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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, 15 Dec 2011 05:33:49 -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/15/t1323945552jc12k2og7xpvhce.htm/, Retrieved Wed, 08 May 2024 15:29:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=155324, Retrieved Wed, 08 May 2024 15:29:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact125

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [] [2010-12-05 17:44:33] [b98453cac15ba1066b407e146608df68]
- RMPD  [Kendall tau Correlation Matrix] [WS10] [2011-12-15 10:04:30] [1111aba4ee34551ac529aa485234fd25]
- RMP       [Multiple Regression] [] [2011-12-15 10:33:49] [6601a4463d1f95e8006e851903a6d39a] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1565	129404	80	500	109	0	20	18	70	63	18158	5636	22622	30	28
1134	130358	46	329	68	1	38	17	68	50	30461	9079	73570	42	39
192	7215	18	72	1	0	0	0	0	0	1423	603	1929	0	0
2033	112861	85	584	146	0	49	22	68	51	25629	8874	36294	54	54
3283	219904	126	1100	124	0	76	30	120	112	48758	17988	62378	86	80
5877	402036	218	1618	267	1	104	31	120	118	129230	21325	167760	157	144
1322	117604	50	442	83	1	37	19	72	59	27376	8325	52443	36	36
1187	126737	49	321	48	0	53	25	96	90	26706	7117	57283	48	48
1463	99729	38	406	87	0	42	30	109	50	26505	7996	36614	45	42
2568	256310	86	818	129	1	62	26	104	79	49801	14218	93268	77	71
1810	113066	69	568	146	2	50	20	54	49	46580	6321	35439	49	49
1915	165392	62	595	113	0	66	30	118	91	48352	19690	72405	77	74
1443	77780	89	529	60	0	38	15	49	32	13899	5659	24044	28	27
2415	152673	84	818	240	4	48	22	88	82	39342	11370	55909	84	83
1254	134368	47	359	50	4	42	17	60	58	27465	4778	44689	31	31
1374	125769	67	419	81	3	47	19	74	65	55211	5954	49319	28	28
1504	123467	50	364	85	0	71	28	112	111	74098	22924	62075	99	98
999	56232	47	284	62	5	0	12	45	36	13497	70	2341	2	2
2222	108458	79	683	127	0	50	28	110	89	38338	14369	40551	41	43
634	22762	21	188	44	0	12	13	39	28	52505	3706	11621	25	24
849	48633	50	291	37	0	16	14	55	35	10663	3147	18741	16	16
2189	182081	83	640	94	0	77	27	102	78	74484	16801	84202	96	95
1469	140857	59	520	127	0	29	25	96	67	28895	2162	15334	23	22
1791	93773	46	532	159	1	38	30	86	61	32827	4721	28024	33	33
1743	133398	78	547	41	1	50	21	78	58	36188	5290	53306	46	45
1180	113933	23	428	153	0	33	17	64	49	28173	6446	37918	59	59
1749	153851	139	561	86	0	49	22	82	77	54926	14711	54819	72	66
1101	140711	75	266	55	0	59	28	100	71	38900	13311	89058	72	70
2391	303844	105	783	78	0	55	26	99	85	88530	13577	103354	62	56
1826	163810	38	754	84	0	42	17	67	56	35482	14634	70239	55	55
1301	123344	40	394	71	0	40	23	87	71	26730	6931	33045	27	27
1433	157640	39	482	111	2	51	20	65	58	29806	9992	63852	41	37
1893	103274	90	593	82	4	45	16	63	34	41799	6185	30905	51	48
2525	193500	105	760	254	0	73	20	80	59	54289	3445	24242	26	26
2033	178768	43	668	66	1	51	21	84	77	36805	12327	78907	65	64
1	0	1	0	0	0	0	0	0	0	0	0	0	0	0
1817	181412	55	855	58	0	46	27	105	75	33146	9898	36005	28	21
1506	92342	47	464	131	3	44	14	51	39	23333	8022	31972	44	44
1820	100023	41	418	258	9	31	29	98	83	47686	10765	35853	36	36
1649	178277	50	607	56	0	71	31	124	123	77783	22717	115301	100	89
1672	145067	58	540	90	2	61	19	75	67	36042	10090	47689	104	101
1433	114146	50	551	57	0	28	30	120	105	34541	12385	34223	35	31
864	86039	25	309	35	2	21	23	84	76	75620	8513	43431	69	65
1683	125481	66	647	53	1	42	21	82	57	60610	5508	52220	73	71
1024	95535	42	321	46	2	44	22	87	82	55041	9628	33863	106	102
1029	129221	78	262	38	2	40	21	78	64	32087	11872	46879	53	53
629	61554	26	180	45	1	15	32	97	57	16356	4186	23228	43	41
1679	168048	82	582	113	0	46	20	80	80	40161	10877	42827	49	46
1715	159121	75	544	104	1	43	26	104	94	55459	17066	65765	38	37
2093	129362	51	758	150	4	47	25	93	72	36679	9175	38167	51	51
658	48188	28	205	37	0	12	22	82	39	22346	2102	14812	14	14
1234	95461	56	317	49	0	46	19	73	60	27377	10807	32615	40	40
2059	229864	64	709	83	0	56	24	87	84	50273	13662	82188	79	77
1725	191094	68	590	67	0	47	26	95	69	32104	9224	51763	52	51
1482	158572	50	537	39	1	48	27	105	102	27016	9001	59325	44	43
1454	111388	47	443	69	6	35	10	37	28	19715	7204	48976	34	33
1620	172614	58	429	58	0	45	26	96	65	33629	6572	43384	47	47
733	63205	18	205	68	0	25	23	88	67	27084	7509	26692	32	31
894	109102	56	310	30	0	47	21	83	80	32352	12920	53279	31	31
2343	137303	74	785	54	10	28	34	124	79	51845	5438	20652	40	40
1503	125304	50	434	65	6	48	29	116	107	26591	11489	38338	42	42
1627	88620	65	602	81	0	32	19	76	60	29677	6661	36735	34	35
1119	95808	48	317	84	11	28	19	65	53	54237	7941	42764	40	40
897	83419	29	288	45	3	31	23	86	59	20284	6173	44331	35	30
855	101723	25	285	52	0	13	22	85	80	22741	5562	41354	11	11
1229	94982	37	391	36	0	38	29	107	89	34178	9492	47879	43	41
1991	143566	61	449	80	8	48	31	124	115	69551	17456	103793	53	53
2393	113325	63	715	144	2	68	21	78	59	29653	9422	52235	82	82
820	81518	32	208	45	0	32	21	83	66	38071	10913	49825	41	41
340	31970	15	101	40	0	5	21	78	42	4157	1283	4105	6	6
2443	192268	102	858	126	3	53	15	59	35	28321	6198	58687	82	81
1030	91261	55	306	75	1	33	9	33	3	40195	4501	40745	47	47
1091	80820	56	360	54	2	54	23	92	72	48158	9560	33187	108	100
1414	85829	59	424	84	1	37	18	52	38	13310	3394	14063	46	46
2192	116322	53	562	86	0	52	31	121	107	78474	9871	37407	38	38
1082	56544	32	292	62	2	0	25	92	73	6386	2419	7190	0	0
1764	116173	51	492	99	1	52	24	99	80	31588	10630	49562	45	45
2072	118781	80	690	63	0	51	22	86	69	61254	8536	76324	57	56
816	60138	23	253	76	0	16	21	75	46	21152	4911	21928	20	18
1121	73422	66	366	92	0	33	26	96	52	41272	9775	27860	56	54
809	67751	57	192	45	0	48	22	81	58	34165	11227	28078	38	37
1699	214002	53	620	57	0	33	26	104	85	37054	6916	49577	42	40
751	51185	24	221	44	0	24	20	76	13	12368	3424	28145	37	37
1309	97181	32	438	132	0	37	25	90	61	23168	8637	36241	36	36
732	45100	39	247	44	0	17	19	75	49	16380	3189	10824	34	34
1327	115801	43	388	67	0	32	22	86	47	41242	8178	46892	53	49
2246	186310	190	541	82	0	55	25	100	93	48450	16739	61264	85	82
968	71960	86	233	71	0	39	22	88	65	20790	6094	22933	36	36
1015	80105	48	333	44	5	31	21	80	64	34585	7237	20787	33	33
1100	103613	41	422	68	0	26	20	73	64	35672	7355	43978	57	55
1300	98707	33	452	54	3	37	23	88	57	52168	9734	51305	50	50
1982	136234	67	584	86	1	66	22	79	61	53933	11225	55593	71	71
1091	136781	52	366	59	0	35	21	81	71	34474	6213	51648	32	31
1107	105863	52	406	74	0	24	12	48	43	43753	4875	30552	45	42
666	42228	32	265	18	0	22	9	33	18	36456	8159	23470	33	31
1903	179997	91	606	156	0	37	32	120	103	51183	11893	77530	53	51
1608	169406	50	491	87	0	86	24	90	76	52742	10754	57299	64	64
223	19349	12	67	15	0	13	1	2	0	3895	786	9604	14	14
1807	160819	87	617	104	1	21	24	96	83	37076	9706	34684	38	37
1466	109510	53	597	54	0	32	25	86	73	24079	7796	41094	39	37
552	43803	24	240	11	0	8	4	15	4	2325	593	3439	8	8
708	47062	19	219	37	0	38	15	48	41	29354	5600	25171	38	38
1079	110845	44	349	80	0	45	21	81	57	30341	7245	23437	24	23
957	92517	52	241	66	1	24	23	84	52	18992	7360	34086	22	22
585	58660	36	136	27	0	23	12	46	24	15292	4574	24649	18	18
596	27676	22	194	59	0	2	16	59	17	5842	522	2342	3	1
980	98550	32	222	113	0	52	24	96	89	28918	10905	45571	49	48
585	43646	24	153	24	0	5	9	29	20	3738	999	3255	5	5
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
975	75566	28	281	58	0	43	25	91	51	95352	9016	30002	47	46
750	57359	48	240	43	0	18	17	63	63	37478	5134	19360	33	33
1071	104330	36	358	45	0	44	18	68	48	26839	6608	43320	44	41
931	70369	47	302	55	0	45	21	84	70	26783	8577	35513	56	57
783	65494	56	267	66	0	29	17	54	32	33392	1543	23536	49	49
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
874	143931	37	287	67	0	32	20	75	72	25446	9803	54438	45	45
1327	117946	66	476	67	0	65	26	87	56	59847	12140	56812	78	78
1831	137332	85	519	118	1	26	27	108	66	28162	6678	33838	51	46
750	84336	33	243	51	0	24	20	80	77	33298	6420	32366	25	25
778	43410	19	292	63	0	7	1	3	3	2781	4	13	1	1
1373	136250	58	410	84	1	62	24	93	73	37121	7979	55082	62	59
807	79015	34	217	35	0	30	14	55	37	22698	5141	31334	29	29
1544	100578	45	448	58	8	49	27	99	57	27615	1311	16612	26	26
685	57586	38	160	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
1336	105757	42	412	51	2	42	16	60	55	23164	8396	47413	43	43
954	103651	25	332	64	0	23	23	88	84	20304	5462	27389	36	36
1283	113402	35	417	96	0	40	28	112	90	34409	7271	30425	43	41
256	11796	9	79	22	0	1	2	8	1	0	0	0	0	0
81	7627	9	25	7	0	0	0	0	0	0	0	0	0	0
1214	121085	49	431	34	0	29	17	52	38	92538	4423	33510	33	32
41	6836	3	11	5	0	0	1	4	0	0	0	0	0	0
1634	139563	46	564	43	5	46	17	57	36	46037	5331	40389	53	53
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
890	95079	42	295	49	0	21	25	91	75	23924	6676	22205	19	18
1203	80763	32	230	44	0	21	26	89	52	52230	1489	17231	26	26
81	7131	4	27	0	1	0	0	0	0	0	0	0	0	0
61	4194	11	14	4	0	0	0	0	0	0	0	0	0	0
849	60378	20	240	40	1	15	15	54	45	8019	3080	11017	16	16
1035	109173	44	251	52	0	47	20	77	66	34542	11409	46741	84	84
964	83484	16	347	47	0	17	19	76	48	21157	6769	39869	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'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155324&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155324&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155324&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Multiple Linear Regression - Estimated Regression Equation
2[t] = + 2161.6434885673 -7.33001618570158`1`[t] + 305.310254127712`3`[t] + 113.522314149144`4`[t] + 29.5793937101484`5`[t] -850.530124315945`6`[t] + 398.09806486197`7`[t] -687.677284161144`8`[t] + 136.928414079113`9`[t] + 432.179559976343`10`[t] + 0.0352085855691625`11`[t] -2.75642059186917`12`[t] + 1.16691888223206`13`[t] + 1130.35628242038`14`[t] -1403.37737242328`15`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
2[t] =  +  2161.6434885673 -7.33001618570158`1`[t] +  305.310254127712`3`[t] +  113.522314149144`4`[t] +  29.5793937101484`5`[t] -850.530124315945`6`[t] +  398.09806486197`7`[t] -687.677284161144`8`[t] +  136.928414079113`9`[t] +  432.179559976343`10`[t] +  0.0352085855691625`11`[t] -2.75642059186917`12`[t] +  1.16691888223206`13`[t] +  1130.35628242038`14`[t] -1403.37737242328`15`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155324&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]2[t] =  +  2161.6434885673 -7.33001618570158`1`[t] +  305.310254127712`3`[t] +  113.522314149144`4`[t] +  29.5793937101484`5`[t] -850.530124315945`6`[t] +  398.09806486197`7`[t] -687.677284161144`8`[t] +  136.928414079113`9`[t] +  432.179559976343`10`[t] +  0.0352085855691625`11`[t] -2.75642059186917`12`[t] +  1.16691888223206`13`[t] +  1130.35628242038`14`[t] -1403.37737242328`15`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155324&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155324&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] = + 2161.6434885673 -7.33001618570158`1`[t] + 305.310254127712`3`[t] + 113.522314149144`4`[t] + 29.5793937101484`5`[t] -850.530124315945`6`[t] + 398.09806486197`7`[t] -687.677284161144`8`[t] + 136.928414079113`9`[t] + 432.179559976343`10`[t] + 0.0352085855691625`11`[t] -2.75642059186917`12`[t] + 1.16691888223206`13`[t] + 1130.35628242038`14`[t] -1403.37737242328`15`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2161.64348856734416.5779590.48940.6253620.312681
