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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

Sat, 25 Dec 2010 21:51:47 +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/25/t12933137717ze49o67qszlcf9.htm/, Retrieved Mon, 29 Apr 2024 01:56:08 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=115466, Retrieved Mon, 29 Apr 2024 01:56:08 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

243

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 - recursive...] [2010-12-11 16:07:41] [033eb2749a430605d9b2be7c4aac4a0c]
-         [Recursive Partitioning (Regression Trees)] [] [2010-12-13 18:24:09] [d7b28a0391ab3b2ddc9f9fba95a43f33]
-             [Recursive Partitioning (Regression Trees)] [] [2010-12-25 21:51:47] [4dbe485270073769796ed1462cddce37] [Current]
- RMPD          [Multiple Regression] [WS10-Multiple Reg...] [2011-12-12 15:40:45] [d3c56829b3e69baec30b0d469b5d7237] 
- R             [Recursive Partitioning (Regression Trees)] [WS10.5] [2011-12-12 22:43:29] [74be16979710d4c4e7c6647856088456] 
-               [Recursive Partitioning (Regression Trees)] [] [2011-12-14 21:04:52] [7d86e24de0a0f8503ecffdef58e8c96c] 
-   PD          [Recursive Partitioning (Regression Trees)] [Workshop 10] [2011-12-15 15:04:31] [bab4e1d4a779bb46523d87231e2a2e96] 
-   PD          [Recursive Partitioning (Regression Trees)] [workshop 10] [2011-12-15 15:05:00] [b1d8f9e536fbb1cbb331f583f7f5f24b] 
-   PD          [Recursive Partitioning (Regression Trees)] [Workshop 10] [2011-12-15 15:04:31] [bab4e1d4a779bb46523d87231e2a2e96] 
-   PD            [Recursive Partitioning (Regression Trees)] [RP] [2011-12-21 13:45:39] [bab4e1d4a779bb46523d87231e2a2e96] 
-   P               [Recursive Partitioning (Regression Trees)] [RP] [2011-12-21 13:59:38] [bab4e1d4a779bb46523d87231e2a2e96] 
- RMPD          [Multiple Regression] [Workshop 10] [2011-12-15 15:10:33] [bab4e1d4a779bb46523d87231e2a2e96] 
- RMPD          [Multiple Regression] [workshop 10] [2011-12-15 15:10:44] [b1d8f9e536fbb1cbb331f583f7f5f24b] 
- RMPD          [Multiple Regression] [Workshop 10] [2011-12-15 15:10:33] [bab4e1d4a779bb46523d87231e2a2e96] 
- RMPD          [Multiple Regression] [Workshop 10] [2011-12-15 15:10:33] [bab4e1d4a779bb46523d87231e2a2e96] 
- RMPD          [Multiple Regression] [Workshop 10] [2011-12-15 15:10:33] [bab4e1d4a779bb46523d87231e2a2e96] 
- RMPD          [Multiple Regression] [Workshop 10] [2011-12-15 15:10:33] [bab4e1d4a779bb46523d87231e2a2e96] 
- RMPD          [Multiple Regression] [Workshop 10] [2011-12-15 15:10:33] [bab4e1d4a779bb46523d87231e2a2e96] 
- RMPD          [Multiple Regression] [Workshop 10] [2011-12-15 15:10:33] [bab4e1d4a779bb46523d87231e2a2e96] 
- RMPD          [Multiple Regression] [workshop 10] [2011-12-15 15:12:05] [bab4e1d4a779bb46523d87231e2a2e96] 
-   PD          [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2011-12-15 19:16:06] [9bc4436833541bde47df2e8d3b08804c] 
-   PD            [Recursive Partitioning (Regression Trees)] [no categorization] [2011-12-19 19:14:30] [9bc4436833541bde47df2e8d3b08804c] 
-   PD            [Recursive Partitioning (Regression Trees)] [with categorizati...] [2011-12-19 19:16:47] [9bc4436833541bde47df2e8d3b08804c] 
-   PD            [Recursive Partitioning (Regression Trees)] [with categorizati...] [2011-12-19 19:56:16] [9bc4436833541bde47df2e8d3b08804c] 
- R PD            [Recursive Partitioning (Regression Trees)] [paper_deel3: Recu...] [2011-12-23 02:14:38] [ce03f21eb3e54b507ec8f2385bd7917e] 
-   PD          [Recursive Partitioning (Regression Trees)] [Recursive partiti...] [2011-12-15 19:35:21] [9bc4436833541bde47df2e8d3b08804c] 
- RMPD          [Kendall tau Correlation Matrix] [] [2011-12-21 12:41:38] [a90f9476280cfbc76b67d22532268624] 
- RMPD            [Recursive Partitioning (Regression Trees)] [] [2011-12-21 18:21:23] [a90f9476280cfbc76b67d22532268624] 
-   P               [Recursive Partitioning (Regression Trees)] [] [2011-12-22 15:42:00] [a90f9476280cfbc76b67d22532268624] 

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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	8 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115466&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115466&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115466&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	8 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Goodness of Fit
Correlation	0.6299
R-squared	0.3968
RMSE	4.4307

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.6299 \tabularnewline
