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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

Wed, 29 Dec 2010 18:00:22 +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/29/t1293645513pif6ipw4m66w57j.htm/, Retrieved Fri, 03 May 2024 05:09:31 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=117006, Retrieved Fri, 03 May 2024 05:09:31 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

131

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]
-   PD      [Recursive Partitioning (Regression Trees)] [Recursive partiti...] [2010-12-27 16:33:37] [d6e648f00513dd750579ba7880c5fbf5]
-   P         [Recursive Partitioning (Regression Trees)] [] [2010-12-27 19:45:37] [e45804683e9a4263debf179d74e04a01]
-                 [Recursive Partitioning (Regression Trees)] [] [2010-12-29 18:00:22] [15be6a7406d91c9912c2afdb984c54ee] [Current]

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Dataseries X:

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Histogram

Boxplots

22	1	27	5	26	49	35
23	1	36	4	25	45	34
27	1	25	4	17	54	13
19	1	27	3	37	36	35
15	2	25	3	35	36	28
29	2	44	3	15	53	32
25	1	50	4	27	46	35
25	1	41	4	36	42	36
21	1	48	5	25	41	27
22	2	43	4	30	45	29
22	2	47	2	27	47	27
24	2	41	3	33	42	28
22	1	44	2	29	45	29
23	2	47	5	30	40	28
19	2	40	3	25	45	30
19	2	46	3	23	40	25
21	1	28	3	26	42	15
20	1	56	3	24	45	33
23	2	49	4	35	47	31
11	2	25	4	39	31	37
21	2	41	4	23	46	37
19	2	26	3	32	34	34
21	1	50	5	29	43	32
23	1	47	4	26	45	21
19	1	52	2	21	42	25
22	2	37	5	35	51	32
19	2	41	3	23	44	28
23	1	45	4	21	47	22
29	2	26	4	28	47	25
27	1	NA	3	30	41	26
18	1	52	4	21	44	34
30	1	46	2	29	51	34
26	1	58	3	28	46	36
20	1	54	5	19	47	36
22	1	29	3	26	46	26
20	2	50	3	33	38	26
21	1	43	2	34	50	34
18	2	30	3	33	48	33
21	2	47	2	40	36	31
27	1	45	3	24	51	33
NA	2	48	1	35	35	22
18	2	48	3	35	49	29
24	2	26	4	32	38	24
24	1	46	5	20	47	37
17	2	NA	3	35	36	32
22	2	50	3	35	47	23
21	1	25	4	21	46	29
23	1	47	2	33	43	35
19	2	47	2	40	53	20
22	1	41	3	22	55	28
19	2	45	2	35	39	26
24	2	41	4	20	55	36
22	2	45	5	28	41	26
26	2	40	3	46	33	33
22	1	29	4	18	52	25
23	2	34	5	22	42	29
27	1	45	5	20	56	32
21	2	52	3	25	46	35
16	2	41	4	31	33	24
21	2	48	3	21	51	31
18	2	45	3	23	46	29
25	1	54	2	26	46	27
20	2	25	3	34	50	29
24	2	26	4	31	46	29
20	1	28	4	23	51	27
24	2	50	4	31	48	34
23	2	48	4	26	44	32
23	2	51	3	36	38	31
22	2	53	3	28	42	31
22	1	37	3	34	39	31
20	1	56	2	25	45	16
14	1	43	3	33	31	25
21	1	34	3	46	29	27
23	1	42	3	24	48	32
17	2	32	3	32	38	28
25	2	31	5	33	55	25
10	1	46	3	42	32	25
25	2	30	5	17	51	36
23	2	47	4	36	53	36
27	2	33	4	40	47	36
16	1	25	4	30	45	27
19	1	25	5	19	33	29
23	2	21	4	33	49	32
19	2	36	5	35	46	29
19	2	50	3	23	42	31
26	2	48	3	15	56	34
19	2	48	2	38	35	27
22	1	25	3	37	40	28
21	1	48	4	23	44	32
22	2	49	5	41	46	33
20	1	27	5	34	46	29
20	1	28	3	38	39	32
20	2	43	2	45	35	35
21	2	48	3	27	48	33
21	2	48	4	46	42	27
14	1	25	1	26	39	16
28	2	49	4	44	39	32
24	1	26	3	36	41	26
24	1	51	3	20	52	32
24	2	25	4	44	45	38
19	1	29	3	27	42	24
19	1	29	4	27	44	26
14	1	43	2	41	33	19
29	2	46	3	30	42	37
22	1	44	3	33	46	25
21	1	25	3	37	45	24
15	1	51	2	30	40	23
23	1	42	5	20	48	28
24	2	53	5	44	32	38
20	1	25	4	20	53	28
25	2	49	2	33	39	28
25	1	51	3	31	45	26
19	2	20	3	23	36	21
23	2	44	3	33	38	35
22	2	38	4	33	49	31
19	1	46	5	32	46	34
24	2	42	4	25	43	30
21	1	29	NA	22	37	30
19	2	46	4	16	48	24
21	2	49	2	36	45	27
18	2	51	3	35	32	26
24	1	38	3	25	46	30
7	1	41	1	27	20	15
24	2	47	3	32	42	28
24	2	44	3	36	45	34
23	2	47	3	51	29	29
24	2	46	3	30	51	26
27	1	44	4	20	55	31
20	2	28	3	29	50	28
20	2	47	4	26	44	33
22	2	28	4	20	41	32
19	1	41	5	40	40	33
18	2	45	4	29	47	31
14	2	46	4	32	42	37
24	1	46	4	33	40	27
29	2	22	3	32	51	19
25	2	33	3	34	43	27
24	1	41	4	24	45	31
20	2	47	5	25	41	38
18	1	25	3	41	41	22
25	2	42	3	39	37	35
21	2	47	3	21	46	35
21	2	50	3	38	38	30
21	1	55	5	28	39	41
23	1	21	3	37	45	25
18	1	NA	3	26	46	28
23	1	52	3	30	39	45
13	2	49	4	25	21	21
23	2	46	4	38	31	33
17	1	NA	4	31	35	25
24	2	45	3	31	49	29
16	2	52	3	27	40	31
23	1	NA	3	21	45	29
20	2	40	4	26	46	31
24	2	49	4	37	45	31
15	1	38	5	28	34	25
20	1	32	5	29	41	27
27	2	46	4	33	43	26
27	2	32	3	41	45	26
19	2	41	3	19	48	23
22	2	43	3	37	43	27
16	1	44	4	36	45	24
21	1	47	5	27	45	35
18	2	28	3	33	34	24
22	1	52	1	29	40	32
18	1	27	2	42	40	24
24	2	45	5	27	55	24
24	1	27	4	47	44	38
19	1	25	4	17	44	36
26	1	28	4	34	48	24
28	1	25	3	32	51	18
23	1	52	4	25	49	34
22	1	44	3	27	33	23
20	2	43	3	37	43	35
20	2	47	4	34	44	22
27	2	52	4	27	44	34
19	2	40	2	37	41	28
23	1	42	3	32	45	34
19	1	45	5	26	44	32
21	1	45	2	29	44	24
13	1	50	5	28	40	34
18	1	49	3	19	48	33
19	1	52	2	46	49	33
23	2	48	3	31	46	29
30	2	51	3	42	49	38
22	2	49	4	33	55	24
23	2	31	4	39	51	25
22	2	43	3	27	46	37
22	2	31	3	35	37	33
23	2	28	4	23	43	30
27	2	43	4	32	41	22
23	2	31	3	22	45	28
18	2	51	3	17	39	24
24	2	58	4	35	38	33
19	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	7 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 & 7 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117006&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117006&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117006&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	7 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.4744
R-squared	0.2251
RMSE	3.2339

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.4744 \tabularnewline
R-squared & 0.2251 \tabularnewline
RMSE & 3.2339 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117006&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.4744[/C][/ROW]
[ROW][C]R-squared[/C][C]0.2251[/C][/ROW]
[ROW][C]RMSE[/C][C]3.2339[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117006&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117006&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	0.4744
R-squared	0.2251
RMSE	3.2339

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	22	21.6293706293706	0.37062937062937
2	23	21.6293706293706	1.37062937062937
3	27	24.4	2.6
4	19	17.6923076923077	1.30769230769231
5	15	17.6923076923077	-2.69230769230769
