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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 computationWed, 14 Dec 2011 09:51:04 -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/14/t13238742906zn8zquhnn2esvi.htm/, Retrieved Wed, 01 May 2024 13:22:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=155018, Retrieved Wed, 01 May 2024 13:22:59 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact89

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [] [2010-12-05 17:44:33] [b98453cac15ba1066b407e146608df68]
- RMPD    [Multiple Regression] [Ws 10 Meervoudige...] [2011-12-14 14:51:04] [242bbde8f74d68805b56d9ecebfdbe63] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1536	127476	78	490	107	0	20	17	66	59	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
2032	112861	84	584	146	0	49	22	68	51	25629	8874	36294	54	54
3231	210171	125	1077	124	0	74	30	120	112	48758	17988	62378	86	80
5777	393802	215	1578	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
1181	126029	48	319	48	0	53	25	96	90	26706	7117	57283	48	48
1462	99729	37	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
1789	156212	59	551	94	0	65	25	98	74	48352	19690	72405	77	74
1334	69952	85	494	57	0	28	15	49	32	13899	5659	24044	28	27
2415	152673	84	818	240	4	48	22	88	82	39342	11370	55909	84	83
1156	125841	44	331	40	4	42	12	45	43	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
2190	108244	77	667	126	0	50	28	110	89	38338	14369	40551	41	43
633	22762	20	188	44	0	12	13	39	28	52505	3706	11621	25	24
838	48554	49	286	37	0	16	14	55	35	10663	3147	18741	16	16
2167	178697	81	633	94	0	76	27	102	78	74484	16801	84202	96	95
1452	139115	58	514	127	0	29	25	96	67	28895	2162	15334	23	22
1790	93773	45	532	159	1	38	30	86	61	32827	4721	28024	33	33
1718	132796	76	540	41	1	50	20	74	55	36188	5290	53306	46	45
1179	113933	22	428	153	0	33	17	64	49	28173	6446	37918	59	59
1688	144781	138	539	86	0	45	22	82	77	54926	14711	54819	72	66
1101	140711	75	266	55	0	59	28	100	71	38900	13311	89058	72	70
2259	283337	102	745	73	0	49	25	95	82	88530	13577	103354	62	56
1768	158146	36	733	79	0	40	16	63	53	35482	14634	70239	55	55
1300	123344	39	394	71	0	40	23	87	71	26730	6931	33045	27	27
1432	157640	38	482	111	2	51	20	65	58	29806	9992	63852	41	37
1791	91279	88	567	71	4	41	11	43	25	41799	6185	30905	51	48
2475	189374	102	746	243	0	73	20	80	59	54289	3445	24242	26	26
1930	167915	42	626	66	1	43	21	84	77	36805	12327	78907	65	64
1	0	1	0	0	0	0	0	0	0	0	0	0	0	0
1782	175403	54	835	58	0	46	27	105	75	33146	9898	36005	28	21
1505	92342	46	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
1648	178277	49	607	56	0	71	31	124	123	77783	22717	115301	100	89
1668	145062	56	539	90	2	61	19	75	67	36042	10090	47689	104	101
1366	110980	47	519	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
1602	119514	62	609	50	1	42	20	78	54	60610	5508	52220	73	71
1023	95535	41	321	46	2	44	22	87	82	55041	9628	33863	106	102
963	109894	73	246	32	2	34	19	70	57	32087	11872	46879	53	53
629	61554	26	180	45	1	15	32	97	57	16356	4186	23228	43	41
1568	156520	77	544	96	0	46	18	72	72	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
1199	91198	54	309	49	0	42	19	73	60	27377	10807	32615	40	40
2059	229864	64	709	83	0	56	24	87	84	50273	13662	82188	79	77
1592	180317	67	542	67	0	41	26	95	69	32104	9224	51763	52	51
1447	150640	48	526	39	1	48	27	105	102	27016	9001	59325	44	43
1342	104416	44	418	68	5	30	10	37	28	19715	7204	48976	34	33
1527	159645	55	409	58	0	44	26	96	65	33629	6572	43384	47	47
670	60368	17	189	59	0	25	21	80	59	27084	7509	26692	32	31
859	100056	55	293	30	0	42	21	83	80	32352	12920	53279	31	31
2329	137214	73	781	54	10	28	34	124	79	51845	5438	20652	40	40
1326	99630	47	383	65	6	33	29	116	107	26591	11489	38338	42	42
1567	84557	62	572	81	0	32	18	72	57	29677	6661	36735	34	35
1081	91199	45	308	81	11	28	16	55	44	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
1939	129700	60	446	80	8	39	31	124	115	69551	17456	103793	53	53
2293	110708	57	690	137	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
993	87611	52	293	74	1	33	9	33	3	40195	4501	40745	47	47
1038	77890	53	349	48	2	48	21	84	68	48158	9560	33187	108	100
1380	83261	58	411	82	1	36	18	52	38	13310	3394	14063	46	46
2186	116290	51	561	86	0	52	31	121	107	78474	9871	37407	38	38
1069	55254	31	289	60	2	0	24	88	69	6386	2419	7190	0	0
1763	116173	50	492	99	1	52	24	99	80	31588	10630	49562	45	45
1995	111488	78	669	63	0	45	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
808	67751	56	192	45	0	48	22	81	58	34165	11227	28078	38	37
1690	213351	51	616	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
685	42311	37	229	43	0	16	19	75	49	16380	3189	10824	34	34
1326	115801	42	388	67	0	32	22	86	47	41242	8178	46892	53	49
2224	183637	182	536	82	0	55	25	100	93	48450	16739	61264	85	82
923	68161	84	220	71	0	36	22	88	65	20790	6094	22933	36	36
967	76441	46	313	44	4	29	21	80	64	34585	7237	20787	33	33
1099	103613	40	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
1872	126527	66	556	86	1	58	22	79	61	53933	11225	55593	71	71
1091	136781	52	366	59	0	35	21	81	71	34474	6213	51648	32	31
1106	105863	51	406	74	0	24	12	48	43	43753	4875	30552	45	42
632	38775	30	254	18	0	18	9	33	18	36456	8159	23470	33	31
1901	179984	89	606	156	0	37	32	120	103	51183	11893	77530	53	51
1580	164808	49	479	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
1698	143902	83	578	104	1	20	24	96	83	37076	9706	34684	38	37
1420	108660	52	581	49	0	32	22	79	70	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
584	58660	35	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
576	43284	22	151	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
880	66016	26	239	54	0	43	22	79	45	95352	9016	30002	47	46
750	57359	48	240	43	0	18	17	63	63	37478	5134	19360	33	33
999	96933	35	323	45	0	41	18	68	48	26839	6608	43320	44	41
931	70369	47	302	55	0	45	21	84	70	26783	8577	35513	56	57
782	65494	55	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
1262	109894	65	442	67	0	58	26	87	56	59847	12140	56812	78	78
1711	122973	81	490	115	1	17	26	104	64	28162	6678	33838	51	46
749	84336	32	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
806	79015	33	217	35	0	30	14	55	37	22698	5141	31334	29	29
1448	92937	42	422	57	8	49	26	96	54	27615	1311	16612	26	26
684	57586	37	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
841	96410	23	293	51	0	18	22	84	81	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
1629	139563	41	564	43	4	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
889	95079	41	295	49	0	21	25	91	75	23924	6676	22205	19	18
1197	80750	31	228	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
970	96971	40	233	49	0	40	18	69	60	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 time10 seconds
R Server'George Udny Yule' @ yule.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 & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155018&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155018&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155018&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 time10 seconds
R Server'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Time[t] = + 2846.51349094432 -8.21319320552047Pageviews[t] + 271.029500060642Logins[t] + 115.773053168956CompendiumViews[t] + 41.7978019564796`CompendiumViews(PRonly)`[t] -1103.30633844618Shared[t] + 427.173628546359Blogs[t] -567.292958123243Reviews[t] + 116.490231472011Submits[t] + 394.702617299329`Submits(+120)`[t] + 0.0360322101919795Characters[t] -2.76330124660822`CW:Revisions`[t] + 1.14546709406017`CW:seconds`[t] + 1219.43310435183`CW:Hyperlinks`[t] -1474.33200711726`CW:blogs`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Time[t] =  +  2846.51349094432 -8.21319320552047Pageviews[t] +  271.029500060642Logins[t] +  115.773053168956CompendiumViews[t] +  41.7978019564796`CompendiumViews(PRonly)`[t] -1103.30633844618Shared[t] +  427.173628546359Blogs[t] -567.292958123243Reviews[t] +  116.490231472011Submits[t] +  394.702617299329`Submits(+120)`[t] +  0.0360322101919795Characters[t] -2.76330124660822`CW:Revisions`[t] +  1.14546709406017`CW:seconds`[t] +  1219.43310435183`CW:Hyperlinks`[t] -1474.33200711726`CW:blogs`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155018&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Time[t] =  +  2846.51349094432 -8.21319320552047Pageviews[t] +  271.029500060642Logins[t] +  115.773053168956CompendiumViews[t] +  41.7978019564796`CompendiumViews(PRonly)`[t] -1103.30633844618Shared[t] +  427.173628546359Blogs[t] -567.292958123243Reviews[t] +  116.490231472011Submits[t] +  394.702617299329`Submits(+120)`[t] +  0.0360322101919795Characters[t] -2.76330124660822`CW:Revisions`[t] +  1.14546709406017`CW:seconds`[t] +  1219.43310435183`CW:Hyperlinks`[t] -1474.33200711726`CW:blogs`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155018&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155018&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
Time[t] = + 2846.51349094432 -8.21319320552047Pageviews[t] + 271.029500060642Logins[t] + 115.773053168956CompendiumViews[t] + 41.7978019564796`CompendiumViews(PRonly)`[t] -1103.30633844618Shared[t] + 427.173628546359Blogs[t] -567.292958123243Reviews[t] + 116.490231472011Submits[t] + 394.702617299329`Submits(+120)`[t] + 0.0360322101919795Characters[t] -2.76330124660822`CW:Revisions`[t] + 1.14546709406017`CW:seconds`[t] + 1219.43310435183`CW:Hyperlinks`[t] -1474.33200711726`CW:blogs`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2846.513490944324243.0149380.67090.5035020.251751
Pageviews-8.2131932055204712.137059-0.67670.4998060.249903
Logins271.029500060642101.1261372.68010.008320.00416
CompendiumViews115.77305316895628.2482984.09847.3e-053.7e-05
`CompendiumViews(PRonly)`41.797801956479657.4589070.72740.4682760.234138
Shared-1103.30633844618876.290776-1.25910.2102810.10514
Blogs427.173628546359182.6296262.3390.0208710.010436
Reviews-567.2929581232431207.082946-0.470.639170.319585
Submits116.490231472011364.2741740.31980.7496470.374823
`Submits(+120)`394.702617299329171.0300472.30780.0226030.011301
Characters0.03603221019197950.1237930.29110.7714660.385733
`CW:Revisions`-2.763301246608220.796301-3.47020.0007080.000354
`CW:seconds`1.145467094060170.1426038.032600
`CW:Hyperlinks`1219.43310435183968.3814811.25920.2102140.105107
`CW:blogs`-1474.33200711726998.707931-1.47620.1423170.071158

