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Author

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R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Sat, 10 Dec 2011 09:33:25 -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/2011/Dec/10/t1323527644g1tlk6e8p79rtoc.htm/, Retrieved Sat, 27 Apr 2024 21:50:18 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=153555, Retrieved Sat, 27 Apr 2024 21:50:18 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

128

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

-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
- RMPD  [Multiple Regression] [WS 10 - Multiple ...] [2010-12-11 15:55:17] [033eb2749a430605d9b2be7c4aac4a0c]
-         [Multiple Regression] [] [2010-12-13 18:21:06] [d7b28a0391ab3b2ddc9f9fba95a43f33]
-           [Multiple Regression] [] [2010-12-25 21:49:52] [2e1e44f0ae3cb9513dc28781dfdb387b]
-   PD        [Multiple Regression] [Workshop 10 (3)] [2011-12-10 14:08:30] [3deae35ae8526e36953f595ad65f3a1f]
- RMP             [Recursive Partitioning (Regression Trees)] [Workshop 10 (4)] [2011-12-10 14:33:25] [7524f34f9c6610426249911bb0d7f59b] [Current]
- R P               [Recursive Partitioning (Regression Trees)] [Workshop 10 (4)] [2011-12-12 15:13:36] [3deae35ae8526e36953f595ad65f3a1f] 
-  M                  [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2013-12-09 21:59:57] [16ce55620e4b91ec00a4b56aea2a2582] 
-   PD              [Recursive Partitioning (Regression Trees)] [Workshop 10 (5)] [2011-12-12 15:18:00] [3deae35ae8526e36953f595ad65f3a1f] 
- R  D                [Recursive Partitioning (Regression Trees)] [Workshop 10 (5)] [2011-12-12 15:28:11] [3deae35ae8526e36953f595ad65f3a1f] 
-   P                   [Recursive Partitioning (Regression Trees)] [Workshop 10 (6)] [2011-12-12 16:25:11] [3deae35ae8526e36953f595ad65f3a1f] 
-  M                      [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2013-12-09 22:16:05] [16ce55620e4b91ec00a4b56aea2a2582] 
-  M                    [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2013-12-09 22:07:36] [16ce55620e4b91ec00a4b56aea2a2582] 

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Dataseries X:

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Histogram

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13	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
12	12	14	14	4
11	6	6	9	4
11	5	16	14	6
11	12	11	12	6
15	11	16	11	5
7	14	12	12	4
11	14	7	13	6
11	12	13	11	4
10	12	11	12	6
14	11	15	16	6
10	11	7	9	4
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
9	11	15	15	7
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
14	12	14	15	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
17	11	15	14	5
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
16	11	14	15	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
14	11	15	15	8
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
15	12	12	16	7
16	11	14	14	6
16	12	14	14	6
11	12	8	16	6
14	12	15	14	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
10	15	15	15	6
15	11	16	14	6
16	12	12	15	8
13	11	12	13	7
16	12	15	16	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	5 seconds
R Server	'George Udny Yule' @ yule.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 & 5 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153555&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153555&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153555&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	5 seconds
R Server	'George Udny Yule' @ yule.wessa.net

Goodness of Fit
Correlation	0.655
R-squared	0.429
RMSE	2.6073

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.655 \tabularnewline
R-squared & 0.429 \tabularnewline
RMSE & 2.6073 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153555&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.655[/C][/ROW]
[ROW][C]R-squared[/C][C]0.429[/C][/ROW]
[ROW][C]RMSE[/C][C]2.6073[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153555&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153555&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	0.655
R-squared	0.429
RMSE	2.6073

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	14	10	4
2	8	12.2857142857143	-4.28571428571429
3	12	14.3529411764706	-2.35294117647059
4	7	12.2857142857143	-5.28571428571429
5	10	10.5555555555556	-0.555555555555555
6	7	10	-3
