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R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Mon, 20 Dec 2010 12:17:02 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/20/t12928473210ect6thmlbwen03.htm/, Retrieved Sat, 04 May 2024 02:51:51 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112867, Retrieved Sat, 04 May 2024 02:51:51 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

186

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

-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
-   PD  [Recursive Partitioning (Regression Trees)] [WS 10 - recursive...] [2010-12-11 16:07:41] [033eb2749a430605d9b2be7c4aac4a0c]
-   PD      [Recursive Partitioning (Regression Trees)] [paper - RP no cat...] [2010-12-20 12:17:02] [a948b7c78e10e31abd3f68e640bbd8ba] [Current]
-   P         [Recursive Partitioning (Regression Trees)] [paper - RP - geen...] [2010-12-21 08:28:20] [033eb2749a430605d9b2be7c4aac4a0c] 
-   P         [Recursive Partitioning (Regression Trees)] [paper - RP - nieu...] [2010-12-21 08:31:35] [033eb2749a430605d9b2be7c4aac4a0c] 
-   P         [Recursive Partitioning (Regression Trees)] [paper - RP - pers...] [2010-12-21 08:35:54] [033eb2749a430605d9b2be7c4aac4a0c] 
-   P         [Recursive Partitioning (Regression Trees)] [paper - RP met ca...] [2010-12-21 08:59:24] [033eb2749a430605d9b2be7c4aac4a0c] 
-   P         [Recursive Partitioning (Regression Trees)] [paper - RP met ca...] [2010-12-21 09:14:23] [033eb2749a430605d9b2be7c4aac4a0c] 
-   P         [Recursive Partitioning (Regression Trees)] [paper - RP met ca...] [2010-12-21 09:27:57] [033eb2749a430605d9b2be7c4aac4a0c] 
-   P         [Recursive Partitioning (Regression Trees)] [paper - RP met ca...] [2010-12-21 09:35:54] [033eb2749a430605d9b2be7c4aac4a0c] 
-   P         [Recursive Partitioning (Regression Trees)] [Paper - crossvali...] [2010-12-21 09:59:23] [033eb2749a430605d9b2be7c4aac4a0c] 
-   P         [Recursive Partitioning (Regression Trees)] [Paper - crossvali...] [2010-12-21 11:02:35] [033eb2749a430605d9b2be7c4aac4a0c] 
-   P         [Recursive Partitioning (Regression Trees)] [Paper - crossvali...] [2010-12-21 11:38:52] [033eb2749a430605d9b2be7c4aac4a0c] 
-   P         [Recursive Partitioning (Regression Trees)] [Paper - crossvali...] [2010-12-21 11:38:52] [033eb2749a430605d9b2be7c4aac4a0c] 

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Dataseries X:

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Histogram

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46	11	52	26	23
44	8	39	25	15
42	10	42	28	25
41	12	35	30	18
48	12	32	28	21
49	10	49	40	19
51	8	33	28	15
47	10	47	27	22
49	11	46	25	19
46	7	40	27	20
51	10	33	32	26
54	9	39	28	26
52	9	37	21	21
52	11	56	40	18
45	12	36	29	19
52	5	24	27	19
56	10	56	31	18
54	11	32	33	19
50	12	41	28	24
35	9	24	26	28
48	3	42	25	20
37	10	47	37	27
47	7	25	13	18
31	9	33	32	19
45	9	43	32	24
47	10	45	38	21
44	9	44	30	22
30	19	46	33	25
40	14	31	22	19
44	5	31	29	15
43	13	42	33	34
51	7	28	31	23
48	8	38	23	19
55	11	59	42	26
48	11	43	35	15
53	12	29	31	15
49	9	38	31	17
44	13	39	38	30
45	12	50	34	19
40	11	44	33	28
44	18	29	23	23
41	8	29	18	23
46	14	36	33	21
47	10	43	26	18
48	13	28	29	19
43	13	39	23	24
46	8	35	18	15
53	10	43	36	20
33	8	28	21	24
47	9	49	31	9
43	10	33	31	20
45	9	39	29	20
49	9	36	24	10
45	9	24	35	44
37	10	47	37	20
42	8	34	29	20
43	11	33	31	11
44	11	43	34	21
39	10	41	38	21
37	23	40	27	19
53	9	39	33	17
48	12	54	36	16
47	9	43	27	14
49	9	45	33	19
47	8	29	24	21
56	9	45	31	16
51	9	47	31	19
43	9	38	23	19
51	11	52	38	16
36	12	34	30	24
55	8	56	39	29
33	9	26	28	21
42	10	42	39	20
43	8	32	19	23
44	9	39	32	18
47	9	37	32	19
43	13	37	35	23
47	11	52	42	19
41	18	31	25	21
53	10	34	11	26
47	14	38	31	13
23	7	29	30	23
43	10	52	30	17
47	9	40	31	30
47	9	47	28	19
49	12	34	34	22
50	8	37	32	14
43	9	43	30	14
44	8	37	27	21
49	13	55	36	21
47	6	36	32	33
39	11	28	27	23
49	10	47	35	30
41	10	38	34	19
40	14	37	32	21
38	13	32	28	25
43	10	47	29	18
55	8	40	18	25
46	10	45	34	21
54	8	37	35	16
47	10	38	34	17
35	7	37	26	23
41	11	35	30	26
53	10	50	35	18
44	8	32	17	19
48	12	32	34	28
49	12	38	30	20
39	11	31	31	29
45	11	27	25	19
34	6	34	16	18
46	14	43	35	24
45	9	28	28	12
53	11	44	42	19
51	10	43	30	25
45	10	53	37	12
50	8	33	26	15
41	9	36	28	25
44	10	46	33	14
43	10	36	29	19
42	12	24	21	23
48	10	50	38	19
45	11	40	18	24
48	16	40	38	20
48	12	32	30	16
53	10	49	35	13
45	13	47	34	20
45	8	28	21	30
50	12	41	30	18
48	10	25	32	22
41	8	46	23	21
53	14	53	31	25
40	9	34	26	18
49	12	40	29	25
46	10	46	28	44
48	9	38	29	12
43	10	51	36	17
53	11	38	36	26
51	11	45	31	18
41	10	41	30	21
45	10	42	29	24
44	20	36	35	20
43	10	41	26	24
34	8	35	25	28
38	8	42	25	20
40	9	35	20	33
48	18	32	27	19

