Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

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

Wed, 14 Dec 2011 08:17:36 -0500

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/14/t1323868706tl8q1fv5zuyxa56.htm/, Retrieved Wed, 01 May 2024 16:10:57 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154932, Retrieved Wed, 01 May 2024 16:10:57 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

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

-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
-   PD    [Recursive Partitioning (Regression Trees)] [regression trees] [2011-12-14 13:17:36] [3ce5305e82abe5b27e1176cc99946857] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

2	41	38	13	12	14
2	39	32	16	11	18
2	30	35	19	15	11
1	31	33	15	6	12
2	34	37	14	13	16
2	35	29	13	10	18
2	39	31	19	12	14
2	34	36	15	14	14
2	36	35	14	12	15
2	37	38	15	6	15
1	38	31	16	10	17
2	36	34	16	12	19
1	38	35	16	12	10
2	39	38	16	11	16
2	33	37	17	15	18
1	32	33	15	12	14
1	36	32	15	10	14
2	38	38	20	12	17
1	39	38	18	11	14
2	32	32	16	12	16
1	32	33	16	11	18
2	31	31	16	12	11
2	39	38	19	13	14
2	37	39	16	11	12
1	39	32	17	9	17
2	41	32	17	13	9
1	36	35	16	10	16
2	33	37	15	14	14
2	33	33	16	12	15
1	34	33	14	10	11
2	31	28	15	12	16
1	27	32	12	8	13
2	37	31	14	10	17
2	34	37	16	12	15
1	34	30	14	12	14
1	32	33	7	7	16
1	29	31	10	6	9
1	36	33	14	12	15
2	29	31	16	10	17
1	35	33	16	10	13
1	37	32	16	10	15
2	34	33	14	12	16
1	38	32	20	15	16
1	35	33	14	10	12
2	38	28	14	10	12
2	37	35	11	12	11
2	38	39	14	13	15
2	33	34	15	11	15
2	36	38	16	11	17
1	38	32	14	12	13
2	32	38	16	14	16
1	32	30	14	10	14
1	32	33	12	12	11
2	34	38	16	13	12
1	32	32	9	5	12
2	37	32	14	6	15
2	39	34	16	12	16
2	29	34	16	12	15
1	37	36	15	11	12
2	35	34	16	10	12
1	30	28	12	7	8
1	38	34	16	12	13
2	34	35	16	14	11
2	31	35	14	11	14
2	34	31	16	12	15
1	35	37	17	13	10
2	36	35	18	14	11
1	30	27	18	11	12
2	39	40	12	12	15
1	35	37	16	12	15
1	38	36	10	8	14
2	31	38	14	11	16
2	34	39	18	14	15
1	38	41	18	14	15
1	34	27	16	12	13
2	39	30	17	9	12
2	37	37	16	13	17
2	34	31	16	11	13
1	28	31	13	12	15
1	37	27	16	12	13
1	33	36	16	12	15
1	37	38	20	12	16
2	35	37	16	12	15
1	37	33	15	12	16
2	32	34	15	11	15
2	33	31	16	10	14
1	38	39	14	9	15
2	33	34	16	12	14
2	29	32	16	12	13
2	33	33	15	12	7
2	31	36	12	9	17
2	36	32	17	15	13
2	35	41	16	12	15
2	32	28	15	12	14
2	29	30	13	12	13
2	39	36	16	10	16
2	37	35	16	13	12
2	35	31	16	9	14
1	37	34	16	12	17
1	32	36	14	10	15
2	38	36	16	14	17
1	37	35	16	11	12
2	36	37	20	15	16
1	32	28	15	11	11
2	33	39	16	11	15
1	40	32	13	12	9
2	38	35	17	12	16
1	41	39	16	12	15
1	36	35	16	11	10
2	43	42	12	7	10
2	30	34	16	12	15
2	31	33	16	14	11
2	32	41	17	11	13
1	32	33	13	11	14
2	37	34	12	10	18
1	37	32	18	13	16
2	33	40	14	13	14
2	34	40	14	8	14
2	33	35	13	11	14
2	38	36	16	12	14
2	33	37	13	11	12
2	31	27	16	13	14
2	38	39	13	12	15
2	37	38	16	14	15
2	33	31	15	13	15
2	31	33	16	15	13
1	39	32	15	10	17
2	44	39	17	11	17
2	33	36	15	9	19
2	35	33	12	11	15
1	32	33	16	10	13
1	28	32	10	11	9
2	40	37	16	8	15
1	27	30	12	11	15
1	37	38	14	12	15
2	32	29	15	12	16
1	28	22	13	9	11
1	34	35	15	11	14
2	30	35	11	10	11
2	35	34	12	8	15
1	31	35	8	9	13
2	32	34	16	8	15
1	30	34	15	9	16
2	30	35	17	15	14
1	31	23	16	11	15
2	40	31	10	8	16
2	32	27	18	13	16
1	36	36	13	12	11
1	32	31	16	12	12
1	35	32	13	9	9
2	38	39	10	7	16
2	42	37	15	13	13
1	34	38	16	9	16
2	35	39	16	6	12
2	35	34	14	8	9
2	33	31	10	8	13
2	36	32	17	15	13
2	32	37	13	6	14
2	33	36	15	9	19
2	34	32	16	11	13
2	32	35	12	8	12
2	34	36	13	8	13

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'AstonUniversity' @ aston.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154932&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154932&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154932&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	4 seconds
R Server	'AstonUniversity' @ aston.wessa.net

Goodness of Fit
Correlation	0.5118
R-squared	0.262
RMSE	1.8342

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.5118 \tabularnewline
R-squared & 0.262 \tabularnewline
RMSE & 1.8342 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154932&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.5118[/C][/ROW]
[ROW][C]R-squared[/C][C]0.262[/C][/ROW]
[ROW][C]RMSE[/C][C]1.8342[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154932&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154932&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	0.5118
R-squared	0.262
RMSE	1.8342

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	12	11.0869565217391	0.913043478260869
2	11	11.0869565217391	-0.0869565217391308
3	15	12.88	2.12
4	6	11.0869565217391	-5.08695652173913
5	13	11.0869565217391	1.91304347826087
6	10	11.0869565217391	-1.08695652173913
7	12	12.88	-0.88