`1`-7.3300161857015812.353433-0.59340.553980.27699
`3`305.310254127712102.9911632.96440.0036130.001806
`4`113.52231414914428.6232963.96610.000126e-05
`5`29.579393710148457.7670240.5120.6094940.304747
`6`-850.530124315945888.195709-0.95760.3400590.17003
`7`398.09806486197192.6812272.06610.0408210.02041
`8`-687.6772841611441228.376749-0.55980.5765690.288284
`9`136.928414079113369.0794470.3710.7112460.355623
`10`432.179559976343175.1656912.46730.0149260.007463
`11`0.03520858556916250.1277820.27550.7833460.391673
`12`-2.756420591869170.836606-3.29480.0012720.000636
`13`1.166918882232060.1483237.867400
`14`1130.356282420381008.2375111.12110.2643190.13216
`15`-1403.377372423281041.573905-1.34740.1802250.090113

\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) & 2161.6434885673 & 4416.577959 & 0.4894 & 0.625362 & 0.312681 \tabularnewline
`1` & -7.33001618570158 & 12.353433 & -0.5934 & 0.55398 & 0.27699 \tabularnewline
`3` & 305.310254127712 & 102.991163 & 2.9644 & 0.003613 & 0.001806 \tabularnewline
`4` & 113.522314149144 & 28.623296 & 3.9661 & 0.00012 & 6e-05 \tabularnewline
`5` & 29.5793937101484 & 57.767024 & 0.512 & 0.609494 & 0.304747 \tabularnewline
`6` & -850.530124315945 & 888.195709 & -0.9576 & 0.340059 & 0.17003 \tabularnewline
`7` & 398.09806486197 & 192.681227 & 2.0661 & 0.040821 & 0.02041 \tabularnewline
`8` & -687.677284161144 & 1228.376749 & -0.5598 & 0.576569 & 0.288284 \tabularnewline
`9` & 136.928414079113 & 369.079447 & 0.371 & 0.711246 & 0.355623 \tabularnewline
`10` & 432.179559976343 & 175.165691 & 2.4673 & 0.014926 & 0.007463 \tabularnewline
`11` & 0.0352085855691625 & 0.127782 & 0.2755 & 0.783346 & 0.391673 \tabularnewline
`12` & -2.75642059186917 & 0.836606 & -3.2948 & 0.001272 & 0.000636 \tabularnewline
`13` & 1.16691888223206 & 0.148323 & 7.8674 & 0 & 0 \tabularnewline
`14` & 1130.35628242038 & 1008.237511 & 1.1211 & 0.264319 & 0.13216 \tabularnewline
`15` & -1403.37737242328 & 1041.573905 & -1.3474 & 0.180225 & 0.090113 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155324&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]2161.6434885673[/C][C]4416.577959[/C][C]0.4894[/C][C]0.625362[/C][C]0.312681[/C][/ROW]
[ROW][C]`1`[/C][C]-7.33001618570158[/C][C]12.353433[/C][C]-0.5934[/C][C]0.55398[/C][C]0.27699[/C][/ROW]
[ROW][C]`3`[/C][C]305.310254127712[/C][C]102.991163[/C][C]2.9644[/C][C]0.003613[/C][C]0.001806[/C][/ROW]
[ROW][C]`4`[/C][C]113.522314149144[/C][C]28.623296[/C][C]3.9661[/C][C]0.00012[/C][C]6e-05[/C][/ROW]
[ROW][C]`5`[/C][C]29.5793937101484[/C][C]57.767024[/C][C]0.512[/C][C]0.609494[/C][C]0.304747[/C][/ROW]
[ROW][C]`6`[/C][C]-850.530124315945[/C][C]888.195709[/C][C]-0.9576[/C][C]0.340059[/C][C]0.17003[/C][/ROW]
[ROW][C]`7`[/C][C]398.09806486197[/C][C]192.681227[/C][C]2.0661[/C][C]0.040821[/C][C]0.02041[/C][/ROW]
[ROW][C]`8`[/C][C]-687.677284161144[/C][C]1228.376749[/C][C]-0.5598[/C][C]0.576569[/C][C]0.288284[/C][/ROW]
[ROW][C]`9`[/C][C]136.928414079113[/C][C]369.079447[/C][C]0.371[/C][C]0.711246[/C][C]0.355623[/C][/ROW]
[ROW][C]`10`[/C][C]432.179559976343[/C][C]175.165691[/C][C]2.4673[/C][C]0.014926[/C][C]0.007463[/C][/ROW]
[ROW][C]`11`[/C][C]0.0352085855691625[/C][C]0.127782[/C][C]0.2755[/C][C]0.783346[/C][C]0.391673[/C][/ROW]
[ROW][C]`12`[/C][C]-2.75642059186917[/C][C]0.836606[/C][C]-3.2948[/C][C]0.001272[/C][C]0.000636[/C][/ROW]
[ROW][C]`13`[/C][C]1.16691888223206[/C][C]0.148323[/C][C]7.8674[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`14`[/C][C]1130.35628242038[/C][C]1008.237511[/C][C]1.1211[/C][C]0.264319[/C][C]0.13216[/C][/ROW]
[ROW][C]`15`[/C][C]-1403.37737242328[/C][C]1041.573905[/C][C]-1.3474[/C][C]0.180225[/C][C]0.090113[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155324&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155324&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)2161.64348856734416.5779590.48940.6253620.312681
`1`-7.3300161857015812.353433-0.59340.553980.27699
`3`305.310254127712102.9911632.96440.0036130.001806
`4`113.52231414914428.6232963.96610.000126e-05
`5`29.579393710148457.7670240.5120.6094940.304747
`6`-850.530124315945888.195709-0.95760.3400590.17003
`7`398.09806486197192.6812272.06610.0408210.02041
`8`-687.6772841611441228.376749-0.55980.5765690.288284
`9`136.928414079113369.0794470.3710.7112460.355623
`10`432.179559976343175.1656912.46730.0149260.007463
`11`0.03520858556916250.1277820.27550.7833460.391673
`12`-2.756420591869170.836606-3.29480.0012720.000636
`13`1.166918882232060.1483237.867400
`14`1130.356282420381008.2375111.12110.2643190.13216
`15`-1403.377372423281041.573905-1.34740.1802250.090113






Multiple Linear Regression - Regression Statistics
Multiple R0.950418415102958
R-squared0.903295163766818
Adjusted R-squared0.892800065260891
F-TEST (value)86.0682882830219
F-TEST (DF numerator)14
F-TEST (DF denominator)129
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation19935.8739215256
Sum Squared Residuals51269639902.9303

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.950418415102958 \tabularnewline
R-squared & 0.903295163766818 \tabularnewline
Adjusted R-squared & 0.892800065260891 \tabularnewline
F-TEST (value) & 86.0682882830219 \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 & 19935.8739215256 \tabularnewline
Sum Squared Residuals & 51269639902.9303 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155324&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.950418415102958[/C][/ROW]
[ROW][C]R-squared[/C][C]0.903295163766818[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.892800065260891[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]86.0682882830219[/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]19935.8739215256[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]51269639902.9303[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155324&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155324&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 R0.950418415102958
R-squared0.903295163766818
Adjusted R-squared0.892800065260891
F-TEST (value)86.0682882830219
F-TEST (DF numerator)14
F-TEST (DF denominator)129
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation19935.8739215256
Sum Squared Residuals51269639902.9303






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1129404113614.66296312915789.3370368708
2130358135401.124291514-5043.12429151422
3721515092.0176918534-7877.01769185336
4112861123607.809330417-10746.8093304167
5219904229434.060705437-9530.06070543733
6402036420791.089543791-18755.0895437914
7117604125924.133570157-8320.13357015716
8126737137292.581096119-10555.5810961185
99972997869.67712499211859.32287500794
10256310219402.98475964336907.0152403565
11113066123974.303967898-10908.3039678982
12165392154178.94716268811213.0528373122
1377780112643.880427097-34863.8804270975
14152673171887.68157565-19214.6815756505
15134368115944.07967300918423.9203269908
16125769139264.147958395-13495.1479583949
17123467108788.57405241814678.4259475822
185623254946.23941393051285.76058606949
19108458140524.154265675-32066.1542656745
202276239620.1713840832-16858.1713840832
214863373934.7007482037-25301.7007482037
22182081176416.8118285065664.1881714935
23140857116724.80831393224132.1916860684
2493773111795.294911914-18022.2949119141
25133398154609.915540887-21211.9155408872
2611393396397.527389828417535.4726101716
27153851161009.513003022-7158.51300302162
28140711149175.376540725-8464.37654072537
29303844239987.06044437963856.9395556215
30163810154724.0490365659085.95096343476
31123344107396.13656953615947.8634304644
32157640142818.07691240114821.9230875989
33103274123111.046186328-19837.0461863276
34193500174867.00344141118632.996558589
35178768171016.7985825327751.20141746812
3602459.6237265093-2459.6237265093
37181412169026.36447731212385.6355226878
389234295202.3883396771-2860.38833967708
3910002394470.93343492225552.06656507779
40178277215791.605222574-37514.6052225737
41145067125242.59852402519824.4014759753
42114146126557.861286874-12411.861286874
438603991417.567623737-5378.56762373703
44125481153047.425603361-27566.4256033609
459553584905.649195213510629.3508047865
4612922196061.052567113133159.9474328869
476155455495.31271787986058.68728212021
48168048146630.20760392221417.792396078
49159121153257.3969797665863.60302023418
50129362141460.36719655-12098.3671965499
514818856439.9834347649-8251.98343476492
529546187136.41588926368324.58411073637
53229864184813.13953507345050.8604649266
54191094146212.6518966644881.3481033405
55158572161588.405674166-3016.40567416572
56111388107414.165724753973.83427525
57172614120545.74324392652068.2567560738
586320566779.1328189097-3574.13281890965
59109102118228.823144779-9126.82314477943
60137303128688.7428297728614.25717022766
61125304116329.4465229598974.55347704121
6288620131692.878763922-43072.8787639216
639580886618.54749045269189.45250954739
6483419102604.041599403-19185.0415994026
65101723104401.877173156-2678.87717315626
6694982120179.194190059-25197.1941900588
67143566178176.228623956-34610.2286239557
68113325150030.067412947-36705.0674129468
698151887258.9736027904-5740.97360279043
703197033041.196391305-1071.19639130505
71192268179374.18180476612893.818195234
729126183995.5388947957265.46110520496
738082097232.762956221-16412.762956221
748582980437.18872411885391.81127588123
75116322139644.178563197-23322.1785631966
765654466183.6692491568-9639.66924915678
77116173132420.55115721-16247.5511572096
78118781191895.855692242-73114.8556922421
796013866392.3830825923-6254.38308259228
807342283778.7861611239-10356.7861611239
816775170949.0164627523-3198.0164627523
82214002155626.11459170558375.8854082948
835118555938.5010956208-4753.5010956208
8497181101658.096551537-4477.09655153684
854510058327.408953399-13227.408953399
86115801106062.6677907139738.3322092869
87186310174197.04915937312112.9508406267
