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Author

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R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Tue, 14 Dec 2010 18:08:05 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/14/t12923500214x6e7hibvegxp00.htm/, Retrieved Thu, 02 May 2024 22:46:16 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109967, Retrieved Thu, 02 May 2024 22:46:16 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

124

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

-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
-   PD    [Recursive Partitioning (Regression Trees)] [WS 10 ] [2010-12-14 18:08:05] [cda497ce08bc921f0aec22acd67c882b] [Current]
-   P       [Recursive Partitioning (Regression Trees)] [Ws 10 - Recursive...] [2010-12-14 22:27:38] [b20a509e36241371274681d9edf773da] 
-   P       [Recursive Partitioning (Regression Trees)] [] [2010-12-21 10:52:19] [f730b099f190102bcd41f590a8dae16d] 
-   P       [Recursive Partitioning (Regression Trees)] [] [2010-12-21 10:55:34] [f730b099f190102bcd41f590a8dae16d] 
-   PD      [Recursive Partitioning (Regression Trees)] [RECURSIVE PARTITI...] [2010-12-22 20:23:18] [b1e5ec7263cdefe98ec76e1a1363de05] 

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Dataseries X:

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Histogram

Boxplots

2	24	14	11	12	24	26
2	25	11	7	8	25	23
2	17	6	17	8	30	25
1	18	12	10	8	19	23
2	18	8	12	9	22	19
2	16	10	12	7	22	29
2	20	10	11	4	25	25
2	16	11	11	11	23	21
2	18	16	12	7	17	22
2	17	11	13	7	21	25
1	23	13	14	12	19	24
2	30	12	16	10	19	18
1	23	8	11	10	15	22
2	18	12	10	8	16	15
2	15	11	11	8	23	22
1	12	4	15	4	27	28
1	21	9	9	9	22	20
2	15	8	11	8	14	12
1	20	8	17	7	22	24
2	31	14	17	11	23	20
1	27	15	11	9	23	21
2	34	16	18	11	21	20
2	21	9	14	13	19	21
2	31	14	10	8	18	23
1	19	11	11	8	20	28
2	16	8	15	9	23	24
1	20	9	15	6	25	24
2	21	9	13	9	19	24
2	22	9	16	9	24	23
1	17	9	13	6	22	23
2	24	10	9	6	25	29
1	25	16	18	16	26	24
2	26	11	18	5	29	18
2	25	8	12	7	32	25
1	17	9	17	9	25	21
1	32	16	9	6	29	26
1	33	11	9	6	28	22
1	13	16	12	5	17	22
2	32	12	18	12	28	22
1	25	12	12	7	29	23
1	29	14	18	10	26	30
2	22	9	14	9	25	23
1	18	10	15	8	14	17
1	17	9	16	5	25	23
2	20	10	10	8	26	23
2	15	12	11	8	20	25
2	20	14	14	10	18	24
2	33	14	9	6	32	24
2	29	10	12	8	25	23
1	23	14	17	7	25	21
2	26	16	5	4	23	24
1	18	9	12	8	21	24
1	20	10	12	8	20	28
2	11	6	6	4	15	16
1	28	8	24	20	30	20
2	26	13	12	8	24	29
2	22	10	12	8	26	27
2	17	8	14	6	24	22
1	12	7	7	4	22	28
2	14	15	13	8	14	16
1	17	9	12	9	24	25
1	21	10	13	6	24	24
2	19	12	14	7	24	28
2	18	13	8	9	24	24
2	10	10	11	5	19	23
1	29	11	9	5	31	30
2	31	8	11	8	22	24
1	19	9	13	8	27	21
2	9	13	10	6	19	25
1	20	11	11	8	25	25
1	28	8	12	7	20	22
2	19	9	9	7	21	23
2	30	9	15	9	27	26
1	29	15	18	11	23	23
1	26	9	15	6	25	25
2	23	10	12	8	20	21
2	13	14	13	6	21	25
2	21	12	14	9	22	24
1	19	12	10	8	23	29
1	28	11	13	6	25	22
1	23	14	13	10	25	27
1	18	6	11	8	17	26
2	21	12	13	8	19	22
1	20	8	16	10	25	24
2	23	14	8	5	19	27
2	21	11	16	7	20	24
1	21	10	11	5	26	24
2	15	14	9	8	23	29
2	28	12	16	14	27	22
2	19	10	12	7	17	21
2	26	14	14	8	17	24
2	10	5	8	6	19	24
2	16	11	9	5	17	23
2	22	10	15	6	22	20
2	19	9	11	10	21	27
2	31	10	21	12	32	26
2	31	16	14	9	21	25
2	29	13	18	12	21	21
1	19	9	12	7	18	21
1	22	10	13	8	18	19
2	23	10	15	10	23	21
1	15	7	12	6	19	21
2	20	9	19	10	20	16
1	18	8	15	10	21	22
2	23	14	11	10	20	29
1	25	14	11	5	17	15
2	21	8	10	7	18	17
1	24	9	13	10	19	15
1	25	14	15	11	22	21
2	17	14	12	6	15	21
2	13	8	12	7	14	19
2	28	8	16	12	18	24
2	21	8	9	11	24	20
1	25	7	18	11	35	17
2	9	6	8	11	29	23
1	16	8	13	5	21	24
2	19	6	17	8	25	14
2	17	11	9	6	20	19
2	25	14	15	9	22	24
2	20	11	8	4	13	13
2	29	11	7	4	26	22
2	14	11	12	7	17	16
2	22	14	14	11	25	19
2	15	8	6	6	20	25
2	19	20	8	7	19	25
2	20	11	17	8	21	23
1	15	8	10	4	22	24
2	20	11	11	8	24	26
2	18	10	14	9	21	26
2	33	14	11	8	26	25
1	22	11	13	11	24	18
1	16	9	12	8	16	21
2	17	9	11	5	23	26
1	16	8	9	4	18	23
1	21	10	12	8	16	23
2	26	13	20	10	26	22
1	18	13	12	6	19	20
1	18	12	13	9	21	13
2	17	8	12	9	21	24
2	22	13	12	13	22	15
1	30	14	9	9	23	14
2	30	12	15	10	29	22
1	24	14	24	20	21	10
2	21	15	7	5	21	24
1	21	13	17	11	23	22
2	29	16	11	6	27	24
2	31	9	17	9	25	19
1	20	9	11	7	21	20
1	16	9	12	9	10	13
1	22	8	14	10	20	20
2	20	7	11	9	26	22
2	28	16	16	8	24	24
1	38	11	21	7	29	29
2	22	9	14	6	19	12
2	20	11	20	13	24	20
2	17	9	13	6	19	21
2	28	14	11	8	24	24
2	22	13	15	10	22	22
2	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	4 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109967&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109967&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109967&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	'RServer@AstonUniversity' @ vre.aston.ac.uk

Goodness of Fit
Correlation	0.5955
R-squared	0.3546
RMSE	2.1679

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.5955 \tabularnewline
R-squared & 0.3546 \tabularnewline
RMSE & 2.1679 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109967&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.5955[/C][/ROW]
