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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Sun, 12 Dec 2010 13:18:21 +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/12/t1292159843ye797bfspmoru4w.htm/, Retrieved Tue, 07 May 2024 05:44:29 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108429, Retrieved Tue, 07 May 2024 05:44:29 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

130

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]
F   PD    [Recursive Partitioning (Regression Trees)] [workshop 10d] [2010-12-12 13:18:21] [3f56c8f677e988de577e4e00a8180a48] [Current]

Feedback Forum

2010-12-19 14:18:21 [48eb36e2c01435ad7e4ea7854a9d98fe] [reply] 
De student past hier op correcte wijze de techniek 'Recursive Partitioning' toe. Ook de interpretatie die gegeven wordt is juist, men ziet inderdaad dat 'mistakes' en 'organization' de belangrijkste verklarende variabelen zijn.

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Dataseries X:

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11	6	6	4	15	16	2	40	37	15	10	77
26	16	5	4	23	24	1	29	31	9	20	63
26	13	20	10	26	22	1	37	35	12	16	73
15	7	12	6	19	21	1	32	36	15	10	76
10	10	11	5	19	23	1	39	32	17	8	90
21	10	12	8	16	23	1	32	30	14	14	67
27	15	11	9	23	21	2	35	34	9	19	69
21	9	9	9	22	20	2	35	34	12	15	70
21	12	13	8	19	22	1	28	22	11	23	54
21	8	9	11	24	20	1	37	27	13	9	54
22	9	14	6	19	12	2	32	27	16	12	76
29	10	12	8	25	23	2	34	33	16	14	75
29	15	18	11	23	23	2	37	38	15	13	76
29	11	9	5	31	30	1	35	37	10	11	80
30	12	15	10	29	22	2	40	31	16	11	89
19	9	12	7	18	21	1	37	36	12	10	73
19	10	12	7	17	21	1	37	38	15	12	74
22	13	12	13	22	15	2	33	31	13	18	78
18	8	15	10	21	22	2	37	34	18	12	76
28	14	11	8	24	24	1	35	33	13	10	69
17	9	13	6	22	23	2	36	38	17	15	74
18	12	10	8	16	15	2	32	28	14	15	82
20	8	17	7	22	24	1	38	34	13	12	77
16	8	13	5	21	24	2	34	32	13	9	84
17	9	17	9	25	21	2	33	34	15	11	75
25	14	15	11	22	21	2	33	39	15	16	79
22	11	13	11	24	18	2	42	37	13	17	79
34	16	18	11	21	20	2	33	34	14	12	69
31	9	17	9	25	19	2	32	41	13	11	88
38	11	21	7	29	29	2	32	32	16	13	57
18	13	12	6	19	20	2	33	35	14	9	69
25	12	12	7	29	23	1	35	33	12	14	52
20	9	15	6	25	24	2	39	32	18	11	86
23	14	8	5	19	27	1	28	32	9	20	66
12	4	15	4	27	28	1	38	32	16	8	54
20	8	16	10	25	24	2	36	37	16	12	85
15	14	9	8	23	29	1	38	31	17	10	79
21	10	13	6	24	24	1	34	27	13	11	84
21	13	17	11	23	22	2	33	31	15	11	73
20	10	11	4	25	25	2	37	37	17	13	70
30	14	9	9	23	14	2	34	31	15	13	54
22	13	15	10	22	22	2	34	40	14	13	70
33	14	9	6	32	24	1	36	35	10	15	54
25	14	15	9	22	24	1	31	35	13	12	69
20	14	14	10	18	24	2	37	35	11	13	68
10	5	8	6	19	24	1	36	35	16	11	76
15	11	11	8	23	22	1	34	38	16	9	71
21	9	14	13	19	21	2	30	35	11	14	66
16	9	12	8	16	21	2	29	34	15	9	67
23	10	15	10	23	21	2	35	37	15	9	71
25	14	11	5	17	15	2	33	37	12	15	54
18	6	11	8	17	26	2	29	31	17	10	76
33	11	9	6	28	22	1	28	31	15	13	77
18	13	8	9	24	24	1	32	33	16	8	71
18	12	13	9	21	13	2	33	37	14	15	69
13	8	12	7	14	19	2	31	36	17	13	73
24	14	24	20	21	10	2	43	42	10	24	46
19	11	11	8	20	28	1	32	28	11	11	66
20	11	11	8	25	25	2	35	41	15	13	77
21	11	16	7	20	24	1	31	23	15	12	77
18	16	12	7	17	22	2	33	33	7	22	70
29	14	18	10	26	30	1	39	32	17	11	86
13	16	12	5	17	22	1	32	33	14	15	38
26	14	14	8	17	24	1	32	33	18	7	66
22	9	16	9	24	23	1	36	32	14	14	75
28	8	24	20	30	20	1	39	38	14	10	64
28	11	13	6	25	22	2	41	32	9	9	80
23	8	11	10	15	22	2	30	35	14	12	86
22	14	14	11	25	19	2	30	35	11	16	54
28	8	16	12	18	24	2	32	34	15	10	54
28	8	12	7	20	22	2	39	34	16	13	74
31	10	21	12	32	26	2	38	38	17	11	88
15	8	11	8	14	12	2	38	39	16	12	85
15	8	6	6	20	25	1	32	32	12	11	63
24	10	9	6	25	29	2	34	39	15	13	81
22	9	14	9	25	23	2	36	35	15	10	74
17	9	16	5	25	23	2	39	36	16	11	80
25	7	18	11	35	17	2	31	28	16	9	80
32	16	9	6	29	26	1	36	36	11	13	60
23	14	13	10	25	27	2	34	38	12	14	62
20	11	17	8	21	23	1	34	35	14	14	63
20	9	11	7	21	20	2	38	39	15	11	89
28	16	16	8	24	24	2	38	36	17	10	76
20	7	11	9	26	22	2	33	36	19	11	81
20	11	11	8	24	26	2	32	34	15	12	72
23	14	11	10	20	29	1	30	34	16	14	84
20	11	20	13	24	20	2	31	27	14	14	76
21	8	10	7	18	17	2	34	37	16	21	76
14	11	12	7	17	16	2	35	33	15	13	72
31	8	11	8	22	24	1	37	34	17	11	81
21	12	14	9	22	24	2	35	39	12	12	72
18	8	12	9	22	19	2	35	29	18	12	78
26	13	12	8	24	29	2	31	33	13	11	79
25	8	12	7	32	25	2	31	35	14	14	52
9	13	10	6	19	25	1	38	36	14	13	67
18	9	12	8	21	24	1	34	30	14	13	74
19	12	10	8	23	29	1	30	27	12	12	73
29	11	7	4	26	22	2	32	37	14	14	69
31	14	10	8	18	23	1	31	33	12	12	67
24	9	13	10	19	15	2	37	32	15	12	76
16	10	12	7	22	29	2	34	35	11	12	77
19	9	13	8	27	21	1	32	33	11	18	63
19	9	9	7	21	23	2	34	37	15	11	84
22	8	14	10	20	20	2	38	36	14	15	90
31	16	14	9	21	25	1	38	39	15	13	75
20	10	12	8	20	28	2	38	35	16	11	76
26	11	18	5	29	18	2	39	31	14	22	53
17	6	17	8	30	25	2	33	37	18	10	87
16	9	12	9	10	13	2	34	36	13	16	69
16	8	15	9	23	24	2	35	31	14	11	78
9	6	8	11	29	23	2	36	32	13	15	54
19	20	8	7	19	25	1	32	33	14	14	58
22	10	12	8	26	27	2	34	36	14	11	80
15	8	10	4	22	24	2	44	39	17	10	74
25	16	18	16	26	24	2	37	39	12	14	56
30	9	15	9	27	26	2	32	29	16	14	82