887196091278.5528347141-19318.5528347141
898010577258.44452310872846.55547689134
90103613110332.229944919-6719.22994491853
9198707109886.112141472-11179.1121414716
92136234140851.645064563-4617.64506456347
93136781131627.1084170875153.89158291301
94105863100339.7141824135523.28581758746
954222852511.1283181668-10283.1283181668
96179997190902.452129988-10905.4521299876
97169406148459.09569989920946.9043001013
981934922357.2978100348-3008.29781003482
99160819134672.69249107226146.3075089279
100109510135310.578876001-25800.5788760013
1014380337506.53707951736296.46292048273
1024706262428.4755315032-15366.4755315032
10311084592168.418313030418676.5816869696
1049251781347.197647056511169.8023529435
1055866054457.12190335014202.87809664994
1062767636984.9336449073-9308.93364490731
10798550101260.933799102-2710.93379910191
1084364631506.920640771312139.0793592287
10902161.64348856729-2161.64348856729
1107556673693.47530112291872.52469887714
1115735971915.2842162438-14556.2842162438
112104330107946.967352353-3616.96735235348
1137036992874.735806134-22505.735806134
1146549477869.620756922-12375.620756922
11536164853.66286335988-1237.66286335988
11602161.64348856729-2161.64348856729
117143931107100.07171808236830.9282819179
118117946126358.754659947-8412.7546599469
119137332126508.23740656710823.7625934328
1208433690285.8728456694-5949.87284566936
1214341040907.17460141282502.82539858719
122136250141317.637753282-5067.63775328246
1237901573408.68733657165606.31266342843
12410057899127.01275755361450.98724244643
1255758643327.001315145414258.9986848546
1261976419788.5359534998-24.5359534998446
127105757110733.200098872-4976.20009887159
12810365191867.803202616311783.1967973837
129113402112262.5327898491139.46721015137
1301179613202.3114807316-1406.31148073164
13176277360.8180743745266.181925625502
132121085104118.07089676816966.929103232
13368364033.722383683292802.27761631671
134139563114846.88657655924716.1134234414
13551185470.91717406582-352.917174065821
1364024832915.28121997757332.71878002248
13702161.64348856729-2161.64348856729
1389507984009.389432118511069.6105678815
1398076366408.868755176714354.1312448233
14071315003.725551747252127.27444825275
14141946780.55526957294-2586.55526957294
1426037852401.36222929177976.63777070834
14310917383442.715794428425730.2842055716
1448348495002.183206886-11518.183206886

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 129404 & 113614.662963129 & 15789.3370368708 \tabularnewline
2 & 130358 & 135401.124291514 & -5043.12429151422 \tabularnewline
3 & 7215 & 15092.0176918534 & -7877.01769185336 \tabularnewline
4 & 112861 & 123607.809330417 & -10746.8093304167 \tabularnewline
5 & 219904 & 229434.060705437 & -9530.06070543733 \tabularnewline
6 & 402036 & 420791.089543791 & -18755.0895437914 \tabularnewline
7 & 117604 & 125924.133570157 & -8320.13357015716 \tabularnewline
8 & 126737 & 137292.581096119 & -10555.5810961185 \tabularnewline
9 & 99729 & 97869.6771249921 & 1859.32287500794 \tabularnewline
10 & 256310 & 219402.984759643 & 36907.0152403565 \tabularnewline
11 & 113066 & 123974.303967898 & -10908.3039678982 \tabularnewline
12 & 165392 & 154178.947162688 & 11213.0528373122 \tabularnewline
13 & 77780 & 112643.880427097 & -34863.8804270975 \tabularnewline
14 & 152673 & 171887.68157565 & -19214.6815756505 \tabularnewline
15 & 134368 & 115944.079673009 & 18423.9203269908 \tabularnewline
16 & 125769 & 139264.147958395 & -13495.1479583949 \tabularnewline
17 & 123467 & 108788.574052418 & 14678.4259475822 \tabularnewline
18 & 56232 & 54946.2394139305 & 1285.76058606949 \tabularnewline
19 & 108458 & 140524.154265675 & -32066.1542656745 \tabularnewline
20 & 22762 & 39620.1713840832 & -16858.1713840832 \tabularnewline
21 & 48633 & 73934.7007482037 & -25301.7007482037 \tabularnewline
22 & 182081 & 176416.811828506 & 5664.1881714935 \tabularnewline
23 & 140857 & 116724.808313932 & 24132.1916860684 \tabularnewline
24 & 93773 & 111795.294911914 & -18022.2949119141 \tabularnewline
25 & 133398 & 154609.915540887 & -21211.9155408872 \tabularnewline
26 & 113933 & 96397.5273898284 & 17535.4726101716 \tabularnewline
27 & 153851 & 161009.513003022 & -7158.51300302162 \tabularnewline
28 & 140711 & 149175.376540725 & -8464.37654072537 \tabularnewline
29 & 303844 & 239987.060444379 & 63856.9395556215 \tabularnewline
30 & 163810 & 154724.049036565 & 9085.95096343476 \tabularnewline
31 & 123344 & 107396.136569536 & 15947.8634304644 \tabularnewline
32 & 157640 & 142818.076912401 & 14821.9230875989 \tabularnewline
33 & 103274 & 123111.046186328 & -19837.0461863276 \tabularnewline
34 & 193500 & 174867.003441411 & 18632.996558589 \tabularnewline
35 & 178768 & 171016.798582532 & 7751.20141746812 \tabularnewline
36 & 0 & 2459.6237265093 & -2459.6237265093 \tabularnewline
37 & 181412 & 169026.364477312 & 12385.6355226878 \tabularnewline
38 & 92342 & 95202.3883396771 & -2860.38833967708 \tabularnewline
39 & 100023 & 94470.9334349222 & 5552.06656507779 \tabularnewline
40 & 178277 & 215791.605222574 & -37514.6052225737 \tabularnewline
41 & 145067 & 125242.598524025 & 19824.4014759753 \tabularnewline
42 & 114146 & 126557.861286874 & -12411.861286874 \tabularnewline
43 & 86039 & 91417.567623737 & -5378.56762373703 \tabularnewline
44 & 125481 & 153047.425603361 & -27566.4256033609 \tabularnewline
45 & 95535 & 84905.6491952135 & 10629.3508047865 \tabularnewline
46 & 129221 & 96061.0525671131 & 33159.9474328869 \tabularnewline
47 & 61554 & 55495.3127178798 & 6058.68728212021 \tabularnewline
48 & 168048 & 146630.207603922 & 21417.792396078 \tabularnewline
49 & 159121 & 153257.396979766 & 5863.60302023418 \tabularnewline
50 & 129362 & 141460.36719655 & -12098.3671965499 \tabularnewline
51 & 48188 & 56439.9834347649 & -8251.98343476492 \tabularnewline
52 & 95461 & 87136.4158892636 & 8324.58411073637 \tabularnewline
53 & 229864 & 184813.139535073 & 45050.8604649266 \tabularnewline
54 & 191094 & 146212.65189666 & 44881.3481033405 \tabularnewline
55 & 158572 & 161588.405674166 & -3016.40567416572 \tabularnewline
56 & 111388 & 107414.16572475 & 3973.83427525 \tabularnewline
57 & 172614 & 120545.743243926 & 52068.2567560738 \tabularnewline
58 & 63205 & 66779.1328189097 & -3574.13281890965 \tabularnewline
59 & 109102 & 118228.823144779 & -9126.82314477943 \tabularnewline
60 & 137303 & 128688.742829772 & 8614.25717022766 \tabularnewline
61 & 125304 & 116329.446522959 & 8974.55347704121 \tabularnewline
62 & 88620 & 131692.878763922 & -43072.8787639216 \tabularnewline
63 & 95808 & 86618.5474904526 & 9189.45250954739 \tabularnewline
64 & 83419 & 102604.041599403 & -19185.0415994026 \tabularnewline
65 & 101723 & 104401.877173156 & -2678.87717315626 \tabularnewline
66 & 94982 & 120179.194190059 & -25197.1941900588 \tabularnewline
67 & 143566 & 178176.228623956 & -34610.2286239557 \tabularnewline
68 & 113325 & 150030.067412947 & -36705.0674129468 \tabularnewline
69 & 81518 & 87258.9736027904 & -5740.97360279043 \tabularnewline
70 & 31970 & 33041.196391305 & -1071.19639130505 \tabularnewline
71 & 192268 & 179374.181804766 & 12893.818195234 \tabularnewline
72 & 91261 & 83995.538894795 & 7265.46110520496 \tabularnewline
73 & 80820 & 97232.762956221 & -16412.762956221 \tabularnewline
74 & 85829 & 80437.1887241188 & 5391.81127588123 \tabularnewline
75 & 116322 & 139644.178563197 & -23322.1785631966 \tabularnewline
76 & 56544 & 66183.6692491568 & -9639.66924915678 \tabularnewline
77 & 116173 & 132420.55115721 & -16247.5511572096 \tabularnewline
78 & 118781 & 191895.855692242 & -73114.8556922421 \tabularnewline
79 & 60138 & 66392.3830825923 & -6254.38308259228 \tabularnewline
80 & 73422 & 83778.7861611239 & -10356.7861611239 \tabularnewline
81 & 67751 & 70949.0164627523 & -3198.0164627523 \tabularnewline
82 & 214002 & 155626.114591705 & 58375.8854082948 \tabularnewline
83 & 51185 & 55938.5010956208 & -4753.5010956208 \tabularnewline
84 & 97181 & 101658.096551537 & -4477.09655153684 \tabularnewline
85 & 45100 & 58327.408953399 & -13227.408953399 \tabularnewline
86 & 115801 & 106062.667790713 & 9738.3322092869 \tabularnewline
87 & 186310 & 174197.049159373 & 12112.9508406267 \tabularnewline
88 & 71960 & 91278.5528347141 & -19318.5528347141 \tabularnewline
89 & 80105 & 77258.4445231087 & 2846.55547689134 \tabularnewline
90 & 103613 & 110332.229944919 & -6719.22994491853 \tabularnewline
91 & 98707 & 109886.112141472 & -11179.1121414716 \tabularnewline
92 & 136234 & 140851.645064563 & -4617.64506456347 \tabularnewline
93 & 136781 & 131627.108417087 & 5153.89158291301 \tabularnewline
94 & 105863 & 100339.714182413 & 5523.28581758746 \tabularnewline
95 & 42228 & 52511.1283181668 & -10283.1283181668 \tabularnewline
96 & 179997 & 190902.452129988 & -10905.4521299876 \tabularnewline
97 & 169406 & 148459.095699899 & 20946.9043001013 \tabularnewline
98 & 19349 & 22357.2978100348 & -3008.29781003482 \tabularnewline
99 & 160819 & 134672.692491072 & 26146.3075089279 \tabularnewline
100 & 109510 & 135310.578876001 & -25800.5788760013 \tabularnewline
101 & 43803 & 37506.5370795173 & 6296.46292048273 \tabularnewline
102 & 47062 & 62428.4755315032 & -15366.4755315032 \tabularnewline
103 & 110845 & 92168.4183130304 & 18676.5816869696 \tabularnewline
104 & 92517 & 81347.1976470565 & 11169.8023529435 \tabularnewline
105 & 58660 & 54457.1219033501 & 4202.87809664994 \tabularnewline
106 & 27676 & 36984.9336449073 & -9308.93364490731 \tabularnewline
107 & 98550 & 101260.933799102 & -2710.93379910191 \tabularnewline
108 & 43646 & 31506.9206407713 & 12139.0793592287 \tabularnewline
109 & 0 & 2161.64348856729 & -2161.64348856729 \tabularnewline
110 & 75566 & 73693.4753011229 & 1872.52469887714 \tabularnewline
111 & 57359 & 71915.2842162438 & -14556.2842162438 \tabularnewline
112 & 104330 & 107946.967352353 & -3616.96735235348 \tabularnewline
113 & 70369 & 92874.735806134 & -22505.735806134 \tabularnewline
114 & 65494 & 77869.620756922 & -12375.620756922 \tabularnewline
115 & 3616 & 4853.66286335988 & -1237.66286335988 \tabularnewline
116 & 0 & 2161.64348856729 & -2161.64348856729 \tabularnewline
117 & 143931 & 107100.071718082 & 36830.9282819179 \tabularnewline
118 & 117946 & 126358.754659947 & -8412.7546599469 \tabularnewline
119 & 137332 & 126508.237406567 & 10823.7625934328 \tabularnewline
120 & 84336 & 90285.8728456694 & -5949.87284566936 \tabularnewline
121 & 43410 & 40907.1746014128 & 2502.82539858719 \tabularnewline
122 & 136250 & 141317.637753282 & -5067.63775328246 \tabularnewline
123 & 79015 & 73408.6873365716 & 5606.31266342843 \tabularnewline