R-squared & 0.3968 \tabularnewline
RMSE & 4.4307 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115466&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.6299[/C][/ROW]
[ROW][C]R-squared[/C][C]0.3968[/C][/ROW]
[ROW][C]RMSE[/C][C]4.4307[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115466&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115466&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	0.6299
R-squared	0.3968
RMSE	4.4307

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	24	25.0714285714286	-1.07142857142857
2	25	20.4230769230769	4.57692307692308
3	17	21.4615384615385	-4.46153846153846
4	18	20.4230769230769	-2.42307692307692
5	18	20.4230769230769	-2.42307692307692
6	16	18.24	-2.24
7	20	18.24	1.76
8	16	20.4230769230769	-4.42307692307692
9	18	18.24	-0.239999999999998
10	17	18.24	-1.24000000000000
11	23	25.0714285714286	-2.07142857142857
12	30	20.4230769230769	9.57692307692308
13	23	20.4230769230769	2.57692307692308
14	18	20.4230769230769	-2.42307692307692
15	15	20.4230769230769	-5.42307692307692
16	12	21.4615384615385	-9.46153846153846
17	21	20.4230769230769	0.576923076923077
18	15	20.4230769230769	-5.42307692307692
19	20	18.24	1.76
20	31	25.0714285714286	5.92857142857143
21	27	25.0714285714286	1.92857142857143
22	34	25.0714285714286	8.92857142857143
23	21	20.4230769230769	0.576923076923077
24	31	25.0714285714286	5.92857142857143
25	19	20.4230769230769	-1.42307692307692
26	16	20.4230769230769	-4.42307692307692
27	20	18.24	1.76
28	21	20.4230769230769	0.576923076923077
29	22	20.4230769230769	1.57692307692308
30	17	18.24	-1.24000000000000
31	24	18.24	5.76
32	25	29.8125	-4.8125
33	26	29.8125	-3.8125
34	25	21.4615384615385	3.53846153846154
35	17	20.4230769230769	-3.42307692307692
36	32	29.8125	2.1875
37	33	29.8125	3.1875
38	13	18.24	-5.24
39	32	29.8125	2.1875
40	25	29.8125	-4.8125
41	29	29.8125	-0.8125
42	22	20.4230769230769	1.57692307692308
43	18	20.4230769230769	-2.42307692307692
44	17	18.24	-1.24000000000000
45	20	21.4615384615385	-1.46153846153846
46	15	20.4230769230769	-5.42307692307692
47	20	25.0714285714286	-5.07142857142857
48	33	29.8125	3.1875
49	29	20.4230769230769	8.57692307692308
50	23	18.24	4.76
51	26	18.24	7.76
52	18	20.4230769230769	-2.42307692307692
53	20	20.4230769230769	-0.423076923076923
54	11	18.24	-7.24
55	28	21.4615384615385	6.53846153846154
56	26	25.0714285714286	0.928571428571427
57	22	21.4615384615385	0.53846153846154
58	17	18.24	-1.24000000000000
59	12	18.24	-6.24
60	14	25.0714285714286	-11.0714285714286
61	17	20.4230769230769	-3.42307692307692
62	21	18.24	2.76
63	19	18.24	0.760000000000002
64	18	25.0714285714286	-7.07142857142857
65	10	18.24	-8.24
66	29	29.8125	-0.8125
67	31	20.4230769230769	10.5769230769231
68	19	21.4615384615385	-2.46153846153846
69	9	18.24	-9.24
70	20	20.4230769230769	-0.423076923076923
71	28	18.24	9.76
72	19	18.24	0.760000000000002
73	30	21.4615384615385	8.53846153846154
74	29	25.0714285714286	3.92857142857143
75	26	18.24	7.76
76	23	20.4230769230769	2.57692307692308
77	13	18.24	-5.24
78	21	20.4230769230769	0.576923076923077
79	19	20.4230769230769	-1.42307692307692
80	28	18.24	9.76
81	23	25.0714285714286	-2.07142857142857
82	18	20.4230769230769	-2.42307692307692
83	21	20.4230769230769	0.576923076923077
84	20	20.4230769230769	-0.423076923076923
85	23	18.24	4.76
86	21	18.24	2.76
87	21	21.4615384615385	-0.46153846153846
88	15	25.0714285714286	-10.0714285714286
89	28	29.8125	-1.8125
90	19	18.24	0.760000000000002
91	26	25.0714285714286	0.928571428571427
92	10	18.24	-8.24
93	16	18.24	-2.24
94	22	18.24	3.76
95	19	20.4230769230769	-1.42307692307692
96	31	21.4615384615385	9.53846153846154
97	31	25.0714285714286	5.92857142857143
98	29	25.0714285714286	3.92857142857143
99	19	18.24	0.760000000000002
100	22	20.4230769230769	1.57692307692308
101	23	20.4230769230769	2.57692307692308
102	15	18.24	-3.24
103	20	20.4230769230769	-0.423076923076923
104	18	20.4230769230769	-2.42307692307692
105	23	25.0714285714286	-2.07142857142857
106	25	18.24	6.76
107	21	18.24	2.76
108	24	20.4230769230769	3.57692307692308
109	25	25.0714285714286	-0.071428571428573
110	17	18.24	-1.24000000000000
111	13	18.24	-5.24
112	28	20.4230769230769	7.57692307692308
113	21	20.4230769230769	0.576923076923077
114	25	21.4615384615385	3.53846153846154
115	9	21.4615384615385	-12.4615384615385
116	16	18.24	-2.24
117	19	20.4230769230769	-1.42307692307692
118	17	18.24	-1.24000000000000
119	25	25.0714285714286	-0.071428571428573
120	20	18.24	1.76
121	29	29.8125	-0.8125
122	14	18.24	-4.24
123	22	25.0714285714286	-3.07142857142857
