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Author's title

Author

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R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Thu, 09 Dec 2010 20:38:11 +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/09/t1291927039megsepsem9amfvq.htm/, Retrieved Mon, 29 Apr 2024 00:32:51 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=107394, Retrieved Mon, 29 Apr 2024 00:32:51 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

216

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]
- R PD    [Recursive Partitioning (Regression Trees)] [Recursive partiti...] [2010-12-09 20:38:11] [9ea95e194e0eb2a674315798620d5bc6] [Current]
- R PD      [Recursive Partitioning (Regression Trees)] [] [2011-12-11 10:14:36] [06c08141d7d783218a8164fd2ea166f2] 
-   P         [Recursive Partitioning (Regression Trees)] [] [2011-12-11 10:26:16] [06c08141d7d783218a8164fd2ea166f2] 
- R  D          [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2011-12-12 21:24:06] [d1ce18d003fa52f731d1c3ce8b58d5f9] 
- R         [Recursive Partitioning (Regression Trees)] [WS10-Recursive Pa...] [2011-12-13 12:57:56] [69d59b79aaf660457acc70a0ef0bfdab] 
- RM          [Recursive Partitioning (Regression Trees)] [] [2011-12-13 13:25:45] [69d59b79aaf660457acc70a0ef0bfdab] 
- RMP       [Notched Boxplots] [] [2011-12-13 13:04:18] [69d59b79aaf660457acc70a0ef0bfdab] 
- R         [Recursive Partitioning (Regression Trees)] [] [2011-12-13 15:50:13] [9401a40688cf36283be626153bc5a38b] 

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Dataseries X:

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Boxplots

1	15	10	77	5	4	15	11	12	13	6
0	12	20	63	6	4	9	12	7	11	4
0	15	16	73	4	10	12	12	13	14	6
0	12	10	76	6	6	15	11	11	12	5
0	14	8	90	3	5	17	11	16	12	5
0	8	14	67	10	8	14	10	10	6	4
1	11	19	69	8	9	9	11	15	10	5
1	15	15	70	3	6	12	9	5	11	3
0	4	23	54	4	8	11	10	4	10	2
0	13	9	54	3	11	13	12	7	12	5
1	19	12	76	5	6	16	12	15	15	6
1	10	14	75	5	8	16	12	5	13	6
1	15	13	76	6	11	15	13	16	18	8
0	6	11	80	5	5	10	9	15	11	6
1	7	11	89	3	10	16	12	13	12	3
0	14	10	73	4	7	12	12	13	13	6
0	16	12	74	8	7	15	12	15	14	6
1	16	18	78	8	13	13	12	15	16	7
1	14	12	76	8	10	18	13	10	16	8
0	15	10	69	5	8	13	11	17	16	6
1	14	15	74	8	6	17	12	14	15	7
1	12	15	82	2	8	14	12	9	13	4
0	9	12	77	0	7	13	15	6	8	4
1	12	9	84	5	5	13	11	11	14	2
1	14	11	75	2	9	15	12	13	15	6
1	12	15	54	7	9	13	10	12	13	6
1	14	16	79	5	11	15	11	10	16	6
1	10	17	79	2	11	13	13	4	13	6
1	14	12	69	12	11	14	6	13	12	6
1	16	11	88	7	9	13	12	15	15	7
1	10	13	57	0	7	16	12	8	11	4
1	8	9	69	2	6	14	10	10	14	3
1	12	11	86	3	6	18	12	8	13	5
1	11	9	65	0	6	15	12	7	13	6
0	8	20	66	9	5	9	11	9	12	4
0	13	8	54	2	4	16	9	14	14	6
1	11	12	85	3	10	16	10	5	13	3
0	12	10	79	1	8	17	12	7	12	3
0	16	11	84	10	6	13	12	16	14	6
1	16	13	70	1	5	17	11	14	15	6
1	13	13	54	4	9	15	12	16	16	6
1	14	13	70	6	10	14	11	15	15	8
0	5	15	54	6	6	10	14	4	5	2
0	14	12	69	4	9	13	10	12	15	6
1	13	13	68	4	10	11	10	8	8	4
1	16	13	68	7	6	11	11	17	16	7
0	14	9	71	7	6	16	11	15	16	6
0	15	9	71	7	6	16	11	16	14	6
1	15	14	66	0	13	11	10	12	16	6
1	11	9	67	3	8	15	10	12	14	5
1	15	9	71	8	10	15	12	13	13	6
1	16	15	54	8	5	12	11	14	14	6
1	13	10	76	10	8	17	8	14	14	5
0	11	13	77	11	6	15	12	15	12	6
0	12	8	71	6	9	16	10	14	13	7
1	12	15	69	2	9	14	7	11	15	5
1	10	13	73	6	7	17	11	13	15	6
1	8	24	46	1	20	10	7	4	13	6
0	9	11	66	5	8	11	11	8	10	4
1	12	13	77	4	8	15	8	13	13	5
0	14	12	77	6	7	15	11	15	14	6
1	12	22	70	6	7	7	12	15	13	6
0	11	11	86	4	10	17	8	8	13	4
0	14	15	38	1	5	14	14	17	18	6
0	7	7	66	6	8	18	14	12	12	4
0	16	14	75	7	9	14	11	13	14	7
1	16	19	80	7	9	12	12	14	16	8
0	11	10	64	2	20	14	14	7	13	6
1	16	9	80	7	6	9	9	16	16	6
1	13	12	86	8	10	14	13	11	15	6
1	11	16	54	5	11	11	8	10	14	5
1	13	13	74	4	7	16	11	14	13	6
1	14	11	88	2	12	17	9	19	12	6
1	15	12	85	0	12	16	12	14	16	4
0	10	11	63	7	8	12	7	8	9	5
1	15	13	81	0	6	15	11	15	15	8
0	11	13	81	5	6	15	12	8	16	6
1	11	10	74	3	9	15	11	8	12	6
1	6	11	80	3	5	16	12	6	11	2
1	11	9	80	3	11	16	9	7	13	2
0	12	13	60	3	6	11	11	16	13	4
0	13	15	65	7	6	15	13	15	14	6
1	12	14	62	6	10	12	12	10	15	6
0	8	14	63	3	8	14	12	8	14	5
1	9	11	89	0	7	15	11	9	12	4
1	10	10	76	2	8	17	12	8	16	4
1	16	11	81	0	9	19	12	14	14	6
1	15	12	72	9	8	15	11	14	13	5
0	14	14	84	10	10	16	11	14	12	6
1	12	14	76	3	13	14	8	15	13	7
1	12	21	76	7	7	16	9	7	12	6
1	10	14	78	3	7	15	11	7	9	4
1	12	13	72	6	7	15	12	12	13	4
0	8	11	81	5	8	17	13	7	10	3
1	16	12	72	0	9	12	12	12	15	8
1	11	12	78	0	9	18	6	6	9	4
1	12	11	79	4	8	13	12	10	13	4
1	9	14	52	0	7	14	11	12	13	5
0	14	13	67	0	6	14	13	13	13	5
0	15	13	74	7	8	14	11	14	15	7
0	8	12	73	3	8	12	12	8	13	4
1	12	14	69	9	4	14	10	14	14	5
0	10	12	67	4	8	12	10	10	11	5
1	16	12	76	4	10	15	11	14	15	8
1	17	12	77	15	7	11	11	15	14	5
0	8	18	63	7	8	11	11	10	15	2
1	9	11	84	8	7	15	9	6	12	5
1	8	15	90	2	10	14	7	9	15	4
0	11	13	75	8	9	15	11	11	14	5
1	16	11	76	7	8	16	12	16	16	7
0	13	11	75	3	8	12	12	14	14	6
1	5	22	53	3	5	14	15	8	12	3
1	15	10	87	6	8	18	11	16	11	5
1	15	11	78	8	9	14	10	16	13	6
1	12	15	54	5	11	13	13	14	12	5
0	12	14	58	6	7	14	13	12	12	6
1	16	11	80	10	8	14	11	16	16	7
1	12	10	74	0	4	17	12	15	13	6
1	10	14	56	5	16	12	12	11	12	6
1	12	14	82	0	9	16	12	6	14	5
1	4	11	64	0	16	15	8	6	4	4
0	11	15	67	5	12	10	5	16	14	6
0	16	11	75	10	8	13	11	16	15	6
0	7	10	69	0	4	15	12	8	12	3
1	9	10	72	5	11	16	12	11	11	4
0	14	16	71	6	11	15	11	12	12	4
1	11	12	54	1	8	14	12	13	11	4
1	10	14	68	5	8	11	10	11	12	5
0	6	15	54	3	12	13	7	9	11	4
1	14	10	71	3	8	17	12	15	13	6
1	11	12	53	6	6	14	12	11	12	6
1	11	15	54	2	8	16	9	12	12	4
0	9	12	71	5	6	15	11	15	15	7
1	16	11	69	6	14	12	12	8	14	4
0	7	10	30	2	10	16	12	7	12	4
0	8	20	53	3	5	8	11	10	12	4
0	10	19	68	7	8	9	11	9	12	4
1	14	17	69	6	12	13	12	13	13	5
1	9	8	54	3	11	19	12	11	11	4
1	13	17	66	6	8	11	11	12	13	7
0	13	11	79	9	8	15	12	5	12	3
0	12	13	67	2	9	11	12	12	14	5
0	11	9	74	5	6	15	8	14	15	5
0	10	10	86	10	5	16	15	15	15	6
1	12	13	63	9	8	15	11	14	13	5
1	14	16	69	8	7	12	11	13	16	6
0	11	12	73	8	4	16	6	14	17	6
0	13	14	69	5	9	15	13	14	13	3
0	14	11	71	9	5	13	12	15	14	6
1	13	13	77	9	9	14	12	13	13	5
1	16	15	74	14	12	11	12	14	16	8
1	13	14	82	5	6	15	12	11	13	6
1	12	14	54	12	4	16	12	14	14	4
1	9	14	54	6	6	14	10	11	13	3
1	14	10	80	6	7	13	12	8	14	4
0	15	8	76	8	9	15	12	12	16	7