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\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 & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112867&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112867&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112867&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	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Goodness of Fit
Correlation	0.2715
R-squared	0.0737
RMSE	5.4265

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.2715 \tabularnewline
R-squared & 0.0737 \tabularnewline
RMSE & 5.4265 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112867&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.2715[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0737[/C][/ROW]
[ROW][C]RMSE[/C][C]5.4265[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112867&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112867&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	0.2715
R-squared	0.0737
RMSE	5.4265

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	46	49.3684210526316	-3.36842105263158
2	44	44.8188976377953	-0.818897637795274
3	42	44.8188976377953	-2.81889763779527
4	41	44.8188976377953	-3.81889763779527
5	48	44.8188976377953	3.18110236220473
6	49	49.3684210526316	-0.368421052631582
7	51	44.8188976377953	6.18110236220473
8	47	44.8188976377953	2.18110236220473
9	49	44.8188976377953	4.18110236220473
10	46	44.8188976377953	1.18110236220473
11	51	44.8188976377953	6.18110236220473
12	54	44.8188976377953	9.18110236220473
13	52	44.8188976377953	7.18110236220473
14	52	49.3684210526316	2.63157894736842
15	45	44.8188976377953	0.181102362204726
16	52	44.8188976377953	7.18110236220473
17	56	49.3684210526316	6.63157894736842
18	54	44.8188976377953	9.18110236220473
19	50	44.8188976377953	5.18110236220473
20	35	44.8188976377953	-9.81889763779527
21	48	44.8188976377953	3.18110236220473
22	37	44.8188976377953	-7.81889763779527
23	47	44.8188976377953	2.18110236220473
24	31	44.8188976377953	-13.8188976377953
25	45	44.8188976377953	0.181102362204726
26	47	44.8188976377953	2.18110236220473
27	44	44.8188976377953	-0.818897637795274
28	30	44.8188976377953	-14.8188976377953
29	40	44.8188976377953	-4.81889763779527
30	44	44.8188976377953	-0.818897637795274
31	43	44.8188976377953	-1.81889763779527
32	51	44.8188976377953	6.18110236220473
33	48	44.8188976377953	3.18110236220473
34	55	49.3684210526316	5.63157894736842
35	48	44.8188976377953	3.18110236220473
36	53	44.8188976377953	8.18110236220473
37	49	44.8188976377953	4.18110236220473
38	44	44.8188976377953	-0.818897637795274
39	45	49.3684210526316	-4.36842105263158
40	40	44.8188976377953	-4.81889763779527
41	44	44.8188976377953	-0.818897637795274
42	41	44.8188976377953	-3.81889763779527
43	46	44.8188976377953	1.18110236220473
44	47	44.8188976377953	2.18110236220473
45	48	44.8188976377953	3.18110236220473
46	43	44.8188976377953	-1.81889763779527
47	46	44.8188976377953	1.18110236220473
48	53	44.8188976377953	8.18110236220473
49	33	44.8188976377953	-11.8188976377953
50	47	49.3684210526316	-2.36842105263158
51	43	44.8188976377953	-1.81889763779527
52	45	44.8188976377953	0.181102362204726
53	49	44.8188976377953	4.18110236220473
54	45	44.8188976377953	0.181102362204726
55	37	44.8188976377953	-7.81889763779527
56	42	44.8188976377953	-2.81889763779527
57	43	44.8188976377953	-1.81889763779527
58	44	44.8188976377953	-0.818897637795274
59	39	44.8188976377953	-5.81889763779527
60	37	44.8188976377953	-7.81889763779527
61	53	44.8188976377953	8.18110236220473
62	48	49.3684210526316	-1.36842105263158
63	47	44.8188976377953	2.18110236220473
64	49	44.8188976377953	4.18110236220473
65	47	44.8188976377953	2.18110236220473
66	56	44.8188976377953	11.1811023622047
67	51	44.8188976377953	6.18110236220473
68	43	44.8188976377953	-1.81889763779527
69	51	49.3684210526316	1.63157894736842
70	36	44.8188976377953	-8.81889763779527
71	55	49.3684210526316	5.63157894736842
72	33	44.8188976377953	-11.8188976377953
73	42	44.8188976377953	-2.81889763779527
74	43	44.8188976377953	-1.81889763779527
75	44	44.8188976377953	-0.818897637795274
76	47	44.8188976377953	2.18110236220473
77	43	44.8188976377953	-1.81889763779527
78	47	49.3684210526316	-2.36842105263158
79	41	44.8188976377953	-3.81889763779527
80	53	44.8188976377953	8.18110236220473
81	47	44.8188976377953	2.18110236220473
82	23	44.8188976377953	-21.8188976377953
83	43	49.3684210526316	-6.36842105263158
84	47	44.8188976377953	2.18110236220473
85	47	44.8188976377953	2.18110236220473
86	49	44.8188976377953	4.18110236220473
87	50	44.8188976377953	5.18110236220473
88	43	44.8188976377953	-1.81889763779527
89	44	44.8188976377953	-0.818897637795274
90	49	49.3684210526316	-0.368421052631582
91	47	44.8188976377953	2.18110236220473
92	39	44.8188976377953	-5.81889763779527
93	49	44.8188976377953	4.18110236220473
94	41	44.8188976377953	-3.81889763779527
95	40	44.8188976377953	-4.81889763779527
96	38	44.8188976377953	-6.81889763779527
97	43	44.8188976377953	-1.81889763779527