[ROW][C]R-squared[/C][C]0.3546[/C][/ROW]
[ROW][C]RMSE[/C][C]2.1679[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109967&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109967&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	0.5955
R-squared	0.3546
RMSE	2.1679

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	12	8.87037037037037	3.12962962962963
2	8	6.52941176470588	1.47058823529412
3	8	7.47272727272727	0.527272727272727
4	8	6.52941176470588	1.47058823529412
5	9	7.47272727272727	1.52727272727273
6	7	7.47272727272727	-0.472727272727273
7	4	7.47272727272727	-3.47272727272727
8	11	7.47272727272727	3.52727272727273
9	7	7.47272727272727	-0.472727272727273
10	7	7.47272727272727	-0.472727272727273
11	12	8.87037037037037	3.12962962962963
12	10	8.87037037037037	1.12962962962963
13	10	8.87037037037037	1.12962962962963
14	8	6.52941176470588	1.47058823529412
15	8	7.47272727272727	0.527272727272727
16	4	7.47272727272727	-3.47272727272727
17	9	6.52941176470588	2.47058823529412
18	8	7.47272727272727	0.527272727272727
19	7	7.47272727272727	-0.472727272727273
20	11	8.87037037037037	2.12962962962963
21	9	8.87037037037037	0.12962962962963
22	11	12.25	-1.25
23	13	8.87037037037037	4.12962962962963
24	8	6.52941176470588	1.47058823529412
25	8	7.47272727272727	0.527272727272727
26	9	7.47272727272727	1.52727272727273
27	6	7.47272727272727	-1.47272727272727
28	9	8.87037037037037	0.12962962962963
29	9	8.87037037037037	0.12962962962963
30	6	7.47272727272727	-1.47272727272727
31	6	6.52941176470588	-0.529411764705882
32	16	12.25	3.75
33	5	12.25	-7.25
34	7	8.87037037037037	-1.87037037037037
35	9	7.47272727272727	1.52727272727273
36	6	6.52941176470588	-0.529411764705882
37	6	6.52941176470588	-0.529411764705882
38	5	7.47272727272727	-2.47272727272727
39	12	12.25	-0.25
40	7	8.87037037037037	-1.87037037037037
41	10	12.25	-2.25
42	9	8.87037037037037	0.12962962962963
43	8	7.47272727272727	0.527272727272727
44	5	7.47272727272727	-2.47272727272727
45	8	6.52941176470588	1.47058823529412
46	8	7.47272727272727	0.527272727272727
47	10	7.47272727272727	2.52727272727273
48	6	6.52941176470588	-0.529411764705882
49	8	8.87037037037037	-0.87037037037037
50	7	8.87037037037037	-1.87037037037037
51	4	6.52941176470588	-2.52941176470588
52	8	7.47272727272727	0.527272727272727
53	8	7.47272727272727	0.527272727272727
54	4	6.52941176470588	-2.52941176470588
55	20	12.25	7.75
56	8	8.87037037037037	-0.87037037037037
57	8	8.87037037037037	-0.87037037037037
58	6	7.47272727272727	-1.47272727272727
59	4	6.52941176470588	-2.52941176470588
60	8	7.47272727272727	0.527272727272727
61	9	7.47272727272727	1.52727272727273
62	6	8.87037037037037	-2.87037037037037
63	7	7.47272727272727	-0.472727272727273
64	9	6.52941176470588	2.47058823529412
65	5	7.47272727272727	-2.47272727272727
66	5	6.52941176470588	-1.52941176470588
67	8	8.87037037037037	-0.87037037037037
68	8	7.47272727272727	0.527272727272727
69	6	6.52941176470588	-0.529411764705882
70	8	7.47272727272727	0.527272727272727
71	7	8.87037037037037	-1.87037037037037
72	7	6.52941176470588	0.470588235294118
73	9	8.87037037037037	0.12962962962963
74	11	12.25	-1.25
75	6	8.87037037037037	-2.87037037037037
76	8	8.87037037037037	-0.87037037037037
77	6	7.47272727272727	-1.47272727272727
78	9	8.87037037037037	0.12962962962963
79	8	6.52941176470588	1.47058823529412
80	6	8.87037037037037	-2.87037037037037
81	10	8.87037037037037	1.12962962962963
82	8	7.47272727272727	0.527272727272727
83	8	8.87037037037037	-0.87037037037037
84	10	7.47272727272727	2.52727272727273
85	5	6.52941176470588	-1.52941176470588
86	7	8.87037037037037	-1.87037037037037
87	5	8.87037037037037	-3.87037037037037
88	8	6.52941176470588	1.47058823529412
89	14	8.87037037037037	5.12962962962963
90	7	7.47272727272727	-0.472727272727273
91	8	8.87037037037037	-0.87037037037037
92	6	6.52941176470588	-0.529411764705882
93	5	6.52941176470588	-1.52941176470588
94	6	8.87037037037037	-2.87037037037037
95	10	7.47272727272727	2.52727272727273
96	12	12.25	-0.25
97	9	8.87037037037037	0.12962962962963
98	12	12.25	-0.25
99	7	7.47272727272727	-0.472727272727273
100	8	8.87037037037037	-0.87037037037037
101	10	8.87037037037037	1.12962962962963
102	6	7.47272727272727	-1.47272727272727
103	10	12.25	-2.25
104	10	7.47272727272727	2.52727272727273
105	10	8.87037037037037	1.12962962962963
106	5	8.87037037037037	-3.87037037037037
107	7	6.52941176470588	0.470588235294118
108	10	8.87037037037037	1.12962962962963
109	11	8.87037037037037	2.12962962962963
110	6	7.47272727272727	-1.47272727272727
111	7	7.47272727272727	-0.472727272727273
112	12	8.87037037037037	3.12962962962963
113	11	6.52941176470588	4.47058823529412
114	11	12.25	-1.25
115	11	6.52941176470588	4.47058823529412
116	5	7.47272727272727	-2.47272727272727
117	8	7.47272727272727	0.527272727272727
118	6	6.52941176470588	-0.529411764705882
119	9	8.87037037037037	0.12962962962963
120	4	6.52941176470588	-2.52941176470588
