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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, 11 Dec 2013 08:48:35 -0500

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/2013/Dec/11/t1386769723u4xrk3so309uaip.htm/, Retrieved Tue, 16 Apr 2024 19:47:13 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=232051, Retrieved Tue, 16 Apr 2024 19:47:13 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

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

-       [Recursive Partitioning (Regression Trees)] [] [2013-12-11 13:48:35] [86ae564e1d54e2fca6d87caa651728c1] [Current]

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Dataseries X:

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Histogram

Boxplots

1 1 41 38 13 12 14
1 1 39 32 16 11 18
1 1 30 35 19 15 11
1 0 31 33 15 6 12
1 1 34 37 14 13 16
1 1 35 29 13 10 18
1 1 39 31 19 12 14
1 1 34 36 15 14 14
1 1 36 35 14 12 15
1 1 37 38 15 9 15
1 0 38 31 16 10 17
1 1 36 34 16 12 19
1 0 38 35 16 12 10
1 1 39 38 16 11 16
1 1 33 37 17 15 18
1 0 32 33 15 12 14
1 0 36 32 15 10 14
1 1 38 38 20 12 17
1 0 39 38 18 11 14
1 1 32 32 16 12 16
1 0 32 33 16 11 18
1 1 31 31 16 12 11
1 1 39 38 19 13 14
1 1 37 39 16 11 12
1 0 39 32 17 12 17
1 1 41 32 17 13 9
1 0 36 35 16 10 16
1 1 33 37 15 14 14
1 1 33 33 16 12 15
1 0 34 33 14 10 11
1 1 31 31 15 12 16
1 0 27 32 12 8 13
1 1 37 31 14 10 17
1 1 34 37 16 12 15
1 0 34 30 14 12 14
1 0 32 33 10 7 16
1 0 29 31 10 9 9
1 0 36 33 14 12 15
1 1 29 31 16 10 17
1 0 35 33 16 10 13
1 0 37 32 16 10 15
1 1 34 33 14 12 16
1 0 38 32 20 15 16
1 0 35 33 14 10 12
1 1 38 28 14 10 15
1 1 37 35 11 12 11
1 1 38 39 14 13 15
1 1 33 34 15 11 15
1 1 36 38 16 11 17
1 0 38 32 14 12 13
1 1 32 38 16 14 16
1 0 32 30 14 10 14
1 0 32 33 12 12 11
1 1 34 38 16 13 12
1 0 32 32 9 5 12
1 1 37 35 14 6 15
1 1 39 34 16 12 16
1 1 29 34 16 12 15
1 0 37 36 15 11 12
1 1 35 34 16 10 12
1 0 30 28 12 7 8
1 0 38 34 16 12 13
1 1 34 35 16 14 11
1 1 31 35 14 11 14
1 1 34 31 16 12 15
1 0 35 37 17 13 10
1 1 36 35 18 14 11
1 0 30 27 18 11 12
1 1 39 40 12 12 15
1 0 35 37 16 12 15
1 0 38 36 10 8 14
1 1 31 38 14 11 16
1 1 34 39 18 14 15
1 0 38 41 18 14 15
1 0 34 27 16 12 13
1 1 39 30 17 9 12
1 1 37 37 16 13 17
1 1 34 31 16 11 13
1 0 28 31 13 12 15
1 0 37 27 16 12 13
1 0 33 36 16 12 15
1 1 35 37 16 12 15
1 0 37 33 15 12 16
1 1 32 34 15 11 15
1 1 33 31 16 10 14
1 0 38 39 14 9 15
1 1 33 34 16 12 14
1 1 29 32 16 12 13
1 1 33 33 15 12 7
1 1 31 36 12 9 17
1 1 36 32 17 15 13
1 1 35 41 16 12 15
1 1 32 28 15 12 14
1 1 29 30 13 12 13
1 1 39 36 16 10 16
1 1 37 35 16 13 12
1 1 35 31 16 9 14
1 0 37 34 16 12 17
1 0 32 36 14 10 15
1 1 38 36 16 14 17
1 0 37 35 16 11 12
1 1 36 37 20 15 16
1 0 32 28 15 11 11
1 1 33 39 16 11 15
1 0 40 32 13 12 9
1 1 38 35 17 12 16
1 0 41 39 16 12 15
1 0 36 35 16 11 10
1 1 43 42 12 7 10
1 1 30 34 16 12 15
1 1 31 33 16 14 11
1 1 32 41 17 11 13
1 1 37 34 12 10 18
1 0 37 32 18 13 16
1 1 33 40 14 13 14
1 1 34 40 14 8 14
1 1 33 35 13 11 14
1 1 38 36 16 12 14
1 0 33 37 13 11 12
1 1 31 27 16 13 14
1 1 38 39 13 12 15
1 1 37 38 16 14 15
1 1 36 31 15 13 15
1 1 31 33 16 15 13
1 0 39 32 15 10 17
1 1 44 39 17 11 17
1 1 33 36 15 9 19
1 1 35 33 12 11 15
1 0 32 33 16 10 13
1 0 28 32 10 11 9
1 1 40 37 16 8 15
1 0 27 30 12 11 15
1 0 37 38 14 12 15
1 1 32 29 15 12 16
1 0 28 22 13 9 11
1 0 34 35 15 11 14
1 1 30 35 11 10 11
1 1 35 34 12 8 15
1 0 31 35 11 9 13
1 1 32 34 16 8 15
1 0 30 37 15 9 16
1 1 30 35 17 15 14
1 0 31 23 16 11 15
1 1 40 31 10 8 16
1 1 32 27 18 13 16
1 0 36 36 13 12 11
1 0 32 31 16 12 12
1 0 35 32 13 9 9
1 1 38 39 10 7 16
1 1 42 37 15 13 13
1 0 34 38 16 9 16
1 1 35 39 16 6 12
1 1 38 34 14 8 9
1 1 33 31 10 8 13
1 1 32 37 13 6 14
1 1 33 36 15 9 19
1 1 34 32 16 11 13
1 1 32 38 12 8 12
0 0 27 26 13 10 10
0 0 31 26 12 8 14
0 0 38 33 17 14 16
0 1 34 39 15 10 10
0 0 24 30 10 8 11
0 0 30 33 14 11 14
0 1 26 25 11 12 12
0 1 34 38 13 12 9
0 0 27 37 16 12 9
0 0 37 31 12 5 11
0 1 36 37 16 12 16
0 0 41 35 12 10 9
0 1 29 25 9 7 13
0 1 36 28 12 12 16
0 0 32 35 15 11 13
0 1 37 33 12 8 9
0 0 30 30 12 9 12
0 1 31 31 14 10 16
0 1 38 37 12 9 11
0 1 36 36 16 12 14
0 0 35 30 11 6 13
0 0 31 36 19 15 15
0 0 38 32 15 12 14
0 1 22 28 8 12 16
0 1 32 36 16 12 13
0 0 36 34 17 11 14
0 1 39 31 12 7 15
0 0 28 28 11 7 13
0 0 32 36 11 5 11
0 1 32 36 14 12 11
0 1 38 40 16 12 14
0 1 32 33 12 3 15
0 1 35 37 16 11 11
0 1 32 32 13 10 15
0 0 37 38 15 12 12
0 1 34 31 16 9 14
0 1 33 37 16 12 14
0 0 33 33 14 9 8
0 0 30 30 16 12 9
0 0 24 30 14 10 15
0 0 34 31 11 9 17
0 0 34 32 12 12 13
0 1 33 34 15 8 15
0 1 34 36 15 11 15
0 1 35 37 16 11 14
0 0 35 36 16 12 16
0 0 36 33 11 10 13
0 0 34 33 15 10 16
0 1 34 33 12 12 9
0 0 41 44 12 12 16
0 0 32 39 15 11 11
0 0 30 32 15 8 10
0 1 35 35 16 12 11
0 0 28 25 14 10 15
0 1 33 35 17 11 17
0 1 39 34 14 10 14
0 0 36 35 13 8 8
0 1 36 39 15 12 15
0 0 35 33 13 12 11
0 0 38 36 14 10 16
0 1 33 32 15 12 10
0 0 31 32 12 9 15
0 1 32 36 8 6 16
0 0 31 32 14 10 19
0 0 33 34 14 9 12
0 0 34 33 11 9 8
0 0 34 35 12 9 11
0 1 34 30 13 6 14
0 0 33 38 10 10 9
0 0 32 34 16 6 15
0 1 41 33 18 14 13
0 1 34 32 13 10 16
0 0 36 31 11 10 11
0 0 37 30 4 6 12
0 0 36 27 13 12 13
0 1 29 31 16 12 10
0 0 37 30 10 7 11
0 0 27 32 12 8 12
0 0 35 35 12 11 8
0 0 28 28 10 3 12
0 0 35 33 13 6 12
0 0 29 35 12 8 11
0 0 32 35 14 9 13
0 1 36 32 10 9 14
0 1 19 21 12 8 10
0 1 21 20 12 9 12
0 0 31 34 11 7 15
0 0 33 32 10 7 13
0 1 36 34 12 6 13
0 1 33 32 16 9 13
0 0 37 33 12 10 12
0 0 34 33 14 11 12
0 0 35 37 16 12 9
0 1 31 32 14 8 9
0 1 37 34 13 11 15
0 1 35 30 4 3 10
0 1 27 30 15 11 14
0 0 34 38 11 12 15
0 0 40 36 11 7 7
0 0 29 32 14 9 14
0 0 38 34 15 12 8
0 1 34 33 14 8 10
0 0 21 27 13 11 13
0 0 36 32 11 8 13
0 1 38 34 15 10 13
0 0 30 29 11 8 8
0 0 35 35 13 7 12
0 1 30 27 13 8 13
0 1 36 33 16 10 12
0 0 34 38 13 8 10
0 1 35 36 16 12 13
0 0 34 33 16 14 12
0 0 32 39 12 7 9
0 1 33 29 7 6 15
0 0 33 32 16 11 13
0 1 26 34 5 4 13
0 0 35 38 16 9 13
0 0 21 17 4 5 15
0 0 38 35 12 9 15
0 0 35 32 15 11 14
0 1 33 34 14 12 15
0 0 37 36 11 9 11
0 0 38 31 16 12 15
0 1 34 35 15 10 14
0 0 27 29 12 9 13
0 1 16 22 6 6 12
0 0 40 41 16 10 16
0 0 36 36 10 9 16
0 1 42 42 15 13 9
0 1 30 33 14 12 14

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	13 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 13 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232051&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]13 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232051&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232051&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	13 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

Goodness of Fit
Correlation	0.5624
R-squared	0.3163
RMSE	3.2245

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.5624 \tabularnewline
R-squared & 0.3163 \tabularnewline
RMSE & 3.2245 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232051&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.5624[/C][/ROW]
[ROW][C]R-squared[/C][C]0.3163[/C][/ROW]
[ROW][C]RMSE[/C][C]3.2245[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232051&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232051&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	0.5624
R-squared	0.3163
RMSE	3.2245

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	38	38.1	-0.100000000000001
2	32	34.8988764044944	-2.89887640449438
3	35	31.1607142857143	3.83928571428572
4	33	31.1607142857143	1.83928571428572
5	37	33.984	3.016
6	29	33.984	-4.984
7	31	34.8988764044944	-3.89887640449438
8	36	33.984	2.016
9	35	33.984	1.016
10	38	33.984	4.016
11	31	34.8988764044944	-3.89887640449438
12	34	34.8988764044944	-0.898876404494381
13	35	34.8988764044944	0.101123595505619
14	38	34.8988764044944	3.10112359550562
15	37	34.8988764044944	2.10112359550562
