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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_regression_trees1.wasp
Title produced by softwareRecursive Partitioning (Regression Trees)
Date of computationThu, 06 Nov 2014 20:02:31 +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/2014/Nov/06/t1415304201vwr69o24thfuly1.htm/, Retrieved Sun, 19 May 2024 15:22:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=252921, Retrieved Sun, 19 May 2024 15:22:46 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact60

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Recursive Partitioning (Regression Trees)] [] [2014-11-06 20:02:31] [63a9f0ea7bb98050796b649e85481845] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
1	0	149	96	18	68	86	7.5	1.8	2.1	1.5	9.0	5.0	4.0	2.0	0.0	1.0	11.0	8.0	7.0	18.0	12.0	20.0	4.0
1	0	152	75	7	55	62	2.5	1.6	1.5	1.8	9.0	6.0	6.0	2.0	1.0	2.0	15.0	18.0	18.0	23.0	20.0	25.0	9.0
1	1	139	70	31	39	70	6.0	2.1	2.0	2.1	8.0	5.0	5.0	1.0	1.0	2.0	19.0	18.0	20.0	23.0	20.0	19.0	4.0
1	0	148	88	39	32	71	6.5	2.2	2.0	2.1	8.0	4.0	6.0	2.0	0.0	2.0	16.0	12.0	9.0	22.0	14.0	18.0	5.0
1	1	158	114	46	62	108	1.0	2.3	2.1	1.9	8.0	5.0	5.0	0.0	0.0	0.0	24.0	24.0	19.0	22.0	25.0	24.0	4.0
1	1	128	69	31	33	64	1.0	2.1	2.0	1.6	8.0	7.0	4.0	0.0	2.0	2.0	15.0	16.0	12.0	19.0	15.0	20.0	4.0
1	1	224	176	67	52	119	5.5	2.7	2.3	2.1	7.0	3.0	0.0	0.0	1.0	1.0	17.0	19.0	16.0	25.0	20.0	20.0	9.0
1	0	159	114	35	62	97	8.5	2.1	2.1	2.1	8.0	4.0	5.0	2.0	1.0	0.0	19.0	16.0	17.0	28.0	21.0	24.0	8.0
1	1	105	121	52	77	129	6.5	2.4	2.1	2.2	9.0	4.0	3.0	2.0	2.0	2.0	19.0	15.0	9.0	16.0	15.0	21.0	11.0
1	1	159	110	77	76	153	4.5	2.9	2.2	1.5	8.0	7.0	5.0	0.0	1.0	0.0	28.0	28.0	28.0	28.0	28.0	28.0	4.0
1	1	167	158	37	41	78	2.0	2.2	2.1	1.9	7.0	6.0	2.0	2.0	1.0	1.0	26.0	21.0	20.0	21.0	11.0	10.0	4.0
1	1	165	116	32	48	80	5.0	2.1	2.1	2.2	9.0	6.0	3.0	3.0	0.0	1.0	15.0	18.0	16.0	22.0	22.0	22.0	6.0
1	1	159	181	36	63	99	0.5	2.2	2.1	1.6	7.0	2.0	4.0	0.0	1.0	1.0	26.0	22.0	22.0	24.0	22.0	19.0	4.0
1	1	119	77	38	30	68	5.0	2.2	2.0	1.5	8.0	4.0	6.0	0.0	1.0	1.0	16.0	19.0	17.0	24.0	27.0	27.0	8.0
1	0	176	141	69	78	147	5.0	2.7	2.3	1.9	8.0	4.0	3.0	2.0	0.0	2.0	24.0	22.0	12.0	26.0	24.0	23.0	4.0
1	0	54	35	21	19	40	2.5	1.9	1.8	0.1	8.0	5.0	4.0	1.0	0.0	0.0	25.0	25.0	18.0	28.0	23.0	24.0	4.0
0	0	91	80	26	31	57	5.0	2.0	2.0	2.2	8.0	3.0	1.0	2.0	0.0	1.0	22.0	20.0	20.0	24.0	24.0	24.0	11.0
1	1	163	152	54	66	120	5.5	2.5	2.2	1.8	8.0	4.0	5.0	1.0	1.0	1.0	15.0	16.0	12.0	20.0	21.0	25.0	4.0
1	0	124	97	36	35	71	3.5	2.2	2.0	1.6	6.0	7.0	4.0	1.0	1.0	2.0	21.0	19.0	16.0	26.0	20.0	24.0	4.0
0	1	137	99	42	42	84	3.0	2.3	2.1	2.2	9.0	5.0	4.0	1.0	0.0	2.0	22.0	18.0	16.0	21.0	19.0	21.0	6.0
1	0	121	84	23	45	68	4.0	1.9	2.0	2.1	7.0	2.0	4.0	0.0	0.0	2.0	27.0	26.0	21.0	28.0	25.0	28.0	6.0
1	1	153	68	34	21	55	0.5	2.1	1.8	1.9	7.0	3.0	3.0	2.0	0.0	1.0	26.0	24.0	15.0	27.0	16.0	28.0	4.0
1	1	148	101	112	25	137	6.5	3.5	2.2	1.6	8.0	6.0	6.0	1.0	0.0	2.0	26.0	20.0	17.0	23.0	24.0	22.0	8.0
1	0	221	107	35	44	79	4.5	2.1	2.2	1.9	7.0	6.0	5.0	1.0	0.0	2.0	22.0	19.0	17.0	24.0	21.0	26.0	5.0
1	1	188	88	47	69	116	7.5	2.3	1.7	2.2	8.0	2.0	5.0	1.0	0.0	2.0	21.0	19.0	17.0	24.0	22.0	26.0	4.0
1	1	149	112	47	54	101	5.5	2.3	2.1	1.8	8.0	7.0	6.0	2.0	2.0	0.0	22.0	23.0	18.0	22.0	25.0	21.0	9.0
1	1	244	171	37	74	111	4.0	2.2	2.3	2.4	3.0	2.0	4.0	0.0	0.0	0.0	20.0	18.0	15.0	21.0	23.0	26.0	4.0
0	1	148	137	109	80	189	7.5	3.5	2.7	2.4	9.0	10.0	6.0	1.0	2.0	2.0	21.0	16.0	20.0	25.0	20.0	23.0	7.0
0	0	92	77	24	42	66	7.0	1.9	1.9	2.5	8.0	4.0	5.0	2.0	0.0	1.0	20.0	18.0	13.0	20.0	21.0	20.0	10.0
1	1	150	66	20	61	81	4.0	1.9	2.0	1.9	8.0	4.0	6.0	3.0	0.0	2.0	22.0	21.0	21.0	21.0	22.0	24.0	4.0
1	0	153	93	22	41	63	5.5	1.9	2.0	2.1	6.0	2.0	5.0	2.0	0.0	1.0	21.0	20.0	12.0	26.0	25.0	25.0	4.0
1	0	94	105	23	46	69	2.5	1.9	1.9	1.9	5.0	4.0	4.0	1.0	0.0	2.0	8.0	15.0	6.0	23.0	23.0	24.0	7.0
1	0	156	131	32	39	71	5.5	2.1	2.0	2.1	8.0	4.0	4.0	0.0	1.0	2.0	22.0	19.0	13.0	21.0	19.0	20.0	12.0
1	1	146	89	7	63	70	0.5	1.6	2.0	1.9	8.0	6.0	6.0	2.0	1.0	2.0	18.0	27.0	6.0	27.0	27.0	23.0	4.0
1	1	132	102	30	34	64	3.5	2.0	2.0	1.5	8.0	7.0	6.0	2.0	1.0	1.0	20.0	19.0	19.0	27.0	21.0	24.0	7.0
1	1	161	161	92	51	143	2.5	3.2	2.1	1.9	9.0	2.0	4.0	2.0	0.0	1.0	24.0	7.0	12.0	25.0	19.0	25.0	5.0
1	1	105	120	43	42	85	4.5	2.3	2.0	2.1	7.0	6.0	6.0	3.0	0.0	1.0	17.0	20.0	14.0	23.0	25.0	23.0	8.0
1	1	97	127	55	31	86	4.5	2.5	1.8	1.5	7.0	3.0	6.0	3.0	1.0	1.0	20.0	20.0	13.0	25.0	16.0	21.0	5.0
1	0	151	77	16	39	55	4.5	1.8	2.0	2.1	3.0	3.0	3.0	1.0	0.0	0.0	23.0	19.0	12.0	23.0	24.0	23.0	4.0
0	1	131	108	49	20	69	6.0	2.4	2.2	2.1	7.0	2.0	4.0	0.0	1.0	0.0	20.0	19.0	17.0	19.0	24.0	21.0	9.0
1	1	166	85	71	49	120	2.5	2.8	2.2	1.8	8.0	5.0	5.0	2.0	1.0	1.0	22.0	20.0	19.0	22.0	18.0	18.0	7.0
1	0	157	168	43	53	96	5.0	2.3	2.1	2.4	8.0	7.0	6.0	2.0	1.0	2.0	19.0	18.0	10.0	24.0	28.0	24.0	4.0
1	1	111	48	29	31	60	0.0	2.0	1.8	2.1	7.0	6.0	6.0	2.0	1.0	1.0	15.0	14.0	10.0	19.0	15.0	18.0	4.0
1	1	145	152	56	39	95	5.0	2.5	1.9	1.9	8.0	4.0	6.0	2.0	1.0	2.0	20.0	17.0	11.0	21.0	17.0	21.0	4.0
1	1	162	75	46	54	100	6.5	2.3	2.1	2.1	8.0	6.0	6.0	1.0	1.0	2.0	22.0	17.0	11.0	27.0	18.0	23.0	4.0
1	1	163	107	19	49	68	5.0	1.8	2.0	1.9	9.0	4.0	6.0	3.0	0.0	2.0	17.0	8.0	10.0	25.0	26.0	25.0	4.0
0	1	59	62	23	34	57	6.0	1.9	1.9	2.4	6.0	3.0	5.0	2.0	0.0	2.0	14.0	9.0	7.0	25.0	18.0	22.0	7.0
1	0	187	121	59	46	105	4.5	2.6	2.2	2.1	9.0	5.0	5.0	2.0	1.0	1.0	24.0	22.0	22.0	23.0	22.0	22.0	4.0
1	1	109	124	30	55	85	5.5	2.0	2.0	2.2	8.0	2.0	3.0	0.0	0.0	2.0	17.0	20.0	12.0	17.0	19.0	23.0	7.0
0	1	90	72	61	42	103	1.0	2.6	2.0	2.2	8.0	3.0	5.0	0.0	1.0	2.0	23.0	20.0	18.0	28.0	17.0	24.0	4.0
1	0	105	40	7	50	57	7.5	1.6	1.7	1.8	8.0	5.0	1.0	0.0	0.0	2.0	25.0	22.0	20.0	25.0	26.0	25.0	4.0
0	1	83	58	38	13	51	6.0	2.2	2.0	2.1	7.0	7.0	5.0	3.0	1.0	2.0	16.0	22.0	9.0	20.0	21.0	22.0	4.0
0	1	116	97	32	37	69	5.0	2.1	2.2	2.4	8.0	4.0	6.0	2.0	2.0	1.0	18.0	22.0	16.0	25.0	26.0	24.0	4.0
0	1	42	88	16	25	41	1.0	1.8	1.7	2.2	7.0	3.0	6.0	0.0	0.0	1.0	20.0	16.0	14.0	21.0	21.0	21.0	8.0
1	1	148	126	19	30	49	5.0	1.8	2.0	2.1	7.0	2.0	4.0	2.0	2.0	2.0	18.0	14.0	11.0	24.0	12.0	24.0	4.0
0	1	155	104	22	28	50	6.5	1.9	2.2	1.5	9.0	5.0	6.0	0.0	0.0	1.0	23.0	24.0	20.0	28.0	20.0	25.0	4.0
1	1	125	148	48	45	93	7.0	2.4	2.0	1.9	7.0	4.0	6.0	0.0	1.0	0.0	24.0	21.0	17.0	20.0	20.0	23.0	4.0
1	1	116	146	23	35	58	4.5	1.9	1.9	1.8	9.0	6.0	6.0	2.0	2.0	2.0	23.0	20.0	14.0	19.0	24.0	27.0	4.0
0	0	128	80	26	28	54	0.0	2.0	2.0	1.8	7.0	4.0	5.0	3.0	0.0	2.0	13.0	20.0	8.0	24.0	24.0	27.0	7.0
1	1	138	97	33	41	74	8.5	2.1	2.0	1.6	6.0	4.0	2.0	0.0	0.0	1.0	20.0	18.0	16.0	21.0	22.0	23.0	12.0
0	0	49	25	9	6	15	3.5	1.7	1.6	1.2	3.0	2.0	2.0	1.0	0.0	0.0	20.0	14.0	11.0	24.0	21.0	18.0	4.0
0	1	96	99	24	45	69	7.5	1.9	2.1	1.8	9.0	9.0	6.0	2.0	1.0	2.0	19.0	19.0	10.0	23.0	20.0	20.0	4.0
1	1	164	118	34	73	107	3.5	2.1	2.1	1.5	9.0	8.0	6.0	2.0	2.0	1.0	22.0	24.0	15.0	18.0	23.0	23.0	4.0
1	0	162	58	48	17	65	6.0	2.4	2.0	2.1	7.0	8.0	5.0	0.0	1.0	2.0	22.0	19.0	15.0	27.0	19.0	24.0	5.0
1	0	99	63	18	40	58	1.5	1.8	1.9	2.4	6.0	3.0	6.0	3.0	1.0	2.0	15.0	16.0	10.0	25.0	24.0	26.0	15.0
1	1	202	139	43	64	107	9.0	2.3	2.2	2.4	9.0	2.0	5.0	2.0	0.0	1.0	17.0	16.0	10.0	20.0	21.0	20.0	5.0
1	0	186	50	33	37	70	3.5	2.1	2.1	1.5	8.0	4.0	4.0	0.0	1.0	2.0	19.0	16.0	18.0	21.0	16.0	23.0	10.0
0	1	66	60	28	25	53	3.5	2.0	1.8	1.8	8.0	2.0	5.0	3.0	0.0	2.0	20.0	14.0	10.0	23.0	17.0	22.0	9.0
1	0	183	152	71	65	136	4.0	2.8	2.3	2.1	7.0	2.0	4.0	2.0	1.0	2.0	22.0	22.0	22.0	27.0	23.0	23.0	8.0
1	1	214	142	26	100	126	6.5	2.0	2.3	2.2	9.0	1.0	5.0	2.0	0.0	2.0	21.0	21.0	16.0	24.0	20.0	17.0	4.0
1	1	188	94	67	28	95	7.5	2.7	2.2	2.1	5.0	4.0	4.0	3.0	1.0	0.0	21.0	15.0	10.0	27.0	19.0	20.0	5.0
0	0	104	66	34	35	69	6.0	2.1	2.1	1.9	6.0	5.0	6.0	0.0	1.0	1.0	16.0	14.0	7.0	24.0	18.0	22.0	4.0
1	0	177	127	80	56	136	5.0	2.9	2.2	2.1	8.0	8.0	5.0	1.0	1.0	2.0	20.0	15.0	16.0	23.0	18.0	18.0	9.0
1	0	126	67	29	29	58	5.5	2.0	1.9	1.9	8.0	4.0	4.0	2.0	0.0	1.0	21.0	14.0	16.0	24.0	21.0	19.0	4.0
0	0	76	90	16	43	59	3.5	1.8	1.8	1.6	8.0	6.0	5.0	1.0	1.0	1.0	20.0	20.0	16.0	21.0	20.0	19.0	10.0
0	1	99	75	59	59	118	7.5	2.6	2.1	2.4	8.0	5.0	5.0	2.0	1.0	2.0	23.0	21.0	22.0	23.0	17.0	16.0	4.0
1	1	157	96	58	52	110	1.0	2.5	1.8	1.9	9.0	6.0	6.0	2.0	1.0	2.0	15.0	17.0	13.0	22.0	20.0	24.0	7.0
1	0	139	128	32	50	82	6.5	2.1	2.0	1.9	7.0	3.0	4.0	0.0	0.0	0.0	18.0	14.0	5.0	27.0	25.0	26.0	4.0
1	1	78	41	47	3	50	NA	2.3	1.7	1.9	8.0	8.0	6.0	3.0	1.0	2.0	22.0	19.0	18.0	24.0	15.0	14.0	6.0
1	0	162	146	43	59	102	6.5	2.3	2.1	2.1	9.0	4.0	2.0	0.0	1.0	0.0	16.0	16.0	10.0	25.0	17.0	25.0	7.0
0	1	108	69	38	27	65	6.5	2.2	2.1	1.8	9.0	6.0	5.0	2.0	1.0	1.0	17.0	13.0	8.0	19.0	17.0	23.0	5.0
1	0	159	186	29	61	90	7.0	2.0	2.1	2.1	8.0	4.0	6.0	1.0	0.0	1.0	24.0	26.0	16.0	24.0	24.0	18.0	4.0
0	0	74	81	36	28	64	3.5	2.2	1.8	2.4	4.0	3.0	5.0	0.0	0.0	0.0	13.0	13.0	8.0	25.0	21.0	22.0	4.0
1	1	110	85	32	51	83	1.5	2.1	2.0	2.1	7.0	8.0	5.0	0.0	2.0	2.0	19.0	18.0	16.0	23.0	22.0	26.0	4.0
0	0	96	54	35	35	70	4.0	2.1	2.1	2.2	8.0	6.0	3.0	1.0	2.0	2.0	20.0	15.0	14.0	23.0	18.0	25.0	4.0
0	0	116	46	21	29	50	7.5	1.9	1.9	2.1	6.0	3.0	3.0	0.0	0.0	0.0	22.0	18.0	15.0	25.0	22.0	26.0	4.0
0	0	87	106	29	48	77	4.5	2.0	2.1	2.2	7.0	5.0	5.0	2.0	1.0	2.0	19.0	21.0	9.0	26.0	20.0	26.0	4.0
0	1	97	34	12	25	37	0.0	1.7	1.0	1.6	7.0	4.0	6.0	1.0	0.0	2.0	21.0	17.0	21.0	26.0	21.0	24.0	6.0
0	0	127	60	37	44	81	3.5	2.2	2.2	2.4	3.0	3.0	2.0	2.0	0.0	0.0	15.0	18.0	7.0	16.0	21.0	22.0	10.0
0	1	106	95	37	64	101	5.5	2.2	2.1	2.1	8.0	7.0	6.0	1.0	0.0	1.0	21.0	20.0	17.0	23.0	20.0	21.0	7.0
0	1	80	57	47	32	79	5.0	2.3	1.9	1.9	8.0	2.0	4.0	1.0	1.0	1.0	24.0	18.0	18.0	26.0	18.0	22.0	4.0
0	0	74	62	51	20	71	4.5	2.4	2.0	2.4	8.0	4.0	5.0	3.0	0.0	2.0	22.0	25.0	16.0	25.0	25.0	28.0	4.0
0	0	91	36	32	28	60	2.5	2.1	1.9	2.1	8.0	6.0	6.0	2.0	1.0	1.0	20.0	20.0	16.0	23.0	23.0	22.0	7.0
0	0	133	56	21	34	55	7.5	1.9	2.0	1.8	5.0	6.0	5.0	0.0	0.0	2.0	21.0	19.0	14.0	26.0	21.0	26.0	4.0
0	1	74	54	13	31	44	7.0	1.7	1.8	2.1	6.0	6.0	5.0	2.0	1.0	1.0	19.0	18.0	15.0	22.0	20.0	20.0	8.0
0	1	114	64	14	26	40	0.0	1.8	2.0	1.8	6.0	4.0	6.0	1.0	1.0	2.0	14.0	12.0	8.0	20.0	21.0	24.0	11.0
0	1	140	76	-2	58	56	4.5	1.5	2.0	1.9	7.0	6.0	5.0	0.0	0.0	2.0	25.0	22.0	22.0	27.0	20.0	21.0	6.0
0	0	95	98	20	23	43	3.0	1.9	2.0	1.9	7.0	5.0	6.0	1.0	1.0	2.0	11.0	16.0	5.0	20.0	22.0	23.0	14.0
0	1	98	88	24	21	45	1.5	1.9	1.8	2.4	7.0	5.0	5.0	0.0	0.0	2.0	17.0	18.0	13.0	22.0	15.0	23.0	5.0
0	0	121	35	11	21	32	3.5	1.7	2.0	1.8	8.0	6.0	4.0	0.0	0.0	2.0	22.0	23.0	22.0	24.0	24.0	23.0	4.0
0	1	126	102	23	33	56	2.5	1.9	1.1	1.8	9.0	8.0	5.0	1.0	1.0	2.0	20.0	20.0	18.0	21.0	22.0	22.0	8.0
0	1	98	61	24	16	40	5.5	1.9	1.8	2.1	8.0	5.0	5.0	2.0	2.0	1.0	22.0	20.0	15.0	24.0	21.0	23.0	9.0
0	1	95	80	14	20	34	8.0	1.8	1.8	2.1	8.0	6.0	5.0	2.0	1.0	2.0	15.0	16.0	11.0	26.0	17.0	21.0	4.0
0	1	110	49	52	37	89	1.0	2.4	2.0	2.4	7.0	4.0	5.0	2.0	1.0	2.0	23.0	22.0	19.0	24.0	23.0	27.0	4.0
0	1	70	78	15	35	50	5.0	1.8	1.9	1.9	9.0	3.0	4.0	2.0	1.0	2.0	20.0	19.0	19.0	24.0	22.0	23.0	5.0
0	0	102	90	23	33	56	4.5	1.9	2.1	1.8	7.0	3.0	5.0	3.0	0.0	1.0	22.0	23.0	21.0	27.0	23.0	26.0	4.0
