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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

Fri, 09 Dec 2011 14:18:30 -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/09/t1323458340y4c5p3gb7fzqsal.htm/, Retrieved Thu, 02 May 2024 23:38:37 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=153437, Retrieved Thu, 02 May 2024 23:38:37 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

102

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]
- R PD  [Recursive Partitioning (Regression Trees)] [Workshop 10 Recur...] [2011-12-09 18:45:00] [de8512d9b386046939a89973b76869e3]
- R  D      [Recursive Partitioning (Regression Trees)] [Workshop 10 Recur...] [2011-12-09 19:18:30] [5c44e6aad476a1bab98fc6774eca4c08] [Current]
- R           [Recursive Partitioning (Regression Trees)] [Workshop 10 Recur...] [2011-12-09 19:22:17] [de8512d9b386046939a89973b76869e3] 
- R  D          [Recursive Partitioning (Regression Trees)] [Workshop 10 Recur...] [2011-12-09 19:32:47] [de8512d9b386046939a89973b76869e3] 
- R               [Recursive Partitioning (Regression Trees)] [Workshop 10 Recur...] [2011-12-09 19:38:35] [de8512d9b386046939a89973b76869e3] 
-  MP               [Recursive Partitioning (Regression Trees)] [Paper SHW Recursi...] [2011-12-16 14:43:06] [74be16979710d4c4e7c6647856088456] 
- RMP               [Multiple Regression] [Paper SHW  MLR 2] [2011-12-16 14:52:16] [de8512d9b386046939a89973b76869e3] 
- R P               [Recursive Partitioning (Regression Trees)] [Paper SHW Recursi...] [2011-12-16 14:58:20] [de8512d9b386046939a89973b76869e3] 
- RMP             [Recursive Partitioning (Regression Trees)] [Paper SHW Recursi...] [2011-12-16 14:42:20] [74be16979710d4c4e7c6647856088456] 
-  MP           [Recursive Partitioning (Regression Trees)] [Paper SHW Recursi...] [2011-12-16 14:41:22] [74be16979710d4c4e7c6647856088456] 
- RMP         [Recursive Partitioning (Regression Trees)] [Paper SHW Recursi...] [2011-12-16 14:40:37] [74be16979710d4c4e7c6647856088456] 

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Dataseries X:

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Histogram

Boxplots

0	21	7	5	13	18	70
1	22	8	8	11	15	58
0	22	9	8	12	19	57
0	21	11	6	12	23	71
0	20	8	5	14	14	64
0	21	10	6	16	17	67
0	21	8	6	16	16	63
0	22	8	8	14	13	64
0	21	9	7	12	27	64
0	21	9	9	16	20	53
1	21	13	10	12	9	62
1	21	12	12	13	15	0
1	23	10	12	12	13	60
1	21	10	9	13	14	65
1	22	10	6	10	12	65
1	21	12	10	12	12	65
1	25	12	6	15	19	54
1	23	14	10	12	15	63
0	22	14	12	12	13	57
0	21	14	11	13	14	65
0	21	15	11	11	12	63
0	21	11	6	14	18	59
0	21	11	10	10	12	58
0	21	15	10	10	18	62
1	21	14	11	13	12	59
1	21	15	7	14	22	69
1	21	14	11	12	14	63
1	22	14	12	12	11	67
1	21	13	10	0	11	58
1	21	12	7	13	12	54
0	21	15	7	15	25	65
0	21	16	12	13	10	54
0	21	17	14	11	9	63
0	21	15	11	12	15	61
0	24	14	12	12	12	63
0	21	14	12	10	16	60
0	22	14	9	12	11	58
0	21	16	11	12	20	67
0	21	18	14	16	16	60
0	22	16	12	13	15	54
0	21	17	12	15	16	67
0	23	14	12	12	13	69
0	20	15	11	14	11	60
1	22	13	11	12	17	60
1	21	16	13	12	13	67
1	23	13	10	11	13	58
1	26	17	12	12	9	56
1	21	15	12	12	14	62
1	23	16	10	13	12	62
1	22	12	10	13	11	71
1	21	16	14	10	0	61
1	21	16	13	11	18	68
1	22	14	11	12	12	61
1	22	17	12	15	16	70
1	21	15	12	12	13	54
1	21	15	12	12	12	65
1	26	16	15	16	17	58
1	22	16	12	13	11	64
1	21	12	11	13	19	68
1	24	16	6	13	12	57
1	22	16	12	13	14	69
1	24	15	14	11	15	70
1	22	16	12	14	17	65
1	23	16	12	17	13	54
1	23	19	14	12	10	62
1	21	14	12	10	11	64
1	21	13	14	12	19	64
1	22	15	11	14	17	63
1	21	16	12	13	12	54
1	22	14	10	13	19	68
1	22	13	10	12	21	65
1	21	16	12	11	12	70
1	25	16	12	11	12	60
0	22	16	12	15	26	54
0	24	17	11	14	19	64
0	22	17	13	16	11	69
0	21	14	12	13	18	63
1	22	20	15	12	13	70
1	23	19	15	11	11	54
1	24	17	14	14	19	58