16	33	33.984	-0.984000000000002
17	32	33.984	-1.984
18	38	34.8988764044944	3.10112359550562
19	38	34.8988764044944	3.10112359550562
20	32	34.8988764044944	-2.89887640449438
21	33	34.8988764044944	-1.89887640449438
22	31	31.1607142857143	-0.160714285714285
23	38	34.8988764044944	3.10112359550562
24	39	34.8988764044944	4.10112359550562
25	32	34.8988764044944	-2.89887640449438
26	32	38.1	-6.1
27	35	34.8988764044944	0.101123595505619
28	37	33.984	3.016
29	33	34.8988764044944	-1.89887640449438
30	33	33.984	-0.984000000000002
31	31	31.1607142857143	-0.160714285714285
32	32	31.1607142857143	0.839285714285715
33	31	33.984	-2.984
34	37	34.8988764044944	2.10112359550562
35	30	33.984	-3.984
36	33	33.984	-0.984000000000002
37	31	31.1607142857143	-0.160714285714285
38	33	33.984	-0.984000000000002
39	31	31.1607142857143	-0.160714285714285
40	33	34.8988764044944	-1.89887640449438
41	32	34.8988764044944	-2.89887640449438
42	33	33.984	-0.984000000000002
43	32	34.8988764044944	-2.89887640449438
44	33	33.984	-0.984000000000002
45	28	33.984	-5.984
46	35	33.984	1.016
47	39	33.984	5.016
48	34	33.984	0.0159999999999982
49	38	34.8988764044944	3.10112359550562
50	32	33.984	-1.984
51	38	34.8988764044944	3.10112359550562
52	30	33.984	-3.984
53	33	33.984	-0.984000000000002
54	38	34.8988764044944	3.10112359550562
55	32	33.984	-1.984
56	35	33.984	1.016
57	34	34.8988764044944	-0.898876404494381
58	34	31.1607142857143	2.83928571428572
59	36	33.984	2.016
60	34	34.8988764044944	-0.898876404494381
61	28	31.1607142857143	-3.16071428571428
62	34	34.8988764044944	-0.898876404494381
63	35	34.8988764044944	0.101123595505619
64	35	31.1607142857143	3.83928571428572
65	31	34.8988764044944	-3.89887640449438
66	37	34.8988764044944	2.10112359550562
67	35	34.8988764044944	0.101123595505619
68	27	31.1607142857143	-4.16071428571428
69	40	33.984	6.016
70	37	34.8988764044944	2.10112359550562
71	36	33.984	2.016
72	38	31.1607142857143	6.83928571428572
73	39	34.8988764044944	4.10112359550562
74	41	34.8988764044944	6.10112359550562
75	27	34.8988764044944	-7.89887640449438
76	30	34.8988764044944	-4.89887640449438
77	37	34.8988764044944	2.10112359550562
78	31	34.8988764044944	-3.89887640449438
79	31	31.1607142857143	-0.160714285714285
80	27	34.8988764044944	-7.89887640449438
81	36	34.8988764044944	1.10112359550562
82	37	34.8988764044944	2.10112359550562
83	33	33.984	-0.984000000000002
84	34	33.984	0.0159999999999982
85	31	34.8988764044944	-3.89887640449438
86	39	33.984	5.016
87	34	34.8988764044944	-0.898876404494381
88	32	31.1607142857143	0.839285714285715
89	33	33.984	-0.984000000000002
90	36	31.1607142857143	4.83928571428572
91	32	34.8988764044944	-2.89887640449438
92	41	34.8988764044944	6.10112359550562
93	28	33.984	-5.984
94	30	31.1607142857143	-1.16071428571428
95	36	34.8988764044944	1.10112359550562
96	35	34.8988764044944	0.101123595505619
97	31	34.8988764044944	-3.89887640449438
98	34	34.8988764044944	-0.898876404494381
99	36	33.984	2.016
100	36	34.8988764044944	1.10112359550562
101	35	34.8988764044944	0.101123595505619
102	37	34.8988764044944	2.10112359550562
103	28	33.984	-5.984
104	39	34.8988764044944	4.10112359550562
105	32	33.984	-1.984
106	35	34.8988764044944	0.101123595505619
107	39	38.1	0.899999999999999
108	35	34.8988764044944	0.101123595505619
109	42	38.1	3.9
110	34	31.1607142857143	2.83928571428572
111	33	31.1607142857143	1.83928571428572
112	41	34.8988764044944	6.10112359550562
113	34	33.984	0.0159999999999982
114	32	34.8988764044944	-2.89887640449438
115	40	33.984	6.016
116	40	33.984	6.016
117	35	33.984	1.016
118	36	34.8988764044944	1.10112359550562
119	37	33.984	3.016
120	27	31.1607142857143	-4.16071428571428
121	39	33.984	5.016
122	38	34.8988764044944	3.10112359550562
123	31	33.984	-2.984
124	33	31.1607142857143	1.83928571428572
125	32	33.984	-1.984
126	39	38.1	0.899999999999999
127	36	33.984	2.016
128	33	33.984	-0.984000000000002
129	33	34.8988764044944	-1.89887640449438
130	32	31.1607142857143	0.839285714285715
131	37	34.8988764044944	2.10112359550562
132	30	31.1607142857143	-1.16071428571428
133	38	33.984	4.016
134	29	33.984	-4.984
135	22	31.1607142857143	-9.16071428571428
136	35	33.984	1.016
137	35	31.1607142857143	3.83928571428572
138	34	33.984	0.0159999999999982
139	35	31.1607142857143	3.83928571428572
140	34	34.8988764044944	-0.898876404494381
141	37	31.1607142857143	5.83928571428572
142	35	31.1607142857143	3.83928571428572
143	23	31.1607142857143	-8.16071428571428
144	31	33.984	-2.984
145	27	34.8988764044944	-7.89887640449438
146	36	33.984	2.016
147	31	34.8988764044944	-3.89887640449438
148	32	33.984	-1.984
149	39	33.984	5.016
150	37	38.1	-1.1
151	38	34.8988764044944	3.10112359550562
152	39	34.8988764044944	4.10112359550562
153	34	33.984	0.0159999999999982
154	31	33.984	-2.984
155	37	33.984	3.016
156	36	33.984	2.016
157	32	34.8988764044944	-2.89887640449438
158	38	33.984	4.016
159	26	31.1607142857143	-5.16071428571428
160	26	31.1607142857143	-5.16071428571428
161	33	34.8988764044944	-1.89887640449438
162	39	33.984	5.016
163	30	24.375	5.625
164	33	31.1607142857143	1.83928571428572
165	25	31.1607142857143	-6.16071428571428
166	38	33.984	4.016
167	37	31.1607142857143	5.83928571428572
168	31	33.984	-2.984
169	37	34.8988764044944	2.10112359550562
170	35	38.1	-3.1
171	25	31.1607142857143	-6.16071428571428
172	28	33.984	-5.984
173	35	33.984	1.016
174	33	33.984	-0.984000000000002
175	30	31.1607142857143	-1.16071428571428
176	31	31.1607142857143	-0.160714285714285
177	37	33.984	3.016
178	36	34.8988764044944	1.10112359550562
179	30	33.984	-3.984
180	36	31.1607142857143	4.83928571428572
181	32	33.984	-1.984
182	28	24.375	3.625
183	36	34.8988764044944	1.10112359550562
184	34	34.8988764044944	-0.898876404494381
185	31	33.984	-2.984
186	28	31.1607142857143	-3.16071428571428
187	36	33.984	2.016
188	36	33.984	2.016
189	40	34.8988764044944	5.10112359550562
190	33	33.984	-0.984000000000002
191	37	34.8988764044944	2.10112359550562
192	32	33.984	-1.984
193	38	33.984	4.016
194	31	34.8988764044944	-3.89887640449438
195	37	34.8988764044944	2.10112359550562
196	33	33.984	-0.984000000000002
197	30	31.1607142857143	-1.16071428571428
198	30	24.375	5.625
199	31	33.984	-2.984
200	32	33.984	-1.984
201	34	33.984	0.0159999999999982
202	36	33.984	2.016
203	37	34.8988764044944	2.10112359550562
204	36	34.8988764044944	1.10112359550562
205	33	33.984	-0.984000000000002
206	33	33.984	-0.984000000000002
207	33	33.984	-0.984000000000002
208	44	38.1	5.9
209	39	33.984	5.016
210	32	31.1607142857143	0.839285714285715
211	35	34.8988764044944	0.101123595505619
212	25	31.1607142857143	-6.16071428571428
213	35	34.8988764044944	0.101123595505619
214	34	33.984	0.0159999999999982
215	35	33.984	1.016
216	39	33.984	5.016
217	33	33.984	-0.984000000000002
218	36	33.984	2.016
219	32	33.984	-1.984
220	32	31.1607142857143	0.839285714285715
221	36	33.984	2.016
222	32	31.1607142857143	0.839285714285715
223	34	33.984	0.0159999999999982
224	33	33.984	-0.984000000000002
225	35	33.984	1.016
226	30	33.984	-3.984
227	38	33.984	4.016
228	34	34.8988764044944	-0.898876404494381
229	33	38.1	-5.1
230	32	33.984	-1.984
231	31	33.984	-2.984
232	30	33.984	-3.984
233	27	33.984	-6.984
234	31	31.1607142857143	-0.160714285714285
235	30	33.984	-3.984
236	32	31.1607142857143	0.839285714285715
237	35	33.984	1.016
238	28	31.1607142857143	-3.16071428571428
239	33	33.984	-0.984000000000002
240	35	31.1607142857143	3.83928571428572
241	35	33.984	1.016
242	32	33.984	-1.984
243	21	24.375	-3.375
244	20	24.375	-4.375
245	34	31.1607142857143	2.83928571428572
246	32	33.984	-1.984
247	34	33.984	0.0159999999999982
248	32	34.8988764044944	-2.89887640449438
249	33	33.984	-0.984000000000002
250	33	33.984	-0.984000000000002