0	1	86	45	19	27	46	3.0	1.8	1.6	1.8	6.0	2.0	0.0	0.0	0.0	0.0	16.0	6.0	4.0	25.0	16.0	27.0	5.0
0	1	130	55	35	41	76	3.0	2.1	2.2	2.2	7.0	4.0	5.0	0.0	0.0	1.0	25.0	19.0	17.0	27.0	18.0	27.0	4.0
0	1	96	96	24	40	64	8.0	1.9	1.9	2.4	8.0	5.0	6.0	0.0	0.0	1.0	18.0	24.0	10.0	19.0	25.0	23.0	4.0
0	0	102	43	39	35	74	2.5	2.2	2.0	1.8	6.0	3.0	1.0	0.0	1.0	0.0	19.0	19.0	13.0	22.0	18.0	23.0	7.0
0	0	100	52	29	28	57	7.0	2.0	2.1	2.4	2.0	4.0	1.0	0.0	1.0	0.0	25.0	15.0	15.0	22.0	14.0	23.0	10.0
0	0	94	60	13	32	45	0.0	1.7	1.3	1.8	4.0	5.0	3.0	3.0	0.0	0.0	21.0	18.0	11.0	25.0	20.0	28.0	4.0
0	0	52	54	8	22	30	1.0	1.7	1.8	1.9	8.0	3.0	3.0	2.0	0.0	2.0	22.0	18.0	20.0	23.0	19.0	24.0	5.0
0	0	98	51	18	44	62	3.5	1.8	1.9	2.4	6.0	5.0	6.0	0.0	0.0	1.0	21.0	22.0	13.0	24.0	18.0	20.0	4.0
0	0	118	51	24	27	51	5.5	1.9	2.1	2.1	8.0	4.0	4.0	2.0	0.0	1.0	22.0	23.0	18.0	24.0	22.0	23.0	4.0
0	1	99	38	19	17	36	5.5	1.8	1.8	1.9	6.0	4.0	5.0	2.0	0.0	2.0	23.0	18.0	20.0	23.0	21.0	22.0	4.0
1	1	48	41	23	12	34	0.5	1	0.75	2.1	7	6	6	0	2	2	20	17	15	22	14	15	6
1	1	50	146	16	45	61	7.5	1	1.5	2.7	7	3	6	2	2	2	6	6	4	24	5	27	4
1	1	150	182	33	37	70	9	4	3	2.1	7	4	6	1	2	1	15	22	9	19	25	23	8
1	1	154	192	32	37	69	9.5	4	2.25	2.1	9	3	6	3	2	2	18	20	18	25	21	23	5
0	0	109	263	37	108	145	8.5	3	3	2.1	7	10	6	3	2	2	24	16	12	26	11	20	4
0	1	68	35	14	10	23	7	2	1.5	2.1	6	4	6	0	0	1	22	16	17	18	20	18	17
1	1	194	439	52	68	120	8	4	3	2.1	8	8	5	3	1	2	21	17	12	24	9	22	4
1	0	158	214	75	72	147	10	4	3	2.1	8	3	6	2	2	2	23	20	16	28	15	20	4
1	1	159	341	72	143	215	7	4	3	2.1	9	5	5	2	0	2	20	23	17	23	23	21	8
1	0	67	58	15	9	24	8.5	2	0.75	2.1	7	4	6	0	0	1	20	18	14	19	21	25	4
1	0	147	292	29	55	84	9	4	3	2.4	6	3	5	2	0	2	18	13	13	19	9	19	7
1	1	39	85	13	17	30	9.5	1	2.25	1.95	8	5	5	3	0	2	25	22	20	27	24	25	4
1	1	100	200	40	37	77	4	3	1.5	2.1	6	3	6	2	0	2	16	20	16	24	16	24	4
1	1	111	158	19	27	46	6	3	1.5	2.1	6	3	4	0	0	2	20	20	15	26	20	22	5
1	1	138	199	24	37	61	8	4	2.25	1.95	9	4	5	0	0	2	14	13	10	21	15	28	7
1	1	101	297	121	58	178	5.5	3	3	2.1	6	3	6	0	0	1	22	16	16	25	18	22	4
0	1	131	227	93	66	160	9.5	4	3	2.4	9	6	6	1	1	1	26	25	21	28	22	21	4
1	1	101	108	36	21	57	7.5	3	1.5	2.1	8	6	5	2	2	2	20	16	15	19	21	23	7
1	1	114	86	23	19	42	7	3	2.25	2.25	8	4	6	3	2	2	17	15	16	20	21	19	11
1	0	165	302	85	78	163	7.5	4	2.25	2.4	9	4	6	0	0	0	22	19	19	26	21	21	7
1	1	114	148	41	35	75	8	3	1.5	2.25	6	4	6	2	0	1	22	19	9	27	20	25	4
1	1	111	178	46	48	94	7	3	2.25	2.55	4	3	4	2	1	2	20	24	19	23	24	23	4
1	1	75	120	18	27	45	7	2	1.5	1.95	8	2	6	1	2	0	17	9	7	18	15	28	4
1	1	82	207	35	43	78	6	2	2.25	2.4	5	5	5	2	0	2	22	22	23	23	24	14	4
1	1	121	157	17	30	47	10	3	2.25	2.1	7	4	6	2	2	2	17	15	14	21	18	23	4
1	1	32	128	4	25	29	2.5	1	3	2.1	9	4	6	2	0	0	22	22	10	23	24	24	4
1	0	150	296	28	69	97	9	4	3	2.4	9	4	5	2	0	2	21	22	16	22	24	25	6
1	1	117	323	44	72	116	8	3	3	2.1	8	3	4	2	1	1	25	24	12	21	15	15	8
0	1	71	79	10	23	32	6	2	1.5	2.1	6	4	5	2	2	1	11	12	10	14	19	23	23
1	1	165	70	38	13	50	8.5	4	3	2.25	8	2	6	1	1	2	19	21	7	24	20	26	4
1	1	154	146	57	61	118	6	4	3	2.25	3	0	0	0	0	0	24	25	20	26	26	21	8
1	1	126	246	23	43	66	9	4	2.25	2.4	8	4	6	2	1	2	17	26	9	24	26	26	6
1	0	138	145	26	22	48	8	4	1.5	2.1	7	3	4	0	0	0	22	19	14	26	18	15	4
1	0	149	196	36	51	86	8	4	2.25	2.1	7	6	6	0	2	2	22	21	12	22	23	23	4
1	0	145	199	22	67	89	9	4	2.25	2.4	9	4	4	2	0	1	17	14	10	20	13	15	7
1	1	120	127	40	36	76	5.5	3	3	2.1	4	4	6	0	0	1	26	28	19	20	16	16	4
1	0	138	91	18	21	39	5	4	0.75	1.95	7	2	4	0	0	0	19	16	16	20	19	20	4
1	0	109	153	31	44	75	7	3	2.25	2.1	6	4	5	0	1	0	20	21	11	18	22	20	4
1	0	132	299	11	45	57	5.5	4	3	2.25	3	2	1	0	1	0	19	16	15	18	21	20	4
1	1	172	228	38	34	72	9	4	3	2.25	8	4	5	3	2	2	21	16	14	25	11	21	10
1	0	169	190	24	36	60	2	4	1.5	2.4	8	3	5	0	0	1	24	25	11	28	23	28	6
1	1	114	180	37	72	109	8.5	3	2.25	2.25	9	6	5	2	2	0	21	21	14	23	18	19	5
1	1	156	212	37	39	76	9	4	3	2.25	8	6	5	3	0	2	19	22	15	20	19	21	5
1	0	172	269	22	43	65	8.5	4	2.25	2.1	8	4	5	0	0	2	13	9	7	22	15	22	4
0	1	68	130	15	25	40	9	2	1.5	2.1	9	5	6	2	2	1	24	20	22	27	8	27	4
0	1	89	179	2	56	58	7.5	2	2.25	2.1	8	4	5	0	1	2	28	19	19	24	15	20	5
1	1	167	243	43	80	123	10	4	2.25	2.7	9	6	6	3	2	2	27	24	22	23	21	17	5
1	0	113	190	31	40	71	9	3	1.5	2.1	7	6	5	2	1	2	22	22	11	20	25	26	5
0	0	115	299	29	73	102	7.5	3	2.25	2.1	7	9	6	2	1	2	23	22	19	22	14	21	5
0	0	78	121	45	34	80	6	2	1.5	2.25	6	4	5	2	1	0	19	12	9	21	21	24	4
0	0	118	137	25	72	97	10.5	3	2.25	2.7	8	8	6	3	1	2	18	17	11	24	18	21	6
0	1	87	305	4	42	46	8.5	2	3	2.4	6	5	5	3	0	2	23	18	17	26	18	25	4
1	0	173	157	31	61	93	8	4	3	2.1	7	4	5	3	0	0	21	10	12	24	12	22	4
1	1	2	96	-4	23	19	10	1	3	2.1	8	4	6	2	2	1	22	22	17	18	24	17	4
0	0	162	183	66	74	140	10.5	4	3	2.4	8	7	6	3	2	1	17	24	10	17	17	14	9
0	1	49	52	61	16	78	6.5	1	1.5	1.95	7	4	6	1	2	2	15	18	17	23	20	23	18
0	0	122	238	32	66	98	9.5	4	2.25	2.7	9	8	6	2	1	2	21	18	13	21	24	28	6
0	1	96	40	31	9	40	8.5	3	1.5	2.1	9	4	6	3	2	1	20	23	11	21	22	24	5
0	0	100	226	39	41	80	7.5	3	2.25	2.25	9	3	6	2	0	1	26	21	19	24	15	22	4
0	0	82	190	19	57	76	5	2	2.25	2.1	6	5	6	2	1	2	19	21	21	22	22	24	11
0	1	100	214	31	48	79	8	3	2.25	2.7	8	8	6	2	2	2	28	28	24	24	26	25	4
0	0	115	145	36	51	87	10	3	3	2.1	9	4	5	1	0	1	21	17	13	24	17	21	10
0	1	141	119	42	53	95	7	4	1.5	2.1	9	10	6	3	1	0	19	21	16	24	23	22	6
1	1	165	222	21	29	49	7.5	4	2.25	1.65	8	5	6	2	2	2	22	21	13	23	19	16	8
1	1	165	222	21	29	49	7.5	4	2.25	1.65	8	5	6	2	2	2	21	20	15	21	21	18	8
0	1	110	159	25	55	80	9.5	3	3	2.1	8	3	6	1	0	2	20	18	15	24	23	27	6
1	1	118	165	32	54	86	6	3	2.25	2.1	8	3	5	1	1	2	19	17	11	19	19	17	8
1	0	158	249	26	43	69	10	4	3	2.1	8	3	3	0	0	2	11	7	7	19	18	25	4
0	1	146	125	28	51	79	7	4	2.25	2.1	9	4	4	1	1	1	17	17	13	23	16	24	4
1	0	49	122	32	20	52	3	1	1.5	2.1	6	5	6	1	0	2	19	14	13	25	23	21	9
0	0	90	186	41	79	120	6	2	3	2.4	9	5	4	2	1	2	20	18	12	24	13	21	9
0	0	121	148	29	39	69	7	3	1.5	2.4	8	4	6	0	0	0	17	14	8	21	18	19	5
1	1	155	274	33	61	94	10	4	3	2.1	8	7	6	3	1	0	21	23	7	18	23	27	4
0	0	104	172	17	55	72	7	3	3	2.25	8	5	3	1	0	1	21	20	17	23	21	28	4
0	1	147	84	13	30	43	3.5	4	3	2.4	8	4	4	1	2	0	12	14	9	20	23	19	15
0	0	110	168	32	55	87	8	3	3	2.1	9	7	4	3	0	2	23	17	18	23	16	23	10
0	0	108	102	30	22	52	10	3	2.25	2.1	9	7	4	3	0	2	22	21	17	23	17	25	9
0	0	113	106	34	37	71	5.5	3	2.25	2.4	9	7	4	3	0	2	22	23	17	23	20	26	7
0	0	115	2	59	2	61	6	3	0.75	2.4	8	7	4	3	0	2	21	24	18	23	18	25	9
0	1	61	139	13	38	51	6.5	1	3	2.1	8	7	4	0	0	0	20	21	12	27	20	25	6
0	1	60	95	23	27	50	6.5	1	0.75	2.1	8	7	6	2	1	2	18	14	14	19	19	24	4
0	1	109	130	10	56	67	8.5	3	1.5	2.4	3	1	4	1	1	0	21	24	22	25	26	24	7
0	1	68	72	5	25	30	4	2	1.5	2.1	6	2	4	2	1	2	24	16	19	25	9	24	4
0	0	111	141	31	39	70	9.5	3	3	2.7	5	3	2	1	0	2	22	21	21	21	23	22	7
0	0	77	113	19	33	52	8	2	1.5	2.1	4	6	5	1	0	1	20	8	10	25	9	21	4
0	1	73	206	32	43	75	8.5	2	2.25	2.1	9	8	6	3	2	2	17	17	16	17	13	17	15
1	0	151	268	30	57	87	5.5	4	3	2.25	8	8	6	1	1	1	19	18	11	22	27	23	4
0	0	89	175	25	43	69	7	2	3	2.1	3	0	1	0	0	0	16	17	15	23	22	17	9
0	0	78	77	48	23	72	9	2	1.5	2.4	6	3	4	1	0	2	19	16	12	27	12	25	4
0	0	110	125	35	44	79	8	3	3	2.25	6	6	5	1	1	2	23	22	21	27	18	19	4
1	1	220	255	67	54	121	10	4	3	2.25	9	5	5	2	0	2	8	17	22	5	6	8	28
0	1	65	111	15	28	43	8	2	1.5	2.1	7	7	6	1	0	1	22	21	20	19	17	14	4
1	0	141	132	22	36	58	6	4	1.5	2.1	6	3	5	0	1	2	23	20	15	24	22	22	4
0	0	117	211	18	39	57	8	3	2.25	2.4	9	3	6	2	0	0	15	20	9	23	22	25	4
1	1	122	92	33	16	50	5	4	1.5	2.25	7	4	6	2	0	1	17	19	15	28	23	28	5
0	0	63	76	46	23	69	9	2	1.5	2.1	8	4	5	3	0	2	21	8	14	25	19	25	4
1	1	44	171	24	40	64	4.5	1	2.25	2.1	8	1	5	0	0	2	25	19	11	27	20	24	4
0	1	52	83	14	24	38	8.5	1	1.5	1.65	8	5	6	2	0	2	18	11	9	16	17	15	12
0	1	62	119	23	29	53	7	1	2.25	1.65	7	3	4	1	0	1	23	15	18	23	18	25	5
0	0	131	266	12	78	90	9.5	4	3	2.7	0	0	0	0	0	0	20	13	12	25	24	24	4
0	1	101	186	38	57	96	8.5	3	3	2.1	6	4	6	1	1	0	21	18	11	26	20	28	6
0	1	42	50	12	37	49	7.5	1	0.75	1.95	9	6	5	2	2	1	21	19	14	24	18	24	6
1	1	152	117	28	27	56	7.5	4	1.5	2.25	9	4	6	1	1	2	24	23	10	23	23	25	5
1	0	107	219	41	61	102	5	3	1.5	2.4	6	1	2	0	1	2	22	20	18	24	27	23	4
0	0	77	246	12	27	40	7	2	2.25	1.95	8	3	5	0	0	2	22	22	11	27	25	26	4
1	0	154	279	31	69	100	8	4	2.25	2.1	8	7	5	2	0	2	23	19	14	25	24	26	4
1	1	103	148	33	34	67	5.5	3	1.5	2.4	5	3	1	0	0	2	17	16	16	19	12	22	10
0	1	96	137	34	44	78	8.5	3	2.25	2.1	6	5	5	1	1	0	15	11	11	19	16	25	7
1	0	154	130	41	21	62	7.5	4	0.75	2.1	6	3	4	1	0	0	24	11	8	14	16	20	4
1	1	175	181	21	34	55	9.5	4	2.25	2.4	9	3	5	2	2	2	22	21	16	24	24	22	4
0	1	57	98	20	39	59	7	1	0.75	2.4	9	6	4	2	1	2	19	14	13	20	23	26	7
0	0	112	226	44	51	96	8	3	2.25	2.4	9	9	6	3	0	2	18	21	12	21	24	20	4
1	0	143	234	52	34	86	8.5	4	3	2.25	6	4	5	0	1	2	21	20	17	28	24	26	4
0	0	49	138	7	31	38	3.5	1	0.75	2.4	4	3	6	0	1	1	20	21	23	26	26	26	12
1	1	110	85	29	13	43	6.5	3	0.75	2.1	8	9	6	2	2	2	19	20	14	19	19	21	5
1	1	131	66	11	12	23	6.5	4	3	2.1	4	5	6	0	1	0	19	19	10	23	28	21	8
1	0	167	236	26	51	77	10.5	4	3	1.8	5	3	6	3	1	1	16	19	16	23	23	24	6
0	0	56	106	24	24	48	8.5	1	3	2.7	8	6	5	2	0	1	18	18	11	21	21	21	17
1	0	137	135	7	19	26	8	4	3	2.1	6	2	6	1	0	1	23	20	16	26	19	18	4
0	1	86	122	60	30	91	10	2	1.5	2.1	8	4	5	3	1	2	22	21	19	25	23	23	5
1	1	121	218	13	81	94	10	3	3	2.4	9	5	5	2	1	1	23	22	17	25	23	26	4
1	0	149	199	20	42	62	9.5	4	3	2.55	7	4	5	2	0	1	20	19	12	24	20	23	5
1	0	168	112	52	22	74	9	4	3	2.55	4	0	0	0	0	0	24	23	17	23	18	25	5
1	0	140	278	28	85	114	10	4	3	2.1	8	2	6	1	1	2	25	16	11	22	20	20	6
0	1	88	94	25	27	52	7.5	2	1.5	2.1	8	5	6	2	1	2	25	23	19	27	28	25	4
1	1	168	113	39	25	64	4.5	4	2.25	2.1	8	3	6	2	0	1	20	18	12	26	21	26	4
1	1	94	84	9	22	31	4.5	2	0.75	2.25	4	0	0	0	0	0	23	23	8	23	25	19	4
1	1	51	86	19	19	38	0.5	1	0.75	2.25	9	5	5	3	0	2	21	20	17	22	18	21	6
0	0	48	62	13	14	27	6.5	1	2.25	2.1	8	6	5	1	0	2	23	20	13	26	24	23	8
1	1	145	222	60	45	105	4.5	4	3	2.1	6	3	5	0	1	1	23	23	17	22	28	24	10
1	1	66	167	19	45	64	5.5	2	2.25	1.95	3	0	0	0	0	0	11	13	7	17	9	6	4
0	1	85	82	34	28	62	5	2	3	2.4	7	3	4	0	1	0	21	21	23	25	22	22	5
1	0	109	207	14	51	65	6	3	2.25	2.1	8	5	6	2	1	2	27	26	18	22	26	21	4
0	0	63	184	17	41	58	4	2	3	2.4	7	4	4	0	0	2	19	18	13	28	28	28	4
0	1	102	83	45	31	76	8	3	1.5	2.4	7	5	5	2	0	1	21	19	17	22	18	24	4
0	0	162	183	66	74	140	10.5	4	3	2.4	8	7	6	3	2	1	16	18	13	21	23	14	16
1	1	128	85	24	24	48	8.5	4	3	2.25	8	4	5	3	1	2	22	19	13	21	22	17	4
0	1	86	89	48	19	68	6.5	2	0.75	1.95	7	8	6	2	1	2	21	18	8	24	15	20	7
0	1	114	225	29	51	80	8	3	1.5	2.1	7	6	6	1	1	2	22	19	16	26	24	28	4
1	0	164	237	-2	73	71	8.5	4	3	2.1	6	4	5	1	0	1	16	13	14	26	12	19	4
1	1	119	102	51	24	76	5.5	3	3	2.55	8	5	5	1	1	0	18	10	13	24	12	24	14
1	0	126	221	2	61	63	7	4	3	2.1	8	5	6	0	1	2	23	21	19	27	20	21	5
1	1	132	128	24	23	46	5	4	2.25	2.1	7	3	6	1	0	2	24	24	15	22	25	21	5
1	1	142	91	40	14	53	3.5	4	2.25	2.1	9	6	6	0	1	2	20	21	15	23	24	26	5
1	0	83	198	20	54	74	5	2	3	1.95	9	3	4	2	0	1	20	23	8	22	23	24	5
0	1	94	204	19	51	70	9	2	1.5	2.25	7	6	5	3	1	1	18	18	14	23	18	26	7
0	0	81	158	16	62	78	8.5	2	2.25	2.4	7	3	2	1	0	2	4	11	7	15	20	25	19
1	1	166	138	20	36	56	5	4	2.25	1.95	8	7	6	2	2	2	14	16	11	20	22	23	16
0	0	110	226	40	59	100	9.5	3	2.25	2.1	8	7	6	3	0	2	22	20	17	22	20	24	4