124 & 100578 & 99127.0127575536 & 1450.98724244643 \tabularnewline
125 & 57586 & 43327.0013151454 & 14258.9986848546 \tabularnewline
126 & 19764 & 19788.5359534998 & -24.5359534998446 \tabularnewline
127 & 105757 & 110733.200098872 & -4976.20009887159 \tabularnewline
128 & 103651 & 91867.8032026163 & 11783.1967973837 \tabularnewline
129 & 113402 & 112262.532789849 & 1139.46721015137 \tabularnewline
130 & 11796 & 13202.3114807316 & -1406.31148073164 \tabularnewline
131 & 7627 & 7360.8180743745 & 266.181925625502 \tabularnewline
132 & 121085 & 104118.070896768 & 16966.929103232 \tabularnewline
133 & 6836 & 4033.72238368329 & 2802.27761631671 \tabularnewline
134 & 139563 & 114846.886576559 & 24716.1134234414 \tabularnewline
135 & 5118 & 5470.91717406582 & -352.917174065821 \tabularnewline
136 & 40248 & 32915.2812199775 & 7332.71878002248 \tabularnewline
137 & 0 & 2161.64348856729 & -2161.64348856729 \tabularnewline
138 & 95079 & 84009.3894321185 & 11069.6105678815 \tabularnewline
139 & 80763 & 66408.8687551767 & 14354.1312448233 \tabularnewline
140 & 7131 & 5003.72555174725 & 2127.27444825275 \tabularnewline
141 & 4194 & 6780.55526957294 & -2586.55526957294 \tabularnewline
142 & 60378 & 52401.3622292917 & 7976.63777070834 \tabularnewline
143 & 109173 & 83442.7157944284 & 25730.2842055716 \tabularnewline
144 & 83484 & 95002.183206886 & -11518.183206886 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155324&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]129404[/C][C]113614.662963129[/C][C]15789.3370368708[/C][/ROW]
[ROW][C]2[/C][C]130358[/C][C]135401.124291514[/C][C]-5043.12429151422[/C][/ROW]
[ROW][C]3[/C][C]7215[/C][C]15092.0176918534[/C][C]-7877.01769185336[/C][/ROW]
[ROW][C]4[/C][C]112861[/C][C]123607.809330417[/C][C]-10746.8093304167[/C][/ROW]
[ROW][C]5[/C][C]219904[/C][C]229434.060705437[/C][C]-9530.06070543733[/C][/ROW]
[ROW][C]6[/C][C]402036[/C][C]420791.089543791[/C][C]-18755.0895437914[/C][/ROW]
[ROW][C]7[/C][C]117604[/C][C]125924.133570157[/C][C]-8320.13357015716[/C][/ROW]
[ROW][C]8[/C][C]126737[/C][C]137292.581096119[/C][C]-10555.5810961185[/C][/ROW]
[ROW][C]9[/C][C]99729[/C][C]97869.6771249921[/C][C]1859.32287500794[/C][/ROW]
[ROW][C]10[/C][C]256310[/C][C]219402.984759643[/C][C]36907.0152403565[/C][/ROW]
[ROW][C]11[/C][C]113066[/C][C]123974.303967898[/C][C]-10908.3039678982[/C][/ROW]
[ROW][C]12[/C][C]165392[/C][C]154178.947162688[/C][C]11213.0528373122[/C][/ROW]
[ROW][C]13[/C][C]77780[/C][C]112643.880427097[/C][C]-34863.8804270975[/C][/ROW]
[ROW][C]14[/C][C]152673[/C][C]171887.68157565[/C][C]-19214.6815756505[/C][/ROW]
[ROW][C]15[/C][C]134368[/C][C]115944.079673009[/C][C]18423.9203269908[/C][/ROW]
[ROW][C]16[/C][C]125769[/C][C]139264.147958395[/C][C]-13495.1479583949[/C][/ROW]
[ROW][C]17[/C][C]123467[/C][C]108788.574052418[/C][C]14678.4259475822[/C][/ROW]
[ROW][C]18[/C][C]56232[/C][C]54946.2394139305[/C][C]1285.76058606949[/C][/ROW]
[ROW][C]19[/C][C]108458[/C][C]140524.154265675[/C][C]-32066.1542656745[/C][/ROW]
[ROW][C]20[/C][C]22762[/C][C]39620.1713840832[/C][C]-16858.1713840832[/C][/ROW]
[ROW][C]21[/C][C]48633[/C][C]73934.7007482037[/C][C]-25301.7007482037[/C][/ROW]
[ROW][C]22[/C][C]182081[/C][C]176416.811828506[/C][C]5664.1881714935[/C][/ROW]
[ROW][C]23[/C][C]140857[/C][C]116724.808313932[/C][C]24132.1916860684[/C][/ROW]
[ROW][C]24[/C][C]93773[/C][C]111795.294911914[/C][C]-18022.2949119141[/C][/ROW]
[ROW][C]25[/C][C]133398[/C][C]154609.915540887[/C][C]-21211.9155408872[/C][/ROW]
[ROW][C]26[/C][C]113933[/C][C]96397.5273898284[/C][C]17535.4726101716[/C][/ROW]
[ROW][C]27[/C][C]153851[/C][C]161009.513003022[/C][C]-7158.51300302162[/C][/ROW]
[ROW][C]28[/C][C]140711[/C][C]149175.376540725[/C][C]-8464.37654072537[/C][/ROW]
[ROW][C]29[/C][C]303844[/C][C]239987.060444379[/C][C]63856.9395556215[/C][/ROW]
[ROW][C]30[/C][C]163810[/C][C]154724.049036565[/C][C]9085.95096343476[/C][/ROW]
[ROW][C]31[/C][C]123344[/C][C]107396.136569536[/C][C]15947.8634304644[/C][/ROW]
[ROW][C]32[/C][C]157640[/C][C]142818.076912401[/C][C]14821.9230875989[/C][/ROW]
[ROW][C]33[/C][C]103274[/C][C]123111.046186328[/C][C]-19837.0461863276[/C][/ROW]
[ROW][C]34[/C][C]193500[/C][C]174867.003441411[/C][C]18632.996558589[/C][/ROW]
[ROW][C]35[/C][C]178768[/C][C]171016.798582532[/C][C]7751.20141746812[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]2459.6237265093[/C][C]-2459.6237265093[/C][/ROW]
[ROW][C]37[/C][C]181412[/C][C]169026.364477312[/C][C]12385.6355226878[/C][/ROW]
[ROW][C]38[/C][C]92342[/C][C]95202.3883396771[/C][C]-2860.38833967708[/C][/ROW]
[ROW][C]39[/C][C]100023[/C][C]94470.9334349222[/C][C]5552.06656507779[/C][/ROW]
[ROW][C]40[/C][C]178277[/C][C]215791.605222574[/C][C]-37514.6052225737[/C][/ROW]
[ROW][C]41[/C][C]145067[/C][C]125242.598524025[/C][C]19824.4014759753[/C][/ROW]
[ROW][C]42[/C][C]114146[/C][C]126557.861286874[/C][C]-12411.861286874[/C][/ROW]
[ROW][C]43[/C][C]86039[/C][C]91417.567623737[/C][C]-5378.56762373703[/C][/ROW]
[ROW][C]44[/C][C]125481[/C][C]153047.425603361[/C][C]-27566.4256033609[/C][/ROW]
[ROW][C]45[/C][C]95535[/C][C]84905.6491952135[/C][C]10629.3508047865[/C][/ROW]
[ROW][C]46[/C][C]129221[/C][C]96061.0525671131[/C][C]33159.9474328869[/C][/ROW]
[ROW][C]47[/C][C]61554[/C][C]55495.3127178798[/C][C]6058.68728212021[/C][/ROW]
[ROW][C]48[/C][C]168048[/C][C]146630.207603922[/C][C]21417.792396078[/C][/ROW]
[ROW][C]49[/C][C]159121[/C][C]153257.396979766[/C][C]5863.60302023418[/C][/ROW]
[ROW][C]50[/C][C]129362[/C][C]141460.36719655[/C][C]-12098.3671965499[/C][/ROW]
[ROW][C]51[/C][C]48188[/C][C]56439.9834347649[/C][C]-8251.98343476492[/C][/ROW]
[ROW][C]52[/C][C]95461[/C][C]87136.4158892636[/C][C]8324.58411073637[/C][/ROW]
[ROW][C]53[/C][C]229864[/C][C]184813.139535073[/C][C]45050.8604649266[/C][/ROW]
[ROW][C]54[/C][C]191094[/C][C]146212.65189666[/C][C]44881.3481033405[/C][/ROW]
[ROW][C]55[/C][C]158572[/C][C]161588.405674166[/C][C]-3016.40567416572[/C][/ROW]
[ROW][C]56[/C][C]111388[/C][C]107414.16572475[/C][C]3973.83427525[/C][/ROW]
[ROW][C]57[/C][C]172614[/C][C]120545.743243926[/C][C]52068.2567560738[/C][/ROW]
[ROW][C]58[/C][C]63205[/C][C]66779.1328189097[/C][C]-3574.13281890965[/C][/ROW]
[ROW][C]59[/C][C]109102[/C][C]118228.823144779[/C][C]-9126.82314477943[/C][/ROW]
[ROW][C]60[/C][C]137303[/C][C]128688.742829772[/C][C]8614.25717022766[/C][/ROW]
[ROW][C]61[/C][C]125304[/C][C]116329.446522959[/C][C]8974.55347704121[/C][/ROW]
[ROW][C]62[/C][C]88620[/C][C]131692.878763922[/C][C]-43072.8787639216[/C][/ROW]
[ROW][C]63[/C][C]95808[/C][C]86618.5474904526[/C][C]9189.45250954739[/C][/ROW]
[ROW][C]64[/C][C]83419[/C][C]102604.041599403[/C][C]-19185.0415994026[/C][/ROW]
[ROW][C]65[/C][C]101723[/C][C]104401.877173156[/C][C]-2678.87717315626[/C][/ROW]
[ROW][C]66[/C][C]94982[/C][C]120179.194190059[/C][C]-25197.1941900588[/C][/ROW]
[ROW][C]67[/C][C]143566[/C][C]178176.228623956[/C][C]-34610.2286239557[/C][/ROW]
[ROW][C]68[/C][C]113325[/C][C]150030.067412947[/C][C]-36705.0674129468[/C][/ROW]
[ROW][C]69[/C][C]81518[/C][C]87258.9736027904[/C][C]-5740.97360279043[/C][/ROW]
[ROW][C]70[/C][C]31970[/C][C]33041.196391305[/C][C]-1071.19639130505[/C][/ROW]
[ROW][C]71[/C][C]192268[/C][C]179374.181804766[/C][C]12893.818195234[/C][/ROW]
[ROW][C]72[/C][C]91261[/C][C]83995.538894795[/C][C]7265.46110520496[/C][/ROW]
[ROW][C]73[/C][C]80820[/C][C]97232.762956221[/C][C]-16412.762956221[/C][/ROW]
[ROW][C]74[/C][C]85829[/C][C]80437.1887241188[/C][C]5391.81127588123[/C][/ROW]
[ROW][C]75[/C][C]116322[/C][C]139644.178563197[/C][C]-23322.1785631966[/C][/ROW]
[ROW][C]76[/C][C]56544[/C][C]66183.6692491568[/C][C]-9639.66924915678[/C][/ROW]
[ROW][C]77[/C][C]116173[/C][C]132420.55115721[/C][C]-16247.5511572096[/C][/ROW]
[ROW][C]78[/C][C]118781[/C][C]191895.855692242[/C][C]-73114.8556922421[/C][/ROW]
[ROW][C]79[/C][C]60138[/C][C]66392.3830825923[/C][C]-6254.38308259228[/C][/ROW]
[ROW][C]80[/C][C]73422[/C][C]83778.7861611239[/C][C]-10356.7861611239[/C][/ROW]
[ROW][C]81[/C][C]67751[/C][C]70949.0164627523[/C][C]-3198.0164627523[/C][/ROW]
[ROW][C]82[/C][C]214002[/C][C]155626.114591705[/C][C]58375.8854082948[/C][/ROW]
[ROW][C]83[/C][C]51185[/C][C]55938.5010956208[/C][C]-4753.5010956208[/C][/ROW]
[ROW][C]84[/C][C]97181[/C][C]101658.096551537[/C][C]-4477.09655153684[/C][/ROW]
[ROW][C]85[/C][C]45100[/C][C]58327.408953399[/C][C]-13227.408953399[/C][/ROW]
[ROW][C]86[/C][C]115801[/C][C]106062.667790713[/C][C]9738.3322092869[/C][/ROW]
[ROW][C]87[/C][C]186310[/C][C]174197.049159373[/C][C]12112.9508406267[/C][/ROW]
[ROW][C]88[/C][C]71960[/C][C]91278.5528347141[/C][C]-19318.5528347141[/C][/ROW]
[ROW][C]89[/C][C]80105[/C][C]77258.4445231087[/C][C]2846.55547689134[/C][/ROW]
[ROW][C]90[/C][C]103613[/C][C]110332.229944919[/C][C]-6719.22994491853[/C][/ROW]
[ROW][C]91[/C][C]98707[/C][C]109886.112141472[/C][C]-11179.1121414716[/C][/ROW]
[ROW][C]92[/C][C]136234[/C][C]140851.645064563[/C][C]-4617.64506456347[/C][/ROW]
[ROW][C]93[/C][C]136781[/C][C]131627.108417087[/C][C]5153.89158291301[/C][/ROW]
[ROW][C]94[/C][C]105863[/C][C]100339.714182413[/C][C]5523.28581758746[/C][/ROW]
[ROW][C]95[/C][C]42228[/C][C]52511.1283181668[/C][C]-10283.1283181668[/C][/ROW]
[ROW][C]96[/C][C]179997[/C][C]190902.452129988[/C][C]-10905.4521299876[/C][/ROW]
[ROW][C]97[/C][C]169406[/C][C]148459.095699899[/C][C]20946.9043001013[/C][/ROW]
[ROW][C]98[/C][C]19349[/C][C]22357.2978100348[/C][C]-3008.29781003482[/C][/ROW]
[ROW][C]99[/C][C]160819[/C][C]134672.692491072[/C][C]26146.3075089279[/C][/ROW]
[ROW][C]100[/C][C]109510[/C][C]135310.578876001[/C][C]-25800.5788760013[/C][/ROW]
[ROW][C]101[/C][C]43803[/C][C]37506.5370795173[/C][C]6296.46292048273[/C][/ROW]
[ROW][C]102[/C][C]47062[/C][C]62428.4755315032[/C][C]-15366.4755315032[/C][/ROW]
[ROW][C]103[/C][C]110845[/C][C]92168.4183130304[/C][C]18676.5816869696[/C][/ROW]
[ROW][C]104[/C][C]92517[/C][C]81347.1976470565[/C][C]11169.8023529435[/C][/ROW]
[ROW][C]105[/C][C]58660[/C][C]54457.1219033501[/C][C]4202.87809664994[/C][/ROW]
[ROW][C]106[/C][C]27676[/C][C]36984.9336449073[/C][C]-9308.93364490731[/C][/ROW]
[ROW][C]107[/C][C]98550[/C][C]101260.933799102[/C][C]-2710.93379910191[/C][/ROW]
[ROW][C]108[/C][C]43646[/C][C]31506.9206407713[/C][C]12139.0793592287[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]2161.64348856729[/C][C]-2161.64348856729[/C][/ROW]
[ROW][C]110[/C][C]75566[/C][C]73693.4753011229[/C][C]1872.52469887714[/C][/ROW]
[ROW][C]111[/C][C]57359[/C][C]71915.2842162438[/C][C]-14556.2842162438[/C][/ROW]
[ROW][C]112[/C][C]104330[/C][C]107946.967352353[/C][C]-3616.96735235348[/C][/ROW]
