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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 17:53:59 +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/t1293645137gjnb40ganp6ze06.htm/, Retrieved Fri, 03 May 2024 11:00:36 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=117000, Retrieved Fri, 03 May 2024 11:00:36 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

135

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)] [] [2010-12-24 13:45:34] [e45804683e9a4263debf179d74e04a01]
-    D      [Recursive Partitioning (Regression Trees)] [] [2010-12-27 16:49:58] [e45804683e9a4263debf179d74e04a01]
-    D        [Recursive Partitioning (Regression Trees)] [] [2010-12-27 18:49:15] [e45804683e9a4263debf179d74e04a01]
-                 [Recursive Partitioning (Regression Trees)] [] [2010-12-29 17:53:59] [15be6a7406d91c9912c2afdb984c54ee] [Current]

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Dataseries X:

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Histogram

Boxplots

18	1	27	5	26	49	35
10	1	36	4	25	45	34
23	1	25	4	17	54	13
14	1	27	3	37	36	35
20	2	25	3	35	36	28
15	2	44	3	15	53	32
18	1	50	4	27	46	35
19	1	41	4	36	42	36
19	1	48	5	25	41	27
14	2	43	4	30	45	29
15	2	47	2	27	47	27
14	2	41	3	33	42	28
16	1	44	2	29	45	29
13	2	47	5	30	40	28
13	2	40	3	25	45	30
14	2	46	3	23	40	25
23	1	28	3	26	42	15
17	1	56	3	24	45	33
14	2	49	4	35	47	31
21	2	25	4	39	31	37
15	2	41	4	23	46	37
19	2	26	3	32	34	34
20	1	50	5	29	43	32
18	1	47	4	26	45	21
13	1	52	2	21	42	25
20	2	37	5	35	51	32
12	2	41	3	23	44	28
17	1	45	4	21	47	22
13	2	26	4	28	47	25
17	1	NA	3	30	41	26
16	1	52	4	21	44	34
20	1	46	2	29	51	34
18	1	58	3	28	46	36
9	1	54	5	19	47	36
14	1	29	3	26	46	26
12	2	50	3	33	38	26
21	1	43	2	34	50	34
16	2	30	3	33	48	33
12	2	47	2	40	36	31
20	1	45	3	24	51	33
18	2	48	1	35	35	22
22	2	48	3	35	49	29
17	2	26	4	32	38	24
16	1	46	5	20	47	37
14	2	NA	3	35	36	32
19	2	50	3	35	47	23
21	1	25	4	21	46	29
18	1	47	2	33	43	35
23	2	47	2	40	53	20
20	1	41	3	22	55	28
10	2	45	2	35	39	26
16	2	41	4	20	55	36
18	2	45	5	28	41	26
12	2	40	3	46	33	33
15	1	29	4	18	52	25
19	2	34	5	22	42	29
11	1	45	5	20	56	32
16	2	52	3	25	46	35
12	2	41	4	31	33	24
18	2	48	3	21	51	31
14	2	45	3	23	46	29
20	1	54	2	26	46	27
15	2	25	3	34	50	29
17	2	26	4	31	46	29
20	1	28	4	23	51	27
14	2	50	4	31	48	34
16	2	48	4	26	44	32
15	2	51	3	36	38	31
17	2	53	3	28	42	31
20	1	37	3	34	39	31
14	1	56	2	25	45	16
20	1	43	3	33	31	25
20	1	34	3	46	29	27
15	1	42	3	24	48	32
21	2	32	3	32	38	28
22	2	31	5	33	55	25
11	1	46	3	42	32	25
20	2	30	5	17	51	36
17	2	47	4	36	53	36
19	2	33	4	40	47	36
17	1	25	4	30	45	27
15	1	25	5	19	33	29
20	2	21	4	33	49	32
12	2	36	5	35	46	29
13	2	50	3	23	42	31
18	2	48	3	15	56	34
19	2	48	2	38	35	27
13	1	25	3	37	40	28
12	1	48	4	23	44	32
16	2	49	5	41	46	33
21	1	27	5	34	46	29
19	1	28	3	38	39	32
19	2	43	2	45	35	35
12	2	48	3	27	48	33
22	2	48	4	46	42	27
9	1	25	1	26	39	16
9	2	49	4	44	39	32
18	1	26	3	36	41	26
14	1	51	3	20	52	32
14	2	25	4	44	45	38
23	1	29	3	27	42	24
19	1	29	4	27	44	26
24	1	43	2	41	33	19
12	2	46	3	30	42	37
20	1	44	3	33	46	25
21	1	25	3	37	45	24
18	1	51	2	30	40	23
20	1	42	5	20	48	28
18	2	53	5	44	32	38
18	1	25	4	20	53	28
17	2	49	2	33	39	28
18	1	51	3	31	45	26
14	2	20	3	23	36	21
23	2	44	3	33	38	35
19	2	38	4	33	49	31
14	1	46	5	32	46	34
17	2	42	4	25	43	30
22	1	29	NA	22	37	30
10	2	46	4	16	48	24
16	2	49	2	36	45	27
14	2	51	3	35	32	26
19	1	38	3	25	46	30
14	1	41	1	27	20	15
18	2	47	3	32	42	28
19	2	44	3	36	45	34
21	2	47	3	51	29	29
13	2	46	3	30	51	26
17	1	44	4	20	55	31
11	2	28	3	29	50	28
16	2	47	4	26	44	33
22	2	28	4	20	41	32
19	1	41	5	40	40	33
17	2	45	4	29	47	31
25	2	46	4	32	42	37
17	1	46	4	33	40	27
23	2	22	3	32	51	19
21	2	33	3	34	43	27
12	1	41	4	24	45	31
18	2	47	5	25	41	38
15	1	25	3	41	41	22
17	2	42	3	39	37	35
11	2	47	3	21	46	35
17	2	50	3	38	38	30
13	1	55	5	28	39	41
17	1	21	3	37	45	25
16	1	NA	3	26	46	28
14	1	52	3	30	39	45
15	2	49	4	25	21	21
20	2	46	4	38	31	33
14	1	NA	4	31	35	25
16	2	45	3	31	49	29
14	2	52	3	27	40	31
13	1	NA	3	21	45	29
15	2	40	4	26	46	31
13	2	49	4	37	45	31
13	1	38	5	28	34	25
23	1	32	5	29	41	27
18	2	46	4	33	43	26
21	2	32	3	41	45	26
14	2	41	3	19	48	23
12	2	43	3	37	43	27
17	1	44	4	36	45	24
11	1	47	5	27	45	35
15	2	28	3	33	34	24
14	1	52	1	29	40	32
19	1	27	2	42	40	24
12	2	45	5	27	55	24
14	1	27	4	47	44	38
18	1	25	4	17	44	36
25	1	28	4	34	48	24
22	1	25	3	32	51	18
15	1	52	4	25	49	34
18	1	44	3	27	33	23
18	2	43	3	37	43	35
12	2	47	4	34	44	22
12	2	52	4	27	44	34
16	2	40	2	37	41	28
22	1	42	3	32	45	34
15	1	45	5	26	44	32
16	1	45	2	29	44	24
11	1	50	5	28	40	34
20	1	49	3	19	48	33
14	1	52	2	46	49	33
20	2	48	3	31	46	29
15	2	51	3	42	49	38
12	2	49	4	33	55	24
18	2	31	4	39	51	25
18	2	43	3	27	46	37
11	2	31	3	35	37	33
13	2	28	4	23	43	30
15	2	43	4	32	41	22
19	2	31	3	22	45	28
13	2	51	3	17	39	24
19	2	58	4	35	38	33
18	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=117000&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=117000&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117000&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	0.2781
R-squared	0.0774
RMSE	3.3877

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.2781 \tabularnewline
R-squared & 0.0774 \tabularnewline
RMSE & 3.3877 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117000&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.2781[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0774[/C][/ROW]
[ROW][C]RMSE[/C][C]3.3877[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117000&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117000&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	0.2781
R-squared	0.0774
RMSE	3.3877

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	18	18.125	-0.125