98	55	44.8188976377953	10.1811023622047
99	46	44.8188976377953	1.18110236220473
100	54	44.8188976377953	9.18110236220473
101	47	44.8188976377953	2.18110236220473
102	35	44.8188976377953	-9.81889763779527
103	41	44.8188976377953	-3.81889763779527
104	53	49.3684210526316	3.63157894736842
105	44	44.8188976377953	-0.818897637795274
106	48	44.8188976377953	3.18110236220473
107	49	44.8188976377953	4.18110236220473
108	39	44.8188976377953	-5.81889763779527
109	45	44.8188976377953	0.181102362204726
110	34	44.8188976377953	-10.8188976377953
111	46	44.8188976377953	1.18110236220473
112	45	44.8188976377953	0.181102362204726
113	53	44.8188976377953	8.18110236220473
114	51	44.8188976377953	6.18110236220473
115	45	49.3684210526316	-4.36842105263158
116	50	44.8188976377953	5.18110236220473
117	41	44.8188976377953	-3.81889763779527
118	44	44.8188976377953	-0.818897637795274
119	43	44.8188976377953	-1.81889763779527
120	42	44.8188976377953	-2.81889763779527
121	48	49.3684210526316	-1.36842105263158
122	45	44.8188976377953	0.181102362204726
123	48	44.8188976377953	3.18110236220473
124	48	44.8188976377953	3.18110236220473
125	53	49.3684210526316	3.63157894736842
126	45	44.8188976377953	0.181102362204726
127	45	44.8188976377953	0.181102362204726
128	50	44.8188976377953	5.18110236220473
129	48	44.8188976377953	3.18110236220473
130	41	44.8188976377953	-3.81889763779527
131	53	49.3684210526316	3.63157894736842
132	40	44.8188976377953	-4.81889763779527
133	49	44.8188976377953	4.18110236220473
134	46	44.8188976377953	1.18110236220473
135	48	44.8188976377953	3.18110236220473
136	43	49.3684210526316	-6.36842105263158
137	53	44.8188976377953	8.18110236220473
138	51	44.8188976377953	6.18110236220473
139	41	44.8188976377953	-3.81889763779527
140	45	44.8188976377953	0.181102362204726
141	44	44.8188976377953	-0.818897637795274
142	43	44.8188976377953	-1.81889763779527
143	34	44.8188976377953	-10.8188976377953
144	38	44.8188976377953	-6.81889763779527
145	40	44.8188976377953	-4.81889763779527
146	48	44.8188976377953	3.18110236220473

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 46 & 49.3684210526316 & -3.36842105263158 \tabularnewline
2 & 44 & 44.8188976377953 & -0.818897637795274 \tabularnewline
3 & 42 & 44.8188976377953 & -2.81889763779527 \tabularnewline
4 & 41 & 44.8188976377953 & -3.81889763779527 \tabularnewline
5 & 48 & 44.8188976377953 & 3.18110236220473 \tabularnewline
6 & 49 & 49.3684210526316 & -0.368421052631582 \tabularnewline
7 & 51 & 44.8188976377953 & 6.18110236220473 \tabularnewline
8 & 47 & 44.8188976377953 & 2.18110236220473 \tabularnewline
9 & 49 & 44.8188976377953 & 4.18110236220473 \tabularnewline
10 & 46 & 44.8188976377953 & 1.18110236220473 \tabularnewline
11 & 51 & 44.8188976377953 & 6.18110236220473 \tabularnewline
12 & 54 & 44.8188976377953 & 9.18110236220473 \tabularnewline
13 & 52 & 44.8188976377953 & 7.18110236220473 \tabularnewline
14 & 52 & 49.3684210526316 & 2.63157894736842 \tabularnewline
15 & 45 & 44.8188976377953 & 0.181102362204726 \tabularnewline
16 & 52 & 44.8188976377953 & 7.18110236220473 \tabularnewline
17 & 56 & 49.3684210526316 & 6.63157894736842 \tabularnewline
18 & 54 & 44.8188976377953 & 9.18110236220473 \tabularnewline
19 & 50 & 44.8188976377953 & 5.18110236220473 \tabularnewline
20 & 35 & 44.8188976377953 & -9.81889763779527 \tabularnewline
21 & 48 & 44.8188976377953 & 3.18110236220473 \tabularnewline
22 & 37 & 44.8188976377953 & -7.81889763779527 \tabularnewline
23 & 47 & 44.8188976377953 & 2.18110236220473 \tabularnewline
24 & 31 & 44.8188976377953 & -13.8188976377953 \tabularnewline
25 & 45 & 44.8188976377953 & 0.181102362204726 \tabularnewline
26 & 47 & 44.8188976377953 & 2.18110236220473 \tabularnewline
27 & 44 & 44.8188976377953 & -0.818897637795274 \tabularnewline
28 & 30 & 44.8188976377953 & -14.8188976377953 \tabularnewline
29 & 40 & 44.8188976377953 & -4.81889763779527 \tabularnewline
30 & 44 & 44.8188976377953 & -0.818897637795274 \tabularnewline
31 & 43 & 44.8188976377953 & -1.81889763779527 \tabularnewline
32 & 51 & 44.8188976377953 & 6.18110236220473 \tabularnewline
33 & 48 & 44.8188976377953 & 3.18110236220473 \tabularnewline
34 & 55 & 49.3684210526316 & 5.63157894736842 \tabularnewline
35 & 48 & 44.8188976377953 & 3.18110236220473 \tabularnewline
36 & 53 & 44.8188976377953 & 8.18110236220473 \tabularnewline
37 & 49 & 44.8188976377953 & 4.18110236220473 \tabularnewline
38 & 44 & 44.8188976377953 & -0.818897637795274 \tabularnewline
39 & 45 & 49.3684210526316 & -4.36842105263158 \tabularnewline
40 & 40 & 44.8188976377953 & -4.81889763779527 \tabularnewline
41 & 44 & 44.8188976377953 & -0.818897637795274 \tabularnewline
42 & 41 & 44.8188976377953 & -3.81889763779527 \tabularnewline
43 & 46 & 44.8188976377953 & 1.18110236220473 \tabularnewline
44 & 47 & 44.8188976377953 & 2.18110236220473 \tabularnewline