124	15	18.24	-3.24
125	19	18.24	0.760000000000002
126	20	20.4230769230769	-0.423076923076923
127	15	18.24	-3.24
128	20	20.4230769230769	-0.423076923076923
129	18	20.4230769230769	-2.42307692307692
130	33	29.8125	3.1875
131	22	20.4230769230769	1.57692307692308
132	16	20.4230769230769	-4.42307692307692
133	17	18.24	-1.24000000000000
134	16	18.24	-2.24
135	21	20.4230769230769	0.576923076923077
136	26	29.8125	-3.8125
137	18	18.24	-0.239999999999998
138	18	20.4230769230769	-2.42307692307692
139	17	20.4230769230769	-3.42307692307692
140	22	25.0714285714286	-3.07142857142857
141	30	25.0714285714286	4.92857142857143
142	30	29.8125	0.1875
143	24	25.0714285714286	-1.07142857142857
144	21	18.24	2.76
145	21	25.0714285714286	-4.07142857142857
146	29	29.8125	-0.8125
147	31	20.4230769230769	10.5769230769231
148	20	18.24	1.76
149	16	20.4230769230769	-4.42307692307692
150	22	20.4230769230769	1.57692307692308
151	20	21.4615384615385	-1.46153846153846
152	28	25.0714285714286	2.92857142857143
153	38	29.8125	8.1875
154	22	18.24	3.76
155	20	20.4230769230769	-0.423076923076923
156	17	18.24	-1.24000000000000
157	28	25.0714285714286	2.92857142857143
158	22	25.0714285714286	-3.07142857142857
159	31	25.0714285714286	5.92857142857143

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 24 & 25.0714285714286 & -1.07142857142857 \tabularnewline
2 & 25 & 20.4230769230769 & 4.57692307692308 \tabularnewline
3 & 17 & 21.4615384615385 & -4.46153846153846 \tabularnewline
4 & 18 & 20.4230769230769 & -2.42307692307692 \tabularnewline
5 & 18 & 20.4230769230769 & -2.42307692307692 \tabularnewline
6 & 16 & 18.24 & -2.24 \tabularnewline
7 & 20 & 18.24 & 1.76 \tabularnewline
8 & 16 & 20.4230769230769 & -4.42307692307692 \tabularnewline
9 & 18 & 18.24 & -0.239999999999998 \tabularnewline
10 & 17 & 18.24 & -1.24000000000000 \tabularnewline
11 & 23 & 25.0714285714286 & -2.07142857142857 \tabularnewline
12 & 30 & 20.4230769230769 & 9.57692307692308 \tabularnewline
13 & 23 & 20.4230769230769 & 2.57692307692308 \tabularnewline
14 & 18 & 20.4230769230769 & -2.42307692307692 \tabularnewline
15 & 15 & 20.4230769230769 & -5.42307692307692 \tabularnewline
16 & 12 & 21.4615384615385 & -9.46153846153846 \tabularnewline
17 & 21 & 20.4230769230769 & 0.576923076923077 \tabularnewline
18 & 15 & 20.4230769230769 & -5.42307692307692 \tabularnewline
19 & 20 & 18.24 & 1.76 \tabularnewline
20 & 31 & 25.0714285714286 & 5.92857142857143 \tabularnewline
21 & 27 & 25.0714285714286 & 1.92857142857143 \tabularnewline
22 & 34 & 25.0714285714286 & 8.92857142857143 \tabularnewline
23 & 21 & 20.4230769230769 & 0.576923076923077 \tabularnewline
24 & 31 & 25.0714285714286 & 5.92857142857143 \tabularnewline
25 & 19 & 20.4230769230769 & -1.42307692307692 \tabularnewline
26 & 16 & 20.4230769230769 & -4.42307692307692 \tabularnewline
27 & 20 & 18.24 & 1.76 \tabularnewline
28 & 21 & 20.4230769230769 & 0.576923076923077 \tabularnewline
29 & 22 & 20.4230769230769 & 1.57692307692308 \tabularnewline
30 & 17 & 18.24 & -1.24000000000000 \tabularnewline
31 & 24 & 18.24 & 5.76 \tabularnewline
32 & 25 & 29.8125 & -4.8125 \tabularnewline
33 & 26 & 29.8125 & -3.8125 \tabularnewline
34 & 25 & 21.4615384615385 & 3.53846153846154 \tabularnewline
35 & 17 & 20.4230769230769 & -3.42307692307692 \tabularnewline
36 & 32 & 29.8125 & 2.1875 \tabularnewline
37 & 33 & 29.8125 & 3.1875 \tabularnewline
38 & 13 & 18.24 & -5.24 \tabularnewline
39 & 32 & 29.8125 & 2.1875 \tabularnewline
40 & 25 & 29.8125 & -4.8125 \tabularnewline
41 & 29 & 29.8125 & -0.8125 \tabularnewline
42 & 22 & 20.4230769230769 & 1.57692307692308 \tabularnewline
43 & 18 & 20.4230769230769 & -2.42307692307692 \tabularnewline
44 & 17 & 18.24 & -1.24000000000000 \tabularnewline
45 & 20 & 21.4615384615385 & -1.46153846153846 \tabularnewline
46 & 15 & 20.4230769230769 & -5.42307692307692 \tabularnewline
47 & 20 & 25.0714285714286 & -5.07142857142857 \tabularnewline
48 & 33 & 29.8125 & 3.1875 \tabularnewline
49 & 29 & 20.4230769230769 & 8.57692307692308 \tabularnewline
50 & 23 & 18.24 & 4.76 \tabularnewline
51 & 26 & 18.24 & 7.76 \tabularnewline
52 & 18 & 20.4230769230769 & -2.42307692307692 \tabularnewline
53 & 20 & 20.4230769230769 & -0.423076923076923 \tabularnewline
54 & 11 & 18.24 & -7.24 \tabularnewline
55 & 28 & 21.4615384615385 & 6.53846153846154 \tabularnewline
56 & 26 & 25.0714285714286 & 0.928571428571427 \tabularnewline
57 & 22 & 21.4615384615385 & 0.53846153846154 \tabularnewline
58 & 17 & 18.24 & -1.24000000000000 \tabularnewline
59 & 12 & 18.24 & -6.24 \tabularnewline