251	37	34.8988764044944	2.10112359550562
252	32	31.1607142857143	0.839285714285715
253	34	33.984	0.0159999999999982
254	30	33.984	-3.984
255	30	31.1607142857143	-1.16071428571428
256	38	33.984	4.016
257	36	33.984	2.016
258	32	31.1607142857143	0.839285714285715
259	34	33.984	0.0159999999999982
260	33	33.984	-0.984000000000002
261	27	24.375	2.625
262	32	33.984	-1.984
263	34	33.984	0.0159999999999982
264	29	31.1607142857143	-2.16071428571428
265	35	33.984	1.016
266	27	31.1607142857143	-4.16071428571428
267	33	34.8988764044944	-1.89887640449438
268	38	33.984	4.016
269	36	34.8988764044944	1.10112359550562
270	33	34.8988764044944	-1.89887640449438
271	39	33.984	5.016
272	29	33.984	-4.984
273	32	34.8988764044944	-2.89887640449438
274	34	31.1607142857143	2.83928571428572
275	38	34.8988764044944	3.10112359550562
276	17	24.375	-7.375
277	35	33.984	1.016
278	32	33.984	-1.984
279	34	33.984	0.0159999999999982
280	36	33.984	2.016
281	31	34.8988764044944	-3.89887640449438
282	35	33.984	1.016
283	29	31.1607142857143	-2.16071428571428
284	22	24.375	-2.375
285	41	34.8988764044944	6.10112359550562
286	36	33.984	2.016
287	42	38.1	3.9
288	33	31.1607142857143	1.83928571428572

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 38 & 38.1 & -0.100000000000001 \tabularnewline
2 & 32 & 34.8988764044944 & -2.89887640449438 \tabularnewline
3 & 35 & 31.1607142857143 & 3.83928571428572 \tabularnewline
4 & 33 & 31.1607142857143 & 1.83928571428572 \tabularnewline
5 & 37 & 33.984 & 3.016 \tabularnewline
6 & 29 & 33.984 & -4.984 \tabularnewline
7 & 31 & 34.8988764044944 & -3.89887640449438 \tabularnewline
8 & 36 & 33.984 & 2.016 \tabularnewline
9 & 35 & 33.984 & 1.016 \tabularnewline
10 & 38 & 33.984 & 4.016 \tabularnewline
11 & 31 & 34.8988764044944 & -3.89887640449438 \tabularnewline
12 & 34 & 34.8988764044944 & -0.898876404494381 \tabularnewline
13 & 35 & 34.8988764044944 & 0.101123595505619 \tabularnewline
14 & 38 & 34.8988764044944 & 3.10112359550562 \tabularnewline
15 & 37 & 34.8988764044944 & 2.10112359550562 \tabularnewline
16 & 33 & 33.984 & -0.984000000000002 \tabularnewline
17 & 32 & 33.984 & -1.984 \tabularnewline
18 & 38 & 34.8988764044944 & 3.10112359550562 \tabularnewline
19 & 38 & 34.8988764044944 & 3.10112359550562 \tabularnewline
20 & 32 & 34.8988764044944 & -2.89887640449438 \tabularnewline
21 & 33 & 34.8988764044944 & -1.89887640449438 \tabularnewline
22 & 31 & 31.1607142857143 & -0.160714285714285 \tabularnewline
23 & 38 & 34.8988764044944 & 3.10112359550562 \tabularnewline
24 & 39 & 34.8988764044944 & 4.10112359550562 \tabularnewline
25 & 32 & 34.8988764044944 & -2.89887640449438 \tabularnewline
26 & 32 & 38.1 & -6.1 \tabularnewline
27 & 35 & 34.8988764044944 & 0.101123595505619 \tabularnewline
28 & 37 & 33.984 & 3.016 \tabularnewline
29 & 33 & 34.8988764044944 & -1.89887640449438 \tabularnewline
30 & 33 & 33.984 & -0.984000000000002 \tabularnewline
31 & 31 & 31.1607142857143 & -0.160714285714285 \tabularnewline
32 & 32 & 31.1607142857143 & 0.839285714285715 \tabularnewline
33 & 31 & 33.984 & -2.984 \tabularnewline
34 & 37 & 34.8988764044944 & 2.10112359550562 \tabularnewline
35 & 30 & 33.984 & -3.984 \tabularnewline
36 & 33 & 33.984 & -0.984000000000002 \tabularnewline
37 & 31 & 31.1607142857143 & -0.160714285714285 \tabularnewline
38 & 33 & 33.984 & -0.984000000000002 \tabularnewline
39 & 31 & 31.1607142857143 & -0.160714285714285 \tabularnewline
40 & 33 & 34.8988764044944 & -1.89887640449438 \tabularnewline
41 & 32 & 34.8988764044944 & -2.89887640449438 \tabularnewline
42 & 33 & 33.984 & -0.984000000000002 \tabularnewline
43 & 32 & 34.8988764044944 & -2.89887640449438 \tabularnewline
44 & 33 & 33.984 & -0.984000000000002 \tabularnewline
45 & 28 & 33.984 & -5.984 \tabularnewline
46 & 35 & 33.984 & 1.016 \tabularnewline
47 & 39 & 33.984 & 5.016 \tabularnewline
48 & 34 & 33.984 & 0.0159999999999982 \tabularnewline
49 & 38 & 34.8988764044944 & 3.10112359550562 \tabularnewline
50 & 32 & 33.984 & -1.984 \tabularnewline
51 & 38 & 34.8988764044944 & 3.10112359550562 \tabularnewline
52 & 30 & 33.984 & -3.984 \tabularnewline
53 & 33 & 33.984 & -0.984000000000002 \tabularnewline
54 & 38 & 34.8988764044944 & 3.10112359550562 \tabularnewline
55 & 32 & 33.984 & -1.984 \tabularnewline
56 & 35 & 33.984 & 1.016 \tabularnewline
57 & 34 & 34.8988764044944 & -0.898876404494381 \tabularnewline
58 & 34 & 31.1607142857143 & 2.83928571428572 \tabularnewline
59 & 36 & 33.984 & 2.016 \tabularnewline
60 & 34 & 34.8988764044944 & -0.898876404494381 \tabularnewline
61 & 28 & 31.1607142857143 & -3.16071428571428 \tabularnewline
62 & 34 & 34.8988764044944 & -0.898876404494381 \tabularnewline
63 & 35 & 34.8988764044944 & 0.101123595505619 \tabularnewline
64 & 35 & 31.1607142857143 & 3.83928571428572 \tabularnewline
65 & 31 & 34.8988764044944 & -3.89887640449438 \tabularnewline
66 & 37 & 34.8988764044944 & 2.10112359550562 \tabularnewline
67 & 35 & 34.8988764044944 & 0.101123595505619 \tabularnewline
68 & 27 & 31.1607142857143 & -4.16071428571428 \tabularnewline
69 & 40 & 33.984 & 6.016 \tabularnewline
70 & 37 & 34.8988764044944 & 2.10112359550562 \tabularnewline
71 & 36 & 33.984 & 2.016 \tabularnewline
72 & 38 & 31.1607142857143 & 6.83928571428572 \tabularnewline
73 & 39 & 34.8988764044944 & 4.10112359550562 \tabularnewline
74 & 41 & 34.8988764044944 & 6.10112359550562 \tabularnewline
75 & 27 & 34.8988764044944 & -7.89887640449438 \tabularnewline
76 & 30 & 34.8988764044944 & -4.89887640449438 \tabularnewline
77 & 37 & 34.8988764044944 & 2.10112359550562 \tabularnewline
78 & 31 & 34.8988764044944 & -3.89887640449438 \tabularnewline
79 & 31 & 31.1607142857143 & -0.160714285714285 \tabularnewline
80 & 27 & 34.8988764044944 & -7.89887640449438 \tabularnewline
81 & 36 & 34.8988764044944 & 1.10112359550562 \tabularnewline
82 & 37 & 34.8988764044944 & 2.10112359550562 \tabularnewline
83 & 33 & 33.984 & -0.984000000000002 \tabularnewline
84 & 34 & 33.984 & 0.0159999999999982 \tabularnewline
85 & 31 & 34.8988764044944 & -3.89887640449438 \tabularnewline
86 & 39 & 33.984 & 5.016 \tabularnewline
87 & 34 & 34.8988764044944 & -0.898876404494381 \tabularnewline
88 & 32 & 31.1607142857143 & 0.839285714285715 \tabularnewline
89 & 33 & 33.984 & -0.984000000000002 \tabularnewline
90 & 36 & 31.1607142857143 & 4.83928571428572 \tabularnewline
91 & 32 & 34.8988764044944 & -2.89887640449438 \tabularnewline
92 & 41 & 34.8988764044944 & 6.10112359550562 \tabularnewline
93 & 28 & 33.984 & -5.984 \tabularnewline
94 & 30 & 31.1607142857143 & -1.16071428571428 \tabularnewline
95 & 36 & 34.8988764044944 & 1.10112359550562 \tabularnewline
96 & 35 & 34.8988764044944 & 0.101123595505619 \tabularnewline
97 & 31 & 34.8988764044944 & -3.89887640449438 \tabularnewline
98 & 34 & 34.8988764044944 & -0.898876404494381 \tabularnewline
99 & 36 & 33.984 & 2.016 \tabularnewline
100 & 36 & 34.8988764044944 & 1.10112359550562 \tabularnewline
101 & 35 & 34.8988764044944 & 0.101123595505619 \tabularnewline
102 & 37 & 34.8988764044944 & 2.10112359550562 \tabularnewline
103 & 28 & 33.984 & -5.984 \tabularnewline
104 & 39 & 34.8988764044944 & 4.10112359550562 \tabularnewline
105 & 32 & 33.984 & -1.984 \tabularnewline
106 & 35 & 34.8988764044944 & 0.101123595505619 \tabularnewline
107 & 39 & 38.1 & 0.899999999999999 \tabularnewline
108 & 35 & 34.8988764044944 & 0.101123595505619 \tabularnewline
109 & 42 & 38.1 & 3.9 \tabularnewline
110 & 34 & 31.1607142857143 & 2.83928571428572 \tabularnewline
111 & 33 & 31.1607142857143 & 1.83928571428572 \tabularnewline
112 & 41 & 34.8988764044944 & 6.10112359550562 \tabularnewline
113 & 34 & 33.984 & 0.0159999999999982 \tabularnewline
114 & 32 & 34.8988764044944 & -2.89887640449438 \tabularnewline