\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) & 2846.51349094432 & 4243.014938 & 0.6709 & 0.503502 & 0.251751 \tabularnewline
Pageviews & -8.21319320552047 & 12.137059 & -0.6767 & 0.499806 & 0.249903 \tabularnewline
Logins & 271.029500060642 & 101.126137 & 2.6801 & 0.00832 & 0.00416 \tabularnewline
CompendiumViews & 115.773053168956 & 28.248298 & 4.0984 & 7.3e-05 & 3.7e-05 \tabularnewline
`CompendiumViews(PRonly)` & 41.7978019564796 & 57.458907 & 0.7274 & 0.468276 & 0.234138 \tabularnewline
Shared & -1103.30633844618 & 876.290776 & -1.2591 & 0.210281 & 0.10514 \tabularnewline
Blogs & 427.173628546359 & 182.629626 & 2.339 & 0.020871 & 0.010436 \tabularnewline
Reviews & -567.292958123243 & 1207.082946 & -0.47 & 0.63917 & 0.319585 \tabularnewline
Submits & 116.490231472011 & 364.274174 & 0.3198 & 0.749647 & 0.374823 \tabularnewline
`Submits(+120)` & 394.702617299329 & 171.030047 & 2.3078 & 0.022603 & 0.011301 \tabularnewline
Characters & 0.0360322101919795 & 0.123793 & 0.2911 & 0.771466 & 0.385733 \tabularnewline
`CW:Revisions` & -2.76330124660822 & 0.796301 & -3.4702 & 0.000708 & 0.000354 \tabularnewline
`CW:seconds` & 1.14546709406017 & 0.142603 & 8.0326 & 0 & 0 \tabularnewline
`CW:Hyperlinks` & 1219.43310435183 & 968.381481 & 1.2592 & 0.210214 & 0.105107 \tabularnewline
`CW:blogs` & -1474.33200711726 & 998.707931 & -1.4762 & 0.142317 & 0.071158 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155018&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]2846.51349094432[/C][C]4243.014938[/C][C]0.6709[/C][C]0.503502[/C][C]0.251751[/C][/ROW]
[ROW][C]Pageviews[/C][C]-8.21319320552047[/C][C]12.137059[/C][C]-0.6767[/C][C]0.499806[/C][C]0.249903[/C][/ROW]
[ROW][C]Logins[/C][C]271.029500060642[/C][C]101.126137[/C][C]2.6801[/C][C]0.00832[/C][C]0.00416[/C][/ROW]
[ROW][C]CompendiumViews[/C][C]115.773053168956[/C][C]28.248298[/C][C]4.0984[/C][C]7.3e-05[/C][C]3.7e-05[/C][/ROW]
[ROW][C]`CompendiumViews(PRonly)`[/C][C]41.7978019564796[/C][C]57.458907[/C][C]0.7274[/C][C]0.468276[/C][C]0.234138[/C][/ROW]
[ROW][C]Shared[/C][C]-1103.30633844618[/C][C]876.290776[/C][C]-1.2591[/C][C]0.210281[/C][C]0.10514[/C][/ROW]
[ROW][C]Blogs[/C][C]427.173628546359[/C][C]182.629626[/C][C]2.339[/C][C]0.020871[/C][C]0.010436[/C][/ROW]
[ROW][C]Reviews[/C][C]-567.292958123243[/C][C]1207.082946[/C][C]-0.47[/C][C]0.63917[/C][C]0.319585[/C][/ROW]
[ROW][C]Submits[/C][C]116.490231472011[/C][C]364.274174[/C][C]0.3198[/C][C]0.749647[/C][C]0.374823[/C][/ROW]
[ROW][C]`Submits(+120)`[/C][C]394.702617299329[/C][C]171.030047[/C][C]2.3078[/C][C]0.022603[/C][C]0.011301[/C][/ROW]
[ROW][C]Characters[/C][C]0.0360322101919795[/C][C]0.123793[/C][C]0.2911[/C][C]0.771466[/C][C]0.385733[/C][/ROW]
[ROW][C]`CW:Revisions`[/C][C]-2.76330124660822[/C][C]0.796301[/C][C]-3.4702[/C][C]0.000708[/C][C]0.000354[/C][/ROW]
[ROW][C]`CW:seconds`[/C][C]1.14546709406017[/C][C]0.142603[/C][C]8.0326[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`CW:Hyperlinks`[/C][C]1219.43310435183[/C][C]968.381481[/C][C]1.2592[/C][C]0.210214[/C][C]0.105107[/C][/ROW]
[ROW][C]`CW:blogs`[/C][C]-1474.33200711726[/C][C]998.707931[/C][C]-1.4762[/C][C]0.142317[/C][C]0.071158[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155018&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155018&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)2846.513490944324243.0149380.67090.5035020.251751
Pageviews-8.2131932055204712.137059-0.67670.4998060.249903
Logins271.029500060642101.1261372.68010.008320.00416
CompendiumViews115.77305316895628.2482984.09847.3e-053.7e-05
`CompendiumViews(PRonly)`41.797801956479657.4589070.72740.4682760.234138
Shared-1103.30633844618876.290776-1.25910.2102810.10514
Blogs427.173628546359182.6296262.3390.0208710.010436
Reviews-567.2929581232431207.082946-0.470.639170.319585
Submits116.490231472011364.2741740.31980.7496470.374823
`Submits(+120)`394.702617299329171.0300472.30780.0226030.011301
Characters0.03603221019197950.1237930.29110.7714660.385733
`CW:Revisions`-2.763301246608220.796301-3.47020.0007080.000354
`CW:seconds`1.145467094060170.1426038.032600
`CW:Hyperlinks`1219.43310435183968.3814811.25920.2102140.105107
`CW:blogs`-1474.33200711726998.707931-1.47620.1423170.071158






Multiple Linear Regression - Regression Statistics
Multiple R0.950900796511658
R-squared0.904212324806506
Adjusted R-squared0.8938167631576
F-TEST (value)86.9806130101332
F-TEST (DF numerator)14
F-TEST (DF denominator)129
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation19256.8532667884
Sum Squared Residuals47836605308.2818

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.950900796511658 \tabularnewline
R-squared & 0.904212324806506 \tabularnewline
Adjusted R-squared & 0.8938167631576 \tabularnewline
F-TEST (value) & 86.9806130101332 \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 & 19256.8532667884 \tabularnewline
Sum Squared Residuals & 47836605308.2818 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155018&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.950900796511658[/C][/ROW]
[ROW][C]R-squared[/C][C]0.904212324806506[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.8938167631576[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]86.9806130101332[/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]19256.8532667884[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]47836605308.2818[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155018&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155018&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 R0.950900796511658
R-squared0.904212324806506
Adjusted R-squared0.8938167631576
F-TEST (value)86.9806130101332
F-TEST (DF numerator)14
F-TEST (DF denominator)129
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation19256.8532667884
Sum Squared Residuals47836605308.2818






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1127476108742.57315465518733.4268453454
2130358134072.293639497-3714.29363949661
3721515120.1782345378-7905.17823453776
4112861123350.933196255-10489.9331962551
5210171223263.68861867-13092.6886186698
6393802410850.327322275-17048.3273222749
7117604124656.662161862-7052.66216186155
8126029134934.728360656-8905.72836065642
99972998614.6091762781114.39082372209
10256310217646.20128299938663.7987170009
11113066124296.06819569-11230.06819569
12156212141137.94863201615074.0513679835
1369952103034.020034245-33082.0200342449
14152673170842.966568347-18169.9665683468
15125841105279.46161036520561.5383896352
16125769136772.517411399-11003.517411399
17123467107711.77910778915755.2208922112
185623252442.87456743383789.12543256618
19108244136358.822752606-28114.8227526058
202276240083.0661879098-17321.0661879098
214855472093.06474559-23539.06474559
22178697173744.5995144054952.40048559485
23139115115533.33007465523581.6699253453
2493773112610.052548644-18837.0525486442
25132796150299.406590468-17503.4065904684
2611393397917.9723059516015.02769405
27144781153682.481924375-8901.48192437499
28140711147445.763319801-6734.76331980117
29283337228524.91030746954812.0896925314
30158146149792.0733514188353.92664858193
31123344106299.8725716917044.1274283098
32157640142574.41743898715065.5825610125
3391279113574.466479729-22295.4664797287
34189374172709.54319962516664.4568003747
35167915161694.3907114346220.60928856639
3603109.32979779947-3109.32979779947
37175403166377.0811514729025.91884852846
389234295106.1057225732-2764.10572257322
3910002393088.53534148856934.4646585115
40178277213773.035529797-35496.0355297972
41145062124698.94596507720363.054034923
42110980120397.332254631-9417.33225463105
438603990520.5797268426-4481.57972684258
44119514146101.777501793-26587.777501793
459553584296.502978787711238.4970212123
4610989485270.115743646424623.8842563536
476155456012.84236110325541.15763889684
48156520136455.75769686520064.2423031347
49159121150107.8132657999013.18673420115
50129362140925.775455644-11563.7754556436
514818856297.0829617419-8109.08296174194
529119883092.03410438288105.9658956172
53229864183444.63703288646419.3629671142
54180317137725.57803360942591.4219663914
55150640157388.025710569-6748.02571056855
56104416101684.3709563942731.62904360589
57159645116637.80370220243007.1962977984
586036861789.8565534696-1421.85655346963
59100056111737.817819074-11681.8178190744
60137214123746.59703240213467.4029675982
6199630100943.31313658-1313.31313657959
6284557125332.523129451-40775.5231294512
639119978529.486396206512669.5136037935
6483419101655.933789869-18236.9337898685
65101723102335.153043774-612.153043773784
6694982118760.748648396-23778.7486483956
67129700167896.05047231-38196.0504723104
68110708146338.185362657-35630.1853626566
698151886059.7198765192-4541.7198765192
703197033148.1441567272-1178.14415672722
71192268178225.07477744914042.9252225508
728761182417.46493043775193.53506956231
737789091704.6695591499-13814.6695591499
748326178846.32352386424414.67647613582
75116290136927.0908243-20637.0908243005
765525461880.3666157136-6626.36661571362
77116173130685.369771608-14512.3697716083
78111488184673.23944866-73185.2394486601
796013866820.2247147614-6682.22471476137
807342283863.4076964306-10441.4076964306
816775170007.6384594595-2256.63845945954
82213351152755.0946298860595.9053701201
835118557290.7758381605-6105.7758381605
8497181102483.049359042-5302.0493590425
854231155201.3228272878-12890.3228272878
86115801105806.441592599994.55840741
87183637165481.45559852118155.5444014785
886816186276.7340679613-18115.7340679613
897644172736.94893110883704.0510688912
90103613109641.321467545-6028.32146754527
9198707108901.792052724-10194.792052724
92126527134303.097087336-7776.09708733649
93136781129868.4162997896912.58370021064
9410586399744.79825221676118.20174778334
953877549666.6180719454-10891.6180719454
96179984187546.413851485-7562.41385148506
97164808147349.69424408317458.3057559172
981934923270.9036859332-3921.90368593321
99143902126461.15348072717440.846519273
100108660132014.92650165-23354.9265016498
1014380337882.43592598565920.56407401437
1024706263309.0888882926-16247.0888882926
10311084592178.02121862618666.978781374
1049251779933.862660531612583.1373394684
1055866053816.71127481074843.2887251893
1062767637835.3782114952-10159.3782114952
10798550100897.436501568-2347.43650156758
1084328430693.893393338712590.1066066613
10902846.51349094435-2846.51349094435