7	16	14.3529411764706	1.64705882352941
8	11	8.5	2.5
9	14	10	4
10	6	8.5	-2.5
11	16	10.5555555555556	5.44444444444444
12	11	10.5555555555556	0.444444444444445
13	16	14.3529411764706	1.64705882352941
14	12	8.5	3.5
15	7	10.5555555555556	-3.55555555555556
16	13	8.5	4.5
17	11	10.5555555555556	0.444444444444445
18	15	14.3529411764706	0.647058823529411
19	7	8.5	-1.5
20	9	8.5	0.5
21	7	8.5	-1.5
22	14	14.3529411764706	-0.352941176470589
23	15	10.5555555555556	4.44444444444444
24	7	10	-3
25	15	14.3529411764706	0.647058823529411
26	17	14.3529411764706	2.64705882352941
27	15	10.5555555555556	4.44444444444444
28	14	12.2857142857143	1.71428571428571
29	14	12.2857142857143	1.71428571428571
30	8	10	-2
31	8	10	-2
32	14	12.2857142857143	1.71428571428571
33	14	14.3529411764706	-0.352941176470589
34	8	8.5	-0.5
35	11	8.5	2.5
36	16	14.3529411764706	1.64705882352941
37	10	12.2857142857143	-2.28571428571429
38	8	10.5555555555556	-2.55555555555556
39	14	12.2857142857143	1.71428571428571
40	16	14.3529411764706	1.64705882352941
41	13	14.3529411764706	-1.35294117647059
42	5	10	-5
43	8	8.5	-0.5
44	10	8.5	1.5
45	8	10.5555555555556	-2.55555555555556
46	13	14.3529411764706	-1.35294117647059
47	15	14.3529411764706	0.647058823529411
48	6	8.5	-2.5
49	12	10.5555555555556	1.44444444444444
50	16	12.2857142857143	3.71428571428571
51	5	10.5555555555556	-5.55555555555556
52	15	10.5555555555556	4.44444444444444
53	12	12.2857142857143	-0.285714285714286
54	8	8.5	-0.5
55	13	14.3529411764706	-1.35294117647059
56	14	12.2857142857143	1.71428571428571
57	12	8.5	3.5
58	16	14.3529411764706	1.64705882352941
59	10	8.5	1.5
60	15	14.3529411764706	0.647058823529411
61	8	8.5	-0.5
62	16	14.3529411764706	1.64705882352941
63	19	14.3529411764706	4.64705882352941
64	14	14.3529411764706	-0.352941176470589
65	6	10.5555555555556	-4.55555555555556
66	13	14.3529411764706	-1.35294117647059
67	15	10.5555555555556	4.44444444444444
68	7	12.2857142857143	-5.28571428571429
69	13	14.3529411764706	-1.35294117647059
70	4	8.5	-4.5
71	14	14.3529411764706	-0.352941176470589
72	13	12.2857142857143	0.714285714285714
73	11	10.5555555555556	0.444444444444445
74	14	10.5555555555556	3.44444444444444
75	12	12.2857142857143	-0.285714285714286
76	15	12.2857142857143	2.71428571428571
77	14	12.2857142857143	1.71428571428571
78	13	12.2857142857143	0.714285714285714
79	8	10	-2
80	6	8.5	-2.5
81	7	8.5	-1.5
82	13	14.3529411764706	-1.35294117647059
83	13	14.3529411764706	-1.35294117647059
84	11	10.5555555555556	0.444444444444445
85	5	10	-5
86	12	12.2857142857143	-0.285714285714286
87	8	8.5	-0.5
88	11	12.2857142857143	-1.28571428571429
89	14	14.3529411764706	-0.352941176470589
90	9	8.5	0.5
91	10	14.3529411764706	-4.35294117647059
92	13	10.5555555555556	2.44444444444444
93	16	14.3529411764706	1.64705882352941
94	16	14.3529411764706	1.64705882352941
95	11	12.2857142857143	-1.28571428571429
96	8	8.5	-0.5
97	4	10.5555555555556	-6.55555555555556
98	7	8.5	-1.5
99	14	10.5555555555556	3.44444444444444
100	11	12.2857142857143	-1.28571428571429
101	17	14.3529411764706	2.64705882352941
102	15	14.3529411764706	0.647058823529411
103	17	14.3529411764706	2.64705882352941
104	5	8.5	-3.5
105	4	8.5	-4.5
106	10	14.3529411764706	-4.35294117647059
107	11	8.5	2.5
108	15	14.3529411764706	0.647058823529411
109	10	8.5	1.5
110	9	8.5	0.5
111	12	10.5555555555556	1.44444444444444
112	15	12.2857142857143	2.71428571428571
113	7	10.5555555555556	-3.55555555555556
114	13	14.3529411764706	-1.35294117647059
115	12	14.3529411764706	-2.35294117647059
116	14	14.3529411764706	-0.352941176470589
117	14	14.3529411764706	-0.352941176470589
118	8	10.5555555555556	-2.55555555555556
119	15	14.3529411764706	0.647058823529411
120	12	10	2
121	12	10	2
122	16	14.3529411764706	1.64705882352941
123	9	8.5	0.5
124	15	12.2857142857143	2.71428571428571
125	15	14.3529411764706	0.647058823529411
126	6	12.2857142857143	-6.28571428571429
127	14	14.3529411764706	-0.352941176470589
128	15	12.2857142857143	2.71428571428571
129	10	10.5555555555556	-0.555555555555555
130	6	8.5	-2.5
131	14	14.3529411764706	-0.352941176470589
132	12	14.3529411764706	-2.35294117647059
133	8	8.5	-0.5
134	11	12.2857142857143	-1.28571428571429
135	13	14.3529411764706	-1.35294117647059
136	9	10	-1
137	15	14.3529411764706	0.647058823529411
138	13	8.5	4.5
139	15	14.3529411764706	0.647058823529411
140	14	12.2857142857143	1.71428571428571
141	16	10	6
142	14	12.2857142857143	1.71428571428571
