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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Sat, 25 Dec 2010 21:09:18 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/25/t12933112362t8r4iozj2fwt5q.htm/, Retrieved Sun, 28 Apr 2024 19:10:08 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=115458, Retrieved Sun, 28 Apr 2024 19:10:08 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

151

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
-   PD  [Recursive Partitioning (Regression Trees)] [recursive partiti...] [2010-12-24 11:07:38] [d6e648f00513dd750579ba7880c5fbf5]
-   PD    [Recursive Partitioning (Regression Trees)] [recursive partiti...] [2010-12-24 13:42:21] [d6e648f00513dd750579ba7880c5fbf5]
-             [Recursive Partitioning (Regression Trees)] [] [2010-12-25 21:09:18] [15be6a7406d91c9912c2afdb984c54ee] [Current]

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Dataseries X:

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Histogram

Boxplots

4	1	27	5	26	49	35
4	1	36	4	25	45	34
5	1	25	4	17	54	13
2	1	27	3	37	36	35
3	2	25	3	35	36	28
5	2	44	3	15	53	32
4	1	50	4	27	46	35
4	1	41	4	36	42	36
4	1	48	5	25	41	27
4	2	43	4	30	45	29
5	2	47	2	27	47	27
4	2	41	3	33	42	28
3	1	44	2	29	45	29
4	2	47	5	30	40	28
3	2	40	3	25	45	30
3	2	46	3	23	40	25
4	1	28	3	26	42	15
3	1	56	3	24	45	33
4	2	49	4	35	47	31
2	2	25	4	39	31	37
4	2	41	4	23	46	37
3	2	26	3	32	34	34
4	1	50	5	29	43	32
4	1	47	4	26	45	21
3	1	52	2	21	42	25
3	2	37	5	35	51	32
2	2	41	3	23	44	28
4	1	45	4	21	47	22
5	2	26	4	28	47	25
4	1	NA	3	30	41	26
2	1	52	4	21	44	34
5	1	46	2	29	51	34
4	1	58	3	28	46	36
3	1	54	5	19	47	36
4	1	29	3	26	46	26
2	2	50	3	33	38	26
3	1	43	2	34	50	34
3	2	30	3	33	48	33
3	2	47	2	40	36	31
5	1	45	3	24	51	33
NA	2	48	1	35	35	22
4	2	48	3	35	49	29
4	2	26	4	32	38	24
4	1	46	5	20	47	37
2	2	NA	3	35	36	32
4	2	50	3	35	47	23
3	1	25	4	21	46	29
4	1	47	2	33	43	35
1	2	47	2	40	53	20
2	1	41	3	22	55	28
2	2	45	2	35	39	26
4	2	41	4	20	55	36
3	2	45	5	28	41	26
4	2	40	3	46	33	33
3	1	29	4	18	52	25
3	2	34	5	22	42	29
5	1	45	5	20	56	32
3	2	52	3	25	46	35
2	2	41	4	31	33	24
1	2	48	3	21	51	31
2	2	45	3	23	46	29
5	1	54	2	26	46	27
4	2	25	3	34	50	29
4	2	26	4	31	46	29
3	1	28	4	23	51	27
4	2	50	4	31	48	34
4	2	48	4	26	44	32
2	2	51	3	36	38	31
3	2	53	3	28	42	31
4	1	37	3	34	39	31
3	1	56	2	25	45	16
2	1	43	3	33	31	25
4	1	34	3	46	29	27
4	1	42	3	24	48	32
3	2	32	3	32	38	28
5	2	31	5	33	55	25
1	1	46	3	42	32	25
3	2	30	5	17	51	36
3	2	47	4	36	53	36
5	2	33	4	40	47	36
2	1	25	4	30	45	27
3	1	25	5	19	33	29
3	2	21	4	33	49	32
4	2	36	5	35	46	29
2	2	50	3	23	42	31
4	2	48	3	15	56	34
3	2	48	2	38	35	27
3	1	25	3	37	40	28
3	1	48	4	23	44	32
2	2	49	5	41	46	33
3	1	27	5	34	46	29
2	1	28	3	38	39	32
4	2	43	2	45	35	35
4	2	48	3	27	48	33
2	2	48	4	46	42	27
1	1	25	1	26	39	16
5	2	49	4	44	39	32
4	1	26	3	36	41	26
4	1	51	3	20	52	32
4	2	25	4	44	45	38
3	1	29	3	27	42	24
3	1	29	4	27	44	26
1	1	43	2	41	33	19
5	2	46	3	30	42	37
3	1	44	3	33	46	25
3	1	25	3	37	45	24
2	1	51	2	30	40	23
4	1	42	5	20	48	28
4	2	53	5	44	32	38
3	1	25	4	20	53	28
4	2	49	2	33	39	28
4	1	51	3	31	45	26
2	2	20	3	23	36	21
3	2	44	3	33	38	35
3	2	38	4	33	49	31
3	1	46	5	32	46	34
4	2	42	4	25	43	30
5	1	29	NA	22	37	30
3	2	46	4	16	48	24
3	2	49	2	36	45	27
2	2	51	3	35	32	26
3	1	38	3	25	46	30
1	1	41	1	27	20	15
4	2	47	3	32	42	28
4	2	44	3	36	45	34
4	2	47	3	51	29	29
3	2	46	3	30	51	26
5	1	44	4	20	55	31
2	2	28	3	29	50	28
2	2	47	4	26	44	33
3	2	28	4	20	41	32
3	1	41	5	40	40	33
2	2	45	4	29	47	31
1	2	46	4	32	42	37
3	1	46	4	33	40	27
5	2	22	3	32	51	19
4	2	33	3	34	43	27
4	1	41	4	24	45	31
4	2	47	5	25	41	38
3	1	25	3	41	41	22
5	2	42	3	39	37	35
3	2	47	3	21	46	35
3	2	50	3	38	38	30
3	1	55	5	28	39	41
3	1	21	3	37	45	25
4	1	NA	3	26	46	28
2	1	52	3	30	39	45
2	2	49	4	25	21	21
4	2	46	4	38	31	33
3	1	NA	4	31	35	25
3	2	45	3	31	49	29
2	2	52	3	27	40	31
3	1	NA	3	21	45	29
3	2	40	4	26	46	31
4	2	49	4	37	45	31
1	1	38	5	28	34	25
1	1	32	5	29	41	27
5	2	46	4	33	43	26
4	2	32	3	41	45	26
3	2	41	3	19	48	23
3	2	43	3	37	43	27
4	1	44	4	36	45	24
3	1	47	5	27	45	35
2	2	28	3	33	34	24
1	1	52	1	29	40	32
1	1	27	2	42	40	24
5	2	45	5	27	55	24
4	1	27	4	47	44	38
3	1	25	4	17	44	36
4	1	28	4	34	48	24
5	1	25	3	32	51	18
4	1	52	4	25	49	34
4	1	44	3	27	33	23
2	2	43	3	37	43	35
3	2	47	4	34	44	22
4	2	52	4	27	44	34
3	2	40	2	37	41	28
4	1	42	3	32	45	34
3	1	45	5	26	44	32
4	1	45	2	29	44	24
1	1	50	5	28	40	34
2	1	49	3	19	48	33
3	1	52	2	46	49	33
3	2	48	3	31	46	29
5	2	51	3	42	49	38
4	2	49	4	33	55	24
3	2	31	4	39	51	25
3	2	43	3	27	46	37
3	2	31	3	35	37	33
3	2	28	4	23	43	30
4	2	43	4	32	41	22
3	2	31	3	22	45	28
2	2	51	3	17	39	24
4	2	58	4	35	38	33
2	2	25	5	34	41	37