30	12	16	10	19	18	2	35	34	15	11	64
24	14	11	12	24	26	1	38	35	10	15	67
20	10	10	8	26	23	1	38	32	13	11	75
12	7	7	4	22	28	1	38	41	15	10	69
31	14	17	11	23	20	2	32	38	16	10	72
25	11	7	8	25	23	2	39	38	14	12	54
23	13	14	12	19	24	1	27	32	13	15	54
23	10	12	8	20	21	2	37	31	17	10	71
26	9	15	6	25	25	2	41	38	14	12	53
14	15	13	8	14	16	2	31	38	16	15	54
18	12	10	8	19	23	1	36	33	15	12	71
28	12	16	14	27	22	2	38	28	12	11	69
19	9	11	10	21	27	1	37	38	16	10	30
21	15	7	5	21	24	1	30	28	8	20	53
18	10	15	8	14	17	1	40	32	9	19	68
29	13	18	12	21	21	2	34	31	13	17	69
16	11	11	11	23	21	2	36	34	19	8	54
22	10	13	8	18	19	2	36	35	11	17	66
15	12	11	8	20	25	1	33	36	15	11	79
21	9	13	9	19	24	1	34	33	11	13	67
17	14	12	6	15	21	1	37	32	15	9	74
17	9	11	5	23	26	1	37	32	16	10	86
33	14	11	8	26	25	2	39	40	15	13	63
17	11	13	7	21	25	2	37	35	12	16	69
20	11	8	4	13	13	1	37	33	16	12	73
17	9	12	9	24	25	1	35	37	15	14	69
16	11	9	5	17	23	1	32	33	13	11	71
18	10	14	9	21	26	2	33	31	14	13	77
32	12	18	12	28	22	2	31	33	11	15	74
22	10	15	6	22	20	2	30	34	15	14	82
19	6	17	8	25	14	2	32	35	12	18	84
29	16	11	6	27	24	2	33	40	14	14	54
23	14	17	7	25	21	2	29	30	13	10	80
17	8	12	9	21	24	2	37	38	15	8	76

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	'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108429&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108429&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108429&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	'George Udny Yule' @ 72.249.76.132

Goodness of Fit
Correlation	0.5398
R-squared	0.2913
RMSE	3.5869

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.5398 \tabularnewline
R-squared & 0.2913 \tabularnewline
RMSE & 3.5869 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108429&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.5398[/C][/ROW]
[ROW][C]R-squared[/C][C]0.2913[/C][/ROW]
[ROW][C]RMSE[/C][C]3.5869[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108429&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108429&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	0.5398
R-squared	0.2913
RMSE	3.5869

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	15	17.2	-2.2
2	23	25.0869565217391	-2.08695652173913
3	26	25.0869565217391	0.913043478260871
4	19	21.5730337078652	-2.57303370786517
5	19	21.5730337078652	-2.57303370786517
6	16	21.5730337078652	-5.57303370786517
7	23	25.0869565217391	-2.08695652173913
8	22	21.5730337078652	0.426966292134832
9	19	21.5730337078652	-2.57303370786517
10	24	21.5730337078652	2.42696629213483
11	19	17.2	1.8
12	25	25.0869565217391	-0.086956521739129
13	23	25.0869565217391	-2.08695652173913
14	31	25.0869565217391	5.91304347826087
15	29	25.0869565217391	3.91304347826087
16	18	21.5730337078652	-3.57303370786517
17	17	21.5730337078652	-4.57303370786517
18	22	17.2	4.8
19	21	21.5730337078652	-0.573033707865168
20	24	25.0869565217391	-1.08695652173913
21	22	21.5730337078652	0.426966292134832
22	16	17.2	-1.2
23	22	21.5730337078652	0.426966292134832
24	21	21.5730337078652	-0.573033707865168
25	25	21.5730337078652	3.42696629213483
26	22	25.0869565217391	-3.08695652173913
27	24	21.5730337078652	2.42696629213483
28	21	25.0869565217391	-4.08695652173913
29	25	25.0869565217391	-0.086956521739129
30	29	25.0869565217391	3.91304347826087
31	19	21.5730337078652	-2.57303370786517
32	29	25.0869565217391	3.91304347826087
33	25	21.5730337078652	3.42696629213483
34	19	21.5730337078652	-2.57303370786517
35	27	21.5730337078652	5.42696629213483
36	25	21.5730337078652	3.42696629213483
37	23	21.5730337078652	1.42696629213483
38	24	21.5730337078652	2.42696629213483
39	23	21.5730337078652	1.42696629213483
40	25	21.5730337078652	3.42696629213483
41	23	25.0869565217391	-2.08695652173913
42	22	21.5730337078652	0.426966292134832
43	32	25.0869565217391	6.91304347826087
44	22	25.0869565217391	-3.08695652173913
45	18	21.5730337078652	-3.57303370786517
46	19	21.5730337078652	-2.57303370786517
47	23	21.5730337078652	1.42696629213483
48	19	21.5730337078652	-2.57303370786517
49	16	21.5730337078652	-5.57303370786517
50	23	21.5730337078652	1.42696629213483
51	17	25.0869565217391	-8.08695652173913
52	17	21.5730337078652	-4.57303370786517
53	28	25.0869565217391	2.91304347826087
54	24	21.5730337078652	2.42696629213483
55	21	17.2	3.8
56	14	21.5730337078652	-7.57303370786517
57	21	17.2	3.8
58	20	21.5730337078652	-1.57303370786517
59	25	21.5730337078652	3.42696629213483
60	20	21.5730337078652	-1.57303370786517
61	17	21.5730337078652	-4.57303370786517
62	26	25.0869565217391	0.913043478260871
63	17	21.5730337078652	-4.57303370786517
64	17	25.0869565217391	-8.08695652173913
65	24	21.5730337078652	2.42696629213483
66	30	25.0869565217391	4.91304347826087
67	25	25.0869565217391	-0.086956521739129
68	15	21.5730337078652	-6.57303370786517
69	25	21.5730337078652	3.42696629213483
70	18	25.0869565217391	-7.08695652173913
71	20	25.0869565217391	-5.08695652173913
72	32	25.0869565217391	6.91304347826087
73	14	17.2	-3.2
74	20	21.5730337078652	-1.57303370786517