60 & 14 & 25.0714285714286 & -11.0714285714286 \tabularnewline
61 & 17 & 20.4230769230769 & -3.42307692307692 \tabularnewline
62 & 21 & 18.24 & 2.76 \tabularnewline
63 & 19 & 18.24 & 0.760000000000002 \tabularnewline
64 & 18 & 25.0714285714286 & -7.07142857142857 \tabularnewline
65 & 10 & 18.24 & -8.24 \tabularnewline
66 & 29 & 29.8125 & -0.8125 \tabularnewline
67 & 31 & 20.4230769230769 & 10.5769230769231 \tabularnewline
68 & 19 & 21.4615384615385 & -2.46153846153846 \tabularnewline
69 & 9 & 18.24 & -9.24 \tabularnewline
70 & 20 & 20.4230769230769 & -0.423076923076923 \tabularnewline
71 & 28 & 18.24 & 9.76 \tabularnewline
72 & 19 & 18.24 & 0.760000000000002 \tabularnewline
73 & 30 & 21.4615384615385 & 8.53846153846154 \tabularnewline
74 & 29 & 25.0714285714286 & 3.92857142857143 \tabularnewline
75 & 26 & 18.24 & 7.76 \tabularnewline
76 & 23 & 20.4230769230769 & 2.57692307692308 \tabularnewline
77 & 13 & 18.24 & -5.24 \tabularnewline
78 & 21 & 20.4230769230769 & 0.576923076923077 \tabularnewline
79 & 19 & 20.4230769230769 & -1.42307692307692 \tabularnewline
80 & 28 & 18.24 & 9.76 \tabularnewline
81 & 23 & 25.0714285714286 & -2.07142857142857 \tabularnewline
82 & 18 & 20.4230769230769 & -2.42307692307692 \tabularnewline
83 & 21 & 20.4230769230769 & 0.576923076923077 \tabularnewline
84 & 20 & 20.4230769230769 & -0.423076923076923 \tabularnewline
85 & 23 & 18.24 & 4.76 \tabularnewline
86 & 21 & 18.24 & 2.76 \tabularnewline
87 & 21 & 21.4615384615385 & -0.46153846153846 \tabularnewline
88 & 15 & 25.0714285714286 & -10.0714285714286 \tabularnewline
89 & 28 & 29.8125 & -1.8125 \tabularnewline
90 & 19 & 18.24 & 0.760000000000002 \tabularnewline
91 & 26 & 25.0714285714286 & 0.928571428571427 \tabularnewline
92 & 10 & 18.24 & -8.24 \tabularnewline
93 & 16 & 18.24 & -2.24 \tabularnewline
94 & 22 & 18.24 & 3.76 \tabularnewline
95 & 19 & 20.4230769230769 & -1.42307692307692 \tabularnewline
96 & 31 & 21.4615384615385 & 9.53846153846154 \tabularnewline
97 & 31 & 25.0714285714286 & 5.92857142857143 \tabularnewline
98 & 29 & 25.0714285714286 & 3.92857142857143 \tabularnewline
99 & 19 & 18.24 & 0.760000000000002 \tabularnewline
100 & 22 & 20.4230769230769 & 1.57692307692308 \tabularnewline
101 & 23 & 20.4230769230769 & 2.57692307692308 \tabularnewline
102 & 15 & 18.24 & -3.24 \tabularnewline
103 & 20 & 20.4230769230769 & -0.423076923076923 \tabularnewline
104 & 18 & 20.4230769230769 & -2.42307692307692 \tabularnewline
105 & 23 & 25.0714285714286 & -2.07142857142857 \tabularnewline
106 & 25 & 18.24 & 6.76 \tabularnewline
107 & 21 & 18.24 & 2.76 \tabularnewline
108 & 24 & 20.4230769230769 & 3.57692307692308 \tabularnewline
109 & 25 & 25.0714285714286 & -0.071428571428573 \tabularnewline
110 & 17 & 18.24 & -1.24000000000000 \tabularnewline
111 & 13 & 18.24 & -5.24 \tabularnewline
112 & 28 & 20.4230769230769 & 7.57692307692308 \tabularnewline
113 & 21 & 20.4230769230769 & 0.576923076923077 \tabularnewline
114 & 25 & 21.4615384615385 & 3.53846153846154 \tabularnewline
115 & 9 & 21.4615384615385 & -12.4615384615385 \tabularnewline
116 & 16 & 18.24 & -2.24 \tabularnewline
117 & 19 & 20.4230769230769 & -1.42307692307692 \tabularnewline
118 & 17 & 18.24 & -1.24000000000000 \tabularnewline
119 & 25 & 25.0714285714286 & -0.071428571428573 \tabularnewline
120 & 20 & 18.24 & 1.76 \tabularnewline
121 & 29 & 29.8125 & -0.8125 \tabularnewline
122 & 14 & 18.24 & -4.24 \tabularnewline
123 & 22 & 25.0714285714286 & -3.07142857142857 \tabularnewline
124 & 15 & 18.24 & -3.24 \tabularnewline
125 & 19 & 18.24 & 0.760000000000002 \tabularnewline
126 & 20 & 20.4230769230769 & -0.423076923076923 \tabularnewline
127 & 15 & 18.24 & -3.24 \tabularnewline
128 & 20 & 20.4230769230769 & -0.423076923076923 \tabularnewline
129 & 18 & 20.4230769230769 & -2.42307692307692 \tabularnewline
130 & 33 & 29.8125 & 3.1875 \tabularnewline
131 & 22 & 20.4230769230769 & 1.57692307692308 \tabularnewline
132 & 16 & 20.4230769230769 & -4.42307692307692 \tabularnewline
133 & 17 & 18.24 & -1.24000000000000 \tabularnewline
134 & 16 & 18.24 & -2.24 \tabularnewline
135 & 21 & 20.4230769230769 & 0.576923076923077 \tabularnewline
136 & 26 & 29.8125 & -3.8125 \tabularnewline
137 & 18 & 18.24 & -0.239999999999998 \tabularnewline
138 & 18 & 20.4230769230769 & -2.42307692307692 \tabularnewline
139 & 17 & 20.4230769230769 & -3.42307692307692 \tabularnewline
140 & 22 & 25.0714285714286 & -3.07142857142857 \tabularnewline
141 & 30 & 25.0714285714286 & 4.92857142857143 \tabularnewline
142 & 30 & 29.8125 & 0.1875 \tabularnewline