1106601667827.0850981753-1811.08509817525
1115735970457.3730559606-13098.3730559606
11296933103109.322803128-6176.32280312789
1137036992119.3003454765-21750.3003454765
1146549478074.571255126-12580.5712551261
11536165390.84367536474-1774.84367536474
11602846.51349094435-2846.51349094435
117143931105917.36642115738013.6335788433
118109894120139.078334172-10245.0783341721
119122973116761.1976168126211.80238318772
1208433688411.0655032072-4075.06550320722
1214341041850.8482558251559.151744175
122136250140683.841165883-4433.8411658833
1237901572752.1210323466262.87896765404
1249293793201.6650609654-264.665060965453
1255758641136.38040329816449.619596702
1261976420133.0052809877-369.005280987649
127105757109426.513770299-3669.51377029853
1289641083026.14499122113383.8550087791
129113402111844.5715543781557.42844562185
1301179613868.0365556422-2072.03655564223
13176277807.42128476221-180.421284762209
132121085103835.23784923417249.7621507659
13368364704.021632105642131.97836789436
134139563113797.04195135925765.9580486413
13551186186.95214019642-1068.95214019642
1364024833304.46153403516943.53846596488
13702846.51349094435-2846.51349094435
1389507982330.551974684212748.4480253158
1398075065643.369931243615106.6300687564
14071315287.928938655381843.07106134462
14141947114.84715826596-2920.84715826596
1426037851917.7442803288460.25571967202
1439697175186.654966482221784.3450335178
1448348495120.5797576593-11636.5797576593

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 127476 & 108742.573154655 & 18733.4268453454 \tabularnewline
2 & 130358 & 134072.293639497 & -3714.29363949661 \tabularnewline
3 & 7215 & 15120.1782345378 & -7905.17823453776 \tabularnewline
4 & 112861 & 123350.933196255 & -10489.9331962551 \tabularnewline
5 & 210171 & 223263.68861867 & -13092.6886186698 \tabularnewline
6 & 393802 & 410850.327322275 & -17048.3273222749 \tabularnewline
7 & 117604 & 124656.662161862 & -7052.66216186155 \tabularnewline
8 & 126029 & 134934.728360656 & -8905.72836065642 \tabularnewline
9 & 99729 & 98614.609176278 & 1114.39082372209 \tabularnewline
10 & 256310 & 217646.201282999 & 38663.7987170009 \tabularnewline
11 & 113066 & 124296.06819569 & -11230.06819569 \tabularnewline
12 & 156212 & 141137.948632016 & 15074.0513679835 \tabularnewline
13 & 69952 & 103034.020034245 & -33082.0200342449 \tabularnewline
14 & 152673 & 170842.966568347 & -18169.9665683468 \tabularnewline
15 & 125841 & 105279.461610365 & 20561.5383896352 \tabularnewline
16 & 125769 & 136772.517411399 & -11003.517411399 \tabularnewline
17 & 123467 & 107711.779107789 & 15755.2208922112 \tabularnewline
18 & 56232 & 52442.8745674338 & 3789.12543256618 \tabularnewline
19 & 108244 & 136358.822752606 & -28114.8227526058 \tabularnewline
20 & 22762 & 40083.0661879098 & -17321.0661879098 \tabularnewline
21 & 48554 & 72093.06474559 & -23539.06474559 \tabularnewline
22 & 178697 & 173744.599514405 & 4952.40048559485 \tabularnewline
23 & 139115 & 115533.330074655 & 23581.6699253453 \tabularnewline
24 & 93773 & 112610.052548644 & -18837.0525486442 \tabularnewline
25 & 132796 & 150299.406590468 & -17503.4065904684 \tabularnewline
26 & 113933 & 97917.97230595 & 16015.02769405 \tabularnewline
27 & 144781 & 153682.481924375 & -8901.48192437499 \tabularnewline
28 & 140711 & 147445.763319801 & -6734.76331980117 \tabularnewline
29 & 283337 & 228524.910307469 & 54812.0896925314 \tabularnewline
30 & 158146 & 149792.073351418 & 8353.92664858193 \tabularnewline
31 & 123344 & 106299.87257169 & 17044.1274283098 \tabularnewline
32 & 157640 & 142574.417438987 & 15065.5825610125 \tabularnewline
33 & 91279 & 113574.466479729 & -22295.4664797287 \tabularnewline
34 & 189374 & 172709.543199625 & 16664.4568003747 \tabularnewline
35 & 167915 & 161694.390711434 & 6220.60928856639 \tabularnewline
36 & 0 & 3109.32979779947 & -3109.32979779947 \tabularnewline
37 & 175403 & 166377.081151472 & 9025.91884852846 \tabularnewline
38 & 92342 & 95106.1057225732 & -2764.10572257322 \tabularnewline
39 & 100023 & 93088.5353414885 & 6934.4646585115 \tabularnewline
40 & 178277 & 213773.035529797 & -35496.0355297972 \tabularnewline
41 & 145062 & 124698.945965077 & 20363.054034923 \tabularnewline
42 & 110980 & 120397.332254631 & -9417.33225463105 \tabularnewline
43 & 86039 & 90520.5797268426 & -4481.57972684258 \tabularnewline
44 & 119514 & 146101.777501793 & -26587.777501793 \tabularnewline
45 & 95535 & 84296.5029787877 & 11238.4970212123 \tabularnewline
46 & 109894 & 85270.1157436464 & 24623.8842563536 \tabularnewline
47 & 61554 & 56012.8423611032 & 5541.15763889684 \tabularnewline
48 & 156520 & 136455.757696865 & 20064.2423031347 \tabularnewline
49 & 159121 & 150107.813265799 & 9013.18673420115 \tabularnewline
50 & 129362 & 140925.775455644 & -11563.7754556436 \tabularnewline
51 & 48188 & 56297.0829617419 & -8109.08296174194 \tabularnewline
52 & 91198 & 83092.0341043828 & 8105.9658956172 \tabularnewline
53 & 229864 & 183444.637032886 & 46419.3629671142 \tabularnewline
54 & 180317 & 137725.578033609 & 42591.4219663914 \tabularnewline
55 & 150640 & 157388.025710569 & -6748.02571056855 \tabularnewline
56 & 104416 & 101684.370956394 & 2731.62904360589 \tabularnewline
57 & 159645 & 116637.803702202 & 43007.1962977984 \tabularnewline
58 & 60368 & 61789.8565534696 & -1421.85655346963 \tabularnewline
59 & 100056 & 111737.817819074 & -11681.8178190744 \tabularnewline
60 & 137214 & 123746.597032402 & 13467.4029675982 \tabularnewline
61 & 99630 & 100943.31313658 & -1313.31313657959 \tabularnewline
62 & 84557 & 125332.523129451 & -40775.5231294512 \tabularnewline
63 & 91199 & 78529.4863962065 & 12669.5136037935 \tabularnewline
64 & 83419 & 101655.933789869 & -18236.9337898685 \tabularnewline
65 & 101723 & 102335.153043774 & -612.153043773784 \tabularnewline
66 & 94982 & 118760.748648396 & -23778.7486483956 \tabularnewline
67 & 129700 & 167896.05047231 & -38196.0504723104 \tabularnewline
68 & 110708 & 146338.185362657 & -35630.1853626566 \tabularnewline
69 & 81518 & 86059.7198765192 & -4541.7198765192 \tabularnewline
70 & 31970 & 33148.1441567272 & -1178.14415672722 \tabularnewline
71 & 192268 & 178225.074777449 & 14042.9252225508 \tabularnewline
72 & 87611 & 82417.4649304377 & 5193.53506956231 \tabularnewline
73 & 77890 & 91704.6695591499 & -13814.6695591499 \tabularnewline
74 & 83261 & 78846.3235238642 & 4414.67647613582 \tabularnewline
75 & 116290 & 136927.0908243 & -20637.0908243005 \tabularnewline
76 & 55254 & 61880.3666157136 & -6626.36661571362 \tabularnewline
77 & 116173 & 130685.369771608 & -14512.3697716083 \tabularnewline
78 & 111488 & 184673.23944866 & -73185.2394486601 \tabularnewline
79 & 60138 & 66820.2247147614 & -6682.22471476137 \tabularnewline
80 & 73422 & 83863.4076964306 & -10441.4076964306 \tabularnewline
81 & 67751 & 70007.6384594595 & -2256.63845945954 \tabularnewline
82 & 213351 & 152755.09462988 & 60595.9053701201 \tabularnewline
83 & 51185 & 57290.7758381605 & -6105.7758381605 \tabularnewline
84 & 97181 & 102483.049359042 & -5302.0493590425 \tabularnewline
85 & 42311 & 55201.3228272878 & -12890.3228272878 \tabularnewline
86 & 115801 & 105806.44159259 & 9994.55840741 \tabularnewline
87 & 183637 & 165481.455598521 & 18155.5444014785 \tabularnewline
88 & 68161 & 86276.7340679613 & -18115.7340679613 \tabularnewline
89 & 76441 & 72736.9489311088 & 3704.0510688912 \tabularnewline
90 & 103613 & 109641.321467545 & -6028.32146754527 \tabularnewline
91 & 98707 & 108901.792052724 & -10194.792052724 \tabularnewline
92 & 126527 & 134303.097087336 & -7776.09708733649 \tabularnewline
93 & 136781 & 129868.416299789 & 6912.58370021064 \tabularnewline
94 & 105863 & 99744.7982522167 & 6118.20174778334 \tabularnewline
95 & 38775 & 49666.6180719454 & -10891.6180719454 \tabularnewline
96 & 179984 & 187546.413851485 & -7562.41385148506 \tabularnewline
97 & 164808 & 147349.694244083 & 17458.3057559172 \tabularnewline
98 & 19349 & 23270.9036859332 & -3921.90368593321 \tabularnewline
99 & 143902 & 126461.153480727 & 17440.846519273 \tabularnewline
100 & 108660 & 132014.92650165 & -23354.9265016498 \tabularnewline
101 & 43803 & 37882.4359259856 & 5920.56407401437 \tabularnewline
102 & 47062 & 63309.0888882926 & -16247.0888882926 \tabularnewline
103 & 110845 & 92178.021218626 & 18666.978781374 \tabularnewline
104 & 92517 & 79933.8626605316 & 12583.1373394684 \tabularnewline
105 & 58660 & 53816.7112748107 & 4843.2887251893 \tabularnewline
106 & 27676 & 37835.3782114952 & -10159.3782114952 \tabularnewline
107 & 98550 & 100897.436501568 & -2347.43650156758 \tabularnewline
108 & 43284 & 30693.8933933387 & 12590.1066066613 \tabularnewline
109 & 0 & 2846.51349094435 & -2846.51349094435 \tabularnewline
110 & 66016 & 67827.0850981753 & -1811.08509817525 \tabularnewline
111 & 57359 & 70457.3730559606 & -13098.3730559606 \tabularnewline
112 & 96933 & 103109.322803128 & -6176.32280312789 \tabularnewline
113 & 70369 & 92119.3003454765 & -21750.3003454765 \tabularnewline
114 & 65494 & 78074.571255126 & -12580.5712551261 \tabularnewline
115 & 3616 & 5390.84367536474 & -1774.84367536474 \tabularnewline
116 & 0 & 2846.51349094435 & -2846.51349094435 \tabularnewline
117 & 143931 & 105917.366421157 & 38013.6335788433 \tabularnewline
118 & 109894 & 120139.078334172 & -10245.0783341721 \tabularnewline
119 & 122973 & 116761.197616812 & 6211.80238318772 \tabularnewline
120 & 84336 & 88411.0655032072 & -4075.06550320722 \tabularnewline
121 & 43410 & 41850.848255825 & 1559.151744175 \tabularnewline
122 & 136250 & 140683.841165883 & -4433.8411658833 \tabularnewline
123 & 79015 & 72752.121032346 & 6262.87896765404 \tabularnewline
124 & 92937 & 93201.6650609654 & -264.665060965453 \tabularnewline
125 & 57586 & 41136.380403298 & 16449.619596702 \tabularnewline
126 & 19764 & 20133.0052809877 & -369.005280987649 \tabularnewline
127 & 105757 & 109426.513770299 & -3669.51377029853 \tabularnewline
128 & 96410 & 83026.144991221 & 13383.8550087791 \tabularnewline
129 & 113402 & 111844.571554378 & 1557.42844562185 \tabularnewline
130 & 11796 & 13868.0365556422 & -2072.03655564223 \tabularnewline
131 & 7627 & 7807.42128476221 & -180.421284762209 \tabularnewline
132 & 121085 & 103835.237849234 & 17249.7621507659 \tabularnewline
133 & 6836 & 4704.02163210564 & 2131.97836789436 \tabularnewline