75	25	21.5730337078652	3.42696629213483
76	25	21.5730337078652	3.42696629213483
77	25	21.5730337078652	3.42696629213483
78	35	25.0869565217391	9.91304347826087
79	29	25.0869565217391	3.91304347826087
80	25	21.5730337078652	3.42696629213483
81	21	21.5730337078652	-0.573033707865168
82	21	21.5730337078652	-0.573033707865168
83	24	25.0869565217391	-1.08695652173913
84	26	21.5730337078652	4.42696629213483
85	24	21.5730337078652	2.42696629213483
86	20	21.5730337078652	-1.57303370786517
87	24	21.5730337078652	2.42696629213483
88	18	17.2	0.8
89	17	17.2	-0.199999999999999
90	22	25.0869565217391	-3.08695652173913
91	22	21.5730337078652	0.426966292134832
92	22	21.5730337078652	0.426966292134832
93	24	25.0869565217391	-1.08695652173913
94	32	25.0869565217391	6.91304347826087
95	19	21.5730337078652	-2.57303370786517
96	21	21.5730337078652	-0.573033707865168
97	23	21.5730337078652	1.42696629213483
98	26	25.0869565217391	0.913043478260871
99	18	25.0869565217391	-7.08695652173913
100	19	17.2	1.8
101	22	21.5730337078652	0.426966292134832
102	27	21.5730337078652	5.42696629213483
103	21	21.5730337078652	-0.573033707865168
104	20	21.5730337078652	-1.57303370786517
105	21	25.0869565217391	-4.08695652173913
106	20	21.5730337078652	-1.57303370786517
107	29	25.0869565217391	3.91304347826087
108	30	21.5730337078652	8.42696629213483
109	10	17.2	-7.2
110	23	21.5730337078652	1.42696629213483
111	29	21.5730337078652	7.42696629213483
112	19	21.5730337078652	-2.57303370786517
113	26	21.5730337078652	4.42696629213483
114	22	21.5730337078652	0.426966292134832
115	26	25.0869565217391	0.913043478260871
116	27	25.0869565217391	1.91304347826087
117	19	25.0869565217391	-6.08695652173913
118	24	21.5730337078652	2.42696629213483
119	26	21.5730337078652	4.42696629213483
120	22	21.5730337078652	0.426966292134832
121	23	25.0869565217391	-2.08695652173913
122	25	25.0869565217391	-0.086956521739129
123	19	21.5730337078652	-2.57303370786517
124	20	21.5730337078652	-1.57303370786517
125	25	25.0869565217391	-0.086956521739129
126	14	17.2	-3.2
127	19	21.5730337078652	-2.57303370786517
128	27	25.0869565217391	1.91304347826087
129	21	21.5730337078652	-0.573033707865168
130	21	21.5730337078652	-0.573033707865168
131	14	17.2	-3.2
132	21	25.0869565217391	-4.08695652173913
133	23	21.5730337078652	1.42696629213483
134	18	21.5730337078652	-3.57303370786517
135	20	21.5730337078652	-1.57303370786517
136	19	21.5730337078652	-2.57303370786517
137	15	21.5730337078652	-6.57303370786517
138	23	21.5730337078652	1.42696629213483
139	26	25.0869565217391	0.913043478260871
140	21	21.5730337078652	-0.573033707865168
141	13	17.2	-4.2
142	24	21.5730337078652	2.42696629213483
143	17	21.5730337078652	-4.57303370786517
144	21	21.5730337078652	-0.573033707865168
145	28	25.0869565217391	2.91304347826087
146	22	21.5730337078652	0.426966292134832
147	25	17.2	7.8
148	27	25.0869565217391	1.91304347826087
149	25	21.5730337078652	3.42696629213483
150	21	21.5730337078652	-0.573033707865168

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 15 & 17.2 & -2.2 \tabularnewline
2 & 23 & 25.0869565217391 & -2.08695652173913 \tabularnewline
3 & 26 & 25.0869565217391 & 0.913043478260871 \tabularnewline
4 & 19 & 21.5730337078652 & -2.57303370786517 \tabularnewline
5 & 19 & 21.5730337078652 & -2.57303370786517 \tabularnewline
6 & 16 & 21.5730337078652 & -5.57303370786517 \tabularnewline
7 & 23 & 25.0869565217391 & -2.08695652173913 \tabularnewline
8 & 22 & 21.5730337078652 & 0.426966292134832 \tabularnewline
9 & 19 & 21.5730337078652 & -2.57303370786517 \tabularnewline
10 & 24 & 21.5730337078652 & 2.42696629213483 \tabularnewline
11 & 19 & 17.2 & 1.8 \tabularnewline
12 & 25 & 25.0869565217391 & -0.086956521739129 \tabularnewline
13 & 23 & 25.0869565217391 & -2.08695652173913 \tabularnewline
14 & 31 & 25.0869565217391 & 5.91304347826087 \tabularnewline
15 & 29 & 25.0869565217391 & 3.91304347826087 \tabularnewline
16 & 18 & 21.5730337078652 & -3.57303370786517 \tabularnewline
17 & 17 & 21.5730337078652 & -4.57303370786517 \tabularnewline
18 & 22 & 17.2 & 4.8 \tabularnewline
19 & 21 & 21.5730337078652 & -0.573033707865168 \tabularnewline
20 & 24 & 25.0869565217391 & -1.08695652173913 \tabularnewline
21 & 22 & 21.5730337078652 & 0.426966292134832 \tabularnewline
22 & 16 & 17.2 & -1.2 \tabularnewline
23 & 22 & 21.5730337078652 & 0.426966292134832 \tabularnewline
24 & 21 & 21.5730337078652 & -0.573033707865168 \tabularnewline
25 & 25 & 21.5730337078652 & 3.42696629213483 \tabularnewline
26 & 22 & 25.0869565217391 & -3.08695652173913 \tabularnewline
27 & 24 & 21.5730337078652 & 2.42696629213483 \tabularnewline
28 & 21 & 25.0869565217391 & -4.08695652173913 \tabularnewline
29 & 25 & 25.0869565217391 & -0.086956521739129 \tabularnewline
30 & 29 & 25.0869565217391 & 3.91304347826087 \tabularnewline
31 & 19 & 21.5730337078652 & -2.57303370786517 \tabularnewline
32 & 29 & 25.0869565217391 & 3.91304347826087 \tabularnewline