143 & 24 & 25.0714285714286 & -1.07142857142857 \tabularnewline
144 & 21 & 18.24 & 2.76 \tabularnewline
145 & 21 & 25.0714285714286 & -4.07142857142857 \tabularnewline
146 & 29 & 29.8125 & -0.8125 \tabularnewline
147 & 31 & 20.4230769230769 & 10.5769230769231 \tabularnewline
148 & 20 & 18.24 & 1.76 \tabularnewline
149 & 16 & 20.4230769230769 & -4.42307692307692 \tabularnewline
150 & 22 & 20.4230769230769 & 1.57692307692308 \tabularnewline
151 & 20 & 21.4615384615385 & -1.46153846153846 \tabularnewline
152 & 28 & 25.0714285714286 & 2.92857142857143 \tabularnewline
153 & 38 & 29.8125 & 8.1875 \tabularnewline
154 & 22 & 18.24 & 3.76 \tabularnewline
155 & 20 & 20.4230769230769 & -0.423076923076923 \tabularnewline
156 & 17 & 18.24 & -1.24000000000000 \tabularnewline
157 & 28 & 25.0714285714286 & 2.92857142857143 \tabularnewline
158 & 22 & 25.0714285714286 & -3.07142857142857 \tabularnewline
159 & 31 & 25.0714285714286 & 5.92857142857143 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115466&T=2

[TABLE]
[ROW][C]Actuals, Predictions, and Residuals[/C][/ROW]
[ROW][C]#[/C][C]Actuals[/C][C]Forecasts[/C][C]Residuals[/C][/ROW]
[ROW][C]1[/C][C]24[/C][C]25.0714285714286[/C][C]-1.07142857142857[/C][/ROW]
[ROW][C]2[/C][C]25[/C][C]20.4230769230769[/C][C]4.57692307692308[/C][/ROW]
[ROW][C]3[/C][C]17[/C][C]21.4615384615385[/C][C]-4.46153846153846[/C][/ROW]
[ROW][C]4[/C][C]18[/C][C]20.4230769230769[/C][C]-2.42307692307692[/C][/ROW]
[ROW][C]5[/C][C]18[/C][C]20.4230769230769[/C][C]-2.42307692307692[/C][/ROW]
[ROW][C]6[/C][C]16[/C][C]18.24[/C][C]-2.24[/C][/ROW]
[ROW][C]7[/C][C]20[/C][C]18.24[/C][C]1.76[/C][/ROW]
[ROW][C]8[/C][C]16[/C][C]20.4230769230769[/C][C]-4.42307692307692[/C][/ROW]
[ROW][C]9[/C][C]18[/C][C]18.24[/C][C]-0.239999999999998[/C][/ROW]
[ROW][C]10[/C][C]17[/C][C]18.24[/C][C]-1.24000000000000[/C][/ROW]
[ROW][C]11[/C][C]23[/C][C]25.0714285714286[/C][C]-2.07142857142857[/C][/ROW]
[ROW][C]12[/C][C]30[/C][C]20.4230769230769[/C][C]9.57692307692308[/C][/ROW]
[ROW][C]13[/C][C]23[/C][C]20.4230769230769[/C][C]2.57692307692308[/C][/ROW]
[ROW][C]14[/C][C]18[/C][C]20.4230769230769[/C][C]-2.42307692307692[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]20.4230769230769[/C][C]-5.42307692307692[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]21.4615384615385[/C][C]-9.46153846153846[/C][/ROW]
[ROW][C]17[/C][C]21[/C][C]20.4230769230769[/C][C]0.576923076923077[/C][/ROW]
[ROW][C]18[/C][C]15[/C][C]20.4230769230769[/C][C]-5.42307692307692[/C][/ROW]
[ROW][C]19[/C][C]20[/C][C]18.24[/C][C]1.76[/C][/ROW]
[ROW][C]20[/C][C]31[/C][C]25.0714285714286[/C][C]5.92857142857143[/C][/ROW]
[ROW][C]21[/C][C]27[/C][C]25.0714285714286[/C][C]1.92857142857143[/C][/ROW]
[ROW][C]22[/C][C]34[/C][C]25.0714285714286[/C][C]8.92857142857143[/C][/ROW]
[ROW][C]23[/C][C]21[/C][C]20.4230769230769[/C][C]0.576923076923077[/C][/ROW]
[ROW][C]24[/C][C]31[/C][C]25.0714285714286[/C][C]5.92857142857143[/C][/ROW]
[ROW][C]25[/C][C]19[/C][C]20.4230769230769[/C][C]-1.42307692307692[/C][/ROW]
[ROW][C]26[/C][C]16[/C][C]20.4230769230769[/C][C]-4.42307692307692[/C][/ROW]
[ROW][C]27[/C][C]20[/C][C]18.24[/C][C]1.76[/C][/ROW]
[ROW][C]28[/C][C]21[/C][C]20.4230769230769[/C][C]0.576923076923077[/C][/ROW]
[ROW][C]29[/C][C]22[/C][C]20.4230769230769[/C][C]1.57692307692308[/C][/ROW]
[ROW][C]30[/C][C]17[/C][C]18.24[/C][C]-1.24000000000000[/C][/ROW]
[ROW][C]31[/C][C]24[/C][C]18.24[/C][C]5.76[/C][/ROW]
[ROW][C]32[/C][C]25[/C][C]29.8125[/C][C]-4.8125[/C][/ROW]
[ROW][C]33[/C][C]26[/C][C]29.8125[/C][C]-3.8125[/C][/ROW]
[ROW][C]34[/C][C]25[/C][C]21.4615384615385[/C][C]3.53846153846154[/C][/ROW]
[ROW][C]35[/C][C]17[/C][C]20.4230769230769[/C][C]-3.42307692307692[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]29.8125[/C][C]2.1875[/C][/ROW]
[ROW][C]37[/C][C]33[/C][C]29.8125[/C][C]3.1875[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]18.24[/C][C]-5.24[/C][/ROW]
[ROW][C]39[/C][C]32[/C][C]29.8125[/C][C]2.1875[/C][/ROW]
[ROW][C]40[/C][C]25[/C][C]29.8125[/C][C]-4.8125[/C][/ROW]
[ROW][C]41[/C][C]29[/C][C]29.8125[/C][C]-0.8125[/C][/ROW]
[ROW][C]42[/C][C]22[/C][C]20.4230769230769[/C][C]1.57692307692308[/C][/ROW]
[ROW][C]43[/C][C]18[/C][C]20.4230769230769[/C][C]-2.42307692307692[/C][/ROW]
[ROW][C]44[/C][C]17[/C][C]18.24[/C][C]-1.24000000000000[/C][/ROW]
[ROW][C]45[/C][C]20[/C][C]21.4615384615385[/C][C]-1.46153846153846[/C][/ROW]
[ROW][C]46[/C][C]15[/C][C]20.4230769230769[/C][C]-5.42307692307692[/C][/ROW]
[ROW][C]47[/C][C]20[/C][C]25.0714285714286[/C][C]-5.07142857142857[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]29.8125[/C][C]3.1875[/C][/ROW]