8	14	11.0869565217391	2.91304347826087
9	12	11.0869565217391	0.913043478260869
10	6	11.0869565217391	-5.08695652173913
11	10	11.0869565217391	-1.08695652173913
12	12	11.0869565217391	0.913043478260869
13	12	11.0869565217391	0.913043478260869
14	11	11.0869565217391	-0.0869565217391308
15	15	12.88	2.12
16	12	11.0869565217391	0.913043478260869
17	10	11.0869565217391	-1.08695652173913
18	12	12.88	-0.88
19	11	12.88	-1.88
20	12	11.0869565217391	0.913043478260869
21	11	11.0869565217391	-0.0869565217391308
22	12	11.0869565217391	0.913043478260869
23	13	12.88	0.119999999999999
24	11	11.0869565217391	-0.0869565217391308
25	9	12.88	-3.88
26	13	12.88	0.119999999999999
27	10	11.0869565217391	-1.08695652173913
28	14	11.0869565217391	2.91304347826087
29	12	11.0869565217391	0.913043478260869
30	10	11.0869565217391	-1.08695652173913
31	12	11.0869565217391	0.913043478260869
32	8	8.81818181818182	-0.818181818181818
33	10	11.0869565217391	-1.08695652173913
34	12	11.0869565217391	0.913043478260869
35	12	11.0869565217391	0.913043478260869
36	7	8.81818181818182	-1.81818181818182
37	6	8.81818181818182	-2.81818181818182
38	12	11.0869565217391	0.913043478260869
39	10	11.0869565217391	-1.08695652173913
40	10	11.0869565217391	-1.08695652173913
41	10	11.0869565217391	-1.08695652173913
42	12	11.0869565217391	0.913043478260869
43	15	12.88	2.12
44	10	11.0869565217391	-1.08695652173913
45	10	11.0869565217391	-1.08695652173913
46	12	8.81818181818182	3.18181818181818
47	13	11.0869565217391	1.91304347826087
48	11	11.0869565217391	-0.0869565217391308
49	11	11.0869565217391	-0.0869565217391308
50	12	11.0869565217391	0.913043478260869
51	14	11.0869565217391	2.91304347826087
52	10	11.0869565217391	-1.08695652173913
53	12	8.81818181818182	3.18181818181818
54	13	11.0869565217391	1.91304347826087
55	5	8.81818181818182	-3.81818181818182
56	6	11.0869565217391	-5.08695652173913
57	12	11.0869565217391	0.913043478260869
58	12	11.0869565217391	0.913043478260869
59	11	11.0869565217391	-0.0869565217391308
60	10	11.0869565217391	-1.08695652173913
61	7	8.81818181818182	-1.81818181818182
62	12	11.0869565217391	0.913043478260869
63	14	11.0869565217391	2.91304347826087
64	11	11.0869565217391	-0.0869565217391308
65	12	11.0869565217391	0.913043478260869
66	13	12.88	0.119999999999999
67	14	12.88	1.12
68	11	12.88	-1.88
69	12	8.81818181818182	3.18181818181818
70	12	11.0869565217391	0.913043478260869
71	8	8.81818181818182	-0.818181818181818
72	11	11.0869565217391	-0.0869565217391308
73	14	12.88	1.12
74	14	12.88	1.12
75	12	11.0869565217391	0.913043478260869
76	9	12.88	-3.88
77	13	11.0869565217391	1.91304347826087
78	11	11.0869565217391	-0.0869565217391308
79	12	11.0869565217391	0.913043478260869
80	12	11.0869565217391	0.913043478260869
81	12	11.0869565217391	0.913043478260869
82	12	12.88	-0.88
83	12	11.0869565217391	0.913043478260869
84	12	11.0869565217391	0.913043478260869
85	11	11.0869565217391	-0.0869565217391308
86	10	11.0869565217391	-1.08695652173913
87	9	11.0869565217391	-2.08695652173913
88	12	11.0869565217391	0.913043478260869
89	12	11.0869565217391	0.913043478260869
90	12	11.0869565217391	0.913043478260869
91	9	8.81818181818182	0.181818181818182
92	15	12.88	2.12
93	12	11.0869565217391	0.913043478260869
94	12	11.0869565217391	0.913043478260869
95	12	11.0869565217391	0.913043478260869
96	10	11.0869565217391	-1.08695652173913
97	13	11.0869565217391	1.91304347826087
98	9	11.0869565217391	-2.08695652173913
99	12	11.0869565217391	0.913043478260869
100	10	11.0869565217391	-1.08695652173913
101	14	11.0869565217391	2.91304347826087
102	11	11.0869565217391	-0.0869565217391308
103	15	12.88	2.12
104	11	11.0869565217391	-0.0869565217391308
105	11	11.0869565217391	-0.0869565217391308
106	12	11.0869565217391	0.913043478260869
107	12	12.88	-0.88
108	12	11.0869565217391	0.913043478260869
109	11	11.0869565217391	-0.0869565217391308
110	7	8.81818181818182	-1.81818181818182
111	12	11.0869565217391	0.913043478260869
112	14	11.0869565217391	2.91304347826087
113	11	12.88	-1.88
114	11	11.0869565217391	-0.0869565217391308
115	10	8.81818181818182	1.18181818181818
116	13	12.88	0.119999999999999
117	13	11.0869565217391	1.91304347826087
118	8	11.0869565217391	-3.08695652173913
119	11	11.0869565217391	-0.0869565217391308
120	12	11.0869565217391	0.913043478260869
121	11	11.0869565217391	-0.0869565217391308
122	13	11.0869565217391	1.91304347826087
123	12	11.0869565217391	0.913043478260869
124	14	11.0869565217391	2.91304347826087
125	13	11.0869565217391	1.91304347826087
126	15	11.0869565217391	3.91304347826087
127	10	11.0869565217391	-1.08695652173913
128	11	12.88	-1.88
129	9	11.0869565217391	-2.08695652173913
130	11	8.81818181818182	2.18181818181818
131	10	11.0869565217391	-1.08695652173913