2	10	15.9568345323741	-5.9568345323741
3	23	18.125	4.875
4	14	18.125	-4.125
5	20	18.125	1.875
6	15	15.9568345323741	-0.956834532374101
7	18	15.9568345323741	2.0431654676259
8	19	15.9568345323741	3.0431654676259
9	19	15.9568345323741	3.0431654676259
10	14	15.9568345323741	-1.9568345323741
11	15	15.9568345323741	-0.956834532374101
12	14	15.9568345323741	-1.9568345323741
13	16	15.9568345323741	0.043165467625899
14	13	15.9568345323741	-2.9568345323741
15	13	15.9568345323741	-2.9568345323741
16	14	15.9568345323741	-1.9568345323741
17	23	18.125	4.875
18	17	15.9568345323741	1.0431654676259
19	14	15.9568345323741	-1.9568345323741
20	21	18.125	2.875
21	15	15.9568345323741	-0.956834532374101
22	19	18.125	0.875
23	20	15.9568345323741	4.0431654676259
24	18	15.9568345323741	2.0431654676259
25	13	15.9568345323741	-2.9568345323741
26	20	15.9568345323741	4.0431654676259
27	12	15.9568345323741	-3.9568345323741
28	17	15.9568345323741	1.0431654676259
29	13	18.125	-5.125
30	17	15.9568345323741	1.0431654676259
31	16	15.9568345323741	0.043165467625899
32	20	15.9568345323741	4.0431654676259
33	18	15.9568345323741	2.0431654676259
34	9	15.9568345323741	-6.9568345323741
35	14	18.125	-4.125
36	12	15.9568345323741	-3.9568345323741
37	21	15.9568345323741	5.0431654676259
38	16	18.125	-2.125
39	12	15.9568345323741	-3.9568345323741
40	20	15.9568345323741	4.0431654676259
41	18	15.9568345323741	2.0431654676259
42	22	15.9568345323741	6.0431654676259
43	17	18.125	-1.125
44	16	15.9568345323741	0.043165467625899
45	14	15.9568345323741	-1.9568345323741
46	19	15.9568345323741	3.0431654676259
47	21	18.125	2.875
48	18	15.9568345323741	2.0431654676259
49	23	15.9568345323741	7.0431654676259
50	20	15.9568345323741	4.0431654676259
51	10	15.9568345323741	-5.9568345323741
52	16	15.9568345323741	0.043165467625899
53	18	15.9568345323741	2.0431654676259
54	12	15.9568345323741	-3.9568345323741
55	15	18.125	-3.125
56	19	18.125	0.875
57	11	15.9568345323741	-4.9568345323741
58	16	15.9568345323741	0.043165467625899
59	12	15.9568345323741	-3.9568345323741
60	18	15.9568345323741	2.0431654676259
61	14	15.9568345323741	-1.9568345323741
62	20	15.9568345323741	4.0431654676259
63	15	18.125	-3.125
64	17	18.125	-1.125
65	20	18.125	1.875
66	14	15.9568345323741	-1.9568345323741
67	16	15.9568345323741	0.043165467625899
68	15	15.9568345323741	-0.956834532374101
69	17	15.9568345323741	1.0431654676259
70	20	15.9568345323741	4.0431654676259
71	14	15.9568345323741	-1.9568345323741
72	20	15.9568345323741	4.0431654676259
73	20	18.125	1.875
74	15	15.9568345323741	-0.956834532374101
75	21	18.125	2.875
76	22	18.125	3.875
77	11	15.9568345323741	-4.9568345323741
78	20	18.125	1.875
79	17	15.9568345323741	1.0431654676259
80	19	18.125	0.875
81	17	18.125	-1.125
82	15	18.125	-3.125
83	20	18.125	1.875
84	12	15.9568345323741	-3.9568345323741
85	13	15.9568345323741	-2.9568345323741
86	18	15.9568345323741	2.0431654676259
87	19	15.9568345323741	3.0431654676259
88	13	18.125	-5.125
89	12	15.9568345323741	-3.9568345323741
90	16	15.9568345323741	0.043165467625899
91	21	18.125	2.875
92	19	18.125	0.875
93	19	15.9568345323741	3.0431654676259
94	12	15.9568345323741	-3.9568345323741
95	22	15.9568345323741	6.0431654676259
96	9	18.125	-9.125
97	9	15.9568345323741	-6.9568345323741
98	18	18.125	-0.125
99	14	15.9568345323741	-1.9568345323741
100	14	18.125	-4.125
101	23	18.125	4.875
102	19	18.125	0.875
103	24	15.9568345323741	8.0431654676259
104	12	15.9568345323741	-3.9568345323741
105	20	15.9568345323741	4.0431654676259
106	21	18.125	2.875
107	18	15.9568345323741	2.0431654676259
108	20	15.9568345323741	4.0431654676259
109	18	15.9568345323741	2.0431654676259
110	18	18.125	-0.125
111	17	15.9568345323741	1.0431654676259
112	18	15.9568345323741	2.0431654676259
113	14	18.125	-4.125
114	23	15.9568345323741	7.0431654676259
115	19	15.9568345323741	3.0431654676259
116	14	15.9568345323741	-1.9568345323741
117	17	15.9568345323741	1.0431654676259
118	22	18.125	3.875
119	10	15.9568345323741	-5.9568345323741
120	16	15.9568345323741	0.043165467625899
121	14	15.9568345323741	-1.9568345323741
122	19	15.9568345323741	3.0431654676259
123	14	15.9568345323741	-1.9568345323741
124	18	15.9568345323741	2.0431654676259
125	19	15.9568345323741	3.0431654676259
126	21	15.9568345323741	5.0431654676259
127	13	15.9568345323741	-2.9568345323741
128	17	15.9568345323741	1.0431654676259
129	11	18.125	-7.125
130	16	15.9568345323741	0.043165467625899
131	22	18.125	3.875
132	19	15.9568345323741	3.0431654676259
133	17	15.9568345323741	1.0431654676259
134	25	15.9568345323741	9.0431654676259
135	17	15.9568345323741	1.0431654676259
136	23	18.125	4.875
137	21	18.125	2.875
138	12	15.9568345323741	-3.9568345323741
139	18	15.9568345323741	2.0431654676259
140	15	18.125	-3.125
141	17	15.9568345323741	1.0431654676259
142	11	15.9568345323741	-4.9568345323741
143	17	15.9568345323741	1.0431654676259
144	13	15.9568345323741	-2.9568345323741
145	17	18.125	-1.125
146	16	15.9568345323741	0.043165467625899
147	14	15.9568345323741	-1.9568345323741
148	15	15.9568345323741	-0.956834532374101
149	20	15.9568345323741	4.0431654676259
150	14	15.9568345323741	-1.9568345323741
151	16	15.9568345323741	0.043165467625899
152	14	15.9568345323741	-1.9568345323741
153	13	15.9568345323741	-2.9568345323741
154	15	15.9568345323741	-0.956834532374101
155	13	15.9568345323741	-2.9568345323741
156	13	15.9568345323741	-2.9568345323741
157	23	18.125	4.875
158	18	15.9568345323741	2.0431654676259
159	21	18.125	2.875
160	14	15.9568345323741	-1.9568345323741
161	12	15.9568345323741	-3.9568345323741
162	17	15.9568345323741	1.0431654676259