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'AstonUniversity' @ aston.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 & 3 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153437&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153437&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153437&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	3 seconds
R Server	'AstonUniversity' @ aston.wessa.net

Goodness of Fit
Correlation	0.7482
R-squared	0.5598
RMSE	1.8662

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.7482 \tabularnewline
R-squared & 0.5598 \tabularnewline
RMSE & 1.8662 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153437&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.7482[/C][/ROW]
[ROW][C]R-squared[/C][C]0.5598[/C][/ROW]
[ROW][C]RMSE[/C][C]1.8662[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153437&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153437&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	0.7482
R-squared	0.5598
RMSE	1.8662

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	7	10.6315789473684	-3.63157894736842
2	8	10.6315789473684	-2.63157894736842
3	9	10.6315789473684	-1.63157894736842
4	11	10.6315789473684	0.368421052631579
5	8	10.6315789473684	-2.63157894736842
6	10	10.6315789473684	-0.631578947368421
7	8	10.6315789473684	-2.63157894736842
8	8	10.6315789473684	-2.63157894736842
9	9	10.6315789473684	-1.63157894736842
10	9	10.6315789473684	-1.63157894736842
11	13	13.2727272727273	-0.272727272727273
12	12	14.8648648648649	-2.86486486486486
13	10	14.8648648648649	-4.86486486486486
14	10	10.6315789473684	-0.631578947368421
15	10	10.6315789473684	-0.631578947368421
16	12	13.2727272727273	-1.27272727272727
17	12	10.6315789473684	1.36842105263158
18	14	13.2727272727273	0.727272727272727
19	14	14.8648648648649	-0.864864864864865
20	14	14.8648648648649	-0.864864864864865
21	15	14.8648648648649	0.135135135135135
22	11	10.6315789473684	0.368421052631579
23	11	13.2727272727273	-2.27272727272727
24	15	13.2727272727273	1.72727272727273
25	14	14.8648648648649	-0.864864864864865
26	15	10.6315789473684	4.36842105263158
27	14	14.8648648648649	-0.864864864864865
28	14	14.8648648648649	-0.864864864864865
29	13	13.2727272727273	-0.272727272727273
30	12	10.6315789473684	1.36842105263158
31	15	10.6315789473684	4.36842105263158
32	16	14.8648648648649	1.13513513513514
33	17	16.8461538461538	0.153846153846153
34	15	14.8648648648649	0.135135135135135
35	14	14.8648648648649	-0.864864864864865
36	14	14.8648648648649	-0.864864864864865
37	14	10.6315789473684	3.36842105263158
38	16	14.8648648648649	1.13513513513514
39	18	16.8461538461538	1.15384615384615
40	16	14.8648648648649	1.13513513513514
41	17	14.8648648648649	2.13513513513514
42	14	14.8648648648649	-0.864864864864865
43	15	14.8648648648649	0.135135135135135
44	13	14.8648648648649	-1.86486486486486
45	16	16.8461538461538	-0.846153846153847
46	13	13.2727272727273	-0.272727272727273
47	17	14.8648648648649	2.13513513513514
48	15	14.8648648648649	0.135135135135135
49	16	13.2727272727273	2.72727272727273
50	12	13.2727272727273	-1.27272727272727
51	16	16.8461538461538	-0.846153846153847
52	16	16.8461538461538	-0.846153846153847
53	14	14.8648648648649	-0.864864864864865
54	17	14.8648648648649	2.13513513513514
55	15	14.8648648648649	0.135135135135135
56	15	14.8648648648649	0.135135135135135
57	16	16.8461538461538	-0.846153846153847
58	16	14.8648648648649	1.13513513513514
59	12	14.8648648648649	-2.86486486486486
60	16	10.6315789473684	5.36842105263158
61	16	14.8648648648649	1.13513513513514
62	15	16.8461538461538	-1.84615384615385
63	16	14.8648648648649	1.13513513513514
64	16	14.8648648648649	1.13513513513514
65	19	16.8461538461538	2.15384615384615
66	14	14.8648648648649	-0.864864864864865
67	13	16.8461538461538	-3.84615384615385
68	15	14.8648648648649	0.135135135135135
69	16	14.8648648648649	1.13513513513514
70	14	13.2727272727273	0.727272727272727
71	13	13.2727272727273	-0.272727272727273
72	16	14.8648648648649	1.13513513513514
73	16	14.8648648648649	1.13513513513514
74	16	14.8648648648649	1.13513513513514
75	17	14.8648648648649	2.13513513513514
76	17	16.8461538461538	0.153846153846153
77	14	14.8648648648649	-0.864864864864865
78	20	16.8461538461538	3.15384615384615
79	19	16.8461538461538	2.15384615384615
80	17	16.8461538461538	0.153846153846153

