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R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Fri, 24 Dec 2010 10:41:30 +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/24/t1293187154gf7hra55ozcei10.htm/, Retrieved Tue, 30 Apr 2024 00:52:43 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114726, Retrieved Tue, 30 Apr 2024 00:52:43 +0000

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No (this computation is public)

User-defined keywords

Estimated Impact

135

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)] [Recursive Partiti...] [2010-12-20 13:28:39] [253127ae8da904b75450fbd69fe4eb21]
-           [Recursive Partitioning (Regression Trees)] [tutorial] [2010-12-24 10:41:30] [65e95fe5923d75db266bc83cb8a34c47] [Current]
F    D        [Recursive Partitioning (Regression Trees)] [Recursive Part.] [2010-12-24 17:04:38] [8e42c8cdf50f15ce85eb45a67cf771d0] 
-               [Recursive Partitioning (Regression Trees)] [] [2010-12-25 09:43:02] [64a7ae6044525e7ca71ecb546c042c9e] 
- R  D        [Recursive Partitioning (Regression Trees)] [WS7: Recursive Pa...] [2010-12-26 11:17:13] [7f54ec67e5798cc59f49446b41e2f221] 
-   P           [Recursive Partitioning (Regression Trees)] [Workshop 10 Recur...] [2010-12-26 19:25:42] [38afc57aa6474689f791e00be1754a89] 

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Dataseries X:

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Histogram

Boxplots

24	14	11	12	24	26
25	11	7	8	25	23
17	6	17	8	30	25
18	12	10	8	19	23
18	8	12	9	22	19
16	10	12	7	22	29
20	10	11	4	25	25
16	11	11	11	23	21
18	16	12	7	17	22
17	11	13	7	21	25
23	13	14	12	19	24
30	12	16	10	19	18
23	8	11	10	15	22
18	12	10	8	16	15
15	11	11	8	23	22
12	4	15	4	27	28
21	9	9	9	22	20
15	8	11	8	14	12
20	8	17	7	22	24
31	14	17	11	23	20
27	15	11	9	23	21
34	16	18	11	21	20
21	9	14	13	19	21
31	14	10	8	18	23
19	11	11	8	20	28
16	8	15	9	23	24
20	9	15	6	25	24
21	9	13	9	19	24
22	9	16	9	24	23
17	9	13	6	22	23
24	10	9	6	25	29
25	16	18	16	26	24
26	11	18	5	29	18
25	8	12	7	32	25
17	9	17	9	25	21
32	16	9	6	29	26
33	11	9	6	28	22
13	16	12	5	17	22
32	12	18	12	28	22
25	12	12	7	29	23
29	14	18	10	26	30
22	9	14	9	25	23
18	10	15	8	14	17
17	9	16	5	25	23
20	10	10	8	26	23
15	12	11	8	20	25
20	14	14	10	18	24
33	14	9	6	32	24
29	10	12	8	25	23
23	14	17	7	25	21
26	16	5	4	23	24
18	9	12	8	21	24
20	10	12	8	20	28
11	6	6	4	15	16
28	8	24	20	30	20
26	13	12	8	24	29
22	10	12	8	26	27
17	8	14	6	24	22
12	7	7	4	22	28
14	15	13	8	14	16
17	9	12	9	24	25
21	10	13	6	24	24
19	12	14	7	24	28
18	13	8	9	24	24
10	10	11	5	19	23
29	11	9	5	31	30
31	8	11	8	22	24
19	9	13	8	27	21
9	13	10	6	19	25
20	11	11	8	25	25
28	8	12	7	20	22
19	9	9	7	21	23
30	9	15	9	27	26
29	15	18	11	23	23
26	9	15	6	25	25
23	10	12	8	20	21
13	14	13	6	21	25
21	12	14	9	22	24
19	12	10	8	23	29
28	11	13	6	25	22
23	14	13	10	25	27
18	6	11	8	17	26
21	12	13	8	19	22
20	8	16	10	25	24
23	14	8	5	19	27
21	11	16	7	20	24
21	10	11	5	26	24
15	14	9	8	23	29
28	12	16	14	27	22
19	10	12	7	17	21
26	14	14	8	17	24
10	5	8	6	19	24
16	11	9	5	17	23
22	10	15	6	22	20
19	9	11	10	21	27
31	10	21	12	32	26
31	16	14	9	21	25
29	13	18	12	21	21
19	9	12	7	18	21
22	10	13	8	18	19
23	10	15	10	23	21
15	7	12	6	19	21
20	9	19	10	20	16
18	8	15	10	21	22
23	14	11	10	20	29
25	14	11	5	17	15
21	8	10	7	18	17
24	9	13	10	19	15
25	14	15	11	22	21
17	14	12	6	15	21
13	8	12	7	14	19
28	8	16	12	18	24
21	8	9	11	24	20
25	7	18	11	35	17
9	6	8	11	29	23
16	8	13	5	21	24
19	6	17	8	25	14
17	11	9	6	20	19
25	14	15	9	22	24
20	11	8	4	13	13
29	11	7	4	26	22
14	11	12	7	17	16
22	14	14	11	25	19
15	8	6	6	20	25
19	20	8	7	19	25
20	11	17	8	21	23
15	8	10	4	22	24
20	11	11	8	24	26
18	10	14	9	21	26
33	14	11	8	26	25
22	11	13	11	24	18
16	9	12	8	16	21
17	9	11	5	23	26
16	8	9	4	18	23
21	10	12	8	16	23
26	13	20	10	26	22
18	13	12	6	19	20
18	12	13	9	21	13
17	8	12	9	21	24
22	13	12	13	22	15
30	14	9	9	23	14
30	12	15	10	29	22
24	14	24	20	21	10
21	15	7	5	21	24
21	13	17	11	23	22
29	16	11	6	27	24
31	9	17	9	25	19
20	9	11	7	21	20
16	9	12	9	10	13
22	8	14	10	20	20
20	7	11	9	26	22
28	16	16	8	24	24
38	11	21	7	29	29
22	9	14	6	19	12
20	11	20	13	24	20
17	9	13	6	19	21
28	14	11	8	24	24
22	13	15	10	22	22
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	5 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 5 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114726&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[ROW][C]R Framework error message[/C][C]The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114726&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114726&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	5 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Goodness of Fit
Correlation	0.6087
R-squared	0.3705
RMSE	2.7251