121	4	6.52941176470588	-2.52941176470588
122	7	7.47272727272727	-0.472727272727273
123	11	8.87037037037037	2.12962962962963
124	6	6.52941176470588	-0.529411764705882
125	7	6.52941176470588	0.470588235294118
126	8	7.47272727272727	0.527272727272727
127	4	6.52941176470588	-2.52941176470588
128	8	7.47272727272727	0.527272727272727
129	9	7.47272727272727	1.52727272727273
130	8	8.87037037037037	-0.87037037037037
131	11	8.87037037037037	2.12962962962963
132	8	7.47272727272727	0.527272727272727
133	5	7.47272727272727	-2.47272727272727
134	4	6.52941176470588	-2.52941176470588
135	8	8.87037037037037	-0.87037037037037
136	10	12.25	-2.25
137	6	7.47272727272727	-1.47272727272727
138	9	7.47272727272727	1.52727272727273
139	9	7.47272727272727	1.52727272727273
140	13	8.87037037037037	4.12962962962963
141	9	6.52941176470588	2.47058823529412
142	10	8.87037037037037	1.12962962962963
143	20	12.25	7.75
144	5	6.52941176470588	-1.52941176470588
145	11	8.87037037037037	2.12962962962963
146	6	8.87037037037037	-2.87037037037037
147	9	8.87037037037037	0.12962962962963
148	7	7.47272727272727	-0.472727272727273
149	9	7.47272727272727	1.52727272727273
150	10	8.87037037037037	1.12962962962963
151	9	7.47272727272727	1.52727272727273
152	8	8.87037037037037	-0.87037037037037
153	7	12.25	-5.25
154	6	8.87037037037037	-2.87037037037037
155	13	12.25	0.75
156	6	7.47272727272727	-1.47272727272727
157	8	8.87037037037037	-0.87037037037037
158	10	8.87037037037037	1.12962962962963
159	16	12.25	3.75

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 12 & 8.87037037037037 & 3.12962962962963 \tabularnewline
2 & 8 & 6.52941176470588 & 1.47058823529412 \tabularnewline
3 & 8 & 7.47272727272727 & 0.527272727272727 \tabularnewline
4 & 8 & 6.52941176470588 & 1.47058823529412 \tabularnewline
5 & 9 & 7.47272727272727 & 1.52727272727273 \tabularnewline
6 & 7 & 7.47272727272727 & -0.472727272727273 \tabularnewline
7 & 4 & 7.47272727272727 & -3.47272727272727 \tabularnewline
8 & 11 & 7.47272727272727 & 3.52727272727273 \tabularnewline
9 & 7 & 7.47272727272727 & -0.472727272727273 \tabularnewline
10 & 7 & 7.47272727272727 & -0.472727272727273 \tabularnewline
11 & 12 & 8.87037037037037 & 3.12962962962963 \tabularnewline
12 & 10 & 8.87037037037037 & 1.12962962962963 \tabularnewline
13 & 10 & 8.87037037037037 & 1.12962962962963 \tabularnewline
14 & 8 & 6.52941176470588 & 1.47058823529412 \tabularnewline
15 & 8 & 7.47272727272727 & 0.527272727272727 \tabularnewline
16 & 4 & 7.47272727272727 & -3.47272727272727 \tabularnewline
17 & 9 & 6.52941176470588 & 2.47058823529412 \tabularnewline
18 & 8 & 7.47272727272727 & 0.527272727272727 \tabularnewline
19 & 7 & 7.47272727272727 & -0.472727272727273 \tabularnewline
20 & 11 & 8.87037037037037 & 2.12962962962963 \tabularnewline
21 & 9 & 8.87037037037037 & 0.12962962962963 \tabularnewline
22 & 11 & 12.25 & -1.25 \tabularnewline
23 & 13 & 8.87037037037037 & 4.12962962962963 \tabularnewline
24 & 8 & 6.52941176470588 & 1.47058823529412 \tabularnewline
25 & 8 & 7.47272727272727 & 0.527272727272727 \tabularnewline
26 & 9 & 7.47272727272727 & 1.52727272727273 \tabularnewline
27 & 6 & 7.47272727272727 & -1.47272727272727 \tabularnewline
28 & 9 & 8.87037037037037 & 0.12962962962963 \tabularnewline
29 & 9 & 8.87037037037037 & 0.12962962962963 \tabularnewline
30 & 6 & 7.47272727272727 & -1.47272727272727 \tabularnewline
31 & 6 & 6.52941176470588 & -0.529411764705882 \tabularnewline
32 & 16 & 12.25 & 3.75 \tabularnewline
33 & 5 & 12.25 & -7.25 \tabularnewline
34 & 7 & 8.87037037037037 & -1.87037037037037 \tabularnewline
35 & 9 & 7.47272727272727 & 1.52727272727273 \tabularnewline
36 & 6 & 6.52941176470588 & -0.529411764705882 \tabularnewline
37 & 6 & 6.52941176470588 & -0.529411764705882 \tabularnewline
38 & 5 & 7.47272727272727 & -2.47272727272727 \tabularnewline
39 & 12 & 12.25 & -0.25 \tabularnewline
40 & 7 & 8.87037037037037 & -1.87037037037037 \tabularnewline
41 & 10 & 12.25 & -2.25 \tabularnewline
42 & 9 & 8.87037037037037 & 0.12962962962963 \tabularnewline
43 & 8 & 7.47272727272727 & 0.527272727272727 \tabularnewline
44 & 5 & 7.47272727272727 & -2.47272727272727 \tabularnewline
45 & 8 & 6.52941176470588 & 1.47058823529412 \tabularnewline
46 & 8 & 7.47272727272727 & 0.527272727272727 \tabularnewline
47 & 10 & 7.47272727272727 & 2.52727272727273 \tabularnewline
48 & 6 & 6.52941176470588 & -0.529411764705882 \tabularnewline
49 & 8 & 8.87037037037037 & -0.87037037037037 \tabularnewline
50 & 7 & 8.87037037037037 & -1.87037037037037 \tabularnewline
51 & 4 & 6.52941176470588 & -2.52941176470588 \tabularnewline
52 & 8 & 7.47272727272727 & 0.527272727272727 \tabularnewline
53 & 8 & 7.47272727272727 & 0.527272727272727 \tabularnewline
54 & 4 & 6.52941176470588 & -2.52941176470588 \tabularnewline
55 & 20 & 12.25 & 7.75 \tabularnewline
56 & 8 & 8.87037037037037 & -0.87037037037037 \tabularnewline
57 & 8 & 8.87037037037037 & -0.87037037037037 \tabularnewline