134 & 139563 & 113797.041951359 & 25765.9580486413 \tabularnewline
135 & 5118 & 6186.95214019642 & -1068.95214019642 \tabularnewline
136 & 40248 & 33304.4615340351 & 6943.53846596488 \tabularnewline
137 & 0 & 2846.51349094435 & -2846.51349094435 \tabularnewline
138 & 95079 & 82330.5519746842 & 12748.4480253158 \tabularnewline
139 & 80750 & 65643.3699312436 & 15106.6300687564 \tabularnewline
140 & 7131 & 5287.92893865538 & 1843.07106134462 \tabularnewline
141 & 4194 & 7114.84715826596 & -2920.84715826596 \tabularnewline
142 & 60378 & 51917.744280328 & 8460.25571967202 \tabularnewline
143 & 96971 & 75186.6549664822 & 21784.3450335178 \tabularnewline
144 & 83484 & 95120.5797576593 & -11636.5797576593 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155018&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]127476[/C][C]108742.573154655[/C][C]18733.4268453454[/C][/ROW]
[ROW][C]2[/C][C]130358[/C][C]134072.293639497[/C][C]-3714.29363949661[/C][/ROW]
[ROW][C]3[/C][C]7215[/C][C]15120.1782345378[/C][C]-7905.17823453776[/C][/ROW]
[ROW][C]4[/C][C]112861[/C][C]123350.933196255[/C][C]-10489.9331962551[/C][/ROW]
[ROW][C]5[/C][C]210171[/C][C]223263.68861867[/C][C]-13092.6886186698[/C][/ROW]
[ROW][C]6[/C][C]393802[/C][C]410850.327322275[/C][C]-17048.3273222749[/C][/ROW]
[ROW][C]7[/C][C]117604[/C][C]124656.662161862[/C][C]-7052.66216186155[/C][/ROW]
[ROW][C]8[/C][C]126029[/C][C]134934.728360656[/C][C]-8905.72836065642[/C][/ROW]
[ROW][C]9[/C][C]99729[/C][C]98614.609176278[/C][C]1114.39082372209[/C][/ROW]
[ROW][C]10[/C][C]256310[/C][C]217646.201282999[/C][C]38663.7987170009[/C][/ROW]
[ROW][C]11[/C][C]113066[/C][C]124296.06819569[/C][C]-11230.06819569[/C][/ROW]
[ROW][C]12[/C][C]156212[/C][C]141137.948632016[/C][C]15074.0513679835[/C][/ROW]
[ROW][C]13[/C][C]69952[/C][C]103034.020034245[/C][C]-33082.0200342449[/C][/ROW]
[ROW][C]14[/C][C]152673[/C][C]170842.966568347[/C][C]-18169.9665683468[/C][/ROW]
[ROW][C]15[/C][C]125841[/C][C]105279.461610365[/C][C]20561.5383896352[/C][/ROW]
[ROW][C]16[/C][C]125769[/C][C]136772.517411399[/C][C]-11003.517411399[/C][/ROW]
[ROW][C]17[/C][C]123467[/C][C]107711.779107789[/C][C]15755.2208922112[/C][/ROW]
[ROW][C]18[/C][C]56232[/C][C]52442.8745674338[/C][C]3789.12543256618[/C][/ROW]
[ROW][C]19[/C][C]108244[/C][C]136358.822752606[/C][C]-28114.8227526058[/C][/ROW]
[ROW][C]20[/C][C]22762[/C][C]40083.0661879098[/C][C]-17321.0661879098[/C][/ROW]
[ROW][C]21[/C][C]48554[/C][C]72093.06474559[/C][C]-23539.06474559[/C][/ROW]
[ROW][C]22[/C][C]178697[/C][C]173744.599514405[/C][C]4952.40048559485[/C][/ROW]
[ROW][C]23[/C][C]139115[/C][C]115533.330074655[/C][C]23581.6699253453[/C][/ROW]
[ROW][C]24[/C][C]93773[/C][C]112610.052548644[/C][C]-18837.0525486442[/C][/ROW]
[ROW][C]25[/C][C]132796[/C][C]150299.406590468[/C][C]-17503.4065904684[/C][/ROW]
[ROW][C]26[/C][C]113933[/C][C]97917.97230595[/C][C]16015.02769405[/C][/ROW]
[ROW][C]27[/C][C]144781[/C][C]153682.481924375[/C][C]-8901.48192437499[/C][/ROW]
[ROW][C]28[/C][C]140711[/C][C]147445.763319801[/C][C]-6734.76331980117[/C][/ROW]
[ROW][C]29[/C][C]283337[/C][C]228524.910307469[/C][C]54812.0896925314[/C][/ROW]
[ROW][C]30[/C][C]158146[/C][C]149792.073351418[/C][C]8353.92664858193[/C][/ROW]
[ROW][C]31[/C][C]123344[/C][C]106299.87257169[/C][C]17044.1274283098[/C][/ROW]
[ROW][C]32[/C][C]157640[/C][C]142574.417438987[/C][C]15065.5825610125[/C][/ROW]
[ROW][C]33[/C][C]91279[/C][C]113574.466479729[/C][C]-22295.4664797287[/C][/ROW]
[ROW][C]34[/C][C]189374[/C][C]172709.543199625[/C][C]16664.4568003747[/C][/ROW]
[ROW][C]35[/C][C]167915[/C][C]161694.390711434[/C][C]6220.60928856639[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]3109.32979779947[/C][C]-3109.32979779947[/C][/ROW]
[ROW][C]37[/C][C]175403[/C][C]166377.081151472[/C][C]9025.91884852846[/C][/ROW]
[ROW][C]38[/C][C]92342[/C][C]95106.1057225732[/C][C]-2764.10572257322[/C][/ROW]
[ROW][C]39[/C][C]100023[/C][C]93088.5353414885[/C][C]6934.4646585115[/C][/ROW]
[ROW][C]40[/C][C]178277[/C][C]213773.035529797[/C][C]-35496.0355297972[/C][/ROW]
[ROW][C]41[/C][C]145062[/C][C]124698.945965077[/C][C]20363.054034923[/C][/ROW]
[ROW][C]42[/C][C]110980[/C][C]120397.332254631[/C][C]-9417.33225463105[/C][/ROW]
[ROW][C]43[/C][C]86039[/C][C]90520.5797268426[/C][C]-4481.57972684258[/C][/ROW]
[ROW][C]44[/C][C]119514[/C][C]146101.777501793[/C][C]-26587.777501793[/C][/ROW]
[ROW][C]45[/C][C]95535[/C][C]84296.5029787877[/C][C]11238.4970212123[/C][/ROW]
[ROW][C]46[/C][C]109894[/C][C]85270.1157436464[/C][C]24623.8842563536[/C][/ROW]
[ROW][C]47[/C][C]61554[/C][C]56012.8423611032[/C][C]5541.15763889684[/C][/ROW]
[ROW][C]48[/C][C]156520[/C][C]136455.757696865[/C][C]20064.2423031347[/C][/ROW]
[ROW][C]49[/C][C]159121[/C][C]150107.813265799[/C][C]9013.18673420115[/C][/ROW]
[ROW][C]50[/C][C]129362[/C][C]140925.775455644[/C][C]-11563.7754556436[/C][/ROW]
[ROW][C]51[/C][C]48188[/C][C]56297.0829617419[/C][C]-8109.08296174194[/C][/ROW]
[ROW][C]52[/C][C]91198[/C][C]83092.0341043828[/C][C]8105.9658956172[/C][/ROW]
[ROW][C]53[/C][C]229864[/C][C]183444.637032886[/C][C]46419.3629671142[/C][/ROW]
[ROW][C]54[/C][C]180317[/C][C]137725.578033609[/C][C]42591.4219663914[/C][/ROW]
[ROW][C]55[/C][C]150640[/C][C]157388.025710569[/C][C]-6748.02571056855[/C][/ROW]
[ROW][C]56[/C][C]104416[/C][C]101684.370956394[/C][C]2731.62904360589[/C][/ROW]
[ROW][C]57[/C][C]159645[/C][C]116637.803702202[/C][C]43007.1962977984[/C][/ROW]
[ROW][C]58[/C][C]60368[/C][C]61789.8565534696[/C][C]-1421.85655346963[/C][/ROW]
[ROW][C]59[/C][C]100056[/C][C]111737.817819074[/C][C]-11681.8178190744[/C][/ROW]
[ROW][C]60[/C][C]137214[/C][C]123746.597032402[/C][C]13467.4029675982[/C][/ROW]
[ROW][C]61[/C][C]99630[/C][C]100943.31313658[/C][C]-1313.31313657959[/C][/ROW]
[ROW][C]62[/C][C]84557[/C][C]125332.523129451[/C][C]-40775.5231294512[/C][/ROW]
[ROW][C]63[/C][C]91199[/C][C]78529.4863962065[/C][C]12669.5136037935[/C][/ROW]
[ROW][C]64[/C][C]83419[/C][C]101655.933789869[/C][C]-18236.9337898685[/C][/ROW]
[ROW][C]65[/C][C]101723[/C][C]102335.153043774[/C][C]-612.153043773784[/C][/ROW]
[ROW][C]66[/C][C]94982[/C][C]118760.748648396[/C][C]-23778.7486483956[/C][/ROW]
[ROW][C]67[/C][C]129700[/C][C]167896.05047231[/C][C]-38196.0504723104[/C][/ROW]
[ROW][C]68[/C][C]110708[/C][C]146338.185362657[/C][C]-35630.1853626566[/C][/ROW]
[ROW][C]69[/C][C]81518[/C][C]86059.7198765192[/C][C]-4541.7198765192[/C][/ROW]
[ROW][C]70[/C][C]31970[/C][C]33148.1441567272[/C][C]-1178.14415672722[/C][/ROW]
[ROW][C]71[/C][C]192268[/C][C]178225.074777449[/C][C]14042.9252225508[/C][/ROW]
[ROW][C]72[/C][C]87611[/C][C]82417.4649304377[/C][C]5193.53506956231[/C][/ROW]
[ROW][C]73[/C][C]77890[/C][C]91704.6695591499[/C][C]-13814.6695591499[/C][/ROW]
[ROW][C]74[/C][C]83261[/C][C]78846.3235238642[/C][C]4414.67647613582[/C][/ROW]
[ROW][C]75[/C][C]116290[/C][C]136927.0908243[/C][C]-20637.0908243005[/C][/ROW]
[ROW][C]76[/C][C]55254[/C][C]61880.3666157136[/C][C]-6626.36661571362[/C][/ROW]
[ROW][C]77[/C][C]116173[/C][C]130685.369771608[/C][C]-14512.3697716083[/C][/ROW]
[ROW][C]78[/C][C]111488[/C][C]184673.23944866[/C][C]-73185.2394486601[/C][/ROW]
[ROW][C]79[/C][C]60138[/C][C]66820.2247147614[/C][C]-6682.22471476137[/C][/ROW]
[ROW][C]80[/C][C]73422[/C][C]83863.4076964306[/C][C]-10441.4076964306[/C][/ROW]
[ROW][C]81[/C][C]67751[/C][C]70007.6384594595[/C][C]-2256.63845945954[/C][/ROW]
[ROW][C]82[/C][C]213351[/C][C]152755.09462988[/C][C]60595.9053701201[/C][/ROW]
[ROW][C]83[/C][C]51185[/C][C]57290.7758381605[/C][C]-6105.7758381605[/C][/ROW]
[ROW][C]84[/C][C]97181[/C][C]102483.049359042[/C][C]-5302.0493590425[/C][/ROW]
[ROW][C]85[/C][C]42311[/C][C]55201.3228272878[/C][C]-12890.3228272878[/C][/ROW]
[ROW][C]86[/C][C]115801[/C][C]105806.44159259[/C][C]9994.55840741[/C][/ROW]
[ROW][C]87[/C][C]183637[/C][C]165481.455598521[/C][C]18155.5444014785[/C][/ROW]
[ROW][C]88[/C][C]68161[/C][C]86276.7340679613[/C][C]-18115.7340679613[/C][/ROW]
[ROW][C]89[/C][C]76441[/C][C]72736.9489311088[/C][C]3704.0510688912[/C][/ROW]
[ROW][C]90[/C][C]103613[/C][C]109641.321467545[/C][C]-6028.32146754527[/C][/ROW]
[ROW][C]91[/C][C]98707[/C][C]108901.792052724[/C][C]-10194.792052724[/C][/ROW]
[ROW][C]92[/C][C]126527[/C][C]134303.097087336[/C][C]-7776.09708733649[/C][/ROW]
[ROW][C]93[/C][C]136781[/C][C]129868.416299789[/C][C]6912.58370021064[/C][/ROW]
[ROW][C]94[/C][C]105863[/C][C]99744.7982522167[/C][C]6118.20174778334[/C][/ROW]
[ROW][C]95[/C][C]38775[/C][C]49666.6180719454[/C][C]-10891.6180719454[/C][/ROW]
[ROW][C]96[/C][C]179984[/C][C]187546.413851485[/C][C]-7562.41385148506[/C][/ROW]
[ROW][C]97[/C][C]164808[/C][C]147349.694244083[/C][C]17458.3057559172[/C][/ROW]
[ROW][C]98[/C][C]19349[/C][C]23270.9036859332[/C][C]-3921.90368593321[/C][/ROW]
[ROW][C]99[/C][C]143902[/C][C]126461.153480727[/C][C]17440.846519273[/C][/ROW]
[ROW][C]100[/C][C]108660[/C][C]132014.92650165[/C][C]-23354.9265016498[/C][/ROW]
[ROW][C]101[/C][C]43803[/C][C]37882.4359259856[/C][C]5920.56407401437[/C][/ROW]
[ROW][C]102[/C][C]47062[/C][C]63309.0888882926[/C][C]-16247.0888882926[/C][/ROW]
[ROW][C]103[/C][C]110845[/C][C]92178.021218626[/C][C]18666.978781374[/C][/ROW]
[ROW][C]104[/C][C]92517[/C][C]79933.8626605316[/C][C]12583.1373394684[/C][/ROW]
[ROW][C]105[/C][C]58660[/C][C]53816.7112748107[/C][C]4843.2887251893[/C][/ROW]
[ROW][C]106[/C][C]27676[/C][C]37835.3782114952[/C][C]-10159.3782114952[/C][/ROW]
[ROW][C]107[/C][C]98550[/C][C]100897.436501568[/C][C]-2347.43650156758[/C][/ROW]
[ROW][C]108[/C][C]43284[/C][C]30693.8933933387[/C][C]12590.1066066613[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]2846.51349094435[/C][C]-2846.51349094435[/C][/ROW]
[ROW][C]110[/C][C]66016[/C][C]67827.0850981753[/C][C]-1811.08509817525[/C][/ROW]
[ROW][C]111[/C][C]57359[/C][C]70457.3730559606[/C][C]-13098.3730559606[/C][/ROW]
[ROW][C]112[/C][C]96933[/C][C]103109.322803128[/C][C]-6176.32280312789[/C][/ROW]
[ROW][C]113[/C][C]70369[/C][C]92119.3003454765[/C][C]-21750.3003454765[/C][/ROW]
[ROW][C]114[/C][C]65494[/C][C]78074.571255126[/C][C]-12580.5712551261[/C][/ROW]
[ROW][C]115[/C][C]3616[/C][C]5390.84367536474[/C][C]-1774.84367536474[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]2846.51349094435[/C][C]-2846.51349094435[/C][/ROW]
[ROW][C]117[/C][C]143931[/C][C]105917.366421157[/C][C]38013.6335788433[/C][/ROW]
[ROW][C]118[/C][C]109894[/C][C]120139.078334172[/C][C]-10245.0783341721[/C][/ROW]