6	29	24.4	4.6
7	25	21.6293706293706	3.37062937062937
8	25	21.6293706293706	3.37062937062937
9	21	21.6293706293706	-0.62937062937063
10	22	21.6293706293706	0.37062937062937
11	22	21.6293706293706	0.37062937062937
12	24	21.6293706293706	2.37062937062937
13	22	21.6293706293706	0.37062937062937
14	23	21.6293706293706	1.37062937062937
15	19	21.6293706293706	-2.62937062937063
16	19	21.6293706293706	-2.62937062937063
17	21	21.6293706293706	-0.62937062937063
18	20	21.6293706293706	-1.62937062937063
19	23	21.6293706293706	1.37062937062937
20	11	17.6923076923077	-6.6923076923077
21	21	21.6293706293706	-0.62937062937063
22	19	17.6923076923077	1.30769230769231
23	21	21.6293706293706	-0.62937062937063
24	23	21.6293706293706	1.37062937062937
25	19	21.6293706293706	-2.62937062937063
26	22	24.4	-2.4
27	19	21.6293706293706	-2.62937062937063
28	23	21.6293706293706	1.37062937062937
29	29	21.6293706293706	7.37062937062937
30	27	21.6293706293706	5.37062937062937
31	18	21.6293706293706	-3.62937062937063
32	30	24.4	5.6
33	26	21.6293706293706	4.37062937062937
34	20	21.6293706293706	-1.62937062937063
35	22	21.6293706293706	0.37062937062937
36	20	21.6293706293706	-1.62937062937063
37	21	21.6293706293706	-0.62937062937063
38	18	21.6293706293706	-3.62937062937063
39	21	17.6923076923077	3.30769230769231
40	27	24.4	2.6
41	18	21.6293706293706	-3.62937062937063
42	24	21.6293706293706	2.37062937062937
43	24	21.6293706293706	2.37062937062937
44	17	17.6923076923077	-0.692307692307693
45	22	21.6293706293706	0.37062937062937
46	21	21.6293706293706	-0.62937062937063
47	23	21.6293706293706	1.37062937062937
48	19	24.4	-5.4
49	22	24.4	-2.4
50	19	21.6293706293706	-2.62937062937063
51	24	24.4	-0.399999999999999
52	22	21.6293706293706	0.37062937062937
53	26	17.6923076923077	8.3076923076923
54	22	24.4	-2.4
55	23	21.6293706293706	1.37062937062937
56	27	24.4	2.6
57	21	21.6293706293706	-0.62937062937063
58	16	17.6923076923077	-1.69230769230769
59	21	24.4	-3.4
60	18	21.6293706293706	-3.62937062937063
61	25	21.6293706293706	3.37062937062937
62	20	21.6293706293706	-1.62937062937063
63	24	21.6293706293706	2.37062937062937
64	20	24.4	-4.4
65	24	21.6293706293706	2.37062937062937
66	23	21.6293706293706	1.37062937062937
67	23	21.6293706293706	1.37062937062937
68	22	21.6293706293706	0.37062937062937
69	22	21.6293706293706	0.37062937062937
70	20	21.6293706293706	-1.62937062937063
71	14	17.6923076923077	-3.69230769230769
72	21	17.6923076923077	3.30769230769231
73	23	21.6293706293706	1.37062937062937
74	17	21.6293706293706	-4.62937062937063
75	25	24.4	0.600000000000001
76	10	17.6923076923077	-7.6923076923077
77	25	24.4	0.600000000000001
78	23	24.4	-1.40000000000000
79	27	21.6293706293706	5.37062937062937
80	16	21.6293706293706	-5.62937062937063
81	19	17.6923076923077	1.30769230769231
82	23	21.6293706293706	1.37062937062937
83	19	21.6293706293706	-2.62937062937063
84	19	21.6293706293706	-2.62937062937063
85	26	24.4	1.6
86	19	17.6923076923077	1.30769230769231
87	22	21.6293706293706	0.37062937062937
88	21	21.6293706293706	-0.62937062937063
89	22	21.6293706293706	0.37062937062937
90	20	21.6293706293706	-1.62937062937063
91	20	21.6293706293706	-1.62937062937063
92	20	17.6923076923077	2.30769230769231
93	21	21.6293706293706	-0.62937062937063
94	21	21.6293706293706	-0.62937062937063
95	14	21.6293706293706	-7.62937062937063
96	28	21.6293706293706	6.37062937062937
97	24	21.6293706293706	2.37062937062937
98	24	24.4	-0.399999999999999
99	24	21.6293706293706	2.37062937062937
100	19	21.6293706293706	-2.62937062937063
101	19	21.6293706293706	-2.62937062937063
102	14	17.6923076923077	-3.69230769230769
103	29	21.6293706293706	7.37062937062937
104	22	21.6293706293706	0.37062937062937
105	21	21.6293706293706	-0.62937062937063
106	15	21.6293706293706	-6.62937062937063
107	23	21.6293706293706	1.37062937062937
108	24	17.6923076923077	6.30769230769231
109	20	24.4	-4.4
110	25	21.6293706293706	3.37062937062937
111	25	21.6293706293706	3.37062937062937
112	19	17.6923076923077	1.30769230769231
113	23	21.6293706293706	1.37062937062937
114	22	21.6293706293706	0.37062937062937
115	19	21.6293706293706	-2.62937062937063
116	24	21.6293706293706	2.37062937062937
117	21	21.6293706293706	-0.62937062937063
118	19	21.6293706293706	-2.62937062937063
119	21	21.6293706293706	-0.62937062937063
120	18	17.6923076923077	0.307692307692307
121	24	21.6293706293706	2.37062937062937
122	7	17.6923076923077	-10.6923076923077
123	24	21.6293706293706	2.37062937062937
124	24	21.6293706293706	2.37062937062937
125	23	17.6923076923077	5.30769230769231
126	24	24.4	-0.399999999999999
127	27	24.4	2.6
128	20	21.6293706293706	-1.62937062937063
129	20	21.6293706293706	-1.62937062937063
130	22	21.6293706293706	0.37062937062937
131	19	21.6293706293706	-2.62937062937063
132	18	21.6293706293706	-3.62937062937063
133	14	21.6293706293706	-7.62937062937063
134	24	21.6293706293706	2.37062937062937
135	29	24.4	4.6
136	25	21.6293706293706	3.37062937062937
137	24	21.6293706293706	2.37062937062937
138	20	21.6293706293706	-1.62937062937063
139	18	21.6293706293706	-3.62937062937063
140	25	21.6293706293706	3.37062937062937
141	21	21.6293706293706	-0.62937062937063
142	21	21.6293706293706	-0.62937062937063
143	21	21.6293706293706	-0.62937062937063
144	23	21.6293706293706	1.37062937062937
145	18	21.6293706293706	-3.62937062937063
146	23	21.6293706293706	1.37062937062937
147	13	17.6923076923077	-4.69230769230769
148	23	17.6923076923077	5.30769230769231
149	17	17.6923076923077	-0.692307692307693
150	24	21.6293706293706	2.37062937062937
151	16	21.6293706293706	-5.62937062937063
152	23	21.6293706293706	1.37062937062937
153	20	21.6293706293706	-1.62937062937063
154	24	21.6293706293706	2.37062937062937
155	15	17.6923076923077	-2.69230769230769
156	20	21.6293706293706	-1.62937062937063
157	27	21.6293706293706	5.37062937062937
158	27	21.6293706293706	5.37062937062937
159	19	21.6293706293706	-2.62937062937063
160	22	21.6293706293706	0.37062937062937
