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R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Tue, 13 Dec 2011 13:32:30 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/13/t13238011638xb3cce1bplptfn.htm/, Retrieved Thu, 02 May 2024 15:32:29 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154613, Retrieved Thu, 02 May 2024 15:32:29 +0000

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IsPrivate?

No (this computation is public)

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Estimated Impact

121

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 19:35:21] [b98453cac15ba1066b407e146608df68]
-   PD  [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2010-12-10 21:32:17] [1429a1a14191a86916b95357f6de790b]
F         [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2010-12-10 21:40:26] [1429a1a14191a86916b95357f6de790b]
-   PD        [Recursive Partitioning (Regression Trees)] [] [2011-12-13 18:32:30] [539ae27d3016cec7ecb6ecd6e9a1efc7] [Current]

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Dataseries X:

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Histogram

Boxplots

3	13	14	13	3
12	12	8	13	5
15	10	12	16	6
12	9	7	12	6
10	10	10	11	5
12	12	7	12	3
15	13	16	18	8
9	12	11	11	4
11	5	16	14	6
12	12	14	14	4
11	6	6	9	4
15	11	16	11	5
11	12	11	12	6
7	14	12	12	4
11	14	7	13	6
11	12	13	11	4
10	12	11	12	6
6	7	9	11	4
11	9	7	13	2
15	11	14	15	7
11	11	15	10	5
12	12	7	11	4
14	12	15	13	6
15	11	17	16	6
13	8	14	14	5
13	9	14	14	6
16	12	8	14	4
13	10	8	8	4
12	10	14	13	7
11	8	8	13	4
9	12	11	11	4
16	11	16	15	6
12	12	10	15	6
10	7	8	9	5
13	11	14	13	6
16	11	16	16	7
14	12	13	13	6
15	9	5	11	3
5	15	8	12	3
8	11	10	12	4
11	11	8	12	6
16	11	13	14	7
9	15	6	8	4
9	11	12	13	5
13	12	16	16	6
10	12	5	13	6
6	9	15	11	6
12	12	12	14	5
8	12	8	13	4
14	13	13	13	5
12	11	14	13	5
11	9	12	12	4
16	9	16	16	6
8	11	10	15	2
15	11	15	15	8
7	12	8	12	3
16	12	16	14	6
14	9	19	12	6
9	9	6	12	5
14	12	13	13	5
11	12	15	12	6
13	12	7	12	5
15	12	13	13	6
5	14	4	5	2
15	11	14	13	5
13	12	13	13	5
11	11	11	14	5
11	6	14	17	6
12	10	12	13	6
12	12	15	13	6
12	13	14	12	5
12	8	13	13	5
14	12	8	14	4
6	12	6	11	2
7	12	7	12	4
14	6	13	12	6
14	11	13	16	6
10	10	11	12	5
13	12	5	12	3
12	13	12	12	6
9	11	8	10	4
12	7	11	15	5
16	11	14	15	8
10	11	9	12	4
14	11	10	16	6
10	11	13	15	6
16	12	16	16	7
15	10	16	13	6
12	11	11	12	5
10	12	8	11	4
8	7	4	13	6
8	13	7	10	3
11	8	14	15	5
13	12	11	13	6
16	11	17	16	7
16	12	15	15	7
14	14	17	18	6
11	10	5	13	3
4	10	4	10	2
14	13	10	16	8
9	10	11	13	3
8	10	10	14	3
8	7	9	15	4
11	10	12	14	5
12	8	15	13	7
11	12	7	13	6
14	12	13	15	6
16	11	14	14	6
15	12	12	16	7
16	12	14	14	6
14	12	15	14	6
11	12	8	16	6
14	11	12	12	4
12	12	12	13	4
14	11	16	12	5
8	11	9	12	4
13	13	15	14	6
16	12	15	14	6
12	12	6	14	5
16	12	14	16	8
12	12	15	13	6
11	8	10	14	5
4	8	6	4	4
16	12	14	16	8
15	11	12	13	6
10	12	8	16	4
13	13	11	15	6
15	12	13	14	6
12	12	9	13	4
14	11	15	14	6
7	12	13	12	3
19	12	15	15	6
12	10	14	14	5
12	11	16	13	4
13	12	14	14	6
15	12	14	16	4
8	10	10	6	4
12	12	10	13	4
10	13	4	13	6
8	12	8	14	5
16	12	12	15	8
13	11	12	13	7
9	11	9	12	4
14	10	12	15	6
14	11	14	12	6
12	11	11	14	2

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'AstonUniversity' @ aston.wessa.net
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 4 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154613&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154613&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154613&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	4 seconds
R Server	'AstonUniversity' @ aston.wessa.net
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Goodness of Fit
Correlation	0.6656
R-squared	0.4431
RMSE	2.2438

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.6656 \tabularnewline
R-squared & 0.4431 \tabularnewline
RMSE & 2.2438 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154613&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.6656[/C][/ROW]
[ROW][C]R-squared[/C][C]0.4431[/C][/ROW]
[ROW][C]RMSE[/C][C]2.2438[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154613&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154613&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	0.6656
R-squared	0.4431
RMSE	2.2438

