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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, 04 Apr 2012 08:07:18 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Apr/04/t1333541317ipbuw61km707osp.htm/, Retrieved Mon, 29 Apr 2024 02:12:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=164291, Retrieved Mon, 29 Apr 2024 02:12:01 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact169

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [OMEC] [2012-04-04 11:30:04] [91ce4971c808115c699d50336245df56]
- RMPD  [Multiple Regression] [Multipele regressie] [2012-04-04 12:00:43] [91ce4971c808115c699d50336245df56]
- R  D      [Multiple Regression] [double log] [2012-04-04 12:07:18] [7a9891c1925ad1e8ddfe52b8c5887b5b] [Current]
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Dataset

Dataseries X:
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Boxplots 
15,27	,21	,90	,85	,92	,59	,62	,98	1,19	,99	-,73	-,05	,17	,57	16,00	13,09	14,63	15,66	16,12
15,18	,20	,90	,84	,95	,58	,62	,96	1,17	,96	-,72	-,03	,17	,59	15,92	13,08	14,52	15,52	16,24
15,13	,19	,89	,86	,94	,58	,61	,97	1,17	,95	-,72	-,03	,19	,57	15,86	13,13	14,65	15,54	16,06
15,14	,20	,89	,82	,95	,59	,60	,98	1,17	,95	-,72	-,05	,17	,57	15,87	13,14	14,64	15,51	15,91
15,10	,19	,89	,81	,95	,58	,59	,97	1,18	,93	-,72	-,04	,17	,59	15,82	13,06	14,56	15,45	15,87
15,17	,20	,88	,84	,96	,57	,61	,97	1,18	,94	-,71	-,04	,17	,60	15,89	13,05	14,54	15,41	15,85
15,11	,19	,88	,82	,95	,56	,61	,97	1,18	,97	-,72	-,06	,16	,60	15,83	13,12	14,53	15,40	15,92
15,09	,19	,88	,79	,95	,57	,58	,94	1,18	,98	-,72	-,04	,17	,60	15,83	13,12	14,57	15,45	15,95
15,10	,17	,88	,81	,95	,57	,59	,94	1,17	,97	-,72	-,03	,16	,58	15,80	13,03	14,51	15,38	15,95
15,06	,17	,87	,85	,94	,55	,58	,96	1,17	,97	-,72	-,03	,17	,58	15,79	13,06	14,51	15,36	15,91
15,03	,18	,88	,86	,93	,56	,55	,91	1,17	,96	-,71	-,04	,19	,59	15,79	13,02	14,49	15,32	15,92
15,03	,17	,87	,80	,92	,53	,55	,88	1,17	,96	-,72	-,05	,16	,57	15,81	13,02	14,55	15,35	15,90
15,13	,17	,86	,84	,92	,54	,57	,92	1,17	,97	-,72	-,04	,19	,59	15,88	12,96	14,60	15,41	15,90
15,02	,18	,88	,82	,95	,57	,57	,91	1,18	,91	-,71	-,04	,19	,60	15,78	13,04	14,50	15,37	15,88
15,01	,19	,86	,83	,97	,57	,55	,92	1,18	,93	-,70	-,03	,20	,59	15,78	13,03	14,52	15,38	15,84
15,04	,19	,85	,84	,97	,57	,57	,93	1,17	,92	-,70	-,04	,17	,59	15,80	13,02	14,57	15,40	15,85
15,02	,18	,85	,89	,96	,57	,56	,92	1,18	,94	-,70	-,06	,20	,58	15,82	12,98	14,61	15,36	15,95
15,00	,18	,86	,90	,97	,57	,58	,92	1,18	,96	-,70	-,05	,22	,59	15,82	13,04	14,59	15,34	15,89
15,13	,19	,86	,91	,98	,57	,58	,91	1,17	,96	-,68	-,06	,21	,60	16,04	12,95	14,86	15,47	16,07
15,06	,19	,88	,90	,97	,58	,58	,91	1,17	,95	-,68	-,07	,20	,59	15,91	12,98	14,71	15,34	15,99
14,90	,19	,86	,89	,95	,58	,58	,92	1,17	,88	-,68	-,02	,21	,60	15,69	13,11	14,49	15,28	15,85
14,91	,18	,85	,86	,94	,57	,56	,90	1,18	,83	-,69	-,07	,19	,55	15,66	13,07	14,58	15,37	15,94
14,92	,19	,86	,85	,95	,56	,57	,92	1,18	,94	-,70	-,07	,20	,57	15,67	13,02	14,54	15,37	15,91
14,97	,19	,85	,77	,94	,58	,57	,92	1,18	,94	-,69	-,04	,21	,58	15,73	13,02	14,58	15,40	15,88
14,97	,19	,85	,80	,95	,59	,57	,92	1,18	1,00	-,71	-,03	,17	,56	15,72	13,07	14,60	15,38	15,95
15,03	,18	,85	,77	,92	,57	,61	,95	1,19	,99	-,71	-,03	,19	,56	15,78	13,04	14,63	15,40	15,95
15,01	,18	,85	,79	,94	,56	,62	,93	1,18	1,01	-,70	-,06	,20	,54	15,75	13,14	14,58	15,37	15,94
15,02	,18	,85	,82	,97	,57	,61	,93	1,19	1,01	-,70	-,05	,20	,56	15,77	13,15	14,58	15,39	15,89
14,98	,18	,86	,80	1,00	,54	,60	,94	1,20	1,06	-,69	-,04	,20	,57	15,73	13,31	14,62	15,43	15,96
15,03	,18	,87	,84	1,01	,59	,60	,95	1,19	1,07	-,69	-,02	,20	,57	15,77	13,37	14,60	15,44	15,98
14,99	,20	,88	,85	1,01	,57	,60	,95	1,20	1,09	-,69	-,02	,20	,57	15,74	13,33	14,61	15,44	15,92
15,05	,20	,87	,85	,98	,59	,61	,95	1,20	1,08	-,69	-,03	,18	,55	15,80	13,32	14,63	15,48	15,94
15,04	,19	,87	,83	,96	,58	,61	,95	1,20	1,07	-,71	-,02	,19	,58	15,78	13,82	14,70	15,52	16,00
15,11	,23	,90	,84	,98	,60	,50	,95	1,21	1,09	-,70	-,02	,18	,54	15,89	13,53	14,66	15,48	16,08
15,14	,23	,88	,84	,99	,59	,50	,93	1,21	1,09	-,70	-,01	,21	,57	15,93	13,49	14,58	15,43	16,02
15,06	,24	,90	,84	,99	,59	,51	,95	1,21	1,08	-,72	-,01	,22	,50	15,83	13,47	14,74	15,59	15,97
15,10	,25	,88	,84	1,00	,56	,50	,93	1,22	1,08	-,69	-,03	,21	,53	15,86	13,81	14,61	15,49	16,02
15,20	,24	,84	,83	1,02	,55	,49	,95	1,20	1,05	-,70	-,02	,21	,55	15,98	13,66	14,65	15,48	16,02
15,13	,25	,88	,84	1,02	,57	,50	,96	1,21	1,07	-,69	-,03	,21	,58	15,92	13,42	14,67	15,54	16,03
15,21	,25	,90	,85	1,00	,58	,50	,97	1,21	1,08	-,67	-,03	,21	,58	15,96	13,44	14,64	15,48	16,15
15,17	,23	,90	,84	,99	,57	,52	,97	1,21	1,08	-,68	-,03	,21	,58	15,94	13,55	14,63	15,49	16,01
15,18	,24	,91	,84	,99	,56	,51	,97	1,20	1,07	-,69	-,02	,21	,59	15,96	13,49	14,79	15,70	16,13
15,21	,23	,90	,84	,99	,56	,51	,97	1,20	1,07	-,68	-,03	,20	,54	15,97	13,44	14,88	15,81	16,12
15,25	,24	,90	,85	,98	,58	,51	,98	1,21	1,05	-,67	-,03	,19	,59	16,03	13,48	14,70	15,65	16,14
15,18	,24	,89	,86	1,01	,56	,49	,95	1,21	1,06	-,68	-,02	,24	,59	15,94	13,60	14,70	15,62	16,23
15,19	,24	,89	,86	1,02	,55	,50	,96	1,21	1,05	-,68	-,02	,20	,58	15,95	13,38	14,68	15,64	16,15
15,25	,25	,90	,85	1,03	,57	,50	,97	1,22	1,05	-,67	-,06	,20	,57	16,02	13,20	14,54	15,49	16,06
15,21	,23	,91	,82	1,02	,58	,52	,96	1,22	1,04	-,68	-,03	,17	,55	15,96	13,29	14,69	15,68	16,04
15,20	,24	,92	,79	1,03	,59	,52	,97	1,22	1,05	-,68	-,03	,17	,56	15,96	13,11	14,71	15,75	16,04
15,28	,24	,92	,81	1,02	,60	,53	,97	1,22	1,06	-,68	-,03	,19	,56	16,04	13,26	14,53	15,52	16,21
15,41	,25	,92	,84	1,01	,58	,53	,97	1,21	1,07	-,67	-,03	,22	,56	16,17	13,21	14,74	15,81	16,24
15,45	,25	,92	,84	1,00	,58	,54	,97	1,22	1,07	-,67	-,02	,22	,58	16,20	13,09	14,88	16,08	16,12
15,31	,24	,93	,86	1,00	,58	,53	,97	1,23	1,05	-,68	-,02	,20	,57	16,06	13,24	14,65	15,72	16,29
15,19	,24	,93	,86	1,01	,57	,52	,96	1,21	1,05	-,65	-,02	,20	,57	15,96	13,23	14,59	15,53	16,11
15,18	,22	,93	,86	1,01	,58	,51	,96	1,22	1,07	-,68	-,04	,16	,56	15,92	13,45	14,70	15,51	16,17
15,26	,22	,93	,85	1,00	,59	,50	,97	1,23	1,07	-,66	-,02	,17	,57	15,98	13,45	14,68	15,49	16,12
15,24	,22	,91	,84	1,00	,60	,51	,97	1,22	1,07	-,67	,00	,18	,51	15,97	13,33	14,61	15,45	16,06
15,14	,22	,90	,82	,98	,59	,50	,96	1,22	1,07	-,66	,01	,18	,56	15,88	13,31	14,67	15,57	16,02
15,08	,21	,89	,83	,98	,58	,49	,96	1,22	1,04	-,67	,02	,18	,59	15,83	13,33	14,63	15,51	16,02
15,12	,21	,89	,83	,98	,56	,48	,96	1,21	1,04	-,67	,01	,17	,59	15,87	13,27	14,61	15,49	16,08
15,11	,21	,89	,83	,99	,57	,50	,95	1,22	1,05	-,68	,01	,16	,61	15,85	13,33	14,54	15,40	16,00
15,08	,21	,88	,83	,98	,57	,47	,89	1,22	,98	-,68	,02	,19	,61	15,82	13,31	14,57	15,37	15,98
15,06	,21	,88	,86	1,00	,57	,47	,90	1,21	1,01	-,68	,01	,20	,62	15,84	13,32	14,50	15,32	15,99
15,17	,21	,89	,85	,99	,58	,47	,93	1,21	1,05	-,69	,01	,19	,61	15,95	13,29	14,58	15,35	16,03
15,11	,22	,88	,85	,97	,58	,46	,93	1,21	1,06	-,70	,01	,19	,60	15,88	13,28	14,63	15,41	16,06
15,03	,22	,90	,83	,98	,58	,49	,95	1,21	1,06	-,69	,02	,21	,61	15,83	13,33	14,53	15,35	15,96
15,02	,23	,90	,81	,99	,61	,50	,92	1,21	1,06	-,69	,02	,20	,61	15,82	13,32	14,54	15,36	15,96
15,02	,23	,90	,80	,99	,65	,50	,93	1,21	1,06	-,68	,02	,19	,60	15,83	13,34	14,56	15,40	16,01
15,04	,24	,89	,82	1,00	,65	,49	,95	1,21	1,05	-,68	,01	,19	,60	15,88	13,27	14,58	15,39	15,99
15,01	,23	,90	,86	1,01	,62	,49	,94	1,21	1,04	-,67	,01	,19	,61	15,86	13,32	14,58	15,36	15,98
15,06	,25	,90	,87	1,02	,57	,52	,96	1,21	1,03	-,63	-,01	,20	,62	15,96	13,34	14,85	15,50	16,20
15,09	,25	,91	,88	1,03	,59	,51	,96	1,20	1,04	-,66	,00	,21	,62	16,01	13,26	14,71	15,29	16,10
15,11	,25	,91	,86	1,01	,59	,53	,97	1,21	1,09	-,68	,01	,21	,62	15,95	13,30	14,59	15,25	15,90
14,94	,24	,89	,86	,99	,59	,50	,97	1,21	1,09	-,69	-,01	,19	,61	15,75	13,39	14,61	15,44	15,98
14,94	,26	,88	,86	,99	,59	,51	,96	1,21	1,08	-,69	,00	,19	,61	15,75	13,41	14,58	15,40	15,96
14,97	,26	,90	,83	,99	,59	,51	,98	1,22	1,08	-,69	,01	,20	,60	15,78	13,41	14,59	15,39	15,96
14,99	,25	,89	,78	1,00	,59	,50	,97	1,22	1,08	-,69	,01	,19	,61	15,78	13,50	14,62	15,41	15,99
15,06	,25	,89	,80	,99	,57	,51	,97	1,21	1,08	-,68	,01	,19	,62	15,85	13,46	14,66	15,49	16,02
15,03	,24	,88	,81	,99	,57	,51	,98	1,22	1,09	-,68	,01	,18	,61	15,82	13,44	14,60	15,43	15,99
15,00	,23	,89	,77	,99	,57	,51	,98	1,22	1,09	-,69	,03	,19	,61	15,80	13,36	14,54	15,41	15,99
15,01	,24	,89	,80	,99	,58	,50	,97	1,22	1,09	-,68	,02	,19	,59	15,79	13,45	14,60	15,41	16,07
15,02	,24	,87	,82	1,00	,57	,49	,97	1,22	1,10	-,66	,02	,20	,60	15,80	13,47	14,67	15,49	16,06
15,03	,24	,87	,81	,99	,59	,52	,98	1,22	1,10	-,66	,03	,20	,59	15,81	13,49	14,63	15,45	16,02
15,08	,24	,88	,81	,98	,59	,51	,98	1,22	1,09	-,66	,03	,19	,59	15,85	13,48	14,62	15,49	16,04
15,13	,26	,88	,82	,99	,59	,52	,98	1,22	1,07	-,66	,02	,20	,57	15,93	13,44	14,59	15,45	16,12
15,15	,25	,86	,82	,98	,58	,51	,98	1,22	1,07	-,70	,02	,19	,55	15,91	13,38	14,65	15,56	16,10
15,14	,26	,87	,84	,99	,60	,51	,98	1,24	1,10	-,71	,02	,21	,57	15,92	13,40	14,62	15,46	16,08
15,10	,26	,86	,85	,98	,59	,51	,98	1,26	1,10	-,71	,02	,20	,48	15,89	13,46	14,59	15,48	16,23
15,12	,26	,87	,83	,99	,58	,51	,95	1,25	1,07	-,72	,03	,22	,55	15,89	13,52	14,68	15,51	16,08
15,23	,26	,86	,83	,99	,58	,51	,98	1,25	1,07	-,72	,02	,21	,54	16,00	13,62	14,74	15,62	16,11
15,24	,26	,87	,79	,98	,58	,51	,98	1,25	1,09	-,70	,02	,23	,58	16,02	13,61	14,70	15,58	16,14
15,19	,25	,98	,76	,98	,58	,52	,97	1,25	1,10	-,67	,02	,21	,56	15,98	13,54	14,68	15,57	16,07
15,21	,25	,91	,76	,97	,59	,50	,95	1,24	1,09	-,69	,03	,21	,57	15,98	13,47	14,63	15,54	16,09
15,33	,26	,96	,75	,97	,62	,54	,97	1,24	1,10	-,69	,02	,19	,53	16,09	13,50	14,68	15,61	16,33
15,21	,26	,97	,75	,95	,66	,55	,97	1,24	1,10	-,69	,02	,18	,53	15,98	13,92	14,75	15,63	16,39
15,19	,27	,98	,77	,94	,59	,54	,99	1,24	1,11	-,69	,02	,18	,55	15,98	13,75	14,76	15,73	16,22
15,32	,27	1,00	,79	,97	,53	,55	,97	1,24	1,11	-,71	,02	,21	,55	16,09	13,64	14,67	15,57	16,24
15,51	,29	1,00	,79	,99	,55	,56	,97	1,24	1,10	-,71	,02	,21	,55	16,26	13,57	14,62	15,48	16,26
15,34	,27	,91	,82	1,00	,55	,55	,98	1,26	1,06	-,72	,00	,20	,58	16,10	13,61	14,65	15,56	16,17
15,23	,26	,88	,84	1,00	,55	,54	,96	1,24	1,07	-,71	,03	,21	,61	16,02	13,50	14,55	15,48	16,34
15,40	,27	,90	,84	1,00	,57	,56	,95	1,24	1,09	-,73	,04	,22	,60	16,18	13,62	14,50	15,42	16,38
15,23	,27	,93	,85	1,00	,56	,54	,95	1,24	1,08	-,72	,03	,19	,60	16,03	13,79	14,56	15,53	16,18
15,30	,27	,94	,84	1,00	,58	,54	,97	1,24	1,08	-,71	,02	,19	,60	16,08	13,51	14,73	15,73	16,45
15,25	,26	,92	,84	1,00	,58	,54	,96	1,24	1,08	-,71	,04	,17	,60	16,04	13,44	14,77	15,85	16,59
15,22	,26	,92	,85	,99	,60	,54	,96	1,24	1,08	-,72	,04	,27	,60	15,99	13,37	14,65	15,68	16,31
15,24	,26	,92	,84	1,00	,60	,53	,95	1,23	1,08	-,73	,03	,20	,60	16,02	13,43	14,70	15,52	16,18
15,17	,26	,92	,78	,99	,61	,51	,97	1,25	1,09	-,73	-,01	,21	,60	15,97	13,62	14,57	15,48	16,19
15,31	,26	,92	,76	1,00	,62	,53	,97	1,25	1,09	-,73	,03	,21	,62	16,09	13,54	14,76	15,63	16,17
15,27	,26	,90	,77	,99	,62	,53	,97	1,25	1,09	-,73	,07	,21	,64	16,04	13,62	14,71	15,67	16,13
15,16	,24	,90	,81	,98	,61	,52	,96	1,25	1,08	-,72	,09	,21	,67	15,92	13,63	14,59	15,51	16,26
15,18	,23	,90	,83	,98	,63	,54	,95	1,26	1,10	-,73	,08	,22	,68	15,91	13,70	14,54	15,44	16,18
15,15	,22	,89	,83	,99	,63	,53	,98	1,25	1,10	-,74	,05	,22	,67	15,91	13,62	14,60	15,47	16,17
15,11	,23	,89	,83	,99	,65	,55	,98	1,26	1,09	-,70	,06	,25	,68	15,87	13,61	14,61	15,51	16,06
15,15	,24	,87	,84	1,00	,65	,54	,97	1,27	1,10	-,73	,04	,25	,67	15,92	13,58	14,58	15,44	16,10
15,11	,23	,87	,84	,99	,66	,55	,97	1,26	1,09	-,73	,06	,23	,67	15,92	13,53	14,54	15,42	16,02
15,20	,23	,87	,85	1,00	,64	,56	,97	1,24	1,10	-,73	,06	,20	,67	16,00	13,52	14,63	15,49	16,08
15,10	,23	,88	,84	1,00	,65	,54	,95	1,25	1,09	-,75	,07	,21	,67	15,90	13,52	14,60	15,43	16,12
15,09	,23	,86	,77	1,00	,64	,54	,86	1,26	1,09	-,74	,08	,22	,67	15,90	13,63	14,50	15,33	16,04
15,07	,23	,87	,63	1,01	,65	,55	,97	1,26	1,08	-,74	,07	,22	,65	15,91	13,55	14,52	15,35	16,03
15,00	,23	,88	,70	1,04	,65	,55	,97	1,26	1,08	-,73	,05	,22	,64	15,82	13,57	14,52	15,35	16,08
15,06	,23	,90	,73	1,01	,65	,54	,97	1,27	1,08	-,73	,04	,24	,66	15,90	13,57	14,59	15,40	16,08
15,03	,24	,91	,80	1,02	,67	,55	,99	1,27	1,10	-,73	,04	,23	,65	15,88	13,56	14,72	15,41	16,04
15,06	,25	,92	,88	1,02	,67	,55	,99	1,27	1,12	-,72	,04	,23	,66	15,96	13,59	14,71	15,44	16,25
15,18	,26	,93	,90	1,03	,68	,58	,99	1,27	1,13	-,70	,04	,22	,64	16,13	13,56	14,74	15,33	16,12
15,13	,26	,93	,89	1,02	,68	,57	,99	1,28	1,12	-,71	,06	,22	,65	16,03	13,57	14,71	15,39	16,00
14,99	,23	,92	,87	1,01	,66	,54	,99	1,27	1,11	-,71	,08	,22	,64	15,77	13,65	14,65	15,53	16,08
14,99	,22	,89	,87	1,01	,66	,55	,99	1,26	1,11	-,65	,08	,22	,64	15,76	13,66	14,62	15,54	16,04
15,03	,23	,93	,87	1,00	,65	,54	,99	1,26	1,10	-,65	,08	,21	,64	15,79	13,64	14,62	15,52	16,04
15,03	,22	,92	,86	1,01	,66	,54	,98	1,27	1,13	-,66	,07	,20	,63	15,78	13,68	14,63	15,49	16,08
15,05	,21	,91	,84	1,00	,65	,54	,98	1,27	1,14	-,62	,06	,21	,63	15,84	13,67	14,67	15,56	16,13