115 & 40 & 33.984 & 6.016 \tabularnewline
116 & 40 & 33.984 & 6.016 \tabularnewline
117 & 35 & 33.984 & 1.016 \tabularnewline
118 & 36 & 34.8988764044944 & 1.10112359550562 \tabularnewline
119 & 37 & 33.984 & 3.016 \tabularnewline
120 & 27 & 31.1607142857143 & -4.16071428571428 \tabularnewline
121 & 39 & 33.984 & 5.016 \tabularnewline
122 & 38 & 34.8988764044944 & 3.10112359550562 \tabularnewline
123 & 31 & 33.984 & -2.984 \tabularnewline
124 & 33 & 31.1607142857143 & 1.83928571428572 \tabularnewline
125 & 32 & 33.984 & -1.984 \tabularnewline
126 & 39 & 38.1 & 0.899999999999999 \tabularnewline
127 & 36 & 33.984 & 2.016 \tabularnewline
128 & 33 & 33.984 & -0.984000000000002 \tabularnewline
129 & 33 & 34.8988764044944 & -1.89887640449438 \tabularnewline
130 & 32 & 31.1607142857143 & 0.839285714285715 \tabularnewline
131 & 37 & 34.8988764044944 & 2.10112359550562 \tabularnewline
132 & 30 & 31.1607142857143 & -1.16071428571428 \tabularnewline
133 & 38 & 33.984 & 4.016 \tabularnewline
134 & 29 & 33.984 & -4.984 \tabularnewline
135 & 22 & 31.1607142857143 & -9.16071428571428 \tabularnewline
136 & 35 & 33.984 & 1.016 \tabularnewline
137 & 35 & 31.1607142857143 & 3.83928571428572 \tabularnewline
138 & 34 & 33.984 & 0.0159999999999982 \tabularnewline
139 & 35 & 31.1607142857143 & 3.83928571428572 \tabularnewline
140 & 34 & 34.8988764044944 & -0.898876404494381 \tabularnewline
141 & 37 & 31.1607142857143 & 5.83928571428572 \tabularnewline
142 & 35 & 31.1607142857143 & 3.83928571428572 \tabularnewline
143 & 23 & 31.1607142857143 & -8.16071428571428 \tabularnewline
144 & 31 & 33.984 & -2.984 \tabularnewline
145 & 27 & 34.8988764044944 & -7.89887640449438 \tabularnewline
146 & 36 & 33.984 & 2.016 \tabularnewline
147 & 31 & 34.8988764044944 & -3.89887640449438 \tabularnewline
148 & 32 & 33.984 & -1.984 \tabularnewline
149 & 39 & 33.984 & 5.016 \tabularnewline
150 & 37 & 38.1 & -1.1 \tabularnewline
151 & 38 & 34.8988764044944 & 3.10112359550562 \tabularnewline
152 & 39 & 34.8988764044944 & 4.10112359550562 \tabularnewline
153 & 34 & 33.984 & 0.0159999999999982 \tabularnewline
154 & 31 & 33.984 & -2.984 \tabularnewline
155 & 37 & 33.984 & 3.016 \tabularnewline
156 & 36 & 33.984 & 2.016 \tabularnewline
157 & 32 & 34.8988764044944 & -2.89887640449438 \tabularnewline
158 & 38 & 33.984 & 4.016 \tabularnewline
159 & 26 & 31.1607142857143 & -5.16071428571428 \tabularnewline
160 & 26 & 31.1607142857143 & -5.16071428571428 \tabularnewline
161 & 33 & 34.8988764044944 & -1.89887640449438 \tabularnewline
162 & 39 & 33.984 & 5.016 \tabularnewline
163 & 30 & 24.375 & 5.625 \tabularnewline
164 & 33 & 31.1607142857143 & 1.83928571428572 \tabularnewline
165 & 25 & 31.1607142857143 & -6.16071428571428 \tabularnewline
166 & 38 & 33.984 & 4.016 \tabularnewline
167 & 37 & 31.1607142857143 & 5.83928571428572 \tabularnewline
168 & 31 & 33.984 & -2.984 \tabularnewline
169 & 37 & 34.8988764044944 & 2.10112359550562 \tabularnewline
170 & 35 & 38.1 & -3.1 \tabularnewline
171 & 25 & 31.1607142857143 & -6.16071428571428 \tabularnewline
172 & 28 & 33.984 & -5.984 \tabularnewline
173 & 35 & 33.984 & 1.016 \tabularnewline
174 & 33 & 33.984 & -0.984000000000002 \tabularnewline
175 & 30 & 31.1607142857143 & -1.16071428571428 \tabularnewline
176 & 31 & 31.1607142857143 & -0.160714285714285 \tabularnewline
177 & 37 & 33.984 & 3.016 \tabularnewline
178 & 36 & 34.8988764044944 & 1.10112359550562 \tabularnewline
179 & 30 & 33.984 & -3.984 \tabularnewline
180 & 36 & 31.1607142857143 & 4.83928571428572 \tabularnewline
181 & 32 & 33.984 & -1.984 \tabularnewline
182 & 28 & 24.375 & 3.625 \tabularnewline
183 & 36 & 34.8988764044944 & 1.10112359550562 \tabularnewline
184 & 34 & 34.8988764044944 & -0.898876404494381 \tabularnewline
185 & 31 & 33.984 & -2.984 \tabularnewline
186 & 28 & 31.1607142857143 & -3.16071428571428 \tabularnewline
187 & 36 & 33.984 & 2.016 \tabularnewline
188 & 36 & 33.984 & 2.016 \tabularnewline
189 & 40 & 34.8988764044944 & 5.10112359550562 \tabularnewline
190 & 33 & 33.984 & -0.984000000000002 \tabularnewline
191 & 37 & 34.8988764044944 & 2.10112359550562 \tabularnewline
192 & 32 & 33.984 & -1.984 \tabularnewline
193 & 38 & 33.984 & 4.016 \tabularnewline
194 & 31 & 34.8988764044944 & -3.89887640449438 \tabularnewline
195 & 37 & 34.8988764044944 & 2.10112359550562 \tabularnewline
196 & 33 & 33.984 & -0.984000000000002 \tabularnewline
197 & 30 & 31.1607142857143 & -1.16071428571428 \tabularnewline
198 & 30 & 24.375 & 5.625 \tabularnewline
199 & 31 & 33.984 & -2.984 \tabularnewline
200 & 32 & 33.984 & -1.984 \tabularnewline
201 & 34 & 33.984 & 0.0159999999999982 \tabularnewline
202 & 36 & 33.984 & 2.016 \tabularnewline
203 & 37 & 34.8988764044944 & 2.10112359550562 \tabularnewline
204 & 36 & 34.8988764044944 & 1.10112359550562 \tabularnewline
205 & 33 & 33.984 & -0.984000000000002 \tabularnewline
206 & 33 & 33.984 & -0.984000000000002 \tabularnewline
207 & 33 & 33.984 & -0.984000000000002 \tabularnewline
208 & 44 & 38.1 & 5.9 \tabularnewline
209 & 39 & 33.984 & 5.016 \tabularnewline
210 & 32 & 31.1607142857143 & 0.839285714285715 \tabularnewline
211 & 35 & 34.8988764044944 & 0.101123595505619 \tabularnewline
212 & 25 & 31.1607142857143 & -6.16071428571428 \tabularnewline
213 & 35 & 34.8988764044944 & 0.101123595505619 \tabularnewline
214 & 34 & 33.984 & 0.0159999999999982 \tabularnewline
215 & 35 & 33.984 & 1.016 \tabularnewline
216 & 39 & 33.984 & 5.016 \tabularnewline
217 & 33 & 33.984 & -0.984000000000002 \tabularnewline
218 & 36 & 33.984 & 2.016 \tabularnewline
219 & 32 & 33.984 & -1.984 \tabularnewline
220 & 32 & 31.1607142857143 & 0.839285714285715 \tabularnewline
221 & 36 & 33.984 & 2.016 \tabularnewline
222 & 32 & 31.1607142857143 & 0.839285714285715 \tabularnewline
223 & 34 & 33.984 & 0.0159999999999982 \tabularnewline
224 & 33 & 33.984 & -0.984000000000002 \tabularnewline
225 & 35 & 33.984 & 1.016 \tabularnewline
226 & 30 & 33.984 & -3.984 \tabularnewline
227 & 38 & 33.984 & 4.016 \tabularnewline
228 & 34 & 34.8988764044944 & -0.898876404494381 \tabularnewline
229 & 33 & 38.1 & -5.1 \tabularnewline
230 & 32 & 33.984 & -1.984 \tabularnewline
231 & 31 & 33.984 & -2.984 \tabularnewline
232 & 30 & 33.984 & -3.984 \tabularnewline
233 & 27 & 33.984 & -6.984 \tabularnewline
234 & 31 & 31.1607142857143 & -0.160714285714285 \tabularnewline
235 & 30 & 33.984 & -3.984 \tabularnewline
236 & 32 & 31.1607142857143 & 0.839285714285715 \tabularnewline
237 & 35 & 33.984 & 1.016 \tabularnewline
238 & 28 & 31.1607142857143 & -3.16071428571428 \tabularnewline
239 & 33 & 33.984 & -0.984000000000002 \tabularnewline
240 & 35 & 31.1607142857143 & 3.83928571428572 \tabularnewline
241 & 35 & 33.984 & 1.016 \tabularnewline
242 & 32 & 33.984 & -1.984 \tabularnewline
243 & 21 & 24.375 & -3.375 \tabularnewline
244 & 20 & 24.375 & -4.375 \tabularnewline
245 & 34 & 31.1607142857143 & 2.83928571428572 \tabularnewline
246 & 32 & 33.984 & -1.984 \tabularnewline
247 & 34 & 33.984 & 0.0159999999999982 \tabularnewline
248 & 32 & 34.8988764044944 & -2.89887640449438 \tabularnewline
249 & 33 & 33.984 & -0.984000000000002 \tabularnewline
250 & 33 & 33.984 & -0.984000000000002 \tabularnewline
251 & 37 & 34.8988764044944 & 2.10112359550562 \tabularnewline
252 & 32 & 31.1607142857143 & 0.839285714285715 \tabularnewline
253 & 34 & 33.984 & 0.0159999999999982 \tabularnewline
254 & 30 & 33.984 & -3.984 \tabularnewline
255 & 30 & 31.1607142857143 & -1.16071428571428 \tabularnewline
256 & 38 & 33.984 & 4.016 \tabularnewline
257 & 36 & 33.984 & 2.016 \tabularnewline
258 & 32 & 31.1607142857143 & 0.839285714285715 \tabularnewline
259 & 34 & 33.984 & 0.0159999999999982 \tabularnewline