143	14	10	4
144	10	8.5	1.5
145	10	10	0
146	4	10.5555555555556	-6.55555555555556
147	8	10.5555555555556	-2.55555555555556
148	15	10.5555555555556	4.44444444444444
149	16	14.3529411764706	1.64705882352941
150	12	14.3529411764706	-2.35294117647059
151	12	12.2857142857143	-0.285714285714286
152	15	14.3529411764706	0.647058823529411
153	9	8.5	0.5
154	12	14.3529411764706	-2.35294117647059
155	14	14.3529411764706	-0.352941176470589
156	11	10	1

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 14 & 10 & 4 \tabularnewline
2 & 8 & 12.2857142857143 & -4.28571428571429 \tabularnewline
3 & 12 & 14.3529411764706 & -2.35294117647059 \tabularnewline
4 & 7 & 12.2857142857143 & -5.28571428571429 \tabularnewline
5 & 10 & 10.5555555555556 & -0.555555555555555 \tabularnewline
6 & 7 & 10 & -3 \tabularnewline
7 & 16 & 14.3529411764706 & 1.64705882352941 \tabularnewline
8 & 11 & 8.5 & 2.5 \tabularnewline
9 & 14 & 10 & 4 \tabularnewline
10 & 6 & 8.5 & -2.5 \tabularnewline
11 & 16 & 10.5555555555556 & 5.44444444444444 \tabularnewline
12 & 11 & 10.5555555555556 & 0.444444444444445 \tabularnewline
13 & 16 & 14.3529411764706 & 1.64705882352941 \tabularnewline
14 & 12 & 8.5 & 3.5 \tabularnewline
15 & 7 & 10.5555555555556 & -3.55555555555556 \tabularnewline
16 & 13 & 8.5 & 4.5 \tabularnewline
17 & 11 & 10.5555555555556 & 0.444444444444445 \tabularnewline
18 & 15 & 14.3529411764706 & 0.647058823529411 \tabularnewline
19 & 7 & 8.5 & -1.5 \tabularnewline
20 & 9 & 8.5 & 0.5 \tabularnewline
21 & 7 & 8.5 & -1.5 \tabularnewline
22 & 14 & 14.3529411764706 & -0.352941176470589 \tabularnewline
23 & 15 & 10.5555555555556 & 4.44444444444444 \tabularnewline
24 & 7 & 10 & -3 \tabularnewline
25 & 15 & 14.3529411764706 & 0.647058823529411 \tabularnewline
26 & 17 & 14.3529411764706 & 2.64705882352941 \tabularnewline
27 & 15 & 10.5555555555556 & 4.44444444444444 \tabularnewline
28 & 14 & 12.2857142857143 & 1.71428571428571 \tabularnewline
29 & 14 & 12.2857142857143 & 1.71428571428571 \tabularnewline
30 & 8 & 10 & -2 \tabularnewline
31 & 8 & 10 & -2 \tabularnewline
32 & 14 & 12.2857142857143 & 1.71428571428571 \tabularnewline
33 & 14 & 14.3529411764706 & -0.352941176470589 \tabularnewline
34 & 8 & 8.5 & -0.5 \tabularnewline
35 & 11 & 8.5 & 2.5 \tabularnewline
36 & 16 & 14.3529411764706 & 1.64705882352941 \tabularnewline
37 & 10 & 12.2857142857143 & -2.28571428571429 \tabularnewline
38 & 8 & 10.5555555555556 & -2.55555555555556 \tabularnewline
39 & 14 & 12.2857142857143 & 1.71428571428571 \tabularnewline
40 & 16 & 14.3529411764706 & 1.64705882352941 \tabularnewline
41 & 13 & 14.3529411764706 & -1.35294117647059 \tabularnewline
42 & 5 & 10 & -5 \tabularnewline
43 & 8 & 8.5 & -0.5 \tabularnewline
44 & 10 & 8.5 & 1.5 \tabularnewline
45 & 8 & 10.5555555555556 & -2.55555555555556 \tabularnewline
46 & 13 & 14.3529411764706 & -1.35294117647059 \tabularnewline
47 & 15 & 14.3529411764706 & 0.647058823529411 \tabularnewline
48 & 6 & 8.5 & -2.5 \tabularnewline
49 & 12 & 10.5555555555556 & 1.44444444444444 \tabularnewline
50 & 16 & 12.2857142857143 & 3.71428571428571 \tabularnewline
51 & 5 & 10.5555555555556 & -5.55555555555556 \tabularnewline
52 & 15 & 10.5555555555556 & 4.44444444444444 \tabularnewline
53 & 12 & 12.2857142857143 & -0.285714285714286 \tabularnewline
54 & 8 & 8.5 & -0.5 \tabularnewline
55 & 13 & 14.3529411764706 & -1.35294117647059 \tabularnewline
56 & 14 & 12.2857142857143 & 1.71428571428571 \tabularnewline
57 & 12 & 8.5 & 3.5 \tabularnewline
58 & 16 & 14.3529411764706 & 1.64705882352941 \tabularnewline
59 & 10 & 8.5 & 1.5 \tabularnewline
60 & 15 & 14.3529411764706 & 0.647058823529411 \tabularnewline
61 & 8 & 8.5 & -0.5 \tabularnewline
62 & 16 & 14.3529411764706 & 1.64705882352941 \tabularnewline
63 & 19 & 14.3529411764706 & 4.64705882352941 \tabularnewline
64 & 14 & 14.3529411764706 & -0.352941176470589 \tabularnewline
65 & 6 & 10.5555555555556 & -4.55555555555556 \tabularnewline
66 & 13 & 14.3529411764706 & -1.35294117647059 \tabularnewline
67 & 15 & 10.5555555555556 & 4.44444444444444 \tabularnewline
68 & 7 & 12.2857142857143 & -5.28571428571429 \tabularnewline
69 & 13 & 14.3529411764706 & -1.35294117647059 \tabularnewline
70 & 4 & 8.5 & -4.5 \tabularnewline
71 & 14 & 14.3529411764706 & -0.352941176470589 \tabularnewline
72 & 13 & 12.2857142857143 & 0.714285714285714 \tabularnewline