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	3	10.7368421052632	-7.73684210526316
2	12	11.1481481481481	0.851851851851851
3	15	13.3518518518519	1.64814814814815
4	12	11.1481481481481	0.851851851851851
5	10	11.1481481481481	-1.14814814814815
6	12	8.9	3.1
7	15	15.0625	-0.0625
8	9	8.9	0.0999999999999996
9	11	13.3518518518519	-2.35185185185185
10	12	10.7368421052632	1.26315789473684
11	11	8.9	2.1
12	15	13.3518518518519	1.64814814814815
13	11	11.1481481481481	-0.148148148148149
14	7	8.9	-1.9
15	11	11.1481481481481	-0.148148148148149
16	11	8.9	2.1
17	10	11.1481481481481	-1.14814814814815
18	6	8.9	-2.9
19	11	10.7368421052632	0.263157894736842
20	15	15.0625	-0.0625
21	11	13.3518518518519	-2.35185185185185
22	12	8.9	3.1
23	14	13.3518518518519	0.648148148148149
24	15	13.3518518518519	1.64814814814815
25	13	13.3518518518519	-0.351851851851851
26	13	13.3518518518519	-0.351851851851851
27	16	10.7368421052632	5.26315789473684
28	13	8.9	4.1
29	12	15.0625	-3.0625
30	11	10.7368421052632	0.263157894736842
31	9	8.9	0.0999999999999996
32	16	13.3518518518519	2.64814814814815
33	12	11.1481481481481	0.851851851851851
34	10	11.1481481481481	-1.14814814814815
35	13	13.3518518518519	-0.351851851851851
36	16	15.0625	0.9375
37	14	13.3518518518519	0.648148148148149
38	15	8.9	6.1
39	5	8.9	-3.9
40	8	8.9	-0.9
41	11	11.1481481481481	-0.148148148148149
42	16	15.0625	0.9375
43	9	8.9	0.0999999999999996
44	9	13.3518518518519	-4.35185185185185
45	13	13.3518518518519	-0.351851851851851
46	10	11.1481481481481	-1.14814814814815
47	6	13.3518518518519	-7.35185185185185
48	12	13.3518518518519	-1.35185185185185
49	8	10.7368421052632	-2.73684210526316
50	14	13.3518518518519	0.648148148148149
51	12	13.3518518518519	-1.35185185185185
52	11	8.9	2.1
53	16	13.3518518518519	2.64814814814815
54	8	10.7368421052632	-2.73684210526316
55	15	15.0625	-0.0625
56	7	8.9	-1.9
57	16	13.3518518518519	2.64814814814815
58	14	13.3518518518519	0.648148148148149
59	9	11.1481481481481	-2.14814814814815
60	14	13.3518518518519	0.648148148148149
61	11	13.3518518518519	-2.35185185185185
62	13	11.1481481481481	1.85185185185185
63	15	13.3518518518519	1.64814814814815
64	5	8.9	-3.9
65	15	13.3518518518519	1.64814814814815
66	13	13.3518518518519	-0.351851851851851
67	11	11.1481481481481	-0.148148148148149
68	11	13.3518518518519	-2.35185185185185
69	12	13.3518518518519	-1.35185185185185
70	12	13.3518518518519	-1.35185185185185
71	12	13.3518518518519	-1.35185185185185
72	12	13.3518518518519	-1.35185185185185
73	14	10.7368421052632	3.26315789473684
74	6	8.9	-2.9
75	7	8.9	-1.9
76	14	13.3518518518519	0.648148148148149
77	14	13.3518518518519	0.648148148148149
78	10	11.1481481481481	-1.14814814814815
79	13	8.9	4.1
80	12	13.3518518518519	-1.35185185185185
81	9	8.9	0.0999999999999996
82	12	11.1481481481481	0.851851851851851
83	16	15.0625	0.9375
84	10	8.9	1.1
85	14	11.1481481481481	2.85185185185185
86	10	13.3518518518519	-3.35185185185185
87	16	15.0625	0.9375
88	15	13.3518518518519	1.64814814814815
89	12	11.1481481481481	0.851851851851851
90	10	8.9	1.1
91	8	11.1481481481481	-3.14814814814815
92	8	8.9	-0.9
93	11	13.3518518518519	-2.35185185185185
94	13	11.1481481481481	1.85185185185185
95	16	15.0625	0.9375
96	16	15.0625	0.9375
97	14	13.3518518518519	0.648148148148149
98	11	10.7368421052632	0.263157894736842
99	4	8.9	-4.9
100	14	11.1481481481481	2.85185185185185
101	9	10.7368421052632	-1.73684210526316
102	8	10.7368421052632	-2.73684210526316
103	8	10.7368421052632	-2.73684210526316
104	11	13.3518518518519	-2.35185185185185
105	12	15.0625	-3.0625
106	11	11.1481481481481	-0.148148148148149
107	14	13.3518518518519	0.648148148148149
108	16	13.3518518518519	2.64814814814815
109	15	15.0625	-0.0625
110	16	13.3518518518519	2.64814814814815
111	14	13.3518518518519	0.648148148148149
112	11	11.1481481481481	-0.148148148148149
113	14	8.9	5.1
114	12	10.7368421052632	1.26315789473684
115	14	13.3518518518519	0.648148148148149
116	8	8.9	-0.9
117	13	13.3518518518519	-0.351851851851851
118	16	13.3518518518519	2.64814814814815
119	12	11.1481481481481	0.851851851851851
120	16	15.0625	0.9375
121	12	13.3518518518519	-1.35185185185185
122	11	11.1481481481481	-0.148148148148149
123	4	8.9	-4.9
124	16	15.0625	0.9375
125	15	13.3518518518519	1.64814814814815
126	10	10.7368421052632	-0.736842105263158
127	13	11.1481481481481	1.85185185185185
128	15	13.3518518518519	1.64814814814815
129	12	10.7368421052632	1.26315789473684
130	14	13.3518518518519	0.648148148148149
131	7	8.9	-1.9
132	19	13.3518518518519	5.64814814814815
133	12	13.3518518518519	-1.35185185185185
134	12	10.7368421052632	1.26315789473684
135	13	13.3518518518519	-0.351851851851851
136	15	10.7368421052632	4.26315789473684
137	8	8.9	-0.9
138	12	10.7368421052632	1.26315789473684
139	10	11.1481481481481	-1.14814814814815
140	8	11.1481481481481	-3.14814814814815
141	16	15.0625	0.9375
142	13	15.0625	-2.0625
143	9	8.9	0.0999999999999996
144	14	13.3518518518519	0.648148148148149
145	14	13.3518518518519	0.648148148148149
146	12	10.7368421052632	1.26315789473684