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 6 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107394&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107394&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107394&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	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.6745
R-squared	0.4549
RMSE	2.1611

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.6745 \tabularnewline
R-squared & 0.4549 \tabularnewline
RMSE & 2.1611 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107394&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.6745[/C][/ROW]
[ROW][C]R-squared[/C][C]0.4549[/C][/ROW]
[ROW][C]RMSE[/C][C]2.1611[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107394&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107394&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	0.6745
R-squared	0.4549
RMSE	2.1611

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	15	12.7931034482759	2.20689655172414
2	12	8.93548387096774	3.06451612903226
3	15	14.32	0.68
4	12	11.1481481481481	0.851851851851851
5	14	12.7931034482759	1.20689655172414
6	8	8.93548387096774	-0.935483870967742
7	11	12.7931034482759	-1.79310344827586
8	15	8.93548387096774	6.06451612903226
9	4	8.93548387096774	-4.93548387096774
10	13	11.1481481481481	1.85185185185185
11	19	14.32	4.68
12	10	11.1481481481481	-1.14814814814815
13	15	14.32	0.68
14	6	12.7931034482759	-6.79310344827586
15	7	8.93548387096774	-1.93548387096774
16	14	12.7931034482759	1.20689655172414
17	16	14.32	1.68
18	16	14.32	1.68
19	14	11.1481481481481	2.85185185185185
20	15	14.32	0.68
21	14	14.32	-0.32
22	12	11.2631578947368	0.736842105263158
23	9	8.93548387096774	0.064516129032258
24	12	11.2631578947368	0.736842105263158
25	14	14.32	-0.32
26	12	12.7931034482759	-0.793103448275861
27	14	11.1481481481481	2.85185185185185
28	10	11.1481481481481	-1.14814814814815
29	14	12.7931034482759	1.20689655172414
30	16	14.32	1.68
31	10	8.93548387096774	1.06451612903226
32	8	11.2631578947368	-3.26315789473684
33	12	11.1481481481481	0.851851851851851
34	11	11.1481481481481	-0.148148148148149
35	8	8.93548387096774	-0.935483870967742
36	13	14.32	-1.32
37	11	11.2631578947368	-0.263157894736842
38	12	8.93548387096774	3.06451612903226
39	16	14.32	1.68
40	16	14.32	1.68
41	13	14.32	-1.32
42	14	14.32	-0.32
43	5	8.93548387096774	-3.93548387096774
44	14	14.32	-0.32
45	13	8.93548387096774	4.06451612903226
46	16	14.32	1.68
47	14	14.32	-0.32
48	15	14.32	0.68
49	15	14.32	0.68
50	11	14.32	-3.32
51	15	12.7931034482759	2.20689655172414
52	16	14.32	1.68
53	13	14.32	-1.32
54	11	12.7931034482759	-1.79310344827586
55	12	12.7931034482759	-0.793103448275861
56	12	11.1481481481481	0.851851851851851
57	10	14.32	-4.32
58	8	11.1481481481481	-3.14814814814815
59	9	8.93548387096774	0.064516129032258
60	12	12.7931034482759	-0.793103448275861
61	14	14.32	-0.32
62	12	12.7931034482759	-0.793103448275861
63	11	11.2631578947368	-0.263157894736842
64	14	14.32	-0.32
65	7	8.93548387096774	-1.93548387096774
66	16	14.32	1.68
67	16	14.32	1.68
68	11	11.1481481481481	-0.148148148148149
69	16	14.32	1.68
70	13	11.1481481481481	1.85185185185185
71	11	11.1481481481481	-0.148148148148149
72	13	12.7931034482759	0.206896551724139
73	14	12.7931034482759	1.20689655172414
74	15	11.2631578947368	3.73684210526316
75	10	11.1481481481481	-1.14814814814815
76	15	14.32	0.68
77	11	11.1481481481481	-0.148148148148149
78	11	11.1481481481481	-0.148148148148149
79	6	8.93548387096774	-2.93548387096774
80	11	11.2631578947368	-0.263157894736842
81	12	11.2631578947368	0.736842105263158
82	13	14.32	-1.32
83	12	11.1481481481481	0.851851851851851
84	8	11.1481481481481	-3.14814814814815
85	9	8.93548387096774	0.064516129032258
86	10	11.2631578947368	-1.26315789473684
87	16	14.32	1.68
88	15	12.7931034482759	2.20689655172414
89	14	12.7931034482759	1.20689655172414
90	12	12.7931034482759	-0.793103448275861
91	12	11.1481481481481	0.851851851851851
92	10	8.93548387096774	1.06451612903226
93	12	11.2631578947368	0.736842105263158
94	8	8.93548387096774	-0.935483870967742
95	16	14.32	1.68
96	11	8.93548387096774	2.06451612903226
97	12	11.2631578947368	0.736842105263158
98	9	12.7931034482759	-3.79310344827586
99	14	12.7931034482759	1.20689655172414
100	15	14.32	0.68
101	8	11.2631578947368	-3.26315789473684
102	12	14.32	-2.32
103	10	11.1481481481481	-1.14814814814815
104	16	14.32	1.68
105	17	14.32	2.68
106	8	11.2631578947368	-3.26315789473684
107	9	11.1481481481481	-2.14814814814815
108	8	11.2631578947368	-3.26315789473684
109	11	11.1481481481481	-0.148148148148149
110	16	14.32	1.68
111	13	14.32	-1.32
112	5	8.93548387096774	-3.93548387096774
113	15	12.7931034482759	2.20689655172414
114	15	12.7931034482759	2.20689655172414
115	12	12.7931034482759	-0.793103448275861
116	12	12.7931034482759	-0.793103448275861
117	16	14.32	1.68
118	12	12.7931034482759	-0.793103448275861
119	10	11.1481481481481	-1.14814814814815
120	12	11.1481481481481	0.851851851851851
121	4	8.93548387096774	-4.93548387096774
122	11	14.32	-3.32
123	16	14.32	1.68
124	7	8.93548387096774	-1.93548387096774
125	9	8.93548387096774	0.064516129032258
126	14	8.93548387096774	5.06451612903226
127	11	8.93548387096774	2.06451612903226
128	10	11.1481481481481	-1.14814814814815
129	6	8.93548387096774	-2.93548387096774
130	14	12.7931034482759	1.20689655172414
131	11	11.1481481481481	-0.148148148148149
132	11	8.93548387096774	2.06451612903226
133	9	14.32	-5.32
134	16	11.2631578947368	4.73684210526316
135	7	8.93548387096774	-1.93548387096774
136	8	8.93548387096774	-0.935483870967742
137	10	8.93548387096774	1.06451612903226
138	14	12.7931034482759	1.20689655172414
139	9	8.93548387096774	0.064516129032258
140	13	12.7931034482759	0.206896551724139
141	13	8.93548387096774	4.06451612903226
142	12	14.32	-2.32
143	11	14.32	-3.32
144	10	14.32	-4.32
145	12	12.7931034482759	-0.793103448275861
146	14	14.32	-0.32
147	11	14.32	-3.32
148	13	11.2631578947368	1.73684210526316
149	14	14.32	-0.32
150	13	12.7931034482759	0.206896551724139
151	16	14.32	1.68
152	13	11.1481481481481	1.85185185185185
153	12	11.2631578947368	0.736842105263158
154	9	11.2631578947368	-2.26315789473684
155	14	11.2631578947368	2.73684210526316
156	15	14.32	0.68