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 7 & 10.6315789473684 & -3.63157894736842 \tabularnewline
2 & 8 & 10.6315789473684 & -2.63157894736842 \tabularnewline
3 & 9 & 10.6315789473684 & -1.63157894736842 \tabularnewline
4 & 11 & 10.6315789473684 & 0.368421052631579 \tabularnewline
5 & 8 & 10.6315789473684 & -2.63157894736842 \tabularnewline
6 & 10 & 10.6315789473684 & -0.631578947368421 \tabularnewline
7 & 8 & 10.6315789473684 & -2.63157894736842 \tabularnewline
8 & 8 & 10.6315789473684 & -2.63157894736842 \tabularnewline
9 & 9 & 10.6315789473684 & -1.63157894736842 \tabularnewline
10 & 9 & 10.6315789473684 & -1.63157894736842 \tabularnewline
11 & 13 & 13.2727272727273 & -0.272727272727273 \tabularnewline
12 & 12 & 14.8648648648649 & -2.86486486486486 \tabularnewline
13 & 10 & 14.8648648648649 & -4.86486486486486 \tabularnewline
14 & 10 & 10.6315789473684 & -0.631578947368421 \tabularnewline
15 & 10 & 10.6315789473684 & -0.631578947368421 \tabularnewline
16 & 12 & 13.2727272727273 & -1.27272727272727 \tabularnewline
17 & 12 & 10.6315789473684 & 1.36842105263158 \tabularnewline
18 & 14 & 13.2727272727273 & 0.727272727272727 \tabularnewline
19 & 14 & 14.8648648648649 & -0.864864864864865 \tabularnewline
20 & 14 & 14.8648648648649 & -0.864864864864865 \tabularnewline
21 & 15 & 14.8648648648649 & 0.135135135135135 \tabularnewline
22 & 11 & 10.6315789473684 & 0.368421052631579 \tabularnewline
23 & 11 & 13.2727272727273 & -2.27272727272727 \tabularnewline
24 & 15 & 13.2727272727273 & 1.72727272727273 \tabularnewline
25 & 14 & 14.8648648648649 & -0.864864864864865 \tabularnewline
26 & 15 & 10.6315789473684 & 4.36842105263158 \tabularnewline
27 & 14 & 14.8648648648649 & -0.864864864864865 \tabularnewline
28 & 14 & 14.8648648648649 & -0.864864864864865 \tabularnewline
29 & 13 & 13.2727272727273 & -0.272727272727273 \tabularnewline
30 & 12 & 10.6315789473684 & 1.36842105263158 \tabularnewline
31 & 15 & 10.6315789473684 & 4.36842105263158 \tabularnewline
32 & 16 & 14.8648648648649 & 1.13513513513514 \tabularnewline
33 & 17 & 16.8461538461538 & 0.153846153846153 \tabularnewline
34 & 15 & 14.8648648648649 & 0.135135135135135 \tabularnewline
35 & 14 & 14.8648648648649 & -0.864864864864865 \tabularnewline
36 & 14 & 14.8648648648649 & -0.864864864864865 \tabularnewline
37 & 14 & 10.6315789473684 & 3.36842105263158 \tabularnewline
38 & 16 & 14.8648648648649 & 1.13513513513514 \tabularnewline
39 & 18 & 16.8461538461538 & 1.15384615384615 \tabularnewline
40 & 16 & 14.8648648648649 & 1.13513513513514 \tabularnewline
41 & 17 & 14.8648648648649 & 2.13513513513514 \tabularnewline
42 & 14 & 14.8648648648649 & -0.864864864864865 \tabularnewline
43 & 15 & 14.8648648648649 & 0.135135135135135 \tabularnewline
44 & 13 & 14.8648648648649 & -1.86486486486486 \tabularnewline
45 & 16 & 16.8461538461538 & -0.846153846153847 \tabularnewline
46 & 13 & 13.2727272727273 & -0.272727272727273 \tabularnewline
47 & 17 & 14.8648648648649 & 2.13513513513514 \tabularnewline
48 & 15 & 14.8648648648649 & 0.135135135135135 \tabularnewline
49 & 16 & 13.2727272727273 & 2.72727272727273 \tabularnewline
