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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

Sat, 11 Dec 2010 14:06:40 +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/11/t1292076298wd0wf11vej9ml7y.htm/, Retrieved Mon, 06 May 2024 16:13:16 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108165, Retrieved Mon, 06 May 2024 16:13:16 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

164

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

-     [Kendall tau Correlation Matrix] [Workshop 10 - Pea...] [2010-12-11 12:06:02] [87d60b8864dc39f7ed759c345edfb471]
-   P   [Kendall tau Correlation Matrix] [Workshop 10 - Ken...] [2010-12-11 12:11:11] [87d60b8864dc39f7ed759c345edfb471]
- RMP       [Recursive Partitioning (Regression Trees)] [Workshop 10 - Rec...] [2010-12-11 14:06:40] [c52f616cc59ab01e55ce1a10b5754887] [Current]
-   P         [Recursive Partitioning (Regression Trees)] [Workshop 10 - Rec...] [2010-12-11 15:27:19] [87d60b8864dc39f7ed759c345edfb471] 
-               [Recursive Partitioning (Regression Trees)] [Workshop 10 - Rec...] [2010-12-11 15:47:28] [87d60b8864dc39f7ed759c345edfb471] 
-   PD            [Recursive Partitioning (Regression Trees)] [Workshop 10 - Rec...] [2010-12-12 09:15:58] [87d60b8864dc39f7ed759c345edfb471] 
-   P               [Recursive Partitioning (Regression Trees)] [Workshop 10 - Rec...] [2010-12-12 09:33:10] [87d60b8864dc39f7ed759c345edfb471] 
-    D                [Recursive Partitioning (Regression Trees)] [blog 2] [2010-12-16 20:10:52] [1afa3497b02a8d7c9f6727c1b17b89b2] 
-   PD              [Recursive Partitioning (Regression Trees)] [Recursive partiti...] [2010-12-17 16:47:59] [87d60b8864dc39f7ed759c345edfb471] 
-               [Recursive Partitioning (Regression Trees)] [Workshop 10 - Rec...] [2010-12-11 16:13:21] [87d60b8864dc39f7ed759c345edfb471] 
-    D        [Recursive Partitioning (Regression Trees)] [Workshop 10 - Rec...] [2010-12-12 09:03:45] [87d60b8864dc39f7ed759c345edfb471] 
-   PD          [Recursive Partitioning (Regression Trees)] [Recursive partiti...] [2010-12-17 16:36:02] [87d60b8864dc39f7ed759c345edfb471] 
-   P             [Recursive Partitioning (Regression Trees)] [Recursive partiti...] [2010-12-21 09:09:25] [87d60b8864dc39f7ed759c345edfb471] 
-   P               [Recursive Partitioning (Regression Trees)] [Recursive partiti...] [2010-12-21 10:24:22] [87d60b8864dc39f7ed759c345edfb471] 

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Dataseries X:

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Histogram

Boxplots

0	24	14	11	12	24	26
0	25	11	7	8	25	23
0	17	6	17	8	30	25
1	18	12	10	8	19	23
1	18	8	12	9	22	19
1	16	10	12	7	22	29
1	20	10	11	4	25	25
1	16	11	11	11	23	21
1	18	16	12	7	17	22
1	17	11	13	7	21	25
0	23	13	14	12	19	24
0	30	12	16	10	19	18
1	23	8	11	10	15	22
1	18	12	10	8	16	15
1	15	11	11	8	23	22
1	12	4	15	4	27	28
0	21	9	9	9	22	20
1	15	8	11	8	14	12
1	20	8	17	7	22	24
0	31	14	17	11	23	20
0	27	15	11	9	23	21
1	34	16	18	11	21	20
1	21	9	14	13	19	21
1	31	14	10	8	18	23
1	19	11	11	8	20	28
0	16	8	15	9	23	24
1	20	9	15	6	25	24
1	21	9	13	9	19	24
1	22	9	16	9	24	23
1	17	9	13	6	22	23
1	24	10	9	6	25	29
0	25	16	18	16	26	24
0	26	11	18	5	29	18
1	25	8	12	7	32	25
1	17	9	17	9	25	21
1	32	16	9	6	29	26
1	33	11	9	6	28	22
1	13	16	12	5	17	22
1	32	12	18	12	28	22
1	25	12	12	7	29	23
1	29	14	18	10	26	30
1	22	9	14	9	25	23
1	18	10	15	8	14	17
1	17	9	16	5	25	23
0	20	10	10	8	26	23
1	15	12	11	8	20	25
1	20	14	14	10	18	24
1	33	14	9	6	32	24
0	29	10	12	8	25	23
1	23	14	17	7	25	21
0	26	16	5	4	23	24
1	18	9	12	8	21	24
0	20	10	12	8	20	28
	11	6	6	4	15	16
1	28	8	24	20	30	20
1	26	13	12	8	24	29
0	22	10	12	8	26	27
1	17	8	14	6	24	22
0	12	7	7	4	22	28
1	14	15	13	8	14	16
1	17	9	12	9	24	25
1	21	10	13	6	24	24
1	19	12	14	7	24	28
1	18	13	8	9	24	24
0	10	10	11	5	19	23
0	29	11	9	5	31	30
1	31	8	11	8	22	24
0	19	9	13	8	27	21
1	9	13	10	6	19	25
1	20	11	11	8	25	25
1	28	8	12	7	20	22
0	19	9	9	7	21	23
0	30	9	15	9	27	26
0	29	15	18	11	23	23
0	26	9	15	6	25	25
0	23	10	12	8	20	21
1	13	14	13	6	21	25
1	21	12	14	9	22	24
1	19	12	10	8	23	29
1	28	11	13	6	25	22
1	23	14	13	10	25	27
1	18	6	11	8	17	26
0	21	12	13	8	19	22
1	20	8	16	10	25	24
1	23	14	8	5	19	27
1	21	11	16	7	20	24
1	21	10	11	5	26	24
1	15	14	9	8	23	29
1	28	12	16	14	27	22
1	19	10	12	7	17	21
1	26	14	14	8	17	24
1	10	5	8	6	19	24
0	16	11	9	5	17	23
1	22	10	15	6	22	20
1	19	9	11	10	21	27
1	31	10	21	12	32	26
0	31	16	14	9	21	25
1	29	13	18	12	21	21
0	19	9	12	7	18	21
1	22	10	13	8	18	19
1	23	10	15	10	23	21
0	15	7	12	6	19	21
0	20	9	19	10	20	16
1	18	8	15	10	21	22
1	23	14	11	10	20	29
1	25	14	11	5	17	15
1	21	8	10	7	18	17
1	24	9	13	10	19	15
1	25	14	15	11	22	21
1	17	14	12	6	15	21
1	13	8	12	7	14	19
1	28	8	16	12	18	24
0	21	8	9	11	24	20
1	25	7	18	11	35	17
0	9	6	8	11	29	23
1	16	8	13	5	21	24
1	19	6	17	8	25	14
1	17	11	9	6	20	19
1	25	14	15	9	22	24
1	20	11	8	4	13	13
1	29	11	7	4	26	22
1	14	11	12	7	17	16
1	22	14	14	11	25	19
1	15	8	6	6	20	25
0	19	20	8	7	19	25
1	20	11	17	8	21	23
0	15	8	10	4	22	24
1	20	11	11	8	24	26
1	18	10	14	9	21	26
1	33	14	11	8	26	25
1	22	11	13	11	24	18
1	16	9	12	8	16	21
1	17	9	11	5	23	26
1	16	8	9	4	18	23
0	21	10	12	8	16	23
0	26	13	20	10	26	22
1	18	13	12	6	19	20
1	18	12	13	9	21	13
1	17	8	12	9	21	24
1	22	13	12	13	22	15
1	30	14	9	9	23	14
0	30	12	15	10	29	22
1	24	14	24	20	21	10
1	21	15	7	5	21	24
1	21	13	17	11	23	22
1	29	16	11	6	27	24
1	31	9	17	9	25	19
1	20	9	11	7	21	20
