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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, 21 Dec 2011 13:58:34 -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/21/t1324493975eyw6o4nugc9zp9m.htm/, Retrieved Tue, 07 May 2024 20:34:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=158951, Retrieved Tue, 07 May 2024 20:34:21 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact58

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-12-21 18:58:34] [1118fb1265e4c78f2f623b6bb1012fba] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1760	158147	89	572	109	0	48	18	70	63	20465	6200	23975	39	37
1592	179043	55	477	74	1	51	20	80	56	33629	10265	85634	46	43
192	7215	18	72	1	0	0	0	0	0	1423	603	1929	0	0
2181	122259	91	618	154	0	49	26	81	60	25629	8874	36294	54	54
3366	222405	130	1124	125	0	76	31	124	116	54002	20323	72255	93	86
6510	468370	245	1833	278	1	122	36	140	138	151036	26258	189748	198	181
1548	150777	54	525	89	1	42	23	88	71	33287	10165	61834	42	42
1507	160529	56	407	59	0	68	30	115	107	31172	8247	68167	59	59
1682	133238	42	506	87	0	52	30	109	50	28113	8683	38462	49	46
2811	275326	91	919	129	1	67	26	104	79	57803	16957	101219	83	77
1942	121821	73	634	158	2	50	24	63	58	49830	8058	43270	49	49
2017	172489	66	625	120	0	71	30	118	91	52143	20488	76183	83	79
1622	86249	95	574	83	0	41	21	68	40	21055	7945	31476	39	37
2971	203892	106	1004	255	4	78	25	100	91	47007	13448	62157	93	92
1358	149839	52	389	50	4	48	17	60	58	28735	5389	46261	31	31
1506	144859	77	454	81	3	54	19	74	65	59147	6185	50063	29	28
1637	134153	53	391	92	0	75	33	132	131	78950	24369	64483	104	103
1077	64149	53	305	72	5	0	15	54	45	13497	70	2341	2	2
2383	122417	83	724	142	0	54	34	134	110	46154	17327	48149	46	48
726	27997	24	221	49	0	13	18	57	41	53249	3878	12743	27	25
903	52197	53	310	40	0	16	15	59	37	10726	3149	18743	16	16
2446	205417	91	742	94	0	83	27	102	78	83700	20517	97057	108	106
1713	188103	70	627	127	0	37	25	96	67	40400	2570	17675	36	35
2027	118698	50	616	164	1	44	34	96	69	33797	5162	33106	33	33
1818	143682	81	572	41	1	50	21	78	58	36205	5299	53311	46	45
1387	140056	27	513	160	0	39	21	80	60	30165	7233	42754	65	64
1966	178026	151	635	92	0	59	25	93	88	58534	15657	59056	80	73
1279	167812	80	322	55	0	77	28	100	71	44663	15329	101621	81	78
2522	317699	114	819	86	0	55	30	112	95	92556	14881	118120	69	63
2084	191623	41	870	90	0	52	20	79	67	40078	16318	79572	69	69
1514	151621	41	427	76	0	50	28	103	84	34711	9556	42744	37	36
1518	167466	42	512	111	2	54	20	65	58	31076	10462	65931	45	41
2088	120433	97	668	87	4	53	17	66	35	74608	7192	38575	62	59
2910	218393	118	872	309	0	76	25	100	74	58092	4362	28795	33	33
2282	202722	50	749	84	1	58	24	96	89	42009	14349	94440	77	76
1	0	1	0	0	0	0	0	0	0	0	0	0	0	0
2008	207163	58	946	58	0	53	27	105	75	36022	10881	38229	34	27
1564	93107	50	483	137	3	44	14	51	39	23333	8022	31972	44	44
2055	129419	46	495	267	9	35	32	108	93	53349	13073	40071	43	43
2106	246427	63	780	60	0	83	31	124	123	92596	26641	132480	117	104
2210	215885	68	686	94	2	97	21	81	73	49598	14426	62797	125	120
1575	126619	55	597	62	0	31	34	136	118	44093	15604	40429	49	44
957	94332	29	350	35	2	25	23	84	76	84205	9184	45545	76	71
2014	168276	75	759	59	1	59	24	92	65	63369	5989	57568	81	78
1089	98202	44	333	51	2	44	23	91	86	60132	11270	39019	111	106
1126	144161	86	299	40	2	40	22	82	67	37403	13958	53866	61	61
744	81293	31	212	49	1	23	35	106	63	24460	7162	38345	56	53
1946	199619	89	661	114	0	63	21	84	84	46456	13275	50210	54	51
2095	196609	81	676	113	1	52	31	124	112	66616	21224	80947	47	46
2560	156589	55	915	171	7	67	26	97	75	41554	10615	43461	55	55
658	48188	28	205	37	0	12	22	82	39	22346	2102	14812	14	14
1558	122425	62	394	51	0	72	21	79	63	30874	12396	37819	44	44
2308	273969	69	777	89	0	62	27	97	93	68701	18717	102738	115	113
2017	234829	77	678	73	0	56	30	107	76	35728	9724	54509	57	55
1686	181731	57	608	49	1	54	33	126	117	29010	9863	62956	48	46
1603	132325	53	497	74	8	35	11	40	30	23110	8374	55411	40	39
1759	189220	61	471	58	0	52	26	96	65	38844	8030	50611	51	51
874	76419	22	260	72	0	25	26	100	78	27084	7509	26692	32	31
1106	134427	64	388	32	0	59	23	91	87	35139	14146	60056	36	36
2779	189286	92	932	59	10	36	38	136	85	57476	7768	25155	47	47
1606	140189	56	472	65	6	50	29	116	107	33277	13823	42840	51	53
1802	102209	73	676	81	0	36	19	76	60	31141	7230	39358	37	38
1300	124234	58	352	84	11	46	19	65	53	61281	10170	47241	52	52
1175	107277	33	400	46	3	53	24	89	62	25820	7573	49611	42	37
1215	153813	32	406	56	0	27	26	97	90	23284	5753	41833	11	11
1230	94982	38	391	36	0	38	29	107	89	35378	9791	48930	47	45
2226	178613	67	483	86	8	68	36	144	135	74990	19365	110600	59	59
2897	138708	65	887	152	2	93	25	90	71	29653	9422	52235	82	82
1007	102378	36	248	48	0	48	24	93	75	64622	12310	53986	49	49
340	31970	15	101	40	0	5	21	78	42	4157	1283	4105	6	6
2704	211635	110	959	135	3	53	19	72	42	29245	6372	59331	83	81
1202	108661	62	369	80	1	36	12	45	8	50008	5413	47796	56	56
1289	99687	62	415	62	2	62	30	120	86	52338	10837	38302	114	105
1535	102900	68	447	89	1	46	21	59	41	13310	3394	14063	46	46
2475	144193	61	627	89	0	67	34	133	118	92901	12964	54414	46	46
1315	74513	41	352	82	2	2	32	117	91	10956	3495	9903	2	2
1927	129080	56	541	111	1	64	27	115	96	34241	11580	53987	51	51
2502	153935	92	811	69	0	71	28	110	89	75043	9970	88937	96	95
817	60138	24	253	76	0	16	21	75	46	21152	4911	21928	20	18
1234	84971	71	395	105	0	34	31	114	60	42249	10138	29487	57	55
917	80478	66	214	49	0	54	26	94	69	42005	14697	35334	49	48
1924	244325	59	714	60	0	39	29	116	95	41152	8464	57596	51	48
852	56486	26	249	49	0	26	23	86	17	14399	4204	29750	40	39
1309	97181	32	438	132	0	37	25	90	61	28263	10226	41029	40	40
928	66367	42	308	49	0	33	22	87	55	17215	3456	12416	36	36
1608	148286	47	516	71	0	32	26	99	55	48140	8895	51158	64	60
2576	222914	219	636	100	0	55	33	132	124	62897	22557	79935	117	114
1123	97180	101	265	71	0	53	22	88	65	22883	6900	26552	40	39
1157	90412	54	361	49	6	39	24	91	73	41622	8620	25807	46	45
1382	143258	48	548	72	0	33	21	77	67	40715	7820	50620	61	59
1563	117794	39	561	59	5	45	28	104	66	65897	12112	61467	59	59
2091	150040	70	606	88	1	72	24	85	67	76542	13178	65292	94	93
1228	151465	56	413	68	0	39	25	94	83	37477	7028	55516	36	35
1265	124626	57	466	81	0	27	15	60	55	53216	6616	42006	51	47
830	51801	36	333	30	0	22	13	46	27	40911	9570	26273	39	36
2215	223008	108	703	166	0	47	36	135	115	57021	14612	90248	62	59
1755	182331	62	527	94	0	95	24	90	76	73116	11219	61476	79	79
223	19349	12	67	15	0	13	1	2	0	3895	786	9604	14	14
2166	187903	98	745	104	3	27	24	96	83	46609	11252	45108	45	42
1927	150561	74	791	61	0	40	31	109	90	29351	9289	47232	43	41
665	53921	28	267	11	0	22	4	15	4	2325	593	3439	8	8
804	58280	22	240	44	0	41	20	64	56	31747	6562	30553	41	41
1131	115944	44	360	84	0	51	23	88	63	32665	8208	24751	25	24
1066	102139	55	272	66	1	27	23	84	52	19249	7488	34458	22	22
710	72904	38	190	27	0	30	12	46	24	15292	4574	24649	18	18
596	27676	22	194	59	0	2	16	59	17	5842	522	2342	3	1
1352	131274	43	304	127	0	79	29	116	105	33994	12840	52739	54	53
971	117451	32	251	32	0	18	10	29	20	13018	1350	6245	6	6
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
1030	85610	31	306	58	0	46	25	91	51	98177	10623	35381	50	49
1129	107175	65	368	57	0	25	21	76	76	37941	5322	19595	33	33
1283	133024	43	435	59	0	50	23	83	59	31032	7987	50848	54	50
1424	136143	59	424	74	0	59	21	84	70	32683	10566	39443	63	64
849	71894	57	287	71	0	36	21	65	38	34545	1900	27023	56	53
78	3616	5	14	5	0	0	0	0	0	0	0	0	0	0
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
924	154806	38	301	70	0	35	23	84	81	27525	10698	61022	49	48
1511	137955	77	543	72	0	68	29	99	64	66856	14884	63528	90	90
1920	149652	93	539	119	1	26	28	112	67	28549	6852	34835	51	46
914	113245	37	287	56	0	36	23	92	89	38610	6873	37172	29	29
778	43410	19	292	63	0	7	1	3	3	2781	4	13	1	1
1663	163584	68	510	92	1	67	29	109	87	41211	9188	62548	68	64
894	89410	39	242	46	0	30	17	71	48	22698	5141	31334	29	29
1734	112383	50	505	60	8	55	29	106	62	41194	4260	20839	27	27
700	60373	39	165	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
1696	146612	54	516	64	3	46	21	80	70	26757	9831	53229	47	47
1021	121052	28	355	66	0	26	25	95	90	22527	5953	29877	44	44
1534	144757	41	512	97	0	48	29	116	91	44810	9435	37310	53	51
256	11796	9	79	22	0	1	2	8	1	0	0	0	0	0
98	10674	9	33	7	0	0	0	0	0	0	0	0	0	0
1318	131263	53	449	37	0	33	18	56	39	100674	7642	50067	40	38
41	6836	3	11	5	0	0	1	4	0	0	0	0	0	0
1769	153278	56	606	48	6	48	21	70	45	57786	6837	47708	57	57
42	5118	3	6	1	0	5	0	0	0	0	0	0	0	0
528	40248	16	183	34	1	8	4	14	7	5444	775	6012	6	6
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
991	112018	43	315	49	0	33	25	91	75	28470	8191	27749	24	22
1296	87635	38	267	44	0	21	26	89	52	61849	1661	47555	34	34
81	7131	4	27	0	1	0	0	0	0	0	0	0	0	0
257	8812	13	97	18	0	0	4	12	1	2179	548	1336	10	10
914	68916	23	251	48	1	15	17	60	49	8019	3080	11017	16	16
1114	120111	47	273	54	0	47	21	80	69	39644	13400	55184	93	93
1079	94127	18	386	50	1	17	22	88	56	23494	8181	43485	28	22

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158951&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158951&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158951&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
TotalnumberofCourseCompendiumViews[t] = + 5854.28185316077 -10.9281487208042NumberofLogins[t] + 315.645603801556TotalNumberofCompendiumViews[t] + 127.41962599569`(PR`[t] + 24.2312449065658`only)`[t] -115.805585666859TotalnumberofCompendiumsthathavebeensharedbyotherAuthors[t] + 294.16462766149TotalNumberofBloggedComputations[t] -533.658275602802TotalNumberofReviewedCompendiums[t] + 35.4158280098801TotalNumberofsubmittedFeedbackMessagesinPeerReviews[t] + 488.35607777416`TotalnumberofsubmittedFeedbackMessagesinPeerReviews(+120characters)`[t] -0.04903276269449`CompendiumWriting:totalnumberofcharacters`[t] -2.24201653991094`CompendiumWriting:totalnumberofrevisions`[t] + 1.22120028181984`CompendiumWriting:totalnumberofseconds`[t] + 910.131004351797`CompendiumWriting:totalnumberofincludedhyperlinks`[t] -1124.5030998214`CompendiumWriting:totalnumberofincludedblogs `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TotalnumberofCourseCompendiumViews[t] =  +  5854.28185316077 -10.9281487208042NumberofLogins[t] +  315.645603801556TotalNumberofCompendiumViews[t] +  127.41962599569`(PR`[t] +  24.2312449065658`only)`[t] -115.805585666859TotalnumberofCompendiumsthathavebeensharedbyotherAuthors[t] +  294.16462766149TotalNumberofBloggedComputations[t] -533.658275602802TotalNumberofReviewedCompendiums[t] +  35.4158280098801TotalNumberofsubmittedFeedbackMessagesinPeerReviews[t] +  488.35607777416`TotalnumberofsubmittedFeedbackMessagesinPeerReviews(+120characters)`[t] -0.04903276269449`CompendiumWriting:totalnumberofcharacters`[t] -2.24201653991094`CompendiumWriting:totalnumberofrevisions`[t] +  1.22120028181984`CompendiumWriting:totalnumberofseconds`[t] +  910.131004351797`CompendiumWriting:totalnumberofincludedhyperlinks`[t] -1124.5030998214`CompendiumWriting:totalnumberofincludedblogs