0	1	64	44	27	24	51	3	2	0.75	2.1	6	6	4	3	1	2	17	20	19	25	25	24	4
1	0	93	196	25	26	52	1.5	2	2.25	1.95	9	5	6	1	1	0	23	26	17	27	28	26	7
0	0	104	83	49	54	102	6	3	1.5	2.1	6	5	5	1	0	1	20	21	12	24	25	23	9
0	1	105	79	39	39	78	0.5	3	2.25	2.1	5	4	4	0	0	2	18	12	12	21	14	20	5
0	1	49	52	61	16	78	6.5	1	1.5	1.95	7	4	6	1	2	2	19	15	18	17	16	16	14
0	0	88	105	19	36	55	7.5	2	0.75	2.1	9	7	6	3	0	2	20	18	16	26	24	24	4
0	1	95	116	67	31	98	4.5	2	1.5	1.95	6	2	1	0	1	0	15	14	15	20	13	20	16
0	1	102	83	45	31	76	8	3	1.5	2.4	7	5	5	2	0	1	24	18	20	22	19	23	10
0	0	99	196	30	42	73	9	3	2.25	2.4	5	4	5	2	1	0	21	16	16	24	18	23	5
0	1	63	153	8	39	47	7.5	2	1.5	2.4	9	2	6	2	2	2	19	19	12	23	16	18	6
0	0	76	157	19	25	45	8.5	2	1.5	1.95	8	5	4	2	0	0	19	7	10	22	8	21	4
0	0	109	75	52	31	83	7	3	3	2.7	4	4	3	0	0	2	27	21	28	28	27	25	4
0	1	117	106	22	38	60	9.5	3	2.25	2.1	9	7	4	3	2	2	23	24	19	21	23	23	4
0	1	57	58	17	31	48	6.5	1	1.5	1.95	8	6	5	2	2	0	23	21	18	24	20	26	5
0	0	120	75	33	17	50	9.5	3	0.75	2.1	7	4	5	0	0	0	20	20	19	28	20	26	4
0	1	73	74	34	22	56	6	2	2.25	1.95	8	5	6	2	2	2	17	22	8	25	26	24	4
0	0	91	185	22	55	77	8	2	3	2.1	1	0	1	0	0	0	21	17	17	24	23	23	5
0	0	108	265	30	62	91	9.5	3	3	2.25	8	7	6	2	1	2	23	19	16	24	24	21	4
0	1	105	131	25	51	76	8	3	1.5	2.7	8	4	4	2	0	2	22	20	18	21	21	23	4
1	0	117	139	38	30	68	8	3	1.5	2.1	9	5	4	3	0	2	16	16	12	20	15	20	5
0	0	119	196	26	49	74	9	3	2.25	2.4	8	6	5	2	0	1	20	20	17	26	22	23	8
0	1	31	78	13	16	29	5	1	0.75	1.35	9	8	3	2	1	1	16	16	13	16	25	24	15

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time15 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 15 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=252921&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]15 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=252921&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=252921&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time15 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Goodness of Fit
Correlation0.5113
R-squared0.2614
RMSE2.1935

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.5113 \tabularnewline
R-squared & 0.2614 \tabularnewline
RMSE & 2.1935 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=252921&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.5113[/C][/ROW]
[ROW][C]R-squared[/C][C]0.2614[/C][/ROW]
[ROW][C]RMSE[/C][C]2.1935[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=252921&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=252921&T=1

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


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

Goodness of Fit
Correlation0.5113
R-squared0.2614
RMSE2.1935






Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
17.55.214723926380372.28527607361963
22.55.21472392638037-2.71472392638037
365.214723926380370.785276073619632
46.55.214723926380371.28527607361963
515.21472392638037-4.21472392638037
615.21472392638037-4.21472392638037
75.55.96052631578947-0.460526315789473
88.55.214723926380373.28527607361963
96.55.214723926380371.28527607361963
104.55.21472392638037-0.714723926380368
1125.96052631578947-3.96052631578947
1255.21472392638037-0.214723926380368
130.55.96052631578947-5.46052631578947
1455.21472392638037-0.214723926380368
1555.96052631578947-0.960526315789473
162.55.21472392638037-2.71472392638037
1755.21472392638037-0.214723926380368
185.55.96052631578947-0.460526315789473
193.55.21472392638037-1.71472392638037
2035.21472392638037-2.21472392638037
2145.21472392638037-1.21472392638037
220.55.21472392638037-4.71472392638037
236.55.214723926380371.28527607361963
244.55.21472392638037-0.714723926380368
257.55.214723926380372.28527607361963
265.55.214723926380370.285276073619632
2745.96052631578947-1.96052631578947
287.58.42307692307692-0.923076923076923
2975.214723926380371.78527607361963
3045.21472392638037-1.21472392638037
315.55.214723926380370.285276073619632
322.55.21472392638037-2.71472392638037
335.55.96052631578947-0.460526315789473
340.55.21472392638037-4.71472392638037
353.55.21472392638037-1.71472392638037
362.58.42307692307692-5.92307692307692
374.55.21472392638037-0.714723926380368
384.55.21472392638037-0.714723926380368
394.55.21472392638037-0.714723926380368
4065.214723926380370.785276073619632
412.55.21472392638037-2.71472392638037
4255.96052631578947-0.960526315789473
4305.21472392638037-5.21472392638037
4455.96052631578947-0.960526315789473
456.55.214723926380371.28527607361963
4655.21472392638037-0.214723926380368
4765.214723926380370.785276073619632
484.55.21472392638037-0.714723926380368
495.55.214723926380370.285276073619632
5015.21472392638037-4.21472392638037
517.55.214723926380372.28527607361963
5265.214723926380370.785276073619632
5355.21472392638037-0.214723926380368
5415.21472392638037-4.21472392638037
5555.21472392638037-0.214723926380368
566.55.214723926380371.28527607361963
5775.960526315789471.03947368421053
584.55.96052631578947-1.46052631578947
5905.21472392638037-5.21472392638037
608.55.214723926380373.28527607361963
613.55.21472392638037-1.71472392638037
627.55.214723926380372.28527607361963
633.55.21472392638037-1.71472392638037
6465.214723926380370.785276073619632
651.55.21472392638037-3.71472392638037
6695.960526315789473.03947368421053
673.55.21472392638037-1.71472392638037
683.55.21472392638037-1.71472392638037
6945.96052631578947-1.96052631578947
706.55.960526315789470.539473684210527
717.55.214723926380372.28527607361963
7265.214723926380370.785276073619632
7355.21472392638037-0.214723926380368
745.55.214723926380370.285276073619632
753.55.21472392638037-1.71472392638037
767.55.214723926380372.28527607361963
7715.21472392638037-4.21472392638037
786.55.214723926380371.28527607361963
796.55.960526315789470.539473684210527
806.55.214723926380371.28527607361963
8175.960526315789471.03947368421053
823.55.21472392638037-1.71472392638037
831.55.21472392638037-3.71472392638037
8445.21472392638037-1.21472392638037
857.55.214723926380372.28527607361963
864.55.21472392638037-0.714723926380368
8705.21472392638037-5.21472392638037
883.55.21472392638037-1.71472392638037
895.55.214723926380370.285276073619632
9055.21472392638037-0.214723926380368
914.55.21472392638037-0.714723926380368
922.55.21472392638037-2.71472392638037
937.55.214723926380372.28527607361963
9475.214723926380371.78527607361963
9505.21472392638037-5.21472392638037
964.55.21472392638037-0.714723926380368
9735.21472392638037-2.21472392638037
981.55.21472392638037-3.71472392638037
993.55.21472392638037-1.71472392638037
1002.55.21472392638037-2.71472392638037
1015.55.214723926380370.285276073619632
10285.214723926380372.78527607361963
10315.21472392638037-4.21472392638037
10455.21472392638037-0.214723926380368
1054.55.21472392638037-0.714723926380368
10635.21472392638037-2.21472392638037
10735.21472392638037-2.21472392638037
10885.214723926380372.78527607361963
1092.55.21472392638037-2.71472392638037
11075.214723926380371.78527607361963
11105.21472392638037-5.21472392638037
11215.21472392638037-4.21472392638037
1133.55.21472392638037-1.71472392638037
1145.55.214723926380370.285276073619632
1155.55.214723926380370.285276073619632
1160.55.21472392638037-4.71472392638037
1177.55.960526315789471.53947368421053
11898.423076923076920.576923076923077
1199.58.423076923076921.07692307692308
1208.58.423076923076920.0769230769230766
12175.214723926380371.78527607361963
12288.42307692307692-0.423076923076923
123108.423076923076921.57692307692308
12478.42307692307692-1.42307692307692
1258.55.214723926380373.28527607361963
12698.423076923076920.576923076923077
1279.55.214723926380374.28527607361963
12848.42307692307692-4.42307692307692
12966.75-0.75
13086.751.25
1315.56.75-1.25
1329.58.423076923076921.07692307692308
1337.55.214723926380372.28527607361963
13475.214723926380371.78527607361963
1357.56.750.75
13688.42307692307692-0.423076923076923
13778.42307692307692-1.42307692307692
13875.214723926380371.78527607361963
13965.960526315789470.0394736842105265
140108.423076923076921.57692307692308
1412.55.21472392638037-2.71472392638037
14298.423076923076920.576923076923077
14388.42307692307692-0.423076923076923
14465.214723926380370.785276073619632
1458.55.214723926380373.28527607361963
14666.75-0.75
14798.423076923076920.576923076923077
14886.751.25
14986.751.25
15098.423076923076920.576923076923077
1515.55.214723926380370.285276073619632
15255.21472392638037-0.214723926380368
15376.750.25
1545.56.75-1.25
15598.423076923076920.576923076923077
15626.75-4.75
1578.58.423076923076920.0769230769230766
15898.423076923076920.576923076923077
1598.56.751.75
16095.960526315789473.03947368421053
1617.55.960526315789471.53947368421053
162108.423076923076921.57692307692308
16398.423076923076920.576923076923077
1647.58.42307692307692-0.923076923076923
16565.214723926380370.785276073619632
16610.58.423076923076922.07692307692308
1678.55.960526315789472.53947368421053
16888.42307692307692-0.423076923076923
169105.214723926380374.78527607361963
17010.58.423076923076922.07692307692308
1716.55.214723926380371.28527607361963
1729.58.423076923076921.07692307692308
1738.55.214723926380373.28527607361963
1747.58.42307692307692-0.923076923076923
17555.96052631578947-0.960526315789473
17688.42307692307692-0.423076923076923
177108.423076923076921.57692307692308
17875.214723926380371.78527607361963
1797.58.42307692307692-0.923076923076923
1807.58.42307692307692-0.923076923076923
1819.58.423076923076921.07692307692308
18268.42307692307692-2.42307692307692
183106.753.25
18475.214723926380371.78527607361963
18535.21472392638037-2.21472392638037
18665.960526315789470.0394736842105265
18776.750.25
188108.423076923076921.57692307692308
18978.42307692307692-1.42307692307692
1903.55.21472392638037-1.71472392638037
19188.42307692307692-0.423076923076923
192105.214723926380374.78527607361963
1935.55.214723926380370.285276073619632
19465.214723926380370.785276073619632
1956.55.960526315789470.539473684210527
1966.55.214723926380371.28527607361963
1978.58.423076923076920.0769230769230766
19845.21472392638037-1.21472392638037
1999.58.423076923076921.07692307692308
20085.214723926380372.78527607361963
2018.55.960526315789472.53947368421053
2025.58.42307692307692-2.92307692307692
20375.960526315789471.03947368421053
20495.214723926380373.78527607361963
20585.214723926380372.78527607361963
206108.423076923076921.57692307692308
20785.214723926380372.78527607361963
20866.75-0.75
20988.42307692307692-0.423076923076923
21055.21472392638037-0.214723926380368
21195.214723926380373.78527607361963
2124.55.96052631578947-1.46052631578947
2138.55.214723926380373.28527607361963
21475.214723926380371.78527607361963
2159.56.752.75
2168.58.423076923076920.0769230769230766
2177.55.214723926380372.28527607361963
2187.55.214723926380372.28527607361963
21956.75-1.75
22075.960526315789471.03947368421053