33 & 25 & 21.5730337078652 & 3.42696629213483 \tabularnewline
34 & 19 & 21.5730337078652 & -2.57303370786517 \tabularnewline
35 & 27 & 21.5730337078652 & 5.42696629213483 \tabularnewline
36 & 25 & 21.5730337078652 & 3.42696629213483 \tabularnewline
37 & 23 & 21.5730337078652 & 1.42696629213483 \tabularnewline
38 & 24 & 21.5730337078652 & 2.42696629213483 \tabularnewline
39 & 23 & 21.5730337078652 & 1.42696629213483 \tabularnewline
40 & 25 & 21.5730337078652 & 3.42696629213483 \tabularnewline
41 & 23 & 25.0869565217391 & -2.08695652173913 \tabularnewline
42 & 22 & 21.5730337078652 & 0.426966292134832 \tabularnewline
43 & 32 & 25.0869565217391 & 6.91304347826087 \tabularnewline
44 & 22 & 25.0869565217391 & -3.08695652173913 \tabularnewline
45 & 18 & 21.5730337078652 & -3.57303370786517 \tabularnewline
46 & 19 & 21.5730337078652 & -2.57303370786517 \tabularnewline
47 & 23 & 21.5730337078652 & 1.42696629213483 \tabularnewline
48 & 19 & 21.5730337078652 & -2.57303370786517 \tabularnewline
49 & 16 & 21.5730337078652 & -5.57303370786517 \tabularnewline
50 & 23 & 21.5730337078652 & 1.42696629213483 \tabularnewline
51 & 17 & 25.0869565217391 & -8.08695652173913 \tabularnewline
52 & 17 & 21.5730337078652 & -4.57303370786517 \tabularnewline
53 & 28 & 25.0869565217391 & 2.91304347826087 \tabularnewline
54 & 24 & 21.5730337078652 & 2.42696629213483 \tabularnewline
55 & 21 & 17.2 & 3.8 \tabularnewline
56 & 14 & 21.5730337078652 & -7.57303370786517 \tabularnewline
57 & 21 & 17.2 & 3.8 \tabularnewline
58 & 20 & 21.5730337078652 & -1.57303370786517 \tabularnewline
59 & 25 & 21.5730337078652 & 3.42696629213483 \tabularnewline
60 & 20 & 21.5730337078652 & -1.57303370786517 \tabularnewline
61 & 17 & 21.5730337078652 & -4.57303370786517 \tabularnewline
62 & 26 & 25.0869565217391 & 0.913043478260871 \tabularnewline
63 & 17 & 21.5730337078652 & -4.57303370786517 \tabularnewline
64 & 17 & 25.0869565217391 & -8.08695652173913 \tabularnewline
65 & 24 & 21.5730337078652 & 2.42696629213483 \tabularnewline
66 & 30 & 25.0869565217391 & 4.91304347826087 \tabularnewline
67 & 25 & 25.0869565217391 & -0.086956521739129 \tabularnewline
68 & 15 & 21.5730337078652 & -6.57303370786517 \tabularnewline
69 & 25 & 21.5730337078652 & 3.42696629213483 \tabularnewline
70 & 18 & 25.0869565217391 & -7.08695652173913 \tabularnewline
71 & 20 & 25.0869565217391 & -5.08695652173913 \tabularnewline
72 & 32 & 25.0869565217391 & 6.91304347826087 \tabularnewline
73 & 14 & 17.2 & -3.2 \tabularnewline
74 & 20 & 21.5730337078652 & -1.57303370786517 \tabularnewline
75 & 25 & 21.5730337078652 & 3.42696629213483 \tabularnewline
76 & 25 & 21.5730337078652 & 3.42696629213483 \tabularnewline
77 & 25 & 21.5730337078652 & 3.42696629213483 \tabularnewline
78 & 35 & 25.0869565217391 & 9.91304347826087 \tabularnewline
79 & 29 & 25.0869565217391 & 3.91304347826087 \tabularnewline
80 & 25 & 21.5730337078652 & 3.42696629213483 \tabularnewline
81 & 21 & 21.5730337078652 & -0.573033707865168 \tabularnewline
82 & 21 & 21.5730337078652 & -0.573033707865168 \tabularnewline
83 & 24 & 25.0869565217391 & -1.08695652173913 \tabularnewline
84 & 26 & 21.5730337078652 & 4.42696629213483 \tabularnewline
85 & 24 & 21.5730337078652 & 2.42696629213483 \tabularnewline
86 & 20 & 21.5730337078652 & -1.57303370786517 \tabularnewline
87 & 24 & 21.5730337078652 & 2.42696629213483 \tabularnewline
88 & 18 & 17.2 & 0.8 \tabularnewline
89 & 17 & 17.2 & -0.199999999999999 \tabularnewline
90 & 22 & 25.0869565217391 & -3.08695652173913 \tabularnewline
91 & 22 & 21.5730337078652 & 0.426966292134832 \tabularnewline
92 & 22 & 21.5730337078652 & 0.426966292134832 \tabularnewline
93 & 24 & 25.0869565217391 & -1.08695652173913 \tabularnewline
94 & 32 & 25.0869565217391 & 6.91304347826087 \tabularnewline
95 & 19 & 21.5730337078652 & -2.57303370786517 \tabularnewline
96 & 21 & 21.5730337078652 & -0.573033707865168 \tabularnewline
97 & 23 & 21.5730337078652 & 1.42696629213483 \tabularnewline
98 & 26 & 25.0869565217391 & 0.913043478260871 \tabularnewline
99 & 18 & 25.0869565217391 & -7.08695652173913 \tabularnewline
100 & 19 & 17.2 & 1.8 \tabularnewline
101 & 22 & 21.5730337078652 & 0.426966292134832 \tabularnewline
102 & 27 & 21.5730337078652 & 5.42696629213483 \tabularnewline
103 & 21 & 21.5730337078652 & -0.573033707865168 \tabularnewline
104 & 20 & 21.5730337078652 & -1.57303370786517 \tabularnewline
105 & 21 & 25.0869565217391 & -4.08695652173913 \tabularnewline
106 & 20 & 21.5730337078652 & -1.57303370786517 \tabularnewline
107 & 29 & 25.0869565217391 & 3.91304347826087 \tabularnewline
108 & 30 & 21.5730337078652 & 8.42696629213483 \tabularnewline
109 & 10 & 17.2 & -7.2 \tabularnewline
110 & 23 & 21.5730337078652 & 1.42696629213483 \tabularnewline
111 & 29 & 21.5730337078652 & 7.42696629213483 \tabularnewline
112 & 19 & 21.5730337078652 & -2.57303370786517 \tabularnewline
113 & 26 & 21.5730337078652 & 4.42696629213483 \tabularnewline
114 & 22 & 21.5730337078652 & 0.426966292134832 \tabularnewline