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 3 & 10.7368421052632 & -7.73684210526316 \tabularnewline
2 & 12 & 11.1481481481481 & 0.851851851851851 \tabularnewline
3 & 15 & 13.3518518518519 & 1.64814814814815 \tabularnewline
4 & 12 & 11.1481481481481 & 0.851851851851851 \tabularnewline
5 & 10 & 11.1481481481481 & -1.14814814814815 \tabularnewline
6 & 12 & 8.9 & 3.1 \tabularnewline
7 & 15 & 15.0625 & -0.0625 \tabularnewline
8 & 9 & 8.9 & 0.0999999999999996 \tabularnewline
9 & 11 & 13.3518518518519 & -2.35185185185185 \tabularnewline
10 & 12 & 10.7368421052632 & 1.26315789473684 \tabularnewline
11 & 11 & 8.9 & 2.1 \tabularnewline
12 & 15 & 13.3518518518519 & 1.64814814814815 \tabularnewline
13 & 11 & 11.1481481481481 & -0.148148148148149 \tabularnewline
14 & 7 & 8.9 & -1.9 \tabularnewline
15 & 11 & 11.1481481481481 & -0.148148148148149 \tabularnewline
16 & 11 & 8.9 & 2.1 \tabularnewline
17 & 10 & 11.1481481481481 & -1.14814814814815 \tabularnewline
18 & 6 & 8.9 & -2.9 \tabularnewline
19 & 11 & 10.7368421052632 & 0.263157894736842 \tabularnewline
20 & 15 & 15.0625 & -0.0625 \tabularnewline
21 & 11 & 13.3518518518519 & -2.35185185185185 \tabularnewline
22 & 12 & 8.9 & 3.1 \tabularnewline
23 & 14 & 13.3518518518519 & 0.648148148148149 \tabularnewline
24 & 15 & 13.3518518518519 & 1.64814814814815 \tabularnewline
25 & 13 & 13.3518518518519 & -0.351851851851851 \tabularnewline
26 & 13 & 13.3518518518519 & -0.351851851851851 \tabularnewline
27 & 16 & 10.7368421052632 & 5.26315789473684 \tabularnewline
28 & 13 & 8.9 & 4.1 \tabularnewline
29 & 12 & 15.0625 & -3.0625 \tabularnewline
30 & 11 & 10.7368421052632 & 0.263157894736842 \tabularnewline
31 & 9 & 8.9 & 0.0999999999999996 \tabularnewline
32 & 16 & 13.3518518518519 & 2.64814814814815 \tabularnewline
33 & 12 & 11.1481481481481 & 0.851851851851851 \tabularnewline
34 & 10 & 11.1481481481481 & -1.14814814814815 \tabularnewline
35 & 13 & 13.3518518518519 & -0.351851851851851 \tabularnewline
36 & 16 & 15.0625 & 0.9375 \tabularnewline
37 & 14 & 13.3518518518519 & 0.648148148148149 \tabularnewline
38 & 15 & 8.9 & 6.1 \tabularnewline
39 & 5 & 8.9 & -3.9 \tabularnewline
40 & 8 & 8.9 & -0.9 \tabularnewline
41 & 11 & 11.1481481481481 & -0.148148148148149 \tabularnewline
42 & 16 & 15.0625 & 0.9375 \tabularnewline
43 & 9 & 8.9 & 0.0999999999999996 \tabularnewline
44 & 9 & 13.3518518518519 & -4.35185185185185 \tabularnewline
45 & 13 & 13.3518518518519 & -0.351851851851851 \tabularnewline
46 & 10 & 11.1481481481481 & -1.14814814814815 \tabularnewline
47 & 6 & 13.3518518518519 & -7.35185185185185 \tabularnewline
48 & 12 & 13.3518518518519 & -1.35185185185185 \tabularnewline
49 & 8 & 10.7368421052632 & -2.73684210526316 \tabularnewline
50 & 14 & 13.3518518518519 & 0.648148148148149 \tabularnewline
51 & 12 & 13.3518518518519 & -1.35185185185185 \tabularnewline
52 & 11 & 8.9 & 2.1 \tabularnewline
53 & 16 & 13.3518518518519 & 2.64814814814815 \tabularnewline
54 & 8 & 10.7368421052632 & -2.73684210526316 \tabularnewline
55 & 15 & 15.0625 & -0.0625 \tabularnewline
56 & 7 & 8.9 & -1.9 \tabularnewline
57 & 16 & 13.3518518518519 & 2.64814814814815 \tabularnewline
58 & 14 & 13.3518518518519 & 0.648148148148149 \tabularnewline
59 & 9 & 11.1481481481481 & -2.14814814814815 \tabularnewline
60 & 14 & 13.3518518518519 & 0.648148148148149 \tabularnewline
61 & 11 & 13.3518518518519 & -2.35185185185185 \tabularnewline
62 & 13 & 11.1481481481481 & 1.85185185185185 \tabularnewline
63 & 15 & 13.3518518518519 & 1.64814814814815 \tabularnewline
64 & 5 & 8.9 & -3.9 \tabularnewline
65 & 15 & 13.3518518518519 & 1.64814814814815 \tabularnewline
66 & 13 & 13.3518518518519 & -0.351851851851851 \tabularnewline
67 & 11 & 11.1481481481481 & -0.148148148148149 \tabularnewline
68 & 11 & 13.3518518518519 & -2.35185185185185 \tabularnewline
69 & 12 & 13.3518518518519 & -1.35185185185185 \tabularnewline
70 & 12 & 13.3518518518519 & -1.35185185185185 \tabularnewline
71 & 12 & 13.3518518518519 & -1.35185185185185 \tabularnewline
72 & 12 & 13.3518518518519 & -1.35185185185185 \tabularnewline
73 & 14 & 10.7368421052632 & 3.26315789473684 \tabularnewline
74 & 6 & 8.9 & -2.9 \tabularnewline
75 & 7 & 8.9 & -1.9 \tabularnewline
76 & 14 & 13.3518518518519 & 0.648148148148149 \tabularnewline
77 & 14 & 13.3518518518519 & 0.648148148148149 \tabularnewline
78 & 10 & 11.1481481481481 & -1.14814814814815 \tabularnewline
79 & 13 & 8.9 & 4.1 \tabularnewline
80 & 12 & 13.3518518518519 & -1.35185185185185 \tabularnewline
81 & 9 & 8.9 & 0.0999999999999996 \tabularnewline
82 & 12 & 11.1481481481481 & 0.851851851851851 \tabularnewline
83 & 16 & 15.0625 & 0.9375 \tabularnewline
84 & 10 & 8.9 & 1.1 \tabularnewline
85 & 14 & 11.1481481481481 & 2.85185185185185 \tabularnewline
86 & 10 & 13.3518518518519 & -3.35185185185185 \tabularnewline
87 & 16 & 15.0625 & 0.9375 \tabularnewline
88 & 15 & 13.3518518518519 & 1.64814814814815 \tabularnewline