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158951&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TotalnumberofCourseCompendiumViews[t] =  +  5854.28185316077 -10.9281487208042NumberofLogins[t] +  315.645603801556TotalNumberofCompendiumViews[t] +  127.41962599569`(PR`[t] +  24.2312449065658`only)`[t] -115.805585666859TotalnumberofCompendiumsthathavebeensharedbyotherAuthors[t] +  294.16462766149TotalNumberofBloggedComputations[t] -533.658275602802TotalNumberofReviewedCompendiums[t] +  35.4158280098801TotalNumberofsubmittedFeedbackMessagesinPeerReviews[t] +  488.35607777416`TotalnumberofsubmittedFeedbackMessagesinPeerReviews(+120characters)`[t] -0.04903276269449`CompendiumWriting:totalnumberofcharacters`[t] -2.24201653991094`CompendiumWriting:totalnumberofrevisions`[t] +  1.22120028181984`CompendiumWriting:totalnumberofseconds`[t] +  910.131004351797`CompendiumWriting:totalnumberofincludedhyperlinks`[t] -1124.5030998214`CompendiumWriting:totalnumberofincludedblogs
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158951&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158951&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
TotalnumberofCourseCompendiumViews[t] = + 5854.28185316077 -10.9281487208042NumberofLogins[t] + 315.645603801556TotalNumberofCompendiumViews[t] + 127.41962599569`(PR`[t] + 24.2312449065658`only)`[t] -115.805585666859TotalnumberofCompendiumsthathavebeensharedbyotherAuthors[t] + 294.16462766149TotalNumberofBloggedComputations[t] -533.658275602802TotalNumberofReviewedCompendiums[t] + 35.4158280098801TotalNumberofsubmittedFeedbackMessagesinPeerReviews[t] + 488.35607777416`TotalnumberofsubmittedFeedbackMessagesinPeerReviews(+120characters)`[t] -0.04903276269449`CompendiumWriting:totalnumberofcharacters`[t] -2.24201653991094`CompendiumWriting:totalnumberofrevisions`[t] + 1.22120028181984`CompendiumWriting:totalnumberofseconds`[t] + 910.131004351797`CompendiumWriting:totalnumberofincludedhyperlinks`[t] -1124.5030998214`CompendiumWriting:totalnumberofincludedblogs `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5854.281853160775174.4055811.13140.2599890.129994
NumberofLogins-10.928148720804213.499629-0.80950.419710.209855
TotalNumberofCompendiumViews315.645603801556107.3505582.94030.0038870.001943
`(PR`127.4196259956930.194394.224.6e-052.3e-05
`only)`24.231244906565863.9506450.37890.7053810.352691
TotalnumberofCompendiumsthathavebeensharedbyotherAuthors-115.805585666859972.968019-0.1190.9054420.452721
TotalNumberofBloggedComputations294.16462766149175.5596071.67560.0962420.048121
TotalNumberofReviewedCompendiums-533.6582756028021198.734219-0.44520.6569320.328466
TotalNumberofsubmittedFeedbackMessagesinPeerReviews35.4158280098801368.9493580.0960.9236770.461838
`TotalnumberofsubmittedFeedbackMessagesinPeerReviews(+120characters)`488.35607777416185.5676512.63170.0095320.004766
`CompendiumWriting:totalnumberofcharacters`-0.049032762694490.134818-0.36370.7166810.35834
`CompendiumWriting:totalnumberofrevisions`-2.242016539910940.824407-2.71960.0074380.003719
`CompendiumWriting:totalnumberofseconds`1.221200281819840.1585937.700200
`CompendiumWriting:totalnumberofincludedhyperlinks`910.1310043517971030.0323380.88360.3785590.189279
`CompendiumWriting:totalnumberofincludedblogs `-1124.50309982141068.166353-1.05270.2944270.147214

\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) & 5854.28185316077 & 5174.405581 & 1.1314 & 0.259989 & 0.129994 \tabularnewline
NumberofLogins & -10.9281487208042 & 13.499629 & -0.8095 & 0.41971 & 0.209855 \tabularnewline
TotalNumberofCompendiumViews & 315.645603801556 & 107.350558 & 2.9403 & 0.003887 & 0.001943 \tabularnewline
`(PR` & 127.41962599569 & 30.19439 & 4.22 & 4.6e-05 & 2.3e-05 \tabularnewline
`only)` & 24.2312449065658 & 63.950645 & 0.3789 & 0.705381 & 0.352691 \tabularnewline
TotalnumberofCompendiumsthathavebeensharedbyotherAuthors & -115.805585666859 & 972.968019 & -0.119 & 0.905442 & 0.452721 \tabularnewline
TotalNumberofBloggedComputations & 294.16462766149 & 175.559607 & 1.6756 & 0.096242 & 0.048121 \tabularnewline
TotalNumberofReviewedCompendiums & -533.658275602802 & 1198.734219 & -0.4452 & 0.656932 & 0.328466 \tabularnewline
TotalNumberofsubmittedFeedbackMessagesinPeerReviews & 35.4158280098801 & 368.949358 & 0.096 & 0.923677 & 0.461838 \tabularnewline
`TotalnumberofsubmittedFeedbackMessagesinPeerReviews(+120characters)` & 488.35607777416 & 185.567651 & 2.6317 & 0.009532 & 0.004766 \tabularnewline
`CompendiumWriting:totalnumberofcharacters` & -0.04903276269449 & 0.134818 & -0.3637 & 0.716681 & 0.35834 \tabularnewline
`CompendiumWriting:totalnumberofrevisions` & -2.24201653991094 & 0.824407 & -2.7196 & 0.007438 & 0.003719 \tabularnewline
`CompendiumWriting:totalnumberofseconds` & 1.22120028181984 & 0.158593 & 7.7002 & 0 & 0 \tabularnewline
`CompendiumWriting:totalnumberofincludedhyperlinks` & 910.131004351797 & 1030.032338 & 0.8836 & 0.378559 & 0.189279 \tabularnewline
`CompendiumWriting:totalnumberofincludedblogs
` & -1124.5030998214 & 1068.166353 & -1.0527 & 0.294427 & 0.147214 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158951&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]5854.28185316077[/C][C]5174.405581[/C][C]1.1314[/C][C]0.259989[/C][C]0.129994[/C][/ROW]
[ROW][C]NumberofLogins[/C][C]-10.9281487208042[/C][C]13.499629[/C][C]-0.8095[/C][C]0.41971[/C][C]0.209855[/C][/ROW]
[ROW][C]TotalNumberofCompendiumViews[/C][C]315.645603801556[/C][C]107.350558[/C][C]2.9403[/C][C]0.003887[/C][C]0.001943[/C][/ROW]
[ROW][C]`(PR`[/C][C]127.41962599569[/C][C]30.19439[/C][C]4.22[/C][C]4.6e-05[/C][C]2.3e-05[/C][/ROW]
[ROW][C]`only)`[/C][C]24.2312449065658[/C][C]63.950645[/C][C]0.3789[/C][C]0.705381[/C][C]0.352691[/C][/ROW]
[ROW][C]TotalnumberofCompendiumsthathavebeensharedbyotherAuthors[/C][C]-115.805585666859[/C][C]972.968019[/C][C]-0.119[/C][C]0.905442[/C][C]0.452721[/C][/ROW]
[ROW][C]TotalNumberofBloggedComputations[/C][C]294.16462766149[/C][C]175.559607[/C][C]1.6756[/C][C]0.096242[/C][C]0.048121[/C][/ROW]
[ROW][C]TotalNumberofReviewedCompendiums[/C][C]-533.658275602802[/C][C]1198.734219[/C][C]-0.4452[/C][C]0.656932[/C][C]0.328466[/C][/ROW]
[ROW][C]TotalNumberofsubmittedFeedbackMessagesinPeerReviews[/C][C]35.4158280098801[/C][C]368.949358[/C][C]0.096[/C][C]0.923677[/C][C]0.461838[/C][/ROW]
[ROW][C]`TotalnumberofsubmittedFeedbackMessagesinPeerReviews(+120characters)`[/C][C]488.35607777416[/C][C]185.567651[/C][C]2.6317[/C][C]0.009532[/C][C]0.004766[/C][/ROW]
[ROW][C]`CompendiumWriting:totalnumberofcharacters`[/C][C]-0.04903276269449[/C][C]0.134818[/C][C]-0.3637[/C][C]0.716681[/C][C]0.35834[/C][/ROW]
[ROW][C]`CompendiumWriting:totalnumberofrevisions`[/C][C]-2.24201653991094[/C][C]0.824407[/C][C]-2.7196[/C][C]0.007438[/C][C]0.003719[/C][/ROW]
[ROW][C]`CompendiumWriting:totalnumberofseconds`[/C][C]1.22120028181984[/C][C]0.158593[/C][C]7.7002[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`CompendiumWriting:totalnumberofincludedhyperlinks`[/C][C]910.131004351797[/C][C]1030.032338[/C][C]0.8836[/C][C]0.378559[/C][C]0.189279[/C][/ROW]
[ROW][C]`CompendiumWriting:totalnumberofincludedblogs
`[/C][C]-1124.5030998214[/C][C]1068.166353[/C][C]-1.0527[/C][C]0.294427[/C][C]0.147214[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158951&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158951&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)5854.281853160775174.4055811.13140.2599890.129994
NumberofLogins-10.928148720804213.499629-0.80950.419710.209855
TotalNumberofCompendiumViews315.645603801556107.3505582.94030.0038870.001943
`(PR`127.4196259956930.194394.224.6e-052.3e-05
`only)`24.231244906565863.9506450.37890.7053810.352691
TotalnumberofCompendiumsthathavebeensharedbyotherAuthors-115.805585666859972.968019-0.1190.9054420.452721
TotalNumberofBloggedComputations294.16462766149175.5596071.67560.0962420.048121
TotalNumberofReviewedCompendiums-533.6582756028021198.734219-0.44520.6569320.328466
TotalNumberofsubmittedFeedbackMessagesinPeerReviews35.4158280098801368.9493580.0960.9236770.461838
`TotalnumberofsubmittedFeedbackMessagesinPeerReviews(+120characters)`488.35607777416185.5676512.63170.0095320.004766
`CompendiumWriting:totalnumberofcharacters`-0.049032762694490.134818-0.36370.7166810.35834
`CompendiumWriting:totalnumberofrevisions`-2.242016539910940.824407-2.71960.0074380.003719
`CompendiumWriting:totalnumberofseconds`1.221200281819840.1585937.700200
`CompendiumWriting:totalnumberofincludedhyperlinks`910.1310043517971030.0323380.88360.3785590.189279
`CompendiumWriting:totalnumberofincludedblogs `-1124.50309982141068.166353-1.05270.2944270.147214






Multiple Linear Regression - Regression Statistics
Multiple R0.949828163788362
R-squared0.902173540725572
Adjusted R-squared0.891556715688037
F-TEST (value)84.9758319964789
F-TEST (DF numerator)14
F-TEST (DF denominator)129
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation23205.7913559056
Sum Squared Residuals69467629066.5435

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.949828163788362 \tabularnewline
R-squared & 0.902173540725572 \tabularnewline
Adjusted R-squared & 0.891556715688037 \tabularnewline
F-TEST (value) & 84.9758319964789 \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 & 23205.7913559056 \tabularnewline
Sum Squared Residuals & 69467629066.5435 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158951&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.949828163788362[/C][/ROW]
[ROW][C]R-squared[/C][C]0.902173540725572[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.891556715688037[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]84.9758319964789[/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]23205.7913559056[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]69467629066.5435[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158951&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158951&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 R0.949828163788362
R-squared0.902173540725572
Adjusted R-squared0.891556715688037
F-TEST (value)84.9758319964789
F-TEST (DF numerator)14
F-TEST (DF denominator)129
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation23205.7913559056
Sum Squared Residuals69467629066.5435






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1158147136260.83783156821886.1621684325
2179043176209.5178132972833.48218670311
3721519570.1282325405-12355.1282325405
4122259137523.51276771-15264.5127677102
5222405251166.973630558-28761.9736305583
6468370483368.209085211-14998.2090852113
7150777154874.916023466-4097.91602346635
8160529171250.760024146-10721.7600241465
9133238113870.83582435419367.1641756461
10275326243775.64893824931550.351061751
11121821146338.82863684-24517.8286368401
12172489171933.921779108555.078220892467
1386249129543.4493188-43294.4493188005
14203892222714.128194281-18822.1281942811
15149839129596.54304507620242.4569549242
16144859152551.973565652-7692.97356565231
17134153128915.2524275965237.74757240352
186414968337.4437072093-4188.44370720934