260 & 33 & 33.984 & -0.984000000000002 \tabularnewline
261 & 27 & 24.375 & 2.625 \tabularnewline
262 & 32 & 33.984 & -1.984 \tabularnewline
263 & 34 & 33.984 & 0.0159999999999982 \tabularnewline
264 & 29 & 31.1607142857143 & -2.16071428571428 \tabularnewline
265 & 35 & 33.984 & 1.016 \tabularnewline
266 & 27 & 31.1607142857143 & -4.16071428571428 \tabularnewline
267 & 33 & 34.8988764044944 & -1.89887640449438 \tabularnewline
268 & 38 & 33.984 & 4.016 \tabularnewline
269 & 36 & 34.8988764044944 & 1.10112359550562 \tabularnewline
270 & 33 & 34.8988764044944 & -1.89887640449438 \tabularnewline
271 & 39 & 33.984 & 5.016 \tabularnewline
272 & 29 & 33.984 & -4.984 \tabularnewline
273 & 32 & 34.8988764044944 & -2.89887640449438 \tabularnewline
274 & 34 & 31.1607142857143 & 2.83928571428572 \tabularnewline
275 & 38 & 34.8988764044944 & 3.10112359550562 \tabularnewline
276 & 17 & 24.375 & -7.375 \tabularnewline
277 & 35 & 33.984 & 1.016 \tabularnewline
278 & 32 & 33.984 & -1.984 \tabularnewline
279 & 34 & 33.984 & 0.0159999999999982 \tabularnewline
280 & 36 & 33.984 & 2.016 \tabularnewline
281 & 31 & 34.8988764044944 & -3.89887640449438 \tabularnewline
282 & 35 & 33.984 & 1.016 \tabularnewline
283 & 29 & 31.1607142857143 & -2.16071428571428 \tabularnewline
284 & 22 & 24.375 & -2.375 \tabularnewline
285 & 41 & 34.8988764044944 & 6.10112359550562 \tabularnewline
286 & 36 & 33.984 & 2.016 \tabularnewline
287 & 42 & 38.1 & 3.9 \tabularnewline
288 & 33 & 31.1607142857143 & 1.83928571428572 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232051&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]38[/C][C]38.1[/C][C]-0.100000000000001[/C][/ROW]
[ROW][C]2[/C][C]32[/C][C]34.8988764044944[/C][C]-2.89887640449438[/C][/ROW]
[ROW][C]3[/C][C]35[/C][C]31.1607142857143[/C][C]3.83928571428572[/C][/ROW]
[ROW][C]4[/C][C]33[/C][C]31.1607142857143[/C][C]1.83928571428572[/C][/ROW]
[ROW][C]5[/C][C]37[/C][C]33.984[/C][C]3.016[/C][/ROW]
[ROW][C]6[/C][C]29[/C][C]33.984[/C][C]-4.984[/C][/ROW]
[ROW][C]7[/C][C]31[/C][C]34.8988764044944[/C][C]-3.89887640449438[/C][/ROW]
[ROW][C]8[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]9[/C][C]35[/C][C]33.984[/C][C]1.016[/C][/ROW]
[ROW][C]10[/C][C]38[/C][C]33.984[/C][C]4.016[/C][/ROW]
[ROW][C]11[/C][C]31[/C][C]34.8988764044944[/C][C]-3.89887640449438[/C][/ROW]
[ROW][C]12[/C][C]34[/C][C]34.8988764044944[/C][C]-0.898876404494381[/C][/ROW]
[ROW][C]13[/C][C]35[/C][C]34.8988764044944[/C][C]0.101123595505619[/C][/ROW]
[ROW][C]14[/C][C]38[/C][C]34.8988764044944[/C][C]3.10112359550562[/C][/ROW]
[ROW][C]15[/C][C]37[/C][C]34.8988764044944[/C][C]2.10112359550562[/C][/ROW]
[ROW][C]16[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]17[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]18[/C][C]38[/C][C]34.8988764044944[/C][C]3.10112359550562[/C][/ROW]
[ROW][C]19[/C][C]38[/C][C]34.8988764044944[/C][C]3.10112359550562[/C][/ROW]
[ROW][C]20[/C][C]32[/C][C]34.8988764044944[/C][C]-2.89887640449438[/C][/ROW]
[ROW][C]21[/C][C]33[/C][C]34.8988764044944[/C][C]-1.89887640449438[/C][/ROW]
[ROW][C]22[/C][C]31[/C][C]31.1607142857143[/C][C]-0.160714285714285[/C][/ROW]
[ROW][C]23[/C][C]38[/C][C]34.8988764044944[/C][C]3.10112359550562[/C][/ROW]
[ROW][C]24[/C][C]39[/C][C]34.8988764044944[/C][C]4.10112359550562[/C][/ROW]
[ROW][C]25[/C][C]32[/C][C]34.8988764044944[/C][C]-2.89887640449438[/C][/ROW]
[ROW][C]26[/C][C]32[/C][C]38.1[/C][C]-6.1[/C][/ROW]
[ROW][C]27[/C][C]35[/C][C]34.8988764044944[/C][C]0.101123595505619[/C][/ROW]
[ROW][C]28[/C][C]37[/C][C]33.984[/C][C]3.016[/C][/ROW]
[ROW][C]29[/C][C]33[/C][C]34.8988764044944[/C][C]-1.89887640449438[/C][/ROW]
[ROW][C]30[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]31.1607142857143[/C][C]-0.160714285714285[/C][/ROW]
[ROW][C]32[/C][C]32[/C][C]31.1607142857143[/C][C]0.839285714285715[/C][/ROW]
[ROW][C]33[/C][C]31[/C][C]33.984[/C][C]-2.984[/C][/ROW]
[ROW][C]34[/C][C]37[/C][C]34.8988764044944[/C][C]2.10112359550562[/C][/ROW]
[ROW][C]35[/C][C]30[/C][C]33.984[/C][C]-3.984[/C][/ROW]
[ROW][C]36[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]37[/C][C]31[/C][C]31.1607142857143[/C][C]-0.160714285714285[/C][/ROW]
[ROW][C]38[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]39[/C][C]31[/C][C]31.1607142857143[/C][C]-0.160714285714285[/C][/ROW]
[ROW][C]40[/C][C]33[/C][C]34.8988764044944[/C][C]-1.89887640449438[/C][/ROW]
[ROW][C]41[/C][C]32[/C][C]34.8988764044944[/C][C]-2.89887640449438[/C][/ROW]
[ROW][C]42[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]43[/C][C]32[/C][C]34.8988764044944[/C][C]-2.89887640449438[/C][/ROW]
[ROW][C]44[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]45[/C][C]28[/C][C]33.984[/C][C]-5.984[/C][/ROW]
[ROW][C]46[/C][C]35[/C][C]33.984[/C][C]1.016[/C][/ROW]
[ROW][C]47[/C][C]39[/C][C]33.984[/C][C]5.016[/C][/ROW]
[ROW][C]48[/C][C]34[/C][C]33.984[/C][C]0.0159999999999982[/C][/ROW]
[ROW][C]49[/C][C]38[/C][C]34.8988764044944[/C][C]3.10112359550562[/C][/ROW]
[ROW][C]50[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]51[/C][C]38[/C][C]34.8988764044944[/C][C]3.10112359550562[/C][/ROW]
[ROW][C]52[/C][C]30[/C][C]33.984[/C][C]-3.984[/C][/ROW]
[ROW][C]53[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]54[/C][C]38[/C][C]34.8988764044944[/C][C]3.10112359550562[/C][/ROW]
[ROW][C]55[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]56[/C][C]35[/C][C]33.984[/C][C]1.016[/C][/ROW]
[ROW][C]57[/C][C]34[/C][C]34.8988764044944[/C][C]-0.898876404494381[/C][/ROW]
[ROW][C]58[/C][C]34[/C][C]31.1607142857143[/C][C]2.83928571428572[/C][/ROW]
[ROW][C]59[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]60[/C][C]34[/C][C]34.8988764044944[/C][C]-0.898876404494381[/C][/ROW]
[ROW][C]61[/C][C]28[/C][C]31.1607142857143[/C][C]-3.16071428571428[/C][/ROW]
[ROW][C]62[/C][C]34[/C][C]34.8988764044944[/C][C]-0.898876404494381[/C][/ROW]
[ROW][C]63[/C][C]35[/C][C]34.8988764044944[/C][C]0.101123595505619[/C][/ROW]
[ROW][C]64[/C][C]35[/C][C]31.1607142857143[/C][C]3.83928571428572[/C][/ROW]
[ROW][C]65[/C][C]31[/C][C]34.8988764044944[/C][C]-3.89887640449438[/C][/ROW]
[ROW][C]66[/C][C]37[/C][C]34.8988764044944[/C][C]2.10112359550562[/C][/ROW]
[ROW][C]67[/C][C]35[/C][C]34.8988764044944[/C][C]0.101123595505619[/C][/ROW]
[ROW][C]68[/C][C]27[/C][C]31.1607142857143[/C][C]-4.16071428571428[/C][/ROW]
[ROW][C]69[/C][C]40[/C][C]33.984[/C][C]6.016[/C][/ROW]
[ROW][C]70[/C][C]37[/C][C]34.8988764044944[/C][C]2.10112359550562[/C][/ROW]
[ROW][C]71[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]72[/C][C]38[/C][C]31.1607142857143[/C][C]6.83928571428572[/C][/ROW]
[ROW][C]73[/C][C]39[/C][C]34.8988764044944[/C][C]4.10112359550562[/C][/ROW]
[ROW][C]74[/C][C]41[/C][C]34.8988764044944[/C][C]6.10112359550562[/C][/ROW]
[ROW][C]75[/C][C]27[/C][C]34.8988764044944[/C][C]-7.89887640449438[/C][/ROW]
[ROW][C]76[/C][C]30[/C][C]34.8988764044944[/C][C]-4.89887640449438[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]34.8988764044944[/C][C]2.10112359550562[/C][/ROW]
[ROW][C]78[/C][C]31[/C][C]34.8988764044944[/C][C]-3.89887640449438[/C][/ROW]
[ROW][C]79[/C][C]31[/C][C]31.1607142857143[/C][C]-0.160714285714285[/C][/ROW]
[ROW][C]80[/C][C]27[/C][C]34.8988764044944[/C][C]-7.89887640449438[/C][/ROW]
[ROW][C]81[/C][C]36[/C][C]34.8988764044944[/C][C]1.10112359550562[/C][/ROW]
[ROW][C]82[/C][C]37[/C][C]34.8988764044944[/C][C]2.10112359550562[/C][/ROW]
[ROW][C]83[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]84[/C][C]34[/C][C]33.984[/C][C]0.0159999999999982[/C][/ROW]
[ROW][C]85[/C][C]31[/C][C]34.8988764044944[/C][C]-3.89887640449438[/C][/ROW]
[ROW][C]86[/C][C]39[/C][C]33.984[/C][C]5.016[/C][/ROW]
[ROW][C]87[/C][C]34[/C][C]34.8988764044944[/C][C]-0.898876404494381[/C][/ROW]
[ROW][C]88[/C][C]32[/C][C]31.1607142857143[/C][C]0.839285714285715[/C][/ROW]