[ROW][C]119[/C][C]122973[/C][C]116761.197616812[/C][C]6211.80238318772[/C][/ROW]
[ROW][C]120[/C][C]84336[/C][C]88411.0655032072[/C][C]-4075.06550320722[/C][/ROW]
[ROW][C]121[/C][C]43410[/C][C]41850.848255825[/C][C]1559.151744175[/C][/ROW]
[ROW][C]122[/C][C]136250[/C][C]140683.841165883[/C][C]-4433.8411658833[/C][/ROW]
[ROW][C]123[/C][C]79015[/C][C]72752.121032346[/C][C]6262.87896765404[/C][/ROW]
[ROW][C]124[/C][C]92937[/C][C]93201.6650609654[/C][C]-264.665060965453[/C][/ROW]
[ROW][C]125[/C][C]57586[/C][C]41136.380403298[/C][C]16449.619596702[/C][/ROW]
[ROW][C]126[/C][C]19764[/C][C]20133.0052809877[/C][C]-369.005280987649[/C][/ROW]
[ROW][C]127[/C][C]105757[/C][C]109426.513770299[/C][C]-3669.51377029853[/C][/ROW]
[ROW][C]128[/C][C]96410[/C][C]83026.144991221[/C][C]13383.8550087791[/C][/ROW]
[ROW][C]129[/C][C]113402[/C][C]111844.571554378[/C][C]1557.42844562185[/C][/ROW]
[ROW][C]130[/C][C]11796[/C][C]13868.0365556422[/C][C]-2072.03655564223[/C][/ROW]
[ROW][C]131[/C][C]7627[/C][C]7807.42128476221[/C][C]-180.421284762209[/C][/ROW]
[ROW][C]132[/C][C]121085[/C][C]103835.237849234[/C][C]17249.7621507659[/C][/ROW]
[ROW][C]133[/C][C]6836[/C][C]4704.02163210564[/C][C]2131.97836789436[/C][/ROW]
[ROW][C]134[/C][C]139563[/C][C]113797.041951359[/C][C]25765.9580486413[/C][/ROW]
[ROW][C]135[/C][C]5118[/C][C]6186.95214019642[/C][C]-1068.95214019642[/C][/ROW]
[ROW][C]136[/C][C]40248[/C][C]33304.4615340351[/C][C]6943.53846596488[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]2846.51349094435[/C][C]-2846.51349094435[/C][/ROW]
[ROW][C]138[/C][C]95079[/C][C]82330.5519746842[/C][C]12748.4480253158[/C][/ROW]
[ROW][C]139[/C][C]80750[/C][C]65643.3699312436[/C][C]15106.6300687564[/C][/ROW]
[ROW][C]140[/C][C]7131[/C][C]5287.92893865538[/C][C]1843.07106134462[/C][/ROW]
[ROW][C]141[/C][C]4194[/C][C]7114.84715826596[/C][C]-2920.84715826596[/C][/ROW]
[ROW][C]142[/C][C]60378[/C][C]51917.744280328[/C][C]8460.25571967202[/C][/ROW]
[ROW][C]143[/C][C]96971[/C][C]75186.6549664822[/C][C]21784.3450335178[/C][/ROW]
[ROW][C]144[/C][C]83484[/C][C]95120.5797576593[/C][C]-11636.5797576593[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155018&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155018&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
1127476108742.57315465518733.4268453454
2130358134072.293639497-3714.29363949661
3721515120.1782345378-7905.17823453776
4112861123350.933196255-10489.9331962551
5210171223263.68861867-13092.6886186698
6393802410850.327322275-17048.3273222749
7117604124656.662161862-7052.66216186155
8126029134934.728360656-8905.72836065642
99972998614.6091762781114.39082372209
10256310217646.20128299938663.7987170009
11113066124296.06819569-11230.06819569
12156212141137.94863201615074.0513679835
1369952103034.020034245-33082.0200342449
14152673170842.966568347-18169.9665683468
15125841105279.46161036520561.5383896352
16125769136772.517411399-11003.517411399
17123467107711.77910778915755.2208922112
185623252442.87456743383789.12543256618
19108244136358.822752606-28114.8227526058
202276240083.0661879098-17321.0661879098
214855472093.06474559-23539.06474559
22178697173744.5995144054952.40048559485
23139115115533.33007465523581.6699253453
2493773112610.052548644-18837.0525486442
25132796150299.406590468-17503.4065904684
2611393397917.9723059516015.02769405
27144781153682.481924375-8901.48192437499
28140711147445.763319801-6734.76331980117
29283337228524.91030746954812.0896925314
30158146149792.0733514188353.92664858193
31123344106299.8725716917044.1274283098
32157640142574.41743898715065.5825610125
3391279113574.466479729-22295.4664797287
34189374172709.54319962516664.4568003747
35167915161694.3907114346220.60928856639
3603109.32979779947-3109.32979779947
37175403166377.0811514729025.91884852846
389234295106.1057225732-2764.10572257322
3910002393088.53534148856934.4646585115
40178277213773.035529797-35496.0355297972
41145062124698.94596507720363.054034923
42110980120397.332254631-9417.33225463105
438603990520.5797268426-4481.57972684258
44119514146101.777501793-26587.777501793
459553584296.502978787711238.4970212123
4610989485270.115743646424623.8842563536
476155456012.84236110325541.15763889684
48156520136455.75769686520064.2423031347
49159121150107.8132657999013.18673420115
50129362140925.775455644-11563.7754556436
514818856297.0829617419-8109.08296174194
529119883092.03410438288105.9658956172
53229864183444.63703288646419.3629671142
54180317137725.57803360942591.4219663914
55150640157388.025710569-6748.02571056855
56104416101684.3709563942731.62904360589
57159645116637.80370220243007.1962977984
586036861789.8565534696-1421.85655346963
59100056111737.817819074-11681.8178190744
60137214123746.59703240213467.4029675982
6199630100943.31313658-1313.31313657959
6284557125332.523129451-40775.5231294512
639119978529.486396206512669.5136037935
6483419101655.933789869-18236.9337898685
65101723102335.153043774-612.153043773784
6694982118760.748648396-23778.7486483956
67129700167896.05047231-38196.0504723104
68110708146338.185362657-35630.1853626566
698151886059.7198765192-4541.7198765192
703197033148.1441567272-1178.14415672722
71192268178225.07477744914042.9252225508
728761182417.46493043775193.53506956231
737789091704.6695591499-13814.6695591499
748326178846.32352386424414.67647613582
75116290136927.0908243-20637.0908243005
765525461880.3666157136-6626.36661571362
77116173130685.369771608-14512.3697716083
78111488184673.23944866-73185.2394486601
796013866820.2247147614-6682.22471476137
807342283863.4076964306-10441.4076964306
816775170007.6384594595-2256.63845945954
82213351152755.0946298860595.9053701201
835118557290.7758381605-6105.7758381605
8497181102483.049359042-5302.0493590425
854231155201.3228272878-12890.3228272878
86115801105806.441592599994.55840741
87183637165481.45559852118155.5444014785
886816186276.7340679613-18115.7340679613
897644172736.94893110883704.0510688912
90103613109641.321467545-6028.32146754527
9198707108901.792052724-10194.792052724
92126527134303.097087336-7776.09708733649
93136781129868.4162997896912.58370021064
9410586399744.79825221676118.20174778334
953877549666.6180719454-10891.6180719454
96179984187546.413851485-7562.41385148506
97164808147349.69424408317458.3057559172
981934923270.9036859332-3921.90368593321
99143902126461.15348072717440.846519273
100108660132014.92650165-23354.9265016498
1014380337882.43592598565920.56407401437
1024706263309.0888882926-16247.0888882926
10311084592178.02121862618666.978781374
1049251779933.862660531612583.1373394684
1055866053816.71127481074843.2887251893
1062767637835.3782114952-10159.3782114952
10798550100897.436501568-2347.43650156758
1084328430693.893393338712590.1066066613
10902846.51349094435-2846.51349094435
1106601667827.0850981753-1811.08509817525
1115735970457.3730559606-13098.3730559606
11296933103109.322803128-6176.32280312789
1137036992119.3003454765-21750.3003454765
1146549478074.571255126-12580.5712551261
11536165390.84367536474-1774.84367536474
11602846.51349094435-2846.51349094435
117143931105917.36642115738013.6335788433
118109894120139.078334172-10245.0783341721
119122973116761.1976168126211.80238318772
1208433688411.0655032072-4075.06550320722
1214341041850.8482558251559.151744175
122136250140683.841165883-4433.8411658833
1237901572752.1210323466262.87896765404
1249293793201.6650609654-264.665060965453
1255758641136.38040329816449.619596702
1261976420133.0052809877-369.005280987649
127105757109426.513770299-3669.51377029853
1289641083026.14499122113383.8550087791
129113402111844.5715543781557.42844562185
1301179613868.0365556422-2072.03655564223
13176277807.42128476221-180.421284762209
132121085103835.23784923417249.7621507659
13368364704.021632105642131.97836789436
134139563113797.04195135925765.9580486413
13551186186.95214019642-1068.95214019642
1364024833304.46153403516943.53846596488
13702846.51349094435-2846.51349094435
1389507982330.551974684212748.4480253158
1398075065643.369931243615106.6300687564
14071315287.928938655381843.07106134462
14141947114.84715826596-2920.84715826596
1426037851917.7442803288460.25571967202
1439697175186.654966482221784.3450335178
1448348495120.5797576593-11636.5797576593






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.8991492661164440.2017014677671120.100850733883556
190.835531594837050.3289368103259020.164468405162951
200.7427600254660880.5144799490678240.257239974533912
210.6428362151383920.7143275697232160.357163784861608
220.7509043687081750.498191262583650.249095631291825
230.8275320828278850.3449358343442290.172467917172115
240.7784208866439680.4431582267120640.221579113356032
250.7130003618971640.5739992762056710.286999638102836
260.6486943758659870.7026112482680270.351305624134013
270.5753398227419420.8493203545161170.424660177258058
280.491197196531320.982394393062640.50880280346868
290.8011252476185980.3977495047628030.198874752381402
300.7494451325076940.5011097349846120.250554867492306
310.714784290892450.5704314182150990.28521570910755
320.6683514857119970.6632970285760070.331648514288004
330.6293725526694780.7412548946610440.370627447330522
340.5897597168219930.8204805663560140.410240283178007
350.5253909180246870.9492181639506260.474609081975313
360.4564067003195830.9128134006391660.543593299680417
370.4875849135289210.9751698270578420.512415086471079
380.4230577201665530.8461154403331050.576942279833447
390.3628201685679380.7256403371358760.637179831432062
400.783331834756210.4333363304875790.21666816524379
410.8336390955358770.3327218089282450.166360904464123
420.8017377923334560.3965244153330890.198262207666544
430.7603301269246810.4793397461506380.239669873075319
440.7897671290610670.4204657418778660.210232870938933
450.7774830056286680.4450339887426630.222516994371332
460.8217886736340890.3564226527318220.178211326365911
470.7981628268695930.4036743462608140.201837173130407
480.7983448749737950.403310250052410.201655125026205
490.7632355885810440.4735288228379120.236764411418956
500.7271986715010110.5456026569979780.272801328498989
510.6910061032918330.6179877934163340.308993896708167
520.6468533051854990.7062933896290030.353146694814501
530.8397772738917020.3204454522165970.160222726108298
540.927613975568380.1447720488632410.0723860244316207
550.9123640120630830.1752719758738340.0876359879369168
560.8903442430144370.2193115139711250.109655756985563