22188.42307692307692-0.423076923076923
2225.56.75-1.25
2238.58.423076923076920.0769230769230766
2247.58.42307692307692-0.923076923076923
2259.58.423076923076921.07692307692308
22675.214723926380371.78527607361963
22788.42307692307692-0.423076923076923
2288.56.751.75
2293.55.96052631578947-2.46052631578947
2306.55.214723926380371.28527607361963
2316.55.214723926380371.28527607361963
23210.58.423076923076922.07692307692308
2338.55.214723926380373.28527607361963
23488.42307692307692-0.423076923076923
235105.214723926380374.78527607361963
236108.423076923076921.57692307692308
2379.58.423076923076921.07692307692308
23895.214723926380373.78527607361963
239108.423076923076921.57692307692308
2407.55.214723926380372.28527607361963
2414.55.21472392638037-0.714723926380368
2424.55.21472392638037-0.714723926380368
2430.55.21472392638037-4.71472392638037
2446.55.214723926380371.28527607361963
2454.56.75-2.25
2465.55.96052631578947-0.460526315789473
24755.21472392638037-0.214723926380368
24868.42307692307692-2.42307692307692
24945.96052631578947-1.96052631578947
25085.214723926380372.78527607361963
25110.58.423076923076922.07692307692308
2528.55.214723926380373.28527607361963
2536.55.214723926380371.28527607361963
25488.42307692307692-0.423076923076923
2558.58.423076923076920.0769230769230766
2565.55.214723926380370.285276073619632
25776.750.25
25855.21472392638037-0.214723926380368
2593.55.21472392638037-1.71472392638037
26055.96052631578947-0.960526315789473
26195.960526315789473.03947368421053
2628.55.960526315789472.53947368421053
26358.42307692307692-3.42307692307692
2649.58.423076923076921.07692307692308
26535.21472392638037-2.21472392638037
2661.55.96052631578947-4.46052631578947
26765.214723926380370.785276073619632
2680.55.21472392638037-4.71472392638037
2696.55.214723926380371.28527607361963
2707.55.214723926380372.28527607361963
2714.55.21472392638037-0.714723926380368
27285.214723926380372.78527607361963
27398.423076923076920.576923076923077
2747.55.960526315789471.53947368421053
2758.55.960526315789472.53947368421053
27675.214723926380371.78527607361963
2779.55.214723926380374.28527607361963
2786.55.214723926380371.28527607361963
2799.55.214723926380374.28527607361963
28065.214723926380370.785276073619632
28185.960526315789472.03947368421053
2829.58.423076923076921.07692307692308
28388.42307692307692-0.423076923076923
28488.42307692307692-0.423076923076923
28598.423076923076920.576923076923077
28655.21472392638037-0.214723926380368

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 7.5 & 5.21472392638037 & 2.28527607361963 \tabularnewline
2 & 2.5 & 5.21472392638037 & -2.71472392638037 \tabularnewline
3 & 6 & 5.21472392638037 & 0.785276073619632 \tabularnewline
4 & 6.5 & 5.21472392638037 & 1.28527607361963 \tabularnewline
5 & 1 & 5.21472392638037 & -4.21472392638037 \tabularnewline
6 & 1 & 5.21472392638037 & -4.21472392638037 \tabularnewline
7 & 5.5 & 5.96052631578947 & -0.460526315789473 \tabularnewline
8 & 8.5 & 5.21472392638037 & 3.28527607361963 \tabularnewline
9 & 6.5 & 5.21472392638037 & 1.28527607361963 \tabularnewline
10 & 4.5 & 5.21472392638037 & -0.714723926380368 \tabularnewline
11 & 2 & 5.96052631578947 & -3.96052631578947 \tabularnewline
12 & 5 & 5.21472392638037 & -0.214723926380368 \tabularnewline
13 & 0.5 & 5.96052631578947 & -5.46052631578947 \tabularnewline
14 & 5 & 5.21472392638037 & -0.214723926380368 \tabularnewline
15 & 5 & 5.96052631578947 & -0.960526315789473 \tabularnewline
16 & 2.5 & 5.21472392638037 & -2.71472392638037 \tabularnewline
17 & 5 & 5.21472392638037 & -0.214723926380368 \tabularnewline
18 & 5.5 & 5.96052631578947 & -0.460526315789473 \tabularnewline
19 & 3.5 & 5.21472392638037 & -1.71472392638037 \tabularnewline
20 & 3 & 5.21472392638037 & -2.21472392638037 \tabularnewline
21 & 4 & 5.21472392638037 & -1.21472392638037 \tabularnewline
22 & 0.5 & 5.21472392638037 & -4.71472392638037 \tabularnewline
23 & 6.5 & 5.21472392638037 & 1.28527607361963 \tabularnewline
24 & 4.5 & 5.21472392638037 & -0.714723926380368 \tabularnewline
25 & 7.5 & 5.21472392638037 & 2.28527607361963 \tabularnewline
26 & 5.5 & 5.21472392638037 & 0.285276073619632 \tabularnewline
27 & 4 & 5.96052631578947 & -1.96052631578947 \tabularnewline
28 & 7.5 & 8.42307692307692 & -0.923076923076923 \tabularnewline
29 & 7 & 5.21472392638037 & 1.78527607361963 \tabularnewline
30 & 4 & 5.21472392638037 & -1.21472392638037 \tabularnewline
31 & 5.5 & 5.21472392638037 & 0.285276073619632 \tabularnewline
32 & 2.5 & 5.21472392638037 & -2.71472392638037 \tabularnewline
33 & 5.5 & 5.96052631578947 & -0.460526315789473 \tabularnewline
34 & 0.5 & 5.21472392638037 & -4.71472392638037 \tabularnewline
35 & 3.5 & 5.21472392638037 & -1.71472392638037 \tabularnewline
36 & 2.5 & 8.42307692307692 & -5.92307692307692 \tabularnewline
37 & 4.5 & 5.21472392638037 & -0.714723926380368 \tabularnewline
38 & 4.5 & 5.21472392638037 & -0.714723926380368 \tabularnewline
39 & 4.5 & 5.21472392638037 & -0.714723926380368 \tabularnewline
40 & 6 & 5.21472392638037 & 0.785276073619632 \tabularnewline
41 & 2.5 & 5.21472392638037 & -2.71472392638037 \tabularnewline
42 & 5 & 5.96052631578947 & -0.960526315789473 \tabularnewline
43 & 0 & 5.21472392638037 & -5.21472392638037 \tabularnewline
44 & 5 & 5.96052631578947 & -0.960526315789473 \tabularnewline
45 & 6.5 & 5.21472392638037 & 1.28527607361963 \tabularnewline
46 & 5 & 5.21472392638037 & -0.214723926380368 \tabularnewline
47 & 6 & 5.21472392638037 & 0.785276073619632 \tabularnewline
48 & 4.5 & 5.21472392638037 & -0.714723926380368 \tabularnewline
49 & 5.5 & 5.21472392638037 & 0.285276073619632 \tabularnewline
50 & 1 & 5.21472392638037 & -4.21472392638037 \tabularnewline
51 & 7.5 & 5.21472392638037 & 2.28527607361963 \tabularnewline
52 & 6 & 5.21472392638037 & 0.785276073619632 \tabularnewline
53 & 5 & 5.21472392638037 & -0.214723926380368 \tabularnewline
54 & 1 & 5.21472392638037 & -4.21472392638037 \tabularnewline
55 & 5 & 5.21472392638037 & -0.214723926380368 \tabularnewline
56 & 6.5 & 5.21472392638037 & 1.28527607361963 \tabularnewline
57 & 7 & 5.96052631578947 & 1.03947368421053 \tabularnewline
58 & 4.5 & 5.96052631578947 & -1.46052631578947 \tabularnewline
59 & 0 & 5.21472392638037 & -5.21472392638037 \tabularnewline
60 & 8.5 & 5.21472392638037 & 3.28527607361963 \tabularnewline
61 & 3.5 & 5.21472392638037 & -1.71472392638037 \tabularnewline
62 & 7.5 & 5.21472392638037 & 2.28527607361963 \tabularnewline
63 & 3.5 & 5.21472392638037 & -1.71472392638037 \tabularnewline
64 & 6 & 5.21472392638037 & 0.785276073619632 \tabularnewline
65 & 1.5 & 5.21472392638037 & -3.71472392638037 \tabularnewline
66 & 9 & 5.96052631578947 & 3.03947368421053 \tabularnewline
67 & 3.5 & 5.21472392638037 & -1.71472392638037 \tabularnewline
68 & 3.5 & 5.21472392638037 & -1.71472392638037 \tabularnewline
69 & 4 & 5.96052631578947 & -1.96052631578947 \tabularnewline
70 & 6.5 & 5.96052631578947 & 0.539473684210527 \tabularnewline
71 & 7.5 & 5.21472392638037 & 2.28527607361963 \tabularnewline
72 & 6 & 5.21472392638037 & 0.785276073619632 \tabularnewline
73 & 5 & 5.21472392638037 & -0.214723926380368 \tabularnewline
74 & 5.5 & 5.21472392638037 & 0.285276073619632 \tabularnewline
75 & 3.5 & 5.21472392638037 & -1.71472392638037 \tabularnewline
76 & 7.5 & 5.21472392638037 & 2.28527607361963 \tabularnewline
77 & 1 & 5.21472392638037 & -4.21472392638037 \tabularnewline
78 & 6.5 & 5.21472392638037 & 1.28527607361963 \tabularnewline
79 & 6.5 & 5.96052631578947 & 0.539473684210527 \tabularnewline
80 & 6.5 & 5.21472392638037 & 1.28527607361963 \tabularnewline
81 & 7 & 5.96052631578947 & 1.03947368421053 \tabularnewline
82 & 3.5 & 5.21472392638037 & -1.71472392638037 \tabularnewline
83 & 1.5 & 5.21472392638037 & -3.71472392638037 \tabularnewline
84 & 4 & 5.21472392638037 & -1.21472392638037 \tabularnewline
85 & 7.5 & 5.21472392638037 & 2.28527607361963 \tabularnewline
86 & 4.5 & 5.21472392638037 & -0.714723926380368 \tabularnewline
87 & 0 & 5.21472392638037 & -5.21472392638037 \tabularnewline
88 & 3.5 & 5.21472392638037 & -1.71472392638037 \tabularnewline
89 & 5.5 & 5.21472392638037 & 0.285276073619632 \tabularnewline
90 & 5 & 5.21472392638037 & -0.214723926380368 \tabularnewline
91 & 4.5 & 5.21472392638037 & -0.714723926380368 \tabularnewline
92 & 2.5 & 5.21472392638037 & -2.71472392638037 \tabularnewline
93 & 7.5 & 5.21472392638037 & 2.28527607361963 \tabularnewline
94 & 7 & 5.21472392638037 & 1.78527607361963 \tabularnewline
95 & 0 & 5.21472392638037 & -5.21472392638037 \tabularnewline
96 & 4.5 & 5.21472392638037 & -0.714723926380368 \tabularnewline
97 & 3 & 5.21472392638037 & -2.21472392638037 \tabularnewline
98 & 1.5 & 5.21472392638037 & -3.71472392638037 \tabularnewline
99 & 3.5 & 5.21472392638037 & -1.71472392638037 \tabularnewline
100 & 2.5 & 5.21472392638037 & -2.71472392638037 \tabularnewline
101 & 5.5 & 5.21472392638037 & 0.285276073619632 \tabularnewline
102 & 8 & 5.21472392638037 & 2.78527607361963 \tabularnewline
103 & 1 & 5.21472392638037 & -4.21472392638037 \tabularnewline
104 & 5 & 5.21472392638037 & -0.214723926380368 \tabularnewline
105 & 4.5 & 5.21472392638037 & -0.714723926380368 \tabularnewline
106 & 3 & 5.21472392638037 & -2.21472392638037 \tabularnewline
107 & 3 & 5.21472392638037 & -2.21472392638037 \tabularnewline
108 & 8 & 5.21472392638037 & 2.78527607361963 \tabularnewline
109 & 2.5 & 5.21472392638037 & -2.71472392638037 \tabularnewline
110 & 7 & 5.21472392638037 & 1.78527607361963 \tabularnewline
111 & 0 & 5.21472392638037 & -5.21472392638037 \tabularnewline
112 & 1 & 5.21472392638037 & -4.21472392638037 \tabularnewline
113 & 3.5 & 5.21472392638037 & -1.71472392638037 \tabularnewline
114 & 5.5 & 5.21472392638037 & 0.285276073619632 \tabularnewline
115 & 5.5 & 5.21472392638037 & 0.285276073619632 \tabularnewline
116 & 0.5 & 5.21472392638037 & -4.71472392638037 \tabularnewline
117 & 7.5 & 5.96052631578947 & 1.53947368421053 \tabularnewline
118 & 9 & 8.42307692307692 & 0.576923076923077 \tabularnewline
119 & 9.5 & 8.42307692307692 & 1.07692307692308 \tabularnewline
120 & 8.5 & 8.42307692307692 & 0.0769230769230766 \tabularnewline
121 & 7 & 5.21472392638037 & 1.78527607361963 \tabularnewline
122 & 8 & 8.42307692307692 & -0.423076923076923 \tabularnewline
123 & 10 & 8.42307692307692 & 1.57692307692308 \tabularnewline
124 & 7 & 8.42307692307692 & -1.42307692307692 \tabularnewline
125 & 8.5 & 5.21472392638037 & 3.28527607361963 \tabularnewline
126 & 9 & 8.42307692307692 & 0.576923076923077 \tabularnewline
127 & 9.5 & 5.21472392638037 & 4.28527607361963 \tabularnewline
128 & 4 & 8.42307692307692 & -4.42307692307692 \tabularnewline
129 & 6 & 6.75 & -0.75 \tabularnewline
130 & 8 & 6.75 & 1.25 \tabularnewline
