Free Statistics

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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, 17 Nov 2010 09:55:05 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/17/t128998774876tbf464wijjhbo.htm/, Retrieved Wed, 17 Aug 2022 17:18:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=96576, Retrieved Wed, 17 Aug 2022 17:18:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact83
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2010-11-17 09:55:05] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
-   PD    [Multiple Regression] [Paper Multiple Li...] [2010-11-19 13:18:31] [6ff9fb24bdca608d2f4f1f9db3f6445e]
-   PD    [Multiple Regression] [] [2010-11-19 14:51:10] [1251ac2db27b84d4a3ba43449388906b]
-   PD    [Multiple Regression] [Workshop 7 - Popu...] [2010-11-19 14:57:03] [6f0e7a2d1a07390e3505a2db8288f975]
-   PD    [Multiple Regression] [Workshop 7 - Addi...] [2010-11-19 15:09:53] [6f0e7a2d1a07390e3505a2db8288f975]
-   PD      [Multiple Regression] [W7] [2010-11-23 17:40:07] [5ddc7dfb25e070b079c4c8fcccc4d42e]
- R PD    [Multiple Regression] [Workshop 7 - Regr...] [2010-11-19 15:10:35] [8b017ffbf7b0eded54d8efebfb3e4cfa]
-   P       [Multiple Regression] [workshop 7 - dumm...] [2010-11-19 15:22:48] [4eaa304e6a28c475ba490fccf4c01ad3]
-           [Multiple Regression] [workshop 7 - tuto...] [2010-11-19 16:27:16] [956e8df26b41c50d9c6c2ec1b6a122a8]
-    D        [Multiple Regression] [WS7 comp 6] [2010-11-23 09:33:39] [dc30d19c3bc2be07fe595ad36c2cf923]
-               [Multiple Regression] [] [2010-12-02 15:22:24] [2e1e44f0ae3cb9513dc28781dfdb387b]
-               [Multiple Regression] [] [2010-12-03 17:49:16] [b07cd1964830aab808142229b1166ece]
-           [Multiple Regression] [workshop 7 multip...] [2010-11-23 14:21:37] [af8eb90b4bf1bcfcc4325c143dbee260]
- RM        [Multiple Regression] [xvcbcvxbcvb] [2011-11-20 14:01:10] [a9671b130b33f9fcb98554992ce4582f]
- R PD    [Multiple Regression] [workshop 7 dummy ...] [2010-11-19 15:14:14] [4eaa304e6a28c475ba490fccf4c01ad3]
- R PD    [Multiple Regression] [Multiple linear r...] [2010-11-20 15:25:08] [97ad38b1c3b35a5feca8b85f7bc7b3ff]
- R         [Multiple Regression] [WS 7 - Blog 4] [2011-11-21 19:50:21] [69d59b79aaf660457acc70a0ef0bfdab]
- RM        [Multiple Regression] [] [2011-11-22 11:08:23] [74be16979710d4c4e7c6647856088456]
-   PD    [Multiple Regression] [] [2010-11-22 10:46:43] [049b50ae610f671f7417ed8e2d1295c1]
-   PD    [Multiple Regression] [Two way anova FMPS] [2010-11-22 18:59:20] [95e8426e0df851c9330605aa1e892ab5]
-           [Multiple Regression] [] [2010-12-01 15:05:12] [42a441ca3193af442aa2201743dfb347]
-             [Multiple Regression] [] [2010-12-03 17:31:11] [5e7b9ab9ddedd2d2f5ce6c303ba3ebe3]
- R PD        [Multiple Regression] [multiple regressi...] [2011-11-24 10:39:28] [ca5872d1d4d784184b94263c274137e3]
- RM        [Multiple Regression] [] [2011-11-24 18:58:20] [7d86e24de0a0f8503ecffdef58e8c96c]
- RM        [Multiple Regression] [ws7 d)] [2011-11-24 20:07:40] [74be16979710d4c4e7c6647856088456]
-   PD    [Multiple Regression] [WS7 verschillende...] [2010-11-22 22:30:04] [49c7a512c56172bc46ae7e93e5b58c1c]
-   PD      [Multiple Regression] [WS7 verschillende...] [2010-11-23 13:14:37] [49c7a512c56172bc46ae7e93e5b58c1c]
-    D        [Multiple Regression] [Paper Multiple re...] [2010-12-18 15:41:02] [49c7a512c56172bc46ae7e93e5b58c1c]
-   PD    [Multiple Regression] [Mini-Tutorial FMP...] [2010-11-23 00:52:41] [3cdf9c5e1f396891d2638627ccb7b98d]
-   PD    [Multiple Regression] [Mini-Tutorial FMP...] [2010-11-23 00:59:48] [3cdf9c5e1f396891d2638627ccb7b98d]
- RMPD    [] [Mini-Tutorial FMP...] [1970-01-01 00:00:00] [3cdf9c5e1f396891d2638627ccb7b98d]
- RMPD    [] [Mini-Tutorial FMP...] [1970-01-01 00:00:00] [3cdf9c5e1f396891d2638627ccb7b98d]
- RMPD    [] [Mini-Tutorial FMP...] [1970-01-01 00:00:00] [3cdf9c5e1f396891d2638627ccb7b98d]
- RMPD    [] [Mini-Tutorial FMP...] [-0001-11-30 00:00:00] [3cdf9c5e1f396891d2638627ccb7b98d]
-   PD    [Multiple Regression] [Mini-Tutorial FMP...] [2010-11-23 01:05:24] [3cdf9c5e1f396891d2638627ccb7b98d]
-   PD    [Multiple Regression] [Mini-Tutorial FMP...] [2010-11-23 01:09:50] [3cdf9c5e1f396891d2638627ccb7b98d]
-   PD    [Multiple Regression] [Mini-Tutorial FMP...] [2010-11-23 01:15:29] [3cdf9c5e1f396891d2638627ccb7b98d]
-           [Multiple Regression] [mini turtorial : ...] [2010-11-24 08:40:50] [2c786c21adba4dd4c8af44dce5258f06]
-           [Multiple Regression] [] [2010-11-24 15:09:48] [afdb2fc47981b6a655b732edc8065db9]
-   PD    [Multiple Regression] [Mini-Tutorial Mul...] [2010-11-23 02:24:42] [2843717cd92615903379c14ebee3c5df]
F   PD      [Multiple Regression] [Mini-tutorial Int...] [2010-11-23 11:55:50] [6ca0fc48dd5333d51a15728999009c83]
-   PD    [Multiple Regression] [twowayanova] [2010-11-23 19:55:57] [3fb95cad3bbcce10c72dbbcc5bec5662]
-   PD    [Multiple Regression] [w7 4] [2010-11-23 21:23:49] [1afa3497b02a8d7c9f6727c1b17b89b2]
F   PD    [Multiple Regression] [Pop] [2010-11-23 22:02:36] [b11c112f8986de933f8b95cd30e75cc2]
-    D    [Multiple Regression] [Multiple Regressi...] [2010-11-23 23:36:51] [b8e188bcc949964bed729335b3416734]
-   PD    [Multiple Regression] [Multiple Regressi...] [2010-11-23 23:46:10] [b8e188bcc949964bed729335b3416734]
-   PD      [Multiple Regression] [Werkloosheid] [2010-11-29 23:29:58] [b8e188bcc949964bed729335b3416734]
-   P         [Multiple Regression] [] [2010-11-30 19:58:06] [b659239b537e56f17142ee5c56ad6265]
-    D        [Multiple Regression] [ACF Nieuwbouw] [2010-12-18 23:41:03] [b8e188bcc949964bed729335b3416734]

[Truncated]
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Dataseries X:
1	1	1	41	41	38	38	13	12	12	14	14	12	12	53	53
1	2	2	39	39	32	32	16	11	11	18	18	11	11	83	83
1	3	3	30	30	35	35	19	15	15	11	11	14	14	66	66
1	4	4	31	31	33	33	15	6	6	12	12	12	12	67	67
1	5	5	34	34	37	37	14	13	13	16	16	21	21	76	76
1	6	6	35	35	29	29	13	10	10	18	18	12	12	78	78
1	7	7	39	39	31	31	19	12	12	14	14	22	22	53	53
1	8	8	34	34	36	36	15	14	14	14	14	11	11	80	80
1	9	9	36	36	35	35	14	12	12	15	15	10	10	74	74
1	10	10	37	37	38	38	15	9	9	15	15	13	13	76	76
1	11	11	38	38	31	31	16	10	10	17	17	10	10	79	79
1	12	12	36	36	34	34	16	12	12	19	19	8	8	54	54
1	13	13	38	38	35	35	16	12	12	10	10	15	15	67	67
1	14	14	39	39	38	38	16	11	11	16	16	14	14	54	54
1	15	15	33	33	37	37	17	15	15	18	18	10	10	87	87
1	16	16	32	32	33	33	15	12	12	14	14	14	14	58	58
1	17	17	36	36	32	32	15	10	10	14	14	14	14	75	75
1	18	18	38	38	38	38	20	12	12	17	17	11	11	88	88
1	19	19	39	39	38	38	18	11	11	14	14	10	10	64	64
1	20	20	32	32	32	32	16	12	12	16	16	13	13	57	57
1	21	21	32	32	33	33	16	11	11	18	18	9.5	9.5	66	66
1	22	22	31	31	31	31	16	12	12	11	11	14	14	68	68
1	23	23	39	39	38	38	19	13	13	14	14	12	12	54	54
1	24	24	37	37	39	39	16	11	11	12	12	14	14	56	56
1	25	25	39	39	32	32	17	12	12	17	17	11	11	86	86
1	26	26	41	41	32	32	17	13	13	9	9	9	9	80	80
1	27	27	36	36	35	35	16	10	10	16	16	11	11	76	76
1	28	28	33	33	37	37	15	14	14	14	14	15	15	69	69
1	29	29	33	33	33	33	16	12	12	15	15	14	14	78	78
1	30	30	34	34	33	33	14	10	10	11	11	13	13	67	67
1	31	31	31	31	31	31	15	12	12	16	16	9	9	80	80
1	32	32	27	27	32	32	12	8	8	13	13	15	15	54	54
1	33	33	37	37	31	31	14	10	10	17	17	10	10	71	71
1	34	34	34	34	37	37	16	12	12	15	15	11	11	84	84
1	35	35	34	34	30	30	14	12	12	14	14	13	13	74	74
1	36	36	32	32	33	33	10	7	7	16	16	8	8	71	71
1	37	37	29	29	31	31	10	9	9	9	9	20	20	63	63
1	38	38	36	36	33	33	14	12	12	15	15	12	12	71	71
1	39	39	29	29	31	31	16	10	10	17	17	10	10	76	76
1	40	40	35	35	33	33	16	10	10	13	13	10	10	69	69
1	41	41	37	37	32	32	16	10	10	15	15	9	9	74	74
1	42	42	34	34	33	33	14	12	12	16	16	14	14	75	75
1	43	43	38	38	32	32	20	15	15	16	16	8	8	54	54
1	44	44	35	35	33	33	14	10	10	12	12	14	14	52	52
1	45	45	38	38	28	28	14	10	10	15	15	11	11	69	69
1	46	46	37	37	35	35	11	12	12	11	11	13	13	68	68
1	47	47	38	38	39	39	14	13	13	15	15	9	9	65	65
1	48	48	33	33	34	34	15	11	11	15	15	11	11	75	75
1	49	49	36	36	38	38	16	11	11	17	17	15	15	74	74
1	50	50	38	38	32	32	14	12	12	13	13	11	11	75	75
1	51	51	32	32	38	38	16	14	14	16	16	10	10	72	72
1	52	52	32	32	30	30	14	10	10	14	14	14	14	67	67
1	53	53	32	32	33	33	12	12	12	11	11	18	18	63	63
1	54	54	34	34	38	38	16	13	13	12	12	14	14	62	62
1	55	55	32	32	32	32	9	5	5	12	12	11	11	63	63
1	56	56	37	37	35	35	14	6	6	15	15	14.5	14.5	76	76
1	57	57	39	39	34	34	16	12	12	16	16	13	13	74	74
1	58	58	29	29	34	34	16	12	12	15	15	9	9	67	67
1	59	59	37	37	36	36	15	11	11	12	12	10	10	73	73
1	60	60	35	35	34	34	16	10	10	12	12	15	15	70	70
1	61	61	30	30	28	28	12	7	7	8	8	20	20	53	53
1	62	62	38	38	34	34	16	12	12	13	13	12	12	77	77
1	63	63	34	34	35	35	16	14	14	11	11	12	12	80	80
1	64	64	31	31	35	35	14	11	11	14	14	14	14	52	52
1	65	65	34	34	31	31	16	12	12	15	15	13	13	54	54
1	66	66	35	35	37	37	17	13	13	10	10	11	11	80	80
1	67	67	36	36	35	35	18	14	14	11	11	17	17	66	66
1	68	68	30	30	27	27	18	11	11	12	12	12	12	73	73
1	69	69	39	39	40	40	12	12	12	15	15	13	13	63	63
1	70	70	35	35	37	37	16	12	12	15	15	14	14	69	69
1	71	71	38	38	36	36	10	8	8	14	14	13	13	67	67
1	72	72	31	31	38	38	14	11	11	16	16	15	15	54	54
1	73	73	34	34	39	39	18	14	14	15	15	13	13	81	81
1	74	74	38	38	41	41	18	14	14	15	15	10	10	69	69
1	75	75	34	34	27	27	16	12	12	13	13	11	11	84	84
1	76	76	39	39	30	30	17	9	9	12	12	19	19	80	80
1	77	77	37	37	37	37	16	13	13	17	17	13	13	70	70
1	78	78	34	34	31	31	16	11	11	13	13	17	17	69	69
1	79	79	28	28	31	31	13	12	12	15	15	13	13	77	77
1	80	80	37	37	27	27	16	12	12	13	13	9	9	54	54
1	81	81	33	33	36	36	16	12	12	15	15	11	11	79	79
1	82	82	35	35	37	37	16	12	12	15	15	9	9	71	71
1	83	83	37	37	33	33	15	12	12	16	16	12	12	73	73
1	84	84	32	32	34	34	15	11	11	15	15	12	12	72	72
1	85	85	33	33	31	31	16	10	10	14	14	13	13	77	77
1	86	86	38	38	39	39	14	9	9	15	15	13	13	75	75
1	87	87	33	33	34	34	16	12	12	14	14	12	12	69	69
1	88	88	29	29	32	32	16	12	12	13	13	15	15	54	54
1	89	89	33	33	33	33	15	12	12	7	7	22	22	70	70
1	90	90	31	31	36	36	12	9	9	17	17	13	13	73	73
1	91	91	36	36	32	32	17	15	15	13	13	15	15	54	54
1	92	92	35	35	41	41	16	12	12	15	15	13	13	77	77
1	93	93	32	32	28	28	15	12	12	14	14	15	15	82	82
1	94	94	29	29	30	30	13	12	12	13	13	12.5	12.5	80	80
1	95	95	39	39	36	36	16	10	10	16	16	11	11	80	80
1	96	96	37	37	35	35	16	13	13	12	12	16	16	69	69
1	97	97	35	35	31	31	16	9	9	14	14	11	11	78	78
1	98	98	37	37	34	34	16	12	12	17	17	11	11	81	81
1	99	99	32	32	36	36	14	10	10	15	15	10	10	76	76
1	100	100	38	38	36	36	16	14	14	17	17	10	10	76	76
1	101	101	37	37	35	35	16	11	11	12	12	16	16	73	73
1	102	102	36	36	37	37	20	15	15	16	16	12	12	85	85
1	103	103	32	32	28	28	15	11	11	11	11	11	11	66	66
1	104	104	33	33	39	39	16	11	11	15	15	16	16	79	79
1	105	105	40	40	32	32	13	12	12	9	9	19	19	68	68
1	106	106	38	38	35	35	17	12	12	16	16	11	11	76	76
1	107	107	41	41	39	39	16	12	12	15	15	16	16	71	71
1	108	108	36	36	35	35	16	11	11	10	10	15	15	54	54
1	109	109	43	43	42	42	12	7	7	10	10	24	24	46	46
1	110	110	30	30	34	34	16	12	12	15	15	14	14	85	85
1	111	111	31	31	33	33	16	14	14	11	11	15	15	74	74
1	112	112	32	32	41	41	17	11	11	13	13	11	11	88	88
1	113	113	32	32	33	33	13	11	11	14	14	15	15	38	38
1	114	114	37	37	34	34	12	10	10	18	18	12	12	76	76
1	115	115	37	37	32	32	18	13	13	16	16	10	10	86	86
1	116	116	33	33	40	40	14	13	13	14	14	14	14	54	54
1	117	117	34	34	40	40	14	8	8	14	14	13	13	67	67
1	118	118	33	33	35	35	13	11	11	14	14	9	9	69	69
1	119	119	38	38	36	36	16	12	12	14	14	15	15	90	90
1	120	120	33	33	37	37	13	11	11	12	12	15	15	54	54
1	121	121	31	31	27	27	16	13	13	14	14	14	14	76	76
1	122	122	38	38	39	39	13	12	12	15	15	11	11	89	89
1	123	123	37	37	38	38	16	14	14	15	15	8	8	76	76
1	124	124	36	36	31	31	15	13	13	15	15	11	11	73	73
1	125	125	31	31	33	33	16	15	15	13	13	11	11	79	79
1	126	126	39	39	32	32	15	10	10	17	17	8	8	90	90
1	127	127	44	44	39	39	17	11	11	17	17	10	10	74	74
1	128	128	33	33	36	36	15	9	9	19	19	11	11	81	81
1	129	129	35	35	33	33	12	11	11	15	15	13	13	72	72
1	130	130	32	32	33	33	16	10	10	13	13	11	11	71	71
1	131	131	28	28	32	32	10	11	11	9	9	20	20	66	66
1	132	132	40	40	37	37	16	8	8	15	15	10	10	77	77
1	133	133	27	27	30	30	12	11	11	15	15	15	15	65	65
1	134	134	37	37	38	38	14	12	12	15	15	12	12	74	74
1	135	135	32	32	29	29	15	12	12	16	16	14	14	85	85
1	136	136	28	28	22	22	13	9	9	11	11	23	23	54	54
1	137	137	34	34	35	35	15	11	11	14	14	14	14	63	63
1	138	138	30	30	35	35	11	10	10	11	11	16	16	54	54
1	139	139	35	35	34	34	12	8	8	15	15	11	11	64	64
1	140	140	31	31	35	35	11	9	9	13	13	12	12	69	69
1	141	141	32	32	34	34	16	8	8	15	15	10	10	54	54
1	142	142	30	30	37	37	15	9	9	16	16	14	14	84	84
1	143	143	30	30	35	35	17	15	15	14	14	12	12	86	86
1	144	144	31	31	23	23	16	11	11	15	15	12	12	77	77
1	145	145	40	40	31	31	10	8	8	16	16	11	11	89	89
1	146	146	32	32	27	27	18	13	13	16	16	12	12	76	76
1	147	147	36	36	36	36	13	12	12	11	11	13	13	60	60
1	148	148	32	32	31	31	16	12	12	12	12	11	11	75	75
1	149	149	35	35	32	32	13	9	9	9	9	19	19	73	73
1	150	150	38	38	39	39	10	7	7	16	16	12	12	85	85
1	151	151	42	42	37	37	15	13	13	13	13	17	17	79	79
1	152	152	34	34	38	38	16	9	9	16	16	9	9	71	71
1	153	153	35	35	39	39	16	6	6	12	12	12	12	72	72
1	154	154	38	38	34	34	14	8	8	9	9	19	19	69	69
1	155	155	33	33	31	31	10	8	8	13	13	18	18	78	78
1	156	156	36	36	32	32	17	15	15	13	13	15	15	54	54
1	157	157	32	32	37	37	13	6	6	14	14	14	14	69	69
1	158	158	33	33	36	36	15	9	9	19	19	11	11	81	81
1	159	159	34	34	32	32	16	11	11	13	13	9	9	84	84
1	160	160	32	32	38	38	12	8	8	12	12	18	18	84	84
1	161	161	34	34	36	36	13	8	8	13	13	16	16	69	69
0	162	0	27	0	26	0	13	10	0	10	0	24	0	66	0
0	163	0	31	0	26	0	12	8	0	14	0	14	0	81	0
0	164	0	38	0	33	0	17	14	0	16	0	20	0	82	0
0	165	0	34	0	39	0	15	10	0	10	0	18	0	72	0
0	166	0	24	0	30	0	10	8	0	11	0	23	0	54	0
0	167	0	30	0	33	0	14	11	0	14	0	12	0	78	0
0	168	0	26	0	25	0	11	12	0	12	0	14	0	74	0
0	169	0	34	0	38	0	13	12	0	9	0	16	0	82	0
0	170	0	27	0	37	0	16	12	0	9	0	18	0	73	0
0	171	0	37	0	31	0	12	5	0	11	0	20	0	55	0
0	172	0	36	0	37	0	16	12	0	16	0	12	0	72	0
0	173	0	41	0	35	0	12	10	0	9	0	12	0	78	0
0	174	0	29	0	25	0	9	7	0	13	0	17	0	59	0
0	175	0	36	0	28	0	12	12	0	16	0	13	0	72	0
0	176	0	32	0	35	0	15	11	0	13	0	9	0	78	0
0	177	0	37	0	33	0	12	8	0	9	0	16	0	68	0
0	178	0	30	0	30	0	12	9	0	12	0	18	0	69	0
0	179	0	31	0	31	0	14	10	0	16	0	10	0	67	0
0	180	0	38	0	37	0	12	9	0	11	0	14	0	74	0
0	181	0	36	0	36	0	16	12	0	14	0	11	0	54	0
0	182	0	35	0	30	0	11	6	0	13	0	9	0	67	0
0	183	0	31	0	36	0	19	15	0	15	0	11	0	70	0
0	184	0	38	0	32	0	15	12	0	14	0	10	0	80	0
0	185	0	22	0	28	0	8	12	0	16	0	11	0	89	0
0	186	0	32	0	36	0	16	12	0	13	0	19	0	76	0
0	187	0	36	0	34	0	17	11	0	14	0	14	0	74	0
0	188	0	39	0	31	0	12	7	0	15	0	12	0	87	0
0	189	0	28	0	28	0	11	7	0	13	0	14	0	54	0
0	190	0	32	0	36	0	11	5	0	11	0	21	0	61	0
0	191	0	32	0	36	0	14	12	0	11	0	13	0	38	0
0	192	0	38	0	40	0	16	12	0	14	0	10	0	75	0
0	193	0	32	0	33	0	12	3	0	15	0	15	0	69	0
0	194	0	35	0	37	0	16	11	0	11	0	16	0	62	0
0	195	0	32	0	32	0	13	10	0	15	0	14	0	72	0
0	196	0	37	0	38	0	15	12	0	12	0	12	0	70	0
0	197	0	34	0	31	0	16	9	0	14	0	19	0	79	0
0	198	0	33	0	37	0	16	12	0	14	0	15	0	87	0
0	199	0	33	0	33	0	14	9	0	8	0	19	0	62	0
0	200	0	26	0	32	0	16	12	0	13	0	13	0	77	0
0	201	0	30	0	30	0	16	12	0	9	0	17	0	69	0
0	202	0	24	0	30	0	14	10	0	15	0	12	0	69	0
0	203	0	34	0	31	0	11	9	0	17	0	11	0	75	0
0	204	0	34	0	32	0	12	12	0	13	0	14	0	54	0
0	205	0	33	0	34	0	15	8	0	15	0	11	0	72	0
0	206	0	34	0	36	0	15	11	0	15	0	13	0	74	0
0	207	0	35	0	37	0	16	11	0	14	0	12	0	85	0
0	208	0	35	0	36	0	16	12	0	16	0	15	0	52	0
0	209	0	36	0	33	0	11	10	0	13	0	14	0	70	0
0	210	0	34	0	33	0	15	10	0	16	0	12	0	84	0
0	211	0	34	0	33	0	12	12	0	9	0	17	0	64	0
0	212	0	41	0	44	0	12	12	0	16	0	11	0	84	0
0	213	0	32	0	39	0	15	11	0	11	0	18	0	87	0
0	214	0	30	0	32	0	15	8	0	10	0	13	0	79	0
0	215	0	35	0	35	0	16	12	0	11	0	17	0	67	0
0	216	0	28	0	25	0	14	10	0	15	0	13	0	65	0
0	217	0	33	0	35	0	17	11	0	17	0	11	0	85	0
0	218	0	39	0	34	0	14	10	0	14	0	12	0	83	0
0	219	0	36	0	35	0	13	8	0	8	0	22	0	61	0
0	220	0	36	0	39	0	15	12	0	15	0	14	0	82	0
0	221	0	35	0	33	0	13	12	0	11	0	12	0	76	0
0	222	0	38	0	36	0	14	10	0	16	0	12	0	58	0
0	223	0	33	0	32	0	15	12	0	10	0	17	0	72	0
0	224	0	31	0	32	0	12	9	0	15	0	9	0	72	0
0	225	0	34	0	36	0	13	9	0	9	0	21	0	38	0
0	226	0	32	0	36	0	8	6	0	16	0	10	0	78	0
0	227	0	31	0	32	0	14	10	0	19	0	11	0	54	0
0	228	0	33	0	34	0	14	9	0	12	0	12	0	63	0
0	229	0	34	0	33	0	11	9	0	8	0	23	0	66	0
0	230	0	34	0	35	0	12	9	0	11	0	13	0	70	0
0	231	0	34	0	30	0	13	6	0	14	0	12	0	71	0
0	232	0	33	0	38	0	10	10	0	9	0	16	0	67	0
0	233	0	32	0	34	0	16	6	0	15	0	9	0	58	0
0	234	0	41	0	33	0	18	14	0	13	0	17	0	72	0
0	235	0	34	0	32	0	13	10	0	16	0	9	0	72	0
0	236	0	36	0	31	0	11	10	0	11	0	14	0	70	0
0	237	0	37	0	30	0	4	6	0	12	0	17	0	76	0
0	238	0	36	0	27	0	13	12	0	13	0	13	0	50	0
0	239	0	29	0	31	0	16	12	0	10	0	11	0	72	0
0	240	0	37	0	30	0	10	7	0	11	0	12	0	72	0
0	241	0	27	0	32	0	12	8	0	12	0	10	0	88	0
0	242	0	35	0	35	0	12	11	0	8	0	19	0	53	0
0	243	0	28	0	28	0	10	3	0	12	0	16	0	58	0
0	244	0	35	0	33	0	13	6	0	12	0	16	0	66	0
0	245	0	37	0	31	0	15	10	0	15	0	14	0	82	0
0	246	0	29	0	35	0	12	8	0	11	0	20	0	69	0
0	247	0	32	0	35	0	14	9	0	13	0	15	0	68	0
0	248	0	36	0	32	0	10	9	0	14	0	23	0	44	0
0	249	0	19	0	21	0	12	8	0	10	0	20	0	56	0
0	250	0	21	0	20	0	12	9	0	12	0	16	0	53	0
0	251	0	31	0	34	0	11	7	0	15	0	14	0	70	0
0	252	0	33	0	32	0	10	7	0	13	0	17	0	78	0
0	253	0	36	0	34	0	12	6	0	13	0	11	0	71	0
0	254	0	33	0	32	0	16	9	0	13	0	13	0	72	0
0	255	0	37	0	33	0	12	10	0	12	0	17	0	68	0
0	256	0	34	0	33	0	14	11	0	12	0	15	0	67	0
0	257	0	35	0	37	0	16	12	0	9	0	21	0	75	0
0	258	0	31	0	32	0	14	8	0	9	0	18	0	62	0
0	259	0	37	0	34	0	13	11	0	15	0	15	0	67	0
0	260	0	35	0	30	0	4	3	0	10	0	8	0	83	0
0	261	0	27	0	30	0	15	11	0	14	0	12	0	64	0
0	262	0	34	0	38	0	11	12	0	15	0	12	0	68	0
0	263	0	40	0	36	0	11	7	0	7	0	22	0	62	0
0	264	0	29	0	32	0	14	9	0	14	0	12	0	72	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time21 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 21 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96576&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]21 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96576&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96576&T=0