45 & 48 & 44.8188976377953 & 3.18110236220473 \tabularnewline
46 & 43 & 44.8188976377953 & -1.81889763779527 \tabularnewline
47 & 46 & 44.8188976377953 & 1.18110236220473 \tabularnewline
48 & 53 & 44.8188976377953 & 8.18110236220473 \tabularnewline
49 & 33 & 44.8188976377953 & -11.8188976377953 \tabularnewline
50 & 47 & 49.3684210526316 & -2.36842105263158 \tabularnewline
51 & 43 & 44.8188976377953 & -1.81889763779527 \tabularnewline
52 & 45 & 44.8188976377953 & 0.181102362204726 \tabularnewline
53 & 49 & 44.8188976377953 & 4.18110236220473 \tabularnewline
54 & 45 & 44.8188976377953 & 0.181102362204726 \tabularnewline
55 & 37 & 44.8188976377953 & -7.81889763779527 \tabularnewline
56 & 42 & 44.8188976377953 & -2.81889763779527 \tabularnewline
57 & 43 & 44.8188976377953 & -1.81889763779527 \tabularnewline
58 & 44 & 44.8188976377953 & -0.818897637795274 \tabularnewline
59 & 39 & 44.8188976377953 & -5.81889763779527 \tabularnewline
60 & 37 & 44.8188976377953 & -7.81889763779527 \tabularnewline
61 & 53 & 44.8188976377953 & 8.18110236220473 \tabularnewline
62 & 48 & 49.3684210526316 & -1.36842105263158 \tabularnewline
63 & 47 & 44.8188976377953 & 2.18110236220473 \tabularnewline
64 & 49 & 44.8188976377953 & 4.18110236220473 \tabularnewline
65 & 47 & 44.8188976377953 & 2.18110236220473 \tabularnewline
66 & 56 & 44.8188976377953 & 11.1811023622047 \tabularnewline
67 & 51 & 44.8188976377953 & 6.18110236220473 \tabularnewline
68 & 43 & 44.8188976377953 & -1.81889763779527 \tabularnewline
69 & 51 & 49.3684210526316 & 1.63157894736842 \tabularnewline
70 & 36 & 44.8188976377953 & -8.81889763779527 \tabularnewline
71 & 55 & 49.3684210526316 & 5.63157894736842 \tabularnewline
72 & 33 & 44.8188976377953 & -11.8188976377953 \tabularnewline
73 & 42 & 44.8188976377953 & -2.81889763779527 \tabularnewline
74 & 43 & 44.8188976377953 & -1.81889763779527 \tabularnewline
75 & 44 & 44.8188976377953 & -0.818897637795274 \tabularnewline
76 & 47 & 44.8188976377953 & 2.18110236220473 \tabularnewline
77 & 43 & 44.8188976377953 & -1.81889763779527 \tabularnewline
78 & 47 & 49.3684210526316 & -2.36842105263158 \tabularnewline
79 & 41 & 44.8188976377953 & -3.81889763779527 \tabularnewline
80 & 53 & 44.8188976377953 & 8.18110236220473 \tabularnewline
81 & 47 & 44.8188976377953 & 2.18110236220473 \tabularnewline
82 & 23 & 44.8188976377953 & -21.8188976377953 \tabularnewline
83 & 43 & 49.3684210526316 & -6.36842105263158 \tabularnewline
84 & 47 & 44.8188976377953 & 2.18110236220473 \tabularnewline
85 & 47 & 44.8188976377953 & 2.18110236220473 \tabularnewline
86 & 49 & 44.8188976377953 & 4.18110236220473 \tabularnewline
87 & 50 & 44.8188976377953 & 5.18110236220473 \tabularnewline
88 & 43 & 44.8188976377953 & -1.81889763779527 \tabularnewline
89 & 44 & 44.8188976377953 & -0.818897637795274 \tabularnewline
90 & 49 & 49.3684210526316 & -0.368421052631582 \tabularnewline
91 & 47 & 44.8188976377953 & 2.18110236220473 \tabularnewline
92 & 39 & 44.8188976377953 & -5.81889763779527 \tabularnewline
93 & 49 & 44.8188976377953 & 4.18110236220473 \tabularnewline
94 & 41 & 44.8188976377953 & -3.81889763779527 \tabularnewline
95 & 40 & 44.8188976377953 & -4.81889763779527 \tabularnewline
96 & 38 & 44.8188976377953 & -6.81889763779527 \tabularnewline
97 & 43 & 44.8188976377953 & -1.81889763779527 \tabularnewline
98 & 55 & 44.8188976377953 & 10.1811023622047 \tabularnewline
99 & 46 & 44.8188976377953 & 1.18110236220473 \tabularnewline
100 & 54 & 44.8188976377953 & 9.18110236220473 \tabularnewline
101 & 47 & 44.8188976377953 & 2.18110236220473 \tabularnewline
102 & 35 & 44.8188976377953 & -9.81889763779527 \tabularnewline
103 & 41 & 44.8188976377953 & -3.81889763779527 \tabularnewline
104 & 53 & 49.3684210526316 & 3.63157894736842 \tabularnewline
105 & 44 & 44.8188976377953 & -0.818897637795274 \tabularnewline
106 & 48 & 44.8188976377953 & 3.18110236220473 \tabularnewline
107 & 49 & 44.8188976377953 & 4.18110236220473 \tabularnewline
108 & 39 & 44.8188976377953 & -5.81889763779527 \tabularnewline
109 & 45 & 44.8188976377953 & 0.181102362204726 \tabularnewline
110 & 34 & 44.8188976377953 & -10.8188976377953 \tabularnewline
111 & 46 & 44.8188976377953 & 1.18110236220473 \tabularnewline
112 & 45 & 44.8188976377953 & 0.181102362204726 \tabularnewline
113 & 53 & 44.8188976377953 & 8.18110236220473 \tabularnewline
114 & 51 & 44.8188976377953 & 6.18110236220473 \tabularnewline
115 & 45 & 49.3684210526316 & -4.36842105263158 \tabularnewline
116 & 50 & 44.8188976377953 & 5.18110236220473 \tabularnewline
117 & 41 & 44.8188976377953 & -3.81889763779527 \tabularnewline
118 & 44 & 44.8188976377953 & -0.818897637795274 \tabularnewline
119 & 43 & 44.8188976377953 & -1.81889763779527 \tabularnewline
120 & 42 & 44.8188976377953 & -2.81889763779527 \tabularnewline