0	16	9	12	9	10	13
0	22	8	14	10	20	20
1	20	7	11	9	26	22
1	28	16	16	8	24	24
1	38	11	21	7	29	29
0	22	9	14	6	19	12
1	20	11	20	13	24	20
0	17	9	13	6	19	21
1	28	14	11	8	24	24
1	22	13	15	10	22	22
0	31	16	19	16	17	20

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=108165&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=108165&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108165&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	0.8356
R-squared	0.6982
RMSE	3.6424

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.8356 \tabularnewline
R-squared & 0.6982 \tabularnewline
RMSE & 3.6424 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108165&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.8356[/C][/ROW]
[ROW][C]R-squared[/C][C]0.6982[/C][/ROW]
[ROW][C]RMSE[/C][C]3.6424[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108165&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108165&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	0.8356
R-squared	0.6982
RMSE	3.6424

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	24	26.1428571428571	-2.14285714285714
2	25	20.6842105263158	4.31578947368421
3	17	20.6842105263158	-3.68421052631579
4	18	20.6842105263158	-2.68421052631579
5	18	20.6842105263158	-2.68421052631579
6	16	20.6842105263158	-4.68421052631579
7	20	20.6842105263158	-0.684210526315791
8	16	20.6842105263158	-4.68421052631579
9	18	26.1428571428571	-8.14285714285714
10	17	20.6842105263158	-3.68421052631579
11	23	20.6842105263158	2.31578947368421
12	30	20.6842105263158	9.3157894736842
13	23	20.6842105263158	2.31578947368421
14	18	20.6842105263158	-2.68421052631579
15	15	20.6842105263158	-5.68421052631579
16	12	20.6842105263158	-8.6842105263158
17	21	20.6842105263158	0.315789473684209
18	15	10.1857142857143	4.81428571428571
19	20	20.6842105263158	-0.684210526315791
20	31	26.1428571428571	4.85714285714286
21	27	26.1428571428571	0.857142857142858
22	34	26.1428571428571	7.85714285714286
23	21	20.6842105263158	0.315789473684209
24	31	26.1428571428571	4.85714285714286
25	19	20.6842105263158	-1.68421052631579
26	16	20.6842105263158	-4.68421052631579
27	20	20.6842105263158	-0.684210526315791
28	21	20.6842105263158	0.315789473684209
29	22	20.6842105263158	1.31578947368421
30	17	20.6842105263158	-3.68421052631579
31	24	20.6842105263158	3.31578947368421
32	25	26.1428571428571	-1.14285714285714
33	26	20.6842105263158	5.31578947368421
34	25	20.6842105263158	4.31578947368421
35	17	20.6842105263158	-3.68421052631579
36	32	26.1428571428571	5.85714285714286
37	33	20.6842105263158	12.3157894736842
38	13	26.1428571428571	-13.1428571428571
39	32	20.6842105263158	11.3157894736842
40	25	20.6842105263158	4.31578947368421
41	29	26.1428571428571	2.85714285714286
42	22	20.6842105263158	1.31578947368421
43	18	20.6842105263158	-2.68421052631579
44	17	20.6842105263158	-3.68421052631579
45	20	20.6842105263158	-0.684210526315791
46	15	20.6842105263158	-5.68421052631579
47	20	26.1428571428571	-6.14285714285714
48	33	26.1428571428571	6.85714285714286
49	29	20.6842105263158	8.3157894736842
50	23	26.1428571428571	-3.14285714285714
51	26	26.1428571428571	-0.142857142857142
52	18	20.6842105263158	-2.68421052631579
53	20	20.6842105263158	-0.684210526315791
54	6	10.1857142857143	-4.18571428571429
55	8	12.0270270270270	-4.02702702702703
56	13	12.0270270270270	0.972972972972974
57	10	10.1857142857143	-0.185714285714285
58	8	10.1857142857143	-2.18571428571429
59	7	10.1857142857143	-3.18571428571429
60	15	10.1857142857143	4.81428571428571
61	9	10.1857142857143	-1.18571428571429
62	10	10.1857142857143	-0.185714285714285
63	12	10.1857142857143	1.81428571428571
64	13	10.1857142857143	2.81428571428571
65	10	10.1857142857143	-0.185714285714285
66	11	12.0270270270270	-1.02702702702703
67	8	12.0270270270270	-4.02702702702703
68	9	10.1857142857143	-1.18571428571429
69	13	10.1857142857143	2.81428571428571
70	11	10.1857142857143	0.814285714285715
71	8	12.0270270270270	-4.02702702702703
72	9	10.1857142857143	-1.18571428571429
73	9	12.0270270270270	-3.02702702702703
74	15	12.0270270270270	2.97297297297297
75	9	12.0270270270270	-3.02702702702703
76	10	12.0270270270270	-2.02702702702703
77	14	10.1857142857143	3.81428571428571
78	12	10.1857142857143	1.81428571428571
79	12	10.1857142857143	1.81428571428571
80	11	12.0270270270270	-1.02702702702703
81	14	12.0270270270270	1.97297297297297
82	6	10.1857142857143	-4.18571428571429
83	12	10.1857142857143	1.81428571428571
84	8	10.1857142857143	-2.18571428571429
85	14	12.0270270270270	1.97297297297297
86	11	10.1857142857143	0.814285714285715
87	10	10.1857142857143	-0.185714285714285
88	14	10.1857142857143	3.81428571428571
89	12	12.0270270270270	-0.0270270270270263
90	10	10.1857142857143	-0.185714285714285
91	14	12.0270270270270	1.97297297297297
92	5	10.1857142857143	-5.18571428571429
93	11	10.1857142857143	0.814285714285715
94	10	10.1857142857143	-0.185714285714285
95	9	10.1857142857143	-1.18571428571429
96	10	12.0270270270270	-2.02702702702703
97	16	12.0270270270270	3.97297297297297
98	13	12.0270270270270	0.972972972972974
99	9	10.1857142857143	-1.18571428571429
100	10	10.1857142857143	-0.185714285714285
101	10	12.0270270270270	-2.02702702702703
102	7	10.1857142857143	-3.18571428571429
103	9	10.1857142857143	-1.18571428571429
104	8	10.1857142857143	-2.18571428571429
105	14	12.0270270270270	1.97297297297297