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115458&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115458&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115458&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.3171
R-squared	0.1006
RMSE	0.9882

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.3171 \tabularnewline
R-squared & 0.1006 \tabularnewline
RMSE & 0.9882 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115458&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.3171[/C][/ROW]
[ROW][C]R-squared[/C][C]0.1006[/C][/ROW]
[ROW][C]RMSE[/C][C]0.9882[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115458&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115458&T=1

As an alternative you can also use a QR Code:

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

Goodness of Fit
Correlation	0.3171
R-squared	0.1006
RMSE	0.9882

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	4	3.46043165467626	0.539568345323741
2	4	3.46043165467626	0.539568345323741
3	5	3.46043165467626	1.53956834532374
4	2	2.72727272727273	-0.727272727272727
5	3	2.72727272727273	0.272727272727273
6	5	3.46043165467626	1.53956834532374
7	4	3.46043165467626	0.539568345323741
8	4	3.46043165467626	0.539568345323741
9	4	3.46043165467626	0.539568345323741
10	4	3.46043165467626	0.539568345323741
11	5	3.46043165467626	1.53956834532374
12	4	3.46043165467626	0.539568345323741
13	3	3.46043165467626	-0.460431654676259
14	4	2.72727272727273	1.27272727272727
15	3	3.46043165467626	-0.460431654676259
16	3	2.72727272727273	0.272727272727273
17	4	3.46043165467626	0.539568345323741
18	3	3.46043165467626	-0.460431654676259
19	4	3.46043165467626	0.539568345323741
20	2	2.72727272727273	-0.727272727272727
21	4	3.46043165467626	0.539568345323741
22	3	2.72727272727273	0.272727272727273
23	4	3.46043165467626	0.539568345323741
24	4	3.46043165467626	0.539568345323741
25	3	3.46043165467626	-0.460431654676259
26	3	3.46043165467626	-0.460431654676259
27	2	3.46043165467626	-1.46043165467626
28	4	3.46043165467626	0.539568345323741
29	5	3.46043165467626	1.53956834532374
30	4	3.46043165467626	0.539568345323741
31	2	3.46043165467626	-1.46043165467626
32	5	3.46043165467626	1.53956834532374
33	4	3.46043165467626	0.539568345323741
34	3	3.46043165467626	-0.460431654676259
35	4	3.46043165467626	0.539568345323741
36	2	2.72727272727273	-0.727272727272727
37	3	3.46043165467626	-0.460431654676259
38	3	3.46043165467626	-0.460431654676259
39	3	2.72727272727273	0.272727272727273
40	5	3.46043165467626	1.53956834532374
41	4	3.46043165467626	0.539568345323741
42	4	2.72727272727273	1.27272727272727
43	4	3.46043165467626	0.539568345323741
44	2	2.72727272727273	-0.727272727272727
45	4	3.46043165467626	0.539568345323741
46	3	3.46043165467626	-0.460431654676259
47	4	3.46043165467626	0.539568345323741
48	1	3.46043165467626	-2.46043165467626
49	2	3.46043165467626	-1.46043165467626
50	2	2.72727272727273	-0.727272727272727
51	4	3.46043165467626	0.539568345323741
52	3	3.46043165467626	-0.460431654676259
53	4	2.72727272727273	1.27272727272727
54	3	3.46043165467626	-0.460431654676259
55	3	3.46043165467626	-0.460431654676259
56	5	3.46043165467626	1.53956834532374
57	3	3.46043165467626	-0.460431654676259
58	2	2.72727272727273	-0.727272727272727
59	1	3.46043165467626	-2.46043165467626
60	2	3.46043165467626	-1.46043165467626
61	5	3.46043165467626	1.53956834532374
62	4	3.46043165467626	0.539568345323741
63	4	3.46043165467626	0.539568345323741
64	3	3.46043165467626	-0.460431654676259
65	4	3.46043165467626	0.539568345323741
66	4	3.46043165467626	0.539568345323741
67	2	2.72727272727273	-0.727272727272727
68	3	3.46043165467626	-0.460431654676259
69	4	2.72727272727273	1.27272727272727
70	3	3.46043165467626	-0.460431654676259
71	2	2.72727272727273	-0.727272727272727
72	4	2.72727272727273	1.27272727272727
73	4	3.46043165467626	0.539568345323741
74	3	2.72727272727273	0.272727272727273
75	5	3.46043165467626	1.53956834532374
76	1	2.72727272727273	-1.72727272727273
77	3	3.46043165467626	-0.460431654676259
78	3	3.46043165467626	-0.460431654676259
79	5	3.46043165467626	1.53956834532374
80	2	3.46043165467626	-1.46043165467626
81	3	2.72727272727273	0.272727272727273
82	3	3.46043165467626	-0.460431654676259
83	4	3.46043165467626	0.539568345323741
84	2	3.46043165467626	-1.46043165467626
85	4	3.46043165467626	0.539568345323741
86	3	2.72727272727273	0.272727272727273
87	3	2.72727272727273	0.272727272727273