[ROW][C]49[/C][C]29[/C][C]20.4230769230769[/C][C]8.57692307692308[/C][/ROW]
[ROW][C]50[/C][C]23[/C][C]18.24[/C][C]4.76[/C][/ROW]
[ROW][C]51[/C][C]26[/C][C]18.24[/C][C]7.76[/C][/ROW]
[ROW][C]52[/C][C]18[/C][C]20.4230769230769[/C][C]-2.42307692307692[/C][/ROW]
[ROW][C]53[/C][C]20[/C][C]20.4230769230769[/C][C]-0.423076923076923[/C][/ROW]
[ROW][C]54[/C][C]11[/C][C]18.24[/C][C]-7.24[/C][/ROW]
[ROW][C]55[/C][C]28[/C][C]21.4615384615385[/C][C]6.53846153846154[/C][/ROW]
[ROW][C]56[/C][C]26[/C][C]25.0714285714286[/C][C]0.928571428571427[/C][/ROW]
[ROW][C]57[/C][C]22[/C][C]21.4615384615385[/C][C]0.53846153846154[/C][/ROW]
[ROW][C]58[/C][C]17[/C][C]18.24[/C][C]-1.24000000000000[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]18.24[/C][C]-6.24[/C][/ROW]
[ROW][C]60[/C][C]14[/C][C]25.0714285714286[/C][C]-11.0714285714286[/C][/ROW]
[ROW][C]61[/C][C]17[/C][C]20.4230769230769[/C][C]-3.42307692307692[/C][/ROW]
[ROW][C]62[/C][C]21[/C][C]18.24[/C][C]2.76[/C][/ROW]
[ROW][C]63[/C][C]19[/C][C]18.24[/C][C]0.760000000000002[/C][/ROW]
[ROW][C]64[/C][C]18[/C][C]25.0714285714286[/C][C]-7.07142857142857[/C][/ROW]
[ROW][C]65[/C][C]10[/C][C]18.24[/C][C]-8.24[/C][/ROW]
[ROW][C]66[/C][C]29[/C][C]29.8125[/C][C]-0.8125[/C][/ROW]
[ROW][C]67[/C][C]31[/C][C]20.4230769230769[/C][C]10.5769230769231[/C][/ROW]
[ROW][C]68[/C][C]19[/C][C]21.4615384615385[/C][C]-2.46153846153846[/C][/ROW]
[ROW][C]69[/C][C]9[/C][C]18.24[/C][C]-9.24[/C][/ROW]
[ROW][C]70[/C][C]20[/C][C]20.4230769230769[/C][C]-0.423076923076923[/C][/ROW]
[ROW][C]71[/C][C]28[/C][C]18.24[/C][C]9.76[/C][/ROW]
[ROW][C]72[/C][C]19[/C][C]18.24[/C][C]0.760000000000002[/C][/ROW]
[ROW][C]73[/C][C]30[/C][C]21.4615384615385[/C][C]8.53846153846154[/C][/ROW]
[ROW][C]74[/C][C]29[/C][C]25.0714285714286[/C][C]3.92857142857143[/C][/ROW]
[ROW][C]75[/C][C]26[/C][C]18.24[/C][C]7.76[/C][/ROW]
[ROW][C]76[/C][C]23[/C][C]20.4230769230769[/C][C]2.57692307692308[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]18.24[/C][C]-5.24[/C][/ROW]
[ROW][C]78[/C][C]21[/C][C]20.4230769230769[/C][C]0.576923076923077[/C][/ROW]
[ROW][C]79[/C][C]19[/C][C]20.4230769230769[/C][C]-1.42307692307692[/C][/ROW]
[ROW][C]80[/C][C]28[/C][C]18.24[/C][C]9.76[/C][/ROW]
[ROW][C]81[/C][C]23[/C][C]25.0714285714286[/C][C]-2.07142857142857[/C][/ROW]
[ROW][C]82[/C][C]18[/C][C]20.4230769230769[/C][C]-2.42307692307692[/C][/ROW]
[ROW][C]83[/C][C]21[/C][C]20.4230769230769[/C][C]0.576923076923077[/C][/ROW]
[ROW][C]84[/C][C]20[/C][C]20.4230769230769[/C][C]-0.423076923076923[/C][/ROW]
[ROW][C]85[/C][C]23[/C][C]18.24[/C][C]4.76[/C][/ROW]
[ROW][C]86[/C][C]21[/C][C]18.24[/C][C]2.76[/C][/ROW]
[ROW][C]87[/C][C]21[/C][C]21.4615384615385[/C][C]-0.46153846153846[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]25.0714285714286[/C][C]-10.0714285714286[/C][/ROW]
[ROW][C]89[/C][C]28[/C][C]29.8125[/C][C]-1.8125[/C][/ROW]
[ROW][C]90[/C][C]19[/C][C]18.24[/C][C]0.760000000000002[/C][/ROW]
[ROW][C]91[/C][C]26[/C][C]25.0714285714286[/C][C]0.928571428571427[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]18.24[/C][C]-8.24[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]18.24[/C][C]-2.24[/C][/ROW]
[ROW][C]94[/C][C]22[/C][C]18.24[/C][C]3.76[/C][/ROW]
[ROW][C]95[/C][C]19[/C][C]20.4230769230769[/C][C]-1.42307692307692[/C][/ROW]
[ROW][C]96[/C][C]31[/C][C]21.4615384615385[/C][C]9.53846153846154[/C][/ROW]
[ROW][C]97[/C][C]31[/C][C]25.0714285714286[/C][C]5.92857142857143[/C][/ROW]
[ROW][C]98[/C][C]29[/C][C]25.0714285714286[/C][C]3.92857142857143[/C][/ROW]
[ROW][C]99[/C][C]19[/C][C]18.24[/C][C]0.760000000000002[/C][/ROW]
[ROW][C]100[/C][C]22[/C][C]20.4230769230769[/C][C]1.57692307692308[/C][/ROW]
[ROW][C]101[/C][C]23[/C][C]20.4230769230769[/C][C]2.57692307692308[/C][/ROW]
[ROW][C]102[/C][C]15[/C][C]18.24[/C][C]-3.24[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]20.4230769230769[/C][C]-0.423076923076923[/C][/ROW]
[ROW][C]104[/C][C]18[/C][C]20.4230769230769[/C][C]-2.42307692307692[/C][/ROW]
[ROW][C]105[/C][C]23[/C][C]25.0714285714286[/C][C]-2.07142857142857[/C][/ROW]
[ROW][C]106[/C][C]25[/C][C]18.24[/C][C]6.76[/C][/ROW]
[ROW][C]107[/C][C]21[/C][C]18.24[/C][C]2.76[/C][/ROW]
[ROW][C]108[/C][C]24[/C][C]20.4230769230769[/C][C]3.57692307692308[/C][/ROW]
[ROW][C]109[/C][C]25[/C][C]25.0714285714286[/C][C]-0.071428571428573[/C][/ROW]
[ROW][C]110[/C][C]17[/C][C]18.24[/C][C]-1.24000000000000[/C][/ROW]
[ROW][C]111[/C][C]13[/C][C]18.24[/C][C]-5.24[/C][/ROW]
[ROW][C]112[/C][C]28[/C][C]20.4230769230769[/C][C]7.57692307692308[/C][/ROW]