161	16	21.6293706293706	-5.62937062937063
162	21	21.6293706293706	-0.62937062937063
163	18	17.6923076923077	0.307692307692307
164	22	21.6293706293706	0.37062937062937
165	18	21.6293706293706	-3.62937062937063
166	24	24.4	-0.399999999999999
167	24	21.6293706293706	2.37062937062937
168	19	21.6293706293706	-2.62937062937063
169	26	21.6293706293706	4.37062937062937
170	28	24.4	3.6
171	23	21.6293706293706	1.37062937062937
172	22	17.6923076923077	4.30769230769231
173	20	21.6293706293706	-1.62937062937063
174	20	21.6293706293706	-1.62937062937063
175	27	21.6293706293706	5.37062937062937
176	19	21.6293706293706	-2.62937062937063
177	23	21.6293706293706	1.37062937062937
178	19	21.6293706293706	-2.62937062937063
179	21	21.6293706293706	-0.62937062937063
180	13	21.6293706293706	-8.62937062937063
181	18	21.6293706293706	-3.62937062937063
182	19	21.6293706293706	-2.62937062937063
183	23	21.6293706293706	1.37062937062937
184	30	21.6293706293706	8.37062937062937
185	22	24.4	-2.4
186	23	24.4	-1.40000000000000
187	22	21.6293706293706	0.37062937062937
188	22	21.6293706293706	0.37062937062937
189	23	21.6293706293706	1.37062937062937
190	27	21.6293706293706	5.37062937062937
191	23	21.6293706293706	1.37062937062937
192	18	21.6293706293706	-3.62937062937063
193	24	21.6293706293706	2.37062937062937
194	19	21.6293706293706	-2.62937062937063

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 22 & 21.6293706293706 & 0.37062937062937 \tabularnewline
2 & 23 & 21.6293706293706 & 1.37062937062937 \tabularnewline
3 & 27 & 24.4 & 2.6 \tabularnewline
4 & 19 & 17.6923076923077 & 1.30769230769231 \tabularnewline
5 & 15 & 17.6923076923077 & -2.69230769230769 \tabularnewline
6 & 29 & 24.4 & 4.6 \tabularnewline
7 & 25 & 21.6293706293706 & 3.37062937062937 \tabularnewline
8 & 25 & 21.6293706293706 & 3.37062937062937 \tabularnewline
9 & 21 & 21.6293706293706 & -0.62937062937063 \tabularnewline
10 & 22 & 21.6293706293706 & 0.37062937062937 \tabularnewline
11 & 22 & 21.6293706293706 & 0.37062937062937 \tabularnewline
12 & 24 & 21.6293706293706 & 2.37062937062937 \tabularnewline
13 & 22 & 21.6293706293706 & 0.37062937062937 \tabularnewline
14 & 23 & 21.6293706293706 & 1.37062937062937 \tabularnewline
15 & 19 & 21.6293706293706 & -2.62937062937063 \tabularnewline
16 & 19 & 21.6293706293706 & -2.62937062937063 \tabularnewline
17 & 21 & 21.6293706293706 & -0.62937062937063 \tabularnewline
18 & 20 & 21.6293706293706 & -1.62937062937063 \tabularnewline
19 & 23 & 21.6293706293706 & 1.37062937062937 \tabularnewline
20 & 11 & 17.6923076923077 & -6.6923076923077 \tabularnewline
21 & 21 & 21.6293706293706 & -0.62937062937063 \tabularnewline
22 & 19 & 17.6923076923077 & 1.30769230769231 \tabularnewline
23 & 21 & 21.6293706293706 & -0.62937062937063 \tabularnewline
24 & 23 & 21.6293706293706 & 1.37062937062937 \tabularnewline
25 & 19 & 21.6293706293706 & -2.62937062937063 \tabularnewline
26 & 22 & 24.4 & -2.4 \tabularnewline
27 & 19 & 21.6293706293706 & -2.62937062937063 \tabularnewline
28 & 23 & 21.6293706293706 & 1.37062937062937 \tabularnewline
29 & 29 & 21.6293706293706 & 7.37062937062937 \tabularnewline
30 & 27 & 21.6293706293706 & 5.37062937062937 \tabularnewline
31 & 18 & 21.6293706293706 & -3.62937062937063 \tabularnewline
32 & 30 & 24.4 & 5.6 \tabularnewline
33 & 26 & 21.6293706293706 & 4.37062937062937 \tabularnewline
34 & 20 & 21.6293706293706 & -1.62937062937063 \tabularnewline
35 & 22 & 21.6293706293706 & 0.37062937062937 \tabularnewline
36 & 20 & 21.6293706293706 & -1.62937062937063 \tabularnewline
37 & 21 & 21.6293706293706 & -0.62937062937063 \tabularnewline
38 & 18 & 21.6293706293706 & -3.62937062937063 \tabularnewline
39 & 21 & 17.6923076923077 & 3.30769230769231 \tabularnewline
40 & 27 & 24.4 & 2.6 \tabularnewline
41 & 18 & 21.6293706293706 & -3.62937062937063 \tabularnewline
42 & 24 & 21.6293706293706 & 2.37062937062937 \tabularnewline
43 & 24 & 21.6293706293706 & 2.37062937062937 \tabularnewline
44 & 17 & 17.6923076923077 & -0.692307692307693 \tabularnewline
45 & 22 & 21.6293706293706 & 0.37062937062937 \tabularnewline
46 & 21 & 21.6293706293706 & -0.62937062937063 \tabularnewline
47 & 23 & 21.6293706293706 & 1.37062937062937 \tabularnewline
48 & 19 & 24.4 & -5.4 \tabularnewline
49 & 22 & 24.4 & -2.4 \tabularnewline
50 & 19 & 21.6293706293706 & -2.62937062937063 \tabularnewline
51 & 24 & 24.4 & -0.399999999999999 \tabularnewline
52 & 22 & 21.6293706293706 & 0.37062937062937 \tabularnewline
53 & 26 & 17.6923076923077 & 8.3076923076923 \tabularnewline
54 & 22 & 24.4 & -2.4 \tabularnewline
55 & 23 & 21.6293706293706 & 1.37062937062937 \tabularnewline
56 & 27 & 24.4 & 2.6 \tabularnewline
57 & 21 & 21.6293706293706 & -0.62937062937063 \tabularnewline
58 & 16 & 17.6923076923077 & -1.69230769230769 \tabularnewline
59 & 21 & 24.4 & -3.4 \tabularnewline
60 & 18 & 21.6293706293706 & -3.62937062937063 \tabularnewline
61 & 25 & 21.6293706293706 & 3.37062937062937 \tabularnewline
62 & 20 & 21.6293706293706 & -1.62937062937063 \tabularnewline
63 & 24 & 21.6293706293706 & 2.37062937062937 \tabularnewline
64 & 20 & 24.4 & -4.4 \tabularnewline
65 & 24 & 21.6293706293706 & 2.37062937062937 \tabularnewline
66 & 23 & 21.6293706293706 & 1.37062937062937 \tabularnewline
67 & 23 & 21.6293706293706 & 1.37062937062937 \tabularnewline
68 & 22 & 21.6293706293706 & 0.37062937062937 \tabularnewline
69 & 22 & 21.6293706293706 & 0.37062937062937 \tabularnewline
70 & 20 & 21.6293706293706 & -1.62937062937063 \tabularnewline
71 & 14 & 17.6923076923077 & -3.69230769230769 \tabularnewline
72 & 21 & 17.6923076923077 & 3.30769230769231 \tabularnewline
73 & 23 & 21.6293706293706 & 1.37062937062937 \tabularnewline
74 & 17 & 21.6293706293706 & -4.62937062937063 \tabularnewline
75 & 25 & 24.4 & 0.600000000000001 \tabularnewline
76 & 10 & 17.6923076923077 & -7.6923076923077 \tabularnewline
77 & 25 & 24.4 & 0.600000000000001 \tabularnewline