As an alternative you can also use a 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 time21 seconds
R Server'George Udny Yule' @ 72.249.76.132







Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 4.02213192467067 + 2.51220871915707Pop[t] -0.0041632636006461t + 0.000172174548079445Pop_t[t] -0.0555568413104866Connected[t] + 0.142876235895839Connected_p[t] + 0.157004790836634Separate[t] -0.171050529312446Separate_p[t] + 0.561688749040414Software[t] -0.0423405001257738Software_p[t] + 0.129559018015446Happiness[t] -0.0990921264717531Happiness_p[t] + 0.0223866216094604Depression[t] -0.105904658670805Depression_p[t] -0.00941064469109043Belonging[t] + 0.0247479684327582Belonging_p[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  4.02213192467067 +  2.51220871915707Pop[t] -0.0041632636006461t +  0.000172174548079445Pop_t[t] -0.0555568413104866Connected[t] +  0.142876235895839Connected_p[t] +  0.157004790836634Separate[t] -0.171050529312446Separate_p[t] +  0.561688749040414Software[t] -0.0423405001257738Software_p[t] +  0.129559018015446Happiness[t] -0.0990921264717531Happiness_p[t] +  0.0223866216094604Depression[t] -0.105904658670805Depression_p[t] -0.00941064469109043Belonging[t] +  0.0247479684327582Belonging_p[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96576&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  4.02213192467067 +  2.51220871915707Pop[t] -0.0041632636006461t +  0.000172174548079445Pop_t[t] -0.0555568413104866Connected[t] +  0.142876235895839Connected_p[t] +  0.157004790836634Separate[t] -0.171050529312446Separate_p[t] +  0.561688749040414Software[t] -0.0423405001257738Software_p[t] +  0.129559018015446Happiness[t] -0.0990921264717531Happiness_p[t] +  0.0223866216094604Depression[t] -0.105904658670805Depression_p[t] -0.00941064469109043Belonging[t] +  0.0247479684327582Belonging_p[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96576&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96576&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 4.02213192467067 + 2.51220871915707Pop[t] -0.0041632636006461t + 0.000172174548079445Pop_t[t] -0.0555568413104866Connected[t] + 0.142876235895839Connected_p[t] + 0.157004790836634Separate[t] -0.171050529312446Separate_p[t] + 0.561688749040414Software[t] -0.0423405001257738Software_p[t] + 0.129559018015446Happiness[t] -0.0990921264717531Happiness_p[t] + 0.0223866216094604Depression[t] -0.105904658670805Depression_p[t] -0.00941064469109043Belonging[t] + 0.0247479684327582Belonging_p[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.022131924670673.3275781.20870.2279190.11396
Pop2.512208719157074.2401550.59250.5540690.277034
t-0.00416326360064610.006372-0.65340.5141130.257057
Pop_t0.0001721745480794450.0071810.0240.9808910.490446
Connected-0.05555684131048660.052264-1.0630.2888130.144406
Connected_p0.1428762358958390.070032.04020.0423880.021194
Separate0.1570047908366340.0597982.62560.0091870.004594
Separate_p-0.1710505293124460.074474-2.29680.0224650.011232
Software0.5616887490404140.0822326.830600
Software_p-0.04234050012577380.109405-0.3870.6990840.349542
Happiness0.1295590180154460.0893821.44950.1484620.074231
Happiness_p-0.09909212647175310.117868-0.84070.4013220.200661
Depression0.02238662160946040.0622070.35990.7192470.359624
Depression_p-0.1059046586708050.084312-1.25610.2102580.105129
Belonging-0.009410644691090430.019054-0.49390.6218160.310908
Belonging_p0.02474796843275820.0246211.00520.3158010.1579

\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) & 4.02213192467067 & 3.327578 & 1.2087 & 0.227919 & 0.11396 \tabularnewline
Pop & 2.51220871915707 & 4.240155 & 0.5925 & 0.554069 & 0.277034 \tabularnewline
t & -0.0041632636006461 & 0.006372 & -0.6534 & 0.514113 & 0.257057 \tabularnewline
Pop_t & 0.000172174548079445 & 0.007181 & 0.024 & 0.980891 & 0.490446 \tabularnewline
Connected & -0.0555568413104866 & 0.052264 & -1.063 & 0.288813 & 0.144406 \tabularnewline
Connected_p & 0.142876235895839 & 0.07003 & 2.0402 & 0.042388 & 0.021194 \tabularnewline
Separate & 0.157004790836634 & 0.059798 & 2.6256 & 0.009187 & 0.004594 \tabularnewline
Separate_p & -0.171050529312446 & 0.074474 & -2.2968 & 0.022465 & 0.011232 \tabularnewline
Software & 0.561688749040414 & 0.082232 & 6.8306 & 0 & 0 \tabularnewline
Software_p & -0.0423405001257738 & 0.109405 & -0.387 & 0.699084 & 0.349542 \tabularnewline
Happiness & 0.129559018015446 & 0.089382 & 1.4495 & 0.148462 & 0.074231 \tabularnewline
Happiness_p & -0.0990921264717531 & 0.117868 & -0.8407 & 0.401322 & 0.200661 \tabularnewline
Depression & 0.0223866216094604 & 0.062207 & 0.3599 & 0.719247 & 0.359624 \tabularnewline
Depression_p & -0.105904658670805 & 0.084312 & -1.2561 & 0.210258 & 0.105129 \tabularnewline
Belonging & -0.00941064469109043 & 0.019054 & -0.4939 & 0.621816 & 0.310908 \tabularnewline
Belonging_p & 0.0247479684327582 & 0.024621 & 1.0052 & 0.315801 & 0.1579 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96576&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]4.02213192467067[/C][C]3.327578[/C][C]1.2087[/C][C]0.227919[/C][C]0.11396[/C][/ROW]
[ROW][C]Pop[/C][C]2.51220871915707[/C][C]4.240155[/C][C]0.5925[/C][C]0.554069[/C][C]0.277034[/C][/ROW]
[ROW][C]t[/C][C]-0.0041632636006461[/C][C]0.006372[/C][C]-0.6534[/C][C]0.514113[/C][C]0.257057[/C][/ROW]
[ROW][C]Pop_t[/C][C]0.000172174548079445[/C][C]0.007181[/C][C]0.024[/C][C]0.980891[/C][C]0.490446[/C][/ROW]
[ROW][C]Connected[/C][C]-0.0555568413104866[/C][C]0.052264[/C][C]-1.063[/C][C]0.288813[/C][C]0.144406[/C][/ROW]
[ROW][C]Connected_p[/C][C]0.142876235895839[/C][C]0.07003[/C][C]2.0402[/C][C]0.042388[/C][C]0.021194[/C][/ROW]
[ROW][C]Separate[/C][C]0.157004790836634[/C][C]0.059798[/C][C]2.6256[/C][C]0.009187[/C][C]0.004594[/C][/ROW]
[ROW][C]Separate_p[/C][C]-0.171050529312446[/C][C]0.074474[/C][C]-2.2968[/C][C]0.022465[/C][C]0.011232[/C][/ROW]
[ROW][C]Software[/C][C]0.561688749040414[/C][C]0.082232[/C][C]6.8306[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Software_p[/C][C]-0.0423405001257738[/C][C]0.109405[/C][C]-0.387[/C][C]0.699084[/C][C]0.349542[/C][/ROW]
[ROW][C]Happiness[/C][C]0.129559018015446[/C][C]0.089382[/C][C]1.4495[/C][C]0.148462[/C][C]0.074231[/C][/ROW]
[ROW][C]Happiness_p[/C][C]-0.0990921264717531[/C][C]0.117868[/C][C]-0.8407[/C][C]0.401322[/C][C]0.200661[/C][/ROW]
[ROW][C]Depression[/C][C]0.0223866216094604[/C][C]0.062207[/C][C]0.3599[/C][C]0.719247[/C][C]0.359624[/C][/ROW]
[ROW][C]Depression_p[/C][C]-0.105904658670805[/C][C]0.084312[/C][C]-1.2561[/C][C]0.210258[/C][C]0.105129[/C][/ROW]
[ROW][C]Belonging[/C][C]-0.00941064469109043[/C][C]0.019054[/C][C]-0.4939[/C][C]0.621816[/C][C]0.310908[/C][/ROW]
[ROW][C]Belonging_p[/C][C]0.0247479684327582[/C][C]0.024621[/C][C]1.0052[/C][C]0.315801[/C][C]0.1579[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96576&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96576&T=2

As an alternative you can also use a 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)4.022131924670673.3275781.20870.2279190.11396
Pop2.512208719157074.2401550.59250.5540690.277034
t-0.00416326360064610.006372-0.65340.5141130.257057
Pop_t0.0001721745480794450.0071810.0240.9808910.490446
Connected-0.05555684131048660.052264-1.0630.2888130.144406
Connected_p0.1428762358958390.070032.04020.0423880.021194
Separate0.1570047908366340.0597982.62560.0091870.004594
Separate_p-0.1710505293124460.074474-2.29680.0224650.011232
Software0.5616887490404140.0822326.830600
Software_p-0.04234050012577380.109405-0.3870.6990840.349542
Happiness0.1295590180154460.0893821.44950.1484620.074231
Happiness_p-0.09909212647175310.117868-0.84070.4013220.200661
Depression0.02238662160946040.0622070.35990.7192470.359624
Depression_p-0.1059046586708050.084312-1.25610.2102580.105129
Belonging-0.009410644691090430.019054-0.49390.6218160.310908
Belonging_p0.02474796843275820.0246211.00520.3158010.1579







Multiple Linear Regression - Regression Statistics
Multiple R0.683693459674527
R-squared0.467436746801724
Adjusted R-squared0.435225259713119
F-TEST (value)14.5114922982577
F-TEST (DF numerator)15
F-TEST (DF denominator)248
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.84570008019649
Sum Squared Residuals844.838978937261

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.683693459674527 \tabularnewline
R-squared & 0.467436746801724 \tabularnewline
Adjusted R-squared & 0.435225259713119 \tabularnewline
F-TEST (value) & 14.5114922982577 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 248 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.84570008019649 \tabularnewline
Sum Squared Residuals & 844.838978937261 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96576&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.683693459674527[/C][/ROW]
[ROW][C]R-squared[/C][C]0.467436746801724[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.435225259713119[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]14.5114922982577[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]248[/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]1.84570008019649[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]844.838978937261[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96576&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96576&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.683693459674527
R-squared0.467436746801724
Adjusted R-squared0.435225259713119
F-TEST (value)14.5114922982577
F-TEST (DF numerator)15
F-TEST (DF denominator)248
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.84570008019649
Sum Squared Residuals844.838978937261