132	11	8.81818181818182	2.18181818181818
133	8	11.0869565217391	-3.08695652173913
134	11	8.81818181818182	2.18181818181818
135	12	11.0869565217391	0.913043478260869
136	12	11.0869565217391	0.913043478260869
137	9	11.0869565217391	-2.08695652173913
138	11	11.0869565217391	-0.0869565217391308
139	10	8.81818181818182	1.18181818181818
140	8	8.81818181818182	-0.818181818181818
141	9	8.81818181818182	0.181818181818182
142	8	11.0869565217391	-3.08695652173913
143	9	11.0869565217391	-2.08695652173913
144	15	12.88	2.12
145	11	11.0869565217391	-0.0869565217391308
146	8	8.81818181818182	-0.818181818181818
147	13	12.88	0.119999999999999
148	12	11.0869565217391	0.913043478260869
149	12	11.0869565217391	0.913043478260869
150	9	11.0869565217391	-2.08695652173913
151	7	8.81818181818182	-1.81818181818182
152	13	11.0869565217391	1.91304347826087
153	9	11.0869565217391	-2.08695652173913
154	6	11.0869565217391	-5.08695652173913
155	8	11.0869565217391	-3.08695652173913
156	8	8.81818181818182	-0.818181818181818
157	15	12.88	2.12
158	6	11.0869565217391	-5.08695652173913
159	9	11.0869565217391	-2.08695652173913
160	11	11.0869565217391	-0.0869565217391308
161	8	8.81818181818182	-0.818181818181818
162	8	11.0869565217391	-3.08695652173913

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
2 & 11 & 11.0869565217391 & -0.0869565217391308 \tabularnewline
3 & 15 & 12.88 & 2.12 \tabularnewline
4 & 6 & 11.0869565217391 & -5.08695652173913 \tabularnewline
5 & 13 & 11.0869565217391 & 1.91304347826087 \tabularnewline
6 & 10 & 11.0869565217391 & -1.08695652173913 \tabularnewline
7 & 12 & 12.88 & -0.88 \tabularnewline
8 & 14 & 11.0869565217391 & 2.91304347826087 \tabularnewline
9 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
10 & 6 & 11.0869565217391 & -5.08695652173913 \tabularnewline
11 & 10 & 11.0869565217391 & -1.08695652173913 \tabularnewline
12 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
13 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
14 & 11 & 11.0869565217391 & -0.0869565217391308 \tabularnewline
15 & 15 & 12.88 & 2.12 \tabularnewline
16 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
17 & 10 & 11.0869565217391 & -1.08695652173913 \tabularnewline
18 & 12 & 12.88 & -0.88 \tabularnewline
19 & 11 & 12.88 & -1.88 \tabularnewline
20 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
21 & 11 & 11.0869565217391 & -0.0869565217391308 \tabularnewline
22 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
23 & 13 & 12.88 & 0.119999999999999 \tabularnewline
24 & 11 & 11.0869565217391 & -0.0869565217391308 \tabularnewline
25 & 9 & 12.88 & -3.88 \tabularnewline
26 & 13 & 12.88 & 0.119999999999999 \tabularnewline
27 & 10 & 11.0869565217391 & -1.08695652173913 \tabularnewline
28 & 14 & 11.0869565217391 & 2.91304347826087 \tabularnewline
29 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
30 & 10 & 11.0869565217391 & -1.08695652173913 \tabularnewline
31 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
32 & 8 & 8.81818181818182 & -0.818181818181818 \tabularnewline
33 & 10 & 11.0869565217391 & -1.08695652173913 \tabularnewline
34 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
35 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
36 & 7 & 8.81818181818182 & -1.81818181818182 \tabularnewline
37 & 6 & 8.81818181818182 & -2.81818181818182 \tabularnewline
38 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
39 & 10 & 11.0869565217391 & -1.08695652173913 \tabularnewline
40 & 10 & 11.0869565217391 & -1.08695652173913 \tabularnewline
41 & 10 & 11.0869565217391 & -1.08695652173913 \tabularnewline
42 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
43 & 15 & 12.88 & 2.12 \tabularnewline
44 & 10 & 11.0869565217391 & -1.08695652173913 \tabularnewline
45 & 10 & 11.0869565217391 & -1.08695652173913 \tabularnewline
46 & 12 & 8.81818181818182 & 3.18181818181818 \tabularnewline
47 & 13 & 11.0869565217391 & 1.91304347826087 \tabularnewline
48 & 11 & 11.0869565217391 & -0.0869565217391308 \tabularnewline
49 & 11 & 11.0869565217391 & -0.0869565217391308 \tabularnewline
50 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
51 & 14 & 11.0869565217391 & 2.91304347826087 \tabularnewline
52 & 10 & 11.0869565217391 & -1.08695652173913 \tabularnewline
53 & 12 & 8.81818181818182 & 3.18181818181818 \tabularnewline
54 & 13 & 11.0869565217391 & 1.91304347826087 \tabularnewline
55 & 5 & 8.81818181818182 & -3.81818181818182 \tabularnewline
56 & 6 & 11.0869565217391 & -5.08695652173913 \tabularnewline
57 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
58 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
59 & 11 & 11.0869565217391 & -0.0869565217391308 \tabularnewline