89 & 12 & 11.1481481481481 & 0.851851851851851 \tabularnewline
90 & 10 & 8.9 & 1.1 \tabularnewline
91 & 8 & 11.1481481481481 & -3.14814814814815 \tabularnewline
92 & 8 & 8.9 & -0.9 \tabularnewline
93 & 11 & 13.3518518518519 & -2.35185185185185 \tabularnewline
94 & 13 & 11.1481481481481 & 1.85185185185185 \tabularnewline
95 & 16 & 15.0625 & 0.9375 \tabularnewline
96 & 16 & 15.0625 & 0.9375 \tabularnewline
97 & 14 & 13.3518518518519 & 0.648148148148149 \tabularnewline
98 & 11 & 10.7368421052632 & 0.263157894736842 \tabularnewline
99 & 4 & 8.9 & -4.9 \tabularnewline
100 & 14 & 11.1481481481481 & 2.85185185185185 \tabularnewline
101 & 9 & 10.7368421052632 & -1.73684210526316 \tabularnewline
102 & 8 & 10.7368421052632 & -2.73684210526316 \tabularnewline
103 & 8 & 10.7368421052632 & -2.73684210526316 \tabularnewline
104 & 11 & 13.3518518518519 & -2.35185185185185 \tabularnewline
105 & 12 & 15.0625 & -3.0625 \tabularnewline
106 & 11 & 11.1481481481481 & -0.148148148148149 \tabularnewline
107 & 14 & 13.3518518518519 & 0.648148148148149 \tabularnewline
108 & 16 & 13.3518518518519 & 2.64814814814815 \tabularnewline
109 & 15 & 15.0625 & -0.0625 \tabularnewline
110 & 16 & 13.3518518518519 & 2.64814814814815 \tabularnewline
111 & 14 & 13.3518518518519 & 0.648148148148149 \tabularnewline
112 & 11 & 11.1481481481481 & -0.148148148148149 \tabularnewline
113 & 14 & 8.9 & 5.1 \tabularnewline
114 & 12 & 10.7368421052632 & 1.26315789473684 \tabularnewline
115 & 14 & 13.3518518518519 & 0.648148148148149 \tabularnewline
116 & 8 & 8.9 & -0.9 \tabularnewline
117 & 13 & 13.3518518518519 & -0.351851851851851 \tabularnewline
118 & 16 & 13.3518518518519 & 2.64814814814815 \tabularnewline
119 & 12 & 11.1481481481481 & 0.851851851851851 \tabularnewline
120 & 16 & 15.0625 & 0.9375 \tabularnewline
121 & 12 & 13.3518518518519 & -1.35185185185185 \tabularnewline
122 & 11 & 11.1481481481481 & -0.148148148148149 \tabularnewline
123 & 4 & 8.9 & -4.9 \tabularnewline
124 & 16 & 15.0625 & 0.9375 \tabularnewline
125 & 15 & 13.3518518518519 & 1.64814814814815 \tabularnewline
126 & 10 & 10.7368421052632 & -0.736842105263158 \tabularnewline
127 & 13 & 11.1481481481481 & 1.85185185185185 \tabularnewline
128 & 15 & 13.3518518518519 & 1.64814814814815 \tabularnewline
129 & 12 & 10.7368421052632 & 1.26315789473684 \tabularnewline
130 & 14 & 13.3518518518519 & 0.648148148148149 \tabularnewline
131 & 7 & 8.9 & -1.9 \tabularnewline
132 & 19 & 13.3518518518519 & 5.64814814814815 \tabularnewline
133 & 12 & 13.3518518518519 & -1.35185185185185 \tabularnewline
134 & 12 & 10.7368421052632 & 1.26315789473684 \tabularnewline
135 & 13 & 13.3518518518519 & -0.351851851851851 \tabularnewline
136 & 15 & 10.7368421052632 & 4.26315789473684 \tabularnewline
137 & 8 & 8.9 & -0.9 \tabularnewline
138 & 12 & 10.7368421052632 & 1.26315789473684 \tabularnewline
139 & 10 & 11.1481481481481 & -1.14814814814815 \tabularnewline
140 & 8 & 11.1481481481481 & -3.14814814814815 \tabularnewline
141 & 16 & 15.0625 & 0.9375 \tabularnewline
142 & 13 & 15.0625 & -2.0625 \tabularnewline
143 & 9 & 8.9 & 0.0999999999999996 \tabularnewline
144 & 14 & 13.3518518518519 & 0.648148148148149 \tabularnewline
145 & 14 & 13.3518518518519 & 0.648148148148149 \tabularnewline
146 & 12 & 10.7368421052632 & 1.26315789473684 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154613&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]3[/C][C]10.7368421052632[/C][C]-7.73684210526316[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]11.1481481481481[/C][C]0.851851851851851[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.3518518518519[/C][C]1.64814814814815[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]11.1481481481481[/C][C]0.851851851851851[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]11.1481481481481[/C][C]-1.14814814814815[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]8.9[/C][C]3.1[/C][/ROW]
[ROW][C]7[/C][C]15[/C][C]15.0625[/C][C]-0.0625[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]8.9[/C][C]0.0999999999999996[/C][/ROW]
[ROW][C]9[/C][C]11[/C][C]13.3518518518519[/C][C]-2.35185185185185[/C][/ROW]
[ROW][C]10[/C][C]12[/C][C]10.7368421052632[/C][C]1.26315789473684[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]8.9[/C][C]2.1[/C][/ROW]
[ROW][C]12[/C][C]15[/C][C]13.3518518518519[/C][C]1.64814814814815[/C][/ROW]
[ROW][C]13[/C][C]11[/C][C]11.1481481481481[/C][C]-0.148148148148149[/C][/ROW]
[ROW][C]14[/C][C]7[/C][C]8.9[/C][C]-1.9[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]11.1481481481481[/C][C]-0.148148148148149[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]8.9[/C][C]2.1[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]11.1481481481481[/C][C]-1.14814814814815[/C][/ROW]
[ROW][C]18[/C][C]6[/C][C]8.9[/C][C]-2.9[/C][/ROW]
[ROW][C]19[/C][C]11[/C][C]10.7368421052632[/C][C]0.263157894736842[/C][/ROW]