106	14	12.0270270270270	1.97297297297297
107	8	10.1857142857143	-2.18571428571429
108	9	12.0270270270270	-3.02702702702703
109	14	12.0270270270270	1.97297297297297
110	14	10.1857142857143	3.81428571428571
111	8	10.1857142857143	-2.18571428571429
112	8	12.0270270270270	-4.02702702702703
113	8	10.1857142857143	-2.18571428571429
114	7	12.0270270270270	-5.02702702702703
115	6	10.1857142857143	-4.18571428571429
116	8	10.1857142857143	-2.18571428571429
117	6	10.1857142857143	-4.18571428571429
118	11	10.1857142857143	0.814285714285715
119	14	12.0270270270270	1.97297297297297
120	11	10.1857142857143	0.814285714285715
121	11	12.0270270270270	-1.02702702702703
122	11	10.1857142857143	0.814285714285715
123	14	10.1857142857143	3.81428571428571
124	8	10.1857142857143	-2.18571428571429
125	20	10.1857142857143	9.81428571428571
126	11	10.1857142857143	0.814285714285715
127	8	10.1857142857143	-2.18571428571429
128	11	10.1857142857143	0.814285714285715
129	10	10.1857142857143	-0.185714285714285
130	14	12.0270270270270	1.97297297297297
131	11	10.1857142857143	0.814285714285715
132	9	10.1857142857143	-1.18571428571429
133	9	10.1857142857143	-1.18571428571429
134	8	10.1857142857143	-2.18571428571429
135	10	10.1857142857143	-0.185714285714285
136	13	12.0270270270270	0.972972972972974
137	13	10.1857142857143	2.81428571428571
138	12	10.1857142857143	1.81428571428571
139	8	10.1857142857143	-2.18571428571429
140	13	10.1857142857143	2.81428571428571
141	14	12.0270270270270	1.97297297297297
142	12	12.0270270270270	-0.0270270270270263
143	14	12.0270270270270	1.97297297297297
144	15	10.1857142857143	4.81428571428571
145	13	10.1857142857143	2.81428571428571
146	16	12.0270270270270	3.97297297297297
147	9	12.0270270270270	-3.02702702702703
148	9	10.1857142857143	-1.18571428571429
149	9	10.1857142857143	-1.18571428571429
150	8	10.1857142857143	-2.18571428571429
151	7	10.1857142857143	-3.18571428571429
152	16	12.0270270270270	3.97297297297297
153	11	12.0270270270270	-1.02702702702703
154	9	10.1857142857143	-1.18571428571429
155	11	10.1857142857143	0.814285714285715
156	9	10.1857142857143	-1.18571428571429
157	14	12.0270270270270	1.97297297297297
158	13	10.1857142857143	2.81428571428571
159	16	12.0270270270270	3.97297297297297

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 24 & 26.1428571428571 & -2.14285714285714 \tabularnewline
2 & 25 & 20.6842105263158 & 4.31578947368421 \tabularnewline
3 & 17 & 20.6842105263158 & -3.68421052631579 \tabularnewline
4 & 18 & 20.6842105263158 & -2.68421052631579 \tabularnewline
5 & 18 & 20.6842105263158 & -2.68421052631579 \tabularnewline
6 & 16 & 20.6842105263158 & -4.68421052631579 \tabularnewline
7 & 20 & 20.6842105263158 & -0.684210526315791 \tabularnewline
8 & 16 & 20.6842105263158 & -4.68421052631579 \tabularnewline
9 & 18 & 26.1428571428571 & -8.14285714285714 \tabularnewline
10 & 17 & 20.6842105263158 & -3.68421052631579 \tabularnewline
11 & 23 & 20.6842105263158 & 2.31578947368421 \tabularnewline
12 & 30 & 20.6842105263158 & 9.3157894736842 \tabularnewline
13 & 23 & 20.6842105263158 & 2.31578947368421 \tabularnewline
14 & 18 & 20.6842105263158 & -2.68421052631579 \tabularnewline
15 & 15 & 20.6842105263158 & -5.68421052631579 \tabularnewline
16 & 12 & 20.6842105263158 & -8.6842105263158 \tabularnewline
17 & 21 & 20.6842105263158 & 0.315789473684209 \tabularnewline
18 & 15 & 10.1857142857143 & 4.81428571428571 \tabularnewline
19 & 20 & 20.6842105263158 & -0.684210526315791 \tabularnewline
20 & 31 & 26.1428571428571 & 4.85714285714286 \tabularnewline
21 & 27 & 26.1428571428571 & 0.857142857142858 \tabularnewline
22 & 34 & 26.1428571428571 & 7.85714285714286 \tabularnewline
23 & 21 & 20.6842105263158 & 0.315789473684209 \tabularnewline
24 & 31 & 26.1428571428571 & 4.85714285714286 \tabularnewline
25 & 19 & 20.6842105263158 & -1.68421052631579 \tabularnewline
26 & 16 & 20.6842105263158 & -4.68421052631579 \tabularnewline
27 & 20 & 20.6842105263158 & -0.684210526315791 \tabularnewline
28 & 21 & 20.6842105263158 & 0.315789473684209 \tabularnewline
29 & 22 & 20.6842105263158 & 1.31578947368421 \tabularnewline
30 & 17 & 20.6842105263158 & -3.68421052631579 \tabularnewline
31 & 24 & 20.6842105263158 & 3.31578947368421 \tabularnewline
32 & 25 & 26.1428571428571 & -1.14285714285714 \tabularnewline
33 & 26 & 20.6842105263158 & 5.31578947368421 \tabularnewline
34 & 25 & 20.6842105263158 & 4.31578947368421 \tabularnewline
35 & 17 & 20.6842105263158 & -3.68421052631579 \tabularnewline
36 & 32 & 26.1428571428571 & 5.85714285714286 \tabularnewline
37 & 33 & 20.6842105263158 & 12.3157894736842 \tabularnewline
38 & 13 & 26.1428571428571 & -13.1428571428571 \tabularnewline
39 & 32 & 20.6842105263158 & 11.3157894736842 \tabularnewline
40 & 25 & 20.6842105263158 & 4.31578947368421 \tabularnewline
41 & 29 & 26.1428571428571 & 2.85714285714286 \tabularnewline
42 & 22 & 20.6842105263158 & 1.31578947368421 \tabularnewline
43 & 18 & 20.6842105263158 & -2.68421052631579 \tabularnewline
44 & 17 & 20.6842105263158 & -3.68421052631579 \tabularnewline
45 & 20 & 20.6842105263158 & -0.684210526315791 \tabularnewline
46 & 15 & 20.6842105263158 & -5.68421052631579 \tabularnewline
47 & 20 & 26.1428571428571 & -6.14285714285714 \tabularnewline
48 & 33 & 26.1428571428571 & 6.85714285714286 \tabularnewline
49 & 29 & 20.6842105263158 & 8.3157894736842 \tabularnewline