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.6087 \tabularnewline
R-squared & 0.3705 \tabularnewline
RMSE & 2.7251 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114726&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.6087[/C][/ROW]
[ROW][C]R-squared[/C][C]0.3705[/C][/ROW]
[ROW][C]RMSE[/C][C]2.7251[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114726&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114726&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	0.6087
R-squared	0.3705
RMSE	2.7251

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	11	18.5	-7.5
2	7	12.1743119266055	-5.1743119266055
3	17	12.1743119266055	4.8256880733945
4	10	12.1743119266055	-2.1743119266055
5	12	12.1743119266055	-0.174311926605505
6	12	12.1743119266055	-0.174311926605505
7	11	8.66666666666667	2.33333333333333
8	11	13.6190476190476	-2.61904761904762
9	12	12.1743119266055	-0.174311926605505
10	13	12.1743119266055	0.825688073394495
11	14	13.6190476190476	0.380952380952381
12	16	16.8	-0.8
13	11	13.6190476190476	-2.61904761904762
14	10	12.1743119266055	-2.1743119266055
15	11	12.1743119266055	-1.1743119266055
16	15	8.66666666666667	6.33333333333333
17	9	12.1743119266055	-3.1743119266055
18	11	12.1743119266055	-1.1743119266055
19	17	12.1743119266055	4.8256880733945
20	17	16.8	0.199999999999999
21	11	12.1743119266055	-1.1743119266055
22	18	16.8	1.2
23	14	13.6190476190476	0.380952380952381
24	10	12.1743119266055	-2.1743119266055
25	11	12.1743119266055	-1.1743119266055
26	15	12.1743119266055	2.8256880733945
27	15	12.1743119266055	2.8256880733945
28	13	12.1743119266055	0.825688073394495
29	16	12.1743119266055	3.8256880733945
30	13	12.1743119266055	0.825688073394495
31	9	12.1743119266055	-3.1743119266055
32	18	18.5	-0.5
33	18	12.1743119266055	5.8256880733945
34	12	12.1743119266055	-0.174311926605505
35	17	12.1743119266055	4.8256880733945
36	9	12.1743119266055	-3.1743119266055
37	9	12.1743119266055	-3.1743119266055
38	12	12.1743119266055	-0.174311926605505
39	18	18.5	-0.5
40	12	12.1743119266055	-0.174311926605505
41	18	16.8	1.2
42	14	12.1743119266055	1.8256880733945
43	15	12.1743119266055	2.8256880733945
44	16	12.1743119266055	3.8256880733945
45	10	12.1743119266055	-2.1743119266055
46	11	12.1743119266055	-1.1743119266055
47	14	13.6190476190476	0.380952380952381
48	9	12.1743119266055	-3.1743119266055
49	12	12.1743119266055	-0.174311926605505
50	17	12.1743119266055	4.8256880733945
51	5	8.66666666666667	-3.66666666666667
52	12	12.1743119266055	-0.174311926605505
53	12	12.1743119266055	-0.174311926605505
54	6	8.66666666666667	-2.66666666666667
55	24	18.5	5.5
56	12	12.1743119266055	-0.174311926605505
57	12	12.1743119266055	-0.174311926605505
58	14	12.1743119266055	1.8256880733945
59	7	8.66666666666667	-1.66666666666667
60	13	12.1743119266055	0.825688073394495
61	12	12.1743119266055	-0.174311926605505
62	13	12.1743119266055	0.825688073394495
63	14	12.1743119266055	1.8256880733945
64	8	12.1743119266055	-4.1743119266055
65	11	12.1743119266055	-1.1743119266055
66	9	12.1743119266055	-3.1743119266055
67	11	12.1743119266055	-1.1743119266055
68	13	12.1743119266055	0.825688073394495
69	10	12.1743119266055	-2.1743119266055
70	11	12.1743119266055	-1.1743119266055
71	12	12.1743119266055	-0.174311926605505
72	9	12.1743119266055	-3.1743119266055
73	15	12.1743119266055	2.8256880733945
74	18	16.8	1.2
75	15	12.1743119266055	2.8256880733945
76	12	12.1743119266055	-0.174311926605505
77	13	12.1743119266055	0.825688073394495
78	14	12.1743119266055	1.8256880733945
79	10	12.1743119266055	-2.1743119266055
80	13	12.1743119266055	0.825688073394495
81	13	13.6190476190476	-0.619047619047619
82	11	12.1743119266055	-1.1743119266055
83	13	12.1743119266055	0.825688073394495
84	16	13.6190476190476	2.38095238095238
85	8	12.1743119266055	-4.1743119266055
86	16	12.1743119266055	3.8256880733945
87	11	12.1743119266055	-1.1743119266055
88	9	12.1743119266055	-3.1743119266055
89	16	18.5	-2.5
90	12	12.1743119266055	-0.174311926605505
91	14	12.1743119266055	1.8256880733945
92	8	12.1743119266055	-4.1743119266055
93	9	12.1743119266055	-3.1743119266055
94	15	12.1743119266055	2.8256880733945
95	11	13.6190476190476	-2.61904761904762
96	21	18.5	2.5
97	14	12.1743119266055	1.8256880733945
98	18	18.5	-0.5
99	12	12.1743119266055	-0.174311926605505
100	13	12.1743119266055	0.825688073394495
101	15	13.6190476190476	1.38095238095238
102	12	12.1743119266055	-0.174311926605505
103	19	13.6190476190476	5.38095238095238
104	15	13.6190476190476	1.38095238095238
105	11	13.6190476190476	-2.61904761904762
106	11	12.1743119266055	-1.1743119266055
107	10	12.1743119266055	-2.1743119266055
108	13	16.8	-3.8
109	15	16.8	-1.8
110	12	12.1743119266055	-0.174311926605505
111	12	12.1743119266055	-0.174311926605505
112	16	18.5	-2.5
113	9	13.6190476190476	-4.61904761904762
114	18	16.8	1.2
115	8	13.6190476190476	-5.61904761904762
116	13	12.1743119266055	0.825688073394495
117	17	12.1743119266055	4.8256880733945
118	9	12.1743119266055	-3.1743119266055
119	15	12.1743119266055	2.8256880733945
120	8	8.66666666666667	-0.666666666666666
121	7	8.66666666666667	-1.66666666666667
122	12	12.1743119266055	-0.174311926605505
123	14	13.6190476190476	0.380952380952381
124	6	12.1743119266055	-6.1743119266055
125	8	12.1743119266055	-4.1743119266055
126	17	12.1743119266055	4.8256880733945
127	10	8.66666666666667	1.33333333333333
128	11	12.1743119266055	-1.1743119266055
129	14	12.1743119266055	1.8256880733945
130	11	12.1743119266055	-1.1743119266055
131	13	13.6190476190476	-0.619047619047619
132	12	12.1743119266055	-0.174311926605505
133	11	12.1743119266055	-1.1743119266055
134	9	8.66666666666667	0.333333333333334
135	12	12.1743119266055	-0.174311926605505
136	20	16.8	3.2
137	12	12.1743119266055	-0.174311926605505
138	13	12.1743119266055	0.825688073394495
139	12	12.1743119266055	-0.174311926605505
140	12	13.6190476190476	-1.61904761904762
141	9	12.1743119266055	-3.1743119266055
142	15	16.8	-1.8
143	24	18.5	5.5
144	7	12.1743119266055	-5.1743119266055
145	17	13.6190476190476	3.38095238095238
146	11	12.1743119266055	-1.1743119266055
147	17	12.1743119266055	4.8256880733945
148	11	12.1743119266055	-1.1743119266055
149	12	12.1743119266055	-0.174311926605505
150	14	13.6190476190476	0.380952380952381
151	11	12.1743119266055	-1.1743119266055
152	16	12.1743119266055	3.8256880733945
153	21	12.1743119266055	8.8256880733945
154	14	12.1743119266055	1.8256880733945
155	20	13.6190476190476	6.38095238095238
156	13	12.1743119266055	0.825688073394495
157	11	12.1743119266055	-1.1743119266055
158	15	13.6190476190476	1.38095238095238
159	19	18.5	0.5