131 & 5.5 & 6.75 & -1.25 \tabularnewline
132 & 9.5 & 8.42307692307692 & 1.07692307692308 \tabularnewline
133 & 7.5 & 5.21472392638037 & 2.28527607361963 \tabularnewline
134 & 7 & 5.21472392638037 & 1.78527607361963 \tabularnewline
135 & 7.5 & 6.75 & 0.75 \tabularnewline
136 & 8 & 8.42307692307692 & -0.423076923076923 \tabularnewline
137 & 7 & 8.42307692307692 & -1.42307692307692 \tabularnewline
138 & 7 & 5.21472392638037 & 1.78527607361963 \tabularnewline
139 & 6 & 5.96052631578947 & 0.0394736842105265 \tabularnewline
140 & 10 & 8.42307692307692 & 1.57692307692308 \tabularnewline
141 & 2.5 & 5.21472392638037 & -2.71472392638037 \tabularnewline
142 & 9 & 8.42307692307692 & 0.576923076923077 \tabularnewline
143 & 8 & 8.42307692307692 & -0.423076923076923 \tabularnewline
144 & 6 & 5.21472392638037 & 0.785276073619632 \tabularnewline
145 & 8.5 & 5.21472392638037 & 3.28527607361963 \tabularnewline
146 & 6 & 6.75 & -0.75 \tabularnewline
147 & 9 & 8.42307692307692 & 0.576923076923077 \tabularnewline
148 & 8 & 6.75 & 1.25 \tabularnewline
149 & 8 & 6.75 & 1.25 \tabularnewline
150 & 9 & 8.42307692307692 & 0.576923076923077 \tabularnewline
151 & 5.5 & 5.21472392638037 & 0.285276073619632 \tabularnewline
152 & 5 & 5.21472392638037 & -0.214723926380368 \tabularnewline
153 & 7 & 6.75 & 0.25 \tabularnewline
154 & 5.5 & 6.75 & -1.25 \tabularnewline
155 & 9 & 8.42307692307692 & 0.576923076923077 \tabularnewline
156 & 2 & 6.75 & -4.75 \tabularnewline
157 & 8.5 & 8.42307692307692 & 0.0769230769230766 \tabularnewline
158 & 9 & 8.42307692307692 & 0.576923076923077 \tabularnewline
159 & 8.5 & 6.75 & 1.75 \tabularnewline
160 & 9 & 5.96052631578947 & 3.03947368421053 \tabularnewline
161 & 7.5 & 5.96052631578947 & 1.53947368421053 \tabularnewline
162 & 10 & 8.42307692307692 & 1.57692307692308 \tabularnewline
163 & 9 & 8.42307692307692 & 0.576923076923077 \tabularnewline
164 & 7.5 & 8.42307692307692 & -0.923076923076923 \tabularnewline
165 & 6 & 5.21472392638037 & 0.785276073619632 \tabularnewline
166 & 10.5 & 8.42307692307692 & 2.07692307692308 \tabularnewline
167 & 8.5 & 5.96052631578947 & 2.53947368421053 \tabularnewline
168 & 8 & 8.42307692307692 & -0.423076923076923 \tabularnewline
169 & 10 & 5.21472392638037 & 4.78527607361963 \tabularnewline
170 & 10.5 & 8.42307692307692 & 2.07692307692308 \tabularnewline
171 & 6.5 & 5.21472392638037 & 1.28527607361963 \tabularnewline
172 & 9.5 & 8.42307692307692 & 1.07692307692308 \tabularnewline
173 & 8.5 & 5.21472392638037 & 3.28527607361963 \tabularnewline
174 & 7.5 & 8.42307692307692 & -0.923076923076923 \tabularnewline
175 & 5 & 5.96052631578947 & -0.960526315789473 \tabularnewline
176 & 8 & 8.42307692307692 & -0.423076923076923 \tabularnewline
177 & 10 & 8.42307692307692 & 1.57692307692308 \tabularnewline
178 & 7 & 5.21472392638037 & 1.78527607361963 \tabularnewline
179 & 7.5 & 8.42307692307692 & -0.923076923076923 \tabularnewline
180 & 7.5 & 8.42307692307692 & -0.923076923076923 \tabularnewline
181 & 9.5 & 8.42307692307692 & 1.07692307692308 \tabularnewline
182 & 6 & 8.42307692307692 & -2.42307692307692 \tabularnewline
183 & 10 & 6.75 & 3.25 \tabularnewline
184 & 7 & 5.21472392638037 & 1.78527607361963 \tabularnewline
185 & 3 & 5.21472392638037 & -2.21472392638037 \tabularnewline
186 & 6 & 5.96052631578947 & 0.0394736842105265 \tabularnewline
187 & 7 & 6.75 & 0.25 \tabularnewline
188 & 10 & 8.42307692307692 & 1.57692307692308 \tabularnewline
189 & 7 & 8.42307692307692 & -1.42307692307692 \tabularnewline
190 & 3.5 & 5.21472392638037 & -1.71472392638037 \tabularnewline
191 & 8 & 8.42307692307692 & -0.423076923076923 \tabularnewline
192 & 10 & 5.21472392638037 & 4.78527607361963 \tabularnewline
193 & 5.5 & 5.21472392638037 & 0.285276073619632 \tabularnewline
194 & 6 & 5.21472392638037 & 0.785276073619632 \tabularnewline
195 & 6.5 & 5.96052631578947 & 0.539473684210527 \tabularnewline
196 & 6.5 & 5.21472392638037 & 1.28527607361963 \tabularnewline
197 & 8.5 & 8.42307692307692 & 0.0769230769230766 \tabularnewline
198 & 4 & 5.21472392638037 & -1.21472392638037 \tabularnewline
199 & 9.5 & 8.42307692307692 & 1.07692307692308 \tabularnewline
200 & 8 & 5.21472392638037 & 2.78527607361963 \tabularnewline
201 & 8.5 & 5.96052631578947 & 2.53947368421053 \tabularnewline
202 & 5.5 & 8.42307692307692 & -2.92307692307692 \tabularnewline
203 & 7 & 5.96052631578947 & 1.03947368421053 \tabularnewline
204 & 9 & 5.21472392638037 & 3.78527607361963 \tabularnewline
205 & 8 & 5.21472392638037 & 2.78527607361963 \tabularnewline
206 & 10 & 8.42307692307692 & 1.57692307692308 \tabularnewline
207 & 8 & 5.21472392638037 & 2.78527607361963 \tabularnewline
208 & 6 & 6.75 & -0.75 \tabularnewline
209 & 8 & 8.42307692307692 & -0.423076923076923 \tabularnewline
210 & 5 & 5.21472392638037 & -0.214723926380368 \tabularnewline
211 & 9 & 5.21472392638037 & 3.78527607361963 \tabularnewline
212 & 4.5 & 5.96052631578947 & -1.46052631578947 \tabularnewline
213 & 8.5 & 5.21472392638037 & 3.28527607361963 \tabularnewline
214 & 7 & 5.21472392638037 & 1.78527607361963 \tabularnewline
215 & 9.5 & 6.75 & 2.75 \tabularnewline
216 & 8.5 & 8.42307692307692 & 0.0769230769230766 \tabularnewline
217 & 7.5 & 5.21472392638037 & 2.28527607361963 \tabularnewline
218 & 7.5 & 5.21472392638037 & 2.28527607361963 \tabularnewline
219 & 5 & 6.75 & -1.75 \tabularnewline
220 & 7 & 5.96052631578947 & 1.03947368421053 \tabularnewline
221 & 8 & 8.42307692307692 & -0.423076923076923 \tabularnewline
222 & 5.5 & 6.75 & -1.25 \tabularnewline
223 & 8.5 & 8.42307692307692 & 0.0769230769230766 \tabularnewline
224 & 7.5 & 8.42307692307692 & -0.923076923076923 \tabularnewline
225 & 9.5 & 8.42307692307692 & 1.07692307692308 \tabularnewline
226 & 7 & 5.21472392638037 & 1.78527607361963 \tabularnewline
227 & 8 & 8.42307692307692 & -0.423076923076923 \tabularnewline
228 & 8.5 & 6.75 & 1.75 \tabularnewline
229 & 3.5 & 5.96052631578947 & -2.46052631578947 \tabularnewline
230 & 6.5 & 5.21472392638037 & 1.28527607361963 \tabularnewline
231 & 6.5 & 5.21472392638037 & 1.28527607361963 \tabularnewline
232 & 10.5 & 8.42307692307692 & 2.07692307692308 \tabularnewline
233 & 8.5 & 5.21472392638037 & 3.28527607361963 \tabularnewline
234 & 8 & 8.42307692307692 & -0.423076923076923 \tabularnewline
235 & 10 & 5.21472392638037 & 4.78527607361963 \tabularnewline
236 & 10 & 8.42307692307692 & 1.57692307692308 \tabularnewline
237 & 9.5 & 8.42307692307692 & 1.07692307692308 \tabularnewline
238 & 9 & 5.21472392638037 & 3.78527607361963 \tabularnewline
239 & 10 & 8.42307692307692 & 1.57692307692308 \tabularnewline
240 & 7.5 & 5.21472392638037 & 2.28527607361963 \tabularnewline
241 & 4.5 & 5.21472392638037 & -0.714723926380368 \tabularnewline
242 & 4.5 & 5.21472392638037 & -0.714723926380368 \tabularnewline
243 & 0.5 & 5.21472392638037 & -4.71472392638037 \tabularnewline
244 & 6.5 & 5.21472392638037 & 1.28527607361963 \tabularnewline
245 & 4.5 & 6.75 & -2.25 \tabularnewline
246 & 5.5 & 5.96052631578947 & -0.460526315789473 \tabularnewline
247 & 5 & 5.21472392638037 & -0.214723926380368 \tabularnewline
248 & 6 & 8.42307692307692 & -2.42307692307692 \tabularnewline
249 & 4 & 5.96052631578947 & -1.96052631578947 \tabularnewline
250 & 8 & 5.21472392638037 & 2.78527607361963 \tabularnewline
251 & 10.5 & 8.42307692307692 & 2.07692307692308 \tabularnewline
252 & 8.5 & 5.21472392638037 & 3.28527607361963 \tabularnewline
253 & 6.5 & 5.21472392638037 & 1.28527607361963 \tabularnewline
254 & 8 & 8.42307692307692 & -0.423076923076923 \tabularnewline
255 & 8.5 & 8.42307692307692 & 0.0769230769230766 \tabularnewline
256 & 5.5 & 5.21472392638037 & 0.285276073619632 \tabularnewline
257 & 7 & 6.75 & 0.25 \tabularnewline
258 & 5 & 5.21472392638037 & -0.214723926380368 \tabularnewline
259 & 3.5 & 5.21472392638037 & -1.71472392638037 \tabularnewline
260 & 5 & 5.96052631578947 & -0.960526315789473 \tabularnewline
261 & 9 & 5.96052631578947 & 3.03947368421053 \tabularnewline
262 & 8.5 & 5.96052631578947 & 2.53947368421053 \tabularnewline
263 & 5 & 8.42307692307692 & -3.42307692307692 \tabularnewline
264 & 9.5 & 8.42307692307692 & 1.07692307692308 \tabularnewline
265 & 3 & 5.21472392638037 & -2.21472392638037 \tabularnewline
266 & 1.5 & 5.96052631578947 & -4.46052631578947 \tabularnewline
267 & 6 & 5.21472392638037 & 0.785276073619632 \tabularnewline
268 & 0.5 & 5.21472392638037 & -4.71472392638037 \tabularnewline
269 & 6.5 & 5.21472392638037 & 1.28527607361963 \tabularnewline
270 & 7.5 & 5.21472392638037 & 2.28527607361963 \tabularnewline
271 & 4.5 & 5.21472392638037 & -0.714723926380368 \tabularnewline
272 & 8 & 5.21472392638037 & 2.78527607361963 \tabularnewline
273 & 9 & 8.42307692307692 & 0.576923076923077 \tabularnewline
274 & 7.5 & 5.96052631578947 & 1.53947368421053 \tabularnewline
275 & 8.5 & 5.96052631578947 & 2.53947368421053 \tabularnewline
276 & 7 & 5.21472392638037 & 1.78527607361963 \tabularnewline
277 & 9.5 & 5.21472392638037 & 4.28527607361963 \tabularnewline
278 & 6.5 & 5.21472392638037 & 1.28527607361963 \tabularnewline
279 & 9.5 & 5.21472392638037 & 4.28527607361963 \tabularnewline
280 & 6 & 5.21472392638037 & 0.785276073619632 \tabularnewline
281 & 8 & 5.96052631578947 & 2.03947368421053 \tabularnewline
282 & 9.5 & 8.42307692307692 & 1.07692307692308 \tabularnewline
283 & 8 & 8.42307692307692 & -0.423076923076923 \tabularnewline
284 & 8 & 8.42307692307692 & -0.423076923076923 \tabularnewline
285 & 9 & 8.42307692307692 & 0.576923076923077 \tabularnewline
286 & 5 & 5.21472392638037 & -0.214723926380368 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=252921&T=2

[TABLE]
[ROW][C]Actuals, Predictions, and Residuals[/C][/ROW]
[ROW][C]#[/C][C]Actuals[/C][C]Forecasts[/C][C]Residuals[/C][/ROW]
[ROW][C]1[/C][C]7.5[/C][C]5.21472392638037[/C][C]2.28527607361963[/C][/ROW]
[ROW][C]2[/C][C]2.5[/C][C]5.21472392638037[/C][C]-2.71472392638037[/C][/ROW]
[ROW][C]3[/C][C]6[/C][C]5.21472392638037[/C][C]0.785276073619632[/C][/ROW]
[ROW][C]4[/C][C]6.5[/C][C]5.21472392638037[/C][C]1.28527607361963[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]5.21472392638037[/C][C]-4.21472392638037[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]5.21472392638037[/C][C]-4.21472392638037[/C][/ROW]
[ROW][C]7[/C][C]5.5[/C][C]5.96052631578947[/C][C]-0.460526315789473[/C][/ROW]
[ROW][C]8[/C][C]8.5[/C][C]5.21472392638037[/C][C]3.28527607361963[/C][/ROW]
[ROW][C]9[/C][C]6.5[/C][C]5.21472392638037[/C][C]1.28527607361963[/C][/ROW]
[ROW][C]10[/C][C]4.5[/C][C]5.21472392638037[/C][C]-0.714723926380368[/C][/ROW]
[ROW][C]11[/C][C]2[/C][C]5.96052631578947[/C][C]-3.96052631578947[/C][/ROW]
[ROW][C]12[/C][C]5[/C][C]5.21472392638037[/C][C]-0.214723926380368[/C][/ROW]
[ROW][C]13[/C][C]0.5[/C][C]5.96052631578947[/C][C]-5.46052631578947[/C][/ROW]