115 & 26 & 25.0869565217391 & 0.913043478260871 \tabularnewline
116 & 27 & 25.0869565217391 & 1.91304347826087 \tabularnewline
117 & 19 & 25.0869565217391 & -6.08695652173913 \tabularnewline
118 & 24 & 21.5730337078652 & 2.42696629213483 \tabularnewline
119 & 26 & 21.5730337078652 & 4.42696629213483 \tabularnewline
120 & 22 & 21.5730337078652 & 0.426966292134832 \tabularnewline
121 & 23 & 25.0869565217391 & -2.08695652173913 \tabularnewline
122 & 25 & 25.0869565217391 & -0.086956521739129 \tabularnewline
123 & 19 & 21.5730337078652 & -2.57303370786517 \tabularnewline
124 & 20 & 21.5730337078652 & -1.57303370786517 \tabularnewline
125 & 25 & 25.0869565217391 & -0.086956521739129 \tabularnewline
126 & 14 & 17.2 & -3.2 \tabularnewline
127 & 19 & 21.5730337078652 & -2.57303370786517 \tabularnewline
128 & 27 & 25.0869565217391 & 1.91304347826087 \tabularnewline
129 & 21 & 21.5730337078652 & -0.573033707865168 \tabularnewline
130 & 21 & 21.5730337078652 & -0.573033707865168 \tabularnewline
131 & 14 & 17.2 & -3.2 \tabularnewline
132 & 21 & 25.0869565217391 & -4.08695652173913 \tabularnewline
133 & 23 & 21.5730337078652 & 1.42696629213483 \tabularnewline
134 & 18 & 21.5730337078652 & -3.57303370786517 \tabularnewline
135 & 20 & 21.5730337078652 & -1.57303370786517 \tabularnewline
136 & 19 & 21.5730337078652 & -2.57303370786517 \tabularnewline
137 & 15 & 21.5730337078652 & -6.57303370786517 \tabularnewline
138 & 23 & 21.5730337078652 & 1.42696629213483 \tabularnewline
139 & 26 & 25.0869565217391 & 0.913043478260871 \tabularnewline
140 & 21 & 21.5730337078652 & -0.573033707865168 \tabularnewline
141 & 13 & 17.2 & -4.2 \tabularnewline
142 & 24 & 21.5730337078652 & 2.42696629213483 \tabularnewline
143 & 17 & 21.5730337078652 & -4.57303370786517 \tabularnewline
144 & 21 & 21.5730337078652 & -0.573033707865168 \tabularnewline
145 & 28 & 25.0869565217391 & 2.91304347826087 \tabularnewline
146 & 22 & 21.5730337078652 & 0.426966292134832 \tabularnewline
147 & 25 & 17.2 & 7.8 \tabularnewline
148 & 27 & 25.0869565217391 & 1.91304347826087 \tabularnewline
149 & 25 & 21.5730337078652 & 3.42696629213483 \tabularnewline
150 & 21 & 21.5730337078652 & -0.573033707865168 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108429&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]15[/C][C]17.2[/C][C]-2.2[/C][/ROW]
[ROW][C]2[/C][C]23[/C][C]25.0869565217391[/C][C]-2.08695652173913[/C][/ROW]
[ROW][C]3[/C][C]26[/C][C]25.0869565217391[/C][C]0.913043478260871[/C][/ROW]
[ROW][C]4[/C][C]19[/C][C]21.5730337078652[/C][C]-2.57303370786517[/C][/ROW]
[ROW][C]5[/C][C]19[/C][C]21.5730337078652[/C][C]-2.57303370786517[/C][/ROW]
[ROW][C]6[/C][C]16[/C][C]21.5730337078652[/C][C]-5.57303370786517[/C][/ROW]
[ROW][C]7[/C][C]23[/C][C]25.0869565217391[/C][C]-2.08695652173913[/C][/ROW]
[ROW][C]8[/C][C]22[/C][C]21.5730337078652[/C][C]0.426966292134832[/C][/ROW]
[ROW][C]9[/C][C]19[/C][C]21.5730337078652[/C][C]-2.57303370786517[/C][/ROW]
[ROW][C]10[/C][C]24[/C][C]21.5730337078652[/C][C]2.42696629213483[/C][/ROW]
[ROW][C]11[/C][C]19[/C][C]17.2[/C][C]1.8[/C][/ROW]
[ROW][C]12[/C][C]25[/C][C]25.0869565217391[/C][C]-0.086956521739129[/C][/ROW]
[ROW][C]13[/C][C]23[/C][C]25.0869565217391[/C][C]-2.08695652173913[/C][/ROW]
[ROW][C]14[/C][C]31[/C][C]25.0869565217391[/C][C]5.91304347826087[/C][/ROW]
[ROW][C]15[/C][C]29[/C][C]25.0869565217391[/C][C]3.91304347826087[/C][/ROW]
[ROW][C]16[/C][C]18[/C][C]21.5730337078652[/C][C]-3.57303370786517[/C][/ROW]
[ROW][C]17[/C][C]17[/C][C]21.5730337078652[/C][C]-4.57303370786517[/C][/ROW]
[ROW][C]18[/C][C]22[/C][C]17.2[/C][C]4.8[/C][/ROW]
[ROW][C]19[/C][C]21[/C][C]21.5730337078652[/C][C]-0.573033707865168[/C][/ROW]
[ROW][C]20[/C][C]24[/C][C]25.0869565217391[/C][C]-1.08695652173913[/C][/ROW]
[ROW][C]21[/C][C]22[/C][C]21.5730337078652[/C][C]0.426966292134832[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]17.2[/C][C]-1.2[/C][/ROW]
[ROW][C]23[/C][C]22[/C][C]21.5730337078652[/C][C]0.426966292134832[/C][/ROW]
[ROW][C]24[/C][C]21[/C][C]21.5730337078652[/C][C]-0.573033707865168[/C][/ROW]
[ROW][C]25[/C][C]25[/C][C]21.5730337078652[/C][C]3.42696629213483[/C][/ROW]
[ROW][C]26[/C][C]22[/C][C]25.0869565217391[/C][C]-3.08695652173913[/C][/ROW]
[ROW][C]27[/C][C]24[/C][C]21.5730337078652[/C][C]2.42696629213483[/C][/ROW]
[ROW][C]28[/C][C]21[/C][C]25.0869565217391[/C][C]-4.08695652173913[/C][/ROW]
[ROW][C]29[/C][C]25[/C][C]25.0869565217391[/C][C]-0.086956521739129[/C][/ROW]
[ROW][C]30[/C][C]29[/C][C]25.0869565217391[/C][C]3.91304347826087[/C][/ROW]
[ROW][C]31[/C][C]19[/C][C]21.5730337078652[/C][C]-2.57303370786517[/C][/ROW]
[ROW][C]32[/C][C]29[/C][C]25.0869565217391[/C][C]3.91304347826087[/C][/ROW]
[ROW][C]33[/C][C]25[/C][C]21.5730337078652[/C][C]3.42696629213483[/C][/ROW]
[ROW][C]34[/C][C]19[/C][C]21.5730337078652[/C][C]-2.57303370786517[/C][/ROW]
[ROW][C]35[/C][C]27[/C][C]21.5730337078652[/C][C]5.42696629213483[/C][/ROW]