Tables (Output of Computation)





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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=164291&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=164291&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=164291&T=0

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


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

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






Multiple Linear Regression - Estimated Regression Equation
QBEPIL[t] = + 0.13092809560212 -0.965391719746462PBEPIL[t] + 0.0726768479056838PBELUX[t] + 0.0249194280100807PBABD[t] -0.175431578573902PBFRU[t] -0.32265123644768PBEPAL[t] -0.126528622098074PBESTO[t] + 0.283699695169786PBEWIT[t] + 0.115370247801717PBENA[t] -0.0127587588377971PCHSAN[t] -0.161931944644183PWABR[t] + 0.110141558184619PSOCOLA[t] -0.165755390119786PSOBIT[t] -0.276345162564919PSPORT[t] + 0.994011328932661BUDBEER[t] + 0.0564059090374617BUDCHIL[t] -0.332643182408528BUDAMB[t] + 0.265561059722925BUDWATER[t] -0.0351291352891992BUDSISSS[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
QBEPIL[t] =  +  0.13092809560212 -0.965391719746462PBEPIL[t] +  0.0726768479056838PBELUX[t] +  0.0249194280100807PBABD[t] -0.175431578573902PBFRU[t] -0.32265123644768PBEPAL[t] -0.126528622098074PBESTO[t] +  0.283699695169786PBEWIT[t] +  0.115370247801717PBENA[t] -0.0127587588377971PCHSAN[t] -0.161931944644183PWABR[t] +  0.110141558184619PSOCOLA[t] -0.165755390119786PSOBIT[t] -0.276345162564919PSPORT[t] +  0.994011328932661BUDBEER[t] +  0.0564059090374617BUDCHIL[t] -0.332643182408528BUDAMB[t] +  0.265561059722925BUDWATER[t] -0.0351291352891992BUDSISSS[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=164291&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]QBEPIL[t] =  +  0.13092809560212 -0.965391719746462PBEPIL[t] +  0.0726768479056838PBELUX[t] +  0.0249194280100807PBABD[t] -0.175431578573902PBFRU[t] -0.32265123644768PBEPAL[t] -0.126528622098074PBESTO[t] +  0.283699695169786PBEWIT[t] +  0.115370247801717PBENA[t] -0.0127587588377971PCHSAN[t] -0.161931944644183PWABR[t] +  0.110141558184619PSOCOLA[t] -0.165755390119786PSOBIT[t] -0.276345162564919PSPORT[t] +  0.994011328932661BUDBEER[t] +  0.0564059090374617BUDCHIL[t] -0.332643182408528BUDAMB[t] +  0.265561059722925BUDWATER[t] -0.0351291352891992BUDSISSS[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=164291&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=164291&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
QBEPIL[t] = + 0.13092809560212 -0.965391719746462PBEPIL[t] + 0.0726768479056838PBELUX[t] + 0.0249194280100807PBABD[t] -0.175431578573902PBFRU[t] -0.32265123644768PBEPAL[t] -0.126528622098074PBESTO[t] + 0.283699695169786PBEWIT[t] + 0.115370247801717PBENA[t] -0.0127587588377971PCHSAN[t] -0.161931944644183PWABR[t] + 0.110141558184619PSOCOLA[t] -0.165755390119786PSOBIT[t] -0.276345162564919PSPORT[t] + 0.994011328932661BUDBEER[t] + 0.0564059090374617BUDCHIL[t] -0.332643182408528BUDAMB[t] + 0.265561059722925BUDWATER[t] -0.0351291352891992BUDSISSS[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.130928095602120.5672310.23080.817880.40894
PBEPIL-0.9653917197464620.169144-5.707500
PBELUX0.07267684790568380.1012290.71790.4742990.23715
PBABD0.02491942801008070.0570640.43670.6631830.331592
PBFRU-0.1754315785739020.127985-1.37070.1732260.086613
PBEPAL-0.322651236447680.098239-3.28440.0013680.000684
PBESTO-0.1265286220980740.080692-1.5680.1197170.059858
PBEWIT0.2836996951697860.1210282.34410.0208530.010426
PBENA0.1153702478017170.1972440.58490.5597950.279897
PCHSAN-0.01275875883779710.079096-0.16130.8721450.436072
PWABR-0.1619319446441830.114502-1.41420.1600940.080047
PSOCOLA0.1101415581846190.1320540.83410.4060340.203017
PSOBIT-0.1657553901197860.133267-1.24380.2161990.108099
PSPORT-0.2763451625649190.08472-3.26180.0014710.000735
BUDBEER0.9940113289326610.03284830.261300
BUDCHIL0.05640590903746170.0187513.00820.0032520.001626
BUDAMB-0.3326431824085280.037748-8.812100
BUDWATER0.2655610597229250.0266889.950600
BUDSISSS-0.03512913528919920.027132-1.29470.1980980.099049