50 & 23 & 26.1428571428571 & -3.14285714285714 \tabularnewline
51 & 26 & 26.1428571428571 & -0.142857142857142 \tabularnewline
52 & 18 & 20.6842105263158 & -2.68421052631579 \tabularnewline
53 & 20 & 20.6842105263158 & -0.684210526315791 \tabularnewline
54 & 6 & 10.1857142857143 & -4.18571428571429 \tabularnewline
55 & 8 & 12.0270270270270 & -4.02702702702703 \tabularnewline
56 & 13 & 12.0270270270270 & 0.972972972972974 \tabularnewline
57 & 10 & 10.1857142857143 & -0.185714285714285 \tabularnewline
58 & 8 & 10.1857142857143 & -2.18571428571429 \tabularnewline
59 & 7 & 10.1857142857143 & -3.18571428571429 \tabularnewline
60 & 15 & 10.1857142857143 & 4.81428571428571 \tabularnewline
61 & 9 & 10.1857142857143 & -1.18571428571429 \tabularnewline
62 & 10 & 10.1857142857143 & -0.185714285714285 \tabularnewline
63 & 12 & 10.1857142857143 & 1.81428571428571 \tabularnewline
64 & 13 & 10.1857142857143 & 2.81428571428571 \tabularnewline
65 & 10 & 10.1857142857143 & -0.185714285714285 \tabularnewline
66 & 11 & 12.0270270270270 & -1.02702702702703 \tabularnewline
67 & 8 & 12.0270270270270 & -4.02702702702703 \tabularnewline
68 & 9 & 10.1857142857143 & -1.18571428571429 \tabularnewline
69 & 13 & 10.1857142857143 & 2.81428571428571 \tabularnewline
70 & 11 & 10.1857142857143 & 0.814285714285715 \tabularnewline
71 & 8 & 12.0270270270270 & -4.02702702702703 \tabularnewline
72 & 9 & 10.1857142857143 & -1.18571428571429 \tabularnewline
73 & 9 & 12.0270270270270 & -3.02702702702703 \tabularnewline
74 & 15 & 12.0270270270270 & 2.97297297297297 \tabularnewline
75 & 9 & 12.0270270270270 & -3.02702702702703 \tabularnewline
76 & 10 & 12.0270270270270 & -2.02702702702703 \tabularnewline
77 & 14 & 10.1857142857143 & 3.81428571428571 \tabularnewline
78 & 12 & 10.1857142857143 & 1.81428571428571 \tabularnewline
79 & 12 & 10.1857142857143 & 1.81428571428571 \tabularnewline
80 & 11 & 12.0270270270270 & -1.02702702702703 \tabularnewline
81 & 14 & 12.0270270270270 & 1.97297297297297 \tabularnewline
82 & 6 & 10.1857142857143 & -4.18571428571429 \tabularnewline
83 & 12 & 10.1857142857143 & 1.81428571428571 \tabularnewline
84 & 8 & 10.1857142857143 & -2.18571428571429 \tabularnewline
85 & 14 & 12.0270270270270 & 1.97297297297297 \tabularnewline
86 & 11 & 10.1857142857143 & 0.814285714285715 \tabularnewline
87 & 10 & 10.1857142857143 & -0.185714285714285 \tabularnewline
88 & 14 & 10.1857142857143 & 3.81428571428571 \tabularnewline
89 & 12 & 12.0270270270270 & -0.0270270270270263 \tabularnewline
90 & 10 & 10.1857142857143 & -0.185714285714285 \tabularnewline
91 & 14 & 12.0270270270270 & 1.97297297297297 \tabularnewline
92 & 5 & 10.1857142857143 & -5.18571428571429 \tabularnewline
93 & 11 & 10.1857142857143 & 0.814285714285715 \tabularnewline
94 & 10 & 10.1857142857143 & -0.185714285714285 \tabularnewline
95 & 9 & 10.1857142857143 & -1.18571428571429 \tabularnewline
96 & 10 & 12.0270270270270 & -2.02702702702703 \tabularnewline
97 & 16 & 12.0270270270270 & 3.97297297297297 \tabularnewline
98 & 13 & 12.0270270270270 & 0.972972972972974 \tabularnewline
99 & 9 & 10.1857142857143 & -1.18571428571429 \tabularnewline
100 & 10 & 10.1857142857143 & -0.185714285714285 \tabularnewline
101 & 10 & 12.0270270270270 & -2.02702702702703 \tabularnewline
102 & 7 & 10.1857142857143 & -3.18571428571429 \tabularnewline
103 & 9 & 10.1857142857143 & -1.18571428571429 \tabularnewline
104 & 8 & 10.1857142857143 & -2.18571428571429 \tabularnewline
105 & 14 & 12.0270270270270 & 1.97297297297297 \tabularnewline
106 & 14 & 12.0270270270270 & 1.97297297297297 \tabularnewline
107 & 8 & 10.1857142857143 & -2.18571428571429 \tabularnewline
108 & 9 & 12.0270270270270 & -3.02702702702703 \tabularnewline
109 & 14 & 12.0270270270270 & 1.97297297297297 \tabularnewline
110 & 14 & 10.1857142857143 & 3.81428571428571 \tabularnewline
111 & 8 & 10.1857142857143 & -2.18571428571429 \tabularnewline
112 & 8 & 12.0270270270270 & -4.02702702702703 \tabularnewline
113 & 8 & 10.1857142857143 & -2.18571428571429 \tabularnewline
114 & 7 & 12.0270270270270 & -5.02702702702703 \tabularnewline
115 & 6 & 10.1857142857143 & -4.18571428571429 \tabularnewline
116 & 8 & 10.1857142857143 & -2.18571428571429 \tabularnewline
117 & 6 & 10.1857142857143 & -4.18571428571429 \tabularnewline
118 & 11 & 10.1857142857143 & 0.814285714285715 \tabularnewline
119 & 14 & 12.0270270270270 & 1.97297297297297 \tabularnewline
120 & 11 & 10.1857142857143 & 0.814285714285715 \tabularnewline
121 & 11 & 12.0270270270270 & -1.02702702702703 \tabularnewline
122 & 11 & 10.1857142857143 & 0.814285714285715 \tabularnewline
123 & 14 & 10.1857142857143 & 3.81428571428571 \tabularnewline
124 & 8 & 10.1857142857143 & -2.18571428571429 \tabularnewline
125 & 20 & 10.1857142857143 & 9.81428571428571 \tabularnewline
126 & 11 & 10.1857142857143 & 0.814285714285715 \tabularnewline
127 & 8 & 10.1857142857143 & -2.18571428571429 \tabularnewline
128 & 11 & 10.1857142857143 & 0.814285714285715 \tabularnewline
129 & 10 & 10.1857142857143 & -0.185714285714285 \tabularnewline
130 & 14 & 12.0270270270270 & 1.97297297297297 \tabularnewline
131 & 11 & 10.1857142857143 & 0.814285714285715 \tabularnewline
132 & 9 & 10.1857142857143 & -1.18571428571429 \tabularnewline
133 & 9 & 10.1857142857143 & -1.18571428571429 \tabularnewline
134 & 8 & 10.1857142857143 & -2.18571428571429 \tabularnewline