[ROW][C]113[/C][C]21[/C][C]20.4230769230769[/C][C]0.576923076923077[/C][/ROW]
[ROW][C]114[/C][C]25[/C][C]21.4615384615385[/C][C]3.53846153846154[/C][/ROW]
[ROW][C]115[/C][C]9[/C][C]21.4615384615385[/C][C]-12.4615384615385[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]18.24[/C][C]-2.24[/C][/ROW]
[ROW][C]117[/C][C]19[/C][C]20.4230769230769[/C][C]-1.42307692307692[/C][/ROW]
[ROW][C]118[/C][C]17[/C][C]18.24[/C][C]-1.24000000000000[/C][/ROW]
[ROW][C]119[/C][C]25[/C][C]25.0714285714286[/C][C]-0.071428571428573[/C][/ROW]
[ROW][C]120[/C][C]20[/C][C]18.24[/C][C]1.76[/C][/ROW]
[ROW][C]121[/C][C]29[/C][C]29.8125[/C][C]-0.8125[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]18.24[/C][C]-4.24[/C][/ROW]
[ROW][C]123[/C][C]22[/C][C]25.0714285714286[/C][C]-3.07142857142857[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]18.24[/C][C]-3.24[/C][/ROW]
[ROW][C]125[/C][C]19[/C][C]18.24[/C][C]0.760000000000002[/C][/ROW]
[ROW][C]126[/C][C]20[/C][C]20.4230769230769[/C][C]-0.423076923076923[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]18.24[/C][C]-3.24[/C][/ROW]
[ROW][C]128[/C][C]20[/C][C]20.4230769230769[/C][C]-0.423076923076923[/C][/ROW]
[ROW][C]129[/C][C]18[/C][C]20.4230769230769[/C][C]-2.42307692307692[/C][/ROW]
[ROW][C]130[/C][C]33[/C][C]29.8125[/C][C]3.1875[/C][/ROW]
[ROW][C]131[/C][C]22[/C][C]20.4230769230769[/C][C]1.57692307692308[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]20.4230769230769[/C][C]-4.42307692307692[/C][/ROW]
[ROW][C]133[/C][C]17[/C][C]18.24[/C][C]-1.24000000000000[/C][/ROW]
[ROW][C]134[/C][C]16[/C][C]18.24[/C][C]-2.24[/C][/ROW]
[ROW][C]135[/C][C]21[/C][C]20.4230769230769[/C][C]0.576923076923077[/C][/ROW]
[ROW][C]136[/C][C]26[/C][C]29.8125[/C][C]-3.8125[/C][/ROW]
[ROW][C]137[/C][C]18[/C][C]18.24[/C][C]-0.239999999999998[/C][/ROW]
[ROW][C]138[/C][C]18[/C][C]20.4230769230769[/C][C]-2.42307692307692[/C][/ROW]
[ROW][C]139[/C][C]17[/C][C]20.4230769230769[/C][C]-3.42307692307692[/C][/ROW]
[ROW][C]140[/C][C]22[/C][C]25.0714285714286[/C][C]-3.07142857142857[/C][/ROW]
[ROW][C]141[/C][C]30[/C][C]25.0714285714286[/C][C]4.92857142857143[/C][/ROW]
[ROW][C]142[/C][C]30[/C][C]29.8125[/C][C]0.1875[/C][/ROW]
[ROW][C]143[/C][C]24[/C][C]25.0714285714286[/C][C]-1.07142857142857[/C][/ROW]
[ROW][C]144[/C][C]21[/C][C]18.24[/C][C]2.76[/C][/ROW]
[ROW][C]145[/C][C]21[/C][C]25.0714285714286[/C][C]-4.07142857142857[/C][/ROW]
[ROW][C]146[/C][C]29[/C][C]29.8125[/C][C]-0.8125[/C][/ROW]
[ROW][C]147[/C][C]31[/C][C]20.4230769230769[/C][C]10.5769230769231[/C][/ROW]
[ROW][C]148[/C][C]20[/C][C]18.24[/C][C]1.76[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]20.4230769230769[/C][C]-4.42307692307692[/C][/ROW]
[ROW][C]150[/C][C]22[/C][C]20.4230769230769[/C][C]1.57692307692308[/C][/ROW]
[ROW][C]151[/C][C]20[/C][C]21.4615384615385[/C][C]-1.46153846153846[/C][/ROW]
[ROW][C]152[/C][C]28[/C][C]25.0714285714286[/C][C]2.92857142857143[/C][/ROW]
[ROW][C]153[/C][C]38[/C][C]29.8125[/C][C]8.1875[/C][/ROW]
[ROW][C]154[/C][C]22[/C][C]18.24[/C][C]3.76[/C][/ROW]
[ROW][C]155[/C][C]20[/C][C]20.4230769230769[/C][C]-0.423076923076923[/C][/ROW]
[ROW][C]156[/C][C]17[/C][C]18.24[/C][C]-1.24000000000000[/C][/ROW]
[ROW][C]157[/C][C]28[/C][C]25.0714285714286[/C][C]2.92857142857143[/C][/ROW]
[ROW][C]158[/C][C]22[/C][C]25.0714285714286[/C][C]-3.07142857142857[/C][/ROW]
[ROW][C]159[/C][C]31[/C][C]25.0714285714286[/C][C]5.92857142857143[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115466&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115466&T=2

As an alternative you can also use a QR Code:

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

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	24	25.0714285714286	-1.07142857142857
2	25	20.4230769230769	4.57692307692308
3	17	21.4615384615385	-4.46153846153846
4	18	20.4230769230769	-2.42307692307692
5	18	20.4230769230769	-2.42307692307692
6	16	18.24	-2.24
7	20	18.24	1.76
8	16	20.4230769230769	-4.42307692307692
9	18	18.24	-0.239999999999998
10	17	18.24	-1.24000000000000
11	23	25.0714285714286	-2.07142857142857
12	30	20.4230769230769	9.57692307692308
13	23	20.4230769230769	2.57692307692308
14	18	20.4230769230769	-2.42307692307692
15	15	20.4230769230769	-5.42307692307692
16	12	21.4615384615385	-9.46153846153846
17	21	20.4230769230769	0.576923076923077
18	15	20.4230769230769	-5.42307692307692
19	20	18.24	1.76
20	31	25.0714285714286	5.92857142857143
21	27	25.0714285714286	1.92857142857143
22	34	25.0714285714286	8.92857142857143
23	21	20.4230769230769	0.576923076923077
24	31	25.0714285714286	5.92857142857143
25	19	20.4230769230769	-1.42307692307692