[ROW][C]89[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]90[/C][C]36[/C][C]31.1607142857143[/C][C]4.83928571428572[/C][/ROW]
[ROW][C]91[/C][C]32[/C][C]34.8988764044944[/C][C]-2.89887640449438[/C][/ROW]
[ROW][C]92[/C][C]41[/C][C]34.8988764044944[/C][C]6.10112359550562[/C][/ROW]
[ROW][C]93[/C][C]28[/C][C]33.984[/C][C]-5.984[/C][/ROW]
[ROW][C]94[/C][C]30[/C][C]31.1607142857143[/C][C]-1.16071428571428[/C][/ROW]
[ROW][C]95[/C][C]36[/C][C]34.8988764044944[/C][C]1.10112359550562[/C][/ROW]
[ROW][C]96[/C][C]35[/C][C]34.8988764044944[/C][C]0.101123595505619[/C][/ROW]
[ROW][C]97[/C][C]31[/C][C]34.8988764044944[/C][C]-3.89887640449438[/C][/ROW]
[ROW][C]98[/C][C]34[/C][C]34.8988764044944[/C][C]-0.898876404494381[/C][/ROW]
[ROW][C]99[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]100[/C][C]36[/C][C]34.8988764044944[/C][C]1.10112359550562[/C][/ROW]
[ROW][C]101[/C][C]35[/C][C]34.8988764044944[/C][C]0.101123595505619[/C][/ROW]
[ROW][C]102[/C][C]37[/C][C]34.8988764044944[/C][C]2.10112359550562[/C][/ROW]
[ROW][C]103[/C][C]28[/C][C]33.984[/C][C]-5.984[/C][/ROW]
[ROW][C]104[/C][C]39[/C][C]34.8988764044944[/C][C]4.10112359550562[/C][/ROW]
[ROW][C]105[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]106[/C][C]35[/C][C]34.8988764044944[/C][C]0.101123595505619[/C][/ROW]
[ROW][C]107[/C][C]39[/C][C]38.1[/C][C]0.899999999999999[/C][/ROW]
[ROW][C]108[/C][C]35[/C][C]34.8988764044944[/C][C]0.101123595505619[/C][/ROW]
[ROW][C]109[/C][C]42[/C][C]38.1[/C][C]3.9[/C][/ROW]
[ROW][C]110[/C][C]34[/C][C]31.1607142857143[/C][C]2.83928571428572[/C][/ROW]
[ROW][C]111[/C][C]33[/C][C]31.1607142857143[/C][C]1.83928571428572[/C][/ROW]
[ROW][C]112[/C][C]41[/C][C]34.8988764044944[/C][C]6.10112359550562[/C][/ROW]
[ROW][C]113[/C][C]34[/C][C]33.984[/C][C]0.0159999999999982[/C][/ROW]
[ROW][C]114[/C][C]32[/C][C]34.8988764044944[/C][C]-2.89887640449438[/C][/ROW]
[ROW][C]115[/C][C]40[/C][C]33.984[/C][C]6.016[/C][/ROW]
[ROW][C]116[/C][C]40[/C][C]33.984[/C][C]6.016[/C][/ROW]
[ROW][C]117[/C][C]35[/C][C]33.984[/C][C]1.016[/C][/ROW]
[ROW][C]118[/C][C]36[/C][C]34.8988764044944[/C][C]1.10112359550562[/C][/ROW]
[ROW][C]119[/C][C]37[/C][C]33.984[/C][C]3.016[/C][/ROW]
[ROW][C]120[/C][C]27[/C][C]31.1607142857143[/C][C]-4.16071428571428[/C][/ROW]
[ROW][C]121[/C][C]39[/C][C]33.984[/C][C]5.016[/C][/ROW]
[ROW][C]122[/C][C]38[/C][C]34.8988764044944[/C][C]3.10112359550562[/C][/ROW]
[ROW][C]123[/C][C]31[/C][C]33.984[/C][C]-2.984[/C][/ROW]
[ROW][C]124[/C][C]33[/C][C]31.1607142857143[/C][C]1.83928571428572[/C][/ROW]
[ROW][C]125[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]126[/C][C]39[/C][C]38.1[/C][C]0.899999999999999[/C][/ROW]
[ROW][C]127[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]128[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]129[/C][C]33[/C][C]34.8988764044944[/C][C]-1.89887640449438[/C][/ROW]
[ROW][C]130[/C][C]32[/C][C]31.1607142857143[/C][C]0.839285714285715[/C][/ROW]
[ROW][C]131[/C][C]37[/C][C]34.8988764044944[/C][C]2.10112359550562[/C][/ROW]
[ROW][C]132[/C][C]30[/C][C]31.1607142857143[/C][C]-1.16071428571428[/C][/ROW]
[ROW][C]133[/C][C]38[/C][C]33.984[/C][C]4.016[/C][/ROW]
[ROW][C]134[/C][C]29[/C][C]33.984[/C][C]-4.984[/C][/ROW]
[ROW][C]135[/C][C]22[/C][C]31.1607142857143[/C][C]-9.16071428571428[/C][/ROW]
[ROW][C]136[/C][C]35[/C][C]33.984[/C][C]1.016[/C][/ROW]
[ROW][C]137[/C][C]35[/C][C]31.1607142857143[/C][C]3.83928571428572[/C][/ROW]
[ROW][C]138[/C][C]34[/C][C]33.984[/C][C]0.0159999999999982[/C][/ROW]
[ROW][C]139[/C][C]35[/C][C]31.1607142857143[/C][C]3.83928571428572[/C][/ROW]
[ROW][C]140[/C][C]34[/C][C]34.8988764044944[/C][C]-0.898876404494381[/C][/ROW]
[ROW][C]141[/C][C]37[/C][C]31.1607142857143[/C][C]5.83928571428572[/C][/ROW]
[ROW][C]142[/C][C]35[/C][C]31.1607142857143[/C][C]3.83928571428572[/C][/ROW]
[ROW][C]143[/C][C]23[/C][C]31.1607142857143[/C][C]-8.16071428571428[/C][/ROW]
[ROW][C]144[/C][C]31[/C][C]33.984[/C][C]-2.984[/C][/ROW]
[ROW][C]145[/C][C]27[/C][C]34.8988764044944[/C][C]-7.89887640449438[/C][/ROW]
[ROW][C]146[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]147[/C][C]31[/C][C]34.8988764044944[/C][C]-3.89887640449438[/C][/ROW]
[ROW][C]148[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]149[/C][C]39[/C][C]33.984[/C][C]5.016[/C][/ROW]
[ROW][C]150[/C][C]37[/C][C]38.1[/C][C]-1.1[/C][/ROW]
[ROW][C]151[/C][C]38[/C][C]34.8988764044944[/C][C]3.10112359550562[/C][/ROW]
[ROW][C]152[/C][C]39[/C][C]34.8988764044944[/C][C]4.10112359550562[/C][/ROW]
[ROW][C]153[/C][C]34[/C][C]33.984[/C][C]0.0159999999999982[/C][/ROW]
[ROW][C]154[/C][C]31[/C][C]33.984[/C][C]-2.984[/C][/ROW]
[ROW][C]155[/C][C]37[/C][C]33.984[/C][C]3.016[/C][/ROW]
[ROW][C]156[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]157[/C][C]32[/C][C]34.8988764044944[/C][C]-2.89887640449438[/C][/ROW]
[ROW][C]158[/C][C]38[/C][C]33.984[/C][C]4.016[/C][/ROW]
[ROW][C]159[/C][C]26[/C][C]31.1607142857143[/C][C]-5.16071428571428[/C][/ROW]
[ROW][C]160[/C][C]26[/C][C]31.1607142857143[/C][C]-5.16071428571428[/C][/ROW]
[ROW][C]161[/C][C]33[/C][C]34.8988764044944[/C][C]-1.89887640449438[/C][/ROW]
[ROW][C]162[/C][C]39[/C][C]33.984[/C][C]5.016[/C][/ROW]
[ROW][C]163[/C][C]30[/C][C]24.375[/C][C]5.625[/C][/ROW]
[ROW][C]164[/C][C]33[/C][C]31.1607142857143[/C][C]1.83928571428572[/C][/ROW]
[ROW][C]165[/C][C]25[/C][C]31.1607142857143[/C][C]-6.16071428571428[/C][/ROW]
[ROW][C]166[/C][C]38[/C][C]33.984[/C][C]4.016[/C][/ROW]
[ROW][C]167[/C][C]37[/C][C]31.1607142857143[/C][C]5.83928571428572[/C][/ROW]
[ROW][C]168[/C][C]31[/C][C]33.984[/C][C]-2.984[/C][/ROW]
[ROW][C]169[/C][C]37[/C][C]34.8988764044944[/C][C]2.10112359550562[/C][/ROW]
[ROW][C]170[/C][C]35[/C][C]38.1[/C][C]-3.1[/C][/ROW]
[ROW][C]171[/C][C]25[/C][C]31.1607142857143[/C][C]-6.16071428571428[/C][/ROW]
[ROW][C]172[/C][C]28[/C][C]33.984[/C][C]-5.984[/C][/ROW]
[ROW][C]173[/C][C]35[/C][C]33.984[/C][C]1.016[/C][/ROW]
[ROW][C]174[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]175[/C][C]30[/C][C]31.1607142857143[/C][C]-1.16071428571428[/C][/ROW]
[ROW][C]176[/C][C]31[/C][C]31.1607142857143[/C][C]-0.160714285714285[/C][/ROW]
[ROW][C]177[/C][C]37[/C][C]33.984[/C][C]3.016[/C][/ROW]
[ROW][C]178[/C][C]36[/C][C]34.8988764044944[/C][C]1.10112359550562[/C][/ROW]
[ROW][C]179[/C][C]30[/C][C]33.984[/C][C]-3.984[/C][/ROW]
[ROW][C]180[/C][C]36[/C][C]31.1607142857143[/C][C]4.83928571428572[/C][/ROW]
[ROW][C]181[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]182[/C][C]28[/C][C]24.375[/C][C]3.625[/C][/ROW]
[ROW][C]183[/C][C]36[/C][C]34.8988764044944[/C][C]1.10112359550562[/C][/ROW]
[ROW][C]184[/C][C]34[/C][C]34.8988764044944[/C][C]-0.898876404494381[/C][/ROW]
[ROW][C]185[/C][C]31[/C][C]33.984[/C][C]-2.984[/C][/ROW]
[ROW][C]186[/C][C]28[/C][C]31.1607142857143[/C][C]-3.16071428571428[/C][/ROW]
[ROW][C]187[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]188[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]189[/C][C]40[/C][C]34.8988764044944[/C][C]5.10112359550562[/C][/ROW]
[ROW][C]190[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]191[/C][C]37[/C][C]34.8988764044944[/C][C]2.10112359550562[/C][/ROW]
[ROW][C]192[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]193[/C][C]38[/C][C]33.984[/C][C]4.016[/C][/ROW]
[ROW][C]194[/C][C]31[/C][C]34.8988764044944[/C][C]-3.89887640449438[/C][/ROW]
[ROW][C]195[/C][C]37[/C][C]34.8988764044944[/C][C]2.10112359550562[/C][/ROW]
[ROW][C]196[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]197[/C][C]30[/C][C]31.1607142857143[/C][C]-1.16071428571428[/C][/ROW]
[ROW][C]198[/C][C]30[/C][C]24.375[/C][C]5.625[/C][/ROW]
[ROW][C]199[/C][C]31[/C][C]33.984[/C][C]-2.984[/C][/ROW]
[ROW][C]200[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]201[/C][C]34[/C][C]33.984[/C][C]0.0159999999999982[/C][/ROW]