88	3	3.46043165467626	-0.460431654676259
89	2	3.46043165467626	-1.46043165467626
90	3	3.46043165467626	-0.460431654676259
91	2	2.72727272727273	-0.727272727272727
92	4	2.72727272727273	1.27272727272727
93	4	3.46043165467626	0.539568345323741
94	2	3.46043165467626	-1.46043165467626
95	1	2.72727272727273	-1.72727272727273
96	5	2.72727272727273	2.27272727272727
97	4	3.46043165467626	0.539568345323741
98	4	3.46043165467626	0.539568345323741
99	4	3.46043165467626	0.539568345323741
100	3	3.46043165467626	-0.460431654676259
101	3	3.46043165467626	-0.460431654676259
102	1	2.72727272727273	-1.72727272727273
103	5	3.46043165467626	1.53956834532374
104	3	3.46043165467626	-0.460431654676259
105	3	3.46043165467626	-0.460431654676259
106	2	2.72727272727273	-0.727272727272727
107	4	3.46043165467626	0.539568345323741
108	4	2.72727272727273	1.27272727272727
109	3	3.46043165467626	-0.460431654676259
110	4	2.72727272727273	1.27272727272727
111	4	3.46043165467626	0.539568345323741
112	2	2.72727272727273	-0.727272727272727
113	3	2.72727272727273	0.272727272727273
114	3	3.46043165467626	-0.460431654676259
115	3	3.46043165467626	-0.460431654676259
116	4	3.46043165467626	0.539568345323741
117	5	2.72727272727273	2.27272727272727
118	3	3.46043165467626	-0.460431654676259
119	3	3.46043165467626	-0.460431654676259
120	2	2.72727272727273	-0.727272727272727
121	3	3.46043165467626	-0.460431654676259
122	1	2.72727272727273	-1.72727272727273
123	4	3.46043165467626	0.539568345323741
124	4	3.46043165467626	0.539568345323741
125	4	2.72727272727273	1.27272727272727
126	3	3.46043165467626	-0.460431654676259
127	5	3.46043165467626	1.53956834532374
128	2	3.46043165467626	-1.46043165467626
129	2	3.46043165467626	-1.46043165467626
130	3	3.46043165467626	-0.460431654676259
131	3	2.72727272727273	0.272727272727273
132	2	3.46043165467626	-1.46043165467626
133	1	3.46043165467626	-2.46043165467626
134	3	2.72727272727273	0.272727272727273
135	5	3.46043165467626	1.53956834532374
136	4	3.46043165467626	0.539568345323741
137	4	3.46043165467626	0.539568345323741
138	4	3.46043165467626	0.539568345323741
139	3	3.46043165467626	-0.460431654676259
140	5	2.72727272727273	2.27272727272727
141	3	3.46043165467626	-0.460431654676259
142	3	2.72727272727273	0.272727272727273
143	3	2.72727272727273	0.272727272727273
144	3	3.46043165467626	-0.460431654676259
145	4	3.46043165467626	0.539568345323741
146	2	2.72727272727273	-0.727272727272727
147	2	2.72727272727273	-0.727272727272727
148	4	2.72727272727273	1.27272727272727
149	3	2.72727272727273	0.272727272727273
150	3	3.46043165467626	-0.460431654676259
151	2	2.72727272727273	-0.727272727272727
152	3	3.46043165467626	-0.460431654676259
153	3	3.46043165467626	-0.460431654676259
154	4	3.46043165467626	0.539568345323741
155	1	2.72727272727273	-1.72727272727273
156	1	3.46043165467626	-2.46043165467626
157	5	3.46043165467626	1.53956834532374
158	4	3.46043165467626	0.539568345323741
159	3	3.46043165467626	-0.460431654676259
160	3	3.46043165467626	-0.460431654676259
161	4	3.46043165467626	0.539568345323741
162	3	3.46043165467626	-0.460431654676259
163	2	2.72727272727273	-0.727272727272727
164	1	2.72727272727273	-1.72727272727273
165	1	2.72727272727273	-1.72727272727273
166	5	3.46043165467626	1.53956834532374
167	4	3.46043165467626	0.539568345323741
168	3	3.46043165467626	-0.460431654676259
169	4	3.46043165467626	0.539568345323741
170	5	3.46043165467626	1.53956834532374
171	4	3.46043165467626	0.539568345323741
172	4	2.72727272727273	1.27272727272727
173	2	3.46043165467626	-1.46043165467626
174	3	3.46043165467626	-0.460431654676259
175	4	3.46043165467626	0.539568345323741
176	3	3.46043165467626	-0.460431654676259
177	4	3.46043165467626	0.539568345323741
178	3	3.46043165467626	-0.460431654676259
179	4	3.46043165467626	0.539568345323741
180	1	2.72727272727273	-1.72727272727273
181	2	3.46043165467626	-1.46043165467626
182	3	3.46043165467626	-0.460431654676259
183	3	3.46043165467626	-0.460431654676259
184	5	3.46043165467626	1.53956834532374
185	4	3.46043165467626	0.539568345323741
186	3	3.46043165467626	-0.460431654676259
187	3	3.46043165467626	-0.460431654676259
188	3	2.72727272727273	0.272727272727273
189	3	3.46043165467626	-0.460431654676259
190	4	3.46043165467626	0.539568345323741
191	3	3.46043165467626	-0.460431654676259
192	2	2.72727272727273	-0.727272727272727
193	4	2.72727272727273	1.27272727272727
194	2	3.46043165467626	-1.46043165467626