19122417163488.103657993-41071.103657993
202799751819.8303933164-23822.8303933164
215219781918.1509112472-29721.1509112472
22205417203901.3187706881515.68122931219
23188103133110.35651983954992.6434801612
24118698133853.491600124-15155.4916001241
25143682162615.515716765-18933.5157167647
26140056122568.80062263417487.199377366
27178026190323.648879154-12297.6488791542
28167812178964.916504582-11152.9165045819
29317699269549.15352977448149.8464702257
30191623193029.79235873-1406.79235873017
31151621125200.86397530526420.1360246951
32167466156444.28340071711021.7165992829
33120433143770.999911156-23337.9999111557
34218393194054.00180401924338.9981959807
35202722210890.299424881-8168.2994248811
3606158.9993082415-6158.9993082415
37207163186796.19811558520366.8018844155
3893107105867.307689946-12760.307689946
39129419116668.5481049912750.4518950099
40246427262949.892578343-16522.8925783425
41215885169207.24445740746677.7555425929
42126619144335.862924852-17716.8629248517
4394332105163.403746493-10831.4037464934
44168276184869.893452435-16593.8934524346
459820298428.680761826-226.680761825995
46144161114757.31053870929403.6894612906
478129379140.42943270662152.57056729344
48199619172062.93242741527556.0675725846
49196609194176.2821942432.71780599972
50156589176504.154145249-19915.1541452487
514818857537.4990463304-9349.49904633041
52122425110821.01914421911603.9808557814
53273969213982.64185373859986.3581462617
54234829170687.32260807964141.677391921
55181731189143.683754801-7412.68375480081
56132325130062.4888488172262.51115118279
57189220134835.7852953554384.2147046497
587641981930.9613643218-5511.96136432179
59134427147159.155009168-12732.1550091683
60189286160598.37552309528687.6244769045
61140189129107.74658609811081.2534139016
62102209151016.693113908-48807.6931139078
63124234107882.66020300916351.3397969907
64107277130337.132586344-23060.1325863443
65153813131909.64878438921903.3512156109
6694982126296.523310738-31314.5233107375
67178613212520.226846851-33907.2268468509
68138708186694.341635168-47986.3416351683
69102378104864.193121047-2486.19312104708
703197034895.8747376981-2925.87473769806
71211635205847.9833373685787.01666263212
72108661102591.3840931366069.6159068638
7399687119558.28931772-19871.2893177199
7410290093028.29774612579871.70225387433
75144193166979.733515394-22786.7335153938
767451386419.3661381455-11906.3661381455
77129080156707.020146271-27627.0201462713
78153935228985.61161974-75050.6116197404
796013869892.9556085668-9754.95560856682
808497195689.9576366663-10718.9576366663
818047882915.965113559-2437.96511355904
82244325184164.0555359560160.9444640496
835648663135.9246079235-6649.92460792349
8497181108395.163046414-11214.1630464135
856636776162.8820614716-9795.88206147164
86148286127439.19964892520846.8003510751
87222914216342.5768634396571.42313656128
8897180108041.77555366-10861.7755536604
899041295695.5929083247-5283.59290832475
90143258142881.187241703376.812758297338
91117794139655.661224784-21861.6612247842
92150040155843.584873588-5803.58487358802
93151465149985.074522111479.92547789043
94124626127704.620101305-3078.62010130487
955180169289.1543084448-17488.1543084448
96223008229629.746251604-6621.74625160392
97182331160514.46559451921816.5344054814
981934926240.9396483245-6891.93964832448
99187903170585.68442527617317.3155747244
100150561181901.580415188-31340.5804151876
1015392149575.58815646714345.41184353291
1025828078914.5967942038-20634.5967942038
10311594497887.725519712418056.2744802875
10410213991376.610290602910762.3897093971
1057290465962.39060260046941.60939739959
1062767637884.90235203-10208.90235203
107131274133112.63946-1838.63946000018
10811745151529.128191997165921.8718080029
10905854.28185316075-5854.28185316075
1108561078080.59069646087529.40930353917
111107175101321.7025609935853.2974390071
112133024132038.962500852985.037499147536
113136143116289.63954324719853.3604567531
1147189491516.0955949033-19622.0955949033
11536168485.14526041829-4869.14526041829
11605854.28185316075-5854.28185316075
117154806128159.72771147226646.2722885278
118137955145506.742165152-7551.74216515212
119149652135534.36375666514117.6362433348
120113245112384.088455039860.911544960929
1214341044835.5330048205-1425.53300482047
122163584170506.401855292-6922.40185529174
1238941085462.39205074263947.60794925737
124112383110386.31242691996.6875731005
1256037346455.281284428313917.7187155717
1261976424820.0332226559-5056.0332226559
127146612142234.4020071444377.59799285584
128121052104593.46550966916458.5344903313
129144757129913.38550295914843.6144970415
1301179616495.2548347619-4699.2548347619
1311067411998.6000849397-1324.60008493965
132131263120165.06626918411097.9337308163
13368367483.94171393458-647.941713934577
134153278137131.04926932816146.9507306721
13551188601.8085574798-3483.80855747979
1364024837344.52168615032903.47831384969
13705854.28185316075-5854.28185316075
13811201897368.124924363514649.8750756365
13987635103650.927067843-16015.9270678426
14071319556.2085381986-2425.2085381986
141881216876.0546907487-8064.05469074875
1426891660275.50463607218640.49536392788
14312011199225.696992481220885.3030075187
14494127108103.593514005-13976.5935140046

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 158147 & 136260.837831568 & 21886.1621684325 \tabularnewline
2 & 179043 & 176209.517813297 & 2833.48218670311 \tabularnewline
3 & 7215 & 19570.1282325405 & -12355.1282325405 \tabularnewline
4 & 122259 & 137523.51276771 & -15264.5127677102 \tabularnewline
5 & 222405 & 251166.973630558 & -28761.9736305583 \tabularnewline
6 & 468370 & 483368.209085211 & -14998.2090852113 \tabularnewline
7 & 150777 & 154874.916023466 & -4097.91602346635 \tabularnewline
8 & 160529 & 171250.760024146 & -10721.7600241465 \tabularnewline
9 & 133238 & 113870.835824354 & 19367.1641756461 \tabularnewline
10 & 275326 & 243775.648938249 & 31550.351061751 \tabularnewline
11 & 121821 & 146338.82863684 & -24517.8286368401 \tabularnewline
12 & 172489 & 171933.921779108 & 555.078220892467 \tabularnewline
13 & 86249 & 129543.4493188 & -43294.4493188005 \tabularnewline
14 & 203892 & 222714.128194281 & -18822.1281942811 \tabularnewline
15 & 149839 & 129596.543045076 & 20242.4569549242 \tabularnewline
16 & 144859 & 152551.973565652 & -7692.97356565231 \tabularnewline
17 & 134153 & 128915.252427596 & 5237.74757240352 \tabularnewline
18 & 64149 & 68337.4437072093 & -4188.44370720934 \tabularnewline
19 & 122417 & 163488.103657993 & -41071.103657993 \tabularnewline
20 & 27997 & 51819.8303933164 & -23822.8303933164 \tabularnewline
21 & 52197 & 81918.1509112472 & -29721.1509112472 \tabularnewline
22 & 205417 & 203901.318770688 & 1515.68122931219 \tabularnewline
23 & 188103 & 133110.356519839 & 54992.6434801612 \tabularnewline
24 & 118698 & 133853.491600124 & -15155.4916001241 \tabularnewline
25 & 143682 & 162615.515716765 & -18933.5157167647 \tabularnewline
26 & 140056 & 122568.800622634 & 17487.199377366 \tabularnewline
27 & 178026 & 190323.648879154 & -12297.6488791542 \tabularnewline
28 & 167812 & 178964.916504582 & -11152.9165045819 \tabularnewline
29 & 317699 & 269549.153529774 & 48149.8464702257 \tabularnewline
30 & 191623 & 193029.79235873 & -1406.79235873017 \tabularnewline
31 & 151621 & 125200.863975305 & 26420.1360246951 \tabularnewline
32 & 167466 & 156444.283400717 & 11021.7165992829 \tabularnewline
33 & 120433 & 143770.999911156 & -23337.9999111557 \tabularnewline
34 & 218393 & 194054.001804019 & 24338.9981959807 \tabularnewline
35 & 202722 & 210890.299424881 & -8168.2994248811 \tabularnewline
36 & 0 & 6158.9993082415 & -6158.9993082415 \tabularnewline
37 & 207163 & 186796.198115585 & 20366.8018844155 \tabularnewline
38 & 93107 & 105867.307689946 & -12760.307689946 \tabularnewline
39 & 129419 & 116668.54810499 & 12750.4518950099 \tabularnewline
40 & 246427 & 262949.892578343 & -16522.8925783425 \tabularnewline
41 & 215885 & 169207.244457407 & 46677.7555425929 \tabularnewline
42 & 126619 & 144335.862924852 & -17716.8629248517 \tabularnewline
43 & 94332 & 105163.403746493 & -10831.4037464934 \tabularnewline
44 & 168276 & 184869.893452435 & -16593.8934524346 \tabularnewline
45 & 98202 & 98428.680761826 & -226.680761825995 \tabularnewline
46 & 144161 & 114757.310538709 & 29403.6894612906 \tabularnewline
47 & 81293 & 79140.4294327066 & 2152.57056729344 \tabularnewline
48 & 199619 & 172062.932427415 & 27556.0675725846 \tabularnewline
49 & 196609 & 194176.282194 & 2432.71780599972 \tabularnewline
50 & 156589 & 176504.154145249 & -19915.1541452487 \tabularnewline
51 & 48188 & 57537.4990463304 & -9349.49904633041 \tabularnewline
52 & 122425 & 110821.019144219 & 11603.9808557814 \tabularnewline
53 & 273969 & 213982.641853738 & 59986.3581462617 \tabularnewline
54 & 234829 & 170687.322608079 & 64141.677391921 \tabularnewline
55 & 181731 & 189143.683754801 & -7412.68375480081 \tabularnewline
56 & 132325 & 130062.488848817 & 2262.51115118279 \tabularnewline
57 & 189220 & 134835.78529535 & 54384.2147046497 \tabularnewline
58 & 76419 & 81930.9613643218 & -5511.96136432179 \tabularnewline
59 & 134427 & 147159.155009168 & -12732.1550091683 \tabularnewline
60 & 189286 & 160598.375523095 & 28687.6244769045 \tabularnewline
61 & 140189 & 129107.746586098 & 11081.2534139016 \tabularnewline
62 & 102209 & 151016.693113908 & -48807.6931139078 \tabularnewline
63 & 124234 & 107882.660203009 & 16351.3397969907 \tabularnewline
64 & 107277 & 130337.132586344 & -23060.1325863443 \tabularnewline
65 & 153813 & 131909.648784389 & 21903.3512156109 \tabularnewline
66 & 94982 & 126296.523310738 & -31314.5233107375 \tabularnewline
67 & 178613 & 212520.226846851 & -33907.2268468509 \tabularnewline
68 & 138708 & 186694.341635168 & -47986.3416351683 \tabularnewline
69 & 102378 & 104864.193121047 & -2486.19312104708 \tabularnewline
70 & 31970 & 34895.8747376981 & -2925.87473769806 \tabularnewline
71 & 211635 & 205847.983337368 & 5787.01666263212 \tabularnewline
72 & 108661 & 102591.384093136 & 6069.6159068638 \tabularnewline
73 & 99687 & 119558.28931772 & -19871.2893177199 \tabularnewline
74 & 102900 & 93028.2977461257 & 9871.70225387433 \tabularnewline
75 & 144193 & 166979.733515394 & -22786.7335153938 \tabularnewline
76 & 74513 & 86419.3661381455 & -11906.3661381455 \tabularnewline
77 & 129080 & 156707.020146271 & -27627.0201462713 \tabularnewline
78 & 153935 & 228985.61161974 & -75050.6116197404 \tabularnewline
79 & 60138 & 69892.9556085668 & -9754.95560856682 \tabularnewline
80 & 84971 & 95689.9576366663 & -10718.9576366663 \tabularnewline
81 & 80478 & 82915.965113559 & -2437.96511355904 \tabularnewline
82 & 244325 & 184164.05553595 & 60160.9444640496 \tabularnewline
83 & 56486 & 63135.9246079235 & -6649.92460792349 \tabularnewline
84 & 97181 & 108395.163046414 & -11214.1630464135 \tabularnewline
85 & 66367 & 76162.8820614716 & -9795.88206147164 \tabularnewline
86 & 148286 & 127439.199648925 & 20846.8003510751 \tabularnewline
87 & 222914 & 216342.576863439 & 6571.42313656128 \tabularnewline