78 & 23 & 24.4 & -1.40000000000000 \tabularnewline
79 & 27 & 21.6293706293706 & 5.37062937062937 \tabularnewline
80 & 16 & 21.6293706293706 & -5.62937062937063 \tabularnewline
81 & 19 & 17.6923076923077 & 1.30769230769231 \tabularnewline
82 & 23 & 21.6293706293706 & 1.37062937062937 \tabularnewline
83 & 19 & 21.6293706293706 & -2.62937062937063 \tabularnewline
84 & 19 & 21.6293706293706 & -2.62937062937063 \tabularnewline
85 & 26 & 24.4 & 1.6 \tabularnewline
86 & 19 & 17.6923076923077 & 1.30769230769231 \tabularnewline
87 & 22 & 21.6293706293706 & 0.37062937062937 \tabularnewline
88 & 21 & 21.6293706293706 & -0.62937062937063 \tabularnewline
89 & 22 & 21.6293706293706 & 0.37062937062937 \tabularnewline
90 & 20 & 21.6293706293706 & -1.62937062937063 \tabularnewline
91 & 20 & 21.6293706293706 & -1.62937062937063 \tabularnewline
92 & 20 & 17.6923076923077 & 2.30769230769231 \tabularnewline
93 & 21 & 21.6293706293706 & -0.62937062937063 \tabularnewline
94 & 21 & 21.6293706293706 & -0.62937062937063 \tabularnewline
95 & 14 & 21.6293706293706 & -7.62937062937063 \tabularnewline
96 & 28 & 21.6293706293706 & 6.37062937062937 \tabularnewline
97 & 24 & 21.6293706293706 & 2.37062937062937 \tabularnewline
98 & 24 & 24.4 & -0.399999999999999 \tabularnewline
99 & 24 & 21.6293706293706 & 2.37062937062937 \tabularnewline
100 & 19 & 21.6293706293706 & -2.62937062937063 \tabularnewline
101 & 19 & 21.6293706293706 & -2.62937062937063 \tabularnewline
102 & 14 & 17.6923076923077 & -3.69230769230769 \tabularnewline
103 & 29 & 21.6293706293706 & 7.37062937062937 \tabularnewline
104 & 22 & 21.6293706293706 & 0.37062937062937 \tabularnewline
105 & 21 & 21.6293706293706 & -0.62937062937063 \tabularnewline
106 & 15 & 21.6293706293706 & -6.62937062937063 \tabularnewline
107 & 23 & 21.6293706293706 & 1.37062937062937 \tabularnewline
108 & 24 & 17.6923076923077 & 6.30769230769231 \tabularnewline
109 & 20 & 24.4 & -4.4 \tabularnewline
110 & 25 & 21.6293706293706 & 3.37062937062937 \tabularnewline
111 & 25 & 21.6293706293706 & 3.37062937062937 \tabularnewline
112 & 19 & 17.6923076923077 & 1.30769230769231 \tabularnewline
113 & 23 & 21.6293706293706 & 1.37062937062937 \tabularnewline
114 & 22 & 21.6293706293706 & 0.37062937062937 \tabularnewline
115 & 19 & 21.6293706293706 & -2.62937062937063 \tabularnewline
116 & 24 & 21.6293706293706 & 2.37062937062937 \tabularnewline
117 & 21 & 21.6293706293706 & -0.62937062937063 \tabularnewline
118 & 19 & 21.6293706293706 & -2.62937062937063 \tabularnewline
119 & 21 & 21.6293706293706 & -0.62937062937063 \tabularnewline
120 & 18 & 17.6923076923077 & 0.307692307692307 \tabularnewline
121 & 24 & 21.6293706293706 & 2.37062937062937 \tabularnewline
122 & 7 & 17.6923076923077 & -10.6923076923077 \tabularnewline
123 & 24 & 21.6293706293706 & 2.37062937062937 \tabularnewline
124 & 24 & 21.6293706293706 & 2.37062937062937 \tabularnewline
125 & 23 & 17.6923076923077 & 5.30769230769231 \tabularnewline
126 & 24 & 24.4 & -0.399999999999999 \tabularnewline
127 & 27 & 24.4 & 2.6 \tabularnewline
128 & 20 & 21.6293706293706 & -1.62937062937063 \tabularnewline
129 & 20 & 21.6293706293706 & -1.62937062937063 \tabularnewline
130 & 22 & 21.6293706293706 & 0.37062937062937 \tabularnewline
131 & 19 & 21.6293706293706 & -2.62937062937063 \tabularnewline
132 & 18 & 21.6293706293706 & -3.62937062937063 \tabularnewline
133 & 14 & 21.6293706293706 & -7.62937062937063 \tabularnewline
134 & 24 & 21.6293706293706 & 2.37062937062937 \tabularnewline
135 & 29 & 24.4 & 4.6 \tabularnewline
136 & 25 & 21.6293706293706 & 3.37062937062937 \tabularnewline
137 & 24 & 21.6293706293706 & 2.37062937062937 \tabularnewline
138 & 20 & 21.6293706293706 & -1.62937062937063 \tabularnewline
139 & 18 & 21.6293706293706 & -3.62937062937063 \tabularnewline
140 & 25 & 21.6293706293706 & 3.37062937062937 \tabularnewline
141 & 21 & 21.6293706293706 & -0.62937062937063 \tabularnewline
142 & 21 & 21.6293706293706 & -0.62937062937063 \tabularnewline
143 & 21 & 21.6293706293706 & -0.62937062937063 \tabularnewline
144 & 23 & 21.6293706293706 & 1.37062937062937 \tabularnewline
145 & 18 & 21.6293706293706 & -3.62937062937063 \tabularnewline
146 & 23 & 21.6293706293706 & 1.37062937062937 \tabularnewline
147 & 13 & 17.6923076923077 & -4.69230769230769 \tabularnewline
148 & 23 & 17.6923076923077 & 5.30769230769231 \tabularnewline
149 & 17 & 17.6923076923077 & -0.692307692307693 \tabularnewline
150 & 24 & 21.6293706293706 & 2.37062937062937 \tabularnewline
151 & 16 & 21.6293706293706 & -5.62937062937063 \tabularnewline
152 & 23 & 21.6293706293706 & 1.37062937062937 \tabularnewline
153 & 20 & 21.6293706293706 & -1.62937062937063 \tabularnewline
154 & 24 & 21.6293706293706 & 2.37062937062937 \tabularnewline
155 & 15 & 17.6923076923077 & -2.69230769230769 \tabularnewline
156 & 20 & 21.6293706293706 & -1.62937062937063 \tabularnewline
157 & 27 & 21.6293706293706 & 5.37062937062937 \tabularnewline
158 & 27 & 21.6293706293706 & 5.37062937062937 \tabularnewline
159 & 19 & 21.6293706293706 & -2.62937062937063 \tabularnewline
160 & 22 & 21.6293706293706 & 0.37062937062937 \tabularnewline
161 & 16 & 21.6293706293706 & -5.62937062937063 \tabularnewline
162 & 21 & 21.6293706293706 & -0.62937062937063 \tabularnewline
163 & 18 & 17.6923076923077 & 0.307692307692307 \tabularnewline
164 & 22 & 21.6293706293706 & 0.37062937062937 \tabularnewline
165 & 18 & 21.6293706293706 & -3.62937062937063 \tabularnewline
166 & 24 & 24.4 & -0.399999999999999 \tabularnewline
167 & 24 & 21.6293706293706 & 2.37062937062937 \tabularnewline
168 & 19 & 21.6293706293706 & -2.62937062937063 \tabularnewline
169 & 26 & 21.6293706293706 & 4.37062937062937 \tabularnewline
170 & 28 & 24.4 & 3.6 \tabularnewline
171 & 23 & 21.6293706293706 & 1.37062937062937 \tabularnewline
172 & 22 & 17.6923076923077 & 4.30769230769231 \tabularnewline
173 & 20 & 21.6293706293706 & -1.62937062937063 \tabularnewline