58 & 6 & 7.47272727272727 & -1.47272727272727 \tabularnewline
59 & 4 & 6.52941176470588 & -2.52941176470588 \tabularnewline
60 & 8 & 7.47272727272727 & 0.527272727272727 \tabularnewline
61 & 9 & 7.47272727272727 & 1.52727272727273 \tabularnewline
62 & 6 & 8.87037037037037 & -2.87037037037037 \tabularnewline
63 & 7 & 7.47272727272727 & -0.472727272727273 \tabularnewline
64 & 9 & 6.52941176470588 & 2.47058823529412 \tabularnewline
65 & 5 & 7.47272727272727 & -2.47272727272727 \tabularnewline
66 & 5 & 6.52941176470588 & -1.52941176470588 \tabularnewline
67 & 8 & 8.87037037037037 & -0.87037037037037 \tabularnewline
68 & 8 & 7.47272727272727 & 0.527272727272727 \tabularnewline
69 & 6 & 6.52941176470588 & -0.529411764705882 \tabularnewline
70 & 8 & 7.47272727272727 & 0.527272727272727 \tabularnewline
71 & 7 & 8.87037037037037 & -1.87037037037037 \tabularnewline
72 & 7 & 6.52941176470588 & 0.470588235294118 \tabularnewline
73 & 9 & 8.87037037037037 & 0.12962962962963 \tabularnewline
74 & 11 & 12.25 & -1.25 \tabularnewline
75 & 6 & 8.87037037037037 & -2.87037037037037 \tabularnewline
76 & 8 & 8.87037037037037 & -0.87037037037037 \tabularnewline
77 & 6 & 7.47272727272727 & -1.47272727272727 \tabularnewline
78 & 9 & 8.87037037037037 & 0.12962962962963 \tabularnewline
79 & 8 & 6.52941176470588 & 1.47058823529412 \tabularnewline
80 & 6 & 8.87037037037037 & -2.87037037037037 \tabularnewline
81 & 10 & 8.87037037037037 & 1.12962962962963 \tabularnewline
82 & 8 & 7.47272727272727 & 0.527272727272727 \tabularnewline
83 & 8 & 8.87037037037037 & -0.87037037037037 \tabularnewline
84 & 10 & 7.47272727272727 & 2.52727272727273 \tabularnewline
85 & 5 & 6.52941176470588 & -1.52941176470588 \tabularnewline
86 & 7 & 8.87037037037037 & -1.87037037037037 \tabularnewline
87 & 5 & 8.87037037037037 & -3.87037037037037 \tabularnewline
88 & 8 & 6.52941176470588 & 1.47058823529412 \tabularnewline
89 & 14 & 8.87037037037037 & 5.12962962962963 \tabularnewline
90 & 7 & 7.47272727272727 & -0.472727272727273 \tabularnewline
91 & 8 & 8.87037037037037 & -0.87037037037037 \tabularnewline
92 & 6 & 6.52941176470588 & -0.529411764705882 \tabularnewline
93 & 5 & 6.52941176470588 & -1.52941176470588 \tabularnewline
94 & 6 & 8.87037037037037 & -2.87037037037037 \tabularnewline
95 & 10 & 7.47272727272727 & 2.52727272727273 \tabularnewline
96 & 12 & 12.25 & -0.25 \tabularnewline
97 & 9 & 8.87037037037037 & 0.12962962962963 \tabularnewline
98 & 12 & 12.25 & -0.25 \tabularnewline
99 & 7 & 7.47272727272727 & -0.472727272727273 \tabularnewline
100 & 8 & 8.87037037037037 & -0.87037037037037 \tabularnewline
101 & 10 & 8.87037037037037 & 1.12962962962963 \tabularnewline
102 & 6 & 7.47272727272727 & -1.47272727272727 \tabularnewline
103 & 10 & 12.25 & -2.25 \tabularnewline
104 & 10 & 7.47272727272727 & 2.52727272727273 \tabularnewline
105 & 10 & 8.87037037037037 & 1.12962962962963 \tabularnewline
106 & 5 & 8.87037037037037 & -3.87037037037037 \tabularnewline
107 & 7 & 6.52941176470588 & 0.470588235294118 \tabularnewline
108 & 10 & 8.87037037037037 & 1.12962962962963 \tabularnewline
109 & 11 & 8.87037037037037 & 2.12962962962963 \tabularnewline
110 & 6 & 7.47272727272727 & -1.47272727272727 \tabularnewline
111 & 7 & 7.47272727272727 & -0.472727272727273 \tabularnewline
112 & 12 & 8.87037037037037 & 3.12962962962963 \tabularnewline
113 & 11 & 6.52941176470588 & 4.47058823529412 \tabularnewline
114 & 11 & 12.25 & -1.25 \tabularnewline
115 & 11 & 6.52941176470588 & 4.47058823529412 \tabularnewline
116 & 5 & 7.47272727272727 & -2.47272727272727 \tabularnewline
117 & 8 & 7.47272727272727 & 0.527272727272727 \tabularnewline
118 & 6 & 6.52941176470588 & -0.529411764705882 \tabularnewline
119 & 9 & 8.87037037037037 & 0.12962962962963 \tabularnewline
120 & 4 & 6.52941176470588 & -2.52941176470588 \tabularnewline
121 & 4 & 6.52941176470588 & -2.52941176470588 \tabularnewline
122 & 7 & 7.47272727272727 & -0.472727272727273 \tabularnewline
123 & 11 & 8.87037037037037 & 2.12962962962963 \tabularnewline
124 & 6 & 6.52941176470588 & -0.529411764705882 \tabularnewline
125 & 7 & 6.52941176470588 & 0.470588235294118 \tabularnewline
126 & 8 & 7.47272727272727 & 0.527272727272727 \tabularnewline
127 & 4 & 6.52941176470588 & -2.52941176470588 \tabularnewline
128 & 8 & 7.47272727272727 & 0.527272727272727 \tabularnewline
129 & 9 & 7.47272727272727 & 1.52727272727273 \tabularnewline
130 & 8 & 8.87037037037037 & -0.87037037037037 \tabularnewline
131 & 11 & 8.87037037037037 & 2.12962962962963 \tabularnewline
132 & 8 & 7.47272727272727 & 0.527272727272727 \tabularnewline
133 & 5 & 7.47272727272727 & -2.47272727272727 \tabularnewline
134 & 4 & 6.52941176470588 & -2.52941176470588 \tabularnewline
135 & 8 & 8.87037037037037 & -0.87037037037037 \tabularnewline
136 & 10 & 12.25 & -2.25 \tabularnewline
137 & 6 & 7.47272727272727 & -1.47272727272727 \tabularnewline
138 & 9 & 7.47272727272727 & 1.52727272727273 \tabularnewline