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
2 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
3 & 5 & 3.46043165467626 & 1.53956834532374 \tabularnewline
4 & 2 & 2.72727272727273 & -0.727272727272727 \tabularnewline
5 & 3 & 2.72727272727273 & 0.272727272727273 \tabularnewline
6 & 5 & 3.46043165467626 & 1.53956834532374 \tabularnewline
7 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
8 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
9 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
10 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
11 & 5 & 3.46043165467626 & 1.53956834532374 \tabularnewline
12 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
13 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
14 & 4 & 2.72727272727273 & 1.27272727272727 \tabularnewline
15 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
16 & 3 & 2.72727272727273 & 0.272727272727273 \tabularnewline
17 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
18 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
19 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
20 & 2 & 2.72727272727273 & -0.727272727272727 \tabularnewline
21 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
22 & 3 & 2.72727272727273 & 0.272727272727273 \tabularnewline
23 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
24 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
25 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
26 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
27 & 2 & 3.46043165467626 & -1.46043165467626 \tabularnewline
28 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
29 & 5 & 3.46043165467626 & 1.53956834532374 \tabularnewline
30 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
31 & 2 & 3.46043165467626 & -1.46043165467626 \tabularnewline
32 & 5 & 3.46043165467626 & 1.53956834532374 \tabularnewline
33 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
34 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
35 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
36 & 2 & 2.72727272727273 & -0.727272727272727 \tabularnewline
37 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
38 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
39 & 3 & 2.72727272727273 & 0.272727272727273 \tabularnewline
40 & 5 & 3.46043165467626 & 1.53956834532374 \tabularnewline
41 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
42 & 4 & 2.72727272727273 & 1.27272727272727 \tabularnewline
43 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
44 & 2 & 2.72727272727273 & -0.727272727272727 \tabularnewline
45 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
46 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
47 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
48 & 1 & 3.46043165467626 & -2.46043165467626 \tabularnewline
49 & 2 & 3.46043165467626 & -1.46043165467626 \tabularnewline
50 & 2 & 2.72727272727273 & -0.727272727272727 \tabularnewline
51 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
52 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
53 & 4 & 2.72727272727273 & 1.27272727272727 \tabularnewline
54 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
55 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
56 & 5 & 3.46043165467626 & 1.53956834532374 \tabularnewline
57 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
58 & 2 & 2.72727272727273 & -0.727272727272727 \tabularnewline
59 & 1 & 3.46043165467626 & -2.46043165467626 \tabularnewline
60 & 2 & 3.46043165467626 & -1.46043165467626 \tabularnewline
61 & 5 & 3.46043165467626 & 1.53956834532374 \tabularnewline
62 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
63 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
64 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
65 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
66 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
67 & 2 & 2.72727272727273 & -0.727272727272727 \tabularnewline
68 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
69 & 4 & 2.72727272727273 & 1.27272727272727 \tabularnewline
70 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
71 & 2 & 2.72727272727273 & -0.727272727272727 \tabularnewline
72 & 4 & 2.72727272727273 & 1.27272727272727 \tabularnewline
73 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
74 & 3 & 2.72727272727273 & 0.272727272727273 \tabularnewline
75 & 5 & 3.46043165467626 & 1.53956834532374 \tabularnewline
76 & 1 & 2.72727272727273 & -1.72727272727273 \tabularnewline
77 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
78 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
79 & 5 & 3.46043165467626 & 1.53956834532374 \tabularnewline
80 & 2 & 3.46043165467626 & -1.46043165467626 \tabularnewline
81 & 3 & 2.72727272727273 & 0.272727272727273 \tabularnewline
82 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
83 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
84 & 2 & 3.46043165467626 & -1.46043165467626 \tabularnewline
85 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
86 & 3 & 2.72727272727273 & 0.272727272727273 \tabularnewline
87 & 3 & 2.72727272727273 & 0.272727272727273 \tabularnewline
88 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
89 & 2 & 3.46043165467626 & -1.46043165467626 \tabularnewline
90 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
91 & 2 & 2.72727272727273 & -0.727272727272727 \tabularnewline
92 & 4 & 2.72727272727273 & 1.27272727272727 \tabularnewline
93 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
94 & 2 & 3.46043165467626 & -1.46043165467626 \tabularnewline
95 & 1 & 2.72727272727273 & -1.72727272727273 \tabularnewline
96 & 5 & 2.72727272727273 & 2.27272727272727 \tabularnewline
97 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
98 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
99 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
100 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
101 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
102 & 1 & 2.72727272727273 & -1.72727272727273 \tabularnewline
103 & 5 & 3.46043165467626 & 1.53956834532374 \tabularnewline
104 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
105 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
106 & 2 & 2.72727272727273 & -0.727272727272727 \tabularnewline
107 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
108 & 4 & 2.72727272727273 & 1.27272727272727 \tabularnewline
109 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
110 & 4 & 2.72727272727273 & 1.27272727272727 \tabularnewline
111 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
112 & 2 & 2.72727272727273 & -0.727272727272727 \tabularnewline
113 & 3 & 2.72727272727273 & 0.272727272727273 \tabularnewline
114 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
115 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
116 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
117 & 5 & 2.72727272727273 & 2.27272727272727 \tabularnewline
118 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
119 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
120 & 2 & 2.72727272727273 & -0.727272727272727 \tabularnewline
121 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
122 & 1 & 2.72727272727273 & -1.72727272727273 \tabularnewline
123 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
124 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
125 & 4 & 2.72727272727273 & 1.27272727272727 \tabularnewline
126 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
127 & 5 & 3.46043165467626 & 1.53956834532374 \tabularnewline
128 & 2 & 3.46043165467626 & -1.46043165467626 \tabularnewline
129 & 2 & 3.46043165467626 & -1.46043165467626 \tabularnewline
130 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
131 & 3 & 2.72727272727273 & 0.272727272727273 \tabularnewline
132 & 2 & 3.46043165467626 & -1.46043165467626 \tabularnewline
133 & 1 & 3.46043165467626 & -2.46043165467626 \tabularnewline
134 & 3 & 2.72727272727273 & 0.272727272727273 \tabularnewline
135 & 5 & 3.46043165467626 & 1.53956834532374 \tabularnewline
136 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
137 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