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 11 & 18.5 & -7.5 \tabularnewline
2 & 7 & 12.1743119266055 & -5.1743119266055 \tabularnewline
3 & 17 & 12.1743119266055 & 4.8256880733945 \tabularnewline
4 & 10 & 12.1743119266055 & -2.1743119266055 \tabularnewline
5 & 12 & 12.1743119266055 & -0.174311926605505 \tabularnewline
6 & 12 & 12.1743119266055 & -0.174311926605505 \tabularnewline
7 & 11 & 8.66666666666667 & 2.33333333333333 \tabularnewline
8 & 11 & 13.6190476190476 & -2.61904761904762 \tabularnewline
9 & 12 & 12.1743119266055 & -0.174311926605505 \tabularnewline
10 & 13 & 12.1743119266055 & 0.825688073394495 \tabularnewline
11 & 14 & 13.6190476190476 & 0.380952380952381 \tabularnewline
12 & 16 & 16.8 & -0.8 \tabularnewline
13 & 11 & 13.6190476190476 & -2.61904761904762 \tabularnewline
14 & 10 & 12.1743119266055 & -2.1743119266055 \tabularnewline
15 & 11 & 12.1743119266055 & -1.1743119266055 \tabularnewline
16 & 15 & 8.66666666666667 & 6.33333333333333 \tabularnewline
17 & 9 & 12.1743119266055 & -3.1743119266055 \tabularnewline
18 & 11 & 12.1743119266055 & -1.1743119266055 \tabularnewline
19 & 17 & 12.1743119266055 & 4.8256880733945 \tabularnewline
20 & 17 & 16.8 & 0.199999999999999 \tabularnewline
21 & 11 & 12.1743119266055 & -1.1743119266055 \tabularnewline
22 & 18 & 16.8 & 1.2 \tabularnewline
23 & 14 & 13.6190476190476 & 0.380952380952381 \tabularnewline
24 & 10 & 12.1743119266055 & -2.1743119266055 \tabularnewline
25 & 11 & 12.1743119266055 & -1.1743119266055 \tabularnewline
26 & 15 & 12.1743119266055 & 2.8256880733945 \tabularnewline
27 & 15 & 12.1743119266055 & 2.8256880733945 \tabularnewline
28 & 13 & 12.1743119266055 & 0.825688073394495 \tabularnewline
29 & 16 & 12.1743119266055 & 3.8256880733945 \tabularnewline
30 & 13 & 12.1743119266055 & 0.825688073394495 \tabularnewline
31 & 9 & 12.1743119266055 & -3.1743119266055 \tabularnewline
32 & 18 & 18.5 & -0.5 \tabularnewline
33 & 18 & 12.1743119266055 & 5.8256880733945 \tabularnewline
34 & 12 & 12.1743119266055 & -0.174311926605505 \tabularnewline
35 & 17 & 12.1743119266055 & 4.8256880733945 \tabularnewline
36 & 9 & 12.1743119266055 & -3.1743119266055 \tabularnewline
37 & 9 & 12.1743119266055 & -3.1743119266055 \tabularnewline
38 & 12 & 12.1743119266055 & -0.174311926605505 \tabularnewline
39 & 18 & 18.5 & -0.5 \tabularnewline
40 & 12 & 12.1743119266055 & -0.174311926605505 \tabularnewline
41 & 18 & 16.8 & 1.2 \tabularnewline
42 & 14 & 12.1743119266055 & 1.8256880733945 \tabularnewline
43 & 15 & 12.1743119266055 & 2.8256880733945 \tabularnewline
44 & 16 & 12.1743119266055 & 3.8256880733945 \tabularnewline
45 & 10 & 12.1743119266055 & -2.1743119266055 \tabularnewline
46 & 11 & 12.1743119266055 & -1.1743119266055 \tabularnewline
47 & 14 & 13.6190476190476 & 0.380952380952381 \tabularnewline
48 & 9 & 12.1743119266055 & -3.1743119266055 \tabularnewline
49 & 12 & 12.1743119266055 & -0.174311926605505 \tabularnewline
50 & 17 & 12.1743119266055 & 4.8256880733945 \tabularnewline
51 & 5 & 8.66666666666667 & -3.66666666666667 \tabularnewline
52 & 12 & 12.1743119266055 & -0.174311926605505 \tabularnewline
53 & 12 & 12.1743119266055 & -0.174311926605505 \tabularnewline
54 & 6 & 8.66666666666667 & -2.66666666666667 \tabularnewline
55 & 24 & 18.5 & 5.5 \tabularnewline
56 & 12 & 12.1743119266055 & -0.174311926605505 \tabularnewline
57 & 12 & 12.1743119266055 & -0.174311926605505 \tabularnewline
58 & 14 & 12.1743119266055 & 1.8256880733945 \tabularnewline
59 & 7 & 8.66666666666667 & -1.66666666666667 \tabularnewline
60 & 13 & 12.1743119266055 & 0.825688073394495 \tabularnewline
61 & 12 & 12.1743119266055 & -0.174311926605505 \tabularnewline
62 & 13 & 12.1743119266055 & 0.825688073394495 \tabularnewline
63 & 14 & 12.1743119266055 & 1.8256880733945 \tabularnewline
64 & 8 & 12.1743119266055 & -4.1743119266055 \tabularnewline
65 & 11 & 12.1743119266055 & -1.1743119266055 \tabularnewline
66 & 9 & 12.1743119266055 & -3.1743119266055 \tabularnewline
67 & 11 & 12.1743119266055 & -1.1743119266055 \tabularnewline
68 & 13 & 12.1743119266055 & 0.825688073394495 \tabularnewline
69 & 10 & 12.1743119266055 & -2.1743119266055 \tabularnewline
70 & 11 & 12.1743119266055 & -1.1743119266055 \tabularnewline
71 & 12 & 12.1743119266055 & -0.174311926605505 \tabularnewline
72 & 9 & 12.1743119266055 & -3.1743119266055 \tabularnewline
73 & 15 & 12.1743119266055 & 2.8256880733945 \tabularnewline
74 & 18 & 16.8 & 1.2 \tabularnewline
75 & 15 & 12.1743119266055 & 2.8256880733945 \tabularnewline
76 & 12 & 12.1743119266055 & -0.174311926605505 \tabularnewline
77 & 13 & 12.1743119266055 & 0.825688073394495 \tabularnewline
78 & 14 & 12.1743119266055 & 1.8256880733945 \tabularnewline
79 & 10 & 12.1743119266055 & -2.1743119266055 \tabularnewline