60 & 10 & 11.0869565217391 & -1.08695652173913 \tabularnewline
61 & 7 & 8.81818181818182 & -1.81818181818182 \tabularnewline
62 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
63 & 14 & 11.0869565217391 & 2.91304347826087 \tabularnewline
64 & 11 & 11.0869565217391 & -0.0869565217391308 \tabularnewline
65 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
66 & 13 & 12.88 & 0.119999999999999 \tabularnewline
67 & 14 & 12.88 & 1.12 \tabularnewline
68 & 11 & 12.88 & -1.88 \tabularnewline
69 & 12 & 8.81818181818182 & 3.18181818181818 \tabularnewline
70 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
71 & 8 & 8.81818181818182 & -0.818181818181818 \tabularnewline
72 & 11 & 11.0869565217391 & -0.0869565217391308 \tabularnewline
73 & 14 & 12.88 & 1.12 \tabularnewline
74 & 14 & 12.88 & 1.12 \tabularnewline
75 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
76 & 9 & 12.88 & -3.88 \tabularnewline
77 & 13 & 11.0869565217391 & 1.91304347826087 \tabularnewline
78 & 11 & 11.0869565217391 & -0.0869565217391308 \tabularnewline
79 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
80 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
81 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
82 & 12 & 12.88 & -0.88 \tabularnewline
83 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
84 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
85 & 11 & 11.0869565217391 & -0.0869565217391308 \tabularnewline
86 & 10 & 11.0869565217391 & -1.08695652173913 \tabularnewline
87 & 9 & 11.0869565217391 & -2.08695652173913 \tabularnewline
88 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
89 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
90 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
91 & 9 & 8.81818181818182 & 0.181818181818182 \tabularnewline
92 & 15 & 12.88 & 2.12 \tabularnewline
93 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
94 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
95 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
96 & 10 & 11.0869565217391 & -1.08695652173913 \tabularnewline
97 & 13 & 11.0869565217391 & 1.91304347826087 \tabularnewline
98 & 9 & 11.0869565217391 & -2.08695652173913 \tabularnewline
99 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
100 & 10 & 11.0869565217391 & -1.08695652173913 \tabularnewline
101 & 14 & 11.0869565217391 & 2.91304347826087 \tabularnewline
102 & 11 & 11.0869565217391 & -0.0869565217391308 \tabularnewline
103 & 15 & 12.88 & 2.12 \tabularnewline
104 & 11 & 11.0869565217391 & -0.0869565217391308 \tabularnewline
105 & 11 & 11.0869565217391 & -0.0869565217391308 \tabularnewline
106 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
107 & 12 & 12.88 & -0.88 \tabularnewline
108 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
109 & 11 & 11.0869565217391 & -0.0869565217391308 \tabularnewline
110 & 7 & 8.81818181818182 & -1.81818181818182 \tabularnewline
111 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
112 & 14 & 11.0869565217391 & 2.91304347826087 \tabularnewline
113 & 11 & 12.88 & -1.88 \tabularnewline
114 & 11 & 11.0869565217391 & -0.0869565217391308 \tabularnewline
115 & 10 & 8.81818181818182 & 1.18181818181818 \tabularnewline
116 & 13 & 12.88 & 0.119999999999999 \tabularnewline
117 & 13 & 11.0869565217391 & 1.91304347826087 \tabularnewline
118 & 8 & 11.0869565217391 & -3.08695652173913 \tabularnewline
119 & 11 & 11.0869565217391 & -0.0869565217391308 \tabularnewline
120 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
121 & 11 & 11.0869565217391 & -0.0869565217391308 \tabularnewline
122 & 13 & 11.0869565217391 & 1.91304347826087 \tabularnewline
123 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
124 & 14 & 11.0869565217391 & 2.91304347826087 \tabularnewline
125 & 13 & 11.0869565217391 & 1.91304347826087 \tabularnewline
126 & 15 & 11.0869565217391 & 3.91304347826087 \tabularnewline
127 & 10 & 11.0869565217391 & -1.08695652173913 \tabularnewline
128 & 11 & 12.88 & -1.88 \tabularnewline
129 & 9 & 11.0869565217391 & -2.08695652173913 \tabularnewline
130 & 11 & 8.81818181818182 & 2.18181818181818 \tabularnewline
131 & 10 & 11.0869565217391 & -1.08695652173913 \tabularnewline
132 & 11 & 8.81818181818182 & 2.18181818181818 \tabularnewline
133 & 8 & 11.0869565217391 & -3.08695652173913 \tabularnewline
134 & 11 & 8.81818181818182 & 2.18181818181818 \tabularnewline
135 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
136 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
137 & 9 & 11.0869565217391 & -2.08695652173913 \tabularnewline
138 & 11 & 11.0869565217391 & -0.0869565217391308 \tabularnewline
139 & 10 & 8.81818181818182 & 1.18181818181818 \tabularnewline