[ROW][C]113[/C][C]70369[/C][C]92874.735806134[/C][C]-22505.735806134[/C][/ROW]
[ROW][C]114[/C][C]65494[/C][C]77869.620756922[/C][C]-12375.620756922[/C][/ROW]
[ROW][C]115[/C][C]3616[/C][C]4853.66286335988[/C][C]-1237.66286335988[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]2161.64348856729[/C][C]-2161.64348856729[/C][/ROW]
[ROW][C]117[/C][C]143931[/C][C]107100.071718082[/C][C]36830.9282819179[/C][/ROW]
[ROW][C]118[/C][C]117946[/C][C]126358.754659947[/C][C]-8412.7546599469[/C][/ROW]
[ROW][C]119[/C][C]137332[/C][C]126508.237406567[/C][C]10823.7625934328[/C][/ROW]
[ROW][C]120[/C][C]84336[/C][C]90285.8728456694[/C][C]-5949.87284566936[/C][/ROW]
[ROW][C]121[/C][C]43410[/C][C]40907.1746014128[/C][C]2502.82539858719[/C][/ROW]
[ROW][C]122[/C][C]136250[/C][C]141317.637753282[/C][C]-5067.63775328246[/C][/ROW]
[ROW][C]123[/C][C]79015[/C][C]73408.6873365716[/C][C]5606.31266342843[/C][/ROW]
[ROW][C]124[/C][C]100578[/C][C]99127.0127575536[/C][C]1450.98724244643[/C][/ROW]
[ROW][C]125[/C][C]57586[/C][C]43327.0013151454[/C][C]14258.9986848546[/C][/ROW]
[ROW][C]126[/C][C]19764[/C][C]19788.5359534998[/C][C]-24.5359534998446[/C][/ROW]
[ROW][C]127[/C][C]105757[/C][C]110733.200098872[/C][C]-4976.20009887159[/C][/ROW]
[ROW][C]128[/C][C]103651[/C][C]91867.8032026163[/C][C]11783.1967973837[/C][/ROW]
[ROW][C]129[/C][C]113402[/C][C]112262.532789849[/C][C]1139.46721015137[/C][/ROW]
[ROW][C]130[/C][C]11796[/C][C]13202.3114807316[/C][C]-1406.31148073164[/C][/ROW]
[ROW][C]131[/C][C]7627[/C][C]7360.8180743745[/C][C]266.181925625502[/C][/ROW]
[ROW][C]132[/C][C]121085[/C][C]104118.070896768[/C][C]16966.929103232[/C][/ROW]
[ROW][C]133[/C][C]6836[/C][C]4033.72238368329[/C][C]2802.27761631671[/C][/ROW]
[ROW][C]134[/C][C]139563[/C][C]114846.886576559[/C][C]24716.1134234414[/C][/ROW]
[ROW][C]135[/C][C]5118[/C][C]5470.91717406582[/C][C]-352.917174065821[/C][/ROW]
[ROW][C]136[/C][C]40248[/C][C]32915.2812199775[/C][C]7332.71878002248[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]2161.64348856729[/C][C]-2161.64348856729[/C][/ROW]
[ROW][C]138[/C][C]95079[/C][C]84009.3894321185[/C][C]11069.6105678815[/C][/ROW]
[ROW][C]139[/C][C]80763[/C][C]66408.8687551767[/C][C]14354.1312448233[/C][/ROW]
[ROW][C]140[/C][C]7131[/C][C]5003.72555174725[/C][C]2127.27444825275[/C][/ROW]
[ROW][C]141[/C][C]4194[/C][C]6780.55526957294[/C][C]-2586.55526957294[/C][/ROW]
[ROW][C]142[/C][C]60378[/C][C]52401.3622292917[/C][C]7976.63777070834[/C][/ROW]
[ROW][C]143[/C][C]109173[/C][C]83442.7157944284[/C][C]25730.2842055716[/C][/ROW]
[ROW][C]144[/C][C]83484[/C][C]95002.183206886[/C][C]-11518.183206886[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155324&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155324&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
1129404113614.66296312915789.3370368708
2130358135401.124291514-5043.12429151422
3721515092.0176918534-7877.01769185336
4112861123607.809330417-10746.8093304167
5219904229434.060705437-9530.06070543733
6402036420791.089543791-18755.0895437914
7117604125924.133570157-8320.13357015716
8126737137292.581096119-10555.5810961185
99972997869.67712499211859.32287500794
10256310219402.98475964336907.0152403565
11113066123974.303967898-10908.3039678982
12165392154178.94716268811213.0528373122
1377780112643.880427097-34863.8804270975
14152673171887.68157565-19214.6815756505
15134368115944.07967300918423.9203269908
16125769139264.147958395-13495.1479583949
17123467108788.57405241814678.4259475822
185623254946.23941393051285.76058606949
19108458140524.154265675-32066.1542656745
202276239620.1713840832-16858.1713840832
214863373934.7007482037-25301.7007482037
22182081176416.8118285065664.1881714935
23140857116724.80831393224132.1916860684
2493773111795.294911914-18022.2949119141
25133398154609.915540887-21211.9155408872
2611393396397.527389828417535.4726101716
27153851161009.513003022-7158.51300302162
28140711149175.376540725-8464.37654072537
29303844239987.06044437963856.9395556215
30163810154724.0490365659085.95096343476
31123344107396.13656953615947.8634304644
32157640142818.07691240114821.9230875989
33103274123111.046186328-19837.0461863276
34193500174867.00344141118632.996558589
35178768171016.7985825327751.20141746812
3602459.6237265093-2459.6237265093
37181412169026.36447731212385.6355226878
389234295202.3883396771-2860.38833967708
3910002394470.93343492225552.06656507779
40178277215791.605222574-37514.6052225737
41145067125242.59852402519824.4014759753
42114146126557.861286874-12411.861286874
438603991417.567623737-5378.56762373703
44125481153047.425603361-27566.4256033609
459553584905.649195213510629.3508047865
4612922196061.052567113133159.9474328869
476155455495.31271787986058.68728212021
48168048146630.20760392221417.792396078
49159121153257.3969797665863.60302023418
50129362141460.36719655-12098.3671965499
514818856439.9834347649-8251.98343476492
529546187136.41588926368324.58411073637
53229864184813.13953507345050.8604649266
54191094146212.6518966644881.3481033405
55158572161588.405674166-3016.40567416572
56111388107414.165724753973.83427525
57172614120545.74324392652068.2567560738
586320566779.1328189097-3574.13281890965
59109102118228.823144779-9126.82314477943
60137303128688.7428297728614.25717022766
61125304116329.4465229598974.55347704121
6288620131692.878763922-43072.8787639216
639580886618.54749045269189.45250954739
6483419102604.041599403-19185.0415994026
65101723104401.877173156-2678.87717315626
6694982120179.194190059-25197.1941900588
67143566178176.228623956-34610.2286239557
68113325150030.067412947-36705.0674129468
698151887258.9736027904-5740.97360279043
703197033041.196391305-1071.19639130505
71192268179374.18180476612893.818195234
729126183995.5388947957265.46110520496
738082097232.762956221-16412.762956221
748582980437.18872411885391.81127588123
75116322139644.178563197-23322.1785631966
765654466183.6692491568-9639.66924915678
77116173132420.55115721-16247.5511572096
78118781191895.855692242-73114.8556922421
796013866392.3830825923-6254.38308259228
807342283778.7861611239-10356.7861611239
816775170949.0164627523-3198.0164627523
82214002155626.11459170558375.8854082948
835118555938.5010956208-4753.5010956208
8497181101658.096551537-4477.09655153684
854510058327.408953399-13227.408953399
86115801106062.6677907139738.3322092869
87186310174197.04915937312112.9508406267
887196091278.5528347141-19318.5528347141
898010577258.44452310872846.55547689134
90103613110332.229944919-6719.22994491853
9198707109886.112141472-11179.1121414716
92136234140851.645064563-4617.64506456347
93136781131627.1084170875153.89158291301
94105863100339.7141824135523.28581758746
954222852511.1283181668-10283.1283181668
96179997190902.452129988-10905.4521299876
97169406148459.09569989920946.9043001013
981934922357.2978100348-3008.29781003482
99160819134672.69249107226146.3075089279
100109510135310.578876001-25800.5788760013
1014380337506.53707951736296.46292048273
1024706262428.4755315032-15366.4755315032
10311084592168.418313030418676.5816869696
1049251781347.197647056511169.8023529435
1055866054457.12190335014202.87809664994
1062767636984.9336449073-9308.93364490731
10798550101260.933799102-2710.93379910191
1084364631506.920640771312139.0793592287
10902161.64348856729-2161.64348856729
1107556673693.47530112291872.52469887714
1115735971915.2842162438-14556.2842162438
112104330107946.967352353-3616.96735235348
1137036992874.735806134-22505.735806134
1146549477869.620756922-12375.620756922
11536164853.66286335988-1237.66286335988
11602161.64348856729-2161.64348856729
117143931107100.07171808236830.9282819179
118117946126358.754659947-8412.7546599469
119137332126508.23740656710823.7625934328
1208433690285.8728456694-5949.87284566936
1214341040907.17460141282502.82539858719
122136250141317.637753282-5067.63775328246
1237901573408.68733657165606.31266342843
12410057899127.01275755361450.98724244643
1255758643327.001315145414258.9986848546
1261976419788.5359534998-24.5359534998446
127105757110733.200098872-4976.20009887159
12810365191867.803202616311783.1967973837
129113402112262.5327898491139.46721015137
1301179613202.3114807316-1406.31148073164
13176277360.8180743745266.181925625502
132121085104118.07089676816966.929103232
13368364033.722383683292802.27761631671
134139563114846.88657655924716.1134234414
13551185470.91717406582-352.917174065821
1364024832915.28121997757332.71878002248
13702161.64348856729-2161.64348856729
1389507984009.389432118511069.6105678815
1398076366408.868755176714354.1312448233
14071315003.725551747252127.27444825275
14141946780.55526957294-2586.55526957294
1426037852401.36222929177976.63777070834
14310917383442.715794428425730.2842055716
1448348495002.183206886-11518.183206886






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.7943914596360430.4112170807279130.205608540363957
190.6876821101984740.6246357796030520.312317889801526
200.5697406532116060.8605186935767880.430259346788394
210.4546378749248630.9092757498497270.545362125075137
220.6423222497483140.7153555005033720.357677750251686
230.7647025890538660.4705948218922690.235297410946134
240.7222702700924260.5554594598151490.277729729907574
250.6585381660342690.6829236679314610.341461833965731
260.5991110683583170.8017778632833660.400888931641683
270.5203022323705560.9593955352588880.479697767629444
280.4362652542171210.8725305084342420.563734745782879
290.7864334176645980.4271331646708030.213566582335402
300.7351328029617690.5297343940764620.264867197038231
310.6968121749328720.6063756501342560.303187825067128
320.6429284076083570.7141431847832870.357071592391643
330.5891715038996430.8216569922007130.410828496100356
340.5848712729977710.8302574540044590.41512872700223
350.5235702907144790.9528594185710410.476429709285521
360.4549603148360810.9099206296721620.545039685163919
370.508442004501220.9831159909975590.49155799549878
380.4501825770398910.9003651540797820.549817422960109
390.3875576648845930.7751153297691850.612442335115407
400.8315954645060850.3368090709878290.168404535493914
410.8677387172932340.2645225654135310.132261282706766
420.8464401692506020.3071196614987960.153559830749398
430.8134678110607940.3730643778784110.186532188939206
440.8368221107372790.3263557785254430.163177889262721
450.8217749849267320.3564500301465350.178225015073268
460.8958269326075560.2083461347848880.104173067392444
470.8773156540161330.2453686919677340.122684345983867
480.8769001516422770.2461996967154450.123099848357723
490.8484082873202110.3031834253595790.151591712679789
500.8203078063142480.3593843873715040.179692193685752