121 & 48 & 49.3684210526316 & -1.36842105263158 \tabularnewline
122 & 45 & 44.8188976377953 & 0.181102362204726 \tabularnewline
123 & 48 & 44.8188976377953 & 3.18110236220473 \tabularnewline
124 & 48 & 44.8188976377953 & 3.18110236220473 \tabularnewline
125 & 53 & 49.3684210526316 & 3.63157894736842 \tabularnewline
126 & 45 & 44.8188976377953 & 0.181102362204726 \tabularnewline
127 & 45 & 44.8188976377953 & 0.181102362204726 \tabularnewline
128 & 50 & 44.8188976377953 & 5.18110236220473 \tabularnewline
129 & 48 & 44.8188976377953 & 3.18110236220473 \tabularnewline
130 & 41 & 44.8188976377953 & -3.81889763779527 \tabularnewline
131 & 53 & 49.3684210526316 & 3.63157894736842 \tabularnewline
132 & 40 & 44.8188976377953 & -4.81889763779527 \tabularnewline
133 & 49 & 44.8188976377953 & 4.18110236220473 \tabularnewline
134 & 46 & 44.8188976377953 & 1.18110236220473 \tabularnewline
135 & 48 & 44.8188976377953 & 3.18110236220473 \tabularnewline
136 & 43 & 49.3684210526316 & -6.36842105263158 \tabularnewline
137 & 53 & 44.8188976377953 & 8.18110236220473 \tabularnewline
138 & 51 & 44.8188976377953 & 6.18110236220473 \tabularnewline
139 & 41 & 44.8188976377953 & -3.81889763779527 \tabularnewline
140 & 45 & 44.8188976377953 & 0.181102362204726 \tabularnewline
141 & 44 & 44.8188976377953 & -0.818897637795274 \tabularnewline
142 & 43 & 44.8188976377953 & -1.81889763779527 \tabularnewline
143 & 34 & 44.8188976377953 & -10.8188976377953 \tabularnewline
144 & 38 & 44.8188976377953 & -6.81889763779527 \tabularnewline
145 & 40 & 44.8188976377953 & -4.81889763779527 \tabularnewline
146 & 48 & 44.8188976377953 & 3.18110236220473 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112867&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]46[/C][C]49.3684210526316[/C][C]-3.36842105263158[/C][/ROW]
[ROW][C]2[/C][C]44[/C][C]44.8188976377953[/C][C]-0.818897637795274[/C][/ROW]
[ROW][C]3[/C][C]42[/C][C]44.8188976377953[/C][C]-2.81889763779527[/C][/ROW]
[ROW][C]4[/C][C]41[/C][C]44.8188976377953[/C][C]-3.81889763779527[/C][/ROW]
[ROW][C]5[/C][C]48[/C][C]44.8188976377953[/C][C]3.18110236220473[/C][/ROW]
[ROW][C]6[/C][C]49[/C][C]49.3684210526316[/C][C]-0.368421052631582[/C][/ROW]
[ROW][C]7[/C][C]51[/C][C]44.8188976377953[/C][C]6.18110236220473[/C][/ROW]
[ROW][C]8[/C][C]47[/C][C]44.8188976377953[/C][C]2.18110236220473[/C][/ROW]
[ROW][C]9[/C][C]49[/C][C]44.8188976377953[/C][C]4.18110236220473[/C][/ROW]
[ROW][C]10[/C][C]46[/C][C]44.8188976377953[/C][C]1.18110236220473[/C][/ROW]
[ROW][C]11[/C][C]51[/C][C]44.8188976377953[/C][C]6.18110236220473[/C][/ROW]
[ROW][C]12[/C][C]54[/C][C]44.8188976377953[/C][C]9.18110236220473[/C][/ROW]
[ROW][C]13[/C][C]52[/C][C]44.8188976377953[/C][C]7.18110236220473[/C][/ROW]
[ROW][C]14[/C][C]52[/C][C]49.3684210526316[/C][C]2.63157894736842[/C][/ROW]
[ROW][C]15[/C][C]45[/C][C]44.8188976377953[/C][C]0.181102362204726[/C][/ROW]
[ROW][C]16[/C][C]52[/C][C]44.8188976377953[/C][C]7.18110236220473[/C][/ROW]
[ROW][C]17[/C][C]56[/C][C]49.3684210526316[/C][C]6.63157894736842[/C][/ROW]
[ROW][C]18[/C][C]54[/C][C]44.8188976377953[/C][C]9.18110236220473[/C][/ROW]
[ROW][C]19[/C][C]50[/C][C]44.8188976377953[/C][C]5.18110236220473[/C][/ROW]
[ROW][C]20[/C][C]35[/C][C]44.8188976377953[/C][C]-9.81889763779527[/C][/ROW]
[ROW][C]21[/C][C]48[/C][C]44.8188976377953[/C][C]3.18110236220473[/C][/ROW]
[ROW][C]22[/C][C]37[/C][C]44.8188976377953[/C][C]-7.81889763779527[/C][/ROW]
[ROW][C]23[/C][C]47[/C][C]44.8188976377953[/C][C]2.18110236220473[/C][/ROW]
[ROW][C]24[/C][C]31[/C][C]44.8188976377953[/C][C]-13.8188976377953[/C][/ROW]
[ROW][C]25[/C][C]45[/C][C]44.8188976377953[/C][C]0.181102362204726[/C][/ROW]
[ROW][C]26[/C][C]47[/C][C]44.8188976377953[/C][C]2.18110236220473[/C][/ROW]
[ROW][C]27[/C][C]44[/C][C]44.8188976377953[/C][C]-0.818897637795274[/C][/ROW]
[ROW][C]28[/C][C]30[/C][C]44.8188976377953[/C][C]-14.8188976377953[/C][/ROW]
[ROW][C]29[/C][C]40[/C][C]44.8188976377953[/C][C]-4.81889763779527[/C][/ROW]
[ROW][C]30[/C][C]44[/C][C]44.8188976377953[/C][C]-0.818897637795274[/C][/ROW]
[ROW][C]31[/C][C]43[/C][C]44.8188976377953[/C][C]-1.81889763779527[/C][/ROW]
[ROW][C]32[/C][C]51[/C][C]44.8188976377953[/C][C]6.18110236220473[/C][/ROW]
[ROW][C]33[/C][C]48[/C][C]44.8188976377953[/C][C]3.18110236220473[/C][/ROW]
[ROW][C]34[/C][C]55[/C][C]49.3684210526316[/C][C]5.63157894736842[/C][/ROW]
[ROW][C]35[/C][C]48[/C][C]44.8188976377953[/C][C]3.18110236220473[/C][/ROW]
[ROW][C]36[/C][C]53[/C][C]44.8188976377953[/C][C]8.18110236220473[/C][/ROW]
[ROW][C]37[/C][C]49[/C][C]44.8188976377953[/C][C]4.18110236220473[/C][/ROW]
[ROW][C]38[/C][C]44[/C][C]44.8188976377953[/C][C]-0.818897637795274[/C][/ROW]
[ROW][C]39[/C][C]45[/C][C]49.3684210526316[/C][C]-4.36842105263158[/C][/ROW]
[ROW][C]40[/C][C]40[/C][C]44.8188976377953[/C][C]-4.81889763779527[/C][/ROW]