[ROW][C]20[/C][C]15[/C][C]15.0625[/C][C]-0.0625[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]13.3518518518519[/C][C]-2.35185185185185[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]8.9[/C][C]3.1[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]13.3518518518519[/C][C]0.648148148148149[/C][/ROW]
[ROW][C]24[/C][C]15[/C][C]13.3518518518519[/C][C]1.64814814814815[/C][/ROW]
[ROW][C]25[/C][C]13[/C][C]13.3518518518519[/C][C]-0.351851851851851[/C][/ROW]
[ROW][C]26[/C][C]13[/C][C]13.3518518518519[/C][C]-0.351851851851851[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]10.7368421052632[/C][C]5.26315789473684[/C][/ROW]
[ROW][C]28[/C][C]13[/C][C]8.9[/C][C]4.1[/C][/ROW]
[ROW][C]29[/C][C]12[/C][C]15.0625[/C][C]-3.0625[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]10.7368421052632[/C][C]0.263157894736842[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]8.9[/C][C]0.0999999999999996[/C][/ROW]
[ROW][C]32[/C][C]16[/C][C]13.3518518518519[/C][C]2.64814814814815[/C][/ROW]
[ROW][C]33[/C][C]12[/C][C]11.1481481481481[/C][C]0.851851851851851[/C][/ROW]
[ROW][C]34[/C][C]10[/C][C]11.1481481481481[/C][C]-1.14814814814815[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]13.3518518518519[/C][C]-0.351851851851851[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]15.0625[/C][C]0.9375[/C][/ROW]
[ROW][C]37[/C][C]14[/C][C]13.3518518518519[/C][C]0.648148148148149[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]8.9[/C][C]6.1[/C][/ROW]
[ROW][C]39[/C][C]5[/C][C]8.9[/C][C]-3.9[/C][/ROW]
[ROW][C]40[/C][C]8[/C][C]8.9[/C][C]-0.9[/C][/ROW]
[ROW][C]41[/C][C]11[/C][C]11.1481481481481[/C][C]-0.148148148148149[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]15.0625[/C][C]0.9375[/C][/ROW]
[ROW][C]43[/C][C]9[/C][C]8.9[/C][C]0.0999999999999996[/C][/ROW]
[ROW][C]44[/C][C]9[/C][C]13.3518518518519[/C][C]-4.35185185185185[/C][/ROW]
[ROW][C]45[/C][C]13[/C][C]13.3518518518519[/C][C]-0.351851851851851[/C][/ROW]
[ROW][C]46[/C][C]10[/C][C]11.1481481481481[/C][C]-1.14814814814815[/C][/ROW]
[ROW][C]47[/C][C]6[/C][C]13.3518518518519[/C][C]-7.35185185185185[/C][/ROW]
[ROW][C]48[/C][C]12[/C][C]13.3518518518519[/C][C]-1.35185185185185[/C][/ROW]
[ROW][C]49[/C][C]8[/C][C]10.7368421052632[/C][C]-2.73684210526316[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]13.3518518518519[/C][C]0.648148148148149[/C][/ROW]
[ROW][C]51[/C][C]12[/C][C]13.3518518518519[/C][C]-1.35185185185185[/C][/ROW]
[ROW][C]52[/C][C]11[/C][C]8.9[/C][C]2.1[/C][/ROW]
[ROW][C]53[/C][C]16[/C][C]13.3518518518519[/C][C]2.64814814814815[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]10.7368421052632[/C][C]-2.73684210526316[/C][/ROW]
[ROW][C]55[/C][C]15[/C][C]15.0625[/C][C]-0.0625[/C][/ROW]
[ROW][C]56[/C][C]7[/C][C]8.9[/C][C]-1.9[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]13.3518518518519[/C][C]2.64814814814815[/C][/ROW]
[ROW][C]58[/C][C]14[/C][C]13.3518518518519[/C][C]0.648148148148149[/C][/ROW]
[ROW][C]59[/C][C]9[/C][C]11.1481481481481[/C][C]-2.14814814814815[/C][/ROW]
[ROW][C]60[/C][C]14[/C][C]13.3518518518519[/C][C]0.648148148148149[/C][/ROW]
[ROW][C]61[/C][C]11[/C][C]13.3518518518519[/C][C]-2.35185185185185[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]11.1481481481481[/C][C]1.85185185185185[/C][/ROW]
[ROW][C]63[/C][C]15[/C][C]13.3518518518519[/C][C]1.64814814814815[/C][/ROW]
[ROW][C]64[/C][C]5[/C][C]8.9[/C][C]-3.9[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]13.3518518518519[/C][C]1.64814814814815[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]13.3518518518519[/C][C]-0.351851851851851[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]11.1481481481481[/C][C]-0.148148148148149[/C][/ROW]
[ROW][C]68[/C][C]11[/C][C]13.3518518518519[/C][C]-2.35185185185185[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]13.3518518518519[/C][C]-1.35185185185185[/C][/ROW]
[ROW][C]70[/C][C]12[/C][C]13.3518518518519[/C][C]-1.35185185185185[/C][/ROW]
[ROW][C]71[/C][C]12[/C][C]13.3518518518519[/C][C]-1.35185185185185[/C][/ROW]
[ROW][C]72[/C][C]12[/C][C]13.3518518518519[/C][C]-1.35185185185185[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]10.7368421052632[/C][C]3.26315789473684[/C][/ROW]
[ROW][C]74[/C][C]6[/C][C]8.9[/C][C]-2.9[/C][/ROW]
[ROW][C]75[/C][C]7[/C][C]8.9[/C][C]-1.9[/C][/ROW]
[ROW][C]76[/C][C]14[/C][C]13.3518518518519[/C][C]0.648148148148149[/C][/ROW]
[ROW][C]77[/C][C]14[/C][C]13.3518518518519[/C][C]0.648148148148149[/C][/ROW]
[ROW][C]78[/C][C]10[/C][C]11.1481481481481[/C][C]-1.14814814814815[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]8.9[/C][C]4.1[/C][/ROW]
[ROW][C]80[/C][C]12[/C][C]13.3518518518519[/C][C]-1.35185185185185[/C][/ROW]
[ROW][C]81[/C][C]9[/C][C]8.9[/C][C]0.0999999999999996[/C][/ROW]
[ROW][C]82[/C][C]12[/C][C]11.1481481481481[/C][C]0.851851851851851[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.0625[/C][C]0.9375[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]8.9[/C][C]1.1[/C][/ROW]