80 & 13 & 12.1743119266055 & 0.825688073394495 \tabularnewline
81 & 13 & 13.6190476190476 & -0.619047619047619 \tabularnewline
82 & 11 & 12.1743119266055 & -1.1743119266055 \tabularnewline
83 & 13 & 12.1743119266055 & 0.825688073394495 \tabularnewline
84 & 16 & 13.6190476190476 & 2.38095238095238 \tabularnewline
85 & 8 & 12.1743119266055 & -4.1743119266055 \tabularnewline
86 & 16 & 12.1743119266055 & 3.8256880733945 \tabularnewline
87 & 11 & 12.1743119266055 & -1.1743119266055 \tabularnewline
88 & 9 & 12.1743119266055 & -3.1743119266055 \tabularnewline
89 & 16 & 18.5 & -2.5 \tabularnewline
90 & 12 & 12.1743119266055 & -0.174311926605505 \tabularnewline
91 & 14 & 12.1743119266055 & 1.8256880733945 \tabularnewline
92 & 8 & 12.1743119266055 & -4.1743119266055 \tabularnewline
93 & 9 & 12.1743119266055 & -3.1743119266055 \tabularnewline
94 & 15 & 12.1743119266055 & 2.8256880733945 \tabularnewline
95 & 11 & 13.6190476190476 & -2.61904761904762 \tabularnewline
96 & 21 & 18.5 & 2.5 \tabularnewline
97 & 14 & 12.1743119266055 & 1.8256880733945 \tabularnewline
98 & 18 & 18.5 & -0.5 \tabularnewline
99 & 12 & 12.1743119266055 & -0.174311926605505 \tabularnewline
100 & 13 & 12.1743119266055 & 0.825688073394495 \tabularnewline
101 & 15 & 13.6190476190476 & 1.38095238095238 \tabularnewline
102 & 12 & 12.1743119266055 & -0.174311926605505 \tabularnewline
103 & 19 & 13.6190476190476 & 5.38095238095238 \tabularnewline
104 & 15 & 13.6190476190476 & 1.38095238095238 \tabularnewline
105 & 11 & 13.6190476190476 & -2.61904761904762 \tabularnewline
106 & 11 & 12.1743119266055 & -1.1743119266055 \tabularnewline
107 & 10 & 12.1743119266055 & -2.1743119266055 \tabularnewline
108 & 13 & 16.8 & -3.8 \tabularnewline
109 & 15 & 16.8 & -1.8 \tabularnewline
110 & 12 & 12.1743119266055 & -0.174311926605505 \tabularnewline
111 & 12 & 12.1743119266055 & -0.174311926605505 \tabularnewline
112 & 16 & 18.5 & -2.5 \tabularnewline
113 & 9 & 13.6190476190476 & -4.61904761904762 \tabularnewline
114 & 18 & 16.8 & 1.2 \tabularnewline
115 & 8 & 13.6190476190476 & -5.61904761904762 \tabularnewline
116 & 13 & 12.1743119266055 & 0.825688073394495 \tabularnewline
117 & 17 & 12.1743119266055 & 4.8256880733945 \tabularnewline
118 & 9 & 12.1743119266055 & -3.1743119266055 \tabularnewline
119 & 15 & 12.1743119266055 & 2.8256880733945 \tabularnewline
120 & 8 & 8.66666666666667 & -0.666666666666666 \tabularnewline
121 & 7 & 8.66666666666667 & -1.66666666666667 \tabularnewline
122 & 12 & 12.1743119266055 & -0.174311926605505 \tabularnewline
123 & 14 & 13.6190476190476 & 0.380952380952381 \tabularnewline
124 & 6 & 12.1743119266055 & -6.1743119266055 \tabularnewline
125 & 8 & 12.1743119266055 & -4.1743119266055 \tabularnewline
126 & 17 & 12.1743119266055 & 4.8256880733945 \tabularnewline
127 & 10 & 8.66666666666667 & 1.33333333333333 \tabularnewline
128 & 11 & 12.1743119266055 & -1.1743119266055 \tabularnewline
129 & 14 & 12.1743119266055 & 1.8256880733945 \tabularnewline
130 & 11 & 12.1743119266055 & -1.1743119266055 \tabularnewline
131 & 13 & 13.6190476190476 & -0.619047619047619 \tabularnewline
132 & 12 & 12.1743119266055 & -0.174311926605505 \tabularnewline
133 & 11 & 12.1743119266055 & -1.1743119266055 \tabularnewline
134 & 9 & 8.66666666666667 & 0.333333333333334 \tabularnewline
135 & 12 & 12.1743119266055 & -0.174311926605505 \tabularnewline
136 & 20 & 16.8 & 3.2 \tabularnewline
137 & 12 & 12.1743119266055 & -0.174311926605505 \tabularnewline
138 & 13 & 12.1743119266055 & 0.825688073394495 \tabularnewline
139 & 12 & 12.1743119266055 & -0.174311926605505 \tabularnewline
140 & 12 & 13.6190476190476 & -1.61904761904762 \tabularnewline
141 & 9 & 12.1743119266055 & -3.1743119266055 \tabularnewline
142 & 15 & 16.8 & -1.8 \tabularnewline
143 & 24 & 18.5 & 5.5 \tabularnewline
144 & 7 & 12.1743119266055 & -5.1743119266055 \tabularnewline
145 & 17 & 13.6190476190476 & 3.38095238095238 \tabularnewline
146 & 11 & 12.1743119266055 & -1.1743119266055 \tabularnewline
147 & 17 & 12.1743119266055 & 4.8256880733945 \tabularnewline
148 & 11 & 12.1743119266055 & -1.1743119266055 \tabularnewline
149 & 12 & 12.1743119266055 & -0.174311926605505 \tabularnewline
150 & 14 & 13.6190476190476 & 0.380952380952381 \tabularnewline
151 & 11 & 12.1743119266055 & -1.1743119266055 \tabularnewline
152 & 16 & 12.1743119266055 & 3.8256880733945 \tabularnewline
153 & 21 & 12.1743119266055 & 8.8256880733945 \tabularnewline
154 & 14 & 12.1743119266055 & 1.8256880733945 \tabularnewline
155 & 20 & 13.6190476190476 & 6.38095238095238 \tabularnewline
156 & 13 & 12.1743119266055 & 0.825688073394495 \tabularnewline
157 & 11 & 12.1743119266055 & -1.1743119266055 \tabularnewline
158 & 15 & 13.6190476190476 & 1.38095238095238 \tabularnewline