[ROW][C]41[/C][C]44[/C][C]44.8188976377953[/C][C]-0.818897637795274[/C][/ROW]
[ROW][C]42[/C][C]41[/C][C]44.8188976377953[/C][C]-3.81889763779527[/C][/ROW]
[ROW][C]43[/C][C]46[/C][C]44.8188976377953[/C][C]1.18110236220473[/C][/ROW]
[ROW][C]44[/C][C]47[/C][C]44.8188976377953[/C][C]2.18110236220473[/C][/ROW]
[ROW][C]45[/C][C]48[/C][C]44.8188976377953[/C][C]3.18110236220473[/C][/ROW]
[ROW][C]46[/C][C]43[/C][C]44.8188976377953[/C][C]-1.81889763779527[/C][/ROW]
[ROW][C]47[/C][C]46[/C][C]44.8188976377953[/C][C]1.18110236220473[/C][/ROW]
[ROW][C]48[/C][C]53[/C][C]44.8188976377953[/C][C]8.18110236220473[/C][/ROW]
[ROW][C]49[/C][C]33[/C][C]44.8188976377953[/C][C]-11.8188976377953[/C][/ROW]
[ROW][C]50[/C][C]47[/C][C]49.3684210526316[/C][C]-2.36842105263158[/C][/ROW]
[ROW][C]51[/C][C]43[/C][C]44.8188976377953[/C][C]-1.81889763779527[/C][/ROW]
[ROW][C]52[/C][C]45[/C][C]44.8188976377953[/C][C]0.181102362204726[/C][/ROW]
[ROW][C]53[/C][C]49[/C][C]44.8188976377953[/C][C]4.18110236220473[/C][/ROW]
[ROW][C]54[/C][C]45[/C][C]44.8188976377953[/C][C]0.181102362204726[/C][/ROW]
[ROW][C]55[/C][C]37[/C][C]44.8188976377953[/C][C]-7.81889763779527[/C][/ROW]
[ROW][C]56[/C][C]42[/C][C]44.8188976377953[/C][C]-2.81889763779527[/C][/ROW]
[ROW][C]57[/C][C]43[/C][C]44.8188976377953[/C][C]-1.81889763779527[/C][/ROW]
[ROW][C]58[/C][C]44[/C][C]44.8188976377953[/C][C]-0.818897637795274[/C][/ROW]
[ROW][C]59[/C][C]39[/C][C]44.8188976377953[/C][C]-5.81889763779527[/C][/ROW]
[ROW][C]60[/C][C]37[/C][C]44.8188976377953[/C][C]-7.81889763779527[/C][/ROW]
[ROW][C]61[/C][C]53[/C][C]44.8188976377953[/C][C]8.18110236220473[/C][/ROW]
[ROW][C]62[/C][C]48[/C][C]49.3684210526316[/C][C]-1.36842105263158[/C][/ROW]
[ROW][C]63[/C][C]47[/C][C]44.8188976377953[/C][C]2.18110236220473[/C][/ROW]
[ROW][C]64[/C][C]49[/C][C]44.8188976377953[/C][C]4.18110236220473[/C][/ROW]
[ROW][C]65[/C][C]47[/C][C]44.8188976377953[/C][C]2.18110236220473[/C][/ROW]
[ROW][C]66[/C][C]56[/C][C]44.8188976377953[/C][C]11.1811023622047[/C][/ROW]
[ROW][C]67[/C][C]51[/C][C]44.8188976377953[/C][C]6.18110236220473[/C][/ROW]
[ROW][C]68[/C][C]43[/C][C]44.8188976377953[/C][C]-1.81889763779527[/C][/ROW]
[ROW][C]69[/C][C]51[/C][C]49.3684210526316[/C][C]1.63157894736842[/C][/ROW]
[ROW][C]70[/C][C]36[/C][C]44.8188976377953[/C][C]-8.81889763779527[/C][/ROW]
[ROW][C]71[/C][C]55[/C][C]49.3684210526316[/C][C]5.63157894736842[/C][/ROW]
[ROW][C]72[/C][C]33[/C][C]44.8188976377953[/C][C]-11.8188976377953[/C][/ROW]
[ROW][C]73[/C][C]42[/C][C]44.8188976377953[/C][C]-2.81889763779527[/C][/ROW]
[ROW][C]74[/C][C]43[/C][C]44.8188976377953[/C][C]-1.81889763779527[/C][/ROW]
[ROW][C]75[/C][C]44[/C][C]44.8188976377953[/C][C]-0.818897637795274[/C][/ROW]
[ROW][C]76[/C][C]47[/C][C]44.8188976377953[/C][C]2.18110236220473[/C][/ROW]
[ROW][C]77[/C][C]43[/C][C]44.8188976377953[/C][C]-1.81889763779527[/C][/ROW]
[ROW][C]78[/C][C]47[/C][C]49.3684210526316[/C][C]-2.36842105263158[/C][/ROW]
[ROW][C]79[/C][C]41[/C][C]44.8188976377953[/C][C]-3.81889763779527[/C][/ROW]
[ROW][C]80[/C][C]53[/C][C]44.8188976377953[/C][C]8.18110236220473[/C][/ROW]
[ROW][C]81[/C][C]47[/C][C]44.8188976377953[/C][C]2.18110236220473[/C][/ROW]
[ROW][C]82[/C][C]23[/C][C]44.8188976377953[/C][C]-21.8188976377953[/C][/ROW]
[ROW][C]83[/C][C]43[/C][C]49.3684210526316[/C][C]-6.36842105263158[/C][/ROW]
[ROW][C]84[/C][C]47[/C][C]44.8188976377953[/C][C]2.18110236220473[/C][/ROW]
[ROW][C]85[/C][C]47[/C][C]44.8188976377953[/C][C]2.18110236220473[/C][/ROW]
[ROW][C]86[/C][C]49[/C][C]44.8188976377953[/C][C]4.18110236220473[/C][/ROW]
[ROW][C]87[/C][C]50[/C][C]44.8188976377953[/C][C]5.18110236220473[/C][/ROW]
[ROW][C]88[/C][C]43[/C][C]44.8188976377953[/C][C]-1.81889763779527[/C][/ROW]
[ROW][C]89[/C][C]44[/C][C]44.8188976377953[/C][C]-0.818897637795274[/C][/ROW]
[ROW][C]90[/C][C]49[/C][C]49.3684210526316[/C][C]-0.368421052631582[/C][/ROW]
[ROW][C]91[/C][C]47[/C][C]44.8188976377953[/C][C]2.18110236220473[/C][/ROW]
[ROW][C]92[/C][C]39[/C][C]44.8188976377953[/C][C]-5.81889763779527[/C][/ROW]
[ROW][C]93[/C][C]49[/C][C]44.8188976377953[/C][C]4.18110236220473[/C][/ROW]
[ROW][C]94[/C][C]41[/C][C]44.8188976377953[/C][C]-3.81889763779527[/C][/ROW]
[ROW][C]95[/C][C]40[/C][C]44.8188976377953[/C][C]-4.81889763779527[/C][/ROW]
[ROW][C]96[/C][C]38[/C][C]44.8188976377953[/C][C]-6.81889763779527[/C][/ROW]
[ROW][C]97[/C][C]43[/C][C]44.8188976377953[/C][C]-1.81889763779527[/C][/ROW]
[ROW][C]98[/C][C]55[/C][C]44.8188976377953[/C][C]10.1811023622047[/C][/ROW]
[ROW][C]99[/C][C]46[/C][C]44.8188976377953[/C][C]1.18110236220473[/C][/ROW]
[ROW][C]100[/C][C]54[/C][C]44.8188976377953[/C][C]9.18110236220473[/C][/ROW]
[ROW][C]101[/C][C]47[/C][C]44.8188976377953[/C][C]2.18110236220473[/C][/ROW]
[ROW][C]102[/C][C]35[/C][C]44.8188976377953[/C][C]-9.81889763779527[/C][/ROW]
[ROW][C]103[/C][C]41[/C][C]44.8188976377953[/C][C]-3.81889763779527[/C][/ROW]