73 & 11 & 10.5555555555556 & 0.444444444444445 \tabularnewline
74 & 14 & 10.5555555555556 & 3.44444444444444 \tabularnewline
75 & 12 & 12.2857142857143 & -0.285714285714286 \tabularnewline
76 & 15 & 12.2857142857143 & 2.71428571428571 \tabularnewline
77 & 14 & 12.2857142857143 & 1.71428571428571 \tabularnewline
78 & 13 & 12.2857142857143 & 0.714285714285714 \tabularnewline
79 & 8 & 10 & -2 \tabularnewline
80 & 6 & 8.5 & -2.5 \tabularnewline
81 & 7 & 8.5 & -1.5 \tabularnewline
82 & 13 & 14.3529411764706 & -1.35294117647059 \tabularnewline
83 & 13 & 14.3529411764706 & -1.35294117647059 \tabularnewline
84 & 11 & 10.5555555555556 & 0.444444444444445 \tabularnewline
85 & 5 & 10 & -5 \tabularnewline
86 & 12 & 12.2857142857143 & -0.285714285714286 \tabularnewline
87 & 8 & 8.5 & -0.5 \tabularnewline
88 & 11 & 12.2857142857143 & -1.28571428571429 \tabularnewline
89 & 14 & 14.3529411764706 & -0.352941176470589 \tabularnewline
90 & 9 & 8.5 & 0.5 \tabularnewline
91 & 10 & 14.3529411764706 & -4.35294117647059 \tabularnewline
92 & 13 & 10.5555555555556 & 2.44444444444444 \tabularnewline
93 & 16 & 14.3529411764706 & 1.64705882352941 \tabularnewline
94 & 16 & 14.3529411764706 & 1.64705882352941 \tabularnewline
95 & 11 & 12.2857142857143 & -1.28571428571429 \tabularnewline
96 & 8 & 8.5 & -0.5 \tabularnewline
97 & 4 & 10.5555555555556 & -6.55555555555556 \tabularnewline
98 & 7 & 8.5 & -1.5 \tabularnewline
99 & 14 & 10.5555555555556 & 3.44444444444444 \tabularnewline
100 & 11 & 12.2857142857143 & -1.28571428571429 \tabularnewline
101 & 17 & 14.3529411764706 & 2.64705882352941 \tabularnewline
102 & 15 & 14.3529411764706 & 0.647058823529411 \tabularnewline
103 & 17 & 14.3529411764706 & 2.64705882352941 \tabularnewline
104 & 5 & 8.5 & -3.5 \tabularnewline
105 & 4 & 8.5 & -4.5 \tabularnewline
106 & 10 & 14.3529411764706 & -4.35294117647059 \tabularnewline
107 & 11 & 8.5 & 2.5 \tabularnewline
108 & 15 & 14.3529411764706 & 0.647058823529411 \tabularnewline
109 & 10 & 8.5 & 1.5 \tabularnewline
110 & 9 & 8.5 & 0.5 \tabularnewline
111 & 12 & 10.5555555555556 & 1.44444444444444 \tabularnewline
112 & 15 & 12.2857142857143 & 2.71428571428571 \tabularnewline
113 & 7 & 10.5555555555556 & -3.55555555555556 \tabularnewline
114 & 13 & 14.3529411764706 & -1.35294117647059 \tabularnewline
115 & 12 & 14.3529411764706 & -2.35294117647059 \tabularnewline
116 & 14 & 14.3529411764706 & -0.352941176470589 \tabularnewline
117 & 14 & 14.3529411764706 & -0.352941176470589 \tabularnewline
118 & 8 & 10.5555555555556 & -2.55555555555556 \tabularnewline
119 & 15 & 14.3529411764706 & 0.647058823529411 \tabularnewline
120 & 12 & 10 & 2 \tabularnewline
121 & 12 & 10 & 2 \tabularnewline
122 & 16 & 14.3529411764706 & 1.64705882352941 \tabularnewline
123 & 9 & 8.5 & 0.5 \tabularnewline
124 & 15 & 12.2857142857143 & 2.71428571428571 \tabularnewline
125 & 15 & 14.3529411764706 & 0.647058823529411 \tabularnewline
126 & 6 & 12.2857142857143 & -6.28571428571429 \tabularnewline
127 & 14 & 14.3529411764706 & -0.352941176470589 \tabularnewline
128 & 15 & 12.2857142857143 & 2.71428571428571 \tabularnewline
129 & 10 & 10.5555555555556 & -0.555555555555555 \tabularnewline
130 & 6 & 8.5 & -2.5 \tabularnewline
131 & 14 & 14.3529411764706 & -0.352941176470589 \tabularnewline
132 & 12 & 14.3529411764706 & -2.35294117647059 \tabularnewline
133 & 8 & 8.5 & -0.5 \tabularnewline
134 & 11 & 12.2857142857143 & -1.28571428571429 \tabularnewline
135 & 13 & 14.3529411764706 & -1.35294117647059 \tabularnewline
136 & 9 & 10 & -1 \tabularnewline
137 & 15 & 14.3529411764706 & 0.647058823529411 \tabularnewline
138 & 13 & 8.5 & 4.5 \tabularnewline
139 & 15 & 14.3529411764706 & 0.647058823529411 \tabularnewline
140 & 14 & 12.2857142857143 & 1.71428571428571 \tabularnewline
141 & 16 & 10 & 6 \tabularnewline
142 & 14 & 12.2857142857143 & 1.71428571428571 \tabularnewline
143 & 14 & 10 & 4 \tabularnewline
144 & 10 & 8.5 & 1.5 \tabularnewline
145 & 10 & 10 & 0 \tabularnewline
146 & 4 & 10.5555555555556 & -6.55555555555556 \tabularnewline
147 & 8 & 10.5555555555556 & -2.55555555555556 \tabularnewline
148 & 15 & 10.5555555555556 & 4.44444444444444 \tabularnewline
149 & 16 & 14.3529411764706 & 1.64705882352941 \tabularnewline
150 & 12 & 14.3529411764706 & -2.35294117647059 \tabularnewline
151 & 12 & 12.2857142857143 & -0.285714285714286 \tabularnewline
152 & 15 & 14.3529411764706 & 0.647058823529411 \tabularnewline