510.788796980226640.422406039546720.21120301977336
520.7523719605320820.4952560789358360.247628039467918
530.8824052895333190.2351894209333620.117594710466681
540.9528900503636110.09421989927277860.0471099496363893
550.9407836914306050.1184326171387890.0592163085693946
560.9255358674100040.1489282651799910.0744641325899957
570.9828959493136460.0342081013727080.017104050686354
580.977163083906540.0456738321869190.0228369160934595
590.9710653814235390.05786923715292230.0289346185764611
600.9623086835678480.07538263286430460.0376913164321523
610.9520963517558780.09580729648824480.0479036482441224
620.9831274128811770.03374517423764560.0168725871188228
630.9777159882201640.04456802355967160.0222840117798358
640.9758194854235660.04836102915286790.0241805145764339
650.9674254653341350.06514906933172930.0325745346658647
660.9707928048223650.05841439035527060.0292071951776353
670.9826037273025230.03479254539495460.0173962726974773
680.9921724445674640.01565511086507280.00782755543253642
690.9890670862635520.02186582747289690.0109329137364485
700.9845717394204620.03085652115907620.0154282605795381
710.9805658922592930.03886821548141360.0194341077407068
720.9745068154134750.05098636917304920.0254931845865246
730.9748716955654170.05025660886916650.0251283044345833
740.9664772722817380.06704545543652380.0335227277182619
750.9750521229795920.04989575404081680.0249478770204084
760.9741423231575490.05171535368490190.0258576768424509
770.9760830096116570.04783398077668650.0239169903883432
780.999994423568531.11528629405459e-055.57643147027296e-06
790.9999895588876542.08822246914297e-051.04411123457148e-05
800.9999828814955823.4237008836163e-051.71185044180815e-05
810.9999711370456675.7725908666391e-052.88629543331955e-05
820.9999995590808468.81838307077613e-074.40919153538807e-07
830.9999991112630861.77747382798119e-068.88736913990597e-07
840.9999982825239443.43495211129054e-061.71747605564527e-06
850.9999970350569935.92988601309181e-062.96494300654591e-06
860.9999948673710391.02652579220579e-055.13262896102897e-06
870.9999904324427931.91351144137189e-059.56755720685946e-06
880.9999927946007911.44107984188399e-057.20539920941997e-06
890.999985955318852.80893622999937e-051.40446811499968e-05
900.9999731824744595.36350510825418e-052.68175255412709e-05
910.9999730123308215.39753383580649e-052.69876691790325e-05
920.9999862607449232.74785101545394e-051.37392550772697e-05
930.9999745856943515.08286112987628e-052.54143056493814e-05
940.9999573651376328.52697247352451e-054.26348623676225e-05
950.99994066959350.0001186608130008445.93304065004221e-05
960.9999726182733065.47634533888619e-052.73817266944309e-05
970.9999746874200515.06251598976636e-052.53125799488318e-05
980.9999487677640130.0001024644719748065.12322359874031e-05
990.9999184368517410.000163126296517698.15631482588448e-05
1000.999964058126217.18837475802769e-053.59418737901385e-05
1010.9999401533515990.00011969329680175.984664840085e-05
1020.9999416333190350.0001167333619305695.83666809652846e-05
1030.9999932780002631.34439994744812e-056.72199973724061e-06
1040.9999851447389752.97105220495475e-051.48552610247738e-05
1050.9999714571042125.70857915759639e-052.8542895787982e-05
1060.9999419009344350.0001161981311293915.80990655646957e-05
1070.999927330336150.0001453393277002397.26696638501197e-05
1080.9998687207341270.0002625585317452180.000131279265872609
1090.9997135226290930.0005729547418131860.000286477370906593
1100.9994981288953490.00100374220930160.000501871104650802
1110.9997536069727880.0004927860544229660.000246393027211483
1120.999686394436750.0006272111264998770.000313605563249939
1130.9996404351533360.0007191296933284610.000359564846664231
1140.9998610535640680.0002778928718644730.000138946435932236
1150.9996519964586820.0006960070826365870.000348003541318294
1160.9991574548420060.001685090315988670.000842545157994337
1170.9999837981316833.24037366347472e-051.62018683173736e-05
1180.9999986066018582.78679628407573e-061.39339814203786e-06
1190.9999987856982212.42860355838824e-061.21430177919412e-06
1200.9999952384300639.5231398733676e-064.7615699366838e-06
1210.9999738106012315.2378797537022e-052.6189398768511e-05
1220.999970553483625.88930327600383e-052.94465163800192e-05
1230.9998886496621260.0002227006757484930.000111350337874246
1240.9997342739934240.0005314520131515080.000265726006575754
1250.9995611084131520.0008777831736961940.000438891586848097
1260.9982719427360770.003456114527846010.001728057263923

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.794391459636043 & 0.411217080727913 & 0.205608540363957 \tabularnewline
19 & 0.687682110198474 & 0.624635779603052 & 0.312317889801526 \tabularnewline
20 & 0.569740653211606 & 0.860518693576788 & 0.430259346788394 \tabularnewline
21 & 0.454637874924863 & 0.909275749849727 & 0.545362125075137 \tabularnewline
22 & 0.642322249748314 & 0.715355500503372 & 0.357677750251686 \tabularnewline
23 & 0.764702589053866 & 0.470594821892269 & 0.235297410946134 \tabularnewline
24 & 0.722270270092426 & 0.555459459815149 & 0.277729729907574 \tabularnewline
25 & 0.658538166034269 & 0.682923667931461 & 0.341461833965731 \tabularnewline
26 & 0.599111068358317 & 0.801777863283366 & 0.400888931641683 \tabularnewline
27 & 0.520302232370556 & 0.959395535258888 & 0.479697767629444 \tabularnewline
28 & 0.436265254217121 & 0.872530508434242 & 0.563734745782879 \tabularnewline
29 & 0.786433417664598 & 0.427133164670803 & 0.213566582335402 \tabularnewline
30 & 0.735132802961769 & 0.529734394076462 & 0.264867197038231 \tabularnewline
31 & 0.696812174932872 & 0.606375650134256 & 0.303187825067128 \tabularnewline
32 & 0.642928407608357 & 0.714143184783287 & 0.357071592391643 \tabularnewline
33 & 0.589171503899643 & 0.821656992200713 & 0.410828496100356 \tabularnewline
34 & 0.584871272997771 & 0.830257454004459 & 0.41512872700223 \tabularnewline
35 & 0.523570290714479 & 0.952859418571041 & 0.476429709285521 \tabularnewline
36 & 0.454960314836081 & 0.909920629672162 & 0.545039685163919 \tabularnewline
37 & 0.50844200450122 & 0.983115990997559 & 0.49155799549878 \tabularnewline
38 & 0.450182577039891 & 0.900365154079782 & 0.549817422960109 \tabularnewline
39 & 0.387557664884593 & 0.775115329769185 & 0.612442335115407 \tabularnewline
40 & 0.831595464506085 & 0.336809070987829 & 0.168404535493914 \tabularnewline
41 & 0.867738717293234 & 0.264522565413531 & 0.132261282706766 \tabularnewline
42 & 0.846440169250602 & 0.307119661498796 & 0.153559830749398 \tabularnewline
43 & 0.813467811060794 & 0.373064377878411 & 0.186532188939206 \tabularnewline
44 & 0.836822110737279 & 0.326355778525443 & 0.163177889262721 \tabularnewline
45 & 0.821774984926732 & 0.356450030146535 & 0.178225015073268 \tabularnewline
46 & 0.895826932607556 & 0.208346134784888 & 0.104173067392444 \tabularnewline
47 & 0.877315654016133 & 0.245368691967734 & 0.122684345983867 \tabularnewline
48 & 0.876900151642277 & 0.246199696715445 & 0.123099848357723 \tabularnewline
49 & 0.848408287320211 & 0.303183425359579 & 0.151591712679789 \tabularnewline
50 & 0.820307806314248 & 0.359384387371504 & 0.179692193685752 \tabularnewline
51 & 0.78879698022664 & 0.42240603954672 & 0.21120301977336 \tabularnewline
52 & 0.752371960532082 & 0.495256078935836 & 0.247628039467918 \tabularnewline
53 & 0.882405289533319 & 0.235189420933362 & 0.117594710466681 \tabularnewline
54 & 0.952890050363611 & 0.0942198992727786 & 0.0471099496363893 \tabularnewline
55 & 0.940783691430605 & 0.118432617138789 & 0.0592163085693946 \tabularnewline
56 & 0.925535867410004 & 0.148928265179991 & 0.0744641325899957 \tabularnewline
57 & 0.982895949313646 & 0.034208101372708 & 0.017104050686354 \tabularnewline
58 & 0.97716308390654 & 0.045673832186919 & 0.0228369160934595 \tabularnewline
59 & 0.971065381423539 & 0.0578692371529223 & 0.0289346185764611 \tabularnewline
60 & 0.962308683567848 & 0.0753826328643046 & 0.0376913164321523 \tabularnewline
61 & 0.952096351755878 & 0.0958072964882448 & 0.0479036482441224 \tabularnewline
62 & 0.983127412881177 & 0.0337451742376456 & 0.0168725871188228 \tabularnewline
63 & 0.977715988220164 & 0.0445680235596716 & 0.0222840117798358 \tabularnewline
64 & 0.975819485423566 & 0.0483610291528679 & 0.0241805145764339 \tabularnewline
65 & 0.967425465334135 & 0.0651490693317293 & 0.0325745346658647 \tabularnewline
66 & 0.970792804822365 & 0.0584143903552706 & 0.0292071951776353 \tabularnewline
67 & 0.982603727302523 & 0.0347925453949546 & 0.0173962726974773 \tabularnewline
68 & 0.992172444567464 & 0.0156551108650728 & 0.00782755543253642 \tabularnewline
69 & 0.989067086263552 & 0.0218658274728969 & 0.0109329137364485 \tabularnewline
70 & 0.984571739420462 & 0.0308565211590762 & 0.0154282605795381 \tabularnewline
71 & 0.980565892259293 & 0.0388682154814136 & 0.0194341077407068 \tabularnewline
72 & 0.974506815413475 & 0.0509863691730492 & 0.0254931845865246 \tabularnewline
73 & 0.974871695565417 & 0.0502566088691665 & 0.0251283044345833 \tabularnewline
74 & 0.966477272281738 & 0.0670454554365238 & 0.0335227277182619 \tabularnewline
75 & 0.975052122979592 & 0.0498957540408168 & 0.0249478770204084 \tabularnewline
76 & 0.974142323157549 & 0.0517153536849019 & 0.0258576768424509 \tabularnewline
77 & 0.976083009611657 & 0.0478339807766865 & 0.0239169903883432 \tabularnewline
78 & 0.99999442356853 & 1.11528629405459e-05 & 5.57643147027296e-06 \tabularnewline
79 & 0.999989558887654 & 2.08822246914297e-05 & 1.04411123457148e-05 \tabularnewline
80 & 0.999982881495582 & 3.4237008836163e-05 & 1.71185044180815e-05 \tabularnewline
81 & 0.999971137045667 & 5.7725908666391e-05 & 2.88629543331955e-05 \tabularnewline
82 & 0.999999559080846 & 8.81838307077613e-07 & 4.40919153538807e-07 \tabularnewline
83 & 0.999999111263086 & 1.77747382798119e-06 & 8.88736913990597e-07 \tabularnewline
84 & 0.999998282523944 & 3.43495211129054e-06 & 1.71747605564527e-06 \tabularnewline
85 & 0.999997035056993 & 5.92988601309181e-06 & 2.96494300654591e-06 \tabularnewline
86 & 0.999994867371039 & 1.02652579220579e-05 & 5.13262896102897e-06 \tabularnewline
87 & 0.999990432442793 & 1.91351144137189e-05 & 9.56755720685946e-06 \tabularnewline
88 & 0.999992794600791 & 1.44107984188399e-05 & 7.20539920941997e-06 \tabularnewline
89 & 0.99998595531885 & 2.80893622999937e-05 & 1.40446811499968e-05 \tabularnewline
90 & 0.999973182474459 & 5.36350510825418e-05 & 2.68175255412709e-05 \tabularnewline
91 & 0.999973012330821 & 5.39753383580649e-05 & 2.69876691790325e-05 \tabularnewline