135 & 10 & 10.1857142857143 & -0.185714285714285 \tabularnewline
136 & 13 & 12.0270270270270 & 0.972972972972974 \tabularnewline
137 & 13 & 10.1857142857143 & 2.81428571428571 \tabularnewline
138 & 12 & 10.1857142857143 & 1.81428571428571 \tabularnewline
139 & 8 & 10.1857142857143 & -2.18571428571429 \tabularnewline
140 & 13 & 10.1857142857143 & 2.81428571428571 \tabularnewline
141 & 14 & 12.0270270270270 & 1.97297297297297 \tabularnewline
142 & 12 & 12.0270270270270 & -0.0270270270270263 \tabularnewline
143 & 14 & 12.0270270270270 & 1.97297297297297 \tabularnewline
144 & 15 & 10.1857142857143 & 4.81428571428571 \tabularnewline
145 & 13 & 10.1857142857143 & 2.81428571428571 \tabularnewline
146 & 16 & 12.0270270270270 & 3.97297297297297 \tabularnewline
147 & 9 & 12.0270270270270 & -3.02702702702703 \tabularnewline
148 & 9 & 10.1857142857143 & -1.18571428571429 \tabularnewline
149 & 9 & 10.1857142857143 & -1.18571428571429 \tabularnewline
150 & 8 & 10.1857142857143 & -2.18571428571429 \tabularnewline
151 & 7 & 10.1857142857143 & -3.18571428571429 \tabularnewline
152 & 16 & 12.0270270270270 & 3.97297297297297 \tabularnewline
153 & 11 & 12.0270270270270 & -1.02702702702703 \tabularnewline
154 & 9 & 10.1857142857143 & -1.18571428571429 \tabularnewline
155 & 11 & 10.1857142857143 & 0.814285714285715 \tabularnewline
156 & 9 & 10.1857142857143 & -1.18571428571429 \tabularnewline
157 & 14 & 12.0270270270270 & 1.97297297297297 \tabularnewline
158 & 13 & 10.1857142857143 & 2.81428571428571 \tabularnewline
159 & 16 & 12.0270270270270 & 3.97297297297297 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108165&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]24[/C][C]26.1428571428571[/C][C]-2.14285714285714[/C][/ROW]
[ROW][C]2[/C][C]25[/C][C]20.6842105263158[/C][C]4.31578947368421[/C][/ROW]
[ROW][C]3[/C][C]17[/C][C]20.6842105263158[/C][C]-3.68421052631579[/C][/ROW]
[ROW][C]4[/C][C]18[/C][C]20.6842105263158[/C][C]-2.68421052631579[/C][/ROW]
[ROW][C]5[/C][C]18[/C][C]20.6842105263158[/C][C]-2.68421052631579[/C][/ROW]
[ROW][C]6[/C][C]16[/C][C]20.6842105263158[/C][C]-4.68421052631579[/C][/ROW]
[ROW][C]7[/C][C]20[/C][C]20.6842105263158[/C][C]-0.684210526315791[/C][/ROW]
[ROW][C]8[/C][C]16[/C][C]20.6842105263158[/C][C]-4.68421052631579[/C][/ROW]
[ROW][C]9[/C][C]18[/C][C]26.1428571428571[/C][C]-8.14285714285714[/C][/ROW]
[ROW][C]10[/C][C]17[/C][C]20.6842105263158[/C][C]-3.68421052631579[/C][/ROW]
[ROW][C]11[/C][C]23[/C][C]20.6842105263158[/C][C]2.31578947368421[/C][/ROW]
[ROW][C]12[/C][C]30[/C][C]20.6842105263158[/C][C]9.3157894736842[/C][/ROW]
[ROW][C]13[/C][C]23[/C][C]20.6842105263158[/C][C]2.31578947368421[/C][/ROW]
[ROW][C]14[/C][C]18[/C][C]20.6842105263158[/C][C]-2.68421052631579[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]20.6842105263158[/C][C]-5.68421052631579[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]20.6842105263158[/C][C]-8.6842105263158[/C][/ROW]
[ROW][C]17[/C][C]21[/C][C]20.6842105263158[/C][C]0.315789473684209[/C][/ROW]
[ROW][C]18[/C][C]15[/C][C]10.1857142857143[/C][C]4.81428571428571[/C][/ROW]
[ROW][C]19[/C][C]20[/C][C]20.6842105263158[/C][C]-0.684210526315791[/C][/ROW]
[ROW][C]20[/C][C]31[/C][C]26.1428571428571[/C][C]4.85714285714286[/C][/ROW]
[ROW][C]21[/C][C]27[/C][C]26.1428571428571[/C][C]0.857142857142858[/C][/ROW]
[ROW][C]22[/C][C]34[/C][C]26.1428571428571[/C][C]7.85714285714286[/C][/ROW]
[ROW][C]23[/C][C]21[/C][C]20.6842105263158[/C][C]0.315789473684209[/C][/ROW]
[ROW][C]24[/C][C]31[/C][C]26.1428571428571[/C][C]4.85714285714286[/C][/ROW]
[ROW][C]25[/C][C]19[/C][C]20.6842105263158[/C][C]-1.68421052631579[/C][/ROW]
[ROW][C]26[/C][C]16[/C][C]20.6842105263158[/C][C]-4.68421052631579[/C][/ROW]
[ROW][C]27[/C][C]20[/C][C]20.6842105263158[/C][C]-0.684210526315791[/C][/ROW]
[ROW][C]28[/C][C]21[/C][C]20.6842105263158[/C][C]0.315789473684209[/C][/ROW]
[ROW][C]29[/C][C]22[/C][C]20.6842105263158[/C][C]1.31578947368421[/C][/ROW]
[ROW][C]30[/C][C]17[/C][C]20.6842105263158[/C][C]-3.68421052631579[/C][/ROW]
[ROW][C]31[/C][C]24[/C][C]20.6842105263158[/C][C]3.31578947368421[/C][/ROW]
[ROW][C]32[/C][C]25[/C][C]26.1428571428571[/C][C]-1.14285714285714[/C][/ROW]
[ROW][C]33[/C][C]26[/C][C]20.6842105263158[/C][C]5.31578947368421[/C][/ROW]
[ROW][C]34[/C][C]25[/C][C]20.6842105263158[/C][C]4.31578947368421[/C][/ROW]
[ROW][C]35[/C][C]17[/C][C]20.6842105263158[/C][C]-3.68421052631579[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]26.1428571428571[/C][C]5.85714285714286[/C][/ROW]
[ROW][C]37[/C][C]33[/C][C]20.6842105263158[/C][C]12.3157894736842[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]26.1428571428571[/C][C]-13.1428571428571[/C][/ROW]
[ROW][C]39[/C][C]32[/C][C]20.6842105263158[/C][C]11.3157894736842[/C][/ROW]
[ROW][C]40[/C][C]25[/C][C]20.6842105263158[/C][C]4.31578947368421[/C][/ROW]
[ROW][C]41[/C][C]29[/C][C]26.1428571428571[/C][C]2.85714285714286[/C][/ROW]
[ROW][C]42[/C][C]22[/C][C]20.6842105263158[/C][C]1.31578947368421[/C][/ROW]
[ROW][C]43[/C][C]18[/C][C]20.6842105263158[/C][C]-2.68421052631579[/C][/ROW]
[ROW][C]44[/C][C]17[/C][C]20.6842105263158[/C][C]-3.68421052631579[/C][/ROW]
[ROW][C]45[/C][C]20[/C][C]20.6842105263158[/C][C]-0.684210526315791[/C][/ROW]
[ROW][C]46[/C][C]15[/C][C]20.6842105263158[/C][C]-5.68421052631579[/C][/ROW]