153 & 9 & 8.5 & 0.5 \tabularnewline
154 & 12 & 14.3529411764706 & -2.35294117647059 \tabularnewline
155 & 14 & 14.3529411764706 & -0.352941176470589 \tabularnewline
156 & 11 & 10 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153555&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]14[/C][C]10[/C][C]4[/C][/ROW]
[ROW][C]2[/C][C]8[/C][C]12.2857142857143[/C][C]-4.28571428571429[/C][/ROW]
[ROW][C]3[/C][C]12[/C][C]14.3529411764706[/C][C]-2.35294117647059[/C][/ROW]
[ROW][C]4[/C][C]7[/C][C]12.2857142857143[/C][C]-5.28571428571429[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]10.5555555555556[/C][C]-0.555555555555555[/C][/ROW]
[ROW][C]6[/C][C]7[/C][C]10[/C][C]-3[/C][/ROW]
[ROW][C]7[/C][C]16[/C][C]14.3529411764706[/C][C]1.64705882352941[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]8.5[/C][C]2.5[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]10[/C][C]4[/C][/ROW]
[ROW][C]10[/C][C]6[/C][C]8.5[/C][C]-2.5[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]10.5555555555556[/C][C]5.44444444444444[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]10.5555555555556[/C][C]0.444444444444445[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]14.3529411764706[/C][C]1.64705882352941[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]8.5[/C][C]3.5[/C][/ROW]
[ROW][C]15[/C][C]7[/C][C]10.5555555555556[/C][C]-3.55555555555556[/C][/ROW]
[ROW][C]16[/C][C]13[/C][C]8.5[/C][C]4.5[/C][/ROW]
[ROW][C]17[/C][C]11[/C][C]10.5555555555556[/C][C]0.444444444444445[/C][/ROW]
[ROW][C]18[/C][C]15[/C][C]14.3529411764706[/C][C]0.647058823529411[/C][/ROW]
[ROW][C]19[/C][C]7[/C][C]8.5[/C][C]-1.5[/C][/ROW]
[ROW][C]20[/C][C]9[/C][C]8.5[/C][C]0.5[/C][/ROW]
[ROW][C]21[/C][C]7[/C][C]8.5[/C][C]-1.5[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]14.3529411764706[/C][C]-0.352941176470589[/C][/ROW]
[ROW][C]23[/C][C]15[/C][C]10.5555555555556[/C][C]4.44444444444444[/C][/ROW]
[ROW][C]24[/C][C]7[/C][C]10[/C][C]-3[/C][/ROW]
[ROW][C]25[/C][C]15[/C][C]14.3529411764706[/C][C]0.647058823529411[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]14.3529411764706[/C][C]2.64705882352941[/C][/ROW]
[ROW][C]27[/C][C]15[/C][C]10.5555555555556[/C][C]4.44444444444444[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]12.2857142857143[/C][C]1.71428571428571[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]12.2857142857143[/C][C]1.71428571428571[/C][/ROW]
[ROW][C]30[/C][C]8[/C][C]10[/C][C]-2[/C][/ROW]
[ROW][C]31[/C][C]8[/C][C]10[/C][C]-2[/C][/ROW]
[ROW][C]32[/C][C]14[/C][C]12.2857142857143[/C][C]1.71428571428571[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.3529411764706[/C][C]-0.352941176470589[/C][/ROW]
[ROW][C]34[/C][C]8[/C][C]8.5[/C][C]-0.5[/C][/ROW]
[ROW][C]35[/C][C]11[/C][C]8.5[/C][C]2.5[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]14.3529411764706[/C][C]1.64705882352941[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]12.2857142857143[/C][C]-2.28571428571429[/C][/ROW]
[ROW][C]38[/C][C]8[/C][C]10.5555555555556[/C][C]-2.55555555555556[/C][/ROW]
[ROW][C]39[/C][C]14[/C][C]12.2857142857143[/C][C]1.71428571428571[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.3529411764706[/C][C]1.64705882352941[/C][/ROW]
[ROW][C]41[/C][C]13[/C][C]14.3529411764706[/C][C]-1.35294117647059[/C][/ROW]
[ROW][C]42[/C][C]5[/C][C]10[/C][C]-5[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]8.5[/C][C]-0.5[/C][/ROW]
[ROW][C]44[/C][C]10[/C][C]8.5[/C][C]1.5[/C][/ROW]
[ROW][C]45[/C][C]8[/C][C]10.5555555555556[/C][C]-2.55555555555556[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]14.3529411764706[/C][C]-1.35294117647059[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]14.3529411764706[/C][C]0.647058823529411[/C][/ROW]
[ROW][C]48[/C][C]6[/C][C]8.5[/C][C]-2.5[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]10.5555555555556[/C][C]1.44444444444444[/C][/ROW]
[ROW][C]50[/C][C]16[/C][C]12.2857142857143[/C][C]3.71428571428571[/C][/ROW]
[ROW][C]51[/C][C]5[/C][C]10.5555555555556[/C][C]-5.55555555555556[/C][/ROW]
[ROW][C]52[/C][C]15[/C][C]10.5555555555556[/C][C]4.44444444444444[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]12.2857142857143[/C][C]-0.285714285714286[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]8.5[/C][C]-0.5[/C][/ROW]