[ROW][C]85[/C][C]14[/C][C]11.1481481481481[/C][C]2.85185185185185[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]13.3518518518519[/C][C]-3.35185185185185[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]15.0625[/C][C]0.9375[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]13.3518518518519[/C][C]1.64814814814815[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]11.1481481481481[/C][C]0.851851851851851[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]8.9[/C][C]1.1[/C][/ROW]
[ROW][C]91[/C][C]8[/C][C]11.1481481481481[/C][C]-3.14814814814815[/C][/ROW]
[ROW][C]92[/C][C]8[/C][C]8.9[/C][C]-0.9[/C][/ROW]
[ROW][C]93[/C][C]11[/C][C]13.3518518518519[/C][C]-2.35185185185185[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]11.1481481481481[/C][C]1.85185185185185[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]15.0625[/C][C]0.9375[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.0625[/C][C]0.9375[/C][/ROW]
[ROW][C]97[/C][C]14[/C][C]13.3518518518519[/C][C]0.648148148148149[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]10.7368421052632[/C][C]0.263157894736842[/C][/ROW]
[ROW][C]99[/C][C]4[/C][C]8.9[/C][C]-4.9[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]11.1481481481481[/C][C]2.85185185185185[/C][/ROW]
[ROW][C]101[/C][C]9[/C][C]10.7368421052632[/C][C]-1.73684210526316[/C][/ROW]
[ROW][C]102[/C][C]8[/C][C]10.7368421052632[/C][C]-2.73684210526316[/C][/ROW]
[ROW][C]103[/C][C]8[/C][C]10.7368421052632[/C][C]-2.73684210526316[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]13.3518518518519[/C][C]-2.35185185185185[/C][/ROW]
[ROW][C]105[/C][C]12[/C][C]15.0625[/C][C]-3.0625[/C][/ROW]
[ROW][C]106[/C][C]11[/C][C]11.1481481481481[/C][C]-0.148148148148149[/C][/ROW]
[ROW][C]107[/C][C]14[/C][C]13.3518518518519[/C][C]0.648148148148149[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]13.3518518518519[/C][C]2.64814814814815[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]15.0625[/C][C]-0.0625[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]13.3518518518519[/C][C]2.64814814814815[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]13.3518518518519[/C][C]0.648148148148149[/C][/ROW]
[ROW][C]112[/C][C]11[/C][C]11.1481481481481[/C][C]-0.148148148148149[/C][/ROW]
[ROW][C]113[/C][C]14[/C][C]8.9[/C][C]5.1[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]10.7368421052632[/C][C]1.26315789473684[/C][/ROW]
[ROW][C]115[/C][C]14[/C][C]13.3518518518519[/C][C]0.648148148148149[/C][/ROW]
[ROW][C]116[/C][C]8[/C][C]8.9[/C][C]-0.9[/C][/ROW]
[ROW][C]117[/C][C]13[/C][C]13.3518518518519[/C][C]-0.351851851851851[/C][/ROW]
[ROW][C]118[/C][C]16[/C][C]13.3518518518519[/C][C]2.64814814814815[/C][/ROW]
[ROW][C]119[/C][C]12[/C][C]11.1481481481481[/C][C]0.851851851851851[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.0625[/C][C]0.9375[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]13.3518518518519[/C][C]-1.35185185185185[/C][/ROW]
[ROW][C]122[/C][C]11[/C][C]11.1481481481481[/C][C]-0.148148148148149[/C][/ROW]
[ROW][C]123[/C][C]4[/C][C]8.9[/C][C]-4.9[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]15.0625[/C][C]0.9375[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]13.3518518518519[/C][C]1.64814814814815[/C][/ROW]
[ROW][C]126[/C][C]10[/C][C]10.7368421052632[/C][C]-0.736842105263158[/C][/ROW]
[ROW][C]127[/C][C]13[/C][C]11.1481481481481[/C][C]1.85185185185185[/C][/ROW]
[ROW][C]128[/C][C]15[/C][C]13.3518518518519[/C][C]1.64814814814815[/C][/ROW]
[ROW][C]129[/C][C]12[/C][C]10.7368421052632[/C][C]1.26315789473684[/C][/ROW]
[ROW][C]130[/C][C]14[/C][C]13.3518518518519[/C][C]0.648148148148149[/C][/ROW]
[ROW][C]131[/C][C]7[/C][C]8.9[/C][C]-1.9[/C][/ROW]
[ROW][C]132[/C][C]19[/C][C]13.3518518518519[/C][C]5.64814814814815[/C][/ROW]
[ROW][C]133[/C][C]12[/C][C]13.3518518518519[/C][C]-1.35185185185185[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]10.7368421052632[/C][C]1.26315789473684[/C][/ROW]
[ROW][C]135[/C][C]13[/C][C]13.3518518518519[/C][C]-0.351851851851851[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]10.7368421052632[/C][C]4.26315789473684[/C][/ROW]
[ROW][C]137[/C][C]8[/C][C]8.9[/C][C]-0.9[/C][/ROW]
[ROW][C]138[/C][C]12[/C][C]10.7368421052632[/C][C]1.26315789473684[/C][/ROW]
[ROW][C]139[/C][C]10[/C][C]11.1481481481481[/C][C]-1.14814814814815[/C][/ROW]
[ROW][C]140[/C][C]8[/C][C]11.1481481481481[/C][C]-3.14814814814815[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]15.0625[/C][C]0.9375[/C][/ROW]
[ROW][C]142[/C][C]13[/C][C]15.0625[/C][C]-2.0625[/C][/ROW]
[ROW][C]143[/C][C]9[/C][C]8.9[/C][C]0.0999999999999996[/C][/ROW]
[ROW][C]144[/C][C]14[/C][C]13.3518518518519[/C][C]0.648148148148149[/C][/ROW]
[ROW][C]145[/C][C]14[/C][C]13.3518518518519[/C][C]0.648148148148149[/C][/ROW]
[ROW][C]146[/C][C]12[/C][C]10.7368421052632[/C][C]1.26315789473684[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154613&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154613&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	3	10.7368421052632	-7.73684210526316