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 0.13092809560212 & 0.567231 & 0.2308 & 0.81788 & 0.40894 \tabularnewline
PBEPIL & -0.965391719746462 & 0.169144 & -5.7075 & 0 & 0 \tabularnewline
PBELUX & 0.0726768479056838 & 0.101229 & 0.7179 & 0.474299 & 0.23715 \tabularnewline
PBABD & 0.0249194280100807 & 0.057064 & 0.4367 & 0.663183 & 0.331592 \tabularnewline
PBFRU & -0.175431578573902 & 0.127985 & -1.3707 & 0.173226 & 0.086613 \tabularnewline
PBEPAL & -0.32265123644768 & 0.098239 & -3.2844 & 0.001368 & 0.000684 \tabularnewline
PBESTO & -0.126528622098074 & 0.080692 & -1.568 & 0.119717 & 0.059858 \tabularnewline
PBEWIT & 0.283699695169786 & 0.121028 & 2.3441 & 0.020853 & 0.010426 \tabularnewline
PBENA & 0.115370247801717 & 0.197244 & 0.5849 & 0.559795 & 0.279897 \tabularnewline
PCHSAN & -0.0127587588377971 & 0.079096 & -0.1613 & 0.872145 & 0.436072 \tabularnewline
PWABR & -0.161931944644183 & 0.114502 & -1.4142 & 0.160094 & 0.080047 \tabularnewline
PSOCOLA & 0.110141558184619 & 0.132054 & 0.8341 & 0.406034 & 0.203017 \tabularnewline
PSOBIT & -0.165755390119786 & 0.133267 & -1.2438 & 0.216199 & 0.108099 \tabularnewline
PSPORT & -0.276345162564919 & 0.08472 & -3.2618 & 0.001471 & 0.000735 \tabularnewline
BUDBEER & 0.994011328932661 & 0.032848 & 30.2613 & 0 & 0 \tabularnewline
BUDCHIL & 0.0564059090374617 & 0.018751 & 3.0082 & 0.003252 & 0.001626 \tabularnewline
BUDAMB & -0.332643182408528 & 0.037748 & -8.8121 & 0 & 0 \tabularnewline
BUDWATER & 0.265561059722925 & 0.026688 & 9.9506 & 0 & 0 \tabularnewline
BUDSISSS & -0.0351291352891992 & 0.027132 & -1.2947 & 0.198098 & 0.099049 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=164291&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]0.13092809560212[/C][C]0.567231[/C][C]0.2308[/C][C]0.81788[/C][C]0.40894[/C][/ROW]
[ROW][C]PBEPIL[/C][C]-0.965391719746462[/C][C]0.169144[/C][C]-5.7075[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]PBELUX[/C][C]0.0726768479056838[/C][C]0.101229[/C][C]0.7179[/C][C]0.474299[/C][C]0.23715[/C][/ROW]
[ROW][C]PBABD[/C][C]0.0249194280100807[/C][C]0.057064[/C][C]0.4367[/C][C]0.663183[/C][C]0.331592[/C][/ROW]
[ROW][C]PBFRU[/C][C]-0.175431578573902[/C][C]0.127985[/C][C]-1.3707[/C][C]0.173226[/C][C]0.086613[/C][/ROW]
[ROW][C]PBEPAL[/C][C]-0.32265123644768[/C][C]0.098239[/C][C]-3.2844[/C][C]0.001368[/C][C]0.000684[/C][/ROW]
[ROW][C]PBESTO[/C][C]-0.126528622098074[/C][C]0.080692[/C][C]-1.568[/C][C]0.119717[/C][C]0.059858[/C][/ROW]
[ROW][C]PBEWIT[/C][C]0.283699695169786[/C][C]0.121028[/C][C]2.3441[/C][C]0.020853[/C][C]0.010426[/C][/ROW]
[ROW][C]PBENA[/C][C]0.115370247801717[/C][C]0.197244[/C][C]0.5849[/C][C]0.559795[/C][C]0.279897[/C][/ROW]
[ROW][C]PCHSAN[/C][C]-0.0127587588377971[/C][C]0.079096[/C][C]-0.1613[/C][C]0.872145[/C][C]0.436072[/C][/ROW]
[ROW][C]PWABR[/C][C]-0.161931944644183[/C][C]0.114502[/C][C]-1.4142[/C][C]0.160094[/C][C]0.080047[/C][/ROW]
[ROW][C]PSOCOLA[/C][C]0.110141558184619[/C][C]0.132054[/C][C]0.8341[/C][C]0.406034[/C][C]0.203017[/C][/ROW]
[ROW][C]PSOBIT[/C][C]-0.165755390119786[/C][C]0.133267[/C][C]-1.2438[/C][C]0.216199[/C][C]0.108099[/C][/ROW]
[ROW][C]PSPORT[/C][C]-0.276345162564919[/C][C]0.08472[/C][C]-3.2618[/C][C]0.001471[/C][C]0.000735[/C][/ROW]
[ROW][C]BUDBEER[/C][C]0.994011328932661[/C][C]0.032848[/C][C]30.2613[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]BUDCHIL[/C][C]0.0564059090374617[/C][C]0.018751[/C][C]3.0082[/C][C]0.003252[/C][C]0.001626[/C][/ROW]
[ROW][C]BUDAMB[/C][C]-0.332643182408528[/C][C]0.037748[/C][C]-8.8121[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]BUDWATER[/C][C]0.265561059722925[/C][C]0.026688[/C][C]9.9506[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]BUDSISSS[/C][C]-0.0351291352891992[/C][C]0.027132[/C][C]-1.2947[/C][C]0.198098[/C][C]0.099049[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=164291&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=164291&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.130928095602120.5672310.23080.817880.40894
PBEPIL-0.9653917197464620.169144-5.707500
PBELUX0.07267684790568380.1012290.71790.4742990.23715
PBABD0.02491942801008070.0570640.43670.6631830.331592
PBFRU-0.1754315785739020.127985-1.37070.1732260.086613
PBEPAL-0.322651236447680.098239-3.28440.0013680.000684
PBESTO-0.1265286220980740.080692-1.5680.1197170.059858
PBEWIT0.2836996951697860.1210282.34410.0208530.010426
PBENA0.1153702478017170.1972440.58490.5597950.279897
PCHSAN-0.01275875883779710.079096-0.16130.8721450.436072
PWABR-0.1619319446441830.114502-1.41420.1600940.080047
PSOCOLA0.1101415581846190.1320540.83410.4060340.203017
PSOBIT-0.1657553901197860.133267-1.24380.2161990.108099
PSPORT-0.2763451625649190.08472-3.26180.0014710.000735
BUDBEER0.9940113289326610.03284830.261300
BUDCHIL0.05640590903746170.0187513.00820.0032520.001626
BUDAMB-0.3326431824085280.037748-8.812100
BUDWATER0.2655610597229250.0266889.950600
BUDSISSS-0.03512913528919920.027132-1.29470.1980980.099049






Multiple Linear Regression - Regression Statistics
Multiple R0.983383587140146
R-squared0.967043279456622
Adjusted R-squared0.961698946395533
F-TEST (value)180.947420080829
F-TEST (DF numerator)18
F-TEST (DF denominator)111
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0222169480518623
Sum Squared Residuals0.0547887986620453

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.983383587140146 \tabularnewline
R-squared & 0.967043279456622 \tabularnewline
Adjusted R-squared & 0.961698946395533 \tabularnewline
F-TEST (value) & 180.947420080829 \tabularnewline
F-TEST (DF numerator) & 18 \tabularnewline
F-TEST (DF denominator) & 111 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0222169480518623 \tabularnewline
Sum Squared Residuals & 0.0547887986620453 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=164291&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.983383587140146[/C][/ROW]
[ROW][C]R-squared[/C][C]0.967043279456622[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.961698946395533[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]180.947420080829[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]18[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]111[/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]0.0222169480518623[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.0547887986620453[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=164291&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=164291&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 R0.983383587140146
R-squared0.967043279456622
Adjusted R-squared0.961698946395533
F-TEST (value)180.947420080829
F-TEST (DF numerator)18
F-TEST (DF denominator)111
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0222169480518623
Sum Squared Residuals0.0547887986620453






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
115.2715.2826400794711-0.0126400794711274
215.1815.1925780798314-0.0125780798314214
315.1315.1217700666550.00822993334495348
415.1415.12248624670380.0175137532961766
515.115.08839907849150.0116009215084856
615.1715.13894697903160.0310530209684475
715.1115.09629413089110.0137058691088807
815.0915.08694104479940.00305895520056644
915.115.07921418446080.0207858155391562
1015.0615.080819268039-0.0208192680389654
1115.0315.0450311776033-0.0150311776032828
1215.0315.0749945416163-0.0449945416162635
1315.1315.1368276592425-0.00682765924252644
1415.0215.0363416643643-0.0163416643642725
1515.0115.0245190142643-0.0145190142643286
1615.0415.03483693401660.00516306598336261
1715.0215.0325888207322-0.0125888207322283
181515.030881321354-0.0308813213540419
1915.1315.1622776257355-0.0322776257354772
2015.0615.05611152429840.00388847570155548
2114.914.9135497983811-0.0135497983811083
2214.9114.89732742938850.0126725706114548
2314.9214.90854280866540.0114571913345518
2414.9714.95374335706460.0162566429353979
2514.9714.94369881740490.0263011825950855
2615.0315.01901758027050.0109824197295447
2715.0114.994668344720.0153316552799766
2815.0215.01243193249610.0075680675038759
2914.9814.9825357622808-0.00253576228077316
3015.0315.02188166756030.00811833243972474
3114.9914.97760608801850.0123939119814932
3215.0515.04463575568750.00536424431249411
3315.0415.0485985609887-0.00859856098873236
3415.1115.1212159389511-0.0112159389511336
3515.1415.1563443701894-0.0163443701893607
3615.0615.0640995976528-0.00409959765278155
3715.115.1078981690388-0.00789816903880273
3815.215.2111813037661-0.0111813037661344
3915.1315.12544118884010.00455881115985261
4015.2115.1576155167480.0523844832519675
4115.1715.1779682767948-0.00796827679482645
4215.1815.187289384333-0.0072893843330293
4315.2115.2157155493957-0.00571554939569099
4415.2515.2706593051376-0.0206593051376366
4515.1815.1658760033440.0141239966559557
4615.1915.1907463944392-0.000746394439168164
4715.2515.24341711836080.00658288163916944
4815.2115.2181136499266-0.0081136499266401
4915.215.2051873948611-0.00518739486107712
5015.2815.2803121739991-0.00031217399911541
5115.4115.40424485120480.0057551487952284
5215.4515.4538606631843-0.00386066318427684
5315.3115.3193450779308-0.0093450779308306
5415.1915.18794024244910.00205975755093465
5515.1815.1468732998620.0331267001379809
5615.2615.2076898603520.0523101396480299
5715.2415.21714844063630.0228515593636781
5815.1415.12947631865860.0105236813414218
5915.0815.0867572186061-0.00675721860609976
6015.1215.1294879583488-0.00948795834878799
6115.1115.10361463197120.00638536802884227
6215.0815.04024592062060.0397540793793959
6315.0615.0633629465879-0.00336294658786801
6415.1715.16400857038190.00599142961813705
6515.1115.09075976427010.0192402357298724
6615.0315.0592055660217-0.0292055660217504
6715.0215.01832569060350.00167430939651892
6815.0215.0240904833439-0.00409048334393806
6915.0415.0555655838472-0.0155655838471837
7015.0115.0431006159508-0.0331006159508107
7115.0615.0674905606465-0.00749056064645165
7215.0915.1040482293134-0.0140482293134122
7315.1115.09115683902880.018843160971234
7414.9414.9594244301162-0.0194244301162209
7514.9414.93770410120970.00229589879034761
7614.9714.9712833624618-0.00128336246183767
7714.9914.97388719901570.0161128009842672
7815.0615.05000100840610.00999899159385736
7915.0315.0415918121439-0.0115918121439201
801515.0433949878574-0.0433949878573777
8115.0115.00486409538440.00513590461557218
8215.0215.00823858261030.0117614173896677
8315.0315.02135336667230.00864663332766472
8415.0815.07932756422180.000672435778162952
8515.1315.1340836232586-0.0040836232586152
8615.1515.14888298001730.0011170199826795
8715.1415.12214283360390.0178571663961068
8815.115.1390672720908-0.0390672720908314
8915.1215.09823005407730.0217699459226564
9015.2315.2325149550511-0.00251495505105216
9115.2415.23708211215780.00291788784216028
9215.1915.2164845074692-0.0264845074691971
9315.2115.2113463290254-0.0013463290253991
9415.3315.31370982904840.0162901709515837
9515.2115.19969913324290.0103008667570881
9615.1915.2365076873175-0.0465076873174865
9715.3215.3337645670741-0.0137645670741451
9815.5115.46041997107170.049580028928307
9915.3415.32952551721280.0104744827872232
10015.2315.2427127599825-0.0127127599825153
10115.415.39298641448220.00701358551781899
10215.2315.2803187711489-0.0503187711488945
10315.315.29828149581720.00171850418280274
10415.2515.2790974722109-0.0290974722109334
10515.2215.21065071752010.00934928247987057
10615.2415.19669212874030.0433078712597349
10715.1715.1913323702071-0.0213323702071388
10815.3115.27430476388640.0356952361136299
10915.2715.25720516926750.0127948307325424
11015.1615.14748296699520.0125170330048152
11115.1815.13767162037550.0423283796244981
11215.1515.13839261170990.0116073882901491
11315.1115.07876011507370.031239884926335
11415.1515.10901376742820.0409862325717554
11515.1115.1284065673756-0.0184065673755863
11615.215.2001273404078-0.000127340407825702
11715.115.09143810500480.00856189499515579
11815.0915.08063518568330.00936481431673854
11915.0715.1118244510617-0.041824451061726
1201515.0178845749721-0.0178845749721367
12115.0615.0873381407234-0.0273381407233893
12215.0315.02089542611020.00910457388983726
12315.0615.0944531141745-0.034453114174491
12415.1815.2137314220676-0.0337314220676075
12515.1315.1501332003326-0.0201332003325514
12614.9914.9942094976461-0.0042094976461133
12714.9914.9942122213109-0.00421222131086009
12815.0315.01887763438320.0111223656168324
12915.0315.00506530686870.0249346931312824
13015.0515.0717147710853-0.0217147710853385