[ROW][C]14[/C][C]5[/C][C]5.21472392638037[/C][C]-0.214723926380368[/C][/ROW]
[ROW][C]15[/C][C]5[/C][C]5.96052631578947[/C][C]-0.960526315789473[/C][/ROW]
[ROW][C]16[/C][C]2.5[/C][C]5.21472392638037[/C][C]-2.71472392638037[/C][/ROW]
[ROW][C]17[/C][C]5[/C][C]5.21472392638037[/C][C]-0.214723926380368[/C][/ROW]
[ROW][C]18[/C][C]5.5[/C][C]5.96052631578947[/C][C]-0.460526315789473[/C][/ROW]
[ROW][C]19[/C][C]3.5[/C][C]5.21472392638037[/C][C]-1.71472392638037[/C][/ROW]
[ROW][C]20[/C][C]3[/C][C]5.21472392638037[/C][C]-2.21472392638037[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]5.21472392638037[/C][C]-1.21472392638037[/C][/ROW]
[ROW][C]22[/C][C]0.5[/C][C]5.21472392638037[/C][C]-4.71472392638037[/C][/ROW]
[ROW][C]23[/C][C]6.5[/C][C]5.21472392638037[/C][C]1.28527607361963[/C][/ROW]
[ROW][C]24[/C][C]4.5[/C][C]5.21472392638037[/C][C]-0.714723926380368[/C][/ROW]
[ROW][C]25[/C][C]7.5[/C][C]5.21472392638037[/C][C]2.28527607361963[/C][/ROW]
[ROW][C]26[/C][C]5.5[/C][C]5.21472392638037[/C][C]0.285276073619632[/C][/ROW]
[ROW][C]27[/C][C]4[/C][C]5.96052631578947[/C][C]-1.96052631578947[/C][/ROW]
[ROW][C]28[/C][C]7.5[/C][C]8.42307692307692[/C][C]-0.923076923076923[/C][/ROW]
[ROW][C]29[/C][C]7[/C][C]5.21472392638037[/C][C]1.78527607361963[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]5.21472392638037[/C][C]-1.21472392638037[/C][/ROW]
[ROW][C]31[/C][C]5.5[/C][C]5.21472392638037[/C][C]0.285276073619632[/C][/ROW]
[ROW][C]32[/C][C]2.5[/C][C]5.21472392638037[/C][C]-2.71472392638037[/C][/ROW]
[ROW][C]33[/C][C]5.5[/C][C]5.96052631578947[/C][C]-0.460526315789473[/C][/ROW]
[ROW][C]34[/C][C]0.5[/C][C]5.21472392638037[/C][C]-4.71472392638037[/C][/ROW]
[ROW][C]35[/C][C]3.5[/C][C]5.21472392638037[/C][C]-1.71472392638037[/C][/ROW]
[ROW][C]36[/C][C]2.5[/C][C]8.42307692307692[/C][C]-5.92307692307692[/C][/ROW]
[ROW][C]37[/C][C]4.5[/C][C]5.21472392638037[/C][C]-0.714723926380368[/C][/ROW]
[ROW][C]38[/C][C]4.5[/C][C]5.21472392638037[/C][C]-0.714723926380368[/C][/ROW]
[ROW][C]39[/C][C]4.5[/C][C]5.21472392638037[/C][C]-0.714723926380368[/C][/ROW]
[ROW][C]40[/C][C]6[/C][C]5.21472392638037[/C][C]0.785276073619632[/C][/ROW]
[ROW][C]41[/C][C]2.5[/C][C]5.21472392638037[/C][C]-2.71472392638037[/C][/ROW]
[ROW][C]42[/C][C]5[/C][C]5.96052631578947[/C][C]-0.960526315789473[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]5.21472392638037[/C][C]-5.21472392638037[/C][/ROW]
[ROW][C]44[/C][C]5[/C][C]5.96052631578947[/C][C]-0.960526315789473[/C][/ROW]
[ROW][C]45[/C][C]6.5[/C][C]5.21472392638037[/C][C]1.28527607361963[/C][/ROW]
[ROW][C]46[/C][C]5[/C][C]5.21472392638037[/C][C]-0.214723926380368[/C][/ROW]
[ROW][C]47[/C][C]6[/C][C]5.21472392638037[/C][C]0.785276073619632[/C][/ROW]
[ROW][C]48[/C][C]4.5[/C][C]5.21472392638037[/C][C]-0.714723926380368[/C][/ROW]
[ROW][C]49[/C][C]5.5[/C][C]5.21472392638037[/C][C]0.285276073619632[/C][/ROW]
[ROW][C]50[/C][C]1[/C][C]5.21472392638037[/C][C]-4.21472392638037[/C][/ROW]
[ROW][C]51[/C][C]7.5[/C][C]5.21472392638037[/C][C]2.28527607361963[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]5.21472392638037[/C][C]0.785276073619632[/C][/ROW]
[ROW][C]53[/C][C]5[/C][C]5.21472392638037[/C][C]-0.214723926380368[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]5.21472392638037[/C][C]-4.21472392638037[/C][/ROW]
[ROW][C]55[/C][C]5[/C][C]5.21472392638037[/C][C]-0.214723926380368[/C][/ROW]
[ROW][C]56[/C][C]6.5[/C][C]5.21472392638037[/C][C]1.28527607361963[/C][/ROW]
[ROW][C]57[/C][C]7[/C][C]5.96052631578947[/C][C]1.03947368421053[/C][/ROW]
[ROW][C]58[/C][C]4.5[/C][C]5.96052631578947[/C][C]-1.46052631578947[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]5.21472392638037[/C][C]-5.21472392638037[/C][/ROW]
[ROW][C]60[/C][C]8.5[/C][C]5.21472392638037[/C][C]3.28527607361963[/C][/ROW]
[ROW][C]61[/C][C]3.5[/C][C]5.21472392638037[/C][C]-1.71472392638037[/C][/ROW]
[ROW][C]62[/C][C]7.5[/C][C]5.21472392638037[/C][C]2.28527607361963[/C][/ROW]
[ROW][C]63[/C][C]3.5[/C][C]5.21472392638037[/C][C]-1.71472392638037[/C][/ROW]
[ROW][C]64[/C][C]6[/C][C]5.21472392638037[/C][C]0.785276073619632[/C][/ROW]
[ROW][C]65[/C][C]1.5[/C][C]5.21472392638037[/C][C]-3.71472392638037[/C][/ROW]
[ROW][C]66[/C][C]9[/C][C]5.96052631578947[/C][C]3.03947368421053[/C][/ROW]
[ROW][C]67[/C][C]3.5[/C][C]5.21472392638037[/C][C]-1.71472392638037[/C][/ROW]
[ROW][C]68[/C][C]3.5[/C][C]5.21472392638037[/C][C]-1.71472392638037[/C][/ROW]
[ROW][C]69[/C][C]4[/C][C]5.96052631578947[/C][C]-1.96052631578947[/C][/ROW]
[ROW][C]70[/C][C]6.5[/C][C]5.96052631578947[/C][C]0.539473684210527[/C][/ROW]
[ROW][C]71[/C][C]7.5[/C][C]5.21472392638037[/C][C]2.28527607361963[/C][/ROW]
[ROW][C]72[/C][C]6[/C][C]5.21472392638037[/C][C]0.785276073619632[/C][/ROW]
[ROW][C]73[/C][C]5[/C][C]5.21472392638037[/C][C]-0.214723926380368[/C][/ROW]
[ROW][C]74[/C][C]5.5[/C][C]5.21472392638037[/C][C]0.285276073619632[/C][/ROW]
[ROW][C]75[/C][C]3.5[/C][C]5.21472392638037[/C][C]-1.71472392638037[/C][/ROW]
[ROW][C]76[/C][C]7.5[/C][C]5.21472392638037[/C][C]2.28527607361963[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]5.21472392638037[/C][C]-4.21472392638037[/C][/ROW]
[ROW][C]78[/C][C]6.5[/C][C]5.21472392638037[/C][C]1.28527607361963[/C][/ROW]
[ROW][C]79[/C][C]6.5[/C][C]5.96052631578947[/C][C]0.539473684210527[/C][/ROW]
[ROW][C]80[/C][C]6.5[/C][C]5.21472392638037[/C][C]1.28527607361963[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]5.96052631578947[/C][C]1.03947368421053[/C][/ROW]
[ROW][C]82[/C][C]3.5[/C][C]5.21472392638037[/C][C]-1.71472392638037[/C][/ROW]
[ROW][C]83[/C][C]1.5[/C][C]5.21472392638037[/C][C]-3.71472392638037[/C][/ROW]
[ROW][C]84[/C][C]4[/C][C]5.21472392638037[/C][C]-1.21472392638037[/C][/ROW]
[ROW][C]85[/C][C]7.5[/C][C]5.21472392638037[/C][C]2.28527607361963[/C][/ROW]
[ROW][C]86[/C][C]4.5[/C][C]5.21472392638037[/C][C]-0.714723926380368[/C][/ROW]
[ROW][C]87[/C][C]0[/C][C]5.21472392638037[/C][C]-5.21472392638037[/C][/ROW]
[ROW][C]88[/C][C]3.5[/C][C]5.21472392638037[/C][C]-1.71472392638037[/C][/ROW]
[ROW][C]89[/C][C]5.5[/C][C]5.21472392638037[/C][C]0.285276073619632[/C][/ROW]
[ROW][C]90[/C][C]5[/C][C]5.21472392638037[/C][C]-0.214723926380368[/C][/ROW]
[ROW][C]91[/C][C]4.5[/C][C]5.21472392638037[/C][C]-0.714723926380368[/C][/ROW]
[ROW][C]92[/C][C]2.5[/C][C]5.21472392638037[/C][C]-2.71472392638037[/C][/ROW]
[ROW][C]93[/C][C]7.5[/C][C]5.21472392638037[/C][C]2.28527607361963[/C][/ROW]
[ROW][C]94[/C][C]7[/C][C]5.21472392638037[/C][C]1.78527607361963[/C][/ROW]
[ROW][C]95[/C][C]0[/C][C]5.21472392638037[/C][C]-5.21472392638037[/C][/ROW]
[ROW][C]96[/C][C]4.5[/C][C]5.21472392638037[/C][C]-0.714723926380368[/C][/ROW]
[ROW][C]97[/C][C]3[/C][C]5.21472392638037[/C][C]-2.21472392638037[/C][/ROW]
[ROW][C]98[/C][C]1.5[/C][C]5.21472392638037[/C][C]-3.71472392638037[/C][/ROW]
[ROW][C]99[/C][C]3.5[/C][C]5.21472392638037[/C][C]-1.71472392638037[/C][/ROW]
[ROW][C]100[/C][C]2.5[/C][C]5.21472392638037[/C][C]-2.71472392638037[/C][/ROW]
[ROW][C]101[/C][C]5.5[/C][C]5.21472392638037[/C][C]0.285276073619632[/C][/ROW]
[ROW][C]102[/C][C]8[/C][C]5.21472392638037[/C][C]2.78527607361963[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]5.21472392638037[/C][C]-4.21472392638037[/C][/ROW]
[ROW][C]104[/C][C]5[/C][C]5.21472392638037[/C][C]-0.214723926380368[/C][/ROW]
[ROW][C]105[/C][C]4.5[/C][C]5.21472392638037[/C][C]-0.714723926380368[/C][/ROW]
[ROW][C]106[/C][C]3[/C][C]5.21472392638037[/C][C]-2.21472392638037[/C][/ROW]
[ROW][C]107[/C][C]3[/C][C]5.21472392638037[/C][C]-2.21472392638037[/C][/ROW]
[ROW][C]108[/C][C]8[/C][C]5.21472392638037[/C][C]2.78527607361963[/C][/ROW]
[ROW][C]109[/C][C]2.5[/C][C]5.21472392638037[/C][C]-2.71472392638037[/C][/ROW]
[ROW][C]110[/C][C]7[/C][C]5.21472392638037[/C][C]1.78527607361963[/C][/ROW]
[ROW][C]111[/C][C]0[/C][C]5.21472392638037[/C][C]-5.21472392638037[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]5.21472392638037[/C][C]-4.21472392638037[/C][/ROW]
[ROW][C]113[/C][C]3.5[/C][C]5.21472392638037[/C][C]-1.71472392638037[/C][/ROW]
[ROW][C]114[/C][C]5.5[/C][C]5.21472392638037[/C][C]0.285276073619632[/C][/ROW]
[ROW][C]115[/C][C]5.5[/C][C]5.21472392638037[/C][C]0.285276073619632[/C][/ROW]
[ROW][C]116[/C][C]0.5[/C][C]5.21472392638037[/C][C]-4.71472392638037[/C][/ROW]
[ROW][C]117[/C][C]7.5[/C][C]5.96052631578947[/C][C]1.53947368421053[/C][/ROW]
[ROW][C]118[/C][C]9[/C][C]8.42307692307692[/C][C]0.576923076923077[/C][/ROW]
[ROW][C]119[/C][C]9.5[/C][C]8.42307692307692[/C][C]1.07692307692308[/C][/ROW]
[ROW][C]120[/C][C]8.5[/C][C]8.42307692307692[/C][C]0.0769230769230766[/C][/ROW]
[ROW][C]121[/C][C]7[/C][C]5.21472392638037[/C][C]1.78527607361963[/C][/ROW]
[ROW][C]122[/C][C]8[/C][C]8.42307692307692[/C][C]-0.423076923076923[/C][/ROW]
[ROW][C]123[/C][C]10[/C][C]8.42307692307692[/C][C]1.57692307692308[/C][/ROW]
[ROW][C]124[/C][C]7[/C][C]8.42307692307692[/C][C]-1.42307692307692[/C][/ROW]
[ROW][C]125[/C][C]8.5[/C][C]5.21472392638037[/C][C]3.28527607361963[/C][/ROW]
[ROW][C]126[/C][C]9[/C][C]8.42307692307692[/C][C]0.576923076923077[/C][/ROW]
[ROW][C]127[/C][C]9.5[/C][C]5.21472392638037[/C][C]4.28527607361963[/C][/ROW]
[ROW][C]128[/C][C]4[/C][C]8.42307692307692[/C][C]-4.42307692307692[/C][/ROW]
[ROW][C]129[/C][C]6[/C][C]6.75[/C][C]-0.75[/C][/ROW]
[ROW][C]130[/C][C]8[/C][C]6.75[/C][C]1.25[/C][/ROW]
[ROW][C]131[/C][C]5.5[/C][C]6.75[/C][C]-1.25[/C][/ROW]
[ROW][C]132[/C][C]9.5[/C][C]8.42307692307692[/C][C]1.07692307692308[/C][/ROW]
[ROW][C]133[/C][C]7.5[/C][C]5.21472392638037[/C][C]2.28527607361963[/C][/ROW]
[ROW][C]134[/C][C]7[/C][C]5.21472392638037[/C][C]1.78527607361963[/C][/ROW]
[ROW][C]135[/C][C]7.5[/C][C]6.75[/C][C]0.75[/C][/ROW]
[ROW][C]136[/C][C]8[/C][C]8.42307692307692[/C][C]-0.423076923076923[/C][/ROW]
[ROW][C]137[/C][C]7[/C][C]8.42307692307692[/C][C]-1.42307692307692[/C][/ROW]
[ROW][C]138[/C][C]7[/C][C]5.21472392638037[/C][C]1.78527607361963[/C][/ROW]
[ROW][C]139[/C][C]6[/C][C]5.96052631578947[/C][C]0.0394736842105265[/C][/ROW]
[ROW][C]140[/C][C]10[/C][C]8.42307692307692[/C][C]1.57692307692308[/C][/ROW]
[ROW][C]141[/C][C]2.5[/C][C]5.21472392638037[/C][C]-2.71472392638037[/C][/ROW]
[ROW][C]142[/C][C]9[/C][C]8.42307692307692[/C][C]0.576923076923077[/C][/ROW]
[ROW][C]143[/C][C]8[/C][C]8.42307692307692[/C][C]-0.423076923076923[/C][/ROW]
[ROW][C]144[/C][C]6[/C][C]5.21472392638037[/C][C]0.785276073619632[/C][/ROW]
[ROW][C]145[/C][C]8.5[/C][C]5.21472392638037[/C][C]3.28527607361963[/C][/ROW]
[ROW][C]146[/C][C]6[/C][C]6.75[/C][C]-0.75[/C][/ROW]
[ROW][C]147[/C][C]9[/C][C]8.42307692307692[/C][C]0.576923076923077[/C][/ROW]
[ROW][C]148[/C][C]8[/C][C]6.75[/C][C]1.25[/C][/ROW]
[ROW][C]149[/C][C]8[/C][C]6.75[/C][C]1.25[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]8.42307692307692[/C][C]0.576923076923077[/C][/ROW]
[ROW][C]151[/C][C]5.5[/C][C]5.21472392638037[/C][C]0.285276073619632[/C][/ROW]