140 & 8 & 8.81818181818182 & -0.818181818181818 \tabularnewline
141 & 9 & 8.81818181818182 & 0.181818181818182 \tabularnewline
142 & 8 & 11.0869565217391 & -3.08695652173913 \tabularnewline
143 & 9 & 11.0869565217391 & -2.08695652173913 \tabularnewline
144 & 15 & 12.88 & 2.12 \tabularnewline
145 & 11 & 11.0869565217391 & -0.0869565217391308 \tabularnewline
146 & 8 & 8.81818181818182 & -0.818181818181818 \tabularnewline
147 & 13 & 12.88 & 0.119999999999999 \tabularnewline
148 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
149 & 12 & 11.0869565217391 & 0.913043478260869 \tabularnewline
150 & 9 & 11.0869565217391 & -2.08695652173913 \tabularnewline
151 & 7 & 8.81818181818182 & -1.81818181818182 \tabularnewline
152 & 13 & 11.0869565217391 & 1.91304347826087 \tabularnewline
153 & 9 & 11.0869565217391 & -2.08695652173913 \tabularnewline
154 & 6 & 11.0869565217391 & -5.08695652173913 \tabularnewline
155 & 8 & 11.0869565217391 & -3.08695652173913 \tabularnewline
156 & 8 & 8.81818181818182 & -0.818181818181818 \tabularnewline
157 & 15 & 12.88 & 2.12 \tabularnewline
158 & 6 & 11.0869565217391 & -5.08695652173913 \tabularnewline
159 & 9 & 11.0869565217391 & -2.08695652173913 \tabularnewline
160 & 11 & 11.0869565217391 & -0.0869565217391308 \tabularnewline
161 & 8 & 8.81818181818182 & -0.818181818181818 \tabularnewline
162 & 8 & 11.0869565217391 & -3.08695652173913 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154932&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]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]11.0869565217391[/C][C]-0.0869565217391308[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]12.88[/C][C]2.12[/C][/ROW]
[ROW][C]4[/C][C]6[/C][C]11.0869565217391[/C][C]-5.08695652173913[/C][/ROW]
[ROW][C]5[/C][C]13[/C][C]11.0869565217391[/C][C]1.91304347826087[/C][/ROW]
[ROW][C]6[/C][C]10[/C][C]11.0869565217391[/C][C]-1.08695652173913[/C][/ROW]
[ROW][C]7[/C][C]12[/C][C]12.88[/C][C]-0.88[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]11.0869565217391[/C][C]2.91304347826087[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]10[/C][C]6[/C][C]11.0869565217391[/C][C]-5.08695652173913[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]11.0869565217391[/C][C]-1.08695652173913[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]13[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]14[/C][C]11[/C][C]11.0869565217391[/C][C]-0.0869565217391308[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]12.88[/C][C]2.12[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]11.0869565217391[/C][C]-1.08695652173913[/C][/ROW]
[ROW][C]18[/C][C]12[/C][C]12.88[/C][C]-0.88[/C][/ROW]
[ROW][C]19[/C][C]11[/C][C]12.88[/C][C]-1.88[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]11.0869565217391[/C][C]-0.0869565217391308[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]23[/C][C]13[/C][C]12.88[/C][C]0.119999999999999[/C][/ROW]
[ROW][C]24[/C][C]11[/C][C]11.0869565217391[/C][C]-0.0869565217391308[/C][/ROW]
[ROW][C]25[/C][C]9[/C][C]12.88[/C][C]-3.88[/C][/ROW]
[ROW][C]26[/C][C]13[/C][C]12.88[/C][C]0.119999999999999[/C][/ROW]
[ROW][C]27[/C][C]10[/C][C]11.0869565217391[/C][C]-1.08695652173913[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]11.0869565217391[/C][C]2.91304347826087[/C][/ROW]
[ROW][C]29[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]30[/C][C]10[/C][C]11.0869565217391[/C][C]-1.08695652173913[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]32[/C][C]8[/C][C]8.81818181818182[/C][C]-0.818181818181818[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]11.0869565217391[/C][C]-1.08695652173913[/C][/ROW]
[ROW][C]34[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]35[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]8.81818181818182[/C][C]-1.81818181818182[/C][/ROW]
[ROW][C]37[/C][C]6[/C][C]8.81818181818182[/C][C]-2.81818181818182[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]11.0869565217391[/C][C]-1.08695652173913[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]11.0869565217391[/C][C]-1.08695652173913[/C][/ROW]
[ROW][C]41[/C][C]10[/C][C]11.0869565217391[/C][C]-1.08695652173913[/C][/ROW]
[ROW][C]42[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]43[/C][C]15[/C][C]12.88[/C][C]2.12[/C][/ROW]
[ROW][C]44[/C][C]10[/C][C]11.0869565217391[/C][C]-1.08695652173913[/C][/ROW]
[ROW][C]45[/C][C]10[/C][C]11.0869565217391[/C][C]-1.08695652173913[/C][/ROW]
[ROW][C]46[/C][C]12[/C][C]8.81818181818182[/C][C]3.18181818181818[/C][/ROW]
[ROW][C]47[/C][C]13[/C][C]11.0869565217391[/C][C]1.91304347826087[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]11.0869565217391[/C][C]-0.0869565217391308[/C][/ROW]