139 & 9 & 7.47272727272727 & 1.52727272727273 \tabularnewline
140 & 13 & 8.87037037037037 & 4.12962962962963 \tabularnewline
141 & 9 & 6.52941176470588 & 2.47058823529412 \tabularnewline
142 & 10 & 8.87037037037037 & 1.12962962962963 \tabularnewline
143 & 20 & 12.25 & 7.75 \tabularnewline
144 & 5 & 6.52941176470588 & -1.52941176470588 \tabularnewline
145 & 11 & 8.87037037037037 & 2.12962962962963 \tabularnewline
146 & 6 & 8.87037037037037 & -2.87037037037037 \tabularnewline
147 & 9 & 8.87037037037037 & 0.12962962962963 \tabularnewline
148 & 7 & 7.47272727272727 & -0.472727272727273 \tabularnewline
149 & 9 & 7.47272727272727 & 1.52727272727273 \tabularnewline
150 & 10 & 8.87037037037037 & 1.12962962962963 \tabularnewline
151 & 9 & 7.47272727272727 & 1.52727272727273 \tabularnewline
152 & 8 & 8.87037037037037 & -0.87037037037037 \tabularnewline
153 & 7 & 12.25 & -5.25 \tabularnewline
154 & 6 & 8.87037037037037 & -2.87037037037037 \tabularnewline
155 & 13 & 12.25 & 0.75 \tabularnewline
156 & 6 & 7.47272727272727 & -1.47272727272727 \tabularnewline
157 & 8 & 8.87037037037037 & -0.87037037037037 \tabularnewline
158 & 10 & 8.87037037037037 & 1.12962962962963 \tabularnewline
159 & 16 & 12.25 & 3.75 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109967&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]8.87037037037037[/C][C]3.12962962962963[/C][/ROW]
[ROW][C]2[/C][C]8[/C][C]6.52941176470588[/C][C]1.47058823529412[/C][/ROW]
[ROW][C]3[/C][C]8[/C][C]7.47272727272727[/C][C]0.527272727272727[/C][/ROW]
[ROW][C]4[/C][C]8[/C][C]6.52941176470588[/C][C]1.47058823529412[/C][/ROW]
[ROW][C]5[/C][C]9[/C][C]7.47272727272727[/C][C]1.52727272727273[/C][/ROW]
[ROW][C]6[/C][C]7[/C][C]7.47272727272727[/C][C]-0.472727272727273[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]7.47272727272727[/C][C]-3.47272727272727[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]7.47272727272727[/C][C]3.52727272727273[/C][/ROW]
[ROW][C]9[/C][C]7[/C][C]7.47272727272727[/C][C]-0.472727272727273[/C][/ROW]
[ROW][C]10[/C][C]7[/C][C]7.47272727272727[/C][C]-0.472727272727273[/C][/ROW]
[ROW][C]11[/C][C]12[/C][C]8.87037037037037[/C][C]3.12962962962963[/C][/ROW]
[ROW][C]12[/C][C]10[/C][C]8.87037037037037[/C][C]1.12962962962963[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]8.87037037037037[/C][C]1.12962962962963[/C][/ROW]
[ROW][C]14[/C][C]8[/C][C]6.52941176470588[/C][C]1.47058823529412[/C][/ROW]
[ROW][C]15[/C][C]8[/C][C]7.47272727272727[/C][C]0.527272727272727[/C][/ROW]
[ROW][C]16[/C][C]4[/C][C]7.47272727272727[/C][C]-3.47272727272727[/C][/ROW]
[ROW][C]17[/C][C]9[/C][C]6.52941176470588[/C][C]2.47058823529412[/C][/ROW]
[ROW][C]18[/C][C]8[/C][C]7.47272727272727[/C][C]0.527272727272727[/C][/ROW]
[ROW][C]19[/C][C]7[/C][C]7.47272727272727[/C][C]-0.472727272727273[/C][/ROW]
[ROW][C]20[/C][C]11[/C][C]8.87037037037037[/C][C]2.12962962962963[/C][/ROW]
[ROW][C]21[/C][C]9[/C][C]8.87037037037037[/C][C]0.12962962962963[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]12.25[/C][C]-1.25[/C][/ROW]
[ROW][C]23[/C][C]13[/C][C]8.87037037037037[/C][C]4.12962962962963[/C][/ROW]
[ROW][C]24[/C][C]8[/C][C]6.52941176470588[/C][C]1.47058823529412[/C][/ROW]
[ROW][C]25[/C][C]8[/C][C]7.47272727272727[/C][C]0.527272727272727[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]7.47272727272727[/C][C]1.52727272727273[/C][/ROW]
[ROW][C]27[/C][C]6[/C][C]7.47272727272727[/C][C]-1.47272727272727[/C][/ROW]
[ROW][C]28[/C][C]9[/C][C]8.87037037037037[/C][C]0.12962962962963[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]8.87037037037037[/C][C]0.12962962962963[/C][/ROW]
[ROW][C]30[/C][C]6[/C][C]7.47272727272727[/C][C]-1.47272727272727[/C][/ROW]
[ROW][C]31[/C][C]6[/C][C]6.52941176470588[/C][C]-0.529411764705882[/C][/ROW]
[ROW][C]32[/C][C]16[/C][C]12.25[/C][C]3.75[/C][/ROW]
[ROW][C]33[/C][C]5[/C][C]12.25[/C][C]-7.25[/C][/ROW]
[ROW][C]34[/C][C]7[/C][C]8.87037037037037[/C][C]-1.87037037037037[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]7.47272727272727[/C][C]1.52727272727273[/C][/ROW]
[ROW][C]36[/C][C]6[/C][C]6.52941176470588[/C][C]-0.529411764705882[/C][/ROW]
[ROW][C]37[/C][C]6[/C][C]6.52941176470588[/C][C]-0.529411764705882[/C][/ROW]
[ROW][C]38[/C][C]5[/C][C]7.47272727272727[/C][C]-2.47272727272727[/C][/ROW]
[ROW][C]39[/C][C]12[/C][C]12.25[/C][C]-0.25[/C][/ROW]
[ROW][C]40[/C][C]7[/C][C]8.87037037037037[/C][C]-1.87037037037037[/C][/ROW]
[ROW][C]41[/C][C]10[/C][C]12.25[/C][C]-2.25[/C][/ROW]
[ROW][C]42[/C][C]9[/C][C]8.87037037037037[/C][C]0.12962962962963[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]7.47272727272727[/C][C]0.527272727272727[/C][/ROW]
[ROW][C]44[/C][C]5[/C][C]7.47272727272727[/C][C]-2.47272727272727[/C][/ROW]
[ROW][C]45[/C][C]8[/C][C]6.52941176470588[/C][C]1.47058823529412[/C][/ROW]
[ROW][C]46[/C][C]8[/C][C]7.47272727272727[/C][C]0.527272727272727[/C][/ROW]
[ROW][C]47[/C][C]10[/C][C]7.47272727272727[/C][C]2.52727272727273[/C][/ROW]
[ROW][C]48[/C][C]6[/C][C]6.52941176470588[/C][C]-0.529411764705882[/C][/ROW]