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.0460838528529-3.04608385285288
21616.0978854720564-0.0978854720563963
31916.61871875636862.38128124363138
41512.26884458802932.73115541197067
51415.6143076175297-1.61430761752974
61315.0952278482479-2.09522784824792
71915.11063832808453.88936167191549
81516.9713242202552-1.97132422025521
91416.1392821471749-2.13928214717492
101514.40254902683560.597450973164355
111615.46104561611020.53895438388982
121616.1235117839571-0.123511783957133
131615.62067067091850.37932932908151
141615.20944768979100.790552310208976
151717.6741165821685-0.674116582168514
161515.1802222027614-0.180222202761411
171514.76159243630510.238407563694865
182016.42800219785453.57199780214553
191815.61600384710292.38399615289714
201615.3074180814340.692581918565983
211615.26131583146810.7386841685319
221615.15902031359790.840979686402122
231916.31832667718252.68167332281749
241614.88965935292741.11034064707260
251716.54098375244340.459016247556603
261717.0622567007993-0.0622567007993099
271615.00636954836510.99363045163488
281516.287354596739-1.28735459673899
291615.55287080604040.447129193959557
301414.3904425234722-0.390442523472174
311515.8770730400500-0.87707304004998
321212.4410863242391-0.441086324239124
331415.1632236724350-1.16322367243502
341615.90662985509380.0933701449062471
351415.6500827322888-1.65008273228881
361013.265086391234-3.26508639123401
371012.7277418177310-2.72774181773096
381415.8385839962544-1.83858399625441
391614.51740860014511.48259139985486
401614.78001356928661.21998643071338
411615.18584544673760.814154553262365
421415.5727609632611-1.57276096326112
432017.66916236156272.33083763843726
441414.1387756696789-0.138775669678926
451415.0696607461849-1.06966074618494
461115.6144856270065-4.61448562700649
471416.5709069707458-2.57090697074581
481515.1481882666103-0.148188266610254
491615.06149671851080.938503281489162
501416.0633090042108-2.06330900421076
511616.6187303550879-0.618730355087908
521414.1780196281422-0.178019628142175
531214.6837657036483-2.68376570364832
541615.65273467634940.347265323650556
55911.6694846725896-2.66948467258963
561412.57777434350911.42222565649092
571616.0036265752432-0.00362657524324481
581615.32268553084720.677314469152835
591515.3869151033687-0.386915103368735
601614.25342629665071.74657370334928
611211.53887566369250.4611243363075
621615.93448106905030.0655189309496733
631616.5909413491474-0.590941349147435
641414.2618668653366-0.261866865336586
651615.24002473894630.759975261053662
661716.17189889622650.828101103773525
671816.11730306441781.88269693558220
681814.69913511195783.30086488804221
691215.6722816230555-3.67228162305553
701615.36965607647770.630343923522344
711013.5866524120327-3.58665241203271
721414.195891330998-0.195891330997995
731816.54853435757361.45146564242638
741816.93223559619491.06776440380514
751615.82251880560930.177481194390746
761713.89498224431103.10501775568903
771616.1954946350854-0.195494635085433
781614.50384625714061.49615374285942
791315.0129915707566-2.01299157075665
801615.77143790597510.228562094024866
811615.56908839480520.430911605194758
821615.77002784063690.229972159363086
831515.8074459225013-0.807445922501296
841514.78765965784620.212340342153845
851614.35647861999501.64352138000496
861414.1571625912085-0.157162591208454
871615.30587517141970.69412482858025
881614.46961712212471.53038287787534
891514.27882744411280.7211725558872
901213.6023588239322-1.60235882393221
911716.62692436380830.373075636191658
921615.43188579041260.568114209587371
931515.2277147708315-0.227714770831518
941315.0813275746976-2.0813275746976
951615.04423723303750.955762766962496
961615.72952952739410.270470472605877
971614.14624948948471.85375051051534
981615.97021736677540.0297826332246363
991414.4087389652808-0.408738965280751
1001617.0669910224862-1.06699102248624
1011614.73222687926871.26777312073132
1022017.33020551365792.66979448634214
1031514.66572992513880.334270074861174
1041614.49821769694741.50178230305259
1051315.1210647666331-2.12106476663310
1061715.90440880021231.09559119978766
1071615.58144924545380.418550754546173
1081614.34814496419761.65185503580237
1091211.90531554876780.094684451232151
1101615.06094993674240.93905006325765
1111615.82292431418580.177075685814236
1121714.84557042888422.15442957111575
1131313.8834738038531-0.883473803853104
1141214.7379256798790-2.73792567987903
1151816.57954634297401.42045365702602
1161415.2281114767074-1.22811147670738
1171412.997601783371.00239821663001
1181314.8993115345838-1.89931153458377
1191615.65819550510370.341804494896255
1201314.0711360179465-1.07113601794654
1211615.55353296477610.44646703522391
1221315.953286738566-2.95328673856601
1231616.9658873937755-0.965887393775536
1241516.1569827481446-1.15698274814463
1251616.7580898664056-0.758089866405574
1261515.4110906664556-0.411090666455589
1271715.85229137592441.14770862407563
1281513.97600467624881.02399532375120
1291214.8005465356512-2.80054653565118
1301614.10601398122161.89398601877845
1311013.3359227827828-3.33592278278282
1321613.93818327066542.06181672933455
1331213.8537668978712-1.85376689787117
1341415.5185421206393-1.51854212063931
1351515.2365070835216-0.236507083521641
1361312.04406001145220.955939988547835
1371514.40118610941360.598813890586377
1381113.1320965306762-2.13209653067623
1391213.2328826440951-1.23288264409513
1401113.3171512856996-2.31715128569959
1411612.89308708187863.10691291812139
1421513.34818269269091.65181730730911
1431716.62514951259640.37485048740358
1441614.69206466204911.30793533795092
1451014.1015702832193-4.10157028321931
1461815.76904499025742.23095500974265
1471314.9873219097028-1.98732190970275
1481615.13184475647920.868155243520766
1491313.0275017473578-0.0275017473578213
1501013.1303945600366-3.13039456003662
1511516.0188472173771-1.01884721737712
1521613.86170861869592.13829138130414
1531612.01586208539183.98413791460823
1541412.66071546501811.33928453498189
1551012.6056861353773-2.60568613537734
1561716.36750357539150.63249642460849
1571311.61391676011681.38608323988323
1581513.85627200467181.1437279953282
1591615.06472645802260.935273541977356
1601212.4616481771048-0.461648177104788
1611312.62783046371590.372169536284084
1621312.7584271073090.241572892691002
1631211.56186916598640.438130834013596
1641716.02200116430780.977998835692165
1651514.20731811040660.792681889593414
1661012.6331863748027-2.63318637480265
1671414.3683314267263-0.368331426726333
1681113.7153437366674-2.71534373666737
1691314.8885990551029-1.88859905510292
1701615.24579793527780.754202064722216
1711210.28549915395901.71450084604098
1721615.51946387742460.480536122575433
1731212.8367523332627-0.836752333262726
174911.0531284391401-2.05312843914015
1751214.1163175907024-2.11631759070238
1761514.33703907052900.662960929471016
1771211.78859249769670.211407502303261
1781212.6880411523742-0.688041152374181
1791413.70497897590840.295021024091621
1801213.0681347026367-1.06813470263669
1811615.21287666098140.787123339018608
1821110.65543835721040.344561642789593
1831917.14638929041181.85361070958823
1841514.19419064063410.805809359365865
185814.6477265300753-6.64772653007528
1861615.25678747987640.743212520123645
1871714.19114571967042.80885428032959
1881211.26498995727910.735010042720903
1891111.4971440575779-0.497144057577914
1901111.2351280597453-0.235128059745261
1911415.2001378944469-1.20013789444691
1921615.46397608197750.536023918022513
193129.93687758322892.06312241677111
1941614.45759801375191.54240198624806
1951313.6527489527911-0.652748952791132
1961515.0215787178556-0.0215787178556023
1971612.73111478028603.26888521971396
1981615.24477170617040.75522829382964
1991412.47498152772441.52501847227564
2001614.76009329963581.23990670036416
2011613.86628866102482.13371133897524
2021413.73750994725160.262490052748416
2031112.9533618586172-1.95336185861718
2041214.5378169642539-2.53781696425391
2051512.67903169423822.32096830576176
2061514.64433937195840.355660628041642
2071614.48616132665701.51383867334304
2081615.52351119692530.47648880307465
2091113.2889439413281-2.28894394132806
2101513.60804914550051.39195085449946
2111214.1204962557417-2.12049625574171
2121216.0388683048002-4.03886830480018
2131514.66868323688610.331316763113950
2141511.82532690439473.17467309560533
2151614.59318204365951.40681795634047
2161412.73200213779121.26799786220875
2171714.60792322403512.39207677596492
2181413.20425622963980.79574377036023
2191312.05393707393230.946062926067694
2201515.4547445845594-0.454744584559391
2211314.0575639701153-1.05756397011527
2221414.0515537515291-0.0515537515290932
2231514.02336300349450.97663699650547
2241212.9139492925952-0.913949292595166
2251313.1823819391275-0.182381939127515
226811.8885006117870-3.88850061178705
2271414.1955491705540-0.195549170553975
2281412.86337075024671.13662924975327
2291112.3464306860680-1.34643068606797
2301212.7834452633280-0.783445263327967
2311310.66607158616872.3339284138313
2321013.6996524618582-3.69965246185824
2331611.58161543910624.41838456089384
2341815.20217171636732.79782828363267
2351313.3927306361125-0.392730636112459
2361112.6034082064065-1.60340820640646
237410.2801833291943-6.28018332919432
2381313.5153843221827-0.515384322182697
2391613.88765363063272.11234636936726
2401010.6255327401344-0.625532740134409
2411211.98685168009140.0131483199086112
2421213.7069303922495-1.70693039224951
243108.903144473420481.09685552657952
2441310.90488836442212.09511163557788
2451512.91569032845892.08430967154114
2461212.5990454991870-0.599045499186962
2471413.14649603337000.853503966630049
2481012.9835984954948-2.98359849549476
2491210.93683641274571.06316358725425
2501211.42404690839420.57595309160577
2511112.1229276563996-1.12292765639964
2521011.4263977997735-1.42639779977352
2531210.93943962805511.06056037194486
2541612.50836615236183.49163384763824
2551212.9982991105830-0.99829911058297
2561413.68713252142640.312867478573634
2571614.48747784698391.51252215301612
2581411.72894151443622.27105848556385
2591314.0536540515759-1.05365405157594
26048.08400355852552-4.08400355852552
2611513.80438982536241.19561017463758
2621115.3209721875729-4.32097218757294
2631111.1048724893516-0.104872489351615
2641412.79613327800321.20386672199677