163	11	15.9568345323741	-4.9568345323741
164	15	18.125	-3.125
165	14	15.9568345323741	-1.9568345323741
166	19	18.125	0.875
167	12	15.9568345323741	-3.9568345323741
168	14	18.125	-4.125
169	18	18.125	-0.125
170	25	18.125	6.875
171	22	18.125	3.875
172	15	15.9568345323741	-0.956834532374101
173	18	15.9568345323741	2.0431654676259
174	18	15.9568345323741	2.0431654676259
175	12	15.9568345323741	-3.9568345323741
176	12	15.9568345323741	-3.9568345323741
177	16	15.9568345323741	0.043165467625899
178	22	15.9568345323741	6.0431654676259
179	15	15.9568345323741	-0.956834532374101
180	16	15.9568345323741	0.043165467625899
181	11	15.9568345323741	-4.9568345323741
182	20	15.9568345323741	4.0431654676259
183	14	15.9568345323741	-1.9568345323741
184	20	15.9568345323741	4.0431654676259
185	15	15.9568345323741	-0.956834532374101
186	12	15.9568345323741	-3.9568345323741
187	18	18.125	-0.125
188	18	15.9568345323741	2.0431654676259
189	11	18.125	-7.125
190	13	18.125	-5.125
191	15	15.9568345323741	-0.956834532374101
192	19	18.125	0.875
193	13	15.9568345323741	-2.9568345323741
194	19	15.9568345323741	3.0431654676259
195	18	18.125	-0.125

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 18 & 18.125 & -0.125 \tabularnewline
2 & 10 & 15.9568345323741 & -5.9568345323741 \tabularnewline
3 & 23 & 18.125 & 4.875 \tabularnewline
4 & 14 & 18.125 & -4.125 \tabularnewline
5 & 20 & 18.125 & 1.875 \tabularnewline
6 & 15 & 15.9568345323741 & -0.956834532374101 \tabularnewline
7 & 18 & 15.9568345323741 & 2.0431654676259 \tabularnewline
8 & 19 & 15.9568345323741 & 3.0431654676259 \tabularnewline
9 & 19 & 15.9568345323741 & 3.0431654676259 \tabularnewline
10 & 14 & 15.9568345323741 & -1.9568345323741 \tabularnewline
11 & 15 & 15.9568345323741 & -0.956834532374101 \tabularnewline
12 & 14 & 15.9568345323741 & -1.9568345323741 \tabularnewline
13 & 16 & 15.9568345323741 & 0.043165467625899 \tabularnewline
14 & 13 & 15.9568345323741 & -2.9568345323741 \tabularnewline
15 & 13 & 15.9568345323741 & -2.9568345323741 \tabularnewline
16 & 14 & 15.9568345323741 & -1.9568345323741 \tabularnewline
17 & 23 & 18.125 & 4.875 \tabularnewline
18 & 17 & 15.9568345323741 & 1.0431654676259 \tabularnewline
19 & 14 & 15.9568345323741 & -1.9568345323741 \tabularnewline
20 & 21 & 18.125 & 2.875 \tabularnewline
21 & 15 & 15.9568345323741 & -0.956834532374101 \tabularnewline
22 & 19 & 18.125 & 0.875 \tabularnewline
23 & 20 & 15.9568345323741 & 4.0431654676259 \tabularnewline
24 & 18 & 15.9568345323741 & 2.0431654676259 \tabularnewline
25 & 13 & 15.9568345323741 & -2.9568345323741 \tabularnewline
26 & 20 & 15.9568345323741 & 4.0431654676259 \tabularnewline
27 & 12 & 15.9568345323741 & -3.9568345323741 \tabularnewline
28 & 17 & 15.9568345323741 & 1.0431654676259 \tabularnewline
29 & 13 & 18.125 & -5.125 \tabularnewline
30 & 17 & 15.9568345323741 & 1.0431654676259 \tabularnewline
31 & 16 & 15.9568345323741 & 0.043165467625899 \tabularnewline
32 & 20 & 15.9568345323741 & 4.0431654676259 \tabularnewline
33 & 18 & 15.9568345323741 & 2.0431654676259 \tabularnewline
34 & 9 & 15.9568345323741 & -6.9568345323741 \tabularnewline
35 & 14 & 18.125 & -4.125 \tabularnewline
36 & 12 & 15.9568345323741 & -3.9568345323741 \tabularnewline
37 & 21 & 15.9568345323741 & 5.0431654676259 \tabularnewline
38 & 16 & 18.125 & -2.125 \tabularnewline
39 & 12 & 15.9568345323741 & -3.9568345323741 \tabularnewline
40 & 20 & 15.9568345323741 & 4.0431654676259 \tabularnewline
41 & 18 & 15.9568345323741 & 2.0431654676259 \tabularnewline
42 & 22 & 15.9568345323741 & 6.0431654676259 \tabularnewline
43 & 17 & 18.125 & -1.125 \tabularnewline
44 & 16 & 15.9568345323741 & 0.043165467625899 \tabularnewline
45 & 14 & 15.9568345323741 & -1.9568345323741 \tabularnewline
46 & 19 & 15.9568345323741 & 3.0431654676259 \tabularnewline
47 & 21 & 18.125 & 2.875 \tabularnewline
48 & 18 & 15.9568345323741 & 2.0431654676259 \tabularnewline
49 & 23 & 15.9568345323741 & 7.0431654676259 \tabularnewline
50 & 20 & 15.9568345323741 & 4.0431654676259 \tabularnewline
51 & 10 & 15.9568345323741 & -5.9568345323741 \tabularnewline
52 & 16 & 15.9568345323741 & 0.043165467625899 \tabularnewline
53 & 18 & 15.9568345323741 & 2.0431654676259 \tabularnewline
54 & 12 & 15.9568345323741 & -3.9568345323741 \tabularnewline
55 & 15 & 18.125 & -3.125 \tabularnewline
56 & 19 & 18.125 & 0.875 \tabularnewline
57 & 11 & 15.9568345323741 & -4.9568345323741 \tabularnewline
58 & 16 & 15.9568345323741 & 0.043165467625899 \tabularnewline
59 & 12 & 15.9568345323741 & -3.9568345323741 \tabularnewline
60 & 18 & 15.9568345323741 & 2.0431654676259 \tabularnewline
61 & 14 & 15.9568345323741 & -1.9568345323741 \tabularnewline
62 & 20 & 15.9568345323741 & 4.0431654676259 \tabularnewline
63 & 15 & 18.125 & -3.125 \tabularnewline
64 & 17 & 18.125 & -1.125 \tabularnewline
65 & 20 & 18.125 & 1.875 \tabularnewline
66 & 14 & 15.9568345323741 & -1.9568345323741 \tabularnewline
67 & 16 & 15.9568345323741 & 0.043165467625899 \tabularnewline
68 & 15 & 15.9568345323741 & -0.956834532374101 \tabularnewline
69 & 17 & 15.9568345323741 & 1.0431654676259 \tabularnewline
70 & 20 & 15.9568345323741 & 4.0431654676259 \tabularnewline
71 & 14 & 15.9568345323741 & -1.9568345323741 \tabularnewline
72 & 20 & 15.9568345323741 & 4.0431654676259 \tabularnewline
73 & 20 & 18.125 & 1.875 \tabularnewline
74 & 15 & 15.9568345323741 & -0.956834532374101 \tabularnewline
75 & 21 & 18.125 & 2.875 \tabularnewline
76 & 22 & 18.125 & 3.875 \tabularnewline
77 & 11 & 15.9568345323741 & -4.9568345323741 \tabularnewline
78 & 20 & 18.125 & 1.875 \tabularnewline