88 & 97180 & 108041.77555366 & -10861.7755536604 \tabularnewline
89 & 90412 & 95695.5929083247 & -5283.59290832475 \tabularnewline
90 & 143258 & 142881.187241703 & 376.812758297338 \tabularnewline
91 & 117794 & 139655.661224784 & -21861.6612247842 \tabularnewline
92 & 150040 & 155843.584873588 & -5803.58487358802 \tabularnewline
93 & 151465 & 149985.07452211 & 1479.92547789043 \tabularnewline
94 & 124626 & 127704.620101305 & -3078.62010130487 \tabularnewline
95 & 51801 & 69289.1543084448 & -17488.1543084448 \tabularnewline
96 & 223008 & 229629.746251604 & -6621.74625160392 \tabularnewline
97 & 182331 & 160514.465594519 & 21816.5344054814 \tabularnewline
98 & 19349 & 26240.9396483245 & -6891.93964832448 \tabularnewline
99 & 187903 & 170585.684425276 & 17317.3155747244 \tabularnewline
100 & 150561 & 181901.580415188 & -31340.5804151876 \tabularnewline
101 & 53921 & 49575.5881564671 & 4345.41184353291 \tabularnewline
102 & 58280 & 78914.5967942038 & -20634.5967942038 \tabularnewline
103 & 115944 & 97887.7255197124 & 18056.2744802875 \tabularnewline
104 & 102139 & 91376.6102906029 & 10762.3897093971 \tabularnewline
105 & 72904 & 65962.3906026004 & 6941.60939739959 \tabularnewline
106 & 27676 & 37884.90235203 & -10208.90235203 \tabularnewline
107 & 131274 & 133112.63946 & -1838.63946000018 \tabularnewline
108 & 117451 & 51529.1281919971 & 65921.8718080029 \tabularnewline
109 & 0 & 5854.28185316075 & -5854.28185316075 \tabularnewline
110 & 85610 & 78080.5906964608 & 7529.40930353917 \tabularnewline
111 & 107175 & 101321.702560993 & 5853.2974390071 \tabularnewline
112 & 133024 & 132038.962500852 & 985.037499147536 \tabularnewline
113 & 136143 & 116289.639543247 & 19853.3604567531 \tabularnewline
114 & 71894 & 91516.0955949033 & -19622.0955949033 \tabularnewline
115 & 3616 & 8485.14526041829 & -4869.14526041829 \tabularnewline
116 & 0 & 5854.28185316075 & -5854.28185316075 \tabularnewline
117 & 154806 & 128159.727711472 & 26646.2722885278 \tabularnewline
118 & 137955 & 145506.742165152 & -7551.74216515212 \tabularnewline
119 & 149652 & 135534.363756665 & 14117.6362433348 \tabularnewline
120 & 113245 & 112384.088455039 & 860.911544960929 \tabularnewline
121 & 43410 & 44835.5330048205 & -1425.53300482047 \tabularnewline
122 & 163584 & 170506.401855292 & -6922.40185529174 \tabularnewline
123 & 89410 & 85462.3920507426 & 3947.60794925737 \tabularnewline
124 & 112383 & 110386.3124269 & 1996.6875731005 \tabularnewline
125 & 60373 & 46455.2812844283 & 13917.7187155717 \tabularnewline
126 & 19764 & 24820.0332226559 & -5056.0332226559 \tabularnewline
127 & 146612 & 142234.402007144 & 4377.59799285584 \tabularnewline
128 & 121052 & 104593.465509669 & 16458.5344903313 \tabularnewline
129 & 144757 & 129913.385502959 & 14843.6144970415 \tabularnewline
130 & 11796 & 16495.2548347619 & -4699.2548347619 \tabularnewline
131 & 10674 & 11998.6000849397 & -1324.60008493965 \tabularnewline
132 & 131263 & 120165.066269184 & 11097.9337308163 \tabularnewline
133 & 6836 & 7483.94171393458 & -647.941713934577 \tabularnewline
134 & 153278 & 137131.049269328 & 16146.9507306721 \tabularnewline
135 & 5118 & 8601.8085574798 & -3483.80855747979 \tabularnewline
136 & 40248 & 37344.5216861503 & 2903.47831384969 \tabularnewline
137 & 0 & 5854.28185316075 & -5854.28185316075 \tabularnewline
138 & 112018 & 97368.1249243635 & 14649.8750756365 \tabularnewline
139 & 87635 & 103650.927067843 & -16015.9270678426 \tabularnewline
140 & 7131 & 9556.2085381986 & -2425.2085381986 \tabularnewline
141 & 8812 & 16876.0546907487 & -8064.05469074875 \tabularnewline
142 & 68916 & 60275.5046360721 & 8640.49536392788 \tabularnewline
143 & 120111 & 99225.6969924812 & 20885.3030075187 \tabularnewline
144 & 94127 & 108103.593514005 & -13976.5935140046 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158951&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]158147[/C][C]136260.837831568[/C][C]21886.1621684325[/C][/ROW]
[ROW][C]2[/C][C]179043[/C][C]176209.517813297[/C][C]2833.48218670311[/C][/ROW]
[ROW][C]3[/C][C]7215[/C][C]19570.1282325405[/C][C]-12355.1282325405[/C][/ROW]
[ROW][C]4[/C][C]122259[/C][C]137523.51276771[/C][C]-15264.5127677102[/C][/ROW]
[ROW][C]5[/C][C]222405[/C][C]251166.973630558[/C][C]-28761.9736305583[/C][/ROW]
[ROW][C]6[/C][C]468370[/C][C]483368.209085211[/C][C]-14998.2090852113[/C][/ROW]
[ROW][C]7[/C][C]150777[/C][C]154874.916023466[/C][C]-4097.91602346635[/C][/ROW]
[ROW][C]8[/C][C]160529[/C][C]171250.760024146[/C][C]-10721.7600241465[/C][/ROW]
[ROW][C]9[/C][C]133238[/C][C]113870.835824354[/C][C]19367.1641756461[/C][/ROW]
[ROW][C]10[/C][C]275326[/C][C]243775.648938249[/C][C]31550.351061751[/C][/ROW]
[ROW][C]11[/C][C]121821[/C][C]146338.82863684[/C][C]-24517.8286368401[/C][/ROW]
[ROW][C]12[/C][C]172489[/C][C]171933.921779108[/C][C]555.078220892467[/C][/ROW]
[ROW][C]13[/C][C]86249[/C][C]129543.4493188[/C][C]-43294.4493188005[/C][/ROW]
[ROW][C]14[/C][C]203892[/C][C]222714.128194281[/C][C]-18822.1281942811[/C][/ROW]
[ROW][C]15[/C][C]149839[/C][C]129596.543045076[/C][C]20242.4569549242[/C][/ROW]
[ROW][C]16[/C][C]144859[/C][C]152551.973565652[/C][C]-7692.97356565231[/C][/ROW]
[ROW][C]17[/C][C]134153[/C][C]128915.252427596[/C][C]5237.74757240352[/C][/ROW]
[ROW][C]18[/C][C]64149[/C][C]68337.4437072093[/C][C]-4188.44370720934[/C][/ROW]
[ROW][C]19[/C][C]122417[/C][C]163488.103657993[/C][C]-41071.103657993[/C][/ROW]
[ROW][C]20[/C][C]27997[/C][C]51819.8303933164[/C][C]-23822.8303933164[/C][/ROW]
[ROW][C]21[/C][C]52197[/C][C]81918.1509112472[/C][C]-29721.1509112472[/C][/ROW]
[ROW][C]22[/C][C]205417[/C][C]203901.318770688[/C][C]1515.68122931219[/C][/ROW]
[ROW][C]23[/C][C]188103[/C][C]133110.356519839[/C][C]54992.6434801612[/C][/ROW]
[ROW][C]24[/C][C]118698[/C][C]133853.491600124[/C][C]-15155.4916001241[/C][/ROW]
[ROW][C]25[/C][C]143682[/C][C]162615.515716765[/C][C]-18933.5157167647[/C][/ROW]
[ROW][C]26[/C][C]140056[/C][C]122568.800622634[/C][C]17487.199377366[/C][/ROW]
[ROW][C]27[/C][C]178026[/C][C]190323.648879154[/C][C]-12297.6488791542[/C][/ROW]
[ROW][C]28[/C][C]167812[/C][C]178964.916504582[/C][C]-11152.9165045819[/C][/ROW]
[ROW][C]29[/C][C]317699[/C][C]269549.153529774[/C][C]48149.8464702257[/C][/ROW]
[ROW][C]30[/C][C]191623[/C][C]193029.79235873[/C][C]-1406.79235873017[/C][/ROW]
[ROW][C]31[/C][C]151621[/C][C]125200.863975305[/C][C]26420.1360246951[/C][/ROW]
[ROW][C]32[/C][C]167466[/C][C]156444.283400717[/C][C]11021.7165992829[/C][/ROW]
[ROW][C]33[/C][C]120433[/C][C]143770.999911156[/C][C]-23337.9999111557[/C][/ROW]
[ROW][C]34[/C][C]218393[/C][C]194054.001804019[/C][C]24338.9981959807[/C][/ROW]
[ROW][C]35[/C][C]202722[/C][C]210890.299424881[/C][C]-8168.2994248811[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]6158.9993082415[/C][C]-6158.9993082415[/C][/ROW]
[ROW][C]37[/C][C]207163[/C][C]186796.198115585[/C][C]20366.8018844155[/C][/ROW]
[ROW][C]38[/C][C]93107[/C][C]105867.307689946[/C][C]-12760.307689946[/C][/ROW]
[ROW][C]39[/C][C]129419[/C][C]116668.54810499[/C][C]12750.4518950099[/C][/ROW]
[ROW][C]40[/C][C]246427[/C][C]262949.892578343[/C][C]-16522.8925783425[/C][/ROW]
[ROW][C]41[/C][C]215885[/C][C]169207.244457407[/C][C]46677.7555425929[/C][/ROW]
[ROW][C]42[/C][C]126619[/C][C]144335.862924852[/C][C]-17716.8629248517[/C][/ROW]
[ROW][C]43[/C][C]94332[/C][C]105163.403746493[/C][C]-10831.4037464934[/C][/ROW]
[ROW][C]44[/C][C]168276[/C][C]184869.893452435[/C][C]-16593.8934524346[/C][/ROW]
[ROW][C]45[/C][C]98202[/C][C]98428.680761826[/C][C]-226.680761825995[/C][/ROW]
[ROW][C]46[/C][C]144161[/C][C]114757.310538709[/C][C]29403.6894612906[/C][/ROW]
[ROW][C]47[/C][C]81293[/C][C]79140.4294327066[/C][C]2152.57056729344[/C][/ROW]
[ROW][C]48[/C][C]199619[/C][C]172062.932427415[/C][C]27556.0675725846[/C][/ROW]
[ROW][C]49[/C][C]196609[/C][C]194176.282194[/C][C]2432.71780599972[/C][/ROW]
[ROW][C]50[/C][C]156589[/C][C]176504.154145249[/C][C]-19915.1541452487[/C][/ROW]
[ROW][C]51[/C][C]48188[/C][C]57537.4990463304[/C][C]-9349.49904633041[/C][/ROW]
[ROW][C]52[/C][C]122425[/C][C]110821.019144219[/C][C]11603.9808557814[/C][/ROW]
[ROW][C]53[/C][C]273969[/C][C]213982.641853738[/C][C]59986.3581462617[/C][/ROW]
[ROW][C]54[/C][C]234829[/C][C]170687.322608079[/C][C]64141.677391921[/C][/ROW]
[ROW][C]55[/C][C]181731[/C][C]189143.683754801[/C][C]-7412.68375480081[/C][/ROW]
[ROW][C]56[/C][C]132325[/C][C]130062.488848817[/C][C]2262.51115118279[/C][/ROW]
[ROW][C]57[/C][C]189220[/C][C]134835.78529535[/C][C]54384.2147046497[/C][/ROW]
[ROW][C]58[/C][C]76419[/C][C]81930.9613643218[/C][C]-5511.96136432179[/C][/ROW]
[ROW][C]59[/C][C]134427[/C][C]147159.155009168[/C][C]-12732.1550091683[/C][/ROW]
[ROW][C]60[/C][C]189286[/C][C]160598.375523095[/C][C]28687.6244769045[/C][/ROW]
[ROW][C]61[/C][C]140189[/C][C]129107.746586098[/C][C]11081.2534139016[/C][/ROW]
[ROW][C]62[/C][C]102209[/C][C]151016.693113908[/C][C]-48807.6931139078[/C][/ROW]
[ROW][C]63[/C][C]124234[/C][C]107882.660203009[/C][C]16351.3397969907[/C][/ROW]
[ROW][C]64[/C][C]107277[/C][C]130337.132586344[/C][C]-23060.1325863443[/C][/ROW]
[ROW][C]65[/C][C]153813[/C][C]131909.648784389[/C][C]21903.3512156109[/C][/ROW]
[ROW][C]66[/C][C]94982[/C][C]126296.523310738[/C][C]-31314.5233107375[/C][/ROW]
[ROW][C]67[/C][C]178613[/C][C]212520.226846851[/C][C]-33907.2268468509[/C][/ROW]
[ROW][C]68[/C][C]138708[/C][C]186694.341635168[/C][C]-47986.3416351683[/C][/ROW]
[ROW][C]69[/C][C]102378[/C][C]104864.193121047[/C][C]-2486.19312104708[/C][/ROW]
[ROW][C]70[/C][C]31970[/C][C]34895.8747376981[/C][C]-2925.87473769806[/C][/ROW]
[ROW][C]71[/C][C]211635[/C][C]205847.983337368[/C][C]5787.01666263212[/C][/ROW]
[ROW][C]72[/C][C]108661[/C][C]102591.384093136[/C][C]6069.6159068638[/C][/ROW]
[ROW][C]73[/C][C]99687[/C][C]119558.28931772[/C][C]-19871.2893177199[/C][/ROW]
[ROW][C]74[/C][C]102900[/C][C]93028.2977461257[/C][C]9871.70225387433[/C][/ROW]
[ROW][C]75[/C][C]144193[/C][C]166979.733515394[/C][C]-22786.7335153938[/C][/ROW]
[ROW][C]76[/C][C]74513[/C][C]86419.3661381455[/C][C]-11906.3661381455[/C][/ROW]
[ROW][C]77[/C][C]129080[/C][C]156707.020146271[/C][C]-27627.0201462713[/C][/ROW]
[ROW][C]78[/C][C]153935[/C][C]228985.61161974[/C][C]-75050.6116197404[/C][/ROW]
[ROW][C]79[/C][C]60138[/C][C]69892.9556085668[/C][C]-9754.95560856682[/C][/ROW]
[ROW][C]80[/C][C]84971[/C][C]95689.9576366663[/C][C]-10718.9576366663[/C][/ROW]
[ROW][C]81[/C][C]80478[/C][C]82915.965113559[/C][C]-2437.96511355904[/C][/ROW]
[ROW][C]82[/C][C]244325[/C][C]184164.05553595[/C][C]60160.9444640496[/C][/ROW]
[ROW][C]83[/C][C]56486[/C][C]63135.9246079235[/C][C]-6649.92460792349[/C][/ROW]
[ROW][C]84[/C][C]97181[/C][C]108395.163046414[/C][C]-11214.1630464135[/C][/ROW]
[ROW][C]85[/C][C]66367[/C][C]76162.8820614716[/C][C]-9795.88206147164[/C][/ROW]
[ROW][C]86[/C][C]148286[/C][C]127439.199648925[/C][C]20846.8003510751[/C][/ROW]
[ROW][C]87[/C][C]222914[/C][C]216342.576863439[/C][C]6571.42313656128[/C][/ROW]
[ROW][C]88[/C][C]97180[/C][C]108041.77555366[/C][C]-10861.7755536604[/C][/ROW]
[ROW][C]89[/C][C]90412[/C][C]95695.5929083247[/C][C]-5283.59290832475[/C][/ROW]