159 & 19 & 18.5 & 0.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114726&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]11[/C][C]18.5[/C][C]-7.5[/C][/ROW]
[ROW][C]2[/C][C]7[/C][C]12.1743119266055[/C][C]-5.1743119266055[/C][/ROW]
[ROW][C]3[/C][C]17[/C][C]12.1743119266055[/C][C]4.8256880733945[/C][/ROW]
[ROW][C]4[/C][C]10[/C][C]12.1743119266055[/C][C]-2.1743119266055[/C][/ROW]
[ROW][C]5[/C][C]12[/C][C]12.1743119266055[/C][C]-0.174311926605505[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]12.1743119266055[/C][C]-0.174311926605505[/C][/ROW]
[ROW][C]7[/C][C]11[/C][C]8.66666666666667[/C][C]2.33333333333333[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]13.6190476190476[/C][C]-2.61904761904762[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]12.1743119266055[/C][C]-0.174311926605505[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]12.1743119266055[/C][C]0.825688073394495[/C][/ROW]
[ROW][C]11[/C][C]14[/C][C]13.6190476190476[/C][C]0.380952380952381[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.8[/C][C]-0.8[/C][/ROW]
[ROW][C]13[/C][C]11[/C][C]13.6190476190476[/C][C]-2.61904761904762[/C][/ROW]
[ROW][C]14[/C][C]10[/C][C]12.1743119266055[/C][C]-2.1743119266055[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]12.1743119266055[/C][C]-1.1743119266055[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]8.66666666666667[/C][C]6.33333333333333[/C][/ROW]
[ROW][C]17[/C][C]9[/C][C]12.1743119266055[/C][C]-3.1743119266055[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]12.1743119266055[/C][C]-1.1743119266055[/C][/ROW]
[ROW][C]19[/C][C]17[/C][C]12.1743119266055[/C][C]4.8256880733945[/C][/ROW]
[ROW][C]20[/C][C]17[/C][C]16.8[/C][C]0.199999999999999[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]12.1743119266055[/C][C]-1.1743119266055[/C][/ROW]
[ROW][C]22[/C][C]18[/C][C]16.8[/C][C]1.2[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]13.6190476190476[/C][C]0.380952380952381[/C][/ROW]
[ROW][C]24[/C][C]10[/C][C]12.1743119266055[/C][C]-2.1743119266055[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]12.1743119266055[/C][C]-1.1743119266055[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]12.1743119266055[/C][C]2.8256880733945[/C][/ROW]
[ROW][C]27[/C][C]15[/C][C]12.1743119266055[/C][C]2.8256880733945[/C][/ROW]
[ROW][C]28[/C][C]13[/C][C]12.1743119266055[/C][C]0.825688073394495[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]12.1743119266055[/C][C]3.8256880733945[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]12.1743119266055[/C][C]0.825688073394495[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]12.1743119266055[/C][C]-3.1743119266055[/C][/ROW]
[ROW][C]32[/C][C]18[/C][C]18.5[/C][C]-0.5[/C][/ROW]
[ROW][C]33[/C][C]18[/C][C]12.1743119266055[/C][C]5.8256880733945[/C][/ROW]
[ROW][C]34[/C][C]12[/C][C]12.1743119266055[/C][C]-0.174311926605505[/C][/ROW]
[ROW][C]35[/C][C]17[/C][C]12.1743119266055[/C][C]4.8256880733945[/C][/ROW]
[ROW][C]36[/C][C]9[/C][C]12.1743119266055[/C][C]-3.1743119266055[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]12.1743119266055[/C][C]-3.1743119266055[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]12.1743119266055[/C][C]-0.174311926605505[/C][/ROW]
[ROW][C]39[/C][C]18[/C][C]18.5[/C][C]-0.5[/C][/ROW]
[ROW][C]40[/C][C]12[/C][C]12.1743119266055[/C][C]-0.174311926605505[/C][/ROW]
[ROW][C]41[/C][C]18[/C][C]16.8[/C][C]1.2[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]12.1743119266055[/C][C]1.8256880733945[/C][/ROW]
[ROW][C]43[/C][C]15[/C][C]12.1743119266055[/C][C]2.8256880733945[/C][/ROW]
[ROW][C]44[/C][C]16[/C][C]12.1743119266055[/C][C]3.8256880733945[/C][/ROW]
[ROW][C]45[/C][C]10[/C][C]12.1743119266055[/C][C]-2.1743119266055[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]12.1743119266055[/C][C]-1.1743119266055[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]13.6190476190476[/C][C]0.380952380952381[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]12.1743119266055[/C][C]-3.1743119266055[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]12.1743119266055[/C][C]-0.174311926605505[/C][/ROW]
[ROW][C]50[/C][C]17[/C][C]12.1743119266055[/C][C]4.8256880733945[/C][/ROW]
[ROW][C]51[/C][C]5[/C][C]8.66666666666667[/C][C]-3.66666666666667[/C][/ROW]
[ROW][C]52[/C][C]12[/C][C]12.1743119266055[/C][C]-0.174311926605505[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]12.1743119266055[/C][C]-0.174311926605505[/C][/ROW]
[ROW][C]54[/C][C]6[/C][C]8.66666666666667[/C][C]-2.66666666666667[/C][/ROW]
[ROW][C]55[/C][C]24[/C][C]18.5[/C][C]5.5[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.1743119266055[/C][C]-0.174311926605505[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]12.1743119266055[/C][C]-0.174311926605505[/C][/ROW]
[ROW][C]58[/C][C]14[/C][C]12.1743119266055[/C][C]1.8256880733945[/C][/ROW]
[ROW][C]59[/C][C]7[/C][C]8.66666666666667[/C][C]-1.66666666666667[/C][/ROW]