92 & 0.999986260744923 & 2.74785101545394e-05 & 1.37392550772697e-05 \tabularnewline
93 & 0.999974585694351 & 5.08286112987628e-05 & 2.54143056493814e-05 \tabularnewline
94 & 0.999957365137632 & 8.52697247352451e-05 & 4.26348623676225e-05 \tabularnewline
95 & 0.9999406695935 & 0.000118660813000844 & 5.93304065004221e-05 \tabularnewline
96 & 0.999972618273306 & 5.47634533888619e-05 & 2.73817266944309e-05 \tabularnewline
97 & 0.999974687420051 & 5.06251598976636e-05 & 2.53125799488318e-05 \tabularnewline
98 & 0.999948767764013 & 0.000102464471974806 & 5.12322359874031e-05 \tabularnewline
99 & 0.999918436851741 & 0.00016312629651769 & 8.15631482588448e-05 \tabularnewline
100 & 0.99996405812621 & 7.18837475802769e-05 & 3.59418737901385e-05 \tabularnewline
101 & 0.999940153351599 & 0.0001196932968017 & 5.984664840085e-05 \tabularnewline
102 & 0.999941633319035 & 0.000116733361930569 & 5.83666809652846e-05 \tabularnewline
103 & 0.999993278000263 & 1.34439994744812e-05 & 6.72199973724061e-06 \tabularnewline
104 & 0.999985144738975 & 2.97105220495475e-05 & 1.48552610247738e-05 \tabularnewline
105 & 0.999971457104212 & 5.70857915759639e-05 & 2.8542895787982e-05 \tabularnewline
106 & 0.999941900934435 & 0.000116198131129391 & 5.80990655646957e-05 \tabularnewline
107 & 0.99992733033615 & 0.000145339327700239 & 7.26696638501197e-05 \tabularnewline
108 & 0.999868720734127 & 0.000262558531745218 & 0.000131279265872609 \tabularnewline
109 & 0.999713522629093 & 0.000572954741813186 & 0.000286477370906593 \tabularnewline
110 & 0.999498128895349 & 0.0010037422093016 & 0.000501871104650802 \tabularnewline
111 & 0.999753606972788 & 0.000492786054422966 & 0.000246393027211483 \tabularnewline
112 & 0.99968639443675 & 0.000627211126499877 & 0.000313605563249939 \tabularnewline
113 & 0.999640435153336 & 0.000719129693328461 & 0.000359564846664231 \tabularnewline
114 & 0.999861053564068 & 0.000277892871864473 & 0.000138946435932236 \tabularnewline
115 & 0.999651996458682 & 0.000696007082636587 & 0.000348003541318294 \tabularnewline
116 & 0.999157454842006 & 0.00168509031598867 & 0.000842545157994337 \tabularnewline
117 & 0.999983798131683 & 3.24037366347472e-05 & 1.62018683173736e-05 \tabularnewline
118 & 0.999998606601858 & 2.78679628407573e-06 & 1.39339814203786e-06 \tabularnewline
119 & 0.999998785698221 & 2.42860355838824e-06 & 1.21430177919412e-06 \tabularnewline
120 & 0.999995238430063 & 9.5231398733676e-06 & 4.7615699366838e-06 \tabularnewline
121 & 0.999973810601231 & 5.2378797537022e-05 & 2.6189398768511e-05 \tabularnewline
122 & 0.99997055348362 & 5.88930327600383e-05 & 2.94465163800192e-05 \tabularnewline
123 & 0.999888649662126 & 0.000222700675748493 & 0.000111350337874246 \tabularnewline
124 & 0.999734273993424 & 0.000531452013151508 & 0.000265726006575754 \tabularnewline
125 & 0.999561108413152 & 0.000877783173696194 & 0.000438891586848097 \tabularnewline
126 & 0.998271942736077 & 0.00345611452784601 & 0.001728057263923 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155324&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]0.794391459636043[/C][C]0.411217080727913[/C][C]0.205608540363957[/C][/ROW]
[ROW][C]19[/C][C]0.687682110198474[/C][C]0.624635779603052[/C][C]0.312317889801526[/C][/ROW]
[ROW][C]20[/C][C]0.569740653211606[/C][C]0.860518693576788[/C][C]0.430259346788394[/C][/ROW]
[ROW][C]21[/C][C]0.454637874924863[/C][C]0.909275749849727[/C][C]0.545362125075137[/C][/ROW]
[ROW][C]22[/C][C]0.642322249748314[/C][C]0.715355500503372[/C][C]0.357677750251686[/C][/ROW]
[ROW][C]23[/C][C]0.764702589053866[/C][C]0.470594821892269[/C][C]0.235297410946134[/C][/ROW]
[ROW][C]24[/C][C]0.722270270092426[/C][C]0.555459459815149[/C][C]0.277729729907574[/C][/ROW]
[ROW][C]25[/C][C]0.658538166034269[/C][C]0.682923667931461[/C][C]0.341461833965731[/C][/ROW]
[ROW][C]26[/C][C]0.599111068358317[/C][C]0.801777863283366[/C][C]0.400888931641683[/C][/ROW]
[ROW][C]27[/C][C]0.520302232370556[/C][C]0.959395535258888[/C][C]0.479697767629444[/C][/ROW]
[ROW][C]28[/C][C]0.436265254217121[/C][C]0.872530508434242[/C][C]0.563734745782879[/C][/ROW]
[ROW][C]29[/C][C]0.786433417664598[/C][C]0.427133164670803[/C][C]0.213566582335402[/C][/ROW]
[ROW][C]30[/C][C]0.735132802961769[/C][C]0.529734394076462[/C][C]0.264867197038231[/C][/ROW]
[ROW][C]31[/C][C]0.696812174932872[/C][C]0.606375650134256[/C][C]0.303187825067128[/C][/ROW]
[ROW][C]32[/C][C]0.642928407608357[/C][C]0.714143184783287[/C][C]0.357071592391643[/C][/ROW]
[ROW][C]33[/C][C]0.589171503899643[/C][C]0.821656992200713[/C][C]0.410828496100356[/C][/ROW]
[ROW][C]34[/C][C]0.584871272997771[/C][C]0.830257454004459[/C][C]0.41512872700223[/C][/ROW]
[ROW][C]35[/C][C]0.523570290714479[/C][C]0.952859418571041[/C][C]0.476429709285521[/C][/ROW]
[ROW][C]36[/C][C]0.454960314836081[/C][C]0.909920629672162[/C][C]0.545039685163919[/C][/ROW]
[ROW][C]37[/C][C]0.50844200450122[/C][C]0.983115990997559[/C][C]0.49155799549878[/C][/ROW]
[ROW][C]38[/C][C]0.450182577039891[/C][C]0.900365154079782[/C][C]0.549817422960109[/C][/ROW]
[ROW][C]39[/C][C]0.387557664884593[/C][C]0.775115329769185[/C][C]0.612442335115407[/C][/ROW]
[ROW][C]40[/C][C]0.831595464506085[/C][C]0.336809070987829[/C][C]0.168404535493914[/C][/ROW]
[ROW][C]41[/C][C]0.867738717293234[/C][C]0.264522565413531[/C][C]0.132261282706766[/C][/ROW]
[ROW][C]42[/C][C]0.846440169250602[/C][C]0.307119661498796[/C][C]0.153559830749398[/C][/ROW]
[ROW][C]43[/C][C]0.813467811060794[/C][C]0.373064377878411[/C][C]0.186532188939206[/C][/ROW]
[ROW][C]44[/C][C]0.836822110737279[/C][C]0.326355778525443[/C][C]0.163177889262721[/C][/ROW]
[ROW][C]45[/C][C]0.821774984926732[/C][C]0.356450030146535[/C][C]0.178225015073268[/C][/ROW]
[ROW][C]46[/C][C]0.895826932607556[/C][C]0.208346134784888[/C][C]0.104173067392444[/C][/ROW]
[ROW][C]47[/C][C]0.877315654016133[/C][C]0.245368691967734[/C][C]0.122684345983867[/C][/ROW]
[ROW][C]48[/C][C]0.876900151642277[/C][C]0.246199696715445[/C][C]0.123099848357723[/C][/ROW]
[ROW][C]49[/C][C]0.848408287320211[/C][C]0.303183425359579[/C][C]0.151591712679789[/C][/ROW]
[ROW][C]50[/C][C]0.820307806314248[/C][C]0.359384387371504[/C][C]0.179692193685752[/C][/ROW]
[ROW][C]51[/C][C]0.78879698022664[/C][C]0.42240603954672[/C][C]0.21120301977336[/C][/ROW]
[ROW][C]52[/C][C]0.752371960532082[/C][C]0.495256078935836[/C][C]0.247628039467918[/C][/ROW]
[ROW][C]53[/C][C]0.882405289533319[/C][C]0.235189420933362[/C][C]0.117594710466681[/C][/ROW]
[ROW][C]54[/C][C]0.952890050363611[/C][C]0.0942198992727786[/C][C]0.0471099496363893[/C][/ROW]
[ROW][C]55[/C][C]0.940783691430605[/C][C]0.118432617138789[/C][C]0.0592163085693946[/C][/ROW]
[ROW][C]56[/C][C]0.925535867410004[/C][C]0.148928265179991[/C][C]0.0744641325899957[/C][/ROW]
[ROW][C]57[/C][C]0.982895949313646[/C][C]0.034208101372708[/C][C]0.017104050686354[/C][/ROW]
[ROW][C]58[/C][C]0.97716308390654[/C][C]0.045673832186919[/C][C]0.0228369160934595[/C][/ROW]
[ROW][C]59[/C][C]0.971065381423539[/C][C]0.0578692371529223[/C][C]0.0289346185764611[/C][/ROW]
[ROW][C]60[/C][C]0.962308683567848[/C][C]0.0753826328643046[/C][C]0.0376913164321523[/C][/ROW]
[ROW][C]61[/C][C]0.952096351755878[/C][C]0.0958072964882448[/C][C]0.0479036482441224[/C][/ROW]
[ROW][C]62[/C][C]0.983127412881177[/C][C]0.0337451742376456[/C][C]0.0168725871188228[/C][/ROW]
[ROW][C]63[/C][C]0.977715988220164[/C][C]0.0445680235596716[/C][C]0.0222840117798358[/C][/ROW]
[ROW][C]64[/C][C]0.975819485423566[/C][C]0.0483610291528679[/C][C]0.0241805145764339[/C][/ROW]
[ROW][C]65[/C][C]0.967425465334135[/C][C]0.0651490693317293[/C][C]0.0325745346658647[/C][/ROW]
[ROW][C]66[/C][C]0.970792804822365[/C][C]0.0584143903552706[/C][C]0.0292071951776353[/C][/ROW]
[ROW][C]67[/C][C]0.982603727302523[/C][C]0.0347925453949546[/C][C]0.0173962726974773[/C][/ROW]
[ROW][C]68[/C][C]0.992172444567464[/C][C]0.0156551108650728[/C][C]0.00782755543253642[/C][/ROW]
[ROW][C]69[/C][C]0.989067086263552[/C][C]0.0218658274728969[/C][C]0.0109329137364485[/C][/ROW]
[ROW][C]70[/C][C]0.984571739420462[/C][C]0.0308565211590762[/C][C]0.0154282605795381[/C][/ROW]
[ROW][C]71[/C][C]0.980565892259293[/C][C]0.0388682154814136[/C][C]0.0194341077407068[/C][/ROW]
[ROW][C]72[/C][C]0.974506815413475[/C][C]0.0509863691730492[/C][C]0.0254931845865246[/C][/ROW]
[ROW][C]73[/C][C]0.974871695565417[/C][C]0.0502566088691665[/C][C]0.0251283044345833[/C][/ROW]
[ROW][C]74[/C][C]0.966477272281738[/C][C]0.0670454554365238[/C][C]0.0335227277182619[/C][/ROW]
[ROW][C]75[/C][C]0.975052122979592[/C][C]0.0498957540408168[/C][C]0.0249478770204084[/C][/ROW]
[ROW][C]76[/C][C]0.974142323157549[/C][C]0.0517153536849019[/C][C]0.0258576768424509[/C][/ROW]
[ROW][C]77[/C][C]0.976083009611657[/C][C]0.0478339807766865[/C][C]0.0239169903883432[/C][/ROW]
[ROW][C]78[/C][C]0.99999442356853[/C][C]1.11528629405459e-05[/C][C]5.57643147027296e-06[/C][/ROW]
[ROW][C]79[/C][C]0.999989558887654[/C][C]2.08822246914297e-05[/C][C]1.04411123457148e-05[/C][/ROW]
[ROW][C]80[/C][C]0.999982881495582[/C][C]3.4237008836163e-05[/C][C]1.71185044180815e-05[/C][/ROW]
[ROW][C]81[/C][C]0.999971137045667[/C][C]5.7725908666391e-05[/C][C]2.88629543331955e-05[/C][/ROW]
[ROW][C]82[/C][C]0.999999559080846[/C][C]8.81838307077613e-07[/C][C]4.40919153538807e-07[/C][/ROW]
[ROW][C]83[/C][C]0.999999111263086[/C][C]1.77747382798119e-06[/C][C]8.88736913990597e-07[/C][/ROW]
[ROW][C]84[/C][C]0.999998282523944[/C][C]3.43495211129054e-06[/C][C]1.71747605564527e-06[/C][/ROW]
[ROW][C]85[/C][C]0.999997035056993[/C][C]5.92988601309181e-06[/C][C]2.96494300654591e-06[/C][/ROW]
[ROW][C]86[/C][C]0.999994867371039[/C][C]1.02652579220579e-05[/C][C]5.13262896102897e-06[/C][/ROW]
[ROW][C]87[/C][C]0.999990432442793[/C][C]1.91351144137189e-05[/C][C]9.56755720685946e-06[/C][/ROW]
[ROW][C]88[/C][C]0.999992794600791[/C][C]1.44107984188399e-05[/C][C]7.20539920941997e-06[/C][/ROW]
[ROW][C]89[/C][C]0.99998595531885[/C][C]2.80893622999937e-05[/C][C]1.40446811499968e-05[/C][/ROW]
[ROW][C]90[/C][C]0.999973182474459[/C][C]5.36350510825418e-05[/C][C]2.68175255412709e-05[/C][/ROW]
[ROW][C]91[/C][C]0.999973012330821[/C][C]5.39753383580649e-05[/C][C]2.69876691790325e-05[/C][/ROW]
[ROW][C]92[/C][C]0.999986260744923[/C][C]2.74785101545394e-05[/C][C]1.37392550772697e-05[/C][/ROW]
[ROW][C]93[/C][C]0.999974585694351[/C][C]5.08286112987628e-05[/C][C]2.54143056493814e-05[/C][/ROW]
[ROW][C]94[/C][C]0.999957365137632[/C][C]8.52697247352451e-05[/C][C]4.26348623676225e-05[/C][/ROW]
[ROW][C]95[/C][C]0.9999406695935[/C][C]0.000118660813000844[/C][C]5.93304065004221e-05[/C][/ROW]