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 15 & 12.7931034482759 & 2.20689655172414 \tabularnewline
2 & 12 & 8.93548387096774 & 3.06451612903226 \tabularnewline
3 & 15 & 14.32 & 0.68 \tabularnewline
4 & 12 & 11.1481481481481 & 0.851851851851851 \tabularnewline
5 & 14 & 12.7931034482759 & 1.20689655172414 \tabularnewline
6 & 8 & 8.93548387096774 & -0.935483870967742 \tabularnewline
7 & 11 & 12.7931034482759 & -1.79310344827586 \tabularnewline
8 & 15 & 8.93548387096774 & 6.06451612903226 \tabularnewline
9 & 4 & 8.93548387096774 & -4.93548387096774 \tabularnewline
10 & 13 & 11.1481481481481 & 1.85185185185185 \tabularnewline
11 & 19 & 14.32 & 4.68 \tabularnewline
12 & 10 & 11.1481481481481 & -1.14814814814815 \tabularnewline
13 & 15 & 14.32 & 0.68 \tabularnewline
14 & 6 & 12.7931034482759 & -6.79310344827586 \tabularnewline
15 & 7 & 8.93548387096774 & -1.93548387096774 \tabularnewline
16 & 14 & 12.7931034482759 & 1.20689655172414 \tabularnewline
17 & 16 & 14.32 & 1.68 \tabularnewline
18 & 16 & 14.32 & 1.68 \tabularnewline
19 & 14 & 11.1481481481481 & 2.85185185185185 \tabularnewline
20 & 15 & 14.32 & 0.68 \tabularnewline
21 & 14 & 14.32 & -0.32 \tabularnewline
22 & 12 & 11.2631578947368 & 0.736842105263158 \tabularnewline
23 & 9 & 8.93548387096774 & 0.064516129032258 \tabularnewline
24 & 12 & 11.2631578947368 & 0.736842105263158 \tabularnewline
25 & 14 & 14.32 & -0.32 \tabularnewline
26 & 12 & 12.7931034482759 & -0.793103448275861 \tabularnewline
27 & 14 & 11.1481481481481 & 2.85185185185185 \tabularnewline
28 & 10 & 11.1481481481481 & -1.14814814814815 \tabularnewline
29 & 14 & 12.7931034482759 & 1.20689655172414 \tabularnewline
30 & 16 & 14.32 & 1.68 \tabularnewline
31 & 10 & 8.93548387096774 & 1.06451612903226 \tabularnewline
32 & 8 & 11.2631578947368 & -3.26315789473684 \tabularnewline
33 & 12 & 11.1481481481481 & 0.851851851851851 \tabularnewline
34 & 11 & 11.1481481481481 & -0.148148148148149 \tabularnewline
35 & 8 & 8.93548387096774 & -0.935483870967742 \tabularnewline
36 & 13 & 14.32 & -1.32 \tabularnewline
37 & 11 & 11.2631578947368 & -0.263157894736842 \tabularnewline
38 & 12 & 8.93548387096774 & 3.06451612903226 \tabularnewline
39 & 16 & 14.32 & 1.68 \tabularnewline
40 & 16 & 14.32 & 1.68 \tabularnewline
41 & 13 & 14.32 & -1.32 \tabularnewline
42 & 14 & 14.32 & -0.32 \tabularnewline
43 & 5 & 8.93548387096774 & -3.93548387096774 \tabularnewline
44 & 14 & 14.32 & -0.32 \tabularnewline
45 & 13 & 8.93548387096774 & 4.06451612903226 \tabularnewline
46 & 16 & 14.32 & 1.68 \tabularnewline
47 & 14 & 14.32 & -0.32 \tabularnewline
48 & 15 & 14.32 & 0.68 \tabularnewline
49 & 15 & 14.32 & 0.68 \tabularnewline
50 & 11 & 14.32 & -3.32 \tabularnewline
51 & 15 & 12.7931034482759 & 2.20689655172414 \tabularnewline
52 & 16 & 14.32 & 1.68 \tabularnewline
53 & 13 & 14.32 & -1.32 \tabularnewline
54 & 11 & 12.7931034482759 & -1.79310344827586 \tabularnewline
55 & 12 & 12.7931034482759 & -0.793103448275861 \tabularnewline
56 & 12 & 11.1481481481481 & 0.851851851851851 \tabularnewline
57 & 10 & 14.32 & -4.32 \tabularnewline
58 & 8 & 11.1481481481481 & -3.14814814814815 \tabularnewline
59 & 9 & 8.93548387096774 & 0.064516129032258 \tabularnewline
60 & 12 & 12.7931034482759 & -0.793103448275861 \tabularnewline
61 & 14 & 14.32 & -0.32 \tabularnewline
62 & 12 & 12.7931034482759 & -0.793103448275861 \tabularnewline
63 & 11 & 11.2631578947368 & -0.263157894736842 \tabularnewline
64 & 14 & 14.32 & -0.32 \tabularnewline
65 & 7 & 8.93548387096774 & -1.93548387096774 \tabularnewline
66 & 16 & 14.32 & 1.68 \tabularnewline
67 & 16 & 14.32 & 1.68 \tabularnewline
68 & 11 & 11.1481481481481 & -0.148148148148149 \tabularnewline
69 & 16 & 14.32 & 1.68 \tabularnewline
70 & 13 & 11.1481481481481 & 1.85185185185185 \tabularnewline
71 & 11 & 11.1481481481481 & -0.148148148148149 \tabularnewline
72 & 13 & 12.7931034482759 & 0.206896551724139 \tabularnewline
73 & 14 & 12.7931034482759 & 1.20689655172414 \tabularnewline
74 & 15 & 11.2631578947368 & 3.73684210526316 \tabularnewline
75 & 10 & 11.1481481481481 & -1.14814814814815 \tabularnewline
76 & 15 & 14.32 & 0.68 \tabularnewline
77 & 11 & 11.1481481481481 & -0.148148148148149 \tabularnewline
78 & 11 & 11.1481481481481 & -0.148148148148149 \tabularnewline
79 & 6 & 8.93548387096774 & -2.93548387096774 \tabularnewline
80 & 11 & 11.2631578947368 & -0.263157894736842 \tabularnewline