26	16	20.4230769230769	-4.42307692307692
27	20	18.24	1.76
28	21	20.4230769230769	0.576923076923077
29	22	20.4230769230769	1.57692307692308
30	17	18.24	-1.24000000000000
31	24	18.24	5.76
32	25	29.8125	-4.8125
33	26	29.8125	-3.8125
34	25	21.4615384615385	3.53846153846154
35	17	20.4230769230769	-3.42307692307692
36	32	29.8125	2.1875
37	33	29.8125	3.1875
38	13	18.24	-5.24
39	32	29.8125	2.1875
40	25	29.8125	-4.8125
41	29	29.8125	-0.8125
42	22	20.4230769230769	1.57692307692308
43	18	20.4230769230769	-2.42307692307692
44	17	18.24	-1.24000000000000
45	20	21.4615384615385	-1.46153846153846
46	15	20.4230769230769	-5.42307692307692
47	20	25.0714285714286	-5.07142857142857
48	33	29.8125	3.1875
49	29	20.4230769230769	8.57692307692308
50	23	18.24	4.76
51	26	18.24	7.76
52	18	20.4230769230769	-2.42307692307692
53	20	20.4230769230769	-0.423076923076923
54	11	18.24	-7.24
55	28	21.4615384615385	6.53846153846154
56	26	25.0714285714286	0.928571428571427
57	22	21.4615384615385	0.53846153846154
58	17	18.24	-1.24000000000000
59	12	18.24	-6.24
60	14	25.0714285714286	-11.0714285714286
61	17	20.4230769230769	-3.42307692307692
62	21	18.24	2.76
63	19	18.24	0.760000000000002
64	18	25.0714285714286	-7.07142857142857
65	10	18.24	-8.24
66	29	29.8125	-0.8125
67	31	20.4230769230769	10.5769230769231
68	19	21.4615384615385	-2.46153846153846
69	9	18.24	-9.24
70	20	20.4230769230769	-0.423076923076923
71	28	18.24	9.76
72	19	18.24	0.760000000000002
73	30	21.4615384615385	8.53846153846154
74	29	25.0714285714286	3.92857142857143
75	26	18.24	7.76
76	23	20.4230769230769	2.57692307692308
77	13	18.24	-5.24
78	21	20.4230769230769	0.576923076923077
79	19	20.4230769230769	-1.42307692307692
80	28	18.24	9.76
81	23	25.0714285714286	-2.07142857142857
82	18	20.4230769230769	-2.42307692307692
83	21	20.4230769230769	0.576923076923077
84	20	20.4230769230769	-0.423076923076923
85	23	18.24	4.76
86	21	18.24	2.76
87	21	21.4615384615385	-0.46153846153846
88	15	25.0714285714286	-10.0714285714286
89	28	29.8125	-1.8125
90	19	18.24	0.760000000000002
91	26	25.0714285714286	0.928571428571427
92	10	18.24	-8.24
93	16	18.24	-2.24
94	22	18.24	3.76
95	19	20.4230769230769	-1.42307692307692
96	31	21.4615384615385	9.53846153846154
97	31	25.0714285714286	5.92857142857143
98	29	25.0714285714286	3.92857142857143
99	19	18.24	0.760000000000002
100	22	20.4230769230769	1.57692307692308
101	23	20.4230769230769	2.57692307692308
102	15	18.24	-3.24
103	20	20.4230769230769	-0.423076923076923
104	18	20.4230769230769	-2.42307692307692
105	23	25.0714285714286	-2.07142857142857
106	25	18.24	6.76
107	21	18.24	2.76
108	24	20.4230769230769	3.57692307692308
109	25	25.0714285714286	-0.071428571428573
110	17	18.24	-1.24000000000000
111	13	18.24	-5.24
112	28	20.4230769230769	7.57692307692308
113	21	20.4230769230769	0.576923076923077
114	25	21.4615384615385	3.53846153846154
115	9	21.4615384615385	-12.4615384615385
116	16	18.24	-2.24
117	19	20.4230769230769	-1.42307692307692
118	17	18.24	-1.24000000000000
119	25	25.0714285714286	-0.071428571428573
120	20	18.24	1.76
121	29	29.8125	-0.8125
122	14	18.24	-4.24
123	22	25.0714285714286	-3.07142857142857
124	15	18.24	-3.24
125	19	18.24	0.760000000000002
126	20	20.4230769230769	-0.423076923076923
127	15	18.24	-3.24
128	20	20.4230769230769	-0.423076923076923
129	18	20.4230769230769	-2.42307692307692
130	33	29.8125	3.1875
131	22	20.4230769230769	1.57692307692308
132	16	20.4230769230769	-4.42307692307692
133	17	18.24	-1.24000000000000
134	16	18.24	-2.24
135	21	20.4230769230769	0.576923076923077
136	26	29.8125	-3.8125
137	18	18.24	-0.239999999999998
138	18	20.4230769230769	-2.42307692307692
139	17	20.4230769230769	-3.42307692307692
140	22	25.0714285714286	-3.07142857142857
141	30	25.0714285714286	4.92857142857143
142	30	29.8125	0.1875
143	24	25.0714285714286	-1.07142857142857
144	21	18.24	2.76
145	21	25.0714285714286	-4.07142857142857
146	29	29.8125	-0.8125
147	31	20.4230769230769	10.5769230769231
148	20	18.24	1.76
149	16	20.4230769230769	-4.42307692307692
150	22	20.4230769230769	1.57692307692308
151	20	21.4615384615385	-1.46153846153846
152	28	25.0714285714286	2.92857142857143
153	38	29.8125	8.1875
154	22	18.24	3.76
155	20	20.4230769230769	-0.423076923076923
156	17	18.24	-1.24000000000000
157	28	25.0714285714286	2.92857142857143
158	22	25.0714285714286	-3.07142857142857
159	31	25.0714285714286	5.92857142857143

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 ; par4 = no ;

Parameters (R input):

par1 = 2 ; par2 = none ; par3 = ; 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