570.9533398341572930.09332033168541460.0466601658427073
580.9399191326258360.1201617347483270.0600808673741636
590.9296134346372120.1407731307255770.0703865653627884
600.9163839533963050.1672320932073910.0836160466036953
610.8949529363393450.2100941273213110.105047063660655
620.9546434573790210.0907130852419570.0453565426209785
630.946231757843780.1075364843124420.053768242156221
640.9425368693037160.1149262613925690.0574631306962843
650.925682181845660.1486356363086790.0743178181543394
660.9324988250065540.1350023499868930.0675011749934464
670.9648982189540040.07020356209199140.0351017810459957
680.9821700521737860.03565989565242760.0178299478262138
690.9758305532962590.04833889340748280.0241694467037414
700.967189534610640.06562093077871760.0328104653893588
710.9616208520927860.07675829581442870.0383791479072143
720.9522258679810520.09554826403789610.0477741320189481
730.9490533854654750.1018932290690490.0509466145345245
740.9347430596009830.1305138807980330.0652569403990166
750.9481719086144590.1036561827710830.0518280913855414
760.9466018794939690.1067962410120630.0533981205060315
770.9481256170085660.1037487659828680.051874382991434
780.9999860351085882.79297828239587e-051.39648914119793e-05
790.9999748215571555.03568856890193e-052.51784428445097e-05
800.9999623205001967.53589996080424e-053.76794998040212e-05
810.9999383224786140.0001233550427709656.16775213854826e-05
820.9999994968222551.00635549069073e-065.03177745345366e-07
830.9999990201721191.95965576211332e-069.7982788105666e-07
840.9999980867919383.82641612369267e-061.91320806184633e-06
850.9999965272946376.94541072674722e-063.47270536337361e-06
860.9999944145175651.11709648702359e-055.58548243511794e-06
870.9999910617369511.78765260972519e-058.93826304862595e-06
880.9999910756835031.7848632993105e-058.9243164965525e-06
890.9999827653980283.44692039433526e-051.72346019716763e-05
900.9999671740137346.56519725320885e-053.28259862660442e-05
910.9999657236981666.85526036686896e-053.42763018343448e-05
920.9999814826936433.7034612713719e-051.85173063568595e-05
930.9999659645664386.8070867124626e-053.4035433562313e-05
940.9999523047666549.53904666911206e-054.76952333455603e-05
950.9999244293170140.0001511413659710787.55706829855392e-05
960.9999678951014686.4209797064697e-053.21048985323485e-05
970.999968123652786.37526944418417e-053.18763472209208e-05
980.9999361556430.0001276887139990696.38443569995347e-05
990.9998864802897580.0002270394204830130.000113519710241506
1000.9999505523642359.88952715298368e-054.94476357649184e-05
1010.999918882231840.0001622355363202378.11177681601183e-05
1020.999919794579180.0001604108416405278.02054208202636e-05
1030.999994734543731.05309125393663e-055.26545626968314e-06
1040.9999881124396222.37751207565567e-051.18875603782783e-05
1050.9999794310609624.1137878075771e-052.05689390378855e-05
1060.9999557012375448.859752491252e-054.429876245626e-05
1070.9999307626136740.0001384747726522516.92373863261255e-05
1080.9998849792669210.0002300414661571710.000115020733078585
1090.9997482666018730.0005034667962542090.000251733398127105
1100.9995424095957980.0009151808084031030.000457590404201551
1110.9996626335408660.0006747329182669740.000337366459133487
1120.99932749877030.001345002459400650.000672501229700324
1130.9990777120010520.001844575997895650.000922287998947823
1140.9990505640045780.00189887199084440.000949435995422202
1150.9979438415167180.00411231696656320.0020561584832816
1160.995734721560160.008530556879679040.00426527843983952
1170.999975365471814.926905637805e-052.4634528189025e-05
1180.9999883345693132.3330861374726e-051.1665430687363e-05
1190.9999948769233681.02461532631926e-055.12307663159628e-06
1200.9999772597399284.5480520144144e-052.2740260072072e-05
1210.9999067874494990.0001864251010025959.32125505012976e-05
1220.9999601072366227.97855267557098e-053.98927633778549e-05
1230.999809856844390.0003802863112219580.000190143155610979
1240.9998608842455340.0002782315089322690.000139115754466135
1250.9997393366160140.0005213267679715720.000260663383985786
1260.998305623250320.003388753499360890.00169437674968045

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.899149266116444 & 0.201701467767112 & 0.100850733883556 \tabularnewline
19 & 0.83553159483705 & 0.328936810325902 & 0.164468405162951 \tabularnewline
20 & 0.742760025466088 & 0.514479949067824 & 0.257239974533912 \tabularnewline
21 & 0.642836215138392 & 0.714327569723216 & 0.357163784861608 \tabularnewline
22 & 0.750904368708175 & 0.49819126258365 & 0.249095631291825 \tabularnewline
23 & 0.827532082827885 & 0.344935834344229 & 0.172467917172115 \tabularnewline
24 & 0.778420886643968 & 0.443158226712064 & 0.221579113356032 \tabularnewline
25 & 0.713000361897164 & 0.573999276205671 & 0.286999638102836 \tabularnewline
26 & 0.648694375865987 & 0.702611248268027 & 0.351305624134013 \tabularnewline
27 & 0.575339822741942 & 0.849320354516117 & 0.424660177258058 \tabularnewline
28 & 0.49119719653132 & 0.98239439306264 & 0.50880280346868 \tabularnewline
29 & 0.801125247618598 & 0.397749504762803 & 0.198874752381402 \tabularnewline
30 & 0.749445132507694 & 0.501109734984612 & 0.250554867492306 \tabularnewline
31 & 0.71478429089245 & 0.570431418215099 & 0.28521570910755 \tabularnewline
32 & 0.668351485711997 & 0.663297028576007 & 0.331648514288004 \tabularnewline
33 & 0.629372552669478 & 0.741254894661044 & 0.370627447330522 \tabularnewline
34 & 0.589759716821993 & 0.820480566356014 & 0.410240283178007 \tabularnewline
35 & 0.525390918024687 & 0.949218163950626 & 0.474609081975313 \tabularnewline
36 & 0.456406700319583 & 0.912813400639166 & 0.543593299680417 \tabularnewline
37 & 0.487584913528921 & 0.975169827057842 & 0.512415086471079 \tabularnewline
38 & 0.423057720166553 & 0.846115440333105 & 0.576942279833447 \tabularnewline
39 & 0.362820168567938 & 0.725640337135876 & 0.637179831432062 \tabularnewline
40 & 0.78333183475621 & 0.433336330487579 & 0.21666816524379 \tabularnewline
41 & 0.833639095535877 & 0.332721808928245 & 0.166360904464123 \tabularnewline
42 & 0.801737792333456 & 0.396524415333089 & 0.198262207666544 \tabularnewline
43 & 0.760330126924681 & 0.479339746150638 & 0.239669873075319 \tabularnewline
44 & 0.789767129061067 & 0.420465741877866 & 0.210232870938933 \tabularnewline
45 & 0.777483005628668 & 0.445033988742663 & 0.222516994371332 \tabularnewline
46 & 0.821788673634089 & 0.356422652731822 & 0.178211326365911 \tabularnewline
47 & 0.798162826869593 & 0.403674346260814 & 0.201837173130407 \tabularnewline
48 & 0.798344874973795 & 0.40331025005241 & 0.201655125026205 \tabularnewline
49 & 0.763235588581044 & 0.473528822837912 & 0.236764411418956 \tabularnewline
50 & 0.727198671501011 & 0.545602656997978 & 0.272801328498989 \tabularnewline
51 & 0.691006103291833 & 0.617987793416334 & 0.308993896708167 \tabularnewline
52 & 0.646853305185499 & 0.706293389629003 & 0.353146694814501 \tabularnewline
53 & 0.839777273891702 & 0.320445452216597 & 0.160222726108298 \tabularnewline
54 & 0.92761397556838 & 0.144772048863241 & 0.0723860244316207 \tabularnewline
55 & 0.912364012063083 & 0.175271975873834 & 0.0876359879369168 \tabularnewline
56 & 0.890344243014437 & 0.219311513971125 & 0.109655756985563 \tabularnewline
57 & 0.953339834157293 & 0.0933203316854146 & 0.0466601658427073 \tabularnewline
58 & 0.939919132625836 & 0.120161734748327 & 0.0600808673741636 \tabularnewline
59 & 0.929613434637212 & 0.140773130725577 & 0.0703865653627884 \tabularnewline
60 & 0.916383953396305 & 0.167232093207391 & 0.0836160466036953 \tabularnewline
61 & 0.894952936339345 & 0.210094127321311 & 0.105047063660655 \tabularnewline
62 & 0.954643457379021 & 0.090713085241957 & 0.0453565426209785 \tabularnewline
63 & 0.94623175784378 & 0.107536484312442 & 0.053768242156221 \tabularnewline
64 & 0.942536869303716 & 0.114926261392569 & 0.0574631306962843 \tabularnewline
65 & 0.92568218184566 & 0.148635636308679 & 0.0743178181543394 \tabularnewline
66 & 0.932498825006554 & 0.135002349986893 & 0.0675011749934464 \tabularnewline
67 & 0.964898218954004 & 0.0702035620919914 & 0.0351017810459957 \tabularnewline
68 & 0.982170052173786 & 0.0356598956524276 & 0.0178299478262138 \tabularnewline
69 & 0.975830553296259 & 0.0483388934074828 & 0.0241694467037414 \tabularnewline
70 & 0.96718953461064 & 0.0656209307787176 & 0.0328104653893588 \tabularnewline
71 & 0.961620852092786 & 0.0767582958144287 & 0.0383791479072143 \tabularnewline
72 & 0.952225867981052 & 0.0955482640378961 & 0.0477741320189481 \tabularnewline
73 & 0.949053385465475 & 0.101893229069049 & 0.0509466145345245 \tabularnewline
74 & 0.934743059600983 & 0.130513880798033 & 0.0652569403990166 \tabularnewline
75 & 0.948171908614459 & 0.103656182771083 & 0.0518280913855414 \tabularnewline
76 & 0.946601879493969 & 0.106796241012063 & 0.0533981205060315 \tabularnewline
77 & 0.948125617008566 & 0.103748765982868 & 0.051874382991434 \tabularnewline
78 & 0.999986035108588 & 2.79297828239587e-05 & 1.39648914119793e-05 \tabularnewline
79 & 0.999974821557155 & 5.03568856890193e-05 & 2.51784428445097e-05 \tabularnewline
80 & 0.999962320500196 & 7.53589996080424e-05 & 3.76794998040212e-05 \tabularnewline
81 & 0.999938322478614 & 0.000123355042770965 & 6.16775213854826e-05 \tabularnewline
82 & 0.999999496822255 & 1.00635549069073e-06 & 5.03177745345366e-07 \tabularnewline
83 & 0.999999020172119 & 1.95965576211332e-06 & 9.7982788105666e-07 \tabularnewline
84 & 0.999998086791938 & 3.82641612369267e-06 & 1.91320806184633e-06 \tabularnewline
85 & 0.999996527294637 & 6.94541072674722e-06 & 3.47270536337361e-06 \tabularnewline
86 & 0.999994414517565 & 1.11709648702359e-05 & 5.58548243511794e-06 \tabularnewline
87 & 0.999991061736951 & 1.78765260972519e-05 & 8.93826304862595e-06 \tabularnewline
88 & 0.999991075683503 & 1.7848632993105e-05 & 8.9243164965525e-06 \tabularnewline
89 & 0.999982765398028 & 3.44692039433526e-05 & 1.72346019716763e-05 \tabularnewline
90 & 0.999967174013734 & 6.56519725320885e-05 & 3.28259862660442e-05 \tabularnewline
91 & 0.999965723698166 & 6.85526036686896e-05 & 3.42763018343448e-05 \tabularnewline
92 & 0.999981482693643 & 3.7034612713719e-05 & 1.85173063568595e-05 \tabularnewline
93 & 0.999965964566438 & 6.8070867124626e-05 & 3.4035433562313e-05 \tabularnewline