[ROW][C]49[/C][C]8[/C][C]8.87037037037037[/C][C]-0.87037037037037[/C][/ROW]
[ROW][C]50[/C][C]7[/C][C]8.87037037037037[/C][C]-1.87037037037037[/C][/ROW]
[ROW][C]51[/C][C]4[/C][C]6.52941176470588[/C][C]-2.52941176470588[/C][/ROW]
[ROW][C]52[/C][C]8[/C][C]7.47272727272727[/C][C]0.527272727272727[/C][/ROW]
[ROW][C]53[/C][C]8[/C][C]7.47272727272727[/C][C]0.527272727272727[/C][/ROW]
[ROW][C]54[/C][C]4[/C][C]6.52941176470588[/C][C]-2.52941176470588[/C][/ROW]
[ROW][C]55[/C][C]20[/C][C]12.25[/C][C]7.75[/C][/ROW]
[ROW][C]56[/C][C]8[/C][C]8.87037037037037[/C][C]-0.87037037037037[/C][/ROW]
[ROW][C]57[/C][C]8[/C][C]8.87037037037037[/C][C]-0.87037037037037[/C][/ROW]
[ROW][C]58[/C][C]6[/C][C]7.47272727272727[/C][C]-1.47272727272727[/C][/ROW]
[ROW][C]59[/C][C]4[/C][C]6.52941176470588[/C][C]-2.52941176470588[/C][/ROW]
[ROW][C]60[/C][C]8[/C][C]7.47272727272727[/C][C]0.527272727272727[/C][/ROW]
[ROW][C]61[/C][C]9[/C][C]7.47272727272727[/C][C]1.52727272727273[/C][/ROW]
[ROW][C]62[/C][C]6[/C][C]8.87037037037037[/C][C]-2.87037037037037[/C][/ROW]
[ROW][C]63[/C][C]7[/C][C]7.47272727272727[/C][C]-0.472727272727273[/C][/ROW]
[ROW][C]64[/C][C]9[/C][C]6.52941176470588[/C][C]2.47058823529412[/C][/ROW]
[ROW][C]65[/C][C]5[/C][C]7.47272727272727[/C][C]-2.47272727272727[/C][/ROW]
[ROW][C]66[/C][C]5[/C][C]6.52941176470588[/C][C]-1.52941176470588[/C][/ROW]
[ROW][C]67[/C][C]8[/C][C]8.87037037037037[/C][C]-0.87037037037037[/C][/ROW]
[ROW][C]68[/C][C]8[/C][C]7.47272727272727[/C][C]0.527272727272727[/C][/ROW]
[ROW][C]69[/C][C]6[/C][C]6.52941176470588[/C][C]-0.529411764705882[/C][/ROW]
[ROW][C]70[/C][C]8[/C][C]7.47272727272727[/C][C]0.527272727272727[/C][/ROW]
[ROW][C]71[/C][C]7[/C][C]8.87037037037037[/C][C]-1.87037037037037[/C][/ROW]
[ROW][C]72[/C][C]7[/C][C]6.52941176470588[/C][C]0.470588235294118[/C][/ROW]
[ROW][C]73[/C][C]9[/C][C]8.87037037037037[/C][C]0.12962962962963[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]12.25[/C][C]-1.25[/C][/ROW]
[ROW][C]75[/C][C]6[/C][C]8.87037037037037[/C][C]-2.87037037037037[/C][/ROW]
[ROW][C]76[/C][C]8[/C][C]8.87037037037037[/C][C]-0.87037037037037[/C][/ROW]
[ROW][C]77[/C][C]6[/C][C]7.47272727272727[/C][C]-1.47272727272727[/C][/ROW]
[ROW][C]78[/C][C]9[/C][C]8.87037037037037[/C][C]0.12962962962963[/C][/ROW]
[ROW][C]79[/C][C]8[/C][C]6.52941176470588[/C][C]1.47058823529412[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]8.87037037037037[/C][C]-2.87037037037037[/C][/ROW]
[ROW][C]81[/C][C]10[/C][C]8.87037037037037[/C][C]1.12962962962963[/C][/ROW]
[ROW][C]82[/C][C]8[/C][C]7.47272727272727[/C][C]0.527272727272727[/C][/ROW]
[ROW][C]83[/C][C]8[/C][C]8.87037037037037[/C][C]-0.87037037037037[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]7.47272727272727[/C][C]2.52727272727273[/C][/ROW]
[ROW][C]85[/C][C]5[/C][C]6.52941176470588[/C][C]-1.52941176470588[/C][/ROW]
[ROW][C]86[/C][C]7[/C][C]8.87037037037037[/C][C]-1.87037037037037[/C][/ROW]
[ROW][C]87[/C][C]5[/C][C]8.87037037037037[/C][C]-3.87037037037037[/C][/ROW]
[ROW][C]88[/C][C]8[/C][C]6.52941176470588[/C][C]1.47058823529412[/C][/ROW]
[ROW][C]89[/C][C]14[/C][C]8.87037037037037[/C][C]5.12962962962963[/C][/ROW]
[ROW][C]90[/C][C]7[/C][C]7.47272727272727[/C][C]-0.472727272727273[/C][/ROW]
[ROW][C]91[/C][C]8[/C][C]8.87037037037037[/C][C]-0.87037037037037[/C][/ROW]
[ROW][C]92[/C][C]6[/C][C]6.52941176470588[/C][C]-0.529411764705882[/C][/ROW]
[ROW][C]93[/C][C]5[/C][C]6.52941176470588[/C][C]-1.52941176470588[/C][/ROW]
[ROW][C]94[/C][C]6[/C][C]8.87037037037037[/C][C]-2.87037037037037[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]7.47272727272727[/C][C]2.52727272727273[/C][/ROW]
[ROW][C]96[/C][C]12[/C][C]12.25[/C][C]-0.25[/C][/ROW]
[ROW][C]97[/C][C]9[/C][C]8.87037037037037[/C][C]0.12962962962963[/C][/ROW]
[ROW][C]98[/C][C]12[/C][C]12.25[/C][C]-0.25[/C][/ROW]
[ROW][C]99[/C][C]7[/C][C]7.47272727272727[/C][C]-0.472727272727273[/C][/ROW]
[ROW][C]100[/C][C]8[/C][C]8.87037037037037[/C][C]-0.87037037037037[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]8.87037037037037[/C][C]1.12962962962963[/C][/ROW]
[ROW][C]102[/C][C]6[/C][C]7.47272727272727[/C][C]-1.47272727272727[/C][/ROW]
[ROW][C]103[/C][C]10[/C][C]12.25[/C][C]-2.25[/C][/ROW]
[ROW][C]104[/C][C]10[/C][C]7.47272727272727[/C][C]2.52727272727273[/C][/ROW]
[ROW][C]105[/C][C]10[/C][C]8.87037037037037[/C][C]1.12962962962963[/C][/ROW]
[ROW][C]106[/C][C]5[/C][C]8.87037037037037[/C][C]-3.87037037037037[/C][/ROW]
[ROW][C]107[/C][C]7[/C][C]6.52941176470588[/C][C]0.470588235294118[/C][/ROW]
[ROW][C]108[/C][C]10[/C][C]8.87037037037037[/C][C]1.12962962962963[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]8.87037037037037[/C][C]2.12962962962963[/C][/ROW]
[ROW][C]110[/C][C]6[/C][C]7.47272727272727[/C][C]-1.47272727272727[/C][/ROW]
[ROW][C]111[/C][C]7[/C][C]7.47272727272727[/C][C]-0.472727272727273[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]8.87037037037037[/C][C]3.12962962962963[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]6.52941176470588[/C][C]4.47058823529412[/C][/ROW]