[ROW][C]104[/C][C]53[/C][C]49.3684210526316[/C][C]3.63157894736842[/C][/ROW]
[ROW][C]105[/C][C]44[/C][C]44.8188976377953[/C][C]-0.818897637795274[/C][/ROW]
[ROW][C]106[/C][C]48[/C][C]44.8188976377953[/C][C]3.18110236220473[/C][/ROW]
[ROW][C]107[/C][C]49[/C][C]44.8188976377953[/C][C]4.18110236220473[/C][/ROW]
[ROW][C]108[/C][C]39[/C][C]44.8188976377953[/C][C]-5.81889763779527[/C][/ROW]
[ROW][C]109[/C][C]45[/C][C]44.8188976377953[/C][C]0.181102362204726[/C][/ROW]
[ROW][C]110[/C][C]34[/C][C]44.8188976377953[/C][C]-10.8188976377953[/C][/ROW]
[ROW][C]111[/C][C]46[/C][C]44.8188976377953[/C][C]1.18110236220473[/C][/ROW]
[ROW][C]112[/C][C]45[/C][C]44.8188976377953[/C][C]0.181102362204726[/C][/ROW]
[ROW][C]113[/C][C]53[/C][C]44.8188976377953[/C][C]8.18110236220473[/C][/ROW]
[ROW][C]114[/C][C]51[/C][C]44.8188976377953[/C][C]6.18110236220473[/C][/ROW]
[ROW][C]115[/C][C]45[/C][C]49.3684210526316[/C][C]-4.36842105263158[/C][/ROW]
[ROW][C]116[/C][C]50[/C][C]44.8188976377953[/C][C]5.18110236220473[/C][/ROW]
[ROW][C]117[/C][C]41[/C][C]44.8188976377953[/C][C]-3.81889763779527[/C][/ROW]
[ROW][C]118[/C][C]44[/C][C]44.8188976377953[/C][C]-0.818897637795274[/C][/ROW]
[ROW][C]119[/C][C]43[/C][C]44.8188976377953[/C][C]-1.81889763779527[/C][/ROW]
[ROW][C]120[/C][C]42[/C][C]44.8188976377953[/C][C]-2.81889763779527[/C][/ROW]
[ROW][C]121[/C][C]48[/C][C]49.3684210526316[/C][C]-1.36842105263158[/C][/ROW]
[ROW][C]122[/C][C]45[/C][C]44.8188976377953[/C][C]0.181102362204726[/C][/ROW]
[ROW][C]123[/C][C]48[/C][C]44.8188976377953[/C][C]3.18110236220473[/C][/ROW]
[ROW][C]124[/C][C]48[/C][C]44.8188976377953[/C][C]3.18110236220473[/C][/ROW]
[ROW][C]125[/C][C]53[/C][C]49.3684210526316[/C][C]3.63157894736842[/C][/ROW]
[ROW][C]126[/C][C]45[/C][C]44.8188976377953[/C][C]0.181102362204726[/C][/ROW]
[ROW][C]127[/C][C]45[/C][C]44.8188976377953[/C][C]0.181102362204726[/C][/ROW]
[ROW][C]128[/C][C]50[/C][C]44.8188976377953[/C][C]5.18110236220473[/C][/ROW]
[ROW][C]129[/C][C]48[/C][C]44.8188976377953[/C][C]3.18110236220473[/C][/ROW]
[ROW][C]130[/C][C]41[/C][C]44.8188976377953[/C][C]-3.81889763779527[/C][/ROW]
[ROW][C]131[/C][C]53[/C][C]49.3684210526316[/C][C]3.63157894736842[/C][/ROW]
[ROW][C]132[/C][C]40[/C][C]44.8188976377953[/C][C]-4.81889763779527[/C][/ROW]
[ROW][C]133[/C][C]49[/C][C]44.8188976377953[/C][C]4.18110236220473[/C][/ROW]
[ROW][C]134[/C][C]46[/C][C]44.8188976377953[/C][C]1.18110236220473[/C][/ROW]
[ROW][C]135[/C][C]48[/C][C]44.8188976377953[/C][C]3.18110236220473[/C][/ROW]
[ROW][C]136[/C][C]43[/C][C]49.3684210526316[/C][C]-6.36842105263158[/C][/ROW]
[ROW][C]137[/C][C]53[/C][C]44.8188976377953[/C][C]8.18110236220473[/C][/ROW]
[ROW][C]138[/C][C]51[/C][C]44.8188976377953[/C][C]6.18110236220473[/C][/ROW]
[ROW][C]139[/C][C]41[/C][C]44.8188976377953[/C][C]-3.81889763779527[/C][/ROW]
[ROW][C]140[/C][C]45[/C][C]44.8188976377953[/C][C]0.181102362204726[/C][/ROW]
[ROW][C]141[/C][C]44[/C][C]44.8188976377953[/C][C]-0.818897637795274[/C][/ROW]
[ROW][C]142[/C][C]43[/C][C]44.8188976377953[/C][C]-1.81889763779527[/C][/ROW]
[ROW][C]143[/C][C]34[/C][C]44.8188976377953[/C][C]-10.8188976377953[/C][/ROW]
[ROW][C]144[/C][C]38[/C][C]44.8188976377953[/C][C]-6.81889763779527[/C][/ROW]
[ROW][C]145[/C][C]40[/C][C]44.8188976377953[/C][C]-4.81889763779527[/C][/ROW]
[ROW][C]146[/C][C]48[/C][C]44.8188976377953[/C][C]3.18110236220473[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112867&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112867&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	46	49.3684210526316	-3.36842105263158
2	44	44.8188976377953	-0.818897637795274
3	42	44.8188976377953	-2.81889763779527
4	41	44.8188976377953	-3.81889763779527
5	48	44.8188976377953	3.18110236220473
6	49	49.3684210526316	-0.368421052631582
7	51	44.8188976377953	6.18110236220473
8	47	44.8188976377953	2.18110236220473
9	49	44.8188976377953	4.18110236220473
10	46	44.8188976377953	1.18110236220473
11	51	44.8188976377953	6.18110236220473
12	54	44.8188976377953	9.18110236220473
13	52	44.8188976377953	7.18110236220473
14	52	49.3684210526316	2.63157894736842
15	45	44.8188976377953	0.181102362204726
16	52	44.8188976377953	7.18110236220473
17	56	49.3684210526316	6.63157894736842
18	54	44.8188976377953	9.18110236220473
19	50	44.8188976377953	5.18110236220473
20	35	44.8188976377953	-9.81889763779527
21	48	44.8188976377953	3.18110236220473
22	37	44.8188976377953	-7.81889763779527
23	47	44.8188976377953	2.18110236220473
24	31	44.8188976377953	-13.8188976377953
25	45	44.8188976377953	0.181102362204726
26	47	44.8188976377953	2.18110236220473
27	44	44.8188976377953	-0.818897637795274
28	30	44.8188976377953	-14.8188976377953
29	40	44.8188976377953	-4.81889763779527
30	44	44.8188976377953	-0.818897637795274
31	43	44.8188976377953	-1.81889763779527
32	51	44.8188976377953	6.18110236220473
33	48	44.8188976377953	3.18110236220473