[ROW][C]152[/C][C]5[/C][C]5.21472392638037[/C][C]-0.214723926380368[/C][/ROW]
[ROW][C]153[/C][C]7[/C][C]6.75[/C][C]0.25[/C][/ROW]
[ROW][C]154[/C][C]5.5[/C][C]6.75[/C][C]-1.25[/C][/ROW]
[ROW][C]155[/C][C]9[/C][C]8.42307692307692[/C][C]0.576923076923077[/C][/ROW]
[ROW][C]156[/C][C]2[/C][C]6.75[/C][C]-4.75[/C][/ROW]
[ROW][C]157[/C][C]8.5[/C][C]8.42307692307692[/C][C]0.0769230769230766[/C][/ROW]
[ROW][C]158[/C][C]9[/C][C]8.42307692307692[/C][C]0.576923076923077[/C][/ROW]
[ROW][C]159[/C][C]8.5[/C][C]6.75[/C][C]1.75[/C][/ROW]
[ROW][C]160[/C][C]9[/C][C]5.96052631578947[/C][C]3.03947368421053[/C][/ROW]
[ROW][C]161[/C][C]7.5[/C][C]5.96052631578947[/C][C]1.53947368421053[/C][/ROW]
[ROW][C]162[/C][C]10[/C][C]8.42307692307692[/C][C]1.57692307692308[/C][/ROW]
[ROW][C]163[/C][C]9[/C][C]8.42307692307692[/C][C]0.576923076923077[/C][/ROW]
[ROW][C]164[/C][C]7.5[/C][C]8.42307692307692[/C][C]-0.923076923076923[/C][/ROW]
[ROW][C]165[/C][C]6[/C][C]5.21472392638037[/C][C]0.785276073619632[/C][/ROW]
[ROW][C]166[/C][C]10.5[/C][C]8.42307692307692[/C][C]2.07692307692308[/C][/ROW]
[ROW][C]167[/C][C]8.5[/C][C]5.96052631578947[/C][C]2.53947368421053[/C][/ROW]
[ROW][C]168[/C][C]8[/C][C]8.42307692307692[/C][C]-0.423076923076923[/C][/ROW]
[ROW][C]169[/C][C]10[/C][C]5.21472392638037[/C][C]4.78527607361963[/C][/ROW]
[ROW][C]170[/C][C]10.5[/C][C]8.42307692307692[/C][C]2.07692307692308[/C][/ROW]
[ROW][C]171[/C][C]6.5[/C][C]5.21472392638037[/C][C]1.28527607361963[/C][/ROW]
[ROW][C]172[/C][C]9.5[/C][C]8.42307692307692[/C][C]1.07692307692308[/C][/ROW]
[ROW][C]173[/C][C]8.5[/C][C]5.21472392638037[/C][C]3.28527607361963[/C][/ROW]
[ROW][C]174[/C][C]7.5[/C][C]8.42307692307692[/C][C]-0.923076923076923[/C][/ROW]
[ROW][C]175[/C][C]5[/C][C]5.96052631578947[/C][C]-0.960526315789473[/C][/ROW]
[ROW][C]176[/C][C]8[/C][C]8.42307692307692[/C][C]-0.423076923076923[/C][/ROW]
[ROW][C]177[/C][C]10[/C][C]8.42307692307692[/C][C]1.57692307692308[/C][/ROW]
[ROW][C]178[/C][C]7[/C][C]5.21472392638037[/C][C]1.78527607361963[/C][/ROW]
[ROW][C]179[/C][C]7.5[/C][C]8.42307692307692[/C][C]-0.923076923076923[/C][/ROW]
[ROW][C]180[/C][C]7.5[/C][C]8.42307692307692[/C][C]-0.923076923076923[/C][/ROW]
[ROW][C]181[/C][C]9.5[/C][C]8.42307692307692[/C][C]1.07692307692308[/C][/ROW]
[ROW][C]182[/C][C]6[/C][C]8.42307692307692[/C][C]-2.42307692307692[/C][/ROW]
[ROW][C]183[/C][C]10[/C][C]6.75[/C][C]3.25[/C][/ROW]
[ROW][C]184[/C][C]7[/C][C]5.21472392638037[/C][C]1.78527607361963[/C][/ROW]
[ROW][C]185[/C][C]3[/C][C]5.21472392638037[/C][C]-2.21472392638037[/C][/ROW]
[ROW][C]186[/C][C]6[/C][C]5.96052631578947[/C][C]0.0394736842105265[/C][/ROW]
[ROW][C]187[/C][C]7[/C][C]6.75[/C][C]0.25[/C][/ROW]
[ROW][C]188[/C][C]10[/C][C]8.42307692307692[/C][C]1.57692307692308[/C][/ROW]
[ROW][C]189[/C][C]7[/C][C]8.42307692307692[/C][C]-1.42307692307692[/C][/ROW]
[ROW][C]190[/C][C]3.5[/C][C]5.21472392638037[/C][C]-1.71472392638037[/C][/ROW]
[ROW][C]191[/C][C]8[/C][C]8.42307692307692[/C][C]-0.423076923076923[/C][/ROW]
[ROW][C]192[/C][C]10[/C][C]5.21472392638037[/C][C]4.78527607361963[/C][/ROW]
[ROW][C]193[/C][C]5.5[/C][C]5.21472392638037[/C][C]0.285276073619632[/C][/ROW]
[ROW][C]194[/C][C]6[/C][C]5.21472392638037[/C][C]0.785276073619632[/C][/ROW]
[ROW][C]195[/C][C]6.5[/C][C]5.96052631578947[/C][C]0.539473684210527[/C][/ROW]
[ROW][C]196[/C][C]6.5[/C][C]5.21472392638037[/C][C]1.28527607361963[/C][/ROW]
[ROW][C]197[/C][C]8.5[/C][C]8.42307692307692[/C][C]0.0769230769230766[/C][/ROW]
[ROW][C]198[/C][C]4[/C][C]5.21472392638037[/C][C]-1.21472392638037[/C][/ROW]
[ROW][C]199[/C][C]9.5[/C][C]8.42307692307692[/C][C]1.07692307692308[/C][/ROW]
[ROW][C]200[/C][C]8[/C][C]5.21472392638037[/C][C]2.78527607361963[/C][/ROW]
[ROW][C]201[/C][C]8.5[/C][C]5.96052631578947[/C][C]2.53947368421053[/C][/ROW]
[ROW][C]202[/C][C]5.5[/C][C]8.42307692307692[/C][C]-2.92307692307692[/C][/ROW]
[ROW][C]203[/C][C]7[/C][C]5.96052631578947[/C][C]1.03947368421053[/C][/ROW]
[ROW][C]204[/C][C]9[/C][C]5.21472392638037[/C][C]3.78527607361963[/C][/ROW]
[ROW][C]205[/C][C]8[/C][C]5.21472392638037[/C][C]2.78527607361963[/C][/ROW]
[ROW][C]206[/C][C]10[/C][C]8.42307692307692[/C][C]1.57692307692308[/C][/ROW]
[ROW][C]207[/C][C]8[/C][C]5.21472392638037[/C][C]2.78527607361963[/C][/ROW]
[ROW][C]208[/C][C]6[/C][C]6.75[/C][C]-0.75[/C][/ROW]
[ROW][C]209[/C][C]8[/C][C]8.42307692307692[/C][C]-0.423076923076923[/C][/ROW]
[ROW][C]210[/C][C]5[/C][C]5.21472392638037[/C][C]-0.214723926380368[/C][/ROW]
[ROW][C]211[/C][C]9[/C][C]5.21472392638037[/C][C]3.78527607361963[/C][/ROW]
[ROW][C]212[/C][C]4.5[/C][C]5.96052631578947[/C][C]-1.46052631578947[/C][/ROW]
[ROW][C]213[/C][C]8.5[/C][C]5.21472392638037[/C][C]3.28527607361963[/C][/ROW]
[ROW][C]214[/C][C]7[/C][C]5.21472392638037[/C][C]1.78527607361963[/C][/ROW]
[ROW][C]215[/C][C]9.5[/C][C]6.75[/C][C]2.75[/C][/ROW]
[ROW][C]216[/C][C]8.5[/C][C]8.42307692307692[/C][C]0.0769230769230766[/C][/ROW]
[ROW][C]217[/C][C]7.5[/C][C]5.21472392638037[/C][C]2.28527607361963[/C][/ROW]
[ROW][C]218[/C][C]7.5[/C][C]5.21472392638037[/C][C]2.28527607361963[/C][/ROW]
[ROW][C]219[/C][C]5[/C][C]6.75[/C][C]-1.75[/C][/ROW]
[ROW][C]220[/C][C]7[/C][C]5.96052631578947[/C][C]1.03947368421053[/C][/ROW]
[ROW][C]221[/C][C]8[/C][C]8.42307692307692[/C][C]-0.423076923076923[/C][/ROW]
[ROW][C]222[/C][C]5.5[/C][C]6.75[/C][C]-1.25[/C][/ROW]
[ROW][C]223[/C][C]8.5[/C][C]8.42307692307692[/C][C]0.0769230769230766[/C][/ROW]
[ROW][C]224[/C][C]7.5[/C][C]8.42307692307692[/C][C]-0.923076923076923[/C][/ROW]
[ROW][C]225[/C][C]9.5[/C][C]8.42307692307692[/C][C]1.07692307692308[/C][/ROW]
[ROW][C]226[/C][C]7[/C][C]5.21472392638037[/C][C]1.78527607361963[/C][/ROW]
[ROW][C]227[/C][C]8[/C][C]8.42307692307692[/C][C]-0.423076923076923[/C][/ROW]
[ROW][C]228[/C][C]8.5[/C][C]6.75[/C][C]1.75[/C][/ROW]
[ROW][C]229[/C][C]3.5[/C][C]5.96052631578947[/C][C]-2.46052631578947[/C][/ROW]
[ROW][C]230[/C][C]6.5[/C][C]5.21472392638037[/C][C]1.28527607361963[/C][/ROW]
[ROW][C]231[/C][C]6.5[/C][C]5.21472392638037[/C][C]1.28527607361963[/C][/ROW]
[ROW][C]232[/C][C]10.5[/C][C]8.42307692307692[/C][C]2.07692307692308[/C][/ROW]
[ROW][C]233[/C][C]8.5[/C][C]5.21472392638037[/C][C]3.28527607361963[/C][/ROW]
[ROW][C]234[/C][C]8[/C][C]8.42307692307692[/C][C]-0.423076923076923[/C][/ROW]
[ROW][C]235[/C][C]10[/C][C]5.21472392638037[/C][C]4.78527607361963[/C][/ROW]
[ROW][C]236[/C][C]10[/C][C]8.42307692307692[/C][C]1.57692307692308[/C][/ROW]
[ROW][C]237[/C][C]9.5[/C][C]8.42307692307692[/C][C]1.07692307692308[/C][/ROW]
[ROW][C]238[/C][C]9[/C][C]5.21472392638037[/C][C]3.78527607361963[/C][/ROW]
[ROW][C]239[/C][C]10[/C][C]8.42307692307692[/C][C]1.57692307692308[/C][/ROW]
[ROW][C]240[/C][C]7.5[/C][C]5.21472392638037[/C][C]2.28527607361963[/C][/ROW]
[ROW][C]241[/C][C]4.5[/C][C]5.21472392638037[/C][C]-0.714723926380368[/C][/ROW]
[ROW][C]242[/C][C]4.5[/C][C]5.21472392638037[/C][C]-0.714723926380368[/C][/ROW]
[ROW][C]243[/C][C]0.5[/C][C]5.21472392638037[/C][C]-4.71472392638037[/C][/ROW]
[ROW][C]244[/C][C]6.5[/C][C]5.21472392638037[/C][C]1.28527607361963[/C][/ROW]
[ROW][C]245[/C][C]4.5[/C][C]6.75[/C][C]-2.25[/C][/ROW]
[ROW][C]246[/C][C]5.5[/C][C]5.96052631578947[/C][C]-0.460526315789473[/C][/ROW]
[ROW][C]247[/C][C]5[/C][C]5.21472392638037[/C][C]-0.214723926380368[/C][/ROW]
[ROW][C]248[/C][C]6[/C][C]8.42307692307692[/C][C]-2.42307692307692[/C][/ROW]
[ROW][C]249[/C][C]4[/C][C]5.96052631578947[/C][C]-1.96052631578947[/C][/ROW]
[ROW][C]250[/C][C]8[/C][C]5.21472392638037[/C][C]2.78527607361963[/C][/ROW]
[ROW][C]251[/C][C]10.5[/C][C]8.42307692307692[/C][C]2.07692307692308[/C][/ROW]
[ROW][C]252[/C][C]8.5[/C][C]5.21472392638037[/C][C]3.28527607361963[/C][/ROW]
[ROW][C]253[/C][C]6.5[/C][C]5.21472392638037[/C][C]1.28527607361963[/C][/ROW]
[ROW][C]254[/C][C]8[/C][C]8.42307692307692[/C][C]-0.423076923076923[/C][/ROW]
[ROW][C]255[/C][C]8.5[/C][C]8.42307692307692[/C][C]0.0769230769230766[/C][/ROW]
[ROW][C]256[/C][C]5.5[/C][C]5.21472392638037[/C][C]0.285276073619632[/C][/ROW]
[ROW][C]257[/C][C]7[/C][C]6.75[/C][C]0.25[/C][/ROW]
[ROW][C]258[/C][C]5[/C][C]5.21472392638037[/C][C]-0.214723926380368[/C][/ROW]
[ROW][C]259[/C][C]3.5[/C][C]5.21472392638037[/C][C]-1.71472392638037[/C][/ROW]
[ROW][C]260[/C][C]5[/C][C]5.96052631578947[/C][C]-0.960526315789473[/C][/ROW]
[ROW][C]261[/C][C]9[/C][C]5.96052631578947[/C][C]3.03947368421053[/C][/ROW]
[ROW][C]262[/C][C]8.5[/C][C]5.96052631578947[/C][C]2.53947368421053[/C][/ROW]
[ROW][C]263[/C][C]5[/C][C]8.42307692307692[/C][C]-3.42307692307692[/C][/ROW]
[ROW][C]264[/C][C]9.5[/C][C]8.42307692307692[/C][C]1.07692307692308[/C][/ROW]
[ROW][C]265[/C][C]3[/C][C]5.21472392638037[/C][C]-2.21472392638037[/C][/ROW]
[ROW][C]266[/C][C]1.5[/C][C]5.96052631578947[/C][C]-4.46052631578947[/C][/ROW]
[ROW][C]267[/C][C]6[/C][C]5.21472392638037[/C][C]0.785276073619632[/C][/ROW]
[ROW][C]268[/C][C]0.5[/C][C]5.21472392638037[/C][C]-4.71472392638037[/C][/ROW]
[ROW][C]269[/C][C]6.5[/C][C]5.21472392638037[/C][C]1.28527607361963[/C][/ROW]
[ROW][C]270[/C][C]7.5[/C][C]5.21472392638037[/C][C]2.28527607361963[/C][/ROW]
[ROW][C]271[/C][C]4.5[/C][C]5.21472392638037[/C][C]-0.714723926380368[/C][/ROW]
[ROW][C]272[/C][C]8[/C][C]5.21472392638037[/C][C]2.78527607361963[/C][/ROW]
[ROW][C]273[/C][C]9[/C][C]8.42307692307692[/C][C]0.576923076923077[/C][/ROW]
[ROW][C]274[/C][C]7.5[/C][C]5.96052631578947[/C][C]1.53947368421053[/C][/ROW]
[ROW][C]275[/C][C]8.5[/C][C]5.96052631578947[/C][C]2.53947368421053[/C][/ROW]
[ROW][C]276[/C][C]7[/C][C]5.21472392638037[/C][C]1.78527607361963[/C][/ROW]
[ROW][C]277[/C][C]9.5[/C][C]5.21472392638037[/C][C]4.28527607361963[/C][/ROW]
[ROW][C]278[/C][C]6.5[/C][C]5.21472392638037[/C][C]1.28527607361963[/C][/ROW]
[ROW][C]279[/C][C]9.5[/C][C]5.21472392638037[/C][C]4.28527607361963[/C][/ROW]
[ROW][C]280[/C][C]6[/C][C]5.21472392638037[/C][C]0.785276073619632[/C][/ROW]
[ROW][C]281[/C][C]8[/C][C]5.96052631578947[/C][C]2.03947368421053[/C][/ROW]
[ROW][C]282[/C][C]9.5[/C][C]8.42307692307692[/C][C]1.07692307692308[/C][/ROW]
[ROW][C]283[/C][C]8[/C][C]8.42307692307692[/C][C]-0.423076923076923[/C][/ROW]
[ROW][C]284[/C][C]8[/C][C]8.42307692307692[/C][C]-0.423076923076923[/C][/ROW]
[ROW][C]285[/C][C]9[/C][C]8.42307692307692[/C][C]0.576923076923077[/C][/ROW]
[ROW][C]286[/C][C]5[/C][C]5.21472392638037[/C][C]-0.214723926380368[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=252921&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=252921&T=2

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


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

Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
17.55.214723926380372.28527607361963
22.55.21472392638037-2.71472392638037
365.214723926380370.785276073619632
46.55.214723926380371.28527607361963
515.21472392638037-4.21472392638037
615.21472392638037-4.21472392638037
