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R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Mon, 12 Dec 2011 14:40:34 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/12/t1323718860ck1g9hpf5brqvzy.htm/, Retrieved Fri, 03 May 2024 07:44:43 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154168, Retrieved Fri, 03 May 2024 07:44:43 +0000

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-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
- R PD    [Recursive Partitioning (Regression Trees)] [WS10 - RP- no cat.] [2011-12-12 19:40:34] [240aada53705cc48eae3e230739818e0] [Current]

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Dataseries X:

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Histogram

Boxplots

1	1	26	21	21	23	17	23	4
1	1	20	16	15	24	17	20	4
1	1	19	19	18	22	18	20	6
1	2	19	18	11	20	21	21	8
1	1	20	16	8	24	20	24	8
1	1	25	23	19	27	28	22	4
1	2	25	17	4	28	19	23	4
1	1	22	12	20	27	22	20	8
1	1	26	19	16	24	16	25	5
1	1	22	16	14	23	18	23	4
1	2	17	19	10	24	25	27	4
1	2	22	20	13	27	17	27	4
1	1	19	13	14	27	14	22	4
1	1	24	20	8	28	11	24	4
1	1	26	27	23	27	27	25	4
1	2	21	17	11	23	20	22	8
1	1	13	8	9	24	22	28	4
1	2	26	25	24	28	22	28	4
1	2	20	26	5	27	21	27	4
1	1	22	13	15	25	23	25	8
1	2	14	19	5	19	17	16	4
1	1	21	15	19	24	24	28	7
1	1	7	5	6	20	14	21	4
1	2	23	16	13	28	17	24	4
1	1	17	14	11	26	23	27	5
1	1	25	24	17	23	24	14	4
1	1	25	24	17	23	24	14	4
1	1	19	9	5	20	8	27	4
1	2	20	19	9	11	22	20	4
1	1	23	19	15	24	23	21	4
1	2	22	25	17	25	25	22	4
1	1	22	19	17	23	21	21	4
1	1	21	18	20	18	24	12	15
1	2	15	15	12	20	15	20	10
1	2	20	12	7	20	22	24	4
1	2	22	21	16	24	21	19	8
1	1	18	12	7	23	25	28	4
1	2	20	15	14	25	16	23	4
1	2	28	28	24	28	28	27	4
1	1	22	25	15	26	23	22	4
1	1	18	19	15	26	21	27	7
1	1	23	20	10	23	21	26	4
1	1	20	24	14	22	26	22	6
1	2	25	26	18	24	22	21	5
1	2	26	25	12	21	21	19	4
1	1	15	12	9	20	18	24	16
1	2	17	12	9	22	12	19	5
1	2	23	15	8	20	25	26	12
1	1	21	17	18	25	17	22	6
1	2	13	14	10	20	24	28	9
1	1	18	16	17	22	15	21	9
1	1	19	11	14	23	13	23	4
1	1	22	20	16	25	26	28	5
1	1	16	11	10	23	16	10	4
1	2	24	22	19	23	24	24	4
1	1	18	20	10	22	21	21	5
1	1	20	19	14	24	20	21	4
1	1	24	17	10	25	14	24	4
1	2	14	21	4	21	25	24	4
1	2	22	23	19	12	25	25	5
1	1	24	18	9	17	20	25	4
1	1	18	17	12	20	22	23	6
1	1	21	27	16	23	20	21	4
1	2	23	25	11	23	26	16	4
1	1	17	19	18	20	18	17	18
1	2	22	22	11	28	22	25	4
1	2	24	24	24	24	24	24	6
1	2	21	20	17	24	17	23	4
1	1	22	19	18	24	24	25	4
1	1	16	11	9	24	20	23	5
1	1	21	22	19	28	19	28	4
1	2	23	22	18	25	20	26	4
1	2	22	16	12	21	15	22	5
1	1	24	20	23	25	23	19	10
1	1	24	24	22	25	26	26	5
1	1	16	16	14	18	22	18	8
1	1	16	16	14	17	20	18	8
1	2	21	22	16	26	24	25	5
1	2	26	24	23	28	26	27	4
1	2	15	16	7	21	21	12	4
1	2	25	27	10	27	25	15	4
1	1	18	11	12	22	13	21	5
1	0	23	21	12	21	20	23	4
1	1	20	20	12	25	22	22	4
1	2	17	20	17	22	23	21	8
1	2	25	27	21	23	28	24	4
1	1	24	20	16	26	22	27	5
1	1	17	12	11	19	20	22	14
1	1	19	8	14	25	6	28	8
1	1	20	21	13	21	21	26	8
1	1	15	18	9	13	20	10	4
1	2	27	24	19	24	18	19	4
1	1	22	16	13	25	23	22	6
1	1	23	18	19	26	20	21	4
1	1	16	20	13	25	24	24	7
1	1	19	20	13	25	22	25	7
1	2	25	19	13	22	21	21	4
1	1	19	17	14	21	18	20	6
1	2	19	16	12	23	21	21	4
0	2	26	26	22	25	23	24	7
0	1	21	15	11	24	23	23	4
0	2	20	22	5	21	15	18	4
0	1	24	17	18	21	21	24	8
0	1	22	23	19	25	24	24	4
0	2	20	21	14	22	23	19	4
0	1	18	19	15	20	21	20	10
0	2	18	14	12	20	21	18	8
0	1	24	17	19	23	20	20	6
0	1	24	12	15	28	11	27	4
0	1	22	24	17	23	22	23	4
0	1	23	18	8	28	27	26	4
0	1	22	20	10	24	25	23	5
0	1	20	16	12	18	18	17	4
0	1	18	20	12	20	20	21	6
0	1	25	22	20	28	24	25	4
0	2	18	12	12	21	10	23	5
0	1	16	16	12	21	27	27	7
0	1	20	17	14	25	21	24	8
0	2	19	22	6	19	21	20	5
0	1	15	12	10	18	18	27	8
0	1	19	14	18	21	15	21	10
0	1	19	23	18	22	24	24	8
0	1	16	15	7	24	22	21	5
0	1	17	17	18	15	14	15	12
0	1	28	28	9	28	28	25	4
0	2	23	20	17	26	18	25	5
0	1	25	23	22	23	26	22	4
0	1	20	13	11	26	17	24	6
0	2	17	18	15	20	19	21	4
0	2	23	23	17	22	22	22	4
0	1	16	19	15	20	18	23	7
0	2	23	23	22	23	24	22	7
0	2	11	12	9	22	15	20	10
0	2	18	16	13	24	18	23	4
0	2	24	23	20	23	26	25	5
0	1	23	13	14	22	11	23	8
0	1	21	22	14	26	26	22	11
0	2	16	18	12	23	21	25	7
0	2	24	23	20	27	23	26	4
0	1	23	20	20	23	23	22	8
0	1	18	10	8	21	15	24	6
0	1	20	17	17	26	22	24	7
0	1	9	18	9	23	26	25	5
0	2	24	15	18	21	16	20	4
0	1	25	23	22	27	20	26	8
0	1	20	17	10	19	18	21	4
0	2	21	17	13	23	22	26	8
0	2	25	22	15	25	16	21	6
0	2	22	20	18	23	19	22	4
0	2	21	20	18	22	20	16	9
0	1	21	19	12	22	19	26	5
0	1	22	18	12	25	23	28	6
0	1	27	22	20	25	24	18	4
0	2	24	20	12	28	25	25	4
0	2	24	22	16	28	21	23	4
0	2	21	18	16	20	21	21	5
0	1	18	16	18	25	23	20	6
0	1	16	16	16	19	27	25	16
0	1	22	16	13	25	23	22	6
0	1	20	16	17	22	18	21	6
0	2	18	17	13	18	16	16	4
0	1	20	18	17	20	16	18	4

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154168&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154168&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154168&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Goodness of Fit
Correlation	0.6791
R-squared	0.4612
RMSE	3.3665

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.6791 \tabularnewline
R-squared & 0.4612 \tabularnewline