[ROW][C]55[/C][C]13[/C][C]14.3529411764706[/C][C]-1.35294117647059[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.2857142857143[/C][C]1.71428571428571[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]8.5[/C][C]3.5[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.3529411764706[/C][C]1.64705882352941[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]8.5[/C][C]1.5[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]14.3529411764706[/C][C]0.647058823529411[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]8.5[/C][C]-0.5[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]14.3529411764706[/C][C]1.64705882352941[/C][/ROW]
[ROW][C]63[/C][C]19[/C][C]14.3529411764706[/C][C]4.64705882352941[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.3529411764706[/C][C]-0.352941176470589[/C][/ROW]
[ROW][C]65[/C][C]6[/C][C]10.5555555555556[/C][C]-4.55555555555556[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]14.3529411764706[/C][C]-1.35294117647059[/C][/ROW]
[ROW][C]67[/C][C]15[/C][C]10.5555555555556[/C][C]4.44444444444444[/C][/ROW]
[ROW][C]68[/C][C]7[/C][C]12.2857142857143[/C][C]-5.28571428571429[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]14.3529411764706[/C][C]-1.35294117647059[/C][/ROW]
[ROW][C]70[/C][C]4[/C][C]8.5[/C][C]-4.5[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]14.3529411764706[/C][C]-0.352941176470589[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]12.2857142857143[/C][C]0.714285714285714[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]10.5555555555556[/C][C]0.444444444444445[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]10.5555555555556[/C][C]3.44444444444444[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]12.2857142857143[/C][C]-0.285714285714286[/C][/ROW]
[ROW][C]76[/C][C]15[/C][C]12.2857142857143[/C][C]2.71428571428571[/C][/ROW]
[ROW][C]77[/C][C]14[/C][C]12.2857142857143[/C][C]1.71428571428571[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]12.2857142857143[/C][C]0.714285714285714[/C][/ROW]
[ROW][C]79[/C][C]8[/C][C]10[/C][C]-2[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]8.5[/C][C]-2.5[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]8.5[/C][C]-1.5[/C][/ROW]
[ROW][C]82[/C][C]13[/C][C]14.3529411764706[/C][C]-1.35294117647059[/C][/ROW]
[ROW][C]83[/C][C]13[/C][C]14.3529411764706[/C][C]-1.35294117647059[/C][/ROW]
[ROW][C]84[/C][C]11[/C][C]10.5555555555556[/C][C]0.444444444444445[/C][/ROW]
[ROW][C]85[/C][C]5[/C][C]10[/C][C]-5[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]12.2857142857143[/C][C]-0.285714285714286[/C][/ROW]
[ROW][C]87[/C][C]8[/C][C]8.5[/C][C]-0.5[/C][/ROW]
[ROW][C]88[/C][C]11[/C][C]12.2857142857143[/C][C]-1.28571428571429[/C][/ROW]
[ROW][C]89[/C][C]14[/C][C]14.3529411764706[/C][C]-0.352941176470589[/C][/ROW]
[ROW][C]90[/C][C]9[/C][C]8.5[/C][C]0.5[/C][/ROW]
[ROW][C]91[/C][C]10[/C][C]14.3529411764706[/C][C]-4.35294117647059[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]10.5555555555556[/C][C]2.44444444444444[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]14.3529411764706[/C][C]1.64705882352941[/C][/ROW]
[ROW][C]94[/C][C]16[/C][C]14.3529411764706[/C][C]1.64705882352941[/C][/ROW]
[ROW][C]95[/C][C]11[/C][C]12.2857142857143[/C][C]-1.28571428571429[/C][/ROW]
[ROW][C]96[/C][C]8[/C][C]8.5[/C][C]-0.5[/C][/ROW]
[ROW][C]97[/C][C]4[/C][C]10.5555555555556[/C][C]-6.55555555555556[/C][/ROW]
[ROW][C]98[/C][C]7[/C][C]8.5[/C][C]-1.5[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]10.5555555555556[/C][C]3.44444444444444[/C][/ROW]
[ROW][C]100[/C][C]11[/C][C]12.2857142857143[/C][C]-1.28571428571429[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]14.3529411764706[/C][C]2.64705882352941[/C][/ROW]
[ROW][C]102[/C][C]15[/C][C]14.3529411764706[/C][C]0.647058823529411[/C][/ROW]
[ROW][C]103[/C][C]17[/C][C]14.3529411764706[/C][C]2.64705882352941[/C][/ROW]
[ROW][C]104[/C][C]5[/C][C]8.5[/C][C]-3.5[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]8.5[/C][C]-4.5[/C][/ROW]
[ROW][C]106[/C][C]10[/C][C]14.3529411764706[/C][C]-4.35294117647059[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]8.5[/C][C]2.5[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]14.3529411764706[/C][C]0.647058823529411[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]8.5[/C][C]1.5[/C][/ROW]
[ROW][C]110[/C][C]9[/C][C]8.5[/C][C]0.5[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]10.5555555555556[/C][C]1.44444444444444[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]12.2857142857143[/C][C]2.71428571428571[/C][/ROW]