[ROW][C]60[/C][C]13[/C][C]12.1743119266055[/C][C]0.825688073394495[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]12.1743119266055[/C][C]-0.174311926605505[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]12.1743119266055[/C][C]0.825688073394495[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]12.1743119266055[/C][C]1.8256880733945[/C][/ROW]
[ROW][C]64[/C][C]8[/C][C]12.1743119266055[/C][C]-4.1743119266055[/C][/ROW]
[ROW][C]65[/C][C]11[/C][C]12.1743119266055[/C][C]-1.1743119266055[/C][/ROW]
[ROW][C]66[/C][C]9[/C][C]12.1743119266055[/C][C]-3.1743119266055[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]12.1743119266055[/C][C]-1.1743119266055[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]12.1743119266055[/C][C]0.825688073394495[/C][/ROW]
[ROW][C]69[/C][C]10[/C][C]12.1743119266055[/C][C]-2.1743119266055[/C][/ROW]
[ROW][C]70[/C][C]11[/C][C]12.1743119266055[/C][C]-1.1743119266055[/C][/ROW]
[ROW][C]71[/C][C]12[/C][C]12.1743119266055[/C][C]-0.174311926605505[/C][/ROW]
[ROW][C]72[/C][C]9[/C][C]12.1743119266055[/C][C]-3.1743119266055[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]12.1743119266055[/C][C]2.8256880733945[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]16.8[/C][C]1.2[/C][/ROW]
[ROW][C]75[/C][C]15[/C][C]12.1743119266055[/C][C]2.8256880733945[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]12.1743119266055[/C][C]-0.174311926605505[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]12.1743119266055[/C][C]0.825688073394495[/C][/ROW]
[ROW][C]78[/C][C]14[/C][C]12.1743119266055[/C][C]1.8256880733945[/C][/ROW]
[ROW][C]79[/C][C]10[/C][C]12.1743119266055[/C][C]-2.1743119266055[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]12.1743119266055[/C][C]0.825688073394495[/C][/ROW]
[ROW][C]81[/C][C]13[/C][C]13.6190476190476[/C][C]-0.619047619047619[/C][/ROW]
[ROW][C]82[/C][C]11[/C][C]12.1743119266055[/C][C]-1.1743119266055[/C][/ROW]
[ROW][C]83[/C][C]13[/C][C]12.1743119266055[/C][C]0.825688073394495[/C][/ROW]
[ROW][C]84[/C][C]16[/C][C]13.6190476190476[/C][C]2.38095238095238[/C][/ROW]
[ROW][C]85[/C][C]8[/C][C]12.1743119266055[/C][C]-4.1743119266055[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]12.1743119266055[/C][C]3.8256880733945[/C][/ROW]
[ROW][C]87[/C][C]11[/C][C]12.1743119266055[/C][C]-1.1743119266055[/C][/ROW]
[ROW][C]88[/C][C]9[/C][C]12.1743119266055[/C][C]-3.1743119266055[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]18.5[/C][C]-2.5[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]12.1743119266055[/C][C]-0.174311926605505[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]12.1743119266055[/C][C]1.8256880733945[/C][/ROW]
[ROW][C]92[/C][C]8[/C][C]12.1743119266055[/C][C]-4.1743119266055[/C][/ROW]
[ROW][C]93[/C][C]9[/C][C]12.1743119266055[/C][C]-3.1743119266055[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]12.1743119266055[/C][C]2.8256880733945[/C][/ROW]
[ROW][C]95[/C][C]11[/C][C]13.6190476190476[/C][C]-2.61904761904762[/C][/ROW]
[ROW][C]96[/C][C]21[/C][C]18.5[/C][C]2.5[/C][/ROW]
[ROW][C]97[/C][C]14[/C][C]12.1743119266055[/C][C]1.8256880733945[/C][/ROW]
[ROW][C]98[/C][C]18[/C][C]18.5[/C][C]-0.5[/C][/ROW]
[ROW][C]99[/C][C]12[/C][C]12.1743119266055[/C][C]-0.174311926605505[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]12.1743119266055[/C][C]0.825688073394495[/C][/ROW]
[ROW][C]101[/C][C]15[/C][C]13.6190476190476[/C][C]1.38095238095238[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]12.1743119266055[/C][C]-0.174311926605505[/C][/ROW]
[ROW][C]103[/C][C]19[/C][C]13.6190476190476[/C][C]5.38095238095238[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]13.6190476190476[/C][C]1.38095238095238[/C][/ROW]
[ROW][C]105[/C][C]11[/C][C]13.6190476190476[/C][C]-2.61904761904762[/C][/ROW]
[ROW][C]106[/C][C]11[/C][C]12.1743119266055[/C][C]-1.1743119266055[/C][/ROW]
[ROW][C]107[/C][C]10[/C][C]12.1743119266055[/C][C]-2.1743119266055[/C][/ROW]
[ROW][C]108[/C][C]13[/C][C]16.8[/C][C]-3.8[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]16.8[/C][C]-1.8[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.1743119266055[/C][C]-0.174311926605505[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]12.1743119266055[/C][C]-0.174311926605505[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]18.5[/C][C]-2.5[/C][/ROW]
[ROW][C]113[/C][C]9[/C][C]13.6190476190476[/C][C]-4.61904761904762[/C][/ROW]
[ROW][C]114[/C][C]18[/C][C]16.8[/C][C]1.2[/C][/ROW]
[ROW][C]115[/C][C]8[/C][C]13.6190476190476[/C][C]-5.61904761904762[/C][/ROW]
[ROW][C]116[/C][C]13[/C][C]12.1743119266055[/C][C]0.825688073394495[/C][/ROW]
[ROW][C]117[/C][C]17[/C][C]12.1743119266055[/C][C]4.8256880733945[/C][/ROW]
[ROW][C]118[/C][C]9[/C][C]12.1743119266055[/C][C]-3.1743119266055[/C][/ROW]
[ROW][C]119[/C][C]15[/C][C]12.1743119266055[/C][C]2.8256880733945[/C][/ROW]