81 & 12 & 11.2631578947368 & 0.736842105263158 \tabularnewline
82 & 13 & 14.32 & -1.32 \tabularnewline
83 & 12 & 11.1481481481481 & 0.851851851851851 \tabularnewline
84 & 8 & 11.1481481481481 & -3.14814814814815 \tabularnewline
85 & 9 & 8.93548387096774 & 0.064516129032258 \tabularnewline
86 & 10 & 11.2631578947368 & -1.26315789473684 \tabularnewline
87 & 16 & 14.32 & 1.68 \tabularnewline
88 & 15 & 12.7931034482759 & 2.20689655172414 \tabularnewline
89 & 14 & 12.7931034482759 & 1.20689655172414 \tabularnewline
90 & 12 & 12.7931034482759 & -0.793103448275861 \tabularnewline
91 & 12 & 11.1481481481481 & 0.851851851851851 \tabularnewline
92 & 10 & 8.93548387096774 & 1.06451612903226 \tabularnewline
93 & 12 & 11.2631578947368 & 0.736842105263158 \tabularnewline
94 & 8 & 8.93548387096774 & -0.935483870967742 \tabularnewline
95 & 16 & 14.32 & 1.68 \tabularnewline
96 & 11 & 8.93548387096774 & 2.06451612903226 \tabularnewline
97 & 12 & 11.2631578947368 & 0.736842105263158 \tabularnewline
98 & 9 & 12.7931034482759 & -3.79310344827586 \tabularnewline
99 & 14 & 12.7931034482759 & 1.20689655172414 \tabularnewline
100 & 15 & 14.32 & 0.68 \tabularnewline
101 & 8 & 11.2631578947368 & -3.26315789473684 \tabularnewline
102 & 12 & 14.32 & -2.32 \tabularnewline
103 & 10 & 11.1481481481481 & -1.14814814814815 \tabularnewline
104 & 16 & 14.32 & 1.68 \tabularnewline
105 & 17 & 14.32 & 2.68 \tabularnewline
106 & 8 & 11.2631578947368 & -3.26315789473684 \tabularnewline
107 & 9 & 11.1481481481481 & -2.14814814814815 \tabularnewline
108 & 8 & 11.2631578947368 & -3.26315789473684 \tabularnewline
109 & 11 & 11.1481481481481 & -0.148148148148149 \tabularnewline
110 & 16 & 14.32 & 1.68 \tabularnewline
111 & 13 & 14.32 & -1.32 \tabularnewline
112 & 5 & 8.93548387096774 & -3.93548387096774 \tabularnewline
113 & 15 & 12.7931034482759 & 2.20689655172414 \tabularnewline
114 & 15 & 12.7931034482759 & 2.20689655172414 \tabularnewline
115 & 12 & 12.7931034482759 & -0.793103448275861 \tabularnewline
116 & 12 & 12.7931034482759 & -0.793103448275861 \tabularnewline
117 & 16 & 14.32 & 1.68 \tabularnewline
118 & 12 & 12.7931034482759 & -0.793103448275861 \tabularnewline
119 & 10 & 11.1481481481481 & -1.14814814814815 \tabularnewline
120 & 12 & 11.1481481481481 & 0.851851851851851 \tabularnewline
121 & 4 & 8.93548387096774 & -4.93548387096774 \tabularnewline
122 & 11 & 14.32 & -3.32 \tabularnewline
123 & 16 & 14.32 & 1.68 \tabularnewline
124 & 7 & 8.93548387096774 & -1.93548387096774 \tabularnewline
125 & 9 & 8.93548387096774 & 0.064516129032258 \tabularnewline
126 & 14 & 8.93548387096774 & 5.06451612903226 \tabularnewline
127 & 11 & 8.93548387096774 & 2.06451612903226 \tabularnewline
128 & 10 & 11.1481481481481 & -1.14814814814815 \tabularnewline
129 & 6 & 8.93548387096774 & -2.93548387096774 \tabularnewline
130 & 14 & 12.7931034482759 & 1.20689655172414 \tabularnewline
131 & 11 & 11.1481481481481 & -0.148148148148149 \tabularnewline
132 & 11 & 8.93548387096774 & 2.06451612903226 \tabularnewline
133 & 9 & 14.32 & -5.32 \tabularnewline
134 & 16 & 11.2631578947368 & 4.73684210526316 \tabularnewline
135 & 7 & 8.93548387096774 & -1.93548387096774 \tabularnewline
136 & 8 & 8.93548387096774 & -0.935483870967742 \tabularnewline
137 & 10 & 8.93548387096774 & 1.06451612903226 \tabularnewline
138 & 14 & 12.7931034482759 & 1.20689655172414 \tabularnewline
139 & 9 & 8.93548387096774 & 0.064516129032258 \tabularnewline
140 & 13 & 12.7931034482759 & 0.206896551724139 \tabularnewline
141 & 13 & 8.93548387096774 & 4.06451612903226 \tabularnewline
142 & 12 & 14.32 & -2.32 \tabularnewline
143 & 11 & 14.32 & -3.32 \tabularnewline
144 & 10 & 14.32 & -4.32 \tabularnewline
145 & 12 & 12.7931034482759 & -0.793103448275861 \tabularnewline
146 & 14 & 14.32 & -0.32 \tabularnewline
147 & 11 & 14.32 & -3.32 \tabularnewline
148 & 13 & 11.2631578947368 & 1.73684210526316 \tabularnewline
149 & 14 & 14.32 & -0.32 \tabularnewline
150 & 13 & 12.7931034482759 & 0.206896551724139 \tabularnewline
151 & 16 & 14.32 & 1.68 \tabularnewline
152 & 13 & 11.1481481481481 & 1.85185185185185 \tabularnewline
153 & 12 & 11.2631578947368 & 0.736842105263158 \tabularnewline
154 & 9 & 11.2631578947368 & -2.26315789473684 \tabularnewline
155 & 14 & 11.2631578947368 & 2.73684210526316 \tabularnewline
156 & 15 & 14.32 & 0.68 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107394&T=2