[ROW][C]90[/C][C]143258[/C][C]142881.187241703[/C][C]376.812758297338[/C][/ROW]
[ROW][C]91[/C][C]117794[/C][C]139655.661224784[/C][C]-21861.6612247842[/C][/ROW]
[ROW][C]92[/C][C]150040[/C][C]155843.584873588[/C][C]-5803.58487358802[/C][/ROW]
[ROW][C]93[/C][C]151465[/C][C]149985.07452211[/C][C]1479.92547789043[/C][/ROW]
[ROW][C]94[/C][C]124626[/C][C]127704.620101305[/C][C]-3078.62010130487[/C][/ROW]
[ROW][C]95[/C][C]51801[/C][C]69289.1543084448[/C][C]-17488.1543084448[/C][/ROW]
[ROW][C]96[/C][C]223008[/C][C]229629.746251604[/C][C]-6621.74625160392[/C][/ROW]
[ROW][C]97[/C][C]182331[/C][C]160514.465594519[/C][C]21816.5344054814[/C][/ROW]
[ROW][C]98[/C][C]19349[/C][C]26240.9396483245[/C][C]-6891.93964832448[/C][/ROW]
[ROW][C]99[/C][C]187903[/C][C]170585.684425276[/C][C]17317.3155747244[/C][/ROW]
[ROW][C]100[/C][C]150561[/C][C]181901.580415188[/C][C]-31340.5804151876[/C][/ROW]
[ROW][C]101[/C][C]53921[/C][C]49575.5881564671[/C][C]4345.41184353291[/C][/ROW]
[ROW][C]102[/C][C]58280[/C][C]78914.5967942038[/C][C]-20634.5967942038[/C][/ROW]
[ROW][C]103[/C][C]115944[/C][C]97887.7255197124[/C][C]18056.2744802875[/C][/ROW]
[ROW][C]104[/C][C]102139[/C][C]91376.6102906029[/C][C]10762.3897093971[/C][/ROW]
[ROW][C]105[/C][C]72904[/C][C]65962.3906026004[/C][C]6941.60939739959[/C][/ROW]
[ROW][C]106[/C][C]27676[/C][C]37884.90235203[/C][C]-10208.90235203[/C][/ROW]
[ROW][C]107[/C][C]131274[/C][C]133112.63946[/C][C]-1838.63946000018[/C][/ROW]
[ROW][C]108[/C][C]117451[/C][C]51529.1281919971[/C][C]65921.8718080029[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]5854.28185316075[/C][C]-5854.28185316075[/C][/ROW]
[ROW][C]110[/C][C]85610[/C][C]78080.5906964608[/C][C]7529.40930353917[/C][/ROW]
[ROW][C]111[/C][C]107175[/C][C]101321.702560993[/C][C]5853.2974390071[/C][/ROW]
[ROW][C]112[/C][C]133024[/C][C]132038.962500852[/C][C]985.037499147536[/C][/ROW]
[ROW][C]113[/C][C]136143[/C][C]116289.639543247[/C][C]19853.3604567531[/C][/ROW]
[ROW][C]114[/C][C]71894[/C][C]91516.0955949033[/C][C]-19622.0955949033[/C][/ROW]
[ROW][C]115[/C][C]3616[/C][C]8485.14526041829[/C][C]-4869.14526041829[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]5854.28185316075[/C][C]-5854.28185316075[/C][/ROW]
[ROW][C]117[/C][C]154806[/C][C]128159.727711472[/C][C]26646.2722885278[/C][/ROW]
[ROW][C]118[/C][C]137955[/C][C]145506.742165152[/C][C]-7551.74216515212[/C][/ROW]
[ROW][C]119[/C][C]149652[/C][C]135534.363756665[/C][C]14117.6362433348[/C][/ROW]
[ROW][C]120[/C][C]113245[/C][C]112384.088455039[/C][C]860.911544960929[/C][/ROW]
[ROW][C]121[/C][C]43410[/C][C]44835.5330048205[/C][C]-1425.53300482047[/C][/ROW]
[ROW][C]122[/C][C]163584[/C][C]170506.401855292[/C][C]-6922.40185529174[/C][/ROW]
[ROW][C]123[/C][C]89410[/C][C]85462.3920507426[/C][C]3947.60794925737[/C][/ROW]
[ROW][C]124[/C][C]112383[/C][C]110386.3124269[/C][C]1996.6875731005[/C][/ROW]
[ROW][C]125[/C][C]60373[/C][C]46455.2812844283[/C][C]13917.7187155717[/C][/ROW]
[ROW][C]126[/C][C]19764[/C][C]24820.0332226559[/C][C]-5056.0332226559[/C][/ROW]
[ROW][C]127[/C][C]146612[/C][C]142234.402007144[/C][C]4377.59799285584[/C][/ROW]
[ROW][C]128[/C][C]121052[/C][C]104593.465509669[/C][C]16458.5344903313[/C][/ROW]
[ROW][C]129[/C][C]144757[/C][C]129913.385502959[/C][C]14843.6144970415[/C][/ROW]
[ROW][C]130[/C][C]11796[/C][C]16495.2548347619[/C][C]-4699.2548347619[/C][/ROW]
[ROW][C]131[/C][C]10674[/C][C]11998.6000849397[/C][C]-1324.60008493965[/C][/ROW]
[ROW][C]132[/C][C]131263[/C][C]120165.066269184[/C][C]11097.9337308163[/C][/ROW]
[ROW][C]133[/C][C]6836[/C][C]7483.94171393458[/C][C]-647.941713934577[/C][/ROW]
[ROW][C]134[/C][C]153278[/C][C]137131.049269328[/C][C]16146.9507306721[/C][/ROW]
[ROW][C]135[/C][C]5118[/C][C]8601.8085574798[/C][C]-3483.80855747979[/C][/ROW]
[ROW][C]136[/C][C]40248[/C][C]37344.5216861503[/C][C]2903.47831384969[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]5854.28185316075[/C][C]-5854.28185316075[/C][/ROW]
[ROW][C]138[/C][C]112018[/C][C]97368.1249243635[/C][C]14649.8750756365[/C][/ROW]
[ROW][C]139[/C][C]87635[/C][C]103650.927067843[/C][C]-16015.9270678426[/C][/ROW]
[ROW][C]140[/C][C]7131[/C][C]9556.2085381986[/C][C]-2425.2085381986[/C][/ROW]
[ROW][C]141[/C][C]8812[/C][C]16876.0546907487[/C][C]-8064.05469074875[/C][/ROW]
[ROW][C]142[/C][C]68916[/C][C]60275.5046360721[/C][C]8640.49536392788[/C][/ROW]
[ROW][C]143[/C][C]120111[/C][C]99225.6969924812[/C][C]20885.3030075187[/C][/ROW]
[ROW][C]144[/C][C]94127[/C][C]108103.593514005[/C][C]-13976.5935140046[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158951&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158951&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
1158147136260.83783156821886.1621684325
2179043176209.5178132972833.48218670311
3721519570.1282325405-12355.1282325405
4122259137523.51276771-15264.5127677102
5222405251166.973630558-28761.9736305583
6468370483368.209085211-14998.2090852113
7150777154874.916023466-4097.91602346635
8160529171250.760024146-10721.7600241465
9133238113870.83582435419367.1641756461
10275326243775.64893824931550.351061751
11121821146338.82863684-24517.8286368401
12172489171933.921779108555.078220892467
1386249129543.4493188-43294.4493188005
14203892222714.128194281-18822.1281942811
15149839129596.54304507620242.4569549242
16144859152551.973565652-7692.97356565231
17134153128915.2524275965237.74757240352
186414968337.4437072093-4188.44370720934
19122417163488.103657993-41071.103657993
202799751819.8303933164-23822.8303933164
215219781918.1509112472-29721.1509112472
22205417203901.3187706881515.68122931219
23188103133110.35651983954992.6434801612
24118698133853.491600124-15155.4916001241
25143682162615.515716765-18933.5157167647
26140056122568.80062263417487.199377366
27178026190323.648879154-12297.6488791542
28167812178964.916504582-11152.9165045819
29317699269549.15352977448149.8464702257
30191623193029.79235873-1406.79235873017
31151621125200.86397530526420.1360246951
32167466156444.28340071711021.7165992829
33120433143770.999911156-23337.9999111557
34218393194054.00180401924338.9981959807
35202722210890.299424881-8168.2994248811
3606158.9993082415-6158.9993082415
37207163186796.19811558520366.8018844155
3893107105867.307689946-12760.307689946
39129419116668.5481049912750.4518950099
40246427262949.892578343-16522.8925783425
41215885169207.24445740746677.7555425929
42126619144335.862924852-17716.8629248517
4394332105163.403746493-10831.4037464934
44168276184869.893452435-16593.8934524346
459820298428.680761826-226.680761825995
46144161114757.31053870929403.6894612906
478129379140.42943270662152.57056729344
48199619172062.93242741527556.0675725846
49196609194176.2821942432.71780599972
50156589176504.154145249-19915.1541452487
514818857537.4990463304-9349.49904633041
52122425110821.01914421911603.9808557814
53273969213982.64185373859986.3581462617
54234829170687.32260807964141.677391921
55181731189143.683754801-7412.68375480081
56132325130062.4888488172262.51115118279
57189220134835.7852953554384.2147046497
587641981930.9613643218-5511.96136432179
59134427147159.155009168-12732.1550091683
60189286160598.37552309528687.6244769045
61140189129107.74658609811081.2534139016
62102209151016.693113908-48807.6931139078
63124234107882.66020300916351.3397969907
64107277130337.132586344-23060.1325863443
65153813131909.64878438921903.3512156109
6694982126296.523310738-31314.5233107375
67178613212520.226846851-33907.2268468509
68138708186694.341635168-47986.3416351683
69102378104864.193121047-2486.19312104708
703197034895.8747376981-2925.87473769806
71211635205847.9833373685787.01666263212
72108661102591.3840931366069.6159068638
7399687119558.28931772-19871.2893177199
7410290093028.29774612579871.70225387433
75144193166979.733515394-22786.7335153938
767451386419.3661381455-11906.3661381455
77129080156707.020146271-27627.0201462713
78153935228985.61161974-75050.6116197404
796013869892.9556085668-9754.95560856682
808497195689.9576366663-10718.9576366663
818047882915.965113559-2437.96511355904
82244325184164.0555359560160.9444640496
835648663135.9246079235-6649.92460792349
8497181108395.163046414-11214.1630464135
856636776162.8820614716-9795.88206147164
86148286127439.19964892520846.8003510751
87222914216342.5768634396571.42313656128
8897180108041.77555366-10861.7755536604
899041295695.5929083247-5283.59290832475
90143258142881.187241703376.812758297338
91117794139655.661224784-21861.6612247842
92150040155843.584873588-5803.58487358802
93151465149985.074522111479.92547789043
94124626127704.620101305-3078.62010130487
955180169289.1543084448-17488.1543084448
96223008229629.746251604-6621.74625160392
97182331160514.46559451921816.5344054814
981934926240.9396483245-6891.93964832448
99187903170585.68442527617317.3155747244
100150561181901.580415188-31340.5804151876
1015392149575.58815646714345.41184353291
1025828078914.5967942038-20634.5967942038
10311594497887.725519712418056.2744802875
10410213991376.610290602910762.3897093971
1057290465962.39060260046941.60939739959
1062767637884.90235203-10208.90235203
107131274133112.63946-1838.63946000018
10811745151529.128191997165921.8718080029
10905854.28185316075-5854.28185316075
1108561078080.59069646087529.40930353917
111107175101321.7025609935853.2974390071
112133024132038.962500852985.037499147536
113136143116289.63954324719853.3604567531
1147189491516.0955949033-19622.0955949033
11536168485.14526041829-4869.14526041829
11605854.28185316075-5854.28185316075
117154806128159.72771147226646.2722885278
118137955145506.742165152-7551.74216515212
119149652135534.36375666514117.6362433348
120113245112384.088455039860.911544960929
1214341044835.5330048205-1425.53300482047
122163584170506.401855292-6922.40185529174
1238941085462.39205074263947.60794925737
124112383110386.31242691996.6875731005
1256037346455.281284428313917.7187155717
1261976424820.0332226559-5056.0332226559
127146612142234.4020071444377.59799285584
128121052104593.46550966916458.5344903313
129144757129913.38550295914843.6144970415
1301179616495.2548347619-4699.2548347619
1311067411998.6000849397-1324.60008493965
132131263120165.06626918411097.9337308163
13368367483.94171393458-647.941713934577
134153278137131.04926932816146.9507306721
13551188601.8085574798-3483.80855747979
1364024837344.52168615032903.47831384969
13705854.28185316075-5854.28185316075
13811201897368.124924363514649.8750756365
13987635103650.927067843-16015.9270678426
14071319556.2085381986-2425.2085381986
141881216876.0546907487-8064.05469074875
1426891660275.50463607218640.49536392788
14312011199225.696992481220885.3030075187
14494127108103.593514005-13976.5935140046






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.8029345433060370.3941309133879260.197065456693963
190.767872823676030.4642543526479390.23212717632397
200.6723226671700710.6553546656598580.327677332829929
210.5596395575988440.8807208848023120.440360442401156
220.5244746719805050.951050656038990.475525328019495
230.8924202432023120.2151595135953760.107579756797688
240.8526743482174410.2946513035651180.147325651782559