75.55.96052631578947-0.460526315789473
88.55.214723926380373.28527607361963
96.55.214723926380371.28527607361963
104.55.21472392638037-0.714723926380368
1125.96052631578947-3.96052631578947
1255.21472392638037-0.214723926380368
130.55.96052631578947-5.46052631578947
1455.21472392638037-0.214723926380368
1555.96052631578947-0.960526315789473
162.55.21472392638037-2.71472392638037
1755.21472392638037-0.214723926380368
185.55.96052631578947-0.460526315789473
193.55.21472392638037-1.71472392638037
2035.21472392638037-2.21472392638037
2145.21472392638037-1.21472392638037
220.55.21472392638037-4.71472392638037
236.55.214723926380371.28527607361963
244.55.21472392638037-0.714723926380368
257.55.214723926380372.28527607361963
265.55.214723926380370.285276073619632
2745.96052631578947-1.96052631578947
287.58.42307692307692-0.923076923076923
2975.214723926380371.78527607361963
3045.21472392638037-1.21472392638037
315.55.214723926380370.285276073619632
322.55.21472392638037-2.71472392638037
335.55.96052631578947-0.460526315789473
340.55.21472392638037-4.71472392638037
353.55.21472392638037-1.71472392638037
362.58.42307692307692-5.92307692307692
374.55.21472392638037-0.714723926380368
384.55.21472392638037-0.714723926380368
394.55.21472392638037-0.714723926380368
4065.214723926380370.785276073619632
412.55.21472392638037-2.71472392638037
4255.96052631578947-0.960526315789473
4305.21472392638037-5.21472392638037
4455.96052631578947-0.960526315789473
456.55.214723926380371.28527607361963
4655.21472392638037-0.214723926380368
4765.214723926380370.785276073619632
484.55.21472392638037-0.714723926380368
495.55.214723926380370.285276073619632
5015.21472392638037-4.21472392638037
517.55.214723926380372.28527607361963
5265.214723926380370.785276073619632
5355.21472392638037-0.214723926380368
5415.21472392638037-4.21472392638037
5555.21472392638037-0.214723926380368
566.55.214723926380371.28527607361963
5775.960526315789471.03947368421053
584.55.96052631578947-1.46052631578947
5905.21472392638037-5.21472392638037
608.55.214723926380373.28527607361963
613.55.21472392638037-1.71472392638037
627.55.214723926380372.28527607361963
633.55.21472392638037-1.71472392638037
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651.55.21472392638037-3.71472392638037
6695.960526315789473.03947368421053
673.55.21472392638037-1.71472392638037
683.55.21472392638037-1.71472392638037
6945.96052631578947-1.96052631578947
706.55.960526315789470.539473684210527
717.55.214723926380372.28527607361963
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12966.75-0.75
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162108.423076923076921.57692307692308
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16565.214723926380370.785276073619632
16610.58.423076923076922.07692307692308
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177108.423076923076921.57692307692308
17875.214723926380371.78527607361963
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1807.58.42307692307692-0.923076923076923
1819.58.423076923076921.07692307692308
18268.42307692307692-2.42307692307692
183106.753.25
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18978.42307692307692-1.42307692307692
1903.55.21472392638037-1.71472392638037
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192105.214723926380374.78527607361963
1935.55.214723926380370.285276073619632
19465.214723926380370.785276073619632
1956.55.960526315789470.539473684210527
1966.55.214723926380371.28527607361963
1978.58.423076923076920.0769230769230766
19845.21472392638037-1.21472392638037
1999.58.423076923076921.07692307692308
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21956.75-1.75
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239108.423076923076921.57692307692308
2407.55.214723926380372.28527607361963
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2424.55.21472392638037-0.714723926380368
2430.55.21472392638037-4.71472392638037
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24868.42307692307692-2.42307692307692
24945.96052631578947-1.96052631578947
25085.214723926380372.78527607361963
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2528.55.214723926380373.28527607361963
2536.55.214723926380371.28527607361963
25488.42307692307692-0.423076923076923
2558.58.423076923076920.0769230769230766
2565.55.214723926380370.285276073619632
25776.750.25
25855.21472392638037-0.214723926380368
2593.55.21472392638037-1.71472392638037
26055.96052631578947-0.960526315789473
26195.960526315789473.03947368421053
2628.55.960526315789472.53947368421053
26358.42307692307692-3.42307692307692
2649.58.423076923076921.07692307692308
26535.21472392638037-2.21472392638037
2661.55.96052631578947-4.46052631578947
26765.214723926380370.785276073619632
2680.55.21472392638037-4.71472392638037
2696.55.214723926380371.28527607361963
2707.55.214723926380372.28527607361963
2714.55.21472392638037-0.714723926380368
27285.214723926380372.78527607361963
27398.423076923076920.576923076923077
2747.55.960526315789471.53947368421053
2758.55.960526315789472.53947368421053
27675.214723926380371.78527607361963
2779.55.214723926380374.28527607361963
2786.55.214723926380371.28527607361963
2799.55.214723926380374.28527607361963
28065.214723926380370.785276073619632
28185.960526315789472.03947368421053
2829.58.423076923076921.07692307692308
28388.42307692307692-0.423076923076923
28488.42307692307692-0.423076923076923
28598.423076923076920.576923076923077
28655.21472392638037-0.214723926380368


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 8 ; par2 = none ; par3 = 3 ; par4 = no ;
Parameters (R input):
par1 = 8 ; par2 = none ; par3 = 3 ; par4 = no ;
R code (references can be found in the software module):
library(party)
library(Hmisc)
par1 <- as.numeric(par1)
par3 <- as.numeric(par3)
x <- data.frame(t(y))
is.data.frame(x)
x <- x[!is.na(x[,par1]),]
k <- length(x[1,])
n <- length(x[,1])
colnames(x)[par1]
x[,par1]
if (par2 == 'kmeans') {
cl <- kmeans(x[,par1], par3)
print(cl)
clm <- matrix(cbind(cl$centers,1:par3),ncol=2)
clm <- clm[sort.list(clm[,1]),]
for (i in 1:par3) {
cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='')
}
cl$cluster <- as.factor(cl$cluster)
print(cl$cluster)
x[,par1] <- cl$cluster
}
if (par2 == 'quantiles') {
x[,par1] <- cut2(x[,par1],g=par3)
}
if (par2 == 'hclust') {
hc <- hclust(dist(x[,par1])^2, 'cen')
print(hc)
memb <- cutree(hc, k = par3)
dum <- c(mean(x[memb==1,par1]))
for (i in 2:par3) {
dum <- c(dum, mean(x[memb==i,par1]))
}
hcm <- matrix(cbind(dum,1:par3),ncol=2)
hcm <- hcm[sort.list(hcm[,1]),]
for (i in 1:par3) {
memb[memb==hcm[i,2]] <- paste('C',i,sep='')
}
memb <- as.factor(memb)
print(memb)
x[,par1] <- memb
}
if (par2=='equal') {
ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep=''))
x[,par1] <- as.factor(ed)
}
table(x[,par1])
colnames(x)
colnames(x)[par1]
x[,par1]
if (par2 == 'none') {
m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
}
load(file='createtable')
if (par2 != 'none') {
m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x)
if (par4=='yes') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
a<-table.element(a,'Prediction (training)',par3+1,TRUE)
a<-table.element(a,'Prediction (testing)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Actual',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
a<-table.row.end(a)
for (i in 1:10) {
ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1))
m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,])
if (i==1) {
m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,])
m.ct.i.actu <- x[ind==1,par1]
m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,])
m.ct.x.actu <- x[ind==2,par1]
} else {
m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,]))
m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1])
m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,]))
m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1])
}
}
print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,]))
numer <- numer + m.ct.i.tab[i,i]
}
print(m.ct.i.cp <- numer / sum(m.ct.i.tab))
print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,]))
numer <- numer + m.ct.x.tab[i,i]
}
print(m.ct.x.cp <- numer / sum(m.ct.x.tab))
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj])
a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4))
for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj])
a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4))
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'Overall',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.i.cp,4))
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.x.cp,4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
}
}
m
bitmap(file='test1.png')
plot(m)
dev.off()
bitmap(file='test1a.png')
plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response')
dev.off()
if (par2 == 'none') {
forec <- predict(m)
result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec))
colnames(result) <- c('Actuals','Forecasts','Residuals')
print(result)
}
if (par2 != 'none') {
print(cbind(as.factor(x[,par1]),predict(m)))
myt <- table(as.factor(x[,par1]),predict(m))
print(myt)
}
bitmap(file='test2.png')
if(par2=='none') {
op <- par(mfrow=c(2,2))
plot(density(result$Actuals),main='Kernel Density Plot of Actuals')
plot(density(result$Residuals),main='Kernel Density Plot of Residuals')
plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals')
plot(density(result$Forecasts),main='Kernel Density Plot of Predictions')
par(op)
}
if(par2!='none') {
plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted')
}
dev.off()
if (par2 == 'none') {
detcoef <- cor(result$Forecasts,result$Actuals)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goodness of Fit',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Correlation',1,TRUE)
a<-table.element(a,round(detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'R-squared',1,TRUE)
a<-table.element(a,round(detcoef*detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'RMSE',1,TRUE)
a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#',header=TRUE)
a<-table.element(a,'Actuals',header=TRUE)
a<-table.element(a,'Forecasts',header=TRUE)
a<-table.element(a,'Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(result$Actuals)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,result$Actuals[i])
a<-table.element(a,result$Forecasts[i])
a<-table.element(a,result$Residuals[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
}
if (par2 != 'none') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
for (i in 1:par3) {
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
}
a<-table.row.end(a)
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (j in 1:par3) {
a<-table.element(a,myt[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}