[ROW][C]113[/C][C]7[/C][C]10.5555555555556[/C][C]-3.55555555555556[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.3529411764706[/C][C]-1.35294117647059[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.3529411764706[/C][C]-2.35294117647059[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]14.3529411764706[/C][C]-0.352941176470589[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]14.3529411764706[/C][C]-0.352941176470589[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]10.5555555555556[/C][C]-2.55555555555556[/C][/ROW]
[ROW][C]119[/C][C]15[/C][C]14.3529411764706[/C][C]0.647058823529411[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]10[/C][C]2[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]10[/C][C]2[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]14.3529411764706[/C][C]1.64705882352941[/C][/ROW]
[ROW][C]123[/C][C]9[/C][C]8.5[/C][C]0.5[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]12.2857142857143[/C][C]2.71428571428571[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]14.3529411764706[/C][C]0.647058823529411[/C][/ROW]
[ROW][C]126[/C][C]6[/C][C]12.2857142857143[/C][C]-6.28571428571429[/C][/ROW]
[ROW][C]127[/C][C]14[/C][C]14.3529411764706[/C][C]-0.352941176470589[/C][/ROW]
[ROW][C]128[/C][C]15[/C][C]12.2857142857143[/C][C]2.71428571428571[/C][/ROW]
[ROW][C]129[/C][C]10[/C][C]10.5555555555556[/C][C]-0.555555555555555[/C][/ROW]
[ROW][C]130[/C][C]6[/C][C]8.5[/C][C]-2.5[/C][/ROW]
[ROW][C]131[/C][C]14[/C][C]14.3529411764706[/C][C]-0.352941176470589[/C][/ROW]
[ROW][C]132[/C][C]12[/C][C]14.3529411764706[/C][C]-2.35294117647059[/C][/ROW]
[ROW][C]133[/C][C]8[/C][C]8.5[/C][C]-0.5[/C][/ROW]
[ROW][C]134[/C][C]11[/C][C]12.2857142857143[/C][C]-1.28571428571429[/C][/ROW]
[ROW][C]135[/C][C]13[/C][C]14.3529411764706[/C][C]-1.35294117647059[/C][/ROW]
[ROW][C]136[/C][C]9[/C][C]10[/C][C]-1[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]14.3529411764706[/C][C]0.647058823529411[/C][/ROW]
[ROW][C]138[/C][C]13[/C][C]8.5[/C][C]4.5[/C][/ROW]
[ROW][C]139[/C][C]15[/C][C]14.3529411764706[/C][C]0.647058823529411[/C][/ROW]
[ROW][C]140[/C][C]14[/C][C]12.2857142857143[/C][C]1.71428571428571[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]10[/C][C]6[/C][/ROW]
[ROW][C]142[/C][C]14[/C][C]12.2857142857143[/C][C]1.71428571428571[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]10[/C][C]4[/C][/ROW]
[ROW][C]144[/C][C]10[/C][C]8.5[/C][C]1.5[/C][/ROW]
[ROW][C]145[/C][C]10[/C][C]10[/C][C]0[/C][/ROW]
[ROW][C]146[/C][C]4[/C][C]10.5555555555556[/C][C]-6.55555555555556[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]10.5555555555556[/C][C]-2.55555555555556[/C][/ROW]
[ROW][C]148[/C][C]15[/C][C]10.5555555555556[/C][C]4.44444444444444[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]14.3529411764706[/C][C]1.64705882352941[/C][/ROW]
[ROW][C]150[/C][C]12[/C][C]14.3529411764706[/C][C]-2.35294117647059[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]12.2857142857143[/C][C]-0.285714285714286[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]14.3529411764706[/C][C]0.647058823529411[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]8.5[/C][C]0.5[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]14.3529411764706[/C][C]-2.35294117647059[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]14.3529411764706[/C][C]-0.352941176470589[/C][/ROW]
[ROW][C]156[/C][C]11[/C][C]10[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153555&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153555&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	14	10	4
2	8	12.2857142857143	-4.28571428571429
3	12	14.3529411764706	-2.35294117647059
4	7	12.2857142857143	-5.28571428571429
5	10	10.5555555555556	-0.555555555555555
6	7	10	-3
7	16	14.3529411764706	1.64705882352941
8	11	8.5	2.5
9	14	10	4
10	6	8.5	-2.5
11	16	10.5555555555556	5.44444444444444
12	11	10.5555555555556	0.444444444444445
13	16	14.3529411764706	1.64705882352941
14	12	8.5	3.5
15	7	10.5555555555556	-3.55555555555556
16	13	8.5	4.5
17	11	10.5555555555556	0.444444444444445
18	15	14.3529411764706	0.647058823529411
19	7	8.5	-1.5
20	9	8.5	0.5
21	7	8.5	-1.5
22	14	14.3529411764706	-0.352941176470589
23	15	10.5555555555556	4.44444444444444
24	7	10	-3
25	15	14.3529411764706	0.647058823529411
26	17	14.3529411764706	2.64705882352941
27	15	10.5555555555556	4.44444444444444
28	14	12.2857142857143	1.71428571428571