250.8212615235060620.3574769529878760.178738476493938
260.7666249242170540.4667501515658920.233375075782946
270.7432969387074810.5134061225850380.256703061292519
280.6774403866468780.6451192267062430.322559613353122
290.8211543696013150.357691260797370.178845630398685
300.7655755849624640.4688488300750710.234424415037536
310.7523121155354490.4953757689291020.247687884464551
320.6906329519587530.6187340960824940.309367048041247
330.6724722818217520.6550554363564970.327527718178248
340.622851474255560.7542970514888790.37714852574444
350.561585491884350.8768290162313010.43841450811565
360.4935094752684430.9870189505368870.506490524731557
370.4625243615197480.9250487230394950.537475638480252
380.4027728215691930.8055456431383860.597227178430807
390.348746279100990.697492558201980.65125372089901
400.5053557753554950.989288449289010.494644224644505
410.6811091218749490.6377817562501010.318890878125051
420.6593056588536190.6813886822927620.340694341146381
430.629558775462940.740882449074120.37044122453706
440.6229364512085540.7541270975828920.377063548791446
450.564525885002570.870948229994860.43547411499743
460.7213617784574040.5572764430851920.278638221542596
470.6755203611671690.6489592776656620.324479638832831
480.6990918840452290.6018162319095410.300908115954771
490.6522894519970090.6954210960059810.347710548002991
500.6334986454662920.7330027090674160.366501354533708
510.5901518303440220.8196963393119570.409848169655978
520.5426194801841320.9147610396317370.457380519815868
530.8288994467184680.3422011065630640.171100553281532
540.9623421842416050.07531563151679090.0376578157583955
550.9531013273485970.09379734530280650.0468986726514033
560.9407839319405890.1184321361188220.0592160680594108
570.9831105594170370.0337788811659270.0168894405829635
580.9776043308090080.04479133838198420.0223956691909921
590.9714478604609050.05710427907819080.0285521395390954
600.9726827756382650.05463444872347090.0273172243617354
610.9647486112461210.07050277750775790.035251388753879
620.9868494793122780.02630104137544370.0131505206877218
630.9836057986721250.03278840265574980.0163942013278749
640.9839757035802450.03204859283951010.016024296419755
650.9833082616083490.03338347678330270.0166917383916513
660.9867131538323120.02657369233537540.0132868461676877
670.9904328639737380.01913427205252430.00956713602626217
680.9971889477550850.005622104489830530.00281105224491527
690.995847535401190.008304929197619970.00415246459880998
700.993919089404770.01216182119046050.00608091059523025
710.9913267663844090.01734646723118230.00867323361559115
720.9888444020471510.02231119590569710.0111555979528486
730.988504020250630.022991959498740.01149597974937
740.9844784877797630.03104302444047470.0155215122202374
750.989650025835570.02069994832885940.0103499741644297
760.9901137186461180.01977256270776480.00988628135388242
770.9943261134547930.01134777309041370.00567388654520687
780.9999932187003461.35625993080303e-056.78129965401514e-06
790.9999878772839712.42454320570433e-051.21227160285216e-05
800.9999796757364664.06485270671454e-052.03242635335727e-05
810.9999630999734767.38000530474265e-053.69000265237132e-05
820.9999983786133193.24277336130685e-061.62138668065342e-06
830.9999970741588265.85168234849635e-062.92584117424817e-06
840.9999957752929128.44941417547627e-064.22470708773813e-06
850.9999925964597661.48070804675601e-057.40354023378004e-06
860.9999933913210941.32173578113675e-056.60867890568375e-06
870.9999905252014521.89495970956759e-059.47479854783795e-06
880.9999906415439661.87169120687174e-059.35845603435871e-06
890.9999842707150813.14585698388218e-051.57292849194109e-05
900.9999763825266594.7234946682178e-052.3617473341089e-05
910.9999647002212797.0599557442927e-053.52997787214635e-05
920.9999783824191254.32351617496735e-052.16175808748368e-05
930.9999640042973787.19914052448316e-053.59957026224158e-05
940.9999307925545820.0001384148908359156.92074454179574e-05
950.9999399874663870.000120025067225246.001253361262e-05
960.9998944352226120.0002111295547764220.000105564777388211
970.9998495097068720.000300980586255460.00015049029312773
980.9997241831159130.0005516337681731190.00027581688408656
990.9995878871722830.0008242256554336180.000412112827716809
1000.9999406332969520.0001187334060952975.93667030476483e-05
1010.9998825690223430.0002348619553134530.000117430977656727
1020.9999563036353448.73927293117651e-054.36963646558826e-05
1030.9999309990360510.0001380019278983326.90009639491662e-05
1040.9998602263813190.0002795472373616970.000139773618680849
1050.9997536457723880.0004927084552241050.000246354227612052
1060.9995310454924850.0009379090150296630.000468954507514831
1070.9995045501113930.0009908997772146680.000495449888607334
1080.9999998450576783.09884644473975e-071.54942322236988e-07
1090.9999995144617269.71076548953802e-074.85538274476901e-07
1100.9999985862060772.82758784611738e-061.41379392305869e-06
1110.9999986212141332.75757173348653e-061.37878586674327e-06
1120.9999990012251731.99754965389507e-069.98774826947535e-07
1130.999998199484723.60103055931868e-061.80051527965934e-06
1140.9999963180111937.36397761357101e-063.68198880678551e-06
1150.9999878767272722.42465454555379e-051.2123272727769e-05
1160.9999610219682367.79560635274603e-053.89780317637301e-05
1170.9999993842878561.23142428847821e-066.15712144239104e-07
1180.9999994813286941.03734261253227e-065.18671306266135e-07
1190.9999976029423744.79411525194395e-062.39705762597198e-06
1200.9999953226144219.35477115735383e-064.67738557867691e-06
1210.9999744368548155.11262903694175e-052.55631451847087e-05
1220.9999198980752760.0001602038494488358.01019247244174e-05
1230.9998649469794610.000270106041078390.000135053020539195
1240.9996737295628490.0006525408743022350.000326270437151118
1250.9978824400640530.004235119871893030.00211755993594651
1260.9970093899924840.005981220015032050.00299061000751603

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.802934543306037 & 0.394130913387926 & 0.197065456693963 \tabularnewline
19 & 0.76787282367603 & 0.464254352647939 & 0.23212717632397 \tabularnewline
20 & 0.672322667170071 & 0.655354665659858 & 0.327677332829929 \tabularnewline
21 & 0.559639557598844 & 0.880720884802312 & 0.440360442401156 \tabularnewline
22 & 0.524474671980505 & 0.95105065603899 & 0.475525328019495 \tabularnewline
23 & 0.892420243202312 & 0.215159513595376 & 0.107579756797688 \tabularnewline
24 & 0.852674348217441 & 0.294651303565118 & 0.147325651782559 \tabularnewline
25 & 0.821261523506062 & 0.357476952987876 & 0.178738476493938 \tabularnewline
26 & 0.766624924217054 & 0.466750151565892 & 0.233375075782946 \tabularnewline
27 & 0.743296938707481 & 0.513406122585038 & 0.256703061292519 \tabularnewline
28 & 0.677440386646878 & 0.645119226706243 & 0.322559613353122 \tabularnewline
29 & 0.821154369601315 & 0.35769126079737 & 0.178845630398685 \tabularnewline
30 & 0.765575584962464 & 0.468848830075071 & 0.234424415037536 \tabularnewline
31 & 0.752312115535449 & 0.495375768929102 & 0.247687884464551 \tabularnewline
32 & 0.690632951958753 & 0.618734096082494 & 0.309367048041247 \tabularnewline
33 & 0.672472281821752 & 0.655055436356497 & 0.327527718178248 \tabularnewline
34 & 0.62285147425556 & 0.754297051488879 & 0.37714852574444 \tabularnewline
35 & 0.56158549188435 & 0.876829016231301 & 0.43841450811565 \tabularnewline
36 & 0.493509475268443 & 0.987018950536887 & 0.506490524731557 \tabularnewline
37 & 0.462524361519748 & 0.925048723039495 & 0.537475638480252 \tabularnewline
38 & 0.402772821569193 & 0.805545643138386 & 0.597227178430807 \tabularnewline
39 & 0.34874627910099 & 0.69749255820198 & 0.65125372089901 \tabularnewline
40 & 0.505355775355495 & 0.98928844928901 & 0.494644224644505 \tabularnewline
41 & 0.681109121874949 & 0.637781756250101 & 0.318890878125051 \tabularnewline
42 & 0.659305658853619 & 0.681388682292762 & 0.340694341146381 \tabularnewline
43 & 0.62955877546294 & 0.74088244907412 & 0.37044122453706 \tabularnewline
44 & 0.622936451208554 & 0.754127097582892 & 0.377063548791446 \tabularnewline
45 & 0.56452588500257 & 0.87094822999486 & 0.43547411499743 \tabularnewline
46 & 0.721361778457404 & 0.557276443085192 & 0.278638221542596 \tabularnewline
47 & 0.675520361167169 & 0.648959277665662 & 0.324479638832831 \tabularnewline
48 & 0.699091884045229 & 0.601816231909541 & 0.300908115954771 \tabularnewline
49 & 0.652289451997009 & 0.695421096005981 & 0.347710548002991 \tabularnewline
50 & 0.633498645466292 & 0.733002709067416 & 0.366501354533708 \tabularnewline
51 & 0.590151830344022 & 0.819696339311957 & 0.409848169655978 \tabularnewline
52 & 0.542619480184132 & 0.914761039631737 & 0.457380519815868 \tabularnewline
53 & 0.828899446718468 & 0.342201106563064 & 0.171100553281532 \tabularnewline
54 & 0.962342184241605 & 0.0753156315167909 & 0.0376578157583955 \tabularnewline
55 & 0.953101327348597 & 0.0937973453028065 & 0.0468986726514033 \tabularnewline
56 & 0.940783931940589 & 0.118432136118822 & 0.0592160680594108 \tabularnewline
57 & 0.983110559417037 & 0.033778881165927 & 0.0168894405829635 \tabularnewline
58 & 0.977604330809008 & 0.0447913383819842 & 0.0223956691909921 \tabularnewline
59 & 0.971447860460905 & 0.0571042790781908 & 0.0285521395390954 \tabularnewline
60 & 0.972682775638265 & 0.0546344487234709 & 0.0273172243617354 \tabularnewline
61 & 0.964748611246121 & 0.0705027775077579 & 0.035251388753879 \tabularnewline
62 & 0.986849479312278 & 0.0263010413754437 & 0.0131505206877218 \tabularnewline
63 & 0.983605798672125 & 0.0327884026557498 & 0.0163942013278749 \tabularnewline
64 & 0.983975703580245 & 0.0320485928395101 & 0.016024296419755 \tabularnewline
65 & 0.983308261608349 & 0.0333834767833027 & 0.0166917383916513 \tabularnewline
66 & 0.986713153832312 & 0.0265736923353754 & 0.0132868461676877 \tabularnewline
67 & 0.990432863973738 & 0.0191342720525243 & 0.00956713602626217 \tabularnewline
68 & 0.997188947755085 & 0.00562210448983053 & 0.00281105224491527 \tabularnewline
69 & 0.99584753540119 & 0.00830492919761997 & 0.00415246459880998 \tabularnewline
70 & 0.99391908940477 & 0.0121618211904605 & 0.00608091059523025 \tabularnewline
71 & 0.991326766384409 & 0.0173464672311823 & 0.00867323361559115 \tabularnewline
72 & 0.988844402047151 & 0.0223111959056971 & 0.0111555979528486 \tabularnewline
73 & 0.98850402025063 & 0.02299195949874 & 0.01149597974937 \tabularnewline
74 & 0.984478487779763 & 0.0310430244404747 & 0.0155215122202374 \tabularnewline
75 & 0.98965002583557 & 0.0206999483288594 & 0.0103499741644297 \tabularnewline
76 & 0.990113718646118 & 0.0197725627077648 & 0.00988628135388242 \tabularnewline
77 & 0.994326113454793 & 0.0113477730904137 & 0.00567388654520687 \tabularnewline
78 & 0.999993218700346 & 1.35625993080303e-05 & 6.78129965401514e-06 \tabularnewline
79 & 0.999987877283971 & 2.42454320570433e-05 & 1.21227160285216e-05 \tabularnewline
80 & 0.999979675736466 & 4.06485270671454e-05 & 2.03242635335727e-05 \tabularnewline
