.6871534261956510.6256931476086980.312846573804349
340.626267961896110.7474640762077790.37373203810389
350.5850213945582960.8299572108834070.414978605441704
360.5179317699803820.9641364600392350.482068230019618
370.5450411867905120.9099176264189770.454958813209488
380.5116033696705920.9767932606588160.488396630329408
390.4540728048575190.9081456097150380.545927195142481
400.6999876269446340.6000247461107320.300012373055366
410.8796640390376060.2406719219247870.120335960962394
420.8539613467303380.2920773065393250.146038653269663
430.832838745593230.3343225088135410.16716125440677
440.8201230190674330.3597539618651340.179876980932567
450.7822920714833850.435415857033230.217707928516615
460.8737329542782660.2525340914434680.126267045721734
470.848664610617610.3026707787647790.15133538938239
480.8738334766776950.252333046644610.126166523322305
490.8585514988776460.2828970022447080.141448501122354
500.8733094904844770.2533810190310450.126690509515523
510.8552818542941070.2894362914117860.144718145705893
520.8275408950763160.3449182098473690.172459104923684
530.9448022349222060.1103955301555870.0551977650777936
540.9709643482820070.0580713034359860.029035651717993
550.96960573244450.06078853511099690.0303942675554985
560.9598431463625940.08031370727481280.0401568536374064
570.987560369731890.02487926053622040.0124396302681102
580.9824757718395110.03504845632097780.0175242281604889
590.9770733654976570.04585326900468650.0229266345023432
600.970752637794310.0584947244113790.0292473622056895
610.9628525976998950.0742948046002110.0371474023001055
620.9733627184576330.05327456308473310.0266372815423665
630.9650866821126830.06982663577463450.0349133178873173
640.9679638124318280.06407237513634460.0320361875681723
650.9613614589843620.07727708203127520.0386385410156376
660.968866972557490.06226605488501810.031133027442509
670.9812383119912140.03752337601757190.0187616880087859
680.9979948612870360.004010277425927630.00200513871296382
690.997079941128760.005840117742481770.00292005887124088
700.995709561514490.008580876971020690.00429043848551035
710.9939174965859660.01216500682806810.00608250341403405
720.994372379619480.01125524076104050.00562762038052023
730.9928134913530350.014373017293930.00718650864696499
740.9901479598559720.01970408028805540.00985204014402768
750.9933089843909620.01338203121807560.00669101560903782
760.9929274892850640.01414502142987120.00707251071493559
770.9935700973774410.01285980524511820.00642990262255909
780.999948203065540.0001035938689184225.17969344592109e-05
790.9999092061974770.0001815876050461729.07938025230862e-05
800.9998575449607980.0002849100784035770.000142455039201788
810.9997622248813520.0004755502372952410.000237775118647621
820.999999855057922.89884161226424e-071.44942080613212e-07
830.9999997218413665.56317267977446e-072.78158633988723e-07
840.9999995010725449.97854912654709e-074.98927456327354e-07
850.9999990316905961.93661880728281e-069.68309403641405e-07
860.9999993326364771.33472704658654e-066.6736352329327e-07
870.9999993438334841.31233303204168e-066.56166516020838e-07
880.9999990502137851.89957243076924e-069.49786215384622e-07
890.9999987688166392.46236672266692e-061.23118336133346e-06
900.9999978684810014.2630379970946e-062.1315189985473e-06
910.9999956475918528.70481629541472e-064.35240814770736e-06
920.9999963408765857.31824683026238e-063.65912341513119e-06
930.9999924408525251.51182949489502e-057.55914747447512e-06
940.9999846982332783.06035334431535e-051.53017667215767e-05
950.9999918869877581.62260244849124e-058.1130122424562e-06
960.9999968060480796.38790384285875e-063.19395192142938e-06
970.9999943358115261.13283769488238e-055.6641884744119e-06
980.9999884118150022.31763699953055e-051.15881849976527e-05
990.9999844475319263.11049361474187e-051.55524680737093e-05
1000.9999995833312228.3333755613003e-074.16668778065015e-07
1010.9999997940288334.11942334714972e-072.05971167357486e-07
1020.9999995044729839.910540347159e-074.9552701735795e-07
1030.999999630918777.38162461353781e-073.6908123067689e-07
1040.9999991402894981.71942100489484e-068.5971050244742e-07
1050.999998995854512.00829097855987e-061.00414548927993e-06
1060.9999974338140665.13237186849584e-062.56618593424792e-06
1070.9999988237645312.35247093804895e-061.17623546902448e-06
1080.9999990857099671.82858006623429e-069.14290033117145e-07
1090.9999973750779495.24984410244332e-062.62492205122166e-06
1100.999992653358861.46932822808839e-057.34664114044196e-06
1110.9999953350681959.32986360965015e-064.66493180482508e-06
1120.999997310146075.37970785864059e-062.6898539293203e-06
1130.9999918809982291.62380035420719e-058.11900177103595e-06
1140.9999760038075574.7992384886706e-052.3996192443353e-05
1150.9999302036262080.0001395927475831176.97963737915583e-05
1160.9998037819534780.0003924360930434880.000196218046521744
1170.999997385593385.22881323804298e-062.61440661902149e-06
1180.9999995143236049.71352791302068e-074.85676395651034e-07
1190.9999986960699632.60786007364667e-061.30393003682333e-06
1200.9999929613091361.4077381727401e-057.03869086370049e-06
1210.9999746109186075.07781627860704e-052.53890813930352e-05
1220.9998678798061890.0002642403876226170.000132120193811308
1230.999957759344628.4481310761689e-054.22406553808445e-05
1240.9997881372499740.0004237255000521510.000211862750026076
1250.9986258081330.002748383734000770.00137419186700039
1260.9966559476243460.006688104751307960.00334405237565398






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level520.477064220183486NOK
5% type I error level630.577981651376147NOK
10% type I error level730.669724770642202NOK

\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 & 52 & 0.477064220183486 & NOK \tabularnewline
5% type I error level & 63 & 0.577981651376147 & NOK \tabularnewline
10% type I error level & 73 & 0.669724770642202 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159387&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]52[/C][C]0.477064220183486[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]63[/C][C]0.577981651376147[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]73[/C][C]0.669724770642202[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159387&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159387&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 level520.477064220183486NOK
5% type I error level630.577981651376147NOK
10% type I error level730.669724770642202NOK


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