[TABLE]
[ROW][C]Actuals, Predictions, and Residuals[/C][/ROW]
[ROW][C]#[/C][C]Actuals[/C][C]Forecasts[/C][C]Residuals[/C][/ROW]
[ROW][C]1[/C][C]15[/C][C]12.7931034482759[/C][C]2.20689655172414[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]8.93548387096774[/C][C]3.06451612903226[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]14.32[/C][C]0.68[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]11.1481481481481[/C][C]0.851851851851851[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]12.7931034482759[/C][C]1.20689655172414[/C][/ROW]
[ROW][C]6[/C][C]8[/C][C]8.93548387096774[/C][C]-0.935483870967742[/C][/ROW]
[ROW][C]7[/C][C]11[/C][C]12.7931034482759[/C][C]-1.79310344827586[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]8.93548387096774[/C][C]6.06451612903226[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]8.93548387096774[/C][C]-4.93548387096774[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]11.1481481481481[/C][C]1.85185185185185[/C][/ROW]
[ROW][C]11[/C][C]19[/C][C]14.32[/C][C]4.68[/C][/ROW]
[ROW][C]12[/C][C]10[/C][C]11.1481481481481[/C][C]-1.14814814814815[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]14.32[/C][C]0.68[/C][/ROW]
[ROW][C]14[/C][C]6[/C][C]12.7931034482759[/C][C]-6.79310344827586[/C][/ROW]
[ROW][C]15[/C][C]7[/C][C]8.93548387096774[/C][C]-1.93548387096774[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]12.7931034482759[/C][C]1.20689655172414[/C][/ROW]
[ROW][C]17[/C][C]16[/C][C]14.32[/C][C]1.68[/C][/ROW]
[ROW][C]18[/C][C]16[/C][C]14.32[/C][C]1.68[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]11.1481481481481[/C][C]2.85185185185185[/C][/ROW]
[ROW][C]20[/C][C]15[/C][C]14.32[/C][C]0.68[/C][/ROW]
[ROW][C]21[/C][C]14[/C][C]14.32[/C][C]-0.32[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]11.2631578947368[/C][C]0.736842105263158[/C][/ROW]
[ROW][C]23[/C][C]9[/C][C]8.93548387096774[/C][C]0.064516129032258[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]11.2631578947368[/C][C]0.736842105263158[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]14.32[/C][C]-0.32[/C][/ROW]
[ROW][C]26[/C][C]12[/C][C]12.7931034482759[/C][C]-0.793103448275861[/C][/ROW]
[ROW][C]27[/C][C]14[/C][C]11.1481481481481[/C][C]2.85185185185185[/C][/ROW]
[ROW][C]28[/C][C]10[/C][C]11.1481481481481[/C][C]-1.14814814814815[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]12.7931034482759[/C][C]1.20689655172414[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]14.32[/C][C]1.68[/C][/ROW]
[ROW][C]31[/C][C]10[/C][C]8.93548387096774[/C][C]1.06451612903226[/C][/ROW]
[ROW][C]32[/C][C]8[/C][C]11.2631578947368[/C][C]-3.26315789473684[/C][/ROW]
[ROW][C]33[/C][C]12[/C][C]11.1481481481481[/C][C]0.851851851851851[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]11.1481481481481[/C][C]-0.148148148148149[/C][/ROW]
[ROW][C]35[/C][C]8[/C][C]8.93548387096774[/C][C]-0.935483870967742[/C][/ROW]
[ROW][C]36[/C][C]13[/C][C]14.32[/C][C]-1.32[/C][/ROW]
[ROW][C]37[/C][C]11[/C][C]11.2631578947368[/C][C]-0.263157894736842[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]8.93548387096774[/C][C]3.06451612903226[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.32[/C][C]1.68[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.32[/C][C]1.68[/C][/ROW]
[ROW][C]41[/C][C]13[/C][C]14.32[/C][C]-1.32[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]14.32[/C][C]-0.32[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]8.93548387096774[/C][C]-3.93548387096774[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.32[/C][C]-0.32[/C][/ROW]
[ROW][C]45[/C][C]13[/C][C]8.93548387096774[/C][C]4.06451612903226[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]14.32[/C][C]1.68[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]14.32[/C][C]-0.32[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.32[/C][C]0.68[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]14.32[/C][C]0.68[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]14.32[/C][C]-3.32[/C][/ROW]
[ROW][C]51[/C][C]15[/C][C]12.7931034482759[/C][C]2.20689655172414[/C][/ROW]
[ROW][C]52[/C][C]16[/C][C]14.32[/C][C]1.68[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]14.32[/C][C]-1.32[/C][/ROW]
[ROW][C]54[/C][C]11[/C][C]12.7931034482759[/C][C]-1.79310344827586[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]12.7931034482759[/C][C]-0.793103448275861[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]11.1481481481481[/C][C]0.851851851851851[/C][/ROW]
[ROW][C]57[/C][C]10[/C][C]14.32[/C][C]-4.32[/C][/ROW]
[ROW][C]58[/C][C]8[/C][C]11.1481481481481[/C][C]-3.14814814814815[/C][/ROW]
[ROW][C]59[/C][C]9[/C][C]8.93548387096774[/C][C]0.064516129032258[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]12.7931034482759[/C][C]-0.793103448275861[/C][/ROW]
[ROW][C]61[/C][C]14[/C][C]14.32[/C][C]-0.32[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]12.7931034482759[/C][C]-0.793103448275861[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]11.2631578947368[/C][C]-0.263157894736842[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.32[/C][C]-0.32[/C][/ROW]
[ROW][C]65[/C][C]7[/C][C]8.93548387096774[/C][C]-1.93548387096774[/C][/ROW]
[ROW][C]66[/C][C]16[/C][C]14.32[/C][C]1.68[/C][/ROW]
[ROW][C]67[/C][C]16[/C][C]14.32[/C][C]1.68[/C][/ROW]
[ROW][C]68[/C][C]11[/C][C]11.1481481481481[/C][C]-0.148148148148149[/C][/ROW]
[ROW][C]69[/C][C]16[/C][C]14.32[/C][C]1.68[/C][/ROW]
[ROW][C]70[/C][C]13[/C][C]11.1481481481481[/C][C]1.85185185185185[/C][/ROW]
[ROW][C]71[/C][C]11[/C][C]11.1481481481481[/C][C]-0.148148148148149[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]12.7931034482759[/C][C]0.206896551724139[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]12.7931034482759[/C][C]1.20689655172414[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]11.2631578947368[/C][C]3.73684210526316[/C][/ROW]
[ROW][C]75[/C][C]10[/C][C]11.1481481481481[/C][C]-1.14814814814815[/C][/ROW]
[ROW][C]76[/C][C]15[/C][C]14.32[/C][C]0.68[/C][/ROW]
[ROW][C]77[/C][C]11[/C][C]11.1481481481481[/C][C]-0.148148148148149[/C][/ROW]
[ROW][C]78[/C][C]11[/C][C]11.1481481481481[/C][C]-0.148148148148149[/C][/ROW]
[ROW][C]79[/C][C]6[/C][C]8.93548387096774[/C][C]-2.93548387096774[/C][/ROW]