94 & 0.999952304766654 & 9.53904666911206e-05 & 4.76952333455603e-05 \tabularnewline
95 & 0.999924429317014 & 0.000151141365971078 & 7.55706829855392e-05 \tabularnewline
96 & 0.999967895101468 & 6.4209797064697e-05 & 3.21048985323485e-05 \tabularnewline
97 & 0.99996812365278 & 6.37526944418417e-05 & 3.18763472209208e-05 \tabularnewline
98 & 0.999936155643 & 0.000127688713999069 & 6.38443569995347e-05 \tabularnewline
99 & 0.999886480289758 & 0.000227039420483013 & 0.000113519710241506 \tabularnewline
100 & 0.999950552364235 & 9.88952715298368e-05 & 4.94476357649184e-05 \tabularnewline
101 & 0.99991888223184 & 0.000162235536320237 & 8.11177681601183e-05 \tabularnewline
102 & 0.99991979457918 & 0.000160410841640527 & 8.02054208202636e-05 \tabularnewline
103 & 0.99999473454373 & 1.05309125393663e-05 & 5.26545626968314e-06 \tabularnewline
104 & 0.999988112439622 & 2.37751207565567e-05 & 1.18875603782783e-05 \tabularnewline
105 & 0.999979431060962 & 4.1137878075771e-05 & 2.05689390378855e-05 \tabularnewline
106 & 0.999955701237544 & 8.859752491252e-05 & 4.429876245626e-05 \tabularnewline
107 & 0.999930762613674 & 0.000138474772652251 & 6.92373863261255e-05 \tabularnewline
108 & 0.999884979266921 & 0.000230041466157171 & 0.000115020733078585 \tabularnewline
109 & 0.999748266601873 & 0.000503466796254209 & 0.000251733398127105 \tabularnewline
110 & 0.999542409595798 & 0.000915180808403103 & 0.000457590404201551 \tabularnewline
111 & 0.999662633540866 & 0.000674732918266974 & 0.000337366459133487 \tabularnewline
112 & 0.9993274987703 & 0.00134500245940065 & 0.000672501229700324 \tabularnewline
113 & 0.999077712001052 & 0.00184457599789565 & 0.000922287998947823 \tabularnewline
114 & 0.999050564004578 & 0.0018988719908444 & 0.000949435995422202 \tabularnewline
115 & 0.997943841516718 & 0.0041123169665632 & 0.0020561584832816 \tabularnewline
116 & 0.99573472156016 & 0.00853055687967904 & 0.00426527843983952 \tabularnewline
117 & 0.99997536547181 & 4.926905637805e-05 & 2.4634528189025e-05 \tabularnewline
118 & 0.999988334569313 & 2.3330861374726e-05 & 1.1665430687363e-05 \tabularnewline
119 & 0.999994876923368 & 1.02461532631926e-05 & 5.12307663159628e-06 \tabularnewline
120 & 0.999977259739928 & 4.5480520144144e-05 & 2.2740260072072e-05 \tabularnewline
121 & 0.999906787449499 & 0.000186425101002595 & 9.32125505012976e-05 \tabularnewline
122 & 0.999960107236622 & 7.97855267557098e-05 & 3.98927633778549e-05 \tabularnewline
123 & 0.99980985684439 & 0.000380286311221958 & 0.000190143155610979 \tabularnewline
124 & 0.999860884245534 & 0.000278231508932269 & 0.000139115754466135 \tabularnewline
125 & 0.999739336616014 & 0.000521326767971572 & 0.000260663383985786 \tabularnewline
126 & 0.99830562325032 & 0.00338875349936089 & 0.00169437674968045 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155018&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]0.899149266116444[/C][C]0.201701467767112[/C][C]0.100850733883556[/C][/ROW]
[ROW][C]19[/C][C]0.83553159483705[/C][C]0.328936810325902[/C][C]0.164468405162951[/C][/ROW]
[ROW][C]20[/C][C]0.742760025466088[/C][C]0.514479949067824[/C][C]0.257239974533912[/C][/ROW]
[ROW][C]21[/C][C]0.642836215138392[/C][C]0.714327569723216[/C][C]0.357163784861608[/C][/ROW]
[ROW][C]22[/C][C]0.750904368708175[/C][C]0.49819126258365[/C][C]0.249095631291825[/C][/ROW]
[ROW][C]23[/C][C]0.827532082827885[/C][C]0.344935834344229[/C][C]0.172467917172115[/C][/ROW]
[ROW][C]24[/C][C]0.778420886643968[/C][C]0.443158226712064[/C][C]0.221579113356032[/C][/ROW]
[ROW][C]25[/C][C]0.713000361897164[/C][C]0.573999276205671[/C][C]0.286999638102836[/C][/ROW]
[ROW][C]26[/C][C]0.648694375865987[/C][C]0.702611248268027[/C][C]0.351305624134013[/C][/ROW]
[ROW][C]27[/C][C]0.575339822741942[/C][C]0.849320354516117[/C][C]0.424660177258058[/C][/ROW]
[ROW][C]28[/C][C]0.49119719653132[/C][C]0.98239439306264[/C][C]0.50880280346868[/C][/ROW]
[ROW][C]29[/C][C]0.801125247618598[/C][C]0.397749504762803[/C][C]0.198874752381402[/C][/ROW]
[ROW][C]30[/C][C]0.749445132507694[/C][C]0.501109734984612[/C][C]0.250554867492306[/C][/ROW]
[ROW][C]31[/C][C]0.71478429089245[/C][C]0.570431418215099[/C][C]0.28521570910755[/C][/ROW]
[ROW][C]32[/C][C]0.668351485711997[/C][C]0.663297028576007[/C][C]0.331648514288004[/C][/ROW]
[ROW][C]33[/C][C]0.629372552669478[/C][C]0.741254894661044[/C][C]0.370627447330522[/C][/ROW]
[ROW][C]34[/C][C]0.589759716821993[/C][C]0.820480566356014[/C][C]0.410240283178007[/C][/ROW]
[ROW][C]35[/C][C]0.525390918024687[/C][C]0.949218163950626[/C][C]0.474609081975313[/C][/ROW]
[ROW][C]36[/C][C]0.456406700319583[/C][C]0.912813400639166[/C][C]0.543593299680417[/C][/ROW]
[ROW][C]37[/C][C]0.487584913528921[/C][C]0.975169827057842[/C][C]0.512415086471079[/C][/ROW]
[ROW][C]38[/C][C]0.423057720166553[/C][C]0.846115440333105[/C][C]0.576942279833447[/C][/ROW]
[ROW][C]39[/C][C]0.362820168567938[/C][C]0.725640337135876[/C][C]0.637179831432062[/C][/ROW]
[ROW][C]40[/C][C]0.78333183475621[/C][C]0.433336330487579[/C][C]0.21666816524379[/C][/ROW]
[ROW][C]41[/C][C]0.833639095535877[/C][C]0.332721808928245[/C][C]0.166360904464123[/C][/ROW]
[ROW][C]42[/C][C]0.801737792333456[/C][C]0.396524415333089[/C][C]0.198262207666544[/C][/ROW]
[ROW][C]43[/C][C]0.760330126924681[/C][C]0.479339746150638[/C][C]0.239669873075319[/C][/ROW]
[ROW][C]44[/C][C]0.789767129061067[/C][C]0.420465741877866[/C][C]0.210232870938933[/C][/ROW]
[ROW][C]45[/C][C]0.777483005628668[/C][C]0.445033988742663[/C][C]0.222516994371332[/C][/ROW]
[ROW][C]46[/C][C]0.821788673634089[/C][C]0.356422652731822[/C][C]0.178211326365911[/C][/ROW]
[ROW][C]47[/C][C]0.798162826869593[/C][C]0.403674346260814[/C][C]0.201837173130407[/C][/ROW]
[ROW][C]48[/C][C]0.798344874973795[/C][C]0.40331025005241[/C][C]0.201655125026205[/C][/ROW]
[ROW][C]49[/C][C]0.763235588581044[/C][C]0.473528822837912[/C][C]0.236764411418956[/C][/ROW]
[ROW][C]50[/C][C]0.727198671501011[/C][C]0.545602656997978[/C][C]0.272801328498989[/C][/ROW]
[ROW][C]51[/C][C]0.691006103291833[/C][C]0.617987793416334[/C][C]0.308993896708167[/C][/ROW]
[ROW][C]52[/C][C]0.646853305185499[/C][C]0.706293389629003[/C][C]0.353146694814501[/C][/ROW]
[ROW][C]53[/C][C]0.839777273891702[/C][C]0.320445452216597[/C][C]0.160222726108298[/C][/ROW]
[ROW][C]54[/C][C]0.92761397556838[/C][C]0.144772048863241[/C][C]0.0723860244316207[/C][/ROW]
[ROW][C]55[/C][C]0.912364012063083[/C][C]0.175271975873834[/C][C]0.0876359879369168[/C][/ROW]
[ROW][C]56[/C][C]0.890344243014437[/C][C]0.219311513971125[/C][C]0.109655756985563[/C][/ROW]
[ROW][C]57[/C][C]0.953339834157293[/C][C]0.0933203316854146[/C][C]0.0466601658427073[/C][/ROW]
[ROW][C]58[/C][C]0.939919132625836[/C][C]0.120161734748327[/C][C]0.0600808673741636[/C][/ROW]
[ROW][C]59[/C][C]0.929613434637212[/C][C]0.140773130725577[/C][C]0.0703865653627884[/C][/ROW]
[ROW][C]60[/C][C]0.916383953396305[/C][C]0.167232093207391[/C][C]0.0836160466036953[/C][/ROW]
[ROW][C]61[/C][C]0.894952936339345[/C][C]0.210094127321311[/C][C]0.105047063660655[/C][/ROW]
[ROW][C]62[/C][C]0.954643457379021[/C][C]0.090713085241957[/C][C]0.0453565426209785[/C][/ROW]
[ROW][C]63[/C][C]0.94623175784378[/C][C]0.107536484312442[/C][C]0.053768242156221[/C][/ROW]
[ROW][C]64[/C][C]0.942536869303716[/C][C]0.114926261392569[/C][C]0.0574631306962843[/C][/ROW]
[ROW][C]65[/C][C]0.92568218184566[/C][C]0.148635636308679[/C][C]0.0743178181543394[/C][/ROW]
[ROW][C]66[/C][C]0.932498825006554[/C][C]0.135002349986893[/C][C]0.0675011749934464[/C][/ROW]
[ROW][C]67[/C][C]0.964898218954004[/C][C]0.0702035620919914[/C][C]0.0351017810459957[/C][/ROW]
[ROW][C]68[/C][C]0.982170052173786[/C][C]0.0356598956524276[/C][C]0.0178299478262138[/C][/ROW]
[ROW][C]69[/C][C]0.975830553296259[/C][C]0.0483388934074828[/C][C]0.0241694467037414[/C][/ROW]
[ROW][C]70[/C][C]0.96718953461064[/C][C]0.0656209307787176[/C][C]0.0328104653893588[/C][/ROW]
[ROW][C]71[/C][C]0.961620852092786[/C][C]0.0767582958144287[/C][C]0.0383791479072143[/C][/ROW]
[ROW][C]72[/C][C]0.952225867981052[/C][C]0.0955482640378961[/C][C]0.0477741320189481[/C][/ROW]
[ROW][C]73[/C][C]0.949053385465475[/C][C]0.101893229069049[/C][C]0.0509466145345245[/C][/ROW]
[ROW][C]74[/C][C]0.934743059600983[/C][C]0.130513880798033[/C][C]0.0652569403990166[/C][/ROW]
[ROW][C]75[/C][C]0.948171908614459[/C][C]0.103656182771083[/C][C]0.0518280913855414[/C][/ROW]
[ROW][C]76[/C][C]0.946601879493969[/C][C]0.106796241012063[/C][C]0.0533981205060315[/C][/ROW]
[ROW][C]77[/C][C]0.948125617008566[/C][C]0.103748765982868[/C][C]0.051874382991434[/C][/ROW]
[ROW][C]78[/C][C]0.999986035108588[/C][C]2.79297828239587e-05[/C][C]1.39648914119793e-05[/C][/ROW]
[ROW][C]79[/C][C]0.999974821557155[/C][C]5.03568856890193e-05[/C][C]2.51784428445097e-05[/C][/ROW]
[ROW][C]80[/C][C]0.999962320500196[/C][C]7.53589996080424e-05[/C][C]3.76794998040212e-05[/C][/ROW]
[ROW][C]81[/C][C]0.999938322478614[/C][C]0.000123355042770965[/C][C]6.16775213854826e-05[/C][/ROW]
[ROW][C]82[/C][C]0.999999496822255[/C][C]1.00635549069073e-06[/C][C]5.03177745345366e-07[/C][/ROW]
[ROW][C]83[/C][C]0.999999020172119[/C][C]1.95965576211332e-06[/C][C]9.7982788105666e-07[/C][/ROW]
[ROW][C]84[/C][C]0.999998086791938[/C][C]3.82641612369267e-06[/C][C]1.91320806184633e-06[/C][/ROW]
[ROW][C]85[/C][C]0.999996527294637[/C][C]6.94541072674722e-06[/C][C]3.47270536337361e-06[/C][/ROW]
[ROW][C]86[/C][C]0.999994414517565[/C][C]1.11709648702359e-05[/C][C]5.58548243511794e-06[/C][/ROW]
[ROW][C]87[/C][C]0.999991061736951[/C][C]1.78765260972519e-05[/C][C]8.93826304862595e-06[/C][/ROW]
[ROW][C]88[/C][C]0.999991075683503[/C][C]1.7848632993105e-05[/C][C]8.9243164965525e-06[/C][/ROW]
[ROW][C]89[/C][C]0.999982765398028[/C][C]3.44692039433526e-05[/C][C]1.72346019716763e-05[/C][/ROW]
[ROW][C]90[/C][C]0.999967174013734[/C][C]6.56519725320885e-05[/C][C]3.28259862660442e-05[/C][/ROW]
[ROW][C]91[/C][C]0.999965723698166[/C][C]6.85526036686896e-05[/C][C]3.42763018343448e-05[/C][/ROW]
[ROW][C]92[/C][C]0.999981482693643[/C][C]3.7034612713719e-05[/C][C]1.85173063568595e-05[/C][/ROW]
[ROW][C]93[/C][C]0.999965964566438[/C][C]6.8070867124626e-05[/C][C]3.4035433562313e-05[/C][/ROW]
[ROW][C]94[/C][C]0.999952304766654[/C][C]9.53904666911206e-05[/C][C]4.76952333455603e-05[/C][/ROW]
[ROW][C]95[/C][C]0.999924429317014[/C][C]0.000151141365971078[/C][C]7.55706829855392e-05[/C][/ROW]