34	55	49.3684210526316	5.63157894736842
35	48	44.8188976377953	3.18110236220473
36	53	44.8188976377953	8.18110236220473
37	49	44.8188976377953	4.18110236220473
38	44	44.8188976377953	-0.818897637795274
39	45	49.3684210526316	-4.36842105263158
40	40	44.8188976377953	-4.81889763779527
41	44	44.8188976377953	-0.818897637795274
42	41	44.8188976377953	-3.81889763779527
43	46	44.8188976377953	1.18110236220473
44	47	44.8188976377953	2.18110236220473
45	48	44.8188976377953	3.18110236220473
46	43	44.8188976377953	-1.81889763779527
47	46	44.8188976377953	1.18110236220473
48	53	44.8188976377953	8.18110236220473
49	33	44.8188976377953	-11.8188976377953
50	47	49.3684210526316	-2.36842105263158
51	43	44.8188976377953	-1.81889763779527
52	45	44.8188976377953	0.181102362204726
53	49	44.8188976377953	4.18110236220473
54	45	44.8188976377953	0.181102362204726
55	37	44.8188976377953	-7.81889763779527
56	42	44.8188976377953	-2.81889763779527
57	43	44.8188976377953	-1.81889763779527
58	44	44.8188976377953	-0.818897637795274
59	39	44.8188976377953	-5.81889763779527
60	37	44.8188976377953	-7.81889763779527
61	53	44.8188976377953	8.18110236220473
62	48	49.3684210526316	-1.36842105263158
63	47	44.8188976377953	2.18110236220473
64	49	44.8188976377953	4.18110236220473
65	47	44.8188976377953	2.18110236220473
66	56	44.8188976377953	11.1811023622047
67	51	44.8188976377953	6.18110236220473
68	43	44.8188976377953	-1.81889763779527
69	51	49.3684210526316	1.63157894736842
70	36	44.8188976377953	-8.81889763779527
71	55	49.3684210526316	5.63157894736842
72	33	44.8188976377953	-11.8188976377953
73	42	44.8188976377953	-2.81889763779527
74	43	44.8188976377953	-1.81889763779527
75	44	44.8188976377953	-0.818897637795274
76	47	44.8188976377953	2.18110236220473
77	43	44.8188976377953	-1.81889763779527
78	47	49.3684210526316	-2.36842105263158
79	41	44.8188976377953	-3.81889763779527
80	53	44.8188976377953	8.18110236220473
81	47	44.8188976377953	2.18110236220473
82	23	44.8188976377953	-21.8188976377953
83	43	49.3684210526316	-6.36842105263158
84	47	44.8188976377953	2.18110236220473
85	47	44.8188976377953	2.18110236220473
86	49	44.8188976377953	4.18110236220473
87	50	44.8188976377953	5.18110236220473
88	43	44.8188976377953	-1.81889763779527
89	44	44.8188976377953	-0.818897637795274
90	49	49.3684210526316	-0.368421052631582
91	47	44.8188976377953	2.18110236220473
92	39	44.8188976377953	-5.81889763779527
93	49	44.8188976377953	4.18110236220473
94	41	44.8188976377953	-3.81889763779527
95	40	44.8188976377953	-4.81889763779527
96	38	44.8188976377953	-6.81889763779527
97	43	44.8188976377953	-1.81889763779527
98	55	44.8188976377953	10.1811023622047
99	46	44.8188976377953	1.18110236220473
100	54	44.8188976377953	9.18110236220473
101	47	44.8188976377953	2.18110236220473
102	35	44.8188976377953	-9.81889763779527
103	41	44.8188976377953	-3.81889763779527
104	53	49.3684210526316	3.63157894736842
105	44	44.8188976377953	-0.818897637795274
106	48	44.8188976377953	3.18110236220473
107	49	44.8188976377953	4.18110236220473
108	39	44.8188976377953	-5.81889763779527
109	45	44.8188976377953	0.181102362204726
110	34	44.8188976377953	-10.8188976377953
111	46	44.8188976377953	1.18110236220473
112	45	44.8188976377953	0.181102362204726
113	53	44.8188976377953	8.18110236220473
114	51	44.8188976377953	6.18110236220473
115	45	49.3684210526316	-4.36842105263158
116	50	44.8188976377953	5.18110236220473
117	41	44.8188976377953	-3.81889763779527
118	44	44.8188976377953	-0.818897637795274
119	43	44.8188976377953	-1.81889763779527
120	42	44.8188976377953	-2.81889763779527
121	48	49.3684210526316	-1.36842105263158
122	45	44.8188976377953	0.181102362204726
123	48	44.8188976377953	3.18110236220473
124	48	44.8188976377953	3.18110236220473
125	53	49.3684210526316	3.63157894736842
126	45	44.8188976377953	0.181102362204726
127	45	44.8188976377953	0.181102362204726
128	50	44.8188976377953	5.18110236220473
129	48	44.8188976377953	3.18110236220473
130	41	44.8188976377953	-3.81889763779527
131	53	49.3684210526316	3.63157894736842
132	40	44.8188976377953	-4.81889763779527
133	49	44.8188976377953	4.18110236220473
134	46	44.8188976377953	1.18110236220473
135	48	44.8188976377953	3.18110236220473
136	43	49.3684210526316	-6.36842105263158
137	53	44.8188976377953	8.18110236220473
138	51	44.8188976377953	6.18110236220473
139	41	44.8188976377953	-3.81889763779527
140	45	44.8188976377953	0.181102362204726
141	44	44.8188976377953	-0.818897637795274
142	43	44.8188976377953	-1.81889763779527
143	34	44.8188976377953	-10.8188976377953
144	38	44.8188976377953	-6.81889763779527
145	40	44.8188976377953	-4.81889763779527
146	48	44.8188976377953	3.18110236220473

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 ; par4 = no ;

Parameters (R input):

par1 = 1 ; par2 = none ; par3 = ; 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