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.0460838528529 & -3.04608385285288 \tabularnewline
2 & 16 & 16.0978854720564 & -0.0978854720563963 \tabularnewline
3 & 19 & 16.6187187563686 & 2.38128124363138 \tabularnewline
4 & 15 & 12.2688445880293 & 2.73115541197067 \tabularnewline
5 & 14 & 15.6143076175297 & -1.61430761752974 \tabularnewline
6 & 13 & 15.0952278482479 & -2.09522784824792 \tabularnewline
7 & 19 & 15.1106383280845 & 3.88936167191549 \tabularnewline
8 & 15 & 16.9713242202552 & -1.97132422025521 \tabularnewline
9 & 14 & 16.1392821471749 & -2.13928214717492 \tabularnewline
10 & 15 & 14.4025490268356 & 0.597450973164355 \tabularnewline
11 & 16 & 15.4610456161102 & 0.53895438388982 \tabularnewline
12 & 16 & 16.1235117839571 & -0.123511783957133 \tabularnewline
13 & 16 & 15.6206706709185 & 0.37932932908151 \tabularnewline
14 & 16 & 15.2094476897910 & 0.790552310208976 \tabularnewline
15 & 17 & 17.6741165821685 & -0.674116582168514 \tabularnewline
16 & 15 & 15.1802222027614 & -0.180222202761411 \tabularnewline
17 & 15 & 14.7615924363051 & 0.238407563694865 \tabularnewline
18 & 20 & 16.4280021978545 & 3.57199780214553 \tabularnewline
19 & 18 & 15.6160038471029 & 2.38399615289714 \tabularnewline
20 & 16 & 15.307418081434 & 0.692581918565983 \tabularnewline
21 & 16 & 15.2613158314681 & 0.7386841685319 \tabularnewline
22 & 16 & 15.1590203135979 & 0.840979686402122 \tabularnewline
23 & 19 & 16.3183266771825 & 2.68167332281749 \tabularnewline
24 & 16 & 14.8896593529274 & 1.11034064707260 \tabularnewline
25 & 17 & 16.5409837524434 & 0.459016247556603 \tabularnewline
26 & 17 & 17.0622567007993 & -0.0622567007993099 \tabularnewline
27 & 16 & 15.0063695483651 & 0.99363045163488 \tabularnewline
28 & 15 & 16.287354596739 & -1.28735459673899 \tabularnewline
29 & 16 & 15.5528708060404 & 0.447129193959557 \tabularnewline
30 & 14 & 14.3904425234722 & -0.390442523472174 \tabularnewline
31 & 15 & 15.8770730400500 & -0.87707304004998 \tabularnewline
32 & 12 & 12.4410863242391 & -0.441086324239124 \tabularnewline
33 & 14 & 15.1632236724350 & -1.16322367243502 \tabularnewline
34 & 16 & 15.9066298550938 & 0.0933701449062471 \tabularnewline
35 & 14 & 15.6500827322888 & -1.65008273228881 \tabularnewline
36 & 10 & 13.265086391234 & -3.26508639123401 \tabularnewline
37 & 10 & 12.7277418177310 & -2.72774181773096 \tabularnewline
38 & 14 & 15.8385839962544 & -1.83858399625441 \tabularnewline
39 & 16 & 14.5174086001451 & 1.48259139985486 \tabularnewline
40 & 16 & 14.7800135692866 & 1.21998643071338 \tabularnewline
41 & 16 & 15.1858454467376 & 0.814154553262365 \tabularnewline
42 & 14 & 15.5727609632611 & -1.57276096326112 \tabularnewline
43 & 20 & 17.6691623615627 & 2.33083763843726 \tabularnewline
44 & 14 & 14.1387756696789 & -0.138775669678926 \tabularnewline
45 & 14 & 15.0696607461849 & -1.06966074618494 \tabularnewline
46 & 11 & 15.6144856270065 & -4.61448562700649 \tabularnewline
47 & 14 & 16.5709069707458 & -2.57090697074581 \tabularnewline
48 & 15 & 15.1481882666103 & -0.148188266610254 \tabularnewline
49 & 16 & 15.0614967185108 & 0.938503281489162 \tabularnewline
50 & 14 & 16.0633090042108 & -2.06330900421076 \tabularnewline
51 & 16 & 16.6187303550879 & -0.618730355087908 \tabularnewline
52 & 14 & 14.1780196281422 & -0.178019628142175 \tabularnewline
53 & 12 & 14.6837657036483 & -2.68376570364832 \tabularnewline
54 & 16 & 15.6527346763494 & 0.347265323650556 \tabularnewline
55 & 9 & 11.6694846725896 & -2.66948467258963 \tabularnewline
56 & 14 & 12.5777743435091 & 1.42222565649092 \tabularnewline
57 & 16 & 16.0036265752432 & -0.00362657524324481 \tabularnewline
58 & 16 & 15.3226855308472 & 0.677314469152835 \tabularnewline
59 & 15 & 15.3869151033687 & -0.386915103368735 \tabularnewline
60 & 16 & 14.2534262966507 & 1.74657370334928 \tabularnewline
61 & 12 & 11.5388756636925 & 0.4611243363075 \tabularnewline
62 & 16 & 15.9344810690503 & 0.0655189309496733 \tabularnewline
63 & 16 & 16.5909413491474 & -0.590941349147435 \tabularnewline
64 & 14 & 14.2618668653366 & -0.261866865336586 \tabularnewline
65 & 16 & 15.2400247389463 & 0.759975261053662 \tabularnewline
66 & 17 & 16.1718988962265 & 0.828101103773525 \tabularnewline
67 & 18 & 16.1173030644178 & 1.88269693558220 \tabularnewline
68 & 18 & 14.6991351119578 & 3.30086488804221 \tabularnewline
69 & 12 & 15.6722816230555 & -3.67228162305553 \tabularnewline
70 & 16 & 15.3696560764777 & 0.630343923522344 \tabularnewline
71 & 10 & 13.5866524120327 & -3.58665241203271 \tabularnewline
72 & 14 & 14.195891330998 & -0.195891330997995 \tabularnewline
73 & 18 & 16.5485343575736 & 1.45146564242638 \tabularnewline
74 & 18 & 16.9322355961949 & 1.06776440380514 \tabularnewline
75 & 16 & 15.8225188056093 & 0.177481194390746 \tabularnewline
76 & 17 & 13.8949822443110 & 3.10501775568903 \tabularnewline
77 & 16 & 16.1954946350854 & -0.195494635085433 \tabularnewline
78 & 16 & 14.5038462571406 & 1.49615374285942 \tabularnewline
79 & 13 & 15.0129915707566 & -2.01299157075665 \tabularnewline
80 & 16 & 15.7714379059751 & 0.228562094024866 \tabularnewline
81 & 16 & 15.5690883948052 & 0.430911605194758 \tabularnewline
82 & 16 & 15.7700278406369 & 0.229972159363086 \tabularnewline
83 & 15 & 15.8074459225013 & -0.807445922501296 \tabularnewline
84 & 15 & 14.7876596578462 & 0.212340342153845 \tabularnewline
85 & 16 & 14.3564786199950 & 1.64352138000496 \tabularnewline
86 & 14 & 14.1571625912085 & -0.157162591208454 \tabularnewline
87 & 16 & 15.3058751714197 & 0.69412482858025 \tabularnewline
88 & 16 & 14.4696171221247 & 1.53038287787534 \tabularnewline
89 & 15 & 14.2788274441128 & 0.7211725558872 \tabularnewline
90 & 12 & 13.6023588239322 & -1.60235882393221 \tabularnewline
91 & 17 & 16.6269243638083 & 0.373075636191658 \tabularnewline
92 & 16 & 15.4318857904126 & 0.568114209587371 \tabularnewline
93 & 15 & 15.2277147708315 & -0.227714770831518 \tabularnewline
94 & 13 & 15.0813275746976 & -2.0813275746976 \tabularnewline
95 & 16 & 15.0442372330375 & 0.955762766962496 \tabularnewline
96 & 16 & 15.7295295273941 & 0.270470472605877 \tabularnewline
97 & 16 & 14.1462494894847 & 1.85375051051534 \tabularnewline
98 & 16 & 15.9702173667754 & 0.0297826332246363 \tabularnewline
99 & 14 & 14.4087389652808 & -0.408738965280751 \tabularnewline
100 & 16 & 17.0669910224862 & -1.06699102248624 \tabularnewline
101 & 16 & 14.7322268792687 & 1.26777312073132 \tabularnewline
102 & 20 & 17.3302055136579 & 2.66979448634214 \tabularnewline
103 & 15 & 14.6657299251388 & 0.334270074861174 \tabularnewline
104 & 16 & 14.4982176969474 & 1.50178230305259 \tabularnewline
105 & 13 & 15.1210647666331 & -2.12106476663310 \tabularnewline
106 & 17 & 15.9044088002123 & 1.09559119978766 \tabularnewline
107 & 16 & 15.5814492454538 & 0.418550754546173 \tabularnewline
108 & 16 & 14.3481449641976 & 1.65185503580237 \tabularnewline
109 & 12 & 11.9053155487678 & 0.094684451232151 \tabularnewline
110 & 16 & 15.0609499367424 & 0.93905006325765 \tabularnewline
111 & 16 & 15.8229243141858 & 0.177075685814236 \tabularnewline
112 & 17 & 14.8455704288842 & 2.15442957111575 \tabularnewline
113 & 13 & 13.8834738038531 & -0.883473803853104 \tabularnewline
114 & 12 & 14.7379256798790 & -2.73792567987903 \tabularnewline
115 & 18 & 16.5795463429740 & 1.42045365702602 \tabularnewline
116 & 14 & 15.2281114767074 & -1.22811147670738 \tabularnewline
117 & 14 & 12.99760178337 & 1.00239821663001 \tabularnewline
118 & 13 & 14.8993115345838 & -1.89931153458377 \tabularnewline
119 & 16 & 15.6581955051037 & 0.341804494896255 \tabularnewline
120 & 13 & 14.0711360179465 & -1.07113601794654 \tabularnewline
121 & 16 & 15.5535329647761 & 0.44646703522391 \tabularnewline
122 & 13 & 15.953286738566 & -2.95328673856601 \tabularnewline
123 & 16 & 16.9658873937755 & -0.965887393775536 \tabularnewline
124 & 15 & 16.1569827481446 & -1.15698274814463 \tabularnewline
125 & 16 & 16.7580898664056 & -0.758089866405574 \tabularnewline
126 & 15 & 15.4110906664556 & -0.411090666455589 \tabularnewline
127 & 17 & 15.8522913759244 & 1.14770862407563 \tabularnewline
128 & 15 & 13.9760046762488 & 1.02399532375120 \tabularnewline
129 & 12 & 14.8005465356512 & -2.80054653565118 \tabularnewline
130 & 16 & 14.1060139812216 & 1.89398601877845 \tabularnewline
131 & 10 & 13.3359227827828 & -3.33592278278282 \tabularnewline
132 & 16 & 13.9381832706654 & 2.06181672933455 \tabularnewline
133 & 12 & 13.8537668978712 & -1.85376689787117 \tabularnewline
134 & 14 & 15.5185421206393 & -1.51854212063931 \tabularnewline
135 & 15 & 15.2365070835216 & -0.236507083521641 \tabularnewline
136 & 13 & 12.0440600114522 & 0.955939988547835 \tabularnewline
137 & 15 & 14.4011861094136 & 0.598813890586377 \tabularnewline
138 & 11 & 13.1320965306762 & -2.13209653067623 \tabularnewline
139 & 12 & 13.2328826440951 & -1.23288264409513 \tabularnewline
140 & 11 & 13.3171512856996 & -2.31715128569959 \tabularnewline
141 & 16 & 12.8930870818786 & 3.10691291812139 \tabularnewline
142 & 15 & 13.3481826926909 & 1.65181730730911 \tabularnewline
143 & 17 & 16.6251495125964 & 0.37485048740358 \tabularnewline
144 & 16 & 14.6920646620491 & 1.30793533795092 \tabularnewline
145 & 10 & 14.1015702832193 & -4.10157028321931 \tabularnewline
146 & 18 & 15.7690449902574 & 2.23095500974265 \tabularnewline
147 & 13 & 14.9873219097028 & -1.98732190970275 \tabularnewline
148 & 16 & 15.1318447564792 & 0.868155243520766 \tabularnewline
149 & 13 & 13.0275017473578 & -0.0275017473578213 \tabularnewline
150 & 10 & 13.1303945600366 & -3.13039456003662 \tabularnewline
151 & 15 & 16.0188472173771 & -1.01884721737712 \tabularnewline
152 & 16 & 13.8617086186959 & 2.13829138130414 \tabularnewline
153 & 16 & 12.0158620853918 & 3.98413791460823 \tabularnewline
154 & 14 & 12.6607154650181 & 1.33928453498189 \tabularnewline
155 & 10 & 12.6056861353773 & -2.60568613537734 \tabularnewline
156 & 17 & 16.3675035753915 & 0.63249642460849 \tabularnewline
157 & 13 & 11.6139167601168 & 1.38608323988323 \tabularnewline
158 & 15 & 13.8562720046718 & 1.1437279953282 \tabularnewline
159 & 16 & 15.0647264580226 & 0.935273541977356 \tabularnewline
160 & 12 & 12.4616481771048 & -0.461648177104788 \tabularnewline
161 & 13 & 12.6278304637159 & 0.372169536284084 \tabularnewline
162 & 13 & 12.758427107309 & 0.241572892691002 \tabularnewline
163 & 12 & 11.5618691659864 & 0.438130834013596 \tabularnewline
164 & 17 & 16.0220011643078 & 0.977998835692165 \tabularnewline
165 & 15 & 14.2073181104066 & 0.792681889593414 \tabularnewline
166 & 10 & 12.6331863748027 & -2.63318637480265 \tabularnewline
167 & 14 & 14.3683314267263 & -0.368331426726333 \tabularnewline
168 & 11 & 13.7153437366674 & -2.71534373666737 \tabularnewline
169 & 13 & 14.8885990551029 & -1.88859905510292 \tabularnewline
170 & 16 & 15.2457979352778 & 0.754202064722216 \tabularnewline
171 & 12 & 10.2854991539590 & 1.71450084604098 \tabularnewline
172 & 16 & 15.5194638774246 & 0.480536122575433 \tabularnewline
173 & 12 & 12.8367523332627 & -0.836752333262726 \tabularnewline
174 & 9 & 11.0531284391401 & -2.05312843914015 \tabularnewline
175 & 12 & 14.1163175907024 & -2.11631759070238 \tabularnewline
176 & 15 & 14.3370390705290 & 0.662960929471016 \tabularnewline
177 & 12 & 11.7885924976967 & 0.211407502303261 \tabularnewline
178 & 12 & 12.6880411523742 & -0.688041152374181 \tabularnewline
179 & 14 & 13.7049789759084 & 0.295021024091621 \tabularnewline
180 & 12 & 13.0681347026367 & -1.06813470263669 \tabularnewline
181 & 16 & 15.2128766609814 & 0.787123339018608 \tabularnewline
182 & 11 & 10.6554383572104 & 0.344561642789593 \tabularnewline
183 & 19 & 17.1463892904118 & 1.85361070958823 \tabularnewline
184 & 15 & 14.1941906406341 & 0.805809359365865 \tabularnewline
185 & 8 & 14.6477265300753 & -6.64772653007528 \tabularnewline
186 & 16 & 15.2567874798764 & 0.743212520123645 \tabularnewline
187 & 17 & 14.1911457196704 & 2.80885428032959 \tabularnewline
188 & 12 & 11.2649899572791 & 0.735010042720903 \tabularnewline
189 & 11 & 11.4971440575779 & -0.497144057577914 \tabularnewline
190 & 11 & 11.2351280597453 & -0.235128059745261 \tabularnewline
191 & 14 & 15.2001378944469 & -1.20013789444691 \tabularnewline
192 & 16 & 15.4639760819775 & 0.536023918022513 \tabularnewline
193 & 12 & 9.9368775832289 & 2.06312241677111 \tabularnewline
194 & 16 & 14.4575980137519 & 1.54240198624806 \tabularnewline
195 & 13 & 13.6527489527911 & -0.652748952791132 \tabularnewline
196 & 15 & 15.0215787178556 & -0.0215787178556023 \tabularnewline
197 & 16 & 12.7311147802860 & 3.26888521971396 \tabularnewline
198 & 16 & 15.2447717061704 & 0.75522829382964 \tabularnewline
199 & 14 & 12.4749815277244 & 1.52501847227564 \tabularnewline
200 & 16 & 14.7600932996358 & 1.23990670036416 \tabularnewline
201 & 16 & 13.8662886610248 & 2.13371133897524 \tabularnewline
202 & 14 & 13.7375099472516 & 0.262490052748416 \tabularnewline
203 & 11 & 12.9533618586172 & -1.95336185861718 \tabularnewline
204 & 12 & 14.5378169642539 & -2.53781696425391 \tabularnewline
205 & 15 & 12.6790316942382 & 2.32096830576176 \tabularnewline
206 & 15 & 14.6443393719584 & 0.355660628041642 \tabularnewline
207 & 16 & 14.4861613266570 & 1.51383867334304 \tabularnewline
208 & 16 & 15.5235111969253 & 0.47648880307465 \tabularnewline
209 & 11 & 13.2889439413281 & -2.28894394132806 \tabularnewline
210 & 15 & 13.6080491455005 & 1.39195085449946 \tabularnewline
211 & 12 & 14.1204962557417 & -2.12049625574171 \tabularnewline
212 & 12 & 16.0388683048002 & -4.03886830480018 \tabularnewline
213 & 15 & 14.6686832368861 & 0.331316763113950 \tabularnewline
214 & 15 & 11.8253269043947 & 3.17467309560533 \tabularnewline
215 & 16 & 14.5931820436595 & 1.40681795634047 \tabularnewline
216 & 14 & 12.7320021377912 & 1.26799786220875 \tabularnewline
217 & 17 & 14.6079232240351 & 2.39207677596492 \tabularnewline
218 & 14 & 13.2042562296398 & 0.79574377036023 \tabularnewline
219 & 13 & 12.0539370739323 & 0.946062926067694 \tabularnewline
220 & 15 & 15.4547445845594 & -0.454744584559391 \tabularnewline
221 & 13 & 14.0575639701153 & -1.05756397011527 \tabularnewline
222 & 14 & 14.0515537515291 & -0.0515537515290932 \tabularnewline
223 & 15 & 14.0233630034945 & 0.97663699650547 \tabularnewline
224 & 12 & 12.9139492925952 & -0.913949292595166 \tabularnewline
225 & 13 & 13.1823819391275 & -0.182381939127515 \tabularnewline
226 & 8 & 11.8885006117870 & -3.88850061178705 \tabularnewline
227 & 14 & 14.1955491705540 & -0.195549170553975 \tabularnewline
228 & 14 & 12.8633707502467 & 1.13662924975327 \tabularnewline
229 & 11 & 12.3464306860680 & -1.34643068606797 \tabularnewline
230 & 12 & 12.7834452633280 & -0.783445263327967 \tabularnewline
231 & 13 & 10.6660715861687 & 2.3339284138313 \tabularnewline
232 & 10 & 13.6996524618582 & -3.69965246185824 \tabularnewline
233 & 16 & 11.5816154391062 & 4.41838456089384 \tabularnewline
234 & 18 & 15.2021717163673 & 2.79782828363267 \tabularnewline
235 & 13 & 13.3927306361125 & -0.392730636112459 \tabularnewline
236 & 11 & 12.6034082064065 & -1.60340820640646 \tabularnewline
237 & 4 & 10.2801833291943 & -6.28018332919432 \tabularnewline
238 & 13 & 13.5153843221827 & -0.515384322182697 \tabularnewline
239 & 16 & 13.8876536306327 & 2.11234636936726 \tabularnewline
240 & 10 & 10.6255327401344 & -0.625532740134409 \tabularnewline
241 & 12 & 11.9868516800914 & 0.0131483199086112 \tabularnewline
242 & 12 & 13.7069303922495 & -1.70693039224951 \tabularnewline
243 & 10 & 8.90314447342048 & 1.09685552657952 \tabularnewline
244 & 13 & 10.9048883644221 & 2.09511163557788 \tabularnewline
245 & 15 & 12.9156903284589 & 2.08430967154114 \tabularnewline
246 & 12 & 12.5990454991870 & -0.599045499186962 \tabularnewline
247 & 14 & 13.1464960333700 & 0.853503966630049 \tabularnewline
248 & 10 & 12.9835984954948 & -2.98359849549476 \tabularnewline
249 & 12 & 10.9368364127457 & 1.06316358725425 \tabularnewline
250 & 12 & 11.4240469083942 & 0.57595309160577 \tabularnewline
251 & 11 & 12.1229276563996 & -1.12292765639964 \tabularnewline
252 & 10 & 11.4263977997735 & -1.42639779977352 \tabularnewline
253 & 12 & 10.9394396280551 & 1.06056037194486 \tabularnewline
254 & 16 & 12.5083661523618 & 3.49163384763824 \tabularnewline
255 & 12 & 12.9982991105830 & -0.99829911058297 \tabularnewline
256 & 14 & 13.6871325214264 & 0.312867478573634 \tabularnewline
257 & 16 & 14.4874778469839 & 1.51252215301612 \tabularnewline
258 & 14 & 11.7289415144362 & 2.27105848556385 \tabularnewline
259 & 13 & 14.0536540515759 & -1.05365405157594 \tabularnewline
260 & 4 & 8.08400355852552 & -4.08400355852552 \tabularnewline
261 & 15 & 13.8043898253624 & 1.19561017463758 \tabularnewline
262 & 11 & 15.3209721875729 & -4.32097218757294 \tabularnewline
263 & 11 & 11.1048724893516 & -0.104872489351615 \tabularnewline
264 & 14 & 12.7961332780032 & 1.20386672199677 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96576&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]13[/C][C]16.0460838528529[/C][C]-3.04608385285288[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]16.0978854720564[/C][C]-0.0978854720563963[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.6187187563686[/C][C]2.38128124363138[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.2688445880293[/C][C]2.73115541197067[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.6143076175297[/C][C]-1.61430761752974[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.0952278482479[/C][C]-2.09522784824792[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.1106383280845[/C][C]3.88936167191549[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.9713242202552[/C][C]-1.97132422025521[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]16.1392821471749[/C][C]-2.13928214717492[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.4025490268356[/C][C]0.597450973164355[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.4610456161102[/C][C]0.53895438388982[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.1235117839571[/C][C]-0.123511783957133[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.6206706709185[/C][C]0.37932932908151[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.2094476897910[/C][C]0.790552310208976[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.6741165821685[/C][C]-0.674116582168514[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.1802222027614[/C][C]-0.180222202761411[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.7615924363051[/C][C]0.238407563694865[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.4280021978545[/C][C]3.57199780214553[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.6160038471029[/C][C]2.38399615289714[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.307418081434[/C][C]0.692581918565983[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.2613158314681[/C][C]0.7386841685319[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]15.1590203135979[/C][C]0.840979686402122[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.3183266771825[/C][C]2.68167332281749[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.8896593529274[/C][C]1.11034064707260[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]16.5409837524434[/C][C]0.459016247556603[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]17.0622567007993[/C][C]-0.0622567007993099[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.0063695483651[/C][C]0.99363045163488[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.287354596739[/C][C]-1.28735459673899[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.5528708060404[/C][C]0.447129193959557[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.3904425234722[/C][C]-0.390442523472174[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.8770730400500[/C][C]-0.87707304004998[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.4410863242391[/C][C]-0.441086324239124[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.1632236724350[/C][C]-1.16322367243502[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.9066298550938[/C][C]0.0933701449062471[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.6500827322888[/C][C]-1.65008273228881[/C][/ROW]
[ROW][C]36[/C][C]10[/C][C]13.265086391234[/C][C]-3.26508639123401[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]12.7277418177310[/C][C]-2.72774181773096[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.8385839962544[/C][C]-1.83858399625441[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.5174086001451[/C][C]1.48259139985486[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.7800135692866[/C][C]1.21998643071338[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.1858454467376[/C][C]0.814154553262365[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.5727609632611[/C][C]-1.57276096326112[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.6691623615627[/C][C]2.33083763843726[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.1387756696789[/C][C]-0.138775669678926[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]15.0696607461849[/C][C]-1.06966074618494[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.6144856270065[/C][C]-4.61448562700649[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.5709069707458[/C][C]-2.57090697074581[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.1481882666103[/C][C]-0.148188266610254[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.0614967185108[/C][C]0.938503281489162[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]16.0633090042108[/C][C]-2.06330900421076[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.6187303550879[/C][C]-0.618730355087908[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.1780196281422[/C][C]-0.178019628142175[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.6837657036483[/C][C]-2.68376570364832[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.6527346763494[/C][C]0.347265323650556[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.6694846725896[/C][C]-2.66948467258963[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.5777743435091[/C][C]1.42222565649092[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.0036265752432[/C][C]-0.00362657524324481[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.3226855308472[/C][C]0.677314469152835[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.3869151033687[/C][C]-0.386915103368735[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.2534262966507[/C][C]1.74657370334928[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.5388756636925[/C][C]0.4611243363075[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.9344810690503[/C][C]0.0655189309496733[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.5909413491474[/C][C]-0.590941349147435[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.2618668653366[/C][C]-0.261866865336586[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.2400247389463[/C][C]0.759975261053662[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]16.1718988962265[/C][C]0.828101103773525[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.1173030644178[/C][C]1.88269693558220[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.6991351119578[/C][C]3.30086488804221[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.6722816230555[/C][C]-3.67228162305553[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.3696560764777[/C][C]0.630343923522344[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.5866524120327[/C][C]-3.58665241203271[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.195891330998[/C][C]-0.195891330997995[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.5485343575736[/C][C]1.45146564242638[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]16.9322355961949[/C][C]1.06776440380514[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.8225188056093[/C][C]0.177481194390746[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.8949822443110[/C][C]3.10501775568903[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.1954946350854[/C][C]-0.195494635085433[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.5038462571406[/C][C]1.49615374285942[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]15.0129915707566[/C][C]-2.01299157075665[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.7714379059751[/C][C]0.228562094024866[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.5690883948052[/C][C]0.430911605194758[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]15.7700278406369[/C][C]0.229972159363086[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.8074459225013[/C][C]-0.807445922501296[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]14.7876596578462[/C][C]0.212340342153845[/C][/ROW]