81 & 0.999963099973476 & 7.38000530474265e-05 & 3.69000265237132e-05 \tabularnewline
82 & 0.999998378613319 & 3.24277336130685e-06 & 1.62138668065342e-06 \tabularnewline
83 & 0.999997074158826 & 5.85168234849635e-06 & 2.92584117424817e-06 \tabularnewline
84 & 0.999995775292912 & 8.44941417547627e-06 & 4.22470708773813e-06 \tabularnewline
85 & 0.999992596459766 & 1.48070804675601e-05 & 7.40354023378004e-06 \tabularnewline
86 & 0.999993391321094 & 1.32173578113675e-05 & 6.60867890568375e-06 \tabularnewline
87 & 0.999990525201452 & 1.89495970956759e-05 & 9.47479854783795e-06 \tabularnewline
88 & 0.999990641543966 & 1.87169120687174e-05 & 9.35845603435871e-06 \tabularnewline
89 & 0.999984270715081 & 3.14585698388218e-05 & 1.57292849194109e-05 \tabularnewline
90 & 0.999976382526659 & 4.7234946682178e-05 & 2.3617473341089e-05 \tabularnewline
91 & 0.999964700221279 & 7.0599557442927e-05 & 3.52997787214635e-05 \tabularnewline
92 & 0.999978382419125 & 4.32351617496735e-05 & 2.16175808748368e-05 \tabularnewline
93 & 0.999964004297378 & 7.19914052448316e-05 & 3.59957026224158e-05 \tabularnewline
94 & 0.999930792554582 & 0.000138414890835915 & 6.92074454179574e-05 \tabularnewline
95 & 0.999939987466387 & 0.00012002506722524 & 6.001253361262e-05 \tabularnewline
96 & 0.999894435222612 & 0.000211129554776422 & 0.000105564777388211 \tabularnewline
97 & 0.999849509706872 & 0.00030098058625546 & 0.00015049029312773 \tabularnewline
98 & 0.999724183115913 & 0.000551633768173119 & 0.00027581688408656 \tabularnewline
99 & 0.999587887172283 & 0.000824225655433618 & 0.000412112827716809 \tabularnewline
100 & 0.999940633296952 & 0.000118733406095297 & 5.93667030476483e-05 \tabularnewline
101 & 0.999882569022343 & 0.000234861955313453 & 0.000117430977656727 \tabularnewline
102 & 0.999956303635344 & 8.73927293117651e-05 & 4.36963646558826e-05 \tabularnewline
103 & 0.999930999036051 & 0.000138001927898332 & 6.90009639491662e-05 \tabularnewline
104 & 0.999860226381319 & 0.000279547237361697 & 0.000139773618680849 \tabularnewline
105 & 0.999753645772388 & 0.000492708455224105 & 0.000246354227612052 \tabularnewline
106 & 0.999531045492485 & 0.000937909015029663 & 0.000468954507514831 \tabularnewline
107 & 0.999504550111393 & 0.000990899777214668 & 0.000495449888607334 \tabularnewline
108 & 0.999999845057678 & 3.09884644473975e-07 & 1.54942322236988e-07 \tabularnewline
109 & 0.999999514461726 & 9.71076548953802e-07 & 4.85538274476901e-07 \tabularnewline
110 & 0.999998586206077 & 2.82758784611738e-06 & 1.41379392305869e-06 \tabularnewline
111 & 0.999998621214133 & 2.75757173348653e-06 & 1.37878586674327e-06 \tabularnewline
112 & 0.999999001225173 & 1.99754965389507e-06 & 9.98774826947535e-07 \tabularnewline
113 & 0.99999819948472 & 3.60103055931868e-06 & 1.80051527965934e-06 \tabularnewline
114 & 0.999996318011193 & 7.36397761357101e-06 & 3.68198880678551e-06 \tabularnewline
115 & 0.999987876727272 & 2.42465454555379e-05 & 1.2123272727769e-05 \tabularnewline
116 & 0.999961021968236 & 7.79560635274603e-05 & 3.89780317637301e-05 \tabularnewline
117 & 0.999999384287856 & 1.23142428847821e-06 & 6.15712144239104e-07 \tabularnewline
118 & 0.999999481328694 & 1.03734261253227e-06 & 5.18671306266135e-07 \tabularnewline
119 & 0.999997602942374 & 4.79411525194395e-06 & 2.39705762597198e-06 \tabularnewline
120 & 0.999995322614421 & 9.35477115735383e-06 & 4.67738557867691e-06 \tabularnewline
121 & 0.999974436854815 & 5.11262903694175e-05 & 2.55631451847087e-05 \tabularnewline
122 & 0.999919898075276 & 0.000160203849448835 & 8.01019247244174e-05 \tabularnewline
123 & 0.999864946979461 & 0.00027010604107839 & 0.000135053020539195 \tabularnewline
124 & 0.999673729562849 & 0.000652540874302235 & 0.000326270437151118 \tabularnewline
125 & 0.997882440064053 & 0.00423511987189303 & 0.00211755993594651 \tabularnewline
126 & 0.997009389992484 & 0.00598122001503205 & 0.00299061000751603 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158951&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]0.802934543306037[/C][C]0.394130913387926[/C][C]0.197065456693963[/C][/ROW]
[ROW][C]19[/C][C]0.76787282367603[/C][C]0.464254352647939[/C][C]0.23212717632397[/C][/ROW]
[ROW][C]20[/C][C]0.672322667170071[/C][C]0.655354665659858[/C][C]0.327677332829929[/C][/ROW]
[ROW][C]21[/C][C]0.559639557598844[/C][C]0.880720884802312[/C][C]0.440360442401156[/C][/ROW]
[ROW][C]22[/C][C]0.524474671980505[/C][C]0.95105065603899[/C][C]0.475525328019495[/C][/ROW]
[ROW][C]23[/C][C]0.892420243202312[/C][C]0.215159513595376[/C][C]0.107579756797688[/C][/ROW]
[ROW][C]24[/C][C]0.852674348217441[/C][C]0.294651303565118[/C][C]0.147325651782559[/C][/ROW]
[ROW][C]25[/C][C]0.821261523506062[/C][C]0.357476952987876[/C][C]0.178738476493938[/C][/ROW]
[ROW][C]26[/C][C]0.766624924217054[/C][C]0.466750151565892[/C][C]0.233375075782946[/C][/ROW]
[ROW][C]27[/C][C]0.743296938707481[/C][C]0.513406122585038[/C][C]0.256703061292519[/C][/ROW]
[ROW][C]28[/C][C]0.677440386646878[/C][C]0.645119226706243[/C][C]0.322559613353122[/C][/ROW]
[ROW][C]29[/C][C]0.821154369601315[/C][C]0.35769126079737[/C][C]0.178845630398685[/C][/ROW]
[ROW][C]30[/C][C]0.765575584962464[/C][C]0.468848830075071[/C][C]0.234424415037536[/C][/ROW]
[ROW][C]31[/C][C]0.752312115535449[/C][C]0.495375768929102[/C][C]0.247687884464551[/C][/ROW]
[ROW][C]32[/C][C]0.690632951958753[/C][C]0.618734096082494[/C][C]0.309367048041247[/C][/ROW]
[ROW][C]33[/C][C]0.672472281821752[/C][C]0.655055436356497[/C][C]0.327527718178248[/C][/ROW]
[ROW][C]34[/C][C]0.62285147425556[/C][C]0.754297051488879[/C][C]0.37714852574444[/C][/ROW]
[ROW][C]35[/C][C]0.56158549188435[/C][C]0.876829016231301[/C][C]0.43841450811565[/C][/ROW]
[ROW][C]36[/C][C]0.493509475268443[/C][C]0.987018950536887[/C][C]0.506490524731557[/C][/ROW]
[ROW][C]37[/C][C]0.462524361519748[/C][C]0.925048723039495[/C][C]0.537475638480252[/C][/ROW]
[ROW][C]38[/C][C]0.402772821569193[/C][C]0.805545643138386[/C][C]0.597227178430807[/C][/ROW]
[ROW][C]39[/C][C]0.34874627910099[/C][C]0.69749255820198[/C][C]0.65125372089901[/C][/ROW]
[ROW][C]40[/C][C]0.505355775355495[/C][C]0.98928844928901[/C][C]0.494644224644505[/C][/ROW]
[ROW][C]41[/C][C]0.681109121874949[/C][C]0.637781756250101[/C][C]0.318890878125051[/C][/ROW]
[ROW][C]42[/C][C]0.659305658853619[/C][C]0.681388682292762[/C][C]0.340694341146381[/C][/ROW]
[ROW][C]43[/C][C]0.62955877546294[/C][C]0.74088244907412[/C][C]0.37044122453706[/C][/ROW]
[ROW][C]44[/C][C]0.622936451208554[/C][C]0.754127097582892[/C][C]0.377063548791446[/C][/ROW]
[ROW][C]45[/C][C]0.56452588500257[/C][C]0.87094822999486[/C][C]0.43547411499743[/C][/ROW]
[ROW][C]46[/C][C]0.721361778457404[/C][C]0.557276443085192[/C][C]0.278638221542596[/C][/ROW]
[ROW][C]47[/C][C]0.675520361167169[/C][C]0.648959277665662[/C][C]0.324479638832831[/C][/ROW]
[ROW][C]48[/C][C]0.699091884045229[/C][C]0.601816231909541[/C][C]0.300908115954771[/C][/ROW]
[ROW][C]49[/C][C]0.652289451997009[/C][C]0.695421096005981[/C][C]0.347710548002991[/C][/ROW]
[ROW][C]50[/C][C]0.633498645466292[/C][C]0.733002709067416[/C][C]0.366501354533708[/C][/ROW]
[ROW][C]51[/C][C]0.590151830344022[/C][C]0.819696339311957[/C][C]0.409848169655978[/C][/ROW]
[ROW][C]52[/C][C]0.542619480184132[/C][C]0.914761039631737[/C][C]0.457380519815868[/C][/ROW]
[ROW][C]53[/C][C]0.828899446718468[/C][C]0.342201106563064[/C][C]0.171100553281532[/C][/ROW]
[ROW][C]54[/C][C]0.962342184241605[/C][C]0.0753156315167909[/C][C]0.0376578157583955[/C][/ROW]
[ROW][C]55[/C][C]0.953101327348597[/C][C]0.0937973453028065[/C][C]0.0468986726514033[/C][/ROW]
[ROW][C]56[/C][C]0.940783931940589[/C][C]0.118432136118822[/C][C]0.0592160680594108[/C][/ROW]
[ROW][C]57[/C][C]0.983110559417037[/C][C]0.033778881165927[/C][C]0.0168894405829635[/C][/ROW]
[ROW][C]58[/C][C]0.977604330809008[/C][C]0.0447913383819842[/C][C]0.0223956691909921[/C][/ROW]
[ROW][C]59[/C][C]0.971447860460905[/C][C]0.0571042790781908[/C][C]0.0285521395390954[/C][/ROW]
[ROW][C]60[/C][C]0.972682775638265[/C][C]0.0546344487234709[/C][C]0.0273172243617354[/C][/ROW]
[ROW][C]61[/C][C]0.964748611246121[/C][C]0.0705027775077579[/C][C]0.035251388753879[/C][/ROW]
[ROW][C]62[/C][C]0.986849479312278[/C][C]0.0263010413754437[/C][C]0.0131505206877218[/C][/ROW]
[ROW][C]63[/C][C]0.983605798672125[/C][C]0.0327884026557498[/C][C]0.0163942013278749[/C][/ROW]
[ROW][C]64[/C][C]0.983975703580245[/C][C]0.0320485928395101[/C][C]0.016024296419755[/C][/ROW]
[ROW][C]65[/C][C]0.983308261608349[/C][C]0.0333834767833027[/C][C]0.0166917383916513[/C][/ROW]
[ROW][C]66[/C][C]0.986713153832312[/C][C]0.0265736923353754[/C][C]0.0132868461676877[/C][/ROW]
[ROW][C]67[/C][C]0.990432863973738[/C][C]0.0191342720525243[/C][C]0.00956713602626217[/C][/ROW]
[ROW][C]68[/C][C]0.997188947755085[/C][C]0.00562210448983053[/C][C]0.00281105224491527[/C][/ROW]
[ROW][C]69[/C][C]0.99584753540119[/C][C]0.00830492919761997[/C][C]0.00415246459880998[/C][/ROW]
[ROW][C]70[/C][C]0.99391908940477[/C][C]0.0121618211904605[/C][C]0.00608091059523025[/C][/ROW]
[ROW][C]71[/C][C]0.991326766384409[/C][C]0.0173464672311823[/C][C]0.00867323361559115[/C][/ROW]
[ROW][C]72[/C][C]0.988844402047151[/C][C]0.0223111959056971[/C][C]0.0111555979528486[/C][/ROW]
[ROW][C]73[/C][C]0.98850402025063[/C][C]0.02299195949874[/C][C]0.01149597974937[/C][/ROW]
[ROW][C]74[/C][C]0.984478487779763[/C][C]0.0310430244404747[/C][C]0.0155215122202374[/C][/ROW]
[ROW][C]75[/C][C]0.98965002583557[/C][C]0.0206999483288594[/C][C]0.0103499741644297[/C][/ROW]
[ROW][C]76[/C][C]0.990113718646118[/C][C]0.0197725627077648[/C][C]0.00988628135388242[/C][/ROW]
[ROW][C]77[/C][C]0.994326113454793[/C][C]0.0113477730904137[/C][C]0.00567388654520687[/C][/ROW]
[ROW][C]78[/C][C]0.999993218700346[/C][C]1.35625993080303e-05[/C][C]6.78129965401514e-06[/C][/ROW]
[ROW][C]79[/C][C]0.999987877283971[/C][C]2.42454320570433e-05[/C][C]1.21227160285216e-05[/C][/ROW]
[ROW][C]80[/C][C]0.999979675736466[/C][C]4.06485270671454e-05[/C][C]2.03242635335727e-05[/C][/ROW]
[ROW][C]81[/C][C]0.999963099973476[/C][C]7.38000530474265e-05[/C][C]3.69000265237132e-05[/C][/ROW]
[ROW][C]82[/C][C]0.999998378613319[/C][C]3.24277336130685e-06[/C][C]1.62138668065342e-06[/C][/ROW]
[ROW][C]83[/C][C]0.999997074158826[/C][C]5.85168234849635e-06[/C][C]2.92584117424817e-06[/C][/ROW]
[ROW][C]84[/C][C]0.999995775292912[/C][C]8.44941417547627e-06[/C][C]4.22470708773813e-06[/C][/ROW]
[ROW][C]85[/C][C]0.999992596459766[/C][C]1.48070804675601e-05[/C][C]7.40354023378004e-06[/C][/ROW]
[ROW][C]86[/C][C]0.999993391321094[/C][C]1.32173578113675e-05[/C][C]6.60867890568375e-06[/C][/ROW]
[ROW][C]87[/C][C]0.999990525201452[/C][C]1.89495970956759e-05[/C][C]9.47479854783795e-06[/C][/ROW]
[ROW][C]88[/C][C]0.999990641543966[/C][C]1.87169120687174e-05[/C][C]9.35845603435871e-06[/C][/ROW]
[ROW][C]89[/C][C]0.999984270715081[/C][C]3.14585698388218e-05[/C][C]1.57292849194109e-05[/C][/ROW]
[ROW][C]90[/C][C]0.999976382526659[/C][C]4.7234946682178e-05[/C][C]2.3617473341089e-05[/C][/ROW]
[ROW][C]91[/C][C]0.999964700221279[/C][C]7.0599557442927e-05[/C][C]3.52997787214635e-05[/C][/ROW]