[ROW][C]120[/C][C]8[/C][C]8.66666666666667[/C][C]-0.666666666666666[/C][/ROW]
[ROW][C]121[/C][C]7[/C][C]8.66666666666667[/C][C]-1.66666666666667[/C][/ROW]
[ROW][C]122[/C][C]12[/C][C]12.1743119266055[/C][C]-0.174311926605505[/C][/ROW]
[ROW][C]123[/C][C]14[/C][C]13.6190476190476[/C][C]0.380952380952381[/C][/ROW]
[ROW][C]124[/C][C]6[/C][C]12.1743119266055[/C][C]-6.1743119266055[/C][/ROW]
[ROW][C]125[/C][C]8[/C][C]12.1743119266055[/C][C]-4.1743119266055[/C][/ROW]
[ROW][C]126[/C][C]17[/C][C]12.1743119266055[/C][C]4.8256880733945[/C][/ROW]
[ROW][C]127[/C][C]10[/C][C]8.66666666666667[/C][C]1.33333333333333[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]12.1743119266055[/C][C]-1.1743119266055[/C][/ROW]
[ROW][C]129[/C][C]14[/C][C]12.1743119266055[/C][C]1.8256880733945[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]12.1743119266055[/C][C]-1.1743119266055[/C][/ROW]
[ROW][C]131[/C][C]13[/C][C]13.6190476190476[/C][C]-0.619047619047619[/C][/ROW]
[ROW][C]132[/C][C]12[/C][C]12.1743119266055[/C][C]-0.174311926605505[/C][/ROW]
[ROW][C]133[/C][C]11[/C][C]12.1743119266055[/C][C]-1.1743119266055[/C][/ROW]
[ROW][C]134[/C][C]9[/C][C]8.66666666666667[/C][C]0.333333333333334[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]12.1743119266055[/C][C]-0.174311926605505[/C][/ROW]
[ROW][C]136[/C][C]20[/C][C]16.8[/C][C]3.2[/C][/ROW]
[ROW][C]137[/C][C]12[/C][C]12.1743119266055[/C][C]-0.174311926605505[/C][/ROW]
[ROW][C]138[/C][C]13[/C][C]12.1743119266055[/C][C]0.825688073394495[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]12.1743119266055[/C][C]-0.174311926605505[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.6190476190476[/C][C]-1.61904761904762[/C][/ROW]
[ROW][C]141[/C][C]9[/C][C]12.1743119266055[/C][C]-3.1743119266055[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]16.8[/C][C]-1.8[/C][/ROW]
[ROW][C]143[/C][C]24[/C][C]18.5[/C][C]5.5[/C][/ROW]
[ROW][C]144[/C][C]7[/C][C]12.1743119266055[/C][C]-5.1743119266055[/C][/ROW]
[ROW][C]145[/C][C]17[/C][C]13.6190476190476[/C][C]3.38095238095238[/C][/ROW]
[ROW][C]146[/C][C]11[/C][C]12.1743119266055[/C][C]-1.1743119266055[/C][/ROW]
[ROW][C]147[/C][C]17[/C][C]12.1743119266055[/C][C]4.8256880733945[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]12.1743119266055[/C][C]-1.1743119266055[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]12.1743119266055[/C][C]-0.174311926605505[/C][/ROW]
[ROW][C]150[/C][C]14[/C][C]13.6190476190476[/C][C]0.380952380952381[/C][/ROW]
[ROW][C]151[/C][C]11[/C][C]12.1743119266055[/C][C]-1.1743119266055[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]12.1743119266055[/C][C]3.8256880733945[/C][/ROW]
[ROW][C]153[/C][C]21[/C][C]12.1743119266055[/C][C]8.8256880733945[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]12.1743119266055[/C][C]1.8256880733945[/C][/ROW]
[ROW][C]155[/C][C]20[/C][C]13.6190476190476[/C][C]6.38095238095238[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]12.1743119266055[/C][C]0.825688073394495[/C][/ROW]
[ROW][C]157[/C][C]11[/C][C]12.1743119266055[/C][C]-1.1743119266055[/C][/ROW]
[ROW][C]158[/C][C]15[/C][C]13.6190476190476[/C][C]1.38095238095238[/C][/ROW]
[ROW][C]159[/C][C]19[/C][C]18.5[/C][C]0.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114726&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114726&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	11	18.5	-7.5
2	7	12.1743119266055	-5.1743119266055
3	17	12.1743119266055	4.8256880733945
4	10	12.1743119266055	-2.1743119266055
5	12	12.1743119266055	-0.174311926605505
6	12	12.1743119266055	-0.174311926605505
7	11	8.66666666666667	2.33333333333333
8	11	13.6190476190476	-2.61904761904762
9	12	12.1743119266055	-0.174311926605505
10	13	12.1743119266055	0.825688073394495
11	14	13.6190476190476	0.380952380952381
12	16	16.8	-0.8
13	11	13.6190476190476	-2.61904761904762
14	10	12.1743119266055	-2.1743119266055
15	11	12.1743119266055	-1.1743119266055
16	15	8.66666666666667	6.33333333333333
17	9	12.1743119266055	-3.1743119266055
18	11	12.1743119266055	-1.1743119266055
19	17	12.1743119266055	4.8256880733945
20	17	16.8	0.199999999999999
21	11	12.1743119266055	-1.1743119266055
22	18	16.8	1.2
23	14	13.6190476190476	0.380952380952381
24	10	12.1743119266055	-2.1743119266055
25	11	12.1743119266055	-1.1743119266055
26	15	12.1743119266055	2.8256880733945
27	15	12.1743119266055	2.8256880733945
28	13	12.1743119266055	0.825688073394495
29	16	12.1743119266055	3.8256880733945
30	13	12.1743119266055	0.825688073394495
31	9	12.1743119266055	-3.1743119266055
32	18	18.5	-0.5
33	18	12.1743119266055	5.8256880733945
34	12	12.1743119266055	-0.174311926605505
35	17	12.1743119266055	4.8256880733945
36	9	12.1743119266055	-3.1743119266055
37	9	12.1743119266055	-3.1743119266055
38	12	12.1743119266055	-0.174311926605505
39	18	18.5	-0.5