[ROW][C]85[/C][C]16[/C][C]14.3564786199950[/C][C]1.64352138000496[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.1571625912085[/C][C]-0.157162591208454[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]15.3058751714197[/C][C]0.69412482858025[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]14.4696171221247[/C][C]1.53038287787534[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]14.2788274441128[/C][C]0.7211725558872[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]13.6023588239322[/C][C]-1.60235882393221[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]16.6269243638083[/C][C]0.373075636191658[/C][/ROW]
[ROW][C]92[/C][C]16[/C][C]15.4318857904126[/C][C]0.568114209587371[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]15.2277147708315[/C][C]-0.227714770831518[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]15.0813275746976[/C][C]-2.0813275746976[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]15.0442372330375[/C][C]0.955762766962496[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.7295295273941[/C][C]0.270470472605877[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]14.1462494894847[/C][C]1.85375051051534[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]15.9702173667754[/C][C]0.0297826332246363[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]14.4087389652808[/C][C]-0.408738965280751[/C][/ROW]
[ROW][C]100[/C][C]16[/C][C]17.0669910224862[/C][C]-1.06699102248624[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]14.7322268792687[/C][C]1.26777312073132[/C][/ROW]
[ROW][C]102[/C][C]20[/C][C]17.3302055136579[/C][C]2.66979448634214[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]14.6657299251388[/C][C]0.334270074861174[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]14.4982176969474[/C][C]1.50178230305259[/C][/ROW]
[ROW][C]105[/C][C]13[/C][C]15.1210647666331[/C][C]-2.12106476663310[/C][/ROW]
[ROW][C]106[/C][C]17[/C][C]15.9044088002123[/C][C]1.09559119978766[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]15.5814492454538[/C][C]0.418550754546173[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]14.3481449641976[/C][C]1.65185503580237[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]11.9053155487678[/C][C]0.094684451232151[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]15.0609499367424[/C][C]0.93905006325765[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]15.8229243141858[/C][C]0.177075685814236[/C][/ROW]
[ROW][C]112[/C][C]17[/C][C]14.8455704288842[/C][C]2.15442957111575[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]13.8834738038531[/C][C]-0.883473803853104[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]14.7379256798790[/C][C]-2.73792567987903[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]16.5795463429740[/C][C]1.42045365702602[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]15.2281114767074[/C][C]-1.22811147670738[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]12.99760178337[/C][C]1.00239821663001[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]14.8993115345838[/C][C]-1.89931153458377[/C][/ROW]
[ROW][C]119[/C][C]16[/C][C]15.6581955051037[/C][C]0.341804494896255[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]14.0711360179465[/C][C]-1.07113601794654[/C][/ROW]
[ROW][C]121[/C][C]16[/C][C]15.5535329647761[/C][C]0.44646703522391[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]15.953286738566[/C][C]-2.95328673856601[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]16.9658873937755[/C][C]-0.965887393775536[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]16.1569827481446[/C][C]-1.15698274814463[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]16.7580898664056[/C][C]-0.758089866405574[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]15.4110906664556[/C][C]-0.411090666455589[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]15.8522913759244[/C][C]1.14770862407563[/C][/ROW]
[ROW][C]128[/C][C]15[/C][C]13.9760046762488[/C][C]1.02399532375120[/C][/ROW]
[ROW][C]129[/C][C]12[/C][C]14.8005465356512[/C][C]-2.80054653565118[/C][/ROW]
[ROW][C]130[/C][C]16[/C][C]14.1060139812216[/C][C]1.89398601877845[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]13.3359227827828[/C][C]-3.33592278278282[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]13.9381832706654[/C][C]2.06181672933455[/C][/ROW]
[ROW][C]133[/C][C]12[/C][C]13.8537668978712[/C][C]-1.85376689787117[/C][/ROW]
[ROW][C]134[/C][C]14[/C][C]15.5185421206393[/C][C]-1.51854212063931[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]15.2365070835216[/C][C]-0.236507083521641[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]12.0440600114522[/C][C]0.955939988547835[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]14.4011861094136[/C][C]0.598813890586377[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]13.1320965306762[/C][C]-2.13209653067623[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]13.2328826440951[/C][C]-1.23288264409513[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]13.3171512856996[/C][C]-2.31715128569959[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]12.8930870818786[/C][C]3.10691291812139[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]13.3481826926909[/C][C]1.65181730730911[/C][/ROW]
[ROW][C]143[/C][C]17[/C][C]16.6251495125964[/C][C]0.37485048740358[/C][/ROW]
[ROW][C]144[/C][C]16[/C][C]14.6920646620491[/C][C]1.30793533795092[/C][/ROW]
[ROW][C]145[/C][C]10[/C][C]14.1015702832193[/C][C]-4.10157028321931[/C][/ROW]
[ROW][C]146[/C][C]18[/C][C]15.7690449902574[/C][C]2.23095500974265[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]14.9873219097028[/C][C]-1.98732190970275[/C][/ROW]
[ROW][C]148[/C][C]16[/C][C]15.1318447564792[/C][C]0.868155243520766[/C][/ROW]
[ROW][C]149[/C][C]13[/C][C]13.0275017473578[/C][C]-0.0275017473578213[/C][/ROW]
[ROW][C]150[/C][C]10[/C][C]13.1303945600366[/C][C]-3.13039456003662[/C][/ROW]
[ROW][C]151[/C][C]15[/C][C]16.0188472173771[/C][C]-1.01884721737712[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]13.8617086186959[/C][C]2.13829138130414[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]12.0158620853918[/C][C]3.98413791460823[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]12.6607154650181[/C][C]1.33928453498189[/C][/ROW]
[ROW][C]155[/C][C]10[/C][C]12.6056861353773[/C][C]-2.60568613537734[/C][/ROW]
[ROW][C]156[/C][C]17[/C][C]16.3675035753915[/C][C]0.63249642460849[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]11.6139167601168[/C][C]1.38608323988323[/C][/ROW]
[ROW][C]158[/C][C]15[/C][C]13.8562720046718[/C][C]1.1437279953282[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]15.0647264580226[/C][C]0.935273541977356[/C][/ROW]
[ROW][C]160[/C][C]12[/C][C]12.4616481771048[/C][C]-0.461648177104788[/C][/ROW]
[ROW][C]161[/C][C]13[/C][C]12.6278304637159[/C][C]0.372169536284084[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.758427107309[/C][C]0.241572892691002[/C][/ROW]
[ROW][C]163[/C][C]12[/C][C]11.5618691659864[/C][C]0.438130834013596[/C][/ROW]
[ROW][C]164[/C][C]17[/C][C]16.0220011643078[/C][C]0.977998835692165[/C][/ROW]
[ROW][C]165[/C][C]15[/C][C]14.2073181104066[/C][C]0.792681889593414[/C][/ROW]
[ROW][C]166[/C][C]10[/C][C]12.6331863748027[/C][C]-2.63318637480265[/C][/ROW]
[ROW][C]167[/C][C]14[/C][C]14.3683314267263[/C][C]-0.368331426726333[/C][/ROW]
[ROW][C]168[/C][C]11[/C][C]13.7153437366674[/C][C]-2.71534373666737[/C][/ROW]
[ROW][C]169[/C][C]13[/C][C]14.8885990551029[/C][C]-1.88859905510292[/C][/ROW]
[ROW][C]170[/C][C]16[/C][C]15.2457979352778[/C][C]0.754202064722216[/C][/ROW]
[ROW][C]171[/C][C]12[/C][C]10.2854991539590[/C][C]1.71450084604098[/C][/ROW]
[ROW][C]172[/C][C]16[/C][C]15.5194638774246[/C][C]0.480536122575433[/C][/ROW]
[ROW][C]173[/C][C]12[/C][C]12.8367523332627[/C][C]-0.836752333262726[/C][/ROW]
[ROW][C]174[/C][C]9[/C][C]11.0531284391401[/C][C]-2.05312843914015[/C][/ROW]
[ROW][C]175[/C][C]12[/C][C]14.1163175907024[/C][C]-2.11631759070238[/C][/ROW]
[ROW][C]176[/C][C]15[/C][C]14.3370390705290[/C][C]0.662960929471016[/C][/ROW]
[ROW][C]177[/C][C]12[/C][C]11.7885924976967[/C][C]0.211407502303261[/C][/ROW]
[ROW][C]178[/C][C]12[/C][C]12.6880411523742[/C][C]-0.688041152374181[/C][/ROW]
[ROW][C]179[/C][C]14[/C][C]13.7049789759084[/C][C]0.295021024091621[/C][/ROW]
[ROW][C]180[/C][C]12[/C][C]13.0681347026367[/C][C]-1.06813470263669[/C][/ROW]
[ROW][C]181[/C][C]16[/C][C]15.2128766609814[/C][C]0.787123339018608[/C][/ROW]
[ROW][C]182[/C][C]11[/C][C]10.6554383572104[/C][C]0.344561642789593[/C][/ROW]
[ROW][C]183[/C][C]19[/C][C]17.1463892904118[/C][C]1.85361070958823[/C][/ROW]
[ROW][C]184[/C][C]15[/C][C]14.1941906406341[/C][C]0.805809359365865[/C][/ROW]
[ROW][C]185[/C][C]8[/C][C]14.6477265300753[/C][C]-6.64772653007528[/C][/ROW]
[ROW][C]186[/C][C]16[/C][C]15.2567874798764[/C][C]0.743212520123645[/C][/ROW]
[ROW][C]187[/C][C]17[/C][C]14.1911457196704[/C][C]2.80885428032959[/C][/ROW]
[ROW][C]188[/C][C]12[/C][C]11.2649899572791[/C][C]0.735010042720903[/C][/ROW]
[ROW][C]189[/C][C]11[/C][C]11.4971440575779[/C][C]-0.497144057577914[/C][/ROW]
[ROW][C]190[/C][C]11[/C][C]11.2351280597453[/C][C]-0.235128059745261[/C][/ROW]
[ROW][C]191[/C][C]14[/C][C]15.2001378944469[/C][C]-1.20013789444691[/C][/ROW]
[ROW][C]192[/C][C]16[/C][C]15.4639760819775[/C][C]0.536023918022513[/C][/ROW]
[ROW][C]193[/C][C]12[/C][C]9.9368775832289[/C][C]2.06312241677111[/C][/ROW]
[ROW][C]194[/C][C]16[/C][C]14.4575980137519[/C][C]1.54240198624806[/C][/ROW]
[ROW][C]195[/C][C]13[/C][C]13.6527489527911[/C][C]-0.652748952791132[/C][/ROW]
[ROW][C]196[/C][C]15[/C][C]15.0215787178556[/C][C]-0.0215787178556023[/C][/ROW]
[ROW][C]197[/C][C]16[/C][C]12.7311147802860[/C][C]3.26888521971396[/C][/ROW]
[ROW][C]198[/C][C]16[/C][C]15.2447717061704[/C][C]0.75522829382964[/C][/ROW]
[ROW][C]199[/C][C]14[/C][C]12.4749815277244[/C][C]1.52501847227564[/C][/ROW]
[ROW][C]200[/C][C]16[/C][C]14.7600932996358[/C][C]1.23990670036416[/C][/ROW]
[ROW][C]201[/C][C]16[/C][C]13.8662886610248[/C][C]2.13371133897524[/C][/ROW]
[ROW][C]202[/C][C]14[/C][C]13.7375099472516[/C][C]0.262490052748416[/C][/ROW]
[ROW][C]203[/C][C]11[/C][C]12.9533618586172[/C][C]-1.95336185861718[/C][/ROW]
[ROW][C]204[/C][C]12[/C][C]14.5378169642539[/C][C]-2.53781696425391[/C][/ROW]
[ROW][C]205[/C][C]15[/C][C]12.6790316942382[/C][C]2.32096830576176[/C][/ROW]
[ROW][C]206[/C][C]15[/C][C]14.6443393719584[/C][C]0.355660628041642[/C][/ROW]
[ROW][C]207[/C][C]16[/C][C]14.4861613266570[/C][C]1.51383867334304[/C][/ROW]
[ROW][C]208[/C][C]16[/C][C]15.5235111969253[/C][C]0.47648880307465[/C][/ROW]
[ROW][C]209[/C][C]11[/C][C]13.2889439413281[/C][C]-2.28894394132806[/C][/ROW]
[ROW][C]210[/C][C]15[/C][C]13.6080491455005[/C][C]1.39195085449946[/C][/ROW]
[ROW][C]211[/C][C]12[/C][C]14.1204962557417[/C][C]-2.12049625574171[/C][/ROW]
[ROW][C]212[/C][C]12[/C][C]16.0388683048002[/C][C]-4.03886830480018[/C][/ROW]
[ROW][C]213[/C][C]15[/C][C]14.6686832368861[/C][C]0.331316763113950[/C][/ROW]
[ROW][C]214[/C][C]15[/C][C]11.8253269043947[/C][C]3.17467309560533[/C][/ROW]
[ROW][C]215[/C][C]16[/C][C]14.5931820436595[/C][C]1.40681795634047[/C][/ROW]
[ROW][C]216[/C][C]14[/C][C]12.7320021377912[/C][C]1.26799786220875[/C][/ROW]
[ROW][C]217[/C][C]17[/C][C]14.6079232240351[/C][C]2.39207677596492[/C][/ROW]
[ROW][C]218[/C][C]14[/C][C]13.2042562296398[/C][C]0.79574377036023[/C][/ROW]
[ROW][C]219[/C][C]13[/C][C]12.0539370739323[/C][C]0.946062926067694[/C][/ROW]
[ROW][C]220[/C][C]15[/C][C]15.4547445845594[/C][C]-0.454744584559391[/C][/ROW]
[ROW][C]221[/C][C]13[/C][C]14.0575639701153[/C][C]-1.05756397011527[/C][/ROW]
[ROW][C]222[/C][C]14[/C][C]14.0515537515291[/C][C]-0.0515537515290932[/C][/ROW]
[ROW][C]223[/C][C]15[/C][C]14.0233630034945[/C][C]0.97663699650547[/C][/ROW]
[ROW][C]224[/C][C]12[/C][C]12.9139492925952[/C][C]-0.913949292595166[/C][/ROW]
[ROW][C]225[/C][C]13[/C][C]13.1823819391275[/C][C]-0.182381939127515[/C][/ROW]
[ROW][C]226[/C][C]8[/C][C]11.8885006117870[/C][C]-3.88850061178705[/C][/ROW]
[ROW][C]227[/C][C]14[/C][C]14.1955491705540[/C][C]-0.195549170553975[/C][/ROW]
[ROW][C]228[/C][C]14[/C][C]12.8633707502467[/C][C]1.13662924975327[/C][/ROW]
[ROW][C]229[/C][C]11[/C][C]12.3464306860680[/C][C]-1.34643068606797[/C][/ROW]
[ROW][C]230[/C][C]12[/C][C]12.7834452633280[/C][C]-0.783445263327967[/C][/ROW]
[ROW][C]231[/C][C]13[/C][C]10.6660715861687[/C][C]2.3339284138313[/C][/ROW]
[ROW][C]232[/C][C]10[/C][C]13.6996524618582[/C][C]-3.69965246185824[/C][/ROW]
[ROW][C]233[/C][C]16[/C][C]11.5816154391062[/C][C]4.41838456089384[/C][/ROW]
[ROW][C]234[/C][C]18[/C][C]15.2021717163673[/C][C]2.79782828363267[/C][/ROW]
[ROW][C]235[/C][C]13[/C][C]13.3927306361125[/C][C]-0.392730636112459[/C][/ROW]
[ROW][C]236[/C][C]11[/C][C]12.6034082064065[/C][C]-1.60340820640646[/C][/ROW]
[ROW][C]237[/C][C]4[/C][C]10.2801833291943[/C][C]-6.28018332919432[/C][/ROW]
[ROW][C]238[/C][C]13[/C][C]13.5153843221827[/C][C]-0.515384322182697[/C][/ROW]
[ROW][C]239[/C][C]16[/C][C]13.8876536306327[/C][C]2.11234636936726[/C][/ROW]
[ROW][C]240[/C][C]10[/C][C]10.6255327401344[/C][C]-0.625532740134409[/C][/ROW]
[ROW][C]241[/C][C]12[/C][C]11.9868516800914[/C][C]0.0131483199086112[/C][/ROW]
[ROW][C]242[/C][C]12[/C][C]13.7069303922495[/C][C]-1.70693039224951[/C][/ROW]
[ROW][C]243[/C][C]10[/C][C]8.90314447342048[/C][C]1.09685552657952[/C][/ROW]
[ROW][C]244[/C][C]13[/C][C]10.9048883644221[/C][C]2.09511163557788[/C][/ROW]
[ROW][C]245[/C][C]15[/C][C]12.9156903284589[/C][C]2.08430967154114[/C][/ROW]
[ROW][C]246[/C][C]12[/C][C]12.5990454991870[/C][C]-0.599045499186962[/C][/ROW]
[ROW][C]247[/C][C]14[/C][C]13.1464960333700[/C][C]0.853503966630049[/C][/ROW]
[ROW][C]248[/C][C]10[/C][C]12.9835984954948[/C][C]-2.98359849549476[/C][/ROW]
[ROW][C]249[/C][C]12[/C][C]10.9368364127457[/C][C]1.06316358725425[/C][/ROW]
[ROW][C]250[/C][C]12[/C][C]11.4240469083942[/C][C]0.57595309160577[/C][/ROW]
[ROW][C]251[/C][C]11[/C][C]12.1229276563996[/C][C]-1.12292765639964[/C][/ROW]
[ROW][C]252[/C][C]10[/C][C]11.4263977997735[/C][C]-1.42639779977352[/C][/ROW]
[ROW][C]253[/C][C]12[/C][C]10.9394396280551[/C][C]1.06056037194486[/C][/ROW]
[ROW][C]254[/C][C]16[/C][C]12.5083661523618[/C][C]3.49163384763824[/C][/ROW]
[ROW][C]255[/C][C]12[/C][C]12.9982991105830[/C][C]-0.99829911058297[/C][/ROW]
[ROW][C]256[/C][C]14[/C][C]13.6871325214264[/C][C]0.312867478573634[/C][/ROW]
[ROW][C]257[/C][C]16[/C][C]14.4874778469839[/C][C]1.51252215301612[/C][/ROW]
[ROW][C]258[/C][C]14[/C][C]11.7289415144362[/C][C]2.27105848556385[/C][/ROW]
[ROW][C]259[/C][C]13[/C][C]14.0536540515759[/C][C]-1.05365405157594[/C][/ROW]
[ROW][C]260[/C][C]4[/C][C]8.08400355852552[/C][C]-4.08400355852552[/C][/ROW]
[ROW][C]261[/C][C]15[/C][C]13.8043898253624[/C][C]1.19561017463758[/C][/ROW]
[ROW][C]262[/C][C]11[/C][C]15.3209721875729[/C][C]-4.32097218757294[/C][/ROW]
[ROW][C]263[/C][C]11[/C][C]11.1048724893516[/C][C]-0.104872489351615[/C][/ROW]
[ROW][C]264[/C][C]14[/C][C]12.7961332780032[/C][C]1.20386672199677[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96576&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96576&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.0460838528529-3.04608385285288
21616.0978854720564-0.0978854720563963
31916.61871875636862.38128124363138
41512.26884458802932.73115541197067
51415.6143076175297-1.61430761752974
61315.0952278482479-2.09522784824792
71915.11063832808453.88936167191549
81516.9713242202552-1.97132422025521
91416.1392821471749-2.13928214717492
101514.40254902683560.597450973164355
111615.46104561611020.53895438388982
121616.1235117839571-0.123511783957133
131615.62067067091850.37932932908151
141615.20944768979100.790552310208976
151717.6741165821685-0.674116582168514
161515.1802222027614-0.180222202761411
171514.76159243630510.238407563694865
182016.42800219785453.57199780214553
191815.61600384710292.38399615289714
201615.3074180814340.692581918565983
211615.26131583146810.7386841685319
221615.15902031359790.840979686402122
231916.31832667718252.68167332281749
241614.88965935292741.11034064707260
251716.54098375244340.459016247556603
261717.0622567007993-0.0622567007993099
271615.00636954836510.99363045163488
281516.287354596739-1.28735459673899
291615.55287080604040.447129193959557
301414.3904425234722-0.390442523472174
311515.8770730400500-0.87707304004998
321212.4410863242391-0.441086324239124
331415.1632236724350-1.16322367243502
341615.90662985509380.0933701449062471
351415.6500827322888-1.65008273228881
361013.265086391234-3.26508639123401
371012.7277418177310-2.72774181773096
381415.8385839962544-1.83858399625441
391614.51740860014511.48259139985486
401614.78001356928661.21998643071338
411615.18584544673760.814154553262365
421415.5727609632611-1.57276096326112
432017.66916236156272.33083763843726
441414.1387756696789-0.138775669678926
451415.0696607461849-1.06966074618494
461115.6144856270065-4.61448562700649
471416.5709069707458-2.57090697074581
481515.1481882666103-0.148188266610254
491615.06149671851080.938503281489162
501416.0633090042108-2.06330900421076
511616.6187303550879-0.618730355087908
521414.1780196281422-0.178019628142175
531214.6837657036483-2.68376570364832
541615.65273467634940.347265323650556
55911.6694846725896-2.66948467258963
561412.57777434350911.42222565649092
571616.0036265752432-0.00362657524324481
581615.32268553084720.677314469152835
591515.3869151033687-0.386915103368735
601614.25342629665071.74657370334928
611211.53887566369250.4611243363075
621615.93448106905030.0655189309496733
631616.5909413491474-0.590941349147435
641414.2618668653366-0.261866865336586
651615.24002473894630.759975261053662
661716.17189889622650.828101103773525
671816.11730306441781.88269693558220
681814.69913511195783.30086488804221
691215.6722816230555-3.67228162305553
701615.36965607647770.630343923522344
711013.5866524120327-3.58665241203271
721414.195891330998-0.195891330997995
731816.54853435757361.45146564242638
741816.93223559619491.06776440380514
751615.82251880560930.177481194390746
761713.89498224431103.10501775568903
771616.1954946350854-0.195494635085433
781614.50384625714061.49615374285942
791315.0129915707566-2.01299157075665
801615.77143790597510.228562094024866
811615.56908839480520.430911605194758
821615.77002784063690.229972159363086
831515.8074459225013-0.807445922501296
841514.78765965784620.212340342153845
851614.35647861999501.64352138000496
861414.1571625912085-0.157162591208454
871615.30587517141970.69412482858025
881614.46961712212471.53038287787534
891514.27882744411280.7211725558872
901213.6023588239322-1.60235882393221
911716.62692436380830.373075636191658
921615.43188579041260.568114209587371
931515.2277147708315-0.227714770831518
941315.0813275746976-2.0813275746976
951615.04423723303750.955762766962496
961615.72952952739410.270470472605877
971614.14624948948471.85375051051534
981615.97021736677540.0297826332246363
991414.4087389652808-0.408738965280751
1001617.0669910224862-1.06699102248624
1011614.73222687926871.26777312073132
1022017.33020551365792.66979448634214
1031514.66572992513880.334270074861174
1041614.49821769694741.50178230305259
1051315.1210647666331-2.12106476663310
1061715.90440880021231.09559119978766
1071615.58144924545380.418550754546173
1081614.34814496419761.65185503580237
1091211.90531554876780.094684451232151
1101615.06094993674240.93905006325765
1111615.82292431418580.177075685814236
1121714.84557042888422.15442957111575
1131313.8834738038531-0.883473803853104
1141214.7379256798790-2.73792567987903
1151816.57954634297401.42045365702602
1161415.2281114767074-1.22811147670738
1171412.997601783371.00239821663001
1181314.8993115345838-1.89931153458377
1191615.65819550510370.341804494896255
1201314.0711360179465-1.07113601794654
1211615.55353296477610.44646703522391
1221315.953286738566-2.95328673856601
1231616.9658873937755-0.965887393775536
1241516.1569827481446-1.15698274814463
1251616.7580898664056-0.758089866405574
1261515.4110906664556-0.411090666455589
1271715.85229137592441.14770862407563
1281513.97600467624881.02399532375120
1291214.8005465356512-2.80054653565118
1301614.10601398122161.89398601877845
1311013.3359227827828-3.33592278278282
1321613.93818327066542.06181672933455
1331213.8537668978712-1.85376689787117
1341415.5185421206393-1.51854212063931
1351515.2365070835216-0.236507083521641
1361312.04406001145220.955939988547835
1371514.40118610941360.598813890586377
1381113.1320965306762-2.13209653067623
1391213.2328826440951-1.23288264409513
1401113.3171512856996-2.31715128569959
1411612.89308708187863.10691291812139
1421513.34818269269091.65181730730911
1431716.62514951259640.37485048740358
1441614.69206466204911.30793533795092
1451014.1015702832193-4.10157028321931
1461815.76904499025742.23095500974265
1471314.9873219097028-1.98732190970275
1481615.13184475647920.868155243520766
1491313.0275017473578-0.0275017473578213
1501013.1303945600366-3.13039456003662
1511516.0188472173771-1.01884721737712
1521613.86170861869592.13829138130414
1531612.01586208539183.98413791460823
1541412.66071546501811.33928453498189
1551012.6056861353773-2.60568613537734
1561716.36750357539150.63249642460849
1571311.61391676011681.38608323988323
1581513.85627200467181.1437279953282
1591615.06472645802260.935273541977356
1601212.4616481771048-0.461648177104788
1611312.62783046371590.372169536284084
1621312.7584271073090.241572892691002
1631211.56186916598640.438130834013596
1641716.02200116430780.977998835692165
1651514.20731811040660.792681889593414
1661012.6331863748027-2.63318637480265
1671414.3683314267263-0.368331426726333
1681113.7153437366674-2.71534373666737
1691314.8885990551029-1.88859905510292
1701615.24579793527780.754202064722216
1711210.28549915395901.71450084604098
1721615.51946387742460.480536122575433
1731212.8367523332627-0.836752333262726
174911.0531284391401-2.05312843914015
1751214.1163175907024-2.11631759070238
1761514.33703907052900.662960929471016
1771211.78859249769670.211407502303261
1781212.6880411523742-0.688041152374181
1791413.70497897590840.295021024091621
1801213.0681347026367-1.06813470263669
1811615.21287666098140.787123339018608
1821110.65543835721040.344561642789593
1831917.14638929041181.85361070958823
1841514.19419064063410.805809359365865
185814.6477265300753-6.64772653007528
1861615.25678747987640.743212520123645
1871714.19114571967042.80885428032959
1881211.26498995727910.735010042720903
1891111.4971440575779-0.497144057577914
1901111.2351280597453-0.235128059745261
1911415.2001378944469-1.20013789444691
1921615.46397608197750.536023918022513
193129.93687758322892.06312241677111
1941614.45759801375191.54240198624806
1951313.6527489527911-0.652748952791132
1961515.0215787178556-0.0215787178556023
1971612.73111478028603.26888521971396
1981615.24477170617040.75522829382964
1991412.47498152772441.52501847227564
2001614.76009329963581.23990670036416
2011613.86628866102482.13371133897524
2021413.73750994725160.262490052748416
2031112.9533618586172-1.95336185861718
2041214.5378169642539-2.53781696425391
2051512.67903169423822.32096830576176
2061514.64433937195840.355660628041642
2071614.48616132665701.51383867334304
2081615.52351119692530.47648880307465
2091113.2889439413281-2.28894394132806
2101513.60804914550051.39195085449946
2111214.1204962557417-2.12049625574171
2121216.0388683048002-4.03886830480018
2131514.66868323688610.331316763113950
2141511.82532690439473.17467309560533
2151614.59318204365951.40681795634047
2161412.73200213779121.26799786220875
2171714.60792322403512.39207677596492
2181413.20425622963980.79574377036023
2191312.05393707393230.946062926067694
2201515.4547445845594-0.454744584559391
2211314.0575639701153-1.05756397011527
2221414.0515537515291-0.0515537515290932
2231514.02336300349450.97663699650547
2241212.9139492925952-0.913949292595166
2251313.1823819391275-0.182381939127515
226811.8885006117870-3.88850061178705
2271414.1955491705540-0.195549170553975
2281412.86337075024671.13662924975327
2291112.3464306860680-1.34643068606797
2301212.7834452633280-0.783445263327967
2311310.66607158616872.3339284138313
2321013.6996524618582-3.69965246185824
2331611.58161543910624.41838456089384
2341815.20217171636732.79782828363267
2351313.3927306361125-0.392730636112459
2361112.6034082064065-1.60340820640646
237410.2801833291943-6.28018332919432
2381313.5153843221827-0.515384322182697
2391613.88765363063272.11234636936726
2401010.6255327401344-0.625532740134409
2411211.98685168009140.0131483199086112
2421213.7069303922495-1.70693039224951
243108.903144473420481.09685552657952
2441310.90488836442212.09511163557788
2451512.91569032845892.08430967154114
2461212.5990454991870-0.599045499186962
2471413.14649603337000.853503966630049
2481012.9835984954948-2.98359849549476
2491210.93683641274571.06316358725425
2501211.42404690839420.57595309160577
2511112.1229276563996-1.12292765639964
2521011.4263977997735-1.42639779977352
2531210.93943962805511.06056037194486
2541612.50836615236183.49163384763824
2551212.9982991105830-0.99829911058297
2561413.68713252142640.312867478573634
2571614.48747784698391.51252215301612
2581411.72894151443622.27105848556385
2591314.0536540515759-1.05365405157594
26048.08400355852552-4.08400355852552
2611513.80438982536241.19561017463758
2621115.3209721875729-4.32097218757294
2631111.1048724893516-0.104872489351615
2641412.79613327800321.20386672199677