138 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
139 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
140 & 5 & 2.72727272727273 & 2.27272727272727 \tabularnewline
141 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
142 & 3 & 2.72727272727273 & 0.272727272727273 \tabularnewline
143 & 3 & 2.72727272727273 & 0.272727272727273 \tabularnewline
144 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
145 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
146 & 2 & 2.72727272727273 & -0.727272727272727 \tabularnewline
147 & 2 & 2.72727272727273 & -0.727272727272727 \tabularnewline
148 & 4 & 2.72727272727273 & 1.27272727272727 \tabularnewline
149 & 3 & 2.72727272727273 & 0.272727272727273 \tabularnewline
150 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
151 & 2 & 2.72727272727273 & -0.727272727272727 \tabularnewline
152 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
153 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
154 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
155 & 1 & 2.72727272727273 & -1.72727272727273 \tabularnewline
156 & 1 & 3.46043165467626 & -2.46043165467626 \tabularnewline
157 & 5 & 3.46043165467626 & 1.53956834532374 \tabularnewline
158 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
159 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
160 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
161 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
162 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
163 & 2 & 2.72727272727273 & -0.727272727272727 \tabularnewline
164 & 1 & 2.72727272727273 & -1.72727272727273 \tabularnewline
165 & 1 & 2.72727272727273 & -1.72727272727273 \tabularnewline
166 & 5 & 3.46043165467626 & 1.53956834532374 \tabularnewline
167 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
168 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
169 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
170 & 5 & 3.46043165467626 & 1.53956834532374 \tabularnewline
171 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
172 & 4 & 2.72727272727273 & 1.27272727272727 \tabularnewline
173 & 2 & 3.46043165467626 & -1.46043165467626 \tabularnewline
174 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
175 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
176 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
177 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
178 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
179 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
180 & 1 & 2.72727272727273 & -1.72727272727273 \tabularnewline
181 & 2 & 3.46043165467626 & -1.46043165467626 \tabularnewline
182 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
183 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
184 & 5 & 3.46043165467626 & 1.53956834532374 \tabularnewline
185 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
186 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
187 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
188 & 3 & 2.72727272727273 & 0.272727272727273 \tabularnewline
189 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
190 & 4 & 3.46043165467626 & 0.539568345323741 \tabularnewline
191 & 3 & 3.46043165467626 & -0.460431654676259 \tabularnewline
192 & 2 & 2.72727272727273 & -0.727272727272727 \tabularnewline
193 & 4 & 2.72727272727273 & 1.27272727272727 \tabularnewline
194 & 2 & 3.46043165467626 & -1.46043165467626 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115458&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]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]2[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]3[/C][C]5[/C][C]3.46043165467626[/C][C]1.53956834532374[/C][/ROW]
[ROW][C]4[/C][C]2[/C][C]2.72727272727273[/C][C]-0.727272727272727[/C][/ROW]
[ROW][C]5[/C][C]3[/C][C]2.72727272727273[/C][C]0.272727272727273[/C][/ROW]
[ROW][C]6[/C][C]5[/C][C]3.46043165467626[/C][C]1.53956834532374[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]8[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]11[/C][C]5[/C][C]3.46043165467626[/C][C]1.53956834532374[/C][/ROW]
[ROW][C]12[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]13[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]14[/C][C]4[/C][C]2.72727272727273[/C][C]1.27272727272727[/C][/ROW]
[ROW][C]15[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]2.72727272727273[/C][C]0.272727272727273[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]18[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]20[/C][C]2[/C][C]2.72727272727273[/C][C]-0.727272727272727[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]22[/C][C]3[/C][C]2.72727272727273[/C][C]0.272727272727273[/C][/ROW]
[ROW][C]23[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]24[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]25[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]26[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]27[/C][C]2[/C][C]3.46043165467626[/C][C]-1.46043165467626[/C][/ROW]
[ROW][C]28[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]29[/C][C]5[/C][C]3.46043165467626[/C][C]1.53956834532374[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]31[/C][C]2[/C][C]3.46043165467626[/C][C]-1.46043165467626[/C][/ROW]
[ROW][C]32[/C][C]5[/C][C]3.46043165467626[/C][C]1.53956834532374[/C][/ROW]
[ROW][C]33[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]34[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]35[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]36[/C][C]2[/C][C]2.72727272727273[/C][C]-0.727272727272727[/C][/ROW]
[ROW][C]37[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]38[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]39[/C][C]3[/C][C]2.72727272727273[/C][C]0.272727272727273[/C][/ROW]
[ROW][C]40[/C][C]5[/C][C]3.46043165467626[/C][C]1.53956834532374[/C][/ROW]
[ROW][C]41[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]42[/C][C]4[/C][C]2.72727272727273[/C][C]1.27272727272727[/C][/ROW]
[ROW][C]43[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]44[/C][C]2[/C][C]2.72727272727273[/C][C]-0.727272727272727[/C][/ROW]
[ROW][C]45[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]46[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]47[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]3.46043165467626[/C][C]-2.46043165467626[/C][/ROW]
[ROW][C]49[/C][C]2[/C][C]3.46043165467626[/C][C]-1.46043165467626[/C][/ROW]
[ROW][C]50[/C][C]2[/C][C]2.72727272727273[/C][C]-0.727272727272727[/C][/ROW]
[ROW][C]51[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]52[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]53[/C][C]4[/C][C]2.72727272727273[/C][C]1.27272727272727[/C][/ROW]
[ROW][C]54[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]55[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]56[/C][C]5[/C][C]3.46043165467626[/C][C]1.53956834532374[/C][/ROW]
[ROW][C]57[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]58[/C][C]2[/C][C]2.72727272727273[/C][C]-0.727272727272727[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]3.46043165467626[/C][C]-2.46043165467626[/C][/ROW]
[ROW][C]60[/C][C]2[/C][C]3.46043165467626[/C][C]-1.46043165467626[/C][/ROW]
[ROW][C]61[/C][C]5[/C][C]3.46043165467626[/C][C]1.53956834532374[/C][/ROW]
[ROW][C]62[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]63[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]64[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]65[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]66[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]67[/C][C]2[/C][C]2.72727272727273[/C][C]-0.727272727272727[/C][/ROW]
[ROW][C]68[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]69[/C][C]4[/C][C]2.72727272727273[/C][C]1.27272727272727[/C][/ROW]
[ROW][C]70[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]71[/C][C]2[/C][C]2.72727272727273[/C][C]-0.727272727272727[/C][/ROW]
[ROW][C]72[/C][C]4[/C][C]2.72727272727273[/C][C]1.27272727272727[/C][/ROW]
[ROW][C]73[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]74[/C][C]3[/C][C]2.72727272727273[/C][C]0.272727272727273[/C][/ROW]