79 & 17 & 15.9568345323741 & 1.0431654676259 \tabularnewline
80 & 19 & 18.125 & 0.875 \tabularnewline
81 & 17 & 18.125 & -1.125 \tabularnewline
82 & 15 & 18.125 & -3.125 \tabularnewline
83 & 20 & 18.125 & 1.875 \tabularnewline
84 & 12 & 15.9568345323741 & -3.9568345323741 \tabularnewline
85 & 13 & 15.9568345323741 & -2.9568345323741 \tabularnewline
86 & 18 & 15.9568345323741 & 2.0431654676259 \tabularnewline
87 & 19 & 15.9568345323741 & 3.0431654676259 \tabularnewline
88 & 13 & 18.125 & -5.125 \tabularnewline
89 & 12 & 15.9568345323741 & -3.9568345323741 \tabularnewline
90 & 16 & 15.9568345323741 & 0.043165467625899 \tabularnewline
91 & 21 & 18.125 & 2.875 \tabularnewline
92 & 19 & 18.125 & 0.875 \tabularnewline
93 & 19 & 15.9568345323741 & 3.0431654676259 \tabularnewline
94 & 12 & 15.9568345323741 & -3.9568345323741 \tabularnewline
95 & 22 & 15.9568345323741 & 6.0431654676259 \tabularnewline
96 & 9 & 18.125 & -9.125 \tabularnewline
97 & 9 & 15.9568345323741 & -6.9568345323741 \tabularnewline
98 & 18 & 18.125 & -0.125 \tabularnewline
99 & 14 & 15.9568345323741 & -1.9568345323741 \tabularnewline
100 & 14 & 18.125 & -4.125 \tabularnewline
101 & 23 & 18.125 & 4.875 \tabularnewline
102 & 19 & 18.125 & 0.875 \tabularnewline
103 & 24 & 15.9568345323741 & 8.0431654676259 \tabularnewline
104 & 12 & 15.9568345323741 & -3.9568345323741 \tabularnewline
105 & 20 & 15.9568345323741 & 4.0431654676259 \tabularnewline
106 & 21 & 18.125 & 2.875 \tabularnewline
107 & 18 & 15.9568345323741 & 2.0431654676259 \tabularnewline
108 & 20 & 15.9568345323741 & 4.0431654676259 \tabularnewline
109 & 18 & 15.9568345323741 & 2.0431654676259 \tabularnewline
110 & 18 & 18.125 & -0.125 \tabularnewline
111 & 17 & 15.9568345323741 & 1.0431654676259 \tabularnewline
112 & 18 & 15.9568345323741 & 2.0431654676259 \tabularnewline
113 & 14 & 18.125 & -4.125 \tabularnewline
114 & 23 & 15.9568345323741 & 7.0431654676259 \tabularnewline
115 & 19 & 15.9568345323741 & 3.0431654676259 \tabularnewline
116 & 14 & 15.9568345323741 & -1.9568345323741 \tabularnewline
117 & 17 & 15.9568345323741 & 1.0431654676259 \tabularnewline
118 & 22 & 18.125 & 3.875 \tabularnewline
119 & 10 & 15.9568345323741 & -5.9568345323741 \tabularnewline
120 & 16 & 15.9568345323741 & 0.043165467625899 \tabularnewline
121 & 14 & 15.9568345323741 & -1.9568345323741 \tabularnewline
122 & 19 & 15.9568345323741 & 3.0431654676259 \tabularnewline
123 & 14 & 15.9568345323741 & -1.9568345323741 \tabularnewline
124 & 18 & 15.9568345323741 & 2.0431654676259 \tabularnewline
125 & 19 & 15.9568345323741 & 3.0431654676259 \tabularnewline
126 & 21 & 15.9568345323741 & 5.0431654676259 \tabularnewline
127 & 13 & 15.9568345323741 & -2.9568345323741 \tabularnewline
128 & 17 & 15.9568345323741 & 1.0431654676259 \tabularnewline
129 & 11 & 18.125 & -7.125 \tabularnewline
130 & 16 & 15.9568345323741 & 0.043165467625899 \tabularnewline
131 & 22 & 18.125 & 3.875 \tabularnewline
132 & 19 & 15.9568345323741 & 3.0431654676259 \tabularnewline
133 & 17 & 15.9568345323741 & 1.0431654676259 \tabularnewline
134 & 25 & 15.9568345323741 & 9.0431654676259 \tabularnewline
135 & 17 & 15.9568345323741 & 1.0431654676259 \tabularnewline
136 & 23 & 18.125 & 4.875 \tabularnewline
137 & 21 & 18.125 & 2.875 \tabularnewline
138 & 12 & 15.9568345323741 & -3.9568345323741 \tabularnewline
139 & 18 & 15.9568345323741 & 2.0431654676259 \tabularnewline
140 & 15 & 18.125 & -3.125 \tabularnewline
141 & 17 & 15.9568345323741 & 1.0431654676259 \tabularnewline
142 & 11 & 15.9568345323741 & -4.9568345323741 \tabularnewline
143 & 17 & 15.9568345323741 & 1.0431654676259 \tabularnewline
144 & 13 & 15.9568345323741 & -2.9568345323741 \tabularnewline
145 & 17 & 18.125 & -1.125 \tabularnewline
146 & 16 & 15.9568345323741 & 0.043165467625899 \tabularnewline
147 & 14 & 15.9568345323741 & -1.9568345323741 \tabularnewline
148 & 15 & 15.9568345323741 & -0.956834532374101 \tabularnewline
149 & 20 & 15.9568345323741 & 4.0431654676259 \tabularnewline
150 & 14 & 15.9568345323741 & -1.9568345323741 \tabularnewline
151 & 16 & 15.9568345323741 & 0.043165467625899 \tabularnewline
152 & 14 & 15.9568345323741 & -1.9568345323741 \tabularnewline
153 & 13 & 15.9568345323741 & -2.9568345323741 \tabularnewline
154 & 15 & 15.9568345323741 & -0.956834532374101 \tabularnewline
155 & 13 & 15.9568345323741 & -2.9568345323741 \tabularnewline
156 & 13 & 15.9568345323741 & -2.9568345323741 \tabularnewline
157 & 23 & 18.125 & 4.875 \tabularnewline
158 & 18 & 15.9568345323741 & 2.0431654676259 \tabularnewline
159 & 21 & 18.125 & 2.875 \tabularnewline
160 & 14 & 15.9568345323741 & -1.9568345323741 \tabularnewline
161 & 12 & 15.9568345323741 & -3.9568345323741 \tabularnewline
162 & 17 & 15.9568345323741 & 1.0431654676259 \tabularnewline
163 & 11 & 15.9568345323741 & -4.9568345323741 \tabularnewline
164 & 15 & 18.125 & -3.125 \tabularnewline
165 & 14 & 15.9568345323741 & -1.9568345323741 \tabularnewline
166 & 19 & 18.125 & 0.875 \tabularnewline
167 & 12 & 15.9568345323741 & -3.9568345323741 \tabularnewline
168 & 14 & 18.125 & -4.125 \tabularnewline
169 & 18 & 18.125 & -0.125 \tabularnewline
170 & 25 & 18.125 & 6.875 \tabularnewline
171 & 22 & 18.125 & 3.875 \tabularnewline
172 & 15 & 15.9568345323741 & -0.956834532374101 \tabularnewline
173 & 18 & 15.9568345323741 & 2.0431654676259 \tabularnewline
174 & 18 & 15.9568345323741 & 2.0431654676259 \tabularnewline
175 & 12 & 15.9568345323741 & -3.9568345323741 \tabularnewline
176 & 12 & 15.9568345323741 & -3.9568345323741 \tabularnewline
177 & 16 & 15.9568345323741 & 0.043165467625899 \tabularnewline