[ROW][C]47[/C][C]20[/C][C]26.1428571428571[/C][C]-6.14285714285714[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]26.1428571428571[/C][C]6.85714285714286[/C][/ROW]
[ROW][C]49[/C][C]29[/C][C]20.6842105263158[/C][C]8.3157894736842[/C][/ROW]
[ROW][C]50[/C][C]23[/C][C]26.1428571428571[/C][C]-3.14285714285714[/C][/ROW]
[ROW][C]51[/C][C]26[/C][C]26.1428571428571[/C][C]-0.142857142857142[/C][/ROW]
[ROW][C]52[/C][C]18[/C][C]20.6842105263158[/C][C]-2.68421052631579[/C][/ROW]
[ROW][C]53[/C][C]20[/C][C]20.6842105263158[/C][C]-0.684210526315791[/C][/ROW]
[ROW][C]54[/C][C]6[/C][C]10.1857142857143[/C][C]-4.18571428571429[/C][/ROW]
[ROW][C]55[/C][C]8[/C][C]12.0270270270270[/C][C]-4.02702702702703[/C][/ROW]
[ROW][C]56[/C][C]13[/C][C]12.0270270270270[/C][C]0.972972972972974[/C][/ROW]
[ROW][C]57[/C][C]10[/C][C]10.1857142857143[/C][C]-0.185714285714285[/C][/ROW]
[ROW][C]58[/C][C]8[/C][C]10.1857142857143[/C][C]-2.18571428571429[/C][/ROW]
[ROW][C]59[/C][C]7[/C][C]10.1857142857143[/C][C]-3.18571428571429[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]10.1857142857143[/C][C]4.81428571428571[/C][/ROW]
[ROW][C]61[/C][C]9[/C][C]10.1857142857143[/C][C]-1.18571428571429[/C][/ROW]
[ROW][C]62[/C][C]10[/C][C]10.1857142857143[/C][C]-0.185714285714285[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]10.1857142857143[/C][C]1.81428571428571[/C][/ROW]
[ROW][C]64[/C][C]13[/C][C]10.1857142857143[/C][C]2.81428571428571[/C][/ROW]
[ROW][C]65[/C][C]10[/C][C]10.1857142857143[/C][C]-0.185714285714285[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]12.0270270270270[/C][C]-1.02702702702703[/C][/ROW]
[ROW][C]67[/C][C]8[/C][C]12.0270270270270[/C][C]-4.02702702702703[/C][/ROW]
[ROW][C]68[/C][C]9[/C][C]10.1857142857143[/C][C]-1.18571428571429[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]10.1857142857143[/C][C]2.81428571428571[/C][/ROW]
[ROW][C]70[/C][C]11[/C][C]10.1857142857143[/C][C]0.814285714285715[/C][/ROW]
[ROW][C]71[/C][C]8[/C][C]12.0270270270270[/C][C]-4.02702702702703[/C][/ROW]
[ROW][C]72[/C][C]9[/C][C]10.1857142857143[/C][C]-1.18571428571429[/C][/ROW]
[ROW][C]73[/C][C]9[/C][C]12.0270270270270[/C][C]-3.02702702702703[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]12.0270270270270[/C][C]2.97297297297297[/C][/ROW]
[ROW][C]75[/C][C]9[/C][C]12.0270270270270[/C][C]-3.02702702702703[/C][/ROW]
[ROW][C]76[/C][C]10[/C][C]12.0270270270270[/C][C]-2.02702702702703[/C][/ROW]
[ROW][C]77[/C][C]14[/C][C]10.1857142857143[/C][C]3.81428571428571[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]10.1857142857143[/C][C]1.81428571428571[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]10.1857142857143[/C][C]1.81428571428571[/C][/ROW]
[ROW][C]80[/C][C]11[/C][C]12.0270270270270[/C][C]-1.02702702702703[/C][/ROW]
[ROW][C]81[/C][C]14[/C][C]12.0270270270270[/C][C]1.97297297297297[/C][/ROW]
[ROW][C]82[/C][C]6[/C][C]10.1857142857143[/C][C]-4.18571428571429[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]10.1857142857143[/C][C]1.81428571428571[/C][/ROW]
[ROW][C]84[/C][C]8[/C][C]10.1857142857143[/C][C]-2.18571428571429[/C][/ROW]
[ROW][C]85[/C][C]14[/C][C]12.0270270270270[/C][C]1.97297297297297[/C][/ROW]
[ROW][C]86[/C][C]11[/C][C]10.1857142857143[/C][C]0.814285714285715[/C][/ROW]
[ROW][C]87[/C][C]10[/C][C]10.1857142857143[/C][C]-0.185714285714285[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]10.1857142857143[/C][C]3.81428571428571[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]12.0270270270270[/C][C]-0.0270270270270263[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]10.1857142857143[/C][C]-0.185714285714285[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]12.0270270270270[/C][C]1.97297297297297[/C][/ROW]
[ROW][C]92[/C][C]5[/C][C]10.1857142857143[/C][C]-5.18571428571429[/C][/ROW]
[ROW][C]93[/C][C]11[/C][C]10.1857142857143[/C][C]0.814285714285715[/C][/ROW]
[ROW][C]94[/C][C]10[/C][C]10.1857142857143[/C][C]-0.185714285714285[/C][/ROW]
[ROW][C]95[/C][C]9[/C][C]10.1857142857143[/C][C]-1.18571428571429[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]12.0270270270270[/C][C]-2.02702702702703[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]12.0270270270270[/C][C]3.97297297297297[/C][/ROW]
[ROW][C]98[/C][C]13[/C][C]12.0270270270270[/C][C]0.972972972972974[/C][/ROW]
[ROW][C]99[/C][C]9[/C][C]10.1857142857143[/C][C]-1.18571428571429[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]10.1857142857143[/C][C]-0.185714285714285[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]12.0270270270270[/C][C]-2.02702702702703[/C][/ROW]
[ROW][C]102[/C][C]7[/C][C]10.1857142857143[/C][C]-3.18571428571429[/C][/ROW]
[ROW][C]103[/C][C]9[/C][C]10.1857142857143[/C][C]-1.18571428571429[/C][/ROW]
[ROW][C]104[/C][C]8[/C][C]10.1857142857143[/C][C]-2.18571428571429[/C][/ROW]
[ROW][C]105[/C][C]14[/C][C]12.0270270270270[/C][C]1.97297297297297[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]12.0270270270270[/C][C]1.97297297297297[/C][/ROW]
[ROW][C]107[/C][C]8[/C][C]10.1857142857143[/C][C]-2.18571428571429[/C][/ROW]
[ROW][C]108[/C][C]9[/C][C]12.0270270270270[/C][C]-3.02702702702703[/C][/ROW]
[ROW][C]109[/C][C]14[/C][C]12.0270270270270[/C][C]1.97297297297297[/C][/ROW]
[ROW][C]110[/C][C]14[/C][C]10.1857142857143[/C][C]3.81428571428571[/C][/ROW]
[ROW][C]111[/C][C]8[/C][C]10.1857142857143[/C][C]-2.18571428571429[/C][/ROW]
[ROW][C]112[/C][C]8[/C][C]12.0270270270270[/C][C]-4.02702702702703[/C][/ROW]