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 15.27 & 15.2826400794711 & -0.0126400794711274 \tabularnewline
2 & 15.18 & 15.1925780798314 & -0.0125780798314214 \tabularnewline
3 & 15.13 & 15.121770066655 & 0.00822993334495348 \tabularnewline
4 & 15.14 & 15.1224862467038 & 0.0175137532961766 \tabularnewline
5 & 15.1 & 15.0883990784915 & 0.0116009215084856 \tabularnewline
6 & 15.17 & 15.1389469790316 & 0.0310530209684475 \tabularnewline
7 & 15.11 & 15.0962941308911 & 0.0137058691088807 \tabularnewline
8 & 15.09 & 15.0869410447994 & 0.00305895520056644 \tabularnewline
9 & 15.1 & 15.0792141844608 & 0.0207858155391562 \tabularnewline
10 & 15.06 & 15.080819268039 & -0.0208192680389654 \tabularnewline
11 & 15.03 & 15.0450311776033 & -0.0150311776032828 \tabularnewline
12 & 15.03 & 15.0749945416163 & -0.0449945416162635 \tabularnewline
13 & 15.13 & 15.1368276592425 & -0.00682765924252644 \tabularnewline
14 & 15.02 & 15.0363416643643 & -0.0163416643642725 \tabularnewline
15 & 15.01 & 15.0245190142643 & -0.0145190142643286 \tabularnewline
16 & 15.04 & 15.0348369340166 & 0.00516306598336261 \tabularnewline
17 & 15.02 & 15.0325888207322 & -0.0125888207322283 \tabularnewline
18 & 15 & 15.030881321354 & -0.0308813213540419 \tabularnewline
19 & 15.13 & 15.1622776257355 & -0.0322776257354772 \tabularnewline
20 & 15.06 & 15.0561115242984 & 0.00388847570155548 \tabularnewline
21 & 14.9 & 14.9135497983811 & -0.0135497983811083 \tabularnewline
22 & 14.91 & 14.8973274293885 & 0.0126725706114548 \tabularnewline
23 & 14.92 & 14.9085428086654 & 0.0114571913345518 \tabularnewline
24 & 14.97 & 14.9537433570646 & 0.0162566429353979 \tabularnewline
25 & 14.97 & 14.9436988174049 & 0.0263011825950855 \tabularnewline
26 & 15.03 & 15.0190175802705 & 0.0109824197295447 \tabularnewline
27 & 15.01 & 14.99466834472 & 0.0153316552799766 \tabularnewline
28 & 15.02 & 15.0124319324961 & 0.0075680675038759 \tabularnewline
29 & 14.98 & 14.9825357622808 & -0.00253576228077316 \tabularnewline
30 & 15.03 & 15.0218816675603 & 0.00811833243972474 \tabularnewline
31 & 14.99 & 14.9776060880185 & 0.0123939119814932 \tabularnewline
32 & 15.05 & 15.0446357556875 & 0.00536424431249411 \tabularnewline
33 & 15.04 & 15.0485985609887 & -0.00859856098873236 \tabularnewline
34 & 15.11 & 15.1212159389511 & -0.0112159389511336 \tabularnewline
35 & 15.14 & 15.1563443701894 & -0.0163443701893607 \tabularnewline
36 & 15.06 & 15.0640995976528 & -0.00409959765278155 \tabularnewline
37 & 15.1 & 15.1078981690388 & -0.00789816903880273 \tabularnewline
38 & 15.2 & 15.2111813037661 & -0.0111813037661344 \tabularnewline
39 & 15.13 & 15.1254411888401 & 0.00455881115985261 \tabularnewline
40 & 15.21 & 15.157615516748 & 0.0523844832519675 \tabularnewline
41 & 15.17 & 15.1779682767948 & -0.00796827679482645 \tabularnewline
42 & 15.18 & 15.187289384333 & -0.0072893843330293 \tabularnewline
43 & 15.21 & 15.2157155493957 & -0.00571554939569099 \tabularnewline
44 & 15.25 & 15.2706593051376 & -0.0206593051376366 \tabularnewline
45 & 15.18 & 15.165876003344 & 0.0141239966559557 \tabularnewline
46 & 15.19 & 15.1907463944392 & -0.000746394439168164 \tabularnewline
47 & 15.25 & 15.2434171183608 & 0.00658288163916944 \tabularnewline
48 & 15.21 & 15.2181136499266 & -0.0081136499266401 \tabularnewline
49 & 15.2 & 15.2051873948611 & -0.00518739486107712 \tabularnewline
50 & 15.28 & 15.2803121739991 & -0.00031217399911541 \tabularnewline
51 & 15.41 & 15.4042448512048 & 0.0057551487952284 \tabularnewline
52 & 15.45 & 15.4538606631843 & -0.00386066318427684 \tabularnewline
53 & 15.31 & 15.3193450779308 & -0.0093450779308306 \tabularnewline
54 & 15.19 & 15.1879402424491 & 0.00205975755093465 \tabularnewline
55 & 15.18 & 15.146873299862 & 0.0331267001379809 \tabularnewline
56 & 15.26 & 15.207689860352 & 0.0523101396480299 \tabularnewline
57 & 15.24 & 15.2171484406363 & 0.0228515593636781 \tabularnewline
58 & 15.14 & 15.1294763186586 & 0.0105236813414218 \tabularnewline
59 & 15.08 & 15.0867572186061 & -0.00675721860609976 \tabularnewline
60 & 15.12 & 15.1294879583488 & -0.00948795834878799 \tabularnewline
61 & 15.11 & 15.1036146319712 & 0.00638536802884227 \tabularnewline
62 & 15.08 & 15.0402459206206 & 0.0397540793793959 \tabularnewline
63 & 15.06 & 15.0633629465879 & -0.00336294658786801 \tabularnewline
64 & 15.17 & 15.1640085703819 & 0.00599142961813705 \tabularnewline
65 & 15.11 & 15.0907597642701 & 0.0192402357298724 \tabularnewline
66 & 15.03 & 15.0592055660217 & -0.0292055660217504 \tabularnewline
67 & 15.02 & 15.0183256906035 & 0.00167430939651892 \tabularnewline
68 & 15.02 & 15.0240904833439 & -0.00409048334393806 \tabularnewline
69 & 15.04 & 15.0555655838472 & -0.0155655838471837 \tabularnewline
70 & 15.01 & 15.0431006159508 & -0.0331006159508107 \tabularnewline
71 & 15.06 & 15.0674905606465 & -0.00749056064645165 \tabularnewline
72 & 15.09 & 15.1040482293134 & -0.0140482293134122 \tabularnewline
73 & 15.11 & 15.0911568390288 & 0.018843160971234 \tabularnewline
74 & 14.94 & 14.9594244301162 & -0.0194244301162209 \tabularnewline
75 & 14.94 & 14.9377041012097 & 0.00229589879034761 \tabularnewline
76 & 14.97 & 14.9712833624618 & -0.00128336246183767 \tabularnewline
77 & 14.99 & 14.9738871990157 & 0.0161128009842672 \tabularnewline
78 & 15.06 & 15.0500010084061 & 0.00999899159385736 \tabularnewline
79 & 15.03 & 15.0415918121439 & -0.0115918121439201 \tabularnewline
80 & 15 & 15.0433949878574 & -0.0433949878573777 \tabularnewline
81 & 15.01 & 15.0048640953844 & 0.00513590461557218 \tabularnewline
82 & 15.02 & 15.0082385826103 & 0.0117614173896677 \tabularnewline
83 & 15.03 & 15.0213533666723 & 0.00864663332766472 \tabularnewline
84 & 15.08 & 15.0793275642218 & 0.000672435778162952 \tabularnewline
85 & 15.13 & 15.1340836232586 & -0.0040836232586152 \tabularnewline
86 & 15.15 & 15.1488829800173 & 0.0011170199826795 \tabularnewline
87 & 15.14 & 15.1221428336039 & 0.0178571663961068 \tabularnewline
88 & 15.1 & 15.1390672720908 & -0.0390672720908314 \tabularnewline
89 & 15.12 & 15.0982300540773 & 0.0217699459226564 \tabularnewline
90 & 15.23 & 15.2325149550511 & -0.00251495505105216 \tabularnewline
91 & 15.24 & 15.2370821121578 & 0.00291788784216028 \tabularnewline
92 & 15.19 & 15.2164845074692 & -0.0264845074691971 \tabularnewline
93 & 15.21 & 15.2113463290254 & -0.0013463290253991 \tabularnewline
94 & 15.33 & 15.3137098290484 & 0.0162901709515837 \tabularnewline
95 & 15.21 & 15.1996991332429 & 0.0103008667570881 \tabularnewline
96 & 15.19 & 15.2365076873175 & -0.0465076873174865 \tabularnewline
97 & 15.32 & 15.3337645670741 & -0.0137645670741451 \tabularnewline
98 & 15.51 & 15.4604199710717 & 0.049580028928307 \tabularnewline
99 & 15.34 & 15.3295255172128 & 0.0104744827872232 \tabularnewline
100 & 15.23 & 15.2427127599825 & -0.0127127599825153 \tabularnewline
101 & 15.4 & 15.3929864144822 & 0.00701358551781899 \tabularnewline
102 & 15.23 & 15.2803187711489 & -0.0503187711488945 \tabularnewline
103 & 15.3 & 15.2982814958172 & 0.00171850418280274 \tabularnewline
104 & 15.25 & 15.2790974722109 & -0.0290974722109334 \tabularnewline
105 & 15.22 & 15.2106507175201 & 0.00934928247987057 \tabularnewline
106 & 15.24 & 15.1966921287403 & 0.0433078712597349 \tabularnewline
107 & 15.17 & 15.1913323702071 & -0.0213323702071388 \tabularnewline
108 & 15.31 & 15.2743047638864 & 0.0356952361136299 \tabularnewline
109 & 15.27 & 15.2572051692675 & 0.0127948307325424 \tabularnewline
110 & 15.16 & 15.1474829669952 & 0.0125170330048152 \tabularnewline
111 & 15.18 & 15.1376716203755 & 0.0423283796244981 \tabularnewline
112 & 15.15 & 15.1383926117099 & 0.0116073882901491 \tabularnewline
113 & 15.11 & 15.0787601150737 & 0.031239884926335 \tabularnewline
114 & 15.15 & 15.1090137674282 & 0.0409862325717554 \tabularnewline
115 & 15.11 & 15.1284065673756 & -0.0184065673755863 \tabularnewline
116 & 15.2 & 15.2001273404078 & -0.000127340407825702 \tabularnewline
117 & 15.1 & 15.0914381050048 & 0.00856189499515579 \tabularnewline
118 & 15.09 & 15.0806351856833 & 0.00936481431673854 \tabularnewline
119 & 15.07 & 15.1118244510617 & -0.041824451061726 \tabularnewline
120 & 15 & 15.0178845749721 & -0.0178845749721367 \tabularnewline
121 & 15.06 & 15.0873381407234 & -0.0273381407233893 \tabularnewline
122 & 15.03 & 15.0208954261102 & 0.00910457388983726 \tabularnewline
123 & 15.06 & 15.0944531141745 & -0.034453114174491 \tabularnewline
124 & 15.18 & 15.2137314220676 & -0.0337314220676075 \tabularnewline
125 & 15.13 & 15.1501332003326 & -0.0201332003325514 \tabularnewline
126 & 14.99 & 14.9942094976461 & -0.0042094976461133 \tabularnewline
127 & 14.99 & 14.9942122213109 & -0.00421222131086009 \tabularnewline
128 & 15.03 & 15.0188776343832 & 0.0111223656168324 \tabularnewline
129 & 15.03 & 15.0050653068687 & 0.0249346931312824 \tabularnewline
130 & 15.05 & 15.0717147710853 & -0.0217147710853385 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=164291&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]15.27[/C][C]15.2826400794711[/C][C]-0.0126400794711274[/C][/ROW]
[ROW][C]2[/C][C]15.18[/C][C]15.1925780798314[/C][C]-0.0125780798314214[/C][/ROW]
[ROW][C]3[/C][C]15.13[/C][C]15.121770066655[/C][C]0.00822993334495348[/C][/ROW]
[ROW][C]4[/C][C]15.14[/C][C]15.1224862467038[/C][C]0.0175137532961766[/C][/ROW]
[ROW][C]5[/C][C]15.1[/C][C]15.0883990784915[/C][C]0.0116009215084856[/C][/ROW]
[ROW][C]6[/C][C]15.17[/C][C]15.1389469790316[/C][C]0.0310530209684475[/C][/ROW]
[ROW][C]7[/C][C]15.11[/C][C]15.0962941308911[/C][C]0.0137058691088807[/C][/ROW]
[ROW][C]8[/C][C]15.09[/C][C]15.0869410447994[/C][C]0.00305895520056644[/C][/ROW]
[ROW][C]9[/C][C]15.1[/C][C]15.0792141844608[/C][C]0.0207858155391562[/C][/ROW]
[ROW][C]10[/C][C]15.06[/C][C]15.080819268039[/C][C]-0.0208192680389654[/C][/ROW]
[ROW][C]11[/C][C]15.03[/C][C]15.0450311776033[/C][C]-0.0150311776032828[/C][/ROW]
[ROW][C]12[/C][C]15.03[/C][C]15.0749945416163[/C][C]-0.0449945416162635[/C][/ROW]
[ROW][C]13[/C][C]15.13[/C][C]15.1368276592425[/C][C]-0.00682765924252644[/C][/ROW]
[ROW][C]14[/C][C]15.02[/C][C]15.0363416643643[/C][C]-0.0163416643642725[/C][/ROW]
[ROW][C]15[/C][C]15.01[/C][C]15.0245190142643[/C][C]-0.0145190142643286[/C][/ROW]
[ROW][C]16[/C][C]15.04[/C][C]15.0348369340166[/C][C]0.00516306598336261[/C][/ROW]
[ROW][C]17[/C][C]15.02[/C][C]15.0325888207322[/C][C]-0.0125888207322283[/C][/ROW]
[ROW][C]18[/C][C]15[/C][C]15.030881321354[/C][C]-0.0308813213540419[/C][/ROW]
[ROW][C]19[/C][C]15.13[/C][C]15.1622776257355[/C][C]-0.0322776257354772[/C][/ROW]
[ROW][C]20[/C][C]15.06[/C][C]15.0561115242984[/C][C]0.00388847570155548[/C][/ROW]
[ROW][C]21[/C][C]14.9[/C][C]14.9135497983811[/C][C]-0.0135497983811083[/C][/ROW]
[ROW][C]22[/C][C]14.91[/C][C]14.8973274293885[/C][C]0.0126725706114548[/C][/ROW]
[ROW][C]23[/C][C]14.92[/C][C]14.9085428086654[/C][C]0.0114571913345518[/C][/ROW]
[ROW][C]24[/C][C]14.97[/C][C]14.9537433570646[/C][C]0.0162566429353979[/C][/ROW]
[ROW][C]25[/C][C]14.97[/C][C]14.9436988174049[/C][C]0.0263011825950855[/C][/ROW]
[ROW][C]26[/C][C]15.03[/C][C]15.0190175802705[/C][C]0.0109824197295447[/C][/ROW]
[ROW][C]27[/C][C]15.01[/C][C]14.99466834472[/C][C]0.0153316552799766[/C][/ROW]
[ROW][C]28[/C][C]15.02[/C][C]15.0124319324961[/C][C]0.0075680675038759[/C][/ROW]
[ROW][C]29[/C][C]14.98[/C][C]14.9825357622808[/C][C]-0.00253576228077316[/C][/ROW]
[ROW][C]30[/C][C]15.03[/C][C]15.0218816675603[/C][C]0.00811833243972474[/C][/ROW]
[ROW][C]31[/C][C]14.99[/C][C]14.9776060880185[/C][C]0.0123939119814932[/C][/ROW]
[ROW][C]32[/C][C]15.05[/C][C]15.0446357556875[/C][C]0.00536424431249411[/C][/ROW]
[ROW][C]33[/C][C]15.04[/C][C]15.0485985609887[/C][C]-0.00859856098873236[/C][/ROW]
[ROW][C]34[/C][C]15.11[/C][C]15.1212159389511[/C][C]-0.0112159389511336[/C][/ROW]
[ROW][C]35[/C][C]15.14[/C][C]15.1563443701894[/C][C]-0.0163443701893607[/C][/ROW]
[ROW][C]36[/C][C]15.06[/C][C]15.0640995976528[/C][C]-0.00409959765278155[/C][/ROW]
[ROW][C]37[/C][C]15.1[/C][C]15.1078981690388[/C][C]-0.00789816903880273[/C][/ROW]
[ROW][C]38[/C][C]15.2[/C][C]15.2111813037661[/C][C]-0.0111813037661344[/C][/ROW]
[ROW][C]39[/C][C]15.13[/C][C]15.1254411888401[/C][C]0.00455881115985261[/C][/ROW]
[ROW][C]40[/C][C]15.21[/C][C]15.157615516748[/C][C]0.0523844832519675[/C][/ROW]
[ROW][C]41[/C][C]15.17[/C][C]15.1779682767948[/C][C]-0.00796827679482645[/C][/ROW]
[ROW][C]42[/C][C]15.18[/C][C]15.187289384333[/C][C]-0.0072893843330293[/C][/ROW]
[ROW][C]43[/C][C]15.21[/C][C]15.2157155493957[/C][C]-0.00571554939569099[/C][/ROW]
[ROW][C]44[/C][C]15.25[/C][C]15.2706593051376[/C][C]-0.0206593051376366[/C][/ROW]
[ROW][C]45[/C][C]15.18[/C][C]15.165876003344[/C][C]0.0141239966559557[/C][/ROW]
[ROW][C]46[/C][C]15.19[/C][C]15.1907463944392[/C][C]-0.000746394439168164[/C][/ROW]