174 & 20 & 21.6293706293706 & -1.62937062937063 \tabularnewline
175 & 27 & 21.6293706293706 & 5.37062937062937 \tabularnewline
176 & 19 & 21.6293706293706 & -2.62937062937063 \tabularnewline
177 & 23 & 21.6293706293706 & 1.37062937062937 \tabularnewline
178 & 19 & 21.6293706293706 & -2.62937062937063 \tabularnewline
179 & 21 & 21.6293706293706 & -0.62937062937063 \tabularnewline
180 & 13 & 21.6293706293706 & -8.62937062937063 \tabularnewline
181 & 18 & 21.6293706293706 & -3.62937062937063 \tabularnewline
182 & 19 & 21.6293706293706 & -2.62937062937063 \tabularnewline
183 & 23 & 21.6293706293706 & 1.37062937062937 \tabularnewline
184 & 30 & 21.6293706293706 & 8.37062937062937 \tabularnewline
185 & 22 & 24.4 & -2.4 \tabularnewline
186 & 23 & 24.4 & -1.40000000000000 \tabularnewline
187 & 22 & 21.6293706293706 & 0.37062937062937 \tabularnewline
188 & 22 & 21.6293706293706 & 0.37062937062937 \tabularnewline
189 & 23 & 21.6293706293706 & 1.37062937062937 \tabularnewline
190 & 27 & 21.6293706293706 & 5.37062937062937 \tabularnewline
191 & 23 & 21.6293706293706 & 1.37062937062937 \tabularnewline
192 & 18 & 21.6293706293706 & -3.62937062937063 \tabularnewline
193 & 24 & 21.6293706293706 & 2.37062937062937 \tabularnewline
194 & 19 & 21.6293706293706 & -2.62937062937063 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117006&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]22[/C][C]21.6293706293706[/C][C]0.37062937062937[/C][/ROW]
[ROW][C]2[/C][C]23[/C][C]21.6293706293706[/C][C]1.37062937062937[/C][/ROW]
[ROW][C]3[/C][C]27[/C][C]24.4[/C][C]2.6[/C][/ROW]
[ROW][C]4[/C][C]19[/C][C]17.6923076923077[/C][C]1.30769230769231[/C][/ROW]
[ROW][C]5[/C][C]15[/C][C]17.6923076923077[/C][C]-2.69230769230769[/C][/ROW]
[ROW][C]6[/C][C]29[/C][C]24.4[/C][C]4.6[/C][/ROW]
[ROW][C]7[/C][C]25[/C][C]21.6293706293706[/C][C]3.37062937062937[/C][/ROW]
[ROW][C]8[/C][C]25[/C][C]21.6293706293706[/C][C]3.37062937062937[/C][/ROW]
[ROW][C]9[/C][C]21[/C][C]21.6293706293706[/C][C]-0.62937062937063[/C][/ROW]
[ROW][C]10[/C][C]22[/C][C]21.6293706293706[/C][C]0.37062937062937[/C][/ROW]
[ROW][C]11[/C][C]22[/C][C]21.6293706293706[/C][C]0.37062937062937[/C][/ROW]
[ROW][C]12[/C][C]24[/C][C]21.6293706293706[/C][C]2.37062937062937[/C][/ROW]
[ROW][C]13[/C][C]22[/C][C]21.6293706293706[/C][C]0.37062937062937[/C][/ROW]
[ROW][C]14[/C][C]23[/C][C]21.6293706293706[/C][C]1.37062937062937[/C][/ROW]
[ROW][C]15[/C][C]19[/C][C]21.6293706293706[/C][C]-2.62937062937063[/C][/ROW]
[ROW][C]16[/C][C]19[/C][C]21.6293706293706[/C][C]-2.62937062937063[/C][/ROW]
[ROW][C]17[/C][C]21[/C][C]21.6293706293706[/C][C]-0.62937062937063[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]21.6293706293706[/C][C]-1.62937062937063[/C][/ROW]
[ROW][C]19[/C][C]23[/C][C]21.6293706293706[/C][C]1.37062937062937[/C][/ROW]
[ROW][C]20[/C][C]11[/C][C]17.6923076923077[/C][C]-6.6923076923077[/C][/ROW]
[ROW][C]21[/C][C]21[/C][C]21.6293706293706[/C][C]-0.62937062937063[/C][/ROW]
[ROW][C]22[/C][C]19[/C][C]17.6923076923077[/C][C]1.30769230769231[/C][/ROW]
[ROW][C]23[/C][C]21[/C][C]21.6293706293706[/C][C]-0.62937062937063[/C][/ROW]
[ROW][C]24[/C][C]23[/C][C]21.6293706293706[/C][C]1.37062937062937[/C][/ROW]
[ROW][C]25[/C][C]19[/C][C]21.6293706293706[/C][C]-2.62937062937063[/C][/ROW]
[ROW][C]26[/C][C]22[/C][C]24.4[/C][C]-2.4[/C][/ROW]
[ROW][C]27[/C][C]19[/C][C]21.6293706293706[/C][C]-2.62937062937063[/C][/ROW]
[ROW][C]28[/C][C]23[/C][C]21.6293706293706[/C][C]1.37062937062937[/C][/ROW]
[ROW][C]29[/C][C]29[/C][C]21.6293706293706[/C][C]7.37062937062937[/C][/ROW]
[ROW][C]30[/C][C]27[/C][C]21.6293706293706[/C][C]5.37062937062937[/C][/ROW]
[ROW][C]31[/C][C]18[/C][C]21.6293706293706[/C][C]-3.62937062937063[/C][/ROW]
[ROW][C]32[/C][C]30[/C][C]24.4[/C][C]5.6[/C][/ROW]
[ROW][C]33[/C][C]26[/C][C]21.6293706293706[/C][C]4.37062937062937[/C][/ROW]
[ROW][C]34[/C][C]20[/C][C]21.6293706293706[/C][C]-1.62937062937063[/C][/ROW]
[ROW][C]35[/C][C]22[/C][C]21.6293706293706[/C][C]0.37062937062937[/C][/ROW]
[ROW][C]36[/C][C]20[/C][C]21.6293706293706[/C][C]-1.62937062937063[/C][/ROW]
[ROW][C]37[/C][C]21[/C][C]21.6293706293706[/C][C]-0.62937062937063[/C][/ROW]
[ROW][C]38[/C][C]18[/C][C]21.6293706293706[/C][C]-3.62937062937063[/C][/ROW]
[ROW][C]39[/C][C]21[/C][C]17.6923076923077[/C][C]3.30769230769231[/C][/ROW]
[ROW][C]40[/C][C]27[/C][C]24.4[/C][C]2.6[/C][/ROW]
[ROW][C]41[/C][C]18[/C][C]21.6293706293706[/C][C]-3.62937062937063[/C][/ROW]
[ROW][C]42[/C][C]24[/C][C]21.6293706293706[/C][C]2.37062937062937[/C][/ROW]
[ROW][C]43[/C][C]24[/C][C]21.6293706293706[/C][C]2.37062937062937[/C][/ROW]
[ROW][C]44[/C][C]17[/C][C]17.6923076923077[/C][C]-0.692307692307693[/C][/ROW]
[ROW][C]45[/C][C]22[/C][C]21.6293706293706[/C][C]0.37062937062937[/C][/ROW]
[ROW][C]46[/C][C]21[/C][C]21.6293706293706[/C][C]-0.62937062937063[/C][/ROW]
[ROW][C]47[/C][C]23[/C][C]21.6293706293706[/C][C]1.37062937062937[/C][/ROW]
[ROW][C]48[/C][C]19[/C][C]24.4[/C][C]-5.4[/C][/ROW]
[ROW][C]49[/C][C]22[/C][C]24.4[/C][C]-2.4[/C][/ROW]
[ROW][C]50[/C][C]19[/C][C]21.6293706293706[/C][C]-2.62937062937063[/C][/ROW]
[ROW][C]51[/C][C]24[/C][C]24.4[/C][C]-0.399999999999999[/C][/ROW]
[ROW][C]52[/C][C]22[/C][C]21.6293706293706[/C][C]0.37062937062937[/C][/ROW]
[ROW][C]53[/C][C]26[/C][C]17.6923076923077[/C][C]8.3076923076923[/C][/ROW]
[ROW][C]54[/C][C]22[/C][C]24.4[/C][C]-2.4[/C][/ROW]
[ROW][C]55[/C][C]23[/C][C]21.6293706293706[/C][C]1.37062937062937[/C][/ROW]
[ROW][C]56[/C][C]27[/C][C]24.4[/C][C]2.6[/C][/ROW]
[ROW][C]57[/C][C]21[/C][C]21.6293706293706[/C][C]-0.62937062937063[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]17.6923076923077[/C][C]-1.69230769230769[/C][/ROW]
[ROW][C]59[/C][C]21[/C][C]24.4[/C][C]-3.4[/C][/ROW]
[ROW][C]60[/C][C]18[/C][C]21.6293706293706[/C][C]-3.62937062937063[/C][/ROW]
[ROW][C]61[/C][C]25[/C][C]21.6293706293706[/C][C]3.37062937062937[/C][/ROW]
[ROW][C]62[/C][C]20[/C][C]21.6293706293706[/C][C]-1.62937062937063[/C][/ROW]