[ROW][C]75[/C][C]5[/C][C]3.46043165467626[/C][C]1.53956834532374[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]2.72727272727273[/C][C]-1.72727272727273[/C][/ROW]
[ROW][C]77[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]78[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]79[/C][C]5[/C][C]3.46043165467626[/C][C]1.53956834532374[/C][/ROW]
[ROW][C]80[/C][C]2[/C][C]3.46043165467626[/C][C]-1.46043165467626[/C][/ROW]
[ROW][C]81[/C][C]3[/C][C]2.72727272727273[/C][C]0.272727272727273[/C][/ROW]
[ROW][C]82[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]83[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]84[/C][C]2[/C][C]3.46043165467626[/C][C]-1.46043165467626[/C][/ROW]
[ROW][C]85[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]86[/C][C]3[/C][C]2.72727272727273[/C][C]0.272727272727273[/C][/ROW]
[ROW][C]87[/C][C]3[/C][C]2.72727272727273[/C][C]0.272727272727273[/C][/ROW]
[ROW][C]88[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]89[/C][C]2[/C][C]3.46043165467626[/C][C]-1.46043165467626[/C][/ROW]
[ROW][C]90[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]91[/C][C]2[/C][C]2.72727272727273[/C][C]-0.727272727272727[/C][/ROW]
[ROW][C]92[/C][C]4[/C][C]2.72727272727273[/C][C]1.27272727272727[/C][/ROW]
[ROW][C]93[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]94[/C][C]2[/C][C]3.46043165467626[/C][C]-1.46043165467626[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]2.72727272727273[/C][C]-1.72727272727273[/C][/ROW]
[ROW][C]96[/C][C]5[/C][C]2.72727272727273[/C][C]2.27272727272727[/C][/ROW]
[ROW][C]97[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]98[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]99[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]100[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]101[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]2.72727272727273[/C][C]-1.72727272727273[/C][/ROW]
[ROW][C]103[/C][C]5[/C][C]3.46043165467626[/C][C]1.53956834532374[/C][/ROW]
[ROW][C]104[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]105[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]106[/C][C]2[/C][C]2.72727272727273[/C][C]-0.727272727272727[/C][/ROW]
[ROW][C]107[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]108[/C][C]4[/C][C]2.72727272727273[/C][C]1.27272727272727[/C][/ROW]
[ROW][C]109[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]110[/C][C]4[/C][C]2.72727272727273[/C][C]1.27272727272727[/C][/ROW]
[ROW][C]111[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]112[/C][C]2[/C][C]2.72727272727273[/C][C]-0.727272727272727[/C][/ROW]
[ROW][C]113[/C][C]3[/C][C]2.72727272727273[/C][C]0.272727272727273[/C][/ROW]
[ROW][C]114[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]115[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]116[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]117[/C][C]5[/C][C]2.72727272727273[/C][C]2.27272727272727[/C][/ROW]
[ROW][C]118[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]119[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]120[/C][C]2[/C][C]2.72727272727273[/C][C]-0.727272727272727[/C][/ROW]
[ROW][C]121[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]122[/C][C]1[/C][C]2.72727272727273[/C][C]-1.72727272727273[/C][/ROW]
[ROW][C]123[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]124[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]125[/C][C]4[/C][C]2.72727272727273[/C][C]1.27272727272727[/C][/ROW]
[ROW][C]126[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]127[/C][C]5[/C][C]3.46043165467626[/C][C]1.53956834532374[/C][/ROW]
[ROW][C]128[/C][C]2[/C][C]3.46043165467626[/C][C]-1.46043165467626[/C][/ROW]
[ROW][C]129[/C][C]2[/C][C]3.46043165467626[/C][C]-1.46043165467626[/C][/ROW]
[ROW][C]130[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]131[/C][C]3[/C][C]2.72727272727273[/C][C]0.272727272727273[/C][/ROW]
[ROW][C]132[/C][C]2[/C][C]3.46043165467626[/C][C]-1.46043165467626[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]3.46043165467626[/C][C]-2.46043165467626[/C][/ROW]
[ROW][C]134[/C][C]3[/C][C]2.72727272727273[/C][C]0.272727272727273[/C][/ROW]
[ROW][C]135[/C][C]5[/C][C]3.46043165467626[/C][C]1.53956834532374[/C][/ROW]
[ROW][C]136[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]137[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]138[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]139[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]140[/C][C]5[/C][C]2.72727272727273[/C][C]2.27272727272727[/C][/ROW]
[ROW][C]141[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]142[/C][C]3[/C][C]2.72727272727273[/C][C]0.272727272727273[/C][/ROW]
[ROW][C]143[/C][C]3[/C][C]2.72727272727273[/C][C]0.272727272727273[/C][/ROW]
[ROW][C]144[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]145[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]146[/C][C]2[/C][C]2.72727272727273[/C][C]-0.727272727272727[/C][/ROW]
[ROW][C]147[/C][C]2[/C][C]2.72727272727273[/C][C]-0.727272727272727[/C][/ROW]
[ROW][C]148[/C][C]4[/C][C]2.72727272727273[/C][C]1.27272727272727[/C][/ROW]
[ROW][C]149[/C][C]3[/C][C]2.72727272727273[/C][C]0.272727272727273[/C][/ROW]
[ROW][C]150[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]151[/C][C]2[/C][C]2.72727272727273[/C][C]-0.727272727272727[/C][/ROW]
[ROW][C]152[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]153[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]154[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]155[/C][C]1[/C][C]2.72727272727273[/C][C]-1.72727272727273[/C][/ROW]
[ROW][C]156[/C][C]1[/C][C]3.46043165467626[/C][C]-2.46043165467626[/C][/ROW]
[ROW][C]157[/C][C]5[/C][C]3.46043165467626[/C][C]1.53956834532374[/C][/ROW]
[ROW][C]158[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]159[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]160[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]161[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]162[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]163[/C][C]2[/C][C]2.72727272727273[/C][C]-0.727272727272727[/C][/ROW]
[ROW][C]164[/C][C]1[/C][C]2.72727272727273[/C][C]-1.72727272727273[/C][/ROW]
[ROW][C]165[/C][C]1[/C][C]2.72727272727273[/C][C]-1.72727272727273[/C][/ROW]
[ROW][C]166[/C][C]5[/C][C]3.46043165467626[/C][C]1.53956834532374[/C][/ROW]
[ROW][C]167[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]168[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]169[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]170[/C][C]5[/C][C]3.46043165467626[/C][C]1.53956834532374[/C][/ROW]
[ROW][C]171[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]172[/C][C]4[/C][C]2.72727272727273[/C][C]1.27272727272727[/C][/ROW]
[ROW][C]173[/C][C]2[/C][C]3.46043165467626[/C][C]-1.46043165467626[/C][/ROW]
[ROW][C]174[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]175[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]176[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]177[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]178[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]179[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]180[/C][C]1[/C][C]2.72727272727273[/C][C]-1.72727272727273[/C][/ROW]
[ROW][C]181[/C][C]2[/C][C]3.46043165467626[/C][C]-1.46043165467626[/C][/ROW]
[ROW][C]182[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]183[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]184[/C][C]5[/C][C]3.46043165467626[/C][C]1.53956834532374[/C][/ROW]
[ROW][C]185[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]186[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]187[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]188[/C][C]3[/C][C]2.72727272727273[/C][C]0.272727272727273[/C][/ROW]
[ROW][C]189[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]190[/C][C]4[/C][C]3.46043165467626[/C][C]0.539568345323741[/C][/ROW]
[ROW][C]191[/C][C]3[/C][C]3.46043165467626[/C][C]-0.460431654676259[/C][/ROW]
[ROW][C]192[/C][C]2[/C][C]2.72727272727273[/C][C]-0.727272727272727[/C][/ROW]
[ROW][C]193[/C][C]4[/C][C]2.72727272727273[/C][C]1.27272727272727[/C][/ROW]
[ROW][C]194[/C][C]2[/C][C]3.46043165467626[/C][C]-1.46043165467626[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115458&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115458&T=2