[ROW][C]96[/C][C]0.999967895101468[/C][C]6.4209797064697e-05[/C][C]3.21048985323485e-05[/C][/ROW]
[ROW][C]97[/C][C]0.99996812365278[/C][C]6.37526944418417e-05[/C][C]3.18763472209208e-05[/C][/ROW]
[ROW][C]98[/C][C]0.999936155643[/C][C]0.000127688713999069[/C][C]6.38443569995347e-05[/C][/ROW]
[ROW][C]99[/C][C]0.999886480289758[/C][C]0.000227039420483013[/C][C]0.000113519710241506[/C][/ROW]
[ROW][C]100[/C][C]0.999950552364235[/C][C]9.88952715298368e-05[/C][C]4.94476357649184e-05[/C][/ROW]
[ROW][C]101[/C][C]0.99991888223184[/C][C]0.000162235536320237[/C][C]8.11177681601183e-05[/C][/ROW]
[ROW][C]102[/C][C]0.99991979457918[/C][C]0.000160410841640527[/C][C]8.02054208202636e-05[/C][/ROW]
[ROW][C]103[/C][C]0.99999473454373[/C][C]1.05309125393663e-05[/C][C]5.26545626968314e-06[/C][/ROW]
[ROW][C]104[/C][C]0.999988112439622[/C][C]2.37751207565567e-05[/C][C]1.18875603782783e-05[/C][/ROW]
[ROW][C]105[/C][C]0.999979431060962[/C][C]4.1137878075771e-05[/C][C]2.05689390378855e-05[/C][/ROW]
[ROW][C]106[/C][C]0.999955701237544[/C][C]8.859752491252e-05[/C][C]4.429876245626e-05[/C][/ROW]
[ROW][C]107[/C][C]0.999930762613674[/C][C]0.000138474772652251[/C][C]6.92373863261255e-05[/C][/ROW]
[ROW][C]108[/C][C]0.999884979266921[/C][C]0.000230041466157171[/C][C]0.000115020733078585[/C][/ROW]
[ROW][C]109[/C][C]0.999748266601873[/C][C]0.000503466796254209[/C][C]0.000251733398127105[/C][/ROW]
[ROW][C]110[/C][C]0.999542409595798[/C][C]0.000915180808403103[/C][C]0.000457590404201551[/C][/ROW]
[ROW][C]111[/C][C]0.999662633540866[/C][C]0.000674732918266974[/C][C]0.000337366459133487[/C][/ROW]
[ROW][C]112[/C][C]0.9993274987703[/C][C]0.00134500245940065[/C][C]0.000672501229700324[/C][/ROW]
[ROW][C]113[/C][C]0.999077712001052[/C][C]0.00184457599789565[/C][C]0.000922287998947823[/C][/ROW]
[ROW][C]114[/C][C]0.999050564004578[/C][C]0.0018988719908444[/C][C]0.000949435995422202[/C][/ROW]
[ROW][C]115[/C][C]0.997943841516718[/C][C]0.0041123169665632[/C][C]0.0020561584832816[/C][/ROW]
[ROW][C]116[/C][C]0.99573472156016[/C][C]0.00853055687967904[/C][C]0.00426527843983952[/C][/ROW]
[ROW][C]117[/C][C]0.99997536547181[/C][C]4.926905637805e-05[/C][C]2.4634528189025e-05[/C][/ROW]
[ROW][C]118[/C][C]0.999988334569313[/C][C]2.3330861374726e-05[/C][C]1.1665430687363e-05[/C][/ROW]
[ROW][C]119[/C][C]0.999994876923368[/C][C]1.02461532631926e-05[/C][C]5.12307663159628e-06[/C][/ROW]
[ROW][C]120[/C][C]0.999977259739928[/C][C]4.5480520144144e-05[/C][C]2.2740260072072e-05[/C][/ROW]
[ROW][C]121[/C][C]0.999906787449499[/C][C]0.000186425101002595[/C][C]9.32125505012976e-05[/C][/ROW]
[ROW][C]122[/C][C]0.999960107236622[/C][C]7.97855267557098e-05[/C][C]3.98927633778549e-05[/C][/ROW]
[ROW][C]123[/C][C]0.99980985684439[/C][C]0.000380286311221958[/C][C]0.000190143155610979[/C][/ROW]
[ROW][C]124[/C][C]0.999860884245534[/C][C]0.000278231508932269[/C][C]0.000139115754466135[/C][/ROW]
[ROW][C]125[/C][C]0.999739336616014[/C][C]0.000521326767971572[/C][C]0.000260663383985786[/C][/ROW]
[ROW][C]126[/C][C]0.99830562325032[/C][C]0.00338875349936089[/C][C]0.00169437674968045[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155018&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155018&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
180.8991492661164440.2017014677671120.100850733883556
190.835531594837050.3289368103259020.164468405162951
200.7427600254660880.5144799490678240.257239974533912
210.6428362151383920.7143275697232160.357163784861608
220.7509043687081750.498191262583650.249095631291825
230.8275320828278850.3449358343442290.172467917172115
240.7784208866439680.4431582267120640.221579113356032
250.7130003618971640.5739992762056710.286999638102836
260.6486943758659870.7026112482680270.351305624134013
270.5753398227419420.8493203545161170.424660177258058
280.491197196531320.982394393062640.50880280346868
290.8011252476185980.3977495047628030.198874752381402
300.7494451325076940.5011097349846120.250554867492306
310.714784290892450.5704314182150990.28521570910755
320.6683514857119970.6632970285760070.331648514288004
330.6293725526694780.7412548946610440.370627447330522
340.5897597168219930.8204805663560140.410240283178007
350.5253909180246870.9492181639506260.474609081975313
360.4564067003195830.9128134006391660.543593299680417
370.4875849135289210.9751698270578420.512415086471079
380.4230577201665530.8461154403331050.576942279833447
390.3628201685679380.7256403371358760.637179831432062
400.783331834756210.4333363304875790.21666816524379
410.8336390955358770.3327218089282450.166360904464123
420.8017377923334560.3965244153330890.198262207666544
430.7603301269246810.4793397461506380.239669873075319
440.7897671290610670.4204657418778660.210232870938933
450.7774830056286680.4450339887426630.222516994371332
460.8217886736340890.3564226527318220.178211326365911
470.7981628268695930.4036743462608140.201837173130407
480.7983448749737950.403310250052410.201655125026205
490.7632355885810440.4735288228379120.236764411418956
500.7271986715010110.5456026569979780.272801328498989
510.6910061032918330.6179877934163340.308993896708167
520.6468533051854990.7062933896290030.353146694814501
530.8397772738917020.3204454522165970.160222726108298
540.927613975568380.1447720488632410.0723860244316207
550.9123640120630830.1752719758738340.0876359879369168
560.8903442430144370.2193115139711250.109655756985563
570.9533398341572930.09332033168541460.0466601658427073
580.9399191326258360.1201617347483270.0600808673741636
590.9296134346372120.1407731307255770.0703865653627884
600.9163839533963050.1672320932073910.0836160466036953
610.8949529363393450.2100941273213110.105047063660655
620.9546434573790210.0907130852419570.0453565426209785
630.946231757843780.1075364843124420.053768242156221
640.9425368693037160.1149262613925690.0574631306962843
650.925682181845660.1486356363086790.0743178181543394
660.9324988250065540.1350023499868930.0675011749934464
670.9648982189540040.07020356209199140.0351017810459957
680.9821700521737860.03565989565242760.0178299478262138
690.9758305532962590.04833889340748280.0241694467037414
700.967189534610640.06562093077871760.0328104653893588
710.9616208520927860.07675829581442870.0383791479072143
720.9522258679810520.09554826403789610.0477741320189481
730.9490533854654750.1018932290690490.0509466145345245
740.9347430596009830.1305138807980330.0652569403990166
750.9481719086144590.1036561827710830.0518280913855414
760.9466018794939690.1067962410120630.0533981205060315
770.9481256170085660.1037487659828680.051874382991434
780.9999860351085882.79297828239587e-051.39648914119793e-05
790.9999748215571555.03568856890193e-052.51784428445097e-05
800.9999623205001967.53589996080424e-053.76794998040212e-05
810.9999383224786140.0001233550427709656.16775213854826e-05
820.9999994968222551.00635549069073e-065.03177745345366e-07
830.9999990201721191.95965576211332e-069.7982788105666e-07
840.9999980867919383.82641612369267e-061.91320806184633e-06
850.9999965272946376.94541072674722e-063.47270536337361e-06
860.9999944145175651.11709648702359e-055.58548243511794e-06
870.9999910617369511.78765260972519e-058.93826304862595e-06
880.9999910756835031.7848632993105e-058.9243164965525e-06
890.9999827653980283.44692039433526e-051.72346019716763e-05
900.9999671740137346.56519725320885e-053.28259862660442e-05
910.9999657236981666.85526036686896e-053.42763018343448e-05
920.9999814826936433.7034612713719e-051.85173063568595e-05
930.9999659645664386.8070867124626e-053.4035433562313e-05
940.9999523047666549.53904666911206e-054.76952333455603e-05
950.9999244293170140.0001511413659710787.55706829855392e-05
960.9999678951014686.4209797064697e-053.21048985323485e-05
970.999968123652786.37526944418417e-053.18763472209208e-05
980.9999361556430.0001276887139990696.38443569995347e-05
990.9998864802897580.0002270394204830130.000113519710241506
1000.9999505523642359.88952715298368e-054.94476357649184e-05
1010.999918882231840.0001622355363202378.11177681601183e-05
1020.999919794579180.0001604108416405278.02054208202636e-05
1030.999994734543731.05309125393663e-055.26545626968314e-06
1040.9999881124396222.37751207565567e-051.18875603782783e-05
1050.9999794310609624.1137878075771e-052.05689390378855e-05
1060.9999557012375448.859752491252e-054.429876245626e-05
1070.9999307626136740.0001384747726522516.92373863261255e-05
1080.9998849792669210.0002300414661571710.000115020733078585
1090.9997482666018730.0005034667962542090.000251733398127105
1100.9995424095957980.0009151808084031030.000457590404201551
1110.9996626335408660.0006747329182669740.000337366459133487
1120.99932749877030.001345002459400650.000672501229700324
1130.9990777120010520.001844575997895650.000922287998947823
1140.9990505640045780.00189887199084440.000949435995422202
1150.9979438415167180.00411231696656320.0020561584832816
1160.995734721560160.008530556879679040.00426527843983952
1170.999975365471814.926905637805e-052.4634528189025e-05
1180.9999883345693132.3330861374726e-051.1665430687363e-05
1190.9999948769233681.02461532631926e-055.12307663159628e-06
1200.9999772597399284.5480520144144e-052.2740260072072e-05
1210.9999067874494990.0001864251010025959.32125505012976e-05
1220.9999601072366227.97855267557098e-053.98927633778549e-05
1230.999809856844390.0003802863112219580.000190143155610979
1240.9998608842455340.0002782315089322690.000139115754466135
1250.9997393366160140.0005213267679715720.000260663383985786
1260.998305623250320.003388753499360890.00169437674968045






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level490.44954128440367NOK
5% type I error level510.467889908256881NOK
10% type I error level570.522935779816514NOK

\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 & 51 & 0.467889908256881 & NOK \tabularnewline
10% type I error level & 57 & 0.522935779816514 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155018&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]51[/C][C]0.467889908256881[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]57[/C][C]0.522935779816514[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155018&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155018&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 level510.467889908256881NOK
10% type I error level570.522935779816514NOK


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):
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')
}