[ROW][C]92[/C][C]0.999978382419125[/C][C]4.32351617496735e-05[/C][C]2.16175808748368e-05[/C][/ROW]
[ROW][C]93[/C][C]0.999964004297378[/C][C]7.19914052448316e-05[/C][C]3.59957026224158e-05[/C][/ROW]
[ROW][C]94[/C][C]0.999930792554582[/C][C]0.000138414890835915[/C][C]6.92074454179574e-05[/C][/ROW]
[ROW][C]95[/C][C]0.999939987466387[/C][C]0.00012002506722524[/C][C]6.001253361262e-05[/C][/ROW]
[ROW][C]96[/C][C]0.999894435222612[/C][C]0.000211129554776422[/C][C]0.000105564777388211[/C][/ROW]
[ROW][C]97[/C][C]0.999849509706872[/C][C]0.00030098058625546[/C][C]0.00015049029312773[/C][/ROW]
[ROW][C]98[/C][C]0.999724183115913[/C][C]0.000551633768173119[/C][C]0.00027581688408656[/C][/ROW]
[ROW][C]99[/C][C]0.999587887172283[/C][C]0.000824225655433618[/C][C]0.000412112827716809[/C][/ROW]
[ROW][C]100[/C][C]0.999940633296952[/C][C]0.000118733406095297[/C][C]5.93667030476483e-05[/C][/ROW]
[ROW][C]101[/C][C]0.999882569022343[/C][C]0.000234861955313453[/C][C]0.000117430977656727[/C][/ROW]
[ROW][C]102[/C][C]0.999956303635344[/C][C]8.73927293117651e-05[/C][C]4.36963646558826e-05[/C][/ROW]
[ROW][C]103[/C][C]0.999930999036051[/C][C]0.000138001927898332[/C][C]6.90009639491662e-05[/C][/ROW]
[ROW][C]104[/C][C]0.999860226381319[/C][C]0.000279547237361697[/C][C]0.000139773618680849[/C][/ROW]
[ROW][C]105[/C][C]0.999753645772388[/C][C]0.000492708455224105[/C][C]0.000246354227612052[/C][/ROW]
[ROW][C]106[/C][C]0.999531045492485[/C][C]0.000937909015029663[/C][C]0.000468954507514831[/C][/ROW]
[ROW][C]107[/C][C]0.999504550111393[/C][C]0.000990899777214668[/C][C]0.000495449888607334[/C][/ROW]
[ROW][C]108[/C][C]0.999999845057678[/C][C]3.09884644473975e-07[/C][C]1.54942322236988e-07[/C][/ROW]
[ROW][C]109[/C][C]0.999999514461726[/C][C]9.71076548953802e-07[/C][C]4.85538274476901e-07[/C][/ROW]
[ROW][C]110[/C][C]0.999998586206077[/C][C]2.82758784611738e-06[/C][C]1.41379392305869e-06[/C][/ROW]
[ROW][C]111[/C][C]0.999998621214133[/C][C]2.75757173348653e-06[/C][C]1.37878586674327e-06[/C][/ROW]
[ROW][C]112[/C][C]0.999999001225173[/C][C]1.99754965389507e-06[/C][C]9.98774826947535e-07[/C][/ROW]
[ROW][C]113[/C][C]0.99999819948472[/C][C]3.60103055931868e-06[/C][C]1.80051527965934e-06[/C][/ROW]
[ROW][C]114[/C][C]0.999996318011193[/C][C]7.36397761357101e-06[/C][C]3.68198880678551e-06[/C][/ROW]
[ROW][C]115[/C][C]0.999987876727272[/C][C]2.42465454555379e-05[/C][C]1.2123272727769e-05[/C][/ROW]
[ROW][C]116[/C][C]0.999961021968236[/C][C]7.79560635274603e-05[/C][C]3.89780317637301e-05[/C][/ROW]
[ROW][C]117[/C][C]0.999999384287856[/C][C]1.23142428847821e-06[/C][C]6.15712144239104e-07[/C][/ROW]
[ROW][C]118[/C][C]0.999999481328694[/C][C]1.03734261253227e-06[/C][C]5.18671306266135e-07[/C][/ROW]
[ROW][C]119[/C][C]0.999997602942374[/C][C]4.79411525194395e-06[/C][C]2.39705762597198e-06[/C][/ROW]
[ROW][C]120[/C][C]0.999995322614421[/C][C]9.35477115735383e-06[/C][C]4.67738557867691e-06[/C][/ROW]
[ROW][C]121[/C][C]0.999974436854815[/C][C]5.11262903694175e-05[/C][C]2.55631451847087e-05[/C][/ROW]
[ROW][C]122[/C][C]0.999919898075276[/C][C]0.000160203849448835[/C][C]8.01019247244174e-05[/C][/ROW]
[ROW][C]123[/C][C]0.999864946979461[/C][C]0.00027010604107839[/C][C]0.000135053020539195[/C][/ROW]
[ROW][C]124[/C][C]0.999673729562849[/C][C]0.000652540874302235[/C][C]0.000326270437151118[/C][/ROW]
[ROW][C]125[/C][C]0.997882440064053[/C][C]0.00423511987189303[/C][C]0.00211755993594651[/C][/ROW]
[ROW][C]126[/C][C]0.997009389992484[/C][C]0.00598122001503205[/C][C]0.00299061000751603[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158951&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158951&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
180.8029345433060370.3941309133879260.197065456693963
190.767872823676030.4642543526479390.23212717632397
200.6723226671700710.6553546656598580.327677332829929
210.5596395575988440.8807208848023120.440360442401156
220.5244746719805050.951050656038990.475525328019495
230.8924202432023120.2151595135953760.107579756797688
240.8526743482174410.2946513035651180.147325651782559
250.8212615235060620.3574769529878760.178738476493938
260.7666249242170540.4667501515658920.233375075782946
270.7432969387074810.5134061225850380.256703061292519
280.6774403866468780.6451192267062430.322559613353122
290.8211543696013150.357691260797370.178845630398685
300.7655755849624640.4688488300750710.234424415037536
310.7523121155354490.4953757689291020.247687884464551
320.6906329519587530.6187340960824940.309367048041247
330.6724722818217520.6550554363564970.327527718178248
340.622851474255560.7542970514888790.37714852574444
350.561585491884350.8768290162313010.43841450811565
360.4935094752684430.9870189505368870.506490524731557
370.4625243615197480.9250487230394950.537475638480252
380.4027728215691930.8055456431383860.597227178430807
390.348746279100990.697492558201980.65125372089901
400.5053557753554950.989288449289010.494644224644505
410.6811091218749490.6377817562501010.318890878125051
420.6593056588536190.6813886822927620.340694341146381
430.629558775462940.740882449074120.37044122453706
440.6229364512085540.7541270975828920.377063548791446
450.564525885002570.870948229994860.43547411499743
460.7213617784574040.5572764430851920.278638221542596
470.6755203611671690.6489592776656620.324479638832831
480.6990918840452290.6018162319095410.300908115954771
490.6522894519970090.6954210960059810.347710548002991
500.6334986454662920.7330027090674160.366501354533708
510.5901518303440220.8196963393119570.409848169655978
520.5426194801841320.9147610396317370.457380519815868
530.8288994467184680.3422011065630640.171100553281532
540.9623421842416050.07531563151679090.0376578157583955
550.9531013273485970.09379734530280650.0468986726514033
560.9407839319405890.1184321361188220.0592160680594108
570.9831105594170370.0337788811659270.0168894405829635
580.9776043308090080.04479133838198420.0223956691909921
590.9714478604609050.05710427907819080.0285521395390954
600.9726827756382650.05463444872347090.0273172243617354
610.9647486112461210.07050277750775790.035251388753879
620.9868494793122780.02630104137544370.0131505206877218
630.9836057986721250.03278840265574980.0163942013278749
640.9839757035802450.03204859283951010.016024296419755
650.9833082616083490.03338347678330270.0166917383916513
660.9867131538323120.02657369233537540.0132868461676877
670.9904328639737380.01913427205252430.00956713602626217
680.9971889477550850.005622104489830530.00281105224491527
690.995847535401190.008304929197619970.00415246459880998
700.993919089404770.01216182119046050.00608091059523025
710.9913267663844090.01734646723118230.00867323361559115
720.9888444020471510.02231119590569710.0111555979528486
730.988504020250630.022991959498740.01149597974937
740.9844784877797630.03104302444047470.0155215122202374
750.989650025835570.02069994832885940.0103499741644297
760.9901137186461180.01977256270776480.00988628135388242
770.9943261134547930.01134777309041370.00567388654520687
780.9999932187003461.35625993080303e-056.78129965401514e-06
790.9999878772839712.42454320570433e-051.21227160285216e-05
800.9999796757364664.06485270671454e-052.03242635335727e-05
810.9999630999734767.38000530474265e-053.69000265237132e-05
820.9999983786133193.24277336130685e-061.62138668065342e-06
830.9999970741588265.85168234849635e-062.92584117424817e-06
840.9999957752929128.44941417547627e-064.22470708773813e-06
850.9999925964597661.48070804675601e-057.40354023378004e-06
860.9999933913210941.32173578113675e-056.60867890568375e-06
870.9999905252014521.89495970956759e-059.47479854783795e-06
880.9999906415439661.87169120687174e-059.35845603435871e-06
890.9999842707150813.14585698388218e-051.57292849194109e-05
900.9999763825266594.7234946682178e-052.3617473341089e-05
910.9999647002212797.0599557442927e-053.52997787214635e-05
920.9999783824191254.32351617496735e-052.16175808748368e-05
930.9999640042973787.19914052448316e-053.59957026224158e-05
940.9999307925545820.0001384148908359156.92074454179574e-05
950.9999399874663870.000120025067225246.001253361262e-05
960.9998944352226120.0002111295547764220.000105564777388211
970.9998495097068720.000300980586255460.00015049029312773
980.9997241831159130.0005516337681731190.00027581688408656
990.9995878871722830.0008242256554336180.000412112827716809
1000.9999406332969520.0001187334060952975.93667030476483e-05
1010.9998825690223430.0002348619553134530.000117430977656727
1020.9999563036353448.73927293117651e-054.36963646558826e-05
1030.9999309990360510.0001380019278983326.90009639491662e-05
1040.9998602263813190.0002795472373616970.000139773618680849
1050.9997536457723880.0004927084552241050.000246354227612052
1060.9995310454924850.0009379090150296630.000468954507514831
1070.9995045501113930.0009908997772146680.000495449888607334
1080.9999998450576783.09884644473975e-071.54942322236988e-07
1090.9999995144617269.71076548953802e-074.85538274476901e-07
1100.9999985862060772.82758784611738e-061.41379392305869e-06
1110.9999986212141332.75757173348653e-061.37878586674327e-06
1120.9999990012251731.99754965389507e-069.98774826947535e-07
1130.999998199484723.60103055931868e-061.80051527965934e-06
1140.9999963180111937.36397761357101e-063.68198880678551e-06
1150.9999878767272722.42465454555379e-051.2123272727769e-05
1160.9999610219682367.79560635274603e-053.89780317637301e-05
1170.9999993842878561.23142428847821e-066.15712144239104e-07
1180.9999994813286941.03734261253227e-065.18671306266135e-07
1190.9999976029423744.79411525194395e-062.39705762597198e-06
1200.9999953226144219.35477115735383e-064.67738557867691e-06
1210.9999744368548155.11262903694175e-052.55631451847087e-05
1220.9999198980752760.0001602038494488358.01019247244174e-05
1230.9998649469794610.000270106041078390.000135053020539195
1240.9996737295628490.0006525408743022350.000326270437151118
1250.9978824400640530.004235119871893030.00211755993594651
1260.9970093899924840.005981220015032050.00299061000751603






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level510.467889908256881NOK
5% type I error level670.614678899082569NOK
10% type I error level720.660550458715596NOK

\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 & 51 & 0.467889908256881 & NOK \tabularnewline
5% type I error level & 67 & 0.614678899082569 & NOK \tabularnewline
10% type I error level & 72 & 0.660550458715596 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158951&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]51[/C][C]0.467889908256881[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]67[/C][C]0.614678899082569[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]72[/C][C]0.660550458715596[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158951&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158951&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 level510.467889908256881NOK
5% type I error level670.614678899082569NOK
10% type I error level720.660550458715596NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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