178 & 22 & 15.9568345323741 & 6.0431654676259 \tabularnewline
179 & 15 & 15.9568345323741 & -0.956834532374101 \tabularnewline
180 & 16 & 15.9568345323741 & 0.043165467625899 \tabularnewline
181 & 11 & 15.9568345323741 & -4.9568345323741 \tabularnewline
182 & 20 & 15.9568345323741 & 4.0431654676259 \tabularnewline
183 & 14 & 15.9568345323741 & -1.9568345323741 \tabularnewline
184 & 20 & 15.9568345323741 & 4.0431654676259 \tabularnewline
185 & 15 & 15.9568345323741 & -0.956834532374101 \tabularnewline
186 & 12 & 15.9568345323741 & -3.9568345323741 \tabularnewline
187 & 18 & 18.125 & -0.125 \tabularnewline
188 & 18 & 15.9568345323741 & 2.0431654676259 \tabularnewline
189 & 11 & 18.125 & -7.125 \tabularnewline
190 & 13 & 18.125 & -5.125 \tabularnewline
191 & 15 & 15.9568345323741 & -0.956834532374101 \tabularnewline
192 & 19 & 18.125 & 0.875 \tabularnewline
193 & 13 & 15.9568345323741 & -2.9568345323741 \tabularnewline
194 & 19 & 15.9568345323741 & 3.0431654676259 \tabularnewline
195 & 18 & 18.125 & -0.125 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117000&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]18[/C][C]18.125[/C][C]-0.125[/C][/ROW]
[ROW][C]2[/C][C]10[/C][C]15.9568345323741[/C][C]-5.9568345323741[/C][/ROW]
[ROW][C]3[/C][C]23[/C][C]18.125[/C][C]4.875[/C][/ROW]
[ROW][C]4[/C][C]14[/C][C]18.125[/C][C]-4.125[/C][/ROW]
[ROW][C]5[/C][C]20[/C][C]18.125[/C][C]1.875[/C][/ROW]
[ROW][C]6[/C][C]15[/C][C]15.9568345323741[/C][C]-0.956834532374101[/C][/ROW]
[ROW][C]7[/C][C]18[/C][C]15.9568345323741[/C][C]2.0431654676259[/C][/ROW]
[ROW][C]8[/C][C]19[/C][C]15.9568345323741[/C][C]3.0431654676259[/C][/ROW]
[ROW][C]9[/C][C]19[/C][C]15.9568345323741[/C][C]3.0431654676259[/C][/ROW]
[ROW][C]10[/C][C]14[/C][C]15.9568345323741[/C][C]-1.9568345323741[/C][/ROW]
[ROW][C]11[/C][C]15[/C][C]15.9568345323741[/C][C]-0.956834532374101[/C][/ROW]
[ROW][C]12[/C][C]14[/C][C]15.9568345323741[/C][C]-1.9568345323741[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.9568345323741[/C][C]0.043165467625899[/C][/ROW]
[ROW][C]14[/C][C]13[/C][C]15.9568345323741[/C][C]-2.9568345323741[/C][/ROW]
[ROW][C]15[/C][C]13[/C][C]15.9568345323741[/C][C]-2.9568345323741[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]15.9568345323741[/C][C]-1.9568345323741[/C][/ROW]
[ROW][C]17[/C][C]23[/C][C]18.125[/C][C]4.875[/C][/ROW]
[ROW][C]18[/C][C]17[/C][C]15.9568345323741[/C][C]1.0431654676259[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]15.9568345323741[/C][C]-1.9568345323741[/C][/ROW]
[ROW][C]20[/C][C]21[/C][C]18.125[/C][C]2.875[/C][/ROW]
[ROW][C]21[/C][C]15[/C][C]15.9568345323741[/C][C]-0.956834532374101[/C][/ROW]
[ROW][C]22[/C][C]19[/C][C]18.125[/C][C]0.875[/C][/ROW]
[ROW][C]23[/C][C]20[/C][C]15.9568345323741[/C][C]4.0431654676259[/C][/ROW]
[ROW][C]24[/C][C]18[/C][C]15.9568345323741[/C][C]2.0431654676259[/C][/ROW]
[ROW][C]25[/C][C]13[/C][C]15.9568345323741[/C][C]-2.9568345323741[/C][/ROW]
[ROW][C]26[/C][C]20[/C][C]15.9568345323741[/C][C]4.0431654676259[/C][/ROW]
[ROW][C]27[/C][C]12[/C][C]15.9568345323741[/C][C]-3.9568345323741[/C][/ROW]
[ROW][C]28[/C][C]17[/C][C]15.9568345323741[/C][C]1.0431654676259[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]18.125[/C][C]-5.125[/C][/ROW]
[ROW][C]30[/C][C]17[/C][C]15.9568345323741[/C][C]1.0431654676259[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]15.9568345323741[/C][C]0.043165467625899[/C][/ROW]
[ROW][C]32[/C][C]20[/C][C]15.9568345323741[/C][C]4.0431654676259[/C][/ROW]
[ROW][C]33[/C][C]18[/C][C]15.9568345323741[/C][C]2.0431654676259[/C][/ROW]
[ROW][C]34[/C][C]9[/C][C]15.9568345323741[/C][C]-6.9568345323741[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]18.125[/C][C]-4.125[/C][/ROW]
[ROW][C]36[/C][C]12[/C][C]15.9568345323741[/C][C]-3.9568345323741[/C][/ROW]
[ROW][C]37[/C][C]21[/C][C]15.9568345323741[/C][C]5.0431654676259[/C][/ROW]
[ROW][C]38[/C][C]16[/C][C]18.125[/C][C]-2.125[/C][/ROW]
[ROW][C]39[/C][C]12[/C][C]15.9568345323741[/C][C]-3.9568345323741[/C][/ROW]
[ROW][C]40[/C][C]20[/C][C]15.9568345323741[/C][C]4.0431654676259[/C][/ROW]
[ROW][C]41[/C][C]18[/C][C]15.9568345323741[/C][C]2.0431654676259[/C][/ROW]
[ROW][C]42[/C][C]22[/C][C]15.9568345323741[/C][C]6.0431654676259[/C][/ROW]
[ROW][C]43[/C][C]17[/C][C]18.125[/C][C]-1.125[/C][/ROW]
[ROW][C]44[/C][C]16[/C][C]15.9568345323741[/C][C]0.043165467625899[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]15.9568345323741[/C][C]-1.9568345323741[/C][/ROW]
[ROW][C]46[/C][C]19[/C][C]15.9568345323741[/C][C]3.0431654676259[/C][/ROW]
[ROW][C]47[/C][C]21[/C][C]18.125[/C][C]2.875[/C][/ROW]
[ROW][C]48[/C][C]18[/C][C]15.9568345323741[/C][C]2.0431654676259[/C][/ROW]
[ROW][C]49[/C][C]23[/C][C]15.9568345323741[/C][C]7.0431654676259[/C][/ROW]
[ROW][C]50[/C][C]20[/C][C]15.9568345323741[/C][C]4.0431654676259[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]15.9568345323741[/C][C]-5.9568345323741[/C][/ROW]
[ROW][C]52[/C][C]16[/C][C]15.9568345323741[/C][C]0.043165467625899[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]15.9568345323741[/C][C]2.0431654676259[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]15.9568345323741[/C][C]-3.9568345323741[/C][/ROW]
[ROW][C]55[/C][C]15[/C][C]18.125[/C][C]-3.125[/C][/ROW]
[ROW][C]56[/C][C]19[/C][C]18.125[/C][C]0.875[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]15.9568345323741[/C][C]-4.9568345323741[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.9568345323741[/C][C]0.043165467625899[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]15.9568345323741[/C][C]-3.9568345323741[/C][/ROW]
[ROW][C]60[/C][C]18[/C][C]15.9568345323741[/C][C]2.0431654676259[/C][/ROW]