2	12	11.1481481481481	0.851851851851851
3	15	13.3518518518519	1.64814814814815
4	12	11.1481481481481	0.851851851851851
5	10	11.1481481481481	-1.14814814814815
6	12	8.9	3.1
7	15	15.0625	-0.0625
8	9	8.9	0.0999999999999996
9	11	13.3518518518519	-2.35185185185185
10	12	10.7368421052632	1.26315789473684
11	11	8.9	2.1
12	15	13.3518518518519	1.64814814814815
13	11	11.1481481481481	-0.148148148148149
14	7	8.9	-1.9
15	11	11.1481481481481	-0.148148148148149
16	11	8.9	2.1
17	10	11.1481481481481	-1.14814814814815
18	6	8.9	-2.9
19	11	10.7368421052632	0.263157894736842
20	15	15.0625	-0.0625
21	11	13.3518518518519	-2.35185185185185
22	12	8.9	3.1
23	14	13.3518518518519	0.648148148148149
24	15	13.3518518518519	1.64814814814815
25	13	13.3518518518519	-0.351851851851851
26	13	13.3518518518519	-0.351851851851851
27	16	10.7368421052632	5.26315789473684
28	13	8.9	4.1
29	12	15.0625	-3.0625
30	11	10.7368421052632	0.263157894736842
31	9	8.9	0.0999999999999996
32	16	13.3518518518519	2.64814814814815
33	12	11.1481481481481	0.851851851851851
34	10	11.1481481481481	-1.14814814814815
35	13	13.3518518518519	-0.351851851851851
36	16	15.0625	0.9375
37	14	13.3518518518519	0.648148148148149
38	15	8.9	6.1
39	5	8.9	-3.9
40	8	8.9	-0.9
41	11	11.1481481481481	-0.148148148148149
42	16	15.0625	0.9375
43	9	8.9	0.0999999999999996
44	9	13.3518518518519	-4.35185185185185
45	13	13.3518518518519	-0.351851851851851
46	10	11.1481481481481	-1.14814814814815
47	6	13.3518518518519	-7.35185185185185
48	12	13.3518518518519	-1.35185185185185
49	8	10.7368421052632	-2.73684210526316
50	14	13.3518518518519	0.648148148148149
51	12	13.3518518518519	-1.35185185185185
52	11	8.9	2.1
53	16	13.3518518518519	2.64814814814815
54	8	10.7368421052632	-2.73684210526316
55	15	15.0625	-0.0625
56	7	8.9	-1.9
57	16	13.3518518518519	2.64814814814815
58	14	13.3518518518519	0.648148148148149
59	9	11.1481481481481	-2.14814814814815
60	14	13.3518518518519	0.648148148148149
61	11	13.3518518518519	-2.35185185185185
62	13	11.1481481481481	1.85185185185185
63	15	13.3518518518519	1.64814814814815
64	5	8.9	-3.9
65	15	13.3518518518519	1.64814814814815
66	13	13.3518518518519	-0.351851851851851
67	11	11.1481481481481	-0.148148148148149
68	11	13.3518518518519	-2.35185185185185
69	12	13.3518518518519	-1.35185185185185
70	12	13.3518518518519	-1.35185185185185
71	12	13.3518518518519	-1.35185185185185
72	12	13.3518518518519	-1.35185185185185
73	14	10.7368421052632	3.26315789473684
74	6	8.9	-2.9
75	7	8.9	-1.9
76	14	13.3518518518519	0.648148148148149
77	14	13.3518518518519	0.648148148148149
78	10	11.1481481481481	-1.14814814814815
79	13	8.9	4.1
80	12	13.3518518518519	-1.35185185185185
81	9	8.9	0.0999999999999996
82	12	11.1481481481481	0.851851851851851
83	16	15.0625	0.9375
84	10	8.9	1.1
85	14	11.1481481481481	2.85185185185185
86	10	13.3518518518519	-3.35185185185185
87	16	15.0625	0.9375
88	15	13.3518518518519	1.64814814814815
89	12	11.1481481481481	0.851851851851851
90	10	8.9	1.1
91	8	11.1481481481481	-3.14814814814815
92	8	8.9	-0.9
93	11	13.3518518518519	-2.35185185185185
94	13	11.1481481481481	1.85185185185185
95	16	15.0625	0.9375
96	16	15.0625	0.9375
97	14	13.3518518518519	0.648148148148149
98	11	10.7368421052632	0.263157894736842
99	4	8.9	-4.9
100	14	11.1481481481481	2.85185185185185
101	9	10.7368421052632	-1.73684210526316
102	8	10.7368421052632	-2.73684210526316
103	8	10.7368421052632	-2.73684210526316
104	11	13.3518518518519	-2.35185185185185
105	12	15.0625	-3.0625
106	11	11.1481481481481	-0.148148148148149
107	14	13.3518518518519	0.648148148148149
108	16	13.3518518518519	2.64814814814815
109	15	15.0625	-0.0625
110	16	13.3518518518519	2.64814814814815
111	14	13.3518518518519	0.648148148148149
112	11	11.1481481481481	-0.148148148148149
113	14	8.9	5.1
114	12	10.7368421052632	1.26315789473684
115	14	13.3518518518519	0.648148148148149
116	8	8.9	-0.9
117	13	13.3518518518519	-0.351851851851851
118	16	13.3518518518519	2.64814814814815
119	12	11.1481481481481	0.851851851851851
120	16	15.0625	0.9375
121	12	13.3518518518519	-1.35185185185185
122	11	11.1481481481481	-0.148148148148149
123	4	8.9	-4.9
124	16	15.0625	0.9375
125	15	13.3518518518519	1.64814814814815
126	10	10.7368421052632	-0.736842105263158
127	13	11.1481481481481	1.85185185185185
128	15	13.3518518518519	1.64814814814815
129	12	10.7368421052632	1.26315789473684
130	14	13.3518518518519	0.648148148148149
131	7	8.9	-1.9
132	19	13.3518518518519	5.64814814814815
133	12	13.3518518518519	-1.35185185185185
134	12	10.7368421052632	1.26315789473684
135	13	13.3518518518519	-0.351851851851851
136	15	10.7368421052632	4.26315789473684
137	8	8.9	-0.9
138	12	10.7368421052632	1.26315789473684
139	10	11.1481481481481	-1.14814814814815
140	8	11.1481481481481	-3.14814814814815
141	16	15.0625	0.9375
142	13	15.0625	-2.0625
143	9	8.9	0.0999999999999996
144	14	13.3518518518519	0.648148148148149
145	14	13.3518518518519	0.648148148148149
146	12	10.7368421052632	1.26315789473684