40	12	12.1743119266055	-0.174311926605505
41	18	16.8	1.2
42	14	12.1743119266055	1.8256880733945
43	15	12.1743119266055	2.8256880733945
44	16	12.1743119266055	3.8256880733945
45	10	12.1743119266055	-2.1743119266055
46	11	12.1743119266055	-1.1743119266055
47	14	13.6190476190476	0.380952380952381
48	9	12.1743119266055	-3.1743119266055
49	12	12.1743119266055	-0.174311926605505
50	17	12.1743119266055	4.8256880733945
51	5	8.66666666666667	-3.66666666666667
52	12	12.1743119266055	-0.174311926605505
53	12	12.1743119266055	-0.174311926605505
54	6	8.66666666666667	-2.66666666666667
55	24	18.5	5.5
56	12	12.1743119266055	-0.174311926605505
57	12	12.1743119266055	-0.174311926605505
58	14	12.1743119266055	1.8256880733945
59	7	8.66666666666667	-1.66666666666667
60	13	12.1743119266055	0.825688073394495
61	12	12.1743119266055	-0.174311926605505
62	13	12.1743119266055	0.825688073394495
63	14	12.1743119266055	1.8256880733945
64	8	12.1743119266055	-4.1743119266055
65	11	12.1743119266055	-1.1743119266055
66	9	12.1743119266055	-3.1743119266055
67	11	12.1743119266055	-1.1743119266055
68	13	12.1743119266055	0.825688073394495
69	10	12.1743119266055	-2.1743119266055
70	11	12.1743119266055	-1.1743119266055
71	12	12.1743119266055	-0.174311926605505
72	9	12.1743119266055	-3.1743119266055
73	15	12.1743119266055	2.8256880733945
74	18	16.8	1.2
75	15	12.1743119266055	2.8256880733945
76	12	12.1743119266055	-0.174311926605505
77	13	12.1743119266055	0.825688073394495
78	14	12.1743119266055	1.8256880733945
79	10	12.1743119266055	-2.1743119266055
80	13	12.1743119266055	0.825688073394495
81	13	13.6190476190476	-0.619047619047619
82	11	12.1743119266055	-1.1743119266055
83	13	12.1743119266055	0.825688073394495
84	16	13.6190476190476	2.38095238095238
85	8	12.1743119266055	-4.1743119266055
86	16	12.1743119266055	3.8256880733945
87	11	12.1743119266055	-1.1743119266055
88	9	12.1743119266055	-3.1743119266055
89	16	18.5	-2.5
90	12	12.1743119266055	-0.174311926605505
91	14	12.1743119266055	1.8256880733945
92	8	12.1743119266055	-4.1743119266055
93	9	12.1743119266055	-3.1743119266055
94	15	12.1743119266055	2.8256880733945
95	11	13.6190476190476	-2.61904761904762
96	21	18.5	2.5
97	14	12.1743119266055	1.8256880733945
98	18	18.5	-0.5
99	12	12.1743119266055	-0.174311926605505
100	13	12.1743119266055	0.825688073394495
101	15	13.6190476190476	1.38095238095238
102	12	12.1743119266055	-0.174311926605505
103	19	13.6190476190476	5.38095238095238
104	15	13.6190476190476	1.38095238095238
105	11	13.6190476190476	-2.61904761904762
106	11	12.1743119266055	-1.1743119266055
107	10	12.1743119266055	-2.1743119266055
108	13	16.8	-3.8
109	15	16.8	-1.8
110	12	12.1743119266055	-0.174311926605505
111	12	12.1743119266055	-0.174311926605505
112	16	18.5	-2.5
113	9	13.6190476190476	-4.61904761904762
114	18	16.8	1.2
115	8	13.6190476190476	-5.61904761904762
116	13	12.1743119266055	0.825688073394495
117	17	12.1743119266055	4.8256880733945
118	9	12.1743119266055	-3.1743119266055
119	15	12.1743119266055	2.8256880733945
120	8	8.66666666666667	-0.666666666666666
121	7	8.66666666666667	-1.66666666666667
122	12	12.1743119266055	-0.174311926605505
123	14	13.6190476190476	0.380952380952381
124	6	12.1743119266055	-6.1743119266055
125	8	12.1743119266055	-4.1743119266055
126	17	12.1743119266055	4.8256880733945
127	10	8.66666666666667	1.33333333333333
128	11	12.1743119266055	-1.1743119266055
129	14	12.1743119266055	1.8256880733945
130	11	12.1743119266055	-1.1743119266055
131	13	13.6190476190476	-0.619047619047619
132	12	12.1743119266055	-0.174311926605505
133	11	12.1743119266055	-1.1743119266055
134	9	8.66666666666667	0.333333333333334
135	12	12.1743119266055	-0.174311926605505
136	20	16.8	3.2
137	12	12.1743119266055	-0.174311926605505
138	13	12.1743119266055	0.825688073394495
139	12	12.1743119266055	-0.174311926605505
140	12	13.6190476190476	-1.61904761904762
141	9	12.1743119266055	-3.1743119266055
142	15	16.8	-1.8
143	24	18.5	5.5
144	7	12.1743119266055	-5.1743119266055
145	17	13.6190476190476	3.38095238095238
146	11	12.1743119266055	-1.1743119266055
147	17	12.1743119266055	4.8256880733945
148	11	12.1743119266055	-1.1743119266055
149	12	12.1743119266055	-0.174311926605505
150	14	13.6190476190476	0.380952380952381
151	11	12.1743119266055	-1.1743119266055
152	16	12.1743119266055	3.8256880733945
153	21	12.1743119266055	8.8256880733945
154	14	12.1743119266055	1.8256880733945
155	20	13.6190476190476	6.38095238095238
156	13	12.1743119266055	0.825688073394495
157	11	12.1743119266055	-1.1743119266055
158	15	13.6190476190476	1.38095238095238
159	19	18.5	0.5

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

Parameters (R input):

par1 = 3 ; par2 = none ; par3 = 3 ; par4 = no ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;

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