[ROW][C]61[/C][C]14[/C][C]15.9568345323741[/C][C]-1.9568345323741[/C][/ROW]
[ROW][C]62[/C][C]20[/C][C]15.9568345323741[/C][C]4.0431654676259[/C][/ROW]
[ROW][C]63[/C][C]15[/C][C]18.125[/C][C]-3.125[/C][/ROW]
[ROW][C]64[/C][C]17[/C][C]18.125[/C][C]-1.125[/C][/ROW]
[ROW][C]65[/C][C]20[/C][C]18.125[/C][C]1.875[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]15.9568345323741[/C][C]-1.9568345323741[/C][/ROW]
[ROW][C]67[/C][C]16[/C][C]15.9568345323741[/C][C]0.043165467625899[/C][/ROW]
[ROW][C]68[/C][C]15[/C][C]15.9568345323741[/C][C]-0.956834532374101[/C][/ROW]
[ROW][C]69[/C][C]17[/C][C]15.9568345323741[/C][C]1.0431654676259[/C][/ROW]
[ROW][C]70[/C][C]20[/C][C]15.9568345323741[/C][C]4.0431654676259[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]15.9568345323741[/C][C]-1.9568345323741[/C][/ROW]
[ROW][C]72[/C][C]20[/C][C]15.9568345323741[/C][C]4.0431654676259[/C][/ROW]
[ROW][C]73[/C][C]20[/C][C]18.125[/C][C]1.875[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]15.9568345323741[/C][C]-0.956834532374101[/C][/ROW]
[ROW][C]75[/C][C]21[/C][C]18.125[/C][C]2.875[/C][/ROW]
[ROW][C]76[/C][C]22[/C][C]18.125[/C][C]3.875[/C][/ROW]
[ROW][C]77[/C][C]11[/C][C]15.9568345323741[/C][C]-4.9568345323741[/C][/ROW]
[ROW][C]78[/C][C]20[/C][C]18.125[/C][C]1.875[/C][/ROW]
[ROW][C]79[/C][C]17[/C][C]15.9568345323741[/C][C]1.0431654676259[/C][/ROW]
[ROW][C]80[/C][C]19[/C][C]18.125[/C][C]0.875[/C][/ROW]
[ROW][C]81[/C][C]17[/C][C]18.125[/C][C]-1.125[/C][/ROW]
[ROW][C]82[/C][C]15[/C][C]18.125[/C][C]-3.125[/C][/ROW]
[ROW][C]83[/C][C]20[/C][C]18.125[/C][C]1.875[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]15.9568345323741[/C][C]-3.9568345323741[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]15.9568345323741[/C][C]-2.9568345323741[/C][/ROW]
[ROW][C]86[/C][C]18[/C][C]15.9568345323741[/C][C]2.0431654676259[/C][/ROW]
[ROW][C]87[/C][C]19[/C][C]15.9568345323741[/C][C]3.0431654676259[/C][/ROW]
[ROW][C]88[/C][C]13[/C][C]18.125[/C][C]-5.125[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]15.9568345323741[/C][C]-3.9568345323741[/C][/ROW]
[ROW][C]90[/C][C]16[/C][C]15.9568345323741[/C][C]0.043165467625899[/C][/ROW]
[ROW][C]91[/C][C]21[/C][C]18.125[/C][C]2.875[/C][/ROW]
[ROW][C]92[/C][C]19[/C][C]18.125[/C][C]0.875[/C][/ROW]
[ROW][C]93[/C][C]19[/C][C]15.9568345323741[/C][C]3.0431654676259[/C][/ROW]
[ROW][C]94[/C][C]12[/C][C]15.9568345323741[/C][C]-3.9568345323741[/C][/ROW]
[ROW][C]95[/C][C]22[/C][C]15.9568345323741[/C][C]6.0431654676259[/C][/ROW]
[ROW][C]96[/C][C]9[/C][C]18.125[/C][C]-9.125[/C][/ROW]
[ROW][C]97[/C][C]9[/C][C]15.9568345323741[/C][C]-6.9568345323741[/C][/ROW]
[ROW][C]98[/C][C]18[/C][C]18.125[/C][C]-0.125[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]15.9568345323741[/C][C]-1.9568345323741[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]18.125[/C][C]-4.125[/C][/ROW]
[ROW][C]101[/C][C]23[/C][C]18.125[/C][C]4.875[/C][/ROW]
[ROW][C]102[/C][C]19[/C][C]18.125[/C][C]0.875[/C][/ROW]
[ROW][C]103[/C][C]24[/C][C]15.9568345323741[/C][C]8.0431654676259[/C][/ROW]
[ROW][C]104[/C][C]12[/C][C]15.9568345323741[/C][C]-3.9568345323741[/C][/ROW]
[ROW][C]105[/C][C]20[/C][C]15.9568345323741[/C][C]4.0431654676259[/C][/ROW]
[ROW][C]106[/C][C]21[/C][C]18.125[/C][C]2.875[/C][/ROW]
[ROW][C]107[/C][C]18[/C][C]15.9568345323741[/C][C]2.0431654676259[/C][/ROW]
[ROW][C]108[/C][C]20[/C][C]15.9568345323741[/C][C]4.0431654676259[/C][/ROW]
[ROW][C]109[/C][C]18[/C][C]15.9568345323741[/C][C]2.0431654676259[/C][/ROW]
[ROW][C]110[/C][C]18[/C][C]18.125[/C][C]-0.125[/C][/ROW]
[ROW][C]111[/C][C]17[/C][C]15.9568345323741[/C][C]1.0431654676259[/C][/ROW]
[ROW][C]112[/C][C]18[/C][C]15.9568345323741[/C][C]2.0431654676259[/C][/ROW]
[ROW][C]113[/C][C]14[/C][C]18.125[/C][C]-4.125[/C][/ROW]
[ROW][C]114[/C][C]23[/C][C]15.9568345323741[/C][C]7.0431654676259[/C][/ROW]
[ROW][C]115[/C][C]19[/C][C]15.9568345323741[/C][C]3.0431654676259[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]15.9568345323741[/C][C]-1.9568345323741[/C][/ROW]
[ROW][C]117[/C][C]17[/C][C]15.9568345323741[/C][C]1.0431654676259[/C][/ROW]
[ROW][C]118[/C][C]22[/C][C]18.125[/C][C]3.875[/C][/ROW]
[ROW][C]119[/C][C]10[/C][C]15.9568345323741[/C][C]-5.9568345323741[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.9568345323741[/C][C]0.043165467625899[/C][/ROW]
[ROW][C]121[/C][C]14[/C][C]15.9568345323741[/C][C]-1.9568345323741[/C][/ROW]
[ROW][C]122[/C][C]19[/C][C]15.9568345323741[/C][C]3.0431654676259[/C][/ROW]
[ROW][C]123[/C][C]14[/C][C]15.9568345323741[/C][C]-1.9568345323741[/C][/ROW]
[ROW][C]124[/C][C]18[/C][C]15.9568345323741[/C][C]2.0431654676259[/C][/ROW]
[ROW][C]125[/C][C]19[/C][C]15.9568345323741[/C][C]3.0431654676259[/C][/ROW]
[ROW][C]126[/C][C]21[/C][C]15.9568345323741[/C][C]5.0431654676259[/C][/ROW]
[ROW][C]127[/C][C]13[/C][C]15.9568345323741[/C][C]-2.9568345323741[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]15.9568345323741[/C][C]1.0431654676259[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]18.125[/C][C]-7.125[/C][/ROW]
[ROW][C]130[/C][C]16[/C][C]15.9568345323741[/C][C]0.043165467625899[/C][/ROW]
[ROW][C]131[/C][C]22[/C][C]18.125[/C][C]3.875[/C][/ROW]
[ROW][C]132[/C][C]19[/C][C]15.9568345323741[/C][C]3.0431654676259[/C][/ROW]
[ROW][C]133[/C][C]17[/C][C]15.9568345323741[/C][C]1.0431654676259[/C][/ROW]
[ROW][C]134[/C][C]25[/C][C]15.9568345323741[/C][C]9.0431654676259[/C][/ROW]
[ROW][C]135[/C][C]17[/C][C]15.9568345323741[/C][C]1.0431654676259[/C][/ROW]
[ROW][C]136[/C][C]23[/C][C]18.125[/C][C]4.875[/C][/ROW]
[ROW][C]137[/C][C]21[/C][C]18.125[/C][C]2.875[/C][/ROW]