[ROW][C]36[/C][C]25[/C][C]21.5730337078652[/C][C]3.42696629213483[/C][/ROW]
[ROW][C]37[/C][C]23[/C][C]21.5730337078652[/C][C]1.42696629213483[/C][/ROW]
[ROW][C]38[/C][C]24[/C][C]21.5730337078652[/C][C]2.42696629213483[/C][/ROW]
[ROW][C]39[/C][C]23[/C][C]21.5730337078652[/C][C]1.42696629213483[/C][/ROW]
[ROW][C]40[/C][C]25[/C][C]21.5730337078652[/C][C]3.42696629213483[/C][/ROW]
[ROW][C]41[/C][C]23[/C][C]25.0869565217391[/C][C]-2.08695652173913[/C][/ROW]
[ROW][C]42[/C][C]22[/C][C]21.5730337078652[/C][C]0.426966292134832[/C][/ROW]
[ROW][C]43[/C][C]32[/C][C]25.0869565217391[/C][C]6.91304347826087[/C][/ROW]
[ROW][C]44[/C][C]22[/C][C]25.0869565217391[/C][C]-3.08695652173913[/C][/ROW]
[ROW][C]45[/C][C]18[/C][C]21.5730337078652[/C][C]-3.57303370786517[/C][/ROW]
[ROW][C]46[/C][C]19[/C][C]21.5730337078652[/C][C]-2.57303370786517[/C][/ROW]
[ROW][C]47[/C][C]23[/C][C]21.5730337078652[/C][C]1.42696629213483[/C][/ROW]
[ROW][C]48[/C][C]19[/C][C]21.5730337078652[/C][C]-2.57303370786517[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]21.5730337078652[/C][C]-5.57303370786517[/C][/ROW]
[ROW][C]50[/C][C]23[/C][C]21.5730337078652[/C][C]1.42696629213483[/C][/ROW]
[ROW][C]51[/C][C]17[/C][C]25.0869565217391[/C][C]-8.08695652173913[/C][/ROW]
[ROW][C]52[/C][C]17[/C][C]21.5730337078652[/C][C]-4.57303370786517[/C][/ROW]
[ROW][C]53[/C][C]28[/C][C]25.0869565217391[/C][C]2.91304347826087[/C][/ROW]
[ROW][C]54[/C][C]24[/C][C]21.5730337078652[/C][C]2.42696629213483[/C][/ROW]
[ROW][C]55[/C][C]21[/C][C]17.2[/C][C]3.8[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]21.5730337078652[/C][C]-7.57303370786517[/C][/ROW]
[ROW][C]57[/C][C]21[/C][C]17.2[/C][C]3.8[/C][/ROW]
[ROW][C]58[/C][C]20[/C][C]21.5730337078652[/C][C]-1.57303370786517[/C][/ROW]
[ROW][C]59[/C][C]25[/C][C]21.5730337078652[/C][C]3.42696629213483[/C][/ROW]
[ROW][C]60[/C][C]20[/C][C]21.5730337078652[/C][C]-1.57303370786517[/C][/ROW]
[ROW][C]61[/C][C]17[/C][C]21.5730337078652[/C][C]-4.57303370786517[/C][/ROW]
[ROW][C]62[/C][C]26[/C][C]25.0869565217391[/C][C]0.913043478260871[/C][/ROW]
[ROW][C]63[/C][C]17[/C][C]21.5730337078652[/C][C]-4.57303370786517[/C][/ROW]
[ROW][C]64[/C][C]17[/C][C]25.0869565217391[/C][C]-8.08695652173913[/C][/ROW]
[ROW][C]65[/C][C]24[/C][C]21.5730337078652[/C][C]2.42696629213483[/C][/ROW]
[ROW][C]66[/C][C]30[/C][C]25.0869565217391[/C][C]4.91304347826087[/C][/ROW]
[ROW][C]67[/C][C]25[/C][C]25.0869565217391[/C][C]-0.086956521739129[/C][/ROW]
[ROW][C]68[/C][C]15[/C][C]21.5730337078652[/C][C]-6.57303370786517[/C][/ROW]
[ROW][C]69[/C][C]25[/C][C]21.5730337078652[/C][C]3.42696629213483[/C][/ROW]
[ROW][C]70[/C][C]18[/C][C]25.0869565217391[/C][C]-7.08695652173913[/C][/ROW]
[ROW][C]71[/C][C]20[/C][C]25.0869565217391[/C][C]-5.08695652173913[/C][/ROW]
[ROW][C]72[/C][C]32[/C][C]25.0869565217391[/C][C]6.91304347826087[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]17.2[/C][C]-3.2[/C][/ROW]
[ROW][C]74[/C][C]20[/C][C]21.5730337078652[/C][C]-1.57303370786517[/C][/ROW]
[ROW][C]75[/C][C]25[/C][C]21.5730337078652[/C][C]3.42696629213483[/C][/ROW]
[ROW][C]76[/C][C]25[/C][C]21.5730337078652[/C][C]3.42696629213483[/C][/ROW]
[ROW][C]77[/C][C]25[/C][C]21.5730337078652[/C][C]3.42696629213483[/C][/ROW]
[ROW][C]78[/C][C]35[/C][C]25.0869565217391[/C][C]9.91304347826087[/C][/ROW]
[ROW][C]79[/C][C]29[/C][C]25.0869565217391[/C][C]3.91304347826087[/C][/ROW]
[ROW][C]80[/C][C]25[/C][C]21.5730337078652[/C][C]3.42696629213483[/C][/ROW]
[ROW][C]81[/C][C]21[/C][C]21.5730337078652[/C][C]-0.573033707865168[/C][/ROW]
[ROW][C]82[/C][C]21[/C][C]21.5730337078652[/C][C]-0.573033707865168[/C][/ROW]
[ROW][C]83[/C][C]24[/C][C]25.0869565217391[/C][C]-1.08695652173913[/C][/ROW]
[ROW][C]84[/C][C]26[/C][C]21.5730337078652[/C][C]4.42696629213483[/C][/ROW]
[ROW][C]85[/C][C]24[/C][C]21.5730337078652[/C][C]2.42696629213483[/C][/ROW]
[ROW][C]86[/C][C]20[/C][C]21.5730337078652[/C][C]-1.57303370786517[/C][/ROW]
[ROW][C]87[/C][C]24[/C][C]21.5730337078652[/C][C]2.42696629213483[/C][/ROW]
[ROW][C]88[/C][C]18[/C][C]17.2[/C][C]0.8[/C][/ROW]
[ROW][C]89[/C][C]17[/C][C]17.2[/C][C]-0.199999999999999[/C][/ROW]
[ROW][C]90[/C][C]22[/C][C]25.0869565217391[/C][C]-3.08695652173913[/C][/ROW]
[ROW][C]91[/C][C]22[/C][C]21.5730337078652[/C][C]0.426966292134832[/C][/ROW]
[ROW][C]92[/C][C]22[/C][C]21.5730337078652[/C][C]0.426966292134832[/C][/ROW]
[ROW][C]93[/C][C]24[/C][C]25.0869565217391[/C][C]-1.08695652173913[/C][/ROW]
[ROW][C]94[/C][C]32[/C][C]25.0869565217391[/C][C]6.91304347826087[/C][/ROW]
[ROW][C]95[/C][C]19[/C][C]21.5730337078652[/C][C]-2.57303370786517[/C][/ROW]
[ROW][C]96[/C][C]21[/C][C]21.5730337078652[/C][C]-0.573033707865168[/C][/ROW]
[ROW][C]97[/C][C]23[/C][C]21.5730337078652[/C][C]1.42696629213483[/C][/ROW]
[ROW][C]98[/C][C]26[/C][C]25.0869565217391[/C][C]0.913043478260871[/C][/ROW]
[ROW][C]99[/C][C]18[/C][C]25.0869565217391[/C][C]-7.08695652173913[/C][/ROW]
[ROW][C]100[/C][C]19[/C][C]17.2[/C][C]1.8[/C][/ROW]
[ROW][C]101[/C][C]22[/C][C]21.5730337078652[/C][C]0.426966292134832[/C][/ROW]