[ROW][C]47[/C][C]15.25[/C][C]15.2434171183608[/C][C]0.00658288163916944[/C][/ROW]
[ROW][C]48[/C][C]15.21[/C][C]15.2181136499266[/C][C]-0.0081136499266401[/C][/ROW]
[ROW][C]49[/C][C]15.2[/C][C]15.2051873948611[/C][C]-0.00518739486107712[/C][/ROW]
[ROW][C]50[/C][C]15.28[/C][C]15.2803121739991[/C][C]-0.00031217399911541[/C][/ROW]
[ROW][C]51[/C][C]15.41[/C][C]15.4042448512048[/C][C]0.0057551487952284[/C][/ROW]
[ROW][C]52[/C][C]15.45[/C][C]15.4538606631843[/C][C]-0.00386066318427684[/C][/ROW]
[ROW][C]53[/C][C]15.31[/C][C]15.3193450779308[/C][C]-0.0093450779308306[/C][/ROW]
[ROW][C]54[/C][C]15.19[/C][C]15.1879402424491[/C][C]0.00205975755093465[/C][/ROW]
[ROW][C]55[/C][C]15.18[/C][C]15.146873299862[/C][C]0.0331267001379809[/C][/ROW]
[ROW][C]56[/C][C]15.26[/C][C]15.207689860352[/C][C]0.0523101396480299[/C][/ROW]
[ROW][C]57[/C][C]15.24[/C][C]15.2171484406363[/C][C]0.0228515593636781[/C][/ROW]
[ROW][C]58[/C][C]15.14[/C][C]15.1294763186586[/C][C]0.0105236813414218[/C][/ROW]
[ROW][C]59[/C][C]15.08[/C][C]15.0867572186061[/C][C]-0.00675721860609976[/C][/ROW]
[ROW][C]60[/C][C]15.12[/C][C]15.1294879583488[/C][C]-0.00948795834878799[/C][/ROW]
[ROW][C]61[/C][C]15.11[/C][C]15.1036146319712[/C][C]0.00638536802884227[/C][/ROW]
[ROW][C]62[/C][C]15.08[/C][C]15.0402459206206[/C][C]0.0397540793793959[/C][/ROW]
[ROW][C]63[/C][C]15.06[/C][C]15.0633629465879[/C][C]-0.00336294658786801[/C][/ROW]
[ROW][C]64[/C][C]15.17[/C][C]15.1640085703819[/C][C]0.00599142961813705[/C][/ROW]
[ROW][C]65[/C][C]15.11[/C][C]15.0907597642701[/C][C]0.0192402357298724[/C][/ROW]
[ROW][C]66[/C][C]15.03[/C][C]15.0592055660217[/C][C]-0.0292055660217504[/C][/ROW]
[ROW][C]67[/C][C]15.02[/C][C]15.0183256906035[/C][C]0.00167430939651892[/C][/ROW]
[ROW][C]68[/C][C]15.02[/C][C]15.0240904833439[/C][C]-0.00409048334393806[/C][/ROW]
[ROW][C]69[/C][C]15.04[/C][C]15.0555655838472[/C][C]-0.0155655838471837[/C][/ROW]
[ROW][C]70[/C][C]15.01[/C][C]15.0431006159508[/C][C]-0.0331006159508107[/C][/ROW]
[ROW][C]71[/C][C]15.06[/C][C]15.0674905606465[/C][C]-0.00749056064645165[/C][/ROW]
[ROW][C]72[/C][C]15.09[/C][C]15.1040482293134[/C][C]-0.0140482293134122[/C][/ROW]
[ROW][C]73[/C][C]15.11[/C][C]15.0911568390288[/C][C]0.018843160971234[/C][/ROW]
[ROW][C]74[/C][C]14.94[/C][C]14.9594244301162[/C][C]-0.0194244301162209[/C][/ROW]
[ROW][C]75[/C][C]14.94[/C][C]14.9377041012097[/C][C]0.00229589879034761[/C][/ROW]
[ROW][C]76[/C][C]14.97[/C][C]14.9712833624618[/C][C]-0.00128336246183767[/C][/ROW]
[ROW][C]77[/C][C]14.99[/C][C]14.9738871990157[/C][C]0.0161128009842672[/C][/ROW]
[ROW][C]78[/C][C]15.06[/C][C]15.0500010084061[/C][C]0.00999899159385736[/C][/ROW]
[ROW][C]79[/C][C]15.03[/C][C]15.0415918121439[/C][C]-0.0115918121439201[/C][/ROW]
[ROW][C]80[/C][C]15[/C][C]15.0433949878574[/C][C]-0.0433949878573777[/C][/ROW]
[ROW][C]81[/C][C]15.01[/C][C]15.0048640953844[/C][C]0.00513590461557218[/C][/ROW]
[ROW][C]82[/C][C]15.02[/C][C]15.0082385826103[/C][C]0.0117614173896677[/C][/ROW]
[ROW][C]83[/C][C]15.03[/C][C]15.0213533666723[/C][C]0.00864663332766472[/C][/ROW]
[ROW][C]84[/C][C]15.08[/C][C]15.0793275642218[/C][C]0.000672435778162952[/C][/ROW]
[ROW][C]85[/C][C]15.13[/C][C]15.1340836232586[/C][C]-0.0040836232586152[/C][/ROW]
[ROW][C]86[/C][C]15.15[/C][C]15.1488829800173[/C][C]0.0011170199826795[/C][/ROW]
[ROW][C]87[/C][C]15.14[/C][C]15.1221428336039[/C][C]0.0178571663961068[/C][/ROW]
[ROW][C]88[/C][C]15.1[/C][C]15.1390672720908[/C][C]-0.0390672720908314[/C][/ROW]
[ROW][C]89[/C][C]15.12[/C][C]15.0982300540773[/C][C]0.0217699459226564[/C][/ROW]
[ROW][C]90[/C][C]15.23[/C][C]15.2325149550511[/C][C]-0.00251495505105216[/C][/ROW]
[ROW][C]91[/C][C]15.24[/C][C]15.2370821121578[/C][C]0.00291788784216028[/C][/ROW]
[ROW][C]92[/C][C]15.19[/C][C]15.2164845074692[/C][C]-0.0264845074691971[/C][/ROW]
[ROW][C]93[/C][C]15.21[/C][C]15.2113463290254[/C][C]-0.0013463290253991[/C][/ROW]
[ROW][C]94[/C][C]15.33[/C][C]15.3137098290484[/C][C]0.0162901709515837[/C][/ROW]
[ROW][C]95[/C][C]15.21[/C][C]15.1996991332429[/C][C]0.0103008667570881[/C][/ROW]
[ROW][C]96[/C][C]15.19[/C][C]15.2365076873175[/C][C]-0.0465076873174865[/C][/ROW]
[ROW][C]97[/C][C]15.32[/C][C]15.3337645670741[/C][C]-0.0137645670741451[/C][/ROW]
[ROW][C]98[/C][C]15.51[/C][C]15.4604199710717[/C][C]0.049580028928307[/C][/ROW]
[ROW][C]99[/C][C]15.34[/C][C]15.3295255172128[/C][C]0.0104744827872232[/C][/ROW]
[ROW][C]100[/C][C]15.23[/C][C]15.2427127599825[/C][C]-0.0127127599825153[/C][/ROW]
[ROW][C]101[/C][C]15.4[/C][C]15.3929864144822[/C][C]0.00701358551781899[/C][/ROW]
[ROW][C]102[/C][C]15.23[/C][C]15.2803187711489[/C][C]-0.0503187711488945[/C][/ROW]
[ROW][C]103[/C][C]15.3[/C][C]15.2982814958172[/C][C]0.00171850418280274[/C][/ROW]
[ROW][C]104[/C][C]15.25[/C][C]15.2790974722109[/C][C]-0.0290974722109334[/C][/ROW]
[ROW][C]105[/C][C]15.22[/C][C]15.2106507175201[/C][C]0.00934928247987057[/C][/ROW]
[ROW][C]106[/C][C]15.24[/C][C]15.1966921287403[/C][C]0.0433078712597349[/C][/ROW]
[ROW][C]107[/C][C]15.17[/C][C]15.1913323702071[/C][C]-0.0213323702071388[/C][/ROW]
[ROW][C]108[/C][C]15.31[/C][C]15.2743047638864[/C][C]0.0356952361136299[/C][/ROW]
[ROW][C]109[/C][C]15.27[/C][C]15.2572051692675[/C][C]0.0127948307325424[/C][/ROW]
[ROW][C]110[/C][C]15.16[/C][C]15.1474829669952[/C][C]0.0125170330048152[/C][/ROW]
[ROW][C]111[/C][C]15.18[/C][C]15.1376716203755[/C][C]0.0423283796244981[/C][/ROW]
[ROW][C]112[/C][C]15.15[/C][C]15.1383926117099[/C][C]0.0116073882901491[/C][/ROW]
[ROW][C]113[/C][C]15.11[/C][C]15.0787601150737[/C][C]0.031239884926335[/C][/ROW]
[ROW][C]114[/C][C]15.15[/C][C]15.1090137674282[/C][C]0.0409862325717554[/C][/ROW]
[ROW][C]115[/C][C]15.11[/C][C]15.1284065673756[/C][C]-0.0184065673755863[/C][/ROW]
[ROW][C]116[/C][C]15.2[/C][C]15.2001273404078[/C][C]-0.000127340407825702[/C][/ROW]
[ROW][C]117[/C][C]15.1[/C][C]15.0914381050048[/C][C]0.00856189499515579[/C][/ROW]
[ROW][C]118[/C][C]15.09[/C][C]15.0806351856833[/C][C]0.00936481431673854[/C][/ROW]
[ROW][C]119[/C][C]15.07[/C][C]15.1118244510617[/C][C]-0.041824451061726[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]15.0178845749721[/C][C]-0.0178845749721367[/C][/ROW]
[ROW][C]121[/C][C]15.06[/C][C]15.0873381407234[/C][C]-0.0273381407233893[/C][/ROW]
[ROW][C]122[/C][C]15.03[/C][C]15.0208954261102[/C][C]0.00910457388983726[/C][/ROW]
[ROW][C]123[/C][C]15.06[/C][C]15.0944531141745[/C][C]-0.034453114174491[/C][/ROW]
[ROW][C]124[/C][C]15.18[/C][C]15.2137314220676[/C][C]-0.0337314220676075[/C][/ROW]
[ROW][C]125[/C][C]15.13[/C][C]15.1501332003326[/C][C]-0.0201332003325514[/C][/ROW]
[ROW][C]126[/C][C]14.99[/C][C]14.9942094976461[/C][C]-0.0042094976461133[/C][/ROW]
[ROW][C]127[/C][C]14.99[/C][C]14.9942122213109[/C][C]-0.00421222131086009[/C][/ROW]
[ROW][C]128[/C][C]15.03[/C][C]15.0188776343832[/C][C]0.0111223656168324[/C][/ROW]
[ROW][C]129[/C][C]15.03[/C][C]15.0050653068687[/C][C]0.0249346931312824[/C][/ROW]
[ROW][C]130[/C][C]15.05[/C][C]15.0717147710853[/C][C]-0.0217147710853385[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=164291&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=164291&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
115.2715.2826400794711-0.0126400794711274
215.1815.1925780798314-0.0125780798314214
315.1315.1217700666550.00822993334495348
415.1415.12248624670380.0175137532961766
515.115.08839907849150.0116009215084856
615.1715.13894697903160.0310530209684475
715.1115.09629413089110.0137058691088807
815.0915.08694104479940.00305895520056644
915.115.07921418446080.0207858155391562
1015.0615.080819268039-0.0208192680389654
1115.0315.0450311776033-0.0150311776032828
1215.0315.0749945416163-0.0449945416162635
1315.1315.1368276592425-0.00682765924252644
1415.0215.0363416643643-0.0163416643642725
1515.0115.0245190142643-0.0145190142643286
1615.0415.03483693401660.00516306598336261
1715.0215.0325888207322-0.0125888207322283
181515.030881321354-0.0308813213540419
1915.1315.1622776257355-0.0322776257354772
2015.0615.05611152429840.00388847570155548
2114.914.9135497983811-0.0135497983811083
2214.9114.89732742938850.0126725706114548
2314.9214.90854280866540.0114571913345518
2414.9714.95374335706460.0162566429353979
2514.9714.94369881740490.0263011825950855
2615.0315.01901758027050.0109824197295447
2715.0114.994668344720.0153316552799766
2815.0215.01243193249610.0075680675038759
2914.9814.9825357622808-0.00253576228077316
3015.0315.02188166756030.00811833243972474
3114.9914.97760608801850.0123939119814932
3215.0515.04463575568750.00536424431249411
3315.0415.0485985609887-0.00859856098873236
3415.1115.1212159389511-0.0112159389511336
3515.1415.1563443701894-0.0163443701893607
3615.0615.0640995976528-0.00409959765278155
3715.115.1078981690388-0.00789816903880273
3815.215.2111813037661-0.0111813037661344
3915.1315.12544118884010.00455881115985261
4015.2115.1576155167480.0523844832519675
4115.1715.1779682767948-0.00796827679482645
4215.1815.187289384333-0.0072893843330293
4315.2115.2157155493957-0.00571554939569099
4415.2515.2706593051376-0.0206593051376366
4515.1815.1658760033440.0141239966559557
4615.1915.1907463944392-0.000746394439168164
4715.2515.24341711836080.00658288163916944
4815.2115.2181136499266-0.0081136499266401
4915.215.2051873948611-0.00518739486107712
5015.2815.2803121739991-0.00031217399911541
5115.4115.40424485120480.0057551487952284
5215.4515.4538606631843-0.00386066318427684
5315.3115.3193450779308-0.0093450779308306
5415.1915.18794024244910.00205975755093465
5515.1815.1468732998620.0331267001379809
5615.2615.2076898603520.0523101396480299
5715.2415.21714844063630.0228515593636781
5815.1415.12947631865860.0105236813414218
5915.0815.0867572186061-0.00675721860609976
6015.1215.1294879583488-0.00948795834878799
6115.1115.10361463197120.00638536802884227
6215.0815.04024592062060.0397540793793959
6315.0615.0633629465879-0.00336294658786801
6415.1715.16400857038190.00599142961813705
6515.1115.09075976427010.0192402357298724
6615.0315.0592055660217-0.0292055660217504
6715.0215.01832569060350.00167430939651892
6815.0215.0240904833439-0.00409048334393806
6915.0415.0555655838472-0.0155655838471837
7015.0115.0431006159508-0.0331006159508107
7115.0615.0674905606465-0.00749056064645165
7215.0915.1040482293134-0.0140482293134122
7315.1115.09115683902880.018843160971234
7414.9414.9594244301162-0.0194244301162209
7514.9414.93770410120970.00229589879034761
7614.9714.9712833624618-0.00128336246183767
7714.9914.97388719901570.0161128009842672
7815.0615.05000100840610.00999899159385736
7915.0315.0415918121439-0.0115918121439201
801515.0433949878574-0.0433949878573777
8115.0115.00486409538440.00513590461557218
8215.0215.00823858261030.0117614173896677
8315.0315.02135336667230.00864663332766472
8415.0815.07932756422180.000672435778162952
8515.1315.1340836232586-0.0040836232586152
8615.1515.14888298001730.0011170199826795
8715.1415.12214283360390.0178571663961068
8815.115.1390672720908-0.0390672720908314
8915.1215.09823005407730.0217699459226564
9015.2315.2325149550511-0.00251495505105216
9115.2415.23708211215780.00291788784216028
9215.1915.2164845074692-0.0264845074691971
9315.2115.2113463290254-0.0013463290253991
9415.3315.31370982904840.0162901709515837
9515.2115.19969913324290.0103008667570881
9615.1915.2365076873175-0.0465076873174865
9715.3215.3337645670741-0.0137645670741451
9815.5115.46041997107170.049580028928307
9915.3415.32952551721280.0104744827872232
10015.2315.2427127599825-0.0127127599825153
10115.415.39298641448220.00701358551781899
10215.2315.2803187711489-0.0503187711488945
10315.315.29828149581720.00171850418280274
10415.2515.2790974722109-0.0290974722109334
10515.2215.21065071752010.00934928247987057
10615.2415.19669212874030.0433078712597349
10715.1715.1913323702071-0.0213323702071388
10815.3115.27430476388640.0356952361136299
10915.2715.25720516926750.0127948307325424
11015.1615.14748296699520.0125170330048152
11115.1815.13767162037550.0423283796244981
11215.1515.13839261170990.0116073882901491
11315.1115.07876011507370.031239884926335
11415.1515.10901376742820.0409862325717554
11515.1115.1284065673756-0.0184065673755863
11615.215.2001273404078-0.000127340407825702
11715.115.09143810500480.00856189499515579
11815.0915.08063518568330.00936481431673854
11915.0715.1118244510617-0.041824451061726
1201515.0178845749721-0.0178845749721367
12115.0615.0873381407234-0.0273381407233893
12215.0315.02089542611020.00910457388983726
12315.0615.0944531141745-0.034453114174491
12415.1815.2137314220676-0.0337314220676075
12515.1315.1501332003326-0.0201332003325514
12614.9914.9942094976461-0.0042094976461133
12714.9914.9942122213109-0.00421222131086009
12815.0315.01887763438320.0111223656168324
12915.0315.00506530686870.0249346931312824
13015.0515.0717147710853-0.0217147710853385