[ROW][C]138[/C][C]12[/C][C]15.9568345323741[/C][C]-3.9568345323741[/C][/ROW]
[ROW][C]139[/C][C]18[/C][C]15.9568345323741[/C][C]2.0431654676259[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]18.125[/C][C]-3.125[/C][/ROW]
[ROW][C]141[/C][C]17[/C][C]15.9568345323741[/C][C]1.0431654676259[/C][/ROW]
[ROW][C]142[/C][C]11[/C][C]15.9568345323741[/C][C]-4.9568345323741[/C][/ROW]
[ROW][C]143[/C][C]17[/C][C]15.9568345323741[/C][C]1.0431654676259[/C][/ROW]
[ROW][C]144[/C][C]13[/C][C]15.9568345323741[/C][C]-2.9568345323741[/C][/ROW]
[ROW][C]145[/C][C]17[/C][C]18.125[/C][C]-1.125[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]15.9568345323741[/C][C]0.043165467625899[/C][/ROW]
[ROW][C]147[/C][C]14[/C][C]15.9568345323741[/C][C]-1.9568345323741[/C][/ROW]
[ROW][C]148[/C][C]15[/C][C]15.9568345323741[/C][C]-0.956834532374101[/C][/ROW]
[ROW][C]149[/C][C]20[/C][C]15.9568345323741[/C][C]4.0431654676259[/C][/ROW]
[ROW][C]150[/C][C]14[/C][C]15.9568345323741[/C][C]-1.9568345323741[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]15.9568345323741[/C][C]0.043165467625899[/C][/ROW]
[ROW][C]152[/C][C]14[/C][C]15.9568345323741[/C][C]-1.9568345323741[/C][/ROW]
[ROW][C]153[/C][C]13[/C][C]15.9568345323741[/C][C]-2.9568345323741[/C][/ROW]
[ROW][C]154[/C][C]15[/C][C]15.9568345323741[/C][C]-0.956834532374101[/C][/ROW]
[ROW][C]155[/C][C]13[/C][C]15.9568345323741[/C][C]-2.9568345323741[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]15.9568345323741[/C][C]-2.9568345323741[/C][/ROW]
[ROW][C]157[/C][C]23[/C][C]18.125[/C][C]4.875[/C][/ROW]
[ROW][C]158[/C][C]18[/C][C]15.9568345323741[/C][C]2.0431654676259[/C][/ROW]
[ROW][C]159[/C][C]21[/C][C]18.125[/C][C]2.875[/C][/ROW]
[ROW][C]160[/C][C]14[/C][C]15.9568345323741[/C][C]-1.9568345323741[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]15.9568345323741[/C][C]-3.9568345323741[/C][/ROW]
[ROW][C]162[/C][C]17[/C][C]15.9568345323741[/C][C]1.0431654676259[/C][/ROW]
[ROW][C]163[/C][C]11[/C][C]15.9568345323741[/C][C]-4.9568345323741[/C][/ROW]
[ROW][C]164[/C][C]15[/C][C]18.125[/C][C]-3.125[/C][/ROW]
[ROW][C]165[/C][C]14[/C][C]15.9568345323741[/C][C]-1.9568345323741[/C][/ROW]
[ROW][C]166[/C][C]19[/C][C]18.125[/C][C]0.875[/C][/ROW]
[ROW][C]167[/C][C]12[/C][C]15.9568345323741[/C][C]-3.9568345323741[/C][/ROW]
[ROW][C]168[/C][C]14[/C][C]18.125[/C][C]-4.125[/C][/ROW]
[ROW][C]169[/C][C]18[/C][C]18.125[/C][C]-0.125[/C][/ROW]
[ROW][C]170[/C][C]25[/C][C]18.125[/C][C]6.875[/C][/ROW]
[ROW][C]171[/C][C]22[/C][C]18.125[/C][C]3.875[/C][/ROW]
[ROW][C]172[/C][C]15[/C][C]15.9568345323741[/C][C]-0.956834532374101[/C][/ROW]
[ROW][C]173[/C][C]18[/C][C]15.9568345323741[/C][C]2.0431654676259[/C][/ROW]
[ROW][C]174[/C][C]18[/C][C]15.9568345323741[/C][C]2.0431654676259[/C][/ROW]
[ROW][C]175[/C][C]12[/C][C]15.9568345323741[/C][C]-3.9568345323741[/C][/ROW]
[ROW][C]176[/C][C]12[/C][C]15.9568345323741[/C][C]-3.9568345323741[/C][/ROW]
[ROW][C]177[/C][C]16[/C][C]15.9568345323741[/C][C]0.043165467625899[/C][/ROW]
[ROW][C]178[/C][C]22[/C][C]15.9568345323741[/C][C]6.0431654676259[/C][/ROW]
[ROW][C]179[/C][C]15[/C][C]15.9568345323741[/C][C]-0.956834532374101[/C][/ROW]
[ROW][C]180[/C][C]16[/C][C]15.9568345323741[/C][C]0.043165467625899[/C][/ROW]
[ROW][C]181[/C][C]11[/C][C]15.9568345323741[/C][C]-4.9568345323741[/C][/ROW]
[ROW][C]182[/C][C]20[/C][C]15.9568345323741[/C][C]4.0431654676259[/C][/ROW]
[ROW][C]183[/C][C]14[/C][C]15.9568345323741[/C][C]-1.9568345323741[/C][/ROW]
[ROW][C]184[/C][C]20[/C][C]15.9568345323741[/C][C]4.0431654676259[/C][/ROW]
[ROW][C]185[/C][C]15[/C][C]15.9568345323741[/C][C]-0.956834532374101[/C][/ROW]
[ROW][C]186[/C][C]12[/C][C]15.9568345323741[/C][C]-3.9568345323741[/C][/ROW]
[ROW][C]187[/C][C]18[/C][C]18.125[/C][C]-0.125[/C][/ROW]
[ROW][C]188[/C][C]18[/C][C]15.9568345323741[/C][C]2.0431654676259[/C][/ROW]
[ROW][C]189[/C][C]11[/C][C]18.125[/C][C]-7.125[/C][/ROW]
[ROW][C]190[/C][C]13[/C][C]18.125[/C][C]-5.125[/C][/ROW]
[ROW][C]191[/C][C]15[/C][C]15.9568345323741[/C][C]-0.956834532374101[/C][/ROW]
[ROW][C]192[/C][C]19[/C][C]18.125[/C][C]0.875[/C][/ROW]
[ROW][C]193[/C][C]13[/C][C]15.9568345323741[/C][C]-2.9568345323741[/C][/ROW]
[ROW][C]194[/C][C]19[/C][C]15.9568345323741[/C][C]3.0431654676259[/C][/ROW]
[ROW][C]195[/C][C]18[/C][C]18.125[/C][C]-0.125[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117000&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117000&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	18	18.125	-0.125
2	10	15.9568345323741	-5.9568345323741
3	23	18.125	4.875
4	14	18.125	-4.125
5	20	18.125	1.875
6	15	15.9568345323741	-0.956834532374101
7	18	15.9568345323741	2.0431654676259
8	19	15.9568345323741	3.0431654676259
9	19	15.9568345323741	3.0431654676259
10	14	15.9568345323741	-1.9568345323741
11	15	15.9568345323741	-0.956834532374101
12	14	15.9568345323741	-1.9568345323741
13	16	15.9568345323741	0.043165467625899
14	13	15.9568345323741	-2.9568345323741
15	13	15.9568345323741	-2.9568345323741
16	14	15.9568345323741	-1.9568345323741
17	23	18.125	4.875
18	17	15.9568345323741	1.0431654676259
19	14	15.9568345323741	-1.9568345323741
20	21	18.125	2.875
21	15	15.9568345323741	-0.956834532374101
22	19	18.125	0.875
23	20	15.9568345323741	4.0431654676259
24	18	15.9568345323741	2.0431654676259
25	13	15.9568345323741	-2.9568345323741
26	20	15.9568345323741	4.0431654676259
27	12	15.9568345323741	-3.9568345323741
28	17	15.9568345323741	1.0431654676259
29	13	18.125	-5.125
30	17	15.9568345323741	1.0431654676259
31	16	15.9568345323741	0.043165467625899
32	20	15.9568345323741	4.0431654676259
33	18	15.9568345323741	2.0431654676259
34	9	15.9568345323741	-6.9568345323741