[ROW][C]102[/C][C]27[/C][C]21.5730337078652[/C][C]5.42696629213483[/C][/ROW]
[ROW][C]103[/C][C]21[/C][C]21.5730337078652[/C][C]-0.573033707865168[/C][/ROW]
[ROW][C]104[/C][C]20[/C][C]21.5730337078652[/C][C]-1.57303370786517[/C][/ROW]
[ROW][C]105[/C][C]21[/C][C]25.0869565217391[/C][C]-4.08695652173913[/C][/ROW]
[ROW][C]106[/C][C]20[/C][C]21.5730337078652[/C][C]-1.57303370786517[/C][/ROW]
[ROW][C]107[/C][C]29[/C][C]25.0869565217391[/C][C]3.91304347826087[/C][/ROW]
[ROW][C]108[/C][C]30[/C][C]21.5730337078652[/C][C]8.42696629213483[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]17.2[/C][C]-7.2[/C][/ROW]
[ROW][C]110[/C][C]23[/C][C]21.5730337078652[/C][C]1.42696629213483[/C][/ROW]
[ROW][C]111[/C][C]29[/C][C]21.5730337078652[/C][C]7.42696629213483[/C][/ROW]
[ROW][C]112[/C][C]19[/C][C]21.5730337078652[/C][C]-2.57303370786517[/C][/ROW]
[ROW][C]113[/C][C]26[/C][C]21.5730337078652[/C][C]4.42696629213483[/C][/ROW]
[ROW][C]114[/C][C]22[/C][C]21.5730337078652[/C][C]0.426966292134832[/C][/ROW]
[ROW][C]115[/C][C]26[/C][C]25.0869565217391[/C][C]0.913043478260871[/C][/ROW]
[ROW][C]116[/C][C]27[/C][C]25.0869565217391[/C][C]1.91304347826087[/C][/ROW]
[ROW][C]117[/C][C]19[/C][C]25.0869565217391[/C][C]-6.08695652173913[/C][/ROW]
[ROW][C]118[/C][C]24[/C][C]21.5730337078652[/C][C]2.42696629213483[/C][/ROW]
[ROW][C]119[/C][C]26[/C][C]21.5730337078652[/C][C]4.42696629213483[/C][/ROW]
[ROW][C]120[/C][C]22[/C][C]21.5730337078652[/C][C]0.426966292134832[/C][/ROW]
[ROW][C]121[/C][C]23[/C][C]25.0869565217391[/C][C]-2.08695652173913[/C][/ROW]
[ROW][C]122[/C][C]25[/C][C]25.0869565217391[/C][C]-0.086956521739129[/C][/ROW]
[ROW][C]123[/C][C]19[/C][C]21.5730337078652[/C][C]-2.57303370786517[/C][/ROW]
[ROW][C]124[/C][C]20[/C][C]21.5730337078652[/C][C]-1.57303370786517[/C][/ROW]
[ROW][C]125[/C][C]25[/C][C]25.0869565217391[/C][C]-0.086956521739129[/C][/ROW]
[ROW][C]126[/C][C]14[/C][C]17.2[/C][C]-3.2[/C][/ROW]
[ROW][C]127[/C][C]19[/C][C]21.5730337078652[/C][C]-2.57303370786517[/C][/ROW]
[ROW][C]128[/C][C]27[/C][C]25.0869565217391[/C][C]1.91304347826087[/C][/ROW]
[ROW][C]129[/C][C]21[/C][C]21.5730337078652[/C][C]-0.573033707865168[/C][/ROW]
[ROW][C]130[/C][C]21[/C][C]21.5730337078652[/C][C]-0.573033707865168[/C][/ROW]
[ROW][C]131[/C][C]14[/C][C]17.2[/C][C]-3.2[/C][/ROW]
[ROW][C]132[/C][C]21[/C][C]25.0869565217391[/C][C]-4.08695652173913[/C][/ROW]
[ROW][C]133[/C][C]23[/C][C]21.5730337078652[/C][C]1.42696629213483[/C][/ROW]
[ROW][C]134[/C][C]18[/C][C]21.5730337078652[/C][C]-3.57303370786517[/C][/ROW]
[ROW][C]135[/C][C]20[/C][C]21.5730337078652[/C][C]-1.57303370786517[/C][/ROW]
[ROW][C]136[/C][C]19[/C][C]21.5730337078652[/C][C]-2.57303370786517[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]21.5730337078652[/C][C]-6.57303370786517[/C][/ROW]
[ROW][C]138[/C][C]23[/C][C]21.5730337078652[/C][C]1.42696629213483[/C][/ROW]
[ROW][C]139[/C][C]26[/C][C]25.0869565217391[/C][C]0.913043478260871[/C][/ROW]
[ROW][C]140[/C][C]21[/C][C]21.5730337078652[/C][C]-0.573033707865168[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]17.2[/C][C]-4.2[/C][/ROW]
[ROW][C]142[/C][C]24[/C][C]21.5730337078652[/C][C]2.42696629213483[/C][/ROW]
[ROW][C]143[/C][C]17[/C][C]21.5730337078652[/C][C]-4.57303370786517[/C][/ROW]
[ROW][C]144[/C][C]21[/C][C]21.5730337078652[/C][C]-0.573033707865168[/C][/ROW]
[ROW][C]145[/C][C]28[/C][C]25.0869565217391[/C][C]2.91304347826087[/C][/ROW]
[ROW][C]146[/C][C]22[/C][C]21.5730337078652[/C][C]0.426966292134832[/C][/ROW]
[ROW][C]147[/C][C]25[/C][C]17.2[/C][C]7.8[/C][/ROW]
[ROW][C]148[/C][C]27[/C][C]25.0869565217391[/C][C]1.91304347826087[/C][/ROW]
[ROW][C]149[/C][C]25[/C][C]21.5730337078652[/C][C]3.42696629213483[/C][/ROW]
[ROW][C]150[/C][C]21[/C][C]21.5730337078652[/C][C]-0.573033707865168[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108429&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108429&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	15	17.2	-2.2
2	23	25.0869565217391	-2.08695652173913
3	26	25.0869565217391	0.913043478260871
4	19	21.5730337078652	-2.57303370786517
5	19	21.5730337078652	-2.57303370786517
6	16	21.5730337078652	-5.57303370786517
7	23	25.0869565217391	-2.08695652173913
8	22	21.5730337078652	0.426966292134832
9	19	21.5730337078652	-2.57303370786517
10	24	21.5730337078652	2.42696629213483
11	19	17.2	1.8
12	25	25.0869565217391	-0.086956521739129
13	23	25.0869565217391	-2.08695652173913
14	31	25.0869565217391	5.91304347826087
15	29	25.0869565217391	3.91304347826087
16	18	21.5730337078652	-3.57303370786517
17	17	21.5730337078652	-4.57303370786517
18	22	17.2	4.8
19	21	21.5730337078652	-0.573033707865168
20	24	25.0869565217391	-1.08695652173913
21	22	21.5730337078652	0.426966292134832
22	16	17.2	-1.2
23	22	21.5730337078652	0.426966292134832
24	21	21.5730337078652	-0.573033707865168
25	25	21.5730337078652	3.42696629213483
26	22	25.0869565217391	-3.08695652173913
27	24	21.5730337078652	2.42696629213483
28	21	25.0869565217391	-4.08695652173913