[ROW][C]114[/C][C]11[/C][C]12.25[/C][C]-1.25[/C][/ROW]
[ROW][C]115[/C][C]11[/C][C]6.52941176470588[/C][C]4.47058823529412[/C][/ROW]
[ROW][C]116[/C][C]5[/C][C]7.47272727272727[/C][C]-2.47272727272727[/C][/ROW]
[ROW][C]117[/C][C]8[/C][C]7.47272727272727[/C][C]0.527272727272727[/C][/ROW]
[ROW][C]118[/C][C]6[/C][C]6.52941176470588[/C][C]-0.529411764705882[/C][/ROW]
[ROW][C]119[/C][C]9[/C][C]8.87037037037037[/C][C]0.12962962962963[/C][/ROW]
[ROW][C]120[/C][C]4[/C][C]6.52941176470588[/C][C]-2.52941176470588[/C][/ROW]
[ROW][C]121[/C][C]4[/C][C]6.52941176470588[/C][C]-2.52941176470588[/C][/ROW]
[ROW][C]122[/C][C]7[/C][C]7.47272727272727[/C][C]-0.472727272727273[/C][/ROW]
[ROW][C]123[/C][C]11[/C][C]8.87037037037037[/C][C]2.12962962962963[/C][/ROW]
[ROW][C]124[/C][C]6[/C][C]6.52941176470588[/C][C]-0.529411764705882[/C][/ROW]
[ROW][C]125[/C][C]7[/C][C]6.52941176470588[/C][C]0.470588235294118[/C][/ROW]
[ROW][C]126[/C][C]8[/C][C]7.47272727272727[/C][C]0.527272727272727[/C][/ROW]
[ROW][C]127[/C][C]4[/C][C]6.52941176470588[/C][C]-2.52941176470588[/C][/ROW]
[ROW][C]128[/C][C]8[/C][C]7.47272727272727[/C][C]0.527272727272727[/C][/ROW]
[ROW][C]129[/C][C]9[/C][C]7.47272727272727[/C][C]1.52727272727273[/C][/ROW]
[ROW][C]130[/C][C]8[/C][C]8.87037037037037[/C][C]-0.87037037037037[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]8.87037037037037[/C][C]2.12962962962963[/C][/ROW]
[ROW][C]132[/C][C]8[/C][C]7.47272727272727[/C][C]0.527272727272727[/C][/ROW]
[ROW][C]133[/C][C]5[/C][C]7.47272727272727[/C][C]-2.47272727272727[/C][/ROW]
[ROW][C]134[/C][C]4[/C][C]6.52941176470588[/C][C]-2.52941176470588[/C][/ROW]
[ROW][C]135[/C][C]8[/C][C]8.87037037037037[/C][C]-0.87037037037037[/C][/ROW]
[ROW][C]136[/C][C]10[/C][C]12.25[/C][C]-2.25[/C][/ROW]
[ROW][C]137[/C][C]6[/C][C]7.47272727272727[/C][C]-1.47272727272727[/C][/ROW]
[ROW][C]138[/C][C]9[/C][C]7.47272727272727[/C][C]1.52727272727273[/C][/ROW]
[ROW][C]139[/C][C]9[/C][C]7.47272727272727[/C][C]1.52727272727273[/C][/ROW]
[ROW][C]140[/C][C]13[/C][C]8.87037037037037[/C][C]4.12962962962963[/C][/ROW]
[ROW][C]141[/C][C]9[/C][C]6.52941176470588[/C][C]2.47058823529412[/C][/ROW]
[ROW][C]142[/C][C]10[/C][C]8.87037037037037[/C][C]1.12962962962963[/C][/ROW]
[ROW][C]143[/C][C]20[/C][C]12.25[/C][C]7.75[/C][/ROW]
[ROW][C]144[/C][C]5[/C][C]6.52941176470588[/C][C]-1.52941176470588[/C][/ROW]
[ROW][C]145[/C][C]11[/C][C]8.87037037037037[/C][C]2.12962962962963[/C][/ROW]
[ROW][C]146[/C][C]6[/C][C]8.87037037037037[/C][C]-2.87037037037037[/C][/ROW]
[ROW][C]147[/C][C]9[/C][C]8.87037037037037[/C][C]0.12962962962963[/C][/ROW]
[ROW][C]148[/C][C]7[/C][C]7.47272727272727[/C][C]-0.472727272727273[/C][/ROW]
[ROW][C]149[/C][C]9[/C][C]7.47272727272727[/C][C]1.52727272727273[/C][/ROW]
[ROW][C]150[/C][C]10[/C][C]8.87037037037037[/C][C]1.12962962962963[/C][/ROW]
[ROW][C]151[/C][C]9[/C][C]7.47272727272727[/C][C]1.52727272727273[/C][/ROW]
[ROW][C]152[/C][C]8[/C][C]8.87037037037037[/C][C]-0.87037037037037[/C][/ROW]
[ROW][C]153[/C][C]7[/C][C]12.25[/C][C]-5.25[/C][/ROW]
[ROW][C]154[/C][C]6[/C][C]8.87037037037037[/C][C]-2.87037037037037[/C][/ROW]
[ROW][C]155[/C][C]13[/C][C]12.25[/C][C]0.75[/C][/ROW]
[ROW][C]156[/C][C]6[/C][C]7.47272727272727[/C][C]-1.47272727272727[/C][/ROW]
[ROW][C]157[/C][C]8[/C][C]8.87037037037037[/C][C]-0.87037037037037[/C][/ROW]
[ROW][C]158[/C][C]10[/C][C]8.87037037037037[/C][C]1.12962962962963[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]12.25[/C][C]3.75[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109967&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109967&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	8.87037037037037	3.12962962962963
2	8	6.52941176470588	1.47058823529412
3	8	7.47272727272727	0.527272727272727
4	8	6.52941176470588	1.47058823529412
5	9	7.47272727272727	1.52727272727273
6	7	7.47272727272727	-0.472727272727273
7	4	7.47272727272727	-3.47272727272727
8	11	7.47272727272727	3.52727272727273
9	7	7.47272727272727	-0.472727272727273
10	7	7.47272727272727	-0.472727272727273
11	12	8.87037037037037	3.12962962962963
12	10	8.87037037037037	1.12962962962963
13	10	8.87037037037037	1.12962962962963
14	8	6.52941176470588	1.47058823529412
15	8	7.47272727272727	0.527272727272727
16	4	7.47272727272727	-3.47272727272727
17	9	6.52941176470588	2.47058823529412
18	8	7.47272727272727	0.527272727272727
19	7	7.47272727272727	-0.472727272727273
20	11	8.87037037037037	2.12962962962963
21	9	8.87037037037037	0.12962962962963
22	11	12.25	-1.25
23	13	8.87037037037037	4.12962962962963
24	8	6.52941176470588	1.47058823529412
25	8	7.47272727272727	0.527272727272727
26	9	7.47272727272727	1.52727272727273
27	6	7.47272727272727	-1.47272727272727
28	9	8.87037037037037	0.12962962962963
29	9	8.87037037037037	0.12962962962963
30	6	7.47272727272727	-1.47272727272727
31	6	6.52941176470588	-0.529411764705882
32	16	12.25	3.75
33	5	12.25	-7.25
34	7	8.87037037037037	-1.87037037037037