[ROW][C]63[/C][C]24[/C][C]21.6293706293706[/C][C]2.37062937062937[/C][/ROW]
[ROW][C]64[/C][C]20[/C][C]24.4[/C][C]-4.4[/C][/ROW]
[ROW][C]65[/C][C]24[/C][C]21.6293706293706[/C][C]2.37062937062937[/C][/ROW]
[ROW][C]66[/C][C]23[/C][C]21.6293706293706[/C][C]1.37062937062937[/C][/ROW]
[ROW][C]67[/C][C]23[/C][C]21.6293706293706[/C][C]1.37062937062937[/C][/ROW]
[ROW][C]68[/C][C]22[/C][C]21.6293706293706[/C][C]0.37062937062937[/C][/ROW]
[ROW][C]69[/C][C]22[/C][C]21.6293706293706[/C][C]0.37062937062937[/C][/ROW]
[ROW][C]70[/C][C]20[/C][C]21.6293706293706[/C][C]-1.62937062937063[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]17.6923076923077[/C][C]-3.69230769230769[/C][/ROW]
[ROW][C]72[/C][C]21[/C][C]17.6923076923077[/C][C]3.30769230769231[/C][/ROW]
[ROW][C]73[/C][C]23[/C][C]21.6293706293706[/C][C]1.37062937062937[/C][/ROW]
[ROW][C]74[/C][C]17[/C][C]21.6293706293706[/C][C]-4.62937062937063[/C][/ROW]
[ROW][C]75[/C][C]25[/C][C]24.4[/C][C]0.600000000000001[/C][/ROW]
[ROW][C]76[/C][C]10[/C][C]17.6923076923077[/C][C]-7.6923076923077[/C][/ROW]
[ROW][C]77[/C][C]25[/C][C]24.4[/C][C]0.600000000000001[/C][/ROW]
[ROW][C]78[/C][C]23[/C][C]24.4[/C][C]-1.40000000000000[/C][/ROW]
[ROW][C]79[/C][C]27[/C][C]21.6293706293706[/C][C]5.37062937062937[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]21.6293706293706[/C][C]-5.62937062937063[/C][/ROW]
[ROW][C]81[/C][C]19[/C][C]17.6923076923077[/C][C]1.30769230769231[/C][/ROW]
[ROW][C]82[/C][C]23[/C][C]21.6293706293706[/C][C]1.37062937062937[/C][/ROW]
[ROW][C]83[/C][C]19[/C][C]21.6293706293706[/C][C]-2.62937062937063[/C][/ROW]
[ROW][C]84[/C][C]19[/C][C]21.6293706293706[/C][C]-2.62937062937063[/C][/ROW]
[ROW][C]85[/C][C]26[/C][C]24.4[/C][C]1.6[/C][/ROW]
[ROW][C]86[/C][C]19[/C][C]17.6923076923077[/C][C]1.30769230769231[/C][/ROW]
[ROW][C]87[/C][C]22[/C][C]21.6293706293706[/C][C]0.37062937062937[/C][/ROW]
[ROW][C]88[/C][C]21[/C][C]21.6293706293706[/C][C]-0.62937062937063[/C][/ROW]
[ROW][C]89[/C][C]22[/C][C]21.6293706293706[/C][C]0.37062937062937[/C][/ROW]
[ROW][C]90[/C][C]20[/C][C]21.6293706293706[/C][C]-1.62937062937063[/C][/ROW]
[ROW][C]91[/C][C]20[/C][C]21.6293706293706[/C][C]-1.62937062937063[/C][/ROW]
[ROW][C]92[/C][C]20[/C][C]17.6923076923077[/C][C]2.30769230769231[/C][/ROW]
[ROW][C]93[/C][C]21[/C][C]21.6293706293706[/C][C]-0.62937062937063[/C][/ROW]
[ROW][C]94[/C][C]21[/C][C]21.6293706293706[/C][C]-0.62937062937063[/C][/ROW]
[ROW][C]95[/C][C]14[/C][C]21.6293706293706[/C][C]-7.62937062937063[/C][/ROW]
[ROW][C]96[/C][C]28[/C][C]21.6293706293706[/C][C]6.37062937062937[/C][/ROW]
[ROW][C]97[/C][C]24[/C][C]21.6293706293706[/C][C]2.37062937062937[/C][/ROW]
[ROW][C]98[/C][C]24[/C][C]24.4[/C][C]-0.399999999999999[/C][/ROW]
[ROW][C]99[/C][C]24[/C][C]21.6293706293706[/C][C]2.37062937062937[/C][/ROW]
[ROW][C]100[/C][C]19[/C][C]21.6293706293706[/C][C]-2.62937062937063[/C][/ROW]
[ROW][C]101[/C][C]19[/C][C]21.6293706293706[/C][C]-2.62937062937063[/C][/ROW]
[ROW][C]102[/C][C]14[/C][C]17.6923076923077[/C][C]-3.69230769230769[/C][/ROW]
[ROW][C]103[/C][C]29[/C][C]21.6293706293706[/C][C]7.37062937062937[/C][/ROW]
[ROW][C]104[/C][C]22[/C][C]21.6293706293706[/C][C]0.37062937062937[/C][/ROW]
[ROW][C]105[/C][C]21[/C][C]21.6293706293706[/C][C]-0.62937062937063[/C][/ROW]
[ROW][C]106[/C][C]15[/C][C]21.6293706293706[/C][C]-6.62937062937063[/C][/ROW]
[ROW][C]107[/C][C]23[/C][C]21.6293706293706[/C][C]1.37062937062937[/C][/ROW]
[ROW][C]108[/C][C]24[/C][C]17.6923076923077[/C][C]6.30769230769231[/C][/ROW]
[ROW][C]109[/C][C]20[/C][C]24.4[/C][C]-4.4[/C][/ROW]
[ROW][C]110[/C][C]25[/C][C]21.6293706293706[/C][C]3.37062937062937[/C][/ROW]
[ROW][C]111[/C][C]25[/C][C]21.6293706293706[/C][C]3.37062937062937[/C][/ROW]
[ROW][C]112[/C][C]19[/C][C]17.6923076923077[/C][C]1.30769230769231[/C][/ROW]
[ROW][C]113[/C][C]23[/C][C]21.6293706293706[/C][C]1.37062937062937[/C][/ROW]
[ROW][C]114[/C][C]22[/C][C]21.6293706293706[/C][C]0.37062937062937[/C][/ROW]
[ROW][C]115[/C][C]19[/C][C]21.6293706293706[/C][C]-2.62937062937063[/C][/ROW]
[ROW][C]116[/C][C]24[/C][C]21.6293706293706[/C][C]2.37062937062937[/C][/ROW]
[ROW][C]117[/C][C]21[/C][C]21.6293706293706[/C][C]-0.62937062937063[/C][/ROW]
[ROW][C]118[/C][C]19[/C][C]21.6293706293706[/C][C]-2.62937062937063[/C][/ROW]
[ROW][C]119[/C][C]21[/C][C]21.6293706293706[/C][C]-0.62937062937063[/C][/ROW]
[ROW][C]120[/C][C]18[/C][C]17.6923076923077[/C][C]0.307692307692307[/C][/ROW]
[ROW][C]121[/C][C]24[/C][C]21.6293706293706[/C][C]2.37062937062937[/C][/ROW]
[ROW][C]122[/C][C]7[/C][C]17.6923076923077[/C][C]-10.6923076923077[/C][/ROW]
[ROW][C]123[/C][C]24[/C][C]21.6293706293706[/C][C]2.37062937062937[/C][/ROW]
[ROW][C]124[/C][C]24[/C][C]21.6293706293706[/C][C]2.37062937062937[/C][/ROW]
[ROW][C]125[/C][C]23[/C][C]17.6923076923077[/C][C]5.30769230769231[/C][/ROW]
[ROW][C]126[/C][C]24[/C][C]24.4[/C][C]-0.399999999999999[/C][/ROW]
[ROW][C]127[/C][C]27[/C][C]24.4[/C][C]2.6[/C][/ROW]
[ROW][C]128[/C][C]20[/C][C]21.6293706293706[/C][C]-1.62937062937063[/C][/ROW]
[ROW][C]129[/C][C]20[/C][C]21.6293706293706[/C][C]-1.62937062937063[/C][/ROW]
[ROW][C]130[/C][C]22[/C][C]21.6293706293706[/C][C]0.37062937062937[/C][/ROW]
[ROW][C]131[/C][C]19[/C][C]21.6293706293706[/C][C]-2.62937062937063[/C][/ROW]
[ROW][C]132[/C][C]18[/C][C]21.6293706293706[/C][C]-3.62937062937063[/C][/ROW]
[ROW][C]133[/C][C]14[/C][C]21.6293706293706[/C][C]-7.62937062937063[/C][/ROW]
[ROW][C]134[/C][C]24[/C][C]21.6293706293706[/C][C]2.37062937062937[/C][/ROW]
[ROW][C]135[/C][C]29[/C][C]24.4[/C][C]4.6[/C][/ROW]
[ROW][C]136[/C][C]25[/C][C]21.6293706293706[/C][C]3.37062937062937[/C][/ROW]
[ROW][C]137[/C][C]24[/C][C]21.6293706293706[/C][C]2.37062937062937[/C][/ROW]
[ROW][C]138[/C][C]20[/C][C]21.6293706293706[/C][C]-1.62937062937063[/C][/ROW]
[ROW][C]139[/C][C]18[/C][C]21.6293706293706[/C][C]-3.62937062937063[/C][/ROW]
[ROW][C]140[/C][C]25[/C][C]21.6293706293706[/C][C]3.37062937062937[/C][/ROW]