[ROW][C]113[/C][C]8[/C][C]10.1857142857143[/C][C]-2.18571428571429[/C][/ROW]
[ROW][C]114[/C][C]7[/C][C]12.0270270270270[/C][C]-5.02702702702703[/C][/ROW]
[ROW][C]115[/C][C]6[/C][C]10.1857142857143[/C][C]-4.18571428571429[/C][/ROW]
[ROW][C]116[/C][C]8[/C][C]10.1857142857143[/C][C]-2.18571428571429[/C][/ROW]
[ROW][C]117[/C][C]6[/C][C]10.1857142857143[/C][C]-4.18571428571429[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]10.1857142857143[/C][C]0.814285714285715[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]12.0270270270270[/C][C]1.97297297297297[/C][/ROW]
[ROW][C]120[/C][C]11[/C][C]10.1857142857143[/C][C]0.814285714285715[/C][/ROW]
[ROW][C]121[/C][C]11[/C][C]12.0270270270270[/C][C]-1.02702702702703[/C][/ROW]
[ROW][C]122[/C][C]11[/C][C]10.1857142857143[/C][C]0.814285714285715[/C][/ROW]
[ROW][C]123[/C][C]14[/C][C]10.1857142857143[/C][C]3.81428571428571[/C][/ROW]
[ROW][C]124[/C][C]8[/C][C]10.1857142857143[/C][C]-2.18571428571429[/C][/ROW]
[ROW][C]125[/C][C]20[/C][C]10.1857142857143[/C][C]9.81428571428571[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]10.1857142857143[/C][C]0.814285714285715[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]10.1857142857143[/C][C]-2.18571428571429[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]10.1857142857143[/C][C]0.814285714285715[/C][/ROW]
[ROW][C]129[/C][C]10[/C][C]10.1857142857143[/C][C]-0.185714285714285[/C][/ROW]
[ROW][C]130[/C][C]14[/C][C]12.0270270270270[/C][C]1.97297297297297[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]10.1857142857143[/C][C]0.814285714285715[/C][/ROW]
[ROW][C]132[/C][C]9[/C][C]10.1857142857143[/C][C]-1.18571428571429[/C][/ROW]
[ROW][C]133[/C][C]9[/C][C]10.1857142857143[/C][C]-1.18571428571429[/C][/ROW]
[ROW][C]134[/C][C]8[/C][C]10.1857142857143[/C][C]-2.18571428571429[/C][/ROW]
[ROW][C]135[/C][C]10[/C][C]10.1857142857143[/C][C]-0.185714285714285[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]12.0270270270270[/C][C]0.972972972972974[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]10.1857142857143[/C][C]2.81428571428571[/C][/ROW]
[ROW][C]138[/C][C]12[/C][C]10.1857142857143[/C][C]1.81428571428571[/C][/ROW]
[ROW][C]139[/C][C]8[/C][C]10.1857142857143[/C][C]-2.18571428571429[/C][/ROW]
[ROW][C]140[/C][C]13[/C][C]10.1857142857143[/C][C]2.81428571428571[/C][/ROW]
[ROW][C]141[/C][C]14[/C][C]12.0270270270270[/C][C]1.97297297297297[/C][/ROW]
[ROW][C]142[/C][C]12[/C][C]12.0270270270270[/C][C]-0.0270270270270263[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]12.0270270270270[/C][C]1.97297297297297[/C][/ROW]
[ROW][C]144[/C][C]15[/C][C]10.1857142857143[/C][C]4.81428571428571[/C][/ROW]
[ROW][C]145[/C][C]13[/C][C]10.1857142857143[/C][C]2.81428571428571[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]12.0270270270270[/C][C]3.97297297297297[/C][/ROW]
[ROW][C]147[/C][C]9[/C][C]12.0270270270270[/C][C]-3.02702702702703[/C][/ROW]
[ROW][C]148[/C][C]9[/C][C]10.1857142857143[/C][C]-1.18571428571429[/C][/ROW]
[ROW][C]149[/C][C]9[/C][C]10.1857142857143[/C][C]-1.18571428571429[/C][/ROW]
[ROW][C]150[/C][C]8[/C][C]10.1857142857143[/C][C]-2.18571428571429[/C][/ROW]
[ROW][C]151[/C][C]7[/C][C]10.1857142857143[/C][C]-3.18571428571429[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]12.0270270270270[/C][C]3.97297297297297[/C][/ROW]
[ROW][C]153[/C][C]11[/C][C]12.0270270270270[/C][C]-1.02702702702703[/C][/ROW]
[ROW][C]154[/C][C]9[/C][C]10.1857142857143[/C][C]-1.18571428571429[/C][/ROW]
[ROW][C]155[/C][C]11[/C][C]10.1857142857143[/C][C]0.814285714285715[/C][/ROW]
[ROW][C]156[/C][C]9[/C][C]10.1857142857143[/C][C]-1.18571428571429[/C][/ROW]
[ROW][C]157[/C][C]14[/C][C]12.0270270270270[/C][C]1.97297297297297[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]10.1857142857143[/C][C]2.81428571428571[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]12.0270270270270[/C][C]3.97297297297297[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108165&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108165&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	24	26.1428571428571	-2.14285714285714
2	25	20.6842105263158	4.31578947368421
3	17	20.6842105263158	-3.68421052631579
4	18	20.6842105263158	-2.68421052631579
5	18	20.6842105263158	-2.68421052631579
6	16	20.6842105263158	-4.68421052631579
7	20	20.6842105263158	-0.684210526315791
8	16	20.6842105263158	-4.68421052631579
9	18	26.1428571428571	-8.14285714285714
10	17	20.6842105263158	-3.68421052631579
11	23	20.6842105263158	2.31578947368421
12	30	20.6842105263158	9.3157894736842
13	23	20.6842105263158	2.31578947368421
14	18	20.6842105263158	-2.68421052631579
15	15	20.6842105263158	-5.68421052631579
16	12	20.6842105263158	-8.6842105263158
17	21	20.6842105263158	0.315789473684209
18	15	10.1857142857143	4.81428571428571
19	20	20.6842105263158	-0.684210526315791
20	31	26.1428571428571	4.85714285714286
21	27	26.1428571428571	0.857142857142858
22	34	26.1428571428571	7.85714285714286
23	21	20.6842105263158	0.315789473684209
24	31	26.1428571428571	4.85714285714286
25	19	20.6842105263158	-1.68421052631579
26	16	20.6842105263158	-4.68421052631579
27	20	20.6842105263158	-0.684210526315791
28	21	20.6842105263158	0.315789473684209
29	22	20.6842105263158	1.31578947368421
30	17	20.6842105263158	-3.68421052631579
31	24	20.6842105263158	3.31578947368421
32	25	26.1428571428571	-1.14285714285714
33	26	20.6842105263158	5.31578947368421