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.998342410934830.003315178130339920.00165758906516996
200.9959061174629930.008187765074013510.00409388253700675
210.9908262820651730.01834743586965310.00917371793482656
220.984925087703940.03014982459211890.0150749122960595
230.9773700103639870.04525997927202580.0226299896360129
240.9685847877411850.06283042451763070.0314152122588153
250.950494221430680.09901155713864150.0495057785693208
260.9300800538382360.1398398923235280.0699199461617642
270.9013016563254890.1973966873490230.0986983436745114
280.9129317320350460.1741365359299090.0870682679649545
290.8806837015055070.2386325969889850.119316298494493
300.8772128846867020.2455742306265950.122787115313298
310.8436563490209630.3126873019580740.156343650979037
320.8323304580631740.3353390838736520.167669541936826
330.8122311193400630.3755377613198730.187768880659937
340.7624885774420330.4750228451159330.237511422557967
350.7444201901770960.5111596196458070.255579809822904
360.8132881432441340.3734237135117320.186711856755866
370.8531152440272310.2937695119455370.146884755972769
380.8343443045511980.3313113908976030.165655695448801
390.8560053337606730.2879893324786540.143994666239327
400.8394663186479170.3210673627041660.160533681352083
410.811148905162920.377702189674160.18885109483708
420.7849083372567120.4301833254865760.215091662743288
430.7912691506890590.4174616986218830.208730849310941
440.7511931188812670.4976137622374660.248806881118733
450.7146455311816930.5707089376366150.285354468818307
460.8660350194900090.2679299610199830.133964980509991
470.8718186551865920.2563626896268160.128181344813408
480.8477741800091910.3044516399816170.152225819990809
490.8380645787075460.3238708425849090.161935421292454
500.8217974879327150.3564050241345690.178202512067285
510.7876071993051780.4247856013896450.212392800694822
520.753308948662060.4933821026758790.246691051337939
530.7520812720111600.4958374559776790.247918727988840
540.7243456859672620.5513086280654770.275654314032738
550.729881422899990.5402371542000210.270118577100010
560.737144611966570.5257107760668610.262855388033431
570.6992661310633890.6014677378732220.300733868936611
580.6824616281470720.6350767437058560.317538371852928
590.6434532977718490.7130934044563020.356546702228151
600.6640825568737770.6718348862524470.335917443126223
610.6349177269625190.7301645460749630.365082273037481
620.5956361920866380.8087276158267240.404363807913362
630.5557219934830950.888556013033810.444278006516905
640.5103929173471210.9792141653057570.489607082652879
650.4758415615447370.9516831230894750.524158438455263
660.4576165856573750.915233171314750.542383414342625
670.4603213562565530.9206427125131070.539678643743447
680.5827819183958710.8344361632082570.417218081604129
690.6845652871430780.6308694257138450.315434712856922
700.6531385039695740.6937229920608520.346861496030426
710.7264777219176010.5470445561647970.273522278082399
720.6903254549427640.6193490901144720.309674545057236
730.6843136355149010.6313727289701980.315686364485099
740.6632903926540870.6734192146918270.336709607345914
750.6246817291673590.7506365416652810.375318270832641
760.6814789954293040.6370420091413910.318521004570696
770.6433646840968350.713270631806330.356635315903165
780.6218990989459270.7562018021081460.378100901054073
790.6264637932641920.7470724134716160.373536206735808
800.5865563254218810.8268873491562370.413443674578119
810.5507547618161910.8984904763676190.449245238183809
820.513169598208470.973660803583060.48683040179153
830.4808343215159760.9616686430319520.519165678484024
840.4420128444701720.8840256889403440.557987155529828
850.4304253977201520.8608507954403050.569574602279848
860.3914702775257440.7829405550514890.608529722474256
870.3576461169247770.7152922338495530.642353883075223
880.3398866492571360.6797732985142720.660113350742864
890.3073320461768030.6146640923536060.692667953823197
900.2945789090786360.5891578181572720.705421090921364
910.2632402627409780.5264805254819560.736759737259022
920.235974293926130.471948587852260.76402570607387
930.2087682755073950.4175365510147910.791231724492604
940.2181970581684150.4363941163368310.781802941831585
950.1976039189393660.3952078378787320.802396081060634
960.1719764842612290.3439529685224590.828023515738771
970.1706978244635090.3413956489270190.82930217553649
980.1468973441803010.2937946883606020.853102655819699
990.1273083251028470.2546166502056930.872691674897153
1000.1149094355470710.2298188710941420.885090564452929
1010.1035147272791030.2070294545582060.896485272720897
1020.1221563133918880.2443126267837750.877843686608112
1030.103742443691260.207484887382520.89625755630874
1040.09814798851361340.1962959770272270.901852011486387
1050.1118713435241490.2237426870482970.888128656475851
1060.1002147637234480.2004295274468960.899785236276552
1070.08769065133532810.1753813026706560.912309348664672
1080.08438409511361730.1687681902272350.915615904886383
1090.07707971052292790.1541594210458560.922920289477072
1100.06831905473647330.1366381094729470.931680945263527
1110.05817858099916840.1163571619983370.941821419000832
1120.06678472590858350.1335694518171670.933215274091416
1130.05740815675417970.1148163135083590.94259184324582
1140.06916809494022740.1383361898804550.930831905059773
1150.06691465427446620.1338293085489320.933085345725534
1160.05911111910381030.1182222382076210.94088888089619
1170.05458950585974790.1091790117194960.945410494140252
1180.05340056121704730.1068011224340950.946599438782953
1190.05188455746379910.1037691149275980.94811544253620
1200.04433770026974280.08867540053948560.955662299730257
1210.03846791820118060.07693583640236120.96153208179882
1220.04628135556232290.09256271112464580.953718644437677
1230.03892563936868830.07785127873737660.961074360631312
1240.03348914302009930.06697828604019860.9665108569799
1250.02774466140314690.05548932280629390.972255338596853
1260.02229561174337500.04459122348675010.977704388256625
1270.02168490289893850.0433698057978770.978315097101061
1280.0201508697043360.0403017394086720.979849130295664
1290.02342959807538170.04685919615076340.976570401924618
1300.02510548585353250.05021097170706510.974894514146467
1310.03414938710303750.0682987742060750.965850612896963
1320.04552771946271640.09105543892543270.954472280537284
1330.04554186665783630.09108373331567260.954458133342164
1340.04011021371484720.08022042742969440.959889786285153
1350.03338553934624080.06677107869248170.96661446065376
1360.02911143939182660.05822287878365330.970888560608173
1370.02559559566124050.05119119132248090.97440440433876
1380.02783573973645470.05567147947290950.972164260263545
1390.02392765787525640.04785531575051280.976072342124744
1400.03184330376932750.0636866075386550.968156696230672
1410.04065571822870550.0813114364574110.959344281771294
1420.03883549235950650.0776709847190130.961164507640494
1430.03195130063876920.06390260127753840.96804869936123
1440.02826672395158010.05653344790316030.97173327604842
1450.04557874729751820.09115749459503650.954421252702482
1460.05508200149677630.1101640029935530.944917998503224
1470.06836062609142520.1367212521828500.931639373908575
1480.05871908397583810.1174381679516760.941280916024162
1490.04932245087327850.0986449017465570.950677549126721
1500.06595038847376710.1319007769475340.934049611526233
1510.0592835602159280.1185671204318560.940716439784072
1520.06073440490579320.1214688098115860.939265595094207
1530.07992990811021710.1598598162204340.920070091889783
1540.06972811169552750.1394562233910550.930271888304472
1550.06789970731612460.1357994146322490.932100292683876
1560.05698171985567130.1139634397113430.943018280144329
1570.04965499969598160.09930999939196310.950345000304018
1580.04218398370976810.08436796741953620.957816016290232
1590.03516993602706960.07033987205413920.96483006397293
1600.02858423161828330.05716846323656670.971415768381717
1610.02300562382099140.04601124764198270.976994376179009
1620.01833275745816690.03666551491633370.981667242541833
1630.01457155848943480.02914311697886950.985428441510565
1640.01194192085095640.02388384170191280.988058079149044
1650.009530738272127690.01906147654425540.990469261727872
1660.00965105037803530.01930210075607060.990348949621965
1670.007481607138499130.01496321427699830.9925183928615
1680.008028104690219710.01605620938043940.99197189530978
1690.007340082027866760.01468016405573350.992659917972133
1700.005956848727893780.01191369745578760.994043151272106
1710.00697948697363210.01395897394726420.993020513026368
1720.005408012898739610.01081602579747920.99459198710126
1730.004288395692250540.008576791384501080.99571160430775
1740.004236450090441890.008472900180883780.995763549909558
1750.004231209160713240.008462418321426490.995768790839287
1760.003502175951533210.007004351903066420.996497824048467
1770.002619411663557810.005238823327115620.997380588336442
1780.002105808883726110.004211617767452230.997894191116274
1790.001616452890686990.003232905781373980.998383547109313
1800.001391319348260930.002782638696521870.99860868065174
1810.001094176173045230.002188352346090450.998905823826955
1820.000793506734611830.001587013469223660.999206493265388
1830.0007847054731048980.001569410946209800.999215294526895
1840.0005798807103629250.001159761420725850.999420119289637
1850.01682086411471670.03364172822943330.983179135885283
1860.01437743826290830.02875487652581670.985622561737092
1870.01698938655710450.0339787731142090.983010613442896
1880.01337775973331790.02675551946663580.986622240266682
1890.01131635151616920.02263270303233840.988683648483831
1900.00896456676155360.01792913352310720.991035433238446
1910.007904796645028430.01580959329005690.992095203354972
1920.006102130213286630.01220426042657330.993897869786713
1930.005608552367255190.01121710473451040.994391447632745
1940.004873788056824670.009747576113649330.995126211943175
1950.003964896689821190.007929793379642380.996035103310179
1960.002897804204192760.005795608408385520.997102195795807
1970.003971742457702950.00794348491540590.996028257542297
1980.002898203853765860.005796407707531720.997101796146234
1990.002446588601850500.004893177203701010.99755341139815
2000.002053784380384950.00410756876076990.997946215619615
2010.001811492046148960.003622984092297930.998188507953851
2020.001389441103695500.002778882207391000.998610558896305
2030.001663395889690050.00332679177938010.99833660411031
2040.002645811590645350.005291623181290710.997354188409355
2050.002640136505073180.005280273010146350.997359863494927
2060.001880583872688390.003761167745376780.998119416127312
2070.001521576365375220.003043152730750440.998478423634625
2080.001078571817168860.002157143634337710.99892142818283
2090.001378861436946960.002757722873893920.998621138563053
2100.001036224283845470.002072448567690930.998963775716154
2110.001323842872877910.002647685745755830.998676157127122
2120.004032497523943840.008064995047887680.995967502476056
2130.002803906835009410.005607813670018830.99719609316499
2140.003704304240152920.007408608480305840.996295695759847
2150.002829670539017280.005659341078034550.997170329460983
2160.002030202005747600.004060404011495210.997969797994252
2170.002165603286302490.004331206572604970.997834396713698
2180.001679637059900340.003359274119800690.9983203629401
2190.001463459206152230.002926918412304460.998536540793848
2200.001007588085430300.002015176170860610.99899241191457
2210.0007379951617153590.001475990323430720.999262004838285
2220.0004748723775266690.0009497447550533390.999525127622473
2230.0003234869091066990.0006469738182133990.999676513090893
2240.0002187256122924670.0004374512245849340.999781274387707
2250.0001329043274539550.0002658086549079110.999867095672546
2260.0003450013167039710.0006900026334079430.999654998683296
2270.0002401043357682270.0004802086715364540.999759895664232
2280.0001499246117902210.0002998492235804430.99985007538821
2299.76700348352581e-050.0001953400696705160.999902329965165
2305.84420960566556e-050.0001168841921133110.999941557903943
2316.14139037689193e-050.0001228278075378390.999938586096231
2320.0002817367328122320.0005634734656244650.999718263267188
2330.001400861178321120.002801722356642250.99859913882168
2340.00228974094929830.00457948189859660.997710259050702
2350.001337847641138040.002675695282276080.998662152358862
2360.000873597338241570.001747194676483140.999126402661758
2370.01918091401295440.03836182802590880.980819085987046
2380.01165371117857170.02330742235714340.988346288821428
2390.007944134142832470.01588826828566490.992055865857168
2400.00453614482226410.00907228964452820.995463855177736
2410.003125833397266390.006251666794532770.996874166602734
2420.005117902687495960.01023580537499190.994882097312504
2430.002930621448106980.005861242896213950.997069378551893
2440.002124768061856010.004249536123712020.997875231938144
2450.001113814824422450.00222762964884490.998886185175577