[ROW][C]80[/C][C]11[/C][C]11.2631578947368[/C][C]-0.263157894736842[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]11.2631578947368[/C][C]0.736842105263158[/C][/ROW]
[ROW][C]82[/C][C]13[/C][C]14.32[/C][C]-1.32[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]11.1481481481481[/C][C]0.851851851851851[/C][/ROW]
[ROW][C]84[/C][C]8[/C][C]11.1481481481481[/C][C]-3.14814814814815[/C][/ROW]
[ROW][C]85[/C][C]9[/C][C]8.93548387096774[/C][C]0.064516129032258[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]11.2631578947368[/C][C]-1.26315789473684[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]14.32[/C][C]1.68[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]12.7931034482759[/C][C]2.20689655172414[/C][/ROW]
[ROW][C]89[/C][C]14[/C][C]12.7931034482759[/C][C]1.20689655172414[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]12.7931034482759[/C][C]-0.793103448275861[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]11.1481481481481[/C][C]0.851851851851851[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]8.93548387096774[/C][C]1.06451612903226[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]11.2631578947368[/C][C]0.736842105263158[/C][/ROW]
[ROW][C]94[/C][C]8[/C][C]8.93548387096774[/C][C]-0.935483870967742[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]14.32[/C][C]1.68[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]8.93548387096774[/C][C]2.06451612903226[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]11.2631578947368[/C][C]0.736842105263158[/C][/ROW]
[ROW][C]98[/C][C]9[/C][C]12.7931034482759[/C][C]-3.79310344827586[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]12.7931034482759[/C][C]1.20689655172414[/C][/ROW]
[ROW][C]100[/C][C]15[/C][C]14.32[/C][C]0.68[/C][/ROW]
[ROW][C]101[/C][C]8[/C][C]11.2631578947368[/C][C]-3.26315789473684[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]14.32[/C][C]-2.32[/C][/ROW]
[ROW][C]103[/C][C]10[/C][C]11.1481481481481[/C][C]-1.14814814814815[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]14.32[/C][C]1.68[/C][/ROW]
[ROW][C]105[/C][C]17[/C][C]14.32[/C][C]2.68[/C][/ROW]
[ROW][C]106[/C][C]8[/C][C]11.2631578947368[/C][C]-3.26315789473684[/C][/ROW]
[ROW][C]107[/C][C]9[/C][C]11.1481481481481[/C][C]-2.14814814814815[/C][/ROW]
[ROW][C]108[/C][C]8[/C][C]11.2631578947368[/C][C]-3.26315789473684[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]11.1481481481481[/C][C]-0.148148148148149[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]14.32[/C][C]1.68[/C][/ROW]
[ROW][C]111[/C][C]13[/C][C]14.32[/C][C]-1.32[/C][/ROW]
[ROW][C]112[/C][C]5[/C][C]8.93548387096774[/C][C]-3.93548387096774[/C][/ROW]
[ROW][C]113[/C][C]15[/C][C]12.7931034482759[/C][C]2.20689655172414[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]12.7931034482759[/C][C]2.20689655172414[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]12.7931034482759[/C][C]-0.793103448275861[/C][/ROW]
[ROW][C]116[/C][C]12[/C][C]12.7931034482759[/C][C]-0.793103448275861[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]14.32[/C][C]1.68[/C][/ROW]
[ROW][C]118[/C][C]12[/C][C]12.7931034482759[/C][C]-0.793103448275861[/C][/ROW]
[ROW][C]119[/C][C]10[/C][C]11.1481481481481[/C][C]-1.14814814814815[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]11.1481481481481[/C][C]0.851851851851851[/C][/ROW]
[ROW][C]121[/C][C]4[/C][C]8.93548387096774[/C][C]-4.93548387096774[/C][/ROW]
[ROW][C]122[/C][C]11[/C][C]14.32[/C][C]-3.32[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]14.32[/C][C]1.68[/C][/ROW]
[ROW][C]124[/C][C]7[/C][C]8.93548387096774[/C][C]-1.93548387096774[/C][/ROW]
[ROW][C]125[/C][C]9[/C][C]8.93548387096774[/C][C]0.064516129032258[/C][/ROW]
[ROW][C]126[/C][C]14[/C][C]8.93548387096774[/C][C]5.06451612903226[/C][/ROW]
[ROW][C]127[/C][C]11[/C][C]8.93548387096774[/C][C]2.06451612903226[/C][/ROW]
[ROW][C]128[/C][C]10[/C][C]11.1481481481481[/C][C]-1.14814814814815[/C][/ROW]
[ROW][C]129[/C][C]6[/C][C]8.93548387096774[/C][C]-2.93548387096774[/C][/ROW]
[ROW][C]130[/C][C]14[/C][C]12.7931034482759[/C][C]1.20689655172414[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]11.1481481481481[/C][C]-0.148148148148149[/C][/ROW]
[ROW][C]132[/C][C]11[/C][C]8.93548387096774[/C][C]2.06451612903226[/C][/ROW]
[ROW][C]133[/C][C]9[/C][C]14.32[/C][C]-5.32[/C][/ROW]
[ROW][C]134[/C][C]16[/C][C]11.2631578947368[/C][C]4.73684210526316[/C][/ROW]
[ROW][C]135[/C][C]7[/C][C]8.93548387096774[/C][C]-1.93548387096774[/C][/ROW]
[ROW][C]136[/C][C]8[/C][C]8.93548387096774[/C][C]-0.935483870967742[/C][/ROW]
[ROW][C]137[/C][C]10[/C][C]8.93548387096774[/C][C]1.06451612903226[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]12.7931034482759[/C][C]1.20689655172414[/C][/ROW]
[ROW][C]139[/C][C]9[/C][C]8.93548387096774[/C][C]0.064516129032258[/C][/ROW]
[ROW][C]140[/C][C]13[/C][C]12.7931034482759[/C][C]0.206896551724139[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]8.93548387096774[/C][C]4.06451612903226[/C][/ROW]
[ROW][C]142[/C][C]12[/C][C]14.32[/C][C]-2.32[/C][/ROW]
[ROW][C]143[/C][C]11[/C][C]14.32[/C][C]-3.32[/C][/ROW]
[ROW][C]144[/C][C]10[/C][C]14.32[/C][C]-4.32[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]12.7931034482759[/C][C]-0.793103448275861[/C][/ROW]
[ROW][C]146[/C][C]14[/C][C]14.32[/C][C]-0.32[/C][/ROW]
[ROW][C]147[/C][C]11[/C][C]14.32[/C][C]-3.32[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]11.2631578947368[/C][C]1.73684210526316[/C][/ROW]
[ROW][C]149[/C][C]14[/C][C]14.32[/C][C]-0.32[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]12.7931034482759[/C][C]0.206896551724139[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]14.32[/C][C]1.68[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]11.1481481481481[/C][C]1.85185185185185[/C][/ROW]
[ROW][C]153[/C][C]12[/C][C]11.2631578947368[/C][C]0.736842105263158[/C][/ROW]
[ROW][C]154[/C][C]9[/C][C]11.2631578947368[/C][C]-2.26315789473684[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]11.2631578947368[/C][C]2.73684210526316[/C][/ROW]
[ROW][C]156[/C][C]15[/C][C]14.32[/C][C]0.68[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107394&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107394&T=2