[ROW][C]202[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]203[/C][C]37[/C][C]34.8988764044944[/C][C]2.10112359550562[/C][/ROW]
[ROW][C]204[/C][C]36[/C][C]34.8988764044944[/C][C]1.10112359550562[/C][/ROW]
[ROW][C]205[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]206[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]207[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]208[/C][C]44[/C][C]38.1[/C][C]5.9[/C][/ROW]
[ROW][C]209[/C][C]39[/C][C]33.984[/C][C]5.016[/C][/ROW]
[ROW][C]210[/C][C]32[/C][C]31.1607142857143[/C][C]0.839285714285715[/C][/ROW]
[ROW][C]211[/C][C]35[/C][C]34.8988764044944[/C][C]0.101123595505619[/C][/ROW]
[ROW][C]212[/C][C]25[/C][C]31.1607142857143[/C][C]-6.16071428571428[/C][/ROW]
[ROW][C]213[/C][C]35[/C][C]34.8988764044944[/C][C]0.101123595505619[/C][/ROW]
[ROW][C]214[/C][C]34[/C][C]33.984[/C][C]0.0159999999999982[/C][/ROW]
[ROW][C]215[/C][C]35[/C][C]33.984[/C][C]1.016[/C][/ROW]
[ROW][C]216[/C][C]39[/C][C]33.984[/C][C]5.016[/C][/ROW]
[ROW][C]217[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]218[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]219[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]220[/C][C]32[/C][C]31.1607142857143[/C][C]0.839285714285715[/C][/ROW]
[ROW][C]221[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]222[/C][C]32[/C][C]31.1607142857143[/C][C]0.839285714285715[/C][/ROW]
[ROW][C]223[/C][C]34[/C][C]33.984[/C][C]0.0159999999999982[/C][/ROW]
[ROW][C]224[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]225[/C][C]35[/C][C]33.984[/C][C]1.016[/C][/ROW]
[ROW][C]226[/C][C]30[/C][C]33.984[/C][C]-3.984[/C][/ROW]
[ROW][C]227[/C][C]38[/C][C]33.984[/C][C]4.016[/C][/ROW]
[ROW][C]228[/C][C]34[/C][C]34.8988764044944[/C][C]-0.898876404494381[/C][/ROW]
[ROW][C]229[/C][C]33[/C][C]38.1[/C][C]-5.1[/C][/ROW]
[ROW][C]230[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]231[/C][C]31[/C][C]33.984[/C][C]-2.984[/C][/ROW]
[ROW][C]232[/C][C]30[/C][C]33.984[/C][C]-3.984[/C][/ROW]
[ROW][C]233[/C][C]27[/C][C]33.984[/C][C]-6.984[/C][/ROW]
[ROW][C]234[/C][C]31[/C][C]31.1607142857143[/C][C]-0.160714285714285[/C][/ROW]
[ROW][C]235[/C][C]30[/C][C]33.984[/C][C]-3.984[/C][/ROW]
[ROW][C]236[/C][C]32[/C][C]31.1607142857143[/C][C]0.839285714285715[/C][/ROW]
[ROW][C]237[/C][C]35[/C][C]33.984[/C][C]1.016[/C][/ROW]
[ROW][C]238[/C][C]28[/C][C]31.1607142857143[/C][C]-3.16071428571428[/C][/ROW]
[ROW][C]239[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]240[/C][C]35[/C][C]31.1607142857143[/C][C]3.83928571428572[/C][/ROW]
[ROW][C]241[/C][C]35[/C][C]33.984[/C][C]1.016[/C][/ROW]
[ROW][C]242[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]243[/C][C]21[/C][C]24.375[/C][C]-3.375[/C][/ROW]
[ROW][C]244[/C][C]20[/C][C]24.375[/C][C]-4.375[/C][/ROW]
[ROW][C]245[/C][C]34[/C][C]31.1607142857143[/C][C]2.83928571428572[/C][/ROW]
[ROW][C]246[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]247[/C][C]34[/C][C]33.984[/C][C]0.0159999999999982[/C][/ROW]
[ROW][C]248[/C][C]32[/C][C]34.8988764044944[/C][C]-2.89887640449438[/C][/ROW]
[ROW][C]249[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]250[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]251[/C][C]37[/C][C]34.8988764044944[/C][C]2.10112359550562[/C][/ROW]
[ROW][C]252[/C][C]32[/C][C]31.1607142857143[/C][C]0.839285714285715[/C][/ROW]
[ROW][C]253[/C][C]34[/C][C]33.984[/C][C]0.0159999999999982[/C][/ROW]
[ROW][C]254[/C][C]30[/C][C]33.984[/C][C]-3.984[/C][/ROW]
[ROW][C]255[/C][C]30[/C][C]31.1607142857143[/C][C]-1.16071428571428[/C][/ROW]
[ROW][C]256[/C][C]38[/C][C]33.984[/C][C]4.016[/C][/ROW]
[ROW][C]257[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]258[/C][C]32[/C][C]31.1607142857143[/C][C]0.839285714285715[/C][/ROW]
[ROW][C]259[/C][C]34[/C][C]33.984[/C][C]0.0159999999999982[/C][/ROW]
[ROW][C]260[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]261[/C][C]27[/C][C]24.375[/C][C]2.625[/C][/ROW]
[ROW][C]262[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]263[/C][C]34[/C][C]33.984[/C][C]0.0159999999999982[/C][/ROW]
[ROW][C]264[/C][C]29[/C][C]31.1607142857143[/C][C]-2.16071428571428[/C][/ROW]
[ROW][C]265[/C][C]35[/C][C]33.984[/C][C]1.016[/C][/ROW]
[ROW][C]266[/C][C]27[/C][C]31.1607142857143[/C][C]-4.16071428571428[/C][/ROW]
[ROW][C]267[/C][C]33[/C][C]34.8988764044944[/C][C]-1.89887640449438[/C][/ROW]
[ROW][C]268[/C][C]38[/C][C]33.984[/C][C]4.016[/C][/ROW]
[ROW][C]269[/C][C]36[/C][C]34.8988764044944[/C][C]1.10112359550562[/C][/ROW]
[ROW][C]270[/C][C]33[/C][C]34.8988764044944[/C][C]-1.89887640449438[/C][/ROW]
[ROW][C]271[/C][C]39[/C][C]33.984[/C][C]5.016[/C][/ROW]
[ROW][C]272[/C][C]29[/C][C]33.984[/C][C]-4.984[/C][/ROW]
[ROW][C]273[/C][C]32[/C][C]34.8988764044944[/C][C]-2.89887640449438[/C][/ROW]
[ROW][C]274[/C][C]34[/C][C]31.1607142857143[/C][C]2.83928571428572[/C][/ROW]
[ROW][C]275[/C][C]38[/C][C]34.8988764044944[/C][C]3.10112359550562[/C][/ROW]
[ROW][C]276[/C][C]17[/C][C]24.375[/C][C]-7.375[/C][/ROW]
[ROW][C]277[/C][C]35[/C][C]33.984[/C][C]1.016[/C][/ROW]
[ROW][C]278[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]279[/C][C]34[/C][C]33.984[/C][C]0.0159999999999982[/C][/ROW]
[ROW][C]280[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]281[/C][C]31[/C][C]34.8988764044944[/C][C]-3.89887640449438[/C][/ROW]
[ROW][C]282[/C][C]35[/C][C]33.984[/C][C]1.016[/C][/ROW]
[ROW][C]283[/C][C]29[/C][C]31.1607142857143[/C][C]-2.16071428571428[/C][/ROW]
[ROW][C]284[/C][C]22[/C][C]24.375[/C][C]-2.375[/C][/ROW]
[ROW][C]285[/C][C]41[/C][C]34.8988764044944[/C][C]6.10112359550562[/C][/ROW]
[ROW][C]286[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]287[/C][C]42[/C][C]38.1[/C][C]3.9[/C][/ROW]
[ROW][C]288[/C][C]33[/C][C]31.1607142857143[/C][C]1.83928571428572[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232051&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232051&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	38	38.1	-0.100000000000001
2	32	34.8988764044944	-2.89887640449438
3	35	31.1607142857143	3.83928571428572
4	33	31.1607142857143	1.83928571428572
5	37	33.984	3.016
6	29	33.984	-4.984
7	31	34.8988764044944	-3.89887640449438
8	36	33.984	2.016
9	35	33.984	1.016
10	38	33.984	4.016
11	31	34.8988764044944	-3.89887640449438
12	34	34.8988764044944	-0.898876404494381
13	35	34.8988764044944	0.101123595505619
14	38	34.8988764044944	3.10112359550562
15	37	34.8988764044944	2.10112359550562
16	33	33.984	-0.984000000000002
17	32	33.984	-1.984
18	38	34.8988764044944	3.10112359550562
19	38	34.8988764044944	3.10112359550562
20	32	34.8988764044944	-2.89887640449438
21	33	34.8988764044944	-1.89887640449438
22	31	31.1607142857143	-0.160714285714285
23	38	34.8988764044944	3.10112359550562
24	39	34.8988764044944	4.10112359550562
25	32	34.8988764044944	-2.89887640449438
26	32	38.1	-6.1
27	35	34.8988764044944	0.101123595505619
28	37	33.984	3.016
29	33	34.8988764044944	-1.89887640449438
30	33	33.984	-0.984000000000002
31	31	31.1607142857143	-0.160714285714285
32	32	31.1607142857143	0.839285714285715
33	31	33.984	-2.984
34	37	34.8988764044944	2.10112359550562
35	30	33.984	-3.984
36	33	33.984	-0.984000000000002
37	31	31.1607142857143	-0.160714285714285
38	33	33.984	-0.984000000000002
39	31	31.1607142857143	-0.160714285714285
40	33	34.8988764044944	-1.89887640449438
41	32	34.8988764044944	-2.89887640449438
42	33	33.984	-0.984000000000002
43	32	34.8988764044944	-2.89887640449438
44	33	33.984	-0.984000000000002
45	28	33.984	-5.984
46	35	33.984	1.016
47	39	33.984	5.016
48	34	33.984	0.0159999999999982
49	38	34.8988764044944	3.10112359550562
50	32	33.984	-1.984
51	38	34.8988764044944	3.10112359550562
52	30	33.984	-3.984
53	33	33.984	-0.984000000000002
54	38	34.8988764044944	3.10112359550562
55	32	33.984	-1.984