[ROW][C]49[/C][C]11[/C][C]11.0869565217391[/C][C]-0.0869565217391308[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]51[/C][C]14[/C][C]11.0869565217391[/C][C]2.91304347826087[/C][/ROW]
[ROW][C]52[/C][C]10[/C][C]11.0869565217391[/C][C]-1.08695652173913[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]8.81818181818182[/C][C]3.18181818181818[/C][/ROW]
[ROW][C]54[/C][C]13[/C][C]11.0869565217391[/C][C]1.91304347826087[/C][/ROW]
[ROW][C]55[/C][C]5[/C][C]8.81818181818182[/C][C]-3.81818181818182[/C][/ROW]
[ROW][C]56[/C][C]6[/C][C]11.0869565217391[/C][C]-5.08695652173913[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]58[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]59[/C][C]11[/C][C]11.0869565217391[/C][C]-0.0869565217391308[/C][/ROW]
[ROW][C]60[/C][C]10[/C][C]11.0869565217391[/C][C]-1.08695652173913[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]8.81818181818182[/C][C]-1.81818181818182[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]11.0869565217391[/C][C]2.91304347826087[/C][/ROW]
[ROW][C]64[/C][C]11[/C][C]11.0869565217391[/C][C]-0.0869565217391308[/C][/ROW]
[ROW][C]65[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]12.88[/C][C]0.119999999999999[/C][/ROW]
[ROW][C]67[/C][C]14[/C][C]12.88[/C][C]1.12[/C][/ROW]
[ROW][C]68[/C][C]11[/C][C]12.88[/C][C]-1.88[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]8.81818181818182[/C][C]3.18181818181818[/C][/ROW]
[ROW][C]70[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]71[/C][C]8[/C][C]8.81818181818182[/C][C]-0.818181818181818[/C][/ROW]
[ROW][C]72[/C][C]11[/C][C]11.0869565217391[/C][C]-0.0869565217391308[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]12.88[/C][C]1.12[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]12.88[/C][C]1.12[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]76[/C][C]9[/C][C]12.88[/C][C]-3.88[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]11.0869565217391[/C][C]1.91304347826087[/C][/ROW]
[ROW][C]78[/C][C]11[/C][C]11.0869565217391[/C][C]-0.0869565217391308[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]80[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]82[/C][C]12[/C][C]12.88[/C][C]-0.88[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]85[/C][C]11[/C][C]11.0869565217391[/C][C]-0.0869565217391308[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]11.0869565217391[/C][C]-1.08695652173913[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]11.0869565217391[/C][C]-2.08695652173913[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]91[/C][C]9[/C][C]8.81818181818182[/C][C]0.181818181818182[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]12.88[/C][C]2.12[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]94[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]11.0869565217391[/C][C]-1.08695652173913[/C][/ROW]
[ROW][C]97[/C][C]13[/C][C]11.0869565217391[/C][C]1.91304347826087[/C][/ROW]
[ROW][C]98[/C][C]9[/C][C]11.0869565217391[/C][C]-2.08695652173913[/C][/ROW]
[ROW][C]99[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]11.0869565217391[/C][C]-1.08695652173913[/C][/ROW]
[ROW][C]101[/C][C]14[/C][C]11.0869565217391[/C][C]2.91304347826087[/C][/ROW]
[ROW][C]102[/C][C]11[/C][C]11.0869565217391[/C][C]-0.0869565217391308[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]12.88[/C][C]2.12[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]11.0869565217391[/C][C]-0.0869565217391308[/C][/ROW]
[ROW][C]105[/C][C]11[/C][C]11.0869565217391[/C][C]-0.0869565217391308[/C][/ROW]
[ROW][C]106[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]107[/C][C]12[/C][C]12.88[/C][C]-0.88[/C][/ROW]
[ROW][C]108[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]11.0869565217391[/C][C]-0.0869565217391308[/C][/ROW]
[ROW][C]110[/C][C]7[/C][C]8.81818181818182[/C][C]-1.81818181818182[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]112[/C][C]14[/C][C]11.0869565217391[/C][C]2.91304347826087[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]12.88[/C][C]-1.88[/C][/ROW]
[ROW][C]114[/C][C]11[/C][C]11.0869565217391[/C][C]-0.0869565217391308[/C][/ROW]
[ROW][C]115[/C][C]10[/C][C]8.81818181818182[/C][C]1.18181818181818[/C][/ROW]
[ROW][C]116[/C][C]13[/C][C]12.88[/C][C]0.119999999999999[/C][/ROW]
[ROW][C]117[/C][C]13[/C][C]11.0869565217391[/C][C]1.91304347826087[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]11.0869565217391[/C][C]-3.08695652173913[/C][/ROW]
[ROW][C]119[/C][C]11[/C][C]11.0869565217391[/C][C]-0.0869565217391308[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]121[/C][C]11[/C][C]11.0869565217391[/C][C]-0.0869565217391308[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]11.0869565217391[/C][C]1.91304347826087[/C][/ROW]