50 & 12 & 13.2727272727273 & -1.27272727272727 \tabularnewline
51 & 16 & 16.8461538461538 & -0.846153846153847 \tabularnewline
52 & 16 & 16.8461538461538 & -0.846153846153847 \tabularnewline
53 & 14 & 14.8648648648649 & -0.864864864864865 \tabularnewline
54 & 17 & 14.8648648648649 & 2.13513513513514 \tabularnewline
55 & 15 & 14.8648648648649 & 0.135135135135135 \tabularnewline
56 & 15 & 14.8648648648649 & 0.135135135135135 \tabularnewline
57 & 16 & 16.8461538461538 & -0.846153846153847 \tabularnewline
58 & 16 & 14.8648648648649 & 1.13513513513514 \tabularnewline
59 & 12 & 14.8648648648649 & -2.86486486486486 \tabularnewline
60 & 16 & 10.6315789473684 & 5.36842105263158 \tabularnewline
61 & 16 & 14.8648648648649 & 1.13513513513514 \tabularnewline
62 & 15 & 16.8461538461538 & -1.84615384615385 \tabularnewline
63 & 16 & 14.8648648648649 & 1.13513513513514 \tabularnewline
64 & 16 & 14.8648648648649 & 1.13513513513514 \tabularnewline
65 & 19 & 16.8461538461538 & 2.15384615384615 \tabularnewline
66 & 14 & 14.8648648648649 & -0.864864864864865 \tabularnewline
67 & 13 & 16.8461538461538 & -3.84615384615385 \tabularnewline
68 & 15 & 14.8648648648649 & 0.135135135135135 \tabularnewline
69 & 16 & 14.8648648648649 & 1.13513513513514 \tabularnewline
70 & 14 & 13.2727272727273 & 0.727272727272727 \tabularnewline
71 & 13 & 13.2727272727273 & -0.272727272727273 \tabularnewline
72 & 16 & 14.8648648648649 & 1.13513513513514 \tabularnewline
73 & 16 & 14.8648648648649 & 1.13513513513514 \tabularnewline
74 & 16 & 14.8648648648649 & 1.13513513513514 \tabularnewline
75 & 17 & 14.8648648648649 & 2.13513513513514 \tabularnewline
76 & 17 & 16.8461538461538 & 0.153846153846153 \tabularnewline
77 & 14 & 14.8648648648649 & -0.864864864864865 \tabularnewline
78 & 20 & 16.8461538461538 & 3.15384615384615 \tabularnewline
79 & 19 & 16.8461538461538 & 2.15384615384615 \tabularnewline
80 & 17 & 16.8461538461538 & 0.153846153846153 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153437&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]7[/C][C]10.6315789473684[/C][C]-3.63157894736842[/C][/ROW]
[ROW][C]2[/C][C]8[/C][C]10.6315789473684[/C][C]-2.63157894736842[/C][/ROW]
[ROW][C]3[/C][C]9[/C][C]10.6315789473684[/C][C]-1.63157894736842[/C][/ROW]
[ROW][C]4[/C][C]11[/C][C]10.6315789473684[/C][C]0.368421052631579[/C][/ROW]
[ROW][C]5[/C][C]8[/C][C]10.6315789473684[/C][C]-2.63157894736842[/C][/ROW]
[ROW][C]6[/C][C]10[/C][C]10.6315789473684[/C][C]-0.631578947368421[/C][/ROW]
[ROW][C]7[/C][C]8[/C][C]10.6315789473684[/C][C]-2.63157894736842[/C][/ROW]
[ROW][C]8[/C][C]8[/C][C]10.6315789473684[/C][C]-2.63157894736842[/C][/ROW]
[ROW][C]9[/C][C]9[/C][C]10.6315789473684[/C][C]-1.63157894736842[/C][/ROW]
[ROW][C]10[/C][C]9[/C][C]10.6315789473684[/C][C]-1.63157894736842[/C][/ROW]
[ROW][C]11[/C][C]13[/C][C]13.2727272727273[/C][C]-0.272727272727273[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]14.8648648648649[/C][C]-2.86486486486486[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]14.8648648648649[/C][C]-4.86486486486486[/C][/ROW]