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
220.5611196663490080.8777606673019830.438880333650991
230.4679082016759860.9358164033519730.532091798324014
240.3859748578857760.7719497157715520.614025142114224
250.2651491823546840.5302983647093680.734850817645316
260.1994849855277340.3989699710554680.800515014472266
270.1472491647139980.2944983294279960.852750835286002
280.1029096648737220.2058193297474450.897090335126278
290.08382361358475960.1676472271695190.91617638641524
300.05383845034457270.1076769006891450.946161549655427
310.03336884118907730.06673768237815450.966631158810923
320.02242725544247830.04485451088495650.977572744557522
330.01781062795906920.03562125591813850.982189372040931
340.01233412055304660.02466824110609320.987665879446953
350.01115511963503970.02231023927007940.98884488036496
360.006131609000176820.01226321800035360.993868390999823
370.004312978907705920.008625957815411830.995687021092294
380.002414028987036340.004828057974072680.997585971012964
390.001281165406461570.002562330812923140.998718834593538
400.008082024973308610.01616404994661720.991917975026691
410.007695203210088540.01539040642017710.992304796789911
420.004709504260386920.009419008520773840.995290495739613
430.002924965345614410.005849930691228810.997075034654386
440.003484560657877780.006969121315755560.996515439342122
450.006044610608392410.01208922121678480.993955389391608
460.003829007834416670.007658015668833330.996170992165583
470.002486829427776840.004973658855553680.997513170572223
480.001460795273941320.002921590547882650.998539204726059
490.001023043575894660.002046087151789310.998976956424105
500.0006308118468594280.001261623693718860.999369188153141
510.0007454294124382340.001490858824876470.999254570587562
520.0008325577553406210.001665115510681240.999167442244659
530.0005144816837630440.001028963367526090.999485518316237
540.0002865930350473220.0005731860700946430.999713406964953
550.001149898410623240.002299796821246480.998850101589377
560.00828978223535540.01657956447071080.991710217764645
570.008132577548251380.01626515509650280.991867422451749
580.006074242693366430.01214848538673290.993925757306634
590.005026443073603640.01005288614720730.994973556926396
600.003686643070429020.007373286140858040.996313356929571
610.003163884260015470.006327768520030940.996836115739985
620.01854519583945740.03709039167891490.981454804160543
630.01298084999979360.02596169999958720.987019150000206
640.009426682156201770.01885336431240350.990573317843798
650.008376343141710850.01675268628342170.991623656858289
660.02000675651234350.04001351302468690.979993243487656
670.01462881011192380.02925762022384750.985371189888076
680.01225791144279080.02451582288558160.987742088557209
690.01244260847648390.02488521695296770.987557391523516
700.02025476552012360.04050953104024710.979745234479876
710.02266465416295270.04532930832590530.977335345837047
720.03765292755525260.07530585511050530.962347072444747
730.03144179378598280.06288358757196550.968558206214017
740.0294392458599990.05887849171999790.970560754140001
750.0214027462165060.04280549243301190.978597253783494
760.01575308862155120.03150617724310250.984246911378449
770.01227573949829480.02455147899658970.987724260501705
780.008589433821869430.01717886764373890.991410566178131
790.007142269544820420.01428453908964080.99285773045518
800.02029580414250180.04059160828500370.979704195857498
810.01393342956836670.02786685913673330.986066570431633
820.01007997922150770.02015995844301540.989920020778492
830.007459062437020230.01491812487404050.99254093756298
840.004823726438854430.009647452877708850.995176273561146
850.003245713913399750.00649142782679950.9967542860866
860.002012616871881450.004025233743762890.997987383128119
870.002415837946324450.004831675892648910.997584162053676
880.003839887829603430.007679775659206850.996160112170397
890.003614913784487080.007229827568974150.996385086215513
900.002685809618868660.005371619237737320.997314190381131
910.001613915096520860.003227830193041720.998386084903479
920.00376398204644620.00752796409289240.996236017953554
930.004072108290698650.00814421658139730.995927891709301
940.002509717712391220.005019435424782450.997490282287609
950.002817306992707560.005634613985415120.997182693007292
960.002675093837474550.005350187674949110.997324906162525
970.007374578843339690.01474915768667940.99262542115666
980.01546397437321560.03092794874643120.984536025626784
990.01260886561940140.02521773123880280.987391134380599
1000.01011221421776930.02022442843553870.989887785782231
1010.0438699611121790.08773992222435810.956130038887821
1020.1813183846090090.3626367692180190.818681615390991
1030.1246912978780530.2493825957561070.875308702121947
1040.0828376406644940.1656752813289880.917162359335506
1050.06153145125584840.1230629025116970.938468548744152
1060.04294663349610880.08589326699221760.957053366503891
1070.3860755759363070.7721511518726140.613924424063693
1080.2589989349052910.5179978698105810.741001065094709