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 = 2 ; par4 = yes ;

Parameters (R input):

par1 = 1 ; par2 = none ; par3 = 2 ; par4 = yes ;

R code (references can be found in the software module):

library(party)
library(Hmisc)
par1 <- as.numeric(par1)
par3 <- as.numeric(par3)
x <- data.frame(t(y))
is.data.frame(x)
x <- x[!is.na(x[,par1]),]
k <- length(x[1,])
n <- length(x[,1])
colnames(x)[par1]
x[,par1]
if (par2 == 'kmeans') {
cl <- kmeans(x[,par1], par3)
print(cl)
clm <- matrix(cbind(cl$centers,1:par3),ncol=2)
clm <- clm[sort.list(clm[,1]),]
for (i in 1:par3) {
cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='')
}
cl$cluster <- as.factor(cl$cluster)
print(cl$cluster)
x[,par1] <- cl$cluster
}
if (par2 == 'quantiles') {
x[,par1] <- cut2(x[,par1],g=par3)
}
if (par2 == 'hclust') {
hc <- hclust(dist(x[,par1])^2, 'cen')
print(hc)
memb <- cutree(hc, k = par3)
dum <- c(mean(x[memb==1,par1]))
for (i in 2:par3) {
dum <- c(dum, mean(x[memb==i,par1]))
}
hcm <- matrix(cbind(dum,1:par3),ncol=2)
hcm <- hcm[sort.list(hcm[,1]),]
for (i in 1:par3) {
memb[memb==hcm[i,2]] <- paste('C',i,sep='')
}
memb <- as.factor(memb)
print(memb)
x[,par1] <- memb
}
if (par2=='equal') {
ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep=''))
x[,par1] <- as.factor(ed)
}
table(x[,par1])
colnames(x)
colnames(x)[par1]
x[,par1]
if (par2 == 'none') {
m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
}
load(file='createtable')
if (par2 != 'none') {
m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x)
if (par4=='yes') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
a<-table.element(a,'Prediction (training)',par3+1,TRUE)
a<-table.element(a,'Prediction (testing)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Actual',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
a<-table.row.end(a)
for (i in 1:10) {
ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1))
m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,])
if (i==1) {
m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,])
m.ct.i.actu <- x[ind==1,par1]
m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,])
m.ct.x.actu <- x[ind==2,par1]
} else {
m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,]))
m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1])
m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,]))
m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1])
}
}
print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,]))
numer <- numer + m.ct.i.tab[i,i]
}
print(m.ct.i.cp <- numer / sum(m.ct.i.tab))
print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,]))
numer <- numer + m.ct.x.tab[i,i]
}
print(m.ct.x.cp <- numer / sum(m.ct.x.tab))
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj])
a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4))
for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj])
a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4))
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'Overall',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.i.cp,4))
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.x.cp,4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
}
}
m
bitmap(file='test1.png')
plot(m)
dev.off()
bitmap(file='test1a.png')
plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response')
dev.off()
if (par2 == 'none') {
forec <- predict(m)
result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec))
colnames(result) <- c('Actuals','Forecasts','Residuals')
print(result)
}
if (par2 != 'none') {
print(cbind(as.factor(x[,par1]),predict(m)))
myt <- table(as.factor(x[,par1]),predict(m))
print(myt)
}
bitmap(file='test2.png')
if(par2=='none') {
op <- par(mfrow=c(2,2))
plot(density(result$Actuals),main='Kernel Density Plot of Actuals')
plot(density(result$Residuals),main='Kernel Density Plot of Residuals')
plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals')
plot(density(result$Forecasts),main='Kernel Density Plot of Predictions')
par(op)
}
if(par2!='none') {
plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted')
}
dev.off()
if (par2 == 'none') {
detcoef <- cor(result$Forecasts,result$Actuals)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goodness of Fit',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Correlation',1,TRUE)
a<-table.element(a,round(detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'R-squared',1,TRUE)
a<-table.element(a,round(detcoef*detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'RMSE',1,TRUE)
a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#',header=TRUE)
a<-table.element(a,'Actuals',header=TRUE)
a<-table.element(a,'Forecasts',header=TRUE)
a<-table.element(a,'Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(result$Actuals)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,result$Actuals[i])
a<-table.element(a,result$Forecasts[i])
a<-table.element(a,result$Residuals[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
}
if (par2 != 'none') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
for (i in 1:par3) {
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
}
a<-table.row.end(a)
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (j in 1:par3) {
a<-table.element(a,myt[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code