[ROW][C]14[/C][C]10[/C][C]10.6315789473684[/C][C]-0.631578947368421[/C][/ROW]
[ROW][C]15[/C][C]10[/C][C]10.6315789473684[/C][C]-0.631578947368421[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]13.2727272727273[/C][C]-1.27272727272727[/C][/ROW]
[ROW][C]17[/C][C]12[/C][C]10.6315789473684[/C][C]1.36842105263158[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]13.2727272727273[/C][C]0.727272727272727[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]14.8648648648649[/C][C]-0.864864864864865[/C][/ROW]
[ROW][C]20[/C][C]14[/C][C]14.8648648648649[/C][C]-0.864864864864865[/C][/ROW]
[ROW][C]21[/C][C]15[/C][C]14.8648648648649[/C][C]0.135135135135135[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]10.6315789473684[/C][C]0.368421052631579[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]13.2727272727273[/C][C]-2.27272727272727[/C][/ROW]
[ROW][C]24[/C][C]15[/C][C]13.2727272727273[/C][C]1.72727272727273[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]14.8648648648649[/C][C]-0.864864864864865[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]10.6315789473684[/C][C]4.36842105263158[/C][/ROW]
[ROW][C]27[/C][C]14[/C][C]14.8648648648649[/C][C]-0.864864864864865[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]14.8648648648649[/C][C]-0.864864864864865[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]13.2727272727273[/C][C]-0.272727272727273[/C][/ROW]
[ROW][C]30[/C][C]12[/C][C]10.6315789473684[/C][C]1.36842105263158[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]10.6315789473684[/C][C]4.36842105263158[/C][/ROW]
[ROW][C]32[/C][C]16[/C][C]14.8648648648649[/C][C]1.13513513513514[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]16.8461538461538[/C][C]0.153846153846153[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]14.8648648648649[/C][C]0.135135135135135[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]14.8648648648649[/C][C]-0.864864864864865[/C][/ROW]
[ROW][C]36[/C][C]14[/C][C]14.8648648648649[/C][C]-0.864864864864865[/C][/ROW]
[ROW][C]37[/C][C]14[/C][C]10.6315789473684[/C][C]3.36842105263158[/C][/ROW]
[ROW][C]38[/C][C]16[/C][C]14.8648648648649[/C][C]1.13513513513514[/C][/ROW]
[ROW][C]39[/C][C]18[/C][C]16.8461538461538[/C][C]1.15384615384615[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.8648648648649[/C][C]1.13513513513514[/C][/ROW]
[ROW][C]41[/C][C]17[/C][C]14.8648648648649[/C][C]2.13513513513514[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]14.8648648648649[/C][C]-0.864864864864865[/C][/ROW]
[ROW][C]43[/C][C]15[/C][C]14.8648648648649[/C][C]0.135135135135135[/C][/ROW]
[ROW][C]44[/C][C]13[/C][C]14.8648648648649[/C][C]-1.86486486486486[/C][/ROW]
[ROW][C]45[/C][C]16[/C][C]16.8461538461538[/C][C]-0.846153846153847[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]13.2727272727273[/C][C]-0.272727272727273[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]14.8648648648649[/C][C]2.13513513513514[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.8648648648649[/C][C]0.135135135135135[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]13.2727272727273[/C][C]2.72727272727273[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]13.2727272727273[/C][C]-1.27272727272727[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.8461538461538[/C][C]-0.846153846153847[/C][/ROW]