29	14	12.2857142857143	1.71428571428571
30	8	10	-2
31	8	10	-2
32	14	12.2857142857143	1.71428571428571
33	14	14.3529411764706	-0.352941176470589
34	8	8.5	-0.5
35	11	8.5	2.5
36	16	14.3529411764706	1.64705882352941
37	10	12.2857142857143	-2.28571428571429
38	8	10.5555555555556	-2.55555555555556
39	14	12.2857142857143	1.71428571428571
40	16	14.3529411764706	1.64705882352941
41	13	14.3529411764706	-1.35294117647059
42	5	10	-5
43	8	8.5	-0.5
44	10	8.5	1.5
45	8	10.5555555555556	-2.55555555555556
46	13	14.3529411764706	-1.35294117647059
47	15	14.3529411764706	0.647058823529411
48	6	8.5	-2.5
49	12	10.5555555555556	1.44444444444444
50	16	12.2857142857143	3.71428571428571
51	5	10.5555555555556	-5.55555555555556
52	15	10.5555555555556	4.44444444444444
53	12	12.2857142857143	-0.285714285714286
54	8	8.5	-0.5
55	13	14.3529411764706	-1.35294117647059
56	14	12.2857142857143	1.71428571428571
57	12	8.5	3.5
58	16	14.3529411764706	1.64705882352941
59	10	8.5	1.5
60	15	14.3529411764706	0.647058823529411
61	8	8.5	-0.5
62	16	14.3529411764706	1.64705882352941
63	19	14.3529411764706	4.64705882352941
64	14	14.3529411764706	-0.352941176470589
65	6	10.5555555555556	-4.55555555555556
66	13	14.3529411764706	-1.35294117647059
67	15	10.5555555555556	4.44444444444444
68	7	12.2857142857143	-5.28571428571429
69	13	14.3529411764706	-1.35294117647059
70	4	8.5	-4.5
71	14	14.3529411764706	-0.352941176470589
72	13	12.2857142857143	0.714285714285714
73	11	10.5555555555556	0.444444444444445
74	14	10.5555555555556	3.44444444444444
75	12	12.2857142857143	-0.285714285714286
76	15	12.2857142857143	2.71428571428571
77	14	12.2857142857143	1.71428571428571
78	13	12.2857142857143	0.714285714285714
79	8	10	-2
80	6	8.5	-2.5
81	7	8.5	-1.5
82	13	14.3529411764706	-1.35294117647059
83	13	14.3529411764706	-1.35294117647059
84	11	10.5555555555556	0.444444444444445
85	5	10	-5
86	12	12.2857142857143	-0.285714285714286
87	8	8.5	-0.5
88	11	12.2857142857143	-1.28571428571429
89	14	14.3529411764706	-0.352941176470589
90	9	8.5	0.5
91	10	14.3529411764706	-4.35294117647059
92	13	10.5555555555556	2.44444444444444
93	16	14.3529411764706	1.64705882352941
94	16	14.3529411764706	1.64705882352941
95	11	12.2857142857143	-1.28571428571429
96	8	8.5	-0.5
97	4	10.5555555555556	-6.55555555555556
98	7	8.5	-1.5
99	14	10.5555555555556	3.44444444444444
100	11	12.2857142857143	-1.28571428571429
101	17	14.3529411764706	2.64705882352941
102	15	14.3529411764706	0.647058823529411
103	17	14.3529411764706	2.64705882352941
104	5	8.5	-3.5
105	4	8.5	-4.5
106	10	14.3529411764706	-4.35294117647059
107	11	8.5	2.5
108	15	14.3529411764706	0.647058823529411
109	10	8.5	1.5
110	9	8.5	0.5
111	12	10.5555555555556	1.44444444444444
112	15	12.2857142857143	2.71428571428571
113	7	10.5555555555556	-3.55555555555556
114	13	14.3529411764706	-1.35294117647059
115	12	14.3529411764706	-2.35294117647059
116	14	14.3529411764706	-0.352941176470589
117	14	14.3529411764706	-0.352941176470589
118	8	10.5555555555556	-2.55555555555556
119	15	14.3529411764706	0.647058823529411
120	12	10	2
121	12	10	2
122	16	14.3529411764706	1.64705882352941
123	9	8.5	0.5
124	15	12.2857142857143	2.71428571428571
125	15	14.3529411764706	0.647058823529411
126	6	12.2857142857143	-6.28571428571429
127	14	14.3529411764706	-0.352941176470589
128	15	12.2857142857143	2.71428571428571
129	10	10.5555555555556	-0.555555555555555
130	6	8.5	-2.5
131	14	14.3529411764706	-0.352941176470589
132	12	14.3529411764706	-2.35294117647059
133	8	8.5	-0.5
134	11	12.2857142857143	-1.28571428571429
135	13	14.3529411764706	-1.35294117647059
136	9	10	-1
137	15	14.3529411764706	0.647058823529411
138	13	8.5	4.5
139	15	14.3529411764706	0.647058823529411
140	14	12.2857142857143	1.71428571428571
141	16	10	6
142	14	12.2857142857143	1.71428571428571
143	14	10	4
144	10	8.5	1.5
145	10	10	0
146	4	10.5555555555556	-6.55555555555556
147	8	10.5555555555556	-2.55555555555556
148	15	10.5555555555556	4.44444444444444
149	16	14.3529411764706	1.64705882352941
150	12	14.3529411764706	-2.35294117647059
151	12	12.2857142857143	-0.285714285714286
152	15	14.3529411764706	0.647058823529411
153	9	8.5	0.5
154	12	14.3529411764706	-2.35294117647059
155	14	14.3529411764706	-0.352941176470589
156	11	10	1

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

Parameters (R input):

par1 = 3 ; par2 = none ; par3 = 4 ; 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