RMSE & 3.3665 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154168&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.6791[/C][/ROW]
[ROW][C]R-squared[/C][C]0.4612[/C][/ROW]
[ROW][C]RMSE[/C][C]3.3665[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154168&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154168&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	0.6791
R-squared	0.4612
RMSE	3.3665

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	21	17.2352941176471	3.76470588235294
2	15	10.0540540540541	4.94594594594595
3	18	13.6578947368421	4.3421052631579
4	11	13.6578947368421	-2.6578947368421
5	8	13.6578947368421	-5.6578947368421
6	19	17.2352941176471	1.76470588235294
7	4	13.3235294117647	-9.32352941176471
8	20	18	2
9	16	13.3235294117647	2.67647058823529
10	14	13.3235294117647	0.676470588235293
11	10	10.0540540540541	-0.0540540540540544
12	13	13.3235294117647	-0.323529411764707
13	14	10.0540540540541	3.94594594594595
14	8	13.3235294117647	-5.32352941176471
15	23	17.2352941176471	5.76470588235294
16	11	13.3235294117647	-2.32352941176471
17	9	10.0540540540541	-1.05405405405405
18	24	21.5	2.5
19	5	10.0540540540541	-5.05405405405405
20	15	13.3235294117647	1.67647058823529
21	5	10.0540540540541	-5.05405405405405
22	19	13.3235294117647	5.67647058823529
23	6	10.0540540540541	-4.05405405405405
24	13	13.3235294117647	-0.323529411764707
25	11	10.0540540540541	0.945945945945946
26	17	17.2352941176471	-0.235294117647058
27	17	17.2352941176471	-0.235294117647058
28	5	10.0540540540541	-5.05405405405405
29	9	10.0540540540541	-1.05405405405405
30	15	18	-3
31	17	17.2352941176471	-0.235294117647058
32	17	18	-1
33	20	18	2
34	12	13.6578947368421	-1.6578947368421
35	7	10.0540540540541	-3.05405405405405
36	16	17.2352941176471	-1.23529411764706
37	7	10.0540540540541	-3.05405405405405
38	14	10.0540540540541	3.94594594594595
39	24	21.5	2.5
40	15	17.2352941176471	-2.23529411764706
41	15	13.6578947368421	1.3421052631579
42	10	13.3235294117647	-3.32352941176471
43	14	13.6578947368421	0.342105263157896
44	18	17.2352941176471	0.764705882352942
45	12	17.2352941176471	-5.23529411764706
46	9	13.6578947368421	-4.6578947368421
47	9	10.0540540540541	-1.05405405405405
48	8	13.3235294117647	-5.32352941176471
49	18	13.3235294117647	4.67647058823529
50	10	13.6578947368421	-3.6578947368421
51	17	13.6578947368421	3.3421052631579
52	14	10.0540540540541	3.94594594594595
53	16	13.3235294117647	2.67647058823529
54	10	10.0540540540541	-0.0540540540540544
55	19	17.2352941176471	1.76470588235294
56	10	10.0540540540541	-0.0540540540540544
57	14	10.0540540540541	3.94594594594595
58	10	13.3235294117647	-3.32352941176471
59	4	10.0540540540541	-6.05405405405405
60	19	17.2352941176471	1.76470588235294
61	9	13.3235294117647	-4.32352941176471
62	12	13.6578947368421	-1.6578947368421
63	16	17.2352941176471	-1.23529411764706
64	11	17.2352941176471	-6.23529411764706
65	18	13.6578947368421	4.3421052631579
66	11	17.2352941176471	-6.23529411764706
67	24	17.2352941176471	6.76470588235294
68	17	13.3235294117647	3.67647058823529
69	18	13.3235294117647	4.67647058823529
70	9	10.0540540540541	-1.05405405405405
71	19	21.5	-2.5
72	18	21.5	-3.5
73	12	13.3235294117647	-1.32352941176471
74	23	18	5
75	22	21.5	0.5
76	14	13.6578947368421	0.342105263157896
77	14	13.6578947368421	0.342105263157896
78	16	17.2352941176471	-1.23529411764706
79	23	21.5	1.5
80	7	10.0540540540541	-3.05405405405405
81	10	17.2352941176471	-7.23529411764706
82	12	10.0540540540541	1.94594594594595
83	12	17.2352941176471	-5.23529411764706
84	12	10.0540540540541	1.94594594594595
85	17	13.6578947368421	3.3421052631579
86	21	17.2352941176471	3.76470588235294
87	16	13.3235294117647	2.67647058823529
88	11	13.6578947368421	-2.6578947368421
89	14	13.6578947368421	0.342105263157896
90	13	13.6578947368421	-0.657894736842104
91	9	10.0540540540541	-1.05405405405405
92	19	17.2352941176471	1.76470588235294
93	13	13.3235294117647	-0.323529411764707
94	19	18	1
95	13	13.6578947368421	-0.657894736842104
96	13	13.6578947368421	-0.657894736842104
97	13	18	-5
98	14	13.6578947368421	0.342105263157896
99	12	10.0540540540541	1.94594594594595
100	22	17.2352941176471	4.76470588235294
101	11	13.3235294117647	-2.32352941176471
102	5	10.0540540540541	-5.05405405405405
103	18	13.3235294117647	4.67647058823529
104	19	17.2352941176471	1.76470588235294
105	14	10.0540540540541	3.94594594594595
106	15	13.6578947368421	1.3421052631579
107	12	13.6578947368421	-1.6578947368421
108	19	18	1
109	15	13.3235294117647	1.67647058823529
110	17	17.2352941176471	-0.235294117647058
111	8	13.3235294117647	-5.32352941176471
112	10	13.3235294117647	-3.32352941176471
113	12	10.0540540540541	1.94594594594595
114	12	13.6578947368421	-1.6578947368421
115	20	17.2352941176471	2.76470588235294
116	12	10.0540540540541	1.94594594594595
117	12	13.6578947368421	-1.6578947368421
118	14	13.6578947368421	0.342105263157896
119	6	10.0540540540541	-4.05405405405405
120	10	13.6578947368421	-3.6578947368421
121	18	13.6578947368421	4.3421052631579
122	18	13.6578947368421	4.3421052631579