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.99834241093483 & 0.00331517813033992 & 0.00165758906516996 \tabularnewline
20 & 0.995906117462993 & 0.00818776507401351 & 0.00409388253700675 \tabularnewline
21 & 0.990826282065173 & 0.0183474358696531 & 0.00917371793482656 \tabularnewline
22 & 0.98492508770394 & 0.0301498245921189 & 0.0150749122960595 \tabularnewline
23 & 0.977370010363987 & 0.0452599792720258 & 0.0226299896360129 \tabularnewline
24 & 0.968584787741185 & 0.0628304245176307 & 0.0314152122588153 \tabularnewline
25 & 0.95049422143068 & 0.0990115571386415 & 0.0495057785693208 \tabularnewline
26 & 0.930080053838236 & 0.139839892323528 & 0.0699199461617642 \tabularnewline
27 & 0.901301656325489 & 0.197396687349023 & 0.0986983436745114 \tabularnewline
28 & 0.912931732035046 & 0.174136535929909 & 0.0870682679649545 \tabularnewline
29 & 0.880683701505507 & 0.238632596988985 & 0.119316298494493 \tabularnewline
30 & 0.877212884686702 & 0.245574230626595 & 0.122787115313298 \tabularnewline
31 & 0.843656349020963 & 0.312687301958074 & 0.156343650979037 \tabularnewline
32 & 0.832330458063174 & 0.335339083873652 & 0.167669541936826 \tabularnewline
33 & 0.812231119340063 & 0.375537761319873 & 0.187768880659937 \tabularnewline
34 & 0.762488577442033 & 0.475022845115933 & 0.237511422557967 \tabularnewline
35 & 0.744420190177096 & 0.511159619645807 & 0.255579809822904 \tabularnewline
36 & 0.813288143244134 & 0.373423713511732 & 0.186711856755866 \tabularnewline
37 & 0.853115244027231 & 0.293769511945537 & 0.146884755972769 \tabularnewline
38 & 0.834344304551198 & 0.331311390897603 & 0.165655695448801 \tabularnewline
39 & 0.856005333760673 & 0.287989332478654 & 0.143994666239327 \tabularnewline
40 & 0.839466318647917 & 0.321067362704166 & 0.160533681352083 \tabularnewline
41 & 0.81114890516292 & 0.37770218967416 & 0.18885109483708 \tabularnewline
42 & 0.784908337256712 & 0.430183325486576 & 0.215091662743288 \tabularnewline
43 & 0.791269150689059 & 0.417461698621883 & 0.208730849310941 \tabularnewline
44 & 0.751193118881267 & 0.497613762237466 & 0.248806881118733 \tabularnewline
45 & 0.714645531181693 & 0.570708937636615 & 0.285354468818307 \tabularnewline
46 & 0.866035019490009 & 0.267929961019983 & 0.133964980509991 \tabularnewline
47 & 0.871818655186592 & 0.256362689626816 & 0.128181344813408 \tabularnewline
48 & 0.847774180009191 & 0.304451639981617 & 0.152225819990809 \tabularnewline
49 & 0.838064578707546 & 0.323870842584909 & 0.161935421292454 \tabularnewline
50 & 0.821797487932715 & 0.356405024134569 & 0.178202512067285 \tabularnewline
51 & 0.787607199305178 & 0.424785601389645 & 0.212392800694822 \tabularnewline
52 & 0.75330894866206 & 0.493382102675879 & 0.246691051337939 \tabularnewline
53 & 0.752081272011160 & 0.495837455977679 & 0.247918727988840 \tabularnewline
54 & 0.724345685967262 & 0.551308628065477 & 0.275654314032738 \tabularnewline
55 & 0.72988142289999 & 0.540237154200021 & 0.270118577100010 \tabularnewline
56 & 0.73714461196657 & 0.525710776066861 & 0.262855388033431 \tabularnewline
57 & 0.699266131063389 & 0.601467737873222 & 0.300733868936611 \tabularnewline
58 & 0.682461628147072 & 0.635076743705856 & 0.317538371852928 \tabularnewline
59 & 0.643453297771849 & 0.713093404456302 & 0.356546702228151 \tabularnewline
60 & 0.664082556873777 & 0.671834886252447 & 0.335917443126223 \tabularnewline
61 & 0.634917726962519 & 0.730164546074963 & 0.365082273037481 \tabularnewline
62 & 0.595636192086638 & 0.808727615826724 & 0.404363807913362 \tabularnewline
63 & 0.555721993483095 & 0.88855601303381 & 0.444278006516905 \tabularnewline
64 & 0.510392917347121 & 0.979214165305757 & 0.489607082652879 \tabularnewline
65 & 0.475841561544737 & 0.951683123089475 & 0.524158438455263 \tabularnewline
66 & 0.457616585657375 & 0.91523317131475 & 0.542383414342625 \tabularnewline
67 & 0.460321356256553 & 0.920642712513107 & 0.539678643743447 \tabularnewline
68 & 0.582781918395871 & 0.834436163208257 & 0.417218081604129 \tabularnewline
69 & 0.684565287143078 & 0.630869425713845 & 0.315434712856922 \tabularnewline
70 & 0.653138503969574 & 0.693722992060852 & 0.346861496030426 \tabularnewline
71 & 0.726477721917601 & 0.547044556164797 & 0.273522278082399 \tabularnewline
72 & 0.690325454942764 & 0.619349090114472 & 0.309674545057236 \tabularnewline
73 & 0.684313635514901 & 0.631372728970198 & 0.315686364485099 \tabularnewline
74 & 0.663290392654087 & 0.673419214691827 & 0.336709607345914 \tabularnewline
75 & 0.624681729167359 & 0.750636541665281 & 0.375318270832641 \tabularnewline
76 & 0.681478995429304 & 0.637042009141391 & 0.318521004570696 \tabularnewline
77 & 0.643364684096835 & 0.71327063180633 & 0.356635315903165 \tabularnewline
78 & 0.621899098945927 & 0.756201802108146 & 0.378100901054073 \tabularnewline
79 & 0.626463793264192 & 0.747072413471616 & 0.373536206735808 \tabularnewline
80 & 0.586556325421881 & 0.826887349156237 & 0.413443674578119 \tabularnewline
81 & 0.550754761816191 & 0.898490476367619 & 0.449245238183809 \tabularnewline
82 & 0.51316959820847 & 0.97366080358306 & 0.48683040179153 \tabularnewline
83 & 0.480834321515976 & 0.961668643031952 & 0.519165678484024 \tabularnewline
84 & 0.442012844470172 & 0.884025688940344 & 0.557987155529828 \tabularnewline
85 & 0.430425397720152 & 0.860850795440305 & 0.569574602279848 \tabularnewline
86 & 0.391470277525744 & 0.782940555051489 & 0.608529722474256 \tabularnewline
87 & 0.357646116924777 & 0.715292233849553 & 0.642353883075223 \tabularnewline
88 & 0.339886649257136 & 0.679773298514272 & 0.660113350742864 \tabularnewline
89 & 0.307332046176803 & 0.614664092353606 & 0.692667953823197 \tabularnewline
90 & 0.294578909078636 & 0.589157818157272 & 0.705421090921364 \tabularnewline
91 & 0.263240262740978 & 0.526480525481956 & 0.736759737259022 \tabularnewline
92 & 0.23597429392613 & 0.47194858785226 & 0.76402570607387 \tabularnewline
93 & 0.208768275507395 & 0.417536551014791 & 0.791231724492604 \tabularnewline
94 & 0.218197058168415 & 0.436394116336831 & 0.781802941831585 \tabularnewline
95 & 0.197603918939366 & 0.395207837878732 & 0.802396081060634 \tabularnewline
96 & 0.171976484261229 & 0.343952968522459 & 0.828023515738771 \tabularnewline
97 & 0.170697824463509 & 0.341395648927019 & 0.82930217553649 \tabularnewline
98 & 0.146897344180301 & 0.293794688360602 & 0.853102655819699 \tabularnewline
99 & 0.127308325102847 & 0.254616650205693 & 0.872691674897153 \tabularnewline
100 & 0.114909435547071 & 0.229818871094142 & 0.885090564452929 \tabularnewline
101 & 0.103514727279103 & 0.207029454558206 & 0.896485272720897 \tabularnewline
102 & 0.122156313391888 & 0.244312626783775 & 0.877843686608112 \tabularnewline
103 & 0.10374244369126 & 0.20748488738252 & 0.89625755630874 \tabularnewline
104 & 0.0981479885136134 & 0.196295977027227 & 0.901852011486387 \tabularnewline
105 & 0.111871343524149 & 0.223742687048297 & 0.888128656475851 \tabularnewline
106 & 0.100214763723448 & 0.200429527446896 & 0.899785236276552 \tabularnewline
107 & 0.0876906513353281 & 0.175381302670656 & 0.912309348664672 \tabularnewline
108 & 0.0843840951136173 & 0.168768190227235 & 0.915615904886383 \tabularnewline
109 & 0.0770797105229279 & 0.154159421045856 & 0.922920289477072 \tabularnewline
110 & 0.0683190547364733 & 0.136638109472947 & 0.931680945263527 \tabularnewline
111 & 0.0581785809991684 & 0.116357161998337 & 0.941821419000832 \tabularnewline
112 & 0.0667847259085835 & 0.133569451817167 & 0.933215274091416 \tabularnewline
113 & 0.0574081567541797 & 0.114816313508359 & 0.94259184324582 \tabularnewline
114 & 0.0691680949402274 & 0.138336189880455 & 0.930831905059773 \tabularnewline
115 & 0.0669146542744662 & 0.133829308548932 & 0.933085345725534 \tabularnewline
116 & 0.0591111191038103 & 0.118222238207621 & 0.94088888089619 \tabularnewline
117 & 0.0545895058597479 & 0.109179011719496 & 0.945410494140252 \tabularnewline
118 & 0.0534005612170473 & 0.106801122434095 & 0.946599438782953 \tabularnewline
119 & 0.0518845574637991 & 0.103769114927598 & 0.94811544253620 \tabularnewline
120 & 0.0443377002697428 & 0.0886754005394856 & 0.955662299730257 \tabularnewline
121 & 0.0384679182011806 & 0.0769358364023612 & 0.96153208179882 \tabularnewline
122 & 0.0462813555623229 & 0.0925627111246458 & 0.953718644437677 \tabularnewline
123 & 0.0389256393686883 & 0.0778512787373766 & 0.961074360631312 \tabularnewline
124 & 0.0334891430200993 & 0.0669782860401986 & 0.9665108569799 \tabularnewline
125 & 0.0277446614031469 & 0.0554893228062939 & 0.972255338596853 \tabularnewline
126 & 0.0222956117433750 & 0.0445912234867501 & 0.977704388256625 \tabularnewline
127 & 0.0216849028989385 & 0.043369805797877 & 0.978315097101061 \tabularnewline
128 & 0.020150869704336 & 0.040301739408672 & 0.979849130295664 \tabularnewline
129 & 0.0234295980753817 & 0.0468591961507634 & 0.976570401924618 \tabularnewline
130 & 0.0251054858535325 & 0.0502109717070651 & 0.974894514146467 \tabularnewline
131 & 0.0341493871030375 & 0.068298774206075 & 0.965850612896963 \tabularnewline
132 & 0.0455277194627164 & 0.0910554389254327 & 0.954472280537284 \tabularnewline
133 & 0.0455418666578363 & 0.0910837333156726 & 0.954458133342164 \tabularnewline
134 & 0.0401102137148472 & 0.0802204274296944 & 0.959889786285153 \tabularnewline
135 & 0.0333855393462408 & 0.0667710786924817 & 0.96661446065376 \tabularnewline
136 & 0.0291114393918266 & 0.0582228787836533 & 0.970888560608173 \tabularnewline
137 & 0.0255955956612405 & 0.0511911913224809 & 0.97440440433876 \tabularnewline
138 & 0.0278357397364547 & 0.0556714794729095 & 0.972164260263545 \tabularnewline
139 & 0.0239276578752564 & 0.0478553157505128 & 0.976072342124744 \tabularnewline
140 & 0.0318433037693275 & 0.063686607538655 & 0.968156696230672 \tabularnewline
141 & 0.0406557182287055 & 0.081311436457411 & 0.959344281771294 \tabularnewline
142 & 0.0388354923595065 & 0.077670984719013 & 0.961164507640494 \tabularnewline
143 & 0.0319513006387692 & 0.0639026012775384 & 0.96804869936123 \tabularnewline
144 & 0.0282667239515801 & 0.0565334479031603 & 0.97173327604842 \tabularnewline
145 & 0.0455787472975182 & 0.0911574945950365 & 0.954421252702482 \tabularnewline
146 & 0.0550820014967763 & 0.110164002993553 & 0.944917998503224 \tabularnewline
147 & 0.0683606260914252 & 0.136721252182850 & 0.931639373908575 \tabularnewline
148 & 0.0587190839758381 & 0.117438167951676 & 0.941280916024162 \tabularnewline
149 & 0.0493224508732785 & 0.098644901746557 & 0.950677549126721 \tabularnewline
150 & 0.0659503884737671 & 0.131900776947534 & 0.934049611526233 \tabularnewline
151 & 0.059283560215928 & 0.118567120431856 & 0.940716439784072 \tabularnewline
152 & 0.0607344049057932 & 0.121468809811586 & 0.939265595094207 \tabularnewline
153 & 0.0799299081102171 & 0.159859816220434 & 0.920070091889783 \tabularnewline
154 & 0.0697281116955275 & 0.139456223391055 & 0.930271888304472 \tabularnewline
155 & 0.0678997073161246 & 0.135799414632249 & 0.932100292683876 \tabularnewline
156 & 0.0569817198556713 & 0.113963439711343 & 0.943018280144329 \tabularnewline
157 & 0.0496549996959816 & 0.0993099993919631 & 0.950345000304018 \tabularnewline
158 & 0.0421839837097681 & 0.0843679674195362 & 0.957816016290232 \tabularnewline
159 & 0.0351699360270696 & 0.0703398720541392 & 0.96483006397293 \tabularnewline
160 & 0.0285842316182833 & 0.0571684632365667 & 0.971415768381717 \tabularnewline
161 & 0.0230056238209914 & 0.0460112476419827 & 0.976994376179009 \tabularnewline
162 & 0.0183327574581669 & 0.0366655149163337 & 0.981667242541833 \tabularnewline
163 & 0.0145715584894348 & 0.0291431169788695 & 0.985428441510565 \tabularnewline
164 & 0.0119419208509564 & 0.0238838417019128 & 0.988058079149044 \tabularnewline
165 & 0.00953073827212769 & 0.0190614765442554 & 0.990469261727872 \tabularnewline
166 & 0.0096510503780353 & 0.0193021007560706 & 0.990348949621965 \tabularnewline
167 & 0.00748160713849913 & 0.0149632142769983 & 0.9925183928615 \tabularnewline
168 & 0.00802810469021971 & 0.0160562093804394 & 0.99197189530978 \tabularnewline
169 & 0.00734008202786676 & 0.0146801640557335 & 0.992659917972133 \tabularnewline
170 & 0.00595684872789378 & 0.0119136974557876 & 0.994043151272106 \tabularnewline
171 & 0.0069794869736321 & 0.0139589739472642 & 0.993020513026368 \tabularnewline
172 & 0.00540801289873961 & 0.0108160257974792 & 0.99459198710126 \tabularnewline
173 & 0.00428839569225054 & 0.00857679138450108 & 0.99571160430775 \tabularnewline
174 & 0.00423645009044189 & 0.00847290018088378 & 0.995763549909558 \tabularnewline
175 & 0.00423120916071324 & 0.00846241832142649 & 0.995768790839287 \tabularnewline
176 & 0.00350217595153321 & 0.00700435190306642 & 0.996497824048467 \tabularnewline
177 & 0.00261941166355781 & 0.00523882332711562 & 0.997380588336442 \tabularnewline
178 & 0.00210580888372611 & 0.00421161776745223 & 0.997894191116274 \tabularnewline
179 & 0.00161645289068699 & 0.00323290578137398 & 0.998383547109313 \tabularnewline
180 & 0.00139131934826093 & 0.00278263869652187 & 0.99860868065174 \tabularnewline
181 & 0.00109417617304523 & 0.00218835234609045 & 0.998905823826955 \tabularnewline
182 & 0.00079350673461183 & 0.00158701346922366 & 0.999206493265388 \tabularnewline
183 & 0.000784705473104898 & 0.00156941094620980 & 0.999215294526895 \tabularnewline
184 & 0.000579880710362925 & 0.00115976142072585 & 0.999420119289637 \tabularnewline
185 & 0.0168208641147167 & 0.0336417282294333 & 0.983179135885283 \tabularnewline
186 & 0.0143774382629083 & 0.0287548765258167 & 0.985622561737092 \tabularnewline
187 & 0.0169893865571045 & 0.033978773114209 & 0.983010613442896 \tabularnewline
188 & 0.0133777597333179 & 0.0267555194666358 & 0.986622240266682 \tabularnewline
189 & 0.0113163515161692 & 0.0226327030323384 & 0.988683648483831 \tabularnewline
190 & 0.0089645667615536 & 0.0179291335231072 & 0.991035433238446 \tabularnewline
191 & 0.00790479664502843 & 0.0158095932900569 & 0.992095203354972 \tabularnewline
192 & 0.00610213021328663 & 0.0122042604265733 & 0.993897869786713 \tabularnewline
193 & 0.00560855236725519 & 0.0112171047345104 & 0.994391447632745 \tabularnewline
194 & 0.00487378805682467 & 0.00974757611364933 & 0.995126211943175 \tabularnewline
195 & 0.00396489668982119 & 0.00792979337964238 & 0.996035103310179 \tabularnewline
196 & 0.00289780420419276 & 0.00579560840838552 & 0.997102195795807 \tabularnewline
197 & 0.00397174245770295 & 0.0079434849154059 & 0.996028257542297 \tabularnewline
198 & 0.00289820385376586 & 0.00579640770753172 & 0.997101796146234 \tabularnewline
199 & 0.00244658860185050 & 0.00489317720370101 & 0.99755341139815 \tabularnewline
200 & 0.00205378438038495 & 0.0041075687607699 & 0.997946215619615 \tabularnewline
201 & 0.00181149204614896 & 0.00362298409229793 & 0.998188507953851 \tabularnewline
202 & 0.00138944110369550 & 0.00277888220739100 & 0.998610558896305 \tabularnewline
203 & 0.00166339588969005 & 0.0033267917793801 & 0.99833660411031 \tabularnewline
204 & 0.00264581159064535 & 0.00529162318129071 & 0.997354188409355 \tabularnewline
205 & 0.00264013650507318 & 0.00528027301014635 & 0.997359863494927 \tabularnewline
206 & 0.00188058387268839 & 0.00376116774537678 & 0.998119416127312 \tabularnewline
207 & 0.00152157636537522 & 0.00304315273075044 & 0.998478423634625 \tabularnewline
208 & 0.00107857181716886 & 0.00215714363433771 & 0.99892142818283 \tabularnewline
209 & 0.00137886143694696 & 0.00275772287389392 & 0.998621138563053 \tabularnewline
210 & 0.00103622428384547 & 0.00207244856769093 & 0.998963775716154 \tabularnewline
211 & 0.00132384287287791 & 0.00264768574575583 & 0.998676157127122 \tabularnewline
212 & 0.00403249752394384 & 0.00806499504788768 & 0.995967502476056 \tabularnewline
213 & 0.00280390683500941 & 0.00560781367001883 & 0.99719609316499 \tabularnewline
214 & 0.00370430424015292 & 0.00740860848030584 & 0.996295695759847 \tabularnewline
215 & 0.00282967053901728 & 0.00565934107803455 & 0.997170329460983 \tabularnewline
216 & 0.00203020200574760 & 0.00406040401149521 & 0.997969797994252 \tabularnewline
217 & 0.00216560328630249 & 0.00433120657260497 & 0.997834396713698 \tabularnewline
218 & 0.00167963705990034 & 0.00335927411980069 & 0.9983203629401 \tabularnewline
219 & 0.00146345920615223 & 0.00292691841230446 & 0.998536540793848 \tabularnewline
220 & 0.00100758808543030 & 0.00201517617086061 & 0.99899241191457 \tabularnewline
221 & 0.000737995161715359 & 0.00147599032343072 & 0.999262004838285 \tabularnewline
222 & 0.000474872377526669 & 0.000949744755053339 & 0.999525127622473 \tabularnewline
223 & 0.000323486909106699 & 0.000646973818213399 & 0.999676513090893 \tabularnewline
224 & 0.000218725612292467 & 0.000437451224584934 & 0.999781274387707 \tabularnewline
225 & 0.000132904327453955 & 0.000265808654907911 & 0.999867095672546 \tabularnewline
226 & 0.000345001316703971 & 0.000690002633407943 & 0.999654998683296 \tabularnewline
227 & 0.000240104335768227 & 0.000480208671536454 & 0.999759895664232 \tabularnewline
228 & 0.000149924611790221 & 0.000299849223580443 & 0.99985007538821 \tabularnewline
229 & 9.76700348352581e-05 & 0.000195340069670516 & 0.999902329965165 \tabularnewline
230 & 5.84420960566556e-05 & 0.000116884192113311 & 0.999941557903943 \tabularnewline
231 & 6.14139037689193e-05 & 0.000122827807537839 & 0.999938586096231 \tabularnewline
232 & 0.000281736732812232 & 0.000563473465624465 & 0.999718263267188 \tabularnewline
233 & 0.00140086117832112 & 0.00280172235664225 & 0.99859913882168 \tabularnewline
234 & 0.0022897409492983 & 0.0045794818985966 & 0.997710259050702 \tabularnewline
235 & 0.00133784764113804 & 0.00267569528227608 & 0.998662152358862 \tabularnewline
236 & 0.00087359733824157 & 0.00174719467648314 & 0.999126402661758 \tabularnewline
237 & 0.0191809140129544 & 0.0383618280259088 & 0.980819085987046 \tabularnewline
238 & 0.0116537111785717 & 0.0233074223571434 & 0.988346288821428 \tabularnewline
239 & 0.00794413414283247 & 0.0158882682856649 & 0.992055865857168 \tabularnewline
240 & 0.0045361448222641 & 0.0090722896445282 & 0.995463855177736 \tabularnewline
241 & 0.00312583339726639 & 0.00625166679453277 & 0.996874166602734 \tabularnewline
242 & 0.00511790268749596 & 0.0102358053749919 & 0.994882097312504 \tabularnewline
243 & 0.00293062144810698 & 0.00586124289621395 & 0.997069378551893 \tabularnewline
244 & 0.00212476806185601 & 0.00424953612371202 & 0.997875231938144 \tabularnewline
245 & 0.00111381482442245 & 0.0022276296488449 & 0.998886185175577 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96576&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]19[/C][C]0.99834241093483[/C][C]0.00331517813033992[/C][C]0.00165758906516996[/C][/ROW]
[ROW][C]20[/C][C]0.995906117462993[/C][C]0.00818776507401351[/C][C]0.00409388253700675[/C][/ROW]
[ROW][C]21[/C][C]0.990826282065173[/C][C]0.0183474358696531[/C][C]0.00917371793482656[/C][/ROW]
[ROW][C]22[/C][C]0.98492508770394[/C][C]0.0301498245921189[/C][C]0.0150749122960595[/C][/ROW]
[ROW][C]23[/C][C]0.977370010363987[/C][C]0.0452599792720258[/C][C]0.0226299896360129[/C][/ROW]
[ROW][C]24[/C][C]0.968584787741185[/C][C]0.0628304245176307[/C][C]0.0314152122588153[/C][/ROW]
[ROW][C]25[/C][C]0.95049422143068[/C][C]0.0990115571386415[/C][C]0.0495057785693208[/C][/ROW]
[ROW][C]26[/C][C]0.930080053838236[/C][C]0.139839892323528[/C][C]0.0699199461617642[/C][/ROW]
[ROW][C]27[/C][C]0.901301656325489[/C][C]0.197396687349023[/C][C]0.0986983436745114[/C][/ROW]
[ROW][C]28[/C][C]0.912931732035046[/C][C]0.174136535929909[/C][C]0.0870682679649545[/C][/ROW]
[ROW][C]29[/C][C]0.880683701505507[/C][C]0.238632596988985[/C][C]0.119316298494493[/C][/ROW]
[ROW][C]30[/C][C]0.877212884686702[/C][C]0.245574230626595[/C][C]0.122787115313298[/C][/ROW]
[ROW][C]31[/C][C]0.843656349020963[/C][C]0.312687301958074[/C][C]0.156343650979037[/C][/ROW]
[ROW][C]32[/C][C]0.832330458063174[/C][C]0.335339083873652[/C][C]0.167669541936826[/C][/ROW]
[ROW][C]33[/C][C]0.812231119340063[/C][C]0.375537761319873[/C][C]0.187768880659937[/C][/ROW]
[ROW][C]34[/C][C]0.762488577442033[/C][C]0.475022845115933[/C][C]0.237511422557967[/C][/ROW]
[ROW][C]35[/C][C]0.744420190177096[/C][C]0.511159619645807[/C][C]0.255579809822904[/C][/ROW]
[ROW][C]36[/C][C]0.813288143244134[/C][C]0.373423713511732[/C][C]0.186711856755866[/C][/ROW]