[ROW][C]96[/C][C]0.999972618273306[/C][C]5.47634533888619e-05[/C][C]2.73817266944309e-05[/C][/ROW]
[ROW][C]97[/C][C]0.999974687420051[/C][C]5.06251598976636e-05[/C][C]2.53125799488318e-05[/C][/ROW]
[ROW][C]98[/C][C]0.999948767764013[/C][C]0.000102464471974806[/C][C]5.12322359874031e-05[/C][/ROW]
[ROW][C]99[/C][C]0.999918436851741[/C][C]0.00016312629651769[/C][C]8.15631482588448e-05[/C][/ROW]
[ROW][C]100[/C][C]0.99996405812621[/C][C]7.18837475802769e-05[/C][C]3.59418737901385e-05[/C][/ROW]
[ROW][C]101[/C][C]0.999940153351599[/C][C]0.0001196932968017[/C][C]5.984664840085e-05[/C][/ROW]
[ROW][C]102[/C][C]0.999941633319035[/C][C]0.000116733361930569[/C][C]5.83666809652846e-05[/C][/ROW]
[ROW][C]103[/C][C]0.999993278000263[/C][C]1.34439994744812e-05[/C][C]6.72199973724061e-06[/C][/ROW]
[ROW][C]104[/C][C]0.999985144738975[/C][C]2.97105220495475e-05[/C][C]1.48552610247738e-05[/C][/ROW]
[ROW][C]105[/C][C]0.999971457104212[/C][C]5.70857915759639e-05[/C][C]2.8542895787982e-05[/C][/ROW]
[ROW][C]106[/C][C]0.999941900934435[/C][C]0.000116198131129391[/C][C]5.80990655646957e-05[/C][/ROW]
[ROW][C]107[/C][C]0.99992733033615[/C][C]0.000145339327700239[/C][C]7.26696638501197e-05[/C][/ROW]
[ROW][C]108[/C][C]0.999868720734127[/C][C]0.000262558531745218[/C][C]0.000131279265872609[/C][/ROW]
[ROW][C]109[/C][C]0.999713522629093[/C][C]0.000572954741813186[/C][C]0.000286477370906593[/C][/ROW]
[ROW][C]110[/C][C]0.999498128895349[/C][C]0.0010037422093016[/C][C]0.000501871104650802[/C][/ROW]
[ROW][C]111[/C][C]0.999753606972788[/C][C]0.000492786054422966[/C][C]0.000246393027211483[/C][/ROW]
[ROW][C]112[/C][C]0.99968639443675[/C][C]0.000627211126499877[/C][C]0.000313605563249939[/C][/ROW]
[ROW][C]113[/C][C]0.999640435153336[/C][C]0.000719129693328461[/C][C]0.000359564846664231[/C][/ROW]
[ROW][C]114[/C][C]0.999861053564068[/C][C]0.000277892871864473[/C][C]0.000138946435932236[/C][/ROW]
[ROW][C]115[/C][C]0.999651996458682[/C][C]0.000696007082636587[/C][C]0.000348003541318294[/C][/ROW]
[ROW][C]116[/C][C]0.999157454842006[/C][C]0.00168509031598867[/C][C]0.000842545157994337[/C][/ROW]
[ROW][C]117[/C][C]0.999983798131683[/C][C]3.24037366347472e-05[/C][C]1.62018683173736e-05[/C][/ROW]
[ROW][C]118[/C][C]0.999998606601858[/C][C]2.78679628407573e-06[/C][C]1.39339814203786e-06[/C][/ROW]
[ROW][C]119[/C][C]0.999998785698221[/C][C]2.42860355838824e-06[/C][C]1.21430177919412e-06[/C][/ROW]
[ROW][C]120[/C][C]0.999995238430063[/C][C]9.5231398733676e-06[/C][C]4.7615699366838e-06[/C][/ROW]
[ROW][C]121[/C][C]0.999973810601231[/C][C]5.2378797537022e-05[/C][C]2.6189398768511e-05[/C][/ROW]
[ROW][C]122[/C][C]0.99997055348362[/C][C]5.88930327600383e-05[/C][C]2.94465163800192e-05[/C][/ROW]
[ROW][C]123[/C][C]0.999888649662126[/C][C]0.000222700675748493[/C][C]0.000111350337874246[/C][/ROW]
[ROW][C]124[/C][C]0.999734273993424[/C][C]0.000531452013151508[/C][C]0.000265726006575754[/C][/ROW]
[ROW][C]125[/C][C]0.999561108413152[/C][C]0.000877783173696194[/C][C]0.000438891586848097[/C][/ROW]
[ROW][C]126[/C][C]0.998271942736077[/C][C]0.00345611452784601[/C][C]0.001728057263923[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155324&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155324&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
180.7943914596360430.4112170807279130.205608540363957
190.6876821101984740.6246357796030520.312317889801526
200.5697406532116060.8605186935767880.430259346788394
210.4546378749248630.9092757498497270.545362125075137
220.6423222497483140.7153555005033720.357677750251686
230.7647025890538660.4705948218922690.235297410946134
240.7222702700924260.5554594598151490.277729729907574
250.6585381660342690.6829236679314610.341461833965731
260.5991110683583170.8017778632833660.400888931641683
270.5203022323705560.9593955352588880.479697767629444
280.4362652542171210.8725305084342420.563734745782879
290.7864334176645980.4271331646708030.213566582335402
300.7351328029617690.5297343940764620.264867197038231
310.6968121749328720.6063756501342560.303187825067128
320.6429284076083570.7141431847832870.357071592391643
330.5891715038996430.8216569922007130.410828496100356
340.5848712729977710.8302574540044590.41512872700223
350.5235702907144790.9528594185710410.476429709285521
360.4549603148360810.9099206296721620.545039685163919
370.508442004501220.9831159909975590.49155799549878
380.4501825770398910.9003651540797820.549817422960109
390.3875576648845930.7751153297691850.612442335115407
400.8315954645060850.3368090709878290.168404535493914
410.8677387172932340.2645225654135310.132261282706766
420.8464401692506020.3071196614987960.153559830749398
430.8134678110607940.3730643778784110.186532188939206
440.8368221107372790.3263557785254430.163177889262721
450.8217749849267320.3564500301465350.178225015073268
460.8958269326075560.2083461347848880.104173067392444
470.8773156540161330.2453686919677340.122684345983867
480.8769001516422770.2461996967154450.123099848357723
490.8484082873202110.3031834253595790.151591712679789
500.8203078063142480.3593843873715040.179692193685752
510.788796980226640.422406039546720.21120301977336
520.7523719605320820.4952560789358360.247628039467918
530.8824052895333190.2351894209333620.117594710466681
540.9528900503636110.09421989927277860.0471099496363893
550.9407836914306050.1184326171387890.0592163085693946
560.9255358674100040.1489282651799910.0744641325899957
570.9828959493136460.0342081013727080.017104050686354
580.977163083906540.0456738321869190.0228369160934595
590.9710653814235390.05786923715292230.0289346185764611
600.9623086835678480.07538263286430460.0376913164321523
610.9520963517558780.09580729648824480.0479036482441224
620.9831274128811770.03374517423764560.0168725871188228
630.9777159882201640.04456802355967160.0222840117798358
640.9758194854235660.04836102915286790.0241805145764339
650.9674254653341350.06514906933172930.0325745346658647
660.9707928048223650.05841439035527060.0292071951776353
670.9826037273025230.03479254539495460.0173962726974773
680.9921724445674640.01565511086507280.00782755543253642
690.9890670862635520.02186582747289690.0109329137364485
700.9845717394204620.03085652115907620.0154282605795381
710.9805658922592930.03886821548141360.0194341077407068
720.9745068154134750.05098636917304920.0254931845865246
730.9748716955654170.05025660886916650.0251283044345833
740.9664772722817380.06704545543652380.0335227277182619
750.9750521229795920.04989575404081680.0249478770204084
760.9741423231575490.05171535368490190.0258576768424509
770.9760830096116570.04783398077668650.0239169903883432
780.999994423568531.11528629405459e-055.57643147027296e-06
790.9999895588876542.08822246914297e-051.04411123457148e-05
800.9999828814955823.4237008836163e-051.71185044180815e-05
810.9999711370456675.7725908666391e-052.88629543331955e-05
820.9999995590808468.81838307077613e-074.40919153538807e-07
830.9999991112630861.77747382798119e-068.88736913990597e-07
840.9999982825239443.43495211129054e-061.71747605564527e-06
850.9999970350569935.92988601309181e-062.96494300654591e-06
860.9999948673710391.02652579220579e-055.13262896102897e-06
870.9999904324427931.91351144137189e-059.56755720685946e-06
880.9999927946007911.44107984188399e-057.20539920941997e-06
890.999985955318852.80893622999937e-051.40446811499968e-05
900.9999731824744595.36350510825418e-052.68175255412709e-05
910.9999730123308215.39753383580649e-052.69876691790325e-05
920.9999862607449232.74785101545394e-051.37392550772697e-05
930.9999745856943515.08286112987628e-052.54143056493814e-05
940.9999573651376328.52697247352451e-054.26348623676225e-05
950.99994066959350.0001186608130008445.93304065004221e-05
960.9999726182733065.47634533888619e-052.73817266944309e-05
970.9999746874200515.06251598976636e-052.53125799488318e-05
980.9999487677640130.0001024644719748065.12322359874031e-05
990.9999184368517410.000163126296517698.15631482588448e-05
1000.999964058126217.18837475802769e-053.59418737901385e-05
1010.9999401533515990.00011969329680175.984664840085e-05
1020.9999416333190350.0001167333619305695.83666809652846e-05
1030.9999932780002631.34439994744812e-056.72199973724061e-06
1040.9999851447389752.97105220495475e-051.48552610247738e-05
1050.9999714571042125.70857915759639e-052.8542895787982e-05
1060.9999419009344350.0001161981311293915.80990655646957e-05
1070.999927330336150.0001453393277002397.26696638501197e-05
1080.9998687207341270.0002625585317452180.000131279265872609
1090.9997135226290930.0005729547418131860.000286477370906593
1100.9994981288953490.00100374220930160.000501871104650802
1110.9997536069727880.0004927860544229660.000246393027211483
1120.999686394436750.0006272111264998770.000313605563249939
1130.9996404351533360.0007191296933284610.000359564846664231
1140.9998610535640680.0002778928718644730.000138946435932236
1150.9996519964586820.0006960070826365870.000348003541318294
1160.9991574548420060.001685090315988670.000842545157994337
1170.9999837981316833.24037366347472e-051.62018683173736e-05
1180.9999986066018582.78679628407573e-061.39339814203786e-06
1190.9999987856982212.42860355838824e-061.21430177919412e-06
1200.9999952384300639.5231398733676e-064.7615699366838e-06
1210.9999738106012315.2378797537022e-052.6189398768511e-05
1220.999970553483625.88930327600383e-052.94465163800192e-05
1230.9998886496621260.0002227006757484930.000111350337874246
1240.9997342739934240.0005314520131515080.000265726006575754
1250.9995611084131520.0008777831736961940.000438891586848097
1260.9982719427360770.003456114527846010.001728057263923






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level490.44954128440367NOK
5% type I error level610.559633027522936NOK
10% type I error level710.651376146788991NOK

\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 & 49 & 0.44954128440367 & NOK \tabularnewline
5% type I error level & 61 & 0.559633027522936 & NOK \tabularnewline
10% type I error level & 71 & 0.651376146788991 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155324&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]49[/C][C]0.44954128440367[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]61[/C][C]0.559633027522936[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]71[/C][C]0.651376146788991[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155324&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155324&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 level490.44954128440367NOK
5% type I error level610.559633027522936NOK
10% type I error level710.651376146788991NOK


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