[ROW][C]123[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]124[/C][C]14[/C][C]11.0869565217391[/C][C]2.91304347826087[/C][/ROW]
[ROW][C]125[/C][C]13[/C][C]11.0869565217391[/C][C]1.91304347826087[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]11.0869565217391[/C][C]3.91304347826087[/C][/ROW]
[ROW][C]127[/C][C]10[/C][C]11.0869565217391[/C][C]-1.08695652173913[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]12.88[/C][C]-1.88[/C][/ROW]
[ROW][C]129[/C][C]9[/C][C]11.0869565217391[/C][C]-2.08695652173913[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]8.81818181818182[/C][C]2.18181818181818[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]11.0869565217391[/C][C]-1.08695652173913[/C][/ROW]
[ROW][C]132[/C][C]11[/C][C]8.81818181818182[/C][C]2.18181818181818[/C][/ROW]
[ROW][C]133[/C][C]8[/C][C]11.0869565217391[/C][C]-3.08695652173913[/C][/ROW]
[ROW][C]134[/C][C]11[/C][C]8.81818181818182[/C][C]2.18181818181818[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]137[/C][C]9[/C][C]11.0869565217391[/C][C]-2.08695652173913[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]11.0869565217391[/C][C]-0.0869565217391308[/C][/ROW]
[ROW][C]139[/C][C]10[/C][C]8.81818181818182[/C][C]1.18181818181818[/C][/ROW]
[ROW][C]140[/C][C]8[/C][C]8.81818181818182[/C][C]-0.818181818181818[/C][/ROW]
[ROW][C]141[/C][C]9[/C][C]8.81818181818182[/C][C]0.181818181818182[/C][/ROW]
[ROW][C]142[/C][C]8[/C][C]11.0869565217391[/C][C]-3.08695652173913[/C][/ROW]
[ROW][C]143[/C][C]9[/C][C]11.0869565217391[/C][C]-2.08695652173913[/C][/ROW]
[ROW][C]144[/C][C]15[/C][C]12.88[/C][C]2.12[/C][/ROW]
[ROW][C]145[/C][C]11[/C][C]11.0869565217391[/C][C]-0.0869565217391308[/C][/ROW]
[ROW][C]146[/C][C]8[/C][C]8.81818181818182[/C][C]-0.818181818181818[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]12.88[/C][C]0.119999999999999[/C][/ROW]
[ROW][C]148[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]11.0869565217391[/C][C]0.913043478260869[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]11.0869565217391[/C][C]-2.08695652173913[/C][/ROW]
[ROW][C]151[/C][C]7[/C][C]8.81818181818182[/C][C]-1.81818181818182[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]11.0869565217391[/C][C]1.91304347826087[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]11.0869565217391[/C][C]-2.08695652173913[/C][/ROW]
[ROW][C]154[/C][C]6[/C][C]11.0869565217391[/C][C]-5.08695652173913[/C][/ROW]
[ROW][C]155[/C][C]8[/C][C]11.0869565217391[/C][C]-3.08695652173913[/C][/ROW]
[ROW][C]156[/C][C]8[/C][C]8.81818181818182[/C][C]-0.818181818181818[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]12.88[/C][C]2.12[/C][/ROW]
[ROW][C]158[/C][C]6[/C][C]11.0869565217391[/C][C]-5.08695652173913[/C][/ROW]
[ROW][C]159[/C][C]9[/C][C]11.0869565217391[/C][C]-2.08695652173913[/C][/ROW]
[ROW][C]160[/C][C]11[/C][C]11.0869565217391[/C][C]-0.0869565217391308[/C][/ROW]
[ROW][C]161[/C][C]8[/C][C]8.81818181818182[/C][C]-0.818181818181818[/C][/ROW]
[ROW][C]162[/C][C]8[/C][C]11.0869565217391[/C][C]-3.08695652173913[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154932&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154932&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	12	11.0869565217391	0.913043478260869
2	11	11.0869565217391	-0.0869565217391308
3	15	12.88	2.12
4	6	11.0869565217391	-5.08695652173913
5	13	11.0869565217391	1.91304347826087
6	10	11.0869565217391	-1.08695652173913
7	12	12.88	-0.88
8	14	11.0869565217391	2.91304347826087
9	12	11.0869565217391	0.913043478260869
10	6	11.0869565217391	-5.08695652173913
11	10	11.0869565217391	-1.08695652173913
12	12	11.0869565217391	0.913043478260869
13	12	11.0869565217391	0.913043478260869
14	11	11.0869565217391	-0.0869565217391308
15	15	12.88	2.12
16	12	11.0869565217391	0.913043478260869
17	10	11.0869565217391	-1.08695652173913
18	12	12.88	-0.88
19	11	12.88	-1.88
20	12	11.0869565217391	0.913043478260869
21	11	11.0869565217391	-0.0869565217391308
22	12	11.0869565217391	0.913043478260869
23	13	12.88	0.119999999999999
24	11	11.0869565217391	-0.0869565217391308
25	9	12.88	-3.88
26	13	12.88	0.119999999999999
27	10	11.0869565217391	-1.08695652173913
28	14	11.0869565217391	2.91304347826087
29	12	11.0869565217391	0.913043478260869
30	10	11.0869565217391	-1.08695652173913
31	12	11.0869565217391	0.913043478260869
32	8	8.81818181818182	-0.818181818181818
33	10	11.0869565217391	-1.08695652173913
34	12	11.0869565217391	0.913043478260869
35	12	11.0869565217391	0.913043478260869
36	7	8.81818181818182	-1.81818181818182
37	6	8.81818181818182	-2.81818181818182
38	12	11.0869565217391	0.913043478260869
39	10	11.0869565217391	-1.08695652173913
40	10	11.0869565217391	-1.08695652173913
41	10	11.0869565217391	-1.08695652173913
42	12	11.0869565217391	0.913043478260869