[ROW][C]37[/C][C]0.853115244027231[/C][C]0.293769511945537[/C][C]0.146884755972769[/C][/ROW]
[ROW][C]38[/C][C]0.834344304551198[/C][C]0.331311390897603[/C][C]0.165655695448801[/C][/ROW]
[ROW][C]39[/C][C]0.856005333760673[/C][C]0.287989332478654[/C][C]0.143994666239327[/C][/ROW]
[ROW][C]40[/C][C]0.839466318647917[/C][C]0.321067362704166[/C][C]0.160533681352083[/C][/ROW]
[ROW][C]41[/C][C]0.81114890516292[/C][C]0.37770218967416[/C][C]0.18885109483708[/C][/ROW]
[ROW][C]42[/C][C]0.784908337256712[/C][C]0.430183325486576[/C][C]0.215091662743288[/C][/ROW]
[ROW][C]43[/C][C]0.791269150689059[/C][C]0.417461698621883[/C][C]0.208730849310941[/C][/ROW]
[ROW][C]44[/C][C]0.751193118881267[/C][C]0.497613762237466[/C][C]0.248806881118733[/C][/ROW]
[ROW][C]45[/C][C]0.714645531181693[/C][C]0.570708937636615[/C][C]0.285354468818307[/C][/ROW]
[ROW][C]46[/C][C]0.866035019490009[/C][C]0.267929961019983[/C][C]0.133964980509991[/C][/ROW]
[ROW][C]47[/C][C]0.871818655186592[/C][C]0.256362689626816[/C][C]0.128181344813408[/C][/ROW]
[ROW][C]48[/C][C]0.847774180009191[/C][C]0.304451639981617[/C][C]0.152225819990809[/C][/ROW]
[ROW][C]49[/C][C]0.838064578707546[/C][C]0.323870842584909[/C][C]0.161935421292454[/C][/ROW]
[ROW][C]50[/C][C]0.821797487932715[/C][C]0.356405024134569[/C][C]0.178202512067285[/C][/ROW]
[ROW][C]51[/C][C]0.787607199305178[/C][C]0.424785601389645[/C][C]0.212392800694822[/C][/ROW]
[ROW][C]52[/C][C]0.75330894866206[/C][C]0.493382102675879[/C][C]0.246691051337939[/C][/ROW]
[ROW][C]53[/C][C]0.752081272011160[/C][C]0.495837455977679[/C][C]0.247918727988840[/C][/ROW]
[ROW][C]54[/C][C]0.724345685967262[/C][C]0.551308628065477[/C][C]0.275654314032738[/C][/ROW]
[ROW][C]55[/C][C]0.72988142289999[/C][C]0.540237154200021[/C][C]0.270118577100010[/C][/ROW]
[ROW][C]56[/C][C]0.73714461196657[/C][C]0.525710776066861[/C][C]0.262855388033431[/C][/ROW]
[ROW][C]57[/C][C]0.699266131063389[/C][C]0.601467737873222[/C][C]0.300733868936611[/C][/ROW]
[ROW][C]58[/C][C]0.682461628147072[/C][C]0.635076743705856[/C][C]0.317538371852928[/C][/ROW]
[ROW][C]59[/C][C]0.643453297771849[/C][C]0.713093404456302[/C][C]0.356546702228151[/C][/ROW]
[ROW][C]60[/C][C]0.664082556873777[/C][C]0.671834886252447[/C][C]0.335917443126223[/C][/ROW]
[ROW][C]61[/C][C]0.634917726962519[/C][C]0.730164546074963[/C][C]0.365082273037481[/C][/ROW]
[ROW][C]62[/C][C]0.595636192086638[/C][C]0.808727615826724[/C][C]0.404363807913362[/C][/ROW]
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[ROW][C]208[/C][C]0.00107857181716886[/C][C]0.00215714363433771[/C][C]0.99892142818283[/C][/ROW]
[ROW][C]209[/C][C]0.00137886143694696[/C][C]0.00275772287389392[/C][C]0.998621138563053[/C][/ROW]
[ROW][C]210[/C][C]0.00103622428384547[/C][C]0.00207244856769093[/C][C]0.998963775716154[/C][/ROW]
[ROW][C]211[/C][C]0.00132384287287791[/C][C]0.00264768574575583[/C][C]0.998676157127122[/C][/ROW]
[ROW][C]212[/C][C]0.00403249752394384[/C][C]0.00806499504788768[/C][C]0.995967502476056[/C][/ROW]
[ROW][C]213[/C][C]0.00280390683500941[/C][C]0.00560781367001883[/C][C]0.99719609316499[/C][/ROW]
[ROW][C]214[/C][C]0.00370430424015292[/C][C]0.00740860848030584[/C][C]0.996295695759847[/C][/ROW]
[ROW][C]215[/C][C]0.00282967053901728[/C][C]0.00565934107803455[/C][C]0.997170329460983[/C][/ROW]
[ROW][C]216[/C][C]0.00203020200574760[/C][C]0.00406040401149521[/C][C]0.997969797994252[/C][/ROW]
[ROW][C]217[/C][C]0.00216560328630249[/C][C]0.00433120657260497[/C][C]0.997834396713698[/C][/ROW]
[ROW][C]218[/C][C]0.00167963705990034[/C][C]0.00335927411980069[/C][C]0.9983203629401[/C][/ROW]
[ROW][C]219[/C][C]0.00146345920615223[/C][C]0.00292691841230446[/C][C]0.998536540793848[/C][/ROW]
[ROW][C]220[/C][C]0.00100758808543030[/C][C]0.00201517617086061[/C][C]0.99899241191457[/C][/ROW]
[ROW][C]221[/C][C]0.000737995161715359[/C][C]0.00147599032343072[/C][C]0.999262004838285[/C][/ROW]
[ROW][C]222[/C][C]0.000474872377526669[/C][C]0.000949744755053339[/C][C]0.999525127622473[/C][/ROW]
[ROW][C]223[/C][C]0.000323486909106699[/C][C]0.000646973818213399[/C][C]0.999676513090893[/C][/ROW]
[ROW][C]224[/C][C]0.000218725612292467[/C][C]0.000437451224584934[/C][C]0.999781274387707[/C][/ROW]
[ROW][C]225[/C][C]0.000132904327453955[/C][C]0.000265808654907911[/C][C]0.999867095672546[/C][/ROW]
[ROW][C]226[/C][C]0.000345001316703971[/C][C]0.000690002633407943[/C][C]0.999654998683296[/C][/ROW]
[ROW][C]227[/C][C]0.000240104335768227[/C][C]0.000480208671536454[/C][C]0.999759895664232[/C][/ROW]
[ROW][C]228[/C][C]0.000149924611790221[/C][C]0.000299849223580443[/C][C]0.99985007538821[/C][/ROW]
[ROW][C]229[/C][C]9.76700348352581e-05[/C][C]0.000195340069670516[/C][C]0.999902329965165[/C][/ROW]
[ROW][C]230[/C][C]5.84420960566556e-05[/C][C]0.000116884192113311[/C][C]0.999941557903943[/C][/ROW]
[ROW][C]231[/C][C]6.14139037689193e-05[/C][C]0.000122827807537839[/C][C]0.999938586096231[/C][/ROW]
[ROW][C]232[/C][C]0.000281736732812232[/C][C]0.000563473465624465[/C][C]0.999718263267188[/C][/ROW]
[ROW][C]233[/C][C]0.00140086117832112[/C][C]0.00280172235664225[/C][C]0.99859913882168[/C][/ROW]
[ROW][C]234[/C][C]0.0022897409492983[/C][C]0.0045794818985966[/C][C]0.997710259050702[/C][/ROW]
[ROW][C]235[/C][C]0.00133784764113804[/C][C]0.00267569528227608[/C][C]0.998662152358862[/C][/ROW]
[ROW][C]236[/C][C]0.00087359733824157[/C][C]0.00174719467648314[/C][C]0.999126402661758[/C][/ROW]
[ROW][C]237[/C][C]0.0191809140129544[/C][C]0.0383618280259088[/C][C]0.980819085987046[/C][/ROW]
[ROW][C]238[/C][C]0.0116537111785717[/C][C]0.0233074223571434[/C][C]0.988346288821428[/C][/ROW]
[ROW][C]239[/C][C]0.00794413414283247[/C][C]0.0158882682856649[/C][C]0.992055865857168[/C][/ROW]
[ROW][C]240[/C][C]0.0045361448222641[/C][C]0.0090722896445282[/C][C]0.995463855177736[/C][/ROW]
[ROW][C]241[/C][C]0.00312583339726639[/C][C]0.00625166679453277[/C][C]0.996874166602734[/C][/ROW]
[ROW][C]242[/C][C]0.00511790268749596[/C][C]0.0102358053749919[/C][C]0.994882097312504[/C][/ROW]
[ROW][C]243[/C][C]0.00293062144810698[/C][C]0.00586124289621395[/C][C]0.997069378551893[/C][/ROW]
[ROW][C]244[/C][C]0.00212476806185601[/C][C]0.00424953612371202[/C][C]0.997875231938144[/C][/ROW]
[ROW][C]245[/C][C]0.00111381482442245[/C][C]0.0022276296488449[/C][C]0.998886185175577[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96576&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96576&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.998342410934830.003315178130339920.00165758906516996
200.9959061174629930.008187765074013510.00409388253700675
210.9908262820651730.01834743586965310.00917371793482656
220.984925087703940.03014982459211890.0150749122960595
230.9773700103639870.04525997927202580.0226299896360129
240.9685847877411850.06283042451763070.0314152122588153
250.950494221430680.09901155713864150.0495057785693208
260.9300800538382360.1398398923235280.0699199461617642
270.9013016563254890.1973966873490230.0986983436745114
280.9129317320350460.1741365359299090.0870682679649545
290.8806837015055070.2386325969889850.119316298494493
300.8772128846867020.2455742306265950.122787115313298
310.8436563490209630.3126873019580740.156343650979037
320.8323304580631740.3353390838736520.167669541936826
330.8122311193400630.3755377613198730.187768880659937
340.7624885774420330.4750228451159330.237511422557967
350.7444201901770960.5111596196458070.255579809822904
360.8132881432441340.3734237135117320.186711856755866
370.8531152440272310.2937695119455370.146884755972769
380.8343443045511980.3313113908976030.165655695448801
390.8560053337606730.2879893324786540.143994666239327
400.8394663186479170.3210673627041660.160533681352083
410.811148905162920.377702189674160.18885109483708
420.7849083372567120.4301833254865760.215091662743288
430.7912691506890590.4174616986218830.208730849310941
440.7511931188812670.4976137622374660.248806881118733
450.7146455311816930.5707089376366150.285354468818307
460.8660350194900090.2679299610199830.133964980509991
470.8718186551865920.2563626896268160.128181344813408
480.8477741800091910.3044516399816170.152225819990809
490.8380645787075460.3238708425849090.161935421292454
500.8217974879327150.3564050241345690.178202512067285
510.7876071993051780.4247856013896450.212392800694822
520.753308948662060.4933821026758790.246691051337939
530.7520812720111600.4958374559776790.247918727988840
540.7243456859672620.5513086280654770.275654314032738
550.729881422899990.5402371542000210.270118577100010
560.737144611966570.5257107760668610.262855388033431
570.6992661310633890.6014677378732220.300733868936611
580.6824616281470720.6350767437058560.317538371852928
590.6434532977718490.7130934044563020.356546702228151
600.6640825568737770.6718348862524470.335917443126223
610.6349177269625190.7301645460749630.365082273037481
620.5956361920866380.8087276158267240.404363807913362
630.5557219934830950.888556013033810.444278006516905
640.5103929173471210.9792141653057570.489607082652879
650.4758415615447370.9516831230894750.524158438455263
660.4576165856573750.915233171314750.542383414342625
670.4603213562565530.9206427125131070.539678643743447
680.5827819183958710.8344361632082570.417218081604129
690.6845652871430780.6308694257138450.315434712856922
700.6531385039695740.6937229920608520.346861496030426
710.7264777219176010.5470445561647970.273522278082399
720.6903254549427640.6193490901144720.309674545057236
730.6843136355149010.6313727289701980.315686364485099
740.6632903926540870.6734192146918270.336709607345914
750.6246817291673590.7506365416652810.375318270832641
760.6814789954293040.6370420091413910.318521004570696
770.6433646840968350.713270631806330.356635315903165
780.6218990989459270.7562018021081460.378100901054073
790.6264637932641920.7470724134716160.373536206735808
800.5865563254218810.8268873491562370.413443674578119
810.5507547618161910.8984904763676190.449245238183809
820.513169598208470.973660803583060.48683040179153
830.4808343215159760.9616686430319520.519165678484024
840.4420128444701720.8840256889403440.557987155529828
850.4304253977201520.8608507954403050.569574602279848
860.3914702775257440.7829405550514890.608529722474256
870.3576461169247770.7152922338495530.642353883075223
880.3398866492571360.6797732985142720.660113350742864
890.3073320461768030.6146640923536060.692667953823197
900.2945789090786360.5891578181572720.705421090921364
910.2632402627409780.5264805254819560.736759737259022
920.235974293926130.471948587852260.76402570607387
930.2087682755073950.4175365510147910.791231724492604
940.2181970581684150.4363941163368310.781802941831585
950.1976039189393660.3952078378787320.802396081060634
960.1719764842612290.3439529685224590.828023515738771
970.1706978244635090.3413956489270190.82930217553649
980.1468973441803010.2937946883606020.853102655819699
990.1273083251028470.2546166502056930.872691674897153
1000.1149094355470710.2298188710941420.885090564452929
1010.1035147272791030.2070294545582060.896485272720897
1020.1221563133918880.2443126267837750.877843686608112
1030.103742443691260.207484887382520.89625755630874
1040.09814798851361340.1962959770272270.901852011486387
1050.1118713435241490.2237426870482970.888128656475851
1060.1002147637234480.2004295274468960.899785236276552
1070.08769065133532810.1753813026706560.912309348664672
1080.08438409511361730.1687681902272350.915615904886383
1090.07707971052292790.1541594210458560.922920289477072
1100.06831905473647330.1366381094729470.931680945263527
1110.05817858099916840.1163571619983370.941821419000832
1120.06678472590858350.1335694518171670.933215274091416
1130.05740815675417970.1148163135083590.94259184324582
1140.06916809494022740.1383361898804550.930831905059773
1150.06691465427446620.1338293085489320.933085345725534
1160.05911111910381030.1182222382076210.94088888089619
1170.05458950585974790.1091790117194960.945410494140252
1180.05340056121704730.1068011224340950.946599438782953
1190.05188455746379910.1037691149275980.94811544253620
1200.04433770026974280.08867540053948560.955662299730257
1210.03846791820118060.07693583640236120.96153208179882
1220.04628135556232290.09256271112464580.953718644437677
1230.03892563936868830.07785127873737660.961074360631312
1240.03348914302009930.06697828604019860.9665108569799
1250.02774466140314690.05548932280629390.972255338596853
1260.02229561174337500.04459122348675010.977704388256625
1270.02168490289893850.0433698057978770.978315097101061
1280.0201508697043360.0403017394086720.979849130295664
1290.02342959807538170.04685919615076340.976570401924618
1300.02510548585353250.05021097170706510.974894514146467
1310.03414938710303750.0682987742060750.965850612896963
1320.04552771946271640.09105543892543270.954472280537284
1330.04554186665783630.09108373331567260.954458133342164
1340.04011021371484720.08022042742969440.959889786285153
1350.03338553934624080.06677107869248170.96661446065376
1360.02911143939182660.05822287878365330.970888560608173
1370.02559559566124050.05119119132248090.97440440433876
1380.02783573973645470.05567147947290950.972164260263545
1390.02392765787525640.04785531575051280.976072342124744
1400.03184330376932750.0636866075386550.968156696230672
1410.04065571822870550.0813114364574110.959344281771294
1420.03883549235950650.0776709847190130.961164507640494
1430.03195130063876920.06390260127753840.96804869936123
1440.02826672395158010.05653344790316030.97173327604842
1450.04557874729751820.09115749459503650.954421252702482
1460.05508200149677630.1101640029935530.944917998503224
1470.06836062609142520.1367212521828500.931639373908575
1480.05871908397583810.1174381679516760.941280916024162
1490.04932245087327850.0986449017465570.950677549126721
1500.06595038847376710.1319007769475340.934049611526233
1510.0592835602159280.1185671204318560.940716439784072
1520.06073440490579320.1214688098115860.939265595094207
1530.07992990811021710.1598598162204340.920070091889783
1540.06972811169552750.1394562233910550.930271888304472
1550.06789970731612460.1357994146322490.932100292683876
1560.05698171985567130.1139634397113430.943018280144329
1570.04965499969598160.09930999939196310.950345000304018
1580.04218398370976810.08436796741953620.957816016290232
1590.03516993602706960.07033987205413920.96483006397293
1600.02858423161828330.05716846323656670.971415768381717
1610.02300562382099140.04601124764198270.976994376179009
1620.01833275745816690.03666551491633370.981667242541833
1630.01457155848943480.02914311697886950.985428441510565
1640.01194192085095640.02388384170191280.988058079149044
1650.009530738272127690.01906147654425540.990469261727872
1660.00965105037803530.01930210075607060.990348949621965
1670.007481607138499130.01496321427699830.9925183928615
1680.008028104690219710.01605620938043940.99197189530978
1690.007340082027866760.01468016405573350.992659917972133
1700.005956848727893780.01191369745578760.994043151272106
1710.00697948697363210.01395897394726420.993020513026368
1720.005408012898739610.01081602579747920.99459198710126
1730.004288395692250540.008576791384501080.99571160430775
1740.004236450090441890.008472900180883780.995763549909558
1750.004231209160713240.008462418321426490.995768790839287
1760.003502175951533210.007004351903066420.996497824048467
1770.002619411663557810.005238823327115620.997380588336442
1780.002105808883726110.004211617767452230.997894191116274
1790.001616452890686990.003232905781373980.998383547109313
1800.001391319348260930.002782638696521870.99860868065174
1810.001094176173045230.002188352346090450.998905823826955
1820.000793506734611830.001587013469223660.999206493265388
1830.0007847054731048980.001569410946209800.999215294526895
1840.0005798807103629250.001159761420725850.999420119289637
1850.01682086411471670.03364172822943330.983179135885283
1860.01437743826290830.02875487652581670.985622561737092
1870.01698938655710450.0339787731142090.983010613442896
1880.01337775973331790.02675551946663580.986622240266682
1890.01131635151616920.02263270303233840.988683648483831
1900.00896456676155360.01792913352310720.991035433238446
1910.007904796645028430.01580959329005690.992095203354972
1920.006102130213286630.01220426042657330.993897869786713
1930.005608552367255190.01121710473451040.994391447632745
1940.004873788056824670.009747576113649330.995126211943175
1950.003964896689821190.007929793379642380.996035103310179
1960.002897804204192760.005795608408385520.997102195795807
1970.003971742457702950.00794348491540590.996028257542297
1980.002898203853765860.005796407707531720.997101796146234
1990.002446588601850500.004893177203701010.99755341139815
2000.002053784380384950.00410756876076990.997946215619615
2010.001811492046148960.003622984092297930.998188507953851
2020.001389441103695500.002778882207391000.998610558896305
2030.001663395889690050.00332679177938010.99833660411031
2040.002645811590645350.005291623181290710.997354188409355
2050.002640136505073180.005280273010146350.997359863494927
2060.001880583872688390.003761167745376780.998119416127312
2070.001521576365375220.003043152730750440.998478423634625
2080.001078571817168860.002157143634337710.99892142818283
2090.001378861436946960.002757722873893920.998621138563053
2100.001036224283845470.002072448567690930.998963775716154
2110.001323842872877910.002647685745755830.998676157127122
2120.004032497523943840.008064995047887680.995967502476056
2130.002803906835009410.005607813670018830.99719609316499
2140.003704304240152920.007408608480305840.996295695759847
2150.002829670539017280.005659341078034550.997170329460983
2160.002030202005747600.004060404011495210.997969797994252
2170.002165603286302490.004331206572604970.997834396713698
2180.001679637059900340.003359274119800690.9983203629401
2190.001463459206152230.002926918412304460.998536540793848
2200.001007588085430300.002015176170860610.99899241191457
2210.0007379951617153590.001475990323430720.999262004838285
2220.0004748723775266690.0009497447550533390.999525127622473
2230.0003234869091066990.0006469738182133990.999676513090893
2240.0002187256122924670.0004374512245849340.999781274387707
2250.0001329043274539550.0002658086549079110.999867095672546
2260.0003450013167039710.0006900026334079430.999654998683296
2270.0002401043357682270.0004802086715364540.999759895664232
2280.0001499246117902210.0002998492235804430.99985007538821
2299.76700348352581e-050.0001953400696705160.999902329965165
2305.84420960566556e-050.0001168841921133110.999941557903943
2316.14139037689193e-050.0001228278075378390.999938586096231
2320.0002817367328122320.0005634734656244650.999718263267188
2330.001400861178321120.002801722356642250.99859913882168
2340.00228974094929830.00457948189859660.997710259050702
2350.001337847641138040.002675695282276080.998662152358862
2360.000873597338241570.001747194676483140.999126402661758
2370.01918091401295440.03836182802590880.980819085987046
2380.01165371117857170.02330742235714340.988346288821428
2390.007944134142832470.01588826828566490.992055865857168
2400.00453614482226410.00907228964452820.995463855177736
2410.003125833397266390.006251666794532770.996874166602734
2420.005117902687495960.01023580537499190.994882097312504
2430.002930621448106980.005861242896213950.997069378551893
2440.002124768061856010.004249536123712020.997875231938144
2450.001113814824422450.00222762964884490.998886185175577







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level620.273127753303965NOK
5% type I error level950.418502202643172NOK
10% type I error level1230.541850220264317NOK

\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 & 62 & 0.273127753303965 & NOK \tabularnewline
5% type I error level & 95 & 0.418502202643172 & NOK \tabularnewline
10% type I error level & 123 & 0.541850220264317 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96576&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]62[/C][C]0.273127753303965[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]95[/C][C]0.418502202643172[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]123[/C][C]0.541850220264317[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96576&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96576&T=6

As an alternative you can also use a 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 level620.273127753303965NOK
5% type I error level950.418502202643172NOK
10% type I error level1230.541850220264317NOK



Parameters (Session):
par1 = 8 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 8 ; 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')
}