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
22 & 0.561119666349008 & 0.877760667301983 & 0.438880333650991 \tabularnewline
23 & 0.467908201675986 & 0.935816403351973 & 0.532091798324014 \tabularnewline
24 & 0.385974857885776 & 0.771949715771552 & 0.614025142114224 \tabularnewline
25 & 0.265149182354684 & 0.530298364709368 & 0.734850817645316 \tabularnewline
26 & 0.199484985527734 & 0.398969971055468 & 0.800515014472266 \tabularnewline
27 & 0.147249164713998 & 0.294498329427996 & 0.852750835286002 \tabularnewline
28 & 0.102909664873722 & 0.205819329747445 & 0.897090335126278 \tabularnewline
29 & 0.0838236135847596 & 0.167647227169519 & 0.91617638641524 \tabularnewline
30 & 0.0538384503445727 & 0.107676900689145 & 0.946161549655427 \tabularnewline
31 & 0.0333688411890773 & 0.0667376823781545 & 0.966631158810923 \tabularnewline
32 & 0.0224272554424783 & 0.0448545108849565 & 0.977572744557522 \tabularnewline
33 & 0.0178106279590692 & 0.0356212559181385 & 0.982189372040931 \tabularnewline
34 & 0.0123341205530466 & 0.0246682411060932 & 0.987665879446953 \tabularnewline
35 & 0.0111551196350397 & 0.0223102392700794 & 0.98884488036496 \tabularnewline
36 & 0.00613160900017682 & 0.0122632180003536 & 0.993868390999823 \tabularnewline
37 & 0.00431297890770592 & 0.00862595781541183 & 0.995687021092294 \tabularnewline
38 & 0.00241402898703634 & 0.00482805797407268 & 0.997585971012964 \tabularnewline
39 & 0.00128116540646157 & 0.00256233081292314 & 0.998718834593538 \tabularnewline
40 & 0.00808202497330861 & 0.0161640499466172 & 0.991917975026691 \tabularnewline
41 & 0.00769520321008854 & 0.0153904064201771 & 0.992304796789911 \tabularnewline
42 & 0.00470950426038692 & 0.00941900852077384 & 0.995290495739613 \tabularnewline
43 & 0.00292496534561441 & 0.00584993069122881 & 0.997075034654386 \tabularnewline
44 & 0.00348456065787778 & 0.00696912131575556 & 0.996515439342122 \tabularnewline
45 & 0.00604461060839241 & 0.0120892212167848 & 0.993955389391608 \tabularnewline
46 & 0.00382900783441667 & 0.00765801566883333 & 0.996170992165583 \tabularnewline
47 & 0.00248682942777684 & 0.00497365885555368 & 0.997513170572223 \tabularnewline
48 & 0.00146079527394132 & 0.00292159054788265 & 0.998539204726059 \tabularnewline
49 & 0.00102304357589466 & 0.00204608715178931 & 0.998976956424105 \tabularnewline
50 & 0.000630811846859428 & 0.00126162369371886 & 0.999369188153141 \tabularnewline
51 & 0.000745429412438234 & 0.00149085882487647 & 0.999254570587562 \tabularnewline
52 & 0.000832557755340621 & 0.00166511551068124 & 0.999167442244659 \tabularnewline
53 & 0.000514481683763044 & 0.00102896336752609 & 0.999485518316237 \tabularnewline
54 & 0.000286593035047322 & 0.000573186070094643 & 0.999713406964953 \tabularnewline
55 & 0.00114989841062324 & 0.00229979682124648 & 0.998850101589377 \tabularnewline
56 & 0.0082897822353554 & 0.0165795644707108 & 0.991710217764645 \tabularnewline
57 & 0.00813257754825138 & 0.0162651550965028 & 0.991867422451749 \tabularnewline
58 & 0.00607424269336643 & 0.0121484853867329 & 0.993925757306634 \tabularnewline
59 & 0.00502644307360364 & 0.0100528861472073 & 0.994973556926396 \tabularnewline
60 & 0.00368664307042902 & 0.00737328614085804 & 0.996313356929571 \tabularnewline
61 & 0.00316388426001547 & 0.00632776852003094 & 0.996836115739985 \tabularnewline
62 & 0.0185451958394574 & 0.0370903916789149 & 0.981454804160543 \tabularnewline
63 & 0.0129808499997936 & 0.0259616999995872 & 0.987019150000206 \tabularnewline
64 & 0.00942668215620177 & 0.0188533643124035 & 0.990573317843798 \tabularnewline
65 & 0.00837634314171085 & 0.0167526862834217 & 0.991623656858289 \tabularnewline
66 & 0.0200067565123435 & 0.0400135130246869 & 0.979993243487656 \tabularnewline
67 & 0.0146288101119238 & 0.0292576202238475 & 0.985371189888076 \tabularnewline
68 & 0.0122579114427908 & 0.0245158228855816 & 0.987742088557209 \tabularnewline
69 & 0.0124426084764839 & 0.0248852169529677 & 0.987557391523516 \tabularnewline
70 & 0.0202547655201236 & 0.0405095310402471 & 0.979745234479876 \tabularnewline
71 & 0.0226646541629527 & 0.0453293083259053 & 0.977335345837047 \tabularnewline
72 & 0.0376529275552526 & 0.0753058551105053 & 0.962347072444747 \tabularnewline
73 & 0.0314417937859828 & 0.0628835875719655 & 0.968558206214017 \tabularnewline
74 & 0.029439245859999 & 0.0588784917199979 & 0.970560754140001 \tabularnewline
75 & 0.021402746216506 & 0.0428054924330119 & 0.978597253783494 \tabularnewline
76 & 0.0157530886215512 & 0.0315061772431025 & 0.984246911378449 \tabularnewline
77 & 0.0122757394982948 & 0.0245514789965897 & 0.987724260501705 \tabularnewline
78 & 0.00858943382186943 & 0.0171788676437389 & 0.991410566178131 \tabularnewline
79 & 0.00714226954482042 & 0.0142845390896408 & 0.99285773045518 \tabularnewline
80 & 0.0202958041425018 & 0.0405916082850037 & 0.979704195857498 \tabularnewline
81 & 0.0139334295683667 & 0.0278668591367333 & 0.986066570431633 \tabularnewline
82 & 0.0100799792215077 & 0.0201599584430154 & 0.989920020778492 \tabularnewline
83 & 0.00745906243702023 & 0.0149181248740405 & 0.99254093756298 \tabularnewline
84 & 0.00482372643885443 & 0.00964745287770885 & 0.995176273561146 \tabularnewline
85 & 0.00324571391339975 & 0.0064914278267995 & 0.9967542860866 \tabularnewline
86 & 0.00201261687188145 & 0.00402523374376289 & 0.997987383128119 \tabularnewline
87 & 0.00241583794632445 & 0.00483167589264891 & 0.997584162053676 \tabularnewline
88 & 0.00383988782960343 & 0.00767977565920685 & 0.996160112170397 \tabularnewline
89 & 0.00361491378448708 & 0.00722982756897415 & 0.996385086215513 \tabularnewline
90 & 0.00268580961886866 & 0.00537161923773732 & 0.997314190381131 \tabularnewline
91 & 0.00161391509652086 & 0.00322783019304172 & 0.998386084903479 \tabularnewline
92 & 0.0037639820464462 & 0.0075279640928924 & 0.996236017953554 \tabularnewline
93 & 0.00407210829069865 & 0.0081442165813973 & 0.995927891709301 \tabularnewline
94 & 0.00250971771239122 & 0.00501943542478245 & 0.997490282287609 \tabularnewline
95 & 0.00281730699270756 & 0.00563461398541512 & 0.997182693007292 \tabularnewline
96 & 0.00267509383747455 & 0.00535018767494911 & 0.997324906162525 \tabularnewline
97 & 0.00737457884333969 & 0.0147491576866794 & 0.99262542115666 \tabularnewline
98 & 0.0154639743732156 & 0.0309279487464312 & 0.984536025626784 \tabularnewline
99 & 0.0126088656194014 & 0.0252177312388028 & 0.987391134380599 \tabularnewline
100 & 0.0101122142177693 & 0.0202244284355387 & 0.989887785782231 \tabularnewline
101 & 0.043869961112179 & 0.0877399222243581 & 0.956130038887821 \tabularnewline
102 & 0.181318384609009 & 0.362636769218019 & 0.818681615390991 \tabularnewline
103 & 0.124691297878053 & 0.249382595756107 & 0.875308702121947 \tabularnewline
104 & 0.082837640664494 & 0.165675281328988 & 0.917162359335506 \tabularnewline
105 & 0.0615314512558484 & 0.123062902511697 & 0.938468548744152 \tabularnewline
106 & 0.0429466334961088 & 0.0858932669922176 & 0.957053366503891 \tabularnewline
107 & 0.386075575936307 & 0.772151151872614 & 0.613924424063693 \tabularnewline
108 & 0.258998934905291 & 0.517997869810581 & 0.741001065094709 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=164291&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]22[/C][C]0.561119666349008[/C][C]0.877760667301983[/C][C]0.438880333650991[/C][/ROW]
[ROW][C]23[/C][C]0.467908201675986[/C][C]0.935816403351973[/C][C]0.532091798324014[/C][/ROW]
[ROW][C]24[/C][C]0.385974857885776[/C][C]0.771949715771552[/C][C]0.614025142114224[/C][/ROW]
[ROW][C]25[/C][C]0.265149182354684[/C][C]0.530298364709368[/C][C]0.734850817645316[/C][/ROW]
[ROW][C]26[/C][C]0.199484985527734[/C][C]0.398969971055468[/C][C]0.800515014472266[/C][/ROW]
[ROW][C]27[/C][C]0.147249164713998[/C][C]0.294498329427996[/C][C]0.852750835286002[/C][/ROW]
[ROW][C]28[/C][C]0.102909664873722[/C][C]0.205819329747445[/C][C]0.897090335126278[/C][/ROW]
[ROW][C]29[/C][C]0.0838236135847596[/C][C]0.167647227169519[/C][C]0.91617638641524[/C][/ROW]
[ROW][C]30[/C][C]0.0538384503445727[/C][C]0.107676900689145[/C][C]0.946161549655427[/C][/ROW]
[ROW][C]31[/C][C]0.0333688411890773[/C][C]0.0667376823781545[/C][C]0.966631158810923[/C][/ROW]
[ROW][C]32[/C][C]0.0224272554424783[/C][C]0.0448545108849565[/C][C]0.977572744557522[/C][/ROW]
[ROW][C]33[/C][C]0.0178106279590692[/C][C]0.0356212559181385[/C][C]0.982189372040931[/C][/ROW]
[ROW][C]34[/C][C]0.0123341205530466[/C][C]0.0246682411060932[/C][C]0.987665879446953[/C][/ROW]
[ROW][C]35[/C][C]0.0111551196350397[/C][C]0.0223102392700794[/C][C]0.98884488036496[/C][/ROW]
[ROW][C]36[/C][C]0.00613160900017682[/C][C]0.0122632180003536[/C][C]0.993868390999823[/C][/ROW]
[ROW][C]37[/C][C]0.00431297890770592[/C][C]0.00862595781541183[/C][C]0.995687021092294[/C][/ROW]
[ROW][C]38[/C][C]0.00241402898703634[/C][C]0.00482805797407268[/C][C]0.997585971012964[/C][/ROW]
[ROW][C]39[/C][C]0.00128116540646157[/C][C]0.00256233081292314[/C][C]0.998718834593538[/C][/ROW]
[ROW][C]40[/C][C]0.00808202497330861[/C][C]0.0161640499466172[/C][C]0.991917975026691[/C][/ROW]
[ROW][C]41[/C][C]0.00769520321008854[/C][C]0.0153904064201771[/C][C]0.992304796789911[/C][/ROW]
[ROW][C]42[/C][C]0.00470950426038692[/C][C]0.00941900852077384[/C][C]0.995290495739613[/C][/ROW]
[ROW][C]43[/C][C]0.00292496534561441[/C][C]0.00584993069122881[/C][C]0.997075034654386[/C][/ROW]
[ROW][C]44[/C][C]0.00348456065787778[/C][C]0.00696912131575556[/C][C]0.996515439342122[/C][/ROW]
[ROW][C]45[/C][C]0.00604461060839241[/C][C]0.0120892212167848[/C][C]0.993955389391608[/C][/ROW]
[ROW][C]46[/C][C]0.00382900783441667[/C][C]0.00765801566883333[/C][C]0.996170992165583[/C][/ROW]
[ROW][C]47[/C][C]0.00248682942777684[/C][C]0.00497365885555368[/C][C]0.997513170572223[/C][/ROW]
[ROW][C]48[/C][C]0.00146079527394132[/C][C]0.00292159054788265[/C][C]0.998539204726059[/C][/ROW]
[ROW][C]49[/C][C]0.00102304357589466[/C][C]0.00204608715178931[/C][C]0.998976956424105[/C][/ROW]
[ROW][C]50[/C][C]0.000630811846859428[/C][C]0.00126162369371886[/C][C]0.999369188153141[/C][/ROW]
[ROW][C]51[/C][C]0.000745429412438234[/C][C]0.00149085882487647[/C][C]0.999254570587562[/C][/ROW]
[ROW][C]52[/C][C]0.000832557755340621[/C][C]0.00166511551068124[/C][C]0.999167442244659[/C][/ROW]
[ROW][C]53[/C][C]0.000514481683763044[/C][C]0.00102896336752609[/C][C]0.999485518316237[/C][/ROW]
[ROW][C]54[/C][C]0.000286593035047322[/C][C]0.000573186070094643[/C][C]0.999713406964953[/C][/ROW]
[ROW][C]55[/C][C]0.00114989841062324[/C][C]0.00229979682124648[/C][C]0.998850101589377[/C][/ROW]
[ROW][C]56[/C][C]0.0082897822353554[/C][C]0.0165795644707108[/C][C]0.991710217764645[/C][/ROW]
[ROW][C]57[/C][C]0.00813257754825138[/C][C]0.0162651550965028[/C][C]0.991867422451749[/C][/ROW]
[ROW][C]58[/C][C]0.00607424269336643[/C][C]0.0121484853867329[/C][C]0.993925757306634[/C][/ROW]
[ROW][C]59[/C][C]0.00502644307360364[/C][C]0.0100528861472073[/C][C]0.994973556926396[/C][/ROW]