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	15	12.7931034482759	2.20689655172414
2	12	8.93548387096774	3.06451612903226
3	15	14.32	0.68
4	12	11.1481481481481	0.851851851851851
5	14	12.7931034482759	1.20689655172414
6	8	8.93548387096774	-0.935483870967742
7	11	12.7931034482759	-1.79310344827586
8	15	8.93548387096774	6.06451612903226
9	4	8.93548387096774	-4.93548387096774
10	13	11.1481481481481	1.85185185185185
11	19	14.32	4.68
12	10	11.1481481481481	-1.14814814814815
13	15	14.32	0.68
14	6	12.7931034482759	-6.79310344827586
15	7	8.93548387096774	-1.93548387096774
16	14	12.7931034482759	1.20689655172414
17	16	14.32	1.68
18	16	14.32	1.68
19	14	11.1481481481481	2.85185185185185
20	15	14.32	0.68
21	14	14.32	-0.32
22	12	11.2631578947368	0.736842105263158
23	9	8.93548387096774	0.064516129032258
24	12	11.2631578947368	0.736842105263158
25	14	14.32	-0.32
26	12	12.7931034482759	-0.793103448275861
27	14	11.1481481481481	2.85185185185185
28	10	11.1481481481481	-1.14814814814815
29	14	12.7931034482759	1.20689655172414
30	16	14.32	1.68
31	10	8.93548387096774	1.06451612903226
32	8	11.2631578947368	-3.26315789473684
33	12	11.1481481481481	0.851851851851851
34	11	11.1481481481481	-0.148148148148149
35	8	8.93548387096774	-0.935483870967742
36	13	14.32	-1.32
37	11	11.2631578947368	-0.263157894736842
38	12	8.93548387096774	3.06451612903226
39	16	14.32	1.68
40	16	14.32	1.68
41	13	14.32	-1.32
42	14	14.32	-0.32
43	5	8.93548387096774	-3.93548387096774
44	14	14.32	-0.32
45	13	8.93548387096774	4.06451612903226
46	16	14.32	1.68
47	14	14.32	-0.32
48	15	14.32	0.68
49	15	14.32	0.68
50	11	14.32	-3.32
51	15	12.7931034482759	2.20689655172414
52	16	14.32	1.68
53	13	14.32	-1.32
54	11	12.7931034482759	-1.79310344827586
55	12	12.7931034482759	-0.793103448275861
56	12	11.1481481481481	0.851851851851851
57	10	14.32	-4.32
58	8	11.1481481481481	-3.14814814814815
59	9	8.93548387096774	0.064516129032258
60	12	12.7931034482759	-0.793103448275861
61	14	14.32	-0.32
62	12	12.7931034482759	-0.793103448275861
63	11	11.2631578947368	-0.263157894736842
64	14	14.32	-0.32
65	7	8.93548387096774	-1.93548387096774
66	16	14.32	1.68
67	16	14.32	1.68
68	11	11.1481481481481	-0.148148148148149
69	16	14.32	1.68
70	13	11.1481481481481	1.85185185185185
71	11	11.1481481481481	-0.148148148148149
72	13	12.7931034482759	0.206896551724139
73	14	12.7931034482759	1.20689655172414
74	15	11.2631578947368	3.73684210526316
75	10	11.1481481481481	-1.14814814814815
76	15	14.32	0.68
77	11	11.1481481481481	-0.148148148148149
78	11	11.1481481481481	-0.148148148148149
79	6	8.93548387096774	-2.93548387096774
80	11	11.2631578947368	-0.263157894736842
81	12	11.2631578947368	0.736842105263158
82	13	14.32	-1.32
83	12	11.1481481481481	0.851851851851851
84	8	11.1481481481481	-3.14814814814815
85	9	8.93548387096774	0.064516129032258
86	10	11.2631578947368	-1.26315789473684
87	16	14.32	1.68
88	15	12.7931034482759	2.20689655172414
89	14	12.7931034482759	1.20689655172414
90	12	12.7931034482759	-0.793103448275861
91	12	11.1481481481481	0.851851851851851
92	10	8.93548387096774	1.06451612903226
93	12	11.2631578947368	0.736842105263158
94	8	8.93548387096774	-0.935483870967742
95	16	14.32	1.68
96	11	8.93548387096774	2.06451612903226
97	12	11.2631578947368	0.736842105263158
98	9	12.7931034482759	-3.79310344827586
99	14	12.7931034482759	1.20689655172414
100	15	14.32	0.68
101	8	11.2631578947368	-3.26315789473684
102	12	14.32	-2.32
103	10	11.1481481481481	-1.14814814814815
104	16	14.32	1.68
105	17	14.32	2.68
106	8	11.2631578947368	-3.26315789473684
107	9	11.1481481481481	-2.14814814814815
108	8	11.2631578947368	-3.26315789473684
109	11	11.1481481481481	-0.148148148148149
110	16	14.32	1.68
111	13	14.32	-1.32
112	5	8.93548387096774	-3.93548387096774
113	15	12.7931034482759	2.20689655172414
114	15	12.7931034482759	2.20689655172414
115	12	12.7931034482759	-0.793103448275861
116	12	12.7931034482759	-0.793103448275861
117	16	14.32	1.68
118	12	12.7931034482759	-0.793103448275861
119	10	11.1481481481481	-1.14814814814815
120	12	11.1481481481481	0.851851851851851
121	4	8.93548387096774	-4.93548387096774
122	11	14.32	-3.32
123	16	14.32	1.68
124	7	8.93548387096774	-1.93548387096774
125	9	8.93548387096774	0.064516129032258
126	14	8.93548387096774	5.06451612903226
127	11	8.93548387096774	2.06451612903226
128	10	11.1481481481481	-1.14814814814815
129	6	8.93548387096774	-2.93548387096774
130	14	12.7931034482759	1.20689655172414
131	11	11.1481481481481	-0.148148148148149
132	11	8.93548387096774	2.06451612903226
133	9	14.32	-5.32
134	16	11.2631578947368	4.73684210526316
135	7	8.93548387096774	-1.93548387096774
136	8	8.93548387096774	-0.935483870967742
137	10	8.93548387096774	1.06451612903226
138	14	12.7931034482759	1.20689655172414
139	9	8.93548387096774	0.064516129032258
140	13	12.7931034482759	0.206896551724139
141	13	8.93548387096774	4.06451612903226
142	12	14.32	-2.32
143	11	14.32	-3.32
144	10	14.32	-4.32
145	12	12.7931034482759	-0.793103448275861
146	14	14.32	-0.32
147	11	14.32	-3.32
148	13	11.2631578947368	1.73684210526316
149	14	14.32	-0.32
150	13	12.7931034482759	0.206896551724139
151	16	14.32	1.68
152	13	11.1481481481481	1.85185185185185
153	12	11.2631578947368	0.736842105263158
154	9	11.2631578947368	-2.26315789473684
155	14	11.2631578947368	2.73684210526316
156	15	14.32	0.68

