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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 20:03:58 +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/t1415304309q648r41u9tcu8ir.htm/, Retrieved Fri, 17 May 2024 03:06:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=252925, Retrieved Fri, 17 May 2024 03:06:07 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact45

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 20:03:58] [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'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=252925&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=252925&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=252925&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'Gertrude Mary Cox' @ cox.wessa.net






10-Fold Cross Validation
Prediction (training)Prediction (testing)
ActualC1C2CVC1C2CV
C112821170.9164107240.8168
C26185890.48865580.4715
Overall--0.718--0.6496

\begin{tabular}{lllllllll}
\hline
10-Fold Cross Validation \tabularnewline
 & Prediction (training) & Prediction (testing) \tabularnewline
Actual & C1 & C2 & CV & C1 & C2 & CV \tabularnewline
C1 & 1282 & 117 & 0.9164 & 107 & 24 & 0.8168 \tabularnewline
C2 & 618 & 589 & 0.488 & 65 & 58 & 0.4715 \tabularnewline
Overall & - & - & 0.718 & - & - & 0.6496 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=252925&T=1

[TABLE]
[ROW][C]10-Fold Cross Validation[/C][/ROW]
[ROW][C][/C][C]Prediction (training)[/C][C]Prediction (testing)[/C][/ROW]
[ROW][C]Actual[/C][C]C1[/C][C]C2[/C][C]CV[/C][C]C1[/C][C]C2[/C][C]CV[/C][/ROW]
[ROW][C]C1[/C][C]1282[/C][C]117[/C][C]0.9164[/C][C]107[/C][C]24[/C][C]0.8168[/C][/ROW]
[ROW][C]C2[/C][C]618[/C][C]589[/C][C]0.488[/C][C]65[/C][C]58[/C][C]0.4715[/C][/ROW]
[ROW][C]Overall[/C][C]-[/C][C]-[/C][C]0.718[/C][C]-[/C][C]-[/C][C]0.6496[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=252925&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=252925&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:

10-Fold Cross Validation
Prediction (training)Prediction (testing)
ActualC1C2CVC1C2CV
C112821170.9164107240.8168
C26185890.48865580.4715
Overall--0.718--0.6496






Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C113320
C25281

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 133 & 20 \tabularnewline
C2 & 52 & 81 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=252925&T=2

[TABLE]
[ROW][C]Confusion Matrix (predicted in columns / actuals in rows)[/C][/ROW]
[ROW][C][/C][C]C1[/C][C]C2[/C][/ROW]
[ROW][C]C1[/C][C]133[/C][C]20[/C][/ROW]
[ROW][C]C2[/C][C]52[/C][C]81[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=252925&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=252925&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:

Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C113320
C25281


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 8 ; par2 = none ; par3 = 3 ; par4 = no ;
Parameters (R input):
par1 = 8 ; par2 = quantiles ; par3 = 2 ; par4 = yes ;
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')
}