[ROW][C]141[/C][C]21[/C][C]21.6293706293706[/C][C]-0.62937062937063[/C][/ROW]
[ROW][C]142[/C][C]21[/C][C]21.6293706293706[/C][C]-0.62937062937063[/C][/ROW]
[ROW][C]143[/C][C]21[/C][C]21.6293706293706[/C][C]-0.62937062937063[/C][/ROW]
[ROW][C]144[/C][C]23[/C][C]21.6293706293706[/C][C]1.37062937062937[/C][/ROW]
[ROW][C]145[/C][C]18[/C][C]21.6293706293706[/C][C]-3.62937062937063[/C][/ROW]
[ROW][C]146[/C][C]23[/C][C]21.6293706293706[/C][C]1.37062937062937[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]17.6923076923077[/C][C]-4.69230769230769[/C][/ROW]
[ROW][C]148[/C][C]23[/C][C]17.6923076923077[/C][C]5.30769230769231[/C][/ROW]
[ROW][C]149[/C][C]17[/C][C]17.6923076923077[/C][C]-0.692307692307693[/C][/ROW]
[ROW][C]150[/C][C]24[/C][C]21.6293706293706[/C][C]2.37062937062937[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]21.6293706293706[/C][C]-5.62937062937063[/C][/ROW]
[ROW][C]152[/C][C]23[/C][C]21.6293706293706[/C][C]1.37062937062937[/C][/ROW]
[ROW][C]153[/C][C]20[/C][C]21.6293706293706[/C][C]-1.62937062937063[/C][/ROW]
[ROW][C]154[/C][C]24[/C][C]21.6293706293706[/C][C]2.37062937062937[/C][/ROW]
[ROW][C]155[/C][C]15[/C][C]17.6923076923077[/C][C]-2.69230769230769[/C][/ROW]
[ROW][C]156[/C][C]20[/C][C]21.6293706293706[/C][C]-1.62937062937063[/C][/ROW]
[ROW][C]157[/C][C]27[/C][C]21.6293706293706[/C][C]5.37062937062937[/C][/ROW]
[ROW][C]158[/C][C]27[/C][C]21.6293706293706[/C][C]5.37062937062937[/C][/ROW]
[ROW][C]159[/C][C]19[/C][C]21.6293706293706[/C][C]-2.62937062937063[/C][/ROW]
[ROW][C]160[/C][C]22[/C][C]21.6293706293706[/C][C]0.37062937062937[/C][/ROW]
[ROW][C]161[/C][C]16[/C][C]21.6293706293706[/C][C]-5.62937062937063[/C][/ROW]
[ROW][C]162[/C][C]21[/C][C]21.6293706293706[/C][C]-0.62937062937063[/C][/ROW]
[ROW][C]163[/C][C]18[/C][C]17.6923076923077[/C][C]0.307692307692307[/C][/ROW]
[ROW][C]164[/C][C]22[/C][C]21.6293706293706[/C][C]0.37062937062937[/C][/ROW]
[ROW][C]165[/C][C]18[/C][C]21.6293706293706[/C][C]-3.62937062937063[/C][/ROW]
[ROW][C]166[/C][C]24[/C][C]24.4[/C][C]-0.399999999999999[/C][/ROW]
[ROW][C]167[/C][C]24[/C][C]21.6293706293706[/C][C]2.37062937062937[/C][/ROW]
[ROW][C]168[/C][C]19[/C][C]21.6293706293706[/C][C]-2.62937062937063[/C][/ROW]
[ROW][C]169[/C][C]26[/C][C]21.6293706293706[/C][C]4.37062937062937[/C][/ROW]
[ROW][C]170[/C][C]28[/C][C]24.4[/C][C]3.6[/C][/ROW]
[ROW][C]171[/C][C]23[/C][C]21.6293706293706[/C][C]1.37062937062937[/C][/ROW]
[ROW][C]172[/C][C]22[/C][C]17.6923076923077[/C][C]4.30769230769231[/C][/ROW]
[ROW][C]173[/C][C]20[/C][C]21.6293706293706[/C][C]-1.62937062937063[/C][/ROW]
[ROW][C]174[/C][C]20[/C][C]21.6293706293706[/C][C]-1.62937062937063[/C][/ROW]
[ROW][C]175[/C][C]27[/C][C]21.6293706293706[/C][C]5.37062937062937[/C][/ROW]
[ROW][C]176[/C][C]19[/C][C]21.6293706293706[/C][C]-2.62937062937063[/C][/ROW]
[ROW][C]177[/C][C]23[/C][C]21.6293706293706[/C][C]1.37062937062937[/C][/ROW]
[ROW][C]178[/C][C]19[/C][C]21.6293706293706[/C][C]-2.62937062937063[/C][/ROW]
[ROW][C]179[/C][C]21[/C][C]21.6293706293706[/C][C]-0.62937062937063[/C][/ROW]
[ROW][C]180[/C][C]13[/C][C]21.6293706293706[/C][C]-8.62937062937063[/C][/ROW]
[ROW][C]181[/C][C]18[/C][C]21.6293706293706[/C][C]-3.62937062937063[/C][/ROW]
[ROW][C]182[/C][C]19[/C][C]21.6293706293706[/C][C]-2.62937062937063[/C][/ROW]
[ROW][C]183[/C][C]23[/C][C]21.6293706293706[/C][C]1.37062937062937[/C][/ROW]
[ROW][C]184[/C][C]30[/C][C]21.6293706293706[/C][C]8.37062937062937[/C][/ROW]
[ROW][C]185[/C][C]22[/C][C]24.4[/C][C]-2.4[/C][/ROW]
[ROW][C]186[/C][C]23[/C][C]24.4[/C][C]-1.40000000000000[/C][/ROW]
[ROW][C]187[/C][C]22[/C][C]21.6293706293706[/C][C]0.37062937062937[/C][/ROW]
[ROW][C]188[/C][C]22[/C][C]21.6293706293706[/C][C]0.37062937062937[/C][/ROW]
[ROW][C]189[/C][C]23[/C][C]21.6293706293706[/C][C]1.37062937062937[/C][/ROW]
[ROW][C]190[/C][C]27[/C][C]21.6293706293706[/C][C]5.37062937062937[/C][/ROW]
[ROW][C]191[/C][C]23[/C][C]21.6293706293706[/C][C]1.37062937062937[/C][/ROW]
[ROW][C]192[/C][C]18[/C][C]21.6293706293706[/C][C]-3.62937062937063[/C][/ROW]
[ROW][C]193[/C][C]24[/C][C]21.6293706293706[/C][C]2.37062937062937[/C][/ROW]
[ROW][C]194[/C][C]19[/C][C]21.6293706293706[/C][C]-2.62937062937063[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117006&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117006&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	22	21.6293706293706	0.37062937062937
2	23	21.6293706293706	1.37062937062937
3	27	24.4	2.6
4	19	17.6923076923077	1.30769230769231
5	15	17.6923076923077	-2.69230769230769
6	29	24.4	4.6
7	25	21.6293706293706	3.37062937062937
8	25	21.6293706293706	3.37062937062937
9	21	21.6293706293706	-0.62937062937063
10	22	21.6293706293706	0.37062937062937
11	22	21.6293706293706	0.37062937062937
12	24	21.6293706293706	2.37062937062937
13	22	21.6293706293706	0.37062937062937
14	23	21.6293706293706	1.37062937062937
15	19	21.6293706293706	-2.62937062937063
16	19	21.6293706293706	-2.62937062937063
17	21	21.6293706293706	-0.62937062937063
18	20	21.6293706293706	-1.62937062937063
19	23	21.6293706293706	1.37062937062937
20	11	17.6923076923077	-6.6923076923077
21	21	21.6293706293706	-0.62937062937063
22	19	17.6923076923077	1.30769230769231
23	21	21.6293706293706	-0.62937062937063
24	23	21.6293706293706	1.37062937062937
25	19	21.6293706293706	-2.62937062937063
26	22	24.4	-2.4
27	19	21.6293706293706	-2.62937062937063
28	23	21.6293706293706	1.37062937062937
29	29	21.6293706293706	7.37062937062937
30	27	21.6293706293706	5.37062937062937
31	18	21.6293706293706	-3.62937062937063
32	30	24.4	5.6
33	26	21.6293706293706	4.37062937062937
34	20	21.6293706293706	-1.62937062937063
35	22	21.6293706293706	0.37062937062937
36	20	21.6293706293706	-1.62937062937063
37	21	21.6293706293706	-0.62937062937063
38	18	21.6293706293706	-3.62937062937063
39	21	17.6923076923077	3.30769230769231
40	27	24.4	2.6
41	18	21.6293706293706	-3.62937062937063
42	24	21.6293706293706	2.37062937062937
43	24	21.6293706293706	2.37062937062937