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 2 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 2 ; par2 = none ; par3 = 3 ; par4 = no ;

R code (references can be found in the software module):

library(party)
library(Hmisc)
par1 <- as.numeric(par1)
par3 <- as.numeric(par3)
x <- data.frame(t(y))
is.data.frame(x)
x <- x[!is.na(x[,par1]),]
k <- length(x[1,])
n <- length(x[,1])
colnames(x)[par1]
x[,par1]
if (par2 == 'kmeans') {
cl <- kmeans(x[,par1], par3)
print(cl)
clm <- matrix(cbind(cl$centers,1:par3),ncol=2)
clm <- clm[sort.list(clm[,1]),]
for (i in 1:par3) {
cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='')
}
cl$cluster <- as.factor(cl$cluster)
print(cl$cluster)
x[,par1] <- cl$cluster
}
if (par2 == 'quantiles') {
x[,par1] <- cut2(x[,par1],g=par3)
}
if (par2 == 'hclust') {
hc <- hclust(dist(x[,par1])^2, 'cen')
print(hc)
memb <- cutree(hc, k = par3)
dum <- c(mean(x[memb==1,par1]))
for (i in 2:par3) {
dum <- c(dum, mean(x[memb==i,par1]))
}
hcm <- matrix(cbind(dum,1:par3),ncol=2)
hcm <- hcm[sort.list(hcm[,1]),]
for (i in 1:par3) {
memb[memb==hcm[i,2]] <- paste('C',i,sep='')
}
memb <- as.factor(memb)
print(memb)
x[,par1] <- memb
}
if (par2=='equal') {
ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep=''))
x[,par1] <- as.factor(ed)
}
table(x[,par1])
colnames(x)
colnames(x)[par1]
x[,par1]
if (par2 == 'none') {
m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
}
load(file='createtable')
if (par2 != 'none') {
m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x)
if (par4=='yes') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
a<-table.element(a,'Prediction (training)',par3+1,TRUE)
a<-table.element(a,'Prediction (testing)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Actual',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
a<-table.row.end(a)
for (i in 1:10) {
ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1))
m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,])
if (i==1) {
m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,])
m.ct.i.actu <- x[ind==1,par1]
m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,])
m.ct.x.actu <- x[ind==2,par1]
} else {
m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,]))
m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1])
m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,]))
m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1])
}
}
print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,]))
numer <- numer + m.ct.i.tab[i,i]
}
print(m.ct.i.cp <- numer / sum(m.ct.i.tab))
print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,]))
numer <- numer + m.ct.x.tab[i,i]
}
print(m.ct.x.cp <- numer / sum(m.ct.x.tab))
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj])
a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4))
for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj])
a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4))
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'Overall',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.i.cp,4))
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.x.cp,4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
}
}
m
bitmap(file='test1.png')
plot(m)
dev.off()
bitmap(file='test1a.png')
plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response')
dev.off()
if (par2 == 'none') {
forec <- predict(m)
result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec))
colnames(result) <- c('Actuals','Forecasts','Residuals')
print(result)
}
if (par2 != 'none') {
print(cbind(as.factor(x[,par1]),predict(m)))
myt <- table(as.factor(x[,par1]),predict(m))
print(myt)
}
bitmap(file='test2.png')
if(par2=='none') {
op <- par(mfrow=c(2,2))
plot(density(result$Actuals),main='Kernel Density Plot of Actuals')
plot(density(result$Residuals),main='Kernel Density Plot of Residuals')
plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals')
plot(density(result$Forecasts),main='Kernel Density Plot of Predictions')
par(op)
}
if(par2!='none') {
plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted')
}
dev.off()
if (par2 == 'none') {
detcoef <- cor(result$Forecasts,result$Actuals)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goodness of Fit',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Correlation',1,TRUE)
a<-table.element(a,round(detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'R-squared',1,TRUE)
a<-table.element(a,round(detcoef*detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'RMSE',1,TRUE)
a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#',header=TRUE)
a<-table.element(a,'Actuals',header=TRUE)
a<-table.element(a,'Forecasts',header=TRUE)
a<-table.element(a,'Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(result$Actuals)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,result$Actuals[i])
a<-table.element(a,result$Forecasts[i])
a<-table.element(a,result$Residuals[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
}
if (par2 != 'none') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
for (i in 1:par3) {
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
}
a<-table.row.end(a)
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (j in 1:par3) {
a<-table.element(a,myt[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code