29	25	25.0869565217391	-0.086956521739129
30	29	25.0869565217391	3.91304347826087
31	19	21.5730337078652	-2.57303370786517
32	29	25.0869565217391	3.91304347826087
33	25	21.5730337078652	3.42696629213483
34	19	21.5730337078652	-2.57303370786517
35	27	21.5730337078652	5.42696629213483
36	25	21.5730337078652	3.42696629213483
37	23	21.5730337078652	1.42696629213483
38	24	21.5730337078652	2.42696629213483
39	23	21.5730337078652	1.42696629213483
40	25	21.5730337078652	3.42696629213483
41	23	25.0869565217391	-2.08695652173913
42	22	21.5730337078652	0.426966292134832
43	32	25.0869565217391	6.91304347826087
44	22	25.0869565217391	-3.08695652173913
45	18	21.5730337078652	-3.57303370786517
46	19	21.5730337078652	-2.57303370786517
47	23	21.5730337078652	1.42696629213483
48	19	21.5730337078652	-2.57303370786517
49	16	21.5730337078652	-5.57303370786517
50	23	21.5730337078652	1.42696629213483
51	17	25.0869565217391	-8.08695652173913
52	17	21.5730337078652	-4.57303370786517
53	28	25.0869565217391	2.91304347826087
54	24	21.5730337078652	2.42696629213483
55	21	17.2	3.8
56	14	21.5730337078652	-7.57303370786517
57	21	17.2	3.8
58	20	21.5730337078652	-1.57303370786517
59	25	21.5730337078652	3.42696629213483
60	20	21.5730337078652	-1.57303370786517
61	17	21.5730337078652	-4.57303370786517
62	26	25.0869565217391	0.913043478260871
63	17	21.5730337078652	-4.57303370786517
64	17	25.0869565217391	-8.08695652173913
65	24	21.5730337078652	2.42696629213483
66	30	25.0869565217391	4.91304347826087
67	25	25.0869565217391	-0.086956521739129
68	15	21.5730337078652	-6.57303370786517
69	25	21.5730337078652	3.42696629213483
70	18	25.0869565217391	-7.08695652173913
71	20	25.0869565217391	-5.08695652173913
72	32	25.0869565217391	6.91304347826087
73	14	17.2	-3.2
74	20	21.5730337078652	-1.57303370786517
75	25	21.5730337078652	3.42696629213483
76	25	21.5730337078652	3.42696629213483
77	25	21.5730337078652	3.42696629213483
78	35	25.0869565217391	9.91304347826087
79	29	25.0869565217391	3.91304347826087
80	25	21.5730337078652	3.42696629213483
81	21	21.5730337078652	-0.573033707865168
82	21	21.5730337078652	-0.573033707865168
83	24	25.0869565217391	-1.08695652173913
84	26	21.5730337078652	4.42696629213483
85	24	21.5730337078652	2.42696629213483
86	20	21.5730337078652	-1.57303370786517
87	24	21.5730337078652	2.42696629213483
88	18	17.2	0.8
89	17	17.2	-0.199999999999999
90	22	25.0869565217391	-3.08695652173913
91	22	21.5730337078652	0.426966292134832
92	22	21.5730337078652	0.426966292134832
93	24	25.0869565217391	-1.08695652173913
94	32	25.0869565217391	6.91304347826087
95	19	21.5730337078652	-2.57303370786517
96	21	21.5730337078652	-0.573033707865168
97	23	21.5730337078652	1.42696629213483
98	26	25.0869565217391	0.913043478260871
99	18	25.0869565217391	-7.08695652173913
100	19	17.2	1.8
101	22	21.5730337078652	0.426966292134832
102	27	21.5730337078652	5.42696629213483
103	21	21.5730337078652	-0.573033707865168
104	20	21.5730337078652	-1.57303370786517
105	21	25.0869565217391	-4.08695652173913
106	20	21.5730337078652	-1.57303370786517
107	29	25.0869565217391	3.91304347826087
108	30	21.5730337078652	8.42696629213483
109	10	17.2	-7.2
110	23	21.5730337078652	1.42696629213483
111	29	21.5730337078652	7.42696629213483
112	19	21.5730337078652	-2.57303370786517
113	26	21.5730337078652	4.42696629213483
114	22	21.5730337078652	0.426966292134832
115	26	25.0869565217391	0.913043478260871
116	27	25.0869565217391	1.91304347826087
117	19	25.0869565217391	-6.08695652173913
118	24	21.5730337078652	2.42696629213483
119	26	21.5730337078652	4.42696629213483
120	22	21.5730337078652	0.426966292134832
121	23	25.0869565217391	-2.08695652173913
122	25	25.0869565217391	-0.086956521739129
123	19	21.5730337078652	-2.57303370786517
124	20	21.5730337078652	-1.57303370786517
125	25	25.0869565217391	-0.086956521739129
126	14	17.2	-3.2
127	19	21.5730337078652	-2.57303370786517
128	27	25.0869565217391	1.91304347826087
129	21	21.5730337078652	-0.573033707865168
130	21	21.5730337078652	-0.573033707865168
131	14	17.2	-3.2
132	21	25.0869565217391	-4.08695652173913
133	23	21.5730337078652	1.42696629213483
134	18	21.5730337078652	-3.57303370786517
135	20	21.5730337078652	-1.57303370786517
136	19	21.5730337078652	-2.57303370786517
137	15	21.5730337078652	-6.57303370786517
138	23	21.5730337078652	1.42696629213483
139	26	25.0869565217391	0.913043478260871
140	21	21.5730337078652	-0.573033707865168
141	13	17.2	-4.2
142	24	21.5730337078652	2.42696629213483
143	17	21.5730337078652	-4.57303370786517
144	21	21.5730337078652	-0.573033707865168
145	28	25.0869565217391	2.91304347826087
146	22	21.5730337078652	0.426966292134832
147	25	17.2	7.8
148	27	25.0869565217391	1.91304347826087
149	25	21.5730337078652	3.42696629213483
150	21	21.5730337078652	-0.573033707865168

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 = 5 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 5 ; 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