As an alternative you can also use a QR Code:

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

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	4	3.46043165467626	0.539568345323741
2	4	3.46043165467626	0.539568345323741
3	5	3.46043165467626	1.53956834532374
4	2	2.72727272727273	-0.727272727272727
5	3	2.72727272727273	0.272727272727273
6	5	3.46043165467626	1.53956834532374
7	4	3.46043165467626	0.539568345323741
8	4	3.46043165467626	0.539568345323741
9	4	3.46043165467626	0.539568345323741
10	4	3.46043165467626	0.539568345323741
11	5	3.46043165467626	1.53956834532374
12	4	3.46043165467626	0.539568345323741
13	3	3.46043165467626	-0.460431654676259
14	4	2.72727272727273	1.27272727272727
15	3	3.46043165467626	-0.460431654676259
16	3	2.72727272727273	0.272727272727273
17	4	3.46043165467626	0.539568345323741
18	3	3.46043165467626	-0.460431654676259
19	4	3.46043165467626	0.539568345323741
20	2	2.72727272727273	-0.727272727272727
21	4	3.46043165467626	0.539568345323741
22	3	2.72727272727273	0.272727272727273
23	4	3.46043165467626	0.539568345323741
24	4	3.46043165467626	0.539568345323741
25	3	3.46043165467626	-0.460431654676259
26	3	3.46043165467626	-0.460431654676259
27	2	3.46043165467626	-1.46043165467626
28	4	3.46043165467626	0.539568345323741
29	5	3.46043165467626	1.53956834532374
30	4	3.46043165467626	0.539568345323741
31	2	3.46043165467626	-1.46043165467626
32	5	3.46043165467626	1.53956834532374
33	4	3.46043165467626	0.539568345323741
34	3	3.46043165467626	-0.460431654676259
35	4	3.46043165467626	0.539568345323741
36	2	2.72727272727273	-0.727272727272727
37	3	3.46043165467626	-0.460431654676259
38	3	3.46043165467626	-0.460431654676259
39	3	2.72727272727273	0.272727272727273
40	5	3.46043165467626	1.53956834532374
41	4	3.46043165467626	0.539568345323741
42	4	2.72727272727273	1.27272727272727
43	4	3.46043165467626	0.539568345323741
44	2	2.72727272727273	-0.727272727272727
45	4	3.46043165467626	0.539568345323741
46	3	3.46043165467626	-0.460431654676259
47	4	3.46043165467626	0.539568345323741
48	1	3.46043165467626	-2.46043165467626
49	2	3.46043165467626	-1.46043165467626
50	2	2.72727272727273	-0.727272727272727
51	4	3.46043165467626	0.539568345323741
52	3	3.46043165467626	-0.460431654676259
53	4	2.72727272727273	1.27272727272727
54	3	3.46043165467626	-0.460431654676259
55	3	3.46043165467626	-0.460431654676259
56	5	3.46043165467626	1.53956834532374
57	3	3.46043165467626	-0.460431654676259
58	2	2.72727272727273	-0.727272727272727
59	1	3.46043165467626	-2.46043165467626
60	2	3.46043165467626	-1.46043165467626
61	5	3.46043165467626	1.53956834532374
62	4	3.46043165467626	0.539568345323741
63	4	3.46043165467626	0.539568345323741
64	3	3.46043165467626	-0.460431654676259
65	4	3.46043165467626	0.539568345323741
66	4	3.46043165467626	0.539568345323741
67	2	2.72727272727273	-0.727272727272727
68	3	3.46043165467626	-0.460431654676259
69	4	2.72727272727273	1.27272727272727
70	3	3.46043165467626	-0.460431654676259
71	2	2.72727272727273	-0.727272727272727
72	4	2.72727272727273	1.27272727272727
73	4	3.46043165467626	0.539568345323741
74	3	2.72727272727273	0.272727272727273
75	5	3.46043165467626	1.53956834532374
76	1	2.72727272727273	-1.72727272727273
77	3	3.46043165467626	-0.460431654676259
78	3	3.46043165467626	-0.460431654676259
79	5	3.46043165467626	1.53956834532374
80	2	3.46043165467626	-1.46043165467626
81	3	2.72727272727273	0.272727272727273
82	3	3.46043165467626	-0.460431654676259
83	4	3.46043165467626	0.539568345323741
84	2	3.46043165467626	-1.46043165467626
85	4	3.46043165467626	0.539568345323741
86	3	2.72727272727273	0.272727272727273
87	3	2.72727272727273	0.272727272727273
88	3	3.46043165467626	-0.460431654676259
89	2	3.46043165467626	-1.46043165467626
90	3	3.46043165467626	-0.460431654676259
91	2	2.72727272727273	-0.727272727272727
92	4	2.72727272727273	1.27272727272727
93	4	3.46043165467626	0.539568345323741
94	2	3.46043165467626	-1.46043165467626
95	1	2.72727272727273	-1.72727272727273
96	5	2.72727272727273	2.27272727272727
97	4	3.46043165467626	0.539568345323741
98	4	3.46043165467626	0.539568345323741
99	4	3.46043165467626	0.539568345323741