34	25	20.6842105263158	4.31578947368421
35	17	20.6842105263158	-3.68421052631579
36	32	26.1428571428571	5.85714285714286
37	33	20.6842105263158	12.3157894736842
38	13	26.1428571428571	-13.1428571428571
39	32	20.6842105263158	11.3157894736842
40	25	20.6842105263158	4.31578947368421
41	29	26.1428571428571	2.85714285714286
42	22	20.6842105263158	1.31578947368421
43	18	20.6842105263158	-2.68421052631579
44	17	20.6842105263158	-3.68421052631579
45	20	20.6842105263158	-0.684210526315791
46	15	20.6842105263158	-5.68421052631579
47	20	26.1428571428571	-6.14285714285714
48	33	26.1428571428571	6.85714285714286
49	29	20.6842105263158	8.3157894736842
50	23	26.1428571428571	-3.14285714285714
51	26	26.1428571428571	-0.142857142857142
52	18	20.6842105263158	-2.68421052631579
53	20	20.6842105263158	-0.684210526315791
54	6	10.1857142857143	-4.18571428571429
55	8	12.0270270270270	-4.02702702702703
56	13	12.0270270270270	0.972972972972974
57	10	10.1857142857143	-0.185714285714285
58	8	10.1857142857143	-2.18571428571429
59	7	10.1857142857143	-3.18571428571429
60	15	10.1857142857143	4.81428571428571
61	9	10.1857142857143	-1.18571428571429
62	10	10.1857142857143	-0.185714285714285
63	12	10.1857142857143	1.81428571428571
64	13	10.1857142857143	2.81428571428571
65	10	10.1857142857143	-0.185714285714285
66	11	12.0270270270270	-1.02702702702703
67	8	12.0270270270270	-4.02702702702703
68	9	10.1857142857143	-1.18571428571429
69	13	10.1857142857143	2.81428571428571
70	11	10.1857142857143	0.814285714285715
71	8	12.0270270270270	-4.02702702702703
72	9	10.1857142857143	-1.18571428571429
73	9	12.0270270270270	-3.02702702702703
74	15	12.0270270270270	2.97297297297297
75	9	12.0270270270270	-3.02702702702703
76	10	12.0270270270270	-2.02702702702703
77	14	10.1857142857143	3.81428571428571
78	12	10.1857142857143	1.81428571428571
79	12	10.1857142857143	1.81428571428571
80	11	12.0270270270270	-1.02702702702703
81	14	12.0270270270270	1.97297297297297
82	6	10.1857142857143	-4.18571428571429
83	12	10.1857142857143	1.81428571428571
84	8	10.1857142857143	-2.18571428571429
85	14	12.0270270270270	1.97297297297297
86	11	10.1857142857143	0.814285714285715
87	10	10.1857142857143	-0.185714285714285
88	14	10.1857142857143	3.81428571428571
89	12	12.0270270270270	-0.0270270270270263
90	10	10.1857142857143	-0.185714285714285
91	14	12.0270270270270	1.97297297297297
92	5	10.1857142857143	-5.18571428571429
93	11	10.1857142857143	0.814285714285715
94	10	10.1857142857143	-0.185714285714285
95	9	10.1857142857143	-1.18571428571429
96	10	12.0270270270270	-2.02702702702703
97	16	12.0270270270270	3.97297297297297
98	13	12.0270270270270	0.972972972972974
99	9	10.1857142857143	-1.18571428571429
100	10	10.1857142857143	-0.185714285714285
101	10	12.0270270270270	-2.02702702702703
102	7	10.1857142857143	-3.18571428571429
103	9	10.1857142857143	-1.18571428571429
104	8	10.1857142857143	-2.18571428571429
105	14	12.0270270270270	1.97297297297297
106	14	12.0270270270270	1.97297297297297
107	8	10.1857142857143	-2.18571428571429
108	9	12.0270270270270	-3.02702702702703
109	14	12.0270270270270	1.97297297297297
110	14	10.1857142857143	3.81428571428571
111	8	10.1857142857143	-2.18571428571429
112	8	12.0270270270270	-4.02702702702703
113	8	10.1857142857143	-2.18571428571429
114	7	12.0270270270270	-5.02702702702703
115	6	10.1857142857143	-4.18571428571429
116	8	10.1857142857143	-2.18571428571429
117	6	10.1857142857143	-4.18571428571429
118	11	10.1857142857143	0.814285714285715
119	14	12.0270270270270	1.97297297297297
120	11	10.1857142857143	0.814285714285715
121	11	12.0270270270270	-1.02702702702703
122	11	10.1857142857143	0.814285714285715
123	14	10.1857142857143	3.81428571428571
124	8	10.1857142857143	-2.18571428571429
125	20	10.1857142857143	9.81428571428571
126	11	10.1857142857143	0.814285714285715
127	8	10.1857142857143	-2.18571428571429
128	11	10.1857142857143	0.814285714285715
129	10	10.1857142857143	-0.185714285714285
130	14	12.0270270270270	1.97297297297297
131	11	10.1857142857143	0.814285714285715
132	9	10.1857142857143	-1.18571428571429
133	9	10.1857142857143	-1.18571428571429
134	8	10.1857142857143	-2.18571428571429
135	10	10.1857142857143	-0.185714285714285
136	13	12.0270270270270	0.972972972972974
137	13	10.1857142857143	2.81428571428571
138	12	10.1857142857143	1.81428571428571
139	8	10.1857142857143	-2.18571428571429
140	13	10.1857142857143	2.81428571428571
141	14	12.0270270270270	1.97297297297297
142	12	12.0270270270270	-0.0270270270270263
143	14	12.0270270270270	1.97297297297297
144	15	10.1857142857143	4.81428571428571
145	13	10.1857142857143	2.81428571428571
146	16	12.0270270270270	3.97297297297297
147	9	12.0270270270270	-3.02702702702703
148	9	10.1857142857143	-1.18571428571429
149	9	10.1857142857143	-1.18571428571429
150	8	10.1857142857143	-2.18571428571429
151	7	10.1857142857143	-3.18571428571429
152	16	12.0270270270270	3.97297297297297
153	11	12.0270270270270	-1.02702702702703
154	9	10.1857142857143	-1.18571428571429
155	11	10.1857142857143	0.814285714285715
156	9	10.1857142857143	-1.18571428571429
157	14	12.0270270270270	1.97297297297297
158	13	10.1857142857143	2.81428571428571
159	16	12.0270270270270	3.97297297297297

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 = 2 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 2 ; 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