[ROW][C]52[/C][C]16[/C][C]16.8461538461538[/C][C]-0.846153846153847[/C][/ROW]
[ROW][C]53[/C][C]14[/C][C]14.8648648648649[/C][C]-0.864864864864865[/C][/ROW]
[ROW][C]54[/C][C]17[/C][C]14.8648648648649[/C][C]2.13513513513514[/C][/ROW]
[ROW][C]55[/C][C]15[/C][C]14.8648648648649[/C][C]0.135135135135135[/C][/ROW]
[ROW][C]56[/C][C]15[/C][C]14.8648648648649[/C][C]0.135135135135135[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.8461538461538[/C][C]-0.846153846153847[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.8648648648649[/C][C]1.13513513513514[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]14.8648648648649[/C][C]-2.86486486486486[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]10.6315789473684[/C][C]5.36842105263158[/C][/ROW]
[ROW][C]61[/C][C]16[/C][C]14.8648648648649[/C][C]1.13513513513514[/C][/ROW]
[ROW][C]62[/C][C]15[/C][C]16.8461538461538[/C][C]-1.84615384615385[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]14.8648648648649[/C][C]1.13513513513514[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]14.8648648648649[/C][C]1.13513513513514[/C][/ROW]
[ROW][C]65[/C][C]19[/C][C]16.8461538461538[/C][C]2.15384615384615[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]14.8648648648649[/C][C]-0.864864864864865[/C][/ROW]
[ROW][C]67[/C][C]13[/C][C]16.8461538461538[/C][C]-3.84615384615385[/C][/ROW]
[ROW][C]68[/C][C]15[/C][C]14.8648648648649[/C][C]0.135135135135135[/C][/ROW]
[ROW][C]69[/C][C]16[/C][C]14.8648648648649[/C][C]1.13513513513514[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]13.2727272727273[/C][C]0.727272727272727[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]13.2727272727273[/C][C]-0.272727272727273[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]14.8648648648649[/C][C]1.13513513513514[/C][/ROW]
[ROW][C]73[/C][C]16[/C][C]14.8648648648649[/C][C]1.13513513513514[/C][/ROW]
[ROW][C]74[/C][C]16[/C][C]14.8648648648649[/C][C]1.13513513513514[/C][/ROW]
[ROW][C]75[/C][C]17[/C][C]14.8648648648649[/C][C]2.13513513513514[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]16.8461538461538[/C][C]0.153846153846153[/C][/ROW]
[ROW][C]77[/C][C]14[/C][C]14.8648648648649[/C][C]-0.864864864864865[/C][/ROW]
[ROW][C]78[/C][C]20[/C][C]16.8461538461538[/C][C]3.15384615384615[/C][/ROW]
[ROW][C]79[/C][C]19[/C][C]16.8461538461538[/C][C]2.15384615384615[/C][/ROW]
[ROW][C]80[/C][C]17[/C][C]16.8461538461538[/C][C]0.153846153846153[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153437&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153437&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	7	10.6315789473684	-3.63157894736842
2	8	10.6315789473684	-2.63157894736842
3	9	10.6315789473684	-1.63157894736842
4	11	10.6315789473684	0.368421052631579
5	8	10.6315789473684	-2.63157894736842
6	10	10.6315789473684	-0.631578947368421
7	8	10.6315789473684	-2.63157894736842
8	8	10.6315789473684	-2.63157894736842
9	9	10.6315789473684	-1.63157894736842