100	3	3.46043165467626	-0.460431654676259
101	3	3.46043165467626	-0.460431654676259
102	1	2.72727272727273	-1.72727272727273
103	5	3.46043165467626	1.53956834532374
104	3	3.46043165467626	-0.460431654676259
105	3	3.46043165467626	-0.460431654676259
106	2	2.72727272727273	-0.727272727272727
107	4	3.46043165467626	0.539568345323741
108	4	2.72727272727273	1.27272727272727
109	3	3.46043165467626	-0.460431654676259
110	4	2.72727272727273	1.27272727272727
111	4	3.46043165467626	0.539568345323741
112	2	2.72727272727273	-0.727272727272727
113	3	2.72727272727273	0.272727272727273
114	3	3.46043165467626	-0.460431654676259
115	3	3.46043165467626	-0.460431654676259
116	4	3.46043165467626	0.539568345323741
117	5	2.72727272727273	2.27272727272727
118	3	3.46043165467626	-0.460431654676259
119	3	3.46043165467626	-0.460431654676259
120	2	2.72727272727273	-0.727272727272727
121	3	3.46043165467626	-0.460431654676259
122	1	2.72727272727273	-1.72727272727273
123	4	3.46043165467626	0.539568345323741
124	4	3.46043165467626	0.539568345323741
125	4	2.72727272727273	1.27272727272727
126	3	3.46043165467626	-0.460431654676259
127	5	3.46043165467626	1.53956834532374
128	2	3.46043165467626	-1.46043165467626
129	2	3.46043165467626	-1.46043165467626
130	3	3.46043165467626	-0.460431654676259
131	3	2.72727272727273	0.272727272727273
132	2	3.46043165467626	-1.46043165467626
133	1	3.46043165467626	-2.46043165467626
134	3	2.72727272727273	0.272727272727273
135	5	3.46043165467626	1.53956834532374
136	4	3.46043165467626	0.539568345323741
137	4	3.46043165467626	0.539568345323741
138	4	3.46043165467626	0.539568345323741
139	3	3.46043165467626	-0.460431654676259
140	5	2.72727272727273	2.27272727272727
141	3	3.46043165467626	-0.460431654676259
142	3	2.72727272727273	0.272727272727273
143	3	2.72727272727273	0.272727272727273
144	3	3.46043165467626	-0.460431654676259
145	4	3.46043165467626	0.539568345323741
146	2	2.72727272727273	-0.727272727272727
147	2	2.72727272727273	-0.727272727272727
148	4	2.72727272727273	1.27272727272727
149	3	2.72727272727273	0.272727272727273
150	3	3.46043165467626	-0.460431654676259
151	2	2.72727272727273	-0.727272727272727
152	3	3.46043165467626	-0.460431654676259
153	3	3.46043165467626	-0.460431654676259
154	4	3.46043165467626	0.539568345323741
155	1	2.72727272727273	-1.72727272727273
156	1	3.46043165467626	-2.46043165467626
157	5	3.46043165467626	1.53956834532374
158	4	3.46043165467626	0.539568345323741
159	3	3.46043165467626	-0.460431654676259
160	3	3.46043165467626	-0.460431654676259
161	4	3.46043165467626	0.539568345323741
162	3	3.46043165467626	-0.460431654676259
163	2	2.72727272727273	-0.727272727272727
164	1	2.72727272727273	-1.72727272727273
165	1	2.72727272727273	-1.72727272727273
166	5	3.46043165467626	1.53956834532374
167	4	3.46043165467626	0.539568345323741
168	3	3.46043165467626	-0.460431654676259
169	4	3.46043165467626	0.539568345323741
170	5	3.46043165467626	1.53956834532374
171	4	3.46043165467626	0.539568345323741
172	4	2.72727272727273	1.27272727272727
173	2	3.46043165467626	-1.46043165467626
174	3	3.46043165467626	-0.460431654676259
175	4	3.46043165467626	0.539568345323741
176	3	3.46043165467626	-0.460431654676259
177	4	3.46043165467626	0.539568345323741
178	3	3.46043165467626	-0.460431654676259
179	4	3.46043165467626	0.539568345323741
180	1	2.72727272727273	-1.72727272727273
181	2	3.46043165467626	-1.46043165467626
182	3	3.46043165467626	-0.460431654676259
183	3	3.46043165467626	-0.460431654676259
184	5	3.46043165467626	1.53956834532374
185	4	3.46043165467626	0.539568345323741
186	3	3.46043165467626	-0.460431654676259
187	3	3.46043165467626	-0.460431654676259
188	3	2.72727272727273	0.272727272727273
189	3	3.46043165467626	-0.460431654676259
190	4	3.46043165467626	0.539568345323741
191	3	3.46043165467626	-0.460431654676259
192	2	2.72727272727273	-0.727272727272727
193	4	2.72727272727273	1.27272727272727
194	2	3.46043165467626	-1.46043165467626

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 1 ; 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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code