44	17	17.6923076923077	-0.692307692307693
45	22	21.6293706293706	0.37062937062937
46	21	21.6293706293706	-0.62937062937063
47	23	21.6293706293706	1.37062937062937
48	19	24.4	-5.4
49	22	24.4	-2.4
50	19	21.6293706293706	-2.62937062937063
51	24	24.4	-0.399999999999999
52	22	21.6293706293706	0.37062937062937
53	26	17.6923076923077	8.3076923076923
54	22	24.4	-2.4
55	23	21.6293706293706	1.37062937062937
56	27	24.4	2.6
57	21	21.6293706293706	-0.62937062937063
58	16	17.6923076923077	-1.69230769230769
59	21	24.4	-3.4
60	18	21.6293706293706	-3.62937062937063
61	25	21.6293706293706	3.37062937062937
62	20	21.6293706293706	-1.62937062937063
63	24	21.6293706293706	2.37062937062937
64	20	24.4	-4.4
65	24	21.6293706293706	2.37062937062937
66	23	21.6293706293706	1.37062937062937
67	23	21.6293706293706	1.37062937062937
68	22	21.6293706293706	0.37062937062937
69	22	21.6293706293706	0.37062937062937
70	20	21.6293706293706	-1.62937062937063
71	14	17.6923076923077	-3.69230769230769
72	21	17.6923076923077	3.30769230769231
73	23	21.6293706293706	1.37062937062937
74	17	21.6293706293706	-4.62937062937063
75	25	24.4	0.600000000000001
76	10	17.6923076923077	-7.6923076923077
77	25	24.4	0.600000000000001
78	23	24.4	-1.40000000000000
79	27	21.6293706293706	5.37062937062937
80	16	21.6293706293706	-5.62937062937063
81	19	17.6923076923077	1.30769230769231
82	23	21.6293706293706	1.37062937062937
83	19	21.6293706293706	-2.62937062937063
84	19	21.6293706293706	-2.62937062937063
85	26	24.4	1.6
86	19	17.6923076923077	1.30769230769231
87	22	21.6293706293706	0.37062937062937
88	21	21.6293706293706	-0.62937062937063
89	22	21.6293706293706	0.37062937062937
90	20	21.6293706293706	-1.62937062937063
91	20	21.6293706293706	-1.62937062937063
92	20	17.6923076923077	2.30769230769231
93	21	21.6293706293706	-0.62937062937063
94	21	21.6293706293706	-0.62937062937063
95	14	21.6293706293706	-7.62937062937063
96	28	21.6293706293706	6.37062937062937
97	24	21.6293706293706	2.37062937062937
98	24	24.4	-0.399999999999999
99	24	21.6293706293706	2.37062937062937
100	19	21.6293706293706	-2.62937062937063
101	19	21.6293706293706	-2.62937062937063
102	14	17.6923076923077	-3.69230769230769
103	29	21.6293706293706	7.37062937062937
104	22	21.6293706293706	0.37062937062937
105	21	21.6293706293706	-0.62937062937063
106	15	21.6293706293706	-6.62937062937063
107	23	21.6293706293706	1.37062937062937
108	24	17.6923076923077	6.30769230769231
109	20	24.4	-4.4
110	25	21.6293706293706	3.37062937062937
111	25	21.6293706293706	3.37062937062937
112	19	17.6923076923077	1.30769230769231
113	23	21.6293706293706	1.37062937062937
114	22	21.6293706293706	0.37062937062937
115	19	21.6293706293706	-2.62937062937063
116	24	21.6293706293706	2.37062937062937
117	21	21.6293706293706	-0.62937062937063
118	19	21.6293706293706	-2.62937062937063
119	21	21.6293706293706	-0.62937062937063
120	18	17.6923076923077	0.307692307692307
121	24	21.6293706293706	2.37062937062937
122	7	17.6923076923077	-10.6923076923077
123	24	21.6293706293706	2.37062937062937
124	24	21.6293706293706	2.37062937062937
125	23	17.6923076923077	5.30769230769231
126	24	24.4	-0.399999999999999
127	27	24.4	2.6
128	20	21.6293706293706	-1.62937062937063
129	20	21.6293706293706	-1.62937062937063
130	22	21.6293706293706	0.37062937062937
131	19	21.6293706293706	-2.62937062937063
132	18	21.6293706293706	-3.62937062937063
133	14	21.6293706293706	-7.62937062937063
134	24	21.6293706293706	2.37062937062937
135	29	24.4	4.6
136	25	21.6293706293706	3.37062937062937
137	24	21.6293706293706	2.37062937062937
138	20	21.6293706293706	-1.62937062937063
139	18	21.6293706293706	-3.62937062937063
140	25	21.6293706293706	3.37062937062937
141	21	21.6293706293706	-0.62937062937063
142	21	21.6293706293706	-0.62937062937063
143	21	21.6293706293706	-0.62937062937063
144	23	21.6293706293706	1.37062937062937
145	18	21.6293706293706	-3.62937062937063
146	23	21.6293706293706	1.37062937062937
147	13	17.6923076923077	-4.69230769230769
148	23	17.6923076923077	5.30769230769231
149	17	17.6923076923077	-0.692307692307693
150	24	21.6293706293706	2.37062937062937
151	16	21.6293706293706	-5.62937062937063
152	23	21.6293706293706	1.37062937062937
153	20	21.6293706293706	-1.62937062937063
154	24	21.6293706293706	2.37062937062937
155	15	17.6923076923077	-2.69230769230769
156	20	21.6293706293706	-1.62937062937063
157	27	21.6293706293706	5.37062937062937
158	27	21.6293706293706	5.37062937062937
159	19	21.6293706293706	-2.62937062937063
160	22	21.6293706293706	0.37062937062937
161	16	21.6293706293706	-5.62937062937063
162	21	21.6293706293706	-0.62937062937063
163	18	17.6923076923077	0.307692307692307
164	22	21.6293706293706	0.37062937062937
165	18	21.6293706293706	-3.62937062937063
166	24	24.4	-0.399999999999999
167	24	21.6293706293706	2.37062937062937
168	19	21.6293706293706	-2.62937062937063
169	26	21.6293706293706	4.37062937062937
170	28	24.4	3.6
171	23	21.6293706293706	1.37062937062937
172	22	17.6923076923077	4.30769230769231
173	20	21.6293706293706	-1.62937062937063
174	20	21.6293706293706	-1.62937062937063
175	27	21.6293706293706	5.37062937062937
176	19	21.6293706293706	-2.62937062937063
177	23	21.6293706293706	1.37062937062937
178	19	21.6293706293706	-2.62937062937063
179	21	21.6293706293706	-0.62937062937063
180	13	21.6293706293706	-8.62937062937063
181	18	21.6293706293706	-3.62937062937063
182	19	21.6293706293706	-2.62937062937063
183	23	21.6293706293706	1.37062937062937
184	30	21.6293706293706	8.37062937062937
185	22	24.4	-2.4
186	23	24.4	-1.40000000000000
187	22	21.6293706293706	0.37062937062937
188	22	21.6293706293706	0.37062937062937
189	23	21.6293706293706	1.37062937062937
190	27	21.6293706293706	5.37062937062937
191	23	21.6293706293706	1.37062937062937
192	18	21.6293706293706	-3.62937062937063
193	24	21.6293706293706	2.37062937062937
194	19	21.6293706293706	-2.62937062937063

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