35	14	18.125	-4.125
36	12	15.9568345323741	-3.9568345323741
37	21	15.9568345323741	5.0431654676259
38	16	18.125	-2.125
39	12	15.9568345323741	-3.9568345323741
40	20	15.9568345323741	4.0431654676259
41	18	15.9568345323741	2.0431654676259
42	22	15.9568345323741	6.0431654676259
43	17	18.125	-1.125
44	16	15.9568345323741	0.043165467625899
45	14	15.9568345323741	-1.9568345323741
46	19	15.9568345323741	3.0431654676259
47	21	18.125	2.875
48	18	15.9568345323741	2.0431654676259
49	23	15.9568345323741	7.0431654676259
50	20	15.9568345323741	4.0431654676259
51	10	15.9568345323741	-5.9568345323741
52	16	15.9568345323741	0.043165467625899
53	18	15.9568345323741	2.0431654676259
54	12	15.9568345323741	-3.9568345323741
55	15	18.125	-3.125
56	19	18.125	0.875
57	11	15.9568345323741	-4.9568345323741
58	16	15.9568345323741	0.043165467625899
59	12	15.9568345323741	-3.9568345323741
60	18	15.9568345323741	2.0431654676259
61	14	15.9568345323741	-1.9568345323741
62	20	15.9568345323741	4.0431654676259
63	15	18.125	-3.125
64	17	18.125	-1.125
65	20	18.125	1.875
66	14	15.9568345323741	-1.9568345323741
67	16	15.9568345323741	0.043165467625899
68	15	15.9568345323741	-0.956834532374101
69	17	15.9568345323741	1.0431654676259
70	20	15.9568345323741	4.0431654676259
71	14	15.9568345323741	-1.9568345323741
72	20	15.9568345323741	4.0431654676259
73	20	18.125	1.875
74	15	15.9568345323741	-0.956834532374101
75	21	18.125	2.875
76	22	18.125	3.875
77	11	15.9568345323741	-4.9568345323741
78	20	18.125	1.875
79	17	15.9568345323741	1.0431654676259
80	19	18.125	0.875
81	17	18.125	-1.125
82	15	18.125	-3.125
83	20	18.125	1.875
84	12	15.9568345323741	-3.9568345323741
85	13	15.9568345323741	-2.9568345323741
86	18	15.9568345323741	2.0431654676259
87	19	15.9568345323741	3.0431654676259
88	13	18.125	-5.125
89	12	15.9568345323741	-3.9568345323741
90	16	15.9568345323741	0.043165467625899
91	21	18.125	2.875
92	19	18.125	0.875
93	19	15.9568345323741	3.0431654676259
94	12	15.9568345323741	-3.9568345323741
95	22	15.9568345323741	6.0431654676259
96	9	18.125	-9.125
97	9	15.9568345323741	-6.9568345323741
98	18	18.125	-0.125
99	14	15.9568345323741	-1.9568345323741
100	14	18.125	-4.125
101	23	18.125	4.875
102	19	18.125	0.875
103	24	15.9568345323741	8.0431654676259
104	12	15.9568345323741	-3.9568345323741
105	20	15.9568345323741	4.0431654676259
106	21	18.125	2.875
107	18	15.9568345323741	2.0431654676259
108	20	15.9568345323741	4.0431654676259
109	18	15.9568345323741	2.0431654676259
110	18	18.125	-0.125
111	17	15.9568345323741	1.0431654676259
112	18	15.9568345323741	2.0431654676259
113	14	18.125	-4.125
114	23	15.9568345323741	7.0431654676259
115	19	15.9568345323741	3.0431654676259
116	14	15.9568345323741	-1.9568345323741
117	17	15.9568345323741	1.0431654676259
118	22	18.125	3.875
119	10	15.9568345323741	-5.9568345323741
120	16	15.9568345323741	0.043165467625899
121	14	15.9568345323741	-1.9568345323741
122	19	15.9568345323741	3.0431654676259
123	14	15.9568345323741	-1.9568345323741
124	18	15.9568345323741	2.0431654676259
125	19	15.9568345323741	3.0431654676259
126	21	15.9568345323741	5.0431654676259
127	13	15.9568345323741	-2.9568345323741
128	17	15.9568345323741	1.0431654676259
129	11	18.125	-7.125
130	16	15.9568345323741	0.043165467625899
131	22	18.125	3.875
132	19	15.9568345323741	3.0431654676259
133	17	15.9568345323741	1.0431654676259
134	25	15.9568345323741	9.0431654676259
135	17	15.9568345323741	1.0431654676259
136	23	18.125	4.875
137	21	18.125	2.875
138	12	15.9568345323741	-3.9568345323741
139	18	15.9568345323741	2.0431654676259
140	15	18.125	-3.125
141	17	15.9568345323741	1.0431654676259
142	11	15.9568345323741	-4.9568345323741
143	17	15.9568345323741	1.0431654676259
144	13	15.9568345323741	-2.9568345323741
145	17	18.125	-1.125
146	16	15.9568345323741	0.043165467625899
147	14	15.9568345323741	-1.9568345323741
148	15	15.9568345323741	-0.956834532374101
149	20	15.9568345323741	4.0431654676259
150	14	15.9568345323741	-1.9568345323741
151	16	15.9568345323741	0.043165467625899
152	14	15.9568345323741	-1.9568345323741
153	13	15.9568345323741	-2.9568345323741
154	15	15.9568345323741	-0.956834532374101
155	13	15.9568345323741	-2.9568345323741
156	13	15.9568345323741	-2.9568345323741
157	23	18.125	4.875
158	18	15.9568345323741	2.0431654676259
159	21	18.125	2.875
160	14	15.9568345323741	-1.9568345323741
161	12	15.9568345323741	-3.9568345323741
162	17	15.9568345323741	1.0431654676259
163	11	15.9568345323741	-4.9568345323741
164	15	18.125	-3.125
165	14	15.9568345323741	-1.9568345323741
166	19	18.125	0.875
167	12	15.9568345323741	-3.9568345323741
168	14	18.125	-4.125
169	18	18.125	-0.125
170	25	18.125	6.875
171	22	18.125	3.875
172	15	15.9568345323741	-0.956834532374101
173	18	15.9568345323741	2.0431654676259
174	18	15.9568345323741	2.0431654676259
175	12	15.9568345323741	-3.9568345323741
176	12	15.9568345323741	-3.9568345323741
177	16	15.9568345323741	0.043165467625899
178	22	15.9568345323741	6.0431654676259
179	15	15.9568345323741	-0.956834532374101
180	16	15.9568345323741	0.043165467625899
181	11	15.9568345323741	-4.9568345323741
182	20	15.9568345323741	4.0431654676259
183	14	15.9568345323741	-1.9568345323741
184	20	15.9568345323741	4.0431654676259
185	15	15.9568345323741	-0.956834532374101
186	12	15.9568345323741	-3.9568345323741
187	18	18.125	-0.125
188	18	15.9568345323741	2.0431654676259
189	11	18.125	-7.125
190	13	18.125	-5.125
191	15	15.9568345323741	-0.956834532374101
192	19	18.125	0.875
193	13	15.9568345323741	-2.9568345323741
194	19	15.9568345323741	3.0431654676259
195	18	18.125	-0.125

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