[ROW][C]60[/C][C]0.00368664307042902[/C][C]0.00737328614085804[/C][C]0.996313356929571[/C][/ROW]
[ROW][C]61[/C][C]0.00316388426001547[/C][C]0.00632776852003094[/C][C]0.996836115739985[/C][/ROW]
[ROW][C]62[/C][C]0.0185451958394574[/C][C]0.0370903916789149[/C][C]0.981454804160543[/C][/ROW]
[ROW][C]63[/C][C]0.0129808499997936[/C][C]0.0259616999995872[/C][C]0.987019150000206[/C][/ROW]
[ROW][C]64[/C][C]0.00942668215620177[/C][C]0.0188533643124035[/C][C]0.990573317843798[/C][/ROW]
[ROW][C]65[/C][C]0.00837634314171085[/C][C]0.0167526862834217[/C][C]0.991623656858289[/C][/ROW]
[ROW][C]66[/C][C]0.0200067565123435[/C][C]0.0400135130246869[/C][C]0.979993243487656[/C][/ROW]
[ROW][C]67[/C][C]0.0146288101119238[/C][C]0.0292576202238475[/C][C]0.985371189888076[/C][/ROW]
[ROW][C]68[/C][C]0.0122579114427908[/C][C]0.0245158228855816[/C][C]0.987742088557209[/C][/ROW]
[ROW][C]69[/C][C]0.0124426084764839[/C][C]0.0248852169529677[/C][C]0.987557391523516[/C][/ROW]
[ROW][C]70[/C][C]0.0202547655201236[/C][C]0.0405095310402471[/C][C]0.979745234479876[/C][/ROW]
[ROW][C]71[/C][C]0.0226646541629527[/C][C]0.0453293083259053[/C][C]0.977335345837047[/C][/ROW]
[ROW][C]72[/C][C]0.0376529275552526[/C][C]0.0753058551105053[/C][C]0.962347072444747[/C][/ROW]
[ROW][C]73[/C][C]0.0314417937859828[/C][C]0.0628835875719655[/C][C]0.968558206214017[/C][/ROW]
[ROW][C]74[/C][C]0.029439245859999[/C][C]0.0588784917199979[/C][C]0.970560754140001[/C][/ROW]
[ROW][C]75[/C][C]0.021402746216506[/C][C]0.0428054924330119[/C][C]0.978597253783494[/C][/ROW]
[ROW][C]76[/C][C]0.0157530886215512[/C][C]0.0315061772431025[/C][C]0.984246911378449[/C][/ROW]
[ROW][C]77[/C][C]0.0122757394982948[/C][C]0.0245514789965897[/C][C]0.987724260501705[/C][/ROW]
[ROW][C]78[/C][C]0.00858943382186943[/C][C]0.0171788676437389[/C][C]0.991410566178131[/C][/ROW]
[ROW][C]79[/C][C]0.00714226954482042[/C][C]0.0142845390896408[/C][C]0.99285773045518[/C][/ROW]
[ROW][C]80[/C][C]0.0202958041425018[/C][C]0.0405916082850037[/C][C]0.979704195857498[/C][/ROW]
[ROW][C]81[/C][C]0.0139334295683667[/C][C]0.0278668591367333[/C][C]0.986066570431633[/C][/ROW]
[ROW][C]82[/C][C]0.0100799792215077[/C][C]0.0201599584430154[/C][C]0.989920020778492[/C][/ROW]
[ROW][C]83[/C][C]0.00745906243702023[/C][C]0.0149181248740405[/C][C]0.99254093756298[/C][/ROW]
[ROW][C]84[/C][C]0.00482372643885443[/C][C]0.00964745287770885[/C][C]0.995176273561146[/C][/ROW]
[ROW][C]85[/C][C]0.00324571391339975[/C][C]0.0064914278267995[/C][C]0.9967542860866[/C][/ROW]
[ROW][C]86[/C][C]0.00201261687188145[/C][C]0.00402523374376289[/C][C]0.997987383128119[/C][/ROW]
[ROW][C]87[/C][C]0.00241583794632445[/C][C]0.00483167589264891[/C][C]0.997584162053676[/C][/ROW]
[ROW][C]88[/C][C]0.00383988782960343[/C][C]0.00767977565920685[/C][C]0.996160112170397[/C][/ROW]
[ROW][C]89[/C][C]0.00361491378448708[/C][C]0.00722982756897415[/C][C]0.996385086215513[/C][/ROW]
[ROW][C]90[/C][C]0.00268580961886866[/C][C]0.00537161923773732[/C][C]0.997314190381131[/C][/ROW]
[ROW][C]91[/C][C]0.00161391509652086[/C][C]0.00322783019304172[/C][C]0.998386084903479[/C][/ROW]
[ROW][C]92[/C][C]0.0037639820464462[/C][C]0.0075279640928924[/C][C]0.996236017953554[/C][/ROW]
[ROW][C]93[/C][C]0.00407210829069865[/C][C]0.0081442165813973[/C][C]0.995927891709301[/C][/ROW]
[ROW][C]94[/C][C]0.00250971771239122[/C][C]0.00501943542478245[/C][C]0.997490282287609[/C][/ROW]
[ROW][C]95[/C][C]0.00281730699270756[/C][C]0.00563461398541512[/C][C]0.997182693007292[/C][/ROW]
[ROW][C]96[/C][C]0.00267509383747455[/C][C]0.00535018767494911[/C][C]0.997324906162525[/C][/ROW]
[ROW][C]97[/C][C]0.00737457884333969[/C][C]0.0147491576866794[/C][C]0.99262542115666[/C][/ROW]
[ROW][C]98[/C][C]0.0154639743732156[/C][C]0.0309279487464312[/C][C]0.984536025626784[/C][/ROW]
[ROW][C]99[/C][C]0.0126088656194014[/C][C]0.0252177312388028[/C][C]0.987391134380599[/C][/ROW]
[ROW][C]100[/C][C]0.0101122142177693[/C][C]0.0202244284355387[/C][C]0.989887785782231[/C][/ROW]
[ROW][C]101[/C][C]0.043869961112179[/C][C]0.0877399222243581[/C][C]0.956130038887821[/C][/ROW]
[ROW][C]102[/C][C]0.181318384609009[/C][C]0.362636769218019[/C][C]0.818681615390991[/C][/ROW]
[ROW][C]103[/C][C]0.124691297878053[/C][C]0.249382595756107[/C][C]0.875308702121947[/C][/ROW]
[ROW][C]104[/C][C]0.082837640664494[/C][C]0.165675281328988[/C][C]0.917162359335506[/C][/ROW]
[ROW][C]105[/C][C]0.0615314512558484[/C][C]0.123062902511697[/C][C]0.938468548744152[/C][/ROW]
[ROW][C]106[/C][C]0.0429466334961088[/C][C]0.0858932669922176[/C][C]0.957053366503891[/C][/ROW]
[ROW][C]107[/C][C]0.386075575936307[/C][C]0.772151151872614[/C][C]0.613924424063693[/C][/ROW]
[ROW][C]108[/C][C]0.258998934905291[/C][C]0.517997869810581[/C][C]0.741001065094709[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=164291&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=164291&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
220.5611196663490080.8777606673019830.438880333650991
230.4679082016759860.9358164033519730.532091798324014
240.3859748578857760.7719497157715520.614025142114224
250.2651491823546840.5302983647093680.734850817645316
260.1994849855277340.3989699710554680.800515014472266
270.1472491647139980.2944983294279960.852750835286002
280.1029096648737220.2058193297474450.897090335126278
290.08382361358475960.1676472271695190.91617638641524
300.05383845034457270.1076769006891450.946161549655427
310.03336884118907730.06673768237815450.966631158810923
320.02242725544247830.04485451088495650.977572744557522
330.01781062795906920.03562125591813850.982189372040931
340.01233412055304660.02466824110609320.987665879446953
350.01115511963503970.02231023927007940.98884488036496
360.006131609000176820.01226321800035360.993868390999823
370.004312978907705920.008625957815411830.995687021092294
380.002414028987036340.004828057974072680.997585971012964
390.001281165406461570.002562330812923140.998718834593538
400.008082024973308610.01616404994661720.991917975026691
410.007695203210088540.01539040642017710.992304796789911
420.004709504260386920.009419008520773840.995290495739613
430.002924965345614410.005849930691228810.997075034654386
440.003484560657877780.006969121315755560.996515439342122
450.006044610608392410.01208922121678480.993955389391608
460.003829007834416670.007658015668833330.996170992165583
470.002486829427776840.004973658855553680.997513170572223
480.001460795273941320.002921590547882650.998539204726059
490.001023043575894660.002046087151789310.998976956424105
500.0006308118468594280.001261623693718860.999369188153141
510.0007454294124382340.001490858824876470.999254570587562
520.0008325577553406210.001665115510681240.999167442244659
530.0005144816837630440.001028963367526090.999485518316237
540.0002865930350473220.0005731860700946430.999713406964953
550.001149898410623240.002299796821246480.998850101589377
560.00828978223535540.01657956447071080.991710217764645
570.008132577548251380.01626515509650280.991867422451749
580.006074242693366430.01214848538673290.993925757306634
590.005026443073603640.01005288614720730.994973556926396
600.003686643070429020.007373286140858040.996313356929571
610.003163884260015470.006327768520030940.996836115739985
620.01854519583945740.03709039167891490.981454804160543
630.01298084999979360.02596169999958720.987019150000206
640.009426682156201770.01885336431240350.990573317843798
650.008376343141710850.01675268628342170.991623656858289
660.02000675651234350.04001351302468690.979993243487656
670.01462881011192380.02925762022384750.985371189888076
680.01225791144279080.02451582288558160.987742088557209
690.01244260847648390.02488521695296770.987557391523516
700.02025476552012360.04050953104024710.979745234479876
710.02266465416295270.04532930832590530.977335345837047
720.03765292755525260.07530585511050530.962347072444747
730.03144179378598280.06288358757196550.968558206214017
740.0294392458599990.05887849171999790.970560754140001
750.0214027462165060.04280549243301190.978597253783494
760.01575308862155120.03150617724310250.984246911378449
770.01227573949829480.02455147899658970.987724260501705
780.008589433821869430.01717886764373890.991410566178131
790.007142269544820420.01428453908964080.99285773045518
800.02029580414250180.04059160828500370.979704195857498
810.01393342956836670.02786685913673330.986066570431633
820.01007997922150770.02015995844301540.989920020778492
830.007459062437020230.01491812487404050.99254093756298
840.004823726438854430.009647452877708850.995176273561146
850.003245713913399750.00649142782679950.9967542860866
860.002012616871881450.004025233743762890.997987383128119
870.002415837946324450.004831675892648910.997584162053676
880.003839887829603430.007679775659206850.996160112170397
890.003614913784487080.007229827568974150.996385086215513
900.002685809618868660.005371619237737320.997314190381131
910.001613915096520860.003227830193041720.998386084903479
920.00376398204644620.00752796409289240.996236017953554
930.004072108290698650.00814421658139730.995927891709301
940.002509717712391220.005019435424782450.997490282287609
950.002817306992707560.005634613985415120.997182693007292
960.002675093837474550.005350187674949110.997324906162525
970.007374578843339690.01474915768667940.99262542115666
980.01546397437321560.03092794874643120.984536025626784
990.01260886561940140.02521773123880280.987391134380599
1000.01011221421776930.02022442843553870.989887785782231
1010.0438699611121790.08773992222435810.956130038887821
1020.1813183846090090.3626367692180190.818681615390991
1030.1246912978780530.2493825957561070.875308702121947
1040.0828376406644940.1656752813289880.917162359335506
1050.06153145125584840.1230629025116970.938468548744152
1060.04294663349610880.08589326699221760.957053366503891
1070.3860755759363070.7721511518726140.613924424063693
1080.2589989349052910.5179978698105810.741001065094709






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level310.35632183908046NOK
5% type I error level660.758620689655172NOK
10% type I error level720.827586206896552NOK

\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 & 31 & 0.35632183908046 & NOK \tabularnewline
5% type I error level & 66 & 0.758620689655172 & NOK \tabularnewline
10% type I error level & 72 & 0.827586206896552 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=164291&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]31[/C][C]0.35632183908046[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]66[/C][C]0.758620689655172[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]72[/C][C]0.827586206896552[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=164291&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=164291&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 level310.35632183908046NOK
5% type I error level660.758620689655172NOK
10% type I error level720.827586206896552NOK


Figures (Output of Computation)

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

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; 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')
}