10	9	10.6315789473684	-1.63157894736842
11	13	13.2727272727273	-0.272727272727273
12	12	14.8648648648649	-2.86486486486486
13	10	14.8648648648649	-4.86486486486486
14	10	10.6315789473684	-0.631578947368421
15	10	10.6315789473684	-0.631578947368421
16	12	13.2727272727273	-1.27272727272727
17	12	10.6315789473684	1.36842105263158
18	14	13.2727272727273	0.727272727272727
19	14	14.8648648648649	-0.864864864864865
20	14	14.8648648648649	-0.864864864864865
21	15	14.8648648648649	0.135135135135135
22	11	10.6315789473684	0.368421052631579
23	11	13.2727272727273	-2.27272727272727
24	15	13.2727272727273	1.72727272727273
25	14	14.8648648648649	-0.864864864864865
26	15	10.6315789473684	4.36842105263158
27	14	14.8648648648649	-0.864864864864865
28	14	14.8648648648649	-0.864864864864865
29	13	13.2727272727273	-0.272727272727273
30	12	10.6315789473684	1.36842105263158
31	15	10.6315789473684	4.36842105263158
32	16	14.8648648648649	1.13513513513514
33	17	16.8461538461538	0.153846153846153
34	15	14.8648648648649	0.135135135135135
35	14	14.8648648648649	-0.864864864864865
36	14	14.8648648648649	-0.864864864864865
37	14	10.6315789473684	3.36842105263158
38	16	14.8648648648649	1.13513513513514
39	18	16.8461538461538	1.15384615384615
40	16	14.8648648648649	1.13513513513514
41	17	14.8648648648649	2.13513513513514
42	14	14.8648648648649	-0.864864864864865
43	15	14.8648648648649	0.135135135135135
44	13	14.8648648648649	-1.86486486486486
45	16	16.8461538461538	-0.846153846153847
46	13	13.2727272727273	-0.272727272727273
47	17	14.8648648648649	2.13513513513514
48	15	14.8648648648649	0.135135135135135
49	16	13.2727272727273	2.72727272727273
50	12	13.2727272727273	-1.27272727272727
51	16	16.8461538461538	-0.846153846153847
52	16	16.8461538461538	-0.846153846153847
53	14	14.8648648648649	-0.864864864864865
54	17	14.8648648648649	2.13513513513514
55	15	14.8648648648649	0.135135135135135
56	15	14.8648648648649	0.135135135135135
57	16	16.8461538461538	-0.846153846153847
58	16	14.8648648648649	1.13513513513514
59	12	14.8648648648649	-2.86486486486486
60	16	10.6315789473684	5.36842105263158
61	16	14.8648648648649	1.13513513513514
62	15	16.8461538461538	-1.84615384615385
63	16	14.8648648648649	1.13513513513514
64	16	14.8648648648649	1.13513513513514
65	19	16.8461538461538	2.15384615384615
66	14	14.8648648648649	-0.864864864864865
67	13	16.8461538461538	-3.84615384615385
68	15	14.8648648648649	0.135135135135135
69	16	14.8648648648649	1.13513513513514
70	14	13.2727272727273	0.727272727272727
71	13	13.2727272727273	-0.272727272727273
72	16	14.8648648648649	1.13513513513514
73	16	14.8648648648649	1.13513513513514
74	16	14.8648648648649	1.13513513513514
75	17	14.8648648648649	2.13513513513514
76	17	16.8461538461538	0.153846153846153
77	14	14.8648648648649	-0.864864864864865
78	20	16.8461538461538	3.15384615384615
79	19	16.8461538461538	2.15384615384615
80	17	16.8461538461538	0.153846153846153

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 = pearson ; par2 = equal ; par3 = 2 ; par4 = no ;

Parameters (R input):

par1 = 3 ; 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