43	15	12.88	2.12
44	10	11.0869565217391	-1.08695652173913
45	10	11.0869565217391	-1.08695652173913
46	12	8.81818181818182	3.18181818181818
47	13	11.0869565217391	1.91304347826087
48	11	11.0869565217391	-0.0869565217391308
49	11	11.0869565217391	-0.0869565217391308
50	12	11.0869565217391	0.913043478260869
51	14	11.0869565217391	2.91304347826087
52	10	11.0869565217391	-1.08695652173913
53	12	8.81818181818182	3.18181818181818
54	13	11.0869565217391	1.91304347826087
55	5	8.81818181818182	-3.81818181818182
56	6	11.0869565217391	-5.08695652173913
57	12	11.0869565217391	0.913043478260869
58	12	11.0869565217391	0.913043478260869
59	11	11.0869565217391	-0.0869565217391308
60	10	11.0869565217391	-1.08695652173913
61	7	8.81818181818182	-1.81818181818182
62	12	11.0869565217391	0.913043478260869
63	14	11.0869565217391	2.91304347826087
64	11	11.0869565217391	-0.0869565217391308
65	12	11.0869565217391	0.913043478260869
66	13	12.88	0.119999999999999
67	14	12.88	1.12
68	11	12.88	-1.88
69	12	8.81818181818182	3.18181818181818
70	12	11.0869565217391	0.913043478260869
71	8	8.81818181818182	-0.818181818181818
72	11	11.0869565217391	-0.0869565217391308
73	14	12.88	1.12
74	14	12.88	1.12
75	12	11.0869565217391	0.913043478260869
76	9	12.88	-3.88
77	13	11.0869565217391	1.91304347826087
78	11	11.0869565217391	-0.0869565217391308
79	12	11.0869565217391	0.913043478260869
80	12	11.0869565217391	0.913043478260869
81	12	11.0869565217391	0.913043478260869
82	12	12.88	-0.88
83	12	11.0869565217391	0.913043478260869
84	12	11.0869565217391	0.913043478260869
85	11	11.0869565217391	-0.0869565217391308
86	10	11.0869565217391	-1.08695652173913
87	9	11.0869565217391	-2.08695652173913
88	12	11.0869565217391	0.913043478260869
89	12	11.0869565217391	0.913043478260869
90	12	11.0869565217391	0.913043478260869
91	9	8.81818181818182	0.181818181818182
92	15	12.88	2.12
93	12	11.0869565217391	0.913043478260869
94	12	11.0869565217391	0.913043478260869
95	12	11.0869565217391	0.913043478260869
96	10	11.0869565217391	-1.08695652173913
97	13	11.0869565217391	1.91304347826087
98	9	11.0869565217391	-2.08695652173913
99	12	11.0869565217391	0.913043478260869
100	10	11.0869565217391	-1.08695652173913
101	14	11.0869565217391	2.91304347826087
102	11	11.0869565217391	-0.0869565217391308
103	15	12.88	2.12
104	11	11.0869565217391	-0.0869565217391308
105	11	11.0869565217391	-0.0869565217391308
106	12	11.0869565217391	0.913043478260869
107	12	12.88	-0.88
108	12	11.0869565217391	0.913043478260869
109	11	11.0869565217391	-0.0869565217391308
110	7	8.81818181818182	-1.81818181818182
111	12	11.0869565217391	0.913043478260869
112	14	11.0869565217391	2.91304347826087
113	11	12.88	-1.88
114	11	11.0869565217391	-0.0869565217391308
115	10	8.81818181818182	1.18181818181818
116	13	12.88	0.119999999999999
117	13	11.0869565217391	1.91304347826087
118	8	11.0869565217391	-3.08695652173913
119	11	11.0869565217391	-0.0869565217391308
120	12	11.0869565217391	0.913043478260869
121	11	11.0869565217391	-0.0869565217391308
122	13	11.0869565217391	1.91304347826087
123	12	11.0869565217391	0.913043478260869
124	14	11.0869565217391	2.91304347826087
125	13	11.0869565217391	1.91304347826087
126	15	11.0869565217391	3.91304347826087
127	10	11.0869565217391	-1.08695652173913
128	11	12.88	-1.88
129	9	11.0869565217391	-2.08695652173913
130	11	8.81818181818182	2.18181818181818
131	10	11.0869565217391	-1.08695652173913
132	11	8.81818181818182	2.18181818181818
133	8	11.0869565217391	-3.08695652173913
134	11	8.81818181818182	2.18181818181818
135	12	11.0869565217391	0.913043478260869
136	12	11.0869565217391	0.913043478260869
137	9	11.0869565217391	-2.08695652173913
138	11	11.0869565217391	-0.0869565217391308
139	10	8.81818181818182	1.18181818181818
140	8	8.81818181818182	-0.818181818181818
141	9	8.81818181818182	0.181818181818182
142	8	11.0869565217391	-3.08695652173913
143	9	11.0869565217391	-2.08695652173913
144	15	12.88	2.12
145	11	11.0869565217391	-0.0869565217391308
146	8	8.81818181818182	-0.818181818181818
147	13	12.88	0.119999999999999
148	12	11.0869565217391	0.913043478260869
149	12	11.0869565217391	0.913043478260869
150	9	11.0869565217391	-2.08695652173913
151	7	8.81818181818182	-1.81818181818182
152	13	11.0869565217391	1.91304347826087
153	9	11.0869565217391	-2.08695652173913
154	6	11.0869565217391	-5.08695652173913
155	8	11.0869565217391	-3.08695652173913
156	8	8.81818181818182	-0.818181818181818
157	15	12.88	2.12
158	6	11.0869565217391	-5.08695652173913
159	9	11.0869565217391	-2.08695652173913
160	11	11.0869565217391	-0.0869565217391308
161	8	8.81818181818182	-0.818181818181818
162	8	11.0869565217391	-3.08695652173913

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