35	9	7.47272727272727	1.52727272727273
36	6	6.52941176470588	-0.529411764705882
37	6	6.52941176470588	-0.529411764705882
38	5	7.47272727272727	-2.47272727272727
39	12	12.25	-0.25
40	7	8.87037037037037	-1.87037037037037
41	10	12.25	-2.25
42	9	8.87037037037037	0.12962962962963
43	8	7.47272727272727	0.527272727272727
44	5	7.47272727272727	-2.47272727272727
45	8	6.52941176470588	1.47058823529412
46	8	7.47272727272727	0.527272727272727
47	10	7.47272727272727	2.52727272727273
48	6	6.52941176470588	-0.529411764705882
49	8	8.87037037037037	-0.87037037037037
50	7	8.87037037037037	-1.87037037037037
51	4	6.52941176470588	-2.52941176470588
52	8	7.47272727272727	0.527272727272727
53	8	7.47272727272727	0.527272727272727
54	4	6.52941176470588	-2.52941176470588
55	20	12.25	7.75
56	8	8.87037037037037	-0.87037037037037
57	8	8.87037037037037	-0.87037037037037
58	6	7.47272727272727	-1.47272727272727
59	4	6.52941176470588	-2.52941176470588
60	8	7.47272727272727	0.527272727272727
61	9	7.47272727272727	1.52727272727273
62	6	8.87037037037037	-2.87037037037037
63	7	7.47272727272727	-0.472727272727273
64	9	6.52941176470588	2.47058823529412
65	5	7.47272727272727	-2.47272727272727
66	5	6.52941176470588	-1.52941176470588
67	8	8.87037037037037	-0.87037037037037
68	8	7.47272727272727	0.527272727272727
69	6	6.52941176470588	-0.529411764705882
70	8	7.47272727272727	0.527272727272727
71	7	8.87037037037037	-1.87037037037037
72	7	6.52941176470588	0.470588235294118
73	9	8.87037037037037	0.12962962962963
74	11	12.25	-1.25
75	6	8.87037037037037	-2.87037037037037
76	8	8.87037037037037	-0.87037037037037
77	6	7.47272727272727	-1.47272727272727
78	9	8.87037037037037	0.12962962962963
79	8	6.52941176470588	1.47058823529412
80	6	8.87037037037037	-2.87037037037037
81	10	8.87037037037037	1.12962962962963
82	8	7.47272727272727	0.527272727272727
83	8	8.87037037037037	-0.87037037037037
84	10	7.47272727272727	2.52727272727273
85	5	6.52941176470588	-1.52941176470588
86	7	8.87037037037037	-1.87037037037037
87	5	8.87037037037037	-3.87037037037037
88	8	6.52941176470588	1.47058823529412
89	14	8.87037037037037	5.12962962962963
90	7	7.47272727272727	-0.472727272727273
91	8	8.87037037037037	-0.87037037037037
92	6	6.52941176470588	-0.529411764705882
93	5	6.52941176470588	-1.52941176470588
94	6	8.87037037037037	-2.87037037037037
95	10	7.47272727272727	2.52727272727273
96	12	12.25	-0.25
97	9	8.87037037037037	0.12962962962963
98	12	12.25	-0.25
99	7	7.47272727272727	-0.472727272727273
100	8	8.87037037037037	-0.87037037037037
101	10	8.87037037037037	1.12962962962963
102	6	7.47272727272727	-1.47272727272727
103	10	12.25	-2.25
104	10	7.47272727272727	2.52727272727273
105	10	8.87037037037037	1.12962962962963
106	5	8.87037037037037	-3.87037037037037
107	7	6.52941176470588	0.470588235294118
108	10	8.87037037037037	1.12962962962963
109	11	8.87037037037037	2.12962962962963
110	6	7.47272727272727	-1.47272727272727
111	7	7.47272727272727	-0.472727272727273
112	12	8.87037037037037	3.12962962962963
113	11	6.52941176470588	4.47058823529412
114	11	12.25	-1.25
115	11	6.52941176470588	4.47058823529412
116	5	7.47272727272727	-2.47272727272727
117	8	7.47272727272727	0.527272727272727
118	6	6.52941176470588	-0.529411764705882
119	9	8.87037037037037	0.12962962962963
120	4	6.52941176470588	-2.52941176470588
121	4	6.52941176470588	-2.52941176470588
122	7	7.47272727272727	-0.472727272727273
123	11	8.87037037037037	2.12962962962963
124	6	6.52941176470588	-0.529411764705882
125	7	6.52941176470588	0.470588235294118
126	8	7.47272727272727	0.527272727272727
127	4	6.52941176470588	-2.52941176470588
128	8	7.47272727272727	0.527272727272727
129	9	7.47272727272727	1.52727272727273
130	8	8.87037037037037	-0.87037037037037
131	11	8.87037037037037	2.12962962962963
132	8	7.47272727272727	0.527272727272727
133	5	7.47272727272727	-2.47272727272727
134	4	6.52941176470588	-2.52941176470588
135	8	8.87037037037037	-0.87037037037037
136	10	12.25	-2.25
137	6	7.47272727272727	-1.47272727272727
138	9	7.47272727272727	1.52727272727273
139	9	7.47272727272727	1.52727272727273
140	13	8.87037037037037	4.12962962962963
141	9	6.52941176470588	2.47058823529412
142	10	8.87037037037037	1.12962962962963
143	20	12.25	7.75
144	5	6.52941176470588	-1.52941176470588
145	11	8.87037037037037	2.12962962962963
146	6	8.87037037037037	-2.87037037037037
147	9	8.87037037037037	0.12962962962963
148	7	7.47272727272727	-0.472727272727273
149	9	7.47272727272727	1.52727272727273
150	10	8.87037037037037	1.12962962962963
151	9	7.47272727272727	1.52727272727273
152	8	8.87037037037037	-0.87037037037037
153	7	12.25	-5.25
154	6	8.87037037037037	-2.87037037037037
155	13	12.25	0.75
156	6	7.47272727272727	-1.47272727272727
157	8	8.87037037037037	-0.87037037037037
158	10	8.87037037037037	1.12962962962963
159	16	12.25	3.75

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 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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