56	35	33.984	1.016
57	34	34.8988764044944	-0.898876404494381
58	34	31.1607142857143	2.83928571428572
59	36	33.984	2.016
60	34	34.8988764044944	-0.898876404494381
61	28	31.1607142857143	-3.16071428571428
62	34	34.8988764044944	-0.898876404494381
63	35	34.8988764044944	0.101123595505619
64	35	31.1607142857143	3.83928571428572
65	31	34.8988764044944	-3.89887640449438
66	37	34.8988764044944	2.10112359550562
67	35	34.8988764044944	0.101123595505619
68	27	31.1607142857143	-4.16071428571428
69	40	33.984	6.016
70	37	34.8988764044944	2.10112359550562
71	36	33.984	2.016
72	38	31.1607142857143	6.83928571428572
73	39	34.8988764044944	4.10112359550562
74	41	34.8988764044944	6.10112359550562
75	27	34.8988764044944	-7.89887640449438
76	30	34.8988764044944	-4.89887640449438
77	37	34.8988764044944	2.10112359550562
78	31	34.8988764044944	-3.89887640449438
79	31	31.1607142857143	-0.160714285714285
80	27	34.8988764044944	-7.89887640449438
81	36	34.8988764044944	1.10112359550562
82	37	34.8988764044944	2.10112359550562
83	33	33.984	-0.984000000000002
84	34	33.984	0.0159999999999982
85	31	34.8988764044944	-3.89887640449438
86	39	33.984	5.016
87	34	34.8988764044944	-0.898876404494381
88	32	31.1607142857143	0.839285714285715
89	33	33.984	-0.984000000000002
90	36	31.1607142857143	4.83928571428572
91	32	34.8988764044944	-2.89887640449438
92	41	34.8988764044944	6.10112359550562
93	28	33.984	-5.984
94	30	31.1607142857143	-1.16071428571428
95	36	34.8988764044944	1.10112359550562
96	35	34.8988764044944	0.101123595505619
97	31	34.8988764044944	-3.89887640449438
98	34	34.8988764044944	-0.898876404494381
99	36	33.984	2.016
100	36	34.8988764044944	1.10112359550562
101	35	34.8988764044944	0.101123595505619
102	37	34.8988764044944	2.10112359550562
103	28	33.984	-5.984
104	39	34.8988764044944	4.10112359550562
105	32	33.984	-1.984
106	35	34.8988764044944	0.101123595505619
107	39	38.1	0.899999999999999
108	35	34.8988764044944	0.101123595505619
109	42	38.1	3.9
110	34	31.1607142857143	2.83928571428572
111	33	31.1607142857143	1.83928571428572
112	41	34.8988764044944	6.10112359550562
113	34	33.984	0.0159999999999982
114	32	34.8988764044944	-2.89887640449438
115	40	33.984	6.016
116	40	33.984	6.016
117	35	33.984	1.016
118	36	34.8988764044944	1.10112359550562
119	37	33.984	3.016
120	27	31.1607142857143	-4.16071428571428
121	39	33.984	5.016
122	38	34.8988764044944	3.10112359550562
123	31	33.984	-2.984
124	33	31.1607142857143	1.83928571428572
125	32	33.984	-1.984
126	39	38.1	0.899999999999999
127	36	33.984	2.016
128	33	33.984	-0.984000000000002
129	33	34.8988764044944	-1.89887640449438
130	32	31.1607142857143	0.839285714285715
131	37	34.8988764044944	2.10112359550562
132	30	31.1607142857143	-1.16071428571428
133	38	33.984	4.016
134	29	33.984	-4.984
135	22	31.1607142857143	-9.16071428571428
136	35	33.984	1.016
137	35	31.1607142857143	3.83928571428572
138	34	33.984	0.0159999999999982
139	35	31.1607142857143	3.83928571428572
140	34	34.8988764044944	-0.898876404494381
141	37	31.1607142857143	5.83928571428572
142	35	31.1607142857143	3.83928571428572
143	23	31.1607142857143	-8.16071428571428
144	31	33.984	-2.984
145	27	34.8988764044944	-7.89887640449438
146	36	33.984	2.016
147	31	34.8988764044944	-3.89887640449438
148	32	33.984	-1.984
149	39	33.984	5.016
150	37	38.1	-1.1
151	38	34.8988764044944	3.10112359550562
152	39	34.8988764044944	4.10112359550562
153	34	33.984	0.0159999999999982
154	31	33.984	-2.984
155	37	33.984	3.016
156	36	33.984	2.016
157	32	34.8988764044944	-2.89887640449438
158	38	33.984	4.016
159	26	31.1607142857143	-5.16071428571428
160	26	31.1607142857143	-5.16071428571428
161	33	34.8988764044944	-1.89887640449438
162	39	33.984	5.016
163	30	24.375	5.625
164	33	31.1607142857143	1.83928571428572
165	25	31.1607142857143	-6.16071428571428
166	38	33.984	4.016
167	37	31.1607142857143	5.83928571428572
168	31	33.984	-2.984
169	37	34.8988764044944	2.10112359550562
170	35	38.1	-3.1
171	25	31.1607142857143	-6.16071428571428
172	28	33.984	-5.984
173	35	33.984	1.016
174	33	33.984	-0.984000000000002
175	30	31.1607142857143	-1.16071428571428
176	31	31.1607142857143	-0.160714285714285
177	37	33.984	3.016
178	36	34.8988764044944	1.10112359550562
179	30	33.984	-3.984
180	36	31.1607142857143	4.83928571428572
181	32	33.984	-1.984
182	28	24.375	3.625
183	36	34.8988764044944	1.10112359550562
184	34	34.8988764044944	-0.898876404494381
185	31	33.984	-2.984
186	28	31.1607142857143	-3.16071428571428
187	36	33.984	2.016
188	36	33.984	2.016
189	40	34.8988764044944	5.10112359550562
190	33	33.984	-0.984000000000002
191	37	34.8988764044944	2.10112359550562
192	32	33.984	-1.984
193	38	33.984	4.016
194	31	34.8988764044944	-3.89887640449438
195	37	34.8988764044944	2.10112359550562
196	33	33.984	-0.984000000000002
197	30	31.1607142857143	-1.16071428571428
198	30	24.375	5.625
199	31	33.984	-2.984
200	32	33.984	-1.984
201	34	33.984	0.0159999999999982
202	36	33.984	2.016
203	37	34.8988764044944	2.10112359550562
204	36	34.8988764044944	1.10112359550562
205	33	33.984	-0.984000000000002
206	33	33.984	-0.984000000000002
207	33	33.984	-0.984000000000002
208	44	38.1	5.9
209	39	33.984	5.016
210	32	31.1607142857143	0.839285714285715
211	35	34.8988764044944	0.101123595505619
212	25	31.1607142857143	-6.16071428571428
213	35	34.8988764044944	0.101123595505619
214	34	33.984	0.0159999999999982
215	35	33.984	1.016
216	39	33.984	5.016
217	33	33.984	-0.984000000000002
218	36	33.984	2.016
219	32	33.984	-1.984
220	32	31.1607142857143	0.839285714285715
221	36	33.984	2.016
222	32	31.1607142857143	0.839285714285715
223	34	33.984	0.0159999999999982
224	33	33.984	-0.984000000000002
225	35	33.984	1.016
226	30	33.984	-3.984
227	38	33.984	4.016
228	34	34.8988764044944	-0.898876404494381
229	33	38.1	-5.1
230	32	33.984	-1.984
231	31	33.984	-2.984
232	30	33.984	-3.984
233	27	33.984	-6.984
234	31	31.1607142857143	-0.160714285714285
235	30	33.984	-3.984
236	32	31.1607142857143	0.839285714285715
237	35	33.984	1.016
238	28	31.1607142857143	-3.16071428571428
239	33	33.984	-0.984000000000002
240	35	31.1607142857143	3.83928571428572
241	35	33.984	1.016
242	32	33.984	-1.984
243	21	24.375	-3.375
244	20	24.375	-4.375
245	34	31.1607142857143	2.83928571428572
246	32	33.984	-1.984
247	34	33.984	0.0159999999999982
248	32	34.8988764044944	-2.89887640449438
249	33	33.984	-0.984000000000002
250	33	33.984	-0.984000000000002
251	37	34.8988764044944	2.10112359550562
252	32	31.1607142857143	0.839285714285715
253	34	33.984	0.0159999999999982
254	30	33.984	-3.984
255	30	31.1607142857143	-1.16071428571428
256	38	33.984	4.016
257	36	33.984	2.016
258	32	31.1607142857143	0.839285714285715
259	34	33.984	0.0159999999999982
260	33	33.984	-0.984000000000002
261	27	24.375	2.625
262	32	33.984	-1.984
263	34	33.984	0.0159999999999982
264	29	31.1607142857143	-2.16071428571428
265	35	33.984	1.016
266	27	31.1607142857143	-4.16071428571428
267	33	34.8988764044944	-1.89887640449438
268	38	33.984	4.016
269	36	34.8988764044944	1.10112359550562
270	33	34.8988764044944	-1.89887640449438
271	39	33.984	5.016
272	29	33.984	-4.984
273	32	34.8988764044944	-2.89887640449438
274	34	31.1607142857143	2.83928571428572
275	38	34.8988764044944	3.10112359550562
276	17	24.375	-7.375
277	35	33.984	1.016
278	32	33.984	-1.984
279	34	33.984	0.0159999999999982
280	36	33.984	2.016
281	31	34.8988764044944	-3.89887640449438
282	35	33.984	1.016
283	29	31.1607142857143	-2.16071428571428
284	22	24.375	-2.375
285	41	34.8988764044944	6.10112359550562
286	36	33.984	2.016
287	42	38.1	3.9
288	33	31.1607142857143	1.83928571428572

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 = 4 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 4 ; 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