123	7	10.0540540540541	-3.05405405405405
124	18	13.6578947368421	4.3421052631579
125	9	17.2352941176471	-8.23529411764706
126	17	13.3235294117647	3.67647058823529
127	22	17.2352941176471	4.76470588235294
128	11	13.6578947368421	-2.6578947368421
129	15	10.0540540540541	4.94594594594595
130	17	17.2352941176471	-0.235294117647058
131	15	13.6578947368421	1.3421052631579
132	22	17.2352941176471	4.76470588235294
133	9	13.6578947368421	-4.6578947368421
134	13	10.0540540540541	2.94594594594595
135	20	17.2352941176471	2.76470588235294
136	14	13.3235294117647	0.676470588235293
137	14	17.2352941176471	-3.23529411764706
138	12	13.6578947368421	-1.6578947368421
139	20	21.5	-1.5
140	20	13.3235294117647	6.67647058823529
141	8	13.6578947368421	-5.6578947368421
142	17	13.6578947368421	3.3421052631579
143	9	10.0540540540541	-1.05405405405405
144	18	18	0
145	22	21.5	0.5
146	10	10.0540540540541	-0.0540540540540544
147	13	13.3235294117647	-0.323529411764707
148	15	17.2352941176471	-2.23529411764706
149	18	13.3235294117647	4.67647058823529
150	18	18	0
151	12	13.3235294117647	-1.32352941176471
152	12	13.3235294117647	-1.32352941176471
153	20	17.2352941176471	2.76470588235294
154	12	13.3235294117647	-1.32352941176471
155	16	17.2352941176471	-1.23529411764706
156	16	18	-2
157	18	13.6578947368421	4.3421052631579
158	16	13.6578947368421	2.3421052631579
159	13	13.3235294117647	-0.323529411764707
160	17	13.6578947368421	3.3421052631579
161	13	10.0540540540541	2.94594594594595
162	17	10.0540540540541	6.94594594594595

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 21 & 17.2352941176471 & 3.76470588235294 \tabularnewline
2 & 15 & 10.0540540540541 & 4.94594594594595 \tabularnewline
3 & 18 & 13.6578947368421 & 4.3421052631579 \tabularnewline
4 & 11 & 13.6578947368421 & -2.6578947368421 \tabularnewline
5 & 8 & 13.6578947368421 & -5.6578947368421 \tabularnewline
6 & 19 & 17.2352941176471 & 1.76470588235294 \tabularnewline
7 & 4 & 13.3235294117647 & -9.32352941176471 \tabularnewline
8 & 20 & 18 & 2 \tabularnewline
9 & 16 & 13.3235294117647 & 2.67647058823529 \tabularnewline
10 & 14 & 13.3235294117647 & 0.676470588235293 \tabularnewline
11 & 10 & 10.0540540540541 & -0.0540540540540544 \tabularnewline
12 & 13 & 13.3235294117647 & -0.323529411764707 \tabularnewline
13 & 14 & 10.0540540540541 & 3.94594594594595 \tabularnewline
14 & 8 & 13.3235294117647 & -5.32352941176471 \tabularnewline
15 & 23 & 17.2352941176471 & 5.76470588235294 \tabularnewline
16 & 11 & 13.3235294117647 & -2.32352941176471 \tabularnewline
17 & 9 & 10.0540540540541 & -1.05405405405405 \tabularnewline
18 & 24 & 21.5 & 2.5 \tabularnewline
19 & 5 & 10.0540540540541 & -5.05405405405405 \tabularnewline
20 & 15 & 13.3235294117647 & 1.67647058823529 \tabularnewline
21 & 5 & 10.0540540540541 & -5.05405405405405 \tabularnewline
22 & 19 & 13.3235294117647 & 5.67647058823529 \tabularnewline
23 & 6 & 10.0540540540541 & -4.05405405405405 \tabularnewline
24 & 13 & 13.3235294117647 & -0.323529411764707 \tabularnewline
25 & 11 & 10.0540540540541 & 0.945945945945946 \tabularnewline
26 & 17 & 17.2352941176471 & -0.235294117647058 \tabularnewline
27 & 17 & 17.2352941176471 & -0.235294117647058 \tabularnewline
28 & 5 & 10.0540540540541 & -5.05405405405405 \tabularnewline
29 & 9 & 10.0540540540541 & -1.05405405405405 \tabularnewline
30 & 15 & 18 & -3 \tabularnewline
31 & 17 & 17.2352941176471 & -0.235294117647058 \tabularnewline
32 & 17 & 18 & -1 \tabularnewline
33 & 20 & 18 & 2 \tabularnewline
34 & 12 & 13.6578947368421 & -1.6578947368421 \tabularnewline
35 & 7 & 10.0540540540541 & -3.05405405405405 \tabularnewline
36 & 16 & 17.2352941176471 & -1.23529411764706 \tabularnewline
37 & 7 & 10.0540540540541 & -3.05405405405405 \tabularnewline
38 & 14 & 10.0540540540541 & 3.94594594594595 \tabularnewline
39 & 24 & 21.5 & 2.5 \tabularnewline
40 & 15 & 17.2352941176471 & -2.23529411764706 \tabularnewline
41 & 15 & 13.6578947368421 & 1.3421052631579 \tabularnewline
42 & 10 & 13.3235294117647 & -3.32352941176471 \tabularnewline
43 & 14 & 13.6578947368421 & 0.342105263157896 \tabularnewline
44 & 18 & 17.2352941176471 & 0.764705882352942 \tabularnewline
45 & 12 & 17.2352941176471 & -5.23529411764706 \tabularnewline
46 & 9 & 13.6578947368421 & -4.6578947368421 \tabularnewline
47 & 9 & 10.0540540540541 & -1.05405405405405 \tabularnewline
48 & 8 & 13.3235294117647 & -5.32352941176471 \tabularnewline
49 & 18 & 13.3235294117647 & 4.67647058823529 \tabularnewline
50 & 10 & 13.6578947368421 & -3.6578947368421 \tabularnewline
51 & 17 & 13.6578947368421 & 3.3421052631579 \tabularnewline
52 & 14 & 10.0540540540541 & 3.94594594594595 \tabularnewline
53 & 16 & 13.3235294117647 & 2.67647058823529 \tabularnewline
54 & 10 & 10.0540540540541 & -0.0540540540540544 \tabularnewline
55 & 19 & 17.2352941176471 & 1.76470588235294 \tabularnewline
56 & 10 & 10.0540540540541 & -0.0540540540540544 \tabularnewline
57 & 14 & 10.0540540540541 & 3.94594594594595 \tabularnewline
58 & 10 & 13.3235294117647 & -3.32352941176471 \tabularnewline
59 & 4 & 10.0540540540541 & -6.05405405405405 \tabularnewline
60 & 19 & 17.2352941176471 & 1.76470588235294 \tabularnewline
61 & 9 & 13.3235294117647 & -4.32352941176471 \tabularnewline
62 & 12 & 13.6578947368421 & -1.6578947368421 \tabularnewline
63 & 16 & 17.2352941176471 & -1.23529411764706 \tabularnewline
64 & 11 & 17.2352941176471 & -6.23529411764706 \tabularnewline
65 & 18 & 13.6578947368421 & 4.3421052631579 \tabularnewline
66 & 11 & 17.2352941176471 & -6.23529411764706 \tabularnewline
67 & 24 & 17.2352941176471 & 6.76470588235294 \tabularnewline
68 & 17 & 13.3235294117647 & 3.67647058823529 \tabularnewline
69 & 18 & 13.3235294117647 & 4.67647058823529 \tabularnewline
70 & 9 & 10.0540540540541 & -1.05405405405405 \tabularnewline
71 & 19 & 21.5 & -2.5 \tabularnewline
72 & 18 & 21.5 & -3.5 \tabularnewline
73 & 12 & 13.3235294117647 & -1.32352941176471 \tabularnewline
74 & 23 & 18 & 5 \tabularnewline
75 & 22 & 21.5 & 0.5 \tabularnewline
76 & 14 & 13.6578947368421 & 0.342105263157896 \tabularnewline
77 & 14 & 13.6578947368421 & 0.342105263157896 \tabularnewline
78 & 16 & 17.2352941176471 & -1.23529411764706 \tabularnewline
79 & 23 & 21.5 & 1.5 \tabularnewline
80 & 7 & 10.0540540540541 & -3.05405405405405 \tabularnewline
81 & 10 & 17.2352941176471 & -7.23529411764706 \tabularnewline
82 & 12 & 10.0540540540541 & 1.94594594594595 \tabularnewline
83 & 12 & 17.2352941176471 & -5.23529411764706 \tabularnewline
84 & 12 & 10.0540540540541 & 1.94594594594595 \tabularnewline
85 & 17 & 13.6578947368421 & 3.3421052631579 \tabularnewline
86 & 21 & 17.2352941176471 & 3.76470588235294 \tabularnewline
87 & 16 & 13.3235294117647 & 2.67647058823529 \tabularnewline
88 & 11 & 13.6578947368421 & -2.6578947368421 \tabularnewline
89 & 14 & 13.6578947368421 & 0.342105263157896 \tabularnewline
90 & 13 & 13.6578947368421 & -0.657894736842104 \tabularnewline
91 & 9 & 10.0540540540541 & -1.05405405405405 \tabularnewline
92 & 19 & 17.2352941176471 & 1.76470588235294 \tabularnewline
93 & 13 & 13.3235294117647 & -0.323529411764707 \tabularnewline
94 & 19 & 18 & 1 \tabularnewline
95 & 13 & 13.6578947368421 & -0.657894736842104 \tabularnewline
96 & 13 & 13.6578947368421 & -0.657894736842104 \tabularnewline
97 & 13 & 18 & -5 \tabularnewline
98 & 14 & 13.6578947368421 & 0.342105263157896 \tabularnewline
99 & 12 & 10.0540540540541 & 1.94594594594595 \tabularnewline
100 & 22 & 17.2352941176471 & 4.76470588235294 \tabularnewline
101 & 11 & 13.3235294117647 & -2.32352941176471 \tabularnewline
102 & 5 & 10.0540540540541 & -5.05405405405405 \tabularnewline
103 & 18 & 13.3235294117647 & 4.67647058823529 \tabularnewline
104 & 19 & 17.2352941176471 & 1.76470588235294 \tabularnewline
105 & 14 & 10.0540540540541 & 3.94594594594595 \tabularnewline
106 & 15 & 13.6578947368421 & 1.3421052631579 \tabularnewline
107 & 12 & 13.6578947368421 & -1.6578947368421 \tabularnewline
108 & 19 & 18 & 1 \tabularnewline
109 & 15 & 13.3235294117647 & 1.67647058823529 \tabularnewline
110 & 17 & 17.2352941176471 & -0.235294117647058 \tabularnewline
111 & 8 & 13.3235294117647 & -5.32352941176471 \tabularnewline
112 & 10 & 13.3235294117647 & -3.32352941176471 \tabularnewline
113 & 12 & 10.0540540540541 & 1.94594594594595 \tabularnewline
114 & 12 & 13.6578947368421 & -1.6578947368421 \tabularnewline
115 & 20 & 17.2352941176471 & 2.76470588235294 \tabularnewline
116 & 12 & 10.0540540540541 & 1.94594594594595 \tabularnewline
117 & 12 & 13.6578947368421 & -1.6578947368421 \tabularnewline
118 & 14 & 13.6578947368421 & 0.342105263157896 \tabularnewline
119 & 6 & 10.0540540540541 & -4.05405405405405 \tabularnewline
120 & 10 & 13.6578947368421 & -3.6578947368421 \tabularnewline
121 & 18 & 13.6578947368421 & 4.3421052631579 \tabularnewline
122 & 18 & 13.6578947368421 & 4.3421052631579 \tabularnewline
123 & 7 & 10.0540540540541 & -3.05405405405405 \tabularnewline
124 & 18 & 13.6578947368421 & 4.3421052631579 \tabularnewline
125 & 9 & 17.2352941176471 & -8.23529411764706 \tabularnewline
126 & 17 & 13.3235294117647 & 3.67647058823529 \tabularnewline
127 & 22 & 17.2352941176471 & 4.76470588235294 \tabularnewline
128 & 11 & 13.6578947368421 & -2.6578947368421 \tabularnewline
129 & 15 & 10.0540540540541 & 4.94594594594595 \tabularnewline
130 & 17 & 17.2352941176471 & -0.235294117647058 \tabularnewline
131 & 15 & 13.6578947368421 & 1.3421052631579 \tabularnewline
132 & 22 & 17.2352941176471 & 4.76470588235294 \tabularnewline
133 & 9 & 13.6578947368421 & -4.6578947368421 \tabularnewline
134 & 13 & 10.0540540540541 & 2.94594594594595 \tabularnewline
135 & 20 & 17.2352941176471 & 2.76470588235294 \tabularnewline
136 & 14 & 13.3235294117647 & 0.676470588235293 \tabularnewline
137 & 14 & 17.2352941176471 & -3.23529411764706 \tabularnewline
138 & 12 & 13.6578947368421 & -1.6578947368421 \tabularnewline
139 & 20 & 21.5 & -1.5 \tabularnewline
140 & 20 & 13.3235294117647 & 6.67647058823529 \tabularnewline
141 & 8 & 13.6578947368421 & -5.6578947368421 \tabularnewline
142 & 17 & 13.6578947368421 & 3.3421052631579 \tabularnewline
143 & 9 & 10.0540540540541 & -1.05405405405405 \tabularnewline
144 & 18 & 18 & 0 \tabularnewline
145 & 22 & 21.5 & 0.5 \tabularnewline
146 & 10 & 10.0540540540541 & -0.0540540540540544 \tabularnewline
147 & 13 & 13.3235294117647 & -0.323529411764707 \tabularnewline
148 & 15 & 17.2352941176471 & -2.23529411764706 \tabularnewline
149 & 18 & 13.3235294117647 & 4.67647058823529 \tabularnewline
150 & 18 & 18 & 0 \tabularnewline
151 & 12 & 13.3235294117647 & -1.32352941176471 \tabularnewline
152 & 12 & 13.3235294117647 & -1.32352941176471 \tabularnewline
153 & 20 & 17.2352941176471 & 2.76470588235294 \tabularnewline
154 & 12 & 13.3235294117647 & -1.32352941176471 \tabularnewline
155 & 16 & 17.2352941176471 & -1.23529411764706 \tabularnewline
156 & 16 & 18 & -2 \tabularnewline
157 & 18 & 13.6578947368421 & 4.3421052631579 \tabularnewline
158 & 16 & 13.6578947368421 & 2.3421052631579 \tabularnewline
159 & 13 & 13.3235294117647 & -0.323529411764707 \tabularnewline
160 & 17 & 13.6578947368421 & 3.3421052631579 \tabularnewline
161 & 13 & 10.0540540540541 & 2.94594594594595 \tabularnewline
162 & 17 & 10.0540540540541 & 6.94594594594595 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154168&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]21[/C][C]17.2352941176471[/C][C]3.76470588235294[/C][/ROW]
[ROW][C]2[/C][C]15[/C][C]10.0540540540541[/C][C]4.94594594594595[/C][/ROW]
[ROW][C]3[/C][C]18[/C][C]13.6578947368421[/C][C]4.3421052631579[/C][/ROW]
[ROW][C]4[/C][C]11[/C][C]13.6578947368421[/C][C]-2.6578947368421[/C][/ROW]
[ROW][C]5[/C][C]8[/C][C]13.6578947368421[/C][C]-5.6578947368421[/C][/ROW]
[ROW][C]6[/C][C]19[/C][C]17.2352941176471[/C][C]1.76470588235294[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]13.3235294117647[/C][C]-9.32352941176471[/C][/ROW]
[ROW][C]8[/C][C]20[/C][C]18[/C][C]2[/C][/ROW]
[ROW][C]9[/C][C]16[/C][C]13.3235294117647[/C][C]2.67647058823529[/C][/ROW]
[ROW][C]10[/C][C]14[/C][C]13.3235294117647[/C][C]0.676470588235293[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]10.0540540540541[/C][C]-0.0540540540540544[/C][/ROW]
[ROW][C]12[/C][C]13[/C][C]13.3235294117647[/C][C]-0.323529411764707[/C][/ROW]
[ROW][C]13[/C][C]14[/C][C]10.0540540540541[/C][C]3.94594594594595[/C][/ROW]
[ROW][C]14[/C][C]8[/C][C]13.3235294117647[/C][C]-5.32352941176471[/C][/ROW]
[ROW][C]15[/C][C]23[/C][C]17.2352941176471[/C][C]5.76470588235294[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]13.3235294117647[/C][C]-2.32352941176471[/C][/ROW]
[ROW][C]17[/C][C]9[/C][C]10.0540540540541[/C][C]-1.05405405405405[/C][/ROW]
[ROW][C]18[/C][C]24[/C][C]21.5[/C][C]2.5[/C][/ROW]
[ROW][C]19[/C][C]5[/C][C]10.0540540540541[/C][C]-5.05405405405405[/C][/ROW]
[ROW][C]20[/C][C]15[/C][C]13.3235294117647[/C][C]1.67647058823529[/C][/ROW]
[ROW][C]21[/C][C]5[/C][C]10.0540540540541[/C][C]-5.05405405405405[/C][/ROW]
[ROW][C]22[/C][C]19[/C][C]13.3235294117647[/C][C]5.67647058823529[/C][/ROW]
[ROW][C]23[/C][C]6[/C][C]10.0540540540541[/C][C]-4.05405405405405[/C][/ROW]
[ROW][C]24[/C][C]13[/C][C]13.3235294117647[/C][C]-0.323529411764707[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]10.0540540540541[/C][C]0.945945945945946[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]17.2352941176471[/C][C]-0.235294117647058[/C][/ROW]
[ROW][C]27[/C][C]17[/C][C]17.2352941176471[/C][C]-0.235294117647058[/C][/ROW]
[ROW][C]28[/C][C]5[/C][C]10.0540540540541[/C][C]-5.05405405405405[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]10.0540540540541[/C][C]-1.05405405405405[/C][/ROW]
[ROW][C]30[/C][C]15[/C][C]18[/C][C]-3[/C][/ROW]
[ROW][C]31[/C][C]17[/C][C]17.2352941176471[/C][C]-0.235294117647058[/C][/ROW]
[ROW][C]32[/C][C]17[/C][C]18[/C][C]-1[/C][/ROW]
[ROW][C]33[/C][C]20[/C][C]18[/C][C]2[/C][/ROW]
[ROW][C]34[/C][C]12[/C][C]13.6578947368421[/C][C]-1.6578947368421[/C][/ROW]
[ROW][C]35[/C][C]7[/C][C]10.0540540540541[/C][C]-3.05405405405405[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]17.2352941176471[/C][C]-1.23529411764706[/C][/ROW]
[ROW][C]37[/C][C]7[/C][C]10.0540540540541[/C][C]-3.05405405405405[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]10.0540540540541[/C][C]3.94594594594595[/C][/ROW]
[ROW][C]39[/C][C]24[/C][C]21.5[/C][C]2.5[/C][/ROW]
[ROW][C]40[/C][C]15[/C][C]17.2352941176471[/C][C]-2.23529411764706[/C][/ROW]
[ROW][C]41[/C][C]15[/C][C]13.6578947368421[/C][C]1.3421052631579[/C][/ROW]
[ROW][C]42[/C][C]10[/C][C]13.3235294117647[/C][C]-3.32352941176471[/C][/ROW]
[ROW][C]43[/C][C]14[/C][C]13.6578947368421[/C][C]0.342105263157896[/C][/ROW]
[ROW][C]44[/C][C]18[/C][C]17.2352941176471[/C][C]0.764705882352942[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]17.2352941176471[/C][C]-5.23529411764706[/C][/ROW]
[ROW][C]46[/C][C]9[/C][C]13.6578947368421[/C][C]-4.6578947368421[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]10.0540540540541[/C][C]-1.05405405405405[/C][/ROW]
[ROW][C]48[/C][C]8[/C][C]13.3235294117647[/C][C]-5.32352941176471[/C][/ROW]
[ROW][C]49[/C][C]18[/C][C]13.3235294117647[/C][C]4.67647058823529[/C][/ROW]
[ROW][C]50[/C][C]10[/C][C]13.6578947368421[/C][C]-3.6578947368421[/C][/ROW]
[ROW][C]51[/C][C]17[/C][C]13.6578947368421[/C][C]3.3421052631579[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]10.0540540540541[/C][C]3.94594594594595[/C][/ROW]
[ROW][C]53[/C][C]16[/C][C]13.3235294117647[/C][C]2.67647058823529[/C][/ROW]
[ROW][C]54[/C][C]10[/C][C]10.0540540540541[/C][C]-0.0540540540540544[/C][/ROW]
[ROW][C]55[/C][C]19[/C][C]17.2352941176471[/C][C]1.76470588235294[/C][/ROW]
[ROW][C]56[/C][C]10[/C][C]10.0540540540541[/C][C]-0.0540540540540544[/C][/ROW]
[ROW][C]57[/C][C]14[/C][C]10.0540540540541[/C][C]3.94594594594595[/C][/ROW]
[ROW][C]58[/C][C]10[/C][C]13.3235294117647[/C][C]-3.32352941176471[/C][/ROW]
[ROW][C]59[/C][C]4[/C][C]10.0540540540541[/C][C]-6.05405405405405[/C][/ROW]
[ROW][C]60[/C][C]19[/C][C]17.2352941176471[/C][C]1.76470588235294[/C][/ROW]
[ROW][C]61[/C][C]9[/C][C]13.3235294117647[/C][C]-4.32352941176471[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]13.6578947368421[/C][C]-1.6578947368421[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]17.2352941176471[/C][C]-1.23529411764706[/C][/ROW]
[ROW][C]64[/C][C]11[/C][C]17.2352941176471[/C][C]-6.23529411764706[/C][/ROW]
[ROW][C]65[/C][C]18[/C][C]13.6578947368421[/C][C]4.3421052631579[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]17.2352941176471[/C][C]-6.23529411764706[/C][/ROW]
[ROW][C]67[/C][C]24[/C][C]17.2352941176471[/C][C]6.76470588235294[/C][/ROW]
[ROW][C]68[/C][C]17[/C][C]13.3235294117647[/C][C]3.67647058823529[/C][/ROW]
[ROW][C]69[/C][C]18[/C][C]13.3235294117647[/C][C]4.67647058823529[/C][/ROW]
[ROW][C]70[/C][C]9[/C][C]10.0540540540541[/C][C]-1.05405405405405[/C][/ROW]
[ROW][C]71[/C][C]19[/C][C]21.5[/C][C]-2.5[/C][/ROW]
[ROW][C]72[/C][C]18[/C][C]21.5[/C][C]-3.5[/C][/ROW]
[ROW][C]73[/C][C]12[/C][C]13.3235294117647[/C][C]-1.32352941176471[/C][/ROW]
[ROW][C]74[/C][C]23[/C][C]18[/C][C]5[/C][/ROW]
[ROW][C]75[/C][C]22[/C][C]21.5[/C][C]0.5[/C][/ROW]
[ROW][C]76[/C][C]14[/C][C]13.6578947368421[/C][C]0.342105263157896[/C][/ROW]
[ROW][C]77[/C][C]14[/C][C]13.6578947368421[/C][C]0.342105263157896[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]17.2352941176471[/C][C]-1.23529411764706[/C][/ROW]
[ROW][C]79[/C][C]23[/C][C]21.5[/C][C]1.5[/C][/ROW]
[ROW][C]80[/C][C]7[/C][C]10.0540540540541[/C][C]-3.05405405405405[/C][/ROW]
[ROW][C]81[/C][C]10[/C][C]17.2352941176471[/C][C]-7.23529411764706[/C][/ROW]
[ROW][C]82[/C][C]12[/C][C]10.0540540540541[/C][C]1.94594594594595[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]17.2352941176471[/C][C]-5.23529411764706[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]10.0540540540541[/C][C]1.94594594594595[/C][/ROW]
[ROW][C]85[/C][C]17[/C][C]13.6578947368421[/C][C]3.3421052631579[/C][/ROW]
[ROW][C]86[/C][C]21[/C][C]17.2352941176471[/C][C]3.76470588235294[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]13.3235294117647[/C][C]2.67647058823529[/C][/ROW]
[ROW][C]88[/C][C]11[/C][C]13.6578947368421[/C][C]-2.6578947368421[/C][/ROW]
[ROW][C]89[/C][C]14[/C][C]13.6578947368421[/C][C]0.342105263157896[/C][/ROW]
[ROW][C]90[/C][C]13[/C][C]13.6578947368421[/C][C]-0.657894736842104[/C][/ROW]
[ROW][C]91[/C][C]9[/C][C]10.0540540540541[/C][C]-1.05405405405405[/C][/ROW]
[ROW][C]92[/C][C]19[/C][C]17.2352941176471[/C][C]1.76470588235294[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]13.3235294117647[/C][C]-0.323529411764707[/C][/ROW]
[ROW][C]94[/C][C]19[/C][C]18[/C][C]1[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]13.6578947368421[/C][C]-0.657894736842104[/C][/ROW]
[ROW][C]96[/C][C]13[/C][C]13.6578947368421[/C][C]-0.657894736842104[/C][/ROW]
[ROW][C]97[/C][C]13[/C][C]18[/C][C]-5[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]13.6578947368421[/C][C]0.342105263157896[/C][/ROW]
[ROW][C]99[/C][C]12[/C][C]10.0540540540541[/C][C]1.94594594594595[/C][/ROW]
[ROW][C]100[/C][C]22[/C][C]17.2352941176471[/C][C]4.76470588235294[/C][/ROW]
[ROW][C]101[/C][C]11[/C][C]13.3235294117647[/C][C]-2.32352941176471[/C][/ROW]
[ROW][C]102[/C][C]5[/C][C]10.0540540540541[/C][C]-5.05405405405405[/C][/ROW]
[ROW][C]103[/C][C]18[/C][C]13.3235294117647[/C][C]4.67647058823529[/C][/ROW]
[ROW][C]104[/C][C]19[/C][C]17.2352941176471[/C][C]1.76470588235294[/C][/ROW]
[ROW][C]105[/C][C]14[/C][C]10.0540540540541[/C][C]3.94594594594595[/C][/ROW]
[ROW][C]106[/C][C]15[/C][C]13.6578947368421[/C][C]1.3421052631579[/C][/ROW]
[ROW][C]107[/C][C]12[/C][C]13.6578947368421[/C][C]-1.6578947368421[/C][/ROW]
[ROW][C]108[/C][C]19[/C][C]18[/C][C]1[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]13.3235294117647[/C][C]1.67647058823529[/C][/ROW]
[ROW][C]110[/C][C]17[/C][C]17.2352941176471[/C][C]-0.235294117647058[/C][/ROW]
[ROW][C]111[/C][C]8[/C][C]13.3235294117647[/C][C]-5.32352941176471[/C][/ROW]
[ROW][C]112[/C][C]10[/C][C]13.3235294117647[/C][C]-3.32352941176471[/C][/ROW]
[ROW][C]113[/C][C]12[/C][C]10.0540540540541[/C][C]1.94594594594595[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]13.6578947368421[/C][C]-1.6578947368421[/C][/ROW]
[ROW][C]115[/C][C]20[/C][C]17.2352941176471[/C][C]2.76470588235294[/C][/ROW]
[ROW][C]116[/C][C]12[/C][C]10.0540540540541[/C][C]1.94594594594595[/C][/ROW]
[ROW][C]117[/C][C]12[/C][C]13.6578947368421[/C][C]-1.6578947368421[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.6578947368421[/C][C]0.342105263157896[/C][/ROW]
[ROW][C]119[/C][C]6[/C][C]10.0540540540541[/C][C]-4.05405405405405[/C][/ROW]
[ROW][C]120[/C][C]10[/C][C]13.6578947368421[/C][C]-3.6578947368421[/C][/ROW]
[ROW][C]121[/C][C]18[/C][C]13.6578947368421[/C][C]4.3421052631579[/C][/ROW]
[ROW][C]122[/C][C]18[/C][C]13.6578947368421[/C][C]4.3421052631579[/C][/ROW]
[ROW][C]123[/C][C]7[/C][C]10.0540540540541[/C][C]-3.05405405405405[/C][/ROW]
[ROW][C]124[/C][C]18[/C][C]13.6578947368421[/C][C]4.3421052631579[/C][/ROW]
[ROW][C]125[/C][C]9[/C][C]17.2352941176471[/C][C]-8.23529411764706[/C][/ROW]
[ROW][C]126[/C][C]17[/C][C]13.3235294117647[/C][C]3.67647058823529[/C][/ROW]
[ROW][C]127[/C][C]22[/C][C]17.2352941176471[/C][C]4.76470588235294[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]13.6578947368421[/C][C]-2.6578947368421[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]10.0540540540541[/C][C]4.94594594594595[/C][/ROW]
[ROW][C]130[/C][C]17[/C][C]17.2352941176471[/C][C]-0.235294117647058[/C][/ROW]
[ROW][C]131[/C][C]15[/C][C]13.6578947368421[/C][C]1.3421052631579[/C][/ROW]
[ROW][C]132[/C][C]22[/C][C]17.2352941176471[/C][C]4.76470588235294[/C][/ROW]
[ROW][C]133[/C][C]9[/C][C]13.6578947368421[/C][C]-4.6578947368421[/C][/ROW]
[ROW][C]134[/C][C]13[/C][C]10.0540540540541[/C][C]2.94594594594595[/C][/ROW]
[ROW][C]135[/C][C]20[/C][C]17.2352941176471[/C][C]2.76470588235294[/C][/ROW]
[ROW][C]136[/C][C]14[/C][C]13.3235294117647[/C][C]0.676470588235293[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]17.2352941176471[/C][C]-3.23529411764706[/C][/ROW]
[ROW][C]138[/C][C]12[/C][C]13.6578947368421[/C][C]-1.6578947368421[/C][/ROW]
[ROW][C]139[/C][C]20[/C][C]21.5[/C][C]-1.5[/C][/ROW]
[ROW][C]140[/C][C]20[/C][C]13.3235294117647[/C][C]6.67647058823529[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.6578947368421[/C][C]-5.6578947368421[/C][/ROW]
[ROW][C]142[/C][C]17[/C][C]13.6578947368421[/C][C]3.3421052631579[/C][/ROW]
[ROW][C]143[/C][C]9[/C][C]10.0540540540541[/C][C]-1.05405405405405[/C][/ROW]
[ROW][C]144[/C][C]18[/C][C]18[/C][C]0[/C][/ROW]
[ROW][C]145[/C][C]22[/C][C]21.5[/C][C]0.5[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]10.0540540540541[/C][C]-0.0540540540540544[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]13.3235294117647[/C][C]-0.323529411764707[/C][/ROW]
[ROW][C]148[/C][C]15[/C][C]17.2352941176471[/C][C]-2.23529411764706[/C][/ROW]
[ROW][C]149[/C][C]18[/C][C]13.3235294117647[/C][C]4.67647058823529[/C][/ROW]
[ROW][C]150[/C][C]18[/C][C]18[/C][C]0[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]13.3235294117647[/C][C]-1.32352941176471[/C][/ROW]
[ROW][C]152[/C][C]12[/C][C]13.3235294117647[/C][C]-1.32352941176471[/C][/ROW]
[ROW][C]153[/C][C]20[/C][C]17.2352941176471[/C][C]2.76470588235294[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]13.3235294117647[/C][C]-1.32352941176471[/C][/ROW]
[ROW][C]155[/C][C]16[/C][C]17.2352941176471[/C][C]-1.23529411764706[/C][/ROW]
[ROW][C]156[/C][C]16[/C][C]18[/C][C]-2[/C][/ROW]
[ROW][C]157[/C][C]18[/C][C]13.6578947368421[/C][C]4.3421052631579[/C][/ROW]
[ROW][C]158[/C][C]16[/C][C]13.6578947368421[/C][C]2.3421052631579[/C][/ROW]
[ROW][C]159[/C][C]13[/C][C]13.3235294117647[/C][C]-0.323529411764707[/C][/ROW]
[ROW][C]160[/C][C]17[/C][C]13.6578947368421[/C][C]3.3421052631579[/C][/ROW]
[ROW][C]161[/C][C]13[/C][C]10.0540540540541[/C][C]2.94594594594595[/C][/ROW]
[ROW][C]162[/C][C]17[/C][C]10.0540540540541[/C][C]6.94594594594595[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154168&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154168&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	21	17.2352941176471	3.76470588235294
2	15	10.0540540540541	4.94594594594595
3	18	13.6578947368421	4.3421052631579
4	11	13.6578947368421	-2.6578947368421
5	8	13.6578947368421	-5.6578947368421
6	19	17.2352941176471	1.76470588235294
7	4	13.3235294117647	-9.32352941176471
8	20	18	2
9	16	13.3235294117647	2.67647058823529
10	14	13.3235294117647	0.676470588235293
11	10	10.0540540540541	-0.0540540540540544
12	13	13.3235294117647	-0.323529411764707
13	14	10.0540540540541	3.94594594594595
14	8	13.3235294117647	-5.32352941176471
15	23	17.2352941176471	5.76470588235294
16	11	13.3235294117647	-2.32352941176471
17	9	10.0540540540541	-1.05405405405405
18	24	21.5	2.5
19	5	10.0540540540541	-5.05405405405405
20	15	13.3235294117647	1.67647058823529
21	5	10.0540540540541	-5.05405405405405
22	19	13.3235294117647	5.67647058823529
23	6	10.0540540540541	-4.05405405405405
24	13	13.3235294117647	-0.323529411764707
25	11	10.0540540540541	0.945945945945946
26	17	17.2352941176471	-0.235294117647058
27	17	17.2352941176471	-0.235294117647058
28	5	10.0540540540541	-5.05405405405405
29	9	10.0540540540541	-1.05405405405405
30	15	18	-3
31	17	17.2352941176471	-0.235294117647058
32	17	18	-1
33	20	18	2
34	12	13.6578947368421	-1.6578947368421
35	7	10.0540540540541	-3.05405405405405
36	16	17.2352941176471	-1.23529411764706
37	7	10.0540540540541	-3.05405405405405
38	14	10.0540540540541	3.94594594594595
39	24	21.5	2.5
40	15	17.2352941176471	-2.23529411764706
41	15	13.6578947368421	1.3421052631579
42	10	13.3235294117647	-3.32352941176471
43	14	13.6578947368421	0.342105263157896
44	18	17.2352941176471	0.764705882352942
45	12	17.2352941176471	-5.23529411764706
46	9	13.6578947368421	-4.6578947368421
47	9	10.0540540540541	-1.05405405405405
48	8	13.3235294117647	-5.32352941176471
49	18	13.3235294117647	4.67647058823529
50	10	13.6578947368421	-3.6578947368421
51	17	13.6578947368421	3.3421052631579
52	14	10.0540540540541	3.94594594594595
53	16	13.3235294117647	2.67647058823529
54	10	10.0540540540541	-0.0540540540540544
55	19	17.2352941176471	1.76470588235294
56	10	10.0540540540541	-0.0540540540540544
57	14	10.0540540540541	3.94594594594595
58	10	13.3235294117647	-3.32352941176471
59	4	10.0540540540541	-6.05405405405405
60	19	17.2352941176471	1.76470588235294
61	9	13.3235294117647	-4.32352941176471
62	12	13.6578947368421	-1.6578947368421
63	16	17.2352941176471	-1.23529411764706
64	11	17.2352941176471	-6.23529411764706
65	18	13.6578947368421	4.3421052631579
66	11	17.2352941176471	-6.23529411764706
67	24	17.2352941176471	6.76470588235294
68	17	13.3235294117647	3.67647058823529
69	18	13.3235294117647	4.67647058823529
70	9	10.0540540540541	-1.05405405405405
71	19	21.5	-2.5
72	18	21.5	-3.5
73	12	13.3235294117647	-1.32352941176471
74	23	18	5
75	22	21.5	0.5
76	14	13.6578947368421	0.342105263157896
77	14	13.6578947368421	0.342105263157896
78	16	17.2352941176471	-1.23529411764706
79	23	21.5	1.5
80	7	10.0540540540541	-3.05405405405405
81	10	17.2352941176471	-7.23529411764706
82	12	10.0540540540541	1.94594594594595
83	12	17.2352941176471	-5.23529411764706
84	12	10.0540540540541	1.94594594594595
85	17	13.6578947368421	3.3421052631579
86	21	17.2352941176471	3.76470588235294
87	16	13.3235294117647	2.67647058823529
88	11	13.6578947368421	-2.6578947368421
89	14	13.6578947368421	0.342105263157896
90	13	13.6578947368421	-0.657894736842104
91	9	10.0540540540541	-1.05405405405405
92	19	17.2352941176471	1.76470588235294
93	13	13.3235294117647	-0.323529411764707
94	19	18	1
95	13	13.6578947368421	-0.657894736842104
96	13	13.6578947368421	-0.657894736842104
97	13	18	-5
98	14	13.6578947368421	0.342105263157896
99	12	10.0540540540541	1.94594594594595
100	22	17.2352941176471	4.76470588235294
101	11	13.3235294117647	-2.32352941176471
102	5	10.0540540540541	-5.05405405405405
103	18	13.3235294117647	4.67647058823529
104	19	17.2352941176471	1.76470588235294
105	14	10.0540540540541	3.94594594594595
106	15	13.6578947368421	1.3421052631579
107	12	13.6578947368421	-1.6578947368421
108	19	18	1
109	15	13.3235294117647	1.67647058823529
110	17	17.2352941176471	-0.235294117647058
111	8	13.3235294117647	-5.32352941176471
112	10	13.3235294117647	-3.32352941176471
113	12	10.0540540540541	1.94594594594595
114	12	13.6578947368421	-1.6578947368421
115	20	17.2352941176471	2.76470588235294
116	12	10.0540540540541	1.94594594594595
117	12	13.6578947368421	-1.6578947368421
118	14	13.6578947368421	0.342105263157896
119	6	10.0540540540541	-4.05405405405405
120	10	13.6578947368421	-3.6578947368421
121	18	13.6578947368421	4.3421052631579
122	18	13.6578947368421	4.3421052631579
123	7	10.0540540540541	-3.05405405405405
124	18	13.6578947368421	4.3421052631579
125	9	17.2352941176471	-8.23529411764706
126	17	13.3235294117647	3.67647058823529
127	22	17.2352941176471	4.76470588235294
128	11	13.6578947368421	-2.6578947368421
129	15	10.0540540540541	4.94594594594595
130	17	17.2352941176471	-0.235294117647058
131	15	13.6578947368421	1.3421052631579
132	22	17.2352941176471	4.76470588235294
133	9	13.6578947368421	-4.6578947368421
134	13	10.0540540540541	2.94594594594595
135	20	17.2352941176471	2.76470588235294
136	14	13.3235294117647	0.676470588235293
137	14	17.2352941176471	-3.23529411764706
138	12	13.6578947368421	-1.6578947368421
139	20	21.5	-1.5
140	20	13.3235294117647	6.67647058823529
141	8	13.6578947368421	-5.6578947368421
142	17	13.6578947368421	3.3421052631579
143	9	10.0540540540541	-1.05405405405405
144	18	18	0
145	22	21.5	0.5
146	10	10.0540540540541	-0.0540540540540544
147	13	13.3235294117647	-0.323529411764707
148	15	17.2352941176471	-2.23529411764706
149	18	13.3235294117647	4.67647058823529
150	18	18	0
151	12	13.3235294117647	-1.32352941176471
152	12	13.3235294117647	-1.32352941176471
153	20	17.2352941176471	2.76470588235294
154	12	13.3235294117647	-1.32352941176471
155	16	17.2352941176471	-1.23529411764706
156	16	18	-2
157	18	13.6578947368421	4.3421052631579
158	16	13.6578947368421	2.3421052631579
159	13	13.3235294117647	-0.323529411764707
160	17	13.6578947368421	3.3421052631579
161	13	10.0540540540541	2.94594594594595
162	17	10.0540540540541	6.94594594594595

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 5 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 5 ; par2 